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

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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.

Introduction

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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How to Write a Research Paper | A Beginner's Guide

A research paper is a piece of academic writing that provides analysis, interpretation, and argument based on in-depth independent research.

Research papers are similar to academic essays , but they are usually longer and more detailed assignments, designed to assess not only your writing skills but also your skills in scholarly research. Writing a research paper requires you to demonstrate a strong knowledge of your topic, engage with a variety of sources, and make an original contribution to the debate.

This step-by-step guide takes you through the entire writing process, from understanding your assignment to proofreading your final draft.

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Table of contents

Understand the assignment, choose a research paper topic, conduct preliminary research, develop a thesis statement, create a research paper outline, write a first draft of the research paper, write the introduction, write a compelling body of text, write the conclusion, the second draft, the revision process, research paper checklist, free lecture slides.

Completing a research paper successfully means accomplishing the specific tasks set out for you. Before you start, make sure you thoroughly understanding the assignment task sheet:

  • Read it carefully, looking for anything confusing you might need to clarify with your professor.
  • Identify the assignment goal, deadline, length specifications, formatting, and submission method.
  • Make a bulleted list of the key points, then go back and cross completed items off as you’re writing.

Carefully consider your timeframe and word limit: be realistic, and plan enough time to research, write, and edit.

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There are many ways to generate an idea for a research paper, from brainstorming with pen and paper to talking it through with a fellow student or professor.

You can try free writing, which involves taking a broad topic and writing continuously for two or three minutes to identify absolutely anything relevant that could be interesting.

You can also gain inspiration from other research. The discussion or recommendations sections of research papers often include ideas for other specific topics that require further examination.

Once you have a broad subject area, narrow it down to choose a topic that interests you, m eets the criteria of your assignment, and i s possible to research. Aim for ideas that are both original and specific:

  • A paper following the chronology of World War II would not be original or specific enough.
  • A paper on the experience of Danish citizens living close to the German border during World War II would be specific and could be original enough.

Note any discussions that seem important to the topic, and try to find an issue that you can focus your paper around. Use a variety of sources , including journals, books, and reliable websites, to ensure you do not miss anything glaring.

Do not only verify the ideas you have in mind, but look for sources that contradict your point of view.

  • Is there anything people seem to overlook in the sources you research?
  • Are there any heated debates you can address?
  • Do you have a unique take on your topic?
  • Have there been some recent developments that build on the extant research?

In this stage, you might find it helpful to formulate some research questions to help guide you. To write research questions, try to finish the following sentence: “I want to know how/what/why…”

A thesis statement is a statement of your central argument — it establishes the purpose and position of your paper. If you started with a research question, the thesis statement should answer it. It should also show what evidence and reasoning you’ll use to support that answer.

The thesis statement should be concise, contentious, and coherent. That means it should briefly summarize your argument in a sentence or two, make a claim that requires further evidence or analysis, and make a coherent point that relates to every part of the paper.

You will probably revise and refine the thesis statement as you do more research, but it can serve as a guide throughout the writing process. Every paragraph should aim to support and develop this central claim.

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A research paper outline is essentially a list of the key topics, arguments, and evidence you want to include, divided into sections with headings so that you know roughly what the paper will look like before you start writing.

A structure outline can help make the writing process much more efficient, so it’s worth dedicating some time to create one.

Your first draft won’t be perfect — you can polish later on. Your priorities at this stage are as follows:

  • Maintaining forward momentum — write now, perfect later.
  • Paying attention to clear organization and logical ordering of paragraphs and sentences, which will help when you come to the second draft.
  • Expressing your ideas as clearly as possible, so you know what you were trying to say when you come back to the text.

You do not need to start by writing the introduction. Begin where it feels most natural for you — some prefer to finish the most difficult sections first, while others choose to start with the easiest part. If you created an outline, use it as a map while you work.

Do not delete large sections of text. If you begin to dislike something you have written or find it doesn’t quite fit, move it to a different document, but don’t lose it completely — you never know if it might come in useful later.

Paragraph structure

Paragraphs are the basic building blocks of research papers. Each one should focus on a single claim or idea that helps to establish the overall argument or purpose of the paper.

Example paragraph

George Orwell’s 1946 essay “Politics and the English Language” has had an enduring impact on thought about the relationship between politics and language. This impact is particularly obvious in light of the various critical review articles that have recently referenced the essay. For example, consider Mark Falcoff’s 2009 article in The National Review Online, “The Perversion of Language; or, Orwell Revisited,” in which he analyzes several common words (“activist,” “civil-rights leader,” “diversity,” and more). Falcoff’s close analysis of the ambiguity built into political language intentionally mirrors Orwell’s own point-by-point analysis of the political language of his day. Even 63 years after its publication, Orwell’s essay is emulated by contemporary thinkers.

Citing sources

It’s also important to keep track of citations at this stage to avoid accidental plagiarism . Each time you use a source, make sure to take note of where the information came from.

You can use our free citation generators to automatically create citations and save your reference list as you go.

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The research paper introduction should address three questions: What, why, and how? After finishing the introduction, the reader should know what the paper is about, why it is worth reading, and how you’ll build your arguments.

What? Be specific about the topic of the paper, introduce the background, and define key terms or concepts.

Why? This is the most important, but also the most difficult, part of the introduction. Try to provide brief answers to the following questions: What new material or insight are you offering? What important issues does your essay help define or answer?

How? To let the reader know what to expect from the rest of the paper, the introduction should include a “map” of what will be discussed, briefly presenting the key elements of the paper in chronological order.

The major struggle faced by most writers is how to organize the information presented in the paper, which is one reason an outline is so useful. However, remember that the outline is only a guide and, when writing, you can be flexible with the order in which the information and arguments are presented.

One way to stay on track is to use your thesis statement and topic sentences . Check:

  • topic sentences against the thesis statement;
  • topic sentences against each other, for similarities and logical ordering;
  • and each sentence against the topic sentence of that paragraph.

Be aware of paragraphs that seem to cover the same things. If two paragraphs discuss something similar, they must approach that topic in different ways. Aim to create smooth transitions between sentences, paragraphs, and sections.

The research paper conclusion is designed to help your reader out of the paper’s argument, giving them a sense of finality.

Trace the course of the paper, emphasizing how it all comes together to prove your thesis statement. Give the paper a sense of finality by making sure the reader understands how you’ve settled the issues raised in the introduction.

You might also discuss the more general consequences of the argument, outline what the paper offers to future students of the topic, and suggest any questions the paper’s argument raises but cannot or does not try to answer.

You should not :

  • Offer new arguments or essential information
  • Take up any more space than necessary
  • Begin with stock phrases that signal you are ending the paper (e.g. “In conclusion”)

There are four main considerations when it comes to the second draft.

  • Check how your vision of the paper lines up with the first draft and, more importantly, that your paper still answers the assignment.
  • Identify any assumptions that might require (more substantial) justification, keeping your reader’s perspective foremost in mind. Remove these points if you cannot substantiate them further.
  • Be open to rearranging your ideas. Check whether any sections feel out of place and whether your ideas could be better organized.
  • If you find that old ideas do not fit as well as you anticipated, you should cut them out or condense them. You might also find that new and well-suited ideas occurred to you during the writing of the first draft — now is the time to make them part of the paper.

The goal during the revision and proofreading process is to ensure you have completed all the necessary tasks and that the paper is as well-articulated as possible. You can speed up the proofreading process by using the AI proofreader .

Global concerns

  • Confirm that your paper completes every task specified in your assignment sheet.
  • Check for logical organization and flow of paragraphs.
  • Check paragraphs against the introduction and thesis statement.

Fine-grained details

Check the content of each paragraph, making sure that:

  • each sentence helps support the topic sentence.
  • no unnecessary or irrelevant information is present.
  • all technical terms your audience might not know are identified.

Next, think about sentence structure , grammatical errors, and formatting . Check that you have correctly used transition words and phrases to show the connections between your ideas. Look for typos, cut unnecessary words, and check for consistency in aspects such as heading formatting and spellings .

Finally, you need to make sure your paper is correctly formatted according to the rules of the citation style you are using. For example, you might need to include an MLA heading  or create an APA title page .

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Checklist: Research paper

I have followed all instructions in the assignment sheet.

My introduction presents my topic in an engaging way and provides necessary background information.

My introduction presents a clear, focused research problem and/or thesis statement .

My paper is logically organized using paragraphs and (if relevant) section headings .

Each paragraph is clearly focused on one central idea, expressed in a clear topic sentence .

Each paragraph is relevant to my research problem or thesis statement.

I have used appropriate transitions  to clarify the connections between sections, paragraphs, and sentences.

My conclusion provides a concise answer to the research question or emphasizes how the thesis has been supported.

My conclusion shows how my research has contributed to knowledge or understanding of my topic.

My conclusion does not present any new points or information essential to my argument.

I have provided an in-text citation every time I refer to ideas or information from a source.

I have included a reference list at the end of my paper, consistently formatted according to a specific citation style .

I have thoroughly revised my paper and addressed any feedback from my professor or supervisor.

I have followed all formatting guidelines (page numbers, headers, spacing, etc.).

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Journal of Writing Research

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  • Linguistics and Language

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journal writing research

The set of journals have been ranked according to their SJR and divided into four equal groups, four quartiles. Q1 (green) comprises the quarter of the journals with the highest values, Q2 (yellow) the second highest values, Q3 (orange) the third highest values and Q4 (red) the lowest values.

The SJR is a size-independent prestige indicator that ranks journals by their 'average prestige per article'. It is based on the idea that 'all citations are not created equal'. SJR is a measure of scientific influence of journals that accounts for both the number of citations received by a journal and the importance or prestige of the journals where such citations come from It measures the scientific influence of the average article in a journal, it expresses how central to the global scientific discussion an average article of the journal is.

Evolution of the number of published documents. All types of documents are considered, including citable and non citable documents.

This indicator counts the number of citations received by documents from a journal and divides them by the total number of documents published in that journal. The chart shows the evolution of the average number of times documents published in a journal in the past two, three and four years have been cited in the current year. The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric.

Evolution of the total number of citations and journal's self-citations received by a journal's published documents during the three previous years. Journal Self-citation is defined as the number of citation from a journal citing article to articles published by the same journal.

Evolution of the number of total citation per document and external citation per document (i.e. journal self-citations removed) received by a journal's published documents during the three previous years. External citations are calculated by subtracting the number of self-citations from the total number of citations received by the journal’s documents.

International Collaboration accounts for the articles that have been produced by researchers from several countries. The chart shows the ratio of a journal's documents signed by researchers from more than one country; that is including more than one country address.

Not every article in a journal is considered primary research and therefore "citable", this chart shows the ratio of a journal's articles including substantial research (research articles, conference papers and reviews) in three year windows vs. those documents other than research articles, reviews and conference papers.

Ratio of a journal's items, grouped in three years windows, that have been cited at least once vs. those not cited during the following year.

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  • About: The Journal of Writing Research (JoWR) is an international peer reviewed journal that publishes high quality theoreti... more The Journal of Writing Research (JoWR) is an international peer reviewed journal that publishes high quality theoretical, empirical, and review papers covering the broad spectrum of writing research: http://www.jowr.org (The Journal of Writing Research (JoWR) is an international peer reviewed journal that publishes high quality theoretical, empirical, and review papers covering the broad spectrum of writing research: http://www.jowr.org) edit
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Research Interests: Basic/Developmental Writing , Writing Studies , and Listening and Writing Skills ()

Research interests: writing studies , cognitive processes , and copy task (), research interests: writing studies , writing pedagogy , and observational learning (), research interests: writing studies (), research interests: functional mri , writing studies , and spelling (), research interests: peer assessment and writing studies (), doi: 10.17239/jowr-2010.02.01.1, more info: http://www.jowr.org/abstracts/vol2_1/klein_kirkpat rick_2010_2_1_abstract.html, page numbers: 1-46, publication date: 2010, publication name: journal of writing research, research interests: corpus linguistics and writing studies (), research interests: plagiarism detection and writing studies (), research interests: writing studies , error correction coding , editing , cognitive processes , keystroke logging , and inputlog, keystroke logging (), research interests: writing studies and error correction coding (), research interests: writing (), research interests: basic/developmental writing and writing (), research interests: gender studies and writing studies (), research interests: writing studies and writing assessment (), research interests: writing studies and metaphor (), research interests: writing studies and collaborative writing (), research interests: second language writing , writing studies , and thinking-aloud protocol (), research interests: writing studies and peer review (), research interests: writing studies , peer review , and computational linguistics & nlp (), research interests: professional communication , writing studies , and communicating bad news (), research interests: writing studies and essays (), research interests: translation studies (), research interests: cognitive psychology , professional writing , writing studies , digital writing , source based writing , and writing in the workplace (), research interests: writing studies and eisenhower (), research interests: writing studies and intervention studies (), research interests: working memory , writing studies , and writing processes (), research interests: writing and keystroke logging (), publication date: 2013, doi: 10.17239/jowr-2015.07.01.02, more info: http://www.jowr.org/abstracts/vol7_1/wilcox_et_al_ 2015_7_1_abstract.html, page numbers: 5-39, publication date: 2015, research interests: science writing , english learners , adolescent writing , and epistemic complexity (), doi: 10.17239/jowr-2015.07.01.03, more info: http://www.jowr.org/abstracts/vol7_1/waschle_et_al _2015_7_1_abstract.html, page numbers: 41-64, research interests: science education , interest , critical reflection , comprehension , and learning journals (), doi: 10.17239/jowr-2015.07.01.04, more info: http://www.jowr.org/abstracts/vol7_1/smirnova_2015 _7_1_abstract.html, page numbers: 65-93, research interests: critical thinking , writing to learn , historical reasoning , argumentation skills , and l1/l2 instruction (), doi: 10.17239/jowr-2015.07.01.05, more info: http://www.jowr.org/abstracts/vol7_1/ortoleva_betr ancourt_2015_7_1_abstract.html, page numbers: 95-122, research interests: computer supported collaborative learning , self-efficacy , vocational education and training , and written peer feedback (), doi: 10.17239/jowr-2015.07.01.06, page numbers: 123-156, research interests: argumentative writing , writing instruction , writing to learn , historical reasoning , and domain-specific instruction (), doi: 10.17239/jowr-2015.07.01.07, page numbers: 157-200, research interests: argumentative writing , writing in the disciplines , collaborative writing , secundary education , and philosophy learning (), doi: 10.17239/jowr-2015.07.02.1, more info: http://www.jowr.org/abstracts/vol7_2/mangen_et_al_ 2015_7_2_abstract.html, page numbers: 227-247, research interests: embodied cognition , handwriting , word memory , keyboard writing , ergonomics of writing , and educational implications of digitization (), doi: 10.17239/jowr-2015.07.02.2, more info: http://www.jowr.org/abstracts/vol7_2/koster_et_al_ 2015_7_2_abstract.html, page numbers: 249-274, research interests: writing , meta-analysis , composition , intervention , and elementary school (), doi: 10.17239/jowr-2015.07.02.03, more info: http://www.jowr.org/abstracts/vol7_2/martinez_et_a l_2015_7_2_abstract.html, page numbers: 275-302, research interests: writing processes , strategy training , reading processes , text quality , and synthesis text (), doi: 10.17239/jowr-2016.07.03.01, more info: http://www.jowr.org/abstracts/vol7_3/klein_boscolo _2016_7_3_abstract.html, page numbers: 311-350, publication date: 2016, research interests: writing , writing skills , cognitive processes , writing to learn , and research methods (), doi: 10.17239/jowr-2016.07.03.02, more info: http://www.jowr.org/abstracts/vol7_3/crossley_mcna mara_2016_7_3_abstract.html, page numbers: 351-370, research interests: coherence , cohesion , elaboration , and essay quality (), doi: 10.17239/jowr-2016.07.03.03, more info: http://www.jowr.org/abstracts/vol7_3/limberg_et_al _2016_7_3_abstract.html, page numbers: 371-396, research interests: questions , case study , coding scheme , writing tutorial , and writing tutoring (), doi: 10.17239/jowr-2016.07.03.04, more info: http://www.jowr.org/abstracts/vol7_3/kellogg_et_al _2016_7_3_abstract.html, page numbers: 397-416, publication name: journal of writing research, research interests: working memory , sentence planning , and sentence generation (), doi: 10.17239/jowr-2016.07.03.05, more info: http://www.jowr.org/abstracts/vol7_3/geisler(1)_20 16_7_3_abstract.html, page numbers: 417-424, research interests: research methodology , rhetorical analysis , text mining , text analysis , and data coding (), doi: 10.17239/jowr-2016.07.03.06, more info: http://www.jowr.org/abstracts/vol7_3/karatsolis_20 16_7_3_abstract.html, page numbers: 425-452, research interests: disciplinarity , novice-expert , citation studies , and verbal data analysis (), doi: 10.17239/jowr-2016.07.03.07, more info: http://www.jowr.org/abstracts/vol7_3/kaufer_et_al_ 2016_7_3_abstract.html, page numbers: 453-483, research interests: corpus analysis , text analysis , common archives , citation research , and dictionary methods (), doi: 10.17239/jowr-2016.07.03.08, more info: http://www.jowr.org/abstracts/vol7_3/omizo_hart-da vidson_2016_7_3_abstract.html, page numbers: 485-509, research interests: computational rhetoric , text processing , citation , and rhetorical moves (), doi: 10.17239/jowr-2016.07.03.09, more info: http://www.jowr.org/abstracts/vol7_3/geisler(2)_20 16_7_3_abstract.html, page numbers: 511-526, research interests: rhetorical analysis (), doi: 10.17239/jowr-2016.08.01.01, more info: http://www.jowr.org/abstracts/vol8_1/kirkpatrick_k lein_2016_8_1_abstract.html, page numbers: 1-47, research interests: strategies , persuasive writing , internet , discourse synthesis , and writing from sources (), doi: 10.17239/jowr-2016.08.01.02, more info: http://www.jowr.org/abstracts/vol8_1/chang_schlepp egrell_2016_8_1_abstract.html, page numbers: 49-80, research interests: academic writing , systemic functional linguistics , explicit learning , authorial stance , and l2 students (), doi: 10.17239/jowr-2016.08.01.03, more info: http://www.jowr.org/abstracts/vol8_1/chang_2016_8_ 1_abstract.html, page numbers: 81-117, research interests: research , peer review , l2 writing , and esl/efl (), doi: 10.17239/jowr-2016.08.01.04, more info: http://www.jowr.org/abstracts/vol8_1/lancaster_201 6_8_1_abstract.html, page numbers: 119-148, research interests: corpus linguistics , writing in the disciplines , hedging , epistemic stance , and discourse-based interviews (), doi: 10.17239/jowr-2016.08.01.05, more info: http://www.jowr.org/abstracts/vol8_1/moore_macarth ur_2016_8_1_abstract.html, page numbers: 149-175, research interests: writing , revision , and automated essay evaluation (), doi: 10.17239/jowr-2016.08.02.02, more info: http://www.jowr.org/abstracts/vol8_2/cuevas_2016_8 _2_abstract.html, page numbers: 205-226, research interests: collaborative writing , controversy , transactional writing belief , and argumentative synthesis (), doi: 10.17239/jowr-2016.08.02.01, more info: http://www.jowr.org/abstracts/vol8_2/vansteendam_2 016_8_2_abstract.html, page numbers: 183-204, research interests: collaborative writing and forms of collaboration in writing (), doi: 10.17239/jowr-2016.08.02.03, more info: http://www.jowr.org/abstracts/vol8_2/patchan_schun n_2016_8_2_abstract.html, page numbers: 227-265, research interests: peer assessment , revision , peer feedback , writing ability , and reviewing ability (), doi: 10.17239/jowr-2016.08.02.04, more info: http://www.jowr.org/abstracts/vol8_2/bommarito_201 6_8_2_abstract.html, page numbers: 267-299, research interests: collaboration , research writing , and doctoral education (), doi: 10.17239/jowr-2016.08.02.05, more info: http://www.jowr.org/abstracts/vol8_2/sturm_2016_8_ 2_abstract.html, page numbers: 301-344, research interests: collaborative writing , writing process , writing knowledge , and struggling adult writers (), doi: 10.17239/jowr-2017.08.03.01, more info: http://www.jowr.org/abstracts/vol8_3/raedts_et_al_ 2017_8_3_abstract.html, page numbers: 399-435, publication date: 2017, research interests: academic writing , observational learning , writing self-efficacy , strategy instruction , and peer modeling (), doi: 10.17239/jowr-2017.08.03.02, more info: http://www.jowr.org/abstracts/vol8_3/nokes_2017_8_ 3_abstract.html, page numbers: 437-467, research interests: teaching history , historical writing , historical literacy , historical reading , and assessments of historical thinking (), doi: 10.17239/jowr-2017.08.03.03, more info: http://www.jowr.org/abstracts/vol8_3/ono_2017_8_3_ abstract.html, page numbers: 469-491, research interests: genre analysis , disciplinary writing , rhetorical structure , literature phd thesis , and perception of supervisor (), doi: 10.17239/jowr-2017.08.03.04, more info: http://www.jowr.org/abstracts/vol8_3/powell_et_al_ 2017_8_3_abstract.html, page numbers: 493-526, research interests: mathematics , writing , mathematical communication , and mathematics writing (), doi: 10.17239/jowr-2017.09.01.01, more info: http://www.jowr.org/abstracts/vol9_1/beers_et_al_2 017_9_1_abstract.html, page numbers: 1-25, research interests: dyslexia , translation , transcription , handwriting , dysgraphia , and 2 more language bursts and keyboarding ( language bursts and keyboarding ), doi: 10.17239/jowr-2017.09.01.02, more info: http://www.jowr.org/abstracts/vol9_1/kuiper_et_al_ 2017_9_1_abstract.html, page numbers: 27-59, research interests: higher education , design-based research , scaffolding , genre-based writing instruction , and embedding writing (), doi: 10.17239/jowr-2017.09.01.03, more info: http://www.jowr.org/abstracts/vol9_1/karlen_2017_9 _1_abstract.html, page numbers: 61-86, research interests: assessment , academic writing , metacognition , and metacognitive strategy knowledge (), doi: 10.17239/jowr-2017.09.02.01, more info: http://www.jowr.org/abstracts/vol9_2/limpo_alves_2 017_9_2_abstract.html, page numbers: 97-125, research interests: self-efficacy , writing , motivation , achievement goals , and beliefs in writing skill malleability (), doi: 10.17239/jowr-2017.09.02.02, more info: http://www.jowr.org/abstracts/vol9_2/wilson_dymoke _2017_9_2_abstract.html, page numbers: 127-150, research interests: poetry , composition , social context , writing development , and poetic writing (), doi: 10.17239/jowr-2017.09.02.03, more info: http://www.jowr.org/abstracts/vol9_2/cerrato_et_al _2017_9_2_abstract.html, page numbers: 151-171, research interests: culture , validation , doctoral education , questionnaire , invariance , and writing conceptions (), doi: 10.17239/jowr-2017.09.02.04, more info: http://www.jowr.org/abstracts/vol9_2/rietdijk_et_a l_2017_9_2_abstract.html, page numbers: 173-225, research interests: primary school , writing instruction , teachers' beliefs , writing performance , and teachers' skills ().

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The International Association for Journal Writing Logo

How to Journal

Your complete guide to getting started with journaling.

Do you want to learn how to journal, but are unsure where to start?  Or you want to know what to write in a journal?  Maybe you’ve heard of creative journaling and are curious what it is? Perhaps you’re a writer and want to journal to deepen your craft?

This comprehensive “How to Journal” article will answer all of your questions about journal writing. For example, what journal writing is, how you can use it, and what benefits you can experience from this type of writing.  It also includes many journal writing prompts to help you get started. Lastly, while journal writing is typically a solitary act, you don’t have to journal alone or in isolation.  This article will tell you where you can get some help and support for your journal writing, including being part of a journal writing community or group.

journal writing research

This Article Covers:

What is Journal Writing?

What can i use journaling for.

  • How to Journal – What are the Benefits?
  • Getting Started with Journaling
  • Creating a Journal Writing Ritual
  • How to Journal – What To Write?
  • How Often Should I Write in my Journal?

Do You Need to Write Regularly in a Journal?

  • How To Journal Consistently –  Creating the Journaling Habit
  • How to Journal – What Help and Support Can I Get?
  • In Conclusion

image of person learning how to journal

Before we talk about how to journal, let’s look at what journaling is.

Journal Writing is the practice of taking time for yourself to write and reflect on your thoughts, feelings and life experiences.  There are many suggestions for how to journal and what to write about. However, the beauty of journal writing is you can do it in your own way. This means you can really make it your own creative and life enhancing practice.

There are lots of people who write in a journal.  I recently heard that 16% of the world’s population regularly writes in a journal. You could loosely test this claim yourself by asking a group of friends or family if they write in a journal and see what percentage say yes.

Each person will give a slightly different answer when asked, “What is journaling?” But in essence, journaling is the simple and profound act of capturing and understanding our lives through expressive writing and story. Expressive writing includes writing about our thoughts and feelings while gaining self-awareness and new discoveries along the way. Journaling is all about exploring and enriching life through narrative, words and creative self-expression through writing.

Journaling is…

  • a powerful tool for personal growth, self-discovery, improved health and creative self-expression
  • a fun and creative life enhancing practice
  • used by many successful people, including Oprah and Jack Canfield (author of Chicken Soup for the Soul books), to achieve success in life and work

“Journal writing is one of the rare forms of writing in which freedom of form and content support each other magically.”   –  Stephanie Dowrick

You can use journal writing to get to know yourself better, solve problems, make life decisions, improve your health and increase feelings of gratitude and joy.  Journaling can also help you heal from stressful life circumstances, deal with grief and loss, or other life transitions. Or just journal for the pure love it!

Journaling is a fun, nourishing and creative practice that simply requires something to write with and write on. Whether it’s a pen and notebook, loose paper, cue cards, you get to choose your journaling tools!

People use journal writing in different ways for a variety of reasons. One person might journal to heal a broken heart writing an unsent letter sharing what they wish they’d said to that person.  Someone else might journal to celebrate their accomplishments and make a list of their recent successes in their journal.

There are also a wide variety of journaling methods and techniques to get the most out of your journaling. You can use it for whatever matters most to you at this time in your life.

How to Journal – What are the Benefits?

There are many evidence-based benefits of journal writing from over 30 years of research in the expressive writing field.  Yes, journal writing is a field of work!

People use the journaling process for many reasons, including to:

  • stimulate a healthier mind and body
  • vent and express thoughts and feelings in a healthy, constructive manner
  • increase self-awareness
  • create clarity for decision-making
  • track progress and personal growth
  • celebrate successes
  • heal emotional pain and trauma
  • increase self-care
  • manage stress and prevent burnout
  • gain broader and multiple perspectives
  • practice writing in a non-judgmental setting
  • improve creative thinking
  • preserve memories
  • get closer to God or a divine energy source

Today, journaling is widely accepted as a means for cultivating wellness as part of a whole person health approach. This includes the emotional, physical, psychological and spiritual dimensions of well-being. Journaling is also being used across various disciplines, such as education, psychology, leadership, business, health, creative writing, coaching and counselling fields, as a powerful tool for learning and growth.

How to Journal – Getting Started

Get organized.

One of the first things to do when you start a journal is get your journaling tools organized.

It can be fun to pick out your favourite pen and an inspiring journal. Look online or go into any book, stationary or office supply store and you’ll find all kinds of journals, pens, markers and other things you might like to use in your journal such as stickers or other creative touches.

So over time, you can experiment with your journaling tools. Do you like blank pages or lined? Would you prefer a small journal or a large sketchbook style journal?  Would you use the same style journal or mix it up and try something new each time you begin a new one?

Sometimes people use loose leaf paper and put their journaling pages in a binder, or write small entries on cue cards. And some people even use big 18 x 24 pages of paper for larger visual journaling entries. You can create a mixed media art journal and much more.

Image of hand starting to write in journal

Just Write!

The key is to pick some simple journaling tools to start with – a pen and notebook – and just start writing.

Your writing will teach you what you need. For example, I used to write in a small lined journal and over the years, my writing longed for larger, open, clear spaces to fill. Now I use an 8 ½ by 11 blank page sketchbook, spiral bound and I keep my pilot pen in the spine of the journal.

Find your own tools and make your own way as you write. The only way to journal, is to write. And then write some more.

Whether you’re an avid journal writer, someone who journaled in the past, or have never written in a journal before:

“There is a Spanish proverb which says: there is no road, we make the road as we walk. I would say the same thing about journal writing: we make the path as we write.” Christina Baldwin

How to Journal – Creating Writing Rituals

What is a journaling writing ritual.

Dr. James Pennebaker, author of Writing to Heal: A Guided Journal for Recovering from Trauma & Emotional Upheaval , suggests some conditions that help enhance the expressive writing process.  His research shows that creating a journal writing ritual is very beneficial.

Being focused, non-judgmental, and connected to your interior world fosters deeper writing. But, it’s not a frame of mind that everyone can simply switch on and off.

The idea behind creating a ritual is to create a unique environment and/or behavior which helps you sink into the best journal writing mindset possible. The purpose of the ritual is to take you away from everyday life. Your ritual contains the cues you create for yourself which help you become relaxed, alert, and reflective.

How do you Create a Journal Writing Ritual?

Here are some suggestions, but remember, the ritual you create to transition into deeper journal writing is uniquely yours.

  • Select some music that creates a sense of serenity. Play it for five minutes, focusing on simply listening to the music. Consider closing your eyes. Do not read your mail or straighten out your desk! You may want to have just one piece of music you use each time as your centering pre-writing ritual. Or choose three or four pieces you love for some variety.
  • Begin with several minutes of a meditation or a prayer. You can write just for the occasion or create something spontaneously each time.
  • Brew a cup of tea or coffee, or pour yourself some fresh juice. Perhaps a glass of wine? Spend a few minutes holding the cup, feeling the warmth, smelling the aromas of your drink and deeply enjoy those sensations.

Write in an environment that’s inspiring for your journal writing

  • This could be by a bright and sunny window or a softly lit corner nestled in a cozy chair.
  • Light a candle and while lighting the candle say an affirmation, your intention or make a wish.

Journal at approximately the same time each day

  • This doesn’t have to be at the same hour each day, but it’s helpful if it’s at the same time in your daily routine. For example half an hour before bed, which will work whether you go to bed at 10pm or at midnight.

The trick, of course, is to find the cues that help you settle in quickly. Initially, experiment with different rituals to see which feels best and then stick with the practice once you’ve found one you like. Remember to use as many of your senses (smell, sight, touch, hearing and taste) as you can when creating your centering ritual.

Image of woman journaling to create a ritual for how to journal article

How to Journal – What To Write

You can write about anything you want to write about. For example write about your day including your thoughts, feelings, problems, challenges, upsets, joys, successes and dreams. Here are some journaling prompts to help you get started:

  • Right now, I am feeling…
  • In the moment, I notice…
  • Currently, I am thinking about…
  • So far, the best part about my week is…

You can also write about what you don’t want to write about—and explore your resistance!

Resistance offers you information about where you’re feeling stuck, perhaps procrastinating, or simply not quite sure how to proceed. Here are some journaling prompts to play with around resistance:

  • At the moment, I don’t really want to write about (and then write about it anyways)…
  • I am feeling resistant because…
  • If I wasn’t feeling resistant, what might be different in my life right now…

You can free write (simply go to the page and start writing) or you can do more structured journal writing activities such as using prompts.

There are many other journal writing techniques and methods such as mind maps, cluster drawings, dialogue writing, captured moments, poetic writing and more that you can learn about and use to keep your journal writing fresh and interesting.

Access our free 7 Servings of Journal Juice for new ideas on what to write about in your journal. And you’ll also receive journal writing prompts, exercises, tips and our inspiring Journaling Museletter .

How To Journal – How Often Should I Write

There are no rules about how often you should write in your journal. Like anything, the more often you do something that’s good for you, the more benefits you get from it. I doubt you would go for one walk around the block and expect to experience significant health benefits from it.

The same is true for journaling. While that one walk would have offered you ‘in the moment’ benefits like time to relax, feeling good from moving your body, fresh air and more, the same is true for journaling.

You could gain a sense of relief, renewal and replenishment from just 10 minutes of writing about your thoughts, feelings and life observations.

Journal Regularly

Much like any other activity that’s good for you like brushing your teeth, meditating or eating a healthy diet, journaling can also be done regularly. Journaling makes a great healthy daily habit.

Set a Timer

I often facilitate timed journal writing exercises in workshops and retreats that I offer. It’s a core part of my Transformational Writing for Wellness Salon , a 6 week group coaching program that takes people into the heart and art of transformational journaling.

So often people say, “I can’t believe how much I wrote in just 5 minutes” or “I can’t believe I gained new insights when I just wrote for 7 minutes!”

Journaling to Cope

Many people only write in their journals when they are going through difficult times. Then once things are going better, they stop writing. This is also a valuable way to use your journal as a life companion to help you cope during stressful or troubled times.

The key is not to get too caught up in “shoulds”: I should journal today, I should journal more often. That’s because ‘shoulds’ can open the door for negative self-talk and feelings of inadequacy and shame. Instead, your journaling practice is best treated like a kind friend. You journal because you want to, and because it’s an enjoyable, or at least helpful, relaxing experience.

It’s a question that most journal writers face at some point. Does it matter if you write often in your journal? Well, whether you write regularly depends on your purpose for writing. Is it to preserve memories? To sort out issues? To track physical or emotional, spiritual, or intellectual progress? Track health symptoms?

If journal writing is pleasurable, then writing is its own reward. If journal writing becomes a task you “should” do, rather than something you enjoy, then you’ll write less consistently.

So part of the issue can be reframed by asking, ”How do I make journal writing pleasurable?” The answer to this question will help you find your own way to make journaling a consistent and enjoyable habit.

How To Journal Consistently –  Creating the Journaling Habit

Think of writing a journal entry as the lowest cost and highest benefit way of taking care of your health. Remember that writing about meaningful events or activities in your life has been proven to positively impact your overall health without major cost of time or money and without having to leave your home!

If you do want to write in your journal on a regular basis and truly create the journaling habit, here are a few ideas to help you keep writing consistently:

Make your journal writing more upbeat

  • Review the good things that have happened in your day—your attitude, your progress toward a goal, a minor victory, even a two-minute interaction with someone that went well.
  • Remind yourself about the good stuff in your life and your good qualities.

Write when you have difficult issues in your life that need to be resolved

  • Who doesn’t experience difficult times? Consider the time that you write in your journal as an oasis of self-nurturing in your day. It’s a time to vent, rant, reflect, and process just for you.

If possible, write at the same time every day

  • Incorporate your writing practice into a daily routine.

Make it short and fun!

  • Write a one-word journal entry that captures your day.
  • It’s a challenge to come up with that one word. You can think about it while you are doing some mindless life maintenance activity—like flossing your teeth, taking out the garbage, or folding clothes.
  • Then once you’ve determined that word, writing your journal entry takes almost no time.

Back to the question: Does it really matter that you write consistently?

Writing consistently helps you maintain your journaling practice. It means that when you re-read your journal, there are enough entries to have meaning and flow.

Your ability to write consistently in your journal will be determined by how you feel and doing what’s right for you. So, while you’re writing and when you finish, notice how you feel.

  • Did you like the process?
  • Were you feeling relaxed and soothed during or after writing?
  • Did you feel at times frustrated, angry, confused, despairing?

This whole spectrum of emotions is simply part of the process of journal writing. I know that I feel better most of the time after I write – like I’ve released a burden or relived a pleasurable part of my day.

How to Journal – What Help and Support Can I Get?

One of the best ways to learn more about how to journal is with the support of a like minded community. When we join with fellow journal writers there are regular opportunities to connect, learn and be inspired about journaling. People who like yoga connect in yoga communities, and the same is true for meditation, scrapbooking, running and more. There is a human instinct to find supportive communities who share our passion or interest, so we can learn and grow together.

At the IAJW, our journal writing community is for extroverts and introverts alike. Perhaps you want the inspiration and support of a community, but would rather sit back quietly and take it all in. Or maybe you want to chat with fellow journal writers live on our monthly Zoom Chats with guest experts. You can gain regular  help and support for your unique approach to journal writing.

People journal writing in group for how to journal article

Join our Online Journal Writing Community

We know there is power in community. So come join fellow journal writers in the International Association for Journal Writing ! We offer a learning and inspirational community for journal writers worldwide. Access monthly online writing circles, interviews with guest experts in the field of journaling and expressive writing, courses, journaling tools, e-books and much more.

We also have our Journal Writing Facebook group . Connect with fellow journal writers, receive journal writing tips and prompts to support you on your unique journal writing journey. Everyone is welcome!

Treat Yourself to a Journal Writing Retreat

Lastly, you might want to join one of our virtual Renew You Writing Retreats . Take 3 hours for yourself to journal in a guided and nourishing way. Whether you want to kick-start or reinvigorate your journaling practice, this retreat gives you time for creative self-care and renewal!

“Wow! What an awesome experience! I must admit I was a tad bit skeptical about an online retreat. But woah! Was I wrong! The Renew You Writing Retreat was so invigorating, uplifting, therapeutic, inspirational….just plain awesomesauce. Have you ever had an experience like that? You go in a little skeptical and come out blown away? Have you had the experience of being deeply inspired through writing and sharing with others? If not, you’re missing out! Thank you, Lynda, for creating such a wonderful space and experience.” Airial W. Dandridge, Certified Life Coach

How to Journal – In Conclusion

If you’ve read this far, I know you’re passionate (or at least curious about) the many benefits of  journal writing. Journaling is an empowering experience because you’re always the expert of your own life. Journaling helps you explore both your inner and outer worlds and make sense of your life experience.

As a Registered Social Worker and Certified Co-Active Life Coach, I have been immersed in human transformation, growth, change and wellness for the past 30 years. I’ve learned many different tools and techniques for self-care, healing and growth through my studies and first-hand experience. Journaling is my go to practice that helps me live an intentional, healthy and happy life. And it has helped many people to do the same! Including you, perhaps?

There is only one way to experience the many benefits of journal writing—pick up your pen and write!

“Writing was the healing place where I could collect bits and pieces, where I could put them together again…written words change us all and make us more than we could ever be without them.” bell hooks

May your journaling support you to live an incredible life!

Authors :  Lynda Monk, Director of IAJW and Ruth Folit, Founder of IAJW , partnered to write this How to Journal article, attempting to answer some of the most common questions that new and, in some cases, even seasoned journal writers have.

23 Comments

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Such a wonderful article. Thank you for sharing!

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Thanks, Diana!

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I went to write lots bits to remember and copied it almost word for word in my common place book,but I love to write and am trying to get back into it,I’m writing for recovery from am 8yr relationship with a covert gaslighting narcissist,and I couldn’t write,let alone relax,I have been out for almost 2yrs,and when I start to feel joy or something didn’t work out and I’m hard on myself,I swear I can feel his presence in my house,he doesn’t know where I am,I left him and moved 2hr away in a different state,the feeling is almost overwhelming

Hi Dixie, personal writing can help heal from painful relationships. It’s great you are getting back into it!

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Thank you both Lynda and Ruth for this wonderfully informative resource. Never too old to learn something new! Thank you both for bringing this to us.

Thanks, Lyn. Glad it offered some new ideas!

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Great article Lynda! You’ve covered so many bases – lots of work, and very informative and knowledgeable as always :) Emma-Louise

Hi Emma, thanks for your kind feedback!

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You two put together a beautiful and accessible piece here. It’s filled with all the vast experience and love you have for journaling. Thanks, Beth

Thanks so much, Beth! Your feedback means a lot to us.

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Lynda, a beautiful gift to receive, words combing thoughts, insightful expressions and creative suggestions. Thank you for sharing a writing world held in heart, pen or typing starts journaling what is seen, felt or sensed from a human inner essence. Whole ❤️ Namaste.

Thank you, Denise! Namaste.

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My name is Jacki Smallwood. I have been watching your sight on Facebook, and all the various gifts you have given while on the sight. I have been in a nursing home for 3 years and in quarantine for the past 11 months, not leaving my room, no guests, no funerals or graduation s. To keep my sanity u journal, I share my journaling with other residents through Messenger to help others cope. I don’t have access to copy machine nor anyone to take it out to staples. I am asking if anyone of your organization would donate material that would help me so much and then share with others. I get 45.00 a month from SS and need every penny for my needs. Anything you can do would be so helpful.

Seniors are a special group often ignored through this Covid.

Thank you for anything you could for me.

Jacky Smallwood

Hi Jacky, thank you for your note and request. I removed your mailing address from your original comment before publishing it for your privacy. I will reach out to you by email. I am glad journaling is helping you during this difficult time. More to follow, Lynda

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Lynda, I’m very grateful to have ran across this article. I used to journal a lot when I was younger and I write poetry and music pretty consistently for the last few years. I have been told journaling could be amazing for me to get over some of my past pains and nasty relationships and getting to know myself, growing into a stronger (as well as better person), and just for my general mental health. So, as I begin to journal this very day, I was writing down many things that I want to include and accomplish with this journal inside the front pages of my book and I happened to run across your article! Now I just want to give you a big thank you BECAUSE I attained a lot of information, ideas, and format to include in my new journaling experience! I’m very excited to embark and I just wanted to let you know again I’m grateful for running across your words.

Chelsea Venice, Florida

Hi Chelsea, thanks for your note and for sharing some of your journaling hopes! I love the serendipity that you found our journaling website. We have lots of free journaling resources, including journaling prompts, that might be helpful along the way. You can find them here if you are interested: https://iajw.org/free-journaling-resources/ Happy journaling!

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Thanks for your article esp the prompts to change the language and freshen up what I usually write.

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wonderful article

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Thank you so much for this article! When I was in my deepest months I would always journal but then once I got better I stopped journaling. I really want to get back into it but instead of writing about the bad in my life, I am going to focus on the good.

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thank you for this article!

You’re welcome, Gwen. Thanks for reading.

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I love the ideas for making journaling more appealing in order to journal more consistently. Sometimes I get so caught up in the “should do’s” that I forget that there really are no rules!

' src=

Very informative article on journaling! I’ve found journaling to be a wonderful practice for self-discovery and personal growth.

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journal writing research

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  • J Athl Train
  • v.41(2); 2006

Journal Writing as a Teaching Technique to Promote Reflection

Stacy e walker.

Ball State University, Muncie, IN

Stacy E. Walker, PhD, ATC, provided conception and design; acquisition and analysis and interpretation of the data; and drafting, critical revision, and final approval of the article.

Objective: To introduce the process of journal writing to promote reflection and discuss the techniques and strategies to implement journal writing in an athletic training education curriculum.

Background: Journal writing can facilitate reflection and allow students to express feelings regarding their educational experiences. The format of this writing can vary depending on the students' needs and the instructor's goals.

Description: Aspects of journal writing assignments are discussed, including different points to take into account before assigning the journals. Lastly, various factors to contemplate are presented when providing feedback to the students regarding their written entries.

Clinical Advantages: Journal writing assignments can benefit students by enhancing reflection, facilitating critical thought, expressing feelings, and writing focused arguments. Journal writing can be adapted into a student's clinical course to assist with bridging the gap between classroom and clinical knowledge. In addition, journals can assist athletic training students with exploring different options for handling daily experiences.

As athletic training students progress through their education, instructors hope that their students have time to not only retain but also to reflect on the knowledge learned. Reflection has been defined as a process regarding thinking about and exploring an issue of concern, which is triggered by an experience. 1 Leaver-Dunn et al 2 stated that reflection distinguishes expert practitioners from their peers. An expert clinician uses information from previous experiences as well as the insights gained from the reflective process to improve decision-making ability. As students progress through their education, they must practice, enhance, and habitually use their reflection skills. Leaver-Dunn et al 2 stated that athletic training educators should seek to facilitate a student's reflection. Although many strategies exist to promote this process, one teaching method that has been used to encourage reflection is journal writing. 3–11 The purpose of this article is to discuss journal writing as a pedagogic technique to promote reflection. I first briefly discuss the process of reflection and the research related to journal writing and then offer strategies for implementing journal writing in an athletic training education curriculum.

PROCESS OF REFLECTION

Once a student has knowledge and becomes proficient at a skill (ie, evaluating an ankle injury), that student possesses knowing-in-action. 12 Knowing-in-action refers to the “know-how” a practitioner reveals while performing an action. Simply put, the practitioner shows competency, or that he or she knows how to perform an orthopaedic assessment, by displaying the appropriate actions. Knowing-in-action assists a student except when a familiar routine produces an unexpected result. Take an example of a senior-level student who has performed various patellofemoral examinations but, during a recent evaluation, had inconclusive results. A student in this situation can become very frustrated. When students come across a new situation such as this, it would be beneficial for them to reflect-on-action, or reflect on that experience after it has happened. Unfortunately, more often than not, no time is designated for students to engage in the activity of reflection. Athletic training educational programs are encouraged to not only foster knowledge in students but also to cultivate reflection to enable our students to learn from past experiences.

An expert practitioner experiments on the spot with previous data or engages in what is called reflection-in-action . 12 Reflection-in-action occurs when an individual reshapes what he or she is doing while doing it. Students, who do not possess an array of previous experiences from which to draw, are not able to reflect-in-action as can skilled practitioners. We hope that as they progress through their education, students will learn to practice, enhance, and learn to habitually use their reflection-in-action skills. Although many strategies exist to facilitate reflection, one teaching method that has been extensively used is journal writing. 3–11 The examples of the reflective processes cited above refer to Schon, 12 but interested readers can also consult Powell 13 and Mezirow 14 for additional processes.

No true definitions of journal writing exist due to the vast number of ways journal writing can be used. In the literature, journal writing is described and explained in many different ways. For the purposes of this article, journal writing refers to any writing that students perform during either a clinical or classroom experience that challenges them to reflect on past situations, as well as consider how they might perform differently should similar situations arise in the future. The goal of any journal writing assignment should guide the written content for the student. For example, a student could reflect on the challenges of designing and administering a rehabilitation program as part of a rehabilitation course. Students can also return to their struggles with matters such as professionalism during any aspect of their clinical experiences. Both assignments encourage the student to reflect on an experience, whether that experience be from classroom content or their clinical experiences.

Journal writing has been used with nursing, 4 5 8 11 physical therapy, 9 15 occupational therapy, 7 and teacher certification 16 17 students. The journal writing topics for this teaching method can range from reflecting on daily clinical experiences (eg, assessments and rehabilitations performed) to summaries of weekly clinical experiences. Widely used, journal writing has been recognized as a method designed to enhance reflection, 3–11 facilitate critical thought, 18–22 express feelings in writing about problems encountered during clinical experiences, 5 23 and practice writing summaries, objectives, and focused arguments. 22 Because of these benefits, educational writing in a clinical journal is a common assignment in nursing programs. 22 23 However, information for the athletic training educator in various teaching methods, including journal writing, is lacking.

JOURNAL WRITING RESEARCH

Most of the research involving journal writing has been qualitative in nature, with the journal entries analyzed for trends. Davies 3 found that in the process of journal writing, students moved from being passive to active learners during their clinical debriefing sessions. Students would come to debriefing sessions with problems or clinical issues partially solved and look to the debriefing sessions for further input and validation. This type of paradigm shift was also reported by Sedlack, 24 who found that journal writing aided in placing responsibility with the student for active engagement and self-directed learning. In addition, the students' self-confidence increased because the journals enabled them to identify their own lack of motivation. 24

Recently, Williams and Wessel 15 used reflective journals with physical therapy students studying chronic musculoskeletal conditions to obtain feedback regarding their learning. Students moved through a “fix-it” mentality to a more client-centered disability focus. Over the course of the 8 weeks, interactions with patients changed students' attitudes and increased students' knowledge about chronic disease.

In another qualitative study, Ritchie 25 reported that after completing 7 weeks of weekly journal entries, physical therapy students were provided with many opportunities for both the student and faculty member to give feedback, ask questions, and offer ideas for further reflection. In addition, bonds of trust were formed, not only between the student and faculty member, but among the students themselves as they learned to begin to trust themselves and the decisions they made. Last, students valued being able to ask the faculty member questions and receive validation without exposing their own perceived weaknesses to their peers. Ibarreta and McLeod 5 also found this need for feedback. Nursing students using journals wanted more feedback and direction from the instructor to gain more confidence regarding decisions made during their practicum.

Wong et al 11 used dialogue and journal writing to assess a system for test coding the level of any reflection. Each student wrote a reflective paper after developing a teaching plan and then carried out that teaching plan at the clinical assignment. A coding scheme was developed to analyze the reflective papers. Students were categorized as nonreflectors, reflectors, or critical reflectors. Of the 45 students in the study, 34 demonstrated reflection and were able to relate their experiences and turn them into new learning opportunities.

In a similar study, 10 during 2 semesters, each student engaged in dialogue 5 times and wrote 4 journal entries in addition to a reflective paper. (Not described were the specific data analysis methods and the specific breakdown of nonreflectors, reflectors, and critical reflectors.) Students moved from a more narrative or descriptive writing style (nonreflector) to expressing frustration and offering solutions to problems (critical reflector). It was felt that journal writing and dialogue were essential to student learning.

JOURNAL WRITING PROCESS

Journal writing can have many different applications based on the goals of the instructor and student. One common use of journal writing is to promote reflection and thought through one-on-one dialogue between the student and instructor. Brown and Sorrell 22 stated that a clinical journal provides guided opportunities for students to “think aloud” on paper and reflect on their own perceptions or understandings of the situations encountered in their practicums. Hahnemann 20 felt that journal writing assignments encourage exploration and risk taking on the part of the student. Before trying solutions to problems in real life, the student can be creative and express feelings and frustrations on paper. Ibarreta and McLeod 5 reported that their students, through journal writing, were expected to apply knowledge gained from prior classroom content and literature relevant to their clinical experiences. Recently, reflective journals 7 were used to emphasize connecting clinical content with thought process and self-awareness.

Holmes 23 stated that by recording and describing experiences, feelings, and thoughts, students are able to recreate their experiences for additional exploration. A student who had a difficult encounter with a student-athlete could write in the journal about the situation and think about what happened. He or she could describe why decisions were made and actions taken, along with feelings and future thoughts and directions. As educators, we must push our students to reflect more deeply. Pushing students to continuously ask themselves why a decision was made or why they feel the way they do about a topic or situation will cause them to look deeper for answers. Why did they perform a certain special test? Why was ultrasound used in the treatment of that injury, and how will that ultrasound affect the inflammation process? What changes could be made to this patient's treatment or future encounters with a specific injury? Davies 3 stated that journal writing provides students with an opportunity to return to their experiences in an attempt to develop new perspectives that can guide future clinical actions. For example, a student, after performing a knee examination and discussing it with the Approved Clinical Instructor, could later write about the entire experience. What would he or she do differently? What did he or she learn? Writing encourages and provides an opportunity for students to reflect on an experience, connect, and think critically about ideas or situations.

Dialogue Between Instructor and Student

As stated previously, journal writing provides a one-on-one dialogue between the instructor and student. 23 Wong et al 11 suggested that instructors and students are partners in the promotion of reflective learning. This dialogue, facilitated by the instructor, should be designed to challenge the student to reflect on his or her experiences. A student who has accomplished a goal or had a positive rehabilitation experience with a patient is allowed to share that information. In addition, this dialogue can also assist with conflicts in a confidential manner. For example, a student could reflect in the written journal about a difficult situation with a coach. Upon reading the journal, the instructor may provide feedback and ask questions, which will ideally push the student to think about future decisions if again faced with a similar situation.

Not only does this one-on-one dialogue assist in challenging the student, but also students valued the feedback to validate their thoughts on new endeavors. 25 26 Because students are unfamiliar with dealing with coaches, let alone being involved in professional conflict, they may be limited in what they perceive as actions and solutions. This unfamiliar problem can leave students feeling that they have no control or power in the situation.

Although students may experience cognitive dissonance when engaging in a written dialogue about a challenging experience they had, the discourse can facilitate different ways of thinking 27 and empower students to handle themselves differently after reflection in the future. 28 Through one-on-one dialogue, students are empowered to not just leave future encounters and experiences to fate. Instead students, after reflection, have thought about their actions and how they would handle themselves or the situation differently in the future, which is reflection-for-action. 28 Reflection should be encouraged and enhanced through one-on-one dialogue via the journal writing process. The journal writing process, however, should be well planned and have explicit student expectations.

EXPECTATIONS AND PLANNING

Before assigning journal writing, the instructor must convey to the students all expectations with regard to completing and grading the journals. 22 29 Table 1 presents many questions that should be asked when contemplating whether to assign journal writing. These questions will provide focus to enable the student to concentrate on the writing and not feel insecure about how the instructor will grade the journal. As stated by Kobert, 29 every effort should be made to ensure that the journal writing is seen as nonthreatening and satisfying. Identifying expectations before starting the first journal will prevent some confusion. It is also imperative for the instructor to consider many facets of the journaling process. The following section discusses factors to consider when planning for the use of journals, including setting student expectations, identifying appropriate topics, journal utilization strategies, and grading systems.

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Journal Utilization Techniques

Depending on the method of use (daily, weekly writing) and the journal's purpose (to enhance critical thinking, promote reflection, etc), the way in which journal writing is used can take many different forms. Table 2 presents general topics followed by subtopics for possible student assignments in the classroom or clinical education setting. These topics can vary depending on the level of student, classroom content, location and type of clinical experience, and deficiencies or needs of the student. Topics may be decided solely by the instructor or through more egalitarian methods with the students' input. Burnard 30 stated that one democratic method of determining topics for journal writing is to discuss this with the class. Preassigned or spontaneous topics could also be used. The advantage of preassigned topics is that the student is aware of the topic and can be thinking about it before writing. On the other hand, some students may have certain spontaneous experiences during their clinical education about which they wish to write. It is important for instructors to experiment with students and classes to determine which methods encourage reflection in students. Some classes as a whole may elect to use journal writing with the spontaneous method.

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Spontaneous topics and experiences can include incidents that interest or concern students during their clinical placements. Unfortunately, due to uncontrollable factors, some students may find this method less challenging than preassigned topics and want to change the method of their journal writing. Journal writing should be viewed as experimental and as a work in progress or a process by which students learn to reflect and, we hope, move from reflection-in-action to reflection-for-action. Simply, the goal is for students to evaluate their actions and reflect on how they could handle the situation differently in the future. Instructors should be ready to adapt the journal writing experience to enhance assignment goals, whether they are reflection, learning, etc.

Journal writing can be time consuming for the student, so one way to show that this writing is valued is to allot some classroom time for the students to write. Hahnemann 20 reported using journal writing for 10 to 15 minutes of each class. Students were asked to write about what they expected to learn from class that day, as well as what had been learned from previous classes. Although allocating 10 to 15 minutes of class time for this purpose may not be feasible in a 50-minute class period, this method could be adapted to 2 to 3 minutes every class period or whatever fits the instructor's schedule.

Brown and Sorrell 22 assigned students to write in their journals during class about difficult concepts or summarize a discussion or argue for or against a treatment. Physical therapy students were assigned to write about at least one learning event that occurred in their clinical placement. 9 Burnard, 30 who assigned weekly writings under 6 headings from which students could choose, also used this type of weekly writing. Pinkstaff 26 asked nursing students enrolled in a public health class to write in their journals on individual topics related to class each week. Qualitative analysis revealed that the students not only improved in the creativity of their writing but the quality of their essay writing skills.

When completing some journal assignments, students should be allowed to write using a freeform style. 20 31 Although this seems nontraditional, it is important to remember that if the focus is on the thought process, then grammar and punctuation should not be a part of the evaluation of the journal. If the focus of the journal is to reflect, then the journal should be a forum where students can write and not worry about punctuation, grammar, and spelling. As stated by Hahnemann, 20 journals are a means by which students should be allowed to experiment and test their wings. Focusing too much attention on grammar and punctuation may lead a student to misinterpret the purpose of the journal writing activity. Instead, the attention should be on the content of what is written and not how it is written. Additional information on grading and feedback is discussed later.

Journal Content and Format

Burnard 30 felt that no guidelines should be given regarding the amount that is written under each heading or journal topic, because it was felt this would be overstructuring; however, students were encouraged to provide regular journal entries for each given topic. Instead of a student's writing about a given topic one time over the course of a week, the student could be encouraged to write after each clinical experience or several times during that week. Brown and Sorrell 22 felt that the maximum length for assignments, such as summaries or critiques, should be 1 to 2 pages. Each instructor must decide what is appropriate for his or her purpose, and students must realize that content is more important than word count. Instructors should also realize that motivation is a factor in journal writing. Paterson 31 pointed out that students are not always interested in all aspects of their clinical experiences, so instructors should not expect all journals to be of the same quality. Some weeks, the student might only meet the basic requirements, whereas in other weeks, the student may write profusely. Different clinical experiences provide more education and invoke more passion than others. The instructor has to decide, based on the goals and objectives of the assignment as well as the clinical experience during a given time frame, the quality and length of journal writing.

Students should also be given instructions as to how and when to turn in and pick up their journal entries. Specific guidelines should be in place that will enable the student to properly submit and collect the journal entries. For example, one guideline may be to have the students collect their journals every Monday by 12:00 pm and to submit them every Friday by 12:00 pm . Another would be to have them submitted during one class period and, after grading, handed back to the students during the next class period. Lastly, other questions must be considered, such as where and how to submit the journal entries (eg, mail box versus e-mail).

JOURNAL GRADING

Jackson 32 and Pinkstaff 26 stated that the single most important factor in the successful use of journaling is allowing the journal to be a safe space for free expression. How can a student be graded for writing about feelings and reactions to specific issues and topics? How do we know he or she is really trying to reflect? Although they should be graded for their thoughts and feelings, it is important the students be informed 22 as to how the journals will fit into their grades. What percentage of their grade will be affected by their journal writing? How will they be graded? Brown and Sorrell 22 suggested a method of grading by which if the student achieves all the goals for the journal, then he or she earns an A or passes that portion of the class the journal fulfills. Hahnemann 20 and Williams et al 9 weighted the journals as 10% of a grade in a course. Hahnemann 20 stated this was done because they felt it would motivate the students to write thoroughly and with meaning. Tryssenaar 7 reported weighting the journals as 20% of the final grade. However the instructor chooses to integrate journal writing into a course, unless the journals have an effect on the grades, students will put very little effort into their writing. 20 Adding a grade to the journals puts value to them and establishes their importance. Although 10% to 20% of a grade has been reported in the literature, it is up to the individual instructor to weight the journals accordingly or in some way to ensure that students feel the journal writing assignments matter and are of significant value. These journals can be a commitment for the student as well as the instructor, but they can potentially provide valuable insight and reflection. The strength of journal entries is related to the students' motivation to engage and participate in their own learning processes. 8 If a student is motivated and active in learning, the process will be seen as an investment instead of time consuming. Wong et al 11 found that willingness, commitment, and open mindedness were attributes that were conducive to reflective learning.

Determining the level of reflectivity is beyond the scope of this manuscript. However, Atkins and Murphy 33 outlined 3 stages of the reflective process that can be used when grading. Stage 1 is triggered by awareness of uncomfortable feelings. The student realizes that knowledge being applied in this situation is not sufficient in and of itself to explain the situation. For example, a student is using ultrasound treatments for tendinitis, but the treatment is producing no therapeutic effect. The student is unsure as to why this is happening and expresses frustration. The second stage is characterized by a critical analysis of the situation. This involves feelings and knowledge, so that new knowledge is applied. Four terms were used to describe this critical thought process: association, validation, integration, and appropriation. The development of a new perspective on the situation is stage 3. The outcome here through learning is reflection. These 3 stages can be a guide when grading a student's written journal entry to determine the level of reflectivity of the student. Educators interested in researching other tools with which to evaluate or grade journals are encouraged to consult the following papers and other works. 8 24 34

JOURNAL FEEDBACK

After writing their first journal entry, students should receive feedback before writing the next entry. 22 One of the most challenging tasks with journal writing is evaluating the student's written work. 20 Judgment and criticism are withheld. Instead, the attempt to write on the student's part is more important than the success of the attempt. 20 Brown and Sorrell 22 agreed to provide 1 to 2 comments about the overall journal. The thought of not providing numerous comments is echoed by Holmes, 23 who stated that when the focus of feedback is detailed, the students lose their sense of purpose and meaning in the writing. Students will shift their focus from constructing a sense of what they are trying to say into worrying about grammar and sentence structure. Table 3 provides some sample follow-up questions that can be used to challenge and encourage students to think and reflect. In addition, as stated by Paterson, 31 a balance must be maintained between giving too many comments and nudging the student into new ways of thinking. Correcting misinformation written by the student is encouraged, but no criticism or judgment should be made of the student's feelings. Annotations might pertain to future questions and comments to expand on in the next journal entry, but the instructor needs to try to avoid excessive grammar and spelling corrections.

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Feedback can be given in various ways. Brown and Sorrell 22 reported using both oral and written feedback. Conferences, also known as debriefing sessions, either individual or group, can be set up to discuss the clinical journal's relationship to reflection, critical thinking, etc. The student and instructor sit down together to discuss the journal along with feedback goals for upcoming writings. In addition, group discussions 8 15 22 at the beginning of a practicum and/or throughout can conserve faculty time, promote the exchange of ideas, and help synthesize information for students. Another way to conserve faculty time is to only grade at random a percentage of the journals that are written after a few weeks of feedback has been given. All of these types of feedback have strong points and limitations. It is up to the instructor to decide what is appropriate and to modify as needed. Last, if a student inquires as to why or how the journal was graded, it is important for the instructor to be able to explain all comments and methods of grading. These grading points are not only justification but can help guide the student to further reflection.

As stated by Riley-Doucet and Wilson, 8 one of the limitations of this type of assignment is the student who procrastinates and doesn't take responsibility for coursework. When a student exhibits this type of behavior, it should be recognized by the instructor and discussed with the student. The student should be given the benefit of the doubt as to the procrastination, and the instructor can approach the student from the perspective that the student is lacking knowledge about reflection and journal writing. Riley-Doucet and Wilson 8 recommended pairing this student with a peer who is comfortable with the journal writing. If this is not possible, another recommendation is to establish small short-term goals for upcoming journal writings, such as considering specific questions when writing the journal. These short-term goals and guiding questions can assist the student in the reflective process.

Examples of questions include the following:

  • How would I have done this differently?
  • Why did I choose to perform the skill the way I did?
  • What was my reasoning in handling that situation that way?

Journal writing is a process, and students may not put much effort into their writing in the beginning. For some students, it will be easy to express their feelings and frustrations. Other students may struggle. Instructors should take into account individual personalities when providing feedback. In addition, the students need to be reminded that the journal writing is a process that takes time. It may take weeks or longer for a student to feel comfortable and trust the instructor. Feedback is a vital aspect in nurturing reflection over time, as the journal writing progresses over weeks and possibly years.

As stated by Kobert, 30 one drawback to journal writing is what makes it so valuable. Students may be reluctant or unable to explore and share intimacies of their own lived experiences with others. They may be more concerned with writing what they think the instructor wants to hear than writing about what is true to them. Writing about issues and feelings puts the student in a very vulnerable position. To promote reflection, he or she must express weakness and insecurity to grow. Students must feel comfortable exposing this vulnerability. Holmes 23 noted the significant responsibility of both the student and instructor to accept differing views while searching for understanding and meaning. Part of encouraging this truthful writing is not only through the previously mentioned feedback procedures but also by maintaining confidentiality to encourage truthfulness. 19 If students are familiar with the instructor and know him or her to be nonjudgmental, they will, more than likely, be more willing to self-disclose in their journal writing. However, if the instructor is new to the students, they will need evidence that the instructor will remain true to his or her word before disclosing too much in a journal entry. Such trust takes time to develop, but if journal writing is seen as a work in progress, this is all part of the journey.

RECOMMENDATIONS

Additional research needs to be conducted investigating journal writing. 4 15 33 Much of the journal writing literature in the allied health field ranges from specific articles about grading 23 and assisting with common problems or pitfalls 31 to general guidelines for using journal writing. 19 20 30 Although this information is useful and often written by professionals speaking from years of experience, more qualitative and quantitative research is needed. Specific research questions include the following:

  • How does journal writing affect the learning of material?
  • Does the type of feedback given to the student affect what is written in journal writing?
  • How do students learn to reflect on their experiences?
  • What variables affect the trust level between the instructor and student to enhance truthful writing?
  • Does maturity affect journal writing and reflection?

CONCLUSIONS

The purpose of this article was to provide an introduction to the process of journal writing to promote reflection. Our students, on a daily basis, encounter experiences that can teach them to reflect during their future practice of athletic training, and we owe it to our students to facilitate their reflection. Course preparation is short in relation to career practice; therefore, as educators, we hope to instill reflective qualities that mature and grow.

Many methods of assigning and grading journal writing were presented in this article. As with any teaching method, there is no right or wrong way to approach journal writing. As the students grow in self-confidence and gain trust in the instructor, they begin to reflect and write about their real concerns. This leads to obtaining valuable feedback to empower our future certified athletic trainers to overcome those real-life concerns. Reflection is the goal, as everyone is rewarded—the student, the patient, the coach, and the instructor. Reflection enables the student to do a better job as a certified athletic trainer. Isn't our real goal to enable all of our students to give thought to their actions and perform with the utmost skill, knowledge, and confidence that they have done their jobs in the best possible manner?

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Recommended Videos

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  • Using ACM Word Template: Video Tutorial  (ACM)
  • From the Editor of IEEE Access: How to Get Published in an Open Access Journal  (IEEE)
  • The Journey of an Article at Springer  (Springer)
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  • 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: orcid.org/0000-0001-9023-0018 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: orcid.org/0000-0003-1080-024X 14 ,
  • Victoria Nieciecki   ORCID: orcid.org/0000-0002-1891-5909 1 , 13 ,
  • Qiyun Zhu   ORCID: orcid.org/0000-0002-3568-6271 7 , 15 , 16 ,
  • Michael Shaffer 4 ,
  • Morgan Panitchpakdi 5 ,
  • Kelly C. Weldon 5 ,
  • Kalen Cantrell   ORCID: orcid.org/0000-0002-6262-1668 17 ,
  • Asa Ben-Hur 18 ,
  • Sasha C. Reed 19 ,
  • Greg C. Humphry 7 ,
  • Gail Ackermann 7 ,
  • Daniel McDonald 7 ,
  • Siu Hung Joshua Chan   ORCID: orcid.org/0000-0002-7707-656X 6 ,
  • Melissa Connor 20 ,
  • Derek Boyd   ORCID: orcid.org/0000-0003-1444-0536 21 , 22 ,
  • Jake Smith 21 , 23 ,
  • Jenna M. S. Watson 21 ,
  • Giovanna Vidoli 21 ,
  • Dawnie Steadman   ORCID: orcid.org/0000-0003-0812-0739 21 ,
  • Aaron M. Lynne 24 ,
  • Sibyl Bucheli 24 ,
  • Pieter C. Dorrestein   ORCID: orcid.org/0000-0002-3003-1030 5 ,
  • Kelly C. Wrighton 4 ,
  • David O. Carter   ORCID: orcid.org/0000-0003-1885-5237 25 ,
  • Rob Knight   ORCID: orcid.org/0000-0002-0975-9019 7 , 17 , 26 , 27 &
  • Jessica L. Metcalf   ORCID: orcid.org/0000-0001-8374-8046 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 ( https://www.ncei.noaa.gov/ ) and monthly total precipitation accumulation over the course of the study was collected from the Weather Underground website ( https://www.wunderground.com/ ) 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 ( http://www.earthmicrobiome.org/ ).

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 (ncbi.nlm.nih.gov/assembly/GCF_000001405.13/) 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 https://gnps.ucsd.edu/ . 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 ( https://edmond.mpdl.mpg.de/dataset.xhtml?persistentId=doi:10.17617/3.UV4FBN ). 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 ; https://doi.org/10.5281/zenodo.7843104 ). 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 reformat.sh script with flag idfilter=0.95 from BBMap (v.38.90) ( https://sourceforge.net/projects/bbmap/ ). Filtered BAM files were input to CoverM (v0.3.2) ( https://github.com/wwood/CoverM ) 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) ( https://github.com/cdanielmachado/HiOrCo ). 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 ( https://github.com/cdanielmachado/carveme ). GEMs from each co-occurring MAG community were input as a microbial community into SMETANA (v1.0.0) ( https://github.com/cdanielmachado/smetana ) 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 ( https://gnps.ucsd.edu ; 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) ( http://biocore.github.io/calour/index.html ) 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 ( https://doi.org/10.5281/zenodo.7843104 ; https://zenodo.org/record/7938240 ) 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 https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=1c73926f2eb5409985cc2e136062db2f . The GNPS database was accessed through https://gnps.ucsd.edu/ . The GreenGenes2 database can be found at https://ftp.microbio.me/greengenes_release/ . SILVA databases can be found at https://www.arb-silva.de/documentation/release-1381/ . 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 ( https://edmond.mpdl.mpg.de/dataset.xhtml?persistentId=doi:10.17617/3.UV4FBN ). The GTDB data can be accessed at https://data.gtdb.ecogenomic.org/releases/ . Source data are provided with this paper.

Code availability

Analysis code, intermediate files and metadata are publicly available on Github ( https://github.com/Metcalf-Lab/2023-Universal-microbial-decomposer-network ). The complete mathematical algorithms for Joint-RPCA can be found in Supplementary Text .

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Acknowledgements

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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Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA

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

Department of Microbiology, University of Tennessee, Knoxville, TN, USA

Zachary M. Burcham

Department of Animal Sciences, Auburn University, Auburn, AL, USA

Aeriel D. Belk

Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, USA

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Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA

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Liat Shenhav

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Anru R. Zhang & Pixu Shi

Department of Computer Science, Duke University, Durham, NC, USA

Anru R. Zhang

Graduate Program in Cell and Molecular Biology, Colorado State University, Fort Collins, CO, USA

Heather L. Deel, Victoria Nieciecki & Jessica L. Metcalf

School of Food Science and Technology, Nanchang University, Nanchang, Jiangxi, China

Zhenjiang Zech Xu

School of Life Sciences, Arizona State University, Tempe, AZ, USA

Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ, USA

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Department of Computer Science, Colorado State University, Fort Collins, CO, USA

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U.S. Geological Survey, Southwest Biological Science Center, Moab, UT, USA

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Forensic Investigation Research Station, Colorado Mesa University, Grand Junction, CO, USA

Melissa Connor

Forensic Anthropology Center, Department of Anthropology, University of Tennessee, Knoxville, TN, USA

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Department of Social, Cultural, and Justice Studies, University of Tennessee at Chattanooga, Chattanooga, TN, USA

Mid-America College of Funeral Service, Jeffersonville, IN, USA

Department of Biological Sciences, Sam Houston State University, Huntsville, TX, USA

Aaron M. Lynne & Sibyl Bucheli

Laboratory of Forensic Taphonomy, Forensic Sciences Unit, School of Natural Sciences and Mathematics, Chaminade University of Honolulu, Honolulu, HI, USA

David O. Carter

Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA

Department of Bioengineering, University of California San Diego, La Jolla, CA, USA

Humans and the Microbiome Program, Canadian Institute for Advanced Research, Toronto, Ontario, Canada

Jessica L. Metcalf

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Contributions

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.

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Correspondence to Jessica L. Metcalf .

Ethics declarations

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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Nature Microbiology thanks Anna Heintz-Buschart, Michael Strickland and Aleksej Zelezniak for their contribution to the peer review of this work.

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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). https://doi.org/10.1038/s41564-023-01580-y

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[Re]considering the male gaze in Italian Baroque sculpture

photo of Baroque style bust statue

The Lamar Dodd School of Art directs us to this essay written by art history student Gabriela Diaz-Jones published in The Classic Journal, the Franklin College Writing intensive Program's journal of undergraduate writing and research, “ Baroque Women in Marble as Intimate or Intricate.” Diaz-Jones explores the objectification of female sitters sculpted in marble during the Italian Baroque era, focusing on two busts, one by Gian Lorenzo Bernini and the other by Alessandro Algardi:

The two artworks are borne from nearly opposite contexts. Algardi created a poised, almost lifeless portrait, befitting of its likely funerary purpose. Bernini, on the other hand, carved a work that is stunningly intimate and energetic, gesturing to his secret sexual relationship with Costanza. Bernini’s invention of the “speaking likeness,” the concept of ownership regarding women’s jewelry and clothing during this period, sexual connotations of women’s hair, the myth and symbolism of Medusa, and the legacy of men’s signing images of women as assertions of ownership all come into play when examining and interrogating these works. The tenor of this paper will be that both busts, while they have entirely opposite approaches to depicting women (formal versus intimate, reserved versus dynamic) are still stunningly alike. In both artworks, male artists used sculpture to construct an idealized version of a woman, either moral or seductive. Ultimately both “constructions” are fictions, not reflective of reality but rather, reflections of the role they wanted these women to play (deceased wife of a patron, or lover.) Bernini and Algardi both brought marble to life in the quintessential Baroque style, but the “life” that they imbued into the rock was, without a doubt, not their subjects’ own.

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Image: Algardi, Alessandro.  Bust of Maria Cerri Capranica , 1640, marble, 90 x 61.3 x 29.2 cm, The J. Paul Getty Museum, Los Angeles, California (Artstor, ITHAKA).

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    This Article Covers: What is Journal Writing? What Can I Use Journaling For? How to Journal - What are the Benefits? Getting Started with Journaling Creating a Journal Writing Ritual How to Journal - What To Write? How Often Should I Write in my Journal? Do You Need to Write Regularly in a Journal?

  17. Archives

    Archives Vol. 15 No. 3 (2024) Early view Vol. 15 No. 2 (2023) Vol. 15 No. 1 (2023) SPECIAL ISSUE: Developing writing across and in school subjects Guest editors: Sara Routarinne, Riitta Juvonen, Johanna Pentikäinen, Arja Kaasinen & Anne-Elina Salo Vol. 14 No. 3 (2023) Vol. 14 No. 2 (2022) Vol. 14 No. 1 (2022) Vol. 13 No. 3 (2022)

  18. Journal Writing as a Teaching Technique to Promote Reflection

    JOURNAL WRITING RESEARCH. Most of the research involving journal writing has been qualitative in nature, with the journal entries analyzed for trends. Davies 3 found that in the process of journal writing, students moved from being passive to active learners during their clinical debriefing sessions. Students would come to debriefing sessions ...

  19. Journal Articles

    This page has information and resources on authoring journal articles and choosing a journal to submit to. Since journal articles are by far the most common venue for submitting research, this page will be the most detailed. Note: If you are a graduate student, ask your supervisor/advisor if there are expectations for where you submit.

  20. ISU Writing Program

    The Grassroots Writing Research Journal editorial team is proud to announce the 27th publication of the journal. GWRJ 14.2 shares literate activity research on texting, temperature blankets, nursing, K-pop, second language writing, and so much more.

  21. Science Research Writing For Non Native Speakers Of English

    science-research-writing-for-non-native-speakers-of-english 3 Downloaded from resources.caih.jhu.edu on 2020-06-01 by guest 2012-10-26 Adrian Wallwork This guide is based on a study of referees' reports and letters from journal editors on the reasons why papers written by

  22. Changing How Writing Is Taught

    Research article First published online May 22, 2019 Changing How Writing Is Taught Steve Graham View all authors and affiliations Volume 43, Issue 1 https://doi.org/10.3102/0091732X18821125 PDF / ePub More Abstract If students are to be successful in school, at work, and in their personal lives, they must learn to write.

  23. Submissions

    A title page, giving the title of your paper, names, professional affiliations, and email addresses for all authors, an abstract (max. 200 words), keywords (max. 5), a suggested running head, and full mailing address for the corresponding author and other authors.

  24. Theorizing is not abstraction but horizontal translation

    Michael Guggenheim is a sociologist who works at the Department of Sociology, Goldsmiths, University of London. He is the co-founder and convenor of the MA Visual Sociology at Goldsmiths. He has published widely on expertise, lay people, disasters, change of use of buildings, environmental research and food and social theory.

  25. Nature

    303 See Other. openresty

  26. Scholars' Writing Is Often Unclear. Why That Matters for the K ...

    Academic incentives reward writing journal articles, working papers, and book chapters, all of which tend to foster bad habits rather than good ones. ... But it's pervasive. I have talented ...

  27. [Re]considering the male gaze in Italian Baroque sculpture

    The Lamar Dodd School of Art directs us to this essay written by art history student Gabriela Diaz-Jones published in The Classic Journal, the Franklin College Writing intensive Program's journal of undergraduate writing and research, " Baroque Women in Marble as Intimate or Intricate." Diaz-Jones explores the objectification of female sitters sculpted in marble during the Italian Baroque ...

  28. Index

    Index. The Journal of Writing Research is a relatively new journal, but is already abstracted and indexed in a series of databases. The editors will make every effort to get the journal included in the most relevant databases in the domain of writing research. listed in the SCOPUS, the largest abstract and citation database of peer-reviewed ...