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Organizing Your Social Sciences Research Paper

  • Choosing a Title
  • Purpose of Guide
  • Design Flaws to Avoid
  • Independent and Dependent Variables
  • Glossary of Research Terms
  • Reading Research Effectively
  • Narrowing a Topic Idea
  • Broadening a Topic Idea
  • Extending the Timeliness of a Topic Idea
  • Academic Writing Style
  • Making an Outline
  • Paragraph Development
  • Research Process Video Series
  • Executive Summary
  • The C.A.R.S. Model
  • Background Information
  • The Research Problem/Question
  • Theoretical Framework
  • Citation Tracking
  • Content Alert Services
  • Evaluating Sources
  • Primary Sources
  • Secondary Sources
  • Tiertiary Sources
  • Scholarly vs. Popular Publications
  • Qualitative Methods
  • Quantitative Methods
  • Insiderness
  • Using Non-Textual Elements
  • Limitations of the Study
  • Common Grammar Mistakes
  • Writing Concisely
  • Avoiding Plagiarism
  • Footnotes or Endnotes?
  • Further Readings
  • Generative AI and Writing
  • USC Libraries Tutorials and Other Guides
  • Bibliography

The title summarizes the main idea or ideas of your study. A good title contains the fewest possible words needed to adequately describe the content and/or purpose of your research paper.

Importance of Choosing a Good Title

The title is the part of a paper that is read the most, and it is usually read first . It is, therefore, the most important element that defines the research study. With this in mind, avoid the following when creating a title:

  • If the title is too long, this usually indicates there are too many unnecessary words. Avoid language, such as, "A Study to Investigate the...," or "An Examination of the...." These phrases are obvious and generally superfluous unless they are necessary to covey the scope, intent, or type of a study.
  • On the other hand, a title which is too short often uses words which are too broad and, thus, does not tell the reader what is being studied. For example, a paper with the title, "African Politics" is so non-specific the title could be the title of a book and so ambiguous that it could refer to anything associated with politics in Africa. A good title should provide information about the focus and/or scope of your research study.
  • In academic writing, catchy phrases or non-specific language may be used, but only if it's within the context of the study [e.g., "Fair and Impartial Jury--Catch as Catch Can"]. However, in most cases, you should avoid including words or phrases that do not help the reader understand the purpose of your paper.
  • Academic writing is a serious and deliberate endeavor. Avoid using humorous or clever journalistic styles of phrasing when creating the title to your paper. Journalistic headlines often use emotional adjectives [e.g., incredible, amazing, effortless] to highlight a problem experienced by the reader or use "trigger words" or interrogative words like how, what, when, or why to persuade people to read the article or click on a link. These approaches are viewed as counter-productive in academic writing. A reader does not need clever or humorous titles to catch their attention because the act of reading research is assumed to be deliberate based on a desire to learn and improve understanding of the problem. In addition, a humorous title can merely detract from the seriousness and authority of your research. 
  • Unlike everywhere else in a college-level social sciences research paper [except when using direct quotes in the text], titles do not have to adhere to rigid grammatical or stylistic standards. For example, it could be appropriate to begin a title with a coordinating conjunction [i.e., and, but, or, nor, for, so, yet] if it makes sense to do so and does not detract from the purpose of the study [e.g., "Yet Another Look at Mutual Fund Tournaments"] or beginning the title with an inflected form of a verb such as those ending in -ing [e.g., "Assessing the Political Landscape: Structure, Cognition, and Power in Organizations"].

Appiah, Kingsley Richard et al. “Structural Organisation of Research Article Titles: A Comparative Study of Titles of Business, Gynaecology and Law.” Advances in Language and Literary Studies 10 (2019); Hartley James. “To Attract or to Inform: What are Titles for?” Journal of Technical Writing and Communication 35 (2005): 203-213; Jaakkola, Maarit. “Journalistic Writing and Style.” In Oxford Research Encyclopedia of Communication . Jon F. Nussbaum, editor. (New York: Oxford University Press, 2018): https://oxfordre.com/communication.

Structure and Writing Style

The following parameters can be used to help you formulate a suitable research paper title:

  • The purpose of the research
  • The scope of the research
  • The narrative tone of the paper [typically defined by the type of the research]
  • The methods used to study the problem

The initial aim of a title is to capture the reader’s attention and to highlight the research problem under investigation.

Create a Working Title Typically, the final title you submit to your professor is created after the research is complete so that the title accurately captures what has been done . The working title should be developed early in the research process because it can help anchor the focus of the study in much the same way the research problem does. Referring back to the working title can help you reorient yourself back to the main purpose of the study if you find yourself drifting off on a tangent while writing. The Final Title Effective titles in research papers have several characteristics that reflect general principles of academic writing.

  • Indicate accurately the subject and scope of the study,
  • Rarely use abbreviations or acronyms unless they are commonly known,
  • Use words that create a positive impression and stimulate reader interest,
  • Use current nomenclature from the field of study,
  • Identify key variables, both dependent and independent,
  • Reveal how the paper will be organized,
  • Suggest a relationship between variables which supports the major hypothesis,
  • Is limited to 5 to 15 substantive words,
  • Does not include redundant phrasing, such as, "A Study of," "An Analysis of" or similar constructions,
  • Takes the form of a question or declarative statement,
  • If you use a quote as part of the title, the source of the quote is cited [usually using an asterisk and footnote],
  • Use correct grammar and capitalization with all first words and last words capitalized, including the first word of a subtitle. All nouns, pronouns, verbs, adjectives, and adverbs that appear between the first and last words of the title are also capitalized, and
  • Rarely uses an exclamation mark at the end of the title.

The Subtitle Subtitles are frequently used in social sciences research papers because it helps the reader understand the scope of the study in relation to how it was designed to address the research problem. Think about what type of subtitle listed below reflects the overall approach to your study and whether you believe a subtitle is needed to emphasize the investigative parameters of your research.

1.  Explains or provides additional context , e.g., "Linguistic Ethnography and the Study of Welfare Institutions as a Flow of Social Practices: The Case of Residential Child Care Institutions as Paradoxical Institutions." [Palomares, Manuel and David Poveda.  Text & Talk: An Interdisciplinary Journal of Language, Discourse and Communication Studies 30 (January 2010): 193-212]

2.  Adds substance to a literary, provocative, or imaginative title or quote , e.g., "Listen to What I Say, Not How I Vote": Congressional Support for the President in Washington and at Home." [Grose, Christian R. and Keesha M. Middlemass. Social Science Quarterly 91 (March 2010): 143-167]

3.  Qualifies the geographic scope of the research , e.g., "The Geopolitics of the Eastern Border of the European Union: The Case of Romania-Moldova-Ukraine." [Marcu, Silvia. Geopolitics 14 (August 2009): 409-432]

4.  Qualifies the temporal scope of the research , e.g., "A Comparison of the Progressive Era and the Depression Years: Societal Influences on Predictions of the Future of the Library, 1895-1940." [Grossman, Hal B. Libraries & the Cultural Record 46 (2011): 102-128]

5.  Focuses on investigating the ideas, theories, or work of a particular individual , e.g., "A Deliberative Conception of Politics: How Francesco Saverio Merlino Related Anarchy and Democracy." [La Torre, Massimo. Sociologia del Diritto 28 (January 2001): 75 - 98]

6.  Identifies the methodology used , e.g. "Student Activism of the 1960s Revisited: A Multivariate Analysis Research Note." [Aron, William S. Social Forces 52 (March 1974): 408-414]

7.  Defines the overarching technique for analyzing the research problem , e.g., "Explaining Territorial Change in Federal Democracies: A Comparative Historical Institutionalist Approach." [ Tillin, Louise. Political Studies 63 (August 2015): 626-641.

With these examples in mind, think about what type of subtitle reflects the overall approach to your study. This will help the reader understand the scope of the study in relation to how it was designed to address the research problem.

Anstey, A. “Writing Style: What's in a Title?” British Journal of Dermatology 170 (May 2014): 1003-1004; Balch, Tucker. How to Compose a Title for Your Research Paper. Augmented Trader blog. School of Interactive Computing, Georgia Tech University; Bavdekar, Sandeep B. “Formulating the Right Title for a Research Article.” Journal of Association of Physicians of India 64 (February 2016); Choosing the Proper Research Paper Titles. AplusReports.com, 2007-2012; Eva, Kevin W. “Titles, Abstracts, and Authors.” In How to Write a Paper . George M. Hall, editor. 5th edition. (Oxford: John Wiley and Sons, 2013), pp. 33-41; Hartley James. “To Attract or to Inform: What are Titles for?” Journal of Technical Writing and Communication 35 (2005): 203-213; General Format. The Writing Lab and The OWL. Purdue University; Kerkut G.A. “Choosing a Title for a Paper.” Comparative Biochemistry and Physiology Part A: Physiology 74 (1983): 1; “Tempting Titles.” In Stylish Academic Writing . Helen Sword, editor. (Cambridge, MA: Harvard University Press, 2012), pp. 63-75; Nundy, Samiran, et al. “How to Choose a Title?” In How to Practice Academic Medicine and Publish from Developing Countries? A Practical Guide . Edited by Samiran Nundy, Atul Kakar, and Zulfiqar A. Bhutta. (Springer Singapore, 2022), pp. 185-192.

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Writing Research Papers

  • Research Paper Structure

Whether you are writing a B.S. Degree Research Paper or completing a research report for a Psychology course, it is highly likely that you will need to organize your research paper in accordance with American Psychological Association (APA) guidelines.  Here we discuss the structure of research papers according to APA style.

Major Sections of a Research Paper in APA Style

A complete research paper in APA style that is reporting on experimental research will typically contain a Title page, Abstract, Introduction, Methods, Results, Discussion, and References sections. 1  Many will also contain Figures and Tables and some will have an Appendix or Appendices.  These sections are detailed as follows (for a more in-depth guide, please refer to " How to Write a Research Paper in APA Style ”, a comprehensive guide developed by Prof. Emma Geller). 2

What is this paper called and who wrote it? – the first page of the paper; this includes the name of the paper, a “running head”, authors, and institutional affiliation of the authors.  The institutional affiliation is usually listed in an Author Note that is placed towards the bottom of the title page.  In some cases, the Author Note also contains an acknowledgment of any funding support and of any individuals that assisted with the research project.

One-paragraph summary of the entire study – typically no more than 250 words in length (and in many cases it is well shorter than that), the Abstract provides an overview of the study.

Introduction

What is the topic and why is it worth studying? – the first major section of text in the paper, the Introduction commonly describes the topic under investigation, summarizes or discusses relevant prior research (for related details, please see the Writing Literature Reviews section of this website), identifies unresolved issues that the current research will address, and provides an overview of the research that is to be described in greater detail in the sections to follow.

What did you do? – a section which details how the research was performed.  It typically features a description of the participants/subjects that were involved, the study design, the materials that were used, and the study procedure.  If there were multiple experiments, then each experiment may require a separate Methods section.  A rule of thumb is that the Methods section should be sufficiently detailed for another researcher to duplicate your research.

What did you find? – a section which describes the data that was collected and the results of any statistical tests that were performed.  It may also be prefaced by a description of the analysis procedure that was used. If there were multiple experiments, then each experiment may require a separate Results section.

What is the significance of your results? – the final major section of text in the paper.  The Discussion commonly features a summary of the results that were obtained in the study, describes how those results address the topic under investigation and/or the issues that the research was designed to address, and may expand upon the implications of those findings.  Limitations and directions for future research are also commonly addressed.

List of articles and any books cited – an alphabetized list of the sources that are cited in the paper (by last name of the first author of each source).  Each reference should follow specific APA guidelines regarding author names, dates, article titles, journal titles, journal volume numbers, page numbers, book publishers, publisher locations, websites, and so on (for more information, please see the Citing References in APA Style page of this website).

Tables and Figures

Graphs and data (optional in some cases) – depending on the type of research being performed, there may be Tables and/or Figures (however, in some cases, there may be neither).  In APA style, each Table and each Figure is placed on a separate page and all Tables and Figures are included after the References.   Tables are included first, followed by Figures.   However, for some journals and undergraduate research papers (such as the B.S. Research Paper or Honors Thesis), Tables and Figures may be embedded in the text (depending on the instructor’s or editor’s policies; for more details, see "Deviations from APA Style" below).

Supplementary information (optional) – in some cases, additional information that is not critical to understanding the research paper, such as a list of experiment stimuli, details of a secondary analysis, or programming code, is provided.  This is often placed in an Appendix.

Variations of Research Papers in APA Style

Although the major sections described above are common to most research papers written in APA style, there are variations on that pattern.  These variations include: 

  • Literature reviews – when a paper is reviewing prior published research and not presenting new empirical research itself (such as in a review article, and particularly a qualitative review), then the authors may forgo any Methods and Results sections. Instead, there is a different structure such as an Introduction section followed by sections for each of the different aspects of the body of research being reviewed, and then perhaps a Discussion section. 
  • Multi-experiment papers – when there are multiple experiments, it is common to follow the Introduction with an Experiment 1 section, itself containing Methods, Results, and Discussion subsections. Then there is an Experiment 2 section with a similar structure, an Experiment 3 section with a similar structure, and so on until all experiments are covered.  Towards the end of the paper there is a General Discussion section followed by References.  Additionally, in multi-experiment papers, it is common for the Results and Discussion subsections for individual experiments to be combined into single “Results and Discussion” sections.

Departures from APA Style

In some cases, official APA style might not be followed (however, be sure to check with your editor, instructor, or other sources before deviating from standards of the Publication Manual of the American Psychological Association).  Such deviations may include:

  • Placement of Tables and Figures  – in some cases, to make reading through the paper easier, Tables and/or Figures are embedded in the text (for example, having a bar graph placed in the relevant Results section). The embedding of Tables and/or Figures in the text is one of the most common deviations from APA style (and is commonly allowed in B.S. Degree Research Papers and Honors Theses; however you should check with your instructor, supervisor, or editor first). 
  • Incomplete research – sometimes a B.S. Degree Research Paper in this department is written about research that is currently being planned or is in progress. In those circumstances, sometimes only an Introduction and Methods section, followed by References, is included (that is, in cases where the research itself has not formally begun).  In other cases, preliminary results are presented and noted as such in the Results section (such as in cases where the study is underway but not complete), and the Discussion section includes caveats about the in-progress nature of the research.  Again, you should check with your instructor, supervisor, or editor first.
  • Class assignments – in some classes in this department, an assignment must be written in APA style but is not exactly a traditional research paper (for instance, a student asked to write about an article that they read, and to write that report in APA style). In that case, the structure of the paper might approximate the typical sections of a research paper in APA style, but not entirely.  You should check with your instructor for further guidelines.

Workshops and Downloadable Resources

  • For in-person discussion of the process of writing research papers, please consider attending this department’s “Writing Research Papers” workshop (for dates and times, please check the undergraduate workshops calendar).

Downloadable Resources

  • How to Write APA Style Research Papers (a comprehensive guide) [ PDF ]
  • Tips for Writing APA Style Research Papers (a brief summary) [ PDF ]
  • Example APA Style Research Paper (for B.S. Degree – empirical research) [ PDF ]
  • Example APA Style Research Paper (for B.S. Degree – literature review) [ PDF ]

Further Resources

How-To Videos     

  • Writing Research Paper Videos

APA Journal Article Reporting Guidelines

  • Appelbaum, M., Cooper, H., Kline, R. B., Mayo-Wilson, E., Nezu, A. M., & Rao, S. M. (2018). Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board task force report . American Psychologist , 73 (1), 3.
  • Levitt, H. M., Bamberg, M., Creswell, J. W., Frost, D. M., Josselson, R., & Suárez-Orozco, C. (2018). Journal article reporting standards for qualitative primary, qualitative meta-analytic, and mixed methods research in psychology: The APA Publications and Communications Board task force report . American Psychologist , 73 (1), 26.  

External Resources

  • Formatting APA Style Papers in Microsoft Word
  • How to Write an APA Style Research Paper from Hamilton University
  • WikiHow Guide to Writing APA Research Papers
  • Sample APA Formatted Paper with Comments
  • Sample APA Formatted Paper
  • Tips for Writing a Paper in APA Style

1 VandenBos, G. R. (Ed). (2010). Publication manual of the American Psychological Association (6th ed.) (pp. 41-60).  Washington, DC: American Psychological Association.

2 geller, e. (2018).  how to write an apa-style research report . [instructional materials]. , prepared by s. c. pan for ucsd psychology.

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  • Formatting Research Papers
  • Using Databases and Finding References
  • What Types of References Are Appropriate?
  • Evaluating References and Taking Notes
  • Citing References
  • Writing a Literature Review
  • Writing Process and Revising
  • Improving Scientific Writing
  • Academic Integrity and Avoiding Plagiarism
  • Writing Research Papers Videos

Enterprise Inquiry

Share your requirements with us and our team will respond to you promptly., writing a research paper: is there a preferred sequence.

In this podcast, discover the most effective sequence for writing a research paper. Join us for expert insights and valuable tips on optimizing your paper's structure and content.

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  • Introduction

In this episode of the Trinka podcast, Dr. KK takes us through the conventional sequence for writing a research paper which is usually structured with a title, abstract, introduction, methods, results, discussion (sometimes with a standalone conclusion section), and references. The acronym IMRAD is often used to refer to this sequence. Writing a research paper in this sequence ensures that the writing has a continuity and flow that is appropriate for the eventual output of the research paper.

Meet the Host

KK

About the Speaker

Dr. krishna kumar venkitachalam.

The Trinka Podcast is hosted by Dr Krishna Kumar Venkitachalam, who prefers to be referred as Dr KK. He is a surgeon by qualification, but is very passionate about science, communication and languages. Also, he has been in the academic publication industry for the last 15 years.

The Usual Sequence of a Research Paper

When it comes to writing a research paper, there are typically two ways to approach it: the usual sequence and the alternative sequence. The usual sequence is the conventional way that most researchers follow, while the alternative sequence is a recommended method that is gaining popularity. In this article, we will explore both sequences in detail.

The usual sequence for writing a research paper is as follows:

  • Methodology

Title: The title is the first thing readers see when they come across a research paper. A good title should be clear, concise, and informative. It should give readers an idea of what the research paper is about without being too long or complicated. A title should also be relevant to the research field in question.

Abstract: The abstract is a brief summary of the research paper. It should include a statement of the research problem, the methods used to address the problem, the main results of the research, and the conclusions drawn from the results. The abstract should be clear, concise, and easy to understand. A well-written abstract can help readers decide whether they want to read the entire research paper or not.

Introduction: The introduction section of a research paper should provide an overview of the research problem and why it is important. It should also include a brief literature review that discusses the previous research that has been done in the area and identifies any gaps that the current research aims to fill. The introduction should end with a clear statement of the research question or hypothesis.

Methods: The methods section should provide a detailed description of the research methodology used in the research. It should describe the participants, materials, and procedures used in the study. This section should be written in enough detail so that other researchers can replicate the study if they wish to do so.

Results: The results section should present the findings of the research in a clear and concise manner. It should include tables, graphs, and other visual aids to help readers understand the data. The results section should also include a discussion of any statistical analyses that were conducted.

Discussion: The discussion section is where the results of the research are interpreted and discussed. The discussion should be focused on answering the research question or hypothesis that was stated in the introduction. It should also discuss the implications of the research and any limitations of the study. Finally, the discussion should end with a clear conclusion that summarizes the main findings of the research.

References: The references section should list all of the sources that were cited in the research paper. It should be formatted according to the citation style used in the research field.

An Alternative Sequence for Writing a Research Paper

While the conventional sequence for writing a research paper is widely used, there are some cases where an alternative sequence may be more appropriate. For example, if the research is focused on a new methodology or technique, it may be more appropriate to start with the methods section before moving on to the introduction and other sections. Alternatively, if the research is focused on a new concept or theory, it may be more appropriate to start with the introduction before moving on to the methods and results sections.

The decision to use an alternative sequence should be based on the specific needs of the research project and the research field in question. It is important to consider the needs of the audience and to ensure that the writing flows smoothly and logically.

The alternative sequence starts with the methodology section, which outlines what will be done, how it will be done, and the tools and techniques that will be used. In medicine, for example, the methodology section is often the first consolidated aspect of any research paper because of the strict adherence to the methodology in clinical trials. The results section explains what was found using the methods outlined in the methodology section.

After the methodology and results sections are completed, the introduction section is written. This approach allows the researcher to write an introduction that accurately reflects the research that has been done, as opposed to guessing what the research will be about before it is conducted. The discussion section follows, putting the findings of the research in the context of the research field. The references section is completed next, followed by the abstract and title.

Advantages of the Alternative Sequence

There are several advantages to using the alternative sequence for writing a research paper. Firstly, starting with the methodology section ensures that the research paper accurately reflects the research that has been conducted. Secondly, completing the results section early on provides a clear picture of what the research paper will ultimately be about. Finally, writing the introduction and discussion sections after the methodology and results sections ensures that they accurately reflect the research that has been conducted.

Marking for Edit and Making the Decision

Once the research paper has been written, it is important to mark it for editing. This means going through the paper and identifying any errors or areas that need improvement. Editing can be done by the author themselves or by a professional editor.

When deciding which sequence to use for writing a research paper, it is important to consider the specific needs of the research project and the research field in question. The conventional sequence is widely used for a reason, but there are cases where an

Both the usual sequence and alternative sequence for writing a research paper have their advantages. The usual sequence follows a more conventional approach, while the alternative sequence takes a more structured approach that ensures that the research paper accurately reflects the research that has been conducted. Ultimately, it is up to the researcher to decide which sequence is best suited for their needs.

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Understanding Language Models: Everything You Need to Know

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13.1 Formatting a Research Paper

Learning objectives.

  • Identify the major components of a research paper written using American Psychological Association (APA) style.
  • Apply general APA style and formatting conventions in a research paper.

In this chapter, you will learn how to use APA style , the documentation and formatting style followed by the American Psychological Association, as well as MLA style , from the Modern Language Association. There are a few major formatting styles used in academic texts, including AMA, Chicago, and Turabian:

  • AMA (American Medical Association) for medicine, health, and biological sciences
  • APA (American Psychological Association) for education, psychology, and the social sciences
  • Chicago—a common style used in everyday publications like magazines, newspapers, and books
  • MLA (Modern Language Association) for English, literature, arts, and humanities
  • Turabian—another common style designed for its universal application across all subjects and disciplines

While all the formatting and citation styles have their own use and applications, in this chapter we focus our attention on the two styles you are most likely to use in your academic studies: APA and MLA.

If you find that the rules of proper source documentation are difficult to keep straight, you are not alone. Writing a good research paper is, in and of itself, a major intellectual challenge. Having to follow detailed citation and formatting guidelines as well may seem like just one more task to add to an already-too-long list of requirements.

Following these guidelines, however, serves several important purposes. First, it signals to your readers that your paper should be taken seriously as a student’s contribution to a given academic or professional field; it is the literary equivalent of wearing a tailored suit to a job interview. Second, it shows that you respect other people’s work enough to give them proper credit for it. Finally, it helps your reader find additional materials if he or she wishes to learn more about your topic.

Furthermore, producing a letter-perfect APA-style paper need not be burdensome. Yes, it requires careful attention to detail. However, you can simplify the process if you keep these broad guidelines in mind:

  • Work ahead whenever you can. Chapter 11 “Writing from Research: What Will I Learn?” includes tips for keeping track of your sources early in the research process, which will save time later on.
  • Get it right the first time. Apply APA guidelines as you write, so you will not have much to correct during the editing stage. Again, putting in a little extra time early on can save time later.
  • Use the resources available to you. In addition to the guidelines provided in this chapter, you may wish to consult the APA website at http://www.apa.org or the Purdue University Online Writing lab at http://owl.english.purdue.edu , which regularly updates its online style guidelines.

General Formatting Guidelines

This chapter provides detailed guidelines for using the citation and formatting conventions developed by the American Psychological Association, or APA. Writers in disciplines as diverse as astrophysics, biology, psychology, and education follow APA style. The major components of a paper written in APA style are listed in the following box.

These are the major components of an APA-style paper:

Body, which includes the following:

  • Headings and, if necessary, subheadings to organize the content
  • In-text citations of research sources
  • References page

All these components must be saved in one document, not as separate documents.

The title page of your paper includes the following information:

  • Title of the paper
  • Author’s name
  • Name of the institution with which the author is affiliated
  • Header at the top of the page with the paper title (in capital letters) and the page number (If the title is lengthy, you may use a shortened form of it in the header.)

List the first three elements in the order given in the previous list, centered about one third of the way down from the top of the page. Use the headers and footers tool of your word-processing program to add the header, with the title text at the left and the page number in the upper-right corner. Your title page should look like the following example.

Beyond the Hype: Evaluating Low-Carb Diets cover page

The next page of your paper provides an abstract , or brief summary of your findings. An abstract does not need to be provided in every paper, but an abstract should be used in papers that include a hypothesis. A good abstract is concise—about one hundred fifty to two hundred fifty words—and is written in an objective, impersonal style. Your writing voice will not be as apparent here as in the body of your paper. When writing the abstract, take a just-the-facts approach, and summarize your research question and your findings in a few sentences.

In Chapter 12 “Writing a Research Paper” , you read a paper written by a student named Jorge, who researched the effectiveness of low-carbohydrate diets. Read Jorge’s abstract. Note how it sums up the major ideas in his paper without going into excessive detail.

Beyond the Hype: Abstract

Write an abstract summarizing your paper. Briefly introduce the topic, state your findings, and sum up what conclusions you can draw from your research. Use the word count feature of your word-processing program to make sure your abstract does not exceed one hundred fifty words.

Depending on your field of study, you may sometimes write research papers that present extensive primary research, such as your own experiment or survey. In your abstract, summarize your research question and your findings, and briefly indicate how your study relates to prior research in the field.

Margins, Pagination, and Headings

APA style requirements also address specific formatting concerns, such as margins, pagination, and heading styles, within the body of the paper. Review the following APA guidelines.

Use these general guidelines to format the paper:

  • Set the top, bottom, and side margins of your paper at 1 inch.
  • Use double-spaced text throughout your paper.
  • Use a standard font, such as Times New Roman or Arial, in a legible size (10- to 12-point).
  • Use continuous pagination throughout the paper, including the title page and the references section. Page numbers appear flush right within your header.
  • Section headings and subsection headings within the body of your paper use different types of formatting depending on the level of information you are presenting. Additional details from Jorge’s paper are provided.

Cover Page

Begin formatting the final draft of your paper according to APA guidelines. You may work with an existing document or set up a new document if you choose. Include the following:

  • Your title page
  • The abstract you created in Note 13.8 “Exercise 1”
  • Correct headers and page numbers for your title page and abstract

APA style uses section headings to organize information, making it easy for the reader to follow the writer’s train of thought and to know immediately what major topics are covered. Depending on the length and complexity of the paper, its major sections may also be divided into subsections, sub-subsections, and so on. These smaller sections, in turn, use different heading styles to indicate different levels of information. In essence, you are using headings to create a hierarchy of information.

The following heading styles used in APA formatting are listed in order of greatest to least importance:

  • Section headings use centered, boldface type. Headings use title case, with important words in the heading capitalized.
  • Subsection headings use left-aligned, boldface type. Headings use title case.
  • The third level uses left-aligned, indented, boldface type. Headings use a capital letter only for the first word, and they end in a period.
  • The fourth level follows the same style used for the previous level, but the headings are boldfaced and italicized.
  • The fifth level follows the same style used for the previous level, but the headings are italicized and not boldfaced.

Visually, the hierarchy of information is organized as indicated in Table 13.1 “Section Headings” .

Table 13.1 Section Headings

A college research paper may not use all the heading levels shown in Table 13.1 “Section Headings” , but you are likely to encounter them in academic journal articles that use APA style. For a brief paper, you may find that level 1 headings suffice. Longer or more complex papers may need level 2 headings or other lower-level headings to organize information clearly. Use your outline to craft your major section headings and determine whether any subtopics are substantial enough to require additional levels of headings.

Working with the document you developed in Note 13.11 “Exercise 2” , begin setting up the heading structure of the final draft of your research paper according to APA guidelines. Include your title and at least two to three major section headings, and follow the formatting guidelines provided above. If your major sections should be broken into subsections, add those headings as well. Use your outline to help you.

Because Jorge used only level 1 headings, his Exercise 3 would look like the following:

Citation Guidelines

In-text citations.

Throughout the body of your paper, include a citation whenever you quote or paraphrase material from your research sources. As you learned in Chapter 11 “Writing from Research: What Will I Learn?” , the purpose of citations is twofold: to give credit to others for their ideas and to allow your reader to follow up and learn more about the topic if desired. Your in-text citations provide basic information about your source; each source you cite will have a longer entry in the references section that provides more detailed information.

In-text citations must provide the name of the author or authors and the year the source was published. (When a given source does not list an individual author, you may provide the source title or the name of the organization that published the material instead.) When directly quoting a source, it is also required that you include the page number where the quote appears in your citation.

This information may be included within the sentence or in a parenthetical reference at the end of the sentence, as in these examples.

Epstein (2010) points out that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (p. 137).

Here, the writer names the source author when introducing the quote and provides the publication date in parentheses after the author’s name. The page number appears in parentheses after the closing quotation marks and before the period that ends the sentence.

Addiction researchers caution that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (Epstein, 2010, p. 137).

Here, the writer provides a parenthetical citation at the end of the sentence that includes the author’s name, the year of publication, and the page number separated by commas. Again, the parenthetical citation is placed after the closing quotation marks and before the period at the end of the sentence.

As noted in the book Junk Food, Junk Science (Epstein, 2010, p. 137), “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive.”

Here, the writer chose to mention the source title in the sentence (an optional piece of information to include) and followed the title with a parenthetical citation. Note that the parenthetical citation is placed before the comma that signals the end of the introductory phrase.

David Epstein’s book Junk Food, Junk Science (2010) pointed out that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (p. 137).

Another variation is to introduce the author and the source title in your sentence and include the publication date and page number in parentheses within the sentence or at the end of the sentence. As long as you have included the essential information, you can choose the option that works best for that particular sentence and source.

Citing a book with a single author is usually a straightforward task. Of course, your research may require that you cite many other types of sources, such as books or articles with more than one author or sources with no individual author listed. You may also need to cite sources available in both print and online and nonprint sources, such as websites and personal interviews. Chapter 13 “APA and MLA Documentation and Formatting” , Section 13.2 “Citing and Referencing Techniques” and Section 13.3 “Creating a References Section” provide extensive guidelines for citing a variety of source types.

Writing at Work

APA is just one of several different styles with its own guidelines for documentation, formatting, and language usage. Depending on your field of interest, you may be exposed to additional styles, such as the following:

  • MLA style. Determined by the Modern Languages Association and used for papers in literature, languages, and other disciplines in the humanities.
  • Chicago style. Outlined in the Chicago Manual of Style and sometimes used for papers in the humanities and the sciences; many professional organizations use this style for publications as well.
  • Associated Press (AP) style. Used by professional journalists.

References List

The brief citations included in the body of your paper correspond to the more detailed citations provided at the end of the paper in the references section. In-text citations provide basic information—the author’s name, the publication date, and the page number if necessary—while the references section provides more extensive bibliographical information. Again, this information allows your reader to follow up on the sources you cited and do additional reading about the topic if desired.

The specific format of entries in the list of references varies slightly for different source types, but the entries generally include the following information:

  • The name(s) of the author(s) or institution that wrote the source
  • The year of publication and, where applicable, the exact date of publication
  • The full title of the source
  • For books, the city of publication
  • For articles or essays, the name of the periodical or book in which the article or essay appears
  • For magazine and journal articles, the volume number, issue number, and pages where the article appears
  • For sources on the web, the URL where the source is located

The references page is double spaced and lists entries in alphabetical order by the author’s last name. If an entry continues for more than one line, the second line and each subsequent line are indented five spaces. Review the following example. ( Chapter 13 “APA and MLA Documentation and Formatting” , Section 13.3 “Creating a References Section” provides extensive guidelines for formatting reference entries for different types of sources.)

References Section

In APA style, book and article titles are formatted in sentence case, not title case. Sentence case means that only the first word is capitalized, along with any proper nouns.

Key Takeaways

  • Following proper citation and formatting guidelines helps writers ensure that their work will be taken seriously, give proper credit to other authors for their work, and provide valuable information to readers.
  • Working ahead and taking care to cite sources correctly the first time are ways writers can save time during the editing stage of writing a research paper.
  • APA papers usually include an abstract that concisely summarizes the paper.
  • APA papers use a specific headings structure to provide a clear hierarchy of information.
  • In APA papers, in-text citations usually include the name(s) of the author(s) and the year of publication.
  • In-text citations correspond to entries in the references section, which provide detailed bibliographical information about a source.

Writing for Success Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Enago Academy

6 Important Tips on Writing a Research Paper Title

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When you are searching for a research study on a particular topic, you probably notice that articles with interesting, descriptive research titles draw you in. By contrast, research paper titles that are not descriptive are usually passed over, even though you may write a good research paper with interesting contents. This shows the importance of coming up with a good title for your research paper when drafting your own manuscript.

Importance of a Research Title

The research title plays a crucial role in the research process, and its importance can be summarized as follows:

Importance of a Research Title

Why do Research Titles Matter?

Before we look at how to title a research paper, let’s look at a research title example that illustrates why a good research paper should have a strong title.

Imagine that you are researching meditation and nursing, and you want to find out if any studies have shown that meditation makes nurses better communicators.  You conduct a keyword search using the keywords “nursing”, “communication”, and “meditation.” You come up with results that have the following titles:

  • Benefits of Meditation for the Nursing Profession: A Quantitative Investigation
  • Why Mindful Nurses Make the Best Communicators
  • Meditation Gurus
  • Nurses on the Move: A Quantitative Report on How Meditation Can Improve Nurse Performance

All four of these research paper titles may describe very similar studies—they could even be titles for the same study! As you can see, they give very different impressions.

  • Title 1 describes the topic and the method of the study but is not particularly catchy.
  • Title 2 partly describes the topic, but does not give any information about the method of the study—it could simply be a theoretical or opinion piece.
  • Title 3 is somewhat catchier but gives almost no information at all about the article.
  • Title 4 begins with a catchy main title and is followed by a subtitle that gives information about the content and method of the study.

As we will see, Title 4 has all the characteristics of a good research title.

Characteristics of a Good Research Title

According to rhetoric scholars Hairston and Keene, making a good title for a paper involves ensuring that the title of the research accomplishes four goals as mentioned below:

  • It should predict the content of the research paper .
  • It should be interesting to the reader .
  • It should reflect the tone of the writing .
  • It should contain important keywords that will make it easier to be located during a keyword search.

Let’s return to the examples in the previous section to see how to make a research title.

As you can see in the table above, only one of the four example titles fulfills all of the criteria of a suitable research paper title.

Related: You’ve chosen your study topic, but having trouble deciding where to publish it? Here’s a comprehensive course to help you identify the right journal .

Tips for Writing an Effective Research Paper Title

When writing a research title, you can use the four criteria listed above as a guide. Here are a few other tips you can use to make sure your title will be part of the recipe for an effective research paper :

  • Make sure your research title describes (a) the topic, (b) the method, (c) the sample, and (d) the results of your study. You can use the following formula:
[ Result ]: A [ method ] study of [ topic ] among [ sample ] Example : Meditation makes nurses perform better: a qualitative study of mindfulness meditation among German nursing students
  • Avoid unnecessary words and jargons. Keep the title statement as concise as possible. You want a title that will be comprehensible even to people who are not experts in your field. Check our article for a detailed list of things to avoid when writing an effective research title .
  • Make sure your title is between 5 and 15 words in length.
  • If you are writing a title for a university assignment or for a particular academic journal, verify that your title conforms to the standards and requirements for that outlet. For example, many journals require that titles fall under a character limit, including spaces. Many universities require that titles take a very specific form, limiting your creativity.
  • Use a descriptive phrase to convey the purpose of your research efficiently.
  • Most importantly, use critical keywords in the title to increase the discoverability of your article.

research paper title sequence

Resources for Further Reading

In addition to the tips above, there are many resources online that you can use to help write your research title. Here is a list of links that you may find useful as you work on creating an excellent research title:

  • The University of Southern California has a guide specific to social science research papers: http://libguides.usc.edu/writingguide/title
  • The Journal of European Psychology Students has a blog article focusing on APA-compliant research paper titles: http://blog.efpsa.org/2012/09/01/how-to-write-a-good-title-for-journal-articles/
  • This article by Kristen Hamlin contains a step-by-step approach to writing titles: http://classroom.synonym.com/choose-title-research-paper-4332.html

Are there any tips or tricks you find useful in crafting research titles? Which tip did you find most useful in this article? Leave a comment to let us know!

  • Hairston, M., & Keene, M. 2003. Successful writing . 5th ed. New York: Norton.
  • University of Southern California. 2017. Organizing your social sciences research paper: choosing a title . [Online] Available at: http://libguides.usc.edu/writingguide/title

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Thank you so much:) Have a nice day!

Thank you so much, it helped me.. God bless..

Thank you for the excellent article and tips for creating a research work, because I always forget about such an essential element as the keywords when forming topics. In particular, I have found a rapid help with the formation of informative and sound titles that also conforms to the standards and requirements.

I am doing a research work on sales girls or shop girls using qualititative method. Basicly I am from Pakistan and writing on the scenario of mycountry. I am really confused about my research title can you kindly give some suggestions and give me an approperaite tilte

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Hi Zubair, Thank you for your question. However, the information you have provided is insufficient for drafting an appropriate title. Information on what exactly you intend to study would be needed in order to draft a meaningful title. Meanwhile, you can try drafting your own title after going through the following articles our website: https://www.enago.com/academy/top-10-tips-on-choosing-an-attractive-research-title/ , https://www.enago.com/academy/writing-a-good-research-title-things-to-avoid/ , https://www.enago.com/academy/write-irresistible-research-paper-title/ We would be happy to give you feedback and suggest changes if required. Did you get a chance to install our free Mobile App? https://www.enago.com/academy/mobile-app/ . Make sure you subscribe to our weekly newsletter https://www.enago.com/academy/subscribe-now/ .

thanks for helping me like this!!

Thank you for this. It helped me improve my research title. I just want to verify to you the title I have just made. “Ensuring the safety: A Quantitative Study of Radio Frequency Identification system among the selected students of ( school’s name ).

(I need your reply asap coz we will be doing the chap. 1 tomorrow. Thank u in advance. 🙂 )

I am actually doing a research paper title. I want to know more further in doing research title. Can you give me some tips on doing a research paper?

Hi Joan, Thank you for your question. We are glad to know that you found our resources useful. Your feedback is very valuable to us. You can try drafting your own title after going through the following articles on our website: https://www.enago.com/academy/top-10-tips-on-choosing-an-attractive-research-title/ , https://www.enago.com/academy/writing-a-good-research-title-things-to-avoid/ , https://www.enago.com/academy/write-irresistible-research-paper-title/

We would be happy to give you feedback and suggest changes if required. Did you get a chance to install our free Mobile App? https://www.enago.com/academy/mobile-app/ . Make sure you subscribe to our weekly newsletter https://www.enago.com/academy/subscribe-now/ .

That really helpful. Thanks alot

Thank you so much. It’s really help me.

Thanks for sharing this tips. Title matters a lot for any article because it contents Keywords of article. It should be eye-catchy. Your article is helpful to select title of any article.

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This blog is very informative for me. Thanks for sharing.

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i’m found in selecting my ma thesis title ,so i’m going to do my final research after the proposal approved. Your post help me find good title.

I need help. I need a research title for my study about early mobilization of the mechanically ventilated patients in the ICU. Any suggestions would be highly appreciated.

Thank you for posting your query on the website. When writing manuscripts, too many scholars neglect the research title. This phrase, along with the abstract, is what people will mostly see and read online. Title research of publications shows that the research paper title does matter a lot. Both bibliometrics and altmetrics tracking of citations are now, for better or worse, used to gauge a paper’s “success” for its author(s) and the journal publishing it. Interesting research topics coupled with good or clever yet accurate research titles can draw more attention to your work from peers and the public alike. You can check through the following search results for titles on similar topics: https://www.google.com/search?q=early+mobilization+of+the+mechanically+ventilated+patients+in+the+icu&rlz=1C1GCEU_enIN907IN907&oq=&aqs=chrome.0.69i59.4920093j0j7&sourceid=chrome&ie=UTF-8 .

We hope this would be helpful in drafting an attractive title for your research paper.

Please let us know in case of any other queries.

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In case the topic is new research before you’re writing. And then to stand out, you end up being different.and be inclined to highlight yourself.

There are many free directories, and more paid lists.

To be honest your article is informative. I search many site to know about writing but I didn’t get the information I needed. I saw your site and I read it. I got some new information from here. I think some of your tips can be applied to those too! Thank you so very much for such informative and useful content.

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Creating effective titles for your scientific publications

Associated data.

You work for months, maybe years, to plan and conduct your study. You write it up carefully, reporting every piece of data accurately. You get the approval of your co-authors and double-check everyone’s conflicts of interest for the disclosure form. You are ready to submit it when you remember that your work needs a title. “No problem,” you say. “I’ll just throw something together.”

Hold on—that’s not a good idea. The title of a scholarly article really does matter, for several reasons ( Video 1 , available online at www.VideoGIE.org ). It is the first thing a reader will see, so it helps him or her decide whether to read the rest of the article ( Fig. 1 ). 1 If you are publishing in a subscription model, it helps the reader decide whether to buy the whole article. Later, when the reader is writing his own article and wants to cite yours, he can find it more easily if you have given it an effective title. If the article is cited more, it will help your H-Index and G-Index, building your reputation and credibility. Furthermore, if your article is highly cited, it helps the publishing journal’s Impact Factor. Journal editors know which authors’ articles are highly cited and will react with interest when they see another article submitted by that author in the future.

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Object name is gr1.jpg

Example of a poor title. It has a problem with grammar (“Are” instead of “Is”), it attempts to be funny, it is in the form of a question, uses abbreviations, does not have clear keywords, and does not make the point of the article clear.

Several elements make up an effective title ( Table 1 ). Studies have shown that shorter titles receive more citations; most recommend 10 to 15 words or between 31 and 40 characters. 2 , 3 , 4 , 5 , 6 Punctuation is important: commas and colons have been shown to increase citations, but articles with question marks or exclamation points are cited less frequently. 7 Keywords that help researchers find your article when they use search algorithms are critical, so make sure that your title accurately reflects the key concepts of your article. 4 , 8 , 9

Table 1

Elements of a good title for a scholarly publication

Avoid abbreviations or jargon in your title. 3 , 4 , 9 People from other fields whose research intersects with yours might cite you if they can find your article, but if you use abbreviations or jargon specific to your field, their searches won’t uncover your article.

Some authors think attracting attention with humor or puns is a good idea, but that practice is actually counterproductive. 3 , 4 , 5 , 9 Your title should reflect the tone of the article and of the journal, and because we are dealing with scholarly publications, that means the title should be formal as well. If you are writing an editorial or opinion piece, you might get away with a less-formal title, but for the most part, making your readers laugh should not be a priority.

Poor grammar and incorrect spelling are jarring and irritating to many readers as well as to editors and reviewers, so check and double check that the title is grammatical and everything is spelled and punctuated correctly. If you are using an editing or translation service to assist you with the composition of your article, be sure to include the title in the content submitted for review to catch errors you may have overlooked.

Above all, remember that your title is a reader’s first impression of your article, so make sure that impression is effective. Do all you can to create a title that is professional and does justice to the article you have worked so hard to create.

All authors disclosed no financial relationships relevant to this publication.

Supplementary data

Creating effective titles for scientific articles takes planning and knowledge. In this video, we discuss the elements of a good title.

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  • Research guides

Writing an Educational Research Paper

Research paper sections, customary parts of an education research paper.

There is no one right style or manner for writing an education paper. Content aside, the writing style and presentation of papers in different educational fields vary greatly. Nevertheless, certain parts are common to most papers, for example:

Title/Cover Page

Contains the paper's title, the author's name, address, phone number, e-mail, and the day's date.

Not every education paper requires an abstract. However, for longer, more complex papers abstracts are particularly useful. Often only 100 to 300 words, the abstract generally provides a broad overview and is never more than a page. It describes the essence, the main theme of the paper. It includes the research question posed, its significance, the methodology, and the main results or findings. Footnotes or cited works are never listed in an abstract. Remember to take great care in composing the abstract. It's the first part of the paper the instructor reads. It must impress with a strong content, good style, and general aesthetic appeal. Never write it hastily or carelessly.

Introduction and Statement of the Problem

A good introduction states the main research problem and thesis argument. What precisely are you studying and why is it important? How original is it? Will it fill a gap in other studies? Never provide a lengthy justification for your topic before it has been explicitly stated.

Limitations of Study

Indicate as soon as possible what you intend to do, and what you are not going to attempt. You may limit the scope of your paper by any number of factors, for example, time, personnel, gender, age, geographic location, nationality, and so on.

Methodology

Discuss your research methodology. Did you employ qualitative or quantitative research methods? Did you administer a questionnaire or interview people? Any field research conducted? How did you collect data? Did you utilize other libraries or archives? And so on.

Literature Review

The research process uncovers what other writers have written about your topic. Your education paper should include a discussion or review of what is known about the subject and how that knowledge was acquired. Once you provide the general and specific context of the existing knowledge, then you yourself can build on others' research. The guide Writing a Literature Review will be helpful here.

Main Body of Paper/Argument

This is generally the longest part of the paper. It's where the author supports the thesis and builds the argument. It contains most of the citations and analysis. This section should focus on a rational development of the thesis with clear reasoning and solid argumentation at all points. A clear focus, avoiding meaningless digressions, provides the essential unity that characterizes a strong education paper.

After spending a great deal of time and energy introducing and arguing the points in the main body of the paper, the conclusion brings everything together and underscores what it all means. A stimulating and informative conclusion leaves the reader informed and well-satisfied. A conclusion that makes sense, when read independently from the rest of the paper, will win praise.

Works Cited/Bibliography

See the Citation guide .

Education research papers often contain one or more appendices. An appendix contains material that is appropriate for enlarging the reader's understanding, but that does not fit very well into the main body of the paper. Such material might include tables, charts, summaries, questionnaires, interview questions, lengthy statistics, maps, pictures, photographs, lists of terms, glossaries, survey instruments, letters, copies of historical documents, and many other types of supplementary material. A paper may have several appendices. They are usually placed after the main body of the paper but before the bibliography or works cited section. They are usually designated by such headings as Appendix A, Appendix B, and so on.

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Advanced Research Methods

Writing the research paper.

  • What Is Research?
  • Library Research
  • Writing a Research Proposal

Before Writing the Paper

Methods, thesis, and hypothesis, clarity, precision, and academic expression, format your paper, typical problems, a few suggestions, avoid plagiarism.

  • Presenting the Research Paper
  • Try to find a subject that really interests you.
  • While you explore the topic, narrow or broaden your target and focus on something that gives the most promising results.
  • Don't choose a huge subject if you have to write a 3 page long paper, and broaden your topic sufficiently if you have to submit at least 25 pages.
  • Consult your class instructor (and your classmates) about the topic.
  • Find primary and secondary sources in the library.
  • Read and critically analyse them.
  • Take notes.
  • Compile surveys, collect data, gather materials for quantitative analysis (if these are good methods to investigate the topic more deeply).
  • Come up with new ideas about the topic. Try to formulate your ideas in a few sentences.
  • Review your notes and other materials and enrich the outline.
  • Try to estimate how long the individual parts will be.
  • Do others understand what you want to say?
  • Do they accept it as new knowledge or relevant and important for a paper?
  • Do they agree that your thoughts will result in a successful paper?
  • Qualitative: gives answers on questions (how, why, when, who, what, etc.) by investigating an issue
  • Quantitative:requires data and the analysis of data as well
  • the essence, the point of the research paper in one or two sentences.
  • a statement that can be proved or disproved.
  • Be specific.
  • Avoid ambiguity.
  • Use predominantly the active voice, not the passive.
  • Deal with one issue in one paragraph.
  • Be accurate.
  • Double-check your data, references, citations and statements.

Academic Expression

  • Don't use familiar style or colloquial/slang expressions.
  • Write in full sentences.
  • Check the meaning of the words if you don't know exactly what they mean.
  • Avoid metaphors.
  • Almost the rough content of every paragraph.
  • The order of the various topics in your paper.
  • On the basis of the outline, start writing a part by planning the content, and then write it down.
  • Put a visible mark (which you will later delete) where you need to quote a source, and write in the citation when you finish writing that part or a bigger part.
  • Does the text make sense?
  • Could you explain what you wanted?
  • Did you write good sentences?
  • Is there something missing?
  • Check the spelling.
  • Complete the citations, bring them in standard format.

Use the guidelines that your instructor requires (MLA, Chicago, APA, Turabian, etc.).

  • Adjust margins, spacing, paragraph indentation, place of page numbers, etc.
  • Standardize the bibliography or footnotes according to the guidelines.

research paper title sequence

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(Based on English Composition 2 from Illinois Valley Community College):

  • Weak organization
  • Poor support and development of ideas
  • Weak use of secondary sources
  • Excessive errors
  • Stylistic weakness

When collecting materials, selecting research topic, and writing the paper:

  • Be systematic and organized (e.g. keep your bibliography neat and organized; write your notes in a neat way, so that you can find them later on.
  • Use your critical thinking ability when you read.
  • Write down your thoughts (so that you can reconstruct them later).
  • Stop when you have a really good idea and think about whether you could enlarge it to a whole research paper. If yes, take much longer notes.
  • When you write down a quotation or summarize somebody else's thoughts in your notes or in the paper, cite the source (i.e. write down the author, title, publication place, year, page number).
  • If you quote or summarize a thought from the internet, cite the internet source.
  • Write an outline that is detailed enough to remind you about the content.
  • Read your paper for yourself or, preferably, somebody else. 
  • When you finish writing, check the spelling;
  • Use the citation form (MLA, Chicago, or other) that your instructor requires and use it everywhere.

Plagiarism : somebody else's words or ideas presented without citation by an author

  • Cite your source every time when you quote a part of somebody's work.
  • Cite your source  every time when you summarize a thought from somebody's work.
  • Cite your source  every time when you use a source (quote or summarize) from the Internet.

Consult the Citing Sources research guide for further details.

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Formatting Research Paper Headings and Subheadings

research paper title sequence

Different style guides have different rules regarding the formatting of headings and subheadings in a paper, but what information you should actually put into your subheadings is a different question and often up to personal taste. Here we quickly summarize general guidelines, different approaches, and what not to do when choosing headings for a research paper.

Does it matter how I name my sections and subsections?

The main sections of a research paper have general headers and are often journal-specific, but some (e.g., the methods and discussion section) can really benefit from subsections with clear and informative headers. The things to keep in mind are thus the general style your paper is supposed to follow (e.g., APA, MLA), the specific guidelines the journal you want to submit to lists in their author instructions , and your personal style (e.g., how much information you want the reader to get from just reading your subsection headers). 

Table of Contents:

  • Style Guides: Rules on Headings and Subheadings
  • What Sections and Subsections Do You Need? 
  • How Should You Name Your Sections and Subsections?
  • Avoid These Common Mistakes

research paper headings

Style Guides: Research Paper Heading and Subheading Format

Headers identify the content within the different sections of your paper and should be as descriptive and concise as possible. That is why the main sections of research articles always have the same or very similar headers ( Introduction, Methods, Results, Discussion ), with no or only small differences between journals. However, you also need to divide the content of some of these sections (e.g., the method section) into smaller subsections (e.g., Participants, Experimental Design, and Statistical Analysis ), and make sure you follow specific journal formatting styles when doing so. 

If the journal you submit to follows APA style , for example, you are allowed to use up to five levels of headings, depending on the length of your paper, the complexity of your work, and your personal preference. To clearly indicate how each subsection fits into the rest of the text, every header level has a different format – but note that headers are usually not numbered because the different formatting already reflects the text hierarchy.

APA style headings example structure

Level 1 Centered, Bold, Title Case

Text begins as a new paragraph.

Level 2 Left-aligned, Bold, Title Case

Level 3 Left-aligned, Bold Italic, Title Case

Level 4     Indented, Bold, Title Case, Period . Text begins on the same                    

                                 line and continues as a regular paragraph.

Level 5     Indented, Bold Italic, Title Case, Period. Text begins on the                           

                                 same line and continues as a regular paragraph.

If you only need one section header (e.g. Methods ) and one level of subsection headers (e.g., Participants, Experimental Design, and Statistical Analysis ), use Level 1 and Level 2 headers. If you need three levels of headings, use Levels 1, 2, and 3 (and so on). Do not skip levels or combine them in a different way. 

If you write a paper in Chicago style or MLA style , then you don’t need to follow such exact rules for headings and subheadings. Your structure just has to be consistent with the general formatting guidelines of both styles (12-pts Times New Roman font, double-spaced text, 0.5-inch indentation for every new paragraph) and consistent throughout your paper. Make sure the different formatting levels indicate a hierarchy (e.g., boldface for level 1 and italics for level 2, or a larger font size for level 1 and smaller font size for level 2). The main specifics regarding Chicago and MLA headings and subheadings are that they should be written in title case (major words capitalized, most minor words lowercase) and not end in a period. Both styles allow you, however, to number your sections and subsections, for example with an Arabic number and a period, followed by a space and then the section name. 

MLA paper headings example structure

1. Introduction

2. Material and Methods

2.1 Subject Recruitment

2.2 Experimental Procedure

2.3 Statistical Analysis

3.1 Experiment 1

3.2 Experiment 2

4. Discussion

5. Conclusion

What research paper headings do you need?

Your paper obviously needs to contain the main sections ( Introduction, Methods, Results, Discussion, and maybe Conclusion ) and you need to make sure that you name them according to the target journal style (have a look at the author guidelines if you are unsure what the journal style is). The differences between journals are subtle, but some want you to combine the results and discussion sections, for example, while others don’t want you to have a separate conclusion section. You also need to check whether the target journal has specific rules on subsections (or no subsections) within these main sections. The introduction section should usually not be subdivided (but some journals do not mind), while the method section, for example, always needs to have clear subsections.

How to Name Your Sections and Subsections

The method section subheadings should be short and descriptive, but how you subdivide this section depends on the structure you choose to present your work – which can be chronological (e.g., Experiment 1, Experiment 2 ) or follow your main topics (e.g., Visual Experiment, Behavioral Experiment, Questionnaire ). Have a look at this article on how to write the methods for a research paper if you need input on what the best structure for your work is. The method subheadings should only be keywords that tell the reader what information is following, not summaries or conclusions. That means that “ Subject Recruitment ” is a good methods section subheading, but “ Subjects Were Screened Using Questionnaires ” is not.  

The subheadings for the result section should then follow the general structure of your method section, but here you can choose what information you want to put in every subheading. Some authors keep it simple and just subdivide their result section into experiments or measures like the method section, but others use the headings to summarize their findings so that the reader is prepared for the details that follow. You could, for example, simply name your subsections “ Anxiety Levels ” and “ Social Behavior ,” if those are the measures you studied and explained in the method section. 

Or, you could provide the reader with a glimpse into the results of the analyses you are going to describe, and instead name these subsections “ Anxiety-Like Behaviors in Mutant Mice ” and “ Normal Social Behaviors in Mutant Mice .” While keeping headings short and simple is always a good idea, such mini-summaries can make your result section much clearer and easier to follow. Just make sure that the target journal you want to submit to does not have a rule against that. 

Common Heading and Subheading Mistakes 

Subheadings are not sentences.

If your heading reads like a full sentence, then you can most probably omit the verb or generally rephrase to shorten it. That also means a heading should not contain punctuation except maybe colons or question marks – definitely don’t put a period at the end, except when you have reached heading level 4 in the APA formatting style (see above) and the rules say so.  

Be consistent

Always check your numbering, for example for spaces and periods before and after numbers (e.g., 3.2. vs 3.2 ), because readability depends on such features. But also make sure that your headings are consistent in structure and content: Switching between short keyword headings (e.g., “ Experiment 2 ”) and summary headings (e.g., “ Mice Do not Recognize People ”) is confusing and never a good idea. Ideally, subheadings within a section all have a similar structure. If your first subsection is called “ Mice Do not Recognize People ,” then “ People Do not Recognize Mice” is a better subheader for the next subsection than “Do People Recognize Mice? ”, because consistency is more important in a research paper than creativity. 

Don’t overdo it

Not every paragraph or every argument needs a subheading. Only use subheadings within a bigger section if you have more than one point to make per heading level, and if subdividing the section really makes the structure clearer overall.

Before submitting your journal manuscript to academic publishers, be sure to get English editing services , including manuscript editing or paper editing from a trusted source. And receive instant proofreading with Wordvice AI, our AI online text editor , which provides unlimited editing while drafting your research work.

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Research Method

Home » Research Paper Title Page – Example and Making Guide

Research Paper Title Page – Example and Making Guide

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Research Paper Title

Research Paper Title Page

Research Paper Title Page is the cover page of a research paper that provides basic information about the paper. It typically includes the title of the research paper, the author’s name, the date of submission, and the name of the institution or department where the research was conducted.

The title page of a research paper typically includes the following information:

  • Title of the research paper
  • Author(s) of the paper (including their name(s), affiliation(s), and contact information)
  • Date of submission or publication
  • Name of the academic institution or organization where the research was conducted (if applicable)
  • Any acknowledgments or funding sources for the research
  • Abstract of the research paper (usually a brief summary of the paper’s main findings or arguments)

Research Paper Title Page Example

Research Paper Title Page

Notes on formatting:

  • The title of your research paper should be centered on the page, and should be written in title case (capitalizing the first letter of each major word).
  • Your name should be written underneath the title, centered on the page.
  • Your institutional affiliation (e.g. the name of your university or research institution) should be written underneath your name, centered on the page.
  • The date of submission should be written underneath your institutional affiliation, centered on the page.

Research Paper Title Page Writing Guide

Here are some guidelines for writing a research paper title page:

  • Title of the paper: The title should be concise and descriptive, reflecting the main idea or focus of the research paper. The title should be centered on the page and in title case (capitalize the first letter of each major word).
  • Author’s name : The author’s name should be written below the title, also centered on the page. Use first name, middle initial, and last name.
  • Institutional affiliation: The institutional affiliation is the name of the university, college, or organization where the research was conducted. It should be listed below the author’s name and centered on the page.
  • Date of submission: The date of submission is the date when the research paper is being submitted for review or publication. It should be written below the institutional affiliation and centered on the page.
  • Running head: A running head is a short version of the title that is used on subsequent pages of the paper. It should be written in all caps and flush left at the top of each page.
  • Page number: The page number should be flush right at the top of each page.
  • Font and spacing: Use a standard font such as Times New Roman or Arial in 12-point size. Double-space the entire title page.

Purpose of Research Paper Title Page

The purpose of the research paper title page is to:

  • Identify the title of the research paper: The title page provides the title of the paper in a clear and concise manner so that readers can quickly understand the topic of the research.
  • Indicate the author(s) of the paper: The title page should include the name(s) of the author(s) who conducted the research and wrote the paper. This information helps to establish credibility and accountability for the research.
  • Provide information about the institutional affiliation: The title page should also include the name of the institution where the research was conducted. This information helps readers understand the context of the research and can be useful for citations and further research.
  • Give the date of the research: The title page should include the date that the research was conducted or the paper was written. This information helps readers understand the currency of the research and can be useful for citing sources.
  • Include other relevant information: Depending on the requirements of the research paper, the title page may also include other relevant information such as the course title, instructor’s name, or a brief abstract of the research.
  • Establish a professional appearance : The title page provides an opportunity to present the research paper in a professional and organized manner. A well-designed title page with all necessary information can make a positive first impression on readers and demonstrate the author’s attention to detail.
  • Facilitate easy referencing: A properly formatted title page can help readers locate the research paper easily in a database, library, or other sources. This is particularly important for academic and scientific research papers that may be referenced frequently by others.
  • Comply with formatting guidelines : Many academic and scientific disciplines have specific formatting guidelines for research papers, including requirements for the title page. Adhering to these guidelines ensures that the research paper is presented in a consistent and standardized format that is familiar to readers in that field.
  • Demonstrate compliance with ethical standards: Some academic institutions require that the title page include a statement of compliance with ethical standards for research, such as human subjects’ protection, data privacy, or animal welfare. This information ensures that the research was conducted in an ethical and responsible manner.

Advantages of Research Paper Title Page

There are several advantages to including a title page in a research paper, including:

  • Professional Appearance: A title page provides a professional appearance to the research paper. It is the first thing that readers see, and it gives them an impression of the paper’s overall quality.
  • Credibility : Including a title page with all the necessary information, such as the author’s name, institutional affiliation, and the date of submission, enhances the credibility of the research paper.
  • Easy Identification: A title page makes it easier for readers to identify the research paper among other papers. It provides important information about the paper, such as the title, author’s name, and institutional affiliation.
  • Easy Access: A title page provides a quick reference for readers who need to cite the research paper in their own work. The necessary information is all in one place and easily accessible.
  • Compliance with Formatting Guidelines: Many academic institutions have specific formatting guidelines for research papers, including the use of a title page. Including a title page ensures compliance with these guidelines and helps avoid any confusion or penalties.

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A Beginner's Guide to Starting the Research Process

Research process steps

When you have to write a thesis or dissertation , it can be hard to know where to begin, but there are some clear steps you can follow.

The research process often begins with a very broad idea for a topic you’d like to know more about. You do some preliminary research to identify a  problem . After refining your research questions , you can lay out the foundations of your research design , leading to a proposal that outlines your ideas and plans.

This article takes you through the first steps of the research process, helping you narrow down your ideas and build up a strong foundation for your research project.

Table of contents

Step 1: choose your topic, step 2: identify a problem, step 3: formulate research questions, step 4: create a research design, step 5: write a research proposal, other interesting articles.

First you have to come up with some ideas. Your thesis or dissertation topic can start out very broad. Think about the general area or field you’re interested in—maybe you already have specific research interests based on classes you’ve taken, or maybe you had to consider your topic when applying to graduate school and writing a statement of purpose .

Even if you already have a good sense of your topic, you’ll need to read widely to build background knowledge and begin narrowing down your ideas. Conduct an initial literature review to begin gathering relevant sources. As you read, take notes and try to identify problems, questions, debates, contradictions and gaps. Your aim is to narrow down from a broad area of interest to a specific niche.

Make sure to consider the practicalities: the requirements of your programme, the amount of time you have to complete the research, and how difficult it will be to access sources and data on the topic. Before moving onto the next stage, it’s a good idea to discuss the topic with your thesis supervisor.

>>Read more about narrowing down a research topic

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research paper title sequence

So you’ve settled on a topic and found a niche—but what exactly will your research investigate, and why does it matter? To give your project focus and purpose, you have to define a research problem .

The problem might be a practical issue—for example, a process or practice that isn’t working well, an area of concern in an organization’s performance, or a difficulty faced by a specific group of people in society.

Alternatively, you might choose to investigate a theoretical problem—for example, an underexplored phenomenon or relationship, a contradiction between different models or theories, or an unresolved debate among scholars.

To put the problem in context and set your objectives, you can write a problem statement . This describes who the problem affects, why research is needed, and how your research project will contribute to solving it.

>>Read more about defining a research problem

Next, based on the problem statement, you need to write one or more research questions . These target exactly what you want to find out. They might focus on describing, comparing, evaluating, or explaining the research problem.

A strong research question should be specific enough that you can answer it thoroughly using appropriate qualitative or quantitative research methods. It should also be complex enough to require in-depth investigation, analysis, and argument. Questions that can be answered with “yes/no” or with easily available facts are not complex enough for a thesis or dissertation.

In some types of research, at this stage you might also have to develop a conceptual framework and testable hypotheses .

>>See research question examples

The research design is a practical framework for answering your research questions. It involves making decisions about the type of data you need, the methods you’ll use to collect and analyze it, and the location and timescale of your research.

There are often many possible paths you can take to answering your questions. The decisions you make will partly be based on your priorities. For example, do you want to determine causes and effects, draw generalizable conclusions, or understand the details of a specific context?

You need to decide whether you will use primary or secondary data and qualitative or quantitative methods . You also need to determine the specific tools, procedures, and materials you’ll use to collect and analyze your data, as well as your criteria for selecting participants or sources.

>>Read more about creating a research design

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Finally, after completing these steps, you are ready to complete a research proposal . The proposal outlines the context, relevance, purpose, and plan of your research.

As well as outlining the background, problem statement, and research questions, the proposal should also include a literature review that shows how your project will fit into existing work on the topic. The research design section describes your approach and explains exactly what you will do.

You might have to get the proposal approved by your supervisor before you get started, and it will guide the process of writing your thesis or dissertation.

>>Read more about writing a research proposal

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

Methodology

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 Statistics

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Research bias

  • Optimism bias
  • Cognitive bias
  • Implicit bias
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International Conference on Information Technology

InCITe 2023: Decision Intelligence pp 233–241 Cite as

Automatic Title Generation with Attention-Based LSTM

  • M. Dhilsath Fathima   ORCID: orcid.org/0000-0002-4491-4352 40 ,
  • M. Seeni Syed Raviyathu Ammal   ORCID: orcid.org/0000-0001-6981-7359 41 ,
  • Prashant Kumar Singh   ORCID: orcid.org/0000-0002-2130-288X 42 ,
  • Sachi Shome 42 ,
  • Manbha Kharsyienlieh 42 &
  • R. Hariharan   ORCID: orcid.org/0000-0003-4198-765X 42  
  • Conference paper
  • First Online: 25 November 2023

65 Accesses

Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 1079)

The objective of this article is to develop a generalised architecture that can generate headings for any type of document, including articles, book paragraphs, newspaper articles, and legal documents. The title of a document provides a concise overview of its content, allowing readers to quickly understand the main idea without focusing on the specifics. The more attractive the title is, the more the reader is bound to read it. Title generation will be helpful to the editors of newspapers or technical writers to easily find the central theme of the whole article. The Sequence-to-Sequence (Seq-to-Seq) model is implemented in this proposed work for generating the title for the project, article, and research articles. The Seq-to-Seq model utilizes an encoder-decoder recurrent neural network that has an attention layer and Long Short-Term Memory (LSTM). The main strength of the developed framework is the adoption of an attention layers to the encoder-decoder architecture, which assigns more weightage to background words. This proposed model achieves 90% prediction accuracy by including an attention layer into the LSTM.

  • Sequence-to-sequence model
  • Long Short-Term Memory (LSTM)
  • Encoder-decoder
  • Attention layer

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Zhang T, Irsan IC, Thung F, Han D, Lo D, Jiang L (2022) Automatic pull request title generation. In: 2022 IEEE international conference on software maintenance and evolution (ICSME). IEEE, pp 71–81

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Guan W, Ni Z, Hu Y, Liang W, Ou C, He J, Liu L et al (2020) Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 382(18):1708–1720

Kaku K, Kikuchi M, Ozono T, Shintani T (2021) Development of an extractive title generation system using titles of papers of top conferences for intermediate English students. arXiv preprint arXiv:2110.04204

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Arxiv title generation datasets (train set and testing set). Kaggle. https://www.kaggle.com/datasets/Cornell-University/arxiv

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Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India

M. Dhilsath Fathima

Department of Information Technology, Mohamed Sathak Engineering College, Kilakarai, Tamil Nadu, India

M. Seeni Syed Raviyathu Ammal

Department of Information Technology, Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India

Prashant Kumar Singh, Sachi Shome, Manbha Kharsyienlieh & R. Hariharan

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Dhilsath Fathima, M., Seeni Syed Raviyathu Ammal, M., Singh, P.K., Shome, S., Kharsyienlieh, M., Hariharan, R. (2023). Automatic Title Generation with Attention-Based LSTM. In: Murthy, B.K., Reddy, B.V.R., Hasteer, N., Van Belle, JP. (eds) Decision Intelligence. InCITe 2023. Lecture Notes in Electrical Engineering, vol 1079. Springer, Singapore. https://doi.org/10.1007/978-981-99-5997-6_20

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Genomic data in the All of Us Research Program

The all of us research program genomics investigators.

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  • Genetic variation
  • Genome-wide association studies

Comprehensively mapping the genetic basis of human disease across diverse individuals is a long-standing goal for the field of human genetics 1 , 2 , 3 , 4 . The All of Us Research Program is a longitudinal cohort study aiming to enrol a diverse group of at least one million individuals across the USA to accelerate biomedical research and improve human health 5 , 6 . Here we describe the programme’s genomics data release of 245,388 clinical-grade genome sequences. This resource is unique in its diversity as 77% of participants are from communities that are historically under-represented in biomedical research and 46% are individuals from under-represented racial and ethnic minorities. All of Us identified more than 1 billion genetic variants, including more than 275 million previously unreported genetic variants, more than 3.9 million of which had coding consequences. Leveraging linkage between genomic data and the longitudinal electronic health record, we evaluated 3,724 genetic variants associated with 117 diseases and found high replication rates across both participants of European ancestry and participants of African ancestry. Summary-level data are publicly available, and individual-level data can be accessed by researchers through the All of Us Researcher Workbench using a unique data passport model with a median time from initial researcher registration to data access of 29 hours. We anticipate that this diverse dataset will advance the promise of genomic medicine for all.

Comprehensively identifying genetic variation and cataloguing its contribution to health and disease, in conjunction with environmental and lifestyle factors, is a central goal of human health research 1 , 2 . A key limitation in efforts to build this catalogue has been the historic under-representation of large subsets of individuals in biomedical research including individuals from diverse ancestries, individuals with disabilities and individuals from disadvantaged backgrounds 3 , 4 . The All of Us Research Program (All of Us) aims to address this gap by enrolling and collecting comprehensive health data on at least one million individuals who reflect the diversity across the USA 5 , 6 . An essential component of All of Us is the generation of whole-genome sequence (WGS) and genotyping data on one million participants. All of Us is committed to making this dataset broadly useful—not only by democratizing access to this dataset across the scientific community but also to return value to the participants themselves by returning individual DNA results, such as genetic ancestry, hereditary disease risk and pharmacogenetics according to clinical standards, to those who wish to receive these research results.

Here we describe the release of WGS data from 245,388 All of Us participants and demonstrate the impact of this high-quality data in genetic and health studies. We carried out a series of data harmonization and quality control (QC) procedures and conducted analyses characterizing the properties of the dataset including genetic ancestry and relatedness. We validated the data by replicating well-established genotype–phenotype associations including low-density lipoprotein cholesterol (LDL-C) and 117 additional diseases. These data are available through the All of Us Researcher Workbench, a cloud platform that embodies and enables programme priorities, facilitating equitable data and compute access while ensuring responsible conduct of research and protecting participant privacy through a passport data access model.

The All of Us Research Program

To accelerate health research, All of Us is committed to curating and releasing research data early and often 6 . Less than five years after national enrolment began in 2018, this fifth data release includes data from more than 413,000 All of Us participants. Summary data are made available through a public Data Browser, and individual-level participant data are made available to researchers through the Researcher Workbench (Fig. 1a and Data availability).

figure 1

a , The All of Us Research Hub contains a publicly accessible Data Browser for exploration of summary phenotypic and genomic data. The Researcher Workbench is a secure cloud-based environment of participant-level data in a Controlled Tier that is widely accessible to researchers. b , All of Us participants have rich phenotype data from a combination of physical measurements, survey responses, EHRs, wearables and genomic data. Dots indicate the presence of the specific data type for the given number of participants. c , Overall summary of participants under-represented in biomedical research (UBR) with data available in the Controlled Tier. The All of Us logo in a is reproduced with permission of the National Institutes of Health’s All of Us Research Program.

Participant data include a rich combination of phenotypic and genomic data (Fig. 1b ). Participants are asked to complete consent for research use of data, sharing of electronic health records (EHRs), donation of biospecimens (blood or saliva, and urine), in-person provision of physical measurements (height, weight and blood pressure) and surveys initially covering demographics, lifestyle and overall health 7 . Participants are also consented for recontact. EHR data, harmonized using the Observational Medical Outcomes Partnership Common Data Model 8 ( Methods ), are available for more than 287,000 participants (69.42%) from more than 50 health care provider organizations. The EHR dataset is longitudinal, with a quarter of participants having 10 years of EHR data (Extended Data Fig. 1 ). Data include 245,388 WGSs and genome-wide genotyping on 312,925 participants. Sequenced and genotyped individuals in this data release were not prioritized on the basis of any clinical or phenotypic feature. Notably, 99% of participants with WGS data also have survey data and physical measurements, and 84% also have EHR data. In this data release, 77% of individuals with genomic data identify with groups historically under-represented in biomedical research, including 46% who self-identify with a racial or ethnic minority group (Fig. 1c , Supplementary Table 1 and Supplementary Note ).

Scaling the All of Us infrastructure

The genomic dataset generated from All of Us participants is a resource for research and discovery and serves as the basis for return of individual health-related DNA results to participants. Consequently, the US Food and Drug Administration determined that All of Us met the criteria for a significant risk device study. As such, the entire All of Us genomics effort from sample acquisition to sequencing meets clinical laboratory standards 9 .

All of Us participants were recruited through a national network of partners, starting in 2018, as previously described 5 . Participants may enrol through All of Us - funded health care provider organizations or direct volunteer pathways and all biospecimens, including blood and saliva, are sent to the central All of Us Biobank for processing and storage. Genomics data for this release were generated from blood-derived DNA. The programme began return of actionable genomic results in December 2022. As of April 2023, approximately 51,000 individuals were sent notifications asking whether they wanted to view their results, and approximately half have accepted. Return continues on an ongoing basis.

The All of Us Data and Research Center maintains all participant information and biospecimen ID linkage to ensure that participant confidentiality and coded identifiers (participant and aliquot level) are used to track each sample through the All of Us genomics workflow. This workflow facilitates weekly automated aliquot and plating requests to the Biobank, supplies relevant metadata for the sample shipments to the Genome Centers, and contains a feedback loop to inform action on samples that fail QC at any stage. Further, the consent status of each participant is checked before sample shipment to confirm that they are still active. Although all participants with genomic data are consented for the same general research use category, the programme accommodates different preferences for the return of genomic data to participants and only data for those individuals who have consented for return of individual health-related DNA results are distributed to the All of Us Clinical Validation Labs for further evaluation and health-related clinical reporting. All participants in All of Us that choose to get health-related DNA results have the option to schedule a genetic counselling appointment to discuss their results. Individuals with positive findings who choose to obtain results are required to schedule an appointment with a genetic counsellor to receive those findings.

Genome sequencing

To satisfy the requirements for clinical accuracy, precision and consistency across DNA sample extraction and sequencing, the All of Us Genome Centers and Biobank harmonized laboratory protocols, established standard QC methodologies and metrics, and conducted a series of validation experiments using previously characterized clinical samples and commercially available reference standards 9 . Briefly, PCR-free barcoded WGS libraries were constructed with the Illumina Kapa HyperPrep kit. Libraries were pooled and sequenced on the Illumina NovaSeq 6000 instrument. After demultiplexing, initial QC analysis is performed with the Illumina DRAGEN pipeline (Supplementary Table 2 ) leveraging lane, library, flow cell, barcode and sample level metrics as well as assessing contamination, mapping quality and concordance to genotyping array data independently processed from a different aliquot of DNA. The Genome Centers use these metrics to determine whether each sample meets programme specifications and then submits sequencing data to the Data and Research Center for further QC, joint calling and distribution to the research community ( Methods ).

This effort to harmonize sequencing methods, multi-level QC and use of identical data processing protocols mitigated the variability in sequencing location and protocols that often leads to batch effects in large genomic datasets 9 . As a result, the data are not only of clinical-grade quality, but also consistent in coverage (≥30× mean) and uniformity across Genome Centers (Supplementary Figs. 1 – 5 ).

Joint calling and variant discovery

We carried out joint calling across the entire All of Us WGS dataset (Extended Data Fig. 2 ). Joint calling leverages information across samples to prune artefact variants, which increases sensitivity, and enables flagging samples with potential issues that were missed during single-sample QC 10 (Supplementary Table 3 ). Scaling conventional approaches to whole-genome joint calling beyond 50,000 individuals is a notable computational challenge 11 , 12 . To address this, we developed a new cloud variant storage solution, the Genomic Variant Store (GVS), which is based on a schema designed for querying and rendering variants in which the variants are stored in GVS and rendered to an analysable variant file, as opposed to the variant file being the primary storage mechanism (Code availability). We carried out QC on the joint call set on the basis of the approach developed for gnomAD 3.1 (ref.  13 ). This included flagging samples with outlying values in eight metrics (Supplementary Table 4 , Supplementary Fig. 2 and Methods ).

To calculate the sensitivity and precision of the joint call dataset, we included four well-characterized samples. We sequenced the National Institute of Standards and Technology reference materials (DNA samples) from the Genome in a Bottle consortium 13 and carried out variant calling as described above. We used the corresponding published set of variant calls for each sample as the ground truth in our sensitivity and precision calculations 14 . The overall sensitivity for single-nucleotide variants was over 98.7% and precision was more than 99.9%. For short insertions or deletions, the sensitivity was over 97% and precision was more than 99.6% (Supplementary Table 5 and Methods ).

The joint call set included more than 1 billion genetic variants. We annotated the joint call dataset on the basis of functional annotation (for example, gene symbol and protein change) using Illumina Nirvana 15 . We defined coding variants as those inducing an amino acid change on a canonical ENSEMBL transcript and found 272,051,104 non-coding and 3,913,722 coding variants that have not been described previously in dbSNP 16 v153 (Extended Data Table 1 ). A total of 3,912,832 (99.98%) of the coding variants are rare (allelic frequency < 0.01) and the remaining 883 (0.02%) are common (allelic frequency > 0.01). Of the coding variants, 454 (0.01%) are common in one or more of the non-European computed ancestries in All of Us, rare among participants of European ancestry, and have an allelic number greater than 1,000 (Extended Data Table 2 and Extended Data Fig. 3 ). The distributions of pathogenic, or likely pathogenic, ClinVar variant counts per participant, stratified by computed ancestry, filtered to only those variants that are found in individuals with an allele count of <40 are shown in Extended Data Fig. 4 . The potential medical implications of these known and new variants with respect to variant pathogenicity by ancestry are highlighted in a companion paper 17 . In particular, we find that the European ancestry subset has the highest rate of pathogenic variation (2.1%), which was twice the rate of pathogenic variation in individuals of East Asian ancestry 17 .The lower frequency of variants in East Asian individuals may be partially explained by the fact the sample size in that group is small and there may be knowledge bias in the variant databases that is reducing the number of findings in some of the less-studied ancestry groups.

Genetic ancestry and relatedness

Genetic ancestry inference confirmed that 51.1% of the All of Us WGS dataset is derived from individuals of non-European ancestry. Briefly, the ancestry categories are based on the same labels used in gnomAD 18 . We trained a classifier on a 16-dimensional principal component analysis (PCA) space of a diverse reference based on 3,202 samples and 151,159 autosomal single-nucleotide polymorphisms. We projected the All of Us samples into the PCA space of the training data, based on the same single-nucleotide polymorphisms from the WGS data, and generated categorical ancestry predictions from the trained classifier ( Methods ). Continuous genetic ancestry fractions for All of Us samples were inferred using the same PCA data, and participants’ patterns of ancestry and admixture were compared to their self-identified race and ethnicity (Fig. 2 and Methods ). Continuous ancestry inference carried out using genome-wide genotypes yields highly concordant estimates.

figure 2

a , b , Uniform manifold approximation and projection (UMAP) representations of All of Us WGS PCA data with self-described race ( a ) and ethnicity ( b ) labels. c , Proportion of genetic ancestry per individual in six distinct and coherent ancestry groups defined by Human Genome Diversity Project and 1000 Genomes samples.

Kinship estimation confirmed that All of Us WGS data consist largely of unrelated individuals with about 85% (215,107) having no first- or second-degree relatives in the dataset (Supplementary Fig. 6 ). As many genomic analyses leverage unrelated individuals, we identified the smallest set of samples that are required to be removed from the remaining individuals that had first- or second-degree relatives and retained one individual from each kindred. This procedure yielded a maximal independent set of 231,442 individuals (about 94%) with genome sequence data in the current release ( Methods ).

Genetic determinants of LDL-C

As a measure of data quality and utility, we carried out a single-variant genome-wide association study (GWAS) for LDL-C, a trait with well-established genomic architecture ( Methods ). Of the 245,388 WGS participants, 91,749 had one or more LDL-C measurements. The All of Us LDL-C GWAS identified 20 well-established genome-wide significant loci, with minimal genomic inflation (Fig. 3 , Extended Data Table 3 and Supplementary Fig. 7 ). We compared the results to those of a recent multi-ethnic LDL-C GWAS in the National Heart, Lung, and Blood Institute (NHLBI) TOPMed study that included 66,329 ancestrally diverse (56% non-European ancestry) individuals 19 . We found a strong correlation between the effect estimates for NHLBI TOPMed genome-wide significant loci and those of All of Us ( R 2  = 0.98, P  < 1.61 × 10 −45 ; Fig. 3 , inset). Notably, the per-locus effect sizes observed in All of Us are decreased compared to those in TOPMed, which is in part due to differences in the underlying statistical model, differences in the ancestral composition of these datasets and differences in laboratory value ascertainment between EHR-derived data and epidemiology studies. A companion manuscript extended this work to identify common and rare genetic associations for three diseases (atrial fibrillation, coronary artery disease and type 2 diabetes) and two quantitative traits (height and LDL-C) in the All of Us dataset and identified very high concordance with previous efforts across all of these diseases and traits 20 .

figure 3

Manhattan plot demonstrating robust replication of 20 well-established LDL-C genetic loci among 91,749 individuals with 1 or more LDL-C measurements. The red horizontal line denotes the genome wide significance threshold of P = 5 × 10 –8 . Inset, effect estimate ( β ) comparison between NHLBI TOPMed LDL-C GWAS ( x  axis) and All of Us LDL-C GWAS ( y  axis) for the subset of 194 independent variants clumped (window 250 kb, r2 0.5) that reached genome-wide significance in NHLBI TOPMed.

Genotype-by-phenotype associations

As another measure of data quality and utility, we tested replication rates of previously reported phenotype–genotype associations in the five predicted genetic ancestry populations present in the Phenotype/Genotype Reference Map (PGRM): AFR, African ancestry; AMR, Latino/admixed American ancestry; EAS, East Asian ancestry; EUR, European ancestry; SAS, South Asian ancestry. The PGRM contains published associations in the GWAS catalogue in these ancestry populations that map to International Classification of Diseases-based phenotype codes 21 . This replication study specifically looked across 4,947 variants, calculating replication rates for powered associations in each ancestry population. The overall replication rates for associations powered at 80% were: 72.0% (18/25) in AFR, 100% (13/13) in AMR, 46.6% (7/15) in EAS, 74.9% (1,064/1,421) in EUR, and 100% (1/1) in SAS. With the exception of the EAS ancestry results, these powered replication rates are comparable to those of the published PGRM analysis where the replication rates of several single-site EHR-linked biobanks ranges from 76% to 85%. These results demonstrate the utility of the data and also highlight opportunities for further work understanding the specifics of the All of Us population and the potential contribution of gene–environment interactions to genotype–phenotype mapping and motivates the development of methods for multi-site EHR phenotype data extraction, harmonization and genetic association studies.

More broadly, the All of Us resource highlights the opportunities to identify genotype–phenotype associations that differ across diverse populations 22 . For example, the Duffy blood group locus ( ACKR1 ) is more prevalent in individuals of AFR ancestry and individuals of AMR ancestry than in individuals of EUR ancestry. Although the phenome-wide association study of this locus highlights the well-established association of the Duffy blood group with lower white blood cell counts both in individuals of AFR and AMR ancestry 23 , 24 , it also revealed genetic-ancestry-specific phenotype patterns, with minimal phenotypic associations in individuals of EAS ancestry and individuals of EUR ancestry (Fig. 4 and Extended Data Table 4 ). Conversely, rs9273363 in the HLA-DQB1 locus is associated with increased risk of type 1 diabetes 25 , 26 and diabetic complications across ancestries, but only associates with increased risk of coeliac disease in individuals of EUR ancestry (Extended Data Fig. 5 ). Similarly, the TCF7L2 locus 27 strongly associates with increased risk of type 2 diabetes and associated complications across several ancestries (Extended Data Fig. 6 ). Association testing results are available in Supplementary Dataset 1 .

figure 4

Results of genetic-ancestry-stratified phenome-wide association analysis among unrelated individuals highlighting ancestry-specific disease associations across the four most common genetic ancestries of participant. Bonferroni-adjusted phenome-wide significance threshold (<2.88 × 10 −5 ) is plotted as a red horizontal line. AFR ( n  = 34,037, minor allele fraction (MAF) 0.82); AMR ( n  = 28,901, MAF 0.10); EAS ( n  = 32,55, MAF 0.003); EUR ( n  = 101,613, MAF 0.007).

The cloud-based Researcher Workbench

All of Us genomic data are available in a secure, access-controlled cloud-based analysis environment: the All of Us Researcher Workbench. Unlike traditional data access models that require per-project approval, access in the Researcher Workbench is governed by a data passport model based on a researcher’s authenticated identity, institutional affiliation, and completion of self-service training and compliance attestation 28 . After gaining access, a researcher may create a new workspace at any time to conduct a study, provided that they comply with all Data Use Policies and self-declare their research purpose. This information is regularly audited and made accessible publicly on the All of Us Research Projects Directory. This streamlined access model is guided by the principles that: participants are research partners and maintaining their privacy and data security is paramount; their data should be made as accessible as possible for authorized researchers; and we should continually seek to remove unnecessary barriers to accessing and using All of Us data.

For researchers at institutions with an existing institutional data use agreement, access can be gained as soon as they complete the required verification and compliance steps. As of August 2023, 556 institutions have agreements in place, allowing more than 5,000 approved researchers to actively work on more than 4,400 projects. The median time for a researcher from initial registration to completion of these requirements is 28.6 h (10th percentile: 48 min, 90th percentile: 14.9 days), a fraction of the weeks to months it can take to assemble a project-specific application and have it reviewed by an access board with conventional access models.

Given that the size of the project’s phenotypic and genomic dataset is expected to reach 4.75 PB in 2023, the use of a central data store and cloud analysis tools will save funders an estimated US$16.5 million per year when compared to the typical approach of allowing researchers to download genomic data. Storing one copy per institution of this data at 556 registered institutions would cost about US$1.16 billion per year. By contrast, storing a central cloud copy costs about US$1.14 million per year, a 99.9% saving. Importantly, cloud infrastructure also democratizes data access particularly for researchers who do not have high-performance local compute resources.

Here we present the All of Us Research Program’s approach to generating diverse clinical-grade genomic data at an unprecedented scale. We present the data release of about 245,000 genome sequences as part of a scalable framework that will grow to include genetic information and health data for one million or more people living across the USA. Our observations permit several conclusions.

First, the All of Us programme is making a notable contribution to improving the study of human biology through purposeful inclusion of under-represented individuals at scale 29 , 30 . Of the participants with genomic data in All of Us, 45.92% self-identified as a non-European race or ethnicity. This diversity enabled identification of more than 275 million new genetic variants across the dataset not previously captured by other large-scale genome aggregation efforts with diverse participants that have submitted variation to dbSNP v153, such as NHLBI TOPMed 31 freeze 8 (Extended Data Table 1 ). In contrast to gnomAD, All of Us permits individual-level genotype access with detailed phenotype data for all participants. Furthermore, unlike many genomics resources, All of Us is uniformly consented for general research use and enables researchers to go from initial account creation to individual-level data access in as little as a few hours. The All of Us cohort is significantly more diverse than those of other large contemporary research studies generating WGS data 32 , 33 . This enables a more equitable future for precision medicine (for example, through constructing polygenic risk scores that are appropriately calibrated to diverse populations 34 , 35 as the eMERGE programme has done leveraging All of Us data 36 , 37 ). Developing new tools and regulatory frameworks to enable analyses across multiple biobanks in the cloud to harness the unique strengths of each is an active area of investigation addressed in a companion paper to this work 38 .

Second, the All of Us Researcher Workbench embodies the programme’s design philosophy of open science, reproducible research, equitable access and transparency to researchers and to research participants 26 . Importantly, for research studies, no group of data users should have privileged access to All of Us resources based on anything other than data protection criteria. Although the All of Us Researcher Workbench initially targeted onboarding US academic, health care and non-profit organizations, it has recently expanded to international researchers. We anticipate further genomic and phenotypic data releases at regular intervals with data available to all researcher communities. We also anticipate additional derived data and functionality to be made available, such as reference data, structural variants and a service for array imputation using the All of Us genomic data.

Third, All of Us enables studying human biology at an unprecedented scale. The programmatic goal of sequencing one million or more genomes has required harnessing the output of multiple sequencing centres. Previous work has focused on achieving functional equivalence in data processing and joint calling pipelines 39 . To achieve clinical-grade data equivalence, All of Us required protocol equivalence at both sequencing production level and data processing across the sequencing centres. Furthermore, previous work has demonstrated the value of joint calling at scale 10 , 18 . The new GVS framework developed by the All of Us programme enables joint calling at extreme scales (Code availability). Finally, the provision of data access through cloud-native tools enables scalable and secure access and analysis to researchers while simultaneously enabling the trust of research participants and transparency underlying the All of Us data passport access model.

The clinical-grade sequencing carried out by All of Us enables not only research, but also the return of value to participants through clinically relevant genetic results and health-related traits to those who opt-in to receiving this information. In the years ahead, we anticipate that this partnership with All of Us participants will enable researchers to move beyond large-scale genomic discovery to understanding the consequences of implementing genomic medicine at scale.

The All of Us cohort

All of Us aims to engage a longitudinal cohort of one million or more US participants, with a focus on including populations that have historically been under-represented in biomedical research. Details of the All of Us cohort have been described previously 5 . Briefly, the primary objective is to build a robust research resource that can facilitate the exploration of biological, clinical, social and environmental determinants of health and disease. The programme will collect and curate health-related data and biospecimens, and these data and biospecimens will be made broadly available for research uses. Health data are obtained through the electronic medical record and through participant surveys. Survey templates can be found on our public website: https://www.researchallofus.org/data-tools/survey-explorer/ . Adults 18 years and older who have the capacity to consent and reside in the USA or a US territory at present are eligible. Informed consent for all participants is conducted in person or through an eConsent platform that includes primary consent, HIPAA Authorization for Research use of EHRs and other external health data, and Consent for Return of Genomic Results. The protocol was reviewed by the Institutional Review Board (IRB) of the All of Us Research Program. The All of Us IRB follows the regulations and guidance of the NIH Office for Human Research Protections for all studies, ensuring that the rights and welfare of research participants are overseen and protected uniformly.

Data accessibility through a ‘data passport’

Authorization for access to participant-level data in All of Us is based on a ‘data passport’ model, through which authorized researchers do not need IRB review for each research project. The data passport is required for gaining data access to the Researcher Workbench and for creating workspaces to carry out research projects using All of Us data. At present, data passports are authorized through a six-step process that includes affiliation with an institution that has signed a Data Use and Registration Agreement, account creation, identity verification, completion of ethics training, and attestation to a data user code of conduct. Results reported follow the All of Us Data and Statistics Dissemination Policy disallowing disclosure of group counts under 20 to protect participant privacy without seeking prior approval 40 .

At present, All of Us gathers EHR data from about 50 health care organizations that are funded to recruit and enrol participants as well as transfer EHR data for those participants who have consented to provide them. Data stewards at each provider organization harmonize their local data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model, and then submit it to the All of Us Data and Research Center (DRC) so that it can be linked with other participant data and further curated for research use. OMOP is a common data model standardizing health information from disparate EHRs to common vocabularies and organized into tables according to data domains. EHR data are updated from the recruitment sites and sent to the DRC quarterly. Updated data releases to the research community occur approximately once a year. Supplementary Table 6 outlines the OMOP concepts collected by the DRC quarterly from the recruitment sites.

Biospecimen collection and processing

Participants who consented to participate in All of Us donated fresh whole blood (4 ml EDTA and 10 ml EDTA) as a primary source of DNA. The All of Us Biobank managed by the Mayo Clinic extracted DNA from 4 ml EDTA whole blood, and DNA was stored at −80 °C at an average concentration of 150 ng µl −1 . The buffy coat isolated from 10 ml EDTA whole blood has been used for extracting DNA in the case of initial extraction failure or absence of 4 ml EDTA whole blood. The Biobank plated 2.4 µg DNA with a concentration of 60 ng µl −1 in duplicate for array and WGS samples. The samples are distributed to All of Us Genome Centers weekly, and a negative (empty well) control and National Institute of Standards and Technology controls are incorporated every two months for QC purposes.

Genome Center sample receipt, accession and QC

On receipt of DNA sample shipments, the All of Us Genome Centers carry out an inspection of the packaging and sample containers to ensure that sample integrity has not been compromised during transport and to verify that the sample containers correspond to the shipping manifest. QC of the submitted samples also includes DNA quantification, using routine procedures to confirm volume and concentration (Supplementary Table 7 ). Any issues or discrepancies are recorded, and affected samples are put on hold until resolved. Samples that meet quality thresholds are accessioned in the Laboratory Information Management System, and sample aliquots are prepared for library construction processing (for example, normalized with respect to concentration and volume).

WGS library construction, sequencing and primary data QC

The DNA sample is first sheared using a Covaris sonicator and is then size-selected using AMPure XP beads to restrict the range of library insert sizes. Using the PCR Free Kapa HyperPrep library construction kit, enzymatic steps are completed to repair the jagged ends of DNA fragments, add proper A-base segments, and ligate indexed adapter barcode sequences onto samples. Excess adaptors are removed using AMPure XP beads for a final clean-up. Libraries are quantified using quantitative PCR with the Illumina Kapa DNA Quantification Kit and then normalized and pooled for sequencing (Supplementary Table 7 ).

Pooled libraries are loaded on the Illumina NovaSeq 6000 instrument. The data from the initial sequencing run are used to QC individual libraries and to remove non-conforming samples from the pipeline. The data are also used to calibrate the pooling volume of each individual library and re-pool the libraries for additional NovaSeq sequencing to reach an average coverage of 30×.

After demultiplexing, WGS analysis occurs on the Illumina DRAGEN platform. The DRAGEN pipeline consists of highly optimized algorithms for mapping, aligning, sorting, duplicate marking and haplotype variant calling and makes use of platform features such as compression and BCL conversion. Alignment uses the GRCh38dh reference genome. QC data are collected at every stage of the analysis protocol, providing high-resolution metrics required to ensure data consistency for large-scale multiplexing. The DRAGEN pipeline produces a large number of metrics that cover lane, library, flow cell, barcode and sample-level metrics for all runs as well as assessing contamination and mapping quality. The All of Us Genome Centers use these metrics to determine pass or fail for each sample before submitting the CRAM files to the All of Us DRC. For mapping and variant calling, all Genome Centers have harmonized on a set of DRAGEN parameters, which ensures consistency in processing (Supplementary Table 2 ).

Every step through the WGS procedure is rigorously controlled by predefined QC measures. Various control mechanisms and acceptance criteria were established during WGS assay validation. Specific metrics for reviewing and releasing genome data are: mean coverage (threshold of ≥30×), genome coverage (threshold of ≥90% at 20×), coverage of hereditary disease risk genes (threshold of ≥95% at 20×), aligned Q30 bases (threshold of ≥8 × 10 10 ), contamination (threshold of ≤1%) and concordance to independently processed array data.

Array genotyping

Samples are processed for genotyping at three All of Us Genome Centers (Broad, Johns Hopkins University and University of Washington). DNA samples are received from the Biobank and the process is facilitated by the All of Us genomics workflow described above. All three centres used an identical array product, scanners, resource files and genotype calling software for array processing to reduce batch effects. Each centre has its own Laboratory Information Management System that manages workflow control, sample and reagent tracking, and centre-specific liquid handling robotics.

Samples are processed using the Illumina Global Diversity Array (GDA) with Illumina Infinium LCG chemistry using the automated protocol and scanned on Illumina iSCANs with Automated Array Loaders. Illumina IAAP software converts raw data (IDAT files; 2 per sample) into a single GTC file per sample using the BPM file (defines strand, probe sequences and illumicode address) and the EGT file (defines the relationship between intensities and genotype calls). Files used for this data release are: GDA-8v1-0_A5.bpm, GDA-8v1-0_A1_ClusterFile.egt, gentrain v3, reference hg19 and gencall cutoff 0.15. The GDA array assays a total of 1,914,935 variant positions including 1,790,654 single-nucleotide variants, 44,172 indels, 9,935 intensity-only probes for CNV calling, and 70,174 duplicates (same position, different probes). Picard GtcToVcf is used to convert the GTC files to VCF format. Resulting VCF and IDAT files are submitted to the DRC for ingestion and further processing. The VCF file contains assay name, chromosome, position, genotype calls, quality score, raw and normalized intensities, B allele frequency and log R ratio values. Each genome centre is running the GDA array under Clinical Laboratory Improvement Amendments-compliant protocols. The GTC files are parsed and metrics are uploaded to in-house Laboratory Information Management System systems for QC review.

At batch level (each set of 96-well plates run together in the laboratory at one time), each genome centre includes positive control samples that are required to have >98% call rate and >99% concordance to existing data to approve release of the batch of data. At the sample level, the call rate and sex are the key QC determinants 41 . Contamination is also measured using BAFRegress 42 and reported out as metadata. Any sample with a call rate below 98% is repeated one time in the laboratory. Genotyped sex is determined by plotting normalized x versus normalized y intensity values for a batch of samples. Any sample discordant with ‘sex at birth’ reported by the All of Us participant is flagged for further detailed review and repeated one time in the laboratory. If several sex-discordant samples are clustered on an array or on a 96-well plate, the entire array or plate will have data production repeated. Samples identified with sex chromosome aneuploidies are also reported back as metadata (XXX, XXY, XYY and so on). A final processing status of ‘pass’, ‘fail’ or ‘abandon’ is determined before release of data to the All of Us DRC. An array sample will pass if the call rate is >98% and the genotyped sex and sex at birth are concordant (or the sex at birth is not applicable). An array sample will fail if the genotyped sex and the sex at birth are discordant. An array sample will have the status of abandon if the call rate is <98% after at least two attempts at the genome centre.

Data from the arrays are used for participant return of genetic ancestry and non-health-related traits for those who consent, and they are also used to facilitate additional QC of the matched WGS data. Contamination is assessed in the array data to determine whether DNA re-extraction is required before WGS. Re-extraction is prompted by level of contamination combined with consent status for return of results. The arrays are also used to confirm sample identity between the WGS data and the matched array data by assessing concordance at 100 unique sites. To establish concordance, a fingerprint file of these 100 sites is provided to the Genome Centers to assess concordance with the same sites in the WGS data before CRAM submission.

Genomic data curation

As seen in Extended Data Fig. 2 , we generate a joint call set for all WGS samples and make these data available in their entirety and by sample subsets to researchers. A breakdown of the frequencies, stratified by computed ancestries for which we had more than 10,000 participants can be found in Extended Data Fig. 3 . The joint call set process allows us to leverage information across samples to improve QC and increase accuracy.

Single-sample QC

If a sample fails single-sample QC, it is excluded from the release and is not reported in this document. These tests detect sample swaps, cross-individual contamination and sample preparation errors. In some cases, we carry out these tests twice (at both the Genome Center and the DRC), for two reasons: to confirm internal consistency between sites; and to mark samples as passing (or failing) QC on the basis of the research pipeline criteria. The single-sample QC process accepts a higher contamination rate than the clinical pipeline (0.03 for the research pipeline versus 0.01 for the clinical pipeline), but otherwise uses identical thresholds. The list of specific QC processes, passing criteria, error modes addressed and an overview of the results can be found in Supplementary Table 3 .

Joint call set QC

During joint calling, we carry out additional QC steps using information that is available across samples including hard thresholds, population outliers, allele-specific filters, and sensitivity and precision evaluation. Supplementary Table 4 summarizes both the steps that we took and the results obtained for the WGS data. More detailed information about the methods and specific parameters can be found in the All of Us Genomic Research Data Quality Report 36 .

Batch effect analysis

We analysed cross-sequencing centre batch effects in the joint call set. To quantify the batch effect, we calculated Cohen’s d (ref.  43 ) for four metrics (insertion/deletion ratio, single-nucleotide polymorphism count, indel count and single-nucleotide polymorphism transition/transversion ratio) across the three genome sequencing centres (Baylor College of Medicine, Broad Institute and University of Washington), stratified by computed ancestry and seven regions of the genome (whole genome, high-confidence calling, repetitive, GC content of >0.85, GC content of <0.15, low mappability, the ACMG59 genes and regions of large duplications (>1 kb)). Using random batches as a control set, all comparisons had a Cohen’s d of <0.35. Here we report any Cohen’s d results >0.5, which we chose before this analysis and is conventionally the threshold of a medium effect size 44 .

We found that there was an effect size in indel counts (Cohen’s d of 0.53) in the entire genome, between Broad Institute and University of Washington, but this was being driven by repetitive and low-mappability regions. We found no batch effects with Cohen’s d of >0.5 in the ratio metrics or in any metrics in the high-confidence calling, low or high GC content, or ACMG59 regions. A complete list of the batch effects with Cohen’s d of >0.5 are found in Supplementary Table 8 .

Sensitivity and precision evaluation

To determine sensitivity and precision, we included four well-characterized control samples (four National Institute of Standards and Technology Genome in a Bottle samples (HG-001, HG-003, HG-004 and HG-005). The samples were sequenced with the same protocol as All of Us. Of note, these samples were not included in data released to researchers. We used the corresponding published set of variant calls for each sample as the ground truth in our sensitivity and precision calculations. We use the high-confidence calling region, defined by Genome in a Bottle v4.2.1, as the source of ground truth. To be called a true positive, a variant must match the chromosome, position, reference allele, alternate allele and zygosity. In cases of sites with multiple alternative alleles, each alternative allele is considered separately. Sensitivity and precision results are reported in Supplementary Table 5 .

Genetic ancestry inference

We computed categorical ancestry for all WGS samples in All of Us and made these available to researchers. These predictions are also the basis for population allele frequency calculations in the Genomic Variants section of the public Data Browser. We used the high-quality set of sites to determine an ancestry label for each sample. The ancestry categories are based on the same labels used in gnomAD 18 , the Human Genome Diversity Project (HGDP) 45 and 1000 Genomes 1 : African (AFR); Latino/admixed American (AMR); East Asian (EAS); Middle Eastern (MID); European (EUR), composed of Finnish (FIN) and Non-Finnish European (NFE); Other (OTH), not belonging to one of the other ancestries or is an admixture; South Asian (SAS).

We trained a random forest classifier 46 on a training set of the HGDP and 1000 Genomes samples variants on the autosome, obtained from gnomAD 11 . We generated the first 16 principal components (PCs) of the training sample genotypes (using the hwe_normalized_pca in Hail) at the high-quality variant sites for use as the feature vector for each training sample. We used the truth labels from the sample metadata, which can be found alongside the VCFs. Note that we do not train the classifier on the samples labelled as Other. We use the label probabilities (‘confidence’) of the classifier on the other ancestries to determine ancestry of Other.

To determine the ancestry of All of Us samples, we project the All of Us samples into the PCA space of the training data and apply the classifier. As a proxy for the accuracy of our All of Us predictions, we look at the concordance between the survey results and the predicted ancestry. The concordance between self-reported ethnicity and the ancestry predictions was 87.7%.

PC data from All of Us samples and the HGDP and 1000 Genomes samples were used to compute individual participant genetic ancestry fractions for All of Us samples using the Rye program. Rye uses PC data to carry out rapid and accurate genetic ancestry inference on biobank-scale datasets 47 . HGDP and 1000 Genomes reference samples were used to define a set of six distinct and coherent ancestry groups—African, East Asian, European, Middle Eastern, Latino/admixed American and South Asian—corresponding to participant self-identified race and ethnicity groups. Rye was run on the first 16 PCs, using the defined reference ancestry groups to assign ancestry group fractions to individual All of Us participant samples.

Relatedness

We calculated the kinship score using the Hail pc_relate function and reported any pairs with a kinship score above 0.1. The kinship score is half of the fraction of the genetic material shared (ranges from 0.0 to 0.5). We determined the maximal independent set 41 for related samples. We identified a maximally unrelated set of 231,442 samples (94%) for kinship scored greater than 0.1.

LDL-C common variant GWAS

The phenotypic data were extracted from the Curated Data Repository (CDR, Control Tier Dataset v7) in the All of Us Researcher Workbench. The All of Us Cohort Builder and Dataset Builder were used to extract all LDL cholesterol measurements from the Lab and Measurements criteria in EHR data for all participants who have WGS data. The most recent measurements were selected as the phenotype and adjusted for statin use 19 , age and sex. A rank-based inverse normal transformation was applied for this continuous trait to increase power and deflate type I error. Analysis was carried out on the Hail MatrixTable representation of the All of Us WGS joint-called data including removing monomorphic variants, variants with a call rate of <95% and variants with extreme Hardy–Weinberg equilibrium values ( P  < 10 −15 ). A linear regression was carried out with REGENIE 48 on variants with a minor allele frequency >5%, further adjusting for relatedness to the first five ancestry PCs. The final analysis included 34,924 participants and 8,589,520 variants.

Genotype-by-phenotype replication

We tested replication rates of known phenotype–genotype associations in three of the four largest populations: EUR, AFR and EAS. The AMR population was not included because they have no registered GWAS. This method is a conceptual extension of the original GWAS × phenome-wide association study, which replicated 66% of powered associations in a single EHR-linked biobank 49 . The PGRM is an expansion of this work by Bastarache et al., based on associations in the GWAS catalogue 50 in June 2020 (ref.  51 ). After directly matching the Experimental Factor Ontology terms to phecodes, the authors identified 8,085 unique loci and 170 unique phecodes that compose the PGRM. They showed replication rates in several EHR-linked biobanks ranging from 76% to 85%. For this analysis, we used the EUR-, and AFR-based maps, considering only catalogue associations that were P  < 5 × 10 −8 significant.

The main tools used were the Python package Hail for data extraction, plink for genomic associations, and the R packages PheWAS and pgrm for further analysis and visualization. The phenotypes, participant-reported sex at birth, and year of birth were extracted from the All of Us CDR (Controlled Tier Dataset v7). These phenotypes were then loaded into a plink-compatible format using the PheWAS package, and related samples were removed by sub-setting to the maximally unrelated dataset ( n  = 231,442). Only samples with EHR data were kept, filtered by selected loci, annotated with demographic and phenotypic information extracted from the CDR and ancestry prediction information provided by All of Us, ultimately resulting in 181,345 participants for downstream analysis. The variants in the PGRM were filtered by a minimum population-specific allele frequency of >1% or population-specific allele count of >100, leaving 4,986 variants. Results for which there were at least 20 cases in the ancestry group were included. Then, a series of Firth logistic regression tests with phecodes as the outcome and variants as the predictor were carried out, adjusting for age, sex (for non-sex-specific phenotypes) and the first three genomic PC features as covariates. The PGRM was annotated with power calculations based on the case counts and reported allele frequencies. Power of 80% or greater was considered powered for this analysis.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The All of Us Research Hub has a tiered data access data passport model with three data access tiers. The Public Tier dataset contains only aggregate data with identifiers removed. These data are available to the public through Data Snapshots ( https://www.researchallofus.org/data-tools/data-snapshots/ ) and the public Data Browser ( https://databrowser.researchallofus.org/ ). The Registered Tier curated dataset contains individual-level data, available only to approved researchers on the Researcher Workbench. At present, the Registered Tier includes data from EHRs, wearables and surveys, as well as physical measurements taken at the time of participant enrolment. The Controlled Tier dataset contains all data in the Registered Tier and additionally genomic data in the form of WGS and genotyping arrays, previously suppressed demographic data fields from EHRs and surveys, and unshifted dates of events. At present, Registered Tier and Controlled Tier data are available to researchers at academic institutions, non-profit institutions, and both non-profit and for-profit health care institutions. Work is underway to begin extending access to additional audiences, including industry-affiliated researchers. Researchers have the option to register for Registered Tier and/or Controlled Tier access by completing the All of Us Researcher Workbench access process, which includes identity verification and All of Us-specific training in research involving human participants ( https://www.researchallofus.org/register/ ). Researchers may create a new workspace at any time to conduct any research study, provided that they comply with all Data Use Policies and self-declare their research purpose. This information is made accessible publicly on the All of Us Research Projects Directory at https://allofus.nih.gov/protecting-data-and-privacy/research-projects-all-us-data .

Code availability

The GVS code is available at https://github.com/broadinstitute/gatk/tree/ah_var_store/scripts/variantstore . The LDL GWAS pipeline is available as a demonstration project in the Featured Workspace Library on the Researcher Workbench ( https://workbench.researchallofus.org/workspaces/aou-rw-5981f9dc/aouldlgwasregeniedsubctv6duplicate/notebooks ).

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Acknowledgements

The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers (OT2 OD026549; OT2 OD026554; OT2 OD026557; OT2 OD026556; OT2 OD026550; OT2 OD 026552; OT2 OD026553; OT2 OD026548; OT2 OD026551; OT2 OD026555); Inter agency agreement AOD 16037; Federally Qualified Health Centers HHSN 263201600085U; Data and Research Center: U2C OD023196; Genome Centers (OT2 OD002748; OT2 OD002750; OT2 OD002751); Biobank: U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: U24 OD023163; Communications and Engagement: OT2 OD023205; OT2 OD023206; and Community Partners (OT2 OD025277; OT2 OD025315; OT2 OD025337; OT2 OD025276). In addition, the All of Us Research Program would not be possible without the partnership of its participants. All of Us and the All of Us logo are service marks of the US Department of Health and Human Services. E.E.E. is an investigator of the Howard Hughes Medical Institute. We acknowledge the foundational contributions of our friend and colleague, the late Deborah A. Nickerson. Debbie’s years of insightful contributions throughout the formation of the All of Us genomics programme are permanently imprinted, and she shares credit for all of the successes of this programme.

Author information

Authors and affiliations.

Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

Alexander G. Bick & Henry R. Condon

Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

Ginger A. Metcalf, Eric Boerwinkle, Richard A. Gibbs, Donna M. Muzny, Eric Venner, Kimberly Walker, Jianhong Hu, Harsha Doddapaneni, Christie L. Kovar, Mullai Murugan, Shannon Dugan, Ziad Khan & Eric Boerwinkle

Vanderbilt Institute of Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA

Kelsey R. Mayo, Jodell E. Linder, Melissa Basford, Ashley Able, Ashley E. Green, Robert J. Carroll, Jennifer Zhang & Yuanyuan Wang

Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Lee Lichtenstein, Anthony Philippakis, Sophie Schwartz, M. Morgan T. Aster, Kristian Cibulskis, Andrea Haessly, Rebecca Asch, Aurora Cremer, Kylee Degatano, Akum Shergill, Laura D. Gauthier, Samuel K. Lee, Aaron Hatcher, George B. Grant, Genevieve R. Brandt, Miguel Covarrubias, Eric Banks & Wail Baalawi

Verily, South San Francisco, CA, USA

Shimon Rura, David Glazer, Moira K. Dillon & C. H. Albach

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

Robert J. Carroll, Paul A. Harris & Dan M. Roden

All of Us Research Program, National Institutes of Health, Bethesda, MD, USA

Anjene Musick, Andrea H. Ramirez, Sokny Lim, Siddhartha Nambiar, Bradley Ozenberger, Anastasia L. Wise, Chris Lunt, Geoffrey S. Ginsburg & Joshua C. Denny

School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA

I. King Jordan, Shashwat Deepali Nagar & Shivam Sharma

Neuroscience Institute, Institute of Translational Genomic Medicine, Morehouse School of Medicine, Atlanta, GA, USA

Robert Meller

Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA

Mine S. Cicek, Stephen N. Thibodeau & Mine S. Cicek

Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Kimberly F. Doheny, Michelle Z. Mawhinney, Sean M. L. Griffith, Elvin Hsu, Hua Ling & Marcia K. Adams

Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA

Evan E. Eichler, Joshua D. Smith, Christian D. Frazar, Colleen P. Davis, Karynne E. Patterson, Marsha M. Wheeler, Sean McGee, Mitzi L. Murray, Valeria Vasta, Dru Leistritz, Matthew A. Richardson, Aparna Radhakrishnan & Brenna W. Ehmen

Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA

Evan E. Eichler

Broad Institute of MIT and Harvard, Cambridge, MA, USA

Stacey Gabriel, Heidi L. Rehm, Niall J. Lennon, Christina Austin-Tse, Eric Banks, Michael Gatzen, Namrata Gupta, Katie Larsson, Sheli McDonough, Steven M. Harrison, Christopher Kachulis, Matthew S. Lebo, Seung Hoan Choi & Xin Wang

Division of Medical Genetics, Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA

Gail P. Jarvik & Elisabeth A. Rosenthal

Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

Dan M. Roden

Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA

Center for Individualized Medicine, Biorepository Program, Mayo Clinic, Rochester, MN, USA

Stephen N. Thibodeau, Ashley L. Blegen, Samantha J. Wirkus, Victoria A. Wagner, Jeffrey G. Meyer & Mine S. Cicek

Color Health, Burlingame, CA, USA

Scott Topper, Cynthia L. Neben, Marcie Steeves & Alicia Y. Zhou

School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA

Eric Boerwinkle

Laboratory for Molecular Medicine, Massachusetts General Brigham Personalized Medicine, Cambridge, MA, USA

Christina Austin-Tse, Emma Henricks & Matthew S. Lebo

Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA

Christina M. Lockwood, Brian H. Shirts, Colin C. Pritchard, Jillian G. Buchan & Niklas Krumm

Manuscript Writing Group

  • Alexander G. Bick
  • , Ginger A. Metcalf
  • , Kelsey R. Mayo
  • , Lee Lichtenstein
  • , Shimon Rura
  • , Robert J. Carroll
  • , Anjene Musick
  • , Jodell E. Linder
  • , I. King Jordan
  • , Shashwat Deepali Nagar
  • , Shivam Sharma
  •  & Robert Meller

All of Us Research Program Genomics Principal Investigators

  • Melissa Basford
  • , Eric Boerwinkle
  • , Mine S. Cicek
  • , Kimberly F. Doheny
  • , Evan E. Eichler
  • , Stacey Gabriel
  • , Richard A. Gibbs
  • , David Glazer
  • , Paul A. Harris
  • , Gail P. Jarvik
  • , Anthony Philippakis
  • , Heidi L. Rehm
  • , Dan M. Roden
  • , Stephen N. Thibodeau
  •  & Scott Topper

Biobank, Mayo

  • Ashley L. Blegen
  • , Samantha J. Wirkus
  • , Victoria A. Wagner
  • , Jeffrey G. Meyer
  •  & Stephen N. Thibodeau

Genome Center: Baylor-Hopkins Clinical Genome Center

  • Donna M. Muzny
  • , Eric Venner
  • , Michelle Z. Mawhinney
  • , Sean M. L. Griffith
  • , Elvin Hsu
  • , Marcia K. Adams
  • , Kimberly Walker
  • , Jianhong Hu
  • , Harsha Doddapaneni
  • , Christie L. Kovar
  • , Mullai Murugan
  • , Shannon Dugan
  • , Ziad Khan
  •  & Richard A. Gibbs

Genome Center: Broad, Color, and Mass General Brigham Laboratory for Molecular Medicine

  • Niall J. Lennon
  • , Christina Austin-Tse
  • , Eric Banks
  • , Michael Gatzen
  • , Namrata Gupta
  • , Emma Henricks
  • , Katie Larsson
  • , Sheli McDonough
  • , Steven M. Harrison
  • , Christopher Kachulis
  • , Matthew S. Lebo
  • , Cynthia L. Neben
  • , Marcie Steeves
  • , Alicia Y. Zhou
  • , Scott Topper
  •  & Stacey Gabriel

Genome Center: University of Washington

  • Gail P. Jarvik
  • , Joshua D. Smith
  • , Christian D. Frazar
  • , Colleen P. Davis
  • , Karynne E. Patterson
  • , Marsha M. Wheeler
  • , Sean McGee
  • , Christina M. Lockwood
  • , Brian H. Shirts
  • , Colin C. Pritchard
  • , Mitzi L. Murray
  • , Valeria Vasta
  • , Dru Leistritz
  • , Matthew A. Richardson
  • , Jillian G. Buchan
  • , Aparna Radhakrishnan
  • , Niklas Krumm
  •  & Brenna W. Ehmen

Data and Research Center

  • Lee Lichtenstein
  • , Sophie Schwartz
  • , M. Morgan T. Aster
  • , Kristian Cibulskis
  • , Andrea Haessly
  • , Rebecca Asch
  • , Aurora Cremer
  • , Kylee Degatano
  • , Akum Shergill
  • , Laura D. Gauthier
  • , Samuel K. Lee
  • , Aaron Hatcher
  • , George B. Grant
  • , Genevieve R. Brandt
  • , Miguel Covarrubias
  • , Melissa Basford
  • , Alexander G. Bick
  • , Ashley Able
  • , Ashley E. Green
  • , Jennifer Zhang
  • , Henry R. Condon
  • , Yuanyuan Wang
  • , Moira K. Dillon
  • , C. H. Albach
  • , Wail Baalawi
  •  & Dan M. Roden

All of Us Research Demonstration Project Teams

  • Seung Hoan Choi
  • , Elisabeth A. Rosenthal

NIH All of Us Research Program Staff

  • Andrea H. Ramirez
  • , Sokny Lim
  • , Siddhartha Nambiar
  • , Bradley Ozenberger
  • , Anastasia L. Wise
  • , Chris Lunt
  • , Geoffrey S. Ginsburg
  •  & Joshua C. Denny

Contributions

The All of Us Biobank (Mayo Clinic) collected, stored and plated participant biospecimens. The All of Us Genome Centers (Baylor-Hopkins Clinical Genome Center; Broad, Color, and Mass General Brigham Laboratory for Molecular Medicine; and University of Washington School of Medicine) generated and QCed the whole-genomic data. The All of Us Data and Research Center (Vanderbilt University Medical Center, Broad Institute of MIT and Harvard, and Verily) generated the WGS joint call set, carried out quality assurance and QC analyses and developed the Researcher Workbench. All of Us Research Demonstration Project Teams contributed analyses. The other All of Us Genomics Investigators and NIH All of Us Research Program Staff provided crucial programmatic support. Members of the manuscript writing group (A.G.B., G.A.M., K.R.M., L.L., S.R., R.J.C. and A.M.) wrote the first draft of this manuscript, which was revised with contributions and feedback from all authors.

Corresponding author

Correspondence to Alexander G. Bick .

Ethics declarations

Competing interests.

D.M.M., G.A.M., E.V., K.W., J.H., H.D., C.L.K., M.M., S.D., Z.K., E. Boerwinkle and R.A.G. declare that Baylor Genetics is a Baylor College of Medicine affiliate that derives revenue from genetic testing. Eric Venner is affiliated with Codified Genomics, a provider of genetic interpretation. E.E.E. is a scientific advisory board member of Variant Bio, Inc. A.G.B. is a scientific advisory board member of TenSixteen Bio. The remaining authors declare no competing interests.

Peer review

Peer review information.

Nature thanks Timothy Frayling and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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Extended data figures and tables

Extended data fig. 1 historic availability of ehr records in all of us v7 controlled tier curated data repository (n = 413,457)..

For better visibility, the plot shows growth starting in 2010.

Extended Data Fig. 2 Overview of the Genomic Data Curation Pipeline for WGS samples.

The Data and Research Center (DRC) performs additional single sample quality control (QC) on the data as it arrives from the Genome Centers. The variants from samples that pass this QC are loaded into the Genomic Variant Store (GVS), where we jointly call the variants and apply additional QC. We apply a joint call set QC process, which is stored with the call set. The entire joint call set is rendered as a Hail Variant Dataset (VDS), which can be accessed from the analysis notebooks in the Researcher Workbench. Subsections of the genome are extracted from the VDS and rendered in different formats with all participants. Auxiliary data can also be accessed through the Researcher Workbench. This includes variant functional annotations, joint call set QC results, predicted ancestry, and relatedness. Auxiliary data are derived from GVS (arrow not shown) and the VDS. The Cohort Builder directly queries GVS when researchers request genomic data for subsets of samples. Aligned reads, as cram files, are available in the Researcher Workbench (not shown). The graphics of the dish, gene and computer and the All of Us logo are reproduced with permission of the National Institutes of Health’s All of Us Research Program.

Extended Data Fig. 3 Proportion of allelic frequencies (AF), stratified by computed ancestry with over 10,000 participants.

Bar counts are not cumulative (eg, “pop AF < 0.01” does not include “pop AF < 0.001”).

Extended Data Fig. 4 Distribution of pathogenic, and likely pathogenic ClinVar variants.

Stratified by ancestry filtered to only those variants that are found in allele count (AC) < 40 individuals for 245,388 short read WGS samples.

Extended Data Fig. 5 Ancestry specific HLA-DQB1 ( rs9273363 ) locus associations in 231,442 unrelated individuals.

Phenome-wide (PheWAS) associations highlight ancestry specific consequences across ancestries.

Extended Data Fig. 6 Ancestry specific TCF7L2 ( rs7903146 ) locus associations in 231,442 unrelated individuals.

Phenome-wide (PheWAS) associations highlight diabetic consequences across ancestries.

Supplementary information

Supplementary information.

Supplementary Figs. 1–7, Tables 1–8 and Note.

Reporting Summary

Supplementary dataset 1.

Associations of ACKR1, HLA-DQB1 and TCF7L2 loci with all Phecodes stratified by genetic ancestry.

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The All of Us Research Program Genomics Investigators. Genomic data in the All of Us Research Program. Nature (2024). https://doi.org/10.1038/s41586-023-06957-x

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Title: chain-of-thought reasoning without prompting.

Abstract: In enhancing the reasoning capabilities of large language models (LLMs), prior research primarily focuses on specific prompting techniques such as few-shot or zero-shot chain-of-thought (CoT) prompting. These methods, while effective, often involve manually intensive prompt engineering. Our study takes a novel approach by asking: Can LLMs reason effectively without prompting? Our findings reveal that, intriguingly, CoT reasoning paths can be elicited from pre-trained LLMs by simply altering the \textit{decoding} process. Rather than conventional greedy decoding, we investigate the top-$k$ alternative tokens, uncovering that CoT paths are frequently inherent in these sequences. This approach not only bypasses the confounders of prompting but also allows us to assess the LLMs' \textit{intrinsic} reasoning abilities. Moreover, we observe that the presence of a CoT in the decoding path correlates with a higher confidence in the model's decoded answer. This confidence metric effectively differentiates between CoT and non-CoT paths. Extensive empirical studies on various reasoning benchmarks show that the proposed CoT-decoding substantially outperforms the standard greedy decoding.

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