research novel method

Creating Novel Methods

As the world’s largest academic clinical research organization, the DCRI has long been at the forefront of developing and testing novel methods that can accelerate research timelines, better partner with patients, and answer more questions. Today, the DCRI is leading the way in use of novel methods and trial designs, such as virtual trials and master protocols, to answer questions more efficiently and improve health around the world—while creating a research process that truly works for patients and sites alike.

Innovate to Accelerate: Putting Novel Methods to the Test

From cutting-edge statistical methodologies to novel networks that enable research based on electronic health records, the DCRI leverages many innovative strategies to accelerate traditional research timelines—while still ensuring reliable and trusted data.

Statistical Innovation: Modeling to Big Data

Past and present members DCRI biostatisticians discuss the innovations that the Biostatistics team has brought to clinical research throughout the institute's history, from modeling techniques to analysis plans for new study designs.

Watch a video featuring Kerry Lee, PhD; Zhen Huang, MS; and Laine Thomas, PhD, talking about the evolution of DCRI’s biostatistics work.

LEARN MORE ABOUT DATA IN CLINICAL RESEARCH

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Discovering More Answers Through Open Science

The pioneering open science effort Supporting Open Access for Researchers (SOAR) paid homage to DCRI's history by making four decades' worth of data from the Duke Cath Lab publicly available for research and education—a first for a university health system.

Watch Frank Rockhold, PhD, talk about the promise of open science and how the DCRI facilitates it via SOAR.

LEARN MORE ABOUT SOAR

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PCORnet: Enabling More Efficient Research

The DCRI serves as the coordinating center for PCORnet®, the National Patient-Centered Clinical Research Network, which aims to improve efficiency of health research nationwide. DCRI led the ADAPTABLE study, which demonstrated research enabled by electronic health records via PCORnet, and has embarked on additional novel studies leveraging the power of PCORnet.

WHAT WE LEARNED FROM ADAPTABLE

Listen to Jenny Cook, MPH, and Lauren Cohen, MA, discuss DCRI research enabled by PCORnet.

Finding Treatments Faster via Master Protocols

DCRI's clinician-researchers are developing studies using master protocols, also known as platform trials, in order to study multiple interventions concurrently and thus enable faster, more efficient research in many clinical areas, including COVID-19. Several factors contribute to DCRI's leadership in the master protocol space, including our range of clinical expertise, our ability to manage operational complexities, and our strong analytics capabilities.

Listen to Christoph Hornik, MD, PhD, MPH, talk about how the DCRI uses our expertise to lead master protocols.

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Accelerating Knowledge Through Partnerships

The DCRI relies on many ongoing partnerships in order to conduct innovative clinical research. One such partnership is with Verily Life Sciences LLC, an Alphabet company, which has enabled large-scale, real-world research such as the HERO research program and Project Baseline, which aims to use longitudinal, real-world data to learn more about health.

Listen to Beth Fraulo, RN, BSN, discuss unique aspects of DCRI’s partnership with Verily and the research it has enabled.

EXPLORE PROJECT BASELINE

Real-World Data for Real-World Questions

The DCRI responded to COVID-19 by creating the HERO Registry, which leveraged PCORnet® to better understand the pandemic's impacts on health care workers, and by co-creating the HERO-TOGETHER study, which tracks real-world safety data for the COVID-19 vaccines.

LEARN MORE ABOUT DCRI'S COVID-19 RESEARCH

Watch Emily O'Brien, PhD, give an overview of HERO.

Engaged and Embedded: Making Research Frictionless for Patients

The DCRI believes that all people should be partners in research. We take the approach of creating bidirectional relationships in research, which is evidenced in the two examples below, in order to make studies more accessible and easier for patients to participate in. Data show that more engaged patients lead to more successful studies—ensuring that, ultimately, we find the answers that patients seek.

Co-Creating Research With Patient Partners

Renee Leverty, BSN, MA, program lead for DCRI Research Together, speaks with patient advocate Fredonia Williams, PhD, about her contributions to DCRI study CONNECT-HF as a patient partner after receiving a new diagnosis of heart failure.

Virtual Trials Bring Research to Patients

DCRI pediatric rheumatologist Stephen Balevic, MD, speaks about DCRI’s partnership with the Childhood Arthritis & Research Alliance (CARRA) with Vincent Del Gaizo, a representative of CARRA. DCRI is leveraging CARRA to conduct a novel virtual trial.

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Knowledge Base

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.

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.

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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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Published: 14 January 2019

Novel methods of qualitative analysis for health policy research

Mireya Martínez-García ORCID: orcid.org/0000-0002-2876-8500 1 na1 ,
Maite Vallejo 1 ,
Enrique Hernández-Lemus 2 &
Jorge Alberto Álvarez-Díaz 3

Health Research Policy and Systems volume 17 , Article number: 6 ( 2019 ) Cite this article

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Currently, thanks to the growing number of public database resources, most evidence on planning and management, healthcare institutions, policies and practices is becoming available to everyone. However, one of the limitations for the advancement of data and literature-driven research has been the lack of flexibility of the methodological resources used in qualitative research. There is a need to incorporate friendly, cheaper and faster tools for the systematic, unbiased analysis of large data corpora, in particular regarding the qualitative aspects of the information (often overlooked).

This article proposes a series of novel techniques, exemplified by the case of the role of Institutional Committees of Bioethics to (1) massively identify the documents relevant to a given issue, (2) extract the fundamental content, focusing on qualitative analysis, (3) synthesize the findings in the published literature, (4) categorize and visualize the evidence, and (5) analyse and report the results.

A critical study of the institutional role of public health policies and practices in Institutional Committees of Bioethics was used as an example application of the method. Interactive strategies were helpful to define and conceptualise variables, propose research questions and refine research interpretation. These methods are additional aids to systematic reviews, pre-coding schemes and construction of a priori diagrams to survey and analyse social science literature.

Conclusions

These novel methods have proven to facilitate the formulation and testing of hypotheses on the subjects to be studied. Such tools may allow important advances going from descriptive approaches to decision-making and even institutional assessment and policy redesign, by pragmatic reason of time and costs.

Peer Review reports

Complexities of policy analysis

Healthcare institutions are complex organisations whose procedures, activities and, ultimately, outcomes should be assessed constantly in order to optimise their functionality in ever-changing environments [ 1 ]. There are several ways to perform qualitative analysis of Health Care Institutions Policies and Practices (HCIPP), including ethnography, ethnomethodology, phenomenology, action research, grounded theory, critical discourse analysis, and evidence-based science, among others [ 2 ]. The Qualitative Research Methodology (QRM), for instance, uses data collected to discover or refine research questions because, usually, performance variables are not fully conceptualised or completely defined [ 3 , 4 ].

As with all research strategies, choosing the best QRM is vital to obtaining the desired results in HCIPP analysis. Computerized Qualitative Analysis of Discourse (CQAD), for instance, is used to extract and synthesise descriptions of search, selection, quality appraisal, analysis and synthesis methods. Additionally, evidence-based non-systematic literature reviews (NSLR), rapid reviews, scoping studies and research syntheses have gained wide acceptance in the QRM [ 5 ].

CQAD and NSLR have some degree of empirical support and classifying evidence of their epistemological strength; both converge in the analytic phase, sharing methodologies for decontextualising and recontextualising data, coding, sorting, identifying themes and relationships, and drawing conclusions [ 2 ]. At this stage, it is useful to assess the strengths and limitations of current approaches to policy analysis and to address how improvement can be achieved in this regard.

Advantages and disadvantages of traditional approaches

A literature search is a key step in carrying out a good reliable HCIPP research. It helps in formulating or refining a research question and planning the strategies of study [ 6 ]. Access to the most relevant articles, with maximum evidence, in a shorter time and with less cost is essential for HCIPP analysis.

Bioethics literature is vast. Researchers use CQAD and NSLR to examine patterns in documents in a replicable and systematic manner. On the one hand, CQAD is used to automate the classification and coding categories in texts or in preparing sets of texts for building up inferences. Additionally, lesser time and cost are advantages, in contrast to the manual limitative analysis of just measuring the number of words and lines. The larger disadvantage of CQAD is the dependence on subjective impressions of a reader [ 7 , 8 ].

On the other hand, two types of non-systematic review have been discussed in relation to bioethics literature, namely Introductory Reviews of Bioethics Literature and Critical Interpretive Reviews of Bioethics literature. These approaches have been quite popular recently since they are faster and easier to implement than systematic reviews of the literature [ 9 , 10 ]. However, some of them have brought scientific and methodological controversies about transparency, rigor, comprehensiveness and reproducibility. Further, these approaches have disadvantages related to the insufficiently focused review scope, diversity of terminology in order to identify all relevant publications and quality assessment [ 5 , 11 ].

Introducing novel methods

This article on novel methods of qualitative analysis is aimed towards policy-makers, bioethics health professionals and researchers. The model has been proposed by pragmatic reasons of time and costs. Many of the processes underlying institutional policies and practices have not been properly investigated; thus, there is a need to incorporate QRM frameworks for such research. Consequently, to address the phenomenon of institutional analysis and to account for its relationship to public health, a systematic model of critical analysis is proposed and exemplified by the case of the role of Institutional Committees of Bioethics (ICB).

Aims of this work and case study outline

As a case study to introduce our methodological proposal, we will analyse the case of HCIPP of ICB . In this subsection, we will provide some information regarding the choice of this case study and the foundations for its analysis.

The bioethical discourse in public policy establishes an important part of the practice in the public healthcare institutions as is the case of ICB. This is so, since recently, science and technology have been increasingly reassessed in ethical terms [ 12 – 14 ]. Ethics has become the decisive semantic form in which government discourses are carried out with greater political relevance, and has since become the dominant discourse [ 15 ]. As a branch of applied ethics, Bioethics has become the political medium for the creation of a moral economy where value commitments are made capable of legitimising the regulatory policies necessary to maintain public confidence in biomedical science and healthcare [ 16 ].

A growing number of studies have explored the role of ICB in various fields, from academic and biotechnological, to medical and legal [ 13 , 14 ]. The strategies for the study of the literary forms of governance of ICB have been separated by opinions, reports, guidelines and consensus statements, focused more on who does things, how and why they do them, yet in isolated form, even when CQAD and NSLR have been implemented [ 17 ]. With this scenario in mind, laying out a comprehensive method has been necessary to improve ICB policy and practice analyses.

A three-stage research design

To address the methodological approach already sketched, we will proceed along the following lines. First, the construction of a preliminary corpus with texts extracted from the Medical Literature Analysis and Retrieval System Online (MEDLINE) PubMed database (Stage I). At this stage, no biased decision as to the content of the corpus was made, except, of course, on the pertinence to the problem under study. Second, two textual exploration techniques were performed simultaneously, namely tracing of the corpus of Medical Subject Headings (MeSH) terms for the construction of a semantic network (Stage II) and an inspection of the corpus MEDLINE to identify a priori codes and categories by means of both manual and automated by CQAD (Stage III). Finally, the main findings were discussed in order to contribute to the generation of a systematic and unbiased methodological proposal to address this social phenomenon. The proposed research strategy is set out below (as it can be seen in Fig. 1 ).

Flow diagram showing the steps followed in this work

First, build a preliminary corpus with the documents extracted from the MEDLINE PubMed database. Second, two text exploration processes were carried out in parallel, namely (1) an exploration of the corpus of MeSH terms for the construction of a semantic network and (2) an exploration of the content obtained from the PubMed corpus to identify preliminary codes and categories. Both by manual and automated processes (cytoscape and Atlas.ti softwares) were used in both cases. Third, two types of visualisations were obtained from the previous processes (semantic network and alluvial diagrams).

Stage I: Documental corpus identification

With the rapid expansion of scientific research, an effective search and the massive integration of new knowledge have become difficult. The development of methods and tools available to researchers has been one of the main lines of research in computer science. Some massive document search tools have been made more precise and a large part of the integrated graphic visualisation tools to show the relationships between authors, topics and appointments, among others, are now available. These innovative search and mass visualisation systems not only facilitate the systematisation of information, they can also help the social sciences researcher to develop a conceptual mapping to identify categories of analysis, as well as emerging categories. With this approach, the conceptualisation of the research problem can be improved, as well as enriching the abstractions and representations of the phenomena in question.

Given that the researchers in social sciences have a lot to read, it is essential not to spend much time searching for potential information. Therefore, it is recommended that these systems of massive document retrieval may be increasingly used, which at the same time allows a simple and rapid systematisation, categorisation and codification of the required knowledge. Several tools, such as the mapping technique, have been developed to graphically represent relationships between document knowledge through networks of concepts, also called semantic networks. These networks consist of nodes and links, wherein nodes represent concepts and links represent connections between documents [ 18 – 20 ].

In this work, a MEDLINE (PubMed) search was first performed to massively identify the documents that may contain terms related to the case study used as an example for the development of this methodology. The search terms entered in these engines were:

Institutionalization of bioethics and public health policy

Institutionalization of bioethics and public health

Institutionalization of bioethics

Bioethics committees and public health policy

Bioethics committees and public policy

Stage II: Semantic network curation

Once this corpus of information was obtained, the two referred processes of exploration of the texts were performed. Firstly, the use of ontology retrieved from the MeSH terms. The importance of this exploration process lies in the compartmentalisation of information, a property that allows the implementation of algorithmic approaches for its analysis, an extremely valuable resource for the massive mining of literature such as the one implemented in this work.

This stage consisted in the qualitative analysis of data extracted from PubMed MEDLINE. In recent times, network-based approaches (semantic or ontology-based networks) to understand complex social, political, biological and technological issues have been developed [ 21 ]. Such approaches are useful since they allow the researcher to have an unbiased, integrated view to discover associations and interactions between the relevant instances involved.

Connectivity maps are built so that source and target nodes are the core concepts in a given corpus and links between these correspond to co-existence of the concepts on a given database, the more instances of repeat co-occurrence, the stronger the link and hence the closer the connection between these concepts (given the underlying corpus, of course).

A previously validated Python code was used to design a reference structure and make way for network analysis with Cytoscape, an open source software platform, to analyse and visualise complex networks of interactions, in this case, semantic. All the source code for general text processing can be found at https://github.com/CSB-IG/literature/tree/master/text_processing . The calculation of the underlying literature-based measures can be found at https://github.com/CSB-IG/bibliometrics .

Once the structured file was processed in a network with the NetworkX Python library, some connectivity maps were built, so that the nodes represented the MeSH terms, and the links between these were the documents (PMID of each publication) that shared MeSH terms among them.

Stage III: Content exploration

The second analysis was performed through the manual selection text content technique, an approach to computer aided CQAD, implemented by the Atlas.ti software. CQAD is a systematic coding and categorisation approach used to explore large amounts of textual and discourse information to determine trends and patterns of words used, their frequency, relationships and structures [ 22 – 24 ]. Atlas.ti is a computer programme for the analysis of qualitative data that allows the import and encoding of textual data as a category that designates broadly semiotic elements [ 25 – 27 ].

All the data within the Atlas.ti tool were organised into a hermeneutic unit (a repository of pdf documents) and, from it, citations, codes and groups of codes were built. The citations were the places where the ideas were stored (a physical location) and the codes were the spaces to store the categories (a way of labelling certain aspects of the data and classifying the information). The Atlas.ti query functions were used to search for coding patterns in the project database.

We used a combined deductive and inductive strategy to construct codes and categories; the deductive approach as a priori defined categories based on a theory or framework and inductive approach as a posteriori built codes and categories [ 8 ].

The operationalisation includes the materialisation of the speeches. In the case of the example, the HCIPP analysis and its relation to the sociopolitical speeches of the ICB, the variables can materialise from an a priori category in the bio-power instruments such as a patronage, clientelism, simulation or authoritarianism. The next step was to adapt these conceptual categories to compare them with the corpus of texts and discourses; while this empirical process was being carried out, other pertinent categories emerged that completed the analysis.

Once the information sources of the database were selected in the Atlas.ti hermeneutic unit, an initial scan of collected information was carried out and the codes and categories were constructed, for the evidentiary stage, for which it was called the Protocol of codification (a table of codes and categories systematised by the software Atlas.ti). The code table and a category map were generated, which was visualised by means alluvial diagram (RAW Graphs) [ 28 ].

These strategies were based on the notion of discovering a possible covert bio-political structure behind an institutionalised symbolic order [ 29 ]. The symbolic struggles and power can determine these hidden orders that exist in all social reality, inherent in different fields of knowledge and capable of revealing the role (or roles) of a certain organisation but veiled by a system of politically correct discourses and endorsed by the scientific bodies in the corpus of related literature.

Stage I Results: documental corpus identification

The search terms entered in the search engine were (the search was made on January 3, 2018):

Institutionalization of bioethics and public health policy (4 results)

Institutionalization of bioethics and public health (18 results)

Institutionalization of bioethics (42 results)

Bioethics committees and public health policy (433 results)

Bioethics committees and public policy (666 results)

The documents were retrieved in plain text (txt) format, a corpus with 770 records was formed, after removing the duplicates ( n = 393), each one of these constituted, among other elements, an identifier of the MEDLINE (PubMed) database (PMID), title, summary, date of publication, name and place of ascription of the authors, as well as the country where the research work took place.

Stage II Results: semantic network analysis

To understand the structure of the network and the interrelationships between its elements, a brief analysis of the connectivity patterns was carried out, such as the number of nodes and the number of connections.

Figure 2 shows a complex network, composed of a single connected element, with a large number of nodes, there is a relatively large number (1996) of interrelated concepts connected by a vast amount (63,488) of interconnections, responsible for the overall conceptual richness of the underlying discourse. Being this a highly clustered graph (average clustering coefficient is 0.808, meaning that more than 80% of all possible triplets of related concepts are actually present in the network), most of the concepts behind the published literature on ICB are tightly connected.

In this Global network a quite complex graph is visually appreciated, composed of a single connected element, with a large number of nodes (MeSH terms) (1996) as well as a large number of links (63,488)

Moreover, the network centralisation statistic equals 0.917, which is indicative that a relatively small number of concepts are responsible for the highly connected structure of the network. If we look at the actual data behind Fig. 2 (To look up for network topological structure and node/link statistics, please refer to Additional file 1 ), we can identify concepts such as Human, United States, Advisory Committees, and Public Policy (with two instances) that possess more than a thousand direct conceptual associations to other terms in this context.

Humans with 1891 connections results obvious since ethics is a human construct, the case of the term United States (1288 links) reflects the fact that an important corpus of research has been published by United States-based researchers and –more importantly– within the geopolitical context of American public health and policy. This fact has to be taken seriously into account when analysing public policy under different national contexts by realising that the public research corpora will be heavily biased towards the United States-like schemes.

The fact that Advisory Committees (1077 links) and Public Policy (with 907 links when considered as a central concept and 792 links as a secondary category) show so many links is also not surprising. What may result more surprising is the fact that important social and philosophical concepts such as Institutionalization/ethics, Communities/health services and Ethics, Medical/education are at the low end of the distribution, all of them with at most 5 links.

If we consider that, on average, each concept in this semantic network is associated to 63.315 other concepts (in the world corpora of published literature in the field as represented in this analysis), the important issues of institutionalisation of ethics, community outreach and medical education are severely disconnected from the main discussions in the current literature.

As the main objective of our work is to show the example of ICB and give an account of their relationship with public policies, we analysed the entire network in a context identified by a priori MeSH terms with the largest number of connections. In this way, we decided to build two subnets based on the following MeSH terms and their first neighbours: Government Regulation (GR-683 connections), Social Control and Formal (SCF-451 connections) see Figs. 3 and 4 . This is so, since, aside from the already commented –and somehow obvious– cases of Humans, United States, Advisory Committees and Public Policy terms, Government Regulation and Social Control are highly connected concepts central to understanding the role played by the ICB.

This figure shows the subnet based on the term MeSH Government Regulation and its first neighbours

This figure shows the subnet based on the term MeSH Social Control, Formal and its first neighbours

Moving on to discuss the Government Regulation sub-network, it is also a large (952 concepts/nodes and 44,091 associations/links) and quite clustered network (clustering coefficient equal to 0.741), with a high centralisation (0.883), which indicates that there are really important concepts (hubs) that link together with most other concepts.

Among such highly central terms, we can mention, aside from the already mentioned global hubs, Federal Government with 587 links, Bioethics (i.e. Bioethics as a secondary subject) with 478 links, as well as Advisory Committees (453 connections), Risk Assessment (441 connections) and Informed Consent (439 links). Interestingly, concepts such as *Government Agencies, Policy and Budget are all under-represented concepts with 10 or less connections in this sub-network, as compared with the average number of neighbours, which is 92.628.

In relation to the Social Control, Formal sub-network, it consists of 1133 concepts and 51,142 relations. With a clustering coefficient of 0.741, and a centralisation of 0.903, this network presented similar connectivity features as the already discussed networks, namely a densely interconnected graph with a small number of highly central concepts. In this case, emerging concepts are Social Values with 567 connections, Government Regulation with 555, and *Bioethical issues with 507 connections, whereas interesting under-represented terms are Health Services/*standards, Safety/*standards and Organisational Culture with 6 links each (versus links on average for this network).

The above described analysis represents just a glimpse of the vast amount of contextual information that can be derived from semantic network studies and will serve as a systematic method for educated hypothesis generations. Such hypotheses can be further pursued by following the tenets of social analysis of discourse and policy assessment, as well as other methods of analysis in the social sciences.

Stage III Results: content analysis codification protocol

One way to know the symbolic order was to establish the dimensions of analysis by categories and codes, see the first column of Fig. 5 . These were determined in at least six possible categories and each of them with multiple codes, which would correspond to the role of the ICB as an organisational model of a public policy.

Model for dimensions of analysis. Columns: (1) Forms of institutionalisation, (2) Forms of governance, (3) Institutional structure, (4) Political discourse, (5) Mechanism power and (6) Symbolic role

The categories were reconstructed from the information collected and a priori analysed, using a content analysis technique according to the recommended procedures by Fairclough [ 30 ]. This analysis, also called text (discourse), focused on identifying the frequency with which certain data appeared for its subsequent synthesis and interpretation. According to the methodological structure of category-wise qualitative analysis, the data were deconstructed, and later gathered in a unit (analytical) structure that allowed identification of its elements (synthesis) [ 31 ].

The a priori categorisation (protocol of codification) was defined as follows:

Forms of institutionalisation (column 1): (1) Normative or advisory committees; (2) Committees of professional medical associations; (3) Committees of care and hospital ethics; and (4) Committees of ethics in research.

Forms of governance (column 2): ICB-Governance mechanisms and their elements have been recognised worldwide by the Universal Declaration of Bioethics (2005).

Institutional structure (column 3): These are the conditions that give legitimacy and consolidation to each ICB, as well as the form that in daily practice takes as a consultant or regulatory entity, whether in the decision of collective, substantive or political processes.

Political discourse (column 4): This content refers to the common object and a series of specific goals which are presented in the medical, scientific, technological, public, social and political fields.

Power mechanism (column 5): As a security device, it might be understood as the combination of knowledge-power-truth that reveals how legal, medical and political discourses can be translated into approved regulatory practices to exercise power not only over the bodies, but on the populations.

Symbolic role (column 6): This symbolic paper could be covered with a veiled power, or by a discursive power, a discourse capable of controlling some minds and in turn controlling some actions. At the beginning of the exploratory phase, the characterisation of some roles could represent the role of ICB as follows: Government elites, Power elite, Control mechanism, Intellectual and moral authority, Discussion forums and Passive actors [ 32 ].

Based on this model, it was intended to describe the process that would give way to the emergence of theoretical structures of this work; implicit in the material compiled and that would integrate it into a logical whole. A model capable of schematising the content of the information had already been constructed, essentially grouped into the following codes: categorisation, structuring, testing and theorising.

The final step of content analysis was the examination of the text/discourse of the ICB; they must be integrated into the relational framework to determine if they really act as government advisors that generate social value. Derived from the previous analysis, several details that gave consistency to the present exploratory work were revealed, for example, the role that public institutions have, as the expression of political forces through which societies propose to solve some of their collective problems. In this case, their role seems to be necessarily influenced by the rules and practices of the political system; however, in the vast majority of political systems, the democratic imperfection itself hinders the representation of institutions [ 33 ].

What do we learn?: Insights on the case study

Institutional text/discourse has an important contribution in social reality. Its analysis was used to approach this reality through a linguistic process, which was used to see beyond the organisational practices. The central question of this work is: what is the role of the ICB in public health policies? To answer this question, it was necessary to dig deep into the text/discourse in order to produce and reproduce the response. In fact, other categories emerged as well as other socially constructed behaviours such as the legitimacy and resistance of institutional actors.

With this analysis, the relevance involved in the identification of texts and discourses revealed social constructions associated with pre-established texts and discourses that predetermine institutional policies and practices with the intention of strengthening its legitimacy [ 23 ].

On the other hand, the mapping of the a priory categories has been used for several purposes, such as the display of a complex structure, the communication of complicated ideas and the demonstration of connections between ideas [ 34 ]. The role that ICB have on health policies and which could modify the social, political and health dynamics, could be systematised using the Atlas.ti software. Once a code table was available, a map of preliminary categories could then generate a visualisation format using an alluvial diagram, seen in Fig. 5 .

When these a priory categories are applied to a critical text/discourse analysis, it may be necessary to recode the information and to re-run the analysis with the new codes and categories to redefine the research hypothesis and its interpretation. The result of this reorganisation forced the modification of the code table generated a priori, so that the category map was also remodelled and, at the same time, it was visualised by means of an alluvial diagram (RAW Graphs), as seen in Fig. 6 .

Alluvial diagram that shows the theoretical proposal and the emergent categories on the legitimisation and the institutional resistance: Columns: (1) Legitimisation, (2) Reduction of uncertainty, (3) Symbolic paper, (4) Resistance. In the column of codes of resistance, it can be seen that there are two that seem to be the most outstanding, namely political support and political will

Now, the main difference between Figs. 5 and 6 , was the replacement of some columns with emerging categories in order to reduce uncertainty resulting in the disaggregation into various codes that we interpreted as being based on the concept of simulation, being this a result of the applied methodology. A question arose as to how can this theoretical concept could be seen or applied to reality. The domination of capitalism demanded a bioethical reflection based on a social analysis. Identifying the uncertainty in social, biological and political systems became essential to understand the central role of the institutions, both to overcome this uncertainty and to propose strategies of struggle.

The institutionalisation of bioethics has been described as a response to a mixture of demands for emerging public concerns (including those about advances in technology and also unethical practices) and to change political contexts in which questions about the mass data or the value of life was debated and translated into principles of rules to guide public life [ 35 ]. The formalism and the attachment to legality have been part of a political text/discourse, but not of the constant practice of the system [ 36 ]. Hence, it conforms to a sort of simulation.

Consequently, a question arose regarding the reconsideration of the case study exemplified in this article, as follows: Is progress being made in this regard or is it simply the appearance of giving attention to these uncertainties, by masking an irritating problem with a promising text/discourse, and at the same time confined to being the object of a generalised political-social control? This question had a close relationship with the case study questions: what is the role of the ICB in public health policies? How does the existing institutional arrangement work? What faculties, scope and limitations of power, as well as the exercise thereof, have been granted to these institutions? And what political and social tendency does it show when exercising its authority over matters that can directly affect health and life?

Based on this approach, it could be argued that institutions cannot directly affect policy outcomes, except by their impact on policy-making processes, from which they are designed, approved and implemented by stakeholders. Through the decision process, the institutions influence the adopted policies, in particular on the capacity to maintain inter-temporal commitments, the quality of the implementation, and the stability and credibility of the policies [ 37 ].

The challenge for this type of analysis to reveal this was then focused on linking citations of the text/discourse in larger conglomerates and higher level of abstraction while disaggregating the different dimensions or categories. This section of the discussion gave an account of how to connect the institutional theory with the forms of text/discourse analysis from a linguistic perspective, i.e. how to differentiate the discourse from the action of the political players. However, the question arose as to what would be the best way to prove that this would be true. One of the strategies employed was to speculate whether the role of ICB had some features of clientelism, patrimonialism, patronage, simulation or authoritarianism, rather than to only analyse the way in which the dynamics between the institution and government structure affect the results of public policies. Additionally, we intended to assess if the issues that generated uncertainty within the framework of bio-politics were priorities for the existing institutional arrangement or simply seemed to give attention to these in terms of maintaining social control. Again, a form of political simulation.

Hence, in order to verify the role of the ICB to reduce uncertainty, the concept of simulation was taken as an example to speculate on how these bodies could respond to the issues of concern, both to society and to national policy, based on the hypothesis that, if these are not attending to, then a social, scientific and even bio-political lack of control on the international scene may occur. The concept of institutional simulation could be used to characterise the authoritarian system, as well as its liberal democracy.

Therefore, derived from a deeper, more rigorous and critical reading of the information obtained, we made an integration and proposed that a column could be added to the theoretical map originally considered regarding the tentative form in which the ICB address the issues of uncertainty that could affect health and life. At the same time, it was proposed that some other columns were no longer necessary for this stage. Thus, the visualisation of the first map of categories (Fig. 5 ), was modified in this second stage as a result of textual and discursive citations that emerged inductively from the information collected Fig. 6 . In the column of codes of resistance, it can be seen that there are two stances that seem to be the most outstanding, namely political support and political will.

Finally, by integrating all the information and the way in which it was reconstructed in the category maps, it was possible to identify, in the text/discourse of the representatives of the ICB that were intervened, a tendency to fight against a system of appearance or simulation.

Derived from this analysis, a map of categories that would contain some of the concepts proposed to prove the traits of resistance, as well as the possible connection with the other codes and categories that emerged in text/discourse analysis, was reconstructed.

How do we learn?: Some advantages and limitations of the proposed approach

Finally, we want to briefly discuss some of the advantages and limitations of the methodology just outlined and exemplified in the study of the role of ICB in public health policy. Namely, the use of computational literature retrieval and classification, the introduction of ontologies –in this case based on PubMed’s MeSH classifier keywords– to build semantic networks and the use of hybrid manual/automated methods for the critical analysis of discourse.

As we have already mentioned, the use of contemporary data science techniques, such as computer-aided semi-automated literature retrieval and classification, provides helpful results since it implies the elimination (or better, the reduction) of sampling biases, resulting from the tendency of researchers to look for information mostly from their preferred sources, some of them ideologically skewed.

There is also the advantage of increased focus, coming from the use of the ontological classification of the concepts, such that conceptual gaps are diminished. For instance, there may be concepts that are closely related but differently enunciated or named in varying cultural circles. The use of ontologies such as the one created by PubMed’s MeSH terms somehow anneals these differences by creating a common language.

Another advantage of the MeSH ontology is, of course, the fact that it allows us to build semantic networks on a global, non-biased way, as terms are linked not by a personal opinion but from a kind of scholarly agreement arising from a large body of peer-reviewed work. Network analysis gives rise to emergent features coming from somehow unexpected conceptual connections that, as in the case of the simulation hypothesis on the role of ICB, are not evident from the study of single instances.

Combining these advantages with the use of hybrid approaches to the critical analysis of discourse allow for unbiased, but still individualised (i.e. human) critiques of the literature in a way that makes evident how the objective and subjective elements of discourse analysis are being carried out. This is highly desirable in the analysis of public policy, and particularly useful in decision-making scenarios.

After enumerating the advantages of using the present approach, we should however mention that, as it is clear, there is no study free of limitations. One particular limitation of this approach is indeed the use of pre-determined ontologies, namely the MeSH system of classifiers. MeSH terms constitute a detailed and structured ontology, which is useful for automated text classification, as it was designed with this in mind. In this regard, specific concepts, relevant to healthcare policy issues, may not be appropriately rendered to a unique MeSH term. As a consequence, the specificity in our description may be partially compromised.

The documental corpus belongs completely to literature published and indexed in the PubMed/Medline database. By this mere fact, there are a number of publication biases introduced. One particularly relevant bias is given by over-representation of papers from the top publishing countries on the subject. Many of them are actually developed countries with their characteristic issues in healthcare policy, which may not reflect the different facets of policy-making and implementation at a worldwide level.

In this article, based on an exhaustive analysis of the literature, following the conceptual tenets of collective health, we developed a novel methodological approach to the problem of critical content analysis. This alternative, which combines novel methodologies of computational data and literature mining and semantic network analysis as well as hybrid manual/automated analysis of discourse, was proposed to study the role of the ICB, as well as some of its expressions in policies, as already discussed.

The challenge of analysing the role of committees in particular, as public bodies, is mainly due to the fact that they are highly dynamic entities. Although their actions can activate transcendental political processes for society, the vast majority of these are intangible and difficult to determine.

This novel approach has allowed us to identify ‘simulation’ as one possible rationale behind the formation of ICB, i.e. one of the reasons behind the creation of ICB may be giving the impression of attending an ethical necessity –to oversee and protect life, society and nature– with political purposes.

We can conclude then that in some cases ICB are formed to attend some bioethical issues to prevent disturbances of the social and institutional order, i.e. to preserve the status quo.

In this work, we introduce a novel, pragmatic approach for the progressive, systematic analysis and exploration of large information corpora. These tools are useful for the study of qualitative data to improve the performance of institutional assessment, and public policy redesign. Interactive strategies are also helpful to perform systematic revisions of the literature, for pattern generation and codification schemes, and for diagrammatic approaches to build models evidencing interactions among concepts and categories not defined a priori.

Abbreviations

Computerized qualitative analysis of discourse

Health care institutions policies and practices

Institutional committees of bioethics

Medical literature analysis and retrieval system online

Medical subject headings

Non-systematic literature reviews

Qualitative research methodology

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Acknowledgments

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This work was supported by CONACYT (grant no.179431/2012) [EHL], as well as by federal funding from the National Institute of Cardiology (Mexico) [MV] and the National Institute of Genomic Medicine (Mexico) [EHL]. [EHL] acknowledges additional support from the 2016 Marcos Moshinsky Chair in the Physical Sciences.

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Mireya Mart\'{i}nez-Garc\'{i}a and Maite Vallejo are joint corresponding authors.

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Sociomedical Research Department, National Institute of Cardiology, Juan Badiano 1, Mexico City, 14080, Mexico

Mireya Martínez-García & Maite Vallejo

Computational Genomics Division, National Institute of Genomic Medicine, Periférico Sur 4809, Mexico City, 14610, Mexico

Enrique Hernández-Lemus

Medical School, Autonomous Metropolitan University, Calzada del Hueso 1100, Mexico City, 04960, Mexico

Jorge Alberto Álvarez-Díaz

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Conceptualization: MM-G, MV, JAÁD. Data curation: MM-G, EH-L. Formal analysis: MMG, EH-L. Funding acquisition: MM-G, EH-L. Investigation: MM-G, EH-L, JAÁ-D. Methodology: MM-G, MV, EH-L. Software: EH-L. Supervision: MV, EH-L. Visualisation: MM-G, MV, EH-L. Writing original draft: MM-G, EH-L. Writing, review and editing: MM-G, MV, EH-L, JAÁ-D. All authors read and approved the final manuscript.

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Martínez-García, M., Vallejo, M., Hernández-Lemus, E. et al. Novel methods of qualitative analysis for health policy research. Health Res Policy Sys 17 , 6 (2019). https://doi.org/10.1186/s12961-018-0404-z

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EDITORIAL article

Editorial: novel approaches for studying creativity in problem-solving and artistic performance.

$\nPhilip A. Fine$

1 School of Psychology and Wellbeing, University of Buckingham, Buckingham, United Kingdom
2 Department of Psychology, Universität Heidelberg, Heidelberg, Germany
3 School of Psychology, Politics and Sociology, Christchurch Canterbury University, Canterbury, United Kingdom
4 Department of Psychology, Macquarie University, Sydney, NSW, Australia

Editorial on the Research Topic Novel Approaches for Studying Creativity in Problem-Solving and Artistic Performance

Introduction

Creativity can be observed across multiple domains of human behavior including problem solving, artistic and athletic engagement, scientific reasoning, decision making, business and marketing, leadership styles, and social interactions. It has a long history of research in many disciplines, and involves a variety of conceptual and methodological approaches. However, given its multi-faceted character, and the multidisciplinary (though not necessarily interdisciplinary) nature of creativity research, it is perhaps unsurprising that such research has tended to examine discrete areas of study, thereby adopting a focused approach that lacks opportunity for cross-fertilization. It is therefore important to encourage interdisciplinary discourse and novel methodological approaches to investigating all aspects of creativity. This can best be achieved by sharing and integrating research ideas, methods, and findings across multiple domains and disciplines, including but not restricted to psychology, neuroscience, philosophy, linguistics, medicine, education, and performance science.

The aim of this Research Topic is to showcase recent creativity research involving new methodological approaches across a range of creativity domains and academic disciplines. Broadly speaking, we see three ways by which such novel methodological approaches can develop. Firstly, adopting technologies such as brain stimulation and EEG allow researchers to investigate creativity in new ways, and new digital research platforms allow researchers to more easily access domain-specific online populations. Secondly, traditional methodologies, already shown to be effective in one field of creativity research, can be employed to investigate hitherto neglected creativity domains. Thirdly, taking advantage of the interdisciplinary nature of creativity research, we can interrogate one domain of creative performance using research perspectives from another, such as viewing medicine as a performance science akin to music ( Kneebone, 2016 ) or investigating insight moments with magic tricks ( Danek et al., 2014 ). This novel juxtaposition of methods from multiple domains and disciplines allows new research questions to be addressed. These three ways of developing novel methodological approaches thus involve: the development of novel methods; the novel application of tried-and-tested methods; and the novel combination of previously separate methodologies.

The Research Topic contains 27 articles (20 Original Research articles, one Case Report, one Review, and five methodological or theoretical contributions). Twelve address questions of creative cognition, covering insight, divergent thinking, and problem solving. Eleven articles investigate creative arts and artistic performance, with a further four addressing other aspects of creativity. Given the focus of the Research Topic, we have decided to address the articles in terms of their methodological approaches, rather than the type of creativity under investigation. Indeed, we hope to encourage the development and ultimately the wider application of those methodological approaches described herein to any aspect or domain of creativity.

Tracking the Process: Physiological Approaches

In line with the increasing pace of technological advancement, several articles utilize physiological techniques to measure and manipulate the creative process, including the electroencephalogram (EEG), and transcranial current stimulation, both direct (tDCS) and alternating (tACS). Dolan et al. employ EEG in both music performers and selected audience members during prepared and improvised renditions of the same piece of classical music, demonstrating what they call an “improvisatory state of mind.” Truelove-Hill et al. measure resting-state EEG in their investigation of the effects of near-future and far-future priming on insight and analytical problem-solving. Di Bernardi Luft et al. use both EEG and tACS in their case study of a professional visual artist with exceptionally vivid spontaneous visual imagery during meditation sessions. They demonstrate increased occipital gamma oscillations during visual imagery, and an effect of alpha tACS on the contents of the artist's images. In another study of musical creativity, Anic et al. investigate the effects of both excitatory and inhibitory tDCS over the left hemisphere primary motor cortex (M1) of pianists who were improvising with their right hands: improvisations under excitatory tDCS were rated as significantly more creative, demonstrating the role of M1 in musical creativity.

Various other articles employ process-tracing methods to probe the creative process. Carey et al. investigate dance in a novel way, using pupillometry (a metric of mental effort) to demonstrate greater pupil dilation in novice, rather than intermediate, dancers as they performed or imagined dance movements. Jankowska et al. use both eye-tracking and think-aloud (verbal protocol) analyses whilst adults completed a creative drawing task, demonstrating methodological synergy between both types of process-tracing and various psychometric measures of drawing creativity. Spiridonov et al. , Loesche et al. , and Dolan et al. all track physical movement during various creative acts. Spiridonov et al. examine the classic 9-dot problem by tracking the position and movement of the solver's index finger on a tablet, and demonstrate specific patterns of motor behavior characterizing the differences between unsuccessful and successful solvers. Similarly, Loesche et al. investigate the chronology of insight moments in a novel insight eliciting task, “Dira,” by tracking the position of the mouse cursor, allowing them to better pinpoint the moment when solutions emerge. Finally, Dolan et al. investigate musical creativity in ensemble playing in various ways, including continuous 3D tracking of the musicians' movement. This enables them to explore movement pattern differences between improvised and prepared renditions, as well as demonstrate, for instance, that the flutist and pianist correlated their fast movements significantly more in an improvised rendition than a classically prepared one.

The Time-Course of Creativity

One common theme, found in 10 articles, is the study of temporal or chronometric aspects of the creative and associated processes. Three articles involving process-tracing, focusing particularly on moment-to-moment aspects of the creative process, have already been mentioned ( Loesche et al. , Spiridonov et al. , and Dolan et al. ). Hass and Beatty directly compare performance on the Alternative Uses Task (AUT) and Consequences Task, showing that both approximate well to an exponential cumulative response time model; they also provide an explanation for why later responses are generally rated as more creative than earlier ones, known as the serial order effect. Kizilirmak et al. measure feelings of warmth (FoW) ratings for Compound Remote Associate Tasks as a function of task difficulty, whether it was successfully solved, and whether the solution (if it occurred) was an example of insight; they demonstrate that FoW ratings increase more abruptly for trials solved with compared to without an insight experience. Kupers et al. measure moment-to-moment ratings of novelty and appropriateness in their study of children's creativity using a novel coding framework. Botella et al. explore the stages of the creative artistic process, which they propose differs from both the creative process and the artistic process, by interviewing visual graphic arts students, integrating their findings into Creative process Report Diaries.

Rather than focusing on the creative process itself, three articles measure the time-course of associated processes. Wang et al. explore the temporal structure of semantic associations in an association chain task and its relationship to divergent thinking. Korovkin et al. use a dual-task procedure to track the temporal dynamics of working memory involvement throughout both insight and non-insight problem-solving experiences. Truelove-Hill et al. investigate the effects of a priming procedure on creative problem-solving by asking problem-solvers to think about the near vs. distant future in order to differentially impact their cognitive style, in accordance with construal level theory. They then apply growth-curve analysis in a novel way to uncover the time-course of these transient priming effects.

Promoting and Measuring Creativity: Psychometric Approaches

Several articles describe novel approaches to promote, track or measure creativity. Three articles propose novel methods for inducing insight. Friedlander and Fine posit a new protocol for eliciting insight moments, that of cryptic crossword solving, drawing parallels between certain cryptic clue mechanisms and problem types already found in the insight literature, such as rebus puzzles, remote associate problems, anagrams, and jokes. Such an approach could be instrumental in exploring individual differences in insight ability, and identifying insight experts. In order to investigate multiple instances of both positive (Aha!) and negative (Uh-oh!) insight experiences, Hill and Kemp use the well-known adversarial game of Connect 4, asking participants to label each move as insight or search (either positive or negative) and collecting concomitant phenomenological ratings. Loesche et al. have developed a new game, “Dira,” based on the existing game “Dixit,” in which participants must find a connection between a short sentence and one of six visual images. However, only the image (or text) over which the mouse is hovering is clearly visible: this allows real-time process-tracing via mouse movements, and provides information about relevant metacognitive and behavioral mechanisms, such as the intensity of the insight moment.

Other cognitive methods applied to creativity research in the current articles include: the use of verbal protocol analysis to probe metacognitive and self-regulation mechanisms together with eye-movement measures during a creative drawing task ( Jankovska et al. ); the measurement of feelings of warmth during insight and non-insight puzzle solving ( Kizilirmak et al. ); and the application of the classic dual-task paradigm to investigate the effect of working memory load on solving insight and non-insight problems ( Korovkin et al. ). Camic et al. also describe the potential utility, for those with dementia, of Visual Thinking Strategies (VTS), an arts-based facilitated learning methodology involving moderated group discussions, permitting individuals to create meaning through viewing visual art.

Two articles probe novel and interesting causal relationships between creativity and other cognitive activities or processes. Having a broad attentional scope has previously been shown to enhance creativity, but Wronska et al. demonstrate the reverse relationship, that divergent thinking can broaden visual attention on a subsequent visual scanning task and enhance peripheral target recognition. Osowiecka and Kolanczyk show that silently reading poetry can both increase and decrease divergent thinking performance, depending on the type of poetic metaphors, the poetic narration style, and individual differences in long-term exposure to poetry.

Several articles explore novel psychometric methods for measuring and otherwise quantifying aspects of creativity. Threadgold et al. present a newly validated normative pool of 84 rebus puzzles freely available for future use in problem-solving and insight studies. Kupers et al. propose a micro-level domain-general systematic coding framework for measuring novelty and appropriateness of creative products on a continual basis. Kershaw et al. apply a novel originality scoring method, the Decision Tree for Originality Assessment in Design (DTOAD), to creative ideation within engineering design. Clements et al. adapt Amabile's Consensual Assessment Technique (CAT; Amabile, 1982 ; Cseh and Jeffries, 2019 ) for online use so as to have a broader reach, by which they investigate the effects of varying levels of dance expertise and experience on ratings of choreographic creativity. Loesche et al. 's exploration of the chronometry of insight moments and Threadgold et al. 's construction of a normative database of rebus puzzles both treat the strength of the Eureka experience as a continuum rather than a dichotomous all-or-none phenomenon, which has generally been a more common approach; similarly, some articles, including Hill and Kemp , and Loesche et al. , consider phenomenological correlates of the insight moment as continua.

Technological and Methodological Advances

In addition to the studies using tDCS, tACS, and EEG already mentioned, two articles in particular employ methods novel to creativity research to increase the reach of their studies. For their direct comparison of the AUT and the Consequences Task, Hass and Beatty's participants were recruited from Amazon Mechanical Turk (MTurk) using psiTurk, an open-access web-app which interfaces with MTurk, allowing online experimental control and response collection. In their study of choreographic creativity, Clements et al. use an online version of the CAT together with a snowball sampling technique in which participants could rate as few or as many as they wished out of 23 randomly ordered short videos: this yielded 2153 individual ratings from 850 raters.

Camic et al. advocate the use of wearable technology for measuring psychophysiological changes on a continuous basis during creative behaviors, particularly where it is important that such data collection is unobtrusive, for instance in persons with dementia. Wearable technology such as wristbands can record 3D position using accelerometers, as well as physiological indices of arousal and stress including heart rate, heart rate variability, skin conductance, and skin temperature. Finally, in their Perspective article, Gobet and Sala advocate the use of methods in Artificial Intelligence (AI), which they argue are less susceptible to mental set issues, in both the design of new experiments and the generation of new theory in relation to the study of creativity.

Investigating Creative People and Populations

Several articles focus more on the creative person, by studying either specific (and sometimes less-studied) populations, or interpersonal aspects of teamwork, ensemble, and co-creativity. Hogan et al. investigate budding fashion designers on a reality television programme in which they are tasked with designing garments. The authors analyze the designers' thinking dispositions using qualitative analysis of the programme transcripts in terms of the 8 Studio Habits of Mind. In a multi-institutional wide-ranging Conceptual Analysis article, Camic et al. explore how we can conceptualize and understand artistic creativity in the dementias, a population easily and undeservedly overlooked in creativity studies. An interesting aspect of the article is their discussion of co-creativity, which focuses on shared processes. Hocking , too, addresses co-creativity, in his dyadic case study of the subjective experience of a professional artist as seen through the eyes of a psychological researcher and thus artistic collaborator, using Interpretative Phenomenological Analysis (IPA). Another case study of an artist ( Di Bernardi Luft et al. ) employs neuroimaging to investigate spontaneous vivid visual imagery, central to this artist's creativity. Though still focusing on the creative process, Kupers et al. present two case studies specifically investigating children's creativity, exemplified by two empirical examples, a music composition task and the solving of a physics problem: their coding framework will no doubt also be applicable to adults (and to other domains of creativity).

Other articles addressed questions of interpersonal interaction with reference to teamwork and ensemble. Reiter-Palmon and Murugavel demonstrate the utility of problem construction in teams by studying the social and cognitive processes involved. Both Bishop and Dolan et al. investigate aspects of ensemble playing and collaborative processes in music performance. Bishop reviews recent literature on collaborative musical creativity, in terms of how ensembles achieve creative spontaneity, through the lenses of embodied music cognition, emergence, and group flow. Dolan et al. explore synchrony of movement in ensemble music performers as a function of the level of improvisation.

Multidisciplinary, Interdisciplinary, and Blended Methodological Approaches

As noted in the introduction to this editorial, one of the main drivers of this Research Topic is that of fostering interdisciplinary cross-fertilization. Two articles explicitly use such a multidisciplinary approach. Wang et al. combine approaches from computational linguistics, complex systems, and creativity research in their investigation of the relationship between semantic association and divergent thinking tasks. Camic et al. 's article about artistic creativity in the dementias is the culmination of a 2-year interdisciplinary study involving research psychologists and neurologists, artists, and media professionals.

Certain articles, although focusing more on a single discipline (often psychology), use a blended approach of multiple methods, some comparing different methodologies directly, such as Hass and Beatty's comparison of the AUT and the Consequences Task. Dolan et al. , in their study of an improvisatory approach to performing classical music, measure various performance-related parameters, post-performance ratings from both performers and audience members, EEG signals again from both performers and selected audience, and 3D motion tracking of the performers' movements. This broad range of measures enables them to demonstrate convergent evidence for differences between improvised and prepared musical performances. Jankowska et al. integrate psychometric, eye-tracking, and verbal protocol analysis in their study of creative drawing. Finally, Carey et al. combine measures of motor imagery, dance performance, and pupillometry to investigate dancers' learning of dance moves.

The Future of Creativity Research

Given the breadth of creativity research, investigating as it does at least the creator, the creative process, the creative product, and environmental influences on creativity ( Rhodes, 1961 ; Abdulla and Cramond, 2017 ), it is important to integrate research ideas, methods, and findings across diverse disciplines. The 27 articles in this Research Topic present a broad picture of contemporary creativity research across multiple disciplines and domains. Separately and together they present a range of novel approaches for studying all aspects of creativity which we hope will encourage further interdisciplinary cross-fertilization. Creativity research is clearly thriving, and through the methodological creativity of developing innovative research methods and approaches, we are in a strong position to advance our understanding of creativity in all its forms.

Author Contributions

PF wrote the first draft of this editorial, and all authors equally contributed to the revisions.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: creativity, problem solving, artistic performance, methodology, novel approach

Citation: Fine PA, Danek AH, Friedlander KJ, Hocking I and Thompson WF (2019) Editorial: Novel Approaches for Studying Creativity in Problem-Solving and Artistic Performance. Front. Psychol. 10:2059. doi: 10.3389/fpsyg.2019.02059

Received: 01 August 2019; Accepted: 23 August 2019; Published: 18 September 2019.

Edited and reviewed by: Aaron Williamon , Royal College of Music, United Kingdom

Copyright © 2019 Fine, Danek, Friedlander, Hocking and Thompson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Philip A. Fine, philip.fine@buckingham.ac.uk

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Introducing a novelty indicator for scientific research: validating the knowledge-based combinatorial approach

Published: 23 June 2021
Volume 126 , pages 6891–6915, ( 2021 )

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Kuniko Matsumoto 1 ,
Sotaro Shibayama 2 ,
Byeongwoo Kang 3 &
Masatsura Igami 1

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Citation counts have long been considered as the primary bibliographic indicator for evaluating the quality of research—a practice premised on the assumption that citation count is reflective of the impact of a scientific publication. However, identifying several limitations in the use of citation counts alone, scholars have advanced the need for multifaceted quality evaluation methods. In this study, we apply a novelty indicator to quantify the degree of citation similarity between a focal paper and a pre-existing same-domain paper from various fields in the natural sciences by proposing a new way of identifying papers that fall into the same domain of focal papers using bibliometric data only. We also conduct a validation analysis, using Japanese survey data, to confirm its usefulness. Employing ordered logit and ordinary least squares regression models, this study tests the consistency between the novelty scores of 1871 Japanese papers published in the natural sciences between 2001 and 2006 and researchers’ subjective judgments of their novelty. The results show statistically positive correlations between novelty scores and researchers’ assessment of research types reflecting aspects of novelty in various natural science fields. As such, this study demonstrates that the proposed novelty indicator is a suitable means of identifying the novelty of various types of natural scientific research.

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How to Write and Publish a Research Paper for a Peer-Reviewed Journal

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The journal field refers the 22 scientific fields in the Essential Science Indicators (ESI) of Thomson Reuters.

The reclassification procedures of multidisciplinary field papers were as follows: (i) collecting the references of a focal paper in the multidisciplinary field; (ii) identifying the scientific field of each reference, where a field was identified based on the scientific fields of a journal; (iii) finding the most frequent scientific field in the references of the focal paper, except for multidisciplinary fields; and (iv) using the most frequent scientific field as the scientific field of the focal paper.

These correspond to focal papers without reference papers or having no same-domain papers. For these focal papers, the novelty scores are not calculable or become zero (the latter case is rare in our study; there are only two observations).

As shown in Tables 2 and 3 , our novelty scores are close to 1 and their variances are small. Previous research indicators (i.e., those used by Dahlin and Behrens ( 2005 ) and Trapido ( 2015 )), which are the basis of our indicators, also have similar features. The small variation in the scores may make it difficult to interpret whether novelty is high or low, especially for the practical use of the indicators. On this point, applying methods such as standardization would help interpret the indicators. Figure 2 is one such example where we adopted percentile representation for the horizontal axis.

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Acknowledgements

We wish to thank Natsuo Onodera for his invaluable insights regarding the measuring of the novelty score.

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Research Unit for Science and Technology Analysis and Indicators, National Institute of Science and Technology Policy (NISTEP), Tokyo, Japan

Kuniko Matsumoto & Masatsura Igami

School of Economics and Management, Lund University, Lund, Sweden

Sotaro Shibayama

Institute of Innovation Research, Hitotsubashi University, Tokyo, Japan

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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by KM. The first draft of the manuscript was written by KM, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Kuniko Matsumoto .

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Matsumoto, K., Shibayama, S., Kang, B. et al. Introducing a novelty indicator for scientific research: validating the knowledge-based combinatorial approach. Scientometrics 126 , 6891–6915 (2021). https://doi.org/10.1007/s11192-021-04049-z

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DOI : https://doi.org/10.1007/s11192-021-04049-z

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On the Fine Art of Researching For Fiction

Jake wolff: how to write beyond the borders of your experience.

The first time I considered the relationship between fiction and research was during a writing workshop—my first—while I watched the professor eviscerate some poor kid’s story about World War II. And yeah, the story was bad. I remember the protagonist being told to “take cover” and then performing several combat rolls to do so.

“You’re college students,” the professor said. “Write about college students.”

Later, better professors would clarify for me that research, with a touch of imagination, can be a perfectly valid substitute for experience. But that’s always where the conversation stopped. If we ever uttered the word “research” in a workshop, we did so in a weaponized way to critique a piece of writing: “This desperately needs more research,” we’d all agree, and then nothing more would be said. We’d all just pretend that everyone in the room already knew how to integrate research into fiction and that the failures of the story were merely a lack of effort rather than skill. Secretly, though, I felt lost.

I knew research was important, and I knew how to research. My questions all had to do with craft. How do I incorporate research into fiction? How do I provide authenticity and detail without turning the story into a lecture? How much research is too much? Too little?

How do I allow research to support the story without feeling obligated to remain in the realm of fact—when I am, after all, trying to write fiction?

I heavily researched my debut novel, in which nearly every chapter is science-oriented, historical, or both. I’d like to share a method I used throughout the research and writing process to help deal with some of my questions. This method is not intended to become a constant fixture in your writing practice. But if you’re looking for ways to balance or check the balance of the amount of research in a given chapter, story, or scene, you might consider these steps: identify, lie, apply.

I recently had a conversation with a former student, now a friend, about a short story he was writing. He told me he was worried he’d packed it too full of historical research.

“Well,” I said, “how much research is in there?”

“Uhhh,” he answered. “I’m not sure?”

That’s what we might call a visualization problem. It’s hard to judge the quantity of something you can’t see.

I’ve faced similar problems in my own work. I once received a note from my editor saying that a certain chapter of my novel read too much like a chemistry textbook. At first, I was baffled—I didn’t think of the chapter as being overly research-forward. But upon reading it again, I realized I had missed the problem. After learning so much about chemistry, I could no longer “see” the amount of research I had crammed into twenty pages.

Literature scholars don’t have this problem because they cite their sources; endnotes, footnotes, and the like don’t merely provide a tool for readers to verify claims, but also provide a visual reminder that research exists within the text. Thankfully, creative writers generally don’t have to worry about proper MLA formatting (though you should absolutely keep track of your sources). Still, finding a quick way to visually mark the research in your fiction is the least exciting but also the most important step in recognizing its role in your work.

Personally, I map my research in blue. So when my editor flagged that chapter for me, I went back to the text and began marking the research. By the end of the process, the chapter was filled with paragraphs that looked like this one:

Progesterone is a steroid hormone that plays an especially important role in pregnancy. Only a few months before Sammy arrived in Littlefield, a group of scientists found the first example of progesterone in plants. They’d used equipment I would never be able to access, nuclear magnetic resonance and mass spectroscopy, to search for the hormone in the leaves of the English Walnut trees. In humans, aging was associated with a drop in progesterone and an increase in tumor formation—perhaps a result of its neurosteroidal function.

My editor was spot-on: this barely qualified as fiction. But I truly hadn’t seen it. As both a writer and teacher, I’m constantly amazed by how blind we can become to our own manuscripts. Of course, this works the other way, too: if you’re writing a story set in medieval England but haven’t supported that setting with any research, you’ll see it during this step. It’s such an easy, obvious exercise, but I know so few writers who do this.

Before moving on, I’ll pause to recommend also highlighting research in other people’s work. If there’s a story or novel you admire that is fairly research-forward, go through a few sections and mark anything that you would have needed research to write. This will help you see the spacing and balance of research in the fiction you’re hoping to emulate.

(Two Truths and a) Lie

You’ve probably heard of the icebreaker Two Truths and a Lie: you tell two truths and one lie about yourself, and then the other players have to guess which is the lie. I’d rather die than play this game in real life, but it works beautifully when adapted as a solo research exercise.

It’s very simple. When I’m trying to (re)balance the research in my fiction, I list two facts I’ve learned from my research and then invent one “fact” that sounds true but isn’t. The idea is to acquaint yourself with the sound of the truth when it comes to a given subject and then to recreate that sound in a fictive sentence. It’s a way to provide balance and productivity, ensuring that you’re continuing to imagine and invent —to be a fiction writer— even as you’re researching.

I still have my notes from the first time I used this exercise. I was researching the ancient Chinese emperor Qin Shi Huang for a work of historical fiction I would later publish in One Story. I was drowning in research, and the story was nearing fifty pages (!) with no end in sight. My story focused on the final years of the emperor’s life, so I made a list of facts related to that period, including these:

1. The emperor was obsessed with finding the elixir of life and executed Confucian scholars who failed to support this obsession.

2. If the emperor coughed, everyone in his presence had to cough in order to mask him as the source.

3. The emperor believed evil spirits were trying to kill him and built secret tunnels to travel in safety from them.

Now, the second of those statements is a lie. My facts were showing me that the emperor was afraid of dying and made other people the victims of that fear—my lie, in turn, creates a usable narrative detail supporting these facts. I ended up using this lie as the opening of the story. I was a graduate student at the time, and when I workshopped the piece, my professor said something about how the opening worked because “It’s the kind of thing you just can’t make up.” I haven’t stopped using this exercise since.

We have some facts; we have some lies. The final step is to integrate these details into the story. We’ll do this by considering their relationship to the beating heart of fiction: conflict. You can use this step with both facts and lies. My problem tends to be an overload of research rather than the opposite, so I’ll show you an example of a lie I used to help provide balance.

In a late chapter in my book, three important characters—Sammy and his current lover Sadiq and his ex- lover Catherine—travel to Rapa Nui (Easter Island). They’ve come to investigate a drug with potential anti-aging properties that originates in the soil there (that’s a fact; the drug is called rapamycin). As I researched travel to Easter Island, my Two Truths and a Lie exercise produced the following lie:

There are only two airports flying into Easter Island; these airports constantly fight with each other.

In reality, while there are two airports serving Easter Island (one in Tahiti; the other in Chile), nearly everyone flies from Chile, and it’s the same airline either way. On its surface, this is the kind of lie I would expect to leave on the cutting room floor—it’s a dry, irrelevant detail.

But when I’m using the ILA method, I try not to pre-judge. Instead, I make a list of the central conflicts in the story or chapter and a list of the facts and lies. Then I look for applications—i.e., for ways in which each detail may feel relevant to the conflicts. To my surprise, I found that the airport lie fit the conflicts of the chapter perfectly:

Ultimately, the airport lie spoke to the characters, all of whom were feeling the painful effects of life’s capriciousness, the way the choices we make can seem under our control but also outside it, arbitrary but also fateful. I used this lie to introduce these opposing forces and to divide the characters: Sammy and Sadiq fly from Tahiti; Catherine flies from Chile.

Two airports in the world offered flights to Rapa Nui—one in Tahiti, to the west, and one in Chile, to the east. Most of the scientists stayed in one of those two countries. There was no real meaning to it. But still, it was hard, in a juvenile way, not to think of the two groups as opposing teams in a faction. There was the Tahiti side, and there was the Chile side, and only one could win.

This sort of schematic—complete with a table and headers—may seem overly rigid to you, to which I’d respond, Gee, you sound like one of my students. What can I say? I’m a rigid guy. But when you’re tackling a research-intensive story, a little rigidity isn’t the worst thing. Narrative structure does not supply itself. It results from the interplay between the conflicts, the characters, and the details used to evoke them. I’m presenting one way, of many, to visualize those relationships whenever you’re feeling lost.

Zora Neal Hurston wrote, “Research is formalized curiosity. It is poking and prying with a purpose.” Maybe that’s why I’m thinking of structure and rigidity—research, for me, is bolstering in this way. It provides form. But it’s also heavy and hard to work with. It doesn’t bend. If you’re struggling with the burden of it, give ILA a shot and see if unsticks whatever is holding you back. If you do try this approach, let me know if it works for you—and if it doesn’t, feel free to lie.

__________________________________

The History of Living Forever by Jake Wolff

Jake Wolff’s The History of Living Forever is out now from FSG.

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Research Process

Navigating the Research Landscape: A Deep Dive into the FINER Method

5 minute read

Table of Contents

Within the complex field of scientific research, the development of a well-structured and feasible research question serves as the fundamental basis of a meaningful investigation. The process of transforming a clinical problem into a structured inquiry is a thorough one, as it sets the direction for the entire research effort. The FINER criteria, which is an acronym representing the fundamental characteristics of a research issue, remains a valuable tool for researchers, particularly those who are in the early stages of their professional journey.

Setting the Stage: The Role of the Research Protocol

Before learning the FINER approach, it’s important to comprehend the study protocol’s structure. This documented plan is essential for funding applications and science. It helps researchers organize their study to be logical, focused, and efficient. The protocol describes the study’s goals, methodology, statistics, and organization, guiding the research from start to finish.

“A good research question should pass the ‘‘So what?’’ test. Getting the answer should contribute usefully to our state of knowledge.” (Hulley et al. 22)

A Closer Look at the FINER Criteria

FINER is an acronym that stands for Feasible, Interesting, Novel, Ethical, and Relevant. These five attributes are essential in ensuring that a research question is not only well-constructed but is also capable of driving a study that’s both meaningful and impactful.

1. Feasible:

Feasibility is all about the practicality of a study. It’s about asking whether the study can be done given the available resources, time, and technology. It’s crucial to consider if the sample size is attainable, if the variables can be measured effectively, and if there are enough resources, including funding and expertise, to carry the study through to completion.

Essential considerations are as follows:

Does the available timeframe allow for the execution of the research?
Does the necessary technology and knowledge exist to facilitate the execution of my study?
Can I pay for it?
Will my study yield the desired level of impact and significance for the intended audience?
Is there sufficient access to the desired group or an adequate number of participants to ensure the attainment of reliable results?

2. Interesting:

A study needs to captivate the attention of not just the researcher but also the wider audience, including peers, collaborators, and potential funders. An interesting study is like a story that needs to be told – it’s compelling, it’s engaging, and it adds a rich layer to the existing body of knowledge. This process can be described as a dynamic interplay between familiar and unfamiliar elements, skillfully arranged to align with established literary works, emerging patterns, and unexplored areas of investigation.

Novelty is about bringing something new to the table. It’s about ensuring that the research contributes fresh insights and perspectives to the existing body of knowledge. A novel study serves as a symbol of innovation, shedding light on uncharted areas, providing new viewpoints, and questioning conventional frameworks.

4. Ethical:

Ethics is the cornerstone of any credible research. It’s about ensuring that the study is conducted with integrity, respect, and responsibility, safeguarding the dignity and well-being of participants. Ethical research is anchored in principles like informed consent, confidentiality, and beneficence, ensuring that the study is conducted with the highest moral standards.

5. Relevant:

Relevance ensures that the research resonates with the real world. It’s about making sure that the findings of the study are not just theoretical but have practical implications, influencing and enriching clinical practice, policy-making, and societal well-being.

Application and Examples of the FINER Criteria

Understanding the theoretical aspects of the FINER criteria is essential, but seeing them applied in real-world research scenarios can offer invaluable insights. Let’s explore how these criteria can be practically employed, drawing from the rich insights provided in the sources shared earlier.

Feasible: Consider a study aiming to explore the impact of a new drug on a specific health condition. The feasibility can be assessed by considering the availability of participants, the accessibility of the drug, the presence of necessary technology and expertise, and the allocation of adequate funding and time.
Interesting: A research question becomes interesting when it addresses a gap in existing knowledge or explores a pressing issue in a specific field. For instance, a study exploring a novel approach to managing a common health condition can captivate the attention of medical professionals, patients, and policymakers.
Novel: Novelty is showcased in a study that brings fresh insights or explores uncharted territories. For example, a research project investigating the genetic basis of a disease previously studied only at the symptomatic level can be considered novel.
Ethical: Ethical considerations are paramount. A study proposing to explore the effects of a new treatment must ensure participants’ safety, informed consent, and data privacy, adhering to established ethical guidelines.
Relevant: Relevance is illustrated in studies that address current challenges or opportunities in a specific field. For instance, a study exploring the efficacy of online therapy sessions can be highly relevant given the increasing reliance on virtual healthcare.

In conclusion…

The FINER criteria aren’t just a checklist – they’re the foundational elements that ensure a study is robust, impactful, and meaningful. In the world of scientific research, where theories and methods intertwine and discoveries emerge, the FINER criteria serve as a guide, ensuring that every step of the research journey is marked by precision, purpose, and impact.

They ensure that every research endeavor is a balanced blend of truth-seeking, impact, and innovation. For the adept researcher, equipped with insights from credible sources, the FINER criteria transform from a set of principles to a powerful tool, turning every research question into a gateway of endless possibilities.

And remember, Elsevier is here to support researchers in making a significant impact. Our Language Editing Plus Service is designed to refine and enhance your manuscript, ensuring clarity, logic, and adherence to journal requirements. With unlimited rounds of language review and comprehensive support throughout the submission process, we’re here to ensure your research not only shines but also reaches its fullest potential. Use the simulator below to get a price quote for your manuscript, based on the total number of words.

The article “FINER: a Research Framework” on the Elsevier Author Services blog is based on the following credible sources:

Hulley SB, Cummings SR, Browner WS, Grady DG, Newman TB. Designing clinical research. 3rd ed. Philadelphia (PA): Lippincott Williams and Wilkins; 2007.

https://edisciplinas.usp.br/pluginfile.php/5486505/mod_resource/content/1/Stephen%20B.%20Hull

ey%2C%20Steven%20R.%20Cummings%2C%20Warren%20S.%20Browner%2C%20Deborah%20G

.%20Grady%2C%20Thomas%20B.%20Newm.pdf

Mohanan, Saritha & Parameswaran, Narayanan. (2022). FINER criteria – What does it mean?. 2. 115. 10.25259/CSDM_123_2022.

https://www.researchgate.net/publication/365592378_FINER_criteria_-_What_does_it_mean

Fandino W. Formulating a good research question: Pearls and pitfalls. Indian J Anaesth. 2019 Aug;63(8):611-616. doi: 10.4103/ija.IJA_198_19. PMID: 31462805; PMCID: PMC6691636.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6691636/

https://ehsanx.github.io/Scientific-Writing-for-Health-Research/research-question.html

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Methods in molecular biology and genetics: looking to the future

Diego a. forero.

1 School of Health and Sport Sciences, Fundación Universitaria del Área Andina, Bogotá, Colombia

Vaibhav Chand

2 Department of Biochemistry and Molecular Genetics, University of Illinois at Chicago, Chicago, USA

Associated Data

Not applicable.

In recent decades, advances in methods in molecular biology and genetics have revolutionized multiple areas of the life and health sciences. However, there remains a global need for the development of more refined and effective methods across these fields of research. In this current Collection, we aim to showcase articles presenting novel molecular biology and genetics techniques developed by scientists from around the world.

A brief overview of the development of methods of molecular biology and genetics

Since ancient times, humankind has recognized the influence of heredity, based on familial resemblance, selective breeding of livestock, and climate-adapted crops. Prior to Gregor Johann Mendel’s work in the nineteenth century, there was no clear scientific theory to explain heredity. Mendel’s work remained essentially theoretical until the discovery of DNA and confirmation of its role as the principal agent of heredity in organisms in the twentieth century [ 1 ]. In addition, the resolution of the DNA structure paved the way for the invention of the Polymerase Chain Reaction (PCR) (by Kary Mullis), nucleotide synthesis [ 2 ] and the Sanger sequencing method [ 3 ] which revolutionized the field of genetics and led to the development of several sub-disciplines, including cytogenetics, biotechnology, bioprocess technology, and molecular biology. Automation of Sanger sequencing led to the Human Genome Project in 1990 [ 1 ], soon followed by sequencing the complete genomes of numerous other species of flora and fauna [ 4 ].

In recent decades, advances in methods in molecular biology and genetics have revolutionized multiple areas of life and health sciences [ 2 ]. As a major example from health sciences, PCR-based methods have advanced our understanding of the aetiology of a myriad of acute and chronic diseases, in addition to allowing the diagnosis of multiple disorders [ 1 , 5 ]. As a recent global application of molecular methods, the PCR-based approaches have led to the processing of hundreds of millions of samples for the analysis of the SARS-CoV-2 virus [ 6 ]. In addition, molecular methods have been key for the creation of multiple companies, products and jobs [ 7 ].

The development of sequencing technologies and their iterative improvements have been instrumental in advancing the understanding of DNA and RNA, their identification, association with various proteins, their covalent modifications, the function of the genes they carry, and the function of the non-coding portion of DNA and RNA in normal and diseased cells, in pathogenic bacteria and viruses, and in plants [ 8 , 9 ]. By producing RNA-based vaccines, we were able to combat the recent SARS-CoV2 pandemic. This was made possible by sequencing and in vitro nucleotide synthesis technologies [ 10 ].

Gene editing technologies, such as restriction endonuclease digestion, transcription activator-like effector nucleases (TALENs), and the clustered regularly interspaced short palindromic repeats (CRISPR-Cas) system, are an additional development in the field of molecular biology that has aided in the understanding of DNA and genes. There is optimism about the use of CRISPR-Cas9 technology in the treatment of a wide variety of diseases, such as cancer, blood-related diseases, hereditary blindness, cystic fibrosis, viral diseases, muscular dystrophy, and Huntington´s disease, due to its precision and its constant improvement, in comparison with other gene-editing technologies [ 15 ].

Need for novel methods in molecular biology and genetics

There is a global need for the development of novel methods for molecular biology and genetics. Particularly, in the area of human health, there is a need for further approaches that facilitate point-of-care molecular analysis (particularly miniaturized and portable platforms), for infectious and non-transmissible diseases [ 11 ], the development of more efficient methods for DNA sequencing [ 3 ], which facilitate cost-effective genome-wide analysis of patients, among others.

In addition, three key factors would also help push this field forward: additional research comparing the performance of different methods for molecular biology [ 12 ], the broader use of reporting standards (such as the Minimum Information for Publication of Quantitative Real-Time PCR Experiments -MIQE-, which describes details of experimental conditions) [ 13 ], and the increased participation of scientists from the Global South.

Although older techniques, such as x-ray crystallography, gene cloning, PCR, and sequencing, have been instrumental in the study of various aspects of genetics, these techniques have several limitations that result in gaps, missing links, and incomplete understanding of the genome. Advances in these techniques are needed to fill in these missing pieces of the puzzle to better comprehend genetics and accelerate the discovery of the causes of various genetically linkeddiseases. From a technological standpoint, the accuracy of sequencing and coverage across the genome remain major issues, especially for GC-rich regions and long homopolymer stretches of DNA. Furthermore, the short read lengths generated by the majority of current platforms severely restrict our ability to accurately characterize large repeat regions, numerous indels, and structural variation, rendering large portions of the genome opaque or inaccurate. Fragmentation of the genome for sequencing continues to be a major source of disruption in the continuity of the correct genomic sequence [ 14 , 15 ].

Recent advances in CRISPR technology provide hope for the medical treatment of cancer and other fatal diseases. Despite significant advances in this field, a number of technical obstacles remain, including off-target activity, insufficient indel or low homology-directed repair (HDR) efficiency, in vivo delivery of the Cas system components, and immune responses. This requires a substantial amount of technological advancement or the creation of new, superior methods to combat severe diseases with minimal side effects [ 14 , 16 ].

Additional considerations

As high-throughput, automated methods commonly produce very large amounts of data, deeper interaction between wet-lab and dry-lab researchers is required, to facilitate the design of efficient assays [ 17 ] and allow effective analysis and interpretation of results. Interdisciplinary collaborations, between biologists, engineers and professionals in the health sciences, might lead to newer and better methods of addressing current and future needs.

Further collaborations between scientists from academia and industry (in addition to researchers from government agencies) [ 18 ] would help to facilitate the development of novel methods, and aid in promoting their implementation around the world. For many countries, the main barrier to the broad use of molecular methods is the high cost of equipment and reagents [ 19 ]. Strategies aimed at lowering costs would be helpful for multiple institutions around the globe. In terms of intellectual property, fair licensing to institutions in the Global South as well as the implementation of Open Innovation and Open Science policies would be appropriate [ 20 ].

Overview of the current collection

In this current Collection, we are calling for articles showcasing novel methods from molecular biology and genetics, written by scientists from around the world. It is our goal to compile a set of articles that will help to address the challenges faced by the fields of molecular biology and genetics and broaden our understanding of genetic disorders and potential treatment strategies. We invite researchers working on such methods to consider submitting to our collection.

Acknowledgements

DAF has been previously supported by research grants from Minciencias and Areandina. VC has been previously supported by research grants from NIH and VA.

Author contributions

DAF and VC wrote an initial draft of the manuscript. All authors read and approved the final manuscript.

Data availability

Declarations.

DAF is a Senior Editorial Board Member of BMC Research Notes. VC is a Guest Editorial Board Member of BMC Research Notes.

DAF is a medical doctor, Ph.D. in Biomedical Sciences and Professor and Research Leader at the School of Health and Sport Sciences, Fundación Universitaria del Área Andina (Bogotá, Colombia). He has worked with multiple methods of molecular biology and genetics and is an author of more than 100 articles in international journals, has been peer reviewer for more than 115 international scientific journals, in addition to being part of editorial boards of several international journals. VC is a Research Assistant Professor in the Department of Biochemistry and Molecular Genetics at the University of Illinois at Chicago. His expertise in Biochemistry, Molecular Biology, Genetics, Oncology, and Cancer Biology is extensive. He is an invited reviewer for more than fourteen international peer review journals and is the author of fourteen articles with high impact.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Diego A. Forero, Email: oc.ude.anidnaera@14orerofd .

Vaibhav Chand, Email: ude.ciu@50dnahcv .

A novel quantitative approach to concept analysis: the internomological network

Affiliation.

1 College of Nursing, University of Colorado, Aurora, USA. [email protected]
PMID: 22592387
PMCID: PMC3422604
DOI: 10.1097/NNR.0b013e318250c199

Background: When a construct such as patients' "transition to self-management" of chronic illness is studied by researchers across multiple disciplines, the meaning of key terms can become confused. This results from inherent problems in language where a term can have multiple meanings (polysemy) and different words can mean the same thing (synonymy).

Objectives: The aim of this study was to test a novel quantitative method for clarifying the meaning of constructs by examining the similarity of published contexts in which they are used.

Methods: Published terms related to the concept transition to self-management of chronic illness were analyzed using the internomological network (INN), a type of latent semantic analysis performed to calculate the mathematical relationships between constructs based on the contexts in which researchers use each term. This novel approach was tested by comparing results with those from concept analysis, a best-practice qualitative approach to clarifying meanings of terms. By comparing results of the 2 methods, the best synonyms of transition to self-management, as well as key antecedent, attribute, and consequence terms, were identified.

Results: Results from INN analysis were consistent with those from concept analysis. The potential synonyms self-management, transition, and adaptation had the greatest utility. Adaptation was the clearest overall synonym but had lower cross-disciplinary use. The terms coping and readiness had more circumscribed meanings. The INN analysis confirmed key features of transition to self-management and suggested related concepts not found by the previous review.

Discussion: The INN analysis is a promising novel methodology that allows researchers to quantify the semantic relationships between constructs. The method works across disciplinary boundaries and may help to integrate the diverse literature on self-management of chronic illness.

Publication types

Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Adaptation, Psychological
Chronic Disease
Nursing Research / methods*
Periodicals as Topic / statistics & numerical data
Self Care / psychology
Terminology as Topic*

Grants and funding

UL1 RR025780/RR/NCRR NIH HHS/United States
UL1 RR025780-02/RR/NCRR NIH HHS/United States
1UL1RR025780-01/RR/NCRR NIH HHS/United States

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Fri, Mar 29, 2024 page3

Academia sinica study creates novel method for calculating climate change.

By Yang Yuan-ting and Jonathan Chin / Staff reporter, with staff writer and CNA

A recent study by Academia Sinica created a novel method for calculating elevation-dependent climate change-induced temperature shifts, allowing scientists to predict environmental impacts with greater precision.

The research was published in the journal Nature on Wednesday with open public access.

The study said myriad species are not migrating fast enough to survive in the changing climate map, even if global warming is curbed as planned, Biodiversity Research Center fellow Shen Sheng-feng (沈聖峰), who led the research, told a news conference in Taipei yesterday.

Academia Sinica researcher Shen Sheng-feng, right, poses with other members of his research team in Taipei yesterday.

Photo: Yang Yuan-ting, Taipei

According to the researcher’s calculations, the isotherm — the boundary line of a temperature zone — is moving upward at 11.67m per year, a rate far higher than previous estimates, he said.

There is a general lack of data on the impact of climate on mountainous regions, where terrain difficulties impede weather station construction and ecological observation, he said.

Study coauthor Chen I-ching (陳一菁), associate professor of life science at National Cheng Kung University, showed in a 2011 study that isotherm shifts outpaced wildlife population’s migration, indicating isotherm calculations were wrong, Sheng said.

The research team utilized satellite data, empirical data on the velocities of species range shifts and modeling based on thermal dynamics to shed light on the relationship between isotherm changes and the vertical relocation speed of species, he said.

The study covered more than 8,600 mountain ranges across 17 regions of the world, including the Yukons in Alaska, the Mediterranean Basin, the Kodar Mountains in Russian Siberia and the Sumatra region in Indonesia, Sheng said.

The accelerated speed of warming shown in the study poses a “severe threat” to the unique species sheltered in the mountains in a troubling sign for the environment, he said.

First author Chen Wei-ping (詹偉平), a post-doctoral fellow at Harvard University, said the study revealed that although Taiwan proper’s mountainous regions are warming more slowly than the global average, the isotherm is rising faster than average due to humidity.

This means climate change would have a stronger impact on Taiwan’s ecological conditions than previously thought, he said.

An Academia Sinica spokesperson said that the research blazed a new trail in climate science and also sounded a call to action for governments to jointly protect vulnerable ecosystems in mountain ranges across the world.

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Published: 22 March 2024

Generative AI for designing and validating easily synthesizable and structurally novel antibiotics

Kyle Swanson ORCID: orcid.org/0000-0002-7385-7844 1 na1 ,
Gary Liu 2 na1 ,
Denise B. Catacutan 2 ,
Autumn Arnold 2 ,
James Zou ORCID: orcid.org/0000-0001-8880-4764 1 , 3 &
Jonathan M. Stokes ORCID: orcid.org/0009-0001-8378-2380 2

Nature Machine Intelligence volume 6 , pages 338–353 ( 2024 ) Cite this article

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Antibiotics
Computational chemistry
Phenotypic screening

The rise of pan-resistant bacteria is creating an urgent need for structurally novel antibiotics. Artificial intelligence methods can discover new antibiotics, but existing methods have notable limitations. Property prediction models, which evaluate molecules one-by-one for a given property, scale poorly to large chemical spaces. Generative models, which directly design molecules, rapidly explore vast chemical spaces but generate molecules that are challenging to synthesize. Here we introduce SyntheMol, a generative model that designs new compounds, which are easy to synthesize, from a chemical space of nearly 30 billion molecules. We apply SyntheMol to design molecules that inhibit the growth of Acinetobacter baumannii , a burdensome Gram-negative bacterial pathogen. We synthesize 58 generated molecules and experimentally validate them, with six structurally novel molecules demonstrating antibacterial activity against A. baumannii and several other phylogenetically diverse bacterial pathogens. This demonstrates the potential of generative artificial intelligence to design structurally novel, synthesizable and effective small-molecule antibiotic candidates from vast chemical spaces, with empirical validation.

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Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans, … Demis Hassabis

De novo design of protein structure and function with RFdiffusion

Joseph L. Watson, David Juergens, … David Baker

Solving olympiad geometry without human demonstrations

Trieu H. Trinh, Yuhuai Wu, … Thang Luong

Data availability

The data used in this paper, including training data and generated molecules, are available in the Supplementary Data . The data, along with trained model checkpoints and LC–MS and 1 H-NMR spectra, are available at https://doi.org/10.5281/zenodo.10257839 (ref. 68 ). The ChEMBL database can be accessed from www.ebi.ac.uk/chembl .

Code availability

Code for data processing and analysis, property prediction model training and SyntheMol molecule generation is available at https://github.com/swansonk14/SyntheMol (ref. 69 ). This code repository makes use of general cheminformatics functions from https://github.com/swansonk14/chemfunc as well as Chemprop model code from https://github.com/chemprop/chemprop .

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Acknowledgements

This research was kindly supported by the Weston Family Foundation (POP and Catalyst to J.M.S.); the David Braley Centre for Antibiotic Discovery (J.M.S.); the Canadian Institutes of Health Research (J.M.S.); a generous gift from M. and M. Heersink (J.M.S.) and the Chan-Zuckerberg Biohub (J.Z.). We thank Y. Moroz for his help accessing and answering our questions about the Enamine REAL Space building blocks, reactions and molecules. We thank G. Dubinina for help obtaining generated compounds. We thank M. Karelina, J. Miguel Hernández-Lobato, A. Tripp and M. Segler for insightful discussions about our property prediction and generative methods. We thank J. Boyce and A. Wright for providing mutant strains of A. baumannii used in this study. K.S. acknowledges support from the Knight-Hennessy scholarship.

Author information

These authors contributed equally: Kyle Swanson, Gary Liu.

Authors and Affiliations

Department of Computer Science, Stanford University, Stanford, CA, USA

Kyle Swanson & James Zou

Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada

Gary Liu, Denise B. Catacutan, Autumn Arnold & Jonathan M. Stokes

Department of Biomedical Data Science, Stanford University, Stanford, CA, USA

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Contributions

Conceptualization was carried out by K.S., G.L., J.Z. and J.M.S. Model development was performed by K.S. and G.L. Biological validation was carried out by D.B.C., A.A. and J.M.S. K.S., G.L., D.B.C., J.Z. and J.M.S. wrote the paper. J.Z. and J.M.S. supervised the work.

Corresponding authors

Correspondence to James Zou or Jonathan M. Stokes .

Ethics declarations

Competing interests.

These authors declare the following competing interests: K.S. is employed part-time by Greenstone Biosciences; J.Z. is on the scientific advisory board of Greenstone Biosciences and J.M.S. is cofounder and scientific director of Phare Bio. The other authors declare no competing interests.

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Nature Machine Intelligence thanks Feixiong Cheng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended data fig. 1 additional property prediction model development..

( a ) Normalized growth of A. baumannii ATCC 17978 in biological duplicate for each of the three training set chemical libraries. Note: compounds with normalized growth > 3 are removed for visual purposes. The R 2 values are the coefficient of determination. ( b ) Receiver operating characteristic (ROC) curves and ( c ) precision-recall (PRC) curves for the Chemprop, Chemprop-RDKit, and random forest models. For each model, the black lines show the performance of each of the ten models in the ensemble and the blue curve shows the average. ( d , e ) Model performance of each of our three property prediction models when generalizing between our three training set libraries. Values on the diagonal are the average test set performance of a model on a single library across tenfold cross-validation. Values on the off-diagonals are the result of applying an ensemble of ten models trained on one library to a different library and evaluating those predictions. ( d ) Performance measured by area under the receiver operating characteristic curve (ROC-AUC). ( e ) Performance measured by area under the precision-recall curve (PRC-AUC).

Extended Data Fig. 2 REAL Space Analysis, Comparisons to Training Set, and Reactions.

( a ) The cumulative percent of molecules in REAL Space that can be produced by each of the 169 REAL chemical reactions. ( b ) The percent of molecules in REAL Space that include each of the REAL building blocks. ( c ) The molecular weight distribution of a random sample of 25,000 REAL molecules (blue), the REAL building blocks (black), and our training set molecules (red). ( d ) The cLogP distribution of a random sample of 25,000 REAL molecules (blue), the REAL building blocks (black), and our training set molecules (red). ( e ) The remaining 5 REAL chemical reactions we used (first 8 in Fig. 4b ), along with the number and percent of REAL molecules produced by each reaction.

Extended Data Fig. 3 REAL Building Block and Full Molecule Scores from Chemprop-RDKit and Random Forest.

( a , b ) The distribution of antibacterial model scores on the REAL building blocks using the ( a ) Chemprop-RDKit or ( b ) random forest models. ( c , d ) The correlation between the antibacterial model score of a REAL molecule and the average antibacterial model score of its constituent building blocks using the ( c ) Chemprop-RDKit or ( d ) random forest models. The R 2 values are the coefficient of determination.

Extended Data Fig. 4 Comparison of Generated Sets with and without Building Block Diversity.

( a – c ) The frequency with which building blocks were used in the generated molecules of SyntheMol, with and without the building block diversity score penalty for ( a ) Chemprop, ( b ) Chemprop-RDKit, and ( c ) random forest.

Extended Data Fig. 5 Model Scores by Rollout from Chemprop-RDKit and Random Forest.

( a , b ) Violin plots of the distribution of antibacterial model scores for every 2,000 rollouts of the MCTS algorithm over 20,000 rollouts. SyntheMol uses the ( a ) Chemprop-RDKit or ( b ) random forest models for antibacterial prediction scores. The lines in each violin indicate the first quartile, the median, and the third quartile.

Extended Data Fig. 6 Additional Analysis of Chemprop Generated and Selected Sets.

( a ) The percent of building blocks that appear at different frequencies among the generated or selected compounds by SyntheMol with Chemprop. Building blocks are assigned to bins on the x-axis based on the number of generated or selected compounds that contain that building block, with the final bin including building blocks that appear in at least six compounds (max 137). ( b ) The distribution of chemical reactions used by the generated or selected compounds by SyntheMol with Chemprop. ( c ) A t-SNE visualization of the training set along with all generated and selected molecules from each of the three property predictor models.

Extended Data Fig. 7 Analysis of Chemprop-RDKit Generated and Selected Sets.

( a ) The percent of building blocks that appear at different frequencies among the generated or selected compounds by SyntheMol with Chemprop-RDKit. Building blocks are assigned to bins on the x-axis based on the number of generated or selected compounds that contain that building block, with the final bin including building blocks that appear in at least six compounds (max 185). ( b ) The distribution of chemical reactions used by the generated or selected compounds by SyntheMol with Chemprop-RDKit. ( c – f ) A comparison of the properties of the 25,828 molecules generated by SyntheMol with the Chemprop-RDKit antibacterial model and the 50 molecules selected from that set after applying post-hoc filters. ( c ) The distribution of nearest neighbour Tversky similarities between the generated or selected compounds and the active molecules in the training set. ( d ) The distribution of nearest neighbor Tversky similarities between the generated or selected compounds and the known antibacterial compounds from ChEMBL. ( e ) The distribution of Chemprop-RDKit antibacterial model scores on the generated or selected compounds, as well as on a random set of 25,000 REAL molecules. ( f ) The distribution of nearest neighbor Tanimoto similarities among the generated or selected compounds.

Extended Data Fig. 8 Analysis of Random Forest Generated and Selected Sets.

( a ) The percent of building blocks that appear at different frequencies among the generated or selected compounds by SyntheMol with random forest. Building blocks are assigned to bins on the x-axis based on the number of generated or selected compounds that contain that building block, with the final bin including building blocks that appear in at least six compounds (max 212). ( b ) The distribution of chemical reactions used by the generated or selected compounds by SyntheMol with random forest. ( c – f ) A comparison of the properties of the 27,396 molecules generated by SyntheMol with the random forest antibacterial model and the 50 molecules selected from that set after applying post-hoc filters. ( c ) The distribution of nearest neighbor Tversky similarities between the generated or selected compounds and the active molecules in the training set. ( d ) The distribution of nearest neighbor Tversky similarities between the generated or selected compounds and the known antibacterial compounds from ChEMBL. ( e ) The distribution of random forest antibacterial model scores on the generated or selected compounds as well as on a random set of 25,000 REAL molecules. ( f ) The distribution of nearest neighbor Tanimoto similarities among the generated or selected compounds.

Extended Data Fig. 9 Additional In Vitro Validation.

( a ) Gram-negative bacterial isolates tested for growth inhibition against SPR 741 or colistin. Experiments were performed in biological duplicate. Error bars represent absolute range of optical density measurements at 600 nm. ( b ) Heat map summarizing MICs of 58 randomly selected compounds from the REAL Space against A. baumannii ATCC 17978 in I) LB medium, II) LB medium + a quarter MIC SPR 741, and III) LB medium + a quarter MIC colistin. Compounds were tested at concentrations from 256 µg/mL to 4 µg/mL in two-fold serial dilutions. Lighter colours indicate lower MIC values for each random REAL molecule. No compounds displayed potent antibacterial activity using the threshold of MIC ≤ 8 µg/mL. Experiments were performed in at least biological duplicate. ( c , d ) Chequerboard analysis to quantify synergy, as defined by FICI, with SPR 741 or colistin against Gram-negative isolates. Chequerboard experiments were performed using two-fold serial dilution series with the maximum and minimum concentrations of the potentiator (x-axis) and compound (y-axis) shown in µg/mL. Darker blue represents higher bacterial growth. Experiments were performed in biological duplicate. The mean growth of each well is shown. ( c ) Chequerboard assays using the six bioactive compounds, in combination with colistin, against A. baumannii ATCC 17978. ( d ) Chequerboard assays using rifampicin – a control antibiotic – in combination with SPR 741 or colistin against a panel of Gram-negative bacterial species.

Extended Data Fig. 10 Toxicity Predictions.

Predictions of the probability of clinical toxicity using an ensemble of ten Chemprop-RDKit models trained on the ClinTox dataset. ‘Non-toxic compounds’ show the toxicity predictions of the model on the non-toxic molecules in the dataset (n = 1,372), where each molecule’s prediction score comes from the one model in the ensemble for which that molecule was in the test set. ‘Toxic compounds’ shows the same toxicity predictions for the toxic molecules in the dataset (n = 112). ‘Selected six’ shows the average prediction of the ensemble of ten models on the six potent generated molecules. Blue horizontal lines represent the mean predictions for each set.

Supplementary information

Supplementary information.

Supplementary Tables 1–6 and Extended Discussion.

Reporting Summary

Supplementary data 1–9.

Supplementary Data 1: Training sets for antibacterial activity and clinical toxicity. Supplementary Data 2: Antibiotic and antibacterial molecules from ChEMBL. Supplementary Data 3: Antibacterial Chemprop/Chemprop-RDKit/random forest models performance summary. Supplementary Data 4: Analysis of the Enamine REAL Space. Supplementary Data 5: cLogP Chemprop model performance summary trained for 1 and 30 epochs. Supplementary Data 6: Molecules generated by SyntheMol, using the antibacterial Chemprop model as a scoring function. Supplementary Data 7: Molecules generated by SyntheMol, using the antibacterial Chemprop-RDKit model as a scoring function. Supplementary Data 8: Molecules generated by SyntheMol, using the antibacterial random forest model as a scoring function. Supplementary Data 9: Molecules selected for synthesis by Enamine.

Supplementary Data

Quality control LC–MS data for SyntheMol-generated compounds.

Quality control LC–MS data for randomly selected control compounds.

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Swanson, K., Liu, G., Catacutan, D.B. et al. Generative AI for designing and validating easily synthesizable and structurally novel antibiotics. Nat Mach Intell 6 , 338–353 (2024). https://doi.org/10.1038/s42256-024-00809-7

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Today, we’re introducing SceneScript , a novel method for reconstructing environments and representing the layout of physical spaces.
SceneScript was trained in simulation using the Aria Synthetic Environments dataset, which is available for academic use.

Imagine a pair of stylish, lightweight glasses that combined contextualized AI with a display that could seamlessly give you access to real-time information when you need it and proactively help you as you go about your day. In order for such a pair of augmented reality (AR) glasses to become reality, the system must be able to understand the layout of your physical environment and how the world is shaped in 3D. That understanding would let AR glasses tailor content to you and your individual context, like seamlessly blending a digital overlay with your physical space or giving you turn-by-turn directions to help you navigate unfamiliar locations.

However, building these 3D scene representations is a complex task. Current MR headsets like Meta Quest 3 create a virtual representation of physical spaces based on raw visual data from cameras or 3D sensors. This raw data is converted into a series of shapes that describe distinct features of the environment, like walls, ceilings, and doors. Typically, these systems rely on pre-defined rules to convert the raw data into shapes. Yet that heuristic approach can often lead to errors, especially in spaces with unique or irregular geometries.

Introducing SceneScript

Today, Reality Labs Research is announcing SceneScript , a novel method of generating scene layouts and representing scenes using language.

Rather than using hard-coded rules to convert raw visual data into an approximation of a room’s architectural elements, SceneScript is trained to directly infer a room’s geometry using end-to-end machine learning.

This results in a representation of physical scenes which is compact , reducing memory requirements to only a few bytes; complete , resulting in crisp geometry, similar to scalable vector graphics; and importantly, interpretable , meaning that we can easily read and edit those representations.

How is SceneScript trained?

Large language models (LLMs) like Llama operate using a technique called next token prediction, in which the AI model predicts the next word in a sentence based on the words that came before it. For example, if you typed the words, “The cat sat on the...,” the model would predict that the next word is likely to be “mat” or “floor.”

SceneScript leverages the same concept of next token prediction used by LLMs. However, instead of predicting a general language token, the SceneScript model predicts the next architectural token, such as ‘wall’ or ‘door.’

By giving the network a large amount of training data, the SceneScript model learns how to encode visual data into a fundamental representation of the scene, which it can then decode into language that describes the room layout. This allows SceneScript to interpret and reconstruct complex environments from visual data and create text descriptions that effectively describe the structure of the scenes that it analyzes.

However, the team required a substantial amount of data to train the network and teach it how physical spaces are typically laid out—and they needed to ensure they were preserving privacy.

This presented a unique challenge.

Training SceneScript in simulation

While LLMs rely on vast amounts of training data that typically comes from a range of publicly available text sources on the web, no such repository of information yet exists for physical spaces at the scale needed for training an end-to-end model. So the Reality Labs Research team had to find another solution.

Instead of relying on data from physical environments, the SceneScript team created a synthetic dataset of indoor environments, called Aria Synthetic Environments . This dataset comprises 100,000 completely unique interior environments, each described using the SceneScript language and paired with a simulated video walking through each scene.

The video rendered through each scene is simulated using the same sensor characteristics as Project Aria , Reality Labs Research’s glasses for accelerating AI and ML research. This approach allows the SceneScript model to be completely trained in simulation, under privacy-preserving conditions. The model can then be validated using physical-world footage from Project Aria glasses, confirming the model’s ability to generalize to actual environments.

Last year, we made the Aria Synthetic Environments dataset available to academic researchers, which we hope will help accelerate public research within this exciting area of study.

Extending SceneScript to describe objects, states, and complex geometry

Another of SceneScript’s strengths is its extensibility .

Simply by adding a few additional parameters to scene language that describes doors in the Aria Synthetic Environments dataset, the network can be trained to accurately predict the degree to which doors are open or closed in physical environments.

Additionally, by adding new features to the architectural language, it’s possible to accurately predict the location of objects and—further still—decompose those objects into their constituent parts.

For example, a sofa could be represented within the SceneScript language as a set of geometric shapes including the cushions, legs, and arms. This level of detail could eventually be used by designers to create AR content that is truly customized to a wide range of physical environments.

Accelerating AR, pushing LLMs forward, and advancing the state of the art in AI and ML research

SceneScript could unlock key use cases for both MR headsets and future AR glasses, like generating the maps needed to provide step-by-step navigation for people who are visually impaired , as demonstrated by Carnegie Mellon University in 2022.

SceneScript also gives LLMs the vocabulary necessary to reason about physical spaces. This could ultimately unlock the potential of next-generation digital assistants, providing them with the physical-world context necessary to answer complex spatial queries. For example, with the ability to reason about physical spaces, we could pose questions to a chat assistant like, “Will this desk fit in my bedroom?” or, “How many pots of paint would it take to paint this room?” Rather than having to find your tape measure, jot down measurements, and do your best to estimate the answer with some back-of-the-napkin math, a chat assistant with access to SceneScript could arrive at the answer in mere fractions of a second.

We believe SceneScript represents a significant milestone on the path to true AR glasses that will bridge the physical and digital worlds. As we dive deeper into this potential at Reality Labs Research, we’re thrilled at the prospect of how this pioneering approach will help shape the future of AI and ML research.

Learn more about SceneScript here .

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Abstract. In recent decades, advances in methods in molecular biology and genetics have revolutionized multiple areas of the life and health sciences. However, there remains a global need for the development of more refined and effective methods across these fields of research. In this current Collection, we aim to showcase articles presenting ...
A novel quantitative approach to concept analysis: the ...
The INN analysis is a promising novel methodology that allows researchers to quantify the semantic relationships between constructs. The method works across disciplinary boundaries and may help to integrate the diverse literature on self-management of chronic illness. Research Support, N.I.H., Extramural.
How can you verify if your research work is novel or not?
If it's also about the development of a new and faster method of finding already existing knowledge, it is also novel, provided the method is not in the literature. If all these are no, then be ...
Top 7 Tips For Researching Your Novel
A set of research files in a novel-writing program such as Scrivener. These kinds of programs are great for storing information, and because your novel and your research are stored in the one place, it's easy to access them simultaneously. (Again, as with any digital method, ensure you back up your files religiously.)
Novel Methods in Toxicology Research
Skin Sensitization Testing Using New Approach Methodologies (Deadline: 19 July 2024) Big Data Calculation and New Findings for Aquatic Toxicology (Deadline: 31 July 2024) Advances in Computational Toxicology and Their Exposure (Deadline: 30 September 2024) Feature Papers in the Novel Methods in Toxicology Research (Deadline: 31 October 2024)
An Overview of Traditional and Novel Tools to Assess Diet
Amoutzopoulos B, Steer T, Roberts C, et al. Traditional methods v. new technologies - dilemmas for dietary assessment in large-scale nutrition surveys and studies: A report following an international panel discussion at the 9th International Conference on Diet and Activity Methods (ICDAM9), Brisbane, 3 September 2015. J Nutr Sci. 2018;7:e11.
Who Wrote This? Columbia Engineers Discover Novel Method to Identify AI
Raidar's remarkable accuracy is noteworthy -- it surpasses previous methods by up to 29%. This leap in performance is achieved using state-of-the-art LLMs to rewrite the input, without needing access to the AI's architecture, algorithms, or training data—a first in the field of AI-generated text detection.
A Novel Technique to Form Human Artificial Chromosomes
Penn Scientists Create Novel Technique to Form Human Artificial Chromosomes Researchers say the method will allow for more efficient laboratory research and expand gene therapy. March 21, 2024. ... allows HACs to be crafted more quickly and precisely, which, in turn, will directly speed up the rate at which DNA research can be done. In time ...
Academia Sinica study creates novel method for calculating climate
A recent study by Academia Sinica created a novel method for calculating elevation-dependent climate change-induced temperature shifts, allowing scientists to predict environmental impacts with greater precision. The research was published in the journal Nature on Wednesday with open public access.
Generative AI for designing and validating easily ...
An overview of our generative AI method, SyntheMol, for designing novel antibiotics. First, we curated a training set of ~13,000 molecules and performed growth inhibition assays to determine their ...
Introducing SceneScript, a novel approach for 3D scene reconstruction
Large Language Models. Introducing SceneScript, a novel approach for 3D scene reconstruction. March 20, 2024. Takeaways. Today, we're introducing SceneScript, a novel method for reconstructing environments and representing the layout of physical spaces. SceneScript was trained in simulation using the Aria Synthetic Environments dataset, which ...
Novel LEADER method topic of Penn State news article
Novel LEADER method topic of Penn State news article. Posted: March 29, 2024. Penn State research published an article about the new LEADER method to nondestructively estimate root depth in the field. This method should facilitate the breeding of crops with greater drought tolerance, better fertilizer recovery, and improved carbon capture.

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Research Methods | Definitions, Types, Examples

Table of contents

Qualitative vs. quantitative data

Primary vs. secondary research

Descriptive vs. experimental data

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Qualitative analysis methods

Quantitative analysis methods

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Novel methods of qualitative analysis for health policy research

Conclusions

Complexities of policy analysis

Advantages and disadvantages of traditional approaches

Introducing novel methods

Aims of this work and case study outline

A three-stage research design

Stage I: Documental corpus identification

Stage II: Semantic network curation

Stage III: Content exploration

Stage I Results: documental corpus identification

Stage II Results: semantic network analysis

Stage III Results: content analysis codification protocol

What do we learn?: Insights on the case study

How do we learn?: Some advantages and limitations of the proposed approach

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Health Research Policy and Systems

EDITORIAL article

Introduction

Tracking the Process: Physiological Approaches

The Time-Course of Creativity

Promoting and Measuring Creativity: Psychometric Approaches

Technological and Methodological Advances

Investigating Creative People and Populations

Multidisciplinary, Interdisciplinary, and Blended Methodological Approaches

The Future of Creativity Research

Author Contributions

Conflict of Interest Statement

Introducing a novelty indicator for scientific research: validating the knowledge-based combinatorial approach

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