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What is Field Research: Definition, Methods, Examples and Advantages

Field Research

What is Field Research?

Field research is defined as a qualitative method of data collection that aims to observe, interact and understand people while they are in a natural environment. For example, nature conservationists observe behavior of animals in their natural surroundings and the way they react to certain scenarios. In the same way, social scientists conducting field research may conduct interviews or observe people from a distance to understand how they behave in a social environment and how they react to situations around them.

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Field research encompasses a diverse range of social research methods including direct observation, limited participation, analysis of documents and other information, informal interviews, surveys etc. Although field research is generally characterized as qualitative research, it often involves multiple aspects of quantitative research in it.

Field research typically begins in a specific setting although the end objective of the study is to observe and analyze the specific behavior of a subject in that setting. The cause and effect of a certain behavior, though, is tough to analyze due to presence of multiple variables in a natural environment. Most of the data collection is based not entirely on cause and effect but mostly on correlation. While field research looks for correlation, the small sample size makes it difficult to establish a causal relationship between two or more variables.

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Methods of Field Research

Field research is typically conducted in 5 distinctive methods. They are:

  • Direct Observation

In this method, the data is collected via an observational method or subjects in a natural environment. In this method, the behavior or outcome of situation is not interfered in any way by the researcher. The advantage of direct observation is that it offers contextual data on people management , situations, interactions and the surroundings. This method of field research is widely used in a public setting or environment but not in a private environment as it raises an ethical dilemma.

  • Participant Observation

In this method of field research, the researcher is deeply involved in the research process, not just purely as an observer, but also as a participant. This method too is conducted in a natural environment but the only difference is the researcher gets involved in the discussions and can mould the direction of the discussions. In this method, researchers live in a comfortable environment with the participants of the research design , to make them comfortable and open up to in-depth discussions.

  • Ethnography

Ethnography is an expanded observation of social research and social perspective and the cultural values of an  entire social setting. In ethnography, entire communities are observed objectively. For example,  if a researcher would like to understand how an Amazon tribe lives their life and operates, he/she may chose to observe them or live amongst them and silently observe their day-to-day behavior.

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  • Qualitative Interviews

Qualitative interviews are close-ended questions that are asked directly to the research subjects. The qualitative interviews could be either informal and conversational, semi-structured, standardized and open-ended or a mix of all the above three. This provides a wealth of data to the researcher that they can sort through. This also helps collect relational data. This method of field research can use a mix of one-on-one interviews, focus groups and text analysis .

LEARN ABOUT: Qualitative Interview

A case study research is an in-depth analysis of a person, situation or event. This method may look difficult to operate, however, it is one of the simplest ways of conducting research as it involves a deep dive and thorough understanding the data collection methods and inferring the data.

Steps in Conducting Field Research

Due to the nature of field research, the magnitude of timelines and costs involved, field research can be very tough to plan, implement and measure. Some basic steps in the management of field research are:

  • Build the Right Team: To be able to conduct field research, having the right team is important. The role of the researcher and any ancillary team members is very important and defining the tasks they have to carry out with defined relevant milestones is important. It is important that the upper management too is vested in the field research for its success.
  • Recruiting People for the Study: The success of the field research depends on the people that the study is being conducted on. Using sampling methods , it is important to derive the people that will be a part of the study.
  • Data Collection Methodology: As spoken in length about above, data collection methods for field research are varied. They could be a mix of surveys, interviews, case studies and observation. All these methods have to be chalked out and the milestones for each method too have to be chalked out at the outset. For example, in the case of a survey, the survey design is important that it is created and tested even before the research begins.
  • Site Visit: A site visit is important to the success of the field research and it is always conducted outside of traditional locations and in the actual natural environment of the respondent/s. Hence, planning a site visit alongwith the methods of data collection is important.
  • Data Analysis: Analysis of the data that is collected is important to validate the premise of the field research and  decide the outcome of the field research.
  • Communicating Results: Once the data is analyzed, it is important to communicate the results to the stakeholders of the research so that it could be actioned upon.

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Field Research Notes

Keeping an ethnographic record is very important in conducting field research. Field notes make up one of the most important aspects of the ethnographic record. The process of field notes begins as the researcher is involved in the observational research process that is to be written down later.

Types of Field Research Notes

The four different kinds of field notes are:

  • Job Notes: This method of taking notes is while the researcher is in the study. This could be in close proximity and in open sight with the subject in study. The notes here are short, concise and in condensed form that can be built on by the researcher later. Most researchers do not prefer this method though due to the fear of feeling that the respondent may not take them seriously.
  • Field Notes Proper: These notes are to be expanded on immediately after the completion of events. The notes have to be detailed and the words have to be as close to possible as the subject being studied.
  • Methodological Notes: These notes contain methods on the research methods used by the researcher, any new proposed research methods and the way to monitor their progress. Methodological notes can be kept with field notes or filed separately but they find their way to the end report of a study.
  • Journals and Diaries: This method of field notes is an insight into the life of the researcher. This tracks all aspects of the researchers life and helps eliminate the Halo effect or any research bias that may have cropped up during the field research.

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Reasons to Conduct Field Research

Field research has been commonly used in the 20th century in the social sciences. But in general, it takes a lot of time to conduct and complete, is expensive and in a lot of cases invasive. So why then is this commonly used and is preferred by researchers to validate data? We look at 4 major reasons:

  • Overcoming lack of data: Field research resolves the major issue of gaps in data. Very often, there is limited to no data about a topic in study, especially in a specific environment analysis . The research problem might be known or suspected but there is no way to validate this without primary research and data. Conducting field research helps not only plug-in gaps in data but collect supporting material and hence is a preferred research method of researchers.
  • Understanding context of the study: In many cases, the data collected is adequate but field research is still conducted. This helps gain insight into the existing data. For example, if the data states that horses from a stable farm generally win races because the horses are pedigreed and the stable owner hires the best jockeys. But conducting field research can throw light into other factors that influence the success like quality of fodder and care provided and conducive weather conditions.
  • Increasing the quality of data: Since this research method uses more than one tool to collect data, the data is of higher quality. Inferences can be made from the data collected and can be statistically analyzed via the triangulation of data.
  • Collecting ancillary data: Field research puts the researchers in a position of localized thinking which opens them new lines of thinking. This can help collect data that the study didn’t account to collect.

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Examples of Field Research

Some examples of field research are:

  • Decipher social metrics in a slum Purely by using observational methods and in-depth interviews, researchers can be part of a community to understand the social metrics and social hierarchy of a slum. This study can also understand the financial independence and day-to-day operational nuances of a slum. The analysis of this data can provide an insight into how different a slum is from structured societies.
  • U nderstand the impact of sports on a child’s development This method of field research takes multiple years to conduct and the sample size can be very large. The data analysis of this research provides insights into how the kids of different geographical locations and backgrounds respond to sports and the impact of sports on their all round development.
  • Study animal migration patterns Field research is used extensively to study flora and fauna. A major use case is scientists monitoring and studying animal migration patterns with the change of seasons. Field research helps collect data across years and that helps draw conclusions about how to safely expedite the safe passage of animals.

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Advantages of Field Research

The advantages of field research are:

  • It is conducted in a real-world and natural environment where there is no tampering of variables and the environment is not doctored.
  • Due to the study being conducted in a comfortable environment, data can be collected even about ancillary topics.
  • The researcher gains a deep understanding into the research subjects due to the proximity to them and hence the research is extensive, thorough and accurate.

Disadvantages of Field Research

The disadvantages of field research are:

  • The studies are expensive and time-consuming and can take years to complete.
  • It is very difficult for the researcher to distance themselves from a bias in the research study.
  • The notes have to be exactly what the researcher says but the nomenclature is very tough to follow.
  • It is an interpretive method and this is subjective and entirely dependent on the ability of the researcher.
  • In this method, it is impossible to control external variables and this constantly alters the nature of the research.

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A guide to field studies

Last updated

18 April 2023

Reviewed by

Cathy Heath

Field studies allow researchers to observe and collect data in real-world settings. Unlike laboratory-based or traditional research methods, field studies enable researchers to investigate complex phenomena within their environment, providing a deeper understanding of the research context.

Researchers can use field studies to investigate a wide range of subjects, from the behavior of animals to the practices of businesses or the experiences of individuals in a particular setting.

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  • What is a field study?

A field study is a research method that involves conducting observations and collecting data in a natural setting. This method includes observing, interviewing, and interacting with participants in their environment, such as a workplace, community, or natural habitat.

Field studies can take many forms, from ethnographic studies involving extended periods of observation and using an anthropological lens to shorter-term studies focusing on specific behaviors or events. Regardless of its form, a successful field study requires careful planning, preparation, and execution to ensure the data collected is valid and reliable.

  • How to plan a field study

Planning a field study is a critical first step in ensuring successful research. Here are some steps to follow when preparing your field study:

1. Define your research question

When developing a good research question , you should make it clear, concise, and specific. It should also be open-ended, allowing for various possible answers rather than a simple yes or no response. Your research question should also be relevant to the broader field of study and contribute new knowledge to the existing literature.

Once you have a defined research question, identify the key variables you need to study and the data you need to collect. It might involve developing a hypothesis or research framework outlining the relationships between different variables and how you’ll measure them in your study.

2. Identify your research site

A research site is a location where you’ll conduct your study and collect data. Here are the types of research sites to consider when planning a field study:

Natural habitats: For environmental or ecological research, you may need to conduct your study in a natural habitat, such as a forest, wetland, or coral reef.

Communities : If your research relates to social or cultural factors, you may need to study a particular community, such as a neighborhood, village, or city.

Organizations : For questions relating to organizational behavior or management, your location will be in a business environment, like a nonprofit or government agency.

Events : If your research question relates to a particular event, you may need to conduct your study at that event, such as, at a protest, festival, or natural disaster.

Ensure your research site represents the population you're studying. For example, if you're exploring cultural beliefs, ensure the community represents the larger population and you have access to a diverse group of participants.

3. Determine your data collection methods

Choosing a suitable method will depend on the research question, the type of data needed, and the characteristics of the participants. Here are some commonly used data collection methods in field studies:

Interviews : You can collect data on people's experiences, perspectives, and attitudes. In some instances, you can use phone or online interviews.

Observations : This method involves watching and recording behaviors and interactions in a specific setting. 

Surveys : By using a survey , you can easily standardize and tailor the questions to provide answers for your research. Respondents can complete the survey in person, by mail, or online.

Document analysis : Organizational reports, letters, diaries, public records, policies, or social media posts can be analyzed to gain context. 

When selecting data collection methods, consider factors such as the availability of participants, the ethical considerations involved, and the resources needed to carry out each method. For example, conducting interviews may require more time and resources than administering a survey.

4. Obtain necessary permissions

Depending on the research location and the nature of the study, you may require permission from local authorities, organizations, or individuals before conducting your research. 

This process is vital when working with human or animal subjects and conducting research in sensitive or protected environments.

Here are some steps you can take to obtain the necessary permissions:

Identify the relevant authorities , including local governments, regulatory bodies, research institutions, or private organizations, to obtain permission for your research.

Reach out to the relevant authorities to explain the nature of your study. Be ready to hand out detailed information about your research. 

If you're conducting research with human participants, you must have their consent . You'll also need to ensure the participants have the right to withdraw from the study at any time.

Obtain necessary permits from regulatory bodies or local authorities. For example, if you're conducting research in a protected area, you may need a research permit from the relevant government agency.

The process of obtaining permissions can be time-consuming, and failure to obtain the necessary permits can lead to legal and ethical issues.

  • Examples of field research

Researchers can apply field research to a wide range of disciplines and phenomena. Here are some examples of field research in different fields:

Anthropology : Anthropologists use field research methods to study different communities' social and cultural practices. For instance, an anthropologist might conduct participant observation in a remote community to understand their customs, beliefs, and practices.

Ecology : Ecologists use field research methods to learn the behavior of organisms and their interactions with the environment. For example, an ecologist might conduct field research on the behavior of birds in their natural habitat to understand their feeding habits, nesting patterns, and migration.

Sociology : Sociologists may use field research methods to study social behavior and interactions. For instance, a sociologist might conduct participant observation in a workplace to understand organizational culture and communication dynamics.

Geography : Geographers use field research methods to study different regions’ physical and human contexts. For example, a geographer might conduct field research on the impact of climate change on a particular ecosystem, such as a forest or wetland.

Psychology : Psychologists use field research methods to study human behavior in natural settings. For instance, a psychologist might conduct field research on the effects of stress on students in a school setting.

Education : Researchers studying education may use field research methods to study teaching and learning in real-world settings. For example, you could use field research to test the effectiveness of a new teaching method in a classroom setting.

By using field research methods, researchers can gain a deeper understanding of the complexities of the natural world, human behavior, and social interaction theory and how they affect each other.

  • Advantages of field research

Field research has several advantages over other research methods, including:

Authenticity : Field research conducted in natural settings allows researchers to observe and study real-life phenomena as it happens. This authenticity enhances the validity and accuracy of the data collected.

Flexibility : Field research methods are flexible and adaptable to different research contexts. Researchers can adjust their strategies to meet the specific needs of their research questions and participants and uncover new insights as the research unfolds.

Rich data : Field research provides rich and detailed data, often including contextual information that’s difficult to capture through other research methods. This depth of knowledge allows for a more comprehensive and nuanced understanding of the research topic.

Novel insights : Field research can lead to discoveries that may not be possible with other research methods. Observing and studying phenomena in natural settings can provide unique perspectives and new understandings of complex issues.

Field research methods can enhance the quality and validity of research findings and lead to new insights and discoveries that may not be possible with other research methods.

  • Disadvantages of field research

While field research has several advantages, there are also some disadvantages that researchers need to consider, including:

Time-consuming : Researchers need to spend time in the field, possibly weeks or months, which can be challenging, especially if the research site is remote or requires travel.

Cost : Conducting field research can be costly, especially if the research site is remote or requires specialized equipment or materials.

Reliance on participants : It may be challenging to recruit participants, and various factors, such as personal circumstances, attitudes, and beliefs, may influence their participation.

Ethical considerations : Field research may raise ethical concerns, mainly if the research involves vulnerable populations or sensitive topics. 

Causality: Researchers may have little control over the environmental or contextual variables they are studying. This can make it difficult to establish causality and then generalize their results with previous research. 

Researchers must carefully weigh the advantages and disadvantages of field research and select the most appropriate research method based on their research question, participants, and context.

What is another name for field study?

Field study is also known as field research or fieldwork. These terms are often used interchangeably and refer to research methods that involve observing and collecting data in natural settings.

What is the difference between a field study and a case study?

Why is field study important.

Field study is critical because it allows researchers to study real-world phenomena in natural settings. This study can also lead to novel insights that may not be possible with other research methods.

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Introduction, what is fieldwork, purpose of fieldwork, physical safety, mental wellbeing and affect, ethical considerations, remote fieldwork, concluding thoughts, acknowledgments, funder information.

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Field Research: A Graduate Student's Guide

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Ezgi Irgil, Anne-Kathrin Kreft, Myunghee Lee, Charmaine N Willis, Kelebogile Zvobgo, Field Research: A Graduate Student's Guide, International Studies Review , Volume 23, Issue 4, December 2021, Pages 1495–1517, https://doi.org/10.1093/isr/viab023

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What is field research? Is it just for qualitative scholars? Must it be done in a foreign country? How much time in the field is “enough”? A lack of disciplinary consensus on what constitutes “field research” or “fieldwork” has left graduate students in political science underinformed and thus underequipped to leverage site-intensive research to address issues of interest and urgency across the subfields. Uneven training in Ph.D. programs has also left early-career researchers underprepared for the logistics of fieldwork, from developing networks and effective sampling strategies to building respondents’ trust, and related issues of funding, physical safety, mental health, research ethics, and crisis response. Based on the experience of five junior scholars, this paper offers answers to questions that graduate students puzzle over, often without the benefit of others’ “lessons learned.” This practical guide engages theory and praxis, in support of an epistemologically and methodologically pluralistic discipline.

¿Qué es la investigación de campo? ¿Es solo para académicos cualitativos? ¿Debe realizarse en un país extranjero? ¿Cuánto tiempo en el terreno es “suficiente”? La falta de consenso disciplinario con respecto a qué constituye la “investigación de campo” o el “trabajo de campo” ha causado que los estudiantes de posgrado en ciencias políticas estén poco informados y, por lo tanto, capacitados de manera insuficiente para aprovechar la investigación exhaustiva en el sitio con el objetivo de abordar los asuntos urgentes y de interés en los subcampos. La capacitación desigual en los programas de doctorado también ha provocado que los investigadores en las primeras etapas de su carrera estén poco preparados para la logística del trabajo de campo, desde desarrollar redes y estrategias de muestreo efectivas hasta generar la confianza de las personas que facilitan la información, y las cuestiones relacionadas con la financiación, la seguridad física, la salud mental, la ética de la investigación y la respuesta a las situaciones de crisis. Con base en la experiencia de cinco académicos novatos, este artículo ofrece respuestas a las preguntas que desconciertan a los estudiantes de posgrado, a menudo, sin el beneficio de las “lecciones aprendidas” de otras personas. Esta guía práctica incluye teoría y praxis, en apoyo de una disciplina pluralista desde el punto de vista epistemológico y metodológico.

En quoi consiste la recherche de terain ? Est-elle uniquement réservée aux chercheurs qualitatifs ? Doit-elle être effectuée dans un pays étranger ? Combien de temps faut-il passer sur le terrain pour que ce soit « suffisant » ? Le manque de consensus disciplinaire sur ce qui constitue une « recherche de terrain » ou un « travail de terrain » a laissé les étudiants diplômés en sciences politiques sous-informés et donc sous-équipés pour tirer parti des recherches de terrain intensives afin d'aborder les questions d'intérêt et d'urgence dans les sous-domaines. L'inégalité de formation des programmes de doctorat a mené à une préparation insuffisante des chercheurs en début de carrière à la logistique du travail de terrain, qu'il s'agisse du développement de réseaux et de stratégies d’échantillonnage efficaces, de l'acquisition de la confiance des personnes interrogées ou des questions de financement, de sécurité physique, de santé mentale, d’éthique de recherche et de réponse aux crises qui y sont associées. Cet article s'appuie sur l'expérience de cinq jeunes chercheurs pour proposer des réponses aux questions que les étudiants diplômés se posent, souvent sans bénéficier des « enseignements tirés » par les autres. Ce guide pratique engage théorie et pratique en soutien à une discipline épistémologiquement et méthodologiquement pluraliste.

Days before embarking on her first field research trip, a Ph.D. student worries about whether she will be able to collect the qualitative data that she needs for her dissertation. Despite sending dozens of emails, she has received only a handful of responses to her interview requests. She wonders if she will be able to gain more traction in-country. Meanwhile, in the midst of drafting her thesis proposal, an M.A. student speculates about the feasibility of his project, given a modest budget. Thousands of miles away from home, a postdoc is concerned about their safety, as protests erupt outside their window and state security forces descend into the streets.

These anecdotes provide a small glimpse into the concerns of early-career researchers undertaking significant projects with a field research component. Many of these fieldwork-related concerns arise from an unfortunate shortage in curricular offerings for qualitative and mixed-method research in political science graduate programs ( Emmons and Moravcsik 2020 ), 1 as well as the scarcity of instructional materials for qualitative and mixed-method research, relative to those available for quantitative research ( Elman, Kapiszewski, and Kirilova 2015 ; Kapiszewski, MacLean, and Read 2015 ; Mosley 2013 ). A recent survey among the leading United States Political Science programs in Comparative Politics and International Relations found that among graduate students who have carried out international fieldwork, 62 percent had not received any formal fieldwork training and only 20 percent felt very or mostly prepared for their fieldwork ( Schwartz and Cronin-Furman 2020 , 7–8). This shortfall in training and instruction means that many young researchers are underprepared for the logistics of fieldwork, from developing networks and effective sampling strategies to building respondents’ trust. In addition, there is a notable lack of preparation around issues of funding, physical safety, mental health, research ethics, and crisis response. This is troubling, as field research is highly valued and, in some parts of the field, it is all but expected, for instance in comparative politics.

Beyond subfield-specific expectations, research that leverages multiple types of data and methods, including fieldwork, is one of the ways that scholars throughout the discipline can more fully answer questions of interest and urgency. Indeed, multimethod work, a critical means by which scholars can parse and evaluate causal pathways, is on the rise ( Weller and Barnes 2016 ). The growing appearance of multimethod research in leading journals and university presses makes adequate training and preparation all the more significant ( Seawright 2016 ; Nexon 2019 ).

We are five political scientists interested in providing graduate students and other early-career researchers helpful resources for field research that we lacked when we first began our work. Each of us has recently completed or will soon complete a Ph.D. at a United States or Swedish university, though we come from many different national backgrounds. We have conducted field research in our home countries and abroad. From Colombia and Guatemala to the United States, from Europe to Turkey, and throughout East and Southeast Asia, we have spanned the globe to investigate civil society activism and transitional justice in post-violence societies, conflict-related sexual violence, social movements, authoritarianism and contentious politics, and the everyday politics and interactions between refugees and host-country citizens.

While some of us have studied in departments that offer strong training in field research methods, most of us have had to self-teach, learning through trial and error. Some of us have also been fortunate to participate in short courses and workshops hosted by universities such as the Consortium for Qualitative Research Methods and interdisciplinary institutions such as the Peace Research Institute Oslo. Recognizing that these opportunities are not available to or feasible for all, and hoping to ease the concerns of our more junior colleagues, we decided to compile our experiences and recommendations for first-time field researchers.

Our experiences in the field differ in several key respects, from the time we spent in the field to the locations we visited, and how we conducted our research. The diversity of our experiences, we hope, will help us reach and assist the broadest possible swath of graduate students interested in field research. Some of us have spent as little as ten days in a given country or as much as several months, in some instances visiting a given field site location just once and in other instances returning several times. At times, we have been able to plan weeks and months in advance. Other times, we have quickly arranged focus groups and impromptu interviews. Other times still, we have completed interviews virtually, when research participants were in remote locations or when we ourselves were unable to travel, of note during the coronavirus pandemic. We have worked in countries where we are fluent or have professional proficiency in the language, and in countries where we have relied on interpreters. We have worked in settings with precarious security as well as in locations that feel as comfortable as home. Our guide is not intended to be prescriptive or exhaustive. What we offer is a set of experience-based suggestions to be implemented as deemed relevant and appropriate by the researcher and their advisor(s).

In terms of the types of research and data sources and collection, we have conducted archival research, interviews, focus groups, and ethnographies with diplomats, bureaucrats, military personnel, ex-combatants, civil society advocates, survivors of political violence, refugees, and ordinary citizens. We have grappled with ethical dilemmas, chief among them how to get useful data for our research projects in ways that exceed the minimal standards of human subjects’ research evaluation panels. Relatedly, we have contemplated how to use our platforms to give back to the individuals and communities who have so generously lent us their time and knowledge, and shared with us their personal and sometimes harrowing stories.

Our target audience is first and foremost graduate students and early-career researchers who are interested in possibly conducting fieldwork but who either (1) do not know the full potential or value of fieldwork, (2) know the potential and value of fieldwork but think that it is excessively cost-prohibitive or otherwise infeasible, or (3) who have the interest, the will, and the means but not necessarily the know-how. We also hope that this resource will be of value to graduate programs, as they endeavor to better support students interested in or already conducting field research. Further, we target instructional faculty and graduate advisors (and other institutional gatekeepers like journal and book reviewers), to show that fieldwork does not have to be year-long, to give just one example. Instead, the length of time spent in the field is a function of the aims and scope of a given project. We also seek to formalize and normalize the idea of remote field research, whether conducted because of security concerns in conflict zones, for instance, or because of health and safety concerns, like the Covid-19 pandemic. Accordingly, researchers in the field for shorter stints or who conduct fieldwork remotely should not be penalized.

We note that several excellent resources on fieldwork such as the bibliography compiled by Advancing Conflict Research (2020) catalogue an impressive list of articles addressing questions such as ethics, safety, mental health, reflexivity, and methods. Further resources can be found about the positionality of the researcher in the field while engaging vulnerable communities, such as in the research field of migration ( Jacobsen and Landau 2003 ; Carling, Bivand Erdal, and Ezzati 2014 ; Nowicka and Cieslik 2014 ; Zapata-Barrero and Yalaz 2019 ). However, little has been written beyond conflict-affected contexts, fragile settings, and vulnerable communities. Moreover, as we consulted different texts and resources, we found no comprehensive guide to fieldwork explicitly written with graduate students in mind. It is this gap that we aim to fill.

In this paper, we address five general categories of questions that graduate students puzzle over, often without the benefit of others’ “lessons learned.” First, What is field research? Is it just for qualitative scholars? Must it be conducted in a foreign country? How much time in the field is “enough”? Second, What is the purpose of fieldwork? When does it make sense to travel to a field site to collect data? How can fieldwork data be used? Third, What are the nuts and bolts? How does one get ready and how can one optimize limited time and financial resources? Fourth, How does one conduct fieldwork safely? What should a researcher do to keep themselves, research assistants, and research subjects safe? What measures should they take to protect their mental health? Fifth, How does one conduct ethical, beneficent field research?

Finally, the Covid-19 pandemic has impressed upon the discipline the volatility of research projects centered around in-person fieldwork. Lockdowns and closed borders left researchers sequestered at home and unable to travel, forced others to cut short any trips already begun, and unexpectedly confined others still to their fieldwork sites. Other factors that may necessitate a (spontaneous) readjustment of planned field research include natural disasters, a deteriorating security situation in the field site, researcher illness, and unexpected changes in personal circumstances. We, therefore, conclude with a section on the promise and potential pitfalls of remote (or virtual) fieldwork. Throughout this guide, we engage theory and praxis to support an epistemologically and methodologically pluralistic discipline.

The concept of “fieldwork” is not well defined in political science. While several symposia discuss the “nuts and bolts” of conducting research in the field within the pages of political science journals, few ever define it ( Ortbals and Rincker 2009 ; Hsueh, Jensenius, and Newsome 2014 ). Defining the concept of fieldwork is important because assumptions about what it is and what it is not underpin any suggestions for conducting it. A lack of disciplinary consensus about what constitutes “fieldwork,” we believe, explains the lack of a unified definition. Below, we discuss three areas of current disagreement about what “fieldwork” is, including the purpose of fieldwork, where it occurs, and how long it should be. We follow this by offering our definition of fieldwork.

First, we find that many in the discipline view fieldwork as squarely in the domain of qualitative research, whether interpretivist or positivist. However, field research can also serve quantitative projects—for example, by providing crucial context, supporting triangulation, or illustrating causal mechanisms. For instance, Kreft (2019) elaborated her theory of women's civil society mobilization in response to conflict-related sexual violence based on interviews she carried out in Colombia. She then examined cross-national patterns through statistical analysis. Conversely, Willis's research on the United States military in East Asia began with quantitative data collection and analysis of protest events before turning to fieldwork to understand why protests occurred in some instances but not others. Researchers can also find quantifiable data in the field that is otherwise unavailable to them at home ( Read 2006 ; Chambers-Ju 2014 ; Jensenius 2014 ). Accordingly, fieldwork is not in the domain of any particular epistemology or methodology as its purpose is to acquire data for further information.

Second, comparative politics and international relations scholars often opine that fieldwork requires leaving the country in which one's institution is based. Instead, we propose that what matters most is the nature of the research project, not the locale. For instance, some of us in the international relations subfield have interviewed representatives of intergovernmental organizations (IGOs) and international nongovernmental organizations (INGOs), whose headquarters are generally located in Global North countries. For someone pursuing a Ph.D. in the United States and writing on transnational advocacy networks, interviews with INGO representatives in New York certainly count as fieldwork ( Zvobgo 2020 ). Similarly, a graduate student who returns to her home country to interview refugees and native citizens is conducting a field study as much as a researcher for whom the context is wholly foreign. Such interviews can provide necessary insights and information that would not have been gained otherwise—one of the key reasons researchers conduct fieldwork in the first place. In other instances, conducting any in-person research is simply not possible, due to financial constraints, safety concerns, or other reasons. For example, the Covid-19 pandemic has forced many researchers to shift their face-to-face research plans to remote data collection, either over the phone or virtually ( Howlett 2021 , 2). For some research projects, gathering data through remote methods may yield the same if not similar information than in-person research ( Howlett 2021 , 3–4). As Howlett (2021 , 11) notes, digital platforms may offer researchers the ability to “embed ourselves in other contexts from a distance” and glimpse into our subjects’ lives in ways similar to in-person research. By adopting a broader definition of fieldwork, researchers can be more flexible in getting access to data sources and interacting with research subjects.

Third, there is a tendency, especially among comparativists, to only count fieldwork that spans the better part of a year; even “surgical strike” field research entails one to three months, according to some scholars ( Ortbals and Rincker 2009 ; Weiss, Hicken, and Kuhonta 2017 ). The emphasis on spending as much time as possible in the field is likely due to ethnographic research traditions, reflected in classics such as James Scott's Weapons of the Weak , which entail year-long stints of research. However, we suggest that the appropriate amount of time in the field should be assessed on a project-by-project basis. Some studies require the researcher to be in the field for long periods; others do not. For example, Willis's research on the discourse around the United States’ military presence in overseas host communities has required months in the field. By contrast, Kreft only needed ten days in New York to carry out interviews with diplomats and United Nations staff, in a context with which she already had some familiarity from a prior internship. Likewise, Zvobgo spent a couple of weeks in her field research sites, conducting interviews with directors and managers of prominent human rights nongovernmental organizations. This population is not so large as to require a whole month or even a few months. This has also been the case for Irgil, as she had spent one month in the field site conducting interviews with ordinary citizens. The goal of the project was to acquire information on citizens’ perceptions of refugees. As we discuss in the next section, when deciding how long to spend in the field, scholars must consider the information their project requires and consider the practicalities of fieldwork, notably cost.

Thus, we highlight three essential points in fieldwork and offer a definition accordingly: fieldwork involves acquiring information, using any set of appropriate data collection techniques, for qualitative, quantitative, or experimental analysis through embedded research whose location and duration is dependent on the project. We argue that adopting such a definition of “fieldwork” is necessary to include the multitude of forms fieldwork can take, including remote methods, whose value and challenges the Covid-19 pandemic has impressed upon the discipline.

When does a researcher need to conduct fieldwork? Fieldwork can be effective for (1) data collection, (2) theory building, and (3) theory testing. First, when a researcher is interested in a research topic, yet they could not find an available and/or reliable data source for the topic, fieldwork could provide the researcher with plenty of options. Some research agendas can require researchers to visit archives to review historical documents. For example, Greitens (2016) visited national archives in the Philippines, South Korea, Taiwan, and the United States to find historical documents about the development of coercive institutions in past authoritarian governments for her book, Dictators and Their Secret Police . Also, newly declassified archival documents can open new possibilities for researchers to examine restricted topics. To illustrate, thanks to the newly released archival records of the Chinese Communist Party's communications, and exchange of visits with the European communist world, Sarotte (2012) was able to study the Party's decision to crack down on Tiananmen protesters, which had previously been deemed as an unstudiable topic due to the limited data.

Other research agendas can require researchers to conduct (semistructured) in-depth interviews to understand human behavior or a situation more closely, for example, by revealing the meanings of concepts for people and showing how people perceive the world. For example, O'Brien and Li (2005) conducted in-depth interviews with activists, elites, and villagers to understand how these actors interact with each other and what are the outcomes of the interaction in contentious movements in rural China. Through research, they revealed that protests have deeply influenced all these actors’ minds, a fact not directly observable without in-depth interviews.

Finally, data collection through fieldwork should not be confined to qualitative data ( Jensenius 2014 ). While some quantitative datasets can be easily compiled or accessed through use of the internet or contact with data-collection agencies, other datasets can only be built or obtained through relationships with “gatekeepers” such as government officials, and thus require researchers to visit the field ( Jensenius 2014 ). Researchers can even collect their own quantitative datasets by launching surveys or quantifying data contained in archives. In a nutshell, fieldwork will allow researchers to use different techniques to collect and access original/primary data sources, whether these are qualitative, quantitative, or experimental in nature, and regardless of the intended method of analysis. 2

But fieldwork is not just for data collection as such. Researchers can accomplish two other fundamental elements of the research process: theory building and theory testing. When a researcher finds a case where existing theories about a phenomenon do not provide plausible explanations, they can build a theory through fieldwork ( Geddes 2003 ). Lee's experience provides a good example. When studying the rise of a protest movement in South Korea for her dissertation, Lee applied commonly discussed social movement theories, grievances, political opportunity, resource mobilization, and repression, to explain the movement's eruption and found that these theories do not offer a convincing explanation for the protest movement. She then moved on to fieldwork and conducted interviews with the movement participants to understand their motivations. Finally, through those interviews, she offered an alternative theory that the protest participants’ collective identity shaped during the authoritarian past played a unifying factor and eventually led them to participate in the movement. Her example shows that theorization can take place through careful review and rigorous inference during fieldwork.

Moreover, researchers can test their theory through fieldwork. Quantitative observational data has limitations in revealing causal mechanisms ( Esarey 2017 ). Therefore, many political scientists turn their attention to conducting field experiments or lab-in-the-field experiments to reveal causality ( Druckman et al. 2006 ; Beath, Christia, and Enikolopov 2013 ; Finseraas and Kotsadam 2017 ), or to leveraging in-depth insights or historical records gained through qualitative or archival research in process-tracing ( Collier 2011 ; Ricks and Liu 2018 ). Surveys and survey experiments may also be useful tools to substantiate a theoretical story or test a theory ( Marston 2020 ). Of course, for most Ph.D. students, especially those not affiliated with more extensive research projects, some of these options will be financially prohibitive.

A central concern for graduate students, especially those working with a small budget and limited time, is optimizing time in the field and integrating remote work. We offer three pieces of advice: have a plan, build in flexibility, and be strategic, focusing on collecting data that are unavailable at home. We also discuss working with local translators or research assistants. Before we turn to these more practical issues arising during fieldwork, we address a no less important issue: funding.

The challenge of securing funds is often overlooked in discussions of what constitutes field research. Months- or year-long in-person research can be cost-prohibitive, something academic gatekeepers must consider when evaluating “what counts” and “what is enough.” Unlike their predecessors, many graduate students today have a significant amount of debt and little savings. 3 Additionally, if researchers are not able to procure funding, they have to pay out of pocket and possibly take on more debt. Not only is in-person fieldwork costly, but researchers may also have to forego working while they are in the field, making long stretches in the field infeasible for some.

For researchers whose fieldwork involves travelling to another location, procuring funding via grants, fellowships, or other sources is a necessity, regardless of how long one plans to be in the field. A good mantra for applying for research funding is “apply early and often” ( Kelsky 2015 , 110). Funding applications take a considerable amount of time to prepare, from writing research statements to requesting letters of recommendation. Even adapting one's materials for different applications takes time. Not only is the application process itself time-consuming, but the time between applying for and receiving funds, if successful, can be quite long, from several months to a year. For example, after defending her prospectus in May 2019, Willis began applying to funding sources for her dissertation, all of which had deadlines between June and September. She received notifications between November and January; however, funds from her successful applications were not available until March and April, almost a year later. 4 Accordingly, we recommend applying for funding as early as possible; this not only increases one's chances of hitting the ground running in the field, but the application process can also help clarify the goals and parameters of one's research.

Graduate students should also apply often for funding opportunities. There are different types of funding for fieldwork: some are larger, more competitive grants such as the National Science Foundation Political Science Doctoral Dissertation Improvement Grant in the United States, others, including sources through one's own institution, are smaller. Some countries, like Sweden, boast a plethora of smaller funding agencies that disburse grants of 20,000–30,0000 Swedish Kronor (approx. 2,500–3,500 U.S. dollars) to Ph.D. students in the social sciences. Listings of potential funding sources are often found on various websites including those belonging to universities, professional organizations (such as the American Political Science Association or the European Consortium for Political Research), and governmental institutions dealing with foreign affairs. Once you have identified fellowships and grants for which you and your project are a good match, we highly recommend soliciting information and advice from colleagues who have successfully applied for them. This can include asking them to share their applications with you, and if possible, to have them, another colleague or set of colleagues read through your project description and research plan (especially for bigger awards) to ensure that you have made the best possible case for why you should be selected. While both large and small pots of funding are worth applying for, many researchers end up funding their fieldwork through several small grants or fellowships. One small award may not be sufficient to fund the entirety of one's fieldwork, but several may. For example, Willis's fieldwork in Japan and South Korea was supported through fellowships within each country. Similarly, Irgil was able to conduct her fieldwork abroad through two different and relatively smaller grants by applying to them each year.

Of course, situations vary in different countries with respect to what kinds of grants from what kinds of funders are available. An essential part of preparing for fieldwork is researching the funding landscape well in advance, even as early as the start of the Ph.D. We encourage first-time field researchers to be aware that universities and departments may themselves not be aware of the full range of possible funds available, so it is always a good idea to do your own research and watch research-related social media channels. The amount of funding needed thereby depends on the nature of one's project and how long one intends to be in the field. As we elaborate in the next section, scholars should think carefully about their project goals, the data required to meet those goals, and the requisite time to attain them. For some projects, even a couple of weeks in the field is sufficient to get the needed information.

Preparing to Enter “the field”

It is important to prepare for the field as much as possible. What kind of preparations do researchers need? For someone conducting interviews with NGO representatives, this might involve identifying the largest possible pool of potential respondents, securing their contact information, sending them study invitation letters, finding a mutually agreeable time to meet, and pulling together short biographies for each interviewee in order to use your time together most effectively. If you plan to travel to conduct interviews, you should reach out to potential respondents roughly four to six weeks prior to your arrival. For individuals who do not respond, you can follow up one to two weeks before you arrive and, if needed, once more when you are there. This is still no guarantee for success, of course. For Kreft, contacting potential interviewees in Colombia initially proved more challenging than anticipated, as many of the people she targeted did not respond to her emails. It turned out that many Colombians have a preference for communicating via phone or, in particular, WhatsApp. Some of those who responded to her emails sent in advance of her field trip asked her to simply be in touch once she was in the country, to set up appointments on short notice. This made planning and arranging her interview schedule more complicated. Therefore, a general piece of advice is to research your target population's preferred communication channels and mediums in the field site if email requests yield no or few responses.

In general, we note for the reader that contacting potential research participants should come after one has designed an interview questionnaire (plus an informed consent protocol) and sought and received, where applicable, approval from institutional review boards (IRBs) or other ethical review procedures in place (both at one's home institution/in the country of the home institution as well as in the country where one plans to conduct research if travelling abroad). The most obvious advantage of having the interview questionnaire in place and having secured all necessary institutional approvals before you start contacting potential interviewees is that you have a clearer idea of the universe of individuals you would like to interview, and for what purpose. Therefore, it is better to start sooner rather than later and be mindful of “high seasons,” when institutional and ethical review boards are receiving, processing, and making decisions on numerous proposals. It may take a few months for them to issue approvals.

On the subject of ethics and review panels, we encourage you to consider talking openly and honestly with your supervisors and/or funders about the situations where a written consent form may not be suitable and might need to be replaced with “verbal consent.” For instance, doing fieldwork in politically unstable contexts, highly scrutinized environments, or vulnerable communities, like refugees, might create obstacles for the interviewees as well as the researcher. The literature discusses the dilemma in offering the interviewees anonymity and requesting signed written consent in addition to the emphasis on total confidentiality ( Jacobsen and Landau 2003 ; Mackenzie, McDowell, and Pittaway 2007 ; Saunders, Kitzinger, and Kitzinger 2015 ). Therefore, in those situations, the researcher might need to take the initiative on how to act while doing the interviews as rigorously as possible. In her fieldwork, Irgil faced this situation as the political context of Turkey did not guarantee that there would not be any adverse consequences for interviewees on both sides of her story: citizens of Turkey and Syrian refugees. Consequently, she took hand-written notes and asked interviewees for their verbal consent in a safe interview atmosphere. This is something respondents greatly appreciated ( Irgil 2020 ).

Ethical considerations, of course, also affect the research design itself, with ramifications for fieldwork. When Kreft began developing her Ph.D. proposal to study women's political and civil society mobilization in response to conflict-related sexual violence, she initially aimed to recruit interviewees from the universe of victims of this violence, to examine variation among those who did and those who did not mobilize politically. As a result of deeper engagement with the literature on researching conflict-related sexual violence, conversations with senior colleagues who had interviewed victims, and critical self-reflection of her status as a researcher (with no background in psychology or social work), she decided to change focus and shift toward representatives of civil society organizations and victims’ associations. This constituted a major reconfiguration of her research design, from one geared toward identifying the factors that drive mobilization of victims toward using insights from interviews to understand better how those mobilize perceive and “make sense” of conflict-related sexual violence. Needless to say, this required alterations to research strategies and interview guides, including reassessing her planned fieldwork. Kreft's primary consideration was not to cause harm to her research participants, particularly in the form of re-traumatization. She opted to speak only with those women who on account of their work are used to speaking about conflict-related sexual violence. In no instance did she inquire about interviewees’ personal experiences with sexual violence, although several brought this up on their own during the interviews.

Finally, if you are conducting research in another country where you have less-than-professional fluency in the language, pre-fieldwork planning should include hiring a translator or research assistant, for example, through an online hiring platform like Upwork, or a local university. Your national embassy or consulate is another option; many diplomatic offices have lists of individuals who they have previously contracted. More generally, establishing contact with a local university can be beneficial, either in the form of a visiting researcher arrangement, which grants access to research groups and facilities like libraries or informally contacting individual researchers. The latter may have valuable insights into the local context, contacts to potential research participants, and they may even be able to recommend translators or research assistants. Kreft, for example, hired local research assistants recommended by researchers at a Bogotá-based university and remunerated them equivalent to the salary they would have received as graduate research assistants at the university, while also covering necessary travel expenses. Irgil, on the other hand, established contacts with native citizens and Syrian gatekeepers, who are shop owners in the area where she conducted her research because she had the opportunity to visit the fieldwork site multiple times.

Depending on the research agenda, researchers may visit national archives, local government offices, etc. Before visiting, researchers should contact these facilities and make sure the materials that they need are accessible. For example, Lee visited the Ronald Reagan Presidential Library Archives to find the United States’ strategic evaluations on South Korea's dictator in the 1980s. Before her visit, she contacted librarians in the archives, telling them her visit plans and her research purpose. Librarians made suggestions on which categories she should start to review based on her research goal, and thus she was able to make a list of categories of the materials she needed, saving her a lot of her time.

Accessibility of and access to certain facilities/libraries can differ depending on locations/countries and types of facilities. Facilities in authoritarian countries might not be easily accessible to foreign researchers. Within democratic countries, some facilities are more restrictive than others. Situations like the pandemic or national holidays can also restrict accessibility. Therefore, researchers are well advised to do preliminary research on whether a certain facility opens during the time they visit and is accessible to researchers regardless of their citizenship status. Moreover, researchers must contact the staff of facilities to know whether identity verification is needed and if so, what kind of documents (photo I.D. or passport) should be exhibited.

Adapting to the Reality of the Field

Researchers need to be flexible because you may meet people you did not make appointments with, come across opportunities you did not expect, or stumble upon new ideas about collecting data in the field. These happenings will enrich your field experience and will ultimately be beneficial for your research. Similarly, researchers should not be discouraged by interviews that do not go according to plan; they present an opportunity to pursue relevant people who can provide an alternative path to your work. Note that planning ahead does not preclude fortuitous encounters or epiphanies. Rather, it provides a structure for them to happen.

If your fieldwork entails travelling abroad, you will also be able to recruit more interviewees once you arrive at your research site. In fact, you may have greater success in-country; not everyone is willing to respond to a cold email from an unknown researcher in a foreign country. In Irgil's fieldwork, she contacted store owners that are known in the area and who know the community. This eased her process of introduction into the community and recruiting interviewees. For Zvobgo, she had fewer than a dozen interviews scheduled when she travelled to Guatemala to study civil society activism and transitional justice since the internal armed conflict. But she was able to recruit additional participants in-country. Interviewees with whom she built a rapport connected her to other NGOs, government offices, and the United Nations country office, sometimes even making the call and scheduling interviews for her. Through snowball sampling, she was able to triple the number of participants. Likewise, snowball sampling was central to Kreft's recruitment of interview partners. Several of her interviewees connected her to highly relevant individuals she would never have been able to identify and contact based on web searches alone.

While in the field, you may nonetheless encounter obstacles that necessitate adjustments to your original plans. Once Kreft had arrived in Colombia, for example, it transpired quickly that carrying out in-person interviews in more remote/rural areas was near impossible given her means, as these were not easily accessible by bus/coach, further complicated by a complex security situation. Instead, she adjusted her research design and shifted her focus to the big cities, where most of the major civil society organizations are based. She complemented the in-person interviews carried out there with a smaller number of phone interviews with civil society activists in rural areas, and she was also able to meet a few activists operating in rural or otherwise inaccessible areas as they were visiting the major cities. The resulting focus on urban settings changed the kinds of generalizations she was able to make based on her fieldwork data and produced a somewhat different study than initially anticipated.

This also has been the case for Irgil, despite her prior arrangements with the Syrian gatekeepers, which required adjustments as in the case of Kreft. Irgil acquired research clearance one year before, during the interviews with native citizens, conducting the interviews with Syrian refugees. She also had her questionnaire ready based on the previously collected data and the media search she had conducted for over a year before travelling to the field site. As she was able to visit the field site multiple times, two months before conducting interviews with Syrian refugees, she developed a schedule with the Syrian gatekeepers and informants. Yet, once she was in the field, influenced by Turkey's recent political events and the policy of increasing control over Syrian refugees, half of the previously agreed informants changed their minds or did not want to participate in interviews. As Irgil was following the policies and the news related to Syrian refugees in Turkey closely, this did not come as that big of a surprise but challenged the previously developed strategy to recruit interviewees. Thus, she changed the strategy of finding interviewees in the field site, such as asking people, almost one by one, whether they would like to participate in the interview. Eventually, she could not find willing Syrian women refugees as she had planned, which resulted in a male-dominant sample. As researchers encounter such situations, it is essential to remind oneself that not everything can go according to plan, that “different” does not equate to “worse,” but that it is important to consider what changes to fieldwork data collection and sampling imply for the study's overall findings and the contribution it makes to the literature.

We should note that conducting interviews is very taxing—especially when opportunities multiply, as in Zvobgo's case. Depending on the project, each interview can take an hour, if not two or more. Hence, you should make a reasonable schedule: we recommend no more than two interviews per day. You do not want to have to cut off an interview because you need to rush to another one, whether the interviews are in-person or remote. And you do not want to be too exhausted to have a robust engagement with your respondent who is generously lending you their time. Limiting the number of interviews per day is also important to ensure that you can write comprehensive and meaningful fieldnotes, which becomes even more essential where it is not possible to audio-record your interviews. Also, be sure to remember to eat, stay hydrated, and try to get enough sleep.

Finally, whether to provide gifts or payments to the subject also requires adapting to the reality of the field. You must think about payments beforehand when you apply for IRB approval (or whatever other ethical review processes may be in place) since these applications usually contain questions about payments. Obviously, the first step is to carefully evaluate whether the gifts and payments provided can harm the subject or are likely to unduly affect the responses they will give in response to your questions. If that is not the case, you have to make payment decisions based on your budget, field situation, and difficulties in recruitment. Usually, payment of respondents is more common in survey research, whereas it is less common in interviews and focus groups.

Nevertheless, payment practices vary depending on the field and the target group. In some cases, it may become a custom to provide small gifts or payments when interviewing a certain group. In other cases, interviewees might be offended if they are provided with money. Therefore, knowing past practices and field situations is important. For example, Lee provided small coffee gift cards to one group while she did not to the other based on previous practices of other researchers. That is, for a particular group, it has become a custom for interviewers to pay interviewees. Sometimes, you may want to reimburse your subject's interview costs such as travel expenses and provide beverages and snacks during the conduct of research, as Kreft did when conducting focus groups in Colombia. To express your gratitude to your respondents, you can prepare small gifts such as your university memorabilia (e.g., notebooks and pens). Since past practices about payments can affect your interactions and interviews with a target group, you want to seek advice from your colleagues and other researchers who had experiences interacting with the target group. If you cannot find researchers who have this knowledge, you can search for published works on the target population to find if the authors share their interview experiences. You may also consider contacting the authors for advice before your interviews.

Researching Strategically

Distinguishing between things that can only be done in person at a particular site and things that can be accomplished later at home is vital. Prioritize the former over the latter. Lee's fieldwork experience serves as a good example. She studied a conservative protest movement called the Taegeukgi Rally in South Korea. She planned to conduct interviews with the rally participants to examine their motivations for participating. But she only had one month in South Korea. So, she focused on things that could only be done in the field: she went to the rally sites, she observed how protests proceeded, which tactics and chants were used, and she met participants and had some casual conversations with them. Then, she used the contacts she made while attending the rallies to create a social network to solicit interviews from ordinary protesters, her target population. She was able to recruit twenty-five interviewees through good rapport with the people she met. The actual interviews proceeded via phone after she returned to the United States. In a nutshell, we advise you not to be obsessed with finishing interviews in the field. Sometimes, it is more beneficial to use your time in the field to build relationships and networks.

Working With Assistants and Translators

A final consideration on logistics is working with research assistants or translators; it affects how you can carry out interviews, focus groups, etc. To what extent constant back-and-forth translation is necessary or advisable depends on the researcher's skills in the interview language and considerations about time and efficiency. For example, Kreft soon realized that she was generally able to follow along quite well during her interviews in Colombia. In order to avoid precious time being lost to translation, she had her research assistant follow the interview guide Kreft had developed, and interjected follow-up questions in Spanish or English (then to be translated) as they arose.

Irgil's and Zvobgo's interviews went a little differently. Irgil's Syrian refugee interviewees in Turkey were native Arabic speakers, and Zvobgo's interviewees in Guatemala were native Spanish speakers. Both Irgil and Zvobgo worked with research assistants. In Irgil's case, her assistant was a Syrian man, who was outside of the area. Meanwhile, Zvobgo's assistant was an undergraduate from her home institution with a Spanish language background. Irgil and Zvobgo began preparing their assistants a couple of months before entering the field, over Skype for Irgil and in-person for Zvobgo. They offered their assistants readings and other resources to provide them with the necessary background to work well. Both Irgil and Zvobgo's research assistants joined them in the interviews and actually did most of the speaking, introducing the principal investigator, explaining the research, and then asking the questions. In Zvobgo's case, interviewee responses were relayed via a professional interpreter whom she had also hired. After every interview, Irgil and Zvobgo and their respective assistants discussed the answers of the interviewees, potential improvements in phrasing, and elaborated on their hand-written interview notes. As a backup, Zvobgo, with the consent of her respondents, had accompanying audio recordings.

Researchers may carry out fieldwork in a country that is considerably less safe than what they are used to, a setting affected by conflict violence or high crime rates, for instance. Feelings of insecurity can be compounded by linguistic barriers, cultural particularities, and being far away from friends and family. Insecurity is also often gendered, differentially affecting women and raising the specter of unwanted sexual advances, street harassment, or even sexual assault ( Gifford and Hall-Clifford 2008 ; Mügge 2013 ). In a recent survey of Political Science graduate students in the United States, about half of those who had done fieldwork internationally reported having encountered safety issues in the field, (54 percent female, 47 percent male), and only 21 percent agreed that their Ph.D. programs had prepared them to carry out their fieldwork safely ( Schwartz and Cronin-Furman 2020 , 8–9).

Preventative measures scholars may adopt in an unsafe context may involve, at their most fundamental, adjustments to everyday routines and habits, restricting one's movements temporally and spatially. Reliance on gatekeepers may also necessitate adopting new strategies, such as a less vehement and cold rejection of unwanted sexual advances than one ordinarily would exhibit, as Mügge (2013) illustratively discusses. At the same time, a competitive academic job market, imperatives to collect novel and useful data, and harmful discourses surrounding dangerous fieldwork also, problematically, shape incentives for junior researchers to relax their own standards of what constitutes acceptable risk ( Gallien 2021 ).

Others have carefully collected a range of safety precautions that field researchers in fragile or conflict-affected settings may take before and during fieldwork ( Hilhorst et al. 2016 ). Therefore, we are more concise in our discussion of recommendations, focusing on the specific situations of graduate students. Apart from ensuring that supervisors and university administrators have the researcher's contact information in the field (and possibly also that of a local contact person), researchers can register with their country's embassy or foreign office and any crisis monitoring and prevention systems it has in place. That way, they will be informed of any possible unfolding emergencies and the authorities have a record of them being in the country.

It may also be advisable to set up more individualized safety protocols with one or two trusted individuals, such as friends, supervisors, or colleagues at home or in the fieldwork setting itself. The latter option makes sense in particular if one has an official affiliation with a local institution for the duration of the fieldwork, which is often advisable. Still, we would also recommend establishing relationships with local researchers in the absence of a formal affiliation. To keep others informed of her whereabouts, Kreft, for instance, made arrangements with her supervisors to be in touch via email at regular intervals to report on progress and wellbeing. This kept her supervisors in the loop, while an interruption in communication would have alerted them early if something were wrong. In addition, she announced planned trips to other parts of the country and granted her supervisors and a colleague at her home institution emergency reading access to her digital calendar. To most of her interviews, she was moreover accompanied by her local research assistant/translator. If the nature of the research, ethical considerations, and the safety situation allow, it might also be possible to bring a local friend along to interviews as an “assistant,” purely for safety reasons. This option needs to be carefully considered already in the planning stage and should, particularly in settings of fragility or if carrying out research on politically exposed individuals, be noted in any ethical and institutional review processes where these are required. Adequate compensation for such an assistant should be ensured. It may also be advisable to put in place an emergency plan, that is, choose emergency contacts back home and “in the field,” know whom to contact if something happens, and know how to get to the nearest hospital or clinic.

We would be remiss if we did not mention that, when in an unfamiliar context, one's safety radar may be misguided, so it is essential to listen to people who know the context. For example, locals can give advice on which means of transport are safe and which are not, a question that is of the utmost importance when traveling to appointments. For example, Kreft was warned that in Colombia regular taxis are often unsafe, especially if waved down in the streets, and that to get to her interviews safely, she should rely on a ride-share service. In one instance, a Colombian friend suggested that when there was no alternative to a regular taxi, Kreft should book through the app and share the order details, including the taxi registration number or license plate, with a friend. Likewise, sharing one's cell phone location with a trusted friend while traveling or when one feels unsafe may be a viable option. Finally, it is prudent to heed the safety recommendations and travel advisories provided by state authorities and embassies to determine when and where it is safe to travel. Especially if researchers have a responsibility not only for themselves but also for research assistants and research participants, safety must be a top priority.

This does not mean that a researcher should be careless in a context they know either. Of course, conducting fieldwork in a context that is known to the researcher offers many advantages. However, one should be prepared to encounter unwanted events too. For instance, Irgil has conducted fieldwork in her country of origin in a city she knows very well. Therefore, access to the site, moving around the site, and blending in has not been a problem; she also has the advantage of speaking the native language. Yet, she took notes of the streets she walked in, as she often returned from the field site after dark and thought she might get confused after a tiring day. She also established a closer relationship with two or three store owners in different parts of the field site if she needed something urgent, like running out of battery. Above all, one should always be aware of one's surroundings and use common sense. If something feels unsafe, chances are it is.

Fieldwork may negatively affect the researcher's mental health and mental wellbeing regardless of where one's “field” is, whether related to concerns about crime and insecurity, linguistic barriers, social isolation, or the practicalities of identifying, contacting and interviewing research participants. Coping with these different sources of stress can be both mentally and physically exhausting. Then there are the things you may hear, see and learn during the research itself, such as gruesome accounts of violence and suffering conveyed in interviews or archival documents one peruses. Kreft and Zvobgo have spoken with women victims of conflict-related sexual violence, who sometimes displayed strong emotions of pain and anger during the interviews. Likewise, Irgil and Willis have spoken with members of other vulnerable populations such as refugees and former sex workers ( Willis 2020 ).

Prior accounts ( Wood 2006 ; Loyle and Simoni 2017 ; Skjelsbæk 2018 ; Hummel and El Kurd 2020 ; Williamson et al. 2020 ; Schulz and Kreft 2021 ) show that it is natural for sensitive research and fieldwork challenges to affect or even (vicariously) traumatize the researcher. By removing researchers from their regular routines and support networks, fieldwork may also exacerbate existing mental health conditions ( Hummel and El Kurd 2020 ). Nonetheless, mental wellbeing is rarely incorporated into fieldwork courses and guidelines, where these exist at all. But even if you know to anticipate some sort of reaction, you rarely know what that reaction will be until you experience it. When researching sensitive or difficult topics, for example, reactions can include sadness, frustration, anger, fear, helplessness, and flashbacks to personal experiences of violence ( Williamson et al. 2020 ). For example, Kreft responded with episodic feelings of depression and both mental and physical exhaustion. But curiously, these reactions emerged most strongly after she had returned from fieldwork and in particular as she spent extended periods analyzing her interview data, reliving some of the more emotional scenes during the interviews and being confronted with accounts of (sexual) violence against women in a concentrated fashion. This is a crucial reminder that fieldwork does not end when one returns home; the after-effects may linger. Likewise, Zvobgo was physically and mentally drained upon her return from the field. Both Kreft and Zvobgo were unable to concentrate for long periods of time and experienced lower-than-normal levels of productivity for weeks afterward, patterns that formal and informal conversations with other scholars confirm to be common ( Schulz and Kreft 2021 ). Furthermore, the boundaries between “field” and “home” are blurred when conducting remote fieldwork ( Howlett 2021 , 11).

Nor are these adverse reactions limited to cases where the researcher has carried out the interviews themselves. Accounts of violence, pain, and suffering transported in reports, secondary literature, or other sources can evoke similar emotional stress, as Kreft experienced when engaging in a concentrated fashion with additional accounts of conflict-related sexual violence in Colombia and with the feminist literature on sexual and gender-based violence in the comfort of her Swedish office. This could also be applicable to Irgil's fieldwork as she interviewed refugees whose traumas have come out during the interviews or recall specific events triggered by the questions. Likewise, Lee has reviewed primary and secondary materials on North Korean defectors in the national archives and these materials contain violent, intense, emotional narratives.

Fortunately, there are several strategies to cope with and manage such adverse consequences. In a candid and insightful piece, other researchers have discussed the usefulness of distractions, sharing with colleagues, counseling, exercise, and, probably less advisable in the long term, comfort eating and drinking ( Williamson et al. 2020 ; see also Loyle and Simoni 2017 ; Hummel and El Kurd 2020 ). Our experiences largely tally with their observations. In this section, we explore some of these in more detail.

First, in the face of adverse consequences on your mental wellbeing, whether in the field or after your return, it is essential to be patient and generous with yourself. Negative effects on the researcher's mental wellbeing can hit in unexpected ways and at unexpected times. Even if you think that certain reactions are disproportionate or unwarranted at that specific moment, they may simply have been building up over a long time. They are legitimate. Second, the importance of taking breaks and finding distractions, whether that is exercise, socializing with friends, reading a good book, or watching a new series, cannot be overstated. It is easy to fall into a mode of thinking that you constantly have to be productive while you are “in the field,” to maximize your time. But as with all other areas in life, balance is key and rest is necessary. Taking your mind off your research and the research questions you puzzle over is also a good way to more fully soak up and appreciate the context in which you find yourself, in the case of in-person fieldwork, and about which you ultimately write.

Third, we cannot stress enough the importance of investing in social relations. Before going on fieldwork, researchers may want to consult others who have done it before them. Try to find (junior) scholars who have done fieldwork on similar kinds of topics or in the same country or countries you are planning to visit. Utilizing colleagues’ contacts and forging connections using social media are valuable strategies to expand your networks (in fact, this very paper is the result of a social media conversation and several of the authors have never met in person). Having been in the same situation before, most field researchers are, in our experience, generous with their time and advice. Before embarking on her first trip to Colombia, Kreft contacted other researchers in her immediate and extended network and received useful advice on questions such as how to move around Bogotá, whom to speak to, and how to find a research assistant. After completing her fieldwork, she has passed on her experiences to others who contacted her before their first fieldwork trip. Informal networks are, in the absence of more formalized fieldwork preparation, your best friend.

In the field, seeking the company of locals and of other researchers who are also doing fieldwork alleviates anxiety and makes fieldwork more enjoyable. Exchanging experiences, advice and potential interviewee contacts with peers can be extremely beneficial and make the many challenges inherent in fieldwork (on difficult topics) seem more manageable. While researchers conducting remote fieldwork may be physically isolated from other researchers, even connecting with others doing remote fieldwork may be comforting. And even when there are no precise solutions to be found, it is heartening or even cathartic to meet others who are in the same boat and with whom you can talk through your experiences. When Kreft shared some of her fieldwork-related struggles with another researcher she had just met in Bogotá and realized that they were encountering very similar challenges, it was like a weight was lifted off her shoulders. Similarly, peer support can help with readjustment after the fieldwork trip, even if it serves only to reassure you that a post-fieldwork dip in productivity and mental wellbeing is entirely natural. Bear in mind that certain challenges are part of the fieldwork experience and that they do not result from inadequacy on the part of the researcher.

Finally, we would like to stress a point made by Inger Skjelsbæk (2018 , 509) and which has not received sufficient attention: as a discipline, we need to take the question of researcher mental wellbeing more seriously—not only in graduate education, fieldwork preparation, and at conferences, but also in reflecting on how it affects the research process itself: “When strong emotions arise, through reading about, coding, or talking to people who have been impacted by [conflict-related sexual violence] (as victims or perpetrators), it may create a feeling of being unprofessional, nonscientific, and too subjective.”

We contend that this is a challenge not only for research on sensitive issues but also for fieldwork more generally. To what extent is it possible, and desirable, to uphold the image of the objective researcher during fieldwork, when we are at our foundation human beings? And going even further, how do the (anticipated) effects of our research on our wellbeing, and the safety precautions we take ( Gifford and Hall-Clifford 2008 ), affect the kinds of questions we ask, the kinds of places we visit and with whom we speak? How do they affect the methods we use and how we interpret our findings? An honest discussion of affective responses to our research in methods sections seems utopian, as emotionality in the research process continues to be silenced and relegated to the personal, often in gendered ways, which in turn is considered unconnected to the objective and scientific research process ( Jamar and Chappuis 2016 ). But as Gifford and Hall-Clifford (2008 , 26) aptly put it: “Graduate education should acknowledge the reality that fieldwork is scholarly but also intimately personal,” and we contend that the two shape each other. Therefore, we encourage political science as a discipline to reflect on researcher wellbeing and affective responses to fieldwork more carefully, and we see the need for methods courses that embrace a more holistic notion of the subjectivity of the researcher.

Interacting with people in the field is one of the most challenging yet rewarding parts of the work that we do, especially in comparison to impersonal, often tedious wrangling and analysis of quantitative data. Field researchers often make personal connections with their interviewees. Consequently, maintaining boundaries can be a bit tricky. Here, we recommend being honest with everyone with whom you interact without overstating the abilities of a researcher. This appears as a challenge in the field, particularly when you empathize with people and when they share profound parts of their lives with you for your research in addition to being “human subjects” ( Fujii 2012 ). For instance, when Irgil interviewed native citizens about the changes in their neighborhood following the arrival of Syrian refugees, many interviewees questioned what she would offer them in return for their participation. Irgil responded that her primary contribution would be her published work. She also noted, however, that academic papers can take a year, sometimes longer, to go through the peer-reviewed process and, once published, many studies have a limited audience. The Syrian refugees posed similar questions. Irgil responded not only with honesty but also, given this population's vulnerable status, she provided them contact information for NGOs with which they could connect if they needed help or answers to specific questions.

For her part, Zvobgo was very upfront with her interviewees about her role as a researcher: she recognized that she is not someone who is on the frontlines of the fight for human rights and transitional justice like they are. All she could/can do is use her platform to amplify their stories, bringing attention to their vital work through her future peer-reviewed publications. She also committed to sending them copies of the work, as electronic journal articles are often inaccessible due to paywalls and university press books are very expensive, especially for nonprofits. Interviewees were very receptive; some were even moved by the degree of self-awareness and the commitment to do right by them. In some cases, this prompted them to share even more, because they knew that the researcher was really there to listen and learn. This is something that junior scholars, and all scholars really, should always remember. We enter the field to be taught. Likewise, Kreft circulated among her interviewees Spanish-language versions of an academic article and a policy brief based on the fieldwork she had carried out in Colombia.

As researchers from the Global North, we recognize a possible power differential between us and our research subjects, and certainly an imbalance in power between the countries where we have been trained and some of the countries where we have done and continue to do field research, particularly in politically dynamic contexts ( Knott 2019 ). This is why we are so concerned with being open and transparent with everyone with whom we come into contact in the field and why we are committed to giving back to those who so generously lend us their time and knowledge. Knott (2019 , 148) summarizes this as “Reflexive openness is a form of transparency that is methodologically and ethically superior to providing access to data in its raw form, at least for qualitative data.”

We also recognize that academics, including in the social sciences and especially those hailing from countries in the Global North, have a long and troubled history of exploiting their power over others for the sake of their research—including failing to be upfront about their research goals, misrepresenting the on-the-ground realities of their field research sites (including remote fieldwork), and publishing essentializing, paternalistic, and damaging views and analyses of the people there. No one should build their career on the backs of others, least of all in a field concerned with the possession and exercise of power. Thus, it is highly crucial to acknowledge the power hierarchies between the researcher and the interviewees, and to reflect on them both in the field and beyond the field upon return.

A major challenge to conducting fieldwork is when researchers’ carefully planned designs do not go as planned due to unforeseen events outside of our control, such as pandemics, natural disasters, deteriorating security situations in the field, or even the researcher falling ill. As the Covid-19 pandemic has made painfully clear, researchers may face situations where in-person research is simply not possible. In some cases, researchers may be barred entry to their fieldwork site; in others, the ethical implications of entering the field greatly outweigh the importance of fieldwork. Such barriers to conducting in-person research require us to reconsider conventional notions of what constitutes fieldwork. Researchers may need to shift their data collection methods, for example, conducting interviews remotely instead of in person. Even while researchers are in the field, they may still need to carry out part of their interviews or surveys virtually or by phone. For example, Kreft (2020) carried out a small number of interviews remotely while she was based in Bogotá, because some of the women's civil society activists with whom she intended to speak were based in parts of the country that were difficult and/or dangerous to access.

Remote field research, which we define as the collection of data over the internet or over the phone where in-person fieldwork is not possible due to security, health or other risks, comes with its own sets of challenges. For one, there may be certain populations that researchers cannot reach remotely due to a lack of internet connectivity or technology such as cellphones and computers. In such instances, there will be a sampling bias toward individuals and groups that do have these resources, a point worth noting when scholars interpret their research findings. In the case of virtual research, the risk of online surveillance, hacking, or wiretapping may also produce reluctance on the part of interviewees to discuss sensitive issues that may compromise their safety. Researchers need to carefully consider how the use of digital technology may increase the risk to research participants and what changes to the research design and any interview guides this necessitates. In general, it is imperative that researchers reflect on how they can ethically use digital technology in their fieldwork ( Van Baalen 2018 ). Remote interviews may also be challenging to arrange for researchers who have not made connections in person with people in their community of interest.

Some of the serendipitous happenings we discussed earlier may also be less likely and snowball sampling more difficult. For example, in phone or virtual interviews, it is harder to build good rapport and trust with interviewees as compared to face-to-face interviews. Accordingly, researchers should be more careful in communicating with interviewees and creating a comfortable interview environment. Especially when dealing with sensitive topics, researchers may have to make several phone calls and sometimes have to open themselves to establishing trust with interviewees. Also, researchers must be careful in protecting interviewees in phone or virtual interviews when they deal with sensitive topics of countries interviewees reside in.

The inability to physically visit one's community of interest may also encourage scholars to critically reflect on how much time in the field is essential to completing their research and to consider creative, alternative means for accessing information to complete their projects. While data collection techniques such as face-to-face interviews and archival work in the field may be ideal in normal times, there exist other data sources that can provide comparably useful information. For example, in her research on the role of framing in the United States base politics, Willis found that social media accounts and websites yielded information useful to her project. Many archives across the world have also been digitized. Researchers may also consider crowdsourcing data from the field among their networks, as fellow academics tend to collect much more data in the field than they ever use in their published works. They may also elect to hire someone, perhaps a graduate student, in a city or a country where they cannot travel and have the individual access, scan, and send archival materials. This final suggestion may prove generally useful to researchers with limited time and financial resources.

Remote qualitative data collection techniques, while they will likely never be “the gold-standard,” also pose several advantages. These techniques may help researchers avoid some of the issues mentioned previously. Remote interviews, for example, are less time-consuming in terms of travel to the interview site ( Archibald et al. 2019 ). The implication is that researchers may have less fatigue from conducting interviews and/or may be able to conduct more interviews. For example, while Willis had little energy to do anything else after an in-person interview (or two) in a given day, she had much more energy after completing remote interviews. Second, remote fieldwork also helps researchers avoid potentially dangerous situations in the field mentioned previously. Lastly, remote fieldwork generally presents fewer financial barriers than in-person research ( Archibald et al. 2019 ). In that sense, considering remote qualitative data collection, a type of “fieldwork” may make fieldwork more accessible to a greater number of scholars.

Many of the substantive, methodological and practical challenges that arise during fieldwork can be anticipated. Proper preparation can help you hit the ground running once you enter your fieldwork destination, whether in-person or virtually. Nonetheless, there is no such thing as being perfectly prepared for the field. Some things will simply be beyond your control, and especially as a newcomer to field research, and you should be prepared for things to not go as planned. New questions will arise, interview participants may cancel appointments, and you might not get the answers you expected. Be ready to make adjustments to research plans, interview guides, or questionnaires. And, be mindful of your affective reactions to the overall fieldwork situation and be gentle with yourself.

We recommend approaching fieldwork as a learning experience as much as, or perhaps even more than, a data collection effort. This also applies to your research topic. While it is prudent always to exercise a healthy amount of skepticism about what people tell you and why, the participants in your research will likely have unique perspectives and knowledge that will challenge yours. Be an attentive listener and remember that they are experts of their own experiences.

We encourage more institutions to offer courses that cover field research preparation and planning, practical advice on safety and wellbeing, and discussion of ethics. Specifically, we align with Schwartz and Cronin-Furman's (2020 , 3) contention “that treating fieldwork preparation as the methodology will improve individual scholars’ experiences and research.” In this article, we outline a set of issue areas in which we think formal preparation is necessary, but we note that our discussion is by no means exhaustive. Formal fieldwork preparation should also extend beyond what we have covered in this article, such as issues of data security and preparing for nonqualitative fieldwork methods. We also note that field research is one area that has yet to be comprehensively addressed in conversations on diversity and equity in the political science discipline and the broader academic profession. In a recent article, Brielle Harbin (2021) begins to fill this gap by sharing her experiences conducting in-person election surveys as a Black woman in a conservative and predominantly white region of the United States and the challenges that she encountered. Beyond race and gender, citizenship, immigration status, one's Ph.D. institution and distance to the field also affect who is able to do what type of field research, where, and for how long. Future research should explore these and related questions in greater detail because limits on who is able to conduct field research constrict the sociological imagination of our field.

While Emmons and Moravcsik (2020) focus on leading Political Science Ph.D. programs in the United States, these trends likely obtain, both in lower ranked institutions in the broader United States as well as in graduate education throughout North America and Europe.

As all the authors have carried out qualitative fieldwork, this is the primary focus of this guide. This does not, however, mean that we exclude quantitative or experimental data collection from our definition of fieldwork.

There is great variation in graduate students’ financial situations, even in the Global North. For example, while higher education is tax-funded in most countries in Europe and Ph.D. students in countries such as Sweden, Norway, Denmark, the Netherlands, and Switzerland receive a comparatively generous full-time salary, healthcare and contributions to pension schemes, Ph.D. programs in other contexts like the United States and the United Kingdom have (high) enrollment fees and rely on scholarships, stipends, or departmental duties like teaching to (partially) offset these, while again others, such as Germany, are commonly financed by part-time (50 percent) employment at the university with tasks substantively unrelated to the dissertation. These different preconditions leave many Ph.D. students struggling financially and even incurring debt, while others are in a more comfortable financial position. Likewise, Ph.D. programs around the globe differ in structure, such as required coursework, duration and supervision relationships. Naturally, all of these factors have a bearing on the extent to which fieldwork is feasible. We acknowledge unequal preconditions across institutions and contexts, and trust that those Ph.D. students interested in pursuing fieldwork are best able to assess the structural and institutional context in which they operate and what this implies for how, when, and how long to carry out fieldwork.

In our experience, this is not only the general cycle for graduate students in North America, but also in Europe and likely elsewhere.

For helpful advice and feedback on earlier drafts, we wish to thank the editors and reviewers at International Studies Review , and Cassandra Emmons. We are also grateful to our interlocuters in Argentina, Canada, Colombia, Germany, Guatemala, Japan, Kenya, Norway, the Philippines, Sierra Leone, South Korea, Spain, Sweden, Turkey, the United Kingdom, and the United States, without whom this reflection on fieldwork would not have been possible. All authors contributed equally to this manuscript.

This material is based upon work supported by the Forskraftstiftelsen Theodor Adelswärds Minne, Knut and Alice Wallenberg Foundation(KAW 2013.0178), National Science Foundation Graduate Research Fellowship Program(DGE-1418060), Southeast Asia Research Group (Pre-Dissertation Fellowship), University at Albany (Initiatives for Women and the Benevolent Association), University of Missouri (John D. Bies International Travel Award Program and Kinder Institute on Constitutional Democracy), University of Southern California (Provost Fellowship in the Social Sciences), Vetenskapsrådet(Diarienummer 2019-06298), Wilhelm och Martina Lundgrens Vetenskapsfond(2016-1102; 2018-2272), and William & Mary (Global Research Institute Pre-doctoral Fellowship).

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Child Care and Early Education Research Connections

Field research.

Field research is a qualitative method of research concerned with understanding and interpreting the social interactions of groups of people, communities, and society by observing and interacting with people in their natural settings. The methods of field research include: direct observation, participant observation, and qualitative interviews. Each of these methods is described here. Terms related to these and other topics in field research are defined in the  Research Glossary .

Direct Observation

Participant observation, qualitative interviews.

Direct observation  is a method of research where the researcher watches and records the activities of individuals or groups engaged in their daily activities. The observations may be unstructured or structured. Unstructured observations involve the researcher observing people and events and recording his/her observations as field notes. Observations are recorded holistically and without the aid of a predetermined guide or protocol. Structured observation, on the other hand, is a technique where a researcher observes people and events using a guide or set protocol that has been developed ahead of time.

Other features of direct observation include:

  • The observer does not actively engage the subjects of the study in conversations or interviews, but instead strives to be unobtrusive and detached from the setting.
  • Data collected through direct observation may include field notes, checklists and rating scales, documents, and photographs or video images.
  • Direct observation is not necessarily an alternative to other types of field methods, such as participant observation or qualitative interviews. Rather, it may be an initial approach to understanding a setting, a group of individuals, or forms of behavior prior to interacting with members or developing interview protocols.
  • Direct observation as a research method is most appropriate in open, public settings where anyone has a right to be or congregate. Conducting direct observation in private or closed settings -- without the knowledge or consent of members -- is more likely to raise ethical concerns.

Participant observation  is a field research method whereby the researcher develops an understanding of a group or setting by taking part in the everyday routines and rituals alongside its members. It was originally developed in the early 20th century by anthropologists researching native societies in developing countries. It is now the principal research method used by ethnographers -- specialists within the fields of anthropology and sociology who focus on recording the details of social life occurring in a setting, community, group, or society. The ethnographer, who often lives among the members for months or years, attempts to build trusting relationships so that he or she becomes part of the social setting. As the ethnographer gains the confidence and trust of the members, many will speak and behave in a natural manner in the presence of the ethnographer.

Data from participant observation studies can take several forms:

  • Field notes are the primary type of data. The researcher takes notes of his/her observations and experiences and later develops them into detailed, formal field notes.
  • Frequently, researchers keep a diary, which is often a more intimate, informal record of the happenings within the setting.
  • The practice of participant observation, with its emphasis on developing relationships with members, often leads to both informal, conversational interviews and more formal, in-depth interviews. The data from these interviews can become part of field notes or may consist of separate interview transcripts.

There are a number of advantages and disadvantages to direct and participant observation studies. Here is a list of some of both. While the advantages and disadvantages apply to both types of studies, their impact and importance may not be the same across the two. For example, researchers engaged in both types of observation will develop a rich, deep understanding of the members of the group and the setting in which social interactions occur, but researchers engaged in participant observation research may gain an even deep understanding. And, participant observers have a greater chance of witnessing a wider range of behaviors and events than those engaged in direct observation.

Advantages of observation studies (observational research):

  • Provide contextual data on settings, interactions, or individuals.
  • A useful tool for generating hypotheses for further study.
  • Source of data on events and phenomena that do not involve verbal interactions (e.g., mother-child nonverbal interactions and contact, physical settings where interactions occur).
  • The researcher develops a rich, deep understanding of a setting and of the members within the setting.

Disadvantages of observation studies:

  • Behaviors observed during direct observation may be unusual or atypical.
  • Significant interactions and events may take place when observer is not present.
  • Certain topics do not necessarily lend themselves to observation (e.g., attitudes, emotions, affection).
  • Reliability of observations can be problematic, especially when multiple observers are involved.
  • The researcher must devote a large amount of time (and resources).
  • The researcher's objectivity may decline as he or she spends more time among the members of the group.
  • The researcher may be faced with a dilemma of choosing between revealing and not revealing his or her identity as a researcher to the members of the group. If he or she introduces him/herself as a researcher, the members may behave differently than if they assume that he or she is just another participant. On the other hand, if the researcher does not, they may feel betrayed upon learning about the research.

Qualitative interviews  are a type of field research method that elicits information and data by directly asking questions of individuals. There are three primary types of qualitative interviews: informal (conversational), semi-structured, and standardized, open-ended. Each is described briefly below along with advantages and disadvantages.

Informal (Conversational) Interviews

  • Frequently occur during participant observation or following direct observation.
  • The researcher begins by conversing with a member of the group of interest. As the conversation unfolds, the researcher formulates specific questions, often spontaneously, and begins asking them informally.
  • Appropriate when the researcher wants maximum flexibility to pursue topics and ideas as they emerge during the exchange

Advantages of informal interviewing:

  • Allows the researcher to be responsive to individual differences and to capture emerging information.
  • Information that is obtained is not constrained by a predetermined set of questions and/or response categories.
  • Permits researcher to delve deeper into a topic and what key terms and constructs mean to study participants.

Disadvantages of informal interviewing:

  • May generate less systematic data, which is difficult to classify and analyze.
  • The researcher might not be able to capture everything that the interviewee is saying and therefore there is potential for important nuance or information to be lost. For example, the researcher might not have a tape recorder at that moment due to the spontaneous nature of these interviews.
  • Quality of the information obtained depends on skills of the interviewer.

Semi-Structured Interviews

  • Prior to the interview, a list of predetermined questions or probes, also known as an interview guide, is developed so that each interviewee will respond to a similar series of questions and topics.
  • Questions are generally open-ended to elicit as much detail and meaning from the interviewee as possible.
  • The researcher is free to pursue and probe other topics as they emerge during the interview.

Advantages of semi-structured interviewing:

  • Systematically captures data across interviewees.
  • The researcher is able to rephrase or explain questions to the interviewee to ensure that everyone understands the questions the same way and probe (follow-up) a response so that an individual's responses are fully explored.
  • Interviewee is allowed the freedom to express his or her views in their own words.

Disadvantages of semi-structured interviewing:

  • Does not offer as much flexibility to respond to new topics that unfold during the interview as the informal interview.
  • Responses to questions that have been asked in slightly different ways can be more difficult to compare and analyze.
  • Interviewer may unconsciously send signals about the types of answers that are expected.

Standardized, Open-Ended Interviews

  • Similar to a survey since questions are carefully scripted and written prior to the interview, which serves to minimize variability in question wording and the way questions are asked.
  • The researcher asks a uniform series of questions in the same order to each interviewee.
  • The questions are open-ended to capture more details and individual differences across interviewees.
  • Particularly appropriate for qualitative studies involving multiple interviewers.

Advantages of standardized interviewing:

  • All questions are asked the same to each study participant. Data are comparable across interviewees.
  • Reduces interviewer effects when several interviewers are used.
  • Standardization helps to facilitate the processing and analysis of the data.

Disadvantages of standardized interviewing:

  • Does not offer as much flexibility to respond to and probe new topics that unfold during the interview.
  • Standardized wording of questions may limit the responses of those being interviewed.

Both standardized and semi-structured interviews involve formally recruiting participants and are typically tape-recorded. The researcher should begin with obtaining informed consent from the interviewee prior to starting the interview. Additionally, the researcher may write a separate field note to describe the interviewee's reactions to the interview, or events that occurred before or after the interview.

See the following for additional information about field research and qualitative research methods.

  • Ethnography, Observational Research and Narrative Inquiry  (PDF)
  • An Introduction to Qualitative Research  (PDF)

The content on this page was prepared by Jerry West. It was last updated March 2019.

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Research Article

Visualizing a field of research: A methodology of systematic scientometric reviews

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Information Science, College of Computing and Informatics, Drexel University, Philadelphia, Pennsylvania, United States of America, Department of Information Science, Yonsei University, Seoul, Republic of Korea

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Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing

Affiliation Department of Information Science, Yonsei University, Seoul, Republic of Korea

  • Chaomei Chen, 

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  • Published: October 31, 2019
  • https://doi.org/10.1371/journal.pone.0223994
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Table 1

Systematic scientometric reviews, empowered by computational and visual analytic approaches, offer opportunities to improve the timeliness, accessibility, and reproducibility of studies of the literature of a field of research. On the other hand, effectively and adequately identifying the most representative body of scholarly publications as the basis of subsequent analyses remains a common bottleneck in the current practice. What can we do to reduce the risk of missing something potentially significant? How can we compare different search strategies in terms of the relevance and specificity of topical areas covered? In this study, we introduce a flexible and generic methodology based on a significant extension of the general conceptual framework of citation indexing for delineating the literature of a research field. The method, through cascading citation expansion, provides a practical connection between studies of science from local and global perspectives. We demonstrate an application of the methodology to the research of literature-based discovery (LBD) and compare five datasets constructed based on three use scenarios and corresponding retrieval strategies, namely a query-based lexical search (one dataset), forward expansions starting from a groundbreaking article of LBD (two datasets), and backward expansions starting from a recently published review article by a prominent expert in LBD (two datasets). We particularly discuss the relevance of areas captured by expansion processes with reference to the query-based scientometric visualization. The method used in this study for comparing bibliometric datasets is applicable to comparative studies of search strategies.

Citation: Chen C, Song M (2019) Visualizing a field of research: A methodology of systematic scientometric reviews. PLoS ONE 14(10): e0223994. https://doi.org/10.1371/journal.pone.0223994

Editor: Wolfgang Glanzel, KU Leuven, BELGIUM

Received: June 12, 2019; Accepted: October 2, 2019; Published: October 31, 2019

Copyright: © 2019 Chen, Song. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript, Supporting Information files, and on Figshare: https://doi.org/10.6084/m9.figshare.9939773.v1 .

Funding: CC acknowledges the support of the SciSIP Program of the National Science Foundation (Award #1633286), the support of Microsoft Azure Sponsorship. Data sourced from Dimensions, an inter-linked research information system provided by Digital Science ( https://www.dimensions.ai ). MS acknowledges the support of the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2018S1A3A2075114) and partial support from the Yonsei University Research Fund of 2019-22-0066. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Systematic reviews play a critical role in scholarly communication [ 1 ]. Systematic reviews typically synthesize findings from original research in a field of study, assess the degree of consensus or the lack of it concerning the state of the art in the field, and identify challenges and future directions. For newcomers to a field of study, a timely and comprehensive systematic review can provide a valuable overview of the intellectual landscape and guide new researchers to pursue their research effectively. For experienced and active researchers, systematic reviews can be instrumental in keeping their knowledge of the field up to date, especially when involving areas that are potentially relevant but fall outside the immediate topic of one’s interest. Researchers have also studied scientific literature to uncover potentially significant but currently overlooked connections between disparate bodies of literature, notably, as demonstrated in the research of literature-based discovery (LBD) [ 2 – 7 ].

A fast-growing trend is the increase of systematic reviews conducted with the assistance of science mapping tools [ 8 ]. A science mapping tool typically takes a set of bibliographic records of a research field and generates an overview of the underlying knowledge domain, e.g. as with CiteSpace [ 9 , 10 ] and VOSviewer [ 11 ]. For example, systematic reviews facilitated by using CiteSpace include a diverse range of research areas such as regenerative medicine [ 12 ], natural disaster research [ 13 ], greenhouse gas emission [ 14 ], and identifying disruptive innovation and emerging technology [ 15 ]. Similarly, VOSviewer was used in reviews of topics such as citizen science [ 16 ] and climate change [ 17 ]. A scientometric overview of a field of research provides a valuable source of input to conducting systematic reviews, especially in situations when relevant and up-to-date systematic reviews may not be readily available or accessible. The quality of the input data therefore is critical to the overall quality of subsequent analyses and reviews. This practical issue is particularly significant when we need to select subsets of articles from a large pool of available data or when we want to limit the scope of a study to specific disciplines as opposed to open to all disciplines.

Considerations concerning the scope of selection have been discussed in the literature in terms of local, global, and hybrid approaches [ 18 ]. Global maps of science by definition provide a comprehensive coverage of all scientific disciplines [ 19 ], whereas local maps typically focus on selected areas of interest. Hybrid maps may use a global map as a base map and superimpose local maps as overlays [ 20 ]. Generating global maps of science requires a substantial array of resources that are not commonly accessible to most of researchers, whereas resources required for generating local maps are more reachable, especially with the increasing accessible science mapping tools. Global maps are valuable as they provide a broad context of a specific research interest. In practice, generating global maps is resource consuming and demanding, for example, requiring direct access to large data sources that are only accessible to a small number of researchers. Consequently, global maps may not be updated as frequently as needed by end users. For example, a significant update of a global model may not be undertaken for 5 years [ 20 ]. The underlying structure of a research field is often subject to a variety of changes as the scientific literature grows over time [ 21 , 22 ]. Therefore, it is reasonable to question whether an existing global model created a few years ago remains a valid representation of the underlying structure for specific analytic tasks in hand, although the answer may differ at different levels of granularity.

In contrast, approaches of localism face different challenges. For example, a common challenge for individual researchers of science mapping tools such as CiteSpace [ 9 , 10 ], VOSviewer [ 11 ], and HistCite [ 23 ] is to select a representative subset of articles from tens of millions or more of scholarly publications in the Web of Science, Scopus, Dimensions, and/or the Lens. Researchers have used lexical search, citation expansion, and hybrid strategies [ 24 – 26 ].

Complex and sophisticated queries are typically constructed with the input from domain experts and iteratively refined as demonstrated in several in-depths studies [ 25 – 27 ]. We refer such strategies as query-based approaches in this article. The role of relevant and sufficient domain expertise in such approaches is critical, which may lead to a double-edged sword. As we will see in this article, using computational approaches to reduce the cognitive burden from domain experts is a noticeable area of research. Following the principles of LBD to emphasize potentially valuable but currently overlooked connections in scientific literature, it is conceivable that there are undiscovered connections where even domain experts’ input may be limited. Reducing the initial reliance on domain expertise can increase the applicability of query-based strategies.

In this article, we propose a flexible computational approach to the construction of a representative dataset of scholarly publications concerning a field of research through iterative citation expansions. Starting from an initial dataset or even a single seed article, the expansion process will automatically expand the initial set by adding articles through citation links in forward, backward, or both directions. This flexibility is particularly valuable in some common scenarios in practice. For example, Swanson’s article on fish oil and Raynaud’s syndrome [ 6 ] is widely considered as a groundbreaking one in LBD research. A researcher may want to retrieve subsequently published articles that are connected to the groundbreaking article through potentially lengthy citation paths. Such expansions are valuable to preserve the continuity of research literature identified across an extensive time span. In contrast, a researcher may come across a recently published review article of LBD and would like to find previously published articles that lead to the state of the knowledge summarized in the review. For example, in 2017, Swanson’s long-term collaborator Smalheiser published an article entitled “Rediscovering Don Swanson: the past, present and future of literature-based discovery” [ 28 ] and researchers may be interested in tracing several generations of relevant articles.

Pragmatically speaking, the ability to expand any set of articles of interest provides a smooth transition across a local-global continuum. The agility of the approach enables us to improve the quality of an input dataset of scientometric studies by refining the context incrementally. We demonstrate the expansion and evaluation of five datasets retrieved by different strategies on literature-based discovery (LBD). The field of LBD is potentially very broad as it is applicable to numerous scientific disciplines. Swanson’s pioneering studies in the 1980s have been a major source of inspiration [ 6 , 7 , 29 , 30 ], followed by a series of studies in collaboration with Smalheiser [ 31 – 33 ], who recently reviewed the past, present, and future of LBD [ 28 ]. Significant developments have also been made by other researchers along this generic framework, for example, [ 34 – 36 ]. It would be challenging to formulate a complex query to capture a diverse range of relevant research activities, some of which may have drifted away from the core literature of LBD.

Judging the relevance of topics is often situational in nature and therefore challenging even with domain expertise. In LBD, the threshold of relevance is by definition lower than other fields because the focus is on undiscovered connections, which in turn leads to a demand for an even higher level of recall for conducting systematic scientometric reviews. Shneider proposed a four-stage model to characterize how a scientific field may evolve, namely, identifying the problem, building tools and instruments, applying tools to the problem, and codifying lessons learned [ 22 , 37 ]. The application stage may also reveal unanticipated problems, which in turn may lead to a new line of research and form a new field of research. The LBD research may continue along the research directions set off by the pioneering studies in LBD. According to Shneider’s four-stage model, new specialties of research may emerge. A systematic review of LBD should open to such new developments as well as the established ones that we are familiar with. What can we gain by using cascading citation expansions? What might we miss if we rely on the simple query-based search strategy alone? What types of biases may be introduced or avoided by cascading expansions as well as by selecting points of departure?

To address these questions, we develop an intuitive visual analytic method to compare multiple search strategies and reveal the strengths and weaknesses of a specific procedure. We compare a total of five datasets in this study, including a dataset from a simple query with phrases of “literature-based discovery” as a baseline reference and four datasets from cascading citation expansions based on two singleton seed article sets using Dimensions as the source of data.

The rest of the article is organized as follows. We characterize existing science mapping approaches in terms globalism and localism, especially their strengths and weaknesses for conducting systematic reviews of relevant literature. We introduce a flexible citation expansion approach–cascading citation expansion–to improve the data quality for conducting systematic scientometric reviews. We first demonstrate what a query-based strategy may reveal about the LBD research, then we combine the five datasets to form a common baseline for comparing the topics covered by the five individual datasets.

Mapping the scientific landscape

Considerations concerning the scope of a study of scientific literature can be characterized as global, local, and hybrid in terms of their intended scope and applications.

Global maps of science aim to represent all scientific disciplines [ 18 ]. Commonly used units of analysis in global maps of science include journals and, to a much less extent, articles. Clusters of journals are typically used to represent disciplines of science.

Global maps of science are valuable for developing an understanding of the structure of scientific knowledge involving all disciplines. Global maps provide a relatively stable representation of the underlying structure of knowledge, which can be rather volatile at finer levels of granularity. Klavans and Boyack compared the structure of 20 global maps of science [ 38 ]. They arrive at a consensus map generated from edges that occur in at least half of the input maps. A stable global representation is suitable to serve the role of an organizing framework to accommodate additional details as overlays [ 19 , 20 , 39 ]. Global maps may reveal insights that may not be possible at a smaller scale. For example, in studies of structural variations caused by scholarly publications, detecting a potentially transformative link in a network representation of the literature relies on the extent to which the contexts of both ends of the link are adequately represented [ 21 ].

On the other hand, the validity of a global map as a base map is likely to decrease over time as subsequent publications may change the underlying knowledge structure considerably [ 21 ]. Given that updating a global map of science is a very resource demanding task and it may be years before a major update becomes available [ 20 ], is the structure shown in a global map still a valid representation at the intended level of granularity? If a global map is updated too often, it may lose its stability value as an organizing framework to sustain overlays. A fundamental question is not necessarily whether the representation is categorically comprehensive, but whether the structural representation as a context for analyzing the literature is adequate and effective.

To localism, the focus is on communicating the structure of a subject matter of interest at the level of scientific inquiry and scholarly communication, including analytic reasoning, hypothesis generation, and argumentation. Many studies of science and systematic scientometric reviews belong to this category. Scientometric studies commonly draw bibliographic data, especially citation data, from long-established sources such as the Web of Science and Scopus. More recent additions include Microsoft Academic Search, Dimensions, and the Lens. Knowledge domain visualization, for example, focuses on knowledge domains as the unit of analysis [ 10 , 40 , 41 ]. The concept of a knowledge domain emphasizes that the boundary of a research field should reflect the underlying structure of knowledge. Relying on organizing structures such as institutions, journals, and even articles along has the risk of missing potentially significant articles.

Query-based search is perhaps by far the most popular strategy for finding articles relevant to a topic of interest or a field of research. A query-based search process typically starts with a list of keywords or phrases provided by an end user. For example, a query of “ literature-based discovery ” OR “ undiscovered public knowledge ” could be a valid starting point to search for articles in the field of LBD. The quality of retrieval is routinely measured in terms of recall and precision. Initial search for systematic reviews tends to give a higher priority to recall than precision.

Formulating a good query is a non-trivial task even for a domain expert because the quality of a query can be affected by several factors, including our current domain knowledge and our motivations of the search [ 24 ]. Furthermore, if our goal is to identify emerging topics and trends in a research field, which is very likely when we conduct a systematic review of the field, then it would be a significant challenge to formulate a complex query effectively in advance. As a result, iteratively refining queries over lessons learned from the performance of previous queries is a common strategy, especially when combined with the input from relevant domain experts [ 25 , 26 ]. Scatter/Gatherer [ 42 ] is an influential early example of iteratively improving the quality of retrieved information based on feedback.

A profound challenge to query-based search is the detection of an implicit semantic connection, or a latent semantic relation. Search systems have utilized additional resources such as WordNet [ 43 ] and domain ontologies [ 44 , 45 ] to enhance users’ original terms with their synonyms and/or closely related concepts. With techniques such as latent semantic indexing [ 46 ] and more recent advances in distributional semantic learning [ 47 ], the relevance of an article to a topic can be established in terms of its distributional properties of language use. The semantic similarity of an article can be detected without an explicit presence of any keywords from users’ original query. This is known as the distributional hypothesis: linguistic items with similar distributions have similar meanings.

Cascading citation expansion

In this article, we conceptualize a unifying framework that accommodates both globalism and localism as special cases on a consistent and continuous spectrum through a combination of query-based search and cascading citation expansion. Under the new conceptualization, the coverage of a local map can grow incrementally as the expansion process may continue as many generations of citation as needed. Thus, the gap between a local map and a global map can be reduced considerably. Table 1 summarizes the major advantages and weaknesses of globalism and localism along with potential benefits that the conceptualized incremental expansion may provide.

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https://doi.org/10.1371/journal.pone.0223994.t001

In this article, we demonstrate the flexibility and extensibility of a incremental expansion approach–cascading citation expansion–by applying this methodology to a few common scenarios in research in relation to a field of research of own interest, i.e. literature-based discovery (LBD).

Citation indexing was originally proposed by Eugene Garfield to tackle the information retrieval problem in the context of scientific literature [ 48 ]. While the nature of a citation may vary widely as many researchers have documented [ 49 ], an instance of a citation from one article to another provides evidence of some potentially significant connections. A unique advantage of a citation-based search method is that it frees us from having to specify a potentially relevant topic in our initial query, which is useful to reduce the risk of missing important relevant topics that we may not be aware of.

We often encounter situations in which we have found a small set of highly relevant articles and yet they are still not fully satisfactory for some reasons. For example, if we were new to a topic of interest and all we have to start with is a systematic review of the topic published many years ago, how can we bring our knowledge up to date? If what we have is a newly published review written by a domain expert, how can we expand the review to find more relevant articles? Another common scenario is when we want to construct a relatively comprehensive survey of a target topic, how can we generate an optimal dataset that is comprehensive enough but also contains the least amount of less relevant articles.

When we encounter these situations, an effective method would enable us to build on what we have found so far and add new articles iteratively. Scatter/Gather [ 42 ] was a dynamic clustering strategy proposed in mid-1990s by allowing users to re-focus on query-specific relevant documents as opposed to query-independent clusters in browsing search results. From the citation indexing point of view, articles that cite any articles in the initial result set are good candidates for further consideration. Thus, an incremental expansion strategy can be built based on these insights to uncover additional relevant articles.

Incremental expansion processes can be performed in parallel or in sequence. When an expansion step is applied to a base set S of articles, articles associated with S through citation links are added to S thus expand the set S. Users may define an inclusion threshold such that the expansion is limited to adding articles with sufficient citations. We refer successive citation expansions as cascading citation expansion [ 50 ]. The concept of cascading citation expansion is intrinsic to the general framework of citation indexing. An expansion process is capable of generating an adequate context for a systematic scientometric review of a research field by incrementally retrieving more and more articles from the literature.

Cascading citation expansion requires a constant programmatic access to a master source of scientific articles. We utilize the Dimensions API to access their collection of over 98 million publications (at the time of writing). A cascading citation expansion process may move forward and backward along citation paths. If the entire universe of all the scientific publications is completely reachable from one article to another, then cascading citation expansion will eventually reach all the publications, which means that one can achieve a coverage as large as we wish. If the entire universe is in fact made of several galaxies that are not reachable through citation links, the question is whether this is desirable to transcend the void and reach other galaxies. Strategies using hybrid lexical and semantic resources may become useful in such situations, but in this study we assume it is acceptable to terminate the expansion process once citation links associated with a chosen starting point are exhausted.

Fig 1 illustrates the cascading citation expansion process. Given an initial set of seed articles, which can be a singleton set, a 1-generation forward citation expansion will add articles that cite any member article of the seed set directly, i.e. with a one-step citation path. A 2-generation forward expansion will add articles connecting to the seed set with two-step citation paths. We include a 5-generation forward expansion in this study.

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https://doi.org/10.1371/journal.pone.0223994.g001

Use scenarios and corresponding search strategies

Here we consider two common scenarios in research. In the first scenario, we have identified a well-known classic work, for example, Swanson’s 1986 article on fish oil and Raynaud’s syndrome [ 6 ], and would like to retrieve all follow-up studies and articles that cited the original work directly or indirectly since 1986. What are the newly developed major topics ever since? What are the hottest and the most far-reaching topics in more recent years? Are there any areas branching off the main paths?

In the second scenario, we are attracted to a very interesting recently published article and we would like to collect relevant studies in the past that lead to the article, i.e., its intellectual base. Smalheiser’s 2017 review of LBD [ 28 ] is an example. Smalheiser has co-authored with Swanson on several landmark studies in the development of literature-based discovery. Smalheiser cited 71 references in his review. What would be a broader context of Smalheiser’s 71-reference review? Are there LBD-relevant topics but excluded by the authoritative domain expert?

Pragmatically, if we were to rely on the simple full text search alone, how much would we miss? Are there topics that we might have missed completely? What would be an optimal search strategy that not only adequately captures the essence of the development of the field but also does in the most efficient way? Borrowing the terminology from information retrieval, an optimal search strategy should maximize the recall and the precision at the same time.

Cascading citation expansion functions are implemented in CiteSpace based on the Dimensions’ API. The expansion process starts with an initial search query in DSL, which is Dimensions’ search language. Users who are familiar with SQL should be able to recognize the resemblance immediately. The result of the initial query forms the initial set of articles. In fact, in addition to publications, one can retrieve grants, patents, and clinical trials from Dimensions. In this study, we concentrate on publications.

Constructing five datasets of literature-based discovery

To demonstrate the flexibility and extensibility of the incremental expansion approach, we take the literature-based discovery (LBD) research as the field to study. We choose LBD for several reasons: 1) we are familiar with the early development of the domain, 2) we are aware of a recent review written by one of the pioneer researchers and we would like to set it in a broader context, and 3) we would like to take this opportunity to demonstrate how one can apply the methodology to a visual exploration of the relevant literature and develop a good understanding of the state of the art. These reasons echo the common scenarios discussed earlier.

Table 2 summarized the construction of the five datasets included in the study, including key parameters such as citation thresholds.

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https://doi.org/10.1371/journal.pone.0223994.t002

Fig 2 illustrates the process of the comparative study of five datasets retrieved based on a query-based search and cascading citation expansions. In this study, we applied a citation filter in cascading citation expansions to the selection of citing and cited articles. Articles with citations below the threshold are filtered out from the expansion processes. These filters provide users with a flexible trade-off option between concentrating on major citation paths with a reduced completion time versus retrieving articles comprehensively with a much longer completion time. Since the distribution of citations of articles follows power law, a comprehensive expansion process may become too long to be viable for a daily use of these functions. The DSL query searched for Swanson’s two articles published in 1986, namely, the fish oil and Raynaud’ syndrome article 1986a [ 6 ] and the undiscovered public knowledge article 1986b [ 7 ]. The query search found 1,777 articles as the set F for Full data search. Swanson 1986a is used as the seed article for two multi-generation forward citation expansions, one for 3 generations (as set S 3 ) and the other for 5 generations (as set S 5 ). S 3 contains 748 articles, whereas S 5 is about 60 times larger, containing 45,178 articles. The other two datasets are expanded from Smalheiser’s 2017 review as the seed, N F and N B , where N is for Neil, Smalheiser’s first name. Smalheiser’s review contains 71 references. At the time of the experiment, a forward expansion from it found two articles that cite the review. The 73 articles form the set N F . The N B set is obtained by applying backward citation expansions on the set N F . The expansion stopped in 1934 with 2,451 articles. The five datasets are combined as the set All 5 , containing 48,298 unique articles. S 5 contributed most of articles to the combination. The five datasets overlap to a different extent. S3 is a subset of S5. NB expands from NF. F overlaps with S5 the most (702 articles out of its 1,777 articles). Each of the five individual datasets and the combined dataset are visualized in CiteSpace as networks of co-cited references with thematic labels for clusters.

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https://doi.org/10.1371/journal.pone.0223994.g002

Given a dataset, its core is defined by references that satisfy two conditions, inspired by [ 24 ]: 1) global citation scores (GCS) in Dimensions are greater than two and 2) the ratio between local citation scores (LCS) within the analyzed dataset to corresponding GCS are greater than or equal to 0.01. According to [ 24 ], the core represents articles with a sufficient specificity to the field of research in question. The core will downplay the role of an article that has a very high GCS but a low LCS because it suggests that the article probably belongs to a field elsewhere. It may be possible for an article to have its citations evenly split across multiple fields, but its LCS/GCS ratio in any of these fields should have a good chance to qualify the article for the core.

Researchers have used main paths of a citation network to study major flows of information or the diffusion of ideas [ 51 , 52 ]. Main paths of a dataset are derived from the corresponding direct citation networks of the dataset. Direct citation networks are generated in CiteSpace with GCS of 1 as the selection threshold. Pajek is used to select main paths based on Search Path Link Count (SPLC) using top 30 key routes found by local search.

Datasets, their cores, main paths, and clusters can be used as the initial seed set for cascading citation expansion. They can all be used as network overlays in CiteSpace to delineate the scope of a research field at various levels of granularity.

Fig 3 shows logarithmically transformed distributions of the five datasets. The distributions shown under the title are the original ones.

  • The F dataset (in blue) is evenly distributed except a surprising peak in 2009, which turns out due to many articles from an encyclopedia. The number of articles each year ranges between 60 and 130.
  • Both S 5 (red) and S 3 (orange) are forward expansions starting with Swanson’s 1986 article on fish oil and Raynaud’s syndrome [ 6 ]. In S 3 , the inclusion threshold was at least 10 citations, whereas it was 20 in S 5 so as to keep the total processing time down. The majority of the articles in S 3 appeared between 2006 and 2018. S 3 had the first peak in 1984. It didn’t return to the same level for the next 10 years until it started to climb up from 2003 and reached the second peak in 2012. In contrast, the distribution of the more extensive forward expansion S 5 shows a steady increase all the way over time.
  • N F , a forward expansion from Smalheiser’s review of Swanson’s work [ 28 ], includes articles that cited the references used in Smalheiser’s review. Its distribution steadily increased between 1986 and 2017 with a rise of 5 over the 31-year span till 2016.
  • N B , a backward expansion from the set N F , contains articles that are cited by N F . The earliest article in N B was published in 1936. A noticeable hump from 1983 followed by a valley around early 1990s. Two peaks appeared in 2006 and 2011, respectively.

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https://doi.org/10.1371/journal.pone.0223994.g003

We will focus on how these datasets differ in terms of networks of co-cited references in the following analysis. There are of course many other ways to conduct scientometric studies based on these datasets, but we will limit to the co-citation networks generated with CiteSpace. It is important to note that co-citation networks in CiteSpace include much more information than a classic co-citation network, notably including various indicators and thematic labels derived from citing articles to clusters of co-cited references, year-by-year concept labels to track the evolution of a cluster, and a hierarchical representation of concept terms extracted from citing articles’ titles and abstracts. Some of these features will be illustrated in the following sections.

The five datasets and the combined dataset are processed in CiteSpace with the consistent configurations. In particular, the link-to-node ratio is 3, look back years is 10, annual citation threshold is 2, and the node selection is based on g-index with a scaling factor of 30. These configuration settings are derived empirically, which tend to identify meaningful patterns. Given a dataset, CiteSpace first develops a network model by synthesizing a time series of annual networks of co-cited references. Then the synthesized network is divided into clusters of cited references. Themes in each clusters are identified based on noun phrases extracted from citing articles’ titles and abstracts. Citing articles to a cluster are defined as articles that cite at least one member of the cluster. Extracted noun phrases are further computed to identify the most representative ones as the thematic labels for their cluster. CiteSpace supports three ways to select cluster labels based on Latent Semantic Indexing, Log-Likelihood Ratio Test, and Mutual Information [ 53 ]. CiteSpace supports a built-in database. Many attributes of datasets can be compared with the database. The core of a dataset, for example, can be identified using SQL queries.

Interactive visualizations in CiteSpace support several views, i.e., types of visualization, including a cluster view, a timeline view, a history view, and a hierarchical view. A network can be superimposed to another network as a layer. A list of references can be superimposed to a network as well. We use this feature to overlay the core and main paths of a dataset to its own network or to a network of another dataset. CiteSpace reports network and cluster properties such as modularity and silhouette scores. The modularity score of a network reflects the clarity of the network structure at the level of decomposed clusters. The silhouette score of a cluster measures the homogeneity of its members. A network with a high modularity and a high average of silhouette scores would be desirable. We will focus on the largest connect component of each network, which is shown as the default visualization. Users may choose to reveal all components of a network if they wish.

Table 3 summarizes various properties of the five individual datasets and the combined set along with their networks and core references. The F dataset, for example, contains 1,777 articles, which in turn cite 30,606 unique references. Among them, 367 references are identified as the core references based on the LCS/GCS ratio and the threshold of 3 for GCS. The resultant network contains 1,269 references as nodes and 5,937 co-citation links. The largest connected component (LCC) consists of 1,029 references, or 74% of the entire network. The modularity with reference to the clusters is 0.76, indicating a relatively high level of clarity. The average silhouette score of 0.34 out of 1.0 is moderate.

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https://doi.org/10.1371/journal.pone.0223994.t003

The modularity of a network measures the clarity of the network structure in terms of how well the entire network can be naturally divided into clusters such that nodes within the same cluster are tightly coupled, whereas nodes in different clusters are loosely coupled. The higher the modularity is, the easier to find such a division. The silhouette score of a network measures the average homogeneity of derived clusters [ 53 ]. The higher the average silhouette score is, the more meaningful a group is in terms of a cluster. The smallest dataset N F has the highest modularity of 0.93. It also has the highest average silhouette score of 0.50. The full text search has the modularity of 0.76, which is slightly lower than N B , but its silhouette value of 0.34 is lower than others except S 5 .

Literature-based discovery

In this section, we visualize the thematic landscape of the field of literature-based discovery from multiple perspectives of the five datasets. We will start with the full text search results and then cascading citation expansions.

Full text search

The query for the full text search on Dimensions consists of ‘literature-based discovery’ and ‘undiscovered public knowledge.’ The phrase ‘literature-based discovery’ is commonly used as the name of the research field. The phrase ‘undiscovered public knowledge’ appears in the titles of two Swanson’s publications in 1986. One is entitled “Fish oil, Raynaud’s Syndrome [ 6 ], and Undiscovered Public Knowledge” in Perspectives in Biology and Medicine and the other is “Undiscovered Public Knowledge” in Library Quarterly [ 7 ].

The full text search found 1,777 records. Dimensions’ export center supports the export of up to 50,000 records to a file in a CSV format for CiteSpace [ 9 , 40 ]. Publication records returned from Dimensions do not include abstracts. We found 431 matched records in PubMed with their abstracts, but in this study the analysis is based on the full set of 1,777 records regardless they have abstracts or not because we primarily focus on the references they cite.

Fig 4 shows an overview map of LBD according to the dataset F. The color of a link indicates the earliest year when two publications were co-cited for the first time in the dataset. In this visualization, the earliest work appeared from the top of the network, whereas the most recent ones appeared at the bottom, although CiteSpace does not utilize any particular layout mechanisms to orient the visualization. The network is decomposed into clusters of references based the strengths of co-citation links. Clusters are numbered in the descending order of their size. The largest one is numbered as #0, followed by #1, and so on. Fig 4 also depicts the core of the F set as an overlay (in green) and its main paths (in red).

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CiteSpace configuration: LRF = 3, LBY = 10, e = 2.0, g-index (k = 30). Network: 1,269 references and 5,937 co-citation links.

https://doi.org/10.1371/journal.pone.0223994.g004

The largest cluster is #0 machine learning. More recent clusters, further down in the visualized network, include #1 semantic predication, and #7 citation network.

Fig 5 shows more features of the dataset F through colormaps of time (a), an overlay of its core (b), its and main paths (c). Labeled nodes on main paths include Swanson DR (1986), which also appears to be one of the oldest core references, Swanson DR (1997), Smalheiser (1998), Webber W (2001), and the most recently Cohen T (2010).

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https://doi.org/10.1371/journal.pone.0223994.g005

Fig 5 also contrasts references cited by Smalheiser’s 2017 review of LBD and the main paths (d) and references cited by another LBD review published in 2017 by Sebastian et al. [ 5 ] in part (e). There are several notable differences. Torvik VI (2007) cited in Smalheiser’s review was not on the main paths. In contrast, a few articles on the main paths such as Hunter L (2006), Zweigenbaum P (2007), and Agarwal P (2008) are not cited in Smalheiser’s review. Similarly, Sebastian et al.’s review also cited Torvik VI (2007) and a few other articles off the main paths, including Kostoff RN (2009), Chen C (2009), and Kostoff RN (2008). The general area of Sebastian et al.’s review overlaps with that of Smalheiser’s review considerably except Sebastian et al.’s review reached further towards cluster #7 citation network. The boundaries of clusters are show in distinct colors in part (f) of Fig 5 . According to the colored cluster areas, the main paths go through #0 machine learning, whereas the two LBD reviews did not.

In Fig 6 , references cited by the two LBD review articles are shown as overlays on a timeline visualization. Each cluster is shown horizontally and advances over time from the left to the right. Both LBD reviews make substantial connections between #1 semantic predication and #6 validating discovery. Given the recency of #1 semantic predication, the role of semantic predication is significant. Our own ongoing research also investigates the role of semantic predication in understanding uncertainties of scientific knowledge [ 22 ]. Sebastian et al.’s review reached further down to #8 biomarker discovery, which was not cited in Smalheiser’s review.

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https://doi.org/10.1371/journal.pone.0223994.g006

Comparing five individual datasets

In order to compare the coverage of individual datasets, we construct a baseline map based on the combined dataset. The base map is created with the same procedure that was applied to individual datasets. The base map is divided into clusters. As a measure of the specificity of a dataset, we calculate the K-L divergence between normalized GCS and LCS scores. A low K-L divergence would suggest that the dataset is representative of the underlying field of research, whereas a high K-L divergence would indicate that the dataset contains many out-of-place articles ( Table 4 ). S 3 has the lowest K-L divergence. F and N F have similar scores. N B and S 5 have higher scores as expected given their size.

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https://doi.org/10.1371/journal.pone.0223994.t004

The overview of a visualized network based on the combined dataset is shown in Fig 7 . The colors in the map on the left depict the time of a link is added. For example, the youngest areas are located towards the lower left of the network, whereas the oldest ones are located near the top. The colors in the map on the right are encoded to depict the membership of clusters. The largest cluster is shown in red, following a rainbow colormap, so that we will know the relative size of a cluster. We will overlay networks from each individual dataset to identify what each search strategy brings unique topics to the overall landscape.

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Modularity: 0.84. Silhouette: 0.34.

https://doi.org/10.1371/journal.pone.0223994.g007

Table 5 lists the distribution of each of the largest 10 clusters in the combined network across the five individual datasets. Thematic labels of each cluster include terms selected by Latent Semantic Indexing (LSI) and by log-likelihood ratio. The former tends to identify common themes, whereas the latter tends to highlight unique themes. The two selections may differ as well as agree. Among the 10 largest clusters, the oldest one is #4 information retrieval with 1990 as the average year of publication. The youngest one is #8 deep learning with 2014 as the average year of publication. The largest cluster #0 systems biology/protein interaction network has the lowest silhouette score, which is expected given its size of 284 references. #6 microRNAs/drosophila melanogaster development has the highest silhouette score of 0.967, followed by the 0.965 of #7 big data, suggesting both them are highly uniformed.

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https://doi.org/10.1371/journal.pone.0223994.t005

The distributions of clusters across individual datasets show that clusters 0–1 and 3–5 are well represented in F with over 50% of the members of these cluster (highlighted in the table). N B is essentially responsible for Cluster #6, whereas S 5 is responsible for #7 big data and #8 deep learning. Independently we can identify the unique contributions associated with #6, #7, and #8 from network overlays shown in Fig 8 .

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https://doi.org/10.1371/journal.pone.0223994.g008

Fig 8 shows a set of network overlays of individual datasets (in red) and core reference overlays of the F and S 5 datasets. The three circles in part g highlight the three unique clusters. #6 is contributed essentially by N B , whereas #7 and #8 are captured by S 5 . The effect of cascading citation expansions is evident. The query-based approach (F) failed to capture #7 big data and #8 deep learning as the 5-generation forward expansion from Swanson’s pioneering article did. Are these clusters relevant enough to be still considered as part of a systematic scientometric review of LBD or rather they should be considered as applications of computational technologies to literature-based discovery? Similarly, #6 microRNAs is missed by forward expansions from Swanson’s 1986 article. What is the basis of its relevance? We will address these questions as follows.

Each cluster can be further analyzed by applying the same visual analytic procedure at the next level, i.e. Level 2. The cluster at the original level is known as a Level-1 cluster. One may continue this drill-down process iteratively as needed. Level-2 clusters are useful for interpreting their Level-1 cluster in terms of more specific topics.

Fig 9 illustrates a few reports from CiteSpace on Cluster #, including a visualization that shows Level-2 clusters of the Level-1 cluster (left), a hierarchy of concepts (top), and year-by-year thematic terms of Level-2 clusters. The hierarchy of concepts, also known as a concept tree, provides a useful context to identify the major themes of a cluster according to the degree of a concept node in the tree, or the number of children in the tree. In this case, RNA interference has the highest degree and it suggests that the cluster’s overarching theme is to do with RNA interference. A concept tree provides an informative context for selecting thematic labels. Labels selected through LSI or LLR do not have the benefit of such contextual information. As shown in Fig 9 , #6 appears to be different from other clusters because connections to the study of scientific literature are not obvious.

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It is a very specific domain on RNA interference.

https://doi.org/10.1371/journal.pone.0223994.g009

Since N B is exclusively responsible for the cluster #6, we overlay references cited by Smalheiser’s review, i.e. its footprints, on a network visualization of N B ( Fig 10 ). The NB network consists of two components that are loosely connected with each other. The footprints of Smalheiser’s review mostly appear in the lower component, indicating that the lower component is strongly relevant to LBD. In contrast, the upper component only contains two footprint references, namely Smalheiser NR (2001) and Lugli G (2005). The two references may hold the key to the formation of #6.

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The upper area is responsible for the formation of Cluster #6, which is in turn due to two references cited in Smalheiser in his 2017 review.

https://doi.org/10.1371/journal.pone.0223994.g010

We examine the full text of Smalheiser’s review for the contexts in which these two references are cited. As it turns out, Smalheiser cited the two references as atypical examples of LBD that “arose haphazardly during the course of laboratory investigation” and they are unlike typical LBD examples, in which complementary bodies of literature were purposefully sought after. Lugli G (2005) was cited in the first example of how Smalheiser and his colleagues put two lines of studies together that involved concepts such as double-stranded RNA, which is featured in the concept tree of #6 in Fig 9 . Smalheiser NR (2001) was cited in the second example of atypical LBD, which was about RNA interference in mammalian brain. It took them a decade to find provisional evidence that may valid the discovery in 2012.

Now it becomes evident that the upper component is connected to LBD through this specific connection. Thus the inclusion of #6 by expansion is reasonable. On the other hand, if the upper component does not contain any other references specifically relevant to LBD, then it seems to be necessary to investigate whether the N B expansion should be cut short in this area.

The query-based search (F) did not capture clusters #7 big data and #8 deep learning. As shown in Fig 11 , the red lines indicate the coverage of F. No red lines even remotely approach to either of the clusters.

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https://doi.org/10.1371/journal.pone.0223994.g011

The relevance of Cluster #8 deep learning is investigated as follows. Fig 12 depicts a drill-down analysis of Cluster #8 deep learning, which is the youngest cluster among the 10 largest Level-1 clusters. The concept tree of the cluster identifies deep learning as the primary theme. More specifically, the concept of deep learning appears in contexts that are relevant to LBD, namely in association with drug discovery and biomedical literature. Level-2 clusters include #0 deep learning, #1 deep learning, #2 ensemble gene selection, #3 neuromorphic computing, #4 drug discovery, and #5 medical record. Year-by-year thematic terms include deep learning for the last four years since 2016 along with domain-specific terms such as radiology, breast ultrasound, and precision medicine. Given the multiple connections to biomedical literature, drug discovery, and other domain-specific terms, the cluster on deep learning should be considered as a relevant development of LBD.

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https://doi.org/10.1371/journal.pone.0223994.g012

Discussions and conclusions

We have proposed and demonstrated a flexible method to improve the quality of data retrieved for systematic scientometric reviews. We have demonstrated how one may use the approach to develop search strategies to meet the needs in common scenarios in practice. The comparisons of network visualization overlays of five datasets have revealed what a commonly used full text search strategy could have missed. Such omissions are likely to be recently emerged topics and missing them in a systematic review may undermine its overall quality. A strategy that combines query-based search and cascading citation expansion is likely to provide a more balanced coverage of a research domain and to reduce the risk of overly relying on topics explicitly specified in the initial queries. A practical implication on finding a representative body of the literature is its potential to uncover emerging topics that are currently connected to the main body of the literature through a chain of weak links. We recommend researchers to consider this strategy in situations when they only have a small number of relevant articles to begin with. As our study demonstrated, a wide variety of articles can serve as a starting point of an expansion process and multiple processes can be utilized and the combination of their results is likely to provide a comprehensive coverage of the underlying thematic landscape of a research field or a discipline.

The present study has some limitations and it raises new questions that need to be addressed in future studies. Our approach implies an assumption that the structure of scientific knowledge can be essentially captured through semantically similar text and/or explicit citation links. Is this assumption valid at the disciplinary level? To what extent does the choice of the seed articles for the expansion process matter? Does the choice of seed articles influence the stability of the expansion process? How many generations of expansion would be optimal?

We have made a few observations and recommendations that are potentially valuable for adapting this type of search strategies to develop a systematic review of a body of scientific literature of interest.

  • Using a combination of multiple cascading citation expansions with different seed articles is recommended to obtain a more balanced representation of a field than using a full text search alone.
  • Multi-generation citation expansions provide a systematic approach to reduce the risk of missing topics that we may not be familiar with or not aware of altogether.
  • Triangulating multiple aggregations of articles such as the core references of a dataset and main paths of a dataset as well as multiple review articles provide useful insights.
  • The flexibility of the approach enables researchers to apply the expansion and visual analytic procedure iteratively at multiple levels of granularity, for example, expanding a cluster, comparing the footprints of review articles with main paths, and drilling down a cluster in terms of Level-2 clusters.
  • Choosing the starting point and an end point of a cascading expansion process may lead to different results, suggesting the complexity of the networks and threshold selections may play important roles in reproducing the results in similar studies.
  • Modularity and cluster silhouette measures can help us to assess the quality of an expansion process.

Comparing multiple networks in the same context allows us to identify the topic areas that are particularly well represented in some of the datasets but not in other ones. Such an understanding of the landscape of a field provides additional insights into the structure and the long-term development of the field.

As a methodology for generating systematic scientometric reviews of a knowledge domain, it bridges the formally mutual exclusive globalism and localism by providing a scalable transition mechanism between them. The most practical contribution of our work is the development and dissemination of a tool that is readily accessible by end users.

Acknowledgments

We are grateful to Neil Smalheiser for his valuable suggestions on the paper. The work is supported by the SciSIP Program of the National Science Foundation (Award #1633286). CC acknowledges the support of Microsoft Azure Sponsorship. Data sourced from Dimensions, an inter-linked research information system provided by Digital Science ( https://www.dimensions.ai ). This work is also supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2018S1A3A2075114). This research is also partially supported by the Yonsei University Research Fund of 2019-22-0066.

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Field Research: A Qualitative Research Technique

66 Field Research: What is it?

Field research is a qualitative method of data collection aimed at understanding, observing, and interacting with people in their natural settings. In the context of research, observation is more than just looking.  It involves looking in a planned and strategic way with a purpose (Palys & Atchison, 2014, p. 189).  As such, when social scientists talk about being in “the field,” they are talking about being out in the real world and involved in the everyday lives of the people they are studying. Sometimes researchers use the terms ethnography or participant observation [1] to refer to this method of data collection; the former is most commonly used in anthropology, while the latter is used commonly in sociology. For our purposes, we will use two main terms: field research and participant observation . You might think of field research as an umbrella term that includes the myriad activities that field researchers engage in when they collect data: they participate, they observe, they usually interview some of the people they observe, and they typically analyze documents or artifacts created by the people they observe.

Researchers conducting participant observation vary in the extent to which they participate or observe. Palys and Atchison (2014, p. 198) refer to this as the “participant-observer continuum,” ranging from complete participant to complete observer.  This continuum is demonstrated in Figure 12.1. However, these researchers, as to do other researchers, question whether a researcher can be at the “complete observer” end of the continuum.  Rather, they contend it is increasingly acknowledged that even as an observer, the researcher is participating in what is being studied and therefore cannot really be a complete observer.

the participant-observer continuum from left to right: complete participant, participant as observer, observer as participant, complete observer

Indeed, it is important to acknowledge that there are pros and cons associated with both aspects of the participant-observer’s role.  For example, depending upon how fully researchers observer their subjects (as opposed to participating), they may miss important aspects of group interaction and may not have the opportunity to fully grasp what life is like for the people they observe. At the same time, sitting back and observing may grant researchers opportunities to see interactions that they would miss were they more involved.

Participation has the benefit of allowing researchers a real taste of life in the group that they study. Some argue that participation is the only way to understand what it is that is being investigated. On the other hand, fully immersed participants may find themselves in situations that they would rather not face but cannot excuse themselves from because they have adopted the role of a fully immersed participant. Further, participants who do not reveal themselves as researchers must face the ethical quandary of possibly deceiving their “subjects.” In reality, much of the field research undertaken lies somewhere near the middle of the observer-participant continuum. Field researchers typically participate to at least some extent in their field sites, but there are also times when they may strictly observe.

Text Attributions

  • This chapter was adapted from Chapter 10.1 in Principles of Sociological Inquiry , which was adapted by the Saylor Academy without attribution to the original authors or publisher, as requested by the licensor. © Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License .

Media Attributions

  • figure12.1 © Palys & Atchison
  • Ethnography is not to be confused with ethnomethodology.  Ethnomethodology will be defined and described in Chapter XIII . ↵

An Introduction to Research Methods in Sociology by Valerie A. Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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10.2: Pros and Cons of Field Research

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Learning Objectives

  • Identify and explain the strengths of field research.
  • Identify and explain the weaknesses of field research.

Field research has many benefits, as well as a set of drawbacks. We’ll explore both here.

Strengths of Field Research

Field research allows researchers to gain firsthand experience and knowledge about the people, events, and processes that they study. No other method offers quite the same kind of closeup lens on everyday life. This close-up on everyday life means that field researchers can obtain very detailed data about people and processes, perhaps more detailed than they can obtain using any other method.

Field research is an excellent method for understanding the role of social context in shaping people’s lives and experiences. It enables a greater understanding of the intricacies and complexities of daily life. Field research may also uncover elements of people’s experiences or of group interactions of which we were not previously aware. This in particular is a unique strength of field research. With other methods, such as interviews and surveys, we certainly can’t expect a respondent to answer a question to which they do not know the answer or to provide us with information of which they are not aware. And because field research typically occurs over an extended period of time, social facts that may not even be immediately revealed to a researcher but that become discovered over time can be uncovered during the course of a field research project.

In sum, the major benefits of field research are the following:

  • It yields very detailed data.
  • It emphasizes the role and relevance of social context.
  • It can uncover social facts that may not be immediately obvious or of which research participants may be unaware.

Weaknesses of Field Research

Earlier I described the fact that field researchers are able to collect very detailed data as a benefit of this method. This benefit, however, does come at a cost. Because a field researcher’s focus is so detailed, it is by necessity also somewhat narrow. Field researchers simply are not able to gather data from as many individuals as, say, a survey researcher can reach. Indeed, field researchers generally sacrifice breadth in exchange for depth. Related to this point is the fact that field research is extremely time intensive.

Field research can also be emotionally taxing. In Chapter 9, I assert that interview research requires, to a certain extent, the development of a relationship between a researcher and her participants. But if interviews and field research both require relationship development, you might say that interviews are more like casual dating while field research is more like a full-blown, committed marriage.

The relationships you develop as a field researcher are sustained over a much longer period than the hour or two it might take you to conduct an interview. Not only do the relationships last longer, but they are also more intimate. A number of field researchers have documented the complexities of relationships with research participants (Arditti, Joest, Lambert-Shute, & Walker, 2010; Keinman & Copp, 1993; MacLeod, 1995).MacLeod, J. (1995). On the making of ain’t no makin’ it. In J. MacLeod (Ed.), Ain’t no makin’ it: Aspirations and attainment in a low-income neighborhood (pp. 270–302). Boulder, CO: Westview Press; Arditti, J. A., Joest, K. A., Lambert-Shute, J., & Walker, L. (2010). The role of emotions in fieldwork: A self-study of family research in a corrections setting. The Qualitative Report, 15, 1387–1414; Keinman, S., & Copp, M. A. (1993). Emotions and fieldwork . Newbury Park, CA: Sage. On the plus side, these relationships can be very rewarding (and yield the rich, detailed data noted as a strength in the preceding discussion). But, as in any relationship, field researchers experience not just the highs but also the lows of daily life and interactions. And participating in day-to-day life with one’s research subjects can result in some tricky ethical quandaries (see Chapter 3 for a discussion of some of these quandaries). It can also be a challenge if your aim is to observe as “objectively” as possible.

Finally, documentation can be challenging for field researchers. Where survey researchers have the questionnaires participants complete and interviewers have recordings, field researchers generally have only themselves to rely on for documenting what they observe. This challenge becomes immediately apparent upon entering the field. It may not be possible to take field notes as you observe, nor will you necessarily know which details to document or which will become the most important details to have noted. And when you take notes after some observation, you may not recall everything exactly as you saw it when you were there.

In sum, the weaknesses of field research include the following:

  • It may lack breadth; gathering very detailed information means being unable to gather data from a very large number of people or groups.
  • It may be emotionally taxing.
  • Documenting observations may be more challenging than with other methods.

KEY TAKEAWAYS

  • Strengths of field research include the fact that it yields very detailed data, it is designed to pay heed to social context, and it can uncover social facts that are not immediately obvious.
  • Weaknesses of field research include that researchers may have to sacrifice breadth for depth, the possibility that the research will be emotionally taxing, and the fact that documenting observations can be challenging.
  • In your opinion, what is the most important strength of field research? What do you view as its greatest weakness? Explain your position.
  • Find an article reporting results from field research. You can do this by using the Sociological Abstracts database, which was introduced in Chapter 4. How do the authors describe the strengths and weaknesses of their study? Are any of the strengths or weaknesses described in this section mentioned in the article? Are there additional strengths or weaknesses not mentioned in this section?
  • What is Field Research: Meaning, Examples, Pros & Cons

Angela Kayode-Sanni

Introduction

Field research is a method of research that deals with understanding and interpreting the social interactions of groups of people and communities by observing and dealing with people in their natural settings. 

The field research methods involve direct observation, participant observation, and qualitative interviews.

Let’s take a deeper look at field research, what it entails, some examples as well as the pros and cons of field research.

What is Field Research

Field research can be defined as a qualitative method of data collection focused on observing, relating, and understanding people while they are in their natural environment. It is somewhat similar to documentaries on Nat Geo wild where the animals are observed in their natural habitat. 

Similarly, social scientists, who are sometimes called men watchers carry out interviews and observe people from a distance to see how they act in a social environment and react to situations around them.

Field research usually begins in a specific setting and the end game is to study, observe and analyze the subject within that setting. It looks at the cause and effect as well as the correlation between the participants and their natural setting. Due to the presence of multiple variables, it is sometimes difficult to properly analyze the results of field research. 

Field research adopts a wide range of social research methods, such as limited participation, direct observation, document analysis, surveys, and informal interviews. Although field research is generally considered qualitative research , it often involves multiple elements of quantitative research.

Methods of Field Research

There are 5 different methods of conducting Field Research and they are as follows;

1. Direct Observation

In this method of research, the researcher watches and records the activities of groups of people or individuals as they go about their daily activities. Direct observation can be structured or unstructured.

 Structured here means that the observation takes place using a guide or process developed before that time. 

Unstructured, on the other hand, means that the researcher conducted the observation, watching people and events, and taking notes as events progressed, without the aid of any predetermined technique.

Some other features of direct observation include the following;

  • The observer does not attempt to actively engage the people being observed in conversations or interviews, rather he or she blends into the crowd and carries out their observation.
  • Data collected include field notes, videos, photographs, rating scales, etc.
  • Direct observation most times occurs in the open, usually public settings, that requires no permission to gain entry. Conducting direct observation in a private setting would raise ethical concerns.
  • The outcome of direct observation is not in any way influenced by the researcher.

2. Participant Observation

This research method has an understanding with a group of individuals, to take part in their daily routines and their scheduled events. In this case, the researcher dwells among the group or community being observed for as long as is necessary to build trust and evoke acceptance.

Data from the participant’s observation take the following varying forms;

  • Field notes are the primary source of data. These notes are taken during the researcher’s observations and from the events they experienced and later developed the notes into formal field notes.
  • A diary is used to record special intimate events that occur within the setting.
  • The process of participant observation is intent on developing relationships with the members which breed conversations that are sometimes formal or informal. Formal here refers to deliberate depth interviews, while informal could stem from everyday conversations that give insight into the study. 

Data from these events can be part of the field notes or separate interview transcripts.

The method of participant observation aims to make the people involved comfortable enough to share what they know freely without any inhibition.

3. Ethnography

Ethnography is a form of field research that carries out observation through social research, social perspective, and the cultural values of a social setting. In this scenario, the observation is carried out objectively, hence the researcher may choose to live within a social environment of a cultural group and silently observe and record their daily routines and behavior.

4. Qualitative Interviews

Qualitative interviews are a type of field research method that gets information by asking direct questions from individuals to gather data on a particular subject. Qualitative interviews are usually conducted via 3 methods namely;

  • Informal Interviews
  • Semi Structured Interviews
  • Standardized Open ended Interviews

Let’s take a look at each of them briefly along with their advantages and disadvantages.

This kind of interview is often conversational and occurs during participant and direct observations.

The interview is triggered, most times spontaneously by conversing with a member of the group on the areas of interest and as the conversation progresses, the researcher fluidly introduces the specific question.

  • Semi-Structured Interviews

In this scenario, the researcher already has a list of prepared questions, that are open-ended and can evoke as much information as possible. The researcher can venture into other topics as the interview progresses, using a call-and-response style.

This method of field research can adopt a mix of one-on-one interviews or focus groups.

  • Standardized, Open-Ended Interviews

These are scripted interviews with the questions prepped and written before the interview following a predetermined order. It is similar to a survey and the questions are open-ended to gather detailed information from the respondents and sometimes it involves multiple interviewers.

5. Case Study

A case study research is a detailed analysis of a person, situation, or event. This method may seem a bit complex, however, it is one of the easiest ways of conducting research. difficult to operate, however, it is one of the simplest ways of researching as it involves only a detailed study of an individual or a group of people or events. Every aspect of the subject life and history is analyzed to identify patterns and causes of behavior.

Steps to Conduct Field Research

Due to the nature of field research, the tight timelines, and the associated costs involved, planning and implementing can be a bit overwhelming. We have put together steps to adopt that would make the whole process hitch free for you.

Set Up The Right Team : To begin your field research, the first step is to have the right team. The role of the researcher and the team members has to be well defined from the start, with the relevant milestones agreed upon to measure progress.

Recruiting People for the Study : The success of field research largely depends on the people being studied. Evaluate the individuals selected for the research to be sure that they tick all the boxes required for successful research in the area of study that is being researched.

Data Collection Methodology : The methodology of data collection adopted must be suited to the area or kind of research being conducted. It could be one of the methods or a combination of two or more methods.

Visit The Site: A prior visit to the site is essential to the success of the field research. This should be done to also help determine the best methodology that would be suitable for the location. 

Data Analysis: Analyzing the data gathered is important to validate the hypothesis of the field research. 

Communicating Results : Once the data is analyzed, communicate the results to the stakeholders involved in the research so that the relevant action required based on the results can be decided and carried out promptly. 

Reasons to Conduct Field Research

Field research has been widely used in the 20th century in the social sciences. However, it can be time-consuming and costly to implement. Despite this fact, there exist a lot of reasons to conduct field research.

Here are 4 major reasons to conduct field research:

Solves the problem of lack of data : Field research fixes the issue of gaps in data, especially in cases where there is very little or no data about a topic. In cases like this, the only way to validate any hypothesis is through primary research and data. Conducting field research solves the problem of data lapses and provides material evidence to support any findings.

Understanding the context of the study : In many cases, the data collected is appropriate, however for a deep understanding of the data gathered there is a need for field research to help understand other factors in the study. For instance, if data show that students from rich homes generally do well academically. 

Conducting field research can bring to the fore other factors like, discipline, well-equipped teachers, motivation from their forebears to excel in whatever they do, etc. but field research is still conducted. 

Increasing data quality: Since this research, method employs the use of multiple tools to collect data and varying methodologies, the quality of data is higher.

Collecting ancillary data : Field research puts the researchers in a position of being at the center of the data collection process, in terms of location, one on one relationship with the participants, etc. This exposes them to new lines of thought that would have hitherto been overlooked and they can now collect data, that was not planned for at the beginning of the study.

Examples of Field Research

1. Interprete social metrics in a slum By employing the use of observation methods and formal interviews, researchers can now understand the social indicators and social hierarchy that exist in a slum.

Financial independence and the way the slum is run daily are part of the study and data collected from these areas can give insight into the way a slum operates differently from structured societies.

2. Understand the impact of sports on a child’s development This method of field research takes years to conduct and the sample size can be quite huge. Data collected and analyzed from this study provides insight into how children from different physical locations and backgrounds are influenced by sports and the impact of sporting activities on a child’s development. 

3. The study of animal migration patterns Field research is used immensely to study flora and fauna. A major use case is scientists observing and studying animal migration patterns alongside the change of seasons and its influence on animal migration patterns.

Field research takes time and uses months and sometimes years to help gather data that show how to safely expedite the passage of animals.

Advantages of Field Research

Field research and the various methodology employed have their pros and cons.

Let’s take a look at some of them.

  • Provide context to the data being analyzed in terms of settings, interactions, or individuals.
  • The source of data does not require or involve verbal interactions, and there is no intrusion of anyone’s personal, space because everything is done quietly, from a distance.
  • The researcher develops a  deep and detailed understanding of a setting and the members within the setting.
  • It is carried out in a real-world and natural environment which eliminates tampering with variables.
  • The study is conducted in a comfortable environment, hence data can be gathered even about an ancillary topic, that would have been undiscovered in other circumstances.
  • The researcher’s deep understanding of the research subjects due to their proximity to them makes the research thorough and precise. 
  • It helps the researcher to be flexible and respond to individual differences while capturing emerging information. Allows the researcher to be responsive to individual differences and to capture emerging information.

Disadvantages of Field Research

  • The researcher might not be able to capture all that is being said and there is the risk of losing information.
  • The quality of the information derived is dependent, on the researcher’s skills.
  • Significant interactions and events may occur when an observer is not present.
  • Some topics cannot easily be interpreted by mere observation.g., attitudes, emotions, affection).
  • The reliability of observations can be complex due to the presence of multiple observers with different interpretations.
  • It requires a lot of time (and resources)and can take years to complete.
  • The researcher may lose objectivity as they spend more time among the members of the group.
  • It is a subjective and interpretive method that is solely dependent on the researcher’s ability.

Field research helps researchers to gain firsthand experience and knowledge about the events, processes, and people, being studied. No other method provides this kind of close-up view of the everyday life of people and events. It is a very detailed method of research and is excellent for understanding the role of social context in shaping the lives, perspectives, and experiences of people. Alongside this, it may uncover aspects of a person that might never have been discovered.

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  • Data Collection
  • field research
  • qualitative research
  • Angela Kayode-Sanni

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Module 2: Sociological Research

Field research, learning outcomes.

  • Explain the three types of field research: participant observation, ethnography, and case studies

The work of sociology rarely happens in limited, confined spaces. Sociologists seldom study subjects in their own offices or laboratories. Rather, sociologists go out into the world. They meet subjects where they live, work, and play. Field research refers to gathering primary data from a natural environment without doing a lab experiment or a survey. It is a research method suited to an interpretive framework rather than to the scientific method. To conduct field research, the sociologist must be willing to step into new environments and observe, participate, or experience those worlds. In field work, the sociologists, rather than the subjects, are the ones out of their element.

The researcher interacts with or observes a person or people and gathers data along the way. The key point in field research is that it takes place in the subject’s natural environment, whether it’s a coffee shop or tribal village, a homeless shelter or the DMV, a hospital, airport, mall, or beach resort.

A man is shown taking notes outside a tent in the mountains.

Figure 1. Sociological researchers travel across countries and cultures to interact with and observe subjects in their natural environments. (Photo courtesy of IMLS Digital Collections and Content/flickr and Olympic National Park)

While field research often begins in a specific setting , the study’s purpose is to observe specific behaviors in that setting. Field work is optimal for observing how people behave. It is less useful, however, for understanding why they behave that way. You can’t really narrow down cause and effect when there are so many variables to be factored into a natural environment.

Many of the data gathered in field research are based not on cause and effect but on correlation. And while field research looks for correlation, its small sample size does not allow for establishing a causal relationship between two variables.

BeyoncÉ and LADY gaga as sociological subjects

Two pictures depict Lady Gaga and Beyoncé performing.

Figure 2. Researchers have used surveys and participant observations to accumulate data on Lady Gaga and Beyonce as multifaceted performers. (Credit a: John Robert Chartlon/flickr, b: Kristopher Harris/flickr.)

Sociologists have studied Lady Gaga and Beyoncé and their impact on music, movies, social media, fan participation, and social equality. In their studies, researchers have used several research methods including secondary analysis, participant observation, and surveys from concert participants.

In their study, Click, Lee & Holiday (2013) interviewed 45 Lady Gaga fans who utilized social media to communicate with the artist. These fans viewed Lady Gaga as a mirror of themselves and a source of inspiration. Like her, they embrace not being a part of mainstream culture. Many of Lady Gaga’s fans are members of the LGBTQ community. They see the “song “Born This Way” as a rallying cry and answer her calls for “Paws Up” with a physical expression of solidarity—outstretched arms and fingers bent and curled to resemble monster claws.”

Sascha Buchanan (2019) made use of participant observation to study the relationship between two fan groups, that of Beyoncé and that of Rihanna. She observed award shows sponsored by iHeartRadio, MTV EMA, and BET that pit one group against another as they competed for Best Fan Army, Biggest Fans, and FANdemonium. Buchanan argues that the media thus sustains a myth of rivalry between the two most commercially successful Black women vocal artists.

Here, we will look at three types of field research: participant observation, ethnography, and the case study.

Participant Observation

In participant observation  research, a sociologist joins people and participates in a group’s routine activities for the purpose of observing them within that context. This method lets researchers experience a specific aspect of social life. A researcher might go to great lengths to get a firsthand look into a trend, institution, or behavior. Researchers temporarily put themselves into roles and record their observations. A researcher might work as a waitress in a diner, live as a homeless person for several weeks, or ride along with police officers as they patrol their regular beat.

Although these researchers try to blend in seamlessly with the population they study, they are still obligated to obtain IRB approval. In keeping with scholarly objectives, the purpose of their observation is different from simply “people watching” at one’s workplace, on the bus or train, or in a public space.

Waitress serves customers in an outdoor café.

Figure 3.  Who is the sociologist in this photo? It’s impossible to tell! In participant observation, researchers immerse themselves in an environment for a time.  (Photo courtesy of zoetnet/flickr)

At the beginning of a field study, researchers might have a question: “What   really goes on in the kitchen of the most popular diner on campus?” or “What is it like to experience homelessness?” Participant observation is a useful method if the researcher wants to explore a certain environment from the inside.

Field researchers simply want to observe and learn. In such a setting, the researcher will be alert and open minded to whatever happens, recording all observations accurately. Soon, as patterns emerge, questions will become more specific, observations will lead to hypotheses, and hypotheses will guide the researcher in shaping data into results.

Some sociologists prefer not to alert people to their presence. The main advantage of covert participant observation is that it allows the researcher access to authentic, natural behaviors of a group’s members. The challenge, however, is gaining access to a setting without disrupting the pattern of others’ beha vior. Becoming an inside member of a group, organization, or subculture takes time and effort. Researchers must pretend to be something they are not. The process could involve role playing, making contacts, networking, or applying for a job. Whenever deception is involved in sociological research, it will be intensely scrutinized and may or may not be approved by an institutional IRB.  

Once inside a group, participation observation research can last months or even years. Sociologists have to balance the types of interpersonal relationships that arise from living and/or working with other people with objectivity as a researcher.  They must keep their purpose in mind and apply the sociological perspective. That way, they illuminate social patterns that are often unrecognized. Because information gathered during participant observation is mostly qualitative, rather than quantitative, the e nd results are often descriptive or interpretive. This type of research is well-suited to learning about the kinds of human behavior or social groups that are not known by the scientific community, who are particularly closed or secretive, or when one is attempting to understand societal structures, as we will see in the following example. 

Nickel and Dimed (2001, 2011)

Journalist Barbara Ehrenreich con ducted participation observation research for her book Nickel and Dimed . One day over lunch with her editor, Ehrenreich mentioned an idea. How can people exist on minimum-wage work? How do low-income workers get by? she wondered aloud. Someone should do a study. To her surprise, her editor responded, Why don’t you do it?

That’s how Ehrenreich found herself joining the ranks of the working class. For several months, she left her comfortable home and lived and worked among people who lacked, for the most part, higher education and marketable job skills. Undercover, she applied for and worked minimum wage jobs as a waitress, a cleaning woman, a nursing home aide, and a retail chain employee. During her participant observation, she used only her income from those jobs to pay for food, clothing, transportation, and shelter.

She discovered the obvious, that it’s almost impossible to get by on minimum wage service work. She also experienced and observed attitudes many middle and upper-class people never think about. She witnessed firsthand the treatment of working class employees. She saw the extreme measures people take to make ends meet and to survive. She described fellow employees who held two or three jobs, worked seven days a week, lived in cars, could not pay to treat chronic health conditions, got randomly fired, submitted to drug tests, and moved in and out of homeless shelters. She brought aspects of that life to light, describing difficult working conditions and the poor treatment that low-wage workers suffer.

Nickel and Dimed: On (Not) Getting By in America , the book she w rote upon her return to her real life as a well-paid writer, has been widely read and used in many college classrooms. The first edition was published in 2001 and a follow-up post-recession edition was published with updated information in 2011. 

About 10 empty office cubicles are shown.

Figure 4. Field research happens in real locations. What type of environment do work spaces foster? What would a sociologist discover after blending in? (Photo courtesy of drewzhrodague/flickr)

Ethnography

Ethnography is a type of social research that involves the extended observation of the social perspective and cultural values of an entire social setting. Ethnogra phies involve objective observation of an entire community, and they often involve participant observation as a research method.

British anthropologist Bronislaw Malinowski, who studied the Trobriand Islanders near Papua New Guinea during World War I, was one of the first anthropologists to engage with the communities they studied and he became known for this methodological contribution, which differed from the detached observations that took place from a distance (i.e., “on the verandas” or “armchair anthropology”). 

Although anthropologists had been doing ethnographic research longer, sociologists were doing ethnographic research in the 20th century, particularly in what became known as The Chicago School at the University of Chicago. William Foote Whyte’s  Street Corner Society:  The Social Structure of an Italian Slum  (1943) is a seminal work of urban ethnography and a “classic” sociological text. 

The heart of an ethnographic study focuses on how subjects view their own social standing and how they understand themselves in relation to a community. An ethnographic study might observe, for example, a small U.S. fishing town, an Inuit community, a village in Thailand, a Buddhist monastery, a private boarding school, or an amusement park. These places all have borders. People live, work, study, or vacation within those borders. People are there for a certain reason and therefore behave in certain ways and respect certain cultural norms. An ethnographer would commit to spending a predetermined amount of time studying every aspect of the chosen place, taking in as much as possible.

A sociologist studying a tribe in the Amazon might watch the way villagers go about their daily lives and then write a paper about it. To observe a spiritual retreat center, an ethnographer might attend as a guest for an extended stay, observe and record data, and collate the material into results.

The Making of Middletown: A Study in Modern U.S. Culture

In 1924, a young married couple named Robert and Helen Lynd undertook an unprecedented ethnography: to apply sociological methods to the study of one U.S. city in order to discover what “ordinary” people in the United States did and believed. Choosing Muncie, Indiana (population about 30,000), as their subject, they moved to the small town and lived there for eighteen months.

Ethnographers had been examining other cultures for decades—groups considered minority or outsider—like gangs, immigrants, and the poor. But no one had studied the so-called average American.

Recording interviews and using surveys to gather data, the Lynds did not sugarcoat or idealize U.S. life (PBS). They objectively stated what they observed. Researching existing sources, they compared Muncie in 1890 to the Muncie they observed in 1924. Most Muncie adults, they found, had grown up on farms but now lived in homes inside the city. From that discovery, the Lynds focused their study on the impact of industrialization and urbanization.

They observed that the workers of Muncie were divided into business class and working class groups. They defined business class as dealing with abstract concepts and symbols, while working class people used tools to create concrete objects. The two classes led different lives with different goals and hopes. However, the Lynds observed, mass production offered both classes the same amenities. Like wealthy families, the working class was now able to own radios, cars, washing machines, telephones, vacuum cleaners, and refrigerators. This was a newly emerging economic and material reality of the 1920s.

Early 20th century black and white photo of a classroom with female students at their desks.

Figure 5. A classroom in Muncie, Indiana, in 1917, five years before John and Helen Lynd began researching this “typical” U.S. community. (Photo courtesy of Don O’Brien/flickr)

As the Lynds worked, they divided their manuscript into six sections: Getting a Living, Making a Home, Training the Young, Using Leisure, Engaging in Religious Practices, and Engaging in Community Activities. Each chapter included subsections such as “The Long Arm of the Job” and “Why Do They Work So Hard?” in the “Getting a Living” chapter.

When the study was completed, the Lynds encountered a big problem. The Rockefeller Foundation, which had commissioned the book, claimed it was useless and refused to publish it. The Lynds asked if they could seek a publisher themselves.

As it turned out, Middletown: A Study in Modern American Culture was not only published in 1929, but also became an instant bestseller, a status unheard of for a sociological study. The book sold out six printings in its first year of publication, and has never gone out of print (PBS).

Nothing like it had ever been done before. Middletown was reviewed on the front page of the New York Times . Readers in the 1920s and 1930s identified with the citizens of Muncie, Indiana, but they were equally fascinated by the sociological methods and the use of scientific data to define ordinary people in the United States. The book was proof that social data were important—and interesting—to the U.S. public.

Institutional Ethnography

Institutional ethnography is an extension of basic ethnographic research principles that focuses intentionally on everyday concrete social relationships. Developed by Canadian sociologist Dorothy E. Smith, institutional ethnography is often considered a feminist-inspired approach to social analysis and primarily considers women’s experiences within male-dominated societies and power structures. Smith’s work challenges sociology’s exclusion of women, both academically and in the study of women’s lives (Fenstermaker, n.d.).

Historically, social science research tended to objectify women and ignore their experiences except as viewed from a male perspective. Modern feminists note that describing women, and other marginalized groups, as subordinates helps those in authority maintain their own dominant positions (Social Sciences and Humanities Research Council of Canada, n.d.). Smith’s three major works explored what she called “the conceptual practices of power” (1990; cited in Fensternmaker, n.d.) and are still considered seminal works in feminist theory and ethnography.

Sometimes a researcher wants to study one specific person or event. A case study is an in-depth analysis of a single event, situation, or individual. To conduct a case study, a researcher examines existing sources like documents and archival records, conducts interviews, or engages in direct observation and even participant observation, if possible.

Researchers might use this method to study a single case of, for example, a foster child, drug lord, cancer patient, criminal, or rape victim. However, a major criticism of the case study method is that a developed study of a single case, while offering depth on a topic, does not provide broad enough evidence to form a generalized conclusion. In other words, it is difficult to make universal claims based on just one person, since one person does not verify a pattern. This is why most sociologists do not use case studies as a primary research method.

However, case studies are useful when the single case is unique. In these instances, a single case study can add tremendous knowledge to a certain discipline. For example, a feral child, also called a “wild child,” is one who grows up isolated from other human beings. Feral children grow up without social contact and language, which are elements crucial to a “civilized” child’s development. These children mimic the behaviors and movements of animals, and often invent their own language. There are only about one hundred cases of “feral children” in the world.

As you may imagine, a feral child is a subject of great interest to researchers. Feral children provide unique information about child development because they have grown up outside of the parameters of “normal” child socialization and language acquisition. And since there are very few feral children, the case study is the most appropriate method for researchers to use in studying the subject.

At age three, a Ukranian girl named Oxana Malaya suffered severe parental neglect. She lived in a shed with dogs, and she ate raw meat and scraps. Five years later, a neighbor called authorities and reported seeing a girl who ran on all fours, barking. Officials brought Oxana into society, where she was cared for and taught some human behaviors, but she never became fully socialized. She has been designated as unable to support herself and now lives in a mental institution (Grice 2011). Case studies like this offer a way for sociologists to collect data that may not be collectable by any other method.

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Visualizing a field of research: A methodology of systematic scientometric reviews

Chaomei Chen

1 Department of Information Science, College of Computing and Informatics, Drexel University, Philadelphia, Pennsylvania, United States of America

2 Department of Information Science, Yonsei University, Seoul, Republic of Korea

Associated Data

All relevant data are within the manuscript, Supporting Information files, and on Figshare: https://doi.org/10.6084/m9.figshare.9939773.v1 .

Systematic scientometric reviews, empowered by computational and visual analytic approaches, offer opportunities to improve the timeliness, accessibility, and reproducibility of studies of the literature of a field of research. On the other hand, effectively and adequately identifying the most representative body of scholarly publications as the basis of subsequent analyses remains a common bottleneck in the current practice. What can we do to reduce the risk of missing something potentially significant? How can we compare different search strategies in terms of the relevance and specificity of topical areas covered? In this study, we introduce a flexible and generic methodology based on a significant extension of the general conceptual framework of citation indexing for delineating the literature of a research field. The method, through cascading citation expansion, provides a practical connection between studies of science from local and global perspectives. We demonstrate an application of the methodology to the research of literature-based discovery (LBD) and compare five datasets constructed based on three use scenarios and corresponding retrieval strategies, namely a query-based lexical search (one dataset), forward expansions starting from a groundbreaking article of LBD (two datasets), and backward expansions starting from a recently published review article by a prominent expert in LBD (two datasets). We particularly discuss the relevance of areas captured by expansion processes with reference to the query-based scientometric visualization. The method used in this study for comparing bibliometric datasets is applicable to comparative studies of search strategies.

Introduction

Systematic reviews play a critical role in scholarly communication [ 1 ]. Systematic reviews typically synthesize findings from original research in a field of study, assess the degree of consensus or the lack of it concerning the state of the art in the field, and identify challenges and future directions. For newcomers to a field of study, a timely and comprehensive systematic review can provide a valuable overview of the intellectual landscape and guide new researchers to pursue their research effectively. For experienced and active researchers, systematic reviews can be instrumental in keeping their knowledge of the field up to date, especially when involving areas that are potentially relevant but fall outside the immediate topic of one’s interest. Researchers have also studied scientific literature to uncover potentially significant but currently overlooked connections between disparate bodies of literature, notably, as demonstrated in the research of literature-based discovery (LBD) [ 2 – 7 ].

A fast-growing trend is the increase of systematic reviews conducted with the assistance of science mapping tools [ 8 ]. A science mapping tool typically takes a set of bibliographic records of a research field and generates an overview of the underlying knowledge domain, e.g. as with CiteSpace [ 9 , 10 ] and VOSviewer [ 11 ]. For example, systematic reviews facilitated by using CiteSpace include a diverse range of research areas such as regenerative medicine [ 12 ], natural disaster research [ 13 ], greenhouse gas emission [ 14 ], and identifying disruptive innovation and emerging technology [ 15 ]. Similarly, VOSviewer was used in reviews of topics such as citizen science [ 16 ] and climate change [ 17 ]. A scientometric overview of a field of research provides a valuable source of input to conducting systematic reviews, especially in situations when relevant and up-to-date systematic reviews may not be readily available or accessible. The quality of the input data therefore is critical to the overall quality of subsequent analyses and reviews. This practical issue is particularly significant when we need to select subsets of articles from a large pool of available data or when we want to limit the scope of a study to specific disciplines as opposed to open to all disciplines.

Considerations concerning the scope of selection have been discussed in the literature in terms of local, global, and hybrid approaches [ 18 ]. Global maps of science by definition provide a comprehensive coverage of all scientific disciplines [ 19 ], whereas local maps typically focus on selected areas of interest. Hybrid maps may use a global map as a base map and superimpose local maps as overlays [ 20 ]. Generating global maps of science requires a substantial array of resources that are not commonly accessible to most of researchers, whereas resources required for generating local maps are more reachable, especially with the increasing accessible science mapping tools. Global maps are valuable as they provide a broad context of a specific research interest. In practice, generating global maps is resource consuming and demanding, for example, requiring direct access to large data sources that are only accessible to a small number of researchers. Consequently, global maps may not be updated as frequently as needed by end users. For example, a significant update of a global model may not be undertaken for 5 years [ 20 ]. The underlying structure of a research field is often subject to a variety of changes as the scientific literature grows over time [ 21 , 22 ]. Therefore, it is reasonable to question whether an existing global model created a few years ago remains a valid representation of the underlying structure for specific analytic tasks in hand, although the answer may differ at different levels of granularity.

In contrast, approaches of localism face different challenges. For example, a common challenge for individual researchers of science mapping tools such as CiteSpace [ 9 , 10 ], VOSviewer [ 11 ], and HistCite [ 23 ] is to select a representative subset of articles from tens of millions or more of scholarly publications in the Web of Science, Scopus, Dimensions, and/or the Lens. Researchers have used lexical search, citation expansion, and hybrid strategies [ 24 – 26 ].

Complex and sophisticated queries are typically constructed with the input from domain experts and iteratively refined as demonstrated in several in-depths studies [ 25 – 27 ]. We refer such strategies as query-based approaches in this article. The role of relevant and sufficient domain expertise in such approaches is critical, which may lead to a double-edged sword. As we will see in this article, using computational approaches to reduce the cognitive burden from domain experts is a noticeable area of research. Following the principles of LBD to emphasize potentially valuable but currently overlooked connections in scientific literature, it is conceivable that there are undiscovered connections where even domain experts’ input may be limited. Reducing the initial reliance on domain expertise can increase the applicability of query-based strategies.

In this article, we propose a flexible computational approach to the construction of a representative dataset of scholarly publications concerning a field of research through iterative citation expansions. Starting from an initial dataset or even a single seed article, the expansion process will automatically expand the initial set by adding articles through citation links in forward, backward, or both directions. This flexibility is particularly valuable in some common scenarios in practice. For example, Swanson’s article on fish oil and Raynaud’s syndrome [ 6 ] is widely considered as a groundbreaking one in LBD research. A researcher may want to retrieve subsequently published articles that are connected to the groundbreaking article through potentially lengthy citation paths. Such expansions are valuable to preserve the continuity of research literature identified across an extensive time span. In contrast, a researcher may come across a recently published review article of LBD and would like to find previously published articles that lead to the state of the knowledge summarized in the review. For example, in 2017, Swanson’s long-term collaborator Smalheiser published an article entitled “Rediscovering Don Swanson: the past, present and future of literature-based discovery” [ 28 ] and researchers may be interested in tracing several generations of relevant articles.

Pragmatically speaking, the ability to expand any set of articles of interest provides a smooth transition across a local-global continuum. The agility of the approach enables us to improve the quality of an input dataset of scientometric studies by refining the context incrementally. We demonstrate the expansion and evaluation of five datasets retrieved by different strategies on literature-based discovery (LBD). The field of LBD is potentially very broad as it is applicable to numerous scientific disciplines. Swanson’s pioneering studies in the 1980s have been a major source of inspiration [ 6 , 7 , 29 , 30 ], followed by a series of studies in collaboration with Smalheiser [ 31 – 33 ], who recently reviewed the past, present, and future of LBD [ 28 ]. Significant developments have also been made by other researchers along this generic framework, for example, [ 34 – 36 ]. It would be challenging to formulate a complex query to capture a diverse range of relevant research activities, some of which may have drifted away from the core literature of LBD.

Judging the relevance of topics is often situational in nature and therefore challenging even with domain expertise. In LBD, the threshold of relevance is by definition lower than other fields because the focus is on undiscovered connections, which in turn leads to a demand for an even higher level of recall for conducting systematic scientometric reviews. Shneider proposed a four-stage model to characterize how a scientific field may evolve, namely, identifying the problem, building tools and instruments, applying tools to the problem, and codifying lessons learned [ 22 , 37 ]. The application stage may also reveal unanticipated problems, which in turn may lead to a new line of research and form a new field of research. The LBD research may continue along the research directions set off by the pioneering studies in LBD. According to Shneider’s four-stage model, new specialties of research may emerge. A systematic review of LBD should open to such new developments as well as the established ones that we are familiar with. What can we gain by using cascading citation expansions? What might we miss if we rely on the simple query-based search strategy alone? What types of biases may be introduced or avoided by cascading expansions as well as by selecting points of departure?

To address these questions, we develop an intuitive visual analytic method to compare multiple search strategies and reveal the strengths and weaknesses of a specific procedure. We compare a total of five datasets in this study, including a dataset from a simple query with phrases of “literature-based discovery” as a baseline reference and four datasets from cascading citation expansions based on two singleton seed article sets using Dimensions as the source of data.

The rest of the article is organized as follows. We characterize existing science mapping approaches in terms globalism and localism, especially their strengths and weaknesses for conducting systematic reviews of relevant literature. We introduce a flexible citation expansion approach–cascading citation expansion–to improve the data quality for conducting systematic scientometric reviews. We first demonstrate what a query-based strategy may reveal about the LBD research, then we combine the five datasets to form a common baseline for comparing the topics covered by the five individual datasets.

Mapping the scientific landscape

Considerations concerning the scope of a study of scientific literature can be characterized as global, local, and hybrid in terms of their intended scope and applications.

Global maps of science aim to represent all scientific disciplines [ 18 ]. Commonly used units of analysis in global maps of science include journals and, to a much less extent, articles. Clusters of journals are typically used to represent disciplines of science.

Global maps of science are valuable for developing an understanding of the structure of scientific knowledge involving all disciplines. Global maps provide a relatively stable representation of the underlying structure of knowledge, which can be rather volatile at finer levels of granularity. Klavans and Boyack compared the structure of 20 global maps of science [ 38 ]. They arrive at a consensus map generated from edges that occur in at least half of the input maps. A stable global representation is suitable to serve the role of an organizing framework to accommodate additional details as overlays [ 19 , 20 , 39 ]. Global maps may reveal insights that may not be possible at a smaller scale. For example, in studies of structural variations caused by scholarly publications, detecting a potentially transformative link in a network representation of the literature relies on the extent to which the contexts of both ends of the link are adequately represented [ 21 ].

On the other hand, the validity of a global map as a base map is likely to decrease over time as subsequent publications may change the underlying knowledge structure considerably [ 21 ]. Given that updating a global map of science is a very resource demanding task and it may be years before a major update becomes available [ 20 ], is the structure shown in a global map still a valid representation at the intended level of granularity? If a global map is updated too often, it may lose its stability value as an organizing framework to sustain overlays. A fundamental question is not necessarily whether the representation is categorically comprehensive, but whether the structural representation as a context for analyzing the literature is adequate and effective.

To localism, the focus is on communicating the structure of a subject matter of interest at the level of scientific inquiry and scholarly communication, including analytic reasoning, hypothesis generation, and argumentation. Many studies of science and systematic scientometric reviews belong to this category. Scientometric studies commonly draw bibliographic data, especially citation data, from long-established sources such as the Web of Science and Scopus. More recent additions include Microsoft Academic Search, Dimensions, and the Lens. Knowledge domain visualization, for example, focuses on knowledge domains as the unit of analysis [ 10 , 40 , 41 ]. The concept of a knowledge domain emphasizes that the boundary of a research field should reflect the underlying structure of knowledge. Relying on organizing structures such as institutions, journals, and even articles along has the risk of missing potentially significant articles.

Query-based search is perhaps by far the most popular strategy for finding articles relevant to a topic of interest or a field of research. A query-based search process typically starts with a list of keywords or phrases provided by an end user. For example, a query of “ literature-based discovery ” OR “ undiscovered public knowledge ” could be a valid starting point to search for articles in the field of LBD. The quality of retrieval is routinely measured in terms of recall and precision. Initial search for systematic reviews tends to give a higher priority to recall than precision.

Formulating a good query is a non-trivial task even for a domain expert because the quality of a query can be affected by several factors, including our current domain knowledge and our motivations of the search [ 24 ]. Furthermore, if our goal is to identify emerging topics and trends in a research field, which is very likely when we conduct a systematic review of the field, then it would be a significant challenge to formulate a complex query effectively in advance. As a result, iteratively refining queries over lessons learned from the performance of previous queries is a common strategy, especially when combined with the input from relevant domain experts [ 25 , 26 ]. Scatter/Gatherer [ 42 ] is an influential early example of iteratively improving the quality of retrieved information based on feedback.

A profound challenge to query-based search is the detection of an implicit semantic connection, or a latent semantic relation. Search systems have utilized additional resources such as WordNet [ 43 ] and domain ontologies [ 44 , 45 ] to enhance users’ original terms with their synonyms and/or closely related concepts. With techniques such as latent semantic indexing [ 46 ] and more recent advances in distributional semantic learning [ 47 ], the relevance of an article to a topic can be established in terms of its distributional properties of language use. The semantic similarity of an article can be detected without an explicit presence of any keywords from users’ original query. This is known as the distributional hypothesis: linguistic items with similar distributions have similar meanings.

Cascading citation expansion

In this article, we conceptualize a unifying framework that accommodates both globalism and localism as special cases on a consistent and continuous spectrum through a combination of query-based search and cascading citation expansion. Under the new conceptualization, the coverage of a local map can grow incrementally as the expansion process may continue as many generations of citation as needed. Thus, the gap between a local map and a global map can be reduced considerably. Table 1 summarizes the major advantages and weaknesses of globalism and localism along with potential benefits that the conceptualized incremental expansion may provide.

In this article, we demonstrate the flexibility and extensibility of a incremental expansion approach–cascading citation expansion–by applying this methodology to a few common scenarios in research in relation to a field of research of own interest, i.e. literature-based discovery (LBD).

Citation indexing was originally proposed by Eugene Garfield to tackle the information retrieval problem in the context of scientific literature [ 48 ]. While the nature of a citation may vary widely as many researchers have documented [ 49 ], an instance of a citation from one article to another provides evidence of some potentially significant connections. A unique advantage of a citation-based search method is that it frees us from having to specify a potentially relevant topic in our initial query, which is useful to reduce the risk of missing important relevant topics that we may not be aware of.

We often encounter situations in which we have found a small set of highly relevant articles and yet they are still not fully satisfactory for some reasons. For example, if we were new to a topic of interest and all we have to start with is a systematic review of the topic published many years ago, how can we bring our knowledge up to date? If what we have is a newly published review written by a domain expert, how can we expand the review to find more relevant articles? Another common scenario is when we want to construct a relatively comprehensive survey of a target topic, how can we generate an optimal dataset that is comprehensive enough but also contains the least amount of less relevant articles.

When we encounter these situations, an effective method would enable us to build on what we have found so far and add new articles iteratively. Scatter/Gather [ 42 ] was a dynamic clustering strategy proposed in mid-1990s by allowing users to re-focus on query-specific relevant documents as opposed to query-independent clusters in browsing search results. From the citation indexing point of view, articles that cite any articles in the initial result set are good candidates for further consideration. Thus, an incremental expansion strategy can be built based on these insights to uncover additional relevant articles.

Incremental expansion processes can be performed in parallel or in sequence. When an expansion step is applied to a base set S of articles, articles associated with S through citation links are added to S thus expand the set S. Users may define an inclusion threshold such that the expansion is limited to adding articles with sufficient citations. We refer successive citation expansions as cascading citation expansion [ 50 ]. The concept of cascading citation expansion is intrinsic to the general framework of citation indexing. An expansion process is capable of generating an adequate context for a systematic scientometric review of a research field by incrementally retrieving more and more articles from the literature.

Cascading citation expansion requires a constant programmatic access to a master source of scientific articles. We utilize the Dimensions API to access their collection of over 98 million publications (at the time of writing). A cascading citation expansion process may move forward and backward along citation paths. If the entire universe of all the scientific publications is completely reachable from one article to another, then cascading citation expansion will eventually reach all the publications, which means that one can achieve a coverage as large as we wish. If the entire universe is in fact made of several galaxies that are not reachable through citation links, the question is whether this is desirable to transcend the void and reach other galaxies. Strategies using hybrid lexical and semantic resources may become useful in such situations, but in this study we assume it is acceptable to terminate the expansion process once citation links associated with a chosen starting point are exhausted.

Fig 1 illustrates the cascading citation expansion process. Given an initial set of seed articles, which can be a singleton set, a 1-generation forward citation expansion will add articles that cite any member article of the seed set directly, i.e. with a one-step citation path. A 2-generation forward expansion will add articles connecting to the seed set with two-step citation paths. We include a 5-generation forward expansion in this study.

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Use scenarios and corresponding search strategies

Here we consider two common scenarios in research. In the first scenario, we have identified a well-known classic work, for example, Swanson’s 1986 article on fish oil and Raynaud’s syndrome [ 6 ], and would like to retrieve all follow-up studies and articles that cited the original work directly or indirectly since 1986. What are the newly developed major topics ever since? What are the hottest and the most far-reaching topics in more recent years? Are there any areas branching off the main paths?

In the second scenario, we are attracted to a very interesting recently published article and we would like to collect relevant studies in the past that lead to the article, i.e., its intellectual base. Smalheiser’s 2017 review of LBD [ 28 ] is an example. Smalheiser has co-authored with Swanson on several landmark studies in the development of literature-based discovery. Smalheiser cited 71 references in his review. What would be a broader context of Smalheiser’s 71-reference review? Are there LBD-relevant topics but excluded by the authoritative domain expert?

Pragmatically, if we were to rely on the simple full text search alone, how much would we miss? Are there topics that we might have missed completely? What would be an optimal search strategy that not only adequately captures the essence of the development of the field but also does in the most efficient way? Borrowing the terminology from information retrieval, an optimal search strategy should maximize the recall and the precision at the same time.

Cascading citation expansion functions are implemented in CiteSpace based on the Dimensions’ API. The expansion process starts with an initial search query in DSL, which is Dimensions’ search language. Users who are familiar with SQL should be able to recognize the resemblance immediately. The result of the initial query forms the initial set of articles. In fact, in addition to publications, one can retrieve grants, patents, and clinical trials from Dimensions. In this study, we concentrate on publications.

Constructing five datasets of literature-based discovery

To demonstrate the flexibility and extensibility of the incremental expansion approach, we take the literature-based discovery (LBD) research as the field to study. We choose LBD for several reasons: 1) we are familiar with the early development of the domain, 2) we are aware of a recent review written by one of the pioneer researchers and we would like to set it in a broader context, and 3) we would like to take this opportunity to demonstrate how one can apply the methodology to a visual exploration of the relevant literature and develop a good understanding of the state of the art. These reasons echo the common scenarios discussed earlier.

Table 2 summarized the construction of the five datasets included in the study, including key parameters such as citation thresholds.

Fig 2 illustrates the process of the comparative study of five datasets retrieved based on a query-based search and cascading citation expansions. In this study, we applied a citation filter in cascading citation expansions to the selection of citing and cited articles. Articles with citations below the threshold are filtered out from the expansion processes. These filters provide users with a flexible trade-off option between concentrating on major citation paths with a reduced completion time versus retrieving articles comprehensively with a much longer completion time. Since the distribution of citations of articles follows power law, a comprehensive expansion process may become too long to be viable for a daily use of these functions. The DSL query searched for Swanson’s two articles published in 1986, namely, the fish oil and Raynaud’ syndrome article 1986a [ 6 ] and the undiscovered public knowledge article 1986b [ 7 ]. The query search found 1,777 articles as the set F for Full data search. Swanson 1986a is used as the seed article for two multi-generation forward citation expansions, one for 3 generations (as set S 3 ) and the other for 5 generations (as set S 5 ). S 3 contains 748 articles, whereas S 5 is about 60 times larger, containing 45,178 articles. The other two datasets are expanded from Smalheiser’s 2017 review as the seed, N F and N B , where N is for Neil, Smalheiser’s first name. Smalheiser’s review contains 71 references. At the time of the experiment, a forward expansion from it found two articles that cite the review. The 73 articles form the set N F . The N B set is obtained by applying backward citation expansions on the set N F . The expansion stopped in 1934 with 2,451 articles. The five datasets are combined as the set All 5 , containing 48,298 unique articles. S 5 contributed most of articles to the combination. The five datasets overlap to a different extent. S3 is a subset of S5. NB expands from NF. F overlaps with S5 the most (702 articles out of its 1,777 articles). Each of the five individual datasets and the combined dataset are visualized in CiteSpace as networks of co-cited references with thematic labels for clusters.

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Given a dataset, its core is defined by references that satisfy two conditions, inspired by [ 24 ]: 1) global citation scores (GCS) in Dimensions are greater than two and 2) the ratio between local citation scores (LCS) within the analyzed dataset to corresponding GCS are greater than or equal to 0.01. According to [ 24 ], the core represents articles with a sufficient specificity to the field of research in question. The core will downplay the role of an article that has a very high GCS but a low LCS because it suggests that the article probably belongs to a field elsewhere. It may be possible for an article to have its citations evenly split across multiple fields, but its LCS/GCS ratio in any of these fields should have a good chance to qualify the article for the core.

Researchers have used main paths of a citation network to study major flows of information or the diffusion of ideas [ 51 , 52 ]. Main paths of a dataset are derived from the corresponding direct citation networks of the dataset. Direct citation networks are generated in CiteSpace with GCS of 1 as the selection threshold. Pajek is used to select main paths based on Search Path Link Count (SPLC) using top 30 key routes found by local search.

Datasets, their cores, main paths, and clusters can be used as the initial seed set for cascading citation expansion. They can all be used as network overlays in CiteSpace to delineate the scope of a research field at various levels of granularity.

Fig 3 shows logarithmically transformed distributions of the five datasets. The distributions shown under the title are the original ones.

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  • The F dataset (in blue) is evenly distributed except a surprising peak in 2009, which turns out due to many articles from an encyclopedia. The number of articles each year ranges between 60 and 130.
  • Both S 5 (red) and S 3 (orange) are forward expansions starting with Swanson’s 1986 article on fish oil and Raynaud’s syndrome [ 6 ]. In S 3 , the inclusion threshold was at least 10 citations, whereas it was 20 in S 5 so as to keep the total processing time down. The majority of the articles in S 3 appeared between 2006 and 2018. S 3 had the first peak in 1984. It didn’t return to the same level for the next 10 years until it started to climb up from 2003 and reached the second peak in 2012. In contrast, the distribution of the more extensive forward expansion S 5 shows a steady increase all the way over time.
  • N F , a forward expansion from Smalheiser’s review of Swanson’s work [ 28 ], includes articles that cited the references used in Smalheiser’s review. Its distribution steadily increased between 1986 and 2017 with a rise of 5 over the 31-year span till 2016.
  • N B , a backward expansion from the set N F , contains articles that are cited by N F . The earliest article in N B was published in 1936. A noticeable hump from 1983 followed by a valley around early 1990s. Two peaks appeared in 2006 and 2011, respectively.

We will focus on how these datasets differ in terms of networks of co-cited references in the following analysis. There are of course many other ways to conduct scientometric studies based on these datasets, but we will limit to the co-citation networks generated with CiteSpace. It is important to note that co-citation networks in CiteSpace include much more information than a classic co-citation network, notably including various indicators and thematic labels derived from citing articles to clusters of co-cited references, year-by-year concept labels to track the evolution of a cluster, and a hierarchical representation of concept terms extracted from citing articles’ titles and abstracts. Some of these features will be illustrated in the following sections.

The five datasets and the combined dataset are processed in CiteSpace with the consistent configurations. In particular, the link-to-node ratio is 3, look back years is 10, annual citation threshold is 2, and the node selection is based on g-index with a scaling factor of 30. These configuration settings are derived empirically, which tend to identify meaningful patterns. Given a dataset, CiteSpace first develops a network model by synthesizing a time series of annual networks of co-cited references. Then the synthesized network is divided into clusters of cited references. Themes in each clusters are identified based on noun phrases extracted from citing articles’ titles and abstracts. Citing articles to a cluster are defined as articles that cite at least one member of the cluster. Extracted noun phrases are further computed to identify the most representative ones as the thematic labels for their cluster. CiteSpace supports three ways to select cluster labels based on Latent Semantic Indexing, Log-Likelihood Ratio Test, and Mutual Information [ 53 ]. CiteSpace supports a built-in database. Many attributes of datasets can be compared with the database. The core of a dataset, for example, can be identified using SQL queries.

Interactive visualizations in CiteSpace support several views, i.e., types of visualization, including a cluster view, a timeline view, a history view, and a hierarchical view. A network can be superimposed to another network as a layer. A list of references can be superimposed to a network as well. We use this feature to overlay the core and main paths of a dataset to its own network or to a network of another dataset. CiteSpace reports network and cluster properties such as modularity and silhouette scores. The modularity score of a network reflects the clarity of the network structure at the level of decomposed clusters. The silhouette score of a cluster measures the homogeneity of its members. A network with a high modularity and a high average of silhouette scores would be desirable. We will focus on the largest connect component of each network, which is shown as the default visualization. Users may choose to reveal all components of a network if they wish.

Table 3 summarizes various properties of the five individual datasets and the combined set along with their networks and core references. The F dataset, for example, contains 1,777 articles, which in turn cite 30,606 unique references. Among them, 367 references are identified as the core references based on the LCS/GCS ratio and the threshold of 3 for GCS. The resultant network contains 1,269 references as nodes and 5,937 co-citation links. The largest connected component (LCC) consists of 1,029 references, or 74% of the entire network. The modularity with reference to the clusters is 0.76, indicating a relatively high level of clarity. The average silhouette score of 0.34 out of 1.0 is moderate.

CiteSpace Configuration: Network: LRF = 3, LBY = 10, e = 2.0, g(30); Core: GCS>2; LCS/GCS≥ 0.01.

The modularity of a network measures the clarity of the network structure in terms of how well the entire network can be naturally divided into clusters such that nodes within the same cluster are tightly coupled, whereas nodes in different clusters are loosely coupled. The higher the modularity is, the easier to find such a division. The silhouette score of a network measures the average homogeneity of derived clusters [ 53 ]. The higher the average silhouette score is, the more meaningful a group is in terms of a cluster. The smallest dataset N F has the highest modularity of 0.93. It also has the highest average silhouette score of 0.50. The full text search has the modularity of 0.76, which is slightly lower than N B , but its silhouette value of 0.34 is lower than others except S 5 .

Literature-based discovery

In this section, we visualize the thematic landscape of the field of literature-based discovery from multiple perspectives of the five datasets. We will start with the full text search results and then cascading citation expansions.

Full text search

The query for the full text search on Dimensions consists of ‘literature-based discovery’ and ‘undiscovered public knowledge.’ The phrase ‘literature-based discovery’ is commonly used as the name of the research field. The phrase ‘undiscovered public knowledge’ appears in the titles of two Swanson’s publications in 1986. One is entitled “Fish oil, Raynaud’s Syndrome [ 6 ], and Undiscovered Public Knowledge” in Perspectives in Biology and Medicine and the other is “Undiscovered Public Knowledge” in Library Quarterly [ 7 ].

The full text search found 1,777 records. Dimensions’ export center supports the export of up to 50,000 records to a file in a CSV format for CiteSpace [ 9 , 40 ]. Publication records returned from Dimensions do not include abstracts. We found 431 matched records in PubMed with their abstracts, but in this study the analysis is based on the full set of 1,777 records regardless they have abstracts or not because we primarily focus on the references they cite.

Fig 4 shows an overview map of LBD according to the dataset F. The color of a link indicates the earliest year when two publications were co-cited for the first time in the dataset. In this visualization, the earliest work appeared from the top of the network, whereas the most recent ones appeared at the bottom, although CiteSpace does not utilize any particular layout mechanisms to orient the visualization. The network is decomposed into clusters of references based the strengths of co-citation links. Clusters are numbered in the descending order of their size. The largest one is numbered as #0, followed by #1, and so on. Fig 4 also depicts the core of the F set as an overlay (in green) and its main paths (in red).

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CiteSpace configuration: LRF = 3, LBY = 10, e = 2.0, g-index (k = 30). Network: 1,269 references and 5,937 co-citation links.

The largest cluster is #0 machine learning. More recent clusters, further down in the visualized network, include #1 semantic predication, and #7 citation network.

Fig 5 shows more features of the dataset F through colormaps of time (a), an overlay of its core (b), its and main paths (c). Labeled nodes on main paths include Swanson DR (1986), which also appears to be one of the oldest core references, Swanson DR (1997), Smalheiser (1998), Webber W (2001), and the most recently Cohen T (2010).

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Fig 5 also contrasts references cited by Smalheiser’s 2017 review of LBD and the main paths (d) and references cited by another LBD review published in 2017 by Sebastian et al. [ 5 ] in part (e). There are several notable differences. Torvik VI (2007) cited in Smalheiser’s review was not on the main paths. In contrast, a few articles on the main paths such as Hunter L (2006), Zweigenbaum P (2007), and Agarwal P (2008) are not cited in Smalheiser’s review. Similarly, Sebastian et al.’s review also cited Torvik VI (2007) and a few other articles off the main paths, including Kostoff RN (2009), Chen C (2009), and Kostoff RN (2008). The general area of Sebastian et al.’s review overlaps with that of Smalheiser’s review considerably except Sebastian et al.’s review reached further towards cluster #7 citation network. The boundaries of clusters are show in distinct colors in part (f) of Fig 5 . According to the colored cluster areas, the main paths go through #0 machine learning, whereas the two LBD reviews did not.

In Fig 6 , references cited by the two LBD review articles are shown as overlays on a timeline visualization. Each cluster is shown horizontally and advances over time from the left to the right. Both LBD reviews make substantial connections between #1 semantic predication and #6 validating discovery. Given the recency of #1 semantic predication, the role of semantic predication is significant. Our own ongoing research also investigates the role of semantic predication in understanding uncertainties of scientific knowledge [ 22 ]. Sebastian et al.’s review reached further down to #8 biomarker discovery, which was not cited in Smalheiser’s review.

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Comparing five individual datasets

In order to compare the coverage of individual datasets, we construct a baseline map based on the combined dataset. The base map is created with the same procedure that was applied to individual datasets. The base map is divided into clusters. As a measure of the specificity of a dataset, we calculate the K-L divergence between normalized GCS and LCS scores. A low K-L divergence would suggest that the dataset is representative of the underlying field of research, whereas a high K-L divergence would indicate that the dataset contains many out-of-place articles ( Table 4 ). S 3 has the lowest K-L divergence. F and N F have similar scores. N B and S 5 have higher scores as expected given their size.

The overview of a visualized network based on the combined dataset is shown in Fig 7 . The colors in the map on the left depict the time of a link is added. For example, the youngest areas are located towards the lower left of the network, whereas the oldest ones are located near the top. The colors in the map on the right are encoded to depict the membership of clusters. The largest cluster is shown in red, following a rainbow colormap, so that we will know the relative size of a cluster. We will overlay networks from each individual dataset to identify what each search strategy brings unique topics to the overall landscape.

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Modularity: 0.84. Silhouette: 0.34.

Table 5 lists the distribution of each of the largest 10 clusters in the combined network across the five individual datasets. Thematic labels of each cluster include terms selected by Latent Semantic Indexing (LSI) and by log-likelihood ratio. The former tends to identify common themes, whereas the latter tends to highlight unique themes. The two selections may differ as well as agree. Among the 10 largest clusters, the oldest one is #4 information retrieval with 1990 as the average year of publication. The youngest one is #8 deep learning with 2014 as the average year of publication. The largest cluster #0 systems biology/protein interaction network has the lowest silhouette score, which is expected given its size of 284 references. #6 microRNAs/drosophila melanogaster development has the highest silhouette score of 0.967, followed by the 0.965 of #7 big data, suggesting both them are highly uniformed.

The distributions of clusters across individual datasets show that clusters 0–1 and 3–5 are well represented in F with over 50% of the members of these cluster (highlighted in the table). N B is essentially responsible for Cluster #6, whereas S 5 is responsible for #7 big data and #8 deep learning. Independently we can identify the unique contributions associated with #6, #7, and #8 from network overlays shown in Fig 8 .

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Fig 8 shows a set of network overlays of individual datasets (in red) and core reference overlays of the F and S 5 datasets. The three circles in part g highlight the three unique clusters. #6 is contributed essentially by N B , whereas #7 and #8 are captured by S 5 . The effect of cascading citation expansions is evident. The query-based approach (F) failed to capture #7 big data and #8 deep learning as the 5-generation forward expansion from Swanson’s pioneering article did. Are these clusters relevant enough to be still considered as part of a systematic scientometric review of LBD or rather they should be considered as applications of computational technologies to literature-based discovery? Similarly, #6 microRNAs is missed by forward expansions from Swanson’s 1986 article. What is the basis of its relevance? We will address these questions as follows.

Each cluster can be further analyzed by applying the same visual analytic procedure at the next level, i.e. Level 2. The cluster at the original level is known as a Level-1 cluster. One may continue this drill-down process iteratively as needed. Level-2 clusters are useful for interpreting their Level-1 cluster in terms of more specific topics.

Fig 9 illustrates a few reports from CiteSpace on Cluster #, including a visualization that shows Level-2 clusters of the Level-1 cluster (left), a hierarchy of concepts (top), and year-by-year thematic terms of Level-2 clusters. The hierarchy of concepts, also known as a concept tree, provides a useful context to identify the major themes of a cluster according to the degree of a concept node in the tree, or the number of children in the tree. In this case, RNA interference has the highest degree and it suggests that the cluster’s overarching theme is to do with RNA interference. A concept tree provides an informative context for selecting thematic labels. Labels selected through LSI or LLR do not have the benefit of such contextual information. As shown in Fig 9 , #6 appears to be different from other clusters because connections to the study of scientific literature are not obvious.

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It is a very specific domain on RNA interference.

Since N B is exclusively responsible for the cluster #6, we overlay references cited by Smalheiser’s review, i.e. its footprints, on a network visualization of N B ( Fig 10 ). The NB network consists of two components that are loosely connected with each other. The footprints of Smalheiser’s review mostly appear in the lower component, indicating that the lower component is strongly relevant to LBD. In contrast, the upper component only contains two footprint references, namely Smalheiser NR (2001) and Lugli G (2005). The two references may hold the key to the formation of #6.

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The upper area is responsible for the formation of Cluster #6, which is in turn due to two references cited in Smalheiser in his 2017 review.

We examine the full text of Smalheiser’s review for the contexts in which these two references are cited. As it turns out, Smalheiser cited the two references as atypical examples of LBD that “arose haphazardly during the course of laboratory investigation” and they are unlike typical LBD examples, in which complementary bodies of literature were purposefully sought after. Lugli G (2005) was cited in the first example of how Smalheiser and his colleagues put two lines of studies together that involved concepts such as double-stranded RNA, which is featured in the concept tree of #6 in Fig 9 . Smalheiser NR (2001) was cited in the second example of atypical LBD, which was about RNA interference in mammalian brain. It took them a decade to find provisional evidence that may valid the discovery in 2012.

Now it becomes evident that the upper component is connected to LBD through this specific connection. Thus the inclusion of #6 by expansion is reasonable. On the other hand, if the upper component does not contain any other references specifically relevant to LBD, then it seems to be necessary to investigate whether the N B expansion should be cut short in this area.

The query-based search (F) did not capture clusters #7 big data and #8 deep learning. As shown in Fig 11 , the red lines indicate the coverage of F. No red lines even remotely approach to either of the clusters.

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The relevance of Cluster #8 deep learning is investigated as follows. Fig 12 depicts a drill-down analysis of Cluster #8 deep learning, which is the youngest cluster among the 10 largest Level-1 clusters. The concept tree of the cluster identifies deep learning as the primary theme. More specifically, the concept of deep learning appears in contexts that are relevant to LBD, namely in association with drug discovery and biomedical literature. Level-2 clusters include #0 deep learning, #1 deep learning, #2 ensemble gene selection, #3 neuromorphic computing, #4 drug discovery, and #5 medical record. Year-by-year thematic terms include deep learning for the last four years since 2016 along with domain-specific terms such as radiology, breast ultrasound, and precision medicine. Given the multiple connections to biomedical literature, drug discovery, and other domain-specific terms, the cluster on deep learning should be considered as a relevant development of LBD.

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Discussions and conclusions

We have proposed and demonstrated a flexible method to improve the quality of data retrieved for systematic scientometric reviews. We have demonstrated how one may use the approach to develop search strategies to meet the needs in common scenarios in practice. The comparisons of network visualization overlays of five datasets have revealed what a commonly used full text search strategy could have missed. Such omissions are likely to be recently emerged topics and missing them in a systematic review may undermine its overall quality. A strategy that combines query-based search and cascading citation expansion is likely to provide a more balanced coverage of a research domain and to reduce the risk of overly relying on topics explicitly specified in the initial queries. A practical implication on finding a representative body of the literature is its potential to uncover emerging topics that are currently connected to the main body of the literature through a chain of weak links. We recommend researchers to consider this strategy in situations when they only have a small number of relevant articles to begin with. As our study demonstrated, a wide variety of articles can serve as a starting point of an expansion process and multiple processes can be utilized and the combination of their results is likely to provide a comprehensive coverage of the underlying thematic landscape of a research field or a discipline.

The present study has some limitations and it raises new questions that need to be addressed in future studies. Our approach implies an assumption that the structure of scientific knowledge can be essentially captured through semantically similar text and/or explicit citation links. Is this assumption valid at the disciplinary level? To what extent does the choice of the seed articles for the expansion process matter? Does the choice of seed articles influence the stability of the expansion process? How many generations of expansion would be optimal?

We have made a few observations and recommendations that are potentially valuable for adapting this type of search strategies to develop a systematic review of a body of scientific literature of interest.

  • Using a combination of multiple cascading citation expansions with different seed articles is recommended to obtain a more balanced representation of a field than using a full text search alone.
  • Multi-generation citation expansions provide a systematic approach to reduce the risk of missing topics that we may not be familiar with or not aware of altogether.
  • Triangulating multiple aggregations of articles such as the core references of a dataset and main paths of a dataset as well as multiple review articles provide useful insights.
  • The flexibility of the approach enables researchers to apply the expansion and visual analytic procedure iteratively at multiple levels of granularity, for example, expanding a cluster, comparing the footprints of review articles with main paths, and drilling down a cluster in terms of Level-2 clusters.
  • Choosing the starting point and an end point of a cascading expansion process may lead to different results, suggesting the complexity of the networks and threshold selections may play important roles in reproducing the results in similar studies.
  • Modularity and cluster silhouette measures can help us to assess the quality of an expansion process.

Comparing multiple networks in the same context allows us to identify the topic areas that are particularly well represented in some of the datasets but not in other ones. Such an understanding of the landscape of a field provides additional insights into the structure and the long-term development of the field.

As a methodology for generating systematic scientometric reviews of a knowledge domain, it bridges the formally mutual exclusive globalism and localism by providing a scalable transition mechanism between them. The most practical contribution of our work is the development and dissemination of a tool that is readily accessible by end users.

Acknowledgments

We are grateful to Neil Smalheiser for his valuable suggestions on the paper. The work is supported by the SciSIP Program of the National Science Foundation (Award #1633286). CC acknowledges the support of Microsoft Azure Sponsorship. Data sourced from Dimensions, an inter-linked research information system provided by Digital Science ( https://www.dimensions.ai ). This work is also supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2018S1A3A2075114). This research is also partially supported by the Yonsei University Research Fund of 2019-22-0066.

Funding Statement

CC acknowledges the support of the SciSIP Program of the National Science Foundation (Award #1633286), the support of Microsoft Azure Sponsorship. Data sourced from Dimensions, an inter-linked research information system provided by Digital Science ( https://www.dimensions.ai ). MS acknowledges the support of the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2018S1A3A2075114) and partial support from the Yonsei University Research Fund of 2019-22-0066. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability

  • PLoS One. 2019; 14(10): e0223994.

Decision Letter 0

PONE-D-19-16569

Visualizing a Field of Research: A Methodology of Systematic Scientometric Reviews

Dear Dr. Chen,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

I would follow Reviewer 2, who points to several majors issues that can be linked to two fundamental problems. In particular, the promised simplification of the conceptualisation of globalism and localism is not convincingly tackled and to a certain extent, the manuscript lacks proper documentation of methodology and its application. I would like to ask the authors to remedy these issues and also to resolve the minor issues according to the reviewer’s comments.

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Reviewer #2: Yes

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Reviewer #1: The authors have demonstrated how to use cascading citation expansion method to generally increase the body of literature to be included and analyzed. The integration between Dimesions and CiteSpace is beneficial to bibliometricians. The method described in the current manuscript offers an alternative solution to define a research landscape, which can be used in parallel with other bibliometric approaches such as cited reference analysis with CRExplorer.

Reviewer #2: This paper compares different ways of creating scientometric datasets in an attempt to identify a method that maximizes coverage of relevant documents while minimizing extraneous material.

I have several major concerns with the paper.

First, the paper is framed in terms of aiding systematic scientometric reviews and in bridging the local-to-global continuum in terms of datasets. However, the paper does little to address either of these issues. In fact, there is little to no referencing of these framing features throughout the results and discussion other than a simple reprisal of the claims at the very end of the paper. So, while the issues used to frame the paper are real, the body of the paper really doesn’t address the issues. I would thus suggest that the paper be framed more simply in terms of differences between datasets, which is what the paper is really about.

Second, and perhaps more importantly, the purpose of the paper is to contrast various ways of creating a dataset. However, the analysis is a full step removed from this because the results of each query (and the superset) are not directly compared. Instead, these results are run through the CiteSpace co-citation black box which hugely amplifies some queries while dramatically reducing the F5 query and the overall space, in addition to being a citation generation removed from all of the queries. Co-citation does tend to remove the cutting edge of research fronts, and since reviews are often focused on the hot or emerging topics, I fail to see how this method could be a great basis for aiding in reviews. I’m not saying that CiteSpace/co-citation should not be used, but I am saying that the authors need to honestly and accurately characterize what they are really doing. This does not currently come through in the paper.

Related to this, the CiteSpace step (bridging Tables 3 and 4) is all of one or two sentences, and the first impression from comparing the numbers of articles/nodes in Tables 3 and 4 is one of bewilderment. What is going on here. How does NF go from 73 articles to 1903 nodes. Also, why does CiteSpace end up with a much smaller set for F5 and smaller sets for the other queries. This all seems to be magic, and much more detail is required for this transition.

Finally, the comparison is qualitative, which it has to be given the lack of ground truth. On the other hand, the authors chose this subject because of their familiarity with it, and yet fail to mention which of the clusters are within vs. without what they would consider LBD to encompass. They seem to favor the F5 expansion. Are there clusters in the F5 that, because of over-expansion, are really outside the field? We are left wanting a more definitive result.

Minor issues:

Michel Zitt wrote about citation expansion several times, most directly in IPM 2006, but also presaged in Scientometrics 1996 and 2003. Many people have used citation expansion – for instance the original Places&Spaces display had a three-generation backward citation expansion based on Nobel Prize winners and others that was displayed on a science map. Author Chen also has a 2006 paper with forward expansion. So this paper is not introducing citation expansion, but is definitely refining it. Claims in this paper should be adjusted accordingly.

Although creation of global maps is indeed “limited to a small number of researchers”, it is also true that a global model is now available to all users of SciVal, which has over a thousand institutional subscribers, and is being widely used for institutional portfolio analysis. While the detailed information in this global model is not used widely to seed systematic reviews, it could be.

The authors claim that local maps are free from the need for stability. I disagree. If there is any comparison to be done from one point in time to another, stability is still needed, and cannot be achieved by a local map.

When it comes to query-based search, I would suggest using a reference to at least one paper by Alan Porter that contains a detailed multi-part query. Also, to be fair to the detailed multi-part query, these queries are typically designed in a multi-step process. A query is run, results are examined to see what should be dropped and/or added, and this is done multiple times, which results in the detailed query with presumable high precision/recall. This should be mentioned. Porter’s paper will mention this process.

Figure 3 would be far easier to interpret if the y-axis used base10 for its log scale.

In the Figure 7 overlay maps, it is very difficult to tell what is overlay and what is basemap. It would be far easier to distinguish the overlay if the basemap were in gray or some other neutral color, and the overlay were colored.

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Reviewer #1: No

Reviewer #2: No

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Author response to Decision Letter 0

16 Sep 2019

Dear Editor,

First, we would like to thank the reviewers for providing their comments.

We have carefully revised the manuscript based on the reviews. Most of the manuscript has been re-written to improve the clarity of the description. Most of the figures are re-generated as well.

We summarize our responses to the reviewers as follows. The text reflects the changes accordingly.

Best wishes,

Chaomei Chen and Min Song

2. We note that Figure 2 in your submission contains a copyrighted image. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright .

Response: Figure 2 was in fact a screenshot of an interface in CiteSpace for connecting to Dimensions. Thus we would be its owner. Regardless, the figure is now replaced with a new Figure 2 to illustrate the workflow of handling the datasets and major analytic tasks.

________________________________________

Response: We are grateful to the reviewers for providing their comments. We have thoroughly revised the study to address these comments. We have in fact gone through the entire process and updated most of the figures and tables along with interpretations and discussions. As a result, the manuscript has been re-written substantially. Please be aware of the possible shifts in text between the revised version and the original version in terms of references to figures and details such as clusters.

Response: Thank you.

Response: Thank you. This is indeed a very accurate outline of the study.

Response: While the paper indeed focuses on the differences between datasets, the motivation for doing so is driven by the lack of flexibility in access the local-global continuum. The cascading citation expansion presented in the paper offers a method that enables us to iteratively expand a set of articles so as to include relevant articles that are not matched by initial search queries. Therefore we believe this paper offers a concrete method in response to the local-global dilemma when researchers try to find publications with high recall (in theory in favor of global approaches) and up-to-date structures (as global structures may not be updated as frequently as needed). As we show in the paper, with cascading citation expansions in different combinations, the boundaries of the topic of interest are no longer limited by initial search.

Another advantage of citation-based expansion is to reduce the burden of having to rely on domain experts in constructing complex queries and thus to shift the focus of domain experts from the question of what we may miss to the question of whether a topic in front of us is relevant enough.

The local-global continuum provides a necessary context of the study also in connection to the principles of literature-based discovery, namely to discovery new connections, including individual domain experts may not be aware of, as one cannot expect to have access to domain experts who know everything about the research area to be surveyed.

We have revised the text with reference to the role of the local-global continuum.

Response: Thank you for your comments. We added direct comparisons between different datasets with new tables (Tables 3, 4). In general, they capture many articles that are not in the F set, i.e. the query search result. For example, the S5 set contains 44,883 articles with only 702 of them found in F. More details can be found in the manuscript.

There are a few reasons for using CiteSpace. First, the original motivation of the study is to improve the quality of systematic scientometric studies supported by CiteSpace and a few other tools such as VOSviewer. Improving the quality of input data is a common need for applications of scientometric tools. Second, CiteSpace allows us to inspect the interrelationships between datasets obtained from different expansions in context so as to contrast which areas are covered well by particular expansions and which areas are underrepresented. The visualization process retains articles with at least 2 citations in at least one year. The resultant map does not represent all the articles in a dataset, only those with sufficient citations. This is a justifiable selection and it is used widely in practice, including global approaches.

Co-citation maps in CiteSpace have grown out of the classic notion of co-citation considerably by incorporating information from citing articles to a great extent. Perhaps we ought to give it a new name to indicate the difference instead of keeping calling it a co-citation network. Notably, clusters shown in the interactive visualization represent a duality between citing articles and cited references. Cluster labels are noun phrases extracted from citing articles to cited references in corresponding clusters. Year-by-Year labels of a cluster provide detailed information on emerging topics of citing articles. For example, clusters such as big data and deep learning are identified in the study. Citing articles to these clusters feature many articles as recent as 2018, considering the data was collected in early 2019. CiteSpace does support functions for generating maps based on bibliographic coupling. In this study, we limit our focus to how cascading citation expansion may affect subsequently generated science maps. We believe citation/co-citation provides an additional vetted information to the bibliometric landscape that alternative methods may not readily reveal. Note that there are a wide variety of possible routes to conduct a visual analytic study of a given dataset. Here these examples are primarily provided to demonstrate the major differences of different strategies of data collection.

As a side note, the ‘black-box’ process in CiteSpace has many user-controllable parameters for users to verify the role of each parameter in the workflow, although the complexity involved in the process is indeed non-trivial in terms of the number of decisions to make when selecting a particular workflow to proceed.

We update the text accordingly to clarify the above points and to provide more detailed descriptions of the steps.

Response: We have revised the descriptions to make them self-contained and added references to publications that describe the relevant information in further detail. The two tables are updated as well.

The Nf dataset contains 73 articles, or source articles, or citing articles. The 73 articles cite 2,239 unique references. 1,903 of them satisfied the selection criteria based on local citation counts and they become the nodes in the corresponding network of references for Nf.

Similarly, S5 contains a much larger number of citing articles, which in turn cited an even larger number of references. References must meet the selection criteria to be retained. As shown in publications such as van Noorden et al. (2014) the majority of the paper mountain has papers with zero citations globally.

We have revised the text accordingly to clarify the details.

Response: We give a higher priority to reduce the risk of missing important relevant articles than including less relevant ones. In part, we share the view with the literature-based discovery in general in that we consider it is critical to bring otherwise disparate bodies of scholarly publications to the attention of researchers. Furthermore, the quality of the coverage directly impacts subsequent analytic tasks, including the assessment of relevance of a given set of articles retrieved.

In the revised version, we particularly discuss the relevance issue with reference to clusters on big data, deep learning, and human metabolisms. The first two clusters are considered relevant with reference to the literature-based discovery research based on the roles they played in those studies (through inspections in cluster explorer), whereas the third one (cluster #6), with a specific focus on domain specific topics such as microRNA and RNA interference and without strong evidence of connections to literature-based discovery, is considered beyond the scope of literature-based discovery.

We have revised the text accordingly.

Response: Michael Zitt (2006) in IPM is indeed relevant and useful. We have added an overlay of the core references of a dataset based on the ratio of local- and global citations. The core list is useful as an additional level of aggregation of references to highlight the boundary of the core references.

Theoretically, the concept of citation expansion is indeed conceivable from the original introduction of citation indexing. Pragmatically, the workflow has remained to be outside of the access of most researchers. We emphasize the practical contribution of the cascading part of the citation expansion, which would require a sustained access to the source database. Therefore, we welcome the new opportunities enabled by the Dimensions API. The 5-generation citation expansions illustrated in the paper may continue further and thus reduce the gap between a local- and a global view of the literature. More importantly to us, our work enables many researchers to adopt the workflow by applying the freely available functions supported in CiteSpace to the vast coverage of the Dimensions platform.

We have adjusted our claims in the revised text accordingly.

Response: Yes, it could. Updating a global model is much more time/effort consuming than creating a local model from scratch. According to Borner et al. (2012), the 2005 USCD map was created in 2007. Its next version 2010 USCD map was reported in 2012, adding six years of data from WoS and 3 years from Scopus. The issue is how long the global maps can stay valid as the structure depicted by the existing model is constantly subject to the changes introduced by new publications and how sensitive the current model to changes that may present only at a finer level of granularity, e.g., as shown in local maps. What we contribute here is a compromise that offers an increased flexibility and agility.

Response: The sentence you referred to is probably this one: “Localized science maps are free from the need to maintain a stable structure.” What we intend to convey here is that for local maps the structure is constructed based on the current data, thus the most recent data is taken into account. In contrast, if a global map is created a few years ago, then it is a legitimate question whether any subsequently published articles might have altered the structure significantly enough, especially at finer levels of granularity.

If we define global maps as the ones featuring all scientific disciplines (Borner et al. 2012), then it is certainly achievable by a local map to make comparisons. What we need in such scenarios is a sufficiently large context to accommodate snapshots taken at different time points. The Link Walkthrough function in CiteSpace allows users to step through the visualized network one time slice at a time so that users can see which areas are covered in a particular time slice.

Response: We have added the following papers to the related work/discussion, two by Porter and his coauthors and one by Kostoff et al. They are in the same category of constructing complex queries through iterative processes. The key strength of such approaches is the input of domain-specific expertise, which also makes it a potential weakness of the method if the domain expertise is not readily accessible. It is cognitively more demanding to think of what is missing in the queries than to review whether specific articles retrieved are indeed relevant. Besides, multi-part complex query approaches can also benefit from cascading citation expansion as the initial burden on the domain experts would be much reduced.

In addition, in consistent with the basic idea of literature-based discovery, it is valuable to undiscover disparate bodies of relevant literature that even domain experts may not be aware of. In the IPM paper you recommended to us , Zitt identified some of the issues with query-based approaches: “The reasons for the absence are various: terms forgotten by experts; terms deliberately excluded because of the fear of noise generated by too general or ambiguous formulation; terms deliberately excluded as deemed out of the scope of the topic.” P. 1526.

• Huang Y, Schuehle J, Porter AL, Youtie J. A systematic method to create search strategies for emerging technologies based on the Web of Science: illustrated for ‘Big Data’. Scientometrics. 2015;105(3):2005-22. doi: 10.1007/s11192-015-1638-y.

• Porter AL, Youtie Y, Shapira P, Schoeneck DJ. Refining search terms for nanotechnology. Journal of Nanopartical Research. 2008;10(5):715-28. doi: 10.1007/s11051-007-9266-y.

• Kostoff RN, Koytcheff RG, Lau CGY. Technical structure of the global nanoscience and nanotechnology literature. Journal of Nanopartical Research. 2007;9(5):701-24.

Response: Thank you for your comments. Revised as such.

Thanks again for reviewers’ comments!

Submitted filename: Response to Reviewers.docx

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Getting to the Source: The Importance of Field Research

An academic and intellectual decline is inevitable without a post-pandemic revival of fieldwork.

Wednesday, April 7, 2021 / By: Alastair Reed, Ph.D. ;  Boglarka Bozsogi

Publication Type: Analysis

Travel restrictions and social distancing practices put in place in response to the COVID-19 pandemic have largely ground field research to a halt. Fieldwork plays an essential but often underappreciated role in both understanding violent extremism and developing policy responses to it. It is vital, therefore, that funders and policymakers support the return of such important work in a post-pandemic world.

Students from the Center for Conflict and Humanitarian Studies conduct a research field visit in Sri Lanka. November 2017. (Center for Conflict and Humanitarian Studies/Wikimedia Commons)

Fieldwork brings important local perspectives to the fore, helping to contextualize conflicts within their wider ecosystems and societal and cultural realities. This forces researchers to challenge their preconceptions and theoretical assumptions as they come face to face with the realities on the ground. And, perhaps most importantly, fieldwork brings to life the human dimension — the human suffering and resilience of the communities affected by violence and the motivations and drivers of the violent actors.

Without understanding the view from the ground, we will continue to struggle to understand violent extremism and develop effective policy responses. 

The Human Side

As many field researchers will admit, there is something about the smell and feel of a place that being on the ground provides and that reading reports and analyzing data cannot capture. On the ground, a researcher has the opportunity to diversify their primary sources and data. They can also better appreciate and absorb the context of the conflict. Without understanding the human side, the unique cultural and societal setting and the physical geography and climate, which together forge the contours within which the violence evolves, we can only have a partial understanding of the conflict ecosystem.

“The value of engagement with human beings cannot be underestimated,” Haroro Ingram, a senior research fellow at the Program on Extremism at George Washington University and member of the RESOLVE Research Advisory Council, told a recent RESOLVE Forum session.

Absorbing the context can help the researcher understand and interpret the collected data, but also to reinterpret what they learned from desk-based studies. The subjective experience of sharing is humbling; it offers an intellectual appreciation not only of the complexity on the ground but also of the breadth and depth of the literature and its gaps.

Researchers are only human and bring along preconceived perceptions, biases and assumptions — implicit or explicit — internalized from academic literature and media reports. Seeing the realities on the ground forces them to confront these preconceived assumptions and challenge, reinterpret or discard them. Theoretical explanations and conceptual analysis can only be tested when applied against the world they purport to explain. Field research gives us a chance to improve and develop our understanding, and a chance to glimpse the unknown unknowns, the missing factors that we cannot see or conceive from our academic ivory towers.

It is easy to overlook the human side — the victims of violence and conflict-affected communities that bear the brunt of the human tragedy of extremism — when researching a conflict from a distance. Observing and talking to the most affected communities reminds us of the horrors of war and the depths of depravity humanity can sink to. However, it also brings to light the human side of violent actors on all sides, an insight into the motivations and drivers that led them down the path to violence. Conflicts are ultimately about people; attempts to understand conflicts need to start with understanding the people that drive them. To do that, field researchers need to adopt a methodical approach, informed by the literature, and ensure their research and findings are triangulated, ethical and trustworthy.

“Mindanao, in the last 50 years, has experienced cycles of failed peace processes that international actors tried to support with a top-down understanding, often from a distance, in the absence of genuine bottom-up, grassroot perspectives,” said Ingram, who focuses his field research on the Middle East and Southeast Asia. “Since the most important actors in the grassroots population do not have electricity, let alone internet, the only effective outreach is getting to the source to build trust, engage with communities respectfully and learn of cultural subtleties through conversations. Collaborative effort, trust and the contribution to research can create actionable, nuanced and effective recommendations for policy and practice,” he added.

Contextual Understanding

Field research strengthens academic rigor, theories and methodologies, complements desk research and brings a different vantage point to understanding conflict. One constant risk in academic research is the tendency to be reductionist, and to focus on an isolated issue and miss the dynamic connections between it and its wider context. It can be appealing to zoom in on a particular violent extremist group and examine a singular aspect, such as ideology and group dynamics, rather than to see it as part of a complex ecosystem and dynamic processes. Conflict contexts often comprise multiple, interlinked armed actors, all influenced by and influencing each other. These contexts are further complicated by cross-cutting dynamics of ethnic, customary, kinship or religious dimensions.

Field research contextualizes the conflict and the issues that matter, helps understand drivers and motivations behind conflict actors and breaks free of embedded preconceptions. It can bring to life the unseen complexities: policemen fighting rebelling siblings, women fleeing insurgent cousins, parents losing children to armed groups, government officials persecuting family members as non-state actors. “People often said: ‘My brother joined that armed group, my cousin is in the police force,’” said Ingram, recalling conversations with locals in conflict areas that may seem, on the surface, to be absurd but that actually reflect a sober, clinical rational choice decision-making. Conflict ecosystems are invariably messy, counterintuitive and seemingly incomprehensible, yet remain the reality we seek to understand.

Sukanya Podder, defense studies senior lecturer at King’s College London and member of the RESOLVE Research Advisory Council, who also participated in the RESOLVE Forum session, conducted research in Mindanao, the Philippines, and Liberia where she focused on children and young people recruited into armed groups. Observing youth relationships with families and commanders in their communities, she was able to break free of preconceptions from media imagery and simplistic assumptions that children join community-based armed groups because they are drugged. Her fieldwork unearthed much more diverse motivations and choices: many children chose to join or decided to refrain of their own will.

Ethics and Safety

With any type of research, ethics and safety must be paramount. Fieldwork poses distinct challenges for each venue, context and participant. “Do no harm” should be the central principle of fieldwork planning to ensure the safety and integrity of researchers, respondents and their communities. Research fatigue is a growing issue that has negative implications on the quality of data. If respondents are wary about the benefits of research and are hesitant to participate, the authenticity of results is harder to determine. Researchers must be careful not to instrumentalize fieldwork and budget enough time and resources for in-depth quality research to produce authentic, reliable and valid data; this data should be periodically updated.

Getting approval from institutional review boards for fieldwork can often be challenging, and rightly so, but this rigor helps researchers address potential challenges and ensure the integrity of their research. While standards procedures, bureaucratic processes, reviews, clearances and preparations may seem taxing, they are indispensable for rich contributions of the highest integrity.  

Strengthening Research and Policy

The effectiveness and ultimate success — however we choose to measure it — of policy approaches to countering violent extremism depend on a thorough understanding of the phenomenon they try to address. Sound research should be the rock on which good policy is built. Podder’s research in West Africa has informed disarmament, demobilization and reintegration programs with a nuanced understanding of the implications of different types of armed groups. Returnees from community-based armed groups or community defense groups found reintegration less problematic, as reconciliation could be locally administered through local, tribal judicial processes. Such findings from field research can avoid wasting money on programs that cannot yield the desired outcome.

Our understanding of violent extremism has benefitted from an interdisciplinary research field where each discipline and method, qualitative and quantitative, brings a new lens to gathering and analyzing data. Collectively, this cross-pollination of research methods has allowed us to see further than one approach alone ever could. Within a complementary and overlapping web of methods, fieldwork has an important but sometimes overlooked role to play. Without a post-pandemic revival of fieldwork, an academic and intellectual decline is inevitable.

Boglarka Bozsogi is executive coordination and network manager at the RESOLVE Network housed at USIP. 

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Field Research : Definition, Examples & Methodology

  • August 19, 2021

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Table of Contents

What is field research.

Field Research is a method of collecting qualitative data with the aim to understand, observe, and interact with people in their natural setting. It requires specialized market research tools . The goal is to understand how a subject behaves in a specific setting to identify how different variables in this setting may be interacting with the subject. Field research is used most in the field of social science, such as anthropology and health care professions, as in these fields it is vital to create a bridge between theory and practice.

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Methods of Field Research

There are 4 main methods of conducting field research, and they are as follows:

  • Ethnography

Ethnography is a kind of fieldwork that aims to record and analyse a particular culture, society, or community. This method defines social anthropology, and it usually involves the complete immersion of an anthropologist in the culture and everyday life of the community they are trying to study.

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      2. Qualitative Interviews

The goal of qualitative interviews is to provide a researcher with a breadth of information that they can sift through in order to make inferences of their sample group. It does so through interviews by directly asking participants questions. There are three types of qualitative interviews; informal, conversational, and open ended.

     3. Direct observation

This method of field research involves researchers gathering information on their subject through close visual inspection in their natural setting. The researcher, and in this case the observer, remains unobtrusive and detached in order to not influence the behavior of their subject. 

     4. Participant Observation 

In this method of field research, the researchers join people by participating in certain group activities relating to their study in order to observe the participants in the context of said activity. 

Steps to conduct Field Research

The following are some key steps taken in conducting field research:

  • Identifying and obtaining a team of researchers who are specialized in the field of research of the study.
  • Identifying the right method of field research for your research topic. The various methods of field research are discussed above. A lot of factors will play a role in deciding what method a researcher chooses, such as duration of the study, financial limitations, and type of study.
  • Visiting the site/setting of the study in order to study the main subjects of the study.
  • Analyzing the data collected through field research.
  • Constructively communicating the results of the field research, whether that be through a research paper or newspaper article etc.

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Reasons to conduct Field Research

The following are a few reasons as to why field research is conducted, typically via market research tools :

  • To understand the context of studies : field research allows researchers to identify the setting of their subjects to draw correlations between how their surroundings may be affecting certain behaviors.
  • To acquire in-depth and high quality data :  Field research provides in-depth information as subjects are observed and analysed for a long period of time.
  • When there is a lack of data on a certain subject : field research can be used to fill gaps in data that may only be filled through in-depth primary research.

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Examples of field research.

The following are real studies conducted using field research in order to answer questions about human behavior in certain settings:  

  • William Foote Whyte used participant observation in his 1942 study to answer the question “How is the social structure of a local “slum” organized?”.  The study involved over 3 years of participation and observations among an Italian community in Boston’s North End.
  • Liebow’s study in 1967 involved twenty months of participation and observations among an African American community in Washington, DC, to answer the question “How do the urban poor live?”.
  • American sociologist, Cheri Jo Pascoe, conducted eighteen months of observations and interviews in a racially diverse working-class high school to answer the question “How is masculinity constructed by and among high school students, and what does this mean for our understanding of gender and sexuality?”.

Advantages of Field Research

  • Can yield detailed data as researchers get to observe their subjects in their own setting.
  • May uncover new social facts : Field research can be used to uncover social facts that may not be easily discernible, and that the research participants may also be unaware of.

No tampering of variables as methods of field research are conducted in natural settings in the real world. Voxco’s mobile offline research software is a powerful tool for conducting field research.

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Disadvantages of Field Research

  • Expensive to collect : most methods of field research involve the researcher to immerse themselves into new settings for long periods of time in order to acquire in-depth data. This can be expensive.
  • Time consuming : Field research is time consuming to conduct.
  • Information gathered may lack breadth : Field research involves in-depth studies and will usually tend to have a small sample group as researchers may be unable to collect in-depth data from large groups of people.

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How to Conduct Field Research Study? – A Complete Guide

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There is a challenge in undergoing a research which involves a vast understanding of the environment and the study of subjects staying in that environment. Although the outcome of this study will help fill in the gaps evidently seen in the literature but the process involves a lot of planning. How does one plan such a humongous research study?  In this article, we will discuss how to conduct a field research and what are the different methods used to streamline the field study !

Research is much more than performing the experiment and analyzing results. It involves gathering raw data and understanding the subject of research in its environment. These type of researches are more elaborate and are the reason for producing real information on a large scale.

Table of Contents

What is Field Research?

Field research is a process where data is collected through a qualitative method. The objective of field study  is to observe and interpret the subject of study in its natural environment. It is used in the field of study of humans and health care professions. Furthermore, it connects theory and practical research study by qualitatively analyzing the data.

Why to Conduct Field Study?

Field study allows researchers to identify and observe the subjects and helps draw correlations between subjects and surroundings, and how the surroundings may influence the behavior.

It gives an in-depth information on subjects because they are observed and analyzed for a long period of time.

Field study allows researchers to fill the gaps in data which can be understood by conducting in-depth primary research.

How is a Field Research different from a Lab Research?

Different methods of field study research.

field of research

There are four main types of methods for conducting a field research .

1. Ethnographic Field Notes

This type of field work is particularly associated with field work that records and analyzes culture, society or community. Most commonly this method of research is used in social anthropology, societies and communities.

2. Qualitative Interviews

Qualitative interviews give researchers detailed information. This vast information is segregated in order to make inferences related to the sample group. This data is gathered by conducting interviews either informally, conversationally or in an open ended interview.

3. Direct Observation

Researchers gather information on their subjects through close visual observation. The researcher can record the observations and events as field notes holistically without a guided protocol. This form of research approach is termed as unstructured observation. However, in a structured observation the researcher uses a guide or set protocols to observe people and events. Furthermore, in direct observation the observer is detached and does not obstruct the research setup. It does not work as an alternative method for conducting field research , and rather works as an initial approach to understand the behavior of the research. This type of method is extensively used in fields of sociology and anthropology wherein the researchers focus on recording social life details in a setting, community, or society.

4. Participant Observation

In this research method, the researcher takes part in the everyday life of the members chosen for observation. This gives the observer a better understanding of the study. Additionally, these observation notes are a primary type of data which the researchers later develop into detailed field notes.

field of research

Steps to Conduct a Field Study

1. identify and acquire researchers of the field.

It is essential to acquire researchers who are specialized in the field of research. Moreover, their experience in the field will help them undergo the further steps of conducting the field research .

2. Identify the topic of research

Post acquiring the researcher, they will work on identifying the topic of research. The researchers are responsible for deciding what topic of research to focus on based on the gaps observed in the existing research literature.

3. Identify the right method of research

After fine tuning the research topic, researchers define the right method to approach the aim and objectives of the research.

4. Visit the site of the study and collect data

Based on the objectives, the observations begin. Observers/Researchers go on field and start collecting data either by visual observation, interviews or staying along with the subjects and experiencing their surroundings to get an in-depth understanding.

5. Analyze the data acquired

The researchers undergo the process of data analysis once the data is collected.

6. Communicate the results

The researchers document a detailed field study report , explaining the data and its outcome. Giving the field study a suitable conclusion.

Advantages of Field Study

The major advantage of field study is that the results represent a greater variety of situations and environments. Researchers yield a detailed data analysis which can be used as primary data for many different research hypotheses. Furthermore, field research has the ability to find newer social facts which the setting or community and the participants may be unaware of. Most importantly, there usually is no tampering of data or variable, as data is collected from the natural setting.

Disadvantages of Field Study

Various methods of field study involve researchers conducting research study and immersing themselves on the research field to gather data. This collection of data can be expensive and time consuming. Moreover, the information acquired is usually undertaken through observation of small groups and this may lack understanding and implications to the larger group of study.

Did you ever conduct a field research? How did you find the process? Which type of field research method did you use? Let us know about it in the comment below.

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Visualizing a field of research: A methodology of systematic scientometric reviews

Affiliations.

  • 1 Department of Information Science, College of Computing and Informatics, Drexel University, Philadelphia, Pennsylvania, United States of America.
  • 2 Department of Information Science, Yonsei University, Seoul, Republic of Korea.
  • PMID: 31671124
  • PMCID: PMC6822756
  • DOI: 10.1371/journal.pone.0223994

Systematic scientometric reviews, empowered by computational and visual analytic approaches, offer opportunities to improve the timeliness, accessibility, and reproducibility of studies of the literature of a field of research. On the other hand, effectively and adequately identifying the most representative body of scholarly publications as the basis of subsequent analyses remains a common bottleneck in the current practice. What can we do to reduce the risk of missing something potentially significant? How can we compare different search strategies in terms of the relevance and specificity of topical areas covered? In this study, we introduce a flexible and generic methodology based on a significant extension of the general conceptual framework of citation indexing for delineating the literature of a research field. The method, through cascading citation expansion, provides a practical connection between studies of science from local and global perspectives. We demonstrate an application of the methodology to the research of literature-based discovery (LBD) and compare five datasets constructed based on three use scenarios and corresponding retrieval strategies, namely a query-based lexical search (one dataset), forward expansions starting from a groundbreaking article of LBD (two datasets), and backward expansions starting from a recently published review article by a prominent expert in LBD (two datasets). We particularly discuss the relevance of areas captured by expansion processes with reference to the query-based scientometric visualization. The method used in this study for comparing bibliometric datasets is applicable to comparative studies of search strategies.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Bibliometrics*
  • Computer Graphics

Associated data

  • figshare/10.6084/m9.figshare.9939773.v1

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

  • Qualitative vs. quantitative : Will your data take the form of words or numbers?
  • Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
  • Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

  • For quantitative data, you can use statistical analysis methods to test relationships between variables.
  • For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

  • From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
  • Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

  • During an experiment .
  • Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Chi square test of independence
  • Statistical power
  • Descriptive statistics
  • Degrees of freedom
  • Pearson correlation
  • Null hypothesis
  • Double-blind study
  • Case-control study
  • Research ethics
  • Data collection
  • Hypothesis testing
  • Structured interviews

Research bias

  • Hawthorne effect
  • Unconscious bias
  • Recall bias
  • Halo effect
  • Self-serving bias
  • Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

  • If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
  • If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
  • If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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What is Research? Definition, Types, Methods and Process

By Nick Jain

Published on: July 25, 2023

What is Research

Table of Contents

What is Research?

Types of research methods, research process: how to conduct research, top 10 best practices for conducting research in 2023.

Research is defined as a meticulous and systematic inquiry process designed to explore and unravel specific subjects or issues with precision. This methodical approach encompasses the thorough collection, rigorous analysis, and insightful interpretation of information, aiming to delve deep into the nuances of a chosen field of study. By adhering to established research methodologies, investigators can draw meaningful conclusions, fostering a profound understanding that contributes significantly to the existing knowledge base. This dedication to systematic inquiry serves as the bedrock of progress, steering advancements across sciences, technology, social sciences, and diverse disciplines. Through the dissemination of meticulously gathered insights, scholars not only inspire collaboration and innovation but also catalyze positive societal change.

In the pursuit of knowledge, researchers embark on a journey of discovery, seeking to unravel the complexities of the world around us. By formulating clear research questions, researchers set the course for their investigations, carefully crafting methodologies to gather relevant data. Whether employing quantitative surveys or qualitative interviews, data collection lies at the heart of every research endeavor. Once the data is collected, researchers meticulously analyze it, employing statistical tools or thematic analysis to identify patterns and draw meaningful insights. These insights, often supported by empirical evidence, contribute to the collective pool of knowledge, enriching our understanding of various phenomena and guiding decision-making processes across diverse fields. Through research, we continually refine our understanding of the universe, laying the foundation for innovation and progress that shape the future.

Research embodies the spirit of curiosity and the pursuit of truth. Here are the key characteristics of research:

  • Systematic Approach: Research follows a well-structured and organized approach, with clearly defined steps and methodologies. It is conducted in a systematic manner to ensure that data is collected, analyzed, and interpreted in a logical and coherent way.
  • Objective and Unbiased: Research is objective and strives to be free from bias or personal opinions. Researchers aim to gather data and draw conclusions based on evidence rather than preconceived notions or beliefs.
  • Empirical Evidence: Research relies on empirical evidence obtained through observations, experiments, surveys, or other data collection methods. This evidence serves as the foundation for drawing conclusions and making informed decisions.
  • Clear Research Question or Problem: Every research study begins with a specific research question or problem that the researcher aims to address. This question provides focus and direction to the entire research process.
  • Replicability: Good research should be replicable, meaning that other researchers should be able to conduct a similar study and obtain similar results when following the same methods.
  • Transparency and Ethics: Research should be conducted with transparency, and researchers should adhere to ethical guidelines and principles. This includes obtaining informed consent from participants, ensuring confidentiality, and avoiding any harm to participants or the environment.
  • Generalizability: Researchers often aim for their findings to be generalizable to a broader population or context. This means that the results of the study can be applied beyond the specific sample or situation studied.
  • Logical and Critical Thinking: Research involves critical thinking to analyze and interpret data, identify patterns, and draw meaningful conclusions. Logical reasoning is essential in formulating hypotheses and designing the study.
  • Contribution to Knowledge: The primary purpose of research is to contribute to the existing body of knowledge in a particular field. Researchers aim to expand understanding, challenge existing theories, or propose new ideas.
  • Peer Review and Publication: Research findings are typically subject to peer review by experts in the field before being published in academic journals or presented at conferences. This process ensures the quality and validity of the research.
  • Iterative Process: Research is often an iterative process, with findings from one study leading to new questions and further research. It is a continuous cycle of discovery and refinement.
  • Practical Application: While some research is theoretical in nature, much of it aims to have practical applications and real-world implications. It can inform policy decisions, improve practices, or address societal challenges.

These key characteristics collectively define research as a rigorous and valuable endeavor that drives progress, knowledge, and innovation in various disciplines.

Types of Research Methods

Research methods refer to the specific approaches and techniques used to collect and analyze data in a research study. There are various types of research methods, and researchers often choose the most appropriate method based on their research question, the nature of the data they want to collect, and the resources available to them. Some common types of research methods include:

1. Quantitative Research: Quantitative research methods focus on collecting and analyzing quantifiable data to draw conclusions. The key methods for conducting quantitative research are:

Surveys- Conducting structured questionnaires or interviews with a large number of participants to gather numerical data.

Experiments-Manipulating variables in a controlled environment to establish cause-and-effect relationships.

Observational Studies- Systematically observing and recording behaviors or phenomena without intervention.

Secondary Data Analysis- Analyzing existing datasets and records to draw new insights or conclusions.

2. Qualitative Research: Qualitative research employs a range of information-gathering methods that are non-numerical, and are instead intellectual in order to provide in-depth insights into the research topic. The key methods are:

Interviews- Conducting in-depth, semi-structured, or unstructured interviews to gain a deeper understanding of participants’ perspectives.

Focus Groups- Group discussions with selected participants to explore their attitudes, beliefs, and experiences on a specific topic.

Ethnography- Immersing in a particular culture or community to observe and understand their behaviors, customs, and beliefs.

Case Studies- In-depth examination of a single individual, group, organization, or event to gain comprehensive insights.

3. Mixed-Methods Research: Combining both quantitative and qualitative research methods in a single study to provide a more comprehensive understanding of the research question.

4. Cross-Sectional Studies: Gathering data from a sample of a population at a specific point in time to understand relationships or differences between variables.

5. Longitudinal Studies: Following a group of participants over an extended period to examine changes and developments over time.

6. Action Research: Collaboratively working with stakeholders to identify and implement solutions to practical problems in real-world settings.

7. Case-Control Studies: Comparing individuals with a particular outcome (cases) to those without the outcome (controls) to identify potential causes or risk factors.

8. Descriptive Research: Describing and summarizing characteristics, behaviors, or patterns without manipulating variables.

9. Correlational Research: Examining the relationship between two or more variables without inferring causation.

10. Grounded Theory: An approach to developing theory based on systematically gathering and analyzing data, allowing the theory to emerge from the data.

11. Surveys and Questionnaires: Administering structured sets of questions to a sample population to gather specific information.

12. Meta-Analysis: A statistical technique that combines the results of multiple studies on the same topic to draw more robust conclusions.

Researchers often choose a research method or a combination of methods that best aligns with their research objectives, resources, and the nature of the data they aim to collect. Each research method has its strengths and limitations, and the choice of method can significantly impact the findings and conclusions of a study.

Learn more: What is Research Design?

Conducting research involves a systematic and organized process that follows specific steps to ensure the collection of reliable and meaningful data. The research process typically consists of the following steps:

Step 1. Identify the Research Topic

Choose a research topic that interests you and aligns with your expertise and resources. Develop clear and focused research questions that you want to answer through your study.

Step 2. Review Existing Research

Conduct a thorough literature review to identify what research has already been done on your chosen topic. This will help you understand the current state of knowledge, identify gaps in the literature, and refine your research questions.

Step 3. Design the Research Methodology

Determine the appropriate research methodology that suits your research questions. Decide whether your study will be qualitative , quantitative , or a mix of both (mixed methods). Also, choose the data collection methods, such as surveys, interviews, experiments, observations, etc.

Step 4. Select the Sample and Participants

If your study involves human participants, decide on the sample size and selection criteria. Obtain ethical approval, if required, and ensure that participants’ rights and privacy are protected throughout the research process.

Step 5. Information Collection

Collect information and data based on your chosen research methodology. Qualitative research has more intellectual information, while quantitative research results are more data-oriented. Ensure that your data collection process is standardized and consistent to maintain the validity of the results.

Step 6. Data Analysis

Analyze the data you have collected using appropriate statistical or qualitative research methods . The type of analysis will depend on the nature of your data and research questions.

Step 7. Interpretation of Results

Interpret the findings of your data analysis. Relate the results to your research questions and consider how they contribute to the existing knowledge in the field.

Step 8. Draw Conclusions

Based on your interpretation of the results, draw meaningful conclusions that answer your research questions. Discuss the implications of your findings and how they align with the existing literature.

Step 9. Discuss Limitations

Acknowledge and discuss any limitations of your study. Addressing limitations demonstrates the validity and reliability of your research.

Step 10. Make Recommendations

If applicable, provide recommendations based on your research findings. These recommendations can be for future research, policy changes, or practical applications.

Step 11. Write the Research Report

Prepare a comprehensive research report detailing all aspects of your study, including the introduction, methodology, results, discussion, conclusion, and references.

Step 12. Peer Review and Revision

If you intend to publish your research, submit your report to peer-reviewed journals. Revise your research report based on the feedback received from reviewers.

Make sure to share your research findings with the broader community through conferences, seminars, or other appropriate channels, this will help contribute to the collective knowledge in your field of study.

Remember that conducting research is a dynamic process, and you may need to revisit and refine various steps as you progress. Good research requires attention to detail, critical thinking, and adherence to ethical principles to ensure the quality and validity of the study.

Learn more: What is Primary Market Research?

Best Practices for Conducting Research

Best practices for conducting research remain rooted in the principles of rigor, transparency, and ethical considerations. Here are the essential best practices to follow when conducting research in 2023:

1. Research Design and Methodology

  • Carefully select and justify the research design and methodology that aligns with your research questions and objectives.
  • Ensure that the chosen methods are appropriate for the data you intend to collect and the type of analysis you plan to perform.
  • Clearly document the research design and methodology to enhance the reproducibility and transparency of your study.

2. Ethical Considerations

  • Obtain approval from relevant research ethics committees or institutional review boards, especially when involving human participants or sensitive data.
  • Prioritize the protection of participants’ rights, privacy, and confidentiality throughout the research process.
  • Provide informed consent to participants, ensuring they understand the study’s purpose, risks, and benefits.

3. Data Collection

  • Ensure the reliability and validity of data collection instruments, such as surveys or interview protocols.
  • Conduct pilot studies or pretests to identify and address any potential issues with data collection procedures.

4. Data Management and Analysis

  • Implement robust data management practices to maintain the integrity and security of research data.
  • Transparently document data analysis procedures, including software and statistical methods used.
  • Use appropriate statistical techniques to analyze the data and avoid data manipulation or cherry-picking results.

5. Transparency and Open Science

  • Embrace open science practices, such as pre-registration of research protocols and sharing data and code openly whenever possible.
  • Clearly report all aspects of your research, including methods, results, and limitations, to enhance the reproducibility of your study.

6. Bias and Confounders

  • Be aware of potential biases in the research process and take steps to minimize them.
  • Consider and address potential confounding variables that could affect the validity of your results.

7. Peer Review

  • Seek peer review from experts in your field before publishing or presenting your research findings.
  • Be receptive to feedback and address any concerns raised by reviewers to improve the quality of your study.

8. Replicability and Generalizability

  • Strive to make your research findings replicable, allowing other researchers to validate your results independently.
  • Clearly state the limitations of your study and the extent to which the findings can be generalized to other populations or contexts.

9. Acknowledging Funding and Conflicts of Interest

  • Disclose any funding sources and potential conflicts of interest that may influence your research or its outcomes.

10. Dissemination and Communication

  • Effectively communicate your research findings to both academic and non-academic audiences using clear and accessible language.
  • Share your research through reputable and open-access platforms to maximize its impact and reach.

By adhering to these best practices, researchers can ensure the integrity and value of their work, contributing to the advancement of knowledge and promoting trust in the research community.

Learn more: What is Consumer Research?

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Research Output Reporting

  • RPDC (HERDC)
  • 2008 FoR Codes (Legacy)
  • Traditional Research Outputs
  • Non-Traditional Research Outputs
  • NTRO Research Statement

Field of Research (FoR) Codes

The Australian and New Zealand Standard Research Classification (ANZSRC) has been developed for use in the collection, analysis and dissemination of research and experimental development (R&D) statistics in Australia and New Zealand.

Note : The FoR classification codes were updated in 2020.  For legacy codes, visit 2008 FoR codes .

The categories in the classification include major fields and related sub-fields of research and emerging areas of study.  The Field of Research (FoR) is a hierarchical classification with three levels, each with its own unique number.

  • Division (2-digit)
  • Group (4-digit)
  • Fields (6-digit).

Note: UniSC does not need you to supply FoR codes to the 6-digit level - only to the 4-digit level.

FoR Codes (Division Level) - 2-digit

30  Agricultural, Veterinary and Food Sciences 31  Biological Sciences 32  Biomedical and Clinical Sciences 33  Built Environment and Design 34  Chemical Sciences 35  Commerce, Management, Tourism and Services 36  Creative Arts and Writing 37  Earth Sciences 38  Economics 39  Education 40  Engineering 41  Environmental Sciences 42  Health Sciences 43  History, Heritage and Archaeology 44  Human Society 45  Indigenous Studies 46  Information and Computing Sciences 47  Language, Communication and Culture 48  Law and Legal Studies 49  Mathematical Sciences 50  Philosophy and Religious Studies 51  Physical Sciences 52  Psychology

FoR Codes (Group Level) - 4-digit

DIVISION 30  AGRICULTURAL, VETERINARY AND FOOD SCIENCES         3001    Agricultural biotechnology     3002    Agriculture, land and farm management     3003    Animal production     3004    Crop and pasture production     3005    Fisheries sciences     3006    Food sciences     3007    Forestry sciences     3008    Horticultural production     3009    Veterinary sciences     3099    Other agricultural, veterinary and food sciences

DIVISION 31  BIOLOGICAL SCIENCES         3101    Biochemistry and cell biology     3102    Bioinformatics and computational biology     3103    Ecology     3104    Evolutionary biology     3105    Genetics     3106    Industrial biotechnology     3107    Microbiology     3108    Plant biology     3109    Zoology     3199    Other biological sciences

DIVISION 32  BIOMEDICAL AND CLINICAL SCIENCES         3201    Cardiovascular medicine and haematology     3202    Clinical sciences     3203    Dentistry     3204    Immunology     3205    Medical biochemistry and metabolomics     3206    Medical biotechnology     3207    Medical microbiology     3208    Medical physiology     3209    Neurosciences     3210    Nutrition and dietetics     3211    Oncology and carcinogenesis     3212    Ophthalmology and optometry     3213    Paediatrics     3214    Pharmacology and pharmaceutical sciences     3215    Reproductive medicine     3299    Other biomedical and clinical sciences

DIVISION 33  BUILT ENVIRONMENT AND DESIGN         3301    Architecture     3302    Building     3303    Design     3304    Urban and regional planning     3399    Other built environment and design

DIVISION 34  CHEMICAL SCIENCES         3401    Analytical chemistry     3402    Inorganic chemistry     3403    Macromolecular and materials chemistry     3404    Medicinal and biomolecular chemistry     3405    Organic chemistry     3406    Physical chemistry     3407    Theoretical and computational chemistry     3499    Other chemical sciences

DIVISION 35  COMMERCE, MANAGEMENT, TOURISM AND SERVICES         3501    Accounting, auditing and accountability     3502    Banking, finance and investment     3503    Business systems in context     3504    Commercial services     3505    Human resources and industrial relations     3506    Marketing     3507    Strategy, management and organisational behaviour     3508    Tourism     3509    Transportation, logistics and supply chains     3599    Other commerce, management, tourism and services

DIVISION 36  CREATIVE ARTS AND WRITING         3601    Art history, theory and criticism     3602    Creative and professional writing     3603    Music     3604    Performing arts     3605    Screen and digital media     3606    Visual arts     3699    Other creative arts and writing

DIVISION 37  EARTH SCIENCES         3701    Atmospheric sciences     3702    Climate change science     3703    Geochemistry     3704    Geoinformatics     3705    Geology     3706    Geophysics     3707    Hydrology     3708    Oceanography     3709    Physical geography and environmental geoscience     3799    Other earth sciences

DIVISION 38  ECONOMICS         3801    Applied economics     3802    Econometrics     3803    Economic theory     3899    Other economics

DIVISION 39  EDUCATION         3901    Curriculum and pedagogy     3902    Education policy, sociology and philosophy     3903    Education systems     3904    Specialist studies in education     3999    Other education

DIVISION 40  ENGINEERING         4001    Aerospace engineering     4002    Automotive engineering     4003    Biomedical engineering     4004    Chemical engineering     4005    Civil engineering     4006    Communications engineering     4007    Control engineering, mechatronics and robotics     4008    Electrical engineering     4009    Electronics, sensors and digital hardware     4010    Engineering practice and education     4011    Environmental engineering     4012    Fluid mechanics and thermal engineering     4013    Geomatic engineering     4014    Manufacturing engineering     4015    Maritime engineering     4016    Materials engineering     4017    Mechanical engineering     4018    Nanotechnology     4019    Resources engineering and extractive metallurgy     4099    Other engineering

DIVISION 41  ENVIRONMENTAL SCIENCES         4101    Climate change impacts and adaptation     4102    Ecological applications     4103    Environmental biotechnology     4104    Environmental management     4105    Pollution and contamination     4106    Soil sciences     4199    Other environmental sciences

DIVISION 42  HEALTH SCIENCES         4201    Allied health and rehabilitation science     4202    Epidemiology     4203    Health services and systems     4204    Midwifery     4205    Nursing     4206    Public health     4207    Sports science and exercise     4208    Traditional, complementary and integrative medicine     4299    Other health sciences

DIVISION 43  HISTORY, HERITAGE AND ARCHAEOLOGY         4301    Archaeology     4302    Heritage, archive and museum studies     4303    Historical studies     4399    Other history, heritage and archaeology

DIVISION 44  HUMAN SOCIETY         4401    Anthropology     4402    Criminology     4403    Demography     4404    Development studies     4405    Gender studies     4406    Human geography     4407    Policy and administration     4408    Political science     4409    Social work     4410    Sociology     4499    Other human society

DIVISION 45  INDIGENOUS STUDIES         4501    Aboriginal and Torres Strait Islander culture, language and history     4502    Aboriginal and Torres Strait Islander education     4503    Aboriginal and Torres Strait Islander environmental knowledges and management     4504    Aboriginal and Torres Strait Islander health and wellbeing     4505    Aboriginal and Torres Strait Islander peoples, society and community     4506    Aboriginal and Torres Strait Islander sciences     4507    Te ahurea, reo me te hītori o te Māori (Māori culture, language and history)     4508    Mātauranga Māori (Māori education)     4509    Ngā mātauranga taiao o te Māori (Māori environmental knowledges)     4510    Te hauora me te oranga o te Māori (Māori health and wellbeing)     4511    Ngā tāngata, te porihanga me ngā hapori o te Māori (Māori peoples, society and community)     4512    Ngā pūtaiao Māori (Māori sciences)     4513    Pacific Peoples culture, language and history     4514    Pacific Peoples education     4515    Pacific Peoples environmental knowledges     4516    Pacific Peoples health and wellbeing     4517    Pacific Peoples sciences     4518    Pacific Peoples society and community     4519    Other Indigenous data, methodologies and global Indigenous studies     4599    Other Indigenous studies

DIVISION 46  INFORMATION AND COMPUTING SCIENCES         4601    Applied computing     4602    Artificial intelligence     4603    Computer vision and multimedia computation     4604    Cybersecurity and privacy     4605    Data management and data science     4606    Distributed computing and systems software     4607    Graphics, augmented reality and games     4608    Human-centred computing     4609    Information systems     4610    Library and information studies     4611    Machine learning     4612    Software engineering     4613    Theory of computation     4699    Other information and computing sciences

DIVISION 47  LANGUAGE, COMMUNICATION AND CULTURE         4701    Communication and media studies     4702    Cultural studies     4703    Language studies     4704    Linguistics     4705    Literary studies     4799    Other language, communication and culture

DIVISION 48  LAW AND LEGAL STUDIES         4801    Commercial law     4802    Environmental and resources law     4803    International and comparative law     4804    Law in context     4805    Legal systems     4806    Private law and civil obligations     4807    Public law     4899    Other law and legal studies

DIVISION 49  MATHEMATICAL SCIENCES         4901    Applied mathematics     4902    Mathematical physics     4903    Numerical and computational mathematics     4904    Pure mathematics     4905    Statistics     4999    Other mathematical sciences

DIVISION 50  PHILOSOPHY AND RELIGIOUS STUDIES         5001    Applied ethics     5002    History and philosophy of specific fields     5003    Philosophy     5004    Religious studies     5005    Theology     5099    Other philosophy and religious studies

DIVISION 51  PHYSICAL SCIENCES         5101    Astronomical sciences     5102    Atomic, molecular and optical physics     5103    Classical physics     5104    Condensed matter physics     5105    Medical and biological physics     5106    Nuclear and plasma physics     5107    Particle and high energy physics     5108    Quantum physics     5109    Space sciences     5110    Synchrotrons and accelerators     5199    Other physical sciences

DIVISION 52  PSYCHOLOGY         5201    Applied and developmental psychology     5202    Biological psychology     5203    Clinical and health psychology     5204    Cognitive and computational psychology     5205    Social and personality psychology     5299    Other psychology

FoR Codes (Field Level) - 6-digit

For a complete list of the 2020 FoR codes to the 6-digit level, please see the  ABS website .

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  • Updated: Jul 6, 2023 9:17 AM
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Advanced Highway Maintenance and Construction Technology | Research Center

Advanced Highway Maintenance and Construction Technology

Caltrans field trials of the intelligent truck-mounted attenuator (itma), what is the need.

Caltrans frequently utilizes a shadow truck, equipped with a Truck Mounted Attenuator (TMA), during highway maintenance and repair endeavors to shield workers from potential collisions with stray vehicles. Despite enhancing worker safety, the nature of these shadow trucks implies that they will encounter impacts from errant vehicles, posing risks to the safety and well-being of the shadow truck driver. To mitigate these risks, there is a necessity to remove Caltrans' shadow truck drivers from the TMA. This move is anticipated to decrease operator injuries resulting from public vehicle impacts with TMA-equipped vehicles within highway work zones. The Intelligent TMA (ITMA) system, which addresses this concern, has undergone successful evaluation in controlled tests on closed sites, including a segment of State Route 905 (SR905). It has also performed with a safety operator on multiple state routes and I-8 near El Centro. To progress towards implementing the ITMA in regular Caltrans operations, it is imperative to conduct controlled field trials on public roads, without an ITMA safety operator, to assess its efficacy.

WHAT ARE WE DOING?

This research project aims to conduct supervised field trials of the ITMA system on California's public roads to validate its practicality. In the standard operation of the ITMA system, the lead vehicle (LV) deposits electronic markers (E-crumb) using Global Positioning System (GPS) technology. The steering, throttle, and braking of the follower vehicle (FV) are then controlled by the Kratos system to track the E-crumb trail of the LV and maintain a predetermined distance. Collaborating with Kratos, the Advanced Highway Maintenance and Construction Technology (AHMCT) Research Center at UC Davis has performed necessary system modifications, formulate a testing strategy, delivered ITMA system training to Caltrans maintenance personnel, overseen field trials, gathered operator feedback via surveys or interviews, and evaluated the system's performance and suitability, including operator acceptance and the identification of any issues.

The initial field trials are being conducted on a secluded Caltrans-operated public road with a safety operator present in the ITMA FV. Following the assessment of these initial trials and agreement on moving forward with the test plan, final field trials will be carried out with the safety operator relocated to the LV, thereby eliminating the operator from the ITMA FV.

WHAT IS OUR GOAL?

The goal of this research project is to validate the safety and efficiency of the Intelligent Truck-Mounted Attenuator (ITMA) under real-world Caltrans operational circumstances and pinpoint any potential issues that might emerge during on-road field trials.

WHAT IS THE BENEFIT?

This research project offers a chance to conduct controlled field tests of the ITMA in closely supervised rural highway operations. By removing the TMA operator from the vehicle, the ITMA has the potential to substantially decrease operator injuries resulting from collisions between public vehicles and TMA-equipped vehicles.

More info can be found here: https://dot.ca.gov/-/media/dot-media/programs/research-innovation-system-information/documents/research-notes/task4159-rns-02-23-a11y.pdf 

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Dozens of Top Scientists Sign Effort to Prevent A.I. Bioweapons

An agreement by more than 90 said, however, that artificial intelligence’s benefit to the field of biology would exceed any potential harm.

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Shelves full of different kinds of gas masks.

By Cade Metz

Reporting from San Francisco

Dario Amodei, chief executive of the high-profile A.I. start-up Anthropic , told Congress last year that new A.I. technology could soon help unskilled but malevolent people create large-scale biological attacks , such as the release of viruses or toxic substances that cause widespread disease and death.

Senators from both parties were alarmed, while A.I. researchers in industry and academia debated how serious the threat might be.

Now, over 90 biologists and other scientists who specialize in A.I. technologies used to design new proteins — the microscopic mechanisms that drive all creations in biology — have signed an agreement that seeks to ensure that their A.I.-aided research will move forward without exposing the world to serious harm.

The biologists, who include the Nobel laureate Frances Arnold and represent labs in the United States and other countries, also argued that the latest technologies would have far more benefits than negatives, including new vaccines and medicines.

“As scientists engaged in this work, we believe the benefits of current A.I. technologies for protein design far outweigh the potential for harm, and we would like to ensure our research remains beneficial for all going forward,” the agreement reads.

The agreement does not seek to suppress the development or distribution of A.I. technologies. Instead, the biologists aim to regulate the use of equipment needed to manufacture new genetic material.

This DNA manufacturing equipment is ultimately what allows for the development of bioweapons, said David Baker, the director of the Institute for Protein Design at the University of Washington, who helped shepherd the agreement.

“Protein design is just the first step in making synthetic proteins,” he said in an interview. “You then have to actually synthesize DNA and move the design from the computer into the real world — and that is the appropriate place to regulate.”

The agreement is one of many efforts to weigh the risks of A.I. against the possible benefits. As some experts warn that A.I. technologies can help spread disinformation, replace jobs at an unusual rate and perhaps even destroy humanity, tech companies, academic labs, regulators and lawmakers are struggling to understand these risks and find ways of addressing them.

Dr. Amodei’s company, Anthropic, builds large language models , or L.L.M.s, the new kind of technology that drives online chatbots . When he testified before Congress, he argued that the technology could soon help attackers build new bioweapons.

But he acknowledged that this was not possible today. Anthropic had recently conducted a detailed study showing that if someone were trying to acquire or design biological weapons, L.L.M.s were marginally more useful than an ordinary internet search engine.

Dr. Amodei and others worry that as companies improve L.L.M.s and combine them with other technologies, a serious threat will arise. He told Congress that this was only two to three years away.

OpenAI, maker of the ChatGPT online chatbot, later ran a similar study that showed L.L.M.s were not significantly more dangerous than search engines. Aleksander Mądry, a professor of computer science at the Massachusetts Institute of Technology and OpenAI’s head of preparedness, said that he expected researchers would continue to improve these systems, but that he had not seen any evidence yet that they would be able to create new bioweapons.

Today’s L.L.M.s are created by analyzing enormous amounts of digital text culled from across the internet. This means that they regurgitate or recombine what is already available online, including existing information on biological attacks. (The New York Times has sued OpenAI and its partner, Microsoft, accusing them of copyright infringement during this process.)

But in an effort to speed the development of new medicines, vaccines and other useful biological materials, researchers are beginning to build similar A.I. systems that can generate new protein designs . Biologists say such technology could also help attackers design biological weapons, but they point out that actually building the weapons would require a multimillion-dollar laboratory, including DNA manufacturing equipment.

“There is some risk that does not require millions of dollars in infrastructure, but those risks have been around for a while and are not related to A.I.,” said Andrew White, a co-founder of the nonprofit Future House and one of the biologists who signed the agreement.

The biologists called for the development of security measures that would prevent DNA manufacturing equipment from being used with harmful materials — though it is unclear how those measures would work. They also called for safety and security reviews of new A.I. models before releasing them.

They did not argue that the technologies should be bottled up.

“These technologies should not be held only by a small number of people or organizations,” said Rama Ranganathan, a professor of biochemistry and molecular biology at the University of Chicago, who also signed the agreement. “The community of scientists should be able to freely explore them and contribute to them.”

Cade Metz writes about artificial intelligence, driverless cars, robotics, virtual reality and other emerging areas of technology. More about Cade Metz

Explore Our Coverage of Artificial Intelligence

News  and Analysis

The Department of Homeland will become the first U.S. federal agency to roll out a comprehensive plan to integrate A.I. into a variety of uses , from fighting crime to helping disaster survivors.

Elon Musk released the raw computer code behind Grok , his version of an A.I. chatbot. The move is an escalation by one of the world’s richest men in a battle to control the future of A.I.

Secretary of State Antony Blinken warned that a malicious “flood” of disinformation  was threatening the world’s democracies, fueled in part by the swift rise of A.I.

The Age of A.I.

By interacting with data about genes and cells, A.I. models have made some surprising discoveries and are learning what it means to be alive. What could they teach us someday ?

Covariant, a robotics start-up, is using the technology behind chatbots  to build robots that learn skills much like ChatGPT does.

When Google released Gemini, a new chatbot, the company quickly faced a backlash. The episode unleashed a fierce debate  about whether A.I. should be guided by social values.

A.I.’s booming growth is radically reshaping an already red-hot data center market, raising questions about whether these sites can be operated sustainably .

Few companies better illustrate how A.I. is changing Silicon Valley deal-making than Anthropic, one of the world’s hottest A.I. start-ups .

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Pokémon Go ‘Weather Week’ 2024 Castform event guide

Castform is the star of this weather-based event

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Castform in its many weather forms in Pokémon Go

Pokémon Go is hosting its yearly “ Weather Week ” event from March 14-18 , promoting Castform and its many different weather-based forms.

During the event, Stardust obtained via catching Pokémon will be doubled. You’ll also get more Stardust for catching weather-boosted Pokémon (with the swirly ring around it when you see it on the overworld). Castform also has an increased chance to be shiny.

Shiny Castform and all of its forms with its normal versions. Shiny Castform is pink instead of grey, Shiny Snow Form Castform is more blue, Shiny Rainy Form Castform is more green, and Shiny Sunny Form Castform is more red.

Below, we list the Pokémon Go “Weather Week” 2024 event perks, like the Collection Challenges.

Pokémon Go ‘Weather Week’ 2024 event Collection Challenges

There are two Collection Challenges for “Weather Week.”

Weather Week Collection Challenge: Castform

Catch Castform in its different forms (as listed below) for a reward:

  • Castform (rainy)
  • Castform (snowy)
  • Castform (sunny)

Rewards : 5,000 Stardust, 1 Incense

Weather Week Collection Challenge

Catch the following to get more rewards:

Rewards : 7,500 Stardust

Pokémon Go ‘Weather Week’ 2024 event Field Research and rewards

Spinning a PokéStop during the event may net you one of these research tasks:

  • Catch 5 Pokémon with weather boost (Paras, Drifloon, Helioptile, or Amaura encounter)
  • Catch 7 Pokémon with weather boost (750 Stardust)
  • Catch 10 Pokémon with weather boost (Castform encounter)

The Castform you get from the “catch 10 Pokémon” encounter may be any of its weather variants.

Pokémon Go ‘Weather Week’ 2024 event boosted spawns

These Pokémon will be spawning more frequently during the event:

There are also some spawns that will only occur during specific weather times:

  • Cacnea (sunny)
  • Sunny Castform (sunny)
  • Lotad (rain)
  • Rainy Castform (rain)
  • Snowy Castform (snow)
  • Snover (snow)
  • Roggenrola (partly cloudy)
  • Spritzee (cloudy)
  • Swablu (windy)
  • Gastly (foggy)

Pokémon Go ‘Weather Week’ 2024 event raid targets

You can find these Pokémon in raids during the event:

  • Poliwhirl (1-star)
  • Gastly (1-star)
  • Hippopotas (1-star)
  • Amaura (1-star)
  • Charizard (3-star)
  • Lickitung (3-star)
  • Drampa (3-star)
  • Tyranitar (mega)
  • Regice (5-star)
  • Pokémon Go guides
  • “World of Wonder” Special Research
  • Wonder Ticket
  • Ditto disguises

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2024 NCAA Tournament bracket West Regional: March Madness predictions, upsets, players to watch

North carolina, arizona and baylor all eye a return to the final four in a stacked regional.

mm-regional-preview-v2-west.png

The 2024 NCAA Tournament field has been announced, which means college basketball fans near and far will spend the next few days until brackets lock on Thursday putting in as much research as possible for their picks. We're here to help you with regional-by-regional primers, turning our attention here to the West Regional with Sweet 16 and Elite Eight games in Los Angeles.

After much debate over which team would earn the final No. 1 seed for the West Regional, North Carolina came away with the top seed, meaning the Tar Heels will have to travel to the other side of the country if they make it to the second weekend. The Tar Heels are seeking their fourth Final Four appearance since 2016.

Pac-12 regular-season champion Arizona was one of those teams vying for the final No. 1 seed with UNC. The Wildcats ended their season with a loss to USC and fell to Oregon in the Pac-12 Tournament semifinals less than a week later. Pac-12 Player of the Year Caleb Love started his career with UNC and could see a matchup with his old team in the regional finals. 

March Madness ® is better with friends, especially when you beat them!  Get your bracket pools ready now  and invite your friends, family and co-workers to play.

This side of the bracket also features two more teams that have been to the Final Four in the last five years. No. 9 seed Michigan State reached the Final Four in 2019, while No. 3 seed Baylor ran the table in the 2021 NCAA Tournament en route to its first national title in program history.

Here's a complete look at the West Regional ahead of this week's action.

Best first-round game 

(8) Mississippi State vs. (9) Michigan State:  The 8-9 game is  always  the obvious pick as the best first-round matchup, but this game stands out. Josh Hubbard has been Mississippi State's best player this season, averaging 17.1 points per game as a freshman. On the flip side, you have a Michigan State team full of veteran guards with guys like Tyson Walker , A.J. Hoggard and Jaden Akins . Michigan State is a different animal in March. Time and time again, no matter how poorly Michigan State plays at the end of the season, coach Tom Izzo always gets his players ready for a tournament run.

Top potential matchup 

(1) North Carolina vs. (2) Arizona:  Again, it feels vanilla to suggest the best potential matchup is between the two best teams from the same corner of the bracket, but that is the case here. The storylines for this matchup would be plentiful. Arizona star Caleb Love would have a chance to get revenge against his old team while potentially guiding his new team back to the Final Four for the first time since 2001. And, oh yeah, the Final Four just so happens to be playing in Glendale, Arizona — located less than 130 miles from the Arizona campus in Tucson. If you love drama, root for this outcome.

Cinderella team that will surprise 

(11) New Mexico :  Based on the aftermath of Selection Sunday, it's clear New Mexico was going to be on the wrong side of the bubble had it lost to San Diego State in the Mountain West title game. The Lobos had to win four games in four days to secure the automatic bid. New Mexico is more than capable of parlaying that performance in Las Vegas to an NCAA tournament run. Jaelen House , the son of former NBA player Eddie House, is averaging 16.1 points this season. Jamal Mashburn Jr . and Donovan Dent are both key contributors from the backcourt, and JT Toppin is one of the top first-year players in the sport. Don't be surprised if the Lobos make a Sweet 16 run.

Team that will make a far-too-early exit 

(4) Alabama : The Crimson Tide's defense is an issue. Alabama ranks No. 346 in scoring defense at 81.1 points per game allowed. That mark is second-to-last among all teams in the Big Six conferences. Alabama's first-round opponent, Charleston , averages 80.5 points per game, good enough for 34th among all Division l teams. Alabama's offense is good enough to score with anyone in the country, but if it wants to avoid an early exit in the tournament the defense has to play better.

Six players to watch  

  • Caleb Love, Arizona:  Love is one of the most fascinating players in this sport. The Pac-12 Player of the Year can score 30 points or shoot 1 of 10 from the floor on a given night. In his most recent outing against Oregon, Love was benched down the stretch while his team attempted a comeback. Love has his faults, but Arizona won't reach its potential without him playing like a star.
  • RJ Davis , North Carolina:  No player on the North Carolina roster benefited more from Love's departure than Davis. The UNC star recently won ACC Player of the Year honors after averaging 21.4 points per game, which ranks 11th among all Division l players. Davis is one of the best pure scorers in the country, and his play style complements a veteran-led UNC team nicely.
  • Mark Sears , Alabama:  After averaging just under 13 points per game for the No. 1 overall seed last season, Sears saw a drastic increase in his production following the departures of  Brandon Miller and Jahvon Quinerly . Sears is averaging 21.1 ppg and is on track to become an All-American. He is shooting an impressive 43.1% on just over five attempts per game. He's the head of the snake on the top-scoring offense in the country.
  • DaRon Holmes ll, Dayton :  For the casual fan, this might be your first experience watching Holmes play. The Dayton big man is one of the most underrated stars in the sport. After bypassing the 2023 NBA Draft and returning to school for another season, Holmes increased his scoring average to just over 20 points. Holmes is also averaging 2.3 blocks. He should be a first-round pick when all is said and done.
  • PJ Hall , Clemson :  Hall is coming off the best statistical season of his career after averaging 18.8 points, 6.7 rebounds, and 1.6 blocks. At 6-foot-10 and 238 pounds, Hall is a menace down low. He has also shown an ability that can stretch the floor. Hall is Clemson's X-factor if it wants to make a run in the tournament.
  • Ja'Kobe Walter , Baylor:  Walter is perhaps the best NBA Draft prospect in this region. The Baylor star freshman is one of the best 3-point shooters (34% on 6.2 attempts per game) in his class and forms a dynamic one-two punch with fellow freshman big man Yves Missi . Walter is a projected lottery pick in the 2024 NBA Draft, and a strong showing at the tournament could do wonders for his stock.

West Regional winner

(2) Arizona:  Who doesn't love a good storyline in college basketball? It would be quite a story if Arizona ran the table and reached the program's first Final Four in 23 years in its home state. Purdue 's first-round exit in 2023 has been a big talking point for the last 12 months, but the Wildcats are also seeking redemption after losing to No. 15 seed Princeton a year ago. Arizona has the talent and depth to take down anyone on this side of the bracket, and it's hard to see them laying another egg in March.

March Madness regional previews: East |  Midwest  | South

Get every pick, every play, every upset and fill out your bracket with our help! Visit SportsLine now to see which teams will make and break your bracket , and see who will cut down the nets, all from the model that beat over 92% of CBS Sports brackets players three of the last five years.

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Mimicking exercise with a pill

FOR IMMEDIATE RELEASE

NEW ORLEANS, March 18, 2024 — Doctors have long prescribed exercise to improve and protect health. In the future, a pill may offer some of the same benefits as exercise. Now, researchers report on new compounds that appear capable of mimicking the physical boost of working out — at least within rodent cells. This discovery could lead to a new way to treat muscle atrophy and other medical conditions in people, including heart failure and neurodegenerative disease.

The researchers will present their results today at the spring meeting of the American Chemical Society (ACS). ACS Spring 2024 is a hybrid meeting being held virtually and in person March 17-21; it features nearly 12,000 presentations on a range of science topics.

Youtube ID: IIcUrSIEa-4

Watch a short Q&A video about this research.

“We cannot replace exercise; exercise is important on all levels,” says Bahaa Elgendy, the project’s principal investigator who is presenting the work at the meeting. “If I can exercise, I should go ahead and get the physical activity. But there are so many cases in which a substitute is needed.”

Exercise benefits both mind and body. In this case, Elgendy, a professor of anesthesiology at Washington University School of Medicine in St. Louis, and his colleagues are hoping to recapitulate its potent physical effects — namely, exercise’s ability to enhance muscle cells’ metabolism and growth, along with improved muscle performance.

A drug that can mimic these effects could offset the muscle atrophy and weakness that can occur as people age or are affected by cancer, certain genetic conditions or other reasons they are unable to carry out regular physical activity. It could also potentially counter the effects of other drugs, such as new weight-loss medications that cause the loss of both fat and muscle, according to Elgendy.  

The metabolic changes associated with exercise kick off with the activation of specialized proteins, known as estrogen-related receptors (ERRs), which come in three forms: ERRα, ERRβ and ERRγ. After about a decade of work, Elgendy and his colleagues developed a compound named SLU-PP-332, which activates all three forms, including the most challenging target, ERRα. This type of ERR regulates exercise-induced stress adaptation and other important physiological processes in muscle. In experiments with mice, the team found this compound increased a fatigue-resistant type of muscle fiber while also improving the animals’ endurance when they ran on a rodent treadmill.

A person exercising on a treadmill.

To identify SLU-PP-332, the researchers scrutinized the structure of the ERRs and how they bind to molecules that activate them. Then, to improve upon their discovery and develop variations that could be patented, Elgendy and his team designed new molecules to strengthen the interaction with the receptors and thus provoke a stronger response than what SLU-PP-332 can provide. When developing the new compounds, the team also optimized the molecules for other desirable characteristics, such as stability and low potential for toxicity.

The team compared the potency of SLU-PP-332 with that of the new compounds by looking at RNA, a measure of gene expression, from about 15,000 genes in cells from rat heart muscle. The new compounds prompted a greater increase in the presence of the RNA, suggesting they more potently simulate the effects of exercise.

Research using SLU-PP-332 suggests targeting ERRs could be useful against specific diseases. Studies in animals with this preliminary compound indicate that it could have a benefit against obesity, heart failure or a decline in kidney function with age. The results in the updated research suggest the new compounds could have similar effects.

ERR activity also appears to counter damaging processes that occur in the brain in patients diagnosed with Alzheimer’s disease and those who have other neurodegenerative conditions. While SLU-PP-332 cannot pass into the brain, some of the new compounds were developed to do so.

“In all of these conditions, ERRs play a major role,” Elgendy says. “If you have a compound that can activate them effectively, you could generate so many beneficial effects.”

Elgendy and his colleagues hope to test the new compounds in animal models through Pelagos Pharmaceuticals, a startup company they have co-founded. They are also looking into the possibility of developing the compounds as potential treatments for neurodegenerative disorders.

The research was supported by the National Institute on Aging of the National Institutes of Health under Award Numbers R21AG065657 and RF1AG077160.

Visit the  ACS Spring 2024 program  to learn more about this presentation, “Exercise in a Pill: Design and Synthesis of Novel ERR Agonists as Exercise Mimetics,” and more scientific presentations.

The American Chemical Society (ACS) is a nonprofit organization chartered by the U.S. Congress. ACS’ mission is to advance the broader chemistry enterprise and its practitioners for the benefit of Earth and all its people. The Society is a global leader in promoting excellence in science education and providing access to chemistry-related information and research through its multiple research solutions, peer-reviewed journals, scientific conferences, eBooks and weekly news periodical Chemical & Engineering News . ACS journals are among the most cited, most trusted and most read within the scientific literature; however, ACS itself does not conduct chemical research. As a leader in scientific information solutions, its CAS division partners with global innovators to accelerate breakthroughs by curating, connecting and analyzing the world’s scientific knowledge. ACS’ main offices are in Washington, D.C., and Columbus, Ohio.

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Note to journalists: Please report that this research was presented at a meeting of the American Chemical Society. ACS does not conduct research, but publishes and publicizes peer-reviewed scientific studies.

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Put Marketing at the Core of Your Growth Strategy

  • Marc Brodherson,
  • Jennifer Ellinas,

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Three ways to use marketing as a lever for growth, according to McKinsey research.

Companies that make the decision to put marketing at the core of their growth strategy outperform the competition, according to McKinsey research. Specifically, both B2C and B2B companies who view branding and advertising as a top two growth strategy are twice as likely to see revenue growth of 5% or more than those that don’t (67% to 33%). Yet their research also showed that few CEOs recognize the potential for marketing as a growth accelerator. They recommend three actions for CEOs to hit the reset button. The first is to define what you need from marketing. While it sounds obvious, their research found that more than half the time CEOs and CMOs in the same company were misaligned on marketing’s primary role. Second, nominate one person to serve as the chief voice of the customer. In two many organizations this is fragmented, and when everyone owns the customer, then no one does. Third, the CEO should function as a growth coach. They should have a handle on the challenges and opportunities of modern marketing, but their job is to draw up the strategy, not toss the ball down the field.

Growth is a perpetual business priority. So it’s imperative that CEOs understand how their marketing function and chief marketing officers (CMOs) can contribute to that goal. Few do — and that misalignment can be costly.

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  • Marc Brodherson is a senior partner in McKinsey & Company’s New York office.
  • Jennifer Ellinas is an associate partner in McKinsey & Company’s Toronto office.
  • Ed See is a partner in McKinsey & Company’s Stamford, Connecticut office.
  • Robert Tas is a partner in McKinsey & Company’s Stamford, Connecticut office.

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IMAGES

  1. Infographic: Steps in the Research Process

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  2. How to Conduct Field Research Study?

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  3. Field Research: What Is It and When to Use It?

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  4. Overview of related research fields.

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  5. How to Conduct Field Research Study?

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  6. What is Field Research?

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VIDEO

  1. What is research

  2. W Field research got raid pass

  3. Anthropological field research

  4. International Field Research

  5. field research experience 🥰#tribhuvanuniversity #students

  6. Let’s see what the field research gives ✨

COMMENTS

  1. Field research

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  2. What is Field Research: Definition, Methods, Examples and Advantages

    Field research is defined as a qualitative method of data collection that aims to observe, interact and understand people while they are in a natural environment. For example, nature conservationists observe behavior of animals in their natural surroundings and the way they react to certain scenarios.

  3. Field Study Guide: Definition, Steps & Examples

    A field study is a research method that involves conducting observations and collecting data in a natural setting. This method includes observing, interviewing, and interacting with participants in their environment, such as a workplace, community, or natural habitat.

  4. Field Research: A Graduate Student's Guide

    A lack of disciplinary consensus on what constitutes "field research" or "fieldwork" has left graduate students in political science underinformed and thus underequipped to leverage site-intensive research to address issues of interest and urgency across the subfields.

  5. Types of Research Designs Compared

    Revised on June 22, 2023. When you start planning a research project, developing research questions and creating a research design, you will have to make various decisions about the type of research you want to do. There are many ways to categorize different types of research.

  6. Field Research

    Field research is a qualitative method of research concerned with understanding and interpreting the social interactions of groups of people, communities, and society by observing and interacting with people in their natural settings. The methods of field research include: direct observation, participant observation, and qualitative interviews.

  7. Visualizing a field of research: A methodology of systematic ...

    Systematic scientometric reviews, empowered by computational and visual analytic approaches, offer opportunities to improve the timeliness, accessibility, and reproducibility of studies of the literature of a field of research. On the other hand, effectively and adequately identifying the most representative body of scholarly publications as the basis of subsequent analyses remains a common ...

  8. 12.1 Field Research: What it is?

    Field research is a qualitative method of data collection aimed at understanding, observing, and interacting with people in their natural settings. In the context of research, observation is more than just looking. It involves looking in a planned and strategic way with a purpose (Palys & Atchison, 2014, p. 189).

  9. Field Research: What is it?

    Field research is a qualitative method of data collection aimed at understanding, observing, and interacting with people in their natural settings. In the context of research, observation is more than just looking. It involves looking in a planned and strategic way with a purpose (Palys & Atchison, 2014, p. 189).

  10. PDF Introduction to Qualitative Field Research

    Field research* is the systematic study, primarily through long-term, face-to-face interactions and observations, of everyday life. A primary goal of field research is to understand daily life from the perspectives of people in a setting or social group of interest to the researcher. Field research is clas-

  11. 10.2: Pros and Cons of Field Research

    Field research is an excellent method for understanding the role of social context in shaping people's lives and experiences. It enables a greater understanding of the intricacies and complexities of daily life. Field research may also uncover elements of people's experiences or of group interactions of which we were not previously aware.

  12. What is Field Research: Meaning, Examples, Pros & Cons

    Field research is a method of research that deals with understanding and interpreting the social interactions of groups of people and communities by observing and dealing with people in their natural settings. The field research methods involve direct observation, participant observation, and qualitative interviews.

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    Field research refers to gathering primary data from a natural environment without doing a lab experiment or a survey. It is a research method suited to an interpretive framework rather than to the scientific method. To conduct field research, the sociologist must be willing to step into new environments and observe, participate, or experience ...

  14. What Is Field Research?: Definition, Types and Examples

    Field research refers to the process and methods of gathering qualitative data about the interactions of people or groups in their natural environments. Social scientists use field research methods to collect information and develop new theories about sociology, human nature and interpersonal interactions.

  15. Visualizing a field of research: A methodology of systematic

    The underlying structure of a research field is often subject to a variety of changes as the scientific literature grows over time [21, 22]. Therefore, it is reasonable to question whether an existing global model created a few years ago remains a valid representation of the underlying structure for specific analytic tasks in hand, although the ...

  16. Getting to the Source: The Importance of Field Research

    Field research strengthens academic rigor, theories and methodologies, complements desk research and brings a different vantage point to understanding conflict. One constant risk in academic research is the tendency to be reductionist, and to focus on an isolated issue and miss the dynamic connections between it and its wider context.

  17. Field Research : Definition, Examples & Methodology

    Field Research is a method of collecting qualitative data with the aim to understand, observe, and interact with people in their natural setting. It requires specialized market research tools.

  18. How to Conduct Field Research Study?

    Field research is a process where data is collected through a qualitative method. The objective of field study is to observe and interpret the subject of study in its natural environment. It is used in the field of study of humans and health care professions.

  19. Visualizing a field of research: A methodology of systematic

    Abstract. Systematic scientometric reviews, empowered by computational and visual analytic approaches, offer opportunities to improve the timeliness, accessibility, and reproducibility of studies of the literature of a field of research. On the other hand, effectively and adequately identifying the most representative body of scholarly ...

  20. Research Methods

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    Research is defined as a meticulous and systematic inquiry process designed to explore and unravel specific subjects or issues with precision. This methodical approach encompasses the thorough collection, rigorous analysis, and insightful interpretation of information, aiming to delve deep into the nuances of a chosen field of study.

  22. What Does a Field Researcher Do? (With Skills and Salary)

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  23. Field of Research (FoR) Codes

    The categories in the classification include major fields and related sub-fields of research and emerging areas of study. The Field of Research (FoR) is a hierarchical classification with three levels, each with its own unique number. Division (2-digit) Group (4-digit) Fields (6-digit).

  24. Caltrans Field Trials of the Intelligent Truck-Mounted Attenuator (ITMA

    This research project offers a chance to conduct controlled field tests of the ITMA in closely supervised rural highway operations. By removing the TMA operator from the vehicle, the ITMA has the potential to substantially decrease operator injuries resulting from collisions between public vehicles and TMA-equipped vehicles.

  25. Dozens of Top Scientists Sign Effort to Prevent A.I. Bioweapons

    An agreement by more than 90 said, however, that artificial intelligence's benefit to the field of biology would exceed any potential harm. Share full article Researchers are trying to tamp down ...

  26. Pokémon Go 'Weather Week' 2024 Castform event guide

    Pokémon Go 'Weather Week' 2024 event Field Research and rewards. Spinning a PokéStop during the event may net you one of these research tasks: Catch 5 Pokémon with weather boost (Paras ...

  27. 2024 NCAA Tournament bracket West Regional: March Madness predictions

    The 2024 NCAA Tournament field has been announced, which means college basketball fans near and far will spend the next few days until brackets lock on Thursday putting in as much research as ...

  28. Mimicking exercise with a pill

    The research was supported by the National Institute on Aging of the National Institutes of Health under Award Numbers R21AG065657 and RF1AG077160. Visit the ACS Spring 2024 program to learn more about this presentation, "Exercise in a Pill: Design and Synthesis of Novel ERR Agonists as Exercise Mimetics," and more scientific presentations.

  29. Put Marketing at the Core of Your Growth Strategy

    Companies that make the decision to put marketing at the core of their growth strategy outperform the competition, according to McKinsey research. Specifically, both B2C and B2B companies who view ...

  30. Printable NCAA Bracket 2024: Downloadable Men's Bracket and Picks

    The NCAA tournament's field of 68 teams is set. A fun regular season and exciting and consequential conference tournaments paved the way for one of the greatest sporting events of the year, and ...