doctoral thesis paper

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Guide to writing your thesis/dissertation, definition of dissertation and thesis.

The dissertation or thesis is a scholarly treatise that substantiates a specific point of view as a result of original research that is conducted by students during their graduate study. At Cornell, the thesis is a requirement for the receipt of the M.A. and M.S. degrees and some professional master’s degrees. The dissertation is a requirement of the Ph.D. degree.

Formatting Requirement and Standards

The Graduate School sets the minimum format for your thesis or dissertation, while you, your special committee, and your advisor/chair decide upon the content and length. Grammar, punctuation, spelling, and other mechanical issues are your sole responsibility. Generally, the thesis and dissertation should conform to the standards of leading academic journals in your field. The Graduate School does not monitor the thesis or dissertation for mechanics, content, or style.

“Papers Option” Dissertation or Thesis

A “papers option” is available only to students in certain fields, which are listed on the Fields Permitting the Use of Papers Option page , or by approved petition. If you choose the papers option, your dissertation or thesis is organized as a series of relatively independent chapters or papers that you have submitted or will be submitting to journals in the field. You must be the only author or the first author of the papers to be used in the dissertation. The papers-option dissertation or thesis must meet all format and submission requirements, and a singular referencing convention must be used throughout.

ProQuest Electronic Submissions

The dissertation and thesis become permanent records of your original research, and in the case of doctoral research, the Graduate School requires publication of the dissertation and abstract in its original form. All Cornell master’s theses and doctoral dissertations require an electronic submission through ProQuest, which fills orders for paper or digital copies of the thesis and dissertation and makes a digital version available online via their subscription database, ProQuest Dissertations & Theses . For master’s theses, only the abstract is available. ProQuest provides worldwide distribution of your work from the master copy. You retain control over your dissertation and are free to grant publishing rights as you see fit. The formatting requirements contained in this guide meet all ProQuest specifications.

Copies of Dissertation and Thesis

Copies of Ph.D. dissertations and master’s theses are also uploaded in PDF format to the Cornell Library Repository, eCommons . A print copy of each master’s thesis and doctoral dissertation is submitted to Cornell University Library by ProQuest.

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How to write a Doctoral Thesis

Prof. HR Ahmad, Department of Biological and Biomedical Sciences, The Aga Khan University, Karachi, Pakistan. E-mail: [email protected]

Note: * Ahmad HR. In: Medical Writing. Eds. SA Jawaid, MH Jafary & SJ Zuberi. PMJA, 1997 Ed II: 133-142.

PATIENT care and teaching are rather well established components of our medical career. However, with the passage of time a third component has started to influence our medical culture, namely research. 1 - 4 How to accept this challenge is a question. 5 Indeed, teaching and research form a dialectic unit, meaning that teaching without a research component is like a soup without salt. It is a well-established fact that the research activity of an institution is directly proportional to the number of qualified and committed PhD candidates. An inspiring infrastructure, laboratory facilities and libraries are pre-requisites for a research culture to grow. 6 - 8 This forms the basis of a generation cycle for an institution, so that research activity and its culture continues to grow from one generation to the next. The main objective of doctoral work in biomedical sciences is to develop a galaxy of scientist physicians and surgeons possessing high degree of humility, selflessness and ethical superiority. Such a programme will add a scholastic dimension to the clinical faculty.

Education in how to write a doctoral thesis or dissertation should be a part of the postgraduate curriculum, parallel to the laboratory work and Journal Club activities during the PhD studies and/or residency levels. 9 , 10 The overall structure of a doctoral thesis is internationally standardized. However, it varies in style and quality, depending upon how original the work is, and how much the author has understood the work. Therefore a thorough discussion with supervisor, colleagues and assistance from other authors through correspondence can be useful sources for consultation.

The choice of a topic for a doctoral thesis is a crucial step. It should be determined by scanning the literature whether the topic is original or similar work has already been done even a hundred years ago. It is the responsibility of both the supervisor and the PhD candidate to sort out this problem by continuous use of internet and a library. 11 The work leading to the PhD degree can originate from research in following spheres: 12

• b) Methodology
• c) Diagnostic
• d) Therapeutic and Management
• e) Epidemiology

The availability of internationally standardized methods, as well as research committed supervisors can enable physicians and surgeons to do PhD work in both basic and clinical health sciences. The importance of research in basic health sciences cannot be overemphasized. It is rather the base of the applied sciences. There are many instances where the elucidation of a mechanism involved in a process awaits the development of an adequate methodology. 13 In such a scenario; a new method is like a new eye. Research activity in the field of (a) and (b) illuminates the research directions for (c) (d) and (e). It is worth noting that sometimes important basic questions can come from (e) and stimulate research activity in the domain of basic health sciences. 14 , 15

Types of Doctoral Thesis

TYPE-I: Book Form: a classical style. The blueprint of this form is shown in Table-I .

Type-I: The Classical Book Form

TYPE-II: Cumulative Doctoral thesis: A modem but quite useful practice.

INTRODUCTION

A book containing the pearls of a PhD work has standardized divisions and formats, where the number of pages should be weighted in terms of content rather than container. The book includes summary, introduction, materials and methods, results, discussion, conclusions, references and acknowledgements.

Two exercises are mandatory before starting a PhD programme:

• Literature survey using a regular library hours and internet surfing
• Familiarization with the hands-on-experience of methodology involved in the work
• The importance of a continuous literature survey using library, internet and direct correspondence with authors across the globe in the same field cannot be over-emphasized. The main goal of this exercise is to pinpoint the unresolved problem in the literature. An attempt to solve this problem now becomes the topic of the PhD thesis. All the relevant references should be collected, and carefully preserved in the form of a card system arranged alphabetically according to themes and authors. The introduction of the thesis should be styled like a review article with a critical analysis of the work of authors in the literature. The aims of the present PhD work can then also be addressed in the form of questions. The objectives would then deal with how to achieve the aims of the proposed study.

MATERIALS / SUBJECTS AND METHODS

Now comes the most crucial and functional part of the doctoral work, the materials/subjects and methods section. This part can be considered as the motor of the PhD work. The reliability, sensitivity and specificity of the motor must be checked before embarking on a long journey. Controlling the controls is the best guide for a precise and authentic work. Usually materials and methods contain components such as a description of the species involved, their number, age, weight and anthropometric parameters, types of surgical procedures and anesthesia if applied, and a detailed description of methodology. Continuous or point measurements should be thoroughly described. However, a dynamic method should always be preferred to static one.

The experimental protocol should be designed after a small pilot study, which is especially advisable in research on human subjects. A detailed and well-thought experimental protocol forms the basis of conditions under which the results would be obtained. Any deviation from the experimental protocol will affect the outcome, and the interpretation of results. It may be noted that great discoveries are usually accidental and without a protocol, based merely on careful observation! However, for the sake of a publication, a protocol has to be designed after the discovery. After having described the different phases of the experimental protocol with the help of a schematic diagram e.g., showing variables, time period and interventions, the selection of a statistical method should be discussed. Negative results should not be disregarded because they represent the boundary conditions of positive results. Sometimes the negative results are the real results.

It is usual practice that most PhD candidates start writing the methodological components first. This is followed by writing the results. The pre-requisites for writing results are that all figures, tables, schematic diagrams of methods and a working model should be ready. They should be designed in such a way that the information content of each figure should, when projected as a frame be visually clear to audience viewing it from a distance of about fifty feet. It is often observed that the presenters themselves have difficulty in deciphering a frame of the Power-Point being projected in a conference.

The results of a doctoral thesis should be treated like a bride. The flow of writing results becomes easier if all figures and tables are well prepared. This promotes the train of thoughts required to analyze the data in a quantitative fashion. The golden rule of writing results of a thesis is to describe what the figure shows. No explanation is required. One should avoid writing anything which is not there in a figure. Before writing one should observe each diagram for some time and make a list of observations in the form of key words. The more one has understood the information content of a figure; the better will be the fluency of writing. The interruption of the flow in writing most often indicates that an author has not understood the results. Discussion with colleagues or reference to the literature is the only remedy, and it functions sometimes like a caesarean procedure.

Statistical methods are good devices to test the degree of authenticity and precision of results if appropriately applied. The application of statistical technique in human studies poses difficulties because of large standard deviations. Outliers must be discussed, if they are excluded for the sake of statistical significance. Large standard deviations can be minimized by increasing the number of observations. If a regression analysis is not weighted, it gives faulty information. The correlation coefficient value can change from 0.7 to 0.4 if the regression analysis is weighted using Fisher’s test. The dissection of effect from artifact should be analysed in such a way that the signal to noise ratio of a parameter should be considered. A competent statistician should always be consulted in order to avoid the danger of distortion of results.

The legend of a figure should be well written. It contains a title, a brief description of variables and interventions, the main effect and a concluding remark conveying the original message. The writing of PhD work is further eased by a well maintained collection of data in the form of log book, original recordings, analyzed references with summaries and compiling the virgin data of the study on master plan sheet to understand the original signals before submitting to the procedures of statistics. The original data belong to the laboratory of an institution where it came into being and should be preserved for 5-7 years in the archive for the sake of brevity.

This is the liveliest part of a thesis. Its main goal is to defend the work by staging a constructive debate with the literature. The golden rule of this written debate should be that a rigid explanation looks backward and a design looks forward. The object is to derive a model out of a jig-saw puzzle of information. It should be designed in such a way that the results of the present study and those of authors from the literature can be better discussed and interpreted. Agreement and disagreement can be better resolved if one considers under what experimental conditions the results were obtained by the various authors. It means that the boundary conditions for each result should be carefully analyzed and compared.

The discussion can be divided into the following parts:

• criticism of material/subjects and methods
• a list of important observations of the present study
• interpretation and comparison of results of other authors using a literature table
• design of a model
• claim of an original research work
• The criticism of the methodological procedure enables a candidate to demonstrate how precisely the research work has been carried out. The interpretation of results depends critically on the strict experimental protocol and methods. For example, an epidemiological work is a study of a population. However, if the population sampling is done regularly at a specific location; the question arises as to how a result derived from a localized place can be applied to the whole population.
• After having discussed at length the strong and weak points of material/subjects and methods, one should list in a telegraphic design the most important observations of the present study. This may form a good agenda to initiate interpretation, argument, reasoning and comparison with results of other authors. The outcome of this constructive debate should permit the design of a working model in the form of a block diagram. All statements should be very carefully referenced. The ratio of agreement and disagreement should indicate the ability of the author to reconcile conflicting data in an objective and quantitative way. Attempts should be made to design a solution out of the given quantum of information. It is also well known that most of the processes of human physiology can only be understood if their time course is known. The dynamic aspect of interpretation of results is therefore more powerful and superior to the static one. 16 Therefore a continuous record of variables should be preferred and sought to reveal the secrets hidden in the kinetics.
• Finally, the discussion should conclude how far the study was successful in answering the questions being posed at the end of the introduction part. Usually a doctoral thesis raises more questions than it answers. In this way research does not come to a standstill and does become a life time engagement for a committed scientist. Also it is important to note that all scientific theses should be quantifiable and falsifiable, otherwise they lose the spirit and fragrance of a scientific research.
• The author’s claim of original work is finally decided by the critical review of his research work by the literature and the number of times being cited. It can be easily read by a high rate of a citation index of a publication and invitation. When a methodological research clicks, one becomes a star overnight.

Type-II: CUMULATIVE DOCTORAL THESES

Another way of writing a doctoral work is a cumulative type of thesis. 11 It consists of a few original publications in refereed journals of repute. It is supplemented by a concise summary about the research work. This type of thesis is usually practiced in Sweden, Germany and other countries. It has the advantage of being doubly refereed by the journals and the faculty of health sciences. Additionally, papers are published during a doctoral work. A declaration has to be given to the faculty of science about the sharing of research work in publications, provided there are co-authors. The weightage should be in favour of the PhD candidate, so that the thesis can ethically be better defended before the team of august research faculty.

ACKNOWLEDGEMENTS

A critical review of this manuscript by Dr. Roger Sutton, Dr. Khalid Khan, Dr. Bukhtiar Shah and Dr. Satwat Hashmi is gratefully acknowledged.

Dedicated to the memory of Mr. Azim Kidwai for his exemplary academic commitment and devotion to the science journalism in Pakistan.

Writing a Postgraduate or Doctoral Thesis: A Step-by-Step Approach

• First Online: 01 October 2023

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• Usha Y. Nayak 4 ,
• Praveen Hoogar 5 ,
• Srinivas Mutalik 4 &
• N. Udupa 6  

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A key characteristic looked after by postgraduate or doctoral students is how they communicate and defend their knowledge. Many candidates believe that there is insufficient instruction on constructing strong arguments. The thesis writing procedure must be meticulously followed to achieve outstanding results. It should be well organized, simple to read, and provide detailed explanations of the core research concepts. Each section in a thesis should be carefully written to make sure that it transitions logically from one to the next in a smooth way and is free of any unclear, cluttered, or redundant elements that make it difficult for the reader to understand what is being tried to convey. In this regard, students must acquire the information and skills to successfully create a strong and effective thesis. A step-by-step description of the thesis/dissertation writing process is provided in this chapter.

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Nayak, U.Y., Hoogar, P., Mutalik, S., Udupa, N. (2023). Writing a Postgraduate or Doctoral Thesis: A Step-by-Step Approach. In: Jagadeesh, G., Balakumar, P., Senatore, F. (eds) The Quintessence of Basic and Clinical Research and Scientific Publishing. Springer, Singapore. https://doi.org/10.1007/978-981-99-1284-1_48

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How to find resources by format

Why use a dissertation or a thesis.

A dissertation is the final large research paper, based on original research, for many disciplines to be able to complete a PhD degree. The thesis is the same idea but for a masters degree.

They are often considered scholarly sources since they are closely supervised by a committee, are directed at an academic audience, are extensively researched, follow research methodology, and are cited in other scholarly work. Often the research is newer or answering questions that are more recent, and can help push scholarship in new directions. 

Search for dissertations and theses

Locating dissertations and theses.

The Proquest Dissertations and Theses Global database includes doctoral dissertations and selected masters theses from major universities worldwide.

• Searchable by subject, author, advisor, title, school, date, etc.
• More information about full text access and requesting through Interlibrary Loan

NDLTD – Networked Digital Library of Theses and Dissertations provides free online access to a over a million theses and dissertations from all over the world.

WorldCat Dissertations and Theses searches library catalogs from across the U.S. and worldwide.

Locating University of Minnesota Dissertations and Theses

Use  Libraries search  and search by title or author and add the word "thesis" in the search box. Write down the library and call number and find it on the shelf. They can be checked out.

Check the  University Digital Conservancy  for online access to dissertations and theses from 2007 to present as well as historic, scanned theses from 1887-1923.

Other Sources for Dissertations and Theses

• Center for Research Libraries
• DART-Europe E-Thesis Portal
• Theses Canada
• Ethos (Great Britain)
• Australasian Digital Theses in Trove
• DiVA (Sweden)
• E-Thesis at the University of Helsinki
• DissOnline (Germany)
• List of libraries worldwide - to search for a thesis when you know the institution and cannot find in the larger collections

University of Minnesota Dissertations and Theses FAQs

What dissertations and theses are available.

With minor exceptions, all doctoral dissertations and all "Plan A" master's theses accepted by the University of Minnesota are available in the University Libraries system. In some cases (see below) only a non-circulating copy in University Archives exists, but for doctoral dissertations from 1940 to date, and for master's theses from 1925 to date, a circulating copy should almost always be available.

"Plan B" papers, accepted in the place of a thesis in many master's degree programs, are not received by the University Libraries and are generally not available. (The only real exceptions are a number of old library school Plan B papers on publishing history, which have been separately cataloged.) In a few cases individual departments may have maintained files of such papers.

In what libraries are U of M dissertations and theses located?

Circulating copies of doctoral dissertations:.

• Use Libraries Search to look for the author or title of the work desired to determine location and call number of a specific dissertation. Circulating copies of U of M doctoral dissertations can be in one of several locations in the library system, depending upon the date and the department for which the dissertation was done. The following are the general rules:
• Dissertations prior to 1940 Circulating copies of U of M dissertations prior to 1940 do not exist (with rare exceptions): for these, only the archival copy (see below) is available. Also, most dissertations prior to 1940 are not cataloged in MNCAT and can only be identified by the departmental listings described below.  
• Dissertations from 1940-1979 Circulating copies of U of M dissertations from 1940 to 1979 will in most cases be held within the Elmer L. Andersen Library, with three major classes of exceptions: dissertations accepted by biological, medical, and related departments are housed in the Health Science Library; science/engineering dissertations from 1970 to date will be located in the Science and Engineering Library (in Walter); and dissertations accepted by agricultural and related departments are available at the Magrath Library or one of the other libraries on the St. Paul campus (the Magrath Library maintains records of locations for such dissertations).  
• Dissertations from 1980-date Circulating copies of U of M dissertations from 1980 to date at present may be located either in Wilson Library (see below) or in storage; consult Libraries Search for location of specific items. Again, exceptions noted above apply here also; dissertations in their respective departments will instead be in Health Science Library or in one of the St. Paul campus libraries.

Circulating copies of master's theses:

• Theses prior to 1925 Circulating copies of U of M master's theses prior to 1925 do not exist (with rare exceptions); for these, only the archival copy (see below) is available.  
• Theses from 1925-1996 Circulating copies of U of M master's theses from 1925 to 1996 may be held in storage; consult Libraries search in specific instances. Once again, there are exceptions and theses in their respective departments will be housed in the Health Science Library or in one of the St. Paul campus libraries.  
• Theses from 1997-date Circulating copies of U of M master's theses from 1997 to date will be located in Wilson Library (see below), except for the same exceptions for Health Science  and St. Paul theses. There is also an exception to the exception: MHA (Masters in Health Administration) theses through 1998 are in the Health Science Library, but those from 1999 on are in Wilson Library.

Archival copies (non-circulating)

Archival (non-circulating) copies of virtually all U of M doctoral dissertations from 1888-1952, and of U of M master's theses from all years up to the present, are maintained by University Archives (located in the Elmer L. Andersen Library). These copies must be consulted on the premises, and it is highly recommended for the present that users make an appointment in advance to ensure that the desired works can be retrieved for them from storage. For dissertations accepted prior to 1940 and for master's theses accepted prior to 1925, University Archives is generally the only option (e.g., there usually will be no circulating copy). Archival copies of U of M doctoral dissertations from 1953 to the present are maintained by Bell and Howell Corporation (formerly University Microfilms Inc.), which produces print or filmed copies from our originals upon request. (There are a very few post-1952 U of M dissertations not available from Bell and Howell; these include such things as music manuscripts and works with color illustrations or extremely large pages that will not photocopy well; in these few cases, our archival copy is retained in University Archives.)

Where is a specific dissertation of thesis located?

To locate a specific dissertation or thesis it is necessary to have its call number. Use Libraries Search for the author or title of the item, just as you would for any other book. Depending on date of acceptance and cataloging, a typical call number for such materials should look something like one of the following:

Dissertations: Plan"A" Theses MnU-D or 378.7M66 MnU-M or 378.7M66 78-342 ODR7617 83-67 OL6156 Libraries Search will also tell the library location (MLAC, Health Science Library, Magrath or another St. Paul campus library, Science and Engineering, Business Reference, Wilson Annex or Wilson Library). Those doctoral dissertations still in Wilson Library (which in all cases should be 1980 or later and will have "MnU-D" numbers) are located in the central section of the third floor. Those master's theses in Wilson (which in all cases will be 1997 or later and will have "MnU-M" numbers) are also located in the central section of the third floor. Both dissertations and theses circulate and can be checked out, like any other books, at the Wilson Circulation desk on the first floor.

How can dissertations and theses accepted by a specific department be located?

Wilson Library contains a series of bound and loose-leaf notebooks, arranged by department and within each department by date, listing dissertations and theses. Information given for each entry includes name of author, title, and date (but not call number, which must be looked up individually). These notebooks are no longer current, but they do cover listings by department from the nineteenth century up to approximately 1992. Many pre-1940 U of M dissertations and pre-1925 U of M master's theses are not cataloged (and exist only as archival copies). Such dissertations can be identified only with these volumes. The books and notebooks are shelved in the general collection under these call numbers: Wilson Ref LD3337 .A5 and Wilson Ref quarto LD3337 .U9x. Major departments of individual degree candidates are also listed under their names in the GRADUATE SCHOOL COMMENCEMENT programs of the U of M, available in University Archives and (for recent years) also in Wilson stacks (LD3361 .U55x).

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• Manuscript Preparation

Know How to Structure Your PhD Thesis

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Table of Contents

In your academic career, few projects are more important than your PhD thesis. Unfortunately, many university professors and advisors assume that their students know how to structure a PhD. Books have literally been written on the subject, but there’s no need to read a book in order to know about PhD thesis paper format and structure. With that said, however, it’s important to understand that your PhD thesis format requirement may not be the same as another student’s. The bottom line is that how to structure a PhD thesis often depends on your university and department guidelines.

But, let’s take a look at a general PhD thesis format. We’ll look at the main sections, and how to connect them to each other. We’ll also examine different hints and tips for each of the sections. As you read through this toolkit, compare it to published PhD theses in your area of study to see how a real-life example looks.

Main Sections of a PhD Thesis

In almost every PhD thesis or dissertation, there are standard sections. Of course, some of these may differ, depending on your university or department requirements, as well as your topic of study, but this will give you a good idea of the basic components of a PhD thesis format.

• Abstract : The abstract is a brief summary that quickly outlines your research, touches on each of the main sections of your thesis, and clearly outlines your contribution to the field by way of your PhD thesis. Even though the abstract is very short, similar to what you’ve seen in published research articles, its impact shouldn’t be underestimated. The abstract is there to answer the most important question to the reviewer. “Why is this important?”
• Introduction : In this section, you help the reviewer understand your entire dissertation, including what your paper is about, why it’s important to the field, a brief description of your methodology, and how your research and the thesis are laid out. Think of your introduction as an expansion of your abstract.
• Literature Review : Within the literature review, you are making a case for your new research by telling the story of the work that’s already been done. You’ll cover a bit about the history of the topic at hand, and how your study fits into the present and future.
• Theory Framework : Here, you explain assumptions related to your study. Here you’re explaining to the review what theoretical concepts you might have used in your research, how it relates to existing knowledge and ideas.
• Methods : This section of a PhD thesis is typically the most detailed and descriptive, depending of course on your research design. Here you’ll discuss the specific techniques you used to get the information you were looking for, in addition to how those methods are relevant and appropriate, as well as how you specifically used each method described.
• Results : Here you present your empirical findings. This section is sometimes also called the “empiracles” chapter. This section is usually pretty straightforward and technical, and full of details. Don’t shortcut this chapter.
• Discussion : This can be a tricky chapter, because it’s where you want to show the reviewer that you know what you’re talking about. You need to speak as a PhD versus a student. The discussion chapter is similar to the empirical/results chapter, but you’re building on those results to push the new information that you learned, prior to making your conclusion.
• Conclusion : Here, you take a step back and reflect on what your original goals and intentions for the research were. You’ll outline them in context of your new findings and expertise.

Tips for your PhD Thesis Format

As you put together your PhD thesis, it’s easy to get a little overwhelmed. Here are some tips that might keep you on track.

• Don’t try to write your PhD as a first-draft. Every great masterwork has typically been edited, and edited, and…edited.
• Work with your thesis supervisor to plan the structure and format of your PhD thesis. Be prepared to rewrite each section, as you work out rough drafts. Don’t get discouraged by this process. It’s typical.
• Make your writing interesting. Academic writing has a reputation of being very dry.
• You don’t have to necessarily work on the chapters and sections outlined above in chronological order. Work on each section as things come up, and while your work on that section is relevant to what you’re doing.
• Don’t rush things. Write a first draft, and leave it for a few days, so you can come back to it with a more critical take. Look at it objectively and carefully grammatical errors, clarity, logic and flow.
• Know what style your references need to be in, and utilize tools out there to organize them in the required format.
• It’s easier to accidentally plagiarize than you think. Make sure you’re referencing appropriately, and check your document for inadvertent plagiarism throughout your writing process.

PhD Thesis Editing Plus

Want some support during your PhD writing process? Our PhD Thesis Editing Plus service includes extensive and detailed editing of your thesis to improve the flow and quality of your writing. Unlimited editing support for guaranteed results. Learn more here , and get started today!

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EBSCO Open Dissertations

EBSCO Open Dissertations makes electronic theses and dissertations (ETDs) more accessible to researchers worldwide. The free portal is designed to benefit universities and their students and make ETDs more discoverable. 

Increasing Discovery & Usage of ETD Research

EBSCO Open Dissertations is a collaboration between EBSCO and BiblioLabs to increase traffic and discoverability of ETD research. You can join the movement and add your theses and dissertations to the database, making them freely available to researchers everywhere while increasing traffic to your institutional repository. 

EBSCO Open Dissertations extends the work started in 2014, when EBSCO and the H.W. Wilson Foundation created American Doctoral Dissertations which contained indexing from the H.W. Wilson print publication, Doctoral Dissertations Accepted by American Universities, 1933-1955. In 2015, the H.W. Wilson Foundation agreed to support the expansion of the scope of the American Doctoral Dissertations database to include records for dissertations and theses from 1955 to the present.

How Does EBSCO Open Dissertations Work?

Your ETD metadata is harvested via OAI and integrated into EBSCO’s platform, where pointers send traffic to your IR.

EBSCO integrates this data into their current subscriber environments and makes the data available on the open web via opendissertations.org .

You might also be interested in:

Doctoral Theses

Academic Commons holds the full text of doctoral theses written since 2011 at Columbia and of theses written for a Doctorate of Education at Teachers College since mid 2018. A selection of dissertations from Union Theological Seminary, and from Columbia before 2011, are also available. You can start exploring theses by selecting one of the doctoral programs below.

• Anthropology (125)
• Anthropology and Education (26)
• Applied Anthropology (27)
• Applied Behavior Analysis (79)
• Applied Physics and Applied Mathematics (131)
• Architecture (36)
• Art History and Archaeology (182)
• Arts and Humanities (125)
• Astronomy (63)
• Behavioral Nutrition (32)
• Biobehavioral Sciences (25)
• Biochemistry and Molecular Biophysics (35)
• Biological Sciences (221)
• Biomedical Engineering (219)
• Biomedical Informatics (58)
• Biostatistics (66)
• Business (205)
• Cellular Physiology and Biophysics (20)
• Cellular, Molecular and Biomedical Studies (189)
• Cellular, Molecular, Structural, and Genetic Studies (12)
• Chemical Engineering (116)
• Chemical Physics (34)
• Chemistry (269)
• Civil Engineering and Engineering Mechanics (118)
• Classical Studies (15)
• Classics (32)
• Clinical Psychology (78)
• Cognitive Studies in Education (92)
• Communication, Media, and Learning Technologies Design (2)
• Communications (44)
• Comparative and International Education (43)
• Computer Science (250)
• Counseling Psychology (65)
• Counseling and Clinical Psychology (4)
• Curriculum and Teaching (89)
• Developmental Psychology (14)
• Earth and Environmental Engineering (89)
• Earth and Environmental Sciences (186)
• East Asian Languages and Cultures (109)
• Ecology, Evolution, and Environmental Biology (65)
• Economics (285)
• Economics and Education (70)
• Education Leadership (17)
• Education Policy (16)
• Electrical Engineering (273)
• English Education (76)
• English and Comparative Literature (179)
• Environmental Health Sciences (47)
• Epidemiology (128)
• French and Romance Philology (49)
• Genetics and Development (66)
• Geological Sciences (1)
• Geology (1)
• Germanic Languages (44)
• Health and Behavior Studies (115)
• History (264)
• History and Education (12)
• Human Development (8)
• Industrial Engineering and Operations Research (131)
• Intellectual Disabilities-Autism (13)
• Interdisciplinary Studies in Education (26)
• International and Transcultural Studies (8)
• Italian (44)
• Kinesiology (14)
• Latin American and Iberian Cultures (58)
• Materials Science and Engineering (34)
• Mathematics (141)
• Mathematics Education (76)
• Mathematics, Science, and Technology (61)
• Measurement and Evaluation (37)
• Mechanical Engineering (150)
• Microbiology, Immunology, and Infection (44)
• Middle Eastern, South Asian, and African Studies (73)
• Music (140)
• Neurobiology and Behavior (200)
• Neuroscience (4)
• Nursing (78)
• Nutritional and Metabolic Biology (55)
• Ophthalmology (1)
• Organization and Leadership (136)
• Pathobiology and Molecular Medicine (53)
• Pathology and Cell Biology (6)
• Pharmacology and Molecular Signaling (39)
• Philosophy (77)
• Philosophy and Education (42)
• Physical Disabilities (13)
• Physics (215)
• Political Science (218)
• Politics and Education (25)
• Population and Family Health (20)
• Psychology (148)
• Pure Science (1)
• Religion (69)
• School Psychology (51)
• Science Education (68)
• Slavic Languages (25)
• Slavic Languages and Literatures (19)
• Social Work (180)
• Social-Organizational Psychology (42)
• Sociology (94)
• Sociology and Education (19)
• Sociomedical Sciences (77)
• Speech and Language Pathology (28)
• Statistics (103)
• Sustainable Development (59)
• Teaching of Social Studies (26)
• Theatre (29)
• Union Theological Seminary (5)
• Urban Planning (44)

Harvard University Theses, Dissertations, and Prize Papers

The Harvard University Archives ’ collection of theses, dissertations, and prize papers document the wide range of academic research undertaken by Harvard students over the course of the University’s history.

Beyond their value as pieces of original research, these collections document the history of American higher education, chronicling both the growth of Harvard as a major research institution as well as the development of numerous academic fields. They are also an important source of biographical information, offering insight into the academic careers of the authors.

Spanning from the ‘theses and quaestiones’ of the 17th and 18th centuries to the current yearly output of student research, they include both the first Harvard Ph.D. dissertation (by William Byerly, Ph.D . 1873) and the dissertation of the first woman to earn a doctorate from Harvard ( Lorna Myrtle Hodgkinson , Ed.D. 1922).

Other highlights include:

• The collection of Mathematical theses, 1782-1839
• The 1895 Ph.D. dissertation of W.E.B. Du Bois, The suppression of the African slave trade in the United States, 1638-1871
• Ph.D. dissertations of astronomer Cecilia Payne-Gaposchkin (Ph.D. 1925) and physicist John Hasbrouck Van Vleck (Ph.D. 1922)
• Undergraduate honors theses of novelist John Updike (A.B. 1954), filmmaker Terrence Malick (A.B. 1966),  and U.S. poet laureate Tracy Smith (A.B. 1994)
• Undergraduate prize papers and dissertations of philosophers Ralph Waldo Emerson (A.B. 1821), George Santayana (Ph.D. 1889), and W.V. Quine (Ph.D. 1932)
• Undergraduate honors theses of U.S. President John F. Kennedy (A.B. 1940) and Chief Justice John Roberts (A.B. 1976)

What does a prize-winning thesis look like?

If you're a Harvard undergraduate writing your own thesis, it can be helpful to review recent prize-winning theses. The Harvard University Archives has made available for digital lending all of the Thomas Hoopes Prize winners from the 2019-2021 academic years.

Accessing These Materials

How to access materials at the Harvard University Archives

How to find and request dissertations, in person or virtually

How to find and request undergraduate honors theses

How to find and request Thomas Temple Hoopes Prize papers

How to find and request Bowdoin Prize papers

• email: Email
• Phone number 617-495-2461

Related Collections

Harvard faculty personal and professional archives, harvard student life collections: arts, sports, politics and social life, access materials at the harvard university archives.

Machine Learning - CMU

PhD Dissertations

[all are .pdf files].

Learning Models that Match Jacob Tyo, 2024

Improving Human Integration across the Machine Learning Pipeline Charvi Rastogi, 2024

Reliable and Practical Machine Learning for Dynamic Healthcare Settings Helen Zhou, 2023

Automatic customization of large-scale spiking network models to neuronal population activity (unavailable) Shenghao Wu, 2023

Estimation of BVk functions from scattered data (unavailable) Addison J. Hu, 2023

Rethinking object categorization in computer vision (unavailable) Jayanth Koushik, 2023

Advances in Statistical Gene Networks Jinjin Tian, 2023 Post-hoc calibration without distributional assumptions Chirag Gupta, 2023

The Role of Noise, Proxies, and Dynamics in Algorithmic Fairness Nil-Jana Akpinar, 2023

Collaborative learning by leveraging siloed data Sebastian Caldas, 2023

Modeling Epidemiological Time Series Aaron Rumack, 2023

Human-Centered Machine Learning: A Statistical and Algorithmic Perspective Leqi Liu, 2023

Uncertainty Quantification under Distribution Shifts Aleksandr Podkopaev, 2023

Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes, and Inferring How to Get There Benjamin Eysenbach, 2023

Comparing Forecasters and Abstaining Classifiers Yo Joong Choe, 2023

Using Task Driven Methods to Uncover Representations of Human Vision and Semantics Aria Yuan Wang, 2023

Data-driven Decisions - An Anomaly Detection Perspective Shubhranshu Shekhar, 2023

Applied Mathematics of the Future Kin G. Olivares, 2023

METHODS AND APPLICATIONS OF EXPLAINABLE MACHINE LEARNING Joon Sik Kim, 2023

NEURAL REASONING FOR QUESTION ANSWERING Haitian Sun, 2023

Principled Machine Learning for Societally Consequential Decision Making Amanda Coston, 2023

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Maxwell B. Wang, 2023

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Darby M. Losey, 2023

Calibrated Conditional Density Models and Predictive Inference via Local Diagnostics David Zhao, 2023

Towards an Application-based Pipeline for Explainability Gregory Plumb, 2022

Objective Criteria for Explainable Machine Learning Chih-Kuan Yeh, 2022

Making Scientific Peer Review Scientific Ivan Stelmakh, 2022

Facets of regularization in high-dimensional learning: Cross-validation, risk monotonization, and model complexity Pratik Patil, 2022

Active Robot Perception using Programmable Light Curtains Siddharth Ancha, 2022

Strategies for Black-Box and Multi-Objective Optimization Biswajit Paria, 2022

Unifying State and Policy-Level Explanations for Reinforcement Learning Nicholay Topin, 2022

Sensor Fusion Frameworks for Nowcasting Maria Jahja, 2022

Equilibrium Approaches to Modern Deep Learning Shaojie Bai, 2022

Towards General Natural Language Understanding with Probabilistic Worldbuilding Abulhair Saparov, 2022

Applications of Point Process Modeling to Spiking Neurons (Unavailable) Yu Chen, 2021

Neural variability: structure, sources, control, and data augmentation Akash Umakantha, 2021

Structure and time course of neural population activity during learning Jay Hennig, 2021

Cross-view Learning with Limited Supervision Yao-Hung Hubert Tsai, 2021

Meta Reinforcement Learning through Memory Emilio Parisotto, 2021

Learning Embodied Agents with Scalably-Supervised Reinforcement Learning Lisa Lee, 2021

Learning to Predict and Make Decisions under Distribution Shift Yifan Wu, 2021

Statistical Game Theory Arun Sai Suggala, 2021

Towards Knowledge-capable AI: Agents that See, Speak, Act and Know Kenneth Marino, 2021

Learning and Reasoning with Fast Semidefinite Programming and Mixing Methods Po-Wei Wang, 2021

Bridging Language in Machines with Language in the Brain Mariya Toneva, 2021

Curriculum Learning Otilia Stretcu, 2021

Principles of Learning in Multitask Settings: A Probabilistic Perspective Maruan Al-Shedivat, 2021

Towards Robust and Resilient Machine Learning Adarsh Prasad, 2021

Towards Training AI Agents with All Types of Experiences: A Unified ML Formalism Zhiting Hu, 2021

Building Intelligent Autonomous Navigation Agents Devendra Chaplot, 2021

Learning to See by Moving: Self-supervising 3D Scene Representations for Perception, Control, and Visual Reasoning Hsiao-Yu Fish Tung, 2021

Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe Collin Politsch, 2020

Causal Inference with Complex Data Structures and Non-Standard Effects Kwhangho Kim, 2020

Networks, Point Processes, and Networks of Point Processes Neil Spencer, 2020

Dissecting neural variability using population recordings, network models, and neurofeedback (Unavailable) Ryan Williamson, 2020

Predicting Health and Safety: Essays in Machine Learning for Decision Support in the Public Sector Dylan Fitzpatrick, 2020

Towards a Unified Framework for Learning and Reasoning Han Zhao, 2020

Learning DAGs with Continuous Optimization Xun Zheng, 2020

Machine Learning and Multiagent Preferences Ritesh Noothigattu, 2020

Learning and Decision Making from Diverse Forms of Information Yichong Xu, 2020

Towards Data-Efficient Machine Learning Qizhe Xie, 2020

Change modeling for understanding our world and the counterfactual one(s) William Herlands, 2020

Machine Learning in High-Stakes Settings: Risks and Opportunities Maria De-Arteaga, 2020

Data Decomposition for Constrained Visual Learning Calvin Murdock, 2020

Structured Sparse Regression Methods for Learning from High-Dimensional Genomic Data Micol Marchetti-Bowick, 2020

Towards Efficient Automated Machine Learning Liam Li, 2020

LEARNING COLLECTIONS OF FUNCTIONS Emmanouil Antonios Platanios, 2020

Provable, structured, and efficient methods for robustness of deep networks to adversarial examples Eric Wong , 2020

Reconstructing and Mining Signals: Algorithms and Applications Hyun Ah Song, 2020

Probabilistic Single Cell Lineage Tracing Chieh Lin, 2020

Graphical network modeling of phase coupling in brain activity (unavailable) Josue Orellana, 2019

Strategic Exploration in Reinforcement Learning - New Algorithms and Learning Guarantees Christoph Dann, 2019 Learning Generative Models using Transformations Chun-Liang Li, 2019

Estimating Probability Distributions and their Properties Shashank Singh, 2019

Post-Inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making Willie Neiswanger, 2019

Accelerating Text-as-Data Research in Computational Social Science Dallas Card, 2019

Multi-view Relationships for Analytics and Inference Eric Lei, 2019

Information flow in networks based on nonstationary multivariate neural recordings Natalie Klein, 2019

Competitive Analysis for Machine Learning & Data Science Michael Spece, 2019

The When, Where and Why of Human Memory Retrieval Qiong Zhang, 2019

Towards Effective and Efficient Learning at Scale Adams Wei Yu, 2019

Towards Literate Artificial Intelligence Mrinmaya Sachan, 2019

Learning Gene Networks Underlying Clinical Phenotypes Under SNP Perturbations From Genome-Wide Data Calvin McCarter, 2019

Unified Models for Dynamical Systems Carlton Downey, 2019

Anytime Prediction and Learning for the Balance between Computation and Accuracy Hanzhang Hu, 2019

Statistical and Computational Properties of Some "User-Friendly" Methods for High-Dimensional Estimation Alnur Ali, 2019

Nonparametric Methods with Total Variation Type Regularization Veeranjaneyulu Sadhanala, 2019

New Advances in Sparse Learning, Deep Networks, and Adversarial Learning: Theory and Applications Hongyang Zhang, 2019

Gradient Descent for Non-convex Problems in Modern Machine Learning Simon Shaolei Du, 2019

Selective Data Acquisition in Learning and Decision Making Problems Yining Wang, 2019

Anomaly Detection in Graphs and Time Series: Algorithms and Applications Bryan Hooi, 2019

Neural dynamics and interactions in the human ventral visual pathway Yuanning Li, 2018

Tuning Hyperparameters without Grad Students: Scaling up Bandit Optimisation Kirthevasan Kandasamy, 2018

Teaching Machines to Classify from Natural Language Interactions Shashank Srivastava, 2018

Statistical Inference for Geometric Data Jisu Kim, 2018

Representation Learning @ Scale Manzil Zaheer, 2018

Diversity-promoting and Large-scale Machine Learning for Healthcare Pengtao Xie, 2018

Distribution and Histogram (DIsH) Learning Junier Oliva, 2018

Stress Detection for Keystroke Dynamics Shing-Hon Lau, 2018

Sublinear-Time Learning and Inference for High-Dimensional Models Enxu Yan, 2018

Neural population activity in the visual cortex: Statistical methods and application Benjamin Cowley, 2018

Efficient Methods for Prediction and Control in Partially Observable Environments Ahmed Hefny, 2018

Learning with Staleness Wei Dai, 2018

Statistical Approach for Functionally Validating Transcription Factor Bindings Using Population SNP and Gene Expression Data Jing Xiang, 2017

New Paradigms and Optimality Guarantees in Statistical Learning and Estimation Yu-Xiang Wang, 2017

Dynamic Question Ordering: Obtaining Useful Information While Reducing User Burden Kirstin Early, 2017

New Optimization Methods for Modern Machine Learning Sashank J. Reddi, 2017

Active Search with Complex Actions and Rewards Yifei Ma, 2017

Why Machine Learning Works George D. Montañez , 2017

Source-Space Analyses in MEG/EEG and Applications to Explore Spatio-temporal Neural Dynamics in Human Vision Ying Yang , 2017

Computational Tools for Identification and Analysis of Neuronal Population Activity Pengcheng Zhou, 2016

Expressive Collaborative Music Performance via Machine Learning Gus (Guangyu) Xia, 2016

Supervision Beyond Manual Annotations for Learning Visual Representations Carl Doersch, 2016

Exploring Weakly Labeled Data Across the Noise-Bias Spectrum Robert W. H. Fisher, 2016

Optimizing Optimization: Scalable Convex Programming with Proximal Operators Matt Wytock, 2016

Combining Neural Population Recordings: Theory and Application William Bishop, 2015

Discovering Compact and Informative Structures through Data Partitioning Madalina Fiterau-Brostean, 2015

Machine Learning in Space and Time Seth R. Flaxman, 2015

The Time and Location of Natural Reading Processes in the Brain Leila Wehbe, 2015

Shape-Constrained Estimation in High Dimensions Min Xu, 2015

Spectral Probabilistic Modeling and Applications to Natural Language Processing Ankur Parikh, 2015 Computational and Statistical Advances in Testing and Learning Aaditya Kumar Ramdas, 2015

Corpora and Cognition: The Semantic Composition of Adjectives and Nouns in the Human Brain Alona Fyshe, 2015

Learning Statistical Features of Scene Images Wooyoung Lee, 2014

Towards Scalable Analysis of Images and Videos Bin Zhao, 2014

Statistical Text Analysis for Social Science Brendan T. O'Connor, 2014

Modeling Large Social Networks in Context Qirong Ho, 2014

Semi-Cooperative Learning in Smart Grid Agents Prashant P. Reddy, 2013

On Learning from Collective Data Liang Xiong, 2013

Exploiting Non-sequence Data in Dynamic Model Learning Tzu-Kuo Huang, 2013

Mathematical Theories of Interaction with Oracles Liu Yang, 2013

Short-Sighted Probabilistic Planning Felipe W. Trevizan, 2013

Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms Lucia Castellanos, 2013

Approximation Algorithms and New Models for Clustering and Learning Pranjal Awasthi, 2013

Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems Mladen Kolar, 2013

Learning with Sparsity: Structures, Optimization and Applications Xi Chen, 2013

GraphLab: A Distributed Abstraction for Large Scale Machine Learning Yucheng Low, 2013

Graph Structured Normal Means Inference James Sharpnack, 2013 (Joint Statistics & ML PhD)

Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data Hai-Son Phuoc Le, 2013

Learning Large-Scale Conditional Random Fields Joseph K. Bradley, 2013

New Statistical Applications for Differential Privacy Rob Hall, 2013 (Joint Statistics & ML PhD)

Parallel and Distributed Systems for Probabilistic Reasoning Joseph Gonzalez, 2012

Spectral Approaches to Learning Predictive Representations Byron Boots, 2012

Attribute Learning using Joint Human and Machine Computation Edith L. M. Law, 2012

Statistical Methods for Studying Genetic Variation in Populations Suyash Shringarpure, 2012

Data Mining Meets HCI: Making Sense of Large Graphs Duen Horng (Polo) Chau, 2012

Learning with Limited Supervision by Input and Output Coding Yi Zhang, 2012

Target Sequence Clustering Benjamin Shih, 2011

Nonparametric Learning in High Dimensions Han Liu, 2010 (Joint Statistics & ML PhD)

Structural Analysis of Large Networks: Observations and Applications Mary McGlohon, 2010

Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy Brian D. Ziebart, 2010

Tractable Algorithms for Proximity Search on Large Graphs Purnamrita Sarkar, 2010

Rare Category Analysis Jingrui He, 2010

Coupled Semi-Supervised Learning Andrew Carlson, 2010

Fast Algorithms for Querying and Mining Large Graphs Hanghang Tong, 2009

Efficient Matrix Models for Relational Learning Ajit Paul Singh, 2009

Exploiting Domain and Task Regularities for Robust Named Entity Recognition Andrew O. Arnold, 2009

Theoretical Foundations of Active Learning Steve Hanneke, 2009

Generalized Learning Factors Analysis: Improving Cognitive Models with Machine Learning Hao Cen, 2009

Detecting Patterns of Anomalies Kaustav Das, 2009

Dynamics of Large Networks Jurij Leskovec, 2008

Computational Methods for Analyzing and Modeling Gene Regulation Dynamics Jason Ernst, 2008

Stacked Graphical Learning Zhenzhen Kou, 2007

Actively Learning Specific Function Properties with Applications to Statistical Inference Brent Bryan, 2007

Approximate Inference, Structure Learning and Feature Estimation in Markov Random Fields Pradeep Ravikumar, 2007

Scalable Graphical Models for Social Networks Anna Goldenberg, 2007

Measure Concentration of Strongly Mixing Processes with Applications Leonid Kontorovich, 2007

Tools for Graph Mining Deepayan Chakrabarti, 2005

Automatic Discovery of Latent Variable Models Ricardo Silva, 2005

Essay、Thesis、Paper和Dissertation区别

说到留学生最头疼的几件大事，莫过于Essay、Thesis、Paper、Dissertation这几件，每到学期即将结束、临近毕业又或者DDL临近的时候，它们都让赶due人抓耳挠腮，苦不堪言，为什么会这样呢？一方面是留学生需要提前克服语言障碍，只有把语言关先过了，才能考虑写的文章是否有深度，是否有专业性，能不能抓人眼球。另一方面，仍然有很多学生不会区分这四种任务的不同需求。写好一篇国外的作业真的这么难吗？

今天就带大家来看看Essay、Thesis、Paper、Dissertation的区别在哪，也让大家能更客观的看待国外的作业，不至于视之为恶魔。第一眼从最常用的中译英角度来看，这四个词与都可以翻译为文章，那它们又有什么区别呢？为何要用四个不同的英文单词来进行区分呢？面对留学生最头疼的大事，我决定将每个单词在牛津词典中的解释翻出来，给大家进行最权威最清晰的区分。

这几个词当中，paper的含义是最广泛的。这个词有很多意思，且它的这些意思当中和写作任务相关的义项也有好几个，并对应了不同类型的文章。它既可以指经过了同行评议（peer review），在期刊（journal）或学术会议（academic conference）上发表的研究论文，也可以指学校给学生布置的学期论文（term paper）。在本科阶段，学期论文（term paper）这个词组的出现频率应该是比较高的，对于文科专业的同学来说尤其。文科的期末考评很少会以答题考试的方式进行，大部分情况都是让学生交一篇学期论文。

Essay这个词也是一个含义非常丰富的词。在用于学术语境下时，essay和另外那三个词最主要的区别就是篇幅。Essay属于篇幅相对短小的综述议论性质的文章，但paper通常会有几千字，thesis和dissertation更是会有上万字。从结构上来看，essay的结构是最为简单的。一般不会有自己的数据（实验/取证），行文会相对自由一些，没有研究型论文那么八股的格式要求。基本等于针对某一个topic做一个相对没那么严谨的讨论甚至猜想。

Thesis & Dissertation 最后thesis和dissertation这两个词就可以放一起说了。

这两个词的意思有很接近的地方，它们都是指篇幅较长，且和高等教育阶段获取学位相关的写作任务。不过这两个词的使用场合有些区别——在英国英语中，本科和硕士论文叫做dissertation，博士论文用thesis；但在美国英语中，本科和硕士论文用thesis，博士论文用dissertation。需要你的论文有实际的价值以及贡献。也就是“现实意义”。硕士毕业论文一般在1万字到1万五千字左右。硕士论文和博士论文的不同之处。对硕士论文和博士论文的要求是不同的。区别不是在于论文的格式，而是在于问题的重要性、对要解决问题的探索层次以及贡献。博士论文当然要求解决更难的问题、作出更具原创性的贡献。硕士论文对知识的贡献可以是类似于对某一知识领域的改进，或者是已有技术在新领域中的应用。博士论文必须对知识作出具有实质性和创新性的贡献。

最后给大家看看与论文和写作相关的那些同义词都是什么意思:

composition:多指学校里老师给学生的作文练习。

article:多指在报刊、杂志上发表的非文艺性的文章，包括新闻报导、学术论文等。

essay:指任何一种非小说性的，篇幅不长、结构简练的文章，如论说文、报道、评论、讽刺性杂文等。

paper:正式用词，多指在学术刊物上发表或在学术会议上宣读的专题论文，也指高等学校的学期论文，或学校里的作文练习。

prose:专指散文。

thesis:既可指毕业论文、学位论文，又可指一般的为阐述学术观点而写的论文。

theme:一般指大学或高中生作为作业所写的篇幅有限、较完整论述某个观点的文章。也可指作品或谈话的主要论题。

dissertation:书面语用词，指独立研究后所写的较为详细的专题文章;也可指学位论文。

Essays on Staffing and Transportation

Christian Braathen, PhD Candidate, Department of Business and Management Science.

PhD Defense: Christian Braathen Essays on Staffing and Transportation

On Monday 27 May 2024 Christian Braathen will hold a trial lecture on a prescribed topic and defend his thesis for the PhD degree at NHH. Title of the thesis: «Essays on Staffing and Transportation»