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

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  • Volume 36 , pages 909–913, ( 2021 )

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Communicating research findings is an essential step in the research process. Often, peer-reviewed journals are the forum for such communication, yet many researchers are never taught how to write a publishable scientific paper. In this article, we explain the basic structure of a scientific paper and describe the information that should be included in each section. We also identify common pitfalls for each section and recommend strategies to avoid them. Further, we give advice about target journal selection and authorship. In the online resource 1 , we provide an example of a high-quality scientific paper, with annotations identifying the elements we describe in this article.

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Writing a scientific paper is an important component of the research process, yet researchers often receive little formal training in scientific writing. This is especially true in low-resource settings. In this article, we explain why choosing a target journal is important, give advice about authorship, provide a basic structure for writing each section of a scientific paper, and describe common pitfalls and recommendations for each section. In the online resource 1 , we also include an annotated journal article that identifies the key elements and writing approaches that we detail here. Before you begin your research, make sure you have ethical clearance from all relevant ethical review boards.

Select a Target Journal Early in the Writing Process

We recommend that you select a “target journal” early in the writing process; a “target journal” is the journal to which you plan to submit your paper. Each journal has a set of core readers and you should tailor your writing to this readership. For example, if you plan to submit a manuscript about vaping during pregnancy to a pregnancy-focused journal, you will need to explain what vaping is because readers of this journal may not have a background in this topic. However, if you were to submit that same article to a tobacco journal, you would not need to provide as much background information about vaping.

Information about a journal’s core readership can be found on its website, usually in a section called “About this journal” or something similar. For example, the Journal of Cancer Education presents such information on the “Aims and Scope” page of its website, which can be found here: .

Peer reviewer guidelines from your target journal are an additional resource that can help you tailor your writing to the journal and provide additional advice about crafting an effective article [ 1 ]. These are not always available, but it is worth a quick web search to find out.

Identify Author Roles Early in the Process

Early in the writing process, identify authors, determine the order of authors, and discuss the responsibilities of each author. Standard author responsibilities have been identified by The International Committee of Medical Journal Editors (ICMJE) [ 2 ]. To set clear expectations about each team member’s responsibilities and prevent errors in communication, we also suggest outlining more detailed roles, such as who will draft each section of the manuscript, write the abstract, submit the paper electronically, serve as corresponding author, and write the cover letter. It is best to formalize this agreement in writing after discussing it, circulating the document to the author team for approval. We suggest creating a title page on which all authors are listed in the agreed-upon order. It may be necessary to adjust authorship roles and order during the development of the paper. If a new author order is agreed upon, be sure to update the title page in the manuscript draft.

In the case where multiple papers will result from a single study, authors should discuss who will author each paper. Additionally, authors should agree on a deadline for each paper and the lead author should take responsibility for producing an initial draft by this deadline.

Structure of the Introduction Section

The introduction section should be approximately three to five paragraphs in length. Look at examples from your target journal to decide the appropriate length. This section should include the elements shown in Fig.  1 . Begin with a general context, narrowing to the specific focus of the paper. Include five main elements: why your research is important, what is already known about the topic, the “gap” or what is not yet known about the topic, why it is important to learn the new information that your research adds, and the specific research aim(s) that your paper addresses. Your research aim should address the gap you identified. Be sure to add enough background information to enable readers to understand your study. Table 1 provides common introduction section pitfalls and recommendations for addressing them.

figure 1

The main elements of the introduction section of an original research article. Often, the elements overlap

Methods Section

The purpose of the methods section is twofold: to explain how the study was done in enough detail to enable its replication and to provide enough contextual detail to enable readers to understand and interpret the results. In general, the essential elements of a methods section are the following: a description of the setting and participants, the study design and timing, the recruitment and sampling, the data collection process, the dataset, the dependent and independent variables, the covariates, the analytic approach for each research objective, and the ethical approval. The hallmark of an exemplary methods section is the justification of why each method was used. Table 2 provides common methods section pitfalls and recommendations for addressing them.

Results Section

The focus of the results section should be associations, or lack thereof, rather than statistical tests. Two considerations should guide your writing here. First, the results should present answers to each part of the research aim. Second, return to the methods section to ensure that the analysis and variables for each result have been explained.

Begin the results section by describing the number of participants in the final sample and details such as the number who were approached to participate, the proportion who were eligible and who enrolled, and the number of participants who dropped out. The next part of the results should describe the participant characteristics. After that, you may organize your results by the aim or by putting the most exciting results first. Do not forget to report your non-significant associations. These are still findings.

Tables and figures capture the reader’s attention and efficiently communicate your main findings [ 3 ]. Each table and figure should have a clear message and should complement, rather than repeat, the text. Tables and figures should communicate all salient details necessary for a reader to understand the findings without consulting the text. Include information on comparisons and tests, as well as information about the sample and timing of the study in the title, legend, or in a footnote. Note that figures are often more visually interesting than tables, so if it is feasible to make a figure, make a figure. To avoid confusing the reader, either avoid abbreviations in tables and figures, or define them in a footnote. Note that there should not be citations in the results section and you should not interpret results here. Table 3 provides common results section pitfalls and recommendations for addressing them.

Discussion Section

Opposite the introduction section, the discussion should take the form of a right-side-up triangle beginning with interpretation of your results and moving to general implications (Fig.  2 ). This section typically begins with a restatement of the main findings, which can usually be accomplished with a few carefully-crafted sentences.

figure 2

Major elements of the discussion section of an original research article. Often, the elements overlap

Next, interpret the meaning or explain the significance of your results, lifting the reader’s gaze from the study’s specific findings to more general applications. Then, compare these study findings with other research. Are these findings in agreement or disagreement with those from other studies? Does this study impart additional nuance to well-accepted theories? Situate your findings within the broader context of scientific literature, then explain the pathways or mechanisms that might give rise to, or explain, the results.

Journals vary in their approach to strengths and limitations sections: some are embedded paragraphs within the discussion section, while some mandate separate section headings. Keep in mind that every study has strengths and limitations. Candidly reporting yours helps readers to correctly interpret your research findings.

The next element of the discussion is a summary of the potential impacts and applications of the research. Should these results be used to optimally design an intervention? Does the work have implications for clinical protocols or public policy? These considerations will help the reader to further grasp the possible impacts of the presented work.

Finally, the discussion should conclude with specific suggestions for future work. Here, you have an opportunity to illuminate specific gaps in the literature that compel further study. Avoid the phrase “future research is necessary” because the recommendation is too general to be helpful to readers. Instead, provide substantive and specific recommendations for future studies. Table 4 provides common discussion section pitfalls and recommendations for addressing them.

Follow the Journal’s Author Guidelines

After you select a target journal, identify the journal’s author guidelines to guide the formatting of your manuscript and references. Author guidelines will often (but not always) include instructions for titles, cover letters, and other components of a manuscript submission. Read the guidelines carefully. If you do not follow the guidelines, your article will be sent back to you.

Finally, do not submit your paper to more than one journal at a time. Even if this is not explicitly stated in the author guidelines of your target journal, it is considered inappropriate and unprofessional.

Your title should invite readers to continue reading beyond the first page [ 4 , 5 ]. It should be informative and interesting. Consider describing the independent and dependent variables, the population and setting, the study design, the timing, and even the main result in your title. Because the focus of the paper can change as you write and revise, we recommend you wait until you have finished writing your paper before composing the title.

Be sure that the title is useful for potential readers searching for your topic. The keywords you select should complement those in your title to maximize the likelihood that a researcher will find your paper through a database search. Avoid using abbreviations in your title unless they are very well known, such as SNP, because it is more likely that someone will use a complete word rather than an abbreviation as a search term to help readers find your paper.

After you have written a complete draft, use the checklist (Fig. 3 ) below to guide your revisions and editing. Additional resources are available on writing the abstract and citing references [ 5 ]. When you feel that your work is ready, ask a trusted colleague or two to read the work and provide informal feedback. The box below provides a checklist that summarizes the key points offered in this article.

figure 3

Checklist for manuscript quality

Data Availability

Michalek AM (2014) Down the rabbit hole…advice to reviewers. J Cancer Educ 29:4–5

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International Committee of Medical Journal Editors. Defining the role of authors and contributors: who is an author? . Accessed 15 January, 2020

Vetto JT (2014) Short and sweet: a short course on concise medical writing. J Cancer Educ 29(1):194–195

Brett M, Kording K (2017) Ten simple rules for structuring papers. PLoS ComputBiol.

Lang TA (2017) Writing a better research article. J Public Health Emerg.

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Ella August is grateful to the Sustainable Sciences Institute for mentoring her in training researchers on writing and publishing their research.

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Busse, C., August, E. How to Write and Publish a Research Paper for a Peer-Reviewed Journal. J Canc Educ 36 , 909–913 (2021).

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How to Write and Publish a Research Paper in 7 Steps

What comes next after you're done with your research? Publishing the results in a journal of course! We tell you how to present your work in the best way possible.

This post is part of a series, which serves to provide hands-on information and resources for authors and editors.

Things have gotten busy in scholarly publishing: These days, a new article gets published in the 50,000 most important peer-reviewed journals every few seconds, while each one takes on average 40 minutes to read. Hundreds of thousands of papers reach the desks of editors and reviewers worldwide each year and 50% of all submissions end up rejected at some stage.

In a nutshell: there is a lot of competition, and the people who decide upon the fate of your manuscript are short on time and overworked. But there are ways to make their lives a little easier and improve your own chances of getting your work published!

Well, it may seem obvious, but before submitting an academic paper, always make sure that it is an excellent reflection of the research you have done and that you present it in the most professional way possible. Incomplete or poorly presented manuscripts can create a great deal of frustration and annoyance for editors who probably won’t even bother wasting the time of the reviewers!

This post will discuss 7 steps to the successful publication of your research paper:

  • Check whether your research is publication-ready
  • Choose an article type
  • Choose a journal
  • Construct your paper
  • Decide the order of authors
  • Check and double-check
  • Submit your paper

1. Check Whether Your Research Is Publication-Ready

Should you publish your research at all?

If your work holds academic value – of course – a well-written scholarly article could open doors to your research community. However, if you are not yet sure, whether your research is ready for publication, here are some key questions to ask yourself depending on your field of expertise:

  • Have you done or found something new and interesting? Something unique?
  • Is the work directly related to a current hot topic?
  • Have you checked the latest results or research in the field?
  • Have you provided solutions to any difficult problems?
  • Have the findings been verified?
  • Have the appropriate controls been performed if required?
  • Are your findings comprehensive?

If the answers to all relevant questions are “yes”, you need to prepare a good, strong manuscript. Remember, a research paper is only useful if it is clearly understood, reproducible and if it is read and used .

2. Choose An Article Type

The first step is to determine which type of paper is most appropriate for your work and what you want to achieve. The following list contains the most important, usually peer-reviewed article types in the natural sciences:

Full original research papers disseminate completed research findings. On average this type of paper is 8-10 pages long, contains five figures, and 25-30 references. Full original research papers are an important part of the process when developing your career.

Review papers present a critical synthesis of a specific research topic. These papers are usually much longer than original papers and will contain numerous references. More often than not, they will be commissioned by journal editors. Reviews present an excellent way to solidify your research career.

Letters, Rapid or Short Communications are often published for the quick and early communication of significant and original advances. They are much shorter than full articles and usually limited in length by the journal. Journals specifically dedicated to short communications or letters are also published in some fields. In these the authors can present short preliminary findings before developing a full-length paper.

3. Choose a Journal

Are you looking for the right place to publish your paper? Find out here whether a De Gruyter journal might be the right fit.

Submit to journals that you already read, that you have a good feel for. If you do so, you will have a better appreciation of both its culture and the requirements of the editors and reviewers.

Other factors to consider are:

  • The specific subject area
  • The aims and scope of the journal
  • The type of manuscript you have written
  • The significance of your work
  • The reputation of the journal
  • The reputation of the editors within the community
  • The editorial/review and production speeds of the journal
  • The community served by the journal
  • The coverage and distribution
  • The accessibility ( open access vs. closed access)

4. Construct Your Paper

Each element of a paper has its purpose, so you should make these sections easy to index and search.

Don’t forget that requirements can differ highly per publication, so always make sure to apply a journal’s specific instructions – or guide – for authors to your manuscript, even to the first draft (text layout, paper citation, nomenclature, figures and table, etc.) It will save you time, and the editor’s.

Also, even in these days of Internet-based publishing, space is still at a premium, so be as concise as possible. As a good journalist would say: “Never use three words when one will do!”

Let’s look at the typical structure of a full research paper, but bear in mind certain subject disciplines may have their own specific requirements so check the instructions for authors on the journal’s home page.

4.1 The Title

It’s important to use the title to tell the reader what your paper is all about! You want to attract their attention, a bit like a newspaper headline does. Be specific and to the point. Keep it informative and concise, and avoid jargon and abbreviations (unless they are universally recognized like DNA, for example).

4.2 The Abstract

This could be termed as the “advertisement” for your article. Make it interesting and easily understood without the reader having to read the whole article. Be accurate and specific, and keep it as brief and concise as possible. Some journals (particularly in the medical fields) will ask you to structure the abstract in distinct, labeled sections, which makes it even more accessible.

A clear abstract will influence whether or not your work is considered and whether an editor should invest more time on it or send it for review.

4.3 Keywords

Keywords are used by abstracting and indexing services, such as PubMed and Web of Science. They are the labels of your manuscript, which make it “searchable” online by other researchers.

Include words or phrases (usually 4-8) that are closely related to your topic but not “too niche” for anyone to find them. Make sure to only use established abbreviations. Think about what scientific terms and its variations your potential readers are likely to use and search for. You can also do a test run of your selected keywords in one of the common academic search engines. Do similar articles to your own appear? Yes? Then that’s a good sign.

4.4 Introduction

This first part of the main text should introduce the problem, as well as any existing solutions you are aware of and the main limitations. Also, state what you hope to achieve with your research.

Do not confuse the introduction with the results, discussion or conclusion.

4.5 Methods

Every research article should include a detailed Methods section (also referred to as “Materials and Methods”) to provide the reader with enough information to be able to judge whether the study is valid and reproducible.

Include detailed information so that a knowledgeable reader can reproduce the experiment. However, use references and supplementary materials to indicate previously published procedures.

4.6 Results

In this section, you will present the essential or primary results of your study. To display them in a comprehensible way, you should use subheadings as well as illustrations such as figures, graphs, tables and photos, as appropriate.

4.7 Discussion

Here you should tell your readers what the results mean .

Do state how the results relate to the study’s aims and hypotheses and how the findings relate to those of other studies. Explain all possible interpretations of your findings and the study’s limitations.

Do not make “grand statements” that are not supported by the data. Also, do not introduce any new results or terms. Moreover, do not ignore work that conflicts or disagrees with your findings. Instead …

Be brave! Address conflicting study results and convince the reader you are the one who is correct.

4.8 Conclusion

Your conclusion isn’t just a summary of what you’ve already written. It should take your paper one step further and answer any unresolved questions.

Sum up what you have shown in your study and indicate possible applications and extensions. The main question your conclusion should answer is: What do my results mean for the research field and my community?

4.9 Acknowledgments and Ethical Statements

It is extremely important to acknowledge anyone who has helped you with your paper, including researchers who supplied materials or reagents (e.g. vectors or antibodies); and anyone who helped with the writing or English, or offered critical comments about the content.

Learn more about academic integrity in our blog post “Scholarly Publication Ethics: 4 Common Mistakes You Want To Avoid” .

Remember to state why people have been acknowledged and ask their permission . Ensure that you acknowledge sources of funding, including any grant or reference numbers.

Furthermore, if you have worked with animals or humans, you need to include information about the ethical approval of your study and, if applicable, whether informed consent was given. Also, state whether you have any competing interests regarding the study (e.g. because of financial or personal relationships.)

4.10 References

The end is in sight, but don’t relax just yet!

De facto, there are often more mistakes in the references than in any other part of the manuscript. It is also one of the most annoying and time-consuming problems for editors.

Remember to cite the main scientific publications on which your work is based. But do not inflate the manuscript with too many references. Avoid excessive – and especially unnecessary – self-citations. Also, avoid excessive citations of publications from the same institute or region.

5. Decide the Order of Authors

In the sciences, the most common way to order the names of the authors is by relative contribution.

Generally, the first author conducts and/or supervises the data analysis and the proper presentation and interpretation of the results. They put the paper together and usually submit the paper to the journal.

Co-authors make intellectual contributions to the data analysis and contribute to data interpretation. They review each paper draft. All of them must be able to present the paper and its results, as well as to defend the implications and discuss study limitations.

Do not leave out authors who should be included or add “gift authors”, i.e. authors who did not contribute significantly.

6. Check and Double-Check

As a final step before submission, ask colleagues to read your work and be constructively critical .

Make sure that the paper is appropriate for the journal – take a last look at their aims and scope. Check if all of the requirements in the instructions for authors are met.

Ensure that the cited literature is balanced. Are the aims, purpose and significance of the results clear?

Conduct a final check for language, either by a native English speaker or an editing service.

7. Submit Your Paper

When you and your co-authors have double-, triple-, quadruple-checked the manuscript: submit it via e-mail or online submission system. Along with your manuscript, submit a cover letter, which highlights the reasons why your paper would appeal to the journal and which ensures that you have received approval of all authors for submission.

It is up to the editors and the peer-reviewers now to provide you with their (ideally constructive and helpful) comments and feedback. Time to take a breather!

If the paper gets rejected, do not despair – it happens to literally everybody. If the journal suggests major or minor revisions, take the chance to provide a thorough response and make improvements as you see fit. If the paper gets accepted, congrats!

It’s now time to get writing and share your hard work – good luck!

If you are interested, check out this related blog post

journal research paper writing

[Title Image by Nick Morrison via Unsplash]

David Sleeman

David Sleeman worked as Senior Journals Manager in the field of Physical Sciences at De Gruyter.

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Writing a Research Paper Introduction | Step-by-Step Guide

Published on September 24, 2022 by Jack Caulfield . Revised on March 27, 2023.

Writing a Research Paper Introduction

The introduction to a research paper is where you set up your topic and approach for the reader. It has several key goals:

  • Present your topic and get the reader interested
  • Provide background or summarize existing research
  • Position your own approach
  • Detail your specific research problem and problem statement
  • Give an overview of the paper’s structure

The introduction looks slightly different depending on whether your paper presents the results of original empirical research or constructs an argument by engaging with a variety of sources.

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Table of contents

Step 1: introduce your topic, step 2: describe the background, step 3: establish your research problem, step 4: specify your objective(s), step 5: map out your paper, research paper introduction examples, frequently asked questions about the research paper introduction.

The first job of the introduction is to tell the reader what your topic is and why it’s interesting or important. This is generally accomplished with a strong opening hook.

The hook is a striking opening sentence that clearly conveys the relevance of your topic. Think of an interesting fact or statistic, a strong statement, a question, or a brief anecdote that will get the reader wondering about your topic.

For example, the following could be an effective hook for an argumentative paper about the environmental impact of cattle farming:

A more empirical paper investigating the relationship of Instagram use with body image issues in adolescent girls might use the following hook:

Don’t feel that your hook necessarily has to be deeply impressive or creative. Clarity and relevance are still more important than catchiness. The key thing is to guide the reader into your topic and situate your ideas.

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This part of the introduction differs depending on what approach your paper is taking.

In a more argumentative paper, you’ll explore some general background here. In a more empirical paper, this is the place to review previous research and establish how yours fits in.

Argumentative paper: Background information

After you’ve caught your reader’s attention, specify a bit more, providing context and narrowing down your topic.

Provide only the most relevant background information. The introduction isn’t the place to get too in-depth; if more background is essential to your paper, it can appear in the body .

Empirical paper: Describing previous research

For a paper describing original research, you’ll instead provide an overview of the most relevant research that has already been conducted. This is a sort of miniature literature review —a sketch of the current state of research into your topic, boiled down to a few sentences.

This should be informed by genuine engagement with the literature. Your search can be less extensive than in a full literature review, but a clear sense of the relevant research is crucial to inform your own work.

Begin by establishing the kinds of research that have been done, and end with limitations or gaps in the research that you intend to respond to.

The next step is to clarify how your own research fits in and what problem it addresses.

Argumentative paper: Emphasize importance

In an argumentative research paper, you can simply state the problem you intend to discuss, and what is original or important about your argument.

Empirical paper: Relate to the literature

In an empirical research paper, try to lead into the problem on the basis of your discussion of the literature. Think in terms of these questions:

  • What research gap is your work intended to fill?
  • What limitations in previous work does it address?
  • What contribution to knowledge does it make?

You can make the connection between your problem and the existing research using phrases like the following.

Now you’ll get into the specifics of what you intend to find out or express in your research paper.

The way you frame your research objectives varies. An argumentative paper presents a thesis statement, while an empirical paper generally poses a research question (sometimes with a hypothesis as to the answer).

Argumentative paper: Thesis statement

The thesis statement expresses the position that the rest of the paper will present evidence and arguments for. It can be presented in one or two sentences, and should state your position clearly and directly, without providing specific arguments for it at this point.

Empirical paper: Research question and hypothesis

The research question is the question you want to answer in an empirical research paper.

Present your research question clearly and directly, with a minimum of discussion at this point. The rest of the paper will be taken up with discussing and investigating this question; here you just need to express it.

A research question can be framed either directly or indirectly.

  • This study set out to answer the following question: What effects does daily use of Instagram have on the prevalence of body image issues among adolescent girls?
  • We investigated the effects of daily Instagram use on the prevalence of body image issues among adolescent girls.

If your research involved testing hypotheses , these should be stated along with your research question. They are usually presented in the past tense, since the hypothesis will already have been tested by the time you are writing up your paper.

For example, the following hypothesis might respond to the research question above:

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journal research paper writing

The final part of the introduction is often dedicated to a brief overview of the rest of the paper.

In a paper structured using the standard scientific “introduction, methods, results, discussion” format, this isn’t always necessary. But if your paper is structured in a less predictable way, it’s important to describe the shape of it for the reader.

If included, the overview should be concise, direct, and written in the present tense.

  • This paper will first discuss several examples of survey-based research into adolescent social media use, then will go on to …
  • This paper first discusses several examples of survey-based research into adolescent social media use, then goes on to …

Full examples of research paper introductions are shown in the tabs below: one for an argumentative paper, the other for an empirical paper.

  • Argumentative paper
  • Empirical paper

Are cows responsible for climate change? A recent study (RIVM, 2019) shows that cattle farmers account for two thirds of agricultural nitrogen emissions in the Netherlands. These emissions result from nitrogen in manure, which can degrade into ammonia and enter the atmosphere. The study’s calculations show that agriculture is the main source of nitrogen pollution, accounting for 46% of the country’s total emissions. By comparison, road traffic and households are responsible for 6.1% each, the industrial sector for 1%. While efforts are being made to mitigate these emissions, policymakers are reluctant to reckon with the scale of the problem. The approach presented here is a radical one, but commensurate with the issue. This paper argues that the Dutch government must stimulate and subsidize livestock farmers, especially cattle farmers, to transition to sustainable vegetable farming. It first establishes the inadequacy of current mitigation measures, then discusses the various advantages of the results proposed, and finally addresses potential objections to the plan on economic grounds.

The rise of social media has been accompanied by a sharp increase in the prevalence of body image issues among women and girls. This correlation has received significant academic attention: Various empirical studies have been conducted into Facebook usage among adolescent girls (Tiggermann & Slater, 2013; Meier & Gray, 2014). These studies have consistently found that the visual and interactive aspects of the platform have the greatest influence on body image issues. Despite this, highly visual social media (HVSM) such as Instagram have yet to be robustly researched. This paper sets out to address this research gap. We investigated the effects of daily Instagram use on the prevalence of body image issues among adolescent girls. It was hypothesized that daily Instagram use would be associated with an increase in body image concerns and a decrease in self-esteem ratings.

The introduction of a research paper includes several key elements:

  • A hook to catch the reader’s interest
  • Relevant background on the topic
  • Details of your research problem

and your problem statement

  • A thesis statement or research question
  • Sometimes an overview of the paper

Don’t feel that you have to write the introduction first. The introduction is often one of the last parts of the research paper you’ll write, along with the conclusion.

This is because it can be easier to introduce your paper once you’ve already written the body ; you may not have the clearest idea of your arguments until you’ve written them, and things can change during the writing process .

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis —a prediction that will be confirmed or disproved by your research.

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The pages in this section provide detailed information about how to write research papers including discussing research papers as a genre, choosing topics, and finding sources.

The Research Paper

There will come a time in most students' careers when they are assigned a research paper. Such an assignment often creates a great deal of unneeded anxiety in the student, which may result in procrastination and a feeling of confusion and inadequacy. This anxiety frequently stems from the fact that many students are unfamiliar and inexperienced with this genre of writing. Never fear—inexperience and unfamiliarity are situations you can change through practice! Writing a research paper is an essential aspect of academics and should not be avoided on account of one's anxiety. In fact, the process of writing a research paper can be one of the more rewarding experiences one may encounter in academics. What is more, many students will continue to do research throughout their careers, which is one of the reasons this topic is so important.

Becoming an experienced researcher and writer in any field or discipline takes a great deal of practice. There are few individuals for whom this process comes naturally. Remember, even the most seasoned academic veterans have had to learn how to write a research paper at some point in their career. Therefore, with diligence, organization, practice, a willingness to learn (and to make mistakes!), and, perhaps most important of all, patience, students will find that they can achieve great things through their research and writing.

The pages in this section cover the following topic areas related to the process of writing a research paper:

  • Genre - This section will provide an overview for understanding the difference between an analytical and argumentative research paper.
  • Choosing a Topic - This section will guide the student through the process of choosing topics, whether the topic be one that is assigned or one that the student chooses themselves.
  • Identifying an Audience - This section will help the student understand the often times confusing topic of audience by offering some basic guidelines for the process.
  • Where Do I Begin - This section concludes the handout by offering several links to resources at Purdue, and also provides an overview of the final stages of writing a research paper.

This document originally came from the Journal of Mammalogy courtesy of Dr. Ronald Barry, a former editor of the journal.

Writing a Research Paper for an Academic Journal: A Five-step Recipe for Perfection

The answer to writing the perfect research paper is as simple as following a step-by-step recipe. Here we bring to you a recipe for effortlessly planning, writing, and publishing your paper as a peer reviewed journal article.

Updated on March 15, 2022

pen with post-it notes on a laptop

As a young researcher, getting your paper published as a journal article is a huge milestone; but producing it may seem like climbing a mountain compared to, perhaps, the theses, essays, or conference papers you have produced in the past.

You may feel overwhelmed with the thought of carrying innumerable equipment and may feel incapable of completing the task. But, in reality, the answer to writing the perfect research paper is as simple as following a recipe with step-by-step instructions.

In this blog, I aim to bring to you the recipe for effortlessly planning, writing, and publishing your paper as a peer reviewed journal article. I will give you the essential information, key points, and resources to keep in mind before you begin the writing process for your research papers.

Secret ingredient 1: Make notes before you begin the writing process

Because I want you to benefit from this article on a personal level, I am going to give away my secret ingredient for producing a good research paper right at the beginning. The one thing that helps me write literally anything is — cue the drum rolls — making notes.

Yes, making notes is the best way to remember and store all that information, which is definitely going to help you throughout the process of writing your paper. So, please pick up a pen and start making notes for writing your research paper.

Step 1. Choose the right research topic

Although it is important to be passionate and curious about your research article topic, it is not enough. Sometimes the sheer excitement of having an idea may take away your ability to focus on and question the novelty, credibility, and potential impact of your research topic.

On the contrary, the first thing that you should do when you write a journal paper is question the novelty, credibility, and potential impact of your research question.

It is also important to remember that your research, along with the aforementioned points, must be original and relevant: It must benefit and interest the scientific community.

All you have to do is perform a thorough literature search in your research field and have a look at what is currently going on in the field of your topic of interest. This step in academic writing is not as daunting as it may seem and, in fact, is quite beneficial for the following reasons:

  • You can determine what is already known about the research topic and the gaps that exist.
  • You can determine the credibility and novelty of your research question by comparing it with previously published papers.
  • If your research question has already been studied or answered before your first draft, you first save a substantial amount of time by avoiding rejections from journals at a much later stage; and second, you can study and aim to bridge the gaps of previous studies, perhaps, by using a different methodology or a bigger sample size.

So, carefully read as much as you can about what has already been published in your field of research; and when you are doing so, make sure that you make lots of relevant notes as you go along in the process. Remember, your study does not necessarily have to be groundbreaking, but it should definitely extend previous knowledge or refute existing statements on the topic.

Secret ingredient 2: Use a thematic approach while drafting your manuscript

For instance, if you are writing about the association between the level of breast cancer awareness and socioeconomic status, open a new Word or Notes file and create subheadings such as “breast cancer awareness in low- and middle-income countries,” “reasons for lack of awareness,” or “ways to increase awareness.”

Under these subheadings, make notes of the information that you think may be suitable to be included in your paper as you carry out your literature review. Ensure that you make a draft reference list so that you don't miss out on the references.

Step 2: Know your audience

Finding your research topic is not synonymous with communicating it, it is merely a step, albeit an important one; however, there are other crucial steps that follow. One of which is identifying your target audience.

Now that you know what your topic of interest is, you need to ask yourself “Who am I trying to benefit with my research?” A general mistake is assuming that your reader knows everything about your research topic. Drafting a peer reviewed journal article often means that your work may reach a wide and varied audience.

Therefore, it is a good idea to ponder over who you want to reach and why, rather than simply delivering chunks of information, facts, and statistics. Along with considering the above factors, evaluate your reader's level of education, expertise, and scientific field as this may help you design and write your manuscript, tailoring it specifically for your target audience.

Here are a few points that you must consider after you have identified your target audience:

  • Shortlist a few target journals: The aims and scope of the journal usually mention their audience. This may help you know your readers and visualize them as you write your manuscript. This will further help you include just the right amount of background and details.
  • View your manuscript from the reader's perspective: Try to think about what they might already know or what they would like more details on.
  • Include the appropriate amount of jargon: Ensure that your article text is familiar to your target audience and use the correct terminology to make your content more relatable for readers - and journal editors as your paper goes through the peer review process.
  • Keep your readers engaged: Write with an aim to fill a knowledge gap or add purpose and value to your reader's intellect. Your manuscript does not necessarily have to be complex, write with a simple yet profound tone, layer (or sub-divide) simple points and build complexity as you go along, rather than stating dry facts.
  • Be specific: It is easy to get carried away and forget the essence of your study. Make sure that you stick to your topic and be as specific as you can to your research topic and audience.

Secret ingredient 3: Clearly define your key terms and key concepts

Do not assume that your audience will know your research topic as well as you do, provide compelling details where it is due. This can be tricky. Using the example from “Secret ingredient 2,” you may not need to define breast cancer while writing about breast cancer awareness. However, while talking about the benefits of awareness, such as early presentation of the disease, it is important to explain these benefits, for instance, in terms of superior survival rates.

Step 3: Structure your research paper with care

After determining the topic of your research and your target audience, your overflowing ideas and information need to be structured in a format generally accepted by journals.

Most academic journals conventionally accept original research articles in the following format: Abstract, followed by the Introduction, Methods, Results, and Discussion sections, also known as the IMRaD, which is a brilliant way of structuring a research paper outline in a simplified and layered format. In brief, these sections comprise the following information:

In closed-access journals, readers have access to the abstract/summary for them to decide if they wish to purchase the research paper. It's an extremely important representative of the entire manuscript.

All information provided in the abstract must be present in the manuscript, it should include a stand-alone summary of the research, the main findings, the abbreviations should be defined separately in this section, and this section should be clear, decluttered, and concise.


This section should begin with a background of the study topic, i.e., what is already known, moving on to the knowledge gaps that exist, and finally, end with how the present study aims to fill these gaps, or any hypotheses that the authors may have proposed.

This section describes, with compelling details, the procedures that were followed to answer the research question.

The ultimate factor to consider while producing the methods section is reproducibility; this section should be detailed enough for other researchers to reproduce your study and validate your results. It should include ethical information (ethical board approval, informed consent, etc.) and must be written in the past tense.

This section typically presents the findings of the study, with no explanations or interpretations. Here, the findings are simply stated alongside figures or tables mentioned in the text in the correct sequential order. Because you are describing what you found, this section is also written in the past tense.

Discussion and conclusion

This section begins with a summary of your findings and is meant for you to interpret your results, compare them with previously published papers, and elaborate on whether your findings are comparable or contradictory to previous literature.

This section also contains the strengths and limitations of your study, and the latter can be used to suggest future research. End this section with a conclusion paragraph, briefly summarizing and highlighting the main findings and novelty of your study.

Step 4: Cite credible research sources

Now that you know who and what you are writing for, it's time to begin the writing process for your research paper. Another crucial factor that determines the quality of your manuscript is the detailed information within. The introduction and discussion sections, which make a massive portion of the manuscript, majorly rely on external sources of information that have already been published.

Therefore, it is absolutely indispensable to extract and cite these statements from appropriate, credible, recent, and relevant literature to support your claims. Here are a few pointers to consider while choosing the right sources:

Cite academic journals

These are the best sources to refer to while writing your research paper, because most articles submitted to top journals are rejected, resulting in high-quality articles being filtered-out. In particular, peer reviewed articles are of the highest quality because they undergo a rigorous process of editorial review, along with revisions until they are judged to be satisfactory.

But not just any book, ideally, the credibility of a book can be judged by whether it is published by an academic publisher, is written by multiple authors who are experts in the field of interest, and is carefully reviewed by multiple editors. It can be beneficial to review the background of the author(s) and check their previous publications.

Cite an official online source

Although it may be difficult to judge the trustworthiness of web content, a few factors may help determine its accuracy. These include demographic data obtained from government websites (.gov), educational resources (.edu), websites that cite other pertinent and trustworthy sources, content meant for education and not product promotion, unbiased sources, or sources with backlinks that are up to date. It is best to avoid referring to online sources such as blogs and Wikipedia.

Do not cite the following sources

While citing sources, you should steer clear from encyclopedias, citing review articles instead of directly citing the original work, referring to sources that you have not read, citing research papers solely from one country (be extensively diverse), anything that is not backed up by evidence, and material with considerable grammatical errors.

Although these sources are generally most appropriate and valid, it is your job to critically read and carefully evaluate all sources prior to citing them.

Step 5: Pick the correct journal

Selecting the correct journal is one of the most crucial steps toward getting published, as it not only determines the weightage of your research but also of your career as a researcher. The journals in which you choose to publish your research are part of your portfolio; it directly or indirectly determines many factors, such as funding, professional advancement, and future collaborations.

The best thing you can do for your work is to pick a peer-reviewed journal. Not only will your paper be polished to the highest quality for editors, but you will also be able to address certain gaps that you may have missed out.

Besides, it always helps to have another perspective, and what better than to have it from an experienced peer?

A common mistake that researchers tend to make is leave the task of choosing the target journal after they have written their paper.

Now, I understand that due to certain factors, it can be challenging to decide what journal you want to publish in before you start drafting your paper, therefore, the best time to make this decision is while you are working on writing your manuscript. Having a target journal in mind while writing your paper has a great deal of benefits.

  • As the most basic benefit, you can know beforehand if your study meets the aims and scope of your desired journal. It will ensure you're not wasting valuable time for editors or yourself.
  • While drafting your manuscript, you could keep in mind the requirements of your target journal, such as the word limit for the main article text and abstract, the maximum number of figures or tables that are allowed, or perhaps, the maximum number of references that you may include.
  • Also, if you choose to submit to an open-access journal, you have ample amount of time to figure out the funding.
  • Another major benefit is that, as mentioned in the previous section, the aims and scope of the journal will give you a fair idea on your target audience and will help you draft your manuscript appropriately.

It is definitely easier to know that your target journal requires the text to be within 3,500 words than spending weeks writing a manuscript that is around, say, 5,000 words, and then spending a substantial amount of time decluttering. Now, while not all journals have very specific requirements, it always helps to short-list a few journals, if not concretely choose one to publish your paper in.

AJE also offers journal recommendation services if you need professional help with finding a target journal.

Secret ingredient 4: Follow the journal guidelines

Perfectly written manuscripts may get rejected by the journal on account of not adhering to their formatting requirements. You can find the author guidelines/instructions on the home page of every journal. Ensure that as you write your manuscript, you follow the journal guidelines such as the word limit, British or American English, formatting references, line spacing, line/page numbering, and so on.

Our ultimate aim is to instill confidence in young researchers like you and help you become independent as you write and communicate your research. With the help of these easy steps and secret ingredients, you are now ready to prepare your flavorful manuscript and serve your research to editors and ultimately the journal readers with a side of impact and a dash of success.

Lubaina Koti, Scientific Writer, BS, Biomedical Sciences, Coventry University

Lubaina Koti, BS

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Successful Scientific Writing and Publishing: A Step-by-Step Approach

John k. iskander.

1 Centers for Disease Control and Prevention, Atlanta, Georgia

Sara Beth Wolicki

2 Association of Schools and Programs of Public Health, Washington, District of Columbia

Rebecca T. Leeb

Paul z. siegel.

Scientific writing and publication are essential to advancing knowledge and practice in public health, but prospective authors face substantial challenges. Authors can overcome barriers, such as lack of understanding about scientific writing and the publishing process, with training and resources. The objective of this article is to provide guidance and practical recommendations to help both inexperienced and experienced authors working in public health settings to more efficiently publish the results of their work in the peer-reviewed literature. We include an overview of basic scientific writing principles, a detailed description of the sections of an original research article, and practical recommendations for selecting a journal and responding to peer review comments. The overall approach and strategies presented are intended to contribute to individual career development while also increasing the external validity of published literature and promoting quality public health science.


Publishing in the peer-reviewed literature is essential to advancing science and its translation to practice in public health ( 1 , 2 ). The public health workforce is diverse and practices in a variety of settings ( 3 ). For some public health professionals, writing and publishing the results of their work is a requirement. Others, such as program managers, policy makers, or health educators, may see publishing as being outside the scope of their responsibilities ( 4 ).

Disseminating new knowledge via writing and publishing is vital both to authors and to the field of public health ( 5 ). On an individual level, publishing is associated with professional development and career advancement ( 6 ). Publications share new research, results, and methods in a trusted format and advance scientific knowledge and practice ( 1 , 7 ). As more public health professionals are empowered to publish, the science and practice of public health will advance ( 1 ).

Unfortunately, prospective authors face barriers to publishing their work, including navigating the process of scientific writing and publishing, which can be time-consuming and cumbersome. Often, public health professionals lack both training opportunities and understanding of the process ( 8 ). To address these barriers and encourage public health professionals to publish their findings, the senior author (P.Z.S.) and others developed Successful Scientific Writing (SSW), a course about scientific writing and publishing. Over the past 30 years, this course has been taught to thousands of public health professionals, as well as hundreds of students at multiple graduate schools of public health. An unpublished longitudinal survey of course participants indicated that two-thirds agreed that SSW had helped them to publish a scientific manuscript or have a conference abstract accepted. The course content has been translated into this manuscript. The objective of this article is to provide prospective authors with the tools needed to write original research articles of high quality that have a good chance of being published.

Basic Recommendations for Scientific Writing

Prospective authors need to know and tailor their writing to the audience. When writing for scientific journals, 4 fundamental recommendations are: clearly stating the usefulness of the study, formulating a key message, limiting unnecessary words, and using strategic sentence structure.

To demonstrate usefulness, focus on how the study addresses a meaningful gap in current knowledge or understanding. What critical piece of information does the study provide that will help solve an important public health problem? For example, if a particular group of people is at higher risk for a specific condition, but the magnitude of that risk is unknown, a study to quantify the risk could be important for measuring the population’s burden of disease.

Scientific articles should have a clear and concise take-home message. Typically, this is expressed in 1 to 2 sentences that summarize the main point of the paper. This message can be used to focus the presentation of background information, results, and discussion of findings. As an early step in the drafting of an article, we recommend writing out the take-home message and sharing it with co-authors for their review and comment. Authors who know their key point are better able to keep their writing within the scope of the article and present information more succinctly. Once an initial draft of the manuscript is complete, the take-home message can be used to review the content and remove needless words, sentences, or paragraphs.

Concise writing improves the clarity of an article. Including additional words or clauses can divert from the main message and confuse the reader. Additionally, journal articles are typically limited by word count. The most important words and phrases to eliminate are those that do not add meaning, or are duplicative. Often, cutting adjectives or parenthetical statements results in a more concise paper that is also easier to read.

Sentence structure strongly influences the readability and comprehension of journal articles. Twenty to 25 words is a reasonable range for maximum sentence length. Limit the number of clauses per sentence, and place the most important or relevant clause at the end of the sentence ( 9 ). Consider the sentences:

  • By using these tips and tricks, an author may write and publish an additional 2 articles a year.
  • An author may write and publish an additional 2 articles a year by using these tips and tricks.

The focus of the first sentence is on the impact of using the tips and tricks, that is, 2 more articles published per year. In contrast, the second sentence focuses on the tips and tricks themselves.

Authors should use the active voice whenever possible. Consider the following example:

  • Active voice: Authors who use the active voice write more clearly.
  • Passive voice: Clarity of writing is promoted by the use of the active voice.

The active voice specifies who is doing the action described in the sentence. Using the active voice improves clarity and understanding, and generally uses fewer words. Scientific writing includes both active and passive voice, but authors should be intentional with their use of either one.

Sections of an Original Research Article

Original research articles make up most of the peer-reviewed literature ( 10 ), follow a standardized format, and are the focus of this article. The 4 main sections are the introduction, methods, results, and discussion, sometimes referred to by the initialism, IMRAD. These 4 sections are referred to as the body of an article. Two additional components of all peer-reviewed articles are the title and the abstract. Each section’s purpose and key components, along with specific recommendations for writing each section, are listed below.

Title. The purpose of a title is twofold: to provide an accurate and informative summary and to attract the target audience. Both prospective readers and database search engines use the title to screen articles for relevance ( 2 ). All titles should clearly state the topic being studied. The topic includes the who, what, when, and where of the study. Along with the topic, select 1 or 2 of the following items to include within the title: methods, results, conclusions, or named data set or study. The items chosen should emphasize what is new and useful about the study. Some sources recommend limiting the title to less than 150 characters ( 2 ). Articles with shorter titles are more frequently cited than articles with longer titles ( 11 ). Several title options are possible for the same study ( Figure ).

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Two examples of title options for a single study.

Abstract . The abstract serves 2 key functions. Journals may screen articles for potential publication by using the abstract alone ( 12 ), and readers may use the abstract to decide whether to read further. Therefore, it is critical to produce an accurate and clear abstract that highlights the major purpose of the study, basic procedures, main findings, and principal conclusions ( 12 ). Most abstracts have a word limit and can be either structured following IMRAD, or unstructured. The abstract needs to stand alone from the article and tell the most important parts of the scientific story up front.

Introduction . The purpose of the introduction is to explain how the study sought to create knowledge that is new and useful. The introduction section may often require only 3 paragraphs. First, describe the scope, nature, or magnitude of the problem being addressed. Next, clearly articulate why better understanding this problem is useful, including what is currently known and the limitations of relevant previous studies. Finally, explain what the present study adds to the knowledge base. Explicitly state whether data were collected in a unique way or obtained from a previously unstudied data set or population. Presenting both the usefulness and novelty of the approach taken will prepare the reader for the remaining sections of the article.

Methods . The methods section provides the information necessary to allow others, given the same data, to recreate the analysis. It describes exactly how data relevant to the study purpose were collected, organized, and analyzed. The methods section describes the process of conducting the study — from how the sample was selected to which statistical methods were used to analyze the data. Authors should clearly name, define, and describe each study variable. Some journals allow detailed methods to be included in an appendix or supplementary document. If the analysis involves a commonly used public health data set, such as the Behavioral Risk Factor Surveillance System ( 13 ), general aspects of the data set can be provided to readers by using references. Because what was done is typically more important than who did it, use of the passive voice is often appropriate when describing methods. For example, “The study was a group randomized, controlled trial. A coin was tossed to select an intervention group and a control group.”

Results . The results section describes the main outcomes of the study or analysis but does not interpret the findings or place them in the context of previous research. It is important that the results be logically organized. Suggested organization strategies include presenting results pertaining to the entire population first, and then subgroup analyses, or presenting results according to increasing complexity of analysis, starting with demographic results before proceeding to univariate and multivariate analyses. Authors wishing to draw special attention to novel or unexpected results can present them first.

One strategy for writing the results section is to start by first drafting the figures and tables. Figures, which typically show trends or relationships, and tables, which show specific data points, should each support a main outcome of the study. Identify the figures and tables that best describe the findings and relate to the study’s purpose, and then develop 1 to 2 sentences summarizing each one. Data not relevant to the study purpose may be excluded, summarized briefly in the text, or included in supplemental data sets. When finalizing figures, ensure that axes are labeled and that readers can understand figures without having to refer to accompanying text.

Discussion . In the discussion section, authors interpret the results of their study within the context of both the related literature and the specific scientific gap the study was intended to fill. The discussion does not introduce results that were not presented in the results section. One way authors can focus their discussion is to limit this section to 4 paragraphs: start by reinforcing the study’s take-home message(s), contextualize key results within the relevant literature, state the study limitations, and lastly, make recommendations for further research or policy and practice changes. Authors can support assertions made in the discussion with either their own findings or by referencing related research. By interpreting their own study results and comparing them to others in the literature, authors can emphasize findings that are unique, useful, and relevant. Present study limitations clearly and without apology. Finally, state the implications of the study and provide recommendations or next steps, for example, further research into remaining gaps or changes to practice or policy. Statements or recommendations regarding policy may use the passive voice, especially in instances where the action to be taken is more important than who will implement the action.

Beginning the Writing Process

The process of writing a scientific article occurs before, during, and after conducting the study or analyses. Conducting a literature review is crucial to confirm the existence of the evidence gap that the planned analysis seeks to fill. Because literature searches are often part of applying for research funding or developing a study protocol, the citations used in the grant application or study proposal can also be used in subsequent manuscripts. Full-text databases such as PubMed Central ( 14 ), NIH RePORT ( 15 ), and CDC Stacks ( 16 ) can be useful when performing literature reviews. Authors should familiarize themselves with databases that are accessible through their institution and any assistance that may be available from reference librarians or interlibrary loan systems. Using citation management software is one way to establish and maintain a working reference list. Authors should clearly understand the distinction between primary and secondary references, and ensure that they are knowledgeable about the content of any primary or secondary reference that they cite.

Review of the literature may continue while organizing the material and writing begins. One way to organize material is to create an outline for the paper. Another way is to begin drafting small sections of the article such as the introduction. Starting a preliminary draft forces authors to establish the scope of their analysis and clearly articulate what is new and novel about the study. Furthermore, using information from the study protocol or proposal allows authors to draft the methods and part of the results sections while the study is in progress. Planning potential data comparisons or drafting “table shells” will help to ensure that the study team has collected all the necessary data. Drafting these preliminary sections early during the writing process and seeking feedback from co-authors and colleagues may help authors avoid potential pitfalls, including misunderstandings about study objectives.

The next step is to conduct the study or analyses and use the resulting data to fill in the draft table shells. The initial results will most likely require secondary analyses, that is, exploring the data in ways in addition to those originally planned. Authors should ensure that they regularly update their methods section to describe all changes to data analysis.

After completing table shells, authors should summarize the key finding of each table or figure in a sentence or two. Presenting preliminary results at meetings, conferences, and internal seminars is an established way to solicit feedback. Authors should pay close attention to questions asked by the audience, treating them as an informal opportunity for peer review. On the basis of the questions and feedback received, authors can incorporate revisions and improvements into subsequent drafts of the manuscript.

The relevant literature should be revisited periodically while writing to ensure knowledge of the most recent publications about the manuscript topic. Authors should focus on content and key message during the process of writing the first draft and should not spend too much time on issues of grammar or style. Drafts, or portions of drafts, should be shared frequently with trusted colleagues. Their recommendations should be reviewed and incorporated when they will improve the manuscript’s overall clarity.

For most authors, revising drafts of the manuscript will be the most time-consuming task involved in writing a paper. By regularly checking in with coauthors and colleagues, authors can adopt a systematic approach to rewriting. When the author has completed a draft of the manuscript, he or she should revisit the key take-home message to ensure that it still matches the final data and analysis. At this point, final comments and approval of the manuscript by coauthors can be sought.

Authors should then seek to identify journals most likely to be interested in considering the study for publication. Initial questions to consider when selecting a journal include:

  • Which audience is most interested in the paper’s message?
  • Would clinicians, public health practitioners, policy makers, scientists, or a broader audience find this useful in their field or practice?
  • Do colleagues have prior experience submitting a manuscript to this journal?
  • Is the journal indexed and peer-reviewed?
  • Is the journal subscription or open-access and are there any processing fees?
  • How competitive is the journal?

Authors should seek to balance the desire to be published in a top-tier journal (eg, Journal of the American Medical Association, BMJ, or Lancet) against the statistical likelihood of rejection. Submitting the paper initially to a journal more focused on the paper’s target audience may result in a greater chance of acceptance, as well as more timely dissemination of findings that can be translated into practice. Most of the 50 to 75 manuscripts published each week by authors from the Centers for Disease Control and Prevention (CDC) are published in specialty and subspecialty journals, rather than in top-tier journals ( 17 ).

The target journal’s website will include author guidelines, which will contain specific information about format requirements (eg, font, line spacing, section order, reference style and limit, table and figure formatting), authorship criteria, article types, and word limits for articles and abstracts.

We recommend returning to the previously drafted abstract and ensuring that it complies with the journal’s format and word limit. Authors should also verify that any changes made to the methods or results sections during the article’s drafting are reflected in the final version of the abstract. The abstract should not be written hurriedly just before submitting the manuscript; it is often apparent to editors and reviewers when this has happened. A cover letter to accompany the submission should be drafted; new and useful findings and the key message should be included.

Before submitting the manuscript and cover letter, authors should perform a final check to ensure that their paper complies with all journal requirements. Journals may elect to reject certain submissions on the basis of review of the abstract, or may send them to peer reviewers (typically 2 or 3) for consultation. Occasionally, on the basis of peer reviews, the journal will request only minor changes before accepting the paper for publication. Much more frequently, authors will receive a request to revise and resubmit their manuscript, taking into account peer review comments. Authors should recognize that while revise-and-resubmit requests may state that the manuscript is not acceptable in its current form, this does not constitute a rejection of the article. Authors have several options in responding to peer review comments:

  • Performing additional analyses and updating the article appropriately
  • Declining to perform additional analyses, but providing an explanation (eg, because the requested analysis goes beyond the scope of the article)
  • Providing updated references
  • Acknowledging reviewer comments that are simply comments without making changes

In addition to submitting a revised manuscript, authors should include a cover letter in which they list peer reviewer comments, along with the revisions they have made to the manuscript and their reply to the comment. The tone of such letters should be thankful and polite, but authors should make clear areas of disagreement with peer reviewers, and explain why they disagree. During the peer review process, authors should continue to consult with colleagues, especially ones who have more experience with the specific journal or with the peer review process.

There is no secret to successful scientific writing and publishing. By adopting a systematic approach and by regularly seeking feedback from trusted colleagues throughout the study, writing, and article submission process, authors can increase their likelihood of not only publishing original research articles of high quality but also becoming more scientifically productive overall.


The authors acknowledge PCD ’s former Associate Editor, Richard A. Goodman, MD, MPH, who, while serving as Editor in Chief of CDC’s Morbidity and Mortality Weekly Report Series, initiated a curriculum on scientific writing for training CDC’s Epidemic Intelligence Service Officers and other CDC public health professionals, and with whom the senior author of this article (P.Z.S.) collaborated in expanding training methods and contents, some of which are contained in this article. The authors acknowledge Juan Carlos Zevallos, MD, for his thoughtful critique and careful editing of previous Successful Scientific Writing materials. We also thank Shira Eisenberg for editorial assistance with the manuscript. This publication was supported by the Cooperative Agreement no. 1U360E000002 from CDC and the Association of Schools and Programs of Public Health. The findings and conclusions of this article do not necessarily represent the official views of CDC or the Association of Schools and Programs of Public Health. Names of journals and citation databases are provided for identification purposes only and do not constitute any endorsement by CDC.

The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions.

Suggested citation for this article: Iskander JK, Wolicki SB, Leeb RT, Siegel PZ. Successful Scientific Writing and Publishing: A Step-by-Step Approach. Prev Chronic Dis 2018;15:180085. DOI: .

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Research Method

Home » Research Paper – Structure, Examples and Writing Guide

Research Paper – Structure, Examples and Writing Guide

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Research Paper

Research Paper


Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.


The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

  • Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
  • Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
  • Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
  • Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
  • Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
  • Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
  • Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
  • Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
  • Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

  • Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
  • Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
  • Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

  • How often do you use social media per day?
  • Less than 30 minutes
  • 30 minutes to 1 hour
  • 1 to 2 hours
  • 2 to 4 hours
  • More than 4 hours
  • Which social media platforms do you use?
  • Others (Please specify)
  • How often do you experience the following on social media?
  • Social comparison (comparing yourself to others)
  • Cyberbullying
  • Fear of Missing Out (FOMO)
  • Have you ever experienced any of the following mental health problems in the past month?
  • Do you think social media use has a positive or negative impact on your mental health?
  • Very positive
  • Somewhat positive
  • Somewhat negative
  • Very negative
  • In your opinion, which factors contribute to the negative impact of social media on mental health?
  • Social comparison
  • In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
  • Education on healthy social media use
  • Counseling for mental health problems caused by social media
  • Social media detox programs
  • Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

  • Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
  • Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
  • Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
  • Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
  • Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

  • For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
  • For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
  • To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
  • To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

  • To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
  • To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
  • To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
  • To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

  • Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
  • Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
  • Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
  • Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
  • Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
  • Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

  • Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
  • Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
  • Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
  • Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
  • Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
  • Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

  • Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
  • Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
  • Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
  • Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
  • Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
  • Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

About the author

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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Critical Writing Program: Decision Making - Spring 2024: Researching the White Paper

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Research the White Paper

Researching the White Paper:

The process of researching and composing a white paper shares some similarities with the kind of research and writing one does for a high school or college research paper. What’s important for writers of white papers to grasp, however, is how much this genre differs from a research paper.  First, the author of a white paper already recognizes that there is a problem to be solved, a decision to be made, and the job of the author is to provide readers with substantive information to help them make some kind of decision--which may include a decision to do more research because major gaps remain. 

Thus, a white paper author would not “brainstorm” a topic. Instead, the white paper author would get busy figuring out how the problem is defined by those who are experiencing it as a problem. Typically that research begins in popular culture--social media, surveys, interviews, newspapers. Once the author has a handle on how the problem is being defined and experienced, its history and its impact, what people in the trenches believe might be the best or worst ways of addressing it, the author then will turn to academic scholarship as well as “grey” literature (more about that later).  Unlike a school research paper, the author does not set out to argue for or against a particular position, and then devote the majority of effort to finding sources to support the selected position.  Instead, the author sets out in good faith to do as much fact-finding as possible, and thus research is likely to present multiple, conflicting, and overlapping perspectives. When people research out of a genuine desire to understand and solve a problem, they listen to every source that may offer helpful information. They will thus have to do much more analysis, synthesis, and sorting of that information, which will often not fall neatly into a “pro” or “con” camp:  Solution A may, for example, solve one part of the problem but exacerbate another part of the problem. Solution C may sound like what everyone wants, but what if it’s built on a set of data that have been criticized by another reliable source?  And so it goes. 

For example, if you are trying to write a white paper on the opioid crisis, you may focus on the value of  providing free, sterilized needles--which do indeed reduce disease, and also provide an opportunity for the health care provider distributing them to offer addiction treatment to the user. However, the free needles are sometimes discarded on the ground, posing a danger to others; or they may be shared; or they may encourage more drug usage. All of those things can be true at once; a reader will want to know about all of these considerations in order to make an informed decision. That is the challenging job of the white paper author.     
 The research you do for your white paper will require that you identify a specific problem, seek popular culture sources to help define the problem, its history, its significance and impact for people affected by it.  You will then delve into academic and grey literature to learn about the way scholars and others with professional expertise answer these same questions. In this way, you will create creating a layered, complex portrait that provides readers with a substantive exploration useful for deliberating and decision-making. You will also likely need to find or create images, including tables, figures, illustrations or photographs, and you will document all of your sources. 

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journal research paper writing

How to Write a Term Paper: 8 Expert Tips for Academic Success 2024

  • Natalie Cowles
  • February 14, 2024

How to Write a Term Paper: 8 Expert Tips for Academic Success 2024

The journey to writing an exceptional term paper is a marathon, not a sprint. It’s a process that tests your research, analytical, and writing skills, all rolled into one challenging assignment. 

But fear not! With the right approach and guidance, crafting a term paper can become not just a means to score high grades but also an opportunity to deepen your understanding of your subject matter and enhance your academic skills.

Table of Contents

1. Truly Knowing What the Assignment Is Asking of You

Truly Knowing What the Assignment Is Asking of You

The first step in navigating the term paper sea is to thoroughly understand the assignment. It may seem straightforward, but many students falter by diving into the research and writing without a clear understanding of what is expected. Read the instructions carefully. 

If anything is unclear, don’t hesitate to ask your professor for clarification or find here a site that may be able to help. Knowing the scope, topic, length, format, and deadline from the outset will set a strong foundation for your work.

2. Choosing a Topic

Selecting the right topic is crucial. It’s the seed from which your term paper will grow. Aim for a topic that is not only interesting to you but also appropriate for the scope of the assignment and your academic level. 

It should be specific enough to be manageable but broad enough to allow for comprehensive research. 

If you find yourself stuck at this stage, consult your professor or peers for suggestions. They can offer perspectives that might not have occurred to you.

3. Conducting Thorough Research

Conducting Thorough Research

Research is the backbone of your term paper. Begin by consulting a variety of sources, including books, academic journals, and reputable websites. Libraries, both physical and digital, are treasure troves of information. 

Utilize databases such as JSTOR or Google Scholar to find relevant academic papers. As you research, keep meticulous records of your sources. This will make citing your references easier and ensure your paper is grounded in credible information.

4. Crafting an Outline

An outline is your roadmap, guiding you through the writing process. It helps organize your thoughts and structure your paper logically. Start with a broad overview, then break down the main sections into more detailed subsections. 

This will help you identify areas that need more research or sections that are too complex and need simplification. An effective outline ensures that every part of your paper serves the overall argument or thesis statement.

5. Writing the Draft

Writing the Draft

With your outline in hand, it’s time to start writing. The introduction should hook the reader, present your thesis statement , and outline the structure of your paper. Each body paragraph should focus on a single idea or piece of evidence, supporting your thesis. 

Use transitions to smoothly navigate from one idea to the next, maintaining a coherent flow throughout. The conclusion should tie everything together, reinforcing your thesis and highlighting the significance of your findings.

The writing process is iterative. Don’t aim for perfection on the first draft. Focus on getting your ideas down on paper; refinement comes later.

6. Revising and Editing

The difference between a good term paper and a great one often lies in the revision stage. Start by reviewing your paper for content and structure. 

Ensure each paragraph contributes to your thesis and that your argument flows logically. Then, move on to editing for clarity, coherence, and conciseness. Pay attention to grammar, punctuation, and style. 

Tools like Grammarly or the Hemingway Editor can be invaluable but don’t rely on them completely. A manual review is irreplaceable.

Finally, check your citations and references. They should adhere to the required format, whether it’s APA , MLA, or Chicago. This not only lends credibility to your paper but also avoids the pitfalls of plagiarism.

7. Handling Feedback

Seek Feedback from Your Professor

If possible, seek feedback from your professor or peers before the final submission. They can offer insights you might have missed and suggest improvements. Be open to criticism; it’s an opportunity for growth, not a personal attack. Use the feedback to refine your paper further.

8. Final Touches and Submission

Before submitting your paper, give it one last review. Check for any errors you might have missed and ensure that it meets all the assignment requirements. Submit your paper with confidence, knowing you’ve put in your best effort.

How Can I Narrow Down a Broad Topic for My Term Paper?

Narrowing down a broad topic requires a bit of brainstorming and preliminary research. Start by reading general sources about your topic to identify specific themes, trends, or issues that interest you. 

Then, consider how these specific angles relate to the broader topic. It can also be helpful to discuss your ideas with your professor or classmates to gain different perspectives. Finally, formulate a research question or thesis statement that reflects the narrowed focus. This approach ensures your topic is manageable and tailored to the assignment’s scope.

What Strategies Can I Use if I’m Struggling to Find Sources for My Topic?

If you’re struggling to find sources, try altering your search terms or using synonyms to expand your search. Consult with a librarian, who can offer expert guidance on searching databases and may suggest resources you hadn’t considered. 

How Do I Balance My Own Ideas with Research Findings in My Term Paper?

How Do I Balance My Own Ideas with Research Findings in My Term Paper?

To balance your own ideas with research findings, start by clearly stating your thesis or main argument. Use research findings to support your ideas, citing evidence that backs up your points. However, don’t just present the research; analyze it. 

Discuss how the evidence supports your thesis, what it means in the context of your argument, and any limitations or counterarguments. Your own analysis and synthesis of the research are what will make your term paper unique and insightful.

Can I Include Visuals in My Term Paper, and How Should I Do So?

Yes, visuals such as graphs, charts, and images can be included in your term paper to support your arguments or illustrate complex ideas. Ensure that each visual is clearly labeled (e.g., Figure 1, Table 1) and accompanied by a caption explaining what it shows.

Refer to the visuals in your text to guide the reader’s attention to them at relevant points in your argument. Always cite the source of the visual in accordance with the citation style you are using.

How Do I Handle Contradictory Evidence in My Term Paper?

Handling contradictory evidence is a crucial part of demonstrating critical thinking . Present the contradictory evidence fairly and objectively, then provide an analysis that explains why it does not undermine your thesis. 

You can argue that the evidence is flawed, outdated, or limited in scope. Alternatively, you can acknowledge the complexity of the issue and refine your thesis to accommodate the nuanced view that emerges from considering all evidence. This approach shows that you have engaged deeply with the material and strengthens your argument.

How Long Should I Spend on Each Stage of Writing My Term Paper?

How Long Should I Spend on Each Stage of Writing My Term Paper?

The time spent on each stage of writing a term paper can vary based on the length of the paper, the complexity of the topic, and your own working style. A balanced approach might involve spending 20% of your time on choosing a topic and conducting initial research, 30% on in-depth research and organizing your findings, 25% on writing the first draft, and 25% on revising, editing, and finalizing the paper.

Adjust these percentages based on your specific needs and deadlines. Remember, starting early and allocating time for each stage can help reduce stress and improve the quality of your work.

Final Words

Writing a term paper is a substantial academic endeavor, but it’s also a deeply rewarding one. It challenges you to think critically, research deeply, and express your thoughts clearly and coherently. By following these steps, you equip yourself with a structured approach to tackle this challenge head-on. 

Remember, academic writing is a skill honed over time. Each term paper is an opportunity to improve, learn, and grow as a scholar. Embrace the process, and you’ll find yourself not just surviving but thriving in the academic world.

  • Academic Writing , Assignment , Education , Research , Students , Term Paper , Tips

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Sample of DNA being pipetted into a petri dish over genetic results

‘The situation has become appalling’: fake scientific papers push research credibility to crisis point

Last year, 10,000 sham papers had to be retracted by academic journals, but experts think this is just the tip of the iceberg

Tens of thousands of bogus research papers are being published in journals in an international scandal that is worsening every year, scientists have warned. Medical research is being compromised, drug development hindered and promising academic research jeopardised thanks to a global wave of sham science that is sweeping laboratories and universities.

Last year the annual number of papers retracted by research journals topped 10,000 for the first time. Most analysts believe the figure is only the tip of an iceberg of scientific fraud .

“The situation has become appalling,” said Professor Dorothy Bishop of Oxford University. “The level of publishing of fraudulent papers is creating serious problems for science. In many fields it is becoming difficult to build up a cumulative approach to a subject, because we lack a solid foundation of trustworthy findings. And it’s getting worse and worse.”

The startling rise in the publication of sham science papers has its roots in China, where young doctors and scientists seeking promotion were required to have published scientific papers. Shadow organisations – known as “paper mills” – began to supply fabricated work for publication in journals there.

The practice has since spread to India, Iran, Russia, former Soviet Union states and eastern Europe, with paper mills supplying ­fabricated studies to more and more journals as increasing numbers of young ­scientists try to boost their careers by claiming false research experience. In some cases, journal editors have been bribed to accept articles, while paper mills have managed to establish their own agents as guest editors who then allow reams of ­falsified work to be published.

Dr Dorothy Bishop sitting in a garden

“Editors are not fulfilling their roles properly, and peer reviewers are not doing their jobs. And some are being paid large sums of money,” said Professor Alison Avenell of Aberdeen University. “It is deeply worrying.”

The products of paper mills often look like regular articles but are based on templates in which names of genes or diseases are slotted in at random among fictitious tables and figures. Worryingly, these articles can then get incorporated into large databases used by those working on drug discovery.

Others are more bizarre and include research unrelated to a journal’s field, making it clear that no peer review has taken place in relation to that article. An example is a paper on Marxist ideology that appeared in the journal Computational and Mathematical Methods in Medicine . Others are distinctive because of the strange language they use, including references to “bosom peril” rather than breast cancer and “Parkinson’s ailment” rather Parkinson’s disease.

Watchdog groups – such as Retraction Watch – have tracked the problem and have noted retractions by journals that were forced to act on occasions when fabrications were uncovered. One study, by Nature , revealed that in 2013 there were just over 1,000 retractions. In 2022, the figure topped 4,000 before jumping to more than 10,000 last year.

Of this last total, more than 8,000 retracted papers had been published in journals owned by Hindawi, a subsidiary of the publisher Wiley, figures that have now forced the company to act. “We will be sunsetting the Hindawi brand and have begun to fully integrate the 200-plus Hindawi journals into Wiley’s ­portfolio,” a Wiley spokesperson told the Observer .

The spokesperson added that Wiley had now identified hundreds of fraudsters present in its portfolio of journals, as well as those who had held guest editorial roles. “We have removed them from our systems and will continue to take a proactive … approach in our efforts to clean up the scholarly record, strengthen our integrity processes and contribute to cross-industry solutions.”

But Wiley insisted it could not tackle the crisis on its own, a message echoed by other publishers, which say they are under siege from paper mills. Academics remain cautious, however. The problem is that in many countries, academics are paid according to the number of papers they have published.

“If you have growing numbers of researchers who are being strongly incentivised to publish just for the sake of publishing, while we have a growing number of journals making money from publishing the resulting articles, you have a perfect storm,” said Professor Marcus Munafo of Bristol University. “That is exactly what we have now.”

The harm done by publishing poor or fabricated research is demonstrated by the anti-parasite drug ivermectin. Early laboratory studies indicated it could be used to treat Covid-19 and it was hailed as a miracle drug. However, it was later found these studies showed clear evidence of fraud, and medical authorities have refused to back it as a treatment for Covid.

“The trouble was, ivermectin was used by anti-vaxxers to say: ‘We don’t need vaccination because we have this wonder drug,’” said Jack Wilkinson at Manchester University. “But many of the trials that underpinned those claims were not authentic.”

Wilkinson added that he and his colleagues were trying to develop protocols that researchers could apply to reveal the authenticity of studies that they might include in their own work. “Some great science came out during the pandemic, but there was an ocean of rubbish research too. We need ways to pinpoint poor data right from the start.”

The danger posed by the rise of the paper mill and fraudulent research papers was also stressed by Professor Malcolm MacLeod of Edinburgh University. “If, as a scientist, I want to check all the papers about a particular drug that might target cancers or stroke cases, it is very hard for me to avoid those that are fabricated. Scientific knowledge is being polluted by made-up material. We are facing a crisis.”

This point was backed by Bishop: “People are building careers on the back of this tidal wave of fraudulent science and could end up running scientific institutes and eventually be used by mainstream journals as reviewers and editors. Corruption is creeping into the system.”

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  • Open access
  • Published: 12 February 2024

A conserved interdomain microbial network underpins cadaver decomposition despite environmental variables

  • Zachary M. Burcham 1 , 2 ,
  • Aeriel D. Belk 1 , 3 ,
  • Bridget B. McGivern   ORCID: 4 ,
  • Amina Bouslimani 5 ,
  • Parsa Ghadermazi 6 ,
  • Cameron Martino 7 ,
  • Liat Shenhav 8 , 9 , 10 ,
  • Anru R. Zhang 11 , 12 ,
  • Pixu Shi 11 ,
  • Alexandra Emmons 1 ,
  • Heather L. Deel 13 ,
  • Zhenjiang Zech Xu   ORCID: 14 ,
  • Victoria Nieciecki   ORCID: 1 , 13 ,
  • Qiyun Zhu   ORCID: 7 , 15 , 16 ,
  • Michael Shaffer 4 ,
  • Morgan Panitchpakdi 5 ,
  • Kelly C. Weldon 5 ,
  • Kalen Cantrell   ORCID: 17 ,
  • Asa Ben-Hur 18 ,
  • Sasha C. Reed 19 ,
  • Greg C. Humphry 7 ,
  • Gail Ackermann 7 ,
  • Daniel McDonald 7 ,
  • Siu Hung Joshua Chan   ORCID: 6 ,
  • Melissa Connor 20 ,
  • Derek Boyd   ORCID: 21 , 22 ,
  • Jake Smith 21 , 23 ,
  • Jenna M. S. Watson 21 ,
  • Giovanna Vidoli 21 ,
  • Dawnie Steadman   ORCID: 21 ,
  • Aaron M. Lynne 24 ,
  • Sibyl Bucheli 24 ,
  • Pieter C. Dorrestein   ORCID: 5 ,
  • Kelly C. Wrighton 4 ,
  • David O. Carter   ORCID: 25 ,
  • Rob Knight   ORCID: 7 , 17 , 26 , 27 &
  • Jessica L. Metcalf   ORCID: 1 , 13 , 28  

Nature Microbiology ( 2024 ) Cite this article

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  • Microbial ecology

Microbial breakdown of organic matter is one of the most important processes on Earth, yet the controls of decomposition are poorly understood. Here we track 36 terrestrial human cadavers in three locations and show that a phylogenetically distinct, interdomain microbial network assembles during decomposition despite selection effects of location, climate and season. We generated a metagenome-assembled genome library from cadaver-associated soils and integrated it with metabolomics data to identify links between taxonomy and function. This universal network of microbial decomposers is characterized by cross-feeding to metabolize labile decomposition products. The key bacterial and fungal decomposers are rare across non-decomposition environments and appear unique to the breakdown of terrestrial decaying flesh, including humans, swine, mice and cattle, with insects as likely important vectors for dispersal. The observed lockstep of microbial interactions further underlies a robust microbial forensic tool with the potential to aid predictions of the time since death.

Decomposition is one of Earth’s most foundational processes, sustaining life through the recycling of dead biological material 1 , 2 . This resource conversion is critical for fuelling core ecosystem functions, such as plant productivity and soil respiration. Microbial networks underpin organic matter breakdown 3 , yet their ecology remains in a black box, obscuring our ability to accurately understand and model ecosystem function, resilience and biogeochemical carbon and nutrient budgets. While DNA-based assessments of decomposer microbial communities have occurred in plant litter 4 , 5 and a few in mammals 6 , 7 , little has been revealed about the microbial ecology of how decomposer microbial communities assemble, interact or function in the ecosystem. Our understanding of how animal remains, or carrion, decompose is in its infancy due to the historical focus on plant litter, which dominates decomposing biomass globally. Nevertheless, an estimated 2 billion metric tons of high-nutrient animal biomass 8 contribute substantially to ecosystem productivity, soil fertility, and a host of other ecosystem functions and attributes 9 , 10 . Carbon and nutrients from carrion biomass can be consumed by invertebrate and vertebrate scavengers, enter the atmosphere as gas, or be metabolized by microbes in situ or via leachate in the surrounding soils 11 , 12 . The proportion of carrion carbon and nutrients entering each resource pool is not well quantified and probably highly variable with substantial contributions to each at an ecosystem scale 2 , 13 . Unlike with plant litter, which is primarily composed of cellulose, animal decomposers must predominantly break down proteins and lipids, which require a vastly different metabolic repertoire. How microbial decomposers assemble to break down these organic compounds is not well understood. For plant litter, it has been proposed that functional redundancy allows different communities of microbes to assemble in any given location 14 and perform similar functions. Alternatively, similar microbial community members, or microbial networks, may assemble across sites to outcompete other community members and thrive on nutrients 15 .

Recent research has demonstrated that microbial community response over the course of terrestrial human cadaver decomposition and across a range of mammals, results in a substantial microbial community change through time that is repeatable across individuals 6 , 7 , 16 , 17 , 18 and appears somewhat similar across different soil types 6 and robust to scavenger activity 16 . These data suggest the potential for universal microbial decomposer networks that assemble in response to mammalian remains. However, it remains unclear how the effects of environmental variability, such as differences in climate, geographic location and season, may affect the assembly processes and interactions of microbial decomposers. Yet understanding and predicting this assembly is important for our understanding of ecosystems and informs practical applications. For example, profiling microbial succession patterns associated with human remains may lead to a novel tool for predicting the postmortem interval (PMI), which has critical societal impact as evidence for death investigations. Within laboratory experiments 6 , 18 , as well as field experiments in single locations 6 , 19 , microbial decomposer community succession is closely linked to PMI at accuracies relevant for forensic applications 6 , 17 , 18 , but these studies do not inform questions of microbial variation across sites, climates and seasons. Consequently, a robust understanding of how microbial ecological patterns of mammalian, and specifically human, decomposition vary is critical for using and improving these important forensic tools. Unlocking the microbial ecology black box for mammal decomposition, or more generally carrion decomposition, could provide actionable knowledge for innovation in agriculture and the human death care industry (for example, composting of bodies) 20 , sustainability (for example, animal mass mortality events) 21 and the forensic sciences (for example, estimating PMI) 22 , as well as guide future research on plant decomposition and maintaining global productivity under anthropogenic change.

To address ecological and forensic research questions on decomposer network assembly and function, we used three willed-body donation anthropological facilities in terrestrial environments across two climate types within the United States (Fig. 1a and Extended Data Fig. 1a,b ) 23 . We asked whether temporal trends in microbial decomposer communities that we previously characterized in a limited experiment using human cadavers at a single geographic location 6 were generalizable across climate, geographic locations and seasons. Over the course of decomposition, we compared the microbial response to decomposition across 36 human bodies within (temperate forest) and between (temperate forest vs semi-arid steppe) climate types. We used multi-omic data (16S and 18S ribosomal (r)RNA gene amplicons, metagenomics and metabolomics) to reveal microbial ecological responses to cadaver decomposition over the first 21 d postmortem (Fig. 1b and Extended Data Fig. 1c ), when decomposition rates are generally fast and dynamic (Fig. 1c , metadata in Supplementary Table 1 ). Here we show that a universal microbial decomposer network assembles despite location, climate and seasonal effects, with evidence of increased metabolic efficiencies to process the ephemeral and abundant lipid- and protein-rich compounds. Key members of the microbial decomposer network are also found associated with swine, cattle and mouse carrion 16 , 24 , 25 , 26 , suggesting that they are not human-specific, but probably general to mammal or animal carrion. Furthermore, the universal microbial network communities underlie a robust microbial-based model for predicting PMI.

figure 1

a , Köppen–Geiger climate map showing ARF and STAFS as ‘temperate without a dry season and hot summer’ and FIRS as ‘arid steppe cold’ adapted from ref. 23 . Thirty-six cadavers in total were placed ( N  = 36), 3 per season for a sum of 12 at each location. b , Upset plot representing the experimental design for the total sample size ( x axis) and number of shared/paired samples ( y axis) for each data type. MetaG, metagenomics; Metab, metabolomics; 18S, 18S rRNA amplicon; 16S, 16S rRNA amplicon. c , Total body score, a visual score of decomposition calculated over the course of decomposition 27 , illustrating how decomposition progresses at each location and by season in triplicate. Dashed lines separate sections of early, active and advanced stages of decomposition as determined by a temperature-based unit of time, accumulated degree day (ADD), calculated by continuously summing the mean daily temperature above 0 °C from left to right. Point transparency increases with days since placement.

Source data

Nutrient-rich cadaver decomposition.

Terrestrial mammalian decomposition is a dynamic process that is partly governed by environmental conditions 1 , 2 . We observed that cadavers placed in the same climate (temperate) decomposed similarly across locations within a season, as determined by a visual total body score (TBS) of decomposition progression (Fig. 1c ) 27 . Cadavers placed in a semi-arid climate (that is, FIRS) generally progressed more slowly through decomposition over the 21 d, which is probably due to decreased temperatures, humidity and precipitation in the semi-arid environment (Extended Data Fig. 1a,b ) 9 , 28 . We observed visual cadaver decomposition progression to be impacted by season, wherein summer was the most consistent across locations (Fig. 1c ). As cadavers and mammalian carrion decompose, they release a complex nutrient pool that impacts the surrounding environment, often resulting in the death and restructuring of nearby plant life 2 , 29 due to generally high inputs of nitrogen 2 , 6 , 9 , 30 , 31 , which is primarily in the form of ammonium 6 , as well as carbon 2 , 6 , 10 , 30 , 31 and phosphorous 9 , 29 . We characterized the cadaver-derived nutrient pool via untargeted metabolomics using liquid chromatography with tandem mass spectrometry (LC–MS/MS) data. Cadaver skin and associated soil metabolite profiles were distinct (Extended Data Fig. 2a,b ). Overall, profiles were largely dominated by likely cadaver-derived lipid-like and protein-like compounds, along with plant-derived lignin-like compounds (Extended Data Fig. 2c,d ). As decomposition progressed, both cadaver-associated soil and skin profiles became enriched in linoleic acids, aleuritic acids, palmitic acids, long-chain fatty acids, fatty amides and general amino acids (Supplementary Tables 2 and 3 ). Furthermore, we estimated a reduction of thermodynamic favourability in the nutrient pool at all locations (Extended Data Fig. 2e,f ), a similar pattern found in the microbial breakdown of plant material in soils 32 . These data suggest that during the first weeks of decomposition, more recalcitrant lipid-like and lipid-derivative nutrients build up within soils as decomposers preferentially utilize labile protein-like resources, but with climate-dependent abundance variations in lipid-like (Extended Data Fig. 2g ) and geographic-dependent variations in protein-like compounds (Extended Data Fig. 2h ). These patterns may also be influenced by the physical properties of soil at each location such as texture, density and stoichiometry.

Cadaver microbial decomposer assembly

The lipid- and protein-rich cadaver nutrient influx is a major ecological disturbance event that attracts scavengers from across the tree of life and initiates the assembly of a specific microbial decomposer community. On the basis of our metabolite data, we hypothesized that soil decomposer microbial communities preferentially shift to efficiently utilize more labile compounds (for example, amino acids from proteins and possibly also carbohydrates such as glycogen, which were not detected via LC–MS/MS metabolomics) and temporarily leave the less-labile compounds (for example, lipids) in the system. By building a metagenome-assembled genome (MAG) database from human decomposition-associated soils (Extended Data Fig. 3a,b and Supplementary Tables 4 – 6 ), we reconstructed genome-scale metabolic models to characterize how potential metabolic efficiencies of soil microbial communities shift in response to three major resources: lipids, amino acids and carbohydrates. Indeed, we found that temperate decomposer metabolic efficiency of labile resources was positively correlated with a temperature-based timeline of decomposition (accumulated degree day (ADD)) (Fig. 2a–c , Extended Data Fig. 3c and Supplementary Tables 7 – 9 ). We found that two MAGs constituted a large portion of the increased amino acid and carbohydrate metabolism efficiencies at temperate locations: Oblitimonas alkaliphila ( Thiopseudomonas alkaliphila ) (Extended Data Fig. 3d ) and Corynebacterium intestinavium (Extended Data Fig. 3e ), respectively. This microbial response is probably an effect of heterogeneous selection (that is, selection driving the community to become different) driving the assemblage of the decomposer community, as heterogeneous selection increases relative to stochastic forces and homogeneous selection during decomposition (Fig. 2d,e , Extended Data Fig. 3f , and Supplementary Tables 10 and 11 ). We further hypothesized that microbe–microbe interactions probably contribute to selection 33 , which we investigated by calculating metabolic competitive and cooperative interaction potentials between our genome-scale metabolic models 34 , 35 . We found that metabolic competition potential initially increased at one temperate and the semi-arid location, suggesting an increase in microbes with similar resource needs (Extended Data Fig. 3g , and Supplementary Tables 12 and 13 ), which was not seen when communities were randomly subsampled within each site and decomposition stage (Extended Data Fig. 3h and Supplementary Table 12 ). Furthermore, we found that communities in temperate climates increased cross-feeding potential (that is, sharing of metabolic products) from early/active to advanced decomposition (Fig. 3a , and Supplementary Tables 12 and 13 ) and had a substantially higher number of cross-feeding exchanges during late decomposition than semi-arid climate communities (Fig. 3b and Supplementary Table 14 ), suggesting the increased potential for metabolic activity. The molecules predicted most for exchange by the models are common by-products of mammalian decomposition 36 , 37 , specifically of protein degradation 38 , and included hydrogen sulfide, acetaldehyde and ammonium, and 56% of the top 25 total exchanged molecules were amino acids. In contrast to temperate locations, semi-arid decomposer communities demonstrated a relatively diminished responsiveness to decomposition stage (Fig. 3c , Extended Data Fig. 4a , and Supplementary Tables 15 and 16 ) and did not significantly shift their metabolism efficiencies (Fig. 2a–c , Extended Data Fig. 3c and Supplementary Tables 7 – 9 ), probably due to a lack of water, which leads to higher metabolic costs 39 , decreased substrate supply 40 and growth 41 . Despite a less measurable microbial response at the semi-arid location, we did detect an increase in cross-feeding potential from early to active decomposition stages, suggesting that the semi-arid community has an increased ability to respond to decomposition nutrients (Fig. 3a , and Supplementary Tables 12 and 13 ) but probably at a smaller scale than temperate locations.

figure 2

a – c , Lipid ( a ), carbohydrate ( b ) and amino acid ( c ) metabolism efficiency as determined by the maximum ATP per C-mol of substrate that can be obtained from each community, plotted against the ADD the community was sampled. ARF n  = 212, STAFS n  = 198 and FIRS n  = 158 biologically independent samples. Data are presented as mean ± 95% confidence interval (CI). Significance was tested with linear mixed-effects models within each location including a random intercept for cadavers with two-tailed ANOVA and no multiple-comparison adjustments. ARF amino acids P  = 6.27 × 10 −23 , STAFS amino acids P  = 6.626 × 10 −10 , STAFS carbohydrate P  = 2.294 × 10 −07 and STAFS lipid P  = 3.591 × 10 −02 . d , Pairwise comparisons to obtain βNTI values focused on successional assembly trends by comparing initial soil at time of cadaver placement to early decomposition soil, then early to active and so on (PL, placement; EA, early; AC, active; AD, advanced) in the 16S rRNA amplicon dataset, showing that strong selection forces are pushing the community to differentiate. ARF n  = 232, STAFS n  = 202 and FIRS n  = 182 biologically independent samples. In boxplots, the lower and upper hinges of the box correspond to the first and third quartiles (the 25th and 75th percentiles); the upper and lower whiskers extend from the hinge to the largest and smallest values no further than 1.5× interquartile range (IQR), respectively; and the centre lines represent the median. The βNTI mean (diamond symbol) change between decomposition stage is represented by connected lines. Dashed lines represent when |βNTI| = 2. A |βNTI| value < 2 indicates stochastic forces (white background) drive community assembly. βNTI values <−2 and >2 indicate homogeneous (blue background) and heterogeneous (yellow background) selection drive assembly, respectively. The width of the violin plot represents the density of the data at different values. Significance was tested with Dunn Kruskal–Wallis H -test, with multiple-comparison P values adjusted using the Benjamini–Hochberg method. e , Representation of heterogeneous selection pressure relative abundance within the total pool of assembly processes increases over decomposition in the 16S rRNA amplicon dataset. Bars were calculated by dividing the number of community comparisons within with βNTI > +2 by the total number of comparisons. * P  < 0.05, ** P  < 0.01 and *** P  < 0.001.

Source Data

figure 3

a , Predicted cross-feeding interactions from MAGs are site-specific and significantly altered over decomposition. ARF n  = 201, STAFS n  = 188 and FIRS n  = 151 biologically independent samples. In boxplots, the lower and upper hinges correspond to the first and third quartiles (the 25th and 75th percentiles); the upper and lower whiskers extend from the hinge to the largest and smallest values no further than 1.5× IQR; the centre lines represent the median. Significance was tested with Dunn Kruskal–Wallis H -test, with multiple-comparison P values adjusted with the Benjamini–Hochberg method. ARF early-active P  = 1.95 × 10 −23 , early-advanced P  = 1.67 × 10 −23 ; STAFS early-active P  = 5.53 × 10 −39 , early-advanced P  = 3.65 × 10 −03 , active-advanced P  = 2.04 × 10 −24 ; FIRS early-active P  = 3.81 × 10 −15 . b , Increased cross-feeding reactions during semi-arid active decomposition and temperate advanced decomposition are summarized to show that compounds such as amino acids (red) are common among the top 25 potential cross-fed molecules from MAGs. c , Phylogenetic turnover in decomposition soil vs control soil shows that temperate climates react quickly to decomposition, while the more arid site does not quickly change (dashed lines represent breaks for early, active (grey shading) and advanced decomposition stages) using the 16S rRNA gene amplicon dataset. ARF n  = 414, STAFS n  = 316 and FIRS n  = 310 biologically independent samples. Data are presented as mean ± 95% CI. Significance was tested using linear mixed-effects models within each location, including a random intercept for cadavers with two-tailed ANOVA and no multiple-comparison adjustments. ARF and STAFS richness P  ≤ 2 × 10 −16 . d , Multi-omic (16S rRNA gene abundances, 18S rRNA gene abundances, MAG abundances, MAG gene abundances, MAG gene functional modules and metabolites) joint-RPCA shows that microbial community ecology is impacted by decomposition stage and geographical location. ** P  < 0.01 and *** P  < 0.001.

We further investigated potential effects of selective environmental conditions via multi-omic, joint robust principal components analysis (joint-RPCA) for dimensionality reduction (see Methods ) 42 , which all (climate, geographic location, season and decomposition stage) significantly shaped the microbial decomposer community ecology (Fig. 3d , Extended Data Fig. 4b–f and Supplementary Table 17 ). Climate (temperate vs semi-arid) along with location (ARF, STAFS, FIRS) significantly shaped the soil microbial community composition (Supplementary Tables 18 – 20 ) and its potential gene function (Supplementary Tables 21 – 22 ). Decomposition soils at temperate sites exhibited strong microbial community phylogenetic turnover (Fig. 3c and Supplementary Table 15 ) and a decrease in microbial richness during decomposition (Extended Data Fig. 4a and Supplementary Table 16 ), while less measurable effects were observed at the semi-arid location (Fig. 3c , Extended Data Fig. 4a , and Supplementary Tables 15 and 16 ). Season appeared to primarily influence soil chemistry as opposed to microbial community composition during decomposition (Supplementary Table 23 ), suggesting possible temperature-associated metabolism changes/limitations of microbial decomposer taxa. Taken together, these data suggest that while stochastic forces play a part in decomposer community assembly, deterministic forces, such as microbial interactions and environmental conditions, also play an important role.

Conserved interdomain soil microbial decomposer network

We discovered a universal network of microbes responding to the cadaver decomposition despite selection effects of climate, location and season on the assembly of the microbial decomposers within the soil. To focus on the universal decomposition effects across locations, we used the joint-RPCA principal component 2 (PC2) scores to generate the universal decomposition network due to their significant change over decomposition stage and reduced impact from location, season and climate (Fig. 4a,b , Extended Data Fig. 4b–f and Supplementary Table 24 ). Therefore, PC2 scores were used to calculate multi-omics of log ratios in late decomposition soil compared to initial and early decomposition soils (Fig. 4c , Extended Data Fig. 4g and Supplementary Table 25 ), which allowed us to identify key co-occurring bacterial and eukaryotic microbial decomposers, bacterial functional pathways and metabolites associated with late decomposition (Fig. 5a , Extended Data Fig. 5 and Supplementary Table 26 ). The organism O. alkaliphila , which is central to the network and a large contributor to the increased amino acid metabolism efficiency at temperate locations (Extended Data Fig. 3d ), may play a key role in terrestrial cadaver decomposition as a controller of labile resource utilization in temperate climates, but little is known about its ecology 43 , 44 , 45 . In addition, most microbial key network decomposers (Fig. 5a ; O. alkaliphila , Ignatzschineria , Wohlfahrtiimonas , Bacteroides , Vagococcus lutrae, Savagea , Acinetobacter rudis and Peptoniphilaceae ) represented unique phylogenetic diversity that was extremely rare or undetected in host-associated or soil microbial communities in American Gut Project (AGP) or Earth Microbiome Project (EMP) data sets (Fig. 5b , Extended Data Fig. 6 , and Supplementary Tables 27 and 28 ). Although the decomposers in the group Bacteroides have previously been assumed to derive from a human gut source 46 , 47 , we find that these are instead probably a specialist group of decomposers distinct from gut-associated Bacteroides (Fig. 5b , Extended Data Fig. 6 , and Supplementary Tables 27 and 28 ). The only strong evidence of key network bacterial decomposers emerging from soil and host-associated environments were in the genera Acinetobacter and Peptoniphilus (Fig. 5b , Extended Data Fig. 6 , and Supplementary Tables 27 and 28 ). We more comprehensively characterized microbial decomposer phylogenetic uniqueness with MAG data, which span previously undescribed bacterial orders, families, genera and species (Extended Data Fig. 3a ). Overall, we find that the soil microbial decomposer network is phylogenetically unique and in extremely low relative abundance in the environment until the cadaver nutrient pool becomes available.

figure 4

a , b , Principal component values show that ( a ) facility variation is primarily explained by principal component 3 (PC3) (that is, least overlap between group scores), while variation caused by ( b ) decomposition stage is explained by PC2. c , Change in log ratio of PC scores within omics datasets (metabolites, MAG abundances, 18S rRNA gene abundances and MAG gene functional modules) from initial soil through advanced decomposition stage soil highlights that decomposition stage progression corresponds to compositional shifts. All data types used the same n  = 374 biologically independent samples. Data are presented as mean ± 95% CI.

figure 5

a , Top 20% correlation values from features responsible for the universal late decomposition log-ratio signal in joint-RPCA PC2 visualized in a co-occurrence network. b , Phylogenetic tree representing ASVs associated with key decomposer nodes from the network placed along the top 50 most abundant ASVs taken from AGP gut, AGP skin, EMP soil and EMP host-associated datasets demonstrates that key decomposers are largely phylogenetically unique. Colour represents taxonomic order (full legend in Extended Data Fig. 6 ); the innermost ring represents decomposer placement, while outer rings represent AGP and EMP ASVs, for which bar height represents ASV rank abundance within each environment. A lack of bars indicates that the ASV was not present within the entire dataset. AGP and EMP ASVs were ranked according to the number of samples they were found in each environment. Decomposer ASVs are numbered clockwise (full taxonomy available in Supplementary Table 27 ).

We hypothesized that specialist decomposer network taxa probably interact to metabolize the nutrient pool, which we explored via estimated cross-feeding capabilities of co-occurring communities. Highlighting the importance of these key taxa, microbial decomposer network members accounted for almost half (42.8%) of predicted late decomposition nutrient exchanges (Figs. 3b and 5a , and Supplementary Table 29 ) with Gammaproteobacteria being prominent as both metabolite donors and receivers. For example, O. alkaliphila has the capability to cross-feed with Ignatzschineria , Acinetobacter , Savagea and Vagococcus lutrae , to which it donates amino acids known to be associated with mammalian decomposition such as aspartate, isoleucine, leucine, tryptophan and valine, along with the lipid metabolism intermediate, sn -Glycero-3-phosphoethanolamine 36 (Supplementary Table 30 ). As a receiver, O. alkaliphilia is predicted to receive essential ferrous ions (Fe 2+ ) from Acinetobacter , Savagea and Vagoccocus along with glutamate, proline and lysine from Ignatzschineria . Further, putrescine, a foul-smelling compound produced during decomposition by the decarboxylation of ornithine and arginine, and arginine/ornithine transport systems were universal functions within our network (Fig. 5a ). Cross-feeding analysis identified multiple potential ornithine and/or arginine exchangers, such as Ignatzschineria , Savagea , Wohlfahrtiimonas and O. alkaliphilia (Supplementary Table 31 ). Putrescine is an interdomain communication molecule probably playing an important role in assembling the universal microbial decomposer network by signalling scavengers such as blow flies 48 , which disperse decomposer microbes, as well as directly signalling other key microbial decomposers, such as fungi 49 , 50 , 51 .

Fungi play an essential role in the breakdown of organic matter; however, their processes and interdomain interactions during cadaver decomposition remain underexplored. Our network analysis identified multiple fungal members that are co-occurring with bacteria, belonging to the Ascomycota phylum (Fig. 5a )—a phylum known for its role in breaking down organic matter 6 , 44 , 52 , 53 . In particular, Yarrowia and Candida are known for their ability to utilize lipids, proteins and carbohydrates 44 , 53 , and both have one of their highest correlations with O. alkaliphila (Fig. 5a and Supplementary Table 25 ). The ability of Yarrowia and Candida to break down lipids and proteins during decomposition may serve as interdomain trophic interactions that allow O. alkaliphila to utilize these resources 44 . For example, Yarrowia and Candida genomes contain biosynthesis capabilities for arginine and ornithine that, if excreted, could be taken up by O. alkaliphilia . The complete genome of O. alkaliphilia (Genbank accession no. CP012358 ) contains the enzyme ornithine decarboxylase, which is responsible for converting ornithine to the key compound putrescine 43 .

Machine learning reveals a predictable microbial decomposer ecology

The assembly of a universal microbial decomposer network suggests the potential to build a robust forensics tool. We demonstrate that the PMI (calculated as ADD) can be accurately predicted directly from microbiome-normalized abundance patterns via random forest regression models (Fig. 6a ). High-resolution taxonomic community structure was the best predictor of PMI (Fig. 6b ), particularly normalized abundances of the 16S rRNA gene at the SILVA database level-7 taxonomic rank (L7) of the skin decomposer microbes (Fig. 6a–c ). Interestingly, 3 out of 4 of the skin-associated decomposer taxa that were most informative for the PMI model had similar normalized abundance trends over decompositions for bodies at all locations, suggesting that skin decomposers are more ubiquitous across climates than soil decomposers (Fig. 6d and Extended Data Fig. 7 ). We hypothesize that this is due to the human skin microbiome being more conserved between individuals than the soil microbiome is between geographic locations 54 . In fact, both skin and soil 16S rRNA-based models had the same top taxon as the most important predictor, Helcococcus seattlensis (Fig. 6d and Extended Data Fig. 7 ). H. seattlensis is a member of the order Tissierellales and family Peptoniphilaceae, both of which were key nodes within the universal decomposer network. In line with our hypothesis, H. seattlensis on the skin showed more-similar abundance trends for cadavers decomposing across both climate types, while H. seattlensis trends in the soil were primarily measurable at temperate locations (Fig. 6e and Extended Data Fig. 8 ). We found that normalized abundances of important soil taxa previously established to be in our universal decomposer network had strong climate signals, further suggesting a diminished responsiveness in semi-arid climates, such as temperate-climate responses with H. seattlensis , O. alkaliphila , Savagea sp., Peptoniphilus stercorisuis , Ignatzschineria sp. and Acinetobacter sp. (Extended Data Fig. 8c,d ). However, we found that the three most important PMI model soil taxa, Peptostreptococcus sp., Sporosarcina sp. and Clostridiales Family XI sp., had increased detection with decomposition in both semi-arid and temperate climates (Extended Data Fig. 8c,d ), suggesting that while strong climate-dependent fluctuations exist, there are microbial members that respond more ubiquitously to decomposition independent of climate. In addition, microbiome-based models and a TBS-based model had comparable average mean absolute errors (MAE) (Extended Data Fig. 9a ); however, 16S rRNA microbiome-based model predictions were on average closer to the actual observed values (that is, smaller average residual values), suggesting a higher accuracy (Fig. 6c and Extended Data Fig. 9a ). Lastly, we confirmed the model accuracy and reliability of PMI prediction using 16S rRNA amplicon data with an independent test set of samples that were collected at a different time from cadavers at locations and climates not represented in our model. We discovered that we could accurately predict the true PMIs of samples better than samples with randomized PMIs at all independent test set locations (Extended Data Fig. 9b,c and Supplementary Table 32 ), confirming the generalizability and robustness of our models in predicting new data from multiple geographies and climates with an accuracy useful for forensic death investigations.

figure 6

a , Cross-validation errors of multi-omic data sets. 16S and 18S rRNA gene data were collapsed to SILVA taxonomic level 7 (L7) and 12 (L12). Boxplots represent average prediction MAE in ADD of individual bodies during nested cross-validation of 36 body dataset. 16S rRNA soil face, soil hip, skin face and skin hip datasets contain n  = 600, 616, 588 and 500 biologically independent samples, respectively. 18S rRNA soil face, soil hip, skin face and skin hip datasets contain n  = 939, 944, 837 and 871 biologically independent samples, respectively. Paired 16S rRNA+18S rRNA soil face, soil hip, skin face and skin hip datasets contain n  = 440, 450, 428 and 356 biologically independent samples, respectively. MAG datasets contain n  = 569 biologically independent samples. Metabolite soil hip and skin hip datasets contain n  = 746 and 748 biologically independent samples, respectively. b , Mean absolute prediction errors are lowest when high-resolution taxonomic data are used for model training and prediction. Data represented contain the same biologically independent samples as in a . In boxplots in a and b , the lower and upper hinges of the boxplot correspond to the first and third quartiles (the 25th and 75th percentiles); the upper and lower whiskers extend from the hinge to the largest and smallest values no further than 1.5× IQR; the centre lines represent the median; the diamond symbol represents the mean. c , Linear regressions of predicted to true ADDs to assess model prediction accuracy show that all sampling locations significantly predict ADD. Data represented contain the same biologically independent samples as in a . Data are presented as mean ± 95% CI. Black dashed lines represent ratio of predicted to real ADD predictions at 1:1. The coloured solid lines represent the linear model calculated from the difference between the predicted and real ADD. d , The most important SILVA L7 taxa driving model accuracy from the best-performing model derived from 16S rRNA gene amplicon data sampled from the skin of the face. e , Comparison of abundance changes of the top important taxon, Helcococcus seattlensis , in skin reveals that low-abundance taxa provide predictive responses. Data plotted with loess regression and represent the same biologically independent samples as in a . Data are presented as mean ± 95% CI. Bact., bacterial; Avg., average; Marg., marginal.

We provide a genome-resolved, comprehensive view of microbial dynamics during cadaver decomposition and shed light on the assembly, interactions and metabolic shifts of a universal microbial decomposer network. We found that initial decomposer community assembly is driven by stochasticity, but deterministic forces increase over the course of decomposition, a finding in agreement with other conceptual models of microbial ecology 33 , 55 , 56 , 57 . These processes led to a decomposer network consisting of phylogenetically unique taxa emerging, regardless of season, location and climate, to synergistically break down organic matter. The ubiquitous decomposer and functional network revealed by our multi-omic data suggests that metabolism is coupled to taxonomy, at least to some extent, for cadaver decomposition ecology. However, the overall composition of microbial decomposer communities did vary between different climates and locations, indicating that some functional redundancy also probably exists. In a study of agricultural crop organic matter decomposition (straw and nutrient amendments), researchers similarly demonstrated that although functional redundancy probably plays a role, key microbial taxa emerge as important plant decomposers 15 , and a meta-analysis of microbial community structure–function relationships in plant litter decay found that community composition had a large effect on mass loss 58 . In terms of climatic controls over cadaver decomposition, temperate locations had a more measurable microbial response (for example, phylogenetic turnover, potential cross-feeding) in soils than the arid location in our study, and plant studies support the idea that climate is a strong determinant of decomposition rates and microbial activity 59 .

Despite the lesser response in the arid location, cadaver decomposer microbial ecologies were similar, suggesting that while climate may act as a strong control, microbial community composition follows similar assembly paths. We find evidence that key interdomain microbial decomposers of cadavers (that is, fungi and bacteria) emerge in diverse environments and probably utilize resource partitioning and cross-feeding to break down a nutrient pulse that is rich in lipids, proteins and carbohydrates. This process would be consistent with dogma within leaf litter ecology that fungal decomposers are typically specialized decomposers of complex substrates while bacteria serve as generalists that decompose a broader nutritional landscape 60 . Thus, we hypothesize that fungi (such as Yarrowia and Candida ) assist in the catabolism of complex, dead organic matter (such as lipids and proteins) into simpler compounds (such as fatty acids and amino acids), which are utilized by bacterial community members, (such as O. alkaliphila ) capable of efficiently metabolizing these by-products. This division of labour coupled with microbial interactions drives the assembly of the microbial decomposer community, in a process reminiscent of ecological dynamics observed in leaf litter decomposition 60 .

We suspect that key network microbial decomposers are probably not specific to decomposition of human cadavers and are, in part, maintained or seeded by insects. Key cadaver bacterial decomposers O. alkaliphila , Ignatzschineria , Wohlfahrtiimonas , Bacteroides , Vagococcus lutrae , Savagea , Acinetobacter rudis and Peptoniphilaceae have been detected in terrestrial decomposition studies of swine, cattle and mice (Supplementary Table 33 ) 16 , 24 , 25 , 26 , and a subset detected in aquatic decomposition 61 . Most key network bacterial decomposers, including the well-known blow fly-associated genera Ignatzschineria and Wohlfahrtiimonas 62 , were rare or not detected in a lab-based mouse decomposition study 6 in which insects were excluded (Supplementary Table 33 ). However, a different lab-based study that excluded blow flies but included carrion beetles 26 detected a subset of these key microbial decomposers, suggesting a role for microbe–insect interactions and dispersal by insects 26 , 48 , 63 . Further evidence implicating insects as important vectors is that all key network bacterial decomposers presented here have been detected on blow flies (Supplementary Table 28 ) 6 , 64 . Furthermore, Ascomycota fungal members, such as Yarrowia and Candida , have been previously detected in association with human, swine and mouse remains 6 , 26 , 44 , 53 . Yarrowia can be vertically transmitted from parent to offspring of carrion beetle 63 and may facilitate beetle consumption of carrion. Taken together, these findings suggest that key microbial decomposer taxa identified in this study of human cadavers are probably more generalizable carrion decomposers and are likely inoculated, at least partly, by insects.

We demonstrate the potential practical application of microbiome tools in forensic science by leveraging microbial community succession patterns and machine learning techniques for accurately predicting PMI. Importantly, the predictive models showcase their generalizability by accurately predicting the PMIs of independent test samples collected from various geographic locations and climates, including for test samples collected from a climate region not represented in the training set of the model. The best-performing model was able to accurately predict PMI within ~±3 calendar days during internal validation and on an independent test set (Supplementary Tables 34 and 35 ), which is a useful timeframe for forensic sciences, enabling investigators to establish crucial timelines and aiding in criminal investigations. Prediction errors are probably due to intrinsic (for example, BMI/total mass) 19 , 24 , 65 and/or extrinsic (for example, scavengers, precipitation) 19 , 26 factors not accounted for in the model, but should be a future area of research for model improvement. For example, total mass has been previously shown not to affect microbial decomposer composition in swine 24 ; however, ref. 19 found that Gammaproteobacteria relative abundance correlated with BMI of humans. Within our study, in which cadavers had highly variable initial total masses (Supplementary Table 1 ), Acinetobacter and Ignatzschineria (within Gammaproteobacteria) were important features in our PMI models, suggesting that it is probably robust to BMI (Extended Data Fig. 7 ). In addition, scavenging by invertebrates and vertebrates is another factor that can affect not only the decomposer microbial composition (for example, carrion beetles) 26 but also the microbes themselves which can shape the scavenger community via volatile organic compounds (for example, repel vertebrates but attract insects 48 , 66 ). A better understanding of which intrinsic and extrinsic factors directly affect microbes that are important features for predicting PMI will be an important next step.

Our improved understanding of the microbial ecology of decomposing human cadavers and its more general implications for the crucial and rarely studied carrion nutrient pool is critical for revising concepts of what should be included in carbon and nutrient budgets and the models used to forecast ecosystem function and change 11 . New insight on the role of carrion decomposition in fuelling carbon and nutrient cycling is needed for conceptual and numerical models of biogeochemical cycles and trophic processes 11 ; this study informs how the assembly and interactions among decomposer microbial communities facilitate the turnover and exchange of resources, and begins unlocking one of the remaining black boxes of ecosystem ecology. Finally, these findings may contribute to society by providing potential for a new forensic tool and for potentially modulating decomposition processes in both agricultural and human death industries via the key microbial decomposers identified here.

Site and donor selection

Outdoor experiments on 36 human cadavers were conducted at three willed-body donation facilities: Colorado Mesa University Forensic Investigation Research Station (FIRS), Sam Houston State University Southeast Texas Applied Forensic Science (STAFS) Facility and University of Tennessee Anthropology Research Facility (ARF). Before the start of the project, a meeting was held at STAFS to demonstrate, discuss and agree on sampling protocols. The Institutional Review Board and the Protection of Human Subjects Committee either determined that review was not required or granted exempt status for donors at each respective facility since the proposed research does not involve human donors as defined by federal regulations. Three deceased human donors were placed supine and unclothed on the soil surface in the spring, summer, fall and winter over the years 2016 and 2017 at each facility ( N  = 36). Bodies were placed on soil with no known previous human decomposition. Before placement, STAFS performed minimal removal of vegetation including raking of leaves and removal of shrubbery, and bodies placed at STAFS were placed in cages made of 1 cm × 1 cm wire fences and wooden frames to prevent vertebrate scavenging. The ARF and FIRS did not remove vegetation or place bodies under cages as standard protocol. Furthermore, bodies were placed no closer than 2.5 m between sternum midpoints. Collection date for each donor can be found in the sample metadata, in addition to cause of death if known, initial condition, autopsy status, weight before placement, age in years if known, estimated age if not known, sex, donor storage type, days donor was stored, time since death to cooling and placement head direction (Supplementary Table 1 ). Donor weight was taken at time of intake at ARF and FIRS but is a self-reported measure either by the donor before death or a family member at STAFS. During daily sampling, daily ambient average temperature and humidity, TBS 27 , scavenging status and insect status were recorded if available or applicable. Human bodies were fully exposed to all weather elements and invertebrate scavengers. Inclusion criteria for the remains were specified before the start of the experiment and required that the remains were in the fresh stage of decomposition and had not been frozen (and not extensively cooled) or autopsied before placement at the facility.

Decomposition metric calculations

The Köppen–Geiger climate classification system characterizes both the ARF and STAFS facilities as temperate without a dry season and hot summer (Cfa) and the FIRS facility as a cold semi-arid steppe (BSk) 23 . Average daily temperatures were collected from the National Centers for Environmental Information (NCEI) website ( ) and monthly total precipitation accumulation over the course of the study was collected from the Weather Underground website ( ) from local weather stations: Grand Junction Regional Airport Station, McGhee Tyson Airport Station and Easterwood Airport Station. Reference 27 TBS quantifies the degree to which decomposition has occurred in three main areas (head, trunk and limbs) 27 . The user assigned values to represent the progress of decomposition on the basis of visual assessment of the cadaver and added these values to generate a TBS at the time of sampling. A maximum score was assigned for each area when the cadaver has reached dry skeletal remains. ADD was estimated using the weather data provided by the NCEI. Degree day on the day of placement was not included, and a base temperature of 0 °C was used. ADD was calculated by adding together all average daily temperatures above 0 °C for all previous days of decomposition, as in ref. 27 , and subtracting the base temperature of 0 °C.

Sample collection and DNA extraction

We sampled the skin surface of the head and torso near the hip along with gravesoils (soils associated with decomposition) associated with each skin site over 21 d of decomposition. Control soil samples were taken of the same soil series and horizon that are not associated with body decomposition (known past or present) from areas within or just outside each facility. We collected swabs of 756 non-decomposition soil (controls), 756 gravesoil near the hip, 756 gravesoil near the face, 756 hip skin and 756 face skin samples ( N  = 3,780). All site samples (skin surface, gravesoil and control soil) were taken using sterile dual-tipped BD SWUBE applicator (REF 281130) swabs as described in ref. 18 , and immediately frozen after each sampling event and kept frozen at −20 °C. Samples were shipped to CU Boulder or Colorado State University overnight on dry ice and immediately stored at −20 °C upon arrival and until DNA extraction. Skin and soil DNA was extracted from a single tip of the dual-tipped swabs using the PowerSoil DNA isolation kit 96-htp (MoBio Laboratories), according to standard EMP protocols ( ).

Amplicon library preparation and sequencing

Bacterial and archaeal communities were characterized using 16S rRNA gene regions while eukaryotic communities were characterized using 18S rRNA gene regions as universal markers, for all successful skin and soil DNA extracts ( n  = 3,547). To survey bacteria and archaea, we used the primer set 515f (5′GTGYCAGCMGCCGCGGTAA) and 806rb (5′GGACTACNVGGGTWTCTAAT) that targets these domains near-universally 67 , 68 , with barcoded primers allowing for multiplexing, following EMP protocols 69 . To survey microbial eukaryotes, we sequenced a subregion of the 18S rRNA gene using the primers 1391f_illumina (5′GTACACACCGCCCGTC) and EukBr_illumina (5′TGATCCTTCTGCAGGTTCACCTAC) targeting the 3′ end of the 18S rRNA gene. 18S rRNA gene primers were adapted from ref. 70 and target a broad range of eukaryotic lineages. We have successfully generated and analysed data using these gene markers previously 6 , 18 . Primers included error-corrected Golay barcodes to allow for multiplexing while preventing misassignment. PCR amplicons were quantified using Picogreen Quant-iT (Invitrogen, Life Technologies) and pooled from each sample to equimolar ratio in a single tube before shipping to the UC San Diego genomics laboratory for sequencing. For both amplicon types, pools were purified using the UltraClean PCR clean-up kit (Qiagen). 16S rRNA pools were sequenced using a 300-cycle kit on the Illumina MiSeq sequencing platform and 18S rRNA gene pools were sequenced using a 300-cycle kit on the Illumina HiSeq 2500 sequencing platform (Illumina). Samples within a sample type (skin vs soil) were randomly assigned to a sequencing run to prevent potential batch effects. Blank DNA extraction and PCR negative controls were included throughout the entire process from DNA extraction to PCR amplification to monitor contamination ( n  = 592 negative controls).

Shotgun metagenomic library preparation and sequencing

Extracted DNA from a subset of hip-associated soil samples ( n  = 756), soil controls ( n  = 9), blank controls ( n  = 102) and no-template PCR controls ( n  = 15) were chosen to undergo shallow shotgun sequencing to provide in-depth investigation of microbial dynamics within decomposition soil (Supplementary Table 4 ). Our standard protocol followed that of ref. 71 and was optimized for an input quantity of 1 ng DNA per reaction. Before library preparation, input DNA was transferred to 384-well plates and quantified using a PicoGreen fluorescence assay (ThermoFisher). Input DNA was then normalized to 1 ng in a volume of 3.5 μl of molecular-grade water using an Echo 550 acoustic liquid-handling robot (Labcyte). Enzyme mixes for fragmentation, end repair and A-tailing, ligation and PCR were prepared and added at 1:8 scale volume using a Mosquito HV micropipetting robot (TTP Labtech). Fragmentation was performed at 37 °C for 20 min, followed by end repair and A-tailing at 65 °C for 30 min. Sequencing adapters and barcode indices were added in two steps, following the iTru adapter protocol 72 . Universal adapter ‘stub’ adapter molecules and ligase mix were first added to the end-repaired DNA using the Mosquito HV robot and ligation performed at 20 °C for 1 h. Unligated adapters and adapter dimers were then removed using AMPure XP magnetic beads and a BlueCat purification robot (BlueCat Bio). A 7.5 μl magnetic bead solution was added to the total adapter-ligated sample volume, washed twice with 70% ethanol and then resuspended in 7 μl molecular-grade water.

Next, individual i7 and i5 indices were added to the adapter-ligated samples using the Echo 550 robot. Because this liquid handler individually addresses wells and we used the full set of 384 unique error-correcting i7 and i5 indices, we generated each plate of 384 libraries without repeating any barcodes, eliminating the problem of sequence misassignment due to barcode swapping (61, 62). To ensure that libraries generated on different plates could be pooled if necessary and to safeguard against the possibility of contamination due to sample carryover between runs, we also iterated the assignment of i7 to i5 indices per run, such that each unique i7:i5 index combination is only repeated once every 147,456 libraries 72 . A volume of 4.5 μl of eluted bead-washed ligated samples was added to 5.5 μl of PCR master mix and PCR-amplified for 15 cycles. The amplified and indexed libraries were then purified again using AMPure XP magnetic beads and the BlueCat robot, resuspended in 10 μl of water and 9 μl of final purified library transferred to a 384-well plate using the Mosquito HTS liquid-handling robot for library quantitation, sequencing and storage. All samples were then normalized on the basis of a PicoGreen fluorescence assay for sequencing.

Samples were originally sequenced on an Illumina HiSeq 4000; however, due to some sequencing failures, samples were resequenced on the Illumina NovaSeq 6000 platform. To ensure that we obtained the best sequencing results possible, we assessed both sequencing runs and added the best-performing sample of the two runs to the final analysis (that is, if sample X provided more reads from the HiSeq run than the NovaSeq run, we added the HiSeq data from that sample to the final analysis and vice versa). Samples were visually assessed to ensure that no batch effects from the two sequencing runs were present in beta diversity analysis. A list of which samples were pulled from the HiSeq vs NovaSeq runs can be found in the sample metadata under the column ‘best_MetaG_run’, with their corresponding read count under ‘MetaG_read_count’ (Supplementary Table 1 ). In total, 762 samples were sequenced, with 25 coming from the HiSeq run and 737 samples coming from the Novaseq run. Raw metagenomic data had adapters removed and were quality filtered using Atropos (v.1.1.24) 73 with cut-offs of q  = 15 and minimum length of 100 nt. All human sequence data were filtered out by aligning against the Genome Reference Consortium Human Build 38 patch release 7 (GRCh37/hg19) reference database released in 21 March 2016 ( and removing all data that matched the reference from the sequence data. Alignment was performed with bowtie2 (v.2.2.3) 74 using the --very-sensitive parameter, and the resulting SAM files were converted to FASTQ format with samtools (v.1.3.1) 75 and bedtools (v.2.26.0) 76 . Metagenomic samples were removed from the analysis if they had <500 k reads. Final metagenomic sample numbers were 569 hip-adjacent soil, 5 soil controls, 102 blank controls and 15 no-template controls.

Metabolite extraction and LC–MS/MS data generation

To investigate the metabolite pools associated with decomposition skin and gravesoils, we performed metabolite extraction on the second tip of the dual-tipped swabs collected from the skin and soil associated with the hip sampling location to ensure all datasets are paired. Skin and soil swab samples were extracted using a solution of 80% methanol. Briefly (with all steps performed on ice), swabs were placed into a pre-labelled 96-well DeepWell plate where A1–D1 were used for a solvent blank and E1–H1 were used for blank clean swabs with extraction solvent added. Swab shafts were cut aseptically and 500 μl of solvent (80% methanol with 0.5 μM sulfamethazine) was added. The DeepWell plate was covered and vortexed for 2 min, followed by 15 min in a water sonication bath. Next, samples were incubated at 4 °C for 2 h, followed by a 12 h incubation at −20 °C. Swab tips were then removed from the solvent and samples were lyophilised. Untargeted metabolomics LC–MS/MS data were generated from each sample. Two types of dataset were generated from each sample: MS1 data for global and statistical analysis and MS/MS data for molecular annotation. Molecular annotation was performed through the GNPS platform . Molecules were annotated with the GNPS reference libraries 77 using accurate parent mass and MS/MS fragmentation pattern according to level 2 or 3 of annotation defined by the 2007 metabolomics standards initiative 78 . If needed and if the authentic chemical standard was available, MS/MS data were collected from the chemical standard and compared to MS/MS spectra of the molecule annotated from the sample (level 1 of annotation).

Amplicon data processing

After data generation, amplicon sequence data were analysed in the Metcalf lab at Colorado State University using the QIIME2 analysis platform v.2020.2 and v.2020.8 (ref. 79 ). In total, 4,139 samples were sequenced, including 592 DNA extraction blank negative and no-template PCR controls. Sequencing resulted in a total of 89,288,561 16S rRNA partial gene reads and 1,543,472,127 18S rRNA partial gene reads. Sequences were quality filtered and demultiplexed using the pre-assigned Golay barcodes. Reads were 150 bp in length. 18S rRNA gene sequences had primers (5′GTAGGTGAACCTGCAGAAGGATCA) removed using cutadapt to ensure that the variable length of the 18S region was processed without primer contamination. Sequences were then classified into amplicon sequence variants (ASVs) in groups of samples that were included on the same sequencing run so the programme could accurately apply the potential error rates from the machine using the Deblur denoising method (v.2020.8.0) 80 . Feature tables and representative sequences obtained from denoising each sequencing run were then merged to create a complete dataset for each amplicon method. Taxonomic identifiers were assigned to the ASVs using the QIIME feature-classifier classify-sklearn method 81 . For the 16S rRNA gene data, these assignments were made using the SILVA 132 99% classifier for the 515fb/806rb gene sequences. ASVs that were assigned to chloroplast or mitochondria (non-microbial sequences) were filtered out of the dataset before continuing analysis. For 18S rRNA data, the RESCRIPt (v.2022.8.0) plugin was used to extract the full 12-level taxonomy from sequences matching the primers from the SILVA 138 99% database, to dereplicate the extracted sequences and to train a classifier to assign labels to ASVs in the feature table 82 . This taxonomy was used to filter out any ASVs that were assigned to Archaea, Streptophyta, Bacteria, Archaeplastida, Arthropoda, Chordata, Mollusca and Mammalia, as well as those that were unassigned, resulting in 5,535 ASVs at a total frequency of 772,483,701. DNA extraction negative and no-template PCR control samples were analysed to determine that contamination within the samples was minimal. Most control samples were low abundance and below the threshold used for rarefaction. The few controls that were above the rarefaction threshold clustered distantly and separately from true samples on principal coordinate analysis (PCoA) and had low alpha diversities, hence samples above the rarefaction depth were considered minimally contaminated and acceptable for analyses. Subsequently, DNA extraction negative and no-template PCR control samples were removed from the dataset and future analyses.

Microbial diversity metrics were generated from both amplicon types using the QIIME2 phylogenetic diversity plugin. The phylogenetic trees were constructed for each amplicon type individually using the fragment-insertion SEPP method 83 against the SILVA 128 99% reference tree. Alpha diversity metrics were calculated using the number of observed features as ASV richness and Faith’s phylogenetic diversity formulas. Statistical comparisons were made using the pairwise Kruskal–Wallis H -test with a Benjamini–Hochberg multiple-testing correction at an alpha level of 0.05 (ref. 84 ). To evaluate beta diversity, the generalized UniFrac method weighted at 0.5 was used to calculate dissimilarity 85 . Statistical comparisons were made using permutational analysis of variance (PERMANOVA) with a multiple-testing correction and an alpha level of 0.05 (ref. 86 ). Taxonomy and alpha diversity visualizations were created using ggplot2 and the viridis package in R 87 , 88 . Beta diversity principal coordinates plots were constructed using the Emperor (v.2022.8.0) plugin in QIIME2 (ref. 89 ). Linear mixed-effects models were used to evaluate the contribution of covariates to a single dependent variable and to test whether community alpha diversity metrics (for example, ASV richness) and beta diversity distances (for example, UniFrac distances) were impacted by decomposition time (that is, ADD) and sampling location (that is, decomposition soil adjacent to the hip and control soil). The response variables were statistically assessed over ADD with sampling site (that is, decomposition soil vs control soil) as an independent variable (fixed effect) and a random intercept for individual bodies to account for repeated measures using the formula: diversity metric ≈ ADD × sampling site + (1|body ID).

Detection of key decomposers in other decomposition studies

16S rRNA gene amplicon sequence data files from refs. 6 , 24 , 25 , 64 , 69 , 90 , 91 were obtained from QIITA 92 under study IDs 10141–10143, 1609, 13114, 10317, 13301 and 11204, respectively. Data obtained from QIITA 92 had been previously demultiplexed and denoised using Deblur 80 and are available on the QIITA 92 study page. Data from ref. 16 were obtained from the NCBI Sequence Read Archive under BioProject PRJNA525153 . Forward reads were imported into QIIME2 (v.2023.5) 79 , demultiplexed and denoised using Deblur (v.1.1.1) 80 . Data from ref. 26 were obtained from the Max Planck Society Edmond repository ( ). Forward reads were imported into QIIME2 (v.2023.5) 79 and demultiplexed. Primers (5′ GTGCCAGCMGCCGCGGTAA) were removed using cutadapt (v.4.4) 93 and the data were denoised using Deblur (v.1.1.1) 80 . ASVs from all studies were assigned taxonomy using a naïve Bayes taxonomy classifier trained on the V4 (515f/806r) region of SILVA 138 99% operational taxonomic units (OTUs). Data tables were imported into Jupyter notebooks (Jupyter Lab v.4.0.5) 94 for further analysis (Python v.3.8.16). A search for the 35 universal PMI decomposer ASVs was conducted within each dataset. This search matched exact ASVs in our dataset to other datasets but did not match similar ASVs that may be classified as the same taxon. The relative abundance of each decomposer ASV was first averaged across all samples within a specific metadata category. The average relative abundances were then summed across each decomposer genus. Prevalence tables were constructed by summing the number of samples across a specific metadata category in which each universal decomposer ASV was present. The presence of Wohlfahrtiimonas was found in the ref. 26 dataset; however, these ASVs were not exact sequence matches to our universal Wohlfahrtiimonas decomposers and probably represent insect-associated strains (Supplementary Table 33 ; Wohlfahrtiimonadaceae column). We searched within the remaining studies for the presence of other ASVs assigned to the Wohlfahrtiimonas genus or ASVs that were assigned to the Wohlfahrtiimonadaceae family but these were unidentified at the genus level. Average relative abundances were calculated as described above.

Community assembly mechanism determination

To investigate the ecological processes driving bacterial assembly, we quantitatively inferred community assembly mechanisms by phylogenetic bin-based null model analysis of 16S rRNA gene amplicon data as described in refs. 95 , 96 . Longitudinal turnover in phylogenetic composition within the decomposition soil between successional stages was quantified using the beta nearest taxon index (βNTI), where a |βNTI| value <+2 indicates that stochastic forces drive community assembly and a value >+2 indicates less than or greater than expected phylogenetic turnover by random chance (deterministic forces). βNTI values <−2 correspond to homogeneous selection and values >+2 correspond to heterogeneous selection. Homogeneous selection refers to communities that are more similar to each other than expected by random chance, while heterogeneous selection refers to communities that are less similar to each other than expected by random chance. Deterministic forces include selection factors such as environmental filtering and biological interactions, while stochastic forces include random factors such as dispersal, birth–death events and immigration.

MAGs generation and classification

To maximize assembly, metagenomes were co-assembled within sites using MEGAHIT (v.1.2.9) 97 with the following flags: –k-min 41 (see Supplementary Tables 4 – 6 for a list of samples used to generate metagenomic data, co-assembly statistics, GTDB taxonomic classification and TPM-normalized count abundance of MAGs within each sample). Assembled scaffolds >2,500 kb were binned into MAGs using MetaBAT2 (v.2.12.1) 98 with default parameters. MAG completion and contamination were assessed using checkM (v.1.1.2) 99 . MAGs were conservatively kept in the local MAG database if they were >50% complete and <10% contaminated. MAGs were dereplicated at 99% identity using dRep (v.2.6.2) 100 . MAG taxonomy was assigned using GTDB-tk (v.2.0.0, r207) 101 . Novel taxonomies were determined as the first un-named taxonomic level in the GTDB classification string (see Supplementary Table 5 for MAG quality and taxonomy information). MAGs and co-assemblies were annotated using DRAM (v.1.0.0) 102 (Supplementary Table 5 ; ). From 575 metagenomes, we recovered 1,130 MAGs, of which 276 were medium or high quality, and dereplicated these at 99% identity into 257 MAGs. This MAG set encompassed novel bacterial orders ( n  = 3), families ( n  = 9), genera ( n  = 28) and species ( n  = 158), providing genomic blueprints for microbial decomposers dominated by Gammaproteobacteria and Actinobacteriota (Supplementary Table 5 ).

MAG and gene abundance mapping

To determine the abundance of the MAGs in each sample, we mapped reads from each sample to the dereplicated MAG set using bowtie2 (v.2.3.5) 74 with the following flags: -D 10 -R 2 -N 1 -L 22 -i S,0,2.50. Output sam files were converted to sorted BAM files using samtools (v.1.9) 75 . BAM files were filtered for reads mapping at 95% identity using the script with flag idfilter=0.95 from BBMap (v.38.90) ( ). Filtered BAM files were input to CoverM (v0.3.2) ( ) in genome mode to output transcripts per million (TPM). To determine the abundance of genes across samples, we clustered the gene nucleotide sequences from the annotated assemblies output by DRAM using MMseqs2 (release 13) easy-linclust (v4e23d5f1d13a435c7b6c9406137ed68ce297e0fc) 103 with the following flags: –min-seq-id 0.95–alignment-mode 3–max-seqs 100000. We then mapped reads to the cluster representative using bowtie2 (ref. 74 ) and filtered them to 95% identity as described above for the MAGs. To determine gene abundance, filtered bams were input to coverM in contig mode to output TPM. Bacterial MAG feature tables were imported into QIIME2 (v.2020.8) 79 . Bacterial features that were not present for a total of 50 times and were found in less than six samples were removed from the dataset to reduce noise. Bacterial feature tables were collapsed at the phylum, class, order, family, genus and species GTDB taxonomic levels. Community diversity was compared between the MAG and 16S rRNA ASV feature tables to ensure that both data types demonstrate the same biological signal. Each table was filtered to contain samples with paired 16S rRNA and metagenomic data (that is, samples with both metagenomic and 16S rRNA data). Bray–Curtis dissimilarity matrices were calculated for the TPM-normalized MAG abundance table and rarified 16S rRNA ASV table. Procrustes/PROTEST 104 , 105 and Mantel tests were performed between the PCoA ordinations and distance matrices, respectively 106 . Results showed that the datasets were not significantly different from each other and confirmed their shared biological signal (Extended Data Fig. 10 ).

Metabolic interaction simulations

Higher-order (20 microbial members) co-occurrence patterns were calculated from the MAG relative frequency tables of each decomposition stage (that is, early, active, advanced) for each facility using HiOrCo (v.1.0.0) (cut-off 0.001) ( ). HiOrCo provides 100 iterations of co-occurring MAG communities to improve simulation accuracy. No significantly co-occurring MAGs were detected at the FIRS facility during advanced decomposition; therefore, we continued the analyses using only early and active decomposition stages at FIRS. CarveMe (v.1.5.1) 107 was used to construct genome-scale metabolic models (GEMs) from each MAG using default parameters ( ). GEMs from each co-occurring MAG community were input as a microbial community into SMETANA (v1.0.0) ( ) to compute several metrics that describe the potential for metabolic cooperative and competitive interactions between community members as described in refs. 34 , 35 . Metrics include metabolic interaction potential (MIP), metabolic resource overlap (MRO), species coupling score (SCS), metabolite uptake score (MUS), metabolite production score (MPS) and SMETANA score. MIP calculates how many metabolites the species can share to decrease their dependency on external resources. MRO is a method of assessing metabolic competition by measuring the overlap between the minimal nutritional requirements of all member species on the basis of their genomes. SCS is a community size-dependent measurement of the dependency of one species in the presence of the others to survive. MUS measures how frequently a species needs to uptake a metabolite to survive. MPS is a binary measurement of the ability of a species to produce a metabolite. The individual SMETANA score is a combination of the SCS, MUS and MPS scores and gives a measure of certainty of a cross-feeding interaction (for example, species A receives metabolite X from species B). Simulations were created on the basis of a minimal medium, calculated using molecular weights, that supports the growth of both organisms, with the inorganic compounds hydrogen, water and phosphate excluded from analysis. A random null model analysis was performed to ensure that changes in co-occurring MAGs within each site and decomposition are driving interaction potential changes. For each site and decomposition stage, 100 20-member communities were generated by random selection without replacement using random.sample(). Simulations to calculate MIP and MRO were performed as above. A detailed investigation into the potential molecules being cross-fed was performed on the late stages of decomposition for each facility: temperate-climate advanced decomposition and semi-arid active decomposition stages.

Metabolic efficiency simulations

Metabolic models and the Constraint Based Reconstruction and Analysis (COBRA) toolbox (v.3.0) 108 were used to simulate differences in metabolic capabilities between samples that are spatiotemporally different. A general base growth medium, M 0 , containing a list of carbohydrates, amino acids, lipids and other vitamins and minerals adapted from a previous study 109 was used. From this base medium, carbohydrate-rich, M 1 , amino acid-rich, M 2 , and lipid-rich, M 3 , media were defined. The carbohydrate-rich medium includes all compounds in the base medium but allows for higher uptake of carbohydrates than proteins and lipids, and vice versa. The COBRA toolbox 108 in MATLAB was used to optimize overall ATP production from M 1 , M 2 and M 3 for each individual MAG in an aerobic condition. This assumption was made because the topsoil conditions in which decomposition happens are relatively aerobic. The calculated maximum ATP yields can be interpreted as the maximum capability of each MAG in extracting ATP from the growth media. Finally, the weighted average of total ATP production from the GEMs in a sample was calculated by multiplying the relative abundance of each MAG by the maximum total ATP production and summing over all of the GEMs in a sample 110 .

Molecular networking and spectral library search

A molecular network was created using the Feature-Based Molecular Networking (FBMN) workflow (v.28.2) 111 on GNPS ( ; ref. 77 ). The mass spectrometry data were first processed with MZMINE2 (v.2.53) 112 and the results were exported to GNPS for FBMN analysis. The precursor ion mass tolerance was set to 0.05 Da and the MS/MS fragment ion tolerance to 0.05 Da. A molecular network was then created where edges were filtered to have a cosine score above 0.7 and >5 matched peaks. Furthermore, edges between two nodes were kept in the network if and only if each of the nodes appeared in each other’s respective top 10 most similar nodes. Finally, the maximum size of a molecular family was set to 100, and the lowest-scoring edges were removed from molecular families until the molecular family size was below this threshold. The spectra in the network were then searched against GNPS spectral libraries 77 , 111 . All matches kept between network spectra and library spectra were required to have a score above 0.7 and at least 6 matched peaks.

Metabolite formula and class prediction

Spectra were downloaded from GNPS and imported to SIRIUS (v.4.4) 113 containing ZODIAC 114 for database-independent molecular formula annotation under default parameters. Formula annotations were kept if the ZODIAC score was at least 0.95 and at least 90% of the MS/MS spectrum intensity was explained by SIRIUS as described by the less-restrictive filtering from ref. 114 . A final list of formula identifications was created by merging ZODIAC identifications with library hits from GNPS (Supplementary Table 36 ). In the cases where a metabolite had both a ZODIAC predicted formula and an assigned library hit, the library hit assignment took precedence. The final formula list contained 604 formula assignments. Organic compound composition was examined in van Krevelen diagrams and assigned to major biochemical classes on the basis of the molar H:C and O:C ratios 115 . Since classification based on molecular ratio does not guarantee that the compound is part of a specific biochemical class, compounds were labelled as chemically similar by adding ‘-like’ to their assigned class (for example, protein-like). Furthermore, compound formulas were used to calculate the nominal oxidation state of carbon on the basis of the molecular abundances of C, H, N, O, P and S as described in ref. 116 (Supplementary Tables 37 and 38 ).

Metabolite feature table processing

The metabolite feature table downloaded from GNPS was normalized using sum normalization, then scaled with pareto scaling 117 and imported in QIIME2 (v.2022.2) 79 . This table contains all library hits, metabolites with predicted formulas and unannotated metabolites. PCoA clustering with Bray–Curtis and Jaccard distances confirmed clustering of processing controls separate from soil and skin samples. Five soil samples were removed for clustering with processing controls. Processing controls were removed from the dataset; then metabolites absent from a minimum of 30 samples were removed to reduce noise. Bray–Curtis and Jaccard beta diversity group comparisons were performed between soil and skin samples using PERMANOVA (perm. = 999). The metabolite feature table was filtered to contain metabolites with chemical formulas based on GNPS library hits and/or predicted chemical formulas from ZODIAC. Differential abundance analyses were performed on these tables from the cadaver-associated soil and skin to test metabolite log-ratio change over decomposition stage using initial, day 0 samples as the reference frame, utilizing the Analysis of Composition of Microbiomes with Bias Correction (ANCOM-BC) 118 QIIME2 (v.2022.2) plugin.

The complete methodology including mathematical formulas for joint-RPCA can be found in Supplementary Text . Briefly, before joint factorization, we first split the dataset into training train and testing sample sets from the total set of shared samples across all input data matrices. The datasets included in this analysis were 16S rRNA gene abundances, 18S rRNA gene abundances, MAG abundances, MAG gene abundances, MAG gene functional modules and metabolites from the hip-adjacent decomposition soil. Each matrix was then transformed through the robust-centred-log-ratio transformation (robust-clr) to centre the data around zero and approximate a normal distribution 42 , 119 . Unlike the traditional clr transformation, the robust-clr handles the sparsity often found in biological data without requiring imputation. The robust-clr transformation was applied to the training and test set matrices independently. The joint factorization used here was built on the OptSpace matrix completion algorithm, which is a singular value decomposition optimized on a local manifold 42 , 119 . A shared matrix was estimated across the shared samples of all input matrices. For each matrix, the observed values were only computed on the non-zero entries and then averaged, such that the minimized shared estimated matrices were optimized across all matrices. The minimization was performed across iterations by gradient descent. To ensure that the rotation of the estimated matrices was consistent, the estimated shared matrix and the matrix of shared eigenvalues across all input matrices were recalculated at each iteration. To prevent overfitting of the joint-factorization, cross-validation of the reconstruction was performed. In this case, all the previously described minimization was performed on only the training set data. The test set data were then projected into the same space using the training set data estimated matrices and the reconstruction of the test data was calculated. Through this, it can be ensured that the minimization error of the training data estimations also minimizes that of the test set data, which is not incorporated into these estimates on each iteration. After the training data estimates were finalized, the test set samples were again projected into the final output to prevent these samples from being lost. The correlations of all features across all input matrices were calculated from the final estimated matrices. Finally, here we treated the joint-RPCA with only one input matrix as the original RPCA 119 but with the additional benefit of the addition of cross-validation for comparison across other methods.

Multi-omics ecological network visualization

The datasets included in this analysis were 18S rRNA gene abundances, MAG abundances, MAG gene functional modules and metabolites from the hip-adjacent decomposition soil. log ratios were generated using the joint-RPCA PC2 scores, chosen on the basis of the sample ordination, to rank each omics feature on the basis of association with either initial non-decomposition and early decomposition soil or late decomposition (that is, active and advanced) soil time periods. The log ratios are the log ratio of the sum of the top N -features raw-counts/table-values over the sum of the bottom N ranked features raw-counts/table-values, based on the PC2 loadings produced from the ordinal analysis since these were observed to change the most by decomposition stage. To prevent sample drop out in the log ratio due to sparsity, as described in refs. 120 , 121 , between 2 and 1,500 numerator and denominator features for each omic were summed such that at least 90% of the sample were retained: metagenomics (MAGs) N -features = 30 (99.2%), 18S N -features = 1,499 (90.1%), metagenomics (gene modules) N -features = 26 (100%) and metabolomics N -features = 238 (100%). The joint-RPCA correlation matrix was subset down to the total initial day zero, early, active or advanced decomposition-associated features used in the log ratios to generate the network visualizations. Only the top 20% of correlations between selected nodes were retained to reduce noise in generating the network visualization.

Phylogenetic tree generation

Redbiom (v.0.3.9) 122 was used to search for all publicly available AGP 90 and EMP 69 studies for samples containing at least 100 counts of a key decomposer. The AGP samples were further filtered to only include gut and skin environments and the EMP samples were limited to only include soil and host environment. Next, the top 50 most abundant ASVs were taken from each environment along with the key decomposers and placed on a phylogenetic tree using Greengenes2 (release 2022.10) 123 . The ASVs were then ranked according to the number of samples they were found in and visualized using EMPress (v.1.2.0) 124 .

Random forest regression modelling

Processed features tables from each ‘omic data type were used for random forest regression modelling with nested cross-validation (CV) to test ADD prediction power. Data were subset so that models were trained and tested for each sampling location separately (for example, soil adjacent to the hip, soil adjacent to the face, skin of the hip and skin of the face). Data were pre-processed for models using calour (v.2018.5.1) ( ) and models were trained/tested using scikit-learn (v.0.24.2) 125 . Features with an abundance of zero in the dataset after filtering were removed. The facilities at which sampling was performed were included as features in the model to determine whether geographical location is important for modelling. Samples from individual bodies were grouped together to prevent samples from a body being split between train and test sets to help prevent overfitting. Nested CV was performed to thoroughly test the accuracy and generalizability of the models. Hyperparameters tested for optimization were: max_depth = [None, 4], max_features = [‘auto’, 0.2] and bootstrap = [True, False]. Nested CV was made of an outer CV loop and an inner CV loop. The outer loop was created by a LeaveOneGroupOut split wherein samples from one of the 36 bodies were set aside for model validation after the inner CV loop completes. The remaining 35 bodies were used for RandomForestRegressor (n_estimators = 500) model training with the inner CV loop. The inner CV loop performed a LeaveOneGroupOut split as well so that 34 bodies were used to train a model, which was tested on the samples from the one withheld body in the inner CV loop. This inner CV was repeated until all 35 bodies within the inner loop were used as a test body once to determine which hyperparameters were best for prediction. The best-performing inner CV model was then used to predict the samples from the 36th body that was withheld at the outer CV loop, which now acts as a validation test set. Model accuracy was determined by calculating the MAE of the predicted ADD relative to the actual ADD of all the validation body samples. The prediction of the samples from the 36th body, which was completely withheld from the training of the model, allowed us to reduce overfitting and gain an estimate of the model accuracy. The entire nested CV process was repeated until each body was used as the outer CV loop validation body one time (that is, 36 iterations). The resulting 36 mean absolute errors of each body were used for determining model accuracy, generalizability and which data type performed the best. To ensure that we were using the complete dataset to determine the important taxa driving the models, the best-performing hyperparameters (bootstrap=False, max_depth=None, max_features=0.2) were used to train a RandomForestRegressor (n_estimators = 1,000) model to extract the important features. Important features were ranked by their relative importance on a scale from 0–1, where the sum of all importances equals 1. A random forest model using TBS from each sampling day as training data for ADD prediction was trained and tested using the same methodology to compare microbiome-based models to a more traditional method of assessing decomposition progression.

Lastly, we confirmed the accuracy and reliability of postmortem interval prediction with an independent test set of samples collected from bodies not represented in our models. The independent test set was collected from hip-adjacent soil and skin of the hip locations across three facilities (ARF, Forensic Anthropology Research Facility in San Marcos, Texas (FARF) and Research on Experimental and Social Thanatology in Quebec, Canada (REST)) (Supplementary Table 39 ). The independent test set was made up of temporal samples taken from each facility. ARF and REST samples consisted of three bodies with three timepoints taken from each body at each facility. At each timepoint, a soil sample was swabbed within the purge and outside the purge, and a skin sample was swabbed from the hip. One ARF body (B3.D4) did not have purge during the first timepoint; therefore, this sample was not collected. FARF provided samples from four bodies. Two bodies (2021.04 and 2021.45) had the same sampling procedure as ARF and REST, while the other two bodies (2021.39 and 2021.44) did not have purge during the first sampling timepoint; hence samples were not collected. Samples were collected, shipped, stored, DNA extracted and 16S rRNA V4 sequenced using the previously described methods. After data generation, amplicon sequence data were analysed in the Metcalf lab using QIIME2 (v.2020.8) 79 . Sequences were quality filtered and demultiplexed using the pre-assigned Golay barcodes. Reads were 150 bp in length. Sequences were then classified into ASVs using the deblur denoising method 80 . Taxonomic identifiers were assigned to the ASVs using the QIIME feature-classifier classify-sklearn method 81 using the SILVA 132 99% classifier for the 515fb/806rb gene sequences. ASVs that were assigned to chloroplast or mitochondria (non-microbial sequences) were filtered out of the dataset before continuing analysis. Data were rarified to 5,000 reads per sample and collapsed to the SILVA database 7-rank taxonomic level (L7). Feature tables were split into soil and skin data; then the validation data table was matched to the original dataset so that sampling location and features were the same (that is, using only taxa found in hip-adjacent soil in both datasets). A random forest regressor model (n_estimators=1000, max_depth=None, bootstrap=False, max_features=0.2) was built and fitted to predict the validation samples’ true ADD measurement. Randomly assigned ADDs were used as a null model.

Statistics and reproducibility

From March 2016 to December 2017, 36 human cadavers were sampled daily starting on the day of placement through 21 d of decomposition. The study encompasses three geographically distinct anthropological research facilities, and 3 cadavers were placed at each facility for each of the four seasons. Swab samples were collected from soil directly adjacent to the hip, face and a control, non-decomposition location. Swab samples were also collected from skin located on the hip and the face. No statistical method was used to predetermine sample size. The samples were randomized during processing. The investigators were not blinded to allocation during experiments and outcome assessment. Samples were excluded if not enough DNA was extracted, sequenced or if sequence quality was poor. Negative controls were included during DNA/metabolite extraction, amplification and library preparation. Linear statistical modelling was performed with linear mixed-effects models to a single dependent variable, and response variables were statistically assessed over ADD with a random intercept for individual bodies to account for repeated measures. Group comparisons were performed using Dunn Kruskal–Wallis H -test with multiple-comparison P values adjusted using the Benjamini–Hochberg method, two-tailed analysis of variance (ANOVA) with no multiple-comparison adjustments, or PERMANOVA with a multiple-testing correction. Differential abundance analyses were performed using ANCOM-BC 118 with initial, day 0 samples as the reference frame. Procrustes/PROTEST 104 , 105 and Mantel tests were performed between PCoA ordinations and distance matrices, respectively 106 .

Reporting summary

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

Data availability

Raw amplicon and metagenomic sequencing data and sample metadata are available on the QIITA open-source microbiome study management platform under study 14989 and ENA accession PRJEB62460 ( ERP147550 ). Dereplicated MAGs and DRAM output can be found publicly on Zenodo ( ; ) and NCBI BioProject PRJNA973116 . The mass spectrometry data were deposited on the MassIVE public repository (accession numbers: MSV000084322 for skin samples and MSV000084463 for soil samples). The molecular networking job can be publicly accessed at . The GNPS database was accessed through . The GreenGenes2 database can be found at . SILVA databases can be found at . The Earth Microbiome Project data and American Gut Project data can be found on EBI under accessions ERP125879 and ERP012803 , respectively. 16S rRNA gene amplicon sequence data files from refs. 6 , 24 , 25 , 64 , 69 , 90 , 91 were obtained from QIITA 92 under study IDs 10141–10143 (ref. 6 ), 1609 (refs. 24 , 25 ), 13114 (ref. 69 ), 10317 (ref. 90 ), 13301 (ref. 64 ) and 11204 (ref. 91 ). Data from ref. 16 were obtained from the NCBI Sequence Read Archive under BioProject PRJNA525153 . Data from ref. 26 were obtained from the Max Planck Society Edmond repository ( ). The GTDB data can be accessed at . Source data are provided with this paper.

Code availability

Analysis code, intermediate files and metadata are publicly available on Github ( ). The complete mathematical algorithms for Joint-RPCA can be found in Supplementary Text .

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Foremost, we thank the willed-body donors for their contribution to science; A. Esterle, K. Otto, H. Archer, C. Carter, R. Reibold, L. Burcham, J. Prenni and the CSU Writes programme for technical and resource contributions; A. Buro, V. Rodriguez, M. Sarles, A. Hartman and A. Uva at SHSU for field contributions. Opinions or points of view expressed here represent a consensus of the authors and do not necessarily represent the official position or policies of the US Department of Justice. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the US Government. Funding was provided by the National Institutes of Justice (2016-DN-BX-0194, J.L.M.; 2015-DN-BX-K016, J.L.M.; GRF STEM 2018-R2-CX-0017, A.D.B.; GRF STEM 2018-R2-CX-0018, H.L.D.), the Canadian Institute for Advanced Research Global Scholar Program (J.L.M.), National Science Foundation Early Career Award (1912915, K. C. Wrighton) and National Institutes of Health T32 Training Award (T32GM132057, V.N.).

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Zachary M. Burcham, Aeriel D. Belk, Alexandra Emmons, Victoria Nieciecki & Jessica L. Metcalf

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Z.M.B., D.O.C., R.K., K. C. Wrighton and J.L.M. conceptualized the project. Z.M.B., A.D.B., B.B.M., A.B., C.M., H.L.D., M.P., K. C. Weldon, G.C.H., G.A., M.C., D.B., J.S., G.V., D.S., A.M.L., S.B., P.C.D., K. C. Wrighton, D.O.C., R.K. and J.L.M. contributed to data curation. Z.M.B., A.D.B., B.B.M., P.G., C.M., L.S., A.R.Z., P.S., A.E., H.L.D., V.N., M.S., K.C. and D.M. conducted formal analysis. K. C. Wrighton, D.O.C., R.K. and J.L.M. acquired funding. A.B., M.P., K. C. Weldon, M.C., D.B., J.S., J.M.S.W., G.V., D.S., A.M.L. and S.B. contributed to project investigation. Z.M.B., A.D.B., B.B.M., A.B., P.G., C.M., L.S., A.R.Z., P.S., Z.Z.X., V.N., Q.Z., M.S., M.P., K. C. Weldon, K.C., A.B.-H., S.H.J.C., M.C., D.B., J.S., G.V., D.S., A.M.L., S.B., P.C.D., K. C. Wrighton, D.O.C., R.K. and J.L.M. developed the methodology. Z.M.B., M.C., G.V., D.S., A.M.L., S.B., P.C.D., K. C. Wrighton, D.O.C., R.K. and J.L.M. administered the project. S.H.J.C., M.C., G.V., D.S., A.M.L., S.B., P.C.D., K. C. Wrighton, D.O.C. and R.K. provided resources. Z.M.B., A.D.B., B.B.M., P.G., C.M., L.S., A.R.Z., P.S., Z.Z.X., M.S., K.C., A.B.-H., D.M. and P.C.D. developed software. S.H.J.C., M.C., G.V., D.S., A.M.L., S.B., P.C.D., K. C. Wrighton, D.O.C. and R.K. supervised the project. Z.M.B., A.D.B., B.B.M., P.G., C.M., M.C., G.V., D.S., A.M.L., S.B., P.C.D., K. C. Wrighton, R.K. and J.L.M. conducted data validation. Z.M.B., A.D.B., B.B.M., P.G., C.M., A.E. and S.C.R. worked on visualization. Z.M.B., A.D.B., A.E., B.B.M., S.C.R., D.O.C. and J.L.M. wrote the original draft. Z.M.B., A.D.B., B.B.M., P.G., C.M., H.L.D., S.C.R., D.M., M.C., S.B., P.C.D., K. C. Wrighton, D.O.C., R.K. and J.L.M. reviewed and edited the manuscript.

Corresponding author

Correspondence to Jessica L. Metcalf .

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Competing interests.

P.C.D. consulted in 2023 for DSM animal health, is a consultant and holds equity in Sirenas and Cybele Microbiome, and is founder and scientific advisor and has equity in Ometa Labs LLC, Arome and Enveda (with approval by UC San Diego). R.K. is affiliated with Gencirq (stock and SAB member), DayTwo (consultant and SAB member), Cybele (stock and consultant), Biomesense (stock, consultant, SAB member), Micronoma (stock, SAB member, co-founder) and Biota (stock, co-founder). The other authors declare no competing interests.

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Extended data

Extended data fig. 1 study information..

Average a ) temperature data and b ) total precipitation per location over experiment with cadaver placement dates. Temperature data was collected from local weather stations reported to the National Centers for Environmental Information. Total monthly precipitation data was collected from Weather Underground. The vertical line represents the date of placement and line color denotes the season the body placement is considered to have been placed. c ) Upset plot illustrating the intersections between sample and omic types after extractions, processing and quality filtering that were used for further analyses. MetaG = metagenomics, Metab = metabolomics, 18S = 18S rRNA amplicon, and 16S = 16S rRNA amplicon.

Extended Data Fig. 2 Metabolome Comparison.

Principal coordinate analysis (PCoA) of a ) Jaccard and b ) Bray-Curtis distances of all unique metabolites and all metabolomic samples show cadaver skin and cadaver-associated soil are significantly different community profiles. n = 1503 biologically independent samples. Significance was determined by PERMANOVA (permutations = 999). Van Krevelen diagram showed a strong presence of lipid-like, protein-like, and lignin-like classes within c ) cadaver-associated soils and d ) cadaver skin. Metabolites that matched database chemical formulas or had a significantly predicted chemical formula were assigned a Van Krevelen organic compound classification by their hydrogen:carbon and oxygen:carbon molar ratios. Colors correspond to organic compound classification. Nominal oxidation state of carbon (NOSC) scores for cadaver-associated e ) soil and f ) cadaver skin metabolites with assigned chemical formulas show significant decrease of thermodynamic favorability at all geographical locations over decomposition time measured by accumulated degree days (ADD). Soil: ARF n = 251, STAFS n = 250, and FIRS n = 245 biologically independent samples. Skin: ARF n = 250, STAFS n = 249, and FIRS n = 249 biologically independent samples. Data are presented as mean values +/− 95% CI. Significance measured with linear mixed-effects models within each location and adding a random intercept for cadavers with two-tailed ANOVA and no multiple comparison adjustments. g ) Lipid-like metabolites show an increased abundance in cadaver-associated soils over decomposition measured by accumulated degree days (ADD) and significantly increase in temperate soils. h ) Protein-like metabolites are less abundant than lipid-like metabolites in cadaver-associated soils over decomposition measured by accumulated degree days (ADD) and significantly decrease in STAFS soil. ARF n = 251, STAFS n = 250, and FIRS n = 245 biologically independent samples. Data are presented as mean values +/− 95% CI. Significance measured with linear mixed-effects models within each location and adding a random intercept for cadavers with two-tailed ANOVA and no multiple comparison adjustments. Metabolite abundance normalized by center log ratio transformation.

Extended Data Fig. 3 Community Assembly.

Sankey diagram of the a ) 257 99% dereplicated, medium to high quality MAGs with Genome Taxonomy Database classifications and b ) the average MAG abundances (given as transcript per million, TPM) at each decomposition stage within each location. Proteobacteria and Bacteroidota representation increases with decomposition while Actinobacteria representation decreases at each location. This MAG set encompassed novel bacterial orders (n=3), families (n=9), genera (n=28), and species (n=158). Proteobacteria is the highest represented phylum. c ) Spearman correlation of the maximum ATP per C-mol for lipids, carbohydrates, and amino acids over ADD at each location represented by circle size. Metabolism efficiency is correlated with ADD in temperate climates. ARF n = 212, STAFS n = 198, and FIRS n = 158 biologically independent samples. Significance measured with linear mixed-effects models within each location and adding a random intercept for cadavers and denoted as p<0.05 (*), p<0.01 (**), and p<0.001 (***). ARF: Amino Acids p = <2e-16, STAFS: Amino Acids p = 1.18e-06, and Carbohydrate p = 4.22e-04. d ) The amino acid metabolism efficiency of the total community that can be attributed to O. alkaliphila and e ) the carbohydrate metabolism efficiency of the total community that can be attributed to C. intestinavium increase over decomposition at temperate locations as a product of the genome’s metabolism efficiency and relative abundance. Data plotted with loess regression as mean values +/− 95% CI. ARF n = 212, STAFS n = 198, and FIRS n = 158 biologically independent samples. f ) Pairwise comparisons to obtain beta nearest taxon index (βNTI) values focused on successional assembly trends by comparing initial non-decomposition soil to early decomposition soil then early to active, etc. (PL = placement, EA = early, AC = active, AD = advanced) in the 16S rRNA amplicon dataset. Relative abundance of assembly forces reveals that heterogeneous selection (βNTI > +2) pressure increases and homogenous selection (βNTI < -2) decreases over decomposition. Stochastic forces are a constant driver of community assembly (+2 > βNTI > -2). g ) Predicted metabolic competition from metagenome-assembled genomes are site-specific and significantly altered over decomposition. STAFS: early-active p = 3.42e-11, early-advanced p = 1.23e-11, active-advanced p = 7.85-41, FIRS: early-active p = 0.042. h ) Predicted metabolic cooperation and competition from metagenome-assembled genomes randomly subsampled into 20-member communities within each site and decomposition serves as a null model comparison signifying the importance of MAG co-occurrence. ARF n = 201, STAFS n = 188, and FIRS n = 151 biologically independent samples. The lower and upper hinges of the boxplot correspond to the first and third quartiles (the 25th and 75th percentiles). The upper whisker extends from the hinge to the largest value no further than 1.5 * IQR from the hinge, and the lower whisker extends from the hinge to the smallest value at most 1.5 * IQR of the hinge. The center of the boxplot is represented by the median. Significance measured with Dunn Kruskal-Wallis H-test with multiple comparison p-values adjusted with the Benjamini-Hochberg method as denoted by p<0.05 (*), p<0.01 (**), and p<0.001 (***).

Extended Data Fig. 4 Multi-omic Integration.

a ) ASV richness comparison between decomposition soil and control soil over the decomposition time frame reveals that bacterial richness decreases significantly at temperate locations. ARF n = 414, STAFS n = 316, and FIRS n = 310 biologically independent samples. Significance measured with linear mixed-effects models within each location and adding a random intercept for cadavers with two-tailed ANOVA and no multiple comparison adjustments. ARF and STAFS richness p = <2e-16. Denoted as p<0.05 (*), p<0.01 (**), and p<0.001 (***). b ) Multi-omic joint-RPCA shows that microbial community ecology is impacted by season and geographical location. Multi-omic Joint-RPCA incorporates soil 16S rRNA, 18S rRNA, metabolomic, and metagenome-assembled genome data. All data types used the same n = 374 biologically independent samples. Multi-omics joint-RPCA principal component scores show that c ) facility variation is primarily explained by principal component 3 (PC3) and PC4, d ) decomposition stage is primarily explained by PC2, e ) season is primarily explained by PC1, and f ) climate is primarily explained by PC3 and PC4 as described by the least overlap of PC values between groups. g ) PC2 from the multi-omics joint-RPCA scores for each geographical location over decomposition stages shows the temperate climate locations are the most dynamic in their microbial ecology. Multi-omic Joint-RPCA incorporates soil 16S rRNA, 18S rRNA, metabolomic, and metagenome-assembled genome data. All data types used the same n = 374 biologically independent samples. Data in panel g are presented as mean values +/− 95% CI.

Extended Data Fig. 5 Universal Initial Non-Decomposition And Early Decomposition Soil Network.

Top 20% of correlations between selected nodes for the universal initial non-decomposition and early decomposition soil log-ratio signal in Joint-RPCA PC2 visualized in co-occurrences network. All data types used the same n = 374 biologically independent samples.

Extended Data Fig. 6 Decomposer ASVs Placed in Current Databases.

Phylogenetic tree representing ASVs associated with the key decomposer nodes from the network placed along within the top 50 most abundant ASVs taken from AGP gut, AGP skin, EMP soil, and EMP host-associated datasets demonstrates key decomposers are largely phylogenetically unique. Innermost ring represents decomposer placement while outer rings represent AGP and EMP ASVs, for which bar height represents ASV rank prevalence within each environment. AGP and EMP ASVs were ranked according to the number of samples they were found in each environment. A lack of bars represents that the ASV was not present within the dataset. Decomposer ASVs are numbered clockwise with full taxonomy available in Supplementary Table 27 .

Extended Data Fig. 7 Important Features for 16S rRNA Random Forest Models.

The 20 most important SILVA level-7 taxa as determined in the 16S rRNA random forest regression models for predicting postmortem interval shows that many of the same taxa appear important for model prediction within all sample types, but some differences do emerge.

Extended Data Fig. 8 Longitudinal Abundances of Important Features.

The 6 most important SILVA level-7 taxa as determined in the 16S rRNA data from the a ) skin of the face, b ) skin of the hip, c ) soil associated with the hip, and d ) soil associated with the face for random forest regression models for predicting postmortem interval. Data plotted by the taxa and the normalized abundance change over ADD at each geographic location. Data plotted with loess regression and 16S rRNA soil face, soil hip, skin face, and skin hip datasets contain n = 600, 616, 588, and 500 biologically independent samples, respectively. Data are presented as mean values +/− 95% CI.

Extended Data Fig. 9 16S rRNA Random Forest Model Validation.

a ) Total body scores (TBS) used to train a random forest model for prediction of PMI (ADD) shows that TBS scores can predict PMI relatively accurately based on a low MAE but have higher variability in their predictions as represented by a higher residual value than microbiome-based models. Models built from 16S rRNA data using SILVA level-7 taxa from the skin and soil associated with the hip were validated with b ) an independent test set of samples that were collected from cadavers at locations and climates not represented in our model and c ) the same data where samples were given randomly assigned ADDs within the range of true ADDs to serve as a null model. Significance measured with linear mixed-effects models within each location and adding a random intercept for cadavers with two-tailed ANOVA and no multiple comparison adjustments. Data are presented as mean values +/− 95% CI.

Extended Data Fig. 10 Diversity Comparison between 16S rRNA and Metagenomic Data.

PCoA ordination plots of Bray-Curtis dissimilarity matrices calculated from paired rarefied 16S rRNA feature abundances (left) and TPM-normalized MAG abundances (right) from the soil adjacent to the hip. Procrustes/PROTEST and mantel tests were performed between the PCoA ordinances and distance matrices, respectively. n = 480 biologically independent samples, respectively.

Supplementary information

Supplementary information.

Legends for Supplementary Tables 1–9, 14–16 and 25–39. Supplementary Tables 10–13 and 17–24, and Text.

Reporting Summary

Supplementary tables.

Supplementary Table 1. Sample metadata. Table includes data taken during intake and over the course of the study. Table 2. ANCOM-BC differential abundance analysis results of cadaver skin metabolite log-ratio change over decomposition stages. Initial day 0 samples were used as the reference level and the intercept. Results include log-ratio changes of day 0 metabolites to early, active and advanced decomposition stages, P values, Holm–Bonferroni-corrected P values ( Q values), standard errors and W values. Table 3. ANCOM-BC differential abundance analysis results of cadaver-associated soil metabolite log-ratio change over decomposition stages. Initial day 0 samples were used as the reference level and the intercept. Results include log-ratio changes of day 0 metabolites to early, active and advanced decomposition stages, P values, Holm–Bonferroni-corrected P values ( Q values), standard errors and W values. Table 4. List of samples used to generate shotgun metagenomic data. Table 5. Assembly statistics and GTDB taxonomic classification of genomic bins (metagenome-assembled genomes; MAGs) co-assembled from the metagenomic samples. Table includes completeness and contamination of each MAG. Table 6. TPM-normalized count abundance of MAGs within metagenomic samples. Table 7. Linear mixed-effects model statistics for testing response variable change of ATP per C-mol amino acids calculated from metagenomic data over ADD at each facility and a random intercept for each individual body to account for repeated measures to test whether the metabolism efficacy shifts within each facility. Formula: ‘ATPm ≈ ADD + (1|body ID)’. Table 8. Linear mixed-effects model statistics for testing response variable change of ATP per C-mol carbohydrates calculated from metagenomic data over ADD at each facility and a random intercept for each individual body to account for repeated measures to test whether the metabolism efficacy shifts within each facility. Formula: ‘ATPm ≈ ADD + (1|body ITable 9. Linear mixed-effects model statistics for testing response variable change of ATP per C-mol lipids calculated from metagenomic data over ADD at each facility and a random intercept for each individual body to account for repeated measures to test whether the metabolism efficacy shifts within each facility. Formula: ‘ATPm ≈ ADD + (1|body ID)’. Table 14. Number of predicted exchanges for cross-fed compounds at each facility during late decomposition. Late decomposition was defined as the advanced decomposition stage at STAFS and ARF and the active decomposition stage at FIRS. Table 15. Linear mixed-effects model statistics for testing response variable change of Generalized UniFrac PC1 distances calculated from 16S rRNA gene data over ADD at each facility with sampling site (that is, soil adjacent to hip vs soil control) as an independent variable (fixed effect) and a random intercept for each individual body to account for repeated measures. The models measure the sampling site and ADD variables individually and the interaction between the variables. The interaction between the variables was used to test whether the sampling sites respond differently to decomposition. Formula: ‘diversity metric ≈ ADD × sampling site + (1|body ID)’. Table 16. Linear mixed-effects model statistics for testing response variable change of ASV richness calculated from 16S rRNA gene data over ADD at each facility with sampling site (that is, soil adjacent to hip vs soil control) as an independent variable (fixed effect) and a random intercept for each individual body to account for repeated measures. The models measure the sampling site and ADD variables individually and the interaction between the variables. The interaction between the variables was used to test whether the sampling sites respond differently to decomposition. Formula: ‘diversity metric ≈ ADD × sampling site + (1|body ID)’. Table 25. Joint-RPCA PC2 correlations calculated between network feature nodes that correspond with late (that is, active and advanced) decomposition soil. Table 26. Joint-RPCA PC2 correlations calculated between network feature nodes in initial, non-decomposition and early decomposition soil. Table 27. 16S rRNA gene ASVs assigned to the same taxonomy as decomposer network taxa. Table includes the phylogenetic tree labels in Fig. 4e, 150-bp-long ASVs and trimmed 100-bp-long ASVs used to explore ASV presence in other studies. Table 28. Presence of universal decomposers in possible human and terrestrial source environments in a few other studies. Table shows the average relative abundance of each decomposer ASV across each sample type. Average relative abundances were then summed for each decomposer genus. Table 29. Cross-feeding statistics for MAGs predicted as cross-feeders during late decomposition. Table includes GTDB taxonomic classification, number of reactions each MAG was considered the compound receiver and/or donor, and the percent responsible for all donations and acceptances during late decomposition. Late decomposition was defined as the advanced decomposition stage at STAFS and ARF and the active decomposition stage at FIRS. Table 30. Cross-feeding exchanges for Oblitimonas alkaliphila during late decomposition. Oblitimonas alkaliphila was not a predicted cross-feeder at FIRS during this timeframe. Table includes MAG ID and taxonomic classification of genomes involved in exchange, compounds exchanged and computed interaction metrics. Table 31. Cross-feeding exchanges for l -arginine or ornithine during late decomposition. Table includes MAG ID and taxonomic classification of genomes involved in exchange, compounds exchanged and computed interaction metrics. Table 32. Model validation results from predicting an independent test set of samples using the 16S rRNA gene at the SILVA database level-7 taxonomic rank random forest regression models for the skin of the hip and soil adjacent to the hip. Errors are represented by MAE in ADD. Table 33. Presence of universal decomposers in a few other studies focused on mammalian decomposition environments. A search for the 35 universal PMI decomposer ASVs was conducted within each dataset. The relative abundance of each decomposer ASV was first averaged across all samples within a specific metadata category. The average relative abundances were then summed across each decomposer genus. Prevalence tables were constructed by summing the number of samples across a specific metadata category in which each universal decomposer ASV was present. Table 34. The average ADD per calendar day calculated for each cadaver at each facility. The average ADD per calendar day was calculated by dividing the final maximum ADD values by the total number of days (that is, 21). The average ADD per day was calculated for each cadaver, season and facility, each climate type and as a study-wide average. Table 35. The average ADD per calendar day calculated for each cadaver at each facility for the independent test set. The average ADD per calendar day was calculated by dividing the final maximum ADD values by the total number of sampling days. The average ADD per day was calculated for each cadaver, facility and as a study-wide average. Table 36. Metabolite identification information for metabolites that had a predicted chemical formula or matched to a compound in the database library. When available, chemical formulas in the database library took precedence over predicted chemical formulas for calculating NOSC and major biochemical classes based on the molar H:C and O:C ratios. Table 37. Soil metabolite feature table normalized with sum normalization then scaled with pareto scaling. Table includes chemical formulas and major biochemical classes based on the molar H:C and O:C ratios. Table 38. Skin metabolite feature table normalized with sum normalization then scaled with pareto scaling. Table includes chemical formulas and major biochemical classes based on the molar H:C and O:C ratios. Table 39. Sample metadata for the machine learning independent test set. Table includes data taken during intake and over the course of the study.

Source Data for Figs. 1–6, Extended Data Figs. 1–6 and Extended Data Fig. 9

SD for Fig. 1. Sample type counts and sample metadata. SD for Fig. 2. ATP per C-mol for each substrate by sample and pairwise beta-NTI calculations. SD for Fig. 3. SMETANA MIP and MRO score calculations, predicted cross-fed metabolites, Faith’s PD calculations and joint-RPCA distance matrix/ordination. SD for Fig. 4. Joint-RPCA distance matrix/ordination and multi-omic log ratios. SD for Fig. 5. Late decomposition multi-omic correlations. SD for Fig. 6. Random forest predictions, 16S rRNA model important features and 16S rRNA SILVA-L7 feature table. SD for ED Fig. 1. Site weather data. SD for ED Fig. 2. Metabolite feature table, chemical formulas and Van Krevelen metabolite classifications. SD for ED Fig. 3. MAG taxonomy and feature table, amino acid and carbohydrate ATP per C-mol per MAG and sample. SD for ED Fig. 4. 16S rRNA calculated richness. SD for ED Fig. 5. Initial/early decomposition multi-omic correlations. SD for ED Fig. 6. Top rank taxa for phylogenetic tree comparing ASVs found during decomposition and in the EMP and AGP datasets. SD for ED Fig. 9. 16S rRNA random forest validation predictions

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Burcham, Z.M., Belk, A.D., McGivern, B.B. et al. A conserved interdomain microbial network underpins cadaver decomposition despite environmental variables. Nat Microbiol (2024).

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  8. The Ultimate Guide to Writing a Research Paper

    A research paper is a type of academic writing that provides an in-depth analysis, evaluation, or interpretation of a single topic, based on empirical evidence. Research papers are similar to analytical essays, except that research papers emphasize the use of statistical data and preexisting research, along with a strict code for citations.

  9. Writing a Research Paper Introduction

    Step 1: Introduce your topic. Step 2: Describe the background. Step 3: Establish your research problem. Step 4: Specify your objective (s) Step 5: Map out your paper. Research paper introduction examples. Frequently asked questions about the research paper introduction.

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

    Writing a scientific paper is an important component of the research process, yet researchers often receive little formal training in scientific writing. This is especially true in low-resource settings.

  11. Essential Guide to Manuscript Writing for Academic Dummies: An Editor's

    Abstract. Writing an effective manuscript is one of the pivotal steps in the successful closure of the research project, and getting it published in a peer-reviewed and indexed journal adds to the academic profile of a researcher. Writing and publishing a scientific paper is a tough task that researchers and academicians must endure in staying ...

  12. Writing a Research Paper

    Writing a research paper is an essential aspect of academics and should not be avoided on account of one's anxiety. In fact, the process of writing a research paper can be one of the more rewarding experiences one may encounter in academics. What is more, many students will continue to do research throughout their careers, which is one of the ...

  13. PDF How to Write Paper in Scientific Journal Style and Format

    The guide addresses four major aspects of writing journal-style scientific papers: (1) Fundamental style considerations; (2) a suggested strategy for efficiently writing up research results; (3) the nuts and bolts of format and content of each section of a paper (part of learning to

  14. Writing for publication: Structure, form, content, and journal

    A recent study which synthesised systematic studies of journal acceptance rates found that somewhere between 35 and 40 per cent of submitted papers are published, meaning that the majority are rejected - although it also noted that there is significant variation between disciplines ( Bjork 2019 ).

  15. Research Papers

    Author (s) Titles (of both articles and the journals they appear, as well as books) Pages for articles (all the pages of the article, including the one you are citing if it's a particular page) Date of publication For books, a place of publication and publisher Direct quotes, word for word with no typos. Note the page numbers carefully.

  16. 8 useful research paper writing tools and resources

    This resource will guide you through writing your paper from start to finish, including preparation and setting your structure, to writing every section of your paper and preparing it for submission. Download your free guide Read the guide to find out all you need to know about: Article structures and formatting

  17. Writing a Research Paper for an Academic Journal: A Five-step ...

    Writing a Research Paper for an Academic Journal: A Five-step Recipe for Perfection The answer to writing the perfect research paper is as simple as following a step-by-step recipe. Here we bring to you a recipe for effortlessly planning, writing, and publishing your paper as a peer reviewed journal article. Updated on March 15, 2022


    build ideas and write papers. - The Writing Process: These features show all the steps taken to write a paper, allowing you to follow it from initial idea to published article. - Into the Essay: Excerpts from actual papers show the ideas from the chapters in action because you learn to write best by getting

  19. Writing a Research Paper course

    Writing a Research Paper. For students and researchers in the natural sciences who are new to scientific writing or wish to improve the quality of their written output. Taught by 17 Nature Portfolio journal Editors. 4.5 hours of learning. 15-minute lessons.

  20. Successful Scientific Writing and Publishing: A Step-by-Step Approach

    We include an overview of basic scientific writing principles, a detailed description of the sections of an original research article, and practical recommendations for selecting a journal and responding to peer review comments.

  21. How to Write a Research Paper for Publication: Outline, Format & Types

    Objective #1 (e.g. summarize the paper, proposed methods, merits, and limitations) Objective #2 (e.g. urge other researchers to test the proposed methods and show recommendations for further research) After creating the outline, you can fill out the details and start writing your first draft.

  22. Research Paper Writing Tips

    Grammarly. '. s Research Paper Writing Guide. Ensure your research papers clearly communicate your ideas. Our research paper resources offer tips on how to approach outlines, abstracts, citations—everything you need to write a polished paper. From keeping your work free of mistakes to flagging when you haven ' t properly cited your ...

  23. Research Paper

    A research paper is a piece of academic writing that provides analysis, interpretation, and argument based on in-depth independent research. About us; ... T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398. Appendix: The survey ...

  24. Researching the White Paper

    Unlike a school research paper, the author does not set out to argue for or against a particular position, and then devote the majority of effort to finding sources to support the selected position. Instead, the author sets out in good faith to do as much fact-finding as possible, and thus research is likely to present multiple, conflicting ...

  25. How to Write a Term Paper: 8 Expert Tips for Academic Success 2024

    It challenges you to think critically, research deeply, and express your thoughts clearly and coherently. By following these steps, you equip yourself with a structured approach to tackle this challenge head-on. Remember, academic writing is a skill honed over time. Each term paper is an opportunity to improve, learn, and grow as a scholar.

  26. 'The situation has become appalling': fake scientific papers push

    Last year, 10,000 sham papers had to be retracted by academic journals, but experts think this is just the tip of the iceberg Tens of thousands of bogus research papers are being published in ...

  27. A conserved interdomain microbial network underpins cadaver

    Recent research has demonstrated that microbial community response over the course of terrestrial human cadaver decomposition and across a range of mammals, results in a substantial microbial ...