title for research methodology

• Affiliate Program

• UNITED STATES
• 台灣 (TAIWAN)
• TÜRKIYE (TURKEY)
• Academic Editing Services
• - Research Paper
• - Journal Manuscript
• - Dissertation
• - College & University Assignments
• Admissions Editing Services
• - Application Essay
• - Personal Statement
• - Recommendation Letter
• - Cover Letter
• - CV/Resume
• Business Editing Services
• - Business Documents
• - Report & Brochure
• - Website & Blog
• Writer Editing Services
• - Script & Screenplay
• Our Editors
• Client Reviews
• Editing & Proofreading Prices
• Wordvice Points
• Partner Discount
• Plagiarism Checker
• APA Citation Generator
• MLA Citation Generator
• Chicago Citation Generator
• Vancouver Citation Generator
• - APA Style
• - MLA Style
• - Chicago Style
• - Vancouver Style
• Writing & Editing Guide
• Academic Resources
• Admissions Resources

How to Make a Research Paper Title with Examples

What is a research paper title and why does it matter?

A research paper title summarizes the aim and purpose of your research study. Making a title for your research is one of the most important decisions when writing an article to publish in journals. The research title is the first thing that journal editors and reviewers see when they look at your paper and the only piece of information that fellow researchers will see in a database or search engine query. Good titles that are concise and contain all the relevant terms have been shown to increase citation counts and Altmetric scores .

Therefore, when you title research work, make sure it captures all of the relevant aspects of your study, including the specific topic and problem being investigated. It also should present these elements in a way that is accessible and will captivate readers. Follow these steps to learn how to make a good research title for your work.

How to Make a Research Paper Title in 5 Steps

You might wonder how you are supposed to pick a title from all the content that your manuscript contains—how are you supposed to choose? What will make your research paper title come up in search engines and what will make the people in your field read it? 

In a nutshell, your research title should accurately capture what you have done, it should sound interesting to the people who work on the same or a similar topic, and it should contain the important title keywords that other researchers use when looking for literature in databases. To make the title writing process as simple as possible, we have broken it down into 5 simple steps.

Step 1: Answer some key questions about your research paper

What does your paper seek to answer and what does it accomplish? Try to answer these questions as briefly as possible. You can create these questions by going through each section of your paper and finding the MOST relevant information to make a research title.

Step 2: Identify research study keywords

Now that you have answers to your research questions, find the most important parts of these responses and make these your study keywords. Note that you should only choose the most important terms for your keywords–journals usually request anywhere from 3 to 8 keywords maximum.

Step 3: Research title writing: use these keywords

“We employed a case study of 60 liver transplant patients around the US aged 20-50 years to assess how waiting list volume affects the outcomes of liver transplantation in patients; results indicate a positive correlation between increased waiting list volume and negative prognosis after the transplant procedure.”

The sentence above is clearly much too long for a research paper title. This is why you will trim and polish your title in the next two steps.

Step 4: Create a working research paper title

To create a working title, remove elements that make it a complete “sentence” but keep everything that is important to what the study is about. Delete all unnecessary and redundant words that are not central to the study or that researchers would most likely not use in a database search.

“ We employed a case study of 60 liver transplant patients around the US aged 20-50 years to assess how the waiting list volume affects the outcome of liver transplantation in patients ; results indicate a positive correlation between increased waiting list volume and a negative prognosis after transplant procedure ”

Now shift some words around for proper syntax and rephrase it a bit to shorten the length and make it leaner and more natural. What you are left with is:

“A case study of 60 liver transplant patients around the US aged 20-50 years assessing the impact of waiting list volume on outcome of transplantation and showing a positive correlation between increased waiting list volume and a negative prognosis” (Word Count: 38)

This text is getting closer to what we want in a research title, which is just the most important information. But note that the word count for this working title is still 38 words, whereas the average length of published journal article titles is 16 words or fewer. Therefore, we should eliminate some words and phrases that are not essential to this title.

Step 5: Remove any nonessential words and phrases from your title

Because the number of patients studied and the exact outcome are not the most essential parts of this paper, remove these elements first:

 “A case study of 60 liver transplant patients around the US aged 20-50 years assessing the impact of waiting list volume on outcomes of transplantation and showing a positive correlation between increased waiting list volume and a negative prognosis” (Word Count: 19)

In addition, the methods used in a study are not usually the most searched-for keywords in databases and represent additional details that you may want to remove to make your title leaner. So what is left is:

“Assessing the impact of waiting list volume on outcome and prognosis in liver transplantation patients” (Word Count: 15)

In this final version of the title, one can immediately recognize the subject and what objectives the study aims to achieve. Note that the most important terms appear at the beginning and end of the title: “Assessing,” which is the main action of the study, is placed at the beginning; and “liver transplantation patients,” the specific subject of the study, is placed at the end.

This will aid significantly in your research paper title being found in search engines and database queries, which means that a lot more researchers will be able to locate your article once it is published. In fact, a 2014 review of more than 150,000 papers submitted to the UK’s Research Excellence Framework (REF) database found the style of a paper’s title impacted the number of citations it would typically receive. In most disciplines, articles with shorter, more concise titles yielded more citations.

Adding a Research Paper Subtitle

If your title might require a subtitle to provide more immediate details about your methodology or sample, you can do this by adding this information after a colon:

“ : a case study of US adult patients ages 20-25”

If we abide strictly by our word count rule this may not be necessary or recommended. But every journal has its own standard formatting and style guidelines for research paper titles, so it is a good idea to be aware of the specific journal author instructions , not just when you write the manuscript but also to decide how to create a good title for it.

Research Paper Title Examples

The title examples in the following table illustrate how a title can be interesting but incomplete, complete by uninteresting, complete and interesting but too informal in tone, or some other combination of these. A good research paper title should meet all the requirements in the four columns below.

Tips on Formulating a Good Research Paper Title

In addition to the steps given above, there are a few other important things you want to keep in mind when it comes to how to write a research paper title, regarding formatting, word count, and content:

• Write the title after you’ve written your paper and abstract
• Include all of the essential terms in your paper
• Keep it short and to the point (~16 words or fewer)
• Avoid unnecessary jargon and abbreviations
• Use keywords that capture the content of your paper
• Never include a period at the end—your title is NOT a sentence

Research Paper Writing Resources

We hope this article has been helpful in teaching you how to craft your research paper title. But you might still want to dig deeper into different journal title formats and categories that might be more suitable for specific article types or need help with writing a cover letter for your manuscript submission.

In addition to getting English proofreading services , including paper editing services , before submission to journals, be sure to visit our academic resources papers. Here you can find dozens of articles on manuscript writing, from drafting an outline to finding a target journal to submit to.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Saudi J Anaesth
• v.13(Suppl 1); 2019 Apr

Writing the title and abstract for a research paper: Being concise, precise, and meticulous is the key

Milind s. tullu.

Department of Pediatrics, Seth G.S. Medical College and KEM Hospital, Parel, Mumbai, Maharashtra, India

This article deals with formulating a suitable title and an appropriate abstract for an original research paper. The “title” and the “abstract” are the “initial impressions” of a research article, and hence they need to be drafted correctly, accurately, carefully, and meticulously. Often both of these are drafted after the full manuscript is ready. Most readers read only the title and the abstract of a research paper and very few will go on to read the full paper. The title and the abstract are the most important parts of a research paper and should be pleasant to read. The “title” should be descriptive, direct, accurate, appropriate, interesting, concise, precise, unique, and should not be misleading. The “abstract” needs to be simple, specific, clear, unbiased, honest, concise, precise, stand-alone, complete, scholarly, (preferably) structured, and should not be misrepresentative. The abstract should be consistent with the main text of the paper, especially after a revision is made to the paper and should include the key message prominently. It is very important to include the most important words and terms (the “keywords”) in the title and the abstract for appropriate indexing purpose and for retrieval from the search engines and scientific databases. Such keywords should be listed after the abstract. One must adhere to the instructions laid down by the target journal with regard to the style and number of words permitted for the title and the abstract.

Introduction

This article deals with drafting a suitable “title” and an appropriate “abstract” for an original research paper. Because the “title” and the “abstract” are the “initial impressions” or the “face” of a research article, they need to be drafted correctly, accurately, carefully, meticulously, and consume time and energy.[ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ] Often, these are drafted after the complete manuscript draft is ready.[ 2 , 3 , 4 , 5 , 9 , 10 , 11 ] Most readers will read only the title and the abstract of a published research paper, and very few “interested ones” (especially, if the paper is of use to them) will go on to read the full paper.[ 1 , 2 ] One must remember to adhere to the instructions laid down by the “target journal” (the journal for which the author is writing) regarding the style and number of words permitted for the title and the abstract.[ 2 , 4 , 5 , 7 , 8 , 9 , 12 ] Both the title and the abstract are the most important parts of a research paper – for editors (to decide whether to process the paper for further review), for reviewers (to get an initial impression of the paper), and for the readers (as these may be the only parts of the paper available freely and hence, read widely).[ 4 , 8 , 12 ] It may be worth for the novice author to browse through titles and abstracts of several prominent journals (and their target journal as well) to learn more about the wording and styles of the titles and abstracts, as well as the aims and scope of the particular journal.[ 5 , 7 , 9 , 13 ]

The details of the title are discussed under the subheadings of importance, types, drafting, and checklist.

Importance of the title

When a reader browses through the table of contents of a journal issue (hard copy or on website), the title is the “ first detail” or “face” of the paper that is read.[ 2 , 3 , 4 , 5 , 6 , 13 ] Hence, it needs to be simple, direct, accurate, appropriate, specific, functional, interesting, attractive/appealing, concise/brief, precise/focused, unambiguous, memorable, captivating, informative (enough to encourage the reader to read further), unique, catchy, and it should not be misleading.[ 1 , 2 , 3 , 4 , 5 , 6 , 9 , 12 ] It should have “just enough details” to arouse the interest and curiosity of the reader so that the reader then goes ahead with studying the abstract and then (if still interested) the full paper.[ 1 , 2 , 4 , 13 ] Journal websites, electronic databases, and search engines use the words in the title and abstract (the “keywords”) to retrieve a particular paper during a search; hence, the importance of these words in accessing the paper by the readers has been emphasized.[ 3 , 4 , 5 , 6 , 12 , 14 ] Such important words (or keywords) should be arranged in appropriate order of importance as per the context of the paper and should be placed at the beginning of the title (rather than the later part of the title, as some search engines like Google may just display only the first six to seven words of the title).[ 3 , 5 , 12 ] Whimsical, amusing, or clever titles, though initially appealing, may be missed or misread by the busy reader and very short titles may miss the essential scientific words (the “keywords”) used by the indexing agencies to catch and categorize the paper.[ 1 , 3 , 4 , 9 ] Also, amusing or hilarious titles may be taken less seriously by the readers and may be cited less often.[ 4 , 15 ] An excessively long or complicated title may put off the readers.[ 3 , 9 ] It may be a good idea to draft the title after the main body of the text and the abstract are drafted.[ 2 , 3 , 4 , 5 ]

Types of titles

Titles can be descriptive, declarative, or interrogative. They can also be classified as nominal, compound, or full-sentence titles.

Descriptive or neutral title

This has the essential elements of the research theme, that is, the patients/subjects, design, interventions, comparisons/control, and outcome, but does not reveal the main result or the conclusion.[ 3 , 4 , 12 , 16 ] Such a title allows the reader to interpret the findings of the research paper in an impartial manner and with an open mind.[ 3 ] These titles also give complete information about the contents of the article, have several keywords (thus increasing the visibility of the article in search engines), and have increased chances of being read and (then) being cited as well.[ 4 ] Hence, such descriptive titles giving a glimpse of the paper are generally preferred.[ 4 , 16 ]

Declarative title

This title states the main finding of the study in the title itself; it reduces the curiosity of the reader, may point toward a bias on the part of the author, and hence is best avoided.[ 3 , 4 , 12 , 16 ]

Interrogative title

This is the one which has a query or the research question in the title.[ 3 , 4 , 16 ] Though a query in the title has the ability to sensationalize the topic, and has more downloads (but less citations), it can be distracting to the reader and is again best avoided for a research article (but can, at times, be used for a review article).[ 3 , 6 , 16 , 17 ]

From a sentence construct point of view, titles may be nominal (capturing only the main theme of the study), compound (with subtitles to provide additional relevant information such as context, design, location/country, temporal aspect, sample size, importance, and a provocative or a literary; for example, see the title of this review), or full-sentence titles (which are longer and indicate an added degree of certainty of the results).[ 4 , 6 , 9 , 16 ] Any of these constructs may be used depending on the type of article, the key message, and the author's preference or judgement.[ 4 ]

Drafting a suitable title

A stepwise process can be followed to draft the appropriate title. The author should describe the paper in about three sentences, avoiding the results and ensuring that these sentences contain important scientific words/keywords that describe the main contents and subject of the paper.[ 1 , 4 , 6 , 12 ] Then the author should join the sentences to form a single sentence, shorten the length (by removing redundant words or adjectives or phrases), and finally edit the title (thus drafted) to make it more accurate, concise (about 10–15 words), and precise.[ 1 , 3 , 4 , 5 , 9 ] Some journals require that the study design be included in the title, and this may be placed (using a colon) after the primary title.[ 2 , 3 , 4 , 14 ] The title should try to incorporate the Patients, Interventions, Comparisons and Outcome (PICO).[ 3 ] The place of the study may be included in the title (if absolutely necessary), that is, if the patient characteristics (such as study population, socioeconomic conditions, or cultural practices) are expected to vary as per the country (or the place of the study) and have a bearing on the possible outcomes.[ 3 , 6 ] Lengthy titles can be boring and appear unfocused, whereas very short titles may not be representative of the contents of the article; hence, optimum length is required to ensure that the title explains the main theme and content of the manuscript.[ 4 , 5 , 9 ] Abbreviations (except the standard or commonly interpreted ones such as HIV, AIDS, DNA, RNA, CDC, FDA, ECG, and EEG) or acronyms should be avoided in the title, as a reader not familiar with them may skip such an article and nonstandard abbreviations may create problems in indexing the article.[ 3 , 4 , 5 , 6 , 9 , 12 ] Also, too much of technical jargon or chemical formulas in the title may confuse the readers and the article may be skipped by them.[ 4 , 9 ] Numerical values of various parameters (stating study period or sample size) should also be avoided in the titles (unless deemed extremely essential).[ 4 ] It may be worthwhile to take an opinion from a impartial colleague before finalizing the title.[ 4 , 5 , 6 ] Thus, multiple factors (which are, at times, a bit conflicting or contrasting) need to be considered while formulating a title, and hence this should not be done in a hurry.[ 4 , 6 ] Many journals ask the authors to draft a “short title” or “running head” or “running title” for printing in the header or footer of the printed paper.[ 3 , 12 ] This is an abridged version of the main title of up to 40–50 characters, may have standard abbreviations, and helps the reader to navigate through the paper.[ 3 , 12 , 14 ]

Checklist for a good title

Table 1 gives a checklist/useful tips for drafting a good title for a research paper.[ 1 , 2 , 3 , 4 , 5 , 6 , 12 ] Table 2 presents some of the titles used by the author of this article in his earlier research papers, and the appropriateness of the titles has been commented upon. As an individual exercise, the reader may try to improvise upon the titles (further) after reading the corresponding abstract and full paper.

Checklist/useful tips for drafting a good title for a research paper

Some titles used by author of this article in his earlier publications and remark/comment on their appropriateness

The Abstract

The details of the abstract are discussed under the subheadings of importance, types, drafting, and checklist.

Importance of the abstract

The abstract is a summary or synopsis of the full research paper and also needs to have similar characteristics like the title. It needs to be simple, direct, specific, functional, clear, unbiased, honest, concise, precise, self-sufficient, complete, comprehensive, scholarly, balanced, and should not be misleading.[ 1 , 2 , 3 , 7 , 8 , 9 , 10 , 11 , 13 , 17 ] Writing an abstract is to extract and summarize (AB – absolutely, STR – straightforward, ACT – actual data presentation and interpretation).[ 17 ] The title and abstracts are the only sections of the research paper that are often freely available to the readers on the journal websites, search engines, and in many abstracting agencies/databases, whereas the full paper may attract a payment per view or a fee for downloading the pdf copy.[ 1 , 2 , 3 , 7 , 8 , 10 , 11 , 13 , 14 ] The abstract is an independent and stand-alone (that is, well understood without reading the full paper) section of the manuscript and is used by the editor to decide the fate of the article and to choose appropriate reviewers.[ 2 , 7 , 10 , 12 , 13 ] Even the reviewers are initially supplied only with the title and the abstract before they agree to review the full manuscript.[ 7 , 13 ] This is the second most commonly read part of the manuscript, and therefore it should reflect the contents of the main text of the paper accurately and thus act as a “real trailer” of the full article.[ 2 , 7 , 11 ] The readers will go through the full paper only if they find the abstract interesting and relevant to their practice; else they may skip the paper if the abstract is unimpressive.[ 7 , 8 , 9 , 10 , 13 ] The abstract needs to highlight the selling point of the manuscript and succeed in luring the reader to read the complete paper.[ 3 , 7 ] The title and the abstract should be constructed using keywords (key terms/important words) from all the sections of the main text.[ 12 ] Abstracts are also used for submitting research papers to a conference for consideration for presentation (as oral paper or poster).[ 9 , 13 , 17 ] Grammatical and typographic errors reflect poorly on the quality of the abstract, may indicate carelessness/casual attitude on part of the author, and hence should be avoided at all times.[ 9 ]

Types of abstracts

The abstracts can be structured or unstructured. They can also be classified as descriptive or informative abstracts.

Structured and unstructured abstracts

Structured abstracts are followed by most journals, are more informative, and include specific subheadings/subsections under which the abstract needs to be composed.[ 1 , 7 , 8 , 9 , 10 , 11 , 13 , 17 , 18 ] These subheadings usually include context/background, objectives, design, setting, participants, interventions, main outcome measures, results, and conclusions.[ 1 ] Some journals stick to the standard IMRAD format for the structure of the abstracts, and the subheadings would include Introduction/Background, Methods, Results, And (instead of Discussion) the Conclusion/s.[ 1 , 2 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 17 , 18 ] Structured abstracts are more elaborate, informative, easy to read, recall, and peer-review, and hence are preferred; however, they consume more space and can have same limitations as an unstructured abstract.[ 7 , 9 , 18 ] The structured abstracts are (possibly) better understood by the reviewers and readers. Anyway, the choice of the type of the abstract and the subheadings of a structured abstract depend on the particular journal style and is not left to the author's wish.[ 7 , 10 , 12 ] Separate subheadings may be necessary for reporting meta-analysis, educational research, quality improvement work, review, or case study.[ 1 ] Clinical trial abstracts need to include the essential items mentioned in the CONSORT (Consolidated Standards Of Reporting Trials) guidelines.[ 7 , 9 , 14 , 19 ] Similar guidelines exist for various other types of studies, including observational studies and for studies of diagnostic accuracy.[ 20 , 21 ] A useful resource for the above guidelines is available at www.equator-network.org (Enhancing the QUAlity and Transparency Of health Research). Unstructured (or non-structured) abstracts are free-flowing, do not have predefined subheadings, and are commonly used for papers that (usually) do not describe original research.[ 1 , 7 , 9 , 10 ]

The four-point structured abstract: This has the following elements which need to be properly balanced with regard to the content/matter under each subheading:[ 9 ]

Background and/or Objectives: This states why the work was undertaken and is usually written in just a couple of sentences.[ 3 , 7 , 8 , 9 , 10 , 12 , 13 ] The hypothesis/study question and the major objectives are also stated under this subheading.[ 3 , 7 , 8 , 9 , 10 , 12 , 13 ]

Methods: This subsection is the longest, states what was done, and gives essential details of the study design, setting, participants, blinding, sample size, sampling method, intervention/s, duration and follow-up, research instruments, main outcome measures, parameters evaluated, and how the outcomes were assessed or analyzed.[ 3 , 7 , 8 , 9 , 10 , 12 , 13 , 14 , 17 ]

Results/Observations/Findings: This subheading states what was found, is longer, is difficult to draft, and needs to mention important details including the number of study participants, results of analysis (of primary and secondary objectives), and include actual data (numbers, mean, median, standard deviation, “P” values, 95% confidence intervals, effect sizes, relative risks, odds ratio, etc.).[ 3 , 7 , 8 , 9 , 10 , 12 , 13 , 14 , 17 ]

Conclusions: The take-home message (the “so what” of the paper) and other significant/important findings should be stated here, considering the interpretation of the research question/hypothesis and results put together (without overinterpreting the findings) and may also include the author's views on the implications of the study.[ 3 , 7 , 8 , 9 , 10 , 12 , 13 , 14 , 17 ]

The eight-point structured abstract: This has the following eight subheadings – Objectives, Study Design, Study Setting, Participants/Patients, Methods/Intervention, Outcome Measures, Results, and Conclusions.[ 3 , 9 , 18 ] The instructions to authors given by the particular journal state whether they use the four- or eight-point abstract or variants thereof.[ 3 , 14 ]

Descriptive and Informative abstracts

Descriptive abstracts are short (75–150 words), only portray what the paper contains without providing any more details; the reader has to read the full paper to know about its contents and are rarely used for original research papers.[ 7 , 10 ] These are used for case reports, reviews, opinions, and so on.[ 7 , 10 ] Informative abstracts (which may be structured or unstructured as described above) give a complete detailed summary of the article contents and truly reflect the actual research done.[ 7 , 10 ]

Drafting a suitable abstract

It is important to religiously stick to the instructions to authors (format, word limit, font size/style, and subheadings) provided by the journal for which the abstract and the paper are being written.[ 7 , 8 , 9 , 10 , 13 ] Most journals allow 200–300 words for formulating the abstract and it is wise to restrict oneself to this word limit.[ 1 , 2 , 3 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 22 ] Though some authors prefer to draft the abstract initially, followed by the main text of the paper, it is recommended to draft the abstract in the end to maintain accuracy and conformity with the main text of the paper (thus maintaining an easy linkage/alignment with title, on one hand, and the introduction section of the main text, on the other hand).[ 2 , 7 , 9 , 10 , 11 ] The authors should check the subheadings (of the structured abstract) permitted by the target journal, use phrases rather than sentences to draft the content of the abstract, and avoid passive voice.[ 1 , 7 , 9 , 12 ] Next, the authors need to get rid of redundant words and edit the abstract (extensively) to the correct word count permitted (every word in the abstract “counts”!).[ 7 , 8 , 9 , 10 , 13 ] It is important to ensure that the key message, focus, and novelty of the paper are not compromised; the rationale of the study and the basis of the conclusions are clear; and that the abstract is consistent with the main text of the paper.[ 1 , 2 , 3 , 7 , 9 , 11 , 12 , 13 , 14 , 17 , 22 ] This is especially important while submitting a revision of the paper (modified after addressing the reviewer's comments), as the changes made in the main (revised) text of the paper need to be reflected in the (revised) abstract as well.[ 2 , 10 , 12 , 14 , 22 ] Abbreviations should be avoided in an abstract, unless they are conventionally accepted or standard; references, tables, or figures should not be cited in the abstract.[ 7 , 9 , 10 , 11 , 13 ] It may be worthwhile not to rush with the abstract and to get an opinion by an impartial colleague on the content of the abstract; and if possible, the full paper (an “informal” peer-review).[ 1 , 7 , 8 , 9 , 11 , 17 ] Appropriate “Keywords” (three to ten words or phrases) should follow the abstract and should be preferably chosen from the Medical Subject Headings (MeSH) list of the U.S. National Library of Medicine ( https://meshb.nlm.nih.gov/search ) and are used for indexing purposes.[ 2 , 3 , 11 , 12 ] These keywords need to be different from the words in the main title (the title words are automatically used for indexing the article) and can be variants of the terms/phrases used in the title, or words from the abstract and the main text.[ 3 , 12 ] The ICMJE (International Committee of Medical Journal Editors; http://www.icmje.org/ ) also recommends publishing the clinical trial registration number at the end of the abstract.[ 7 , 14 ]

Checklist for a good abstract

Table 3 gives a checklist/useful tips for formulating a good abstract for a research paper.[ 1 , 2 , 3 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 17 , 22 ]

Checklist/useful tips for formulating a good abstract for a research paper

Concluding Remarks

This review article has given a detailed account of the importance and types of titles and abstracts. It has also attempted to give useful hints for drafting an appropriate title and a complete abstract for a research paper. It is hoped that this review will help the authors in their career in medical writing.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

Acknowledgement

The author thanks Dr. Hemant Deshmukh - Dean, Seth G.S. Medical College & KEM Hospital, for granting permission to publish this manuscript.

How To Write The Methodology Chapter

The what, why & how explained simply (with examples).

By: Jenna Crossley (PhD) | Reviewed By: Dr. Eunice Rautenbach | September 2021 (Updated April 2023)

So, you’ve pinned down your research topic and undertaken a review of the literature – now it’s time to write up the methodology section of your dissertation, thesis or research paper . But what exactly is the methodology chapter all about – and how do you go about writing one? In this post, we’ll unpack the topic, step by step .

Overview: The Methodology Chapter

• The purpose  of the methodology chapter
• Why you need to craft this chapter (really) well
• How to write and structure the chapter
• Methodology chapter example
• Essential takeaways

What (exactly) is the methodology chapter?

The methodology chapter is where you outline the philosophical underpinnings of your research and outline the specific methodological choices you’ve made. The point of the methodology chapter is to tell the reader exactly how you designed your study and, just as importantly, why you did it this way.

Importantly, this chapter should comprehensively describe and justify all the methodological choices you made in your study. For example, the approach you took to your research (i.e., qualitative, quantitative or mixed), who  you collected data from (i.e., your sampling strategy), how you collected your data and, of course, how you analysed it. If that sounds a little intimidating, don’t worry – we’ll explain all these methodological choices in this post .

Why is the methodology chapter important?

The methodology chapter plays two important roles in your dissertation or thesis:

Firstly, it demonstrates your understanding of research theory, which is what earns you marks. A flawed research design or methodology would mean flawed results. So, this chapter is vital as it allows you to show the marker that you know what you’re doing and that your results are credible .

Secondly, the methodology chapter is what helps to make your study replicable. In other words, it allows other researchers to undertake your study using the same methodological approach, and compare their findings to yours. This is very important within academic research, as each study builds on previous studies.

The methodology chapter is also important in that it allows you to identify and discuss any methodological issues or problems you encountered (i.e., research limitations ), and to explain how you mitigated the impacts of these. Every research project has its limitations , so it’s important to acknowledge these openly and highlight your study’s value despite its limitations . Doing so demonstrates your understanding of research design, which will earn you marks. We’ll discuss limitations in a bit more detail later in this post, so stay tuned!

Need a helping hand?

How to write up the methodology chapter

First off, it’s worth noting that the exact structure and contents of the methodology chapter will vary depending on the field of research (e.g., humanities, chemistry or engineering) as well as the university . So, be sure to always check the guidelines provided by your institution for clarity and, if possible, review past dissertations from your university. Here we’re going to discuss a generic structure for a methodology chapter typically found in the sciences.

Before you start writing, it’s always a good idea to draw up a rough outline to guide your writing. Don’t just start writing without knowing what you’ll discuss where. If you do, you’ll likely end up with a disjointed, ill-flowing narrative . You’ll then waste a lot of time rewriting in an attempt to try to stitch all the pieces together. Do yourself a favour and start with the end in mind .

Section 1 – Introduction

As with all chapters in your dissertation or thesis, the methodology chapter should have a brief introduction. In this section, you should remind your readers what the focus of your study is, especially the research aims . As we’ve discussed many times on the blog, your methodology needs to align with your research aims, objectives and research questions. Therefore, it’s useful to frontload this component to remind the reader (and yourself!) what you’re trying to achieve.

In this section, you can also briefly mention how you’ll structure the chapter. This will help orient the reader and provide a bit of a roadmap so that they know what to expect. You don’t need a lot of detail here – just a brief outline will do.

Section 2 – The Methodology

The next section of your chapter is where you’ll present the actual methodology. In this section, you need to detail and justify the key methodological choices you’ve made in a logical, intuitive fashion. Importantly, this is the heart of your methodology chapter, so you need to get specific – don’t hold back on the details here. This is not one of those “less is more” situations.

Let’s take a look at the most common components you’ll likely need to cover. 

Methodological Choice #1 – Research Philosophy

Research philosophy refers to the underlying beliefs (i.e., the worldview) regarding how data about a phenomenon should be gathered , analysed and used . The research philosophy will serve as the core of your study and underpin all of the other research design choices, so it’s critically important that you understand which philosophy you’ll adopt and why you made that choice. If you’re not clear on this, take the time to get clarity before you make any further methodological choices.

While several research philosophies exist, two commonly adopted ones are positivism and interpretivism . These two sit roughly on opposite sides of the research philosophy spectrum.

Positivism states that the researcher can observe reality objectively and that there is only one reality, which exists independently of the observer. As a consequence, it is quite commonly the underlying research philosophy in quantitative studies and is oftentimes the assumed philosophy in the physical sciences.

Contrasted with this, interpretivism , which is often the underlying research philosophy in qualitative studies, assumes that the researcher performs a role in observing the world around them and that reality is unique to each observer . In other words, reality is observed subjectively .

These are just two philosophies (there are many more), but they demonstrate significantly different approaches to research and have a significant impact on all the methodological choices. Therefore, it’s vital that you clearly outline and justify your research philosophy at the beginning of your methodology chapter, as it sets the scene for everything that follows.

Methodological Choice #2 – Research Type

The next thing you would typically discuss in your methodology section is the research type. The starting point for this is to indicate whether the research you conducted is inductive or deductive .

Inductive research takes a bottom-up approach , where the researcher begins with specific observations or data and then draws general conclusions or theories from those observations. Therefore these studies tend to be exploratory in terms of approach.

Conversely , d eductive research takes a top-down approach , where the researcher starts with a theory or hypothesis and then tests it using specific observations or data. Therefore these studies tend to be confirmatory in approach.

Related to this, you’ll need to indicate whether your study adopts a qualitative, quantitative or mixed  approach. As we’ve mentioned, there’s a strong link between this choice and your research philosophy, so make sure that your choices are tightly aligned . When you write this section up, remember to clearly justify your choices, as they form the foundation of your study.

Methodological Choice #3 – Research Strategy

Next, you’ll need to discuss your research strategy (also referred to as a research design ). This methodological choice refers to the broader strategy in terms of how you’ll conduct your research, based on the aims of your study.

Several research strategies exist, including experimental , case studies , ethnography , grounded theory, action research , and phenomenology . Let’s take a look at two of these, experimental and ethnographic, to see how they contrast.

Experimental research makes use of the scientific method , where one group is the control group (in which no variables are manipulated ) and another is the experimental group (in which a specific variable is manipulated). This type of research is undertaken under strict conditions in a controlled, artificial environment (e.g., a laboratory). By having firm control over the environment, experimental research typically allows the researcher to establish causation between variables. Therefore, it can be a good choice if you have research aims that involve identifying causal relationships.

Ethnographic research , on the other hand, involves observing and capturing the experiences and perceptions of participants in their natural environment (for example, at home or in the office). In other words, in an uncontrolled environment.  Naturally, this means that this research strategy would be far less suitable if your research aims involve identifying causation, but it would be very valuable if you’re looking to explore and examine a group culture, for example.

As you can see, the right research strategy will depend largely on your research aims and research questions – in other words, what you’re trying to figure out. Therefore, as with every other methodological choice, it’s essential to justify why you chose the research strategy you did.

Methodological Choice #4 – Time Horizon

The next thing you’ll need to detail in your methodology chapter is the time horizon. There are two options here: cross-sectional and longitudinal . In other words, whether the data for your study were all collected at one point in time (cross-sectional) or at multiple points in time (longitudinal).

The choice you make here depends again on your research aims, objectives and research questions. If, for example, you aim to assess how a specific group of people’s perspectives regarding a topic change over time , you’d likely adopt a longitudinal time horizon.

Another important factor to consider is simply whether you have the time necessary to adopt a longitudinal approach (which could involve collecting data over multiple months or even years). Oftentimes, the time pressures of your degree program will force your hand into adopting a cross-sectional time horizon, so keep this in mind.

Methodological Choice #5 – Sampling Strategy

Next, you’ll need to discuss your sampling strategy . There are two main categories of sampling, probability and non-probability sampling.

Probability sampling involves a random (and therefore representative) selection of participants from a population, whereas non-probability sampling entails selecting participants in a non-random  (and therefore non-representative) manner. For example, selecting participants based on ease of access (this is called a convenience sample).

The right sampling approach depends largely on what you’re trying to achieve in your study. Specifically, whether you trying to develop findings that are generalisable to a population or not. Practicalities and resource constraints also play a large role here, as it can oftentimes be challenging to gain access to a truly random sample. In the video below, we explore some of the most common sampling strategies.

Methodological Choice #6 – Data Collection Method

Next up, you’ll need to explain how you’ll go about collecting the necessary data for your study. Your data collection method (or methods) will depend on the type of data that you plan to collect – in other words, qualitative or quantitative data.

Typically, quantitative research relies on surveys , data generated by lab equipment, analytics software or existing datasets. Qualitative research, on the other hand, often makes use of collection methods such as interviews , focus groups , participant observations, and ethnography.

So, as you can see, there is a tight link between this section and the design choices you outlined in earlier sections. Strong alignment between these sections, as well as your research aims and questions is therefore very important.

Methodological Choice #7 – Data Analysis Methods/Techniques

The final major methodological choice that you need to address is that of analysis techniques . In other words, how you’ll go about analysing your date once you’ve collected it. Here it’s important to be very specific about your analysis methods and/or techniques – don’t leave any room for interpretation. Also, as with all choices in this chapter, you need to justify each choice you make.

What exactly you discuss here will depend largely on the type of study you’re conducting (i.e., qualitative, quantitative, or mixed methods). For qualitative studies, common analysis methods include content analysis , thematic analysis and discourse analysis . In the video below, we explain each of these in plain language.

For quantitative studies, you’ll almost always make use of descriptive statistics , and in many cases, you’ll also use inferential statistical techniques (e.g., correlation and regression analysis). In the video below, we unpack some of the core concepts involved in descriptive and inferential statistics.

In this section of your methodology chapter, it’s also important to discuss how you prepared your data for analysis, and what software you used (if any). For example, quantitative data will often require some initial preparation such as removing duplicates or incomplete responses . Similarly, qualitative data will often require transcription and perhaps even translation. As always, remember to state both what you did and why you did it.

Section 3 – The Methodological Limitations

With the key methodological choices outlined and justified, the next step is to discuss the limitations of your design. No research methodology is perfect – there will always be trade-offs between the “ideal” methodology and what’s practical and viable, given your constraints. Therefore, this section of your methodology chapter is where you’ll discuss the trade-offs you had to make, and why these were justified given the context.

Methodological limitations can vary greatly from study to study, ranging from common issues such as time and budget constraints to issues of sample or selection bias . For example, you may find that you didn’t manage to draw in enough respondents to achieve the desired sample size (and therefore, statistically significant results), or your sample may be skewed heavily towards a certain demographic, thereby negatively impacting representativeness .

In this section, it’s important to be critical of the shortcomings of your study. There’s no use trying to hide them (your marker will be aware of them regardless). By being critical, you’ll demonstrate to your marker that you have a strong understanding of research theory, so don’t be shy here. At the same time, don’t beat your study to death . State the limitations, why these were justified, how you mitigated their impacts to the best degree possible, and how your study still provides value despite these limitations .

Section 4 – Concluding Summary

Finally, it’s time to wrap up the methodology chapter with a brief concluding summary. In this section, you’ll want to concisely summarise what you’ve presented in the chapter. Here, it can be a good idea to use a figure to summarise the key decisions, especially if your university recommends using a specific model (for example, Saunders’ Research Onion ).

Importantly, this section needs to be brief – a paragraph or two maximum (it’s a summary, after all). Also, make sure that when you write up your concluding summary, you include only what you’ve already discussed in your chapter; don’t add any new information.

Methodology Chapter Example

In the video below, we walk you through an example of a high-quality research methodology chapter from a dissertation. We also unpack our free methodology chapter template so that you can see how best to structure your chapter.

Wrapping Up

And there you have it – the methodology chapter in a nutshell. As we’ve mentioned, the exact contents and structure of this chapter can vary between universities , so be sure to check in with your institution before you start writing. If possible, try to find dissertations or theses from former students of your specific degree program – this will give you a strong indication of the expectations and norms when it comes to the methodology chapter (and all the other chapters!).

Also, remember the golden rule of the methodology chapter – justify every choice ! Make sure that you clearly explain the “why” for every “what”, and reference credible methodology textbooks or academic sources to back up your justifications.

If you need a helping hand with your research methodology (or any other component of your research), be sure to check out our private coaching service , where we hold your hand through every step of the research journey. Until next time, good luck!

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

52 Comments

highly appreciated.

This was very helpful!

This was helpful

Thanks ,it is a very useful idea.

Thanks ,it is very useful idea.

Thank you so much, this information is very useful.

Thank you very much. I must say the information presented was succinct, coherent and invaluable. It is well put together and easy to comprehend. I have a great guide to create the research methodology for my dissertation.

Highly clear and useful.

I understand a bit on the explanation above. I want to have some coach but I’m still student and don’t have any budget to hire one. A lot of question I want to ask.

Thank you so much. This concluded my day plan. Thank you so much.

Thanks it was helpful

Great information. It would be great though if you could show us practical examples.

Thanks so much for this information. God bless and be with you

Thank you so so much. Indeed it was helpful

This is EXCELLENT!

I was totally confused by other explanations. Thank you so much!.

justdoing my research now , thanks for the guidance.

Thank uuuu! These contents are really valued for me!

This is powerful …I really like it

Highly useful and clear, thank you so much.

Highly appreciated. Good guide

That was helpful. Thanks

This is very useful.Thank you

Very helpful information. Thank you

This is exactly what I was looking for. The explanation is so detailed and easy to comprehend. Well done and thank you.

Great job. You just summarised everything in the easiest and most comprehensible way possible. Thanks a lot.

Thank you very much for the ideas you have given this will really help me a lot. Thank you and God Bless.

Such great effort …….very grateful thank you

Please accept my sincere gratitude. I have to say that the information that was delivered was congruent, concise, and quite helpful. It is clear and straightforward, making it simple to understand. I am in possession of an excellent manual that will assist me in developing the research methods for my dissertation.

Thank you for your great explanation. It really helped me construct my methodology paper.

thank you for simplifieng the methodoly, It was realy helpful

Very helpful!

Thank you for your great explanation.

The explanation I have been looking for. So clear Thank you

Thank you very much .this was more enlightening.

helped me create the in depth and thorough methodology for my dissertation

Thank you for the great explaination.please construct one methodology for me

I appreciate you for the explanation of methodology. Please construct one methodology on the topic: The effects influencing students dropout among schools for my thesis

This helped me complete my methods section of my dissertation with ease. I have managed to write a thorough and concise methodology!

its so good in deed

wow …what an easy to follow presentation. very invaluable content shared. utmost important.

Peace be upon you, I am Dr. Ahmed Khedr, a former part-time professor at Al-Azhar University in Cairo, Egypt. I am currently teaching research methods, and I have been dealing with your esteemed site for several years, and I found that despite my long experience with research methods sites, it is one of the smoothest sites for evaluating the material for students, For this reason, I relied on it a lot in teaching and translated most of what was written into Arabic and published it on my own page on Facebook. Thank you all… Everything I posted on my page is provided with the names of the writers of Grad coach, the title of the article, and the site. My best regards.

A remarkably simple and useful guide, thank you kindly.

I real appriciate your short and remarkable chapter summary

Bravo! Very helpful guide.

Only true experts could provide such helpful, fantastic, and inspiring knowledge about Methodology. Thank you very much! God be with you and us all!

highly appreciate your effort.

This is a very well thought out post. Very informative and a great read.

THANKS SO MUCH FOR SHARING YOUR NICE IDEA

I love you Emma, you are simply amazing with clear explanations with complete information. GradCoach really helped me to do my assignment here in Auckland. Mostly, Emma make it so simple and enjoyable

Thank you very much for this informative and synthesised version.

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

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

The methods section describes actions taken to investigate a research problem and the rationale for the application of specific procedures or techniques used to identify, select, process, and analyze information applied to understanding the problem, thereby, allowing the reader to critically evaluate a study’s overall validity and reliability. The methodology section of a research paper answers two main questions: How was the data collected or generated? And, how was it analyzed? The writing should be direct and precise and always written in the past tense.

Kallet, Richard H. "How to Write the Methods Section of a Research Paper." Respiratory Care 49 (October 2004): 1229-1232.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

• Readers need to know how the data was obtained because the method you chose affects the results and, by extension, how you interpreted their significance in the discussion section of your paper.
• Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and, as a consequence, undermines the value of your analysis of the findings.
• In most cases, there are a variety of different methods you can choose to investigate a research problem. The methodology section of your paper should clearly articulate the reasons why you have chosen a particular procedure or technique.
• The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a multiple choice questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
• The method must be appropriate to fulfilling the overall aims of the study. For example, you need to ensure that you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
• The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring. For any problems that do arise, you must describe the ways in which they were minimized or why these problems do not impact in any meaningful way your interpretation of the findings.
• In the social and behavioral sciences, it is important to always provide sufficient information to allow other researchers to adopt or replicate your methodology. This information is particularly important when a new method has been developed or an innovative use of an existing method is utilized.

Bem, Daryl J. Writing the Empirical Journal Article. Psychology Writing Center. University of Washington; Denscombe, Martyn. The Good Research Guide: For Small-Scale Social Research Projects . 5th edition. Buckingham, UK: Open University Press, 2014; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.

Structure and Writing Style

I.  Groups of Research Methods

There are two main groups of research methods in the social sciences:

• The e mpirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences . This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for formulating hypotheses that need to be tested. This approach is focused on explanation.
• The i nterpretative group of methods is focused on understanding phenomenon in a comprehensive, holistic way . Interpretive methods focus on analytically disclosing the meaning-making practices of human subjects [the why, how, or by what means people do what they do], while showing how those practices arrange so that it can be used to generate observable outcomes. Interpretive methods allow you to recognize your connection to the phenomena under investigation. However, the interpretative group requires careful examination of variables because it focuses more on subjective knowledge.

II.  Content

The introduction to your methodology section should begin by restating the research problem and underlying assumptions underpinning your study. This is followed by situating the methods you used to gather, analyze, and process information within the overall “tradition” of your field of study and within the particular research design you have chosen to study the problem. If the method you choose lies outside of the tradition of your field [i.e., your review of the literature demonstrates that the method is not commonly used], provide a justification for how your choice of methods specifically addresses the research problem in ways that have not been utilized in prior studies.

The remainder of your methodology section should describe the following:

• Decisions made in selecting the data you have analyzed or, in the case of qualitative research, the subjects and research setting you have examined,
• Tools and methods used to identify and collect information, and how you identified relevant variables,
• The ways in which you processed the data and the procedures you used to analyze that data, and
• The specific research tools or strategies that you utilized to study the underlying hypothesis and research questions.

In addition, an effectively written methodology section should:

• Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
• Indicate how the approach fits the overall research design . Your methods for gathering data should have a clear connection to your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is not suitable to achieving the stated objective of your paper.
• Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom. Also be sure to explain how older data is still relevant to investigating the current research problem.
• Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors? Describe how you plan to obtain an accurate assessment of relationships, patterns, trends, distributions, and possible contradictions found in the data.
• Provide background and a rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
• Provide a justification for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of data being used? If other data sources exist, explain why the data you chose is most appropriate to addressing the research problem.
• Provide a justification for case study selection . A common method of analyzing research problems in the social sciences is to analyze specific cases. These can be a person, place, event, phenomenon, or other type of subject of analysis that are either examined as a singular topic of in-depth investigation or multiple topics of investigation studied for the purpose of comparing or contrasting findings. In either method, you should explain why a case or cases were chosen and how they specifically relate to the research problem.
• Describe potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

NOTE:   Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. If necessary, consider using appendices for raw data.

ANOTHER NOTE: If you are conducting a qualitative analysis of a research problem , the methodology section generally requires a more elaborate description of the methods used as well as an explanation of the processes applied to gathering and analyzing of data than is generally required for studies using quantitative methods. Because you are the primary instrument for generating the data [e.g., through interviews or observations], the process for collecting that data has a significantly greater impact on producing the findings. Therefore, qualitative research requires a more detailed description of the methods used.

YET ANOTHER NOTE:   If your study involves interviews, observations, or other qualitative techniques involving human subjects , you may be required to obtain approval from the university's Office for the Protection of Research Subjects before beginning your research. This is not a common procedure for most undergraduate level student research assignments. However, i f your professor states you need approval, you must include a statement in your methods section that you received official endorsement and adequate informed consent from the office and that there was a clear assessment and minimization of risks to participants and to the university. This statement informs the reader that your study was conducted in an ethical and responsible manner. In some cases, the approval notice is included as an appendix to your paper.

III.  Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but concise. Do not provide any background information that does not directly help the reader understand why a particular method was chosen, how the data was gathered or obtained, and how the data was analyzed in relation to the research problem [note: analyzed, not interpreted! Save how you interpreted the findings for the discussion section]. With this in mind, the page length of your methods section will generally be less than any other section of your paper except the conclusion.

Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. An exception to this rule is if you select an unconventional methodological approach; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall process of discovery.

Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data, or, gaps will exist in existing data or archival materials. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose.

Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].

It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in and of itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.

Azevedo, L.F. et al. "How to Write a Scientific Paper: Writing the Methods Section." Revista Portuguesa de Pneumologia 17 (2011): 232-238; Blair Lorrie. “Choosing a Methodology.” In Writing a Graduate Thesis or Dissertation , Teaching Writing Series. (Rotterdam: Sense Publishers 2016), pp. 49-72; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Kallet, Richard H. “How to Write the Methods Section of a Research Paper.” Respiratory Care 49 (October 2004):1229-1232; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Rudestam, Kjell Erik and Rae R. Newton. “The Method Chapter: Describing Your Research Plan.” In Surviving Your Dissertation: A Comprehensive Guide to Content and Process . (Thousand Oaks, Sage Publications, 2015), pp. 87-115; What is Interpretive Research. Institute of Public and International Affairs, University of Utah; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University; Methods and Materials. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.

To locate data and statistics, GO HERE .

Another Writing Tip

Knowing the Relationship Between Theories and Methods

There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.

Introspectively engage in an ongoing dialectic between the application of theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.

Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics . Part 1, Chapter 3. Boise State University; The Theory-Method Relationship. S-Cool Revision. United Kingdom.

Yet Another Writing Tip

Methods and the Methodology

Do not confuse the terms "methods" and "methodology." As Schneider notes, a method refers to the technical steps taken to do research . Descriptions of methods usually include defining and stating why you have chosen specific techniques to investigate a research problem, followed by an outline of the procedures you used to systematically select, gather, and process the data [remember to always save the interpretation of data for the discussion section of your paper].

The methodology refers to a discussion of the underlying reasoning why particular methods were used . This discussion includes describing the theoretical concepts that inform the choice of methods to be applied, placing the choice of methods within the more general nature of academic work, and reviewing its relevance to examining the research problem. The methodology section also includes a thorough review of the methods other scholars have used to study the topic.

Bryman, Alan. "Of Methods and Methodology." Qualitative Research in Organizations and Management: An International Journal 3 (2008): 159-168; Schneider, Florian. “What's in a Methodology: The Difference between Method, Methodology, and Theory…and How to Get the Balance Right?” PoliticsEastAsia.com. Chinese Department, University of Leiden, Netherlands.

• << Previous: Scholarly vs. Popular Publications
• Next: Qualitative Methods >>
• Last Updated: Jul 3, 2024 10:07 AM
• URL: https://libguides.usc.edu/writingguide

• Resources Home 🏠
• Try SciSpace Copilot
• Search research papers
• Add Copilot Extension
• Try AI Detector
• Try Paraphraser
• Try Citation Generator
• April Papers
• June Papers
• July Papers

Here's What You Need to Understand About Research Methodology

Table of Contents

Research methodology involves a systematic and well-structured approach to conducting scholarly or scientific inquiries. Knowing the significance of research methodology and its different components is crucial as it serves as the basis for any study.

Typically, your research topic will start as a broad idea you want to investigate more thoroughly. Once you’ve identified a research problem and created research questions , you must choose the appropriate methodology and frameworks to address those questions effectively.

What is the definition of a research methodology?

Research methodology is the process or the way you intend to execute your study. The methodology section of a research paper outlines how you plan to conduct your study. It covers various steps such as collecting data, statistical analysis, observing participants, and other procedures involved in the research process

The methods section should give a description of the process that will convert your idea into a study. Additionally, the outcomes of your process must provide valid and reliable results resonant with the aims and objectives of your research. This thumb rule holds complete validity, no matter whether your paper has inclinations for qualitative or quantitative usage.

Studying research methods used in related studies can provide helpful insights and direction for your own research. Now easily discover papers related to your topic on SciSpace and utilize our AI research assistant, Copilot , to quickly review the methodologies applied in different papers.

The need for a good research methodology

While deciding on your approach towards your research, the reason or factors you weighed in choosing a particular problem and formulating a research topic need to be validated and explained. A research methodology helps you do exactly that. Moreover, a good research methodology lets you build your argument to validate your research work performed through various data collection methods, analytical methods, and other essential points.

Just imagine it as a strategy documented to provide an overview of what you intend to do.

While undertaking any research writing or performing the research itself, you may get drifted in not something of much importance. In such a case, a research methodology helps you to get back to your outlined work methodology.

A research methodology helps in keeping you accountable for your work. Additionally, it can help you evaluate whether your work is in sync with your original aims and objectives or not. Besides, a good research methodology enables you to navigate your research process smoothly and swiftly while providing effective planning to achieve your desired results.

What is the basic structure of a research methodology?

Usually, you must ensure to include the following stated aspects while deciding over the basic structure of your research methodology:

1. Your research procedure

Explain what research methods you’re going to use. Whether you intend to proceed with quantitative or qualitative, or a composite of both approaches, you need to state that explicitly. The option among the three depends on your research’s aim, objectives, and scope.

2. Provide the rationality behind your chosen approach

Based on logic and reason, let your readers know why you have chosen said research methodologies. Additionally, you have to build strong arguments supporting why your chosen research method is the best way to achieve the desired outcome.

3. Explain your mechanism

The mechanism encompasses the research methods or instruments you will use to develop your research methodology. It usually refers to your data collection methods. You can use interviews, surveys, physical questionnaires, etc., of the many available mechanisms as research methodology instruments. The data collection method is determined by the type of research and whether the data is quantitative data(includes numerical data) or qualitative data (perception, morale, etc.) Moreover, you need to put logical reasoning behind choosing a particular instrument.

4. Significance of outcomes

The results will be available once you have finished experimenting. However, you should also explain how you plan to use the data to interpret the findings. This section also aids in understanding the problem from within, breaking it down into pieces, and viewing the research problem from various perspectives.

5. Reader’s advice

Anything that you feel must be explained to spread more awareness among readers and focus groups must be included and described in detail. You should not just specify your research methodology on the assumption that a reader is aware of the topic.  

All the relevant information that explains and simplifies your research paper must be included in the methodology section. If you are conducting your research in a non-traditional manner, give a logical justification and list its benefits.

6. Explain your sample space

Include information about the sample and sample space in the methodology section. The term "sample" refers to a smaller set of data that a researcher selects or chooses from a larger group of people or focus groups using a predetermined selection method. Let your readers know how you are going to distinguish between relevant and non-relevant samples. How you figured out those exact numbers to back your research methodology, i.e. the sample spacing of instruments, must be discussed thoroughly.

For example, if you are going to conduct a survey or interview, then by what procedure will you select the interviewees (or sample size in case of surveys), and how exactly will the interview or survey be conducted.

7. Challenges and limitations

This part, which is frequently assumed to be unnecessary, is actually very important. The challenges and limitations that your chosen strategy inherently possesses must be specified while you are conducting different types of research.

The importance of a good research methodology

You must have observed that all research papers, dissertations, or theses carry a chapter entirely dedicated to research methodology. This section helps maintain your credibility as a better interpreter of results rather than a manipulator.

A good research methodology always explains the procedure, data collection methods and techniques, aim, and scope of the research. In a research study, it leads to a well-organized, rationality-based approach, while the paper lacking it is often observed as messy or disorganized.

You should pay special attention to validating your chosen way towards the research methodology. This becomes extremely important in case you select an unconventional or a distinct method of execution.

Curating and developing a strong, effective research methodology can assist you in addressing a variety of situations, such as:

• When someone tries to duplicate or expand upon your research after few years.
• If a contradiction or conflict of facts occurs at a later time. This gives you the security you need to deal with these contradictions while still being able to defend your approach.
• Gaining a tactical approach in getting your research completed in time. Just ensure you are using the right approach while drafting your research methodology, and it can help you achieve your desired outcomes. Additionally, it provides a better explanation and understanding of the research question itself.
• Documenting the results so that the final outcome of the research stays as you intended it to be while starting.

Instruments you could use while writing a good research methodology

As a researcher, you must choose which tools or data collection methods that fit best in terms of the relevance of your research. This decision has to be wise.

There exists many research equipments or tools that you can use to carry out your research process. These are classified as:

a. Interviews (One-on-One or a Group)

An interview aimed to get your desired research outcomes can be undertaken in many different ways. For example, you can design your interview as structured, semi-structured, or unstructured. What sets them apart is the degree of formality in the questions. On the other hand, in a group interview, your aim should be to collect more opinions and group perceptions from the focus groups on a certain topic rather than looking out for some formal answers.

In surveys, you are in better control if you specifically draft the questions you seek the response for. For example, you may choose to include free-style questions that can be answered descriptively, or you may provide a multiple-choice type response for questions. Besides, you can also opt to choose both ways, deciding what suits your research process and purpose better.

c. Sample Groups

Similar to the group interviews, here, you can select a group of individuals and assign them a topic to discuss or freely express their opinions over that. You can simultaneously note down the answers and later draft them appropriately, deciding on the relevance of every response.

d. Observations

If your research domain is humanities or sociology, observations are the best-proven method to draw your research methodology. Of course, you can always include studying the spontaneous response of the participants towards a situation or conducting the same but in a more structured manner. A structured observation means putting the participants in a situation at a previously decided time and then studying their responses.

Of all the tools described above, it is you who should wisely choose the instruments and decide what’s the best fit for your research. You must not restrict yourself from multiple methods or a combination of a few instruments if appropriate in drafting a good research methodology.

Types of research methodology

A research methodology exists in various forms. Depending upon their approach, whether centered around words, numbers, or both, methodologies are distinguished as qualitative, quantitative, or an amalgamation of both.

1. Qualitative research methodology

When a research methodology primarily focuses on words and textual data, then it is generally referred to as qualitative research methodology. This type is usually preferred among researchers when the aim and scope of the research are mainly theoretical and explanatory.

The instruments used are observations, interviews, and sample groups. You can use this methodology if you are trying to study human behavior or response in some situations. Generally, qualitative research methodology is widely used in sociology, psychology, and other related domains.

2. Quantitative research methodology

If your research is majorly centered on data, figures, and stats, then analyzing these numerical data is often referred to as quantitative research methodology. You can use quantitative research methodology if your research requires you to validate or justify the obtained results.

In quantitative methods, surveys, tests, experiments, and evaluations of current databases can be advantageously used as instruments If your research involves testing some hypothesis, then use this methodology.

3. Amalgam methodology

As the name suggests, the amalgam methodology uses both quantitative and qualitative approaches. This methodology is used when a part of the research requires you to verify the facts and figures, whereas the other part demands you to discover the theoretical and explanatory nature of the research question.

The instruments for the amalgam methodology require you to conduct interviews and surveys, including tests and experiments. The outcome of this methodology can be insightful and valuable as it provides precise test results in line with theoretical explanations and reasoning.

The amalgam method, makes your work both factual and rational at the same time.

Final words: How to decide which is the best research methodology?

If you have kept your sincerity and awareness intact with the aims and scope of research well enough, you must have got an idea of which research methodology suits your work best.

Before deciding which research methodology answers your research question, you must invest significant time in reading and doing your homework for that. Taking references that yield relevant results should be your first approach to establishing a research methodology.

Moreover, you should never refrain from exploring other options. Before setting your work in stone, you must try all the available options as it explains why the choice of research methodology that you finally make is more appropriate than the other available options.

You should always go for a quantitative research methodology if your research requires gathering large amounts of data, figures, and statistics. This research methodology will provide you with results if your research paper involves the validation of some hypothesis.

Whereas, if  you are looking for more explanations, reasons, opinions, and public perceptions around a theory, you must use qualitative research methodology.The choice of an appropriate research methodology ultimately depends on what you want to achieve through your research.

Frequently Asked Questions (FAQs) about Research Methodology

1. how to write a research methodology.

You can always provide a separate section for research methodology where you should specify details about the methods and instruments used during the research, discussions on result analysis, including insights into the background information, and conveying the research limitations.

2. What are the types of research methodology?

There generally exists four types of research methodology i.e.

• Observation
• Experimental
• Derivational

3. What is the true meaning of research methodology?

The set of techniques or procedures followed to discover and analyze the information gathered to validate or justify a research outcome is generally called Research Methodology.

4. Where lies the importance of research methodology?

Your research methodology directly reflects the validity of your research outcomes and how well-informed your research work is. Moreover, it can help future researchers cite or refer to your research if they plan to use a similar research methodology.

You might also like

Consensus GPT vs. SciSpace GPT: Choose the Best GPT for Research

Literature Review and Theoretical Framework: Understanding the Differences

Using AI for research: A beginner’s guide

6 Important Tips on Writing a Research Paper Title

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

Importance of a Research Title

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

Why do Research Titles Matter?

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

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

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

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

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

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

Characteristics of a Good Research Title

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

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

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

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

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

Tips for Writing an Effective Research Paper Title

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

• Make sure your research title describes (a) the topic, (b) the method, (c) the sample, and (d) the results of your study. You can use the following formula:

[ Result ]: A [ method ] study of [ topic ] among [ sample ] Example : Meditation makes nurses perform better: a qualitative study of mindfulness meditation among German nursing students

• Avoid unnecessary words and jargons. Keep the title statement as concise as possible. You want a title that will be comprehensible even to people who are not experts in your field. Check our article for a detailed list of things to avoid when writing an effective research title .
• Make sure your title is between 5 and 15 words in length.
• If you are writing a title for a university assignment or for a particular academic journal, verify that your title conforms to the standards and requirements for that outlet. For example, many journals require that titles fall under a character limit, including spaces. Many universities require that titles take a very specific form, limiting your creativity.
• Use a descriptive phrase to convey the purpose of your research efficiently.
• Most importantly, use critical keywords in the title to increase the discoverability of your article.

Resources for Further Reading

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

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

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

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

Thank you so much:) Have a nice day!

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

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

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

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

thanks for helping me like this!!

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

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

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

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

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

That really helpful. Thanks alot

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

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

nice blog that you have shared

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

nice information that you have shared

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

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

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

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

Please let us know in case of any other queries.

I’ve been surfing online more than 3 hours these days, but I never found any interesting article like yours. It is lovely worth enough for me. In my opinion, if all website owners and bloggers made just right content material as you did, the internet will be much more helpful than ever before.

Wonderful article! We will bee linking to this particularly great post on our site. Keep up the good writing.

Wow that was odd. I just wrote an very long comment but after I clicked submit my comment didn’t show up. Grrrr… well I’m not writing all that over again. Anyhow, just wanted to say fantastic blog!

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

There are many free directories, and more paid lists.

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

Nice and well written content you have shared with us. thanks a lot!

Thanks for sharing these tips… Rockwide

Its helpful. a person can grab knowledge through it.

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

• Reporting Research

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for data interpretation

In research, choosing the right approach to understand data is crucial for deriving meaningful insights.…

Comparing Cross Sectional and Longitudinal Studies: 5 steps for choosing the right approach

The process of choosing the right research design can put ourselves at the crossroads of…

• Career Corner

Unlocking the Power of Networking in Academic Conferences

Embarking on your first academic conference experience? Fear not, we got you covered! Academic conferences…

Research Recommendations – Guiding policy-makers for evidence-based decision making

Research recommendations play a crucial role in guiding scholars and researchers toward fruitful avenues of…

• AI in Academia

15 Research Methodology Examples

Tio Gabunia (B.Arch, M.Arch)

Tio Gabunia is an academic writer and architect based in Tbilisi. He has studied architecture, design, and urban planning at the Georgian Technical University and the University of Lisbon. He has worked in these fields in Georgia, Portugal, and France. Most of Tio’s writings concern philosophy. Other writings include architecture, sociology, urban planning, and economics.

Learn about our Editorial Process

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

Research methodologies can roughly be categorized into three group: quantitative, qualitative, and mixed-methods.

• Qualitative Research : This methodology is based on obtaining deep, contextualized, non-numerical data. It can occur, for example, through open-ended questioning of research particiapnts in order to understand human behavior. It’s all about describing and analyzing subjective phenomena such as emotions or experiences.
• Quantitative Research: This methodology is rationally-based and relies heavily on numerical analysis of empirical data . With quantitative research, you aim for objectivity by creating hypotheses and testing them through experiments or surveys, which allow for statistical analyses.
• Mixed-Methods Research: Mixed-methods research combines both previous types into one project. We have more flexibility when designing our research study with mixed methods since we can use multiple approaches depending on our needs at each time. Using mixed methods can help us validate our results and offer greater predictability than just either type of methodology alone could provide.

Below are research methodologies that fit into each category.

Qualitative Research Methodologies

1. case study.

Conducts an in-depth examination of a specific case, individual, or event to understand a phenomenon.

Instead of examining a whole population for numerical trend data, case study researchers seek in-depth explanations of one event.

The benefit of case study research is its ability to elucidate overlooked details of interesting cases of a phenomenon (Busetto, Wick & Gumbinger, 2020). It offers deep insights for empathetic, reflective, and thoughtful understandings of that phenomenon.

However, case study findings aren’t transferrable to new contexts or for population-wide predictions. Instead, they inform practitioner understandings for nuanced, deep approaches to future instances (Liamputtong, 2020).

2. Grounded Theory

Grounded theory involves generating hypotheses and theories through the collection and interpretation of data (Faggiolani, n.d.). Its distinguishing features is that it doesn’t test a hypothesis generated prior to analysis, but rather generates a hypothesis or ‘theory’ that emerges from the data.

It also involves the application of inductive reasoning and is often contrasted with the hypothetico-deductive model of scientific research. This research methodology was developed by Barney Glaser and Anselm Strauss in the 1960s (Glaser & Strauss, 2009). 

The basic difference between traditional scientific approaches to research and grounded theory is that the latter begins with a question, then collects data, and the theoretical framework is said to emerge later from this data.

By contrast, scientists usually begin with an existing theoretical framework , develop hypotheses, and only then start collecting data to verify or falsify the hypotheses.

3. Ethnography

In ethnographic research , the researcher immerses themselves within the group they are studying, often for long periods of time.

This type of research aims to understand the shared beliefs, practices, and values of a particular community by immersing the researcher within the cultural group.

Although ethnographic research cannot predict or identify trends in an entire population, it can create detailed explanations of cultural practices and comparisons between social and cultural groups.

When a person conducts an ethnographic study of themselves or their own culture, it can be considered autoethnography .

Its strength lies in producing comprehensive accounts of groups of people and their interactions.

Common methods researchers use during an ethnographic study include participant observation , thick description, unstructured interviews, and field notes vignettes. These methods can provide detailed and contextualized descriptions of their subjects.

Example Study

Liquidated: An Ethnography of Wall Street by Karen Ho involves an anthropologist who embeds herself with Wall Street firms to study the culture of Wall Street bankers and how this culture affects the broader economy and world.

4. Phenomenology

Phenomenology to understand and describe individuals’ lived experiences concerning a specific phenomenon.

As a research methodology typically used in the social sciences , phenomenology involves the study of social reality as a product of intersubjectivity (the intersection of people’s cognitive perspectives) (Zahavi & Overgaard, n.d.).

This philosophical approach was first developed by Edmund Husserl.

5. Narrative Research

Narrative research explores personal stories and experiences to understand their meanings and interpretations.

It is also known as narrative inquiry and narrative analysis(Riessman, 1993).

This approach to research uses qualitative material like journals, field notes, letters, interviews, texts, photos, etc., as its data.

It is aimed at understanding the way people create meaning through narratives (Clandinin & Connelly, 2004).

6. Discourse Analysis

A discourse analysis examines the structure, patterns, and functions of language in context to understand how the text produces social constructs.

This methodology is common in critical theory , poststructuralism , and postmodernism. Its aim is to understand how language constructs discourses (roughly interpreted as “ways of thinking and constructing knowledge”).

As a qualitative methodology , its focus is on developing themes through close textual analysis rather than using numerical methods. Common methods for extracting data include semiotics and linguistic analysis.

7. Action Research

Action research involves researchers working collaboratively with stakeholders to address problems, develop interventions, and evaluate effectiveness.

Action research is a methodology and philosophy of research that is common in the social sciences.

The term was first coined in 1944 by Kurt Lewin, a German-American psychologist who also introduced applied research and group communication (Altrichter & Gstettner, 1993).

Lewin originally defined action research as involving two primary processes: taking action and doing research (Lewin, 1946).

Action research involves planning, action, and information-seeking about the result of the action.

Since Lewin’s original formulation, many different theoretical approaches to action research have been developed. These include action science, participatory action research, cooperative inquiry, and living educational theory among others.

Using Digital Sandbox Gaming to Improve Creativity Within Boys’ Writing (Ellison & Drew, 2019) is a study conducted by a school teacher who used video games to help teach his students English. It involved action research, where he interviewed his students to see if the use of games as stimuli for storytelling helped draw them into the learning experience, and iterated on his teaching style based on their feedback (disclaimer: I am the second author of this study).

See More: Examples of Qualitative Research

Quantitative Research Methodologies

8. experimental design.

As the name suggests, this type of research is based on testing hypotheses in experimental settings by manipulating variables and observing their effects on other variables.

The main benefit lies in its ability to manipulate specific variables to determine their effect on outcomes which is a great method for those looking for causational links in their research.

This is common, for example, in high-school science labs, where students are asked to introduce a variable into a setting in order to examine its effect.

9. Non-Experimental Design

Non-experimental design observes and measures associations between variables without manipulating them.

It can take, for example, the form of a ‘fly on the wall’ observation of a phenomenon, allowing researchers to examine authentic settings and changes that occur naturally in the environment.

10. Cross-Sectional Design

Cross-sectional design involves analyzing variables pertaining to a specific time period and at that exact moment.

This approach allows for an extensive examination and comparison of distinct and independent subjects, thereby offering advantages over qualitative methodologies such as case studies or surveys.

While cross-sectional design can be extremely useful in taking a ‘snapshot in time’, as a standalone method, it is not useful for examining changes in subjects after an intervention. The next methodology addresses this issue.

The prime example of this type of study is a census. A population census is mailed out to every house in the country, and each household must complete the census on the same evening. This allows the government to gather a snapshot of the nation’s demographics, beliefs, religion, and so on.

11. Longitudinal Design

Longitudinal research gathers data from the same subjects over an extended period to analyze changes and development.

In contrast to cross-sectional tactics, longitudinal designs examine variables more than once, over a pre-determined time span, allowing for multiple data points to be taken at different times.

A cross-sectional design is also useful for examining cohort effects , by comparing differences or changes in multiple different generations’ beliefs over time.

With multiple data points collected over extended periods ,it’s possible to examine continuous changes within things like population dynamics or consumer behavior. This makes detailed analysis of change possible.

12. Quasi-Experimental Design

Quasi-experimental design involves manipulating variables for analysis, but uses pre-existing groups of subjects rather than random groups.

Because the groups of research participants already exist, they cannot be randomly assigned to a cohort as with a true experimental design study. This makes inferring a causal relationship more difficult, but is nonetheless often more feasible in real-life settings.

Quasi-experimental designs are generally considered inferior to true experimental designs.

13. Correlational Research

Correlational research examines the relationships between two or more variables, determining the strength and direction of their association.

Similar to quasi-experimental methods, this type of research focuses on relationship differences between variables.

This approach provides a fast and easy way to make initial hypotheses based on either positive or negative correlation trends that can be observed within dataset.

Methods used for data analysis may include statistic correlations such as Pearson’s or Spearman’s.

Mixed-Methods Research Methodologies

14. sequential explanatory design (quan→qual).

This methodology involves conducting quantitative analysis first, then supplementing it with a qualitative study.

It begins by collecting quantitative data that is then analyzed to determine any significant patterns or trends.

Secondly, qualitative methods are employed. Their intent is to help interpret and expand the quantitative results.

This offers greater depth into understanding both large and smaller aspects of research questions being addressed.

The rationale behind this approach is to ensure that your data collection generates richer context for gaining insight into the particular issue across different levels, integrating in one study, qualitative exploration as well as statistical procedures.

15. Sequential Exploratory Design (QUAL→QUAN)

This methodology goes in the other direction, starting with qualitative analysis and ending with quantitative analysis.

It starts with qualitative research that delves deeps into complex areas and gathers rich information through interviewing or observing participants.

After this stage of exploration comes to an end, quantitative techniques are used to analyze the collected data through inferential statistics.

The idea is that a qualitative study can arm the researchers with a strong hypothesis testing framework, which they can then apply to a larger sample size using qualitative methods.

When I first took research classes, I had a lot of trouble distinguishing between methodologies and methods.

The key is to remember that the methodology sets the direction, while the methods are the specific tools to be used. A good analogy is transport: first you need to choose a mode (public transport, private transport, motorized transit, non-motorized transit), then you can choose a tool (bus, car, bike, on foot).

While research methodologies can be split into three types, each type has many different nuanced methodologies that can be chosen, before you then choose the methods – or tools – to use in the study. Each has its own strengths and weaknesses, so choose wisely!

Altrichter, H., & Gstettner, P. (1993). Action Research: A closed chapter in the history of German social science? Educational Action Research , 1 (3), 329–360. https://doi.org/10.1080/0965079930010302

Audi, R. (1999). The Cambridge dictionary of philosophy . Cambridge ; New York : Cambridge University Press. http://archive.org/details/cambridgediction00audi

Clandinin, D. J., & Connelly, F. M. (2004). Narrative Inquiry: Experience and Story in Qualitative Research . John Wiley & Sons.

Creswell, J. W. (2008). Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research . Pearson/Merrill Prentice Hall.

Faggiolani, C. (n.d.). Perceived Identity: Applying Grounded Theory in Libraries . https://doi.org/10.4403/jlis.it-4592

Gauch, H. G. (2002). Scientific Method in Practice . Cambridge University Press.

Glaser, B. G., & Strauss, A. L. (2009). The Discovery of Grounded Theory: Strategies for Qualitative Research . Transaction Publishers.

Kothari, C. R. (2004). Research Methodology: Methods and Techniques . New Age International.

Kuada, J. (2012). Research Methodology: A Project Guide for University Students . Samfundslitteratur.

Lewin, K. (1946). Action research and minority problems. Journal of Social Issues , 2,  4 , 34–46. https://doi.org/10.1111/j.1540-4560.1946.tb02295.x

Mills, J., Bonner, A., & Francis, K. (2006). The Development of Constructivist Grounded Theory. International Journal of Qualitative Methods , 5 (1), 25–35. https://doi.org/10.1177/160940690600500103

Mingers, J., & Willcocks, L. (2017). An integrative semiotic methodology for IS research. Information and Organization , 27 (1), 17–36. https://doi.org/10.1016/j.infoandorg.2016.12.001

OECD. (2015). Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development . Organisation for Economic Co-operation and Development. https://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en

Peirce, C. S. (1992). The Essential Peirce, Volume 1: Selected Philosophical Writings (1867–1893) . Indiana University Press.

Reese, W. L. (1980). Dictionary of Philosophy and Religion: Eastern and Western Thought . Humanities Press.

Riessman, C. K. (1993). Narrative analysis . Sage Publications, Inc.

Saussure, F. de, & Riedlinger, A. (1959). Course in General Linguistics . Philosophical Library.

Thomas, C. G. (2021). Research Methodology and Scientific Writing . Springer Nature.

Zahavi, D., & Overgaard, S. (n.d.). Phenomenological Sociology—The Subjectivity of Everyday Life .

• Tio Gabunia (B.Arch, M.Arch) #molongui-disabled-link 6 Types of Societies (With 21 Examples)
• Tio Gabunia (B.Arch, M.Arch) #molongui-disabled-link 25 Public Health Policy Examples
• Tio Gabunia (B.Arch, M.Arch) #molongui-disabled-link 15 Cultural Differences Examples
• Tio Gabunia (B.Arch, M.Arch) #molongui-disabled-link Social Interaction Types & Examples (Sociology)

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 25 Classroom Wall Decoration Ideas
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 31 Cute & Cozy Play Corner Ideas
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 24 Steiner-Waldorf Classroom Design Ideas
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 25 Kindergarten Decoration Ideas

Leave a Comment Cancel Reply

Your email address will not be published. Required fields are marked *

Get science-backed answers as you write with Paperpal's Research feature

How to Write an Abstract in Research Papers (with Examples)

An abstract in research papers is a keyword-rich summary usually not exceeding 200-350 words. It can be considered the “face” of research papers because it creates an initial impression on the readers. While searching databases (such as PubMed) for research papers, a title is usually the first selection criterion for readers. If the title matches their search criteria, then the readers read the abstract, which sets the tone of the paper. Titles and abstracts are often the only freely available parts of research papers on journal websites. The pdf versions of full articles need to be purchased. Journal reviewers are often provided with only the title and abstract before they agree to review the complete paper. [ 1]  

Abstracts in research papers provide readers with a quick insight into what the paper is about to help them decide whether they want to read it further or not. Abstracts are the main selling points of articles and therefore should be carefully drafted, accurately highlighting the important aspects. [ 2]  

This article will help you identify the important components and provide tips on how to write an abstract in research papers effectively

What is an Abstract?  

An abstract in research papers can be defined as a synopsis of the paper. It should be clear, direct, self-contained, specific, unbiased, and concise. These summaries are published along with the complete research paper and are also submitted to conferences for consideration for presentation.  

Abstracts are of four types and journals can follow any of these formats: [ 2]  

• Structured  
• Unstructured  
• Descriptive  
• Informative  

Structured abstracts are used by most journals because they are more organized and have clear sections, usually including introduction/background; objective; design, settings, and participants (or materials and methods); outcomes and measures; results; and conclusion. These headings may differ based on the journal or the type of paper. Clinical trial abstracts should include the essential items mentioned in the CONSORT (Consolidated Standards Of Reporting Trials) guidelines.  

Figure 1. Structured abstract example [3] 

Unstructured abstracts are common in social science, humanities, and physical science journals. They usually have one paragraph and no specific structure or subheadings. These abstracts are commonly used for research papers that don’t report original work and therefore have a more flexible and narrative style.  

Figure 2. Unstructured abstract example [3] 

Descriptive abstracts are short (75–150 words) and provide an outline with only the most important points of research papers. They are used for shorter articles such as case reports, reviews, and opinions where space is at a premium, and rarely for original investigations. These abstracts don’t present the results but mainly list the topics covered.  

Here’s a sample abstract . [ 4]  

“Design of a Radio-Based System for Distribution Automation”  

A new survey by the Maryland Public Utilities Commission suggests that utilities have not effectively explained to consumers the benefits of smart meters. The two-year study of 86,000 consumers concludes that the long-term benefits of smart meters will not be realized until consumers understand the benefits of shifting some of their power usage to off-peak hours in response to the data they receive from their meters. The study presents recommendations for utilities and municipal governments to improve customer understanding of how to use the smart meters effectively.  

Keywords: smart meters, distribution systems, load, customer attitudes, power consumption, utilities  

Informative abstracts (structured or unstructured) give a complete detailed summary, including the main results, of the research paper and may or may not have subsections.   

Figure 3. Informative abstract example [5] 

Purpose of Abstracts in Research    

Abstracts in research have two main purposes—selection and indexing. [ 6,7]  

• Selection : Abstracts allow interested readers to quickly decide the relevance of a paper to gauge if they should read it completely.   
• Indexing : Most academic journal databases accessed through libraries enable you to search abstracts, allowing for quick retrieval of relevant articles and avoiding unnecessary search results. Therefore, abstracts must necessarily include the keywords that researchers may use to search for articles.  

Thus, a well-written, keyword-rich abstract can p ique readers’ interest and curiosity and help them decide whether they want to read the complete paper. It can also direct readers to articles of potential clinical and research interest during an online search.  

Contents of Abstracts in Research  

Abstracts in research papers summarize the main points of an article and are broadly categorized into four or five sections. Here are some details on how to write an abstract .   

Introduction/Background and/or Objectives  

This section should provide the following information:  

• What is already known about the subject?  
• What is not known about the subject or what does the study aim to investigate?  

The hypothesis or research question and objectives should be mentioned here. The Background sets the context for the rest of the paper and its length should be short so that the word count could be saved for the Results or other information directly pertaining to the study. The objective should be written in present or past simple tense.  

Examples:  

The antidepressant efficacy of desvenlafaxine (DV) has been established in 8-week, randomized controlled trials. The present study examined the continued efficacy of DV across 6 months of maintenance treatment . [ 1]  

Objective: To describe gastric and breast cancer risk estimates for individuals with CDH1 variants.  

Design, Setting, and Participants (or Materials and Methods)  

This section should provide information on the processes used and should be written in past simple tense because the process is already completed.  

A few important questions to be answered include:  

• What was the research design and setting?  
• What was the sample size and how were the participants sampled?  
• What treatments did the participants receive?  
• What were the data collection and data analysis dates?  
• What was the primary outcome measure?  

Hazard ratios (HRs) were estimated for each cancer type and used to calculate cumulative risks and risks per decade of life up to age 80 years.  

This section, written in either present or past simple tense, should be the longest and should describe the main findings of the study. Here’s an example of how descriptive the sentences should be:  

Avoid: Response rates differed significantly between diabetic and nondiabetic patients.  

Better: The response rate was higher in nondiabetic than in diabetic patients (49% vs 30%, respectively; P<0.01).  

This section should include the following information:  

• Total number of patients (included, excluded [exclusion criteria])  
• Primary and secondary outcomes, expressed in words, and supported by numerical data  
• Data on adverse outcomes  

Example: [ 8]  

In total, 10.9% of students were reported to have favorable study skills. The minimum score was found for preparation for examination domain. Also, a significantly positive correlation was observed between students’ study skills and their Grade Point Average (GPA) of previous term (P=0.001, r=0.269) and satisfaction with study skills (P=0.001, r=0.493).  

Conclusions  

Here, authors should mention the importance of their findings and also the practical and theoretical implications, which would benefit readers referring to this paper for their own research. Present simple tense should be used here.  

Examples: [ 1,8]  

The 9.3% prevalence of bipolar spectrum disorders in students at an arts university is substantially higher than general population estimates. These findings strengthen the oft-expressed hypothesis linking creativity with affective psychopathology.  

The findings indicated that students’ study skills need to be improved. Given the significant relationship between study skills and GPA, as an index of academic achievement, and satisfaction, it is necessary to promote the students’ study skills. These skills are suggested to be reinforced, with more emphasis on weaker domains.  

When to Write an Abstract  

In addition to knowing how to write an abstract , you should also know when to write an abstract . It’s best to write abstracts once the paper is completed because this would make it easier for authors to extract relevant parts from every section.  

Abstracts are usually required for: [ 7]    

• submitting articles to journals  
• applying for research grants   
• writing book proposals  
• completing and submitting dissertations  
• submitting proposals for conference papers  

Mostly, the author of the entire work writes the abstract (the first author, in works with multiple authors). However, there are professional abstracting services that hire writers to draft abstracts of other people’s work.   

How to Write an Abstract (Step-by-Step Process)  

Here are some key steps on how to write an abstract in research papers: [ 9]  

• Write the abstract after you’ve finished writing your paper.  
• Select the major objectives/hypotheses and conclusions from your Introduction and Conclusion sections.  
• Select key sentences from your Methods section.  
• Identify the major results from the Results section.  
• Paraphrase or re-write the sentences selected in steps 2, 3, and 4 in your own words into one or two paragraphs in the following sequence: Introduction/Objective, Methods, Results, and Conclusions. The headings may differ among journals, but the content remains the same.  
• Ensure that this draft does not contain: a.   new information that is not present in the paper b.   undefined abbreviations c.   a discussion of previous literature or reference citations d.   unnecessary details about the methods used  
• Remove all extra information and connect your sentences to ensure that the information flows well, preferably in the following order: purpose; basic study design, methodology and techniques used; major findings; summary of your interpretations, conclusions, and implications. Use section headings for structured abstracts.  
• Ensure consistency between the information presented in the abstract and the paper.  
• Check to see if the final abstract meets the guidelines of the target journal (word limit, type of abstract, recommended subheadings, etc.) and if all the required information has been included.  

Choosing Keywords for Abstracts  

Keywords [ 2] are the important and repeatedly used words and phrases in research papers and can help indexers and search engines find papers relevant to your requirements. Easy retrieval would help in reaching a wider audience and eventually gain more citations. In the fields of medicine and health, keywords should preferably be chosen from the Medical Subject Headings (MeSH) list of the US National Library of Medicine because they are used for indexing. These keywords need to be different from the words in the main title (automatically used for indexing) but can be variants of the terms/phrases used in the title, abstract, and the main text. Keywords should represent the content of your manuscript and be specific to your subject area.  

Basic tips for authors [ 10,11]  

• Read through your paper and highlight key terms or phrases that are most relevant and frequently used in your field, to ensure familiarity.  
• Several journals provide instructions about the length (eg, 3 words in a keyword) and maximum number of keywords allowed and other related rules. Create a list of keywords based on these instructions and include specific phrases containing 2 to 4 words. A longer string of words would yield generic results irrelevant to your field.  
• Use abbreviations, acronyms, and initializations if these would be more familiar.  
• Search with your keywords to ensure the results fit with your article and assess how helpful they would be to readers.  
• Narrow down your keywords to about five to ten, to ensure accuracy.  
• Finalize your list based on the maximum number allowed.  

  Few examples: [ 12]  

Important Tips for Writing an Abstract  

Here are a few tips on how to write an abstract to ensure that your abstract is complete, concise, and accurate. [ 1,2]  

• Write the abstract last.  
• Follow journal-specific formatting guidelines or Instructions to Authors strictly to ensure acceptance for publication.  
• Proofread the final draft meticulously to avoid grammatical or typographical errors.  
• Ensure that the terms or data mentioned in the abstract are consistent with the main text.  
• Include appropriate keywords at the end.

Do not include:  

• New information  
• Text citations to references  
• Citations to tables and figures  
• Generic statements  
• Abbreviations unless necessary, like a trial or study name  

Key Takeaways    

Here’s a quick snapshot of all the important aspects of how to write an abstract . [2]

• An abstract in research is a summary of the paper and describes only the main aspects. Typically, abstracts are about 200-350 words long.  
• Abstracts are of four types—structured, unstructured, descriptive, and informative.  
• Abstracts should be simple, clear, concise, independent, and unbiased (present both favorable and adverse outcomes).  
• They should adhere to the prescribed journal format, including word limits, section headings, number of keywords, fonts used, etc.  
• The terminology should be consistent with the main text.   
• Although the section heading names may differ for journals, every abstract should include a background and objective, analysis methods, primary results, and conclusions.  
• Nonstandard abbreviations, references, and URLs shouldn’t be included.  
• Only relevant and specific keywords should be used to ensure focused searches and higher citation frequency.  
• Abstracts should be written last after completing the main paper.  

Frequently Asked Questions   

Q1. Do all journals have different guidelines for abstracts?  

A1. Yes, all journals have their own specific guidelines for writing abstracts; a few examples are given in the following table. [ 6,13,14,15]  

Q2. What are the common mistakes to avoid when writing an abstract?  

A2. Listed below are a few mistakes that authors may make inadvertently while writing abstracts.  

• Copying sentences from the paper verbatim  

An abstract is a summary, which should be created by paraphrasing your own work or writing in your own words. Extracting sentences from every section and combining them into one paragraph cannot be considered summarizing.  

• Not adhering to the formatting guidelines  

Journals have special instructions for writing abstracts, such as word limits and section headings. These should be followed strictly to avoid rejections.  

• Not including the right amount of details in every section  

Both too little and too much information could discourage readers. For instance, if the Background has very little information, the readers may not get sufficient context to appreciate your research. Similarly, incomplete information in the Methods and a text-heavy Results section without supporting numerical data may affect the credibility of your research.  

• Including citations, standard abbreviations, and detailed measurements  

Typically, abstracts shouldn’t include these elements—citations, URLs, and abbreviations. Only nonstandard abbreviations are allowed or those that would be more familiar to readers than the expansions.  

• Including new information  

Abstracts should strictly include only the same information mentioned in the main text. Any new information should first be added to the text and then to the abstract only if necessary or if permitted by the word limit.  

• Not including keywords  

Keywords are essential for indexing and searching and should be included to increase the frequency of retrieval and citation.  

Q3. What is the difference between abstracts in research papers and conference abstracts? [16]  

A3. The table summarizes the main differences between research and conference abstracts.  

  Thus, abstracts are essential “trailers” that can market your research to a wide audience. The better and more complete the abstract the more are the chances of your paper being read and cited. By following our checklist and ensuring that all key elements are included, you can create a well-structured abstract that summarizes your paper accurately.  

References  

• Andrade C. How to write a good abstract for a scientific paper or conference presentation. Indian J Psychiatry . 2011; 53(2):172-175. Accessed June 14, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3136027/  
• Tullu MS. Writing the title and abstract for a research paper: Being concise, precise, and meticulous is the key. 2019; 13(Suppl 1): S12-S17. Accessed June 14, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6398294/  
• Zawia J. Writing an Academic Paper? Get to know Abstracts vs. Structured Abstracts. Medium. Published October 16, 2023. Accessed June 16, 2024. https://medium.com/@jamala.zawia/writing-an-academic-paper-get-to-know-abstracts-vs-structured-abstracts-11ed86888367  
• Markel M and Selber S. Technical Communication, 12 th edition. 2018; pp. 482. Bedford/St Martin’s.  
• Abstracts. Arkansas State University. Accessed June 17, 2024. https://www.astate.edu/a/global-initiatives/online/a-state-online-services/online-writing-center/resources/How%20to%20Write%20an%20Abstract1.pdf  
• AMA Manual of Style. 11 th edition. Oxford University Press.  
• Writing an Abstract. The University of Melbourne. Accessed June 16, 2024. https://services.unimelb.edu.au/__data/assets/pdf_file/0007/471274/Writing_an_Abstract_Update_051112.pdf  
• 10 Good Abstract Examples that will Kickstart Your Brain. Kibin Essay Writing Blog. Published April 5, 2017. Accessed June 17, 2024. https://www.kibin.com/essay-writing-blog/10-good-abstract-examples/  
• A 10-step guide to make your research paper abstract more effective. Editage Insights. Published October 16, 2013. Accessed June 17, 2024. https://www.editage.com/insights/a-10-step-guide-to-make-your-research-paper-abstract-more-effective  
• Using keywords to write your title and abstract. Taylor & Francis Author Services. Accessed June 15, 2024. https://authorservices.taylorandfrancis.com/publishing-your-research/writing-your-paper/using-keywords-to-write-title-and-abstract/  
• How to choose and use keywords in research papers. Paperpal by Editage blog. Published March 10, 2023. Accessed June 17, 2024. https://paperpal.com/blog/researcher-resources/phd-pointers/how-to-choose-and-use-keywords-in-research-papers  
• Title, abstract and keywords. Springer. Accessed June 16, 2024. https://www.springer.com/it/authors-editors/authorandreviewertutorials/writing-a-journal-manuscript/title-abstract-and-keywords/10285522  
• Abstract and keywords guide. APA Style, 7 th edition. Accessed June 18, 2024. https://apastyle.apa.org/instructional-aids/abstract-keywords-guide.pdf  
• Abstract guidelines. American Society for Microbiology. Accessed June 18, 2024. https://asm.org/events/asm-microbe/present/abstract-guidelines  
• Guidelines for conference abstracts. The Lancet. Accessed June 16, 2024. https://www.thelancet.com/pb/assets/raw/Lancet/pdfs/Abstract_Guidelines_2013.pdf  
• Is a conference abstract the same as a paper abstract? Global Conference Alliance, Inc. Accessed June 18, 2024. https://globalconference.ca/is-a-conference-abstract-the-same-as-a-paper-abstract/  

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

Experience the future of academic writing – Sign up to Paperpal and start writing for free!  

Related Reads:

• What are Journal Guidelines on Using Generative AI Tools
• How to Write a High-Quality Conference Paper

How to Write Dissertation Acknowledgements?

• How to Write the First Draft of a Research Paper with Paperpal? 

Top 7 AI Tools for Research 2024

You may also like, maintaining academic integrity with paperpal’s generative ai writing..., research funding basics: what should a grant proposal..., how to structure an essay, leveraging generative ai to enhance student understanding of..., how to write a good hook for essays,..., addressing peer review feedback and mastering manuscript revisions..., how paperpal can boost comprehension and foster interdisciplinary..., what is the importance of a concept paper..., how to write the first draft of a....

• Methodology
• Research Methodology

Writing Research Methodology Chapter

• Kathmandu University

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations

• Sara Delmont
• Jennifer Mason
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

Crack the Code: A Simple Guide to Reading Scientific Articles

By Jin Chow

Co-founder of Polygence, Forbes 30 Under 30 for Education

6 minute read

Science articles and research papers might seem like a maze of complicated words and confusing graphs, especially when you’re new to them, but fear not! We're here to help you decode these treasure troves of knowledge and learn how to read a scientific article. Initially, it’s important not to approach a scientific article or scientific literature like a textbook, where you read without pause for reflection. 

It’s highly recommended that you highlight and jot down notes as you navigate the scientific research article. Taking notes while you read scientific papers keeps you on track and enhances comprehension. Now, let’s dive into the exciting world of reading scientific articles step by step as well as make it easier to decide if you are going to use scientific journals in your research. 

Do your own research through Polygence!

Polygence pairs you with an expert mentor in your area of passion. Together, you work to create a high quality research project that is uniquely your own.

1. Unveil the Mystery: Title & Abstract 

Imagine you're about to embark on an adventure. Start by reading the title and abstract of the scientific research paper – they're like your treasure map! The title gives you a sneak peek of the scientific paper, while the abstract sums up the whole journey in a few sentences. The abstract of the journal article is like the trailer for a movie, giving you an idea of what to expect without revealing all the details. 

When reading an abstract, focus on the main objective of the study, the key findings, and any major implications as well as limitations. Skimming articles for research also helps you determine if the article aligns with your interests or research, especially when dealing with multiple lengthy articles. 

2. Connect the Dots: Discussion or Conclusion 

Now, let’s dive into the deep waters of either the discussion or the conclusion section. If the article is about a topic you know, head straight to the discussion to find out what all the fuss is about. Scientists chat about their findings and what they mean for the world. It’s like getting to the end of a mystery novel and revealing the solution! 

If the topic is new to you, follow the path of the conclusion. Think of it as the last chapter of a fantastic story. Scientists sum up their journey, tell you what they learned, and suggest where the adventure might lead next. It’s your chance to grasp the big picture without delving into all 

the scientific jargon. Pay attention to any limitations the scientists mention; this shows their honesty and helps you understand the study’s scope. 

3. The Recipe of Discovery: Methods 

If you’re diving into a topic you’re familiar with, start with the methods section. Imagine this as your secret recipe for a scientific experiment. Scientists spill the beans on how they conducted their research – what tools they used, how they gathered data, and more. It’s like peeking into a magician’s hat to learn their tricks! Don’t worry if you don’t understand every technical term; the goal is to get a general idea of how the study was conducted. 

4. The Opening Act: Introduction 

But if the topic is new, begin with the introduction. This is like the opening act of a play. Scientists tell you what they’re exploring and why it matters. It’s your chance to understand the questions they’re trying to answer and why those questions are exciting. Pay attention to any prior research they reference, as this will help you understand the foundation of their study.

Tip: As you read the introduction, jot down key points, questions, and any connections you make with your existing knowledge. Seek definitions for pivotal concepts. If the study involves specialized terms, the introduction might provide definitions or explanations to ensure readers understand them. Maintain these on sticky notes or separate paper for reference, aiding your assimilation of the article’s contents as these words will often appear throughout the article. 

5. The Heart of the Journey: Results 

The results section is where the heart of the journey lies. Scientists unveil what they found, often using graphs, charts, or tables. These are like the clues in a treasure hunt – they show you the evidence behind the discoveries. Look for patterns, trends, and any significant differences. Remember, it’s okay to take your time and go back to the methods section if you need to clarify how the data was collected. Don’t worry if you’re not sure about everything – even scientists take time to piece it all together! 

A proven college admissions edge

Polygence alumni had a 90% admissions rate to R1 universities in 2023. Polygence provides high schoolers a personalized, flexible research experience proven to boost your admission odds. Get matched to a mentor now!"

6. Deciding to Use the Article 

After reading through these sections, you’ll have a better understanding of the study. Now, it’s time to decide whether the article is useful for your needs. Ask yourself if the research addresses your questions or supports your ideas. Consider whether the methods used are appropriate and if the conclusions are supported by the evidence presented. Remember, not all articles will be a perfect fit, and that’s okay. Each piece of the puzzle you encounter adds to your knowledge and critical thinking skills. 

7. Embrace the Adventure: Take Your Time 

Remember, you're not racing against time. Take your time to absorb the information. If you find unfamiliar words, don't worry. Use a dictionary or ask your teacher for help. Like any great adventure, understanding comes with practice. 

8. Join the Scientific Expedition: Ask Questions 

Are questions popping up? Don’t hesitate to ask! Talk to your teachers, classmates, or even online communities. Science is all about curiosity, and questions are your superpower. Or you can use those questions for your research – they might lead you to a new adventure! 

9. Verify the Sources: Check Citations 

Examine references to see if the authors are building upon previous work and how their study fits into the broader scientific context. 

10. Take Notes 

Summarize key points and your understanding of the article. Note any questions or areas that need further exploration. 

Next Steps with Polygence

Congratulations! You’ve just uncovered the secrets of reading scientific articles, one step at a time. So, dear explorers, arm yourself with curiosity and embark on the journey of reading scientific articles. Reading top neuroscience journals , medical journals , and scientific articles might feel like a journey into the unknown, but with the right approach, it becomes an exciting adventure. 

Start with the abstract to get a sense of the study’s purpose, then dive into the discussion or conclusion. Then uncover the methods or introduction depending on your familiarity with the topic. Explore the results and graphs like a treasure map. 

As you read more articles, you’ll become more skilled at deciphering their secrets and unlocking the wealth of knowledge they hold. From there, you can explore how to potentially publish and showcase your own research article . Happy reading, intrepid young scientists!

General Catalog

Bibliography and methods of research, italian 201.

CHAPTER 48—HUMANE METHODS OF LIVESTOCK SLAUGHTER

§1901. Findings and declaration of policy

The Congress finds that the use of humane methods in the slaughter of livestock prevents needless suffering; results in safer and better working conditions for persons engaged in the slaughtering industry; brings about improvement of products and economies in slaughtering operations; and produces other benefits for producers, processors, and consumers which tend to expedite an orderly flow of livestock and livestock products in interstate and foreign commerce. It is therefore declared to be the policy of the United States that the slaughtering of livestock and the handling of livestock in connection with slaughter shall be carried out only by humane methods.

(Pub. L. 85–765, §1, Aug. 27, 1958, 72 Stat. 862.)

Statutory Notes and Related Subsidiaries

Short title of 1978 amendment.

For citation of Pub. L. 95–445, Oct. 10, 1978, 92 Stat. 1069, as the "Humane Methods of Slaughter Act of 1978", see Short Title of 1978 Amendment note set out under section 601 of Title 21, Food and Drugs.

Enforcement of Humane Methods of Slaughter Act of 1958

Pub. L. 107–171, title X, §10305, May 13, 2002, 116 Stat. 493, provided that:

"(a) Sense of Congress .—It is the sense of Congress that the Secretary of Agriculture should—

"(1) continue tracking the number of violations of Public Law 85–765 (7 U.S.C. 1901 et seq.; commonly known as the 'Humane Methods of Slaughter Act of 1958') and report the results and relevant trends annually to Congress; and

"(2) fully enforce Public Law 85–765 by ensuring that humane methods in the slaughter of livestock—

"(A) prevent needless suffering;

"(B) result in safer and better working conditions for persons engaged in slaughtering operations;

"(C) bring about improvement of products and economies in slaughtering operations; and

"(D) produce other benefits for producers, processors, and consumers that tend to expedite an orderly flow of livestock and livestock products in interstate and foreign commerce.

"(b) United States Policy .—It is the policy of the United States that the slaughtering of livestock and the handling of livestock in connection with slaughter shall be carried out only by humane methods, as provided by Public Law 85–765."

Commercial Transportation of Equine for Slaughter

Pub. L. 104–127, title IX, subtitle A, Apr. 4, 1996, 110 Stat. 1184, provided that:

"SEC. 901. FINDINGS.

"Because of the unique and special needs of equine being transported to slaughter, Congress finds that it is appropriate for the Secretary of Agriculture to issue guidelines for the regulation of the commercial transportation of equine for slaughter by persons regularly engaged in that activity within the United States.

"SEC. 902. DEFINITIONS.

"In this subtitle:

"(1) Commercial transportation .—The term 'commercial transportation' means the regular operation for profit of a transport business that uses trucks, tractors, trailers, or semitrailers, or any combination thereof, propelled or drawn by mechanical power on any highway or public road.

"(2) Equine for slaughter .—The term 'equine for slaughter' means any member of the Equidae family being transferred to a slaughter facility, including an assembly point, feedlot, or stockyard.

"(3) Person .—The term 'person'—

"(A) means any individual, partnership, corporation, or cooperative association that regularly engages in the commercial transportation of equine for slaughter; but

"(B) does not include any individual or other entity referred to in subparagraph (A) that occasionally transports equine for slaughter incidental to the principal activity of the individual or other entity in production agriculture.

"SEC. 903. REGULATION OF COMMERCIAL TRANSPORTATION OF EQUINE FOR SLAUGHTER.

"(a) In General .—Subject to the availability of appropriations, the Secretary of Agriculture may issue guidelines for the regulation of the commercial transportation of equine for slaughter by persons regularly engaged in that activity within the United States.

"(b) Issues for Review .—In carrying out this section, the Secretary of Agriculture shall review the food, water, and rest provided to equine for slaughter in transit, the segregation of stallions from other equine during transit, and such other issues as the Secretary considers appropriate.

"(c) Additional Authority .—In carrying out this section, the Secretary of Agriculture may—

"(1) require any person to maintain such records and reports as the Secretary considers necessary;

"(2) conduct such investigations and inspections as the Secretary considers necessary; and

"(3) establish and enforce appropriate and effective civil penalties.

"SEC. 904. LIMITATION OF AUTHORITY TO EQUINE FOR SLAUGHTER.

"Nothing in this subtitle authorizes the Secretary of Agriculture to regulate the routine or regular transportation, to slaughter or elsewhere, of—

"(1) livestock other than equine; or

"(2) poultry.

"SEC. 905. EFFECTIVE DATE.

"This subtitle shall become effective on the first day of the first month that begins 30 days or more after the date of enactment of this Act [Apr. 4, 1996]."

§1902. Humane methods

No method of slaughtering or handling in connection with slaughtering shall be deemed to comply with the public policy of the United States unless it is humane. Either of the following two methods of slaughtering and handling are hereby found to be humane:

(a) in the case of cattle, calves, horses, mules, sheep, swine, and other livestock, all animals are rendered insensible to pain by a single blow or gunshot or an electrical, chemical or other means that is rapid and effective, before being shackled, hoisted, thrown, cast, or cut; or

(b) by slaughtering in accordance with the ritual requirements of the Jewish faith or any other religious faith that prescribes a method of slaughter whereby the animal suffers loss of consciousness by anemia of the brain caused by the simultaneous and instantaneous severance of the carotid arteries with a sharp instrument and handling in connection with such slaughtering.

(Pub. L. 85–765, §2, Aug. 27, 1958, 72 Stat. 862; Pub. L. 95–445, §5(a), Oct. 10, 1978, 92 Stat. 1069.)

Editorial Notes

1978 —Par. (b). Pub. L. 95–445 inserted "and handling in connection with such slaughtering" at end.

Effective Date of 1978 Amendment

Amendment by Pub. L. 95–445 effective one year after Oct. 10, 1978, and nonapplicability during not to exceed additional 18 months in hardship cases, see sec. 7 of Pub. L. 95–445 set out as a note under section 603 of Title 21, Food and Drugs.

§1903. Repealed. Pub. L. 95–445, §5(b), Oct. 10, 1978, 92 Stat. 1069

Section, Pub. L. 85–765, §3, Aug. 27, 1958, 72 Stat. 862, related to limitations on Government procurement and price support, modifications during national emergency, and statements of eligibility.

Effective Date of Repeal

Repeal effective one year after Oct. 10, 1978, and nonapplicability during not to exceed additional 18 months in hardship cases, see sec. 7 of Pub. L. 95–445 set out as an Effective Date of 1978 Amendment note under section 603 of Title 21, Food and Drugs.

Contracts For or Procurement of Livestock Products During the Period From June 30, 1960, to August 30, 1960

Pub. L. 86–547, June 29, 1960, 74 Stat. 255, permitted any agency or instrumentality of the United States, during the period from June 30, 1960, to August 30, 1960, to contract for or procure livestock products produced or processed by a slaughterer or processor which slaughters or handles for slaughter livestock by methods other than those designated and approved by the Secretary of Agriculture if such slaughterer or processor has contracted for the purchase of the equipment necessary to enable him to adopt such methods but such equipment has not been delivered to him.

§1904. Methods research; designation of methods

In furtherance of the policy expressed herein the Secretary is authorized and directed—

(a) to conduct, assist, and foster research, investigation, and experimentation to develop and determine methods of slaughter and the handling of livestock in connection with slaughter which are practicable with reference to the speed and scope of slaughtering operations and humane with reference to other existing methods and then current scientific knowledge; and

(b) on or before March 1, 1959, and at such times thereafter as he deems advisable, to designate methods of slaughter and of handling in connection with slaughter which, with respect to each species of livestock, conform to the policy stated in this chapter. If he deems it more effective, the Secretary may make any such designation by designating methods which are not in conformity with such policy. Designations by the Secretary subsequent to March 1, 1959, shall become effective 180 days after their publication in the Federal Register.

(Pub. L. 85–765, §4, Aug. 27, 1958, 72 Stat. 863; Pub. L. 95–445, §5(b)–(e), Oct. 10, 1978, 92 Stat. 1069.)

1978 —Par. (a). Pub. L. 95–445, §5(d), inserted "and" after the semicolon at end.

Par. (b). Pub. L. 95–445, §5(c), (e), struck out "for purposes of section 1903 of this title" before "180 days", and substituted a period for the semicolon at end.

Par. (c). Pub. L. 95–445, §5(b), repealed par. (c).

§1905. Repealed. Pub. L. 95–445, §5(b), Oct. 10, 1978, 92 Stat. 1069

Section, Pub. L. 85–765, §5, Aug. 27, 1958, 72 Stat. 863, related to establishment, composition, functions, compensation, meetings, and reports of advisory committees.

§1906. Exemption of ritual slaughter

Nothing in this chapter shall be construed to prohibit, abridge, or in any way hinder the religious freedom of any person or group. Notwithstanding any other provision of this chapter, in order to protect freedom of religion, ritual slaughter and the handling or other preparation of livestock for ritual slaughter are exempted from the terms of this chapter. For the purposes of this section the term "ritual slaughter" means slaughter in accordance with section 1902(b) of this title.

(Pub. L. 85–765, §6, Aug. 27, 1958, 72 Stat. 864.)

§1907. Practices involving nonambulatory livestock

The Secretary of Agriculture shall investigate and submit to Congress a report on—

(1) the scope of nonambulatory livestock;

(2) the causes that render livestock nonambulatory;

(3) the humane treatment of nonambulatory livestock; and

(4) the extent to which nonambulatory livestock may present handling and disposition problems for stockyards, market agencies, and dealers.

(b) Authority

Based on the findings of the report, if the Secretary determines it necessary, the Secretary shall promulgate regulations to provide for the humane treatment, handling, and disposition of nonambulatory livestock by stockyards, market agencies, and dealers.

(c) Administration and enforcement

For the purpose of administering and enforcing any regulations promulgated under subsection (b), the authorities provided under sections 10414 [7 U.S.C. 8313] and 10415 [7 U.S.C. 8314] shall apply to the regulations in a similar manner as those sections apply to the Animal Health Protection Act [7 U.S.C. 8301 et seq.]. Any person that violates regulations promulgated under subsection (b) shall be subject to penalties provided in section 10414.

(Pub. L. 107–171, title X, §10815, May 13, 2002, 116 Stat. 532.)

References in Text

The Animal Health Protection Act, referred to in subsec. (c), is subtitle E (§§10401–10418) of title X of Pub. L. 107–171, May 13, 2002, 116 Stat. 494, which is classified principally to chapter 109 (§8301 et seq.) of this title. For complete classification of this Act to the Code, see Short Title note set out under section 8301 of this title and Tables.

Codification

Section was enacted as part of the Farm Security and Rural Investment Act of 2002 and not as part of Pub. L. 85–765, which comprises this chapter.

• Privacy Policy

Home » 500+ Quantitative Research Titles and Topics

500+ Quantitative Research Titles and Topics

Table of Contents

Quantitative research involves collecting and analyzing numerical data to identify patterns, trends, and relationships among variables. This method is widely used in social sciences, psychology , economics , and other fields where researchers aim to understand human behavior and phenomena through statistical analysis. If you are looking for a quantitative research topic, there are numerous areas to explore, from analyzing data on a specific population to studying the effects of a particular intervention or treatment. In this post, we will provide some ideas for quantitative research topics that may inspire you and help you narrow down your interests.

Quantitative Research Titles

Quantitative Research Titles are as follows:

Business and Economics

• “Statistical Analysis of Supply Chain Disruptions on Retail Sales”
• “Quantitative Examination of Consumer Loyalty Programs in the Fast Food Industry”
• “Predicting Stock Market Trends Using Machine Learning Algorithms”
• “Influence of Workplace Environment on Employee Productivity: A Quantitative Study”
• “Impact of Economic Policies on Small Businesses: A Regression Analysis”
• “Customer Satisfaction and Profit Margins: A Quantitative Correlation Study”
• “Analyzing the Role of Marketing in Brand Recognition: A Statistical Overview”
• “Quantitative Effects of Corporate Social Responsibility on Consumer Trust”
• “Price Elasticity of Demand for Luxury Goods: A Case Study”
• “The Relationship Between Fiscal Policy and Inflation Rates: A Time-Series Analysis”
• “Factors Influencing E-commerce Conversion Rates: A Quantitative Exploration”
• “Examining the Correlation Between Interest Rates and Consumer Spending”
• “Standardized Testing and Academic Performance: A Quantitative Evaluation”
• “Teaching Strategies and Student Learning Outcomes in Secondary Schools: A Quantitative Study”
• “The Relationship Between Extracurricular Activities and Academic Success”
• “Influence of Parental Involvement on Children’s Educational Achievements”
• “Digital Literacy in Primary Schools: A Quantitative Assessment”
• “Learning Outcomes in Blended vs. Traditional Classrooms: A Comparative Analysis”
• “Correlation Between Teacher Experience and Student Success Rates”
• “Analyzing the Impact of Classroom Technology on Reading Comprehension”
• “Gender Differences in STEM Fields: A Quantitative Analysis of Enrollment Data”
• “The Relationship Between Homework Load and Academic Burnout”
• “Assessment of Special Education Programs in Public Schools”
• “Role of Peer Tutoring in Improving Academic Performance: A Quantitative Study”

Medicine and Health Sciences

• “The Impact of Sleep Duration on Cardiovascular Health: A Cross-sectional Study”
• “Analyzing the Efficacy of Various Antidepressants: A Meta-Analysis”
• “Patient Satisfaction in Telehealth Services: A Quantitative Assessment”
• “Dietary Habits and Incidence of Heart Disease: A Quantitative Review”
• “Correlations Between Stress Levels and Immune System Functioning”
• “Smoking and Lung Function: A Quantitative Analysis”
• “Influence of Physical Activity on Mental Health in Older Adults”
• “Antibiotic Resistance Patterns in Community Hospitals: A Quantitative Study”
• “The Efficacy of Vaccination Programs in Controlling Disease Spread: A Time-Series Analysis”
• “Role of Social Determinants in Health Outcomes: A Quantitative Exploration”
• “Impact of Hospital Design on Patient Recovery Rates”
• “Quantitative Analysis of Dietary Choices and Obesity Rates in Children”

Social Sciences

• “Examining Social Inequality through Wage Distribution: A Quantitative Study”
• “Impact of Parental Divorce on Child Development: A Longitudinal Study”
• “Social Media and its Effect on Political Polarization: A Quantitative Analysis”
• “The Relationship Between Religion and Social Attitudes: A Statistical Overview”
• “Influence of Socioeconomic Status on Educational Achievement”
• “Quantifying the Effects of Community Programs on Crime Reduction”
• “Public Opinion and Immigration Policies: A Quantitative Exploration”
• “Analyzing the Gender Representation in Political Offices: A Quantitative Study”
• “Impact of Mass Media on Public Opinion: A Regression Analysis”
• “Influence of Urban Design on Social Interactions in Communities”
• “The Role of Social Support in Mental Health Outcomes: A Quantitative Analysis”
• “Examining the Relationship Between Substance Abuse and Employment Status”

Engineering and Technology

• “Performance Evaluation of Different Machine Learning Algorithms in Autonomous Vehicles”
• “Material Science: A Quantitative Analysis of Stress-Strain Properties in Various Alloys”
• “Impacts of Data Center Cooling Solutions on Energy Consumption”
• “Analyzing the Reliability of Renewable Energy Sources in Grid Management”
• “Optimization of 5G Network Performance: A Quantitative Assessment”
• “Quantifying the Effects of Aerodynamics on Fuel Efficiency in Commercial Airplanes”
• “The Relationship Between Software Complexity and Bug Frequency”
• “Machine Learning in Predictive Maintenance: A Quantitative Analysis”
• “Wearable Technologies and their Impact on Healthcare Monitoring”
• “Quantitative Assessment of Cybersecurity Measures in Financial Institutions”
• “Analysis of Noise Pollution from Urban Transportation Systems”
• “The Influence of Architectural Design on Energy Efficiency in Buildings”

Quantitative Research Topics

Quantitative Research Topics are as follows:

• The effects of social media on self-esteem among teenagers.
• A comparative study of academic achievement among students of single-sex and co-educational schools.
• The impact of gender on leadership styles in the workplace.
• The correlation between parental involvement and academic performance of students.
• The effect of mindfulness meditation on stress levels in college students.
• The relationship between employee motivation and job satisfaction.
• The effectiveness of online learning compared to traditional classroom learning.
• The correlation between sleep duration and academic performance among college students.
• The impact of exercise on mental health among adults.
• The relationship between social support and psychological well-being among cancer patients.
• The effect of caffeine consumption on sleep quality.
• A comparative study of the effectiveness of cognitive-behavioral therapy and pharmacotherapy in treating depression.
• The relationship between physical attractiveness and job opportunities.
• The correlation between smartphone addiction and academic performance among high school students.
• The impact of music on memory recall among adults.
• The effectiveness of parental control software in limiting children’s online activity.
• The relationship between social media use and body image dissatisfaction among young adults.
• The correlation between academic achievement and parental involvement among minority students.
• The impact of early childhood education on academic performance in later years.
• The effectiveness of employee training and development programs in improving organizational performance.
• The relationship between socioeconomic status and access to healthcare services.
• The correlation between social support and academic achievement among college students.
• The impact of technology on communication skills among children.
• The effectiveness of mindfulness-based stress reduction programs in reducing symptoms of anxiety and depression.
• The relationship between employee turnover and organizational culture.
• The correlation between job satisfaction and employee engagement.
• The impact of video game violence on aggressive behavior among children.
• The effectiveness of nutritional education in promoting healthy eating habits among adolescents.
• The relationship between bullying and academic performance among middle school students.
• The correlation between teacher expectations and student achievement.
• The impact of gender stereotypes on career choices among high school students.
• The effectiveness of anger management programs in reducing violent behavior.
• The relationship between social support and recovery from substance abuse.
• The correlation between parent-child communication and adolescent drug use.
• The impact of technology on family relationships.
• The effectiveness of smoking cessation programs in promoting long-term abstinence.
• The relationship between personality traits and academic achievement.
• The correlation between stress and job performance among healthcare professionals.
• The impact of online privacy concerns on social media use.
• The effectiveness of cognitive-behavioral therapy in treating anxiety disorders.
• The relationship between teacher feedback and student motivation.
• The correlation between physical activity and academic performance among elementary school students.
• The impact of parental divorce on academic achievement among children.
• The effectiveness of diversity training in improving workplace relationships.
• The relationship between childhood trauma and adult mental health.
• The correlation between parental involvement and substance abuse among adolescents.
• The impact of social media use on romantic relationships among young adults.
• The effectiveness of assertiveness training in improving communication skills.
• The relationship between parental expectations and academic achievement among high school students.
• The correlation between sleep quality and mood among adults.
• The impact of video game addiction on academic performance among college students.
• The effectiveness of group therapy in treating eating disorders.
• The relationship between job stress and job performance among teachers.
• The correlation between mindfulness and emotional regulation.
• The impact of social media use on self-esteem among college students.
• The effectiveness of parent-teacher communication in promoting academic achievement among elementary school students.
• The impact of renewable energy policies on carbon emissions
• The relationship between employee motivation and job performance
• The effectiveness of psychotherapy in treating eating disorders
• The correlation between physical activity and cognitive function in older adults
• The effect of childhood poverty on adult health outcomes
• The impact of urbanization on biodiversity conservation
• The relationship between work-life balance and employee job satisfaction
• The effectiveness of eye movement desensitization and reprocessing (EMDR) in treating trauma
• The correlation between parenting styles and child behavior
• The effect of social media on political polarization
• The impact of foreign aid on economic development
• The relationship between workplace diversity and organizational performance
• The effectiveness of dialectical behavior therapy in treating borderline personality disorder
• The correlation between childhood abuse and adult mental health outcomes
• The effect of sleep deprivation on cognitive function
• The impact of trade policies on international trade and economic growth
• The relationship between employee engagement and organizational commitment
• The effectiveness of cognitive therapy in treating postpartum depression
• The correlation between family meals and child obesity rates
• The effect of parental involvement in sports on child athletic performance
• The impact of social entrepreneurship on sustainable development
• The relationship between emotional labor and job burnout
• The effectiveness of art therapy in treating dementia
• The correlation between social media use and academic procrastination
• The effect of poverty on childhood educational attainment
• The impact of urban green spaces on mental health
• The relationship between job insecurity and employee well-being
• The effectiveness of virtual reality exposure therapy in treating anxiety disorders
• The correlation between childhood trauma and substance abuse
• The effect of screen time on children’s social skills
• The impact of trade unions on employee job satisfaction
• The relationship between cultural intelligence and cross-cultural communication
• The effectiveness of acceptance and commitment therapy in treating chronic pain
• The correlation between childhood obesity and adult health outcomes
• The effect of gender diversity on corporate performance
• The impact of environmental regulations on industry competitiveness.
• The impact of renewable energy policies on greenhouse gas emissions
• The relationship between workplace diversity and team performance
• The effectiveness of group therapy in treating substance abuse
• The correlation between parental involvement and social skills in early childhood
• The effect of technology use on sleep patterns
• The impact of government regulations on small business growth
• The relationship between job satisfaction and employee turnover
• The effectiveness of virtual reality therapy in treating anxiety disorders
• The correlation between parental involvement and academic motivation in adolescents
• The effect of social media on political engagement
• The impact of urbanization on mental health
• The relationship between corporate social responsibility and consumer trust
• The correlation between early childhood education and social-emotional development
• The effect of screen time on cognitive development in young children
• The impact of trade policies on global economic growth
• The relationship between workplace diversity and innovation
• The effectiveness of family therapy in treating eating disorders
• The correlation between parental involvement and college persistence
• The effect of social media on body image and self-esteem
• The impact of environmental regulations on business competitiveness
• The relationship between job autonomy and job satisfaction
• The effectiveness of virtual reality therapy in treating phobias
• The correlation between parental involvement and academic achievement in college
• The effect of social media on sleep quality
• The impact of immigration policies on social integration
• The relationship between workplace diversity and employee well-being
• The effectiveness of psychodynamic therapy in treating personality disorders
• The correlation between early childhood education and executive function skills
• The effect of parental involvement on STEM education outcomes
• The impact of trade policies on domestic employment rates
• The relationship between job insecurity and mental health
• The effectiveness of exposure therapy in treating PTSD
• The correlation between parental involvement and social mobility
• The effect of social media on intergroup relations
• The impact of urbanization on air pollution and respiratory health.
• The relationship between emotional intelligence and leadership effectiveness
• The effectiveness of cognitive-behavioral therapy in treating depression
• The correlation between early childhood education and language development
• The effect of parental involvement on academic achievement in STEM fields
• The impact of trade policies on income inequality
• The relationship between workplace diversity and customer satisfaction
• The effectiveness of mindfulness-based therapy in treating anxiety disorders
• The correlation between parental involvement and civic engagement in adolescents
• The effect of social media on mental health among teenagers
• The impact of public transportation policies on traffic congestion
• The relationship between job stress and job performance
• The effectiveness of group therapy in treating depression
• The correlation between early childhood education and cognitive development
• The effect of parental involvement on academic motivation in college
• The impact of environmental regulations on energy consumption
• The relationship between workplace diversity and employee engagement
• The effectiveness of art therapy in treating PTSD
• The correlation between parental involvement and academic success in vocational education
• The effect of social media on academic achievement in college
• The impact of tax policies on economic growth
• The relationship between job flexibility and work-life balance
• The effectiveness of acceptance and commitment therapy in treating anxiety disorders
• The correlation between early childhood education and social competence
• The effect of parental involvement on career readiness in high school
• The impact of immigration policies on crime rates
• The relationship between workplace diversity and employee retention
• The effectiveness of play therapy in treating trauma
• The correlation between parental involvement and academic success in online learning
• The effect of social media on body dissatisfaction among women
• The impact of urbanization on public health infrastructure
• The relationship between job satisfaction and job performance
• The effectiveness of eye movement desensitization and reprocessing therapy in treating PTSD
• The correlation between early childhood education and social skills in adolescence
• The effect of parental involvement on academic achievement in the arts
• The impact of trade policies on foreign investment
• The relationship between workplace diversity and decision-making
• The effectiveness of exposure and response prevention therapy in treating OCD
• The correlation between parental involvement and academic success in special education
• The impact of zoning laws on affordable housing
• The relationship between job design and employee motivation
• The effectiveness of cognitive rehabilitation therapy in treating traumatic brain injury
• The correlation between early childhood education and social-emotional learning
• The effect of parental involvement on academic achievement in foreign language learning
• The impact of trade policies on the environment
• The relationship between workplace diversity and creativity
• The effectiveness of emotion-focused therapy in treating relationship problems
• The correlation between parental involvement and academic success in music education
• The effect of social media on interpersonal communication skills
• The impact of public health campaigns on health behaviors
• The relationship between job resources and job stress
• The effectiveness of equine therapy in treating substance abuse
• The correlation between early childhood education and self-regulation
• The effect of parental involvement on academic achievement in physical education
• The impact of immigration policies on cultural assimilation
• The relationship between workplace diversity and conflict resolution
• The effectiveness of schema therapy in treating personality disorders
• The correlation between parental involvement and academic success in career and technical education
• The effect of social media on trust in government institutions
• The impact of urbanization on public transportation systems
• The relationship between job demands and job stress
• The correlation between early childhood education and executive functioning
• The effect of parental involvement on academic achievement in computer science
• The effectiveness of cognitive processing therapy in treating PTSD
• The correlation between parental involvement and academic success in homeschooling
• The effect of social media on cyberbullying behavior
• The impact of urbanization on air quality
• The effectiveness of dance therapy in treating anxiety disorders
• The correlation between early childhood education and math achievement
• The effect of parental involvement on academic achievement in health education
• The impact of global warming on agriculture
• The effectiveness of narrative therapy in treating depression
• The correlation between parental involvement and academic success in character education
• The effect of social media on political participation
• The impact of technology on job displacement
• The relationship between job resources and job satisfaction
• The effectiveness of art therapy in treating addiction
• The correlation between early childhood education and reading comprehension
• The effect of parental involvement on academic achievement in environmental education
• The impact of income inequality on social mobility
• The relationship between workplace diversity and organizational culture
• The effectiveness of solution-focused brief therapy in treating anxiety disorders
• The correlation between parental involvement and academic success in physical therapy education
• The effect of social media on misinformation
• The impact of green energy policies on economic growth
• The relationship between job demands and employee well-being
• The correlation between early childhood education and science achievement
• The effect of parental involvement on academic achievement in religious education
• The impact of gender diversity on corporate governance
• The relationship between workplace diversity and ethical decision-making
• The correlation between parental involvement and academic success in dental hygiene education
• The effect of social media on self-esteem among adolescents
• The impact of renewable energy policies on energy security
• The effect of parental involvement on academic achievement in social studies
• The impact of trade policies on job growth
• The relationship between workplace diversity and leadership styles
• The correlation between parental involvement and academic success in online vocational training
• The effect of social media on self-esteem among men
• The impact of urbanization on air pollution levels
• The effectiveness of music therapy in treating depression
• The correlation between early childhood education and math skills
• The effect of parental involvement on academic achievement in language arts
• The impact of immigration policies on labor market outcomes
• The effectiveness of hypnotherapy in treating phobias
• The effect of social media on political engagement among young adults
• The impact of urbanization on access to green spaces
• The relationship between job crafting and job satisfaction
• The effectiveness of exposure therapy in treating specific phobias
• The correlation between early childhood education and spatial reasoning
• The effect of parental involvement on academic achievement in business education
• The impact of trade policies on economic inequality
• The effectiveness of narrative therapy in treating PTSD
• The correlation between parental involvement and academic success in nursing education
• The effect of social media on sleep quality among adolescents
• The impact of urbanization on crime rates
• The relationship between job insecurity and turnover intentions
• The effectiveness of pet therapy in treating anxiety disorders
• The correlation between early childhood education and STEM skills
• The effect of parental involvement on academic achievement in culinary education
• The impact of immigration policies on housing affordability
• The relationship between workplace diversity and employee satisfaction
• The effectiveness of mindfulness-based stress reduction in treating chronic pain
• The correlation between parental involvement and academic success in art education
• The effect of social media on academic procrastination among college students
• The impact of urbanization on public safety services.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

500+ Sports Research Topics

500+ Statistics Research Topics

500+ Google Scholar Research Topics

500+ Psychology Research Paper Topics

500+ Physics Research Topics

500+ Nursing Research Topic Ideas

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 02 July 2024

High-dimensional single-cell analysis of human natural killer cell heterogeneity

• Lucas Rebuffet   ORCID: orcid.org/0009-0009-0641-3942 1 ,
• Janine E. Melsen   ORCID: orcid.org/0000-0001-5322-7194 2 , 3 ,
• Bertrand Escalière 1 ,
• Daniela Basurto-Lozada   ORCID: orcid.org/0000-0003-3943-8424 4 , 5 ,
• Avinash Bhandoola   ORCID: orcid.org/0000-0002-4657-8372 6 ,
• Niklas K. Björkström 7 ,
• Yenan T. Bryceson   ORCID: orcid.org/0000-0002-7783-9934 8 , 9 , 10 ,
• Roberta Castriconi   ORCID: orcid.org/0000-0003-2806-1115 11 , 12 ,
• Frank Cichocki   ORCID: orcid.org/0000-0003-3043-0653 13 ,
• Marco Colonna   ORCID: orcid.org/0000-0001-5222-4987 14 ,
• Daniel M. Davis   ORCID: orcid.org/0000-0002-9182-291X 15 ,
• Andreas Diefenbach   ORCID: orcid.org/0000-0002-9176-9530 16 , 17 ,
• Yi Ding 6 ,
• Muzlifah Haniffa   ORCID: orcid.org/0000-0002-3927-2084 4 , 5 , 18 ,
• Amir Horowitz 19 , 20 ,
• Lewis L. Lanier   ORCID: orcid.org/0000-0003-1308-3952 21 ,
• Karl-Johan Malmberg   ORCID: orcid.org/0000-0002-8718-9373 7 , 22 , 23 ,
• Jeffrey S. Miller   ORCID: orcid.org/0000-0002-0339-4944 13 ,
• Lorenzo Moretta   ORCID: orcid.org/0000-0003-4658-1747 24 ,
• Emilie Narni-Mancinelli   ORCID: orcid.org/0000-0002-7001-1026 1 ,
• Luke A. J. O’Neill 25 ,
• Chiara Romagnani   ORCID: orcid.org/0000-0002-5167-7463 26 , 27 , 28 ,
• Dylan G. Ryan 29 ,
• Simona Sivori 11 , 30 ,
• Dan Sun   ORCID: orcid.org/0000-0003-2147-9308 7 ,
• Constance Vagne 31 &
• Eric Vivier   ORCID: orcid.org/0000-0001-7022-8287 1 , 31 , 32 , 33  

Nature Immunology ( 2024 ) Cite this article

10k Accesses

49 Altmetric

Metrics details

• Innate lymphoid cells
• RNA sequencing
• Tumour immunology

Natural killer (NK) cells are innate lymphoid cells (ILCs) contributing to immune responses to microbes and tumors. Historically, their classification hinged on a limited array of surface protein markers. Here, we used single-cell RNA sequencing (scRNA-seq) and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) to dissect the heterogeneity of NK cells. We identified three prominent NK cell subsets in healthy human blood: NK1, NK2 and NK3, further differentiated into six distinct subgroups. Our findings delineate the molecular characteristics, key transcription factors, biological functions, metabolic traits and cytokine responses of each subgroup. These data also suggest two separate ontogenetic origins for NK cells, leading to divergent transcriptional trajectories. Furthermore, we analyzed the distribution of NK cell subsets in the lung, tonsils and intraepithelial lymphocytes isolated from healthy individuals and in 22 tumor types. This standardized terminology aims at fostering clarity and consistency in future research, thereby improving cross-study comparisons.

Similar content being viewed by others

Single-cell profiling reveals the trajectories of natural killer cell differentiation in bone marrow and a stress signature induced by acute myeloid leukemia

Heterogeneity of human bone marrow and blood natural killer cells defined by single-cell transcriptome

Single-cell transcriptomics of human T cells reveals tissue and activation signatures in health and disease

NK cells are lymphocytes of the innate immune system that belong to the ILC family 1 . NK cells were initially recognized for their capability to identify and eliminate virus-infected and tumor cells independently of prior sensitization, but their multifaceted roles have since been acknowledged. These include not only direct immune responses, but also regulatory functions that influence the adaptive immune system.

The heterogeneity of NK cells is central to their varied functions. Over time, researchers have identified distinct NK cell subgroups, each characterized by unique functional potentials and developmental pathways. These traditional classification methods mainly relied on surface marker expression. Along this line, human NK cells are typically divided into two main categories on the basis of the density of CD56, the 140-kDa isoform of the neural cell adhesion molecule (NCAM) 2 , on the cell surface: CD56 bright and CD56 dim NK cells. Further distinctions in the CD56 dim population are made on the basis of expression of the CD57 carbohydrate moiety 3 on the cell surface and the absence of CD94–NKG2A and CD62L; cells with these features comprise a more mature subset 4 , 5 , 6 , 7 . Additionally, adaptive NK cells, which make up a distinct NK cell subset demonstrating characteristics akin to those of adaptive immune cells, emerge in certain immune contexts, such as human cytomegalovirus (HCMV) encounters 8 , 9 . The advent of advanced single-cell technologies, namely scRNA-seq and CITE-seq, has precipitated a paradigm shift in our understanding of NK cells. These technologies reveal that the NK cell landscape is more intricate and nuanced than previously understood and is marked by subtle distinctions. However, despite these advancements, a unified and standardized description of NK cell heterogeneity remains elusive. Current definitions vary between laboratories and could lead to discrepancies in scientific literature. This lack of standardized terminology creates major challenges, particularly in translating research across model systems or cohorts of people.

The increasing relevance of NK cells in therapeutic approaches, especially in NK-cell-based immunotherapy against cancer, underscores the necessity of a comprehensive understanding of their heterogeneity. Misinterpretation or neglect of specific NK cell subsets could have substantial implications, potentially affecting the effectiveness or safety of therapies. In this study, we integrated scRNA-seq and CITE-seq data from ~225,000 NK cells (718 donors) to establish a baseline classification of NK cells in the blood, lung, tonsil and intraepithelial lymphocytes of healthy individuals, and in 22 tumor types. These data were extracted from 7 distinct publicly available datasets. The accession code of each of the datasets used is listed in Supplementary Table 3 and ‘Data Availability’. This classification is intended to serve as a reference point for future studies, thereby facilitating a more standardized approach to understanding and using NK cells in both research and clinical settings.

Human circulating NK cells comprise three main populations

To systematically and comprehensively categorize human blood NK cells, we used a high-dimensional CITE-seq dataset, encompassing 228 antibody-derived tags (ADTs) and the transcriptional profiles of 5,708 NK cells from eight healthy donors 10 . To effectively integrate both RNA- and protein-expression data, we used the weighted nearest neighbors (WNN) method 10 . Initially, we isolated non-proliferating NK cells at the baseline and then reclustered them to elucidate the foundational heterogeneity among blood NK cells. Our analysis revealed three primary NK cell subsets: NK1, NK2 and NK3 (Fig. 1a ). We subsequently analyzed their transcriptional (Fig. 1b ) and proteomic signatures (Fig. 1c,d ).

Based on dataset 5. a , WNN and UMAP (WNN_UMAP) visualization of NK cells sorted from healthy human blood with clusters identified by unsupervised hierarchical clustering (based on scRNA-seq and expression of 228 surface proteins). b , Dot plot of the 20 most distinguishing genes expressed for the three major subsets of human blood NK cells. Gene expression was analyzed using the using the two-sided Wilcoxon rank-sum test with Bonferroni adjustment. Ribosomal genes and mitochondrial genes were removed for clarity. The color indicates the Z -score scaled gene expression levels. c , Dot plot of the most distinguishing proteins expressed for the three major subsets of human blood NK cells. Protein expression was analyzed using the two-sided Wilcoxon rank-sum test with Bonferroni adjustment. Alternative protein names are shown in parentheses. The color indicates the Z -score scaled protein expression levels. d , WNN_UMAP visualization of the surface expression of the major discriminating proteins expressed at the surface of NK1, NK2 and NK3 cells. a.u., arbitrary units.

The NK1 cluster was marked by high protein expression of CD16, CX3CR1, CD161, β7-integrin and CD38 (Fig. 1c,d ). Its transcriptional profile highlighted genes corresponding to these proteins, along with elevated levels of genes encoding cytotoxic molecules ( GZMB and PRF1 ) and markers of NK cell maturity, such as CD160 , CD247 , ADGRG1 , NKG7 , FCER1G , LAIR2 , SPON2 , CLIC3 and CHST2 (Fig. 1b ). Cells in the NK1 cluster express lower levels of CD56 compared to cells in the NK2 cluster and lower levels of CD57 compared to cells in the NK3 cluster.

The NK2 cluster was defined by high expression of CD56, CD27, CD44, CD54, CD45RB, CD314 (NKG2D) and CD335 (NKp46) and little or no expression of CD16 and CD57 at the protein level (Fig. 1c,d ). At the transcriptome level, NK2 cells showed pronounced expression of ribosomal genes (RPL and RPS gene families, Supplementary Table 1 ) and genes encoding proteins involved in protein synthesis and structural integrity ( EEF1A1 , TPT1 ), indicative of heightened protein synthesis and proliferative capacity. This subset also expressed various genes encoding cytokine receptors ( IL2RB , IL7R ), membrane receptors ( KLRC1 encoding NKG2A), transcription factors ( TCF7 ), soluble factors that modulate immune responses ( XCL1 , XCL2 , AREG ) and molecules implicated in cell migration and tissue homing ( CD44 , GPR183 , SELL ), along with granzyme K ( GZMK ) (Fig. 1b ). Expression of the classic NK cell markers CD57 and CD16 was reduced or absent on NK2 cells compared with NK1 and NK3 cells, indicating that the NK2 population comprised CD56 bright and early-stage CD56 dim NK cells.

For the NK3 cluster, the protein-expression profile included CD16, CD57, CD271 ( NGFR ), CD2, CD18, CD49d and inhibitory killer cell immunoglobulin-like receptors (KIRs) (CD158e, CD158b), with lower expression levels of CD56, NKp30, NKp46, CD161 and CD122 (Fig. 1c,d ). Transcriptionally, NK3 cells were characterized by the preferential expression of genes encoding transcription factors ( PRDM1 (encoding BLIMP1) and ZBTB38 ), surface molecules and receptors ( CD2 and KLRC2 (encoding NKG2C)), CD3 chain transcripts ( CD3D , CD3E , CD3G ), secreted cytokines and chemokines ( IL32 , CCL5 ) and granzyme H ( GZMH ) (Fig. 1b ). Altogether, the combined protein and transcriptional signature of the NK3 cluster closely resembles that of adaptive NK cells, and this cluster’s preferential expression of CD57 and PRDM1 suggests that it also includes mature CD57 + CD56 dim NK cells that are not produced in response to HCMV. We then confirmed the robustness of the classification of human blood NK cells into the NK1, NK2 and NK3 clusters by applying the derived transcriptional signatures to blood NK cells from other available datasets 11 (Extended Data Figs. 1a–c and 2a–c ).

The three primary NK cell populations can be split into six subsets

To further delineate the heterogeneity of blood NK cells, we integrated scRNA-seq data from sorted NK cells from 13 healthy individuals across four datasets using the same RNA-seq protocol (10× genomics v2 chemistry protocol), therefore including 36,270 cells after high-quality cell filtering. This procedure resulted in the identification of eight well-defined clusters (Extended Data Fig. 3a–d ). Three clusters (1, 3 and 8) shared an NK3 signature marked by genes such as KLRC2 (encoding NKG2C), CD52 , IL32 and GZMH (Supplementary Table 1 ) and were enriched in cells expressing NKG2C on their surface (Extended Data Fig. 3e ). Notably, NK3B (cluster 1) was distinguished by expression of members of the HLA-D gene family, CD74 , CCL5 , CD7 and KLRC1 , and NK3A exhibited enhanced cytotoxic capabilities (through expression of GZMA , GZMB and PRF1 ) (Supplementary Table 1 ). Previous data have shown that there is dramatic epigenetic and transcriptional heterogeneity within adaptive NK cells in HCMV + individuals. This heterogeneity was observed within the same person and across different people, reflecting the clonality of adaptive NK cells rather than functionally distinct programs 12 . We consolidated these three clusters into a single cluster for subsequent analyzes. This led to a final configuration of six clusters (Fig. 2a and Extended Data Fig. 3f ). The integrated dataset in our study can be explored at: https://collections.cellatlas.io/meta-nk .

Based on datasets 1–4a. a , UMAP visualization of NK cells sorted from healthy human blood, with clusters identified by unsupervised hierarchical clustering. b , Bar graph showing the proportion of cells within each cluster in all donors. Blue and pink bars are shown under HCMV-positive and HCMV-negative individuals, respectively. c , Violin plot of the scoring of the six NK clusters with respect to established NK1, NK2 and NK3 signatures ( n  = 13 samples). In the violin plots, the point is the median value. The error bars present the median +/- standard deviation. d , Violin plot of the scoring of the NK3 clusters with the NK3 signature in HCMV-positive and HCMV-negative individuals ( n  = 13 samples). e , Dot plot of the 20 most distinguishing genes for each subset of NK cells. Gene expression was analyzed using the two-sided Wilcoxon rank-sum test with Bonferroni adjustment. Ribosomal genes and mitochondrial genes were removed for clarity. The color indicates the Z -score scaled gene expression levels.

Upon confirming that batch correction was adequate and ensuring that our final cluster designations were free from batch effects both at the dataset (Extended Data Fig. 4a ) and donor (Fig. 2b ) levels, we scored all CD45 pos populations from dataset 5 with the previously defined 13 13-gene signature ( CD160 , CD244 , CHST12 , CST7 , GNLY , IL18RAP , IL2RB , KLRC1 , KLRC3 , KLRD1 , KLRF1 , PRF1 , XCL2 ) that is characteristic of human NK cells, therefore validating the ability of this signature to discriminate NK cells from other subsets (Extended Data Fig. 4b ). We also used the 13-gene signature to score the six subsets of NK cells, thus verifying the robustness of this signature across all NK populations (Extended Data Fig. 4c ). Then, we evaluated each cluster against established transcriptional signatures for NK1, NK2 and NK3 (Fig. 2c ). Three clusters (cluster 2, cluster 4 and cluster 0) exhibited a strong correlation with the NK1 signature, prompting their reclassification as NK1A, NK1B and NK1C, respectively. As expected, the cluster containing the NK3 subpopulations displayed a clear association with the NK3 signature. Notably, a higher NK3 score was observed in NK3 cells derived from HCMV + individuals (Fig. 2d ). Simultaneously, cluster 6 showed a strong correlation with NK2 signature, justifying its classification as NK2. Finally, cluster 5 displayed an intermediate association with both NK1 and NK2 signatures and was thus renamed intermediate NK (NKint). This reassignment is consistent with the gene-expression patterns delineated by pre-defined signatures, facilitating a clearer understanding of the functional landscape within the blood NK cell repertoire.

Analysis of the top 20 defining markers for the six clusters (Fig. 2e ) provided a detailed transcriptional profile for each cluster. NK1 cells, as noted in Figure 1b , showed a core signature indicative of chemokines ( CCL3 , CCL4 ) and proteins critical for cytotoxicity and its regulation ( PRF1 , GZMA , GZMB , NKG7 ), cytoskeletal dynamics ( RAC2 , ARPC2 , CFL1 ) and cellular adhesion ( ITGB2 , CALR ). NK1 subpopulations expressed unique subset-specific markers. NK1A was characterized by high expression of CXCR4 and the JUN and JUNB , which encode AP-1 transcription factors; NK1B was distinguished by the surface marker CD160 , the long non-coding RNA NEAT1 and the interferon-induced transmembrane protein 1 IFITM1 ; NK1C exhibited enhanced cytotoxic potential, with higher levels of granzyme and perforin transcripts, a distinct expression profile related to prostaglandin metabolism ( PTGDS , AKR1C3 ) and the most active cytoskeletal profile ( ACTB , ACTG1 , CFL1 , RAC2 , ARPC2 ).

NK2 and NKint populations, whose core signatures shared genes encoding chemokines ( XCL1 , XCL2 ), granzymes ( GZMK ), proteins involved in transcription and signaling regulation ( NFKBIA , FOS , BTG1 , GAS5 ) and protein synthesis ( TPT1 , EEF gene family) and surface proteins ( CD44 , CD74 , CD7 , KLRC1 ), also displayed distinct markers. NK2 expressed LTB , SELL , GNLY and IL7R , whereas NKint exhibited strong expression of CXCR4 , JUNB , ZFP36 , IER2 and EIF3G .

NK3, along with the previously defined signature ( KLRC2 , CCL5 , GZMH , IL32 , CD3E , CD3D , S100A4 , LGALS1 ), expressed additional markers ( CD52 , TMSB4X ) and shared certain ones with the NK2 population, such as NKG2E ( KLRC3 ) and granulysin ( GNLY ). This intricate transcriptional landscape underscores the diverse functionalities and regulatory mechanisms at play within the NK cell subsets.

Further investigation into the distribution of these populations among the 13 healthy donors revealed a predominance of NK1 cells, constituting approximately 60% ± 12% of circulating NK cells. NK2 and NK3 cells represented 17% ± 7% and 24% ± 14%, respectively (Supplementary Table 2 ). A more granular analysis at the subpopulation level (Fig. 2b and Extended Data Fig. 3f ) showed that nearly half of the NK1 population was made up of NK1C cells, translating to 26% ± 6% of all circulating NK cells. The NK2 population represented a minor fraction of total NK cells (6% ± 4%) as compared to NKint (11% ± 4% of total NK cells). The NK3 cluster, characterized by distinctive expression of markers indicative of both adaptive and terminally mature NK cells (such as PRDM1 and B3GAT1 (encoding an enzyme key for the biosynthesis of CD57)) along with genes uniquely associated with adaptive NK cells ( CD3E and ZBTB38 ) (Fig. 1c,d ), exhibited considerable variability in its prevalence across individuals (Fig. 2b ). Our study’s approach to cluster identification was conducted without consideration of HCMV status. Consequently, to discern the potential impact of HCMV on the NK3 cluster, we conducted separate analyzes of the frequency and predictive scores of NK3 cells in individuals positive for HCMV (HCMV + ) and in those without HCMV (HCMV − ). Notably, cells in the NK3 cluster were observed in both HCMV + and HCMV − donors (Fig. 2b and Extended Data Fig. 3f ). However, a higher NK3 score was predominantly observed in NK3 cells derived from HCMV + individuals (Fig. 2d ). Altogether, these insights provide a quantitative perspective on the distribution and variability of NK cell subsets in the bloodstream.

Molecular features of NK cell subsets

After confirming that the six subpopulations expressed the populations’ defining markers (Extended Data Fig. 4d–f ), we computed the z scores for the expression levels of various pertinent markers in the NK cell subpopulations, considering only those genes expressed above a defined threshold—detection in more than 5% of circulating NK cells—for inclusion in the heatmap. This approach yielded a heatmap that, beyond previously identified markers, unveiled additional distinctive characteristics for the subpopulations (Fig. 3a ).

Based on datasets 1–4a. a , Heatmap showing the differential expression of markers of interest among NK cell subsets. The color scale is based on z -score-scaled gene expression. The z-score distribution ranges from −2 (blue) to 2 (red). b , c , Selected GO terms showing enrichment in the three major populations (and six major subsets) of healthy human blood NK cells. Benjamini–Hochberg-corrected −log 10 ( P ) values were calculated by a hypergeometric test. The black dotted line indicates the significance threshold, which is −log 10 (0.05). d , Heatmap showing the differential enrichment for selected metabolic pathways among NK cell subsets. The color scale is based on the z score of the normalized enrichment score for each metabolic pathway. e , Assessment ( z scores) of the response to different cytokines and chemokines in each subgroup, quantified by Cytosig. IFN, interferon; PGE2, prostaglandin E2. P values were computed by comparing z scores in one NK subset with those in other subsets using two-sided Student’s t -tests, and –log 10 ( P ) values exceeding 10 were capped at 10 to facilitate visualization.

We examined cytokine and chemokine production and found that NK1 subpopulations were characterized by robust transcription of CCL4 , CCL3 , CCL4L2 and IL16 , whereas NK2 and NKint cells exhibited predominant transcription of FLT3LG along with XCL1 and XCL2 . Finally, NK3 cells were marked by high transcription levels of IFNG , IL32 and CCL5 . Differential expression was also apparent in chemotaxis receptors and cell–cell adhesion proteins. In particular, the subsets were distinguished by different patterns of sphingosine-1-phosphate receptors ( S1PR1 for NK2, S1PR4 for NK3 and S1PR5 for NK1). Furthermore, the CXC chemokine receptor family had a role in distinguishing the subpopulations ( CXCR2 and CX3CR1 for NK1, CXCR3 for NK2 and CXCR4 for both NK1A and NKint). Classic activating receptors of NK cells also exhibited subset-specific expression patterns. High levels of NKp46 ( NCR1 ), CD160 , NKp30 ( NCR3 ) and signaling lymphocyte activation molecule receptor genes were characteristic of the NK1 population. NK2 shared a pronounced expression of NKG2D ( KLRK1 ) with NK1A and NK1B. As expected, NKG2C ( KLRC2 ) was predominantly expressed by the NK3 population. Inhibitory-receptor expression profiles diverged between subsets. NK1 cells expressed higher levels of inhibitory KIRs, along with TIM3 ( HAVCR2 ), CD161 ( KLRB1 ) and SIGLEC7 , whereas NK2 and NKint cells had an elevated expression level of NKG2A ( KLRC1 ), and NK3 cells appeared have higher expression levels of TIGIT . In terms of cytokine-receptor expression, NK1 populations exhibited heightened expression levels of TGFBR1 , TGFBR2 and TGFBR3 , as well as IL12RB1 , IL10RB and IL2RG . By contrast, NK2 cells were characterized by a preference for IL2RB (consistent with their strong expression of CD122 at the protein level, Fig. 1c ), IL10RA , and a distinct expression of IL18R and its accessory protein IL18RAP . These findings suggest that the subsets have varying levels of sensitivity to cytokines and chemokines.

The observed cytotoxic profiles were in line with prior observations: NK1 populations exhibited a spectrum of cytotoxic molecules and associated proteins— GZMA , GZMB , PRF1 , NKG7 , GSDMD and FASLG —whereas NK2 and NKint displayed strong expression of GZMK and TRAIL ( TNFSF10 ). The NK3 subset expressed intermediate levels of cytotoxic molecules and was distinguished by high GZMH expression.

Activation markers also served as differential markers, with CD69 being most prominent in NK1B and NKint, whereas TNFRSF18 (encoding GITR) was more pronounced in NK2. Moreover, classic markers of NK maturation aligned with earlier descriptions: CD56 ( NCAM1 ) was more prevalent in NK2, and CD16 ( FCGR3A ) expression increased progressively from NK2 to NK1C. In addition, CD11B ( ITGAM ) levels were higher in NK1B, whereas CD11C ( ITGAX ) expression was more pronounced in NK2.

Gene Ontology (GO) term enrichment analysis revealed distinct functional specializations within NK subpopulations. NK1 cells were primarily involved in processes such as cell–cell adhesion, activation response, signaling, cytoskeletal activity and cell-mediated cytotoxicity (Fig. 3b ). These findings underscore NK1 cells’ have pivotal cytotoxic effector functions. By contrast, NK2 cells were linked to enhanced chemotaxis regulation and leukocyte differentiation, suggestive of their ability to infiltrate tissues and an ongoing maturation process. NK3 cells displayed an upregulation in leukocyte activation. We then explored the functions of the six main NK cell subpopulations (Fig. 3c ). NK1B cells were found to be highly responsive to activation through surface receptors, indicating their potential as primary targets in immunotherapeutic strategies. Both NK1A and NK1B populations were significantly enriched for production of tumor necrosis factor (TNF) and cytokines. Notably, the NK1C subset seems to be the most cytotoxic, as indicated by its pronounced cytoskeletal activity and cell-killing signature. An intriguing discovery was the considerable enrichment of tricarboxylic acid (TCA) cycle activities in the NK1C subset, prompting further investigation using single-cell gene set variation analysis (scGSVA).

Clustering analysis based on metabolic-pathway-enrichment analysis separated the NK cell subpopulations into two broad categories, with NK1B and NK1C clustering more closely together and separately from the NK1A, NK2, NK3 and NKint subsets (Fig. 3d ). The NK1C subset appears to be ‘hypermetabolic,’ with notable enrichment across the central carbon metabolism, including glycolysis and the TCA cycle, and mitochondrial oxidative phosphorylation (OXPHOS), which could support enhanced cytotoxic activity. Similarly, the NK1B subset also exhibits enrichment in the TCA cycle and OXPHOS, albeit to a lesser extent than does NK1C, in contrast to the other NK cell subpopulations. In addition, NK1B cells are more clearly defined by an enrichment in the mTOR signaling pathway. Finally, cysteine and methionine metabolism are enriched in the NK2 subset. Decomposition analysis of this pathway (Extended Data Fig. 5a ) found that the signature was in part driven by high expression levels of lactate dehydrogenase B (LDHB), spermine synthase (SMS) and 3-mercaptopyruvate sulfurtransferase (MPST). LDHB (which preferentially converts lactate to pyruvate and NAD + to NADH) seems to be the predominant isoform of lactate dehydrogenase in the NK2 subset; LDHA (which preferentially converts pyruvate to lactate and NADH to NAD + ) is highly expressed across the other NK subpopulations. These data suggest that different metabolic profiles underlie NK cell subpopulations and warrant further investigation.

To elucidate the varying responses of the six NK cell subsets to cytokine stimulation, we used the cytokine signaling analyzer (CytoSig) 14 , which predicts the responsiveness of cells to cytokine signals. This analysis indicated that the NK2 population exhibits a pronounced reaction to interleukin-18 (IL-18), consistent with the strong expression of IL18R and its associated protein IL18RAP in this subset (Fig. 3e ). NKint, NK1A and NK1B cells seemed to be more susceptible to IL-10 and PGE2, which are signals that can dampen immune responses, in particular in the tumor microenvironment 15 , 16 . By contrast, NK1B and NK3 showed a greater response to transforming growth factor beta (TGF-β). TGF-β is notorious for its immunosuppressive effects on NK cells 17 , particularly within the tumor microenvironment, where it can hinder their cytotoxic functions 18 . Consistent with previous studies, NK3 showed reduced sensitivity to IL-12 (ref. 9 ). Finally, NK1C cells demonstrated the most robust response to a suite of cytokines, namely IL-2, IL-15 and IL-12, that is traditionally associated with the activation and proliferation of NK cells 15 .

Transcriptional trajectories of NK cell subpopulations

The comprehensive examination of six NK cell subsets has revealed not only their distinctive characteristics in terms of markers, cytokine response and functionalities, but also a continuum in their transcriptional landscapes, particularly between the NKint and NK1A subsets. This continuum seems to bridge the transcriptional states of NKint with NK1C. To investigate the potential transcriptional pathways connecting these subsets, we performed a multifaceted analysis.

First, RNA velocity was used to predict the future states of individual cells. This analysis indicated that the majority of NK2 cells would likely persist as NK2 cells, forming a specific NK2 trajectory (Fig. 4a,b ). However, it also pointed towards a potential differentiation pathway from NKint into NK1C. This path was characterized by a clear pseudotime progression from NKint to NK1C, transitioning through intermediary populations (NK1A and NK1B) (Fig. 4a,b ). NK3 cells, which exhibit clonal-like transcriptional dynamics owing to their interaction with HCMV 12 , were excluded from the following trajectory analysis, to avoid having their unique transcriptional behavior skew the findings. Further trajectory analysis using diffusion maps (Destiny 19 ) and trajectory inference (Monocle3 (ref. 20 )) corroborated the pathway suggested by the RNA-velocity analysis (Fig. 4c–g ). Pseudotime inference clearly outlined a trajectory from NKint to NK1C (Fig. 4d,e,g ). Notably, the pseudotime inferred through diffusion-map analysis highlighted a considerable gap between NK2 and NKint (Fig. 4e ), reinforcing the concept of two distinct trajectories: one in which NK2 cells predominantly remain NK2, and another leading from NKint to NK1C. This latter trajectory aligns with the metabolism-based unsupervised clustering previously discussed (Fig. 3d ), which grouped NK1B and NK1C closely together owing to their strong central carbon metabolism activity. By contrast, NKint and NK1A clustered together and exhibited lower metabolic activity.

a – e , Based on dataset 4a. f – g , Based on datasets 1–4a. a , b , RNA-velocity analysis and pseudotime inference based on velocity analysis in a representative sample. c , d , Confirmation of trajectories and pseudotime analysis using a diffusion-map approach (Destiny analysis of dataset 4a). e , Plot of pseudotime derived from diffusion-map analysis across all NK cell subgroups. f , Monocle-derived trajectory performed on the NK1A, NK1B and NK1C subsets and projected onto the UMAP, colored by clusters. g , Monocle-derived trajectory performed on NK1 subpopulations and projected onto the UMAP colored by pseudotime (inferred by Monocle3).

Building on the Monocle analysis, we homed in on the top 150 genes that exhibited significant changes along the NK cell maturation trajectory from NKint to NK1C, as indicated by a q value below 0.05 and a high Moran’s I correlation score. This detailed examination revealed nine gene modules, each of which was sequentially activated as the cells progressed through maturation stages (Extended Data Fig. 6a ).

The RNA-velocity analysis also predicts another major developmental pathway, indicating that a considerable portion of NK2 cells is likely to maintain the NK2-cell state. Consistent with this possibility, evidence has been presented suggesting that mouse NK cell populations arise from two distinct lineages: a primary progenitor, known as the early NK cell progenitor (ENKP), and an alternative one, called the innate lymphoid common progenitor (ILCP), which is also capable of giving rise to other types of ILCs 21 . By mapping the transcriptional module scores of human blood ENKPs onto a uniform manifold approximation and projection (UMAP) representation, we observed that both NK1 and NK3 populations displayed transcriptional signatures that closely align with those of NK cells originating from ENKPs (Fig. 5a,b ). In addition, the scoring of NK1, NK2 and NK3 subsets on the basis of recently available blood human ILCP signatures 22 revealed that their signature is enriched in NK2s (Fig. 5c,d ). Altogether, these observations support the existence of two divergent ontogenic pathways: one for NK1 and NK3, originating from ENKPs, and another for NK2, originating from ILCPs.

Based on datasets 1–4a. a , UMAP visualization of the module score of individual cells scored with signatures derived from the main NK cell progenitor (ENKP) identified in mice. b , Violin plots of the module scores of individual cells scored with ENKP signatures. Data are shown as median ± s.d. ( n  = 13 samples). b , d , In the violin plots the point is the median value. The error bars present the median +/- standard deviation. c , UMAP visualization of the module score of individual cells scored with human blood ILCP signatures. d , Violin plots of the module scores of individual cells scored with signatures of human blood ILCPs Data are shown as median ± s.d. ( n  = 13 samples).

To elucidate the master regulatory genes that define the six NK cell subpopulations, we conducted a gene regulatory network analysis using the single-cell regulatory network inference and clustering (SCENIC) workflow 23 . The initial step involved cataloging the regulons identified in our dataset. Each regulon consists of a transcription factor or cofactor and its associated target genes (Extended Data Fig. 7a ). Next, we compared our list of regulons with a more robust database of verified transcription factors 24 . This comparison was crucial for excluding unreliable transcription factors and proteins that bind to RNA and DNA non-specifically, and to focus our analysis solely on bona fide transcription factors. Unsupervised clustering based on regulon activity first revealed two striking features: first, that NK2 branched away from the other subsets, supporting the theory of a distinct ontological origin for NK2. Second, clustering first grouped the NKint and NK1A subpopulations, suggesting that they might represent early stages of NK1 cell differentiation, then grouped the NK1B cells that appeared more differentiated, and finally included the NK1C and NK3 subsets that correspond to more advanced NK cell states.

This sequential differentiation pattern highlights the complex regulatory mechanisms that govern NK cell development and differentiation. Transcription factors that are pivotal in NK cell maturation 25 , such as T-bet ( TBX21 ) and BLIMP1 ( PRDM1 ) 26 , 27 , showed a progressive increase across NK2, NKint, NK1A, NK1B to NK1C continuum. Conversely, MYC , TCF7 , RUNX2 and GATA3 were predominantly expressed in NK2 subsets, aligning with previous research findings 28 . NK3 was distinguished by robust expression of ASCL2 and KLF6 , and the continued presence of BLIMP1 ( PRDM1 ). Therefore, the observed expression pattern of key master regulators of maturation substantiates the hypothesis that there are distinct lineages of NK cell progenitors.

Distribution of NK cell subsets in healthy tissue

NK cells are found in tissues in addition to the peripheral blood 29 . The link between circulating NK cells, tissue-infiltrating NK cells and tissue-resident ILCs is an emerging area of research. ILCs vary greatly depending on their environment and the local signals, such as cytokines, that they are exposed to, resulting in distinct ILC profiles in different tissues and diseases 1 . A detailed description of these ILC variations was recently published 22 . We therefore analyzed the scRNA-seq data in the earlier study 22 to investigate the distribution of NK1, NK2 and NK3 subsets in tonsils, lungs and intraepithelial lymphocytes (IELs) isolated from healthy individuals (Fig. 6a ). Remarkably, the NK1 and NK2 signatures coincided with the CD56 dim and CD56 bright subsets, respectively, identified in lung, tonsil and IELs (Fig. 6b ). More specifically, the vast majority of the different subgroups of CD56 dim and CD56 bright cells in these tissues could be characterized as NK1 and NK2, respectively (Extended Data Fig. 8a ). A few discrete subsets of NK cells in the tonsils (labeled JUNhi, ILC1-like NK, HSP + ) and lungs (labeled cyclic NKs, NK HSP, ILC1) could not be assigned to the NK1, NK2 or NK3 subsets. Because we had removed two subsets, cyclic NK cells and NK cells that exhibited characteristics of stress, for the analysis that led to the identification of the NK1, NK2 and NK3 subsets, we expected that some subsets, namely tonsil JUNhi, tonsil HSP + , lung NK HSP and cyclic NKs in the lung, could not be annotated. Notably, the tonsil ILC1-like and lung ILC1 subsets also did not match any of the NK1, NK2 and NK3 profiles, confirming that the later transcriptomic signatures preferentially resemble those of NK cells. However, the partial enrichment of lung ILC1s with NK2 signatures reinforces the idea that there is a shared ontology between these two populations (Extended Data Fig. 8a ). Finally, our data show the similarities between the IEL ILC1 and NK3 subsets (Fig. 6a,b and Extended Data Fig. 8a ), as illustrated in particular by the strong expression of PRDM1 in both the IEL ILC1 and NK3 subsets. These results indicate that the similarities between IEL ILC1s and NK cells and the divergence of IEL ILC1s from other tissue-resident ILC1s should be reanalyzed.

Based on dataset 7. a , UMAP visualization of the main populations of group 1 ILCs present in PBMCs, tonsil, lung and IEL, colored by their main populations and by their cluster and tissues (as defined in dataset 7). b , UMAP visualization of the module score of individual cells scored with signatures of NK1, NK2 and NK3 of ILC populations present in tonsil, lung and IELs. iel_prdm1, Intestinal intraepithelial lymphocytes; PRDM1+ NK cells; lung_bright, lung CD56bright NK cells; lung_dim, lung CD56bright NK cells; lung_znf NK, lung ZNF683+ NK cells; pbmc_bright, blood CD56bright NK cells; pbmc_dim, blood CD56dim NK cells; tonsil_bright, tonsil CD56bright NK cells; tonsil_dim, tonsil CD56dim NK cells; tonsil_znfNK, tonsil ZNF683+ NK cells.

Distribution of NK cell subsets in cancer

An important point of our analysis was to provide a benchmark for future comparisons with diseased conditions. Therefore, we analyzed the distribution patterns of NK1, NK2 and NK3 cell subsets in 22 cancer types (Fig. 7 ). To that end, we used a classical label-transfer approach (see Methods ). After verifying the accuracy of the method used to annotate the subgroups (Extended Data Fig. 9a–e ), we investigated the proportions and transcriptional proximity of these subsets across tissues and cancer types. The distribution of NK cell subsets in these 22 tumors varies by tumor type (Fig. 7a , top panel). This distribution does not correlate with that found in the blood (Fig. 7a , bottom panel). This difference between circulating and tumor-associated NK cells was confirmed by principal component analysis (PCA) (Fig. 7b , PC1), as was the accuracy of NK1, NK2 and NK3 annotation at the tumor bed (Fig. 8a,b , PC2 and PC3). The divergence between NK2 and the other subsets was also confirmed in blood from people with cancer, but the influence of the tumor on the distinction between the NK1, NK2 and NK3 subsets is stronger at the tumor bed than in the blood (Fig. 8a,b ). The Spearman correlation calculated across NK groups, type of cancer and tissues and their unsupervised hierarchical clustering confirmed that NK cells first segregate by tissue type and then by the subset to which they belong (Extended Data Fig. 10a ). The better grouping of NK1s, NK2s and NK3s in the tumor bed than in the blood also suggests an exacerbated phenotype in tumor conditions.

Based on datasets 1–4a and 6. a , Bar graph showing the proportion of the three main NK populations in the blood and at the tumor bed in 22 cancer types ( n  = 676 samples). b , PCA on tumor-infiltrating and blood NK cells, grouped by NK population, cancer conditions and tissue. The PCA is based on the mean expression levels of the 2,000 genes most differentially expressed across tissue and conditions. Groups are colored on the basis of their tissue of origin. PC1 and PC2 explained 13.7% and 11% of the variance, respectively ( n  = 676 samples). MELA, melanoma; MM, multiple myeloma; RC, renal carcinoma; FTC, fallopian tube carcinoma; CLL, chronic lymphocytic leukemia; ALL, acute lymphocytic leukemia; PACA, pancreatic carcinoma; THCA,: thyroid carcinoma; LC, lung cancer; HCC, hepatocellular carcinoma; PRAD, prostate cancer; GC, gastric cancer; CRC, colorectal cancer; ICC, intrahepatic cholangiocarcinoma; HNSCC, head and neck squamous cell carcinoma; OV, ovarian cancer; BRCA, breast cancer; UCEC, uterine corpus endometrial carcinoma; ESCA, esophageal cancer; NB, neuroblastoma; NPC, nasopharyngeal carcinoma; BCC, basal cell carcinoma.

Based on datasets 1–4a and 6. a , PCA of blood NK cells, grouped by NK population and cancer conditions. The PCA is based on the mean expression levels of the 2,000 genes most differentially expressed across tissue and conditions. Groups are colored on the basis of NK cell subsets. PC2 and PC3 explained 8.1% and 7% of the variance, respectively ( n  = 676 samples). b , PCA of tumor-infiltrating NK cells, grouped by NK population and cancer conditions. The PCA is based on the mean expression levels of the 2,000 genes most differentially expressed across tissues and conditions. Groups are colored on the basis of NK cell subsets. PC2 and PC3 explained 13.9% and 12.7% of the variance, respectively ( n  = 676 samples).

Although scRNA-seq and CITE-seq have considerably advanced exploration of the diversity of human NK cells, definitions of their cell types and subtypes have varied across publications for several reasons, including differing experimental protocols, data-acquisition methods and analysis tools. This has led to complexity in the literature and even disagreement as to whether certain cell subsets are real or artifacts arising from a particular processing methodology. For example, what seems to be an NK cell subtype could be the result of a stress response that was triggered during cell isolation or by culture conditions. Thus, it is important to establish a consensus framework for a basic set of NK cell types by pooling datasets obtained from multiple laboratories and analyzing them holistically.

The integration of CITE-seq and scRNA-seq NK cell data in our meta-analysis, including data from a total of more than 225,000 NK cells, led us to discern three major NK cell populations in peripheral blood, herein called NK1, NK2 and NK3. These populations are highly enriched in canonical CD56 dim , canonical CD56 bright and HCMV-driven adaptive NK cells, respectively. The gene-expression profile of the NK1 population described here aligns with that of the previously described hNK_Bl1 cells, with strong expression of FGFBP2 , GZMB , SPON2 and FCGR3A 13 . The gene-expression profile of the NK2 population overlaps with that of hNK_Bl2, defined by high levels of COTL1 , CD44 , XCL1 , LTB and GZMK . Notably, the equivalents of NK1 and NK2 have also been characterized in mouse blood: mNK_Bl1 and mNK_Bl2, respectively 13 . NK3 cells exhibited a pattern of gene expression overlapping with that of previously described HCMV-driven adaptive NK cells, defined by high levels of KLRC2 , CD3E and ZBTB38 (refs. 12 , 30 ). However, although these adaptive genes are the main drivers of the NK3 cluster signature, cells assigned to the NK3 cluster in our study can be also found at lower frequencies in HCMV – individuals. Therefore, the NK3 cluster defined here is not limited to HCMV-driven adaptive NK cells. Additionally, considering that the transcriptional signature that is exclusive to adaptive NK cells is limited relative to the level of epigenetic remodeling that these cells undergo, the next step in resolving adaptive NK cell identity might be at the epigenetic level through single-cell ATAC sequencing methods 12 . This highlights the benefits of combining multimodal single-cell approaches when defining distinct cell subsets. Alternatively, adaptive NK cells can also be distinguished from canonical CD56 dim NK cells on the basis of deficient PLZF expression 8 , 9 .

The NK1 population could be reliably divided into three subsets, called NK1A, NK1B and NK1C; an NKint population with an intermediate phenotype between NK1 and NK2 was also characterized. In line with our meta-study, recently published scRNA-seq datasets also delineated multiple subclusters. The NK1A subset exhibited the highest expression levels of CXCR4 , JUN and JUNB , mirroring the description of the previously published active CD56 dim (ref. 31 ) and intermediate CD56 dim (ref. 32 ) clusters. CD160 and IFITM1 were most abundant in the NK1B subset, a population that was predicted to have the highest response to chemokines and cytokines. The NK1C subset displayed the highest expression levels of PRF1 , PFN1 , ACTB and NKG7 , concordant with the descriptions of mature and terminal CD56 dim (ref. 31 ), late CD56 dim (ref. 32 ), cluster 2 (ref. 33 ) and CD56 dim CD57 + NK cells 5 , 30 , 34 .

Previous studies have reported an intermediate subset linking CD56 bright (NK2) and CD56 dim NK cells 12 , 31 , 32 , 35 . This intermediate subset shares a core signature including expression of CD44 , XCL1 and GZMK , but is distinguished by elevated expression of CXCR4 , in line with our description of NKint. The high expression of KLRC1 (NKG2A) but low expression of CD56 indicates that the NKint population has strong similarities with the early NKG2A + KIR − CD56 dim NK cell population 5 , 30 . The intermediate expression of CD56, which lies between that of NK2 and NK1 and the lower levels of perforin and granzyme B, as well as the expression of CD27, which was detected at the beginning of the putative transcription pathway connecting NKint to NK1C, also point to a previously defined CD27 + CD56 dim/bright CD94 + NK population 36 .

Our comprehensive trajectory studies revealed two distinct developmental pathways for NK cells. The first trajectory indicates a path through which NK2 cells can maintain their identity; the second involves a progressive maturation process in which NKint cells evolve into the NK1A, NK1B and NK1C stages. For NK2 cells, which seem to derive from ILCPs, it is noteworthy previous research has identified a medullary population of human NK progenitors, termed NK0 (ref. 37 ), and that the human NK0 signature matches that of ILCPs, suggesting that NK0s might correspond to medullary ILCPs. As with NK1 cells, their maturation is associated with a notable shift in the transcriptional landscape, characterized in particular by an increase in expression of cytotoxicity-related genes such as GZMA , GZMB and PRF1 . Concurrently, we observed an escalation in central carbon metabolism activities along this maturation trajectory. This escalation is marked by enhanced glycolysis, TCA cycle activity and OXPHOS. The module score analysis further corroborates these distinct developmental paths, revealing a strong association between the NK2 population and blood ILCPs, as evidenced by their shared signature markers including SELL , CD44 , LTB , IL7R and GPR183 (Figs. 2e and 5c,d ). Similarly, NK1 and NK3 populations show a pronounced connection to ENKPs, aligning with the observation that Ly49H + NK cells in mice, which respond to mouse CMV and are analogous to the human adaptive NK cells included in the NK3 subset, predominantly originate from ENKPs 21 . At the level of transcription factors, NK1 and NK3 have unique characteristics akin to certain ENKP traits, such as reduced expression of GATA3 , EOMES and TCF7 alongside an increased expression of KLF2 . This multifaceted analysis underscores the intricate pathways and mechanisms governing NK cell differentiation and functionality.

Our investigation also sheds light on several molecular dimensions of NK cell biology, warranting additional research. A key finding is the distinct profiles of granzymes and perforin across the three primary NK populations. NK1 cells exhibit robust expression of GZMA , GZMB and PRF1 , which have been extensively studied 38 , 39 . Conversely, NK2 cells predominantly express GZMK , known for its role in caspase-independent apoptosis 40 , 41 and in controlling autoimmunity 42 . The NK3 subset is characterized by expression of GZMH , encoding granzyme H, which also initiates caspase-independent cell death 43 and is effective in inducing rapid apoptosis in tumor cells 44 . This underlines the considerable antitumor potential of NK3 cells. But more remains to be understood about the biology of granzymes, as illustrated by the recent demonstration of the role of granzyme A in triggering production of gasdermin-B 45 . Another noteworthy finding was the cytokine profile of the NK2 subset that predominantly transcribed FLT3LG along with XCL1 and XCL2 , which encode proteins that attract dendritic cells and promote their antigen-presentation function 46 , 47 . The integrin profile of the NK1 subset and the change in their expression along the NK1-maturation trajectory suggest that these integrins could be instrumental in enhancing contact interactions with other cells, regulating NK1 cytotoxicity or facilitating NK1 cells’ entry into tissues (for example, ITGB7 dimerizes with ITGA4 to adhere to MAdCAM-1 for intestinal entry) 48 . A better understanding of the mechanism of expression and regulation of these integrins could have major clinical applications, such as enhancing antitumor immunity in colorectal cancer 49 .

Our results also indicate that there are notable differences in cytokine responses among NK cell subsets. In the context of adoptive NK cell therapy, IL-21 conditioning enhances proliferation, cytotoxicity and production of interferon-γ and TNF in NK cells 50 . The stronger response of NK1B and NK1C subsets to IL-21 makes them particularly promising for NK cell-based therapies. Additionally, IL-15 has been shown to boost NK cell metabolism and longevity 51 , aligning with the characteristics of the NK1C subset, which exhibits strong metabolic activity and a pronounced response to IL-15. These cytokine responses can be further exploited through the use of cytokine-armed NK cell engagers 52 , enhancing our understanding of subset-specific responses to improve and diversify these new therapeutic approaches.

Our data also show that the gene profiles of the NK1, NK2 and NK3 subsets extend beyond the peripheral blood of healthy individuals, and allowed us to describe the heterogeneity of NK cells in tissues. Indeed, we were able to identify NK1, NK2 and NK3 cell subsets in the lung, tonsils and IELs. The relevance of NK1, NK2 and NK3 profiles was also illustrated by distinguishing between subsets of tissue-infiltrating NK cells and ILC1s.

Notably, we were also able to analyze the distribution of NK cell subsets in 22 cancer types. This showed that the distribution of NK cell subsets varies depending on the tumor type and does not show a strict correlation with the distribution in the blood. The immediate implication of this observation is the relative value of monitoring NK cells in peripheral blood to assess NK cell immunity in people with cancer. Interestingly, the proportion of NK2 cells was increased in most tumors tested, particularly in ovarian cancer, breast cancer, endometrial carcinoma of the uterus, esophageal cancer, neuroblastoma, nasopharyngeal carcinoma and basal cell carcinoma. Although NK cell dysfunction at the tumor bed is well established 53 , no specific profile corresponding to dysfunctional NK cells has been characterized. The reported impairment of the cytolytic capacity of NK cells at the tumor bed is consistent with a shift towards the NK2 profile, in which the expression of molecules involved in the cytolytic machinery is low. It is also important to consider the metabolic profile of NK2 and its response to cytokines compared with NK1 and NK3. In particular, several therapeutic agents have been developed to stimulate NK cells using cytokines or mutant cytokines 54 , such as NK cell engagers armed with IL-2 variants 52 , and it is crucial to consider the cytokine sensitivity of tumor-associated NK cells.

The NK cell atlas presented here not only serves as a reference for future studies on NK cells in blood in health and disease, but is also a tool for understanding NK cell diversity in tissues in relation to circulating NK cells, the ontogeny of NK cells in tissues and the relationship between NK cells and ILC1s in tissues in health and disease.

scRNA-seq data retrieval and preprocessing

For datasets 1–4, scRNA-seq data were retrieved from the studies referenced in Supplementary Table 3 . Single-cell sequencing data were aligned with the GRCh38 human reference genome and quantified using Cell Ranger (v6.1.2, 10x Genomics). The preliminary filtered data generated from Cell Ranger were used for downstream filtering and analyzes. First, each sample was examined individually to remove low-quality cells and cell contaminations. Genes detected in more than three cells were retained, and cells expressing fewer than 200 distinct features were removed. Then, for each sample, data were normalized and scaled and cells were clustered following the standard Seurat protocol. The remaining contaminations were identified using the SingleR package (v1.4.1). The detailed metadata (including patient identifier and CMV status) were retrieved from the original studies. For dataset 4, because the original data were enriched at a ratio of 1:1 between NKG2C + and NKG2C − NK cells, the samples were downsampled to match the initial biological ratio of each sample (donor). For datasets 5, 6 and 7, the preprocessed Seurat objects were used.

Batch-effect correction and unsupervised clustering

The samples were then merged. To reduce the batch effect during the clustering process, the 11,965 genes present in each of the samples were kept for the clustering step of the analysis. To account for the difference in sequencing depth between samples, count data were normalized using the Multibatchnorm function with the parameter ‘batch= sample’ of batchelor (v1.10.0). The top 5,000 highly variable genes (HVGs) were identified in each sample using the FindVariableFeatures function in Seurat (v4.0.0). Then, to choose the 2,000 best features to keep for integration, the SelectIntegrationFeatures function of Seurat was used with the parameter setting ‘nfeatures = 2000’. Gene expression was then scaled and centered using the ScaleData function of the Seurat library. Next, PCA was performed on the HVG matrix to reduce noise and reveal the main axes of variation using the RunPCA function, and the top 30 components were retained for analysis. The batch effects were corrected using harmony (v0.1.0) correction algorithm across samples 55 . UMAP dimensional reduction and the shared nearest neighbor graph were calculated on harmony-corrected PCA embeddings. The resolution parameter of the FindClusters function of Seurat was chosen to maximize the mean sc3 stability of the clustering for a granularity ranging from k  = 0.5 to k  = 1.4. The cluster of proliferating cells was identified using the CellCycleScoring function of Seurat. Cells in these clusters were then removed, and a new UMAP visualization was calculated to better visualize the remaining clusters and cells. The final object used for the analysis of datasets 1–4 is available at: https://collections.cellatlas.io/meta-nk .

The cluster-specific marker genes were identified using the FindAllMarkers function of Seurat with the parameter ‘method= wilcox, only.pos = TRUE, min.pct = 0.2, logfc.threshold = 0.25’.

Scoring with signatures

To score cells with respect to specific signatures, the top 20 cluster-specific markers (calculated as defined above) were entered into the AddModuleScore function. In brief, the mean expression level for each gene in the defined expression programs was calculated for each cell, and the aggregated expression of control gene sets was then subtracted. All analyzed genes were binned on the basis of the mean expression level, and control genes were randomly selected from each bin.

RNA-velocity analysis

To limit batch effects and to take into account the differences in the quality of the samples, the RNA-velocity analysis 56 was carried out separately on the different samples. First, the spliced and unspliced unique molecular identifiers were recounted using the Python package velocyto 57 (v0.2.2). Subsequently, RNA velocity was estimated using the scvelo function implemented in the R package velociraptor (v3.18). Velocity calculations were restricted to genes previously used for data integration. To facilitate visualization, velocity pseudotimes were projected onto the UMAP coordinates.

Diffusion-map analysis

Diffusion-map algorithms implemented in the R package destiny 19 (v3.4.0) were used to infer pseudotime. We removed NK3 cells from the analysis owing to their specific quasi-clonal dynamic. To eliminate the dataset batch effect, the analysis was performed on the biggest dataset (dataset 4) alone. To prevent individual batch effect (at the sample level), the RunFastMNN function implemented in the R package batchelor 58 (v1.10.0) was used. The corrected expression matrix was then used as input to generate diffusion maps using the DiffusionMap function with the parameters set to ‘censor_val = 30, censor_range = c(30,40)’. The Destiny algorithm automatically identified three ‘root’ cells. We selected the first root cell as the main root because it is located at the start of the directed streamline inferred by RNA velocity, and we then calculated the diffusion pseudotime for all the cells using the DPT function.

Transcriptional trajectory analysis

To confirm the identified transcriptional trajectories and to better understand the changes along the trajectory from NKint to NK1C, we performed pseudotime analysis using Monocle3 (ref. 20 ) v1.3.1 on every sample from datasets 1–4a together. NK3 and NK2 cells were removed from the analysis, to focus only on the NK1-maturation process. The learn_graph function was run with the parameter ‘ncenter = 150’ to prevent over-branching of the trajectory. The starting point of the trajectory was chosen as the endpoint of the branch in the NKint population, as identified by the RNA-velocity and diffusion-map analyses. The pseudotime was then calculated using the order_cells function. Then, we performed Moran’s I test to detect significant genes showing correlation along the principal graph, selected the top 150 genes with a q value < 0.05 and the highest Moran’s I correlation score and plotted their expression ( z score) along the pseudotime using the Heatmap function of the ComplexHeatmap library (v2.6.2).

SCENIC analysis

Activated regulons in the different subsets were analyzed by SCENIC 23 (v0.12.1). The data analyzed for the identification of the main six NK subpopulations was used as input for the python implementation of the SCENIC algorithm (pyscenic) 59 . In brief, the gene–gene co-expression relationships between transcription factors and their potential targets were inferred using the grn function with the gene regulatory network reconstruction algorithm ‘grnboost2’ selected. A transcription factor and its target genes together make up a regulon. Then, ctx was used to refine the regulons by using targets that do not have an enrichment for a corresponding motif of the transcription factor, effectively separating direct from indirect targets on the basis of the presence of a cis -regulatory footprint. Next, the command aucell was used to calculate the regulon activity for each cell. Then, the list of regulons was cross-checked with a robust database of verified transcription factors 24 to remove unreliable transcription factors and proteins that bind to RNA and DNA non-specifically, and to limit the analysis to bona fide transcription factors. The regulon activity was then scaled and centered before visualization using the Heatmap function of the ComplexHeatmap library.

ENKP signatures scoring

To score the cells with ENKP-derived NK cell signatures, we extracted the lists of the most representative genes differentially expressed in ENKP-derived NK cells and converted them to their human equivalent. Because they do not have a human equivalent, and owing to their evolutionary convergence, the genes in the Klra family were replaced with the equivalent human KIR genes 60 . Cells were then scored using the AddModuleScore function on the 20 most significant genes. For ILCP scoring, gene signatures were directly retrieved from the original publication 22 .

GO enrichment analysis

We performed GO enrichment analysis with the clusterProfiler package (v3.18.1). Eight descriptions of interest were chosen among the top 20 most discriminating GO annotations for each cluster. Enrichment scores ( P values) for the eight selected GO annotations were calculated by a hypergeometric statistical test with a significance threshold of 0.05. The data were plotted as the −log 10 ( P ) values after Benjamini–Hochberg correction. The significance threshold was set at −log 10 (0.05).

Cytokine responsiveness

To compare cytokine responsiveness across NK cell subsets, we normalized the raw gene counts to log 2 -scaled counts per million, followed by mean centralization to enable direct comparison across cells. The data were then analyzed in CytoSig 14 (v0.0.3), with the parameter -s 2 to include a more comprehensive set of signatures. P values were derived from the comparison of z scores between one NK cell subset and the others, using Student’s t -tests.

CITE-seq analysis

For the analysis of CITE-seq data (dataset 5), data that had been preprocessed as described in ref. 10 were used. In brief, after removing the cells with an outlier number of features (genes and or ADTs), HTODemux was used to detect and remove doublets 61 . Then, the batch correction was performed using SCTransform followed by the reciprocal PCA workflow 62 . The same process was used for ADTs, but normalization was performed by CLR transformation within each cell. Then, the PCA was run on both RNA and protein modalities, and the top 40 or top 50 dimensions, respectively, were used to construct k -nearest neighbor graphs. This graph was then used as input for the WNN procedure 62 . On the basis of the author’s annotations, NK cells were extracted and proliferating NK cells were removed. Cells that arose after vaccination (on days 3 and 7) were also removed from the analysis, so only untreated cells were kept. The remaining 5,708 NK cells were reclustered with very high granularity ( k  = 0.2). Differentially expressed genes and ADTs were identified using the two-sided Wilcoxon rank-sum test with Bonferroni adjustment calculated using the FindAllMarkers function, as described above. For better visualization of ADT expression on the UMAP, the FeaturePlot function of Seurat was used with the parameters ‘min.cutoff = ‘q01’, max.cutoff = ‘q99’’ to prevent outliers from affecting the color scale too strongly.

Optimization of clustering

To determine the most appropriate granularity of clustering in an unbiased way, the clustree package was used to quantify the SC3 stability metric. This metric is used to evaluate clustering stability at various levels of detail 63 , 64 . This approach measures how consistently cell groupings hold up across different clustering resolutions and quantifies the stability of each cluster at chosen levels of granularity (Extended Data Fig. 3a ). By pinpointing the granularity that maximized SC3 stability, we determined the most reliable clustering configuration (Extended Data Fig. 3b ), ultimately settling on a granularity value of 0.7, which corresponded to 11 clusters (Extended Data Fig. 3c ).

Metabolic-pathway analysis

To compare metabolism across subsets, the scGSVA ( https://github.com/guokai8/scGSVA ), which is the single-cell implementation of GSVA 65 , was used. For this study, only the major metabolic pathways for which multiple genes were sufficiently detected (for example detected in more than 10% of the cells in at least of the NK populations) were retained.

Enhanced identity prediction through label transfer

To obtain classifications of NK1, NK2 and NK3 cells in dataset 7, we used Seurat’s established protocol for label transfer. Initially, a reference was constructed using datasets 1–4, enabling the annotation transfer to dataset 7. Of note, NKint cells were categorized as NK1, reflecting their initial position in the NK1-maturation trajectory. Through the integration and label transfer process (see https://satijalab.org ), we examined the method’s reliability and annotation precision by applying it to datasets 1–4. We then assessed the labeling accuracy on a 20% subset of each population, which was excluded from reference training (Extended Data Fig. 9a ). This evaluation demonstrated a minimum prediction accuracy of 86% across populations, with NK1 identification being particularly accurate (90.7% accuracy). The integrity of label transfer to dataset 7 was further assessed by examining the highest prediction score for individual cells within both blood and tumor environments (Extended Data Fig. 9b,c ). This confirmed that the NK1 population was the most confidently predicted. Additionally, we assessed the cells’ congruence with NK1, NK2 and NK3 signatures, grouping them by their predicted identities to confirm the enrichment of each predicted population with its corresponding signature (Extended Data Fig. 9d,e ).

PCA and covariance analysis

For PCA analysis on dataset 7, a structured three-step approach was adopted. Initially, cells were categorized by tissue type (tumor or blood) and cancer classification. Following normalization, the top 2,000 variable features within each category were identified using the FindVariableFeatures function. Subsequently, the FindIntegrationFeatures function pinpointed the 2,000 most variable genes across categories. Post-scaling, we computed the mean expression of these 2,000 selected features for cell groups, classified by predicted identity, cancer type and tissue, using Seurat’s AverageExpression function. The data were then scaled again for PCA analysis, which was conducted with the ade4 library. This procedure was replicated for tumor and blood NK cells independently and included a Kruskal–Wallis test to determine the principal components (PC2 and PC3) that best differentiated the three primary NK cell populations in both blood and tumor contexts. For covariance analysis, the same preparatory steps were used, followed by calculation of the Spearman correlation among each group using the cor function. The Pheatmap package was used for the visualization of these correlations.

Reporting summary

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

Data availability

All the scRNA-seq and CITE-seq data used in this study have been deposited in the Gene Expression Omnibus. The accession code for each of the datasets used is listed in Supplementary Table 3 . Datasets 1–7 correspond to the following accession numbers, respectively: GSE119562 , GSE130430 , GSE184329 , GSE197037 , GSE164378 , GSE212890 and GSE240441 . Single-cell sequencing data were aligned with the GRCh38 human reference genome. To make our data more accessible to the broader research community, we have created an interactive portal ( https://collections.cellatlas.io/meta-nk ) designed for easy analysis and visualization of our single-cell data.

Code availability

All the custom code used in this study has been deposited on GitHub ( https://github.com/RebuffetLucas/Meta_NK_Project ).

Vivier, E. et al. Innate lymphoid cells: 10 years on. Cell 174 , 1054–1066 (2018).

Article   CAS   PubMed   Google Scholar  

Lanier, L. L., Testi, R., Bindl, J. & Phillips, J. H. Identity of Leu-19 (CD56) leukocyte differentiation antigen and neural cell adhesion molecule. J. Exp. Med. 169 , 2233–2238 (1989).

Phillips, J. H. & Lanier, L. L. A model for the differentiation of human natural killer cells. Studies on the in vitro activation of Leu-11 + granular lymphocytes with a natural killer-sensitive tumor cell, K562. J. Exp. Med. 161 , 1464–1482 (1985).

Beziat, V., Descours, B., Parizot, C., Debre, P. & Vieillard, V. NK cell terminal differentiation: correlated stepwise decrease of NKG2A and acquisition of KIRs. PLoS ONE 5 , e11966 (2010).

Article   PubMed   PubMed Central   Google Scholar  

Bjorkstrom, N. K. et al. Expression patterns of NKG2A, KIR, and CD57 define a process of CD56 dim NK-cell differentiation uncoupled from NK-cell education. Blood 116 , 3853–3864 (2010).

Article   PubMed   Google Scholar  

Juelke, K. et al. CD62L expression identifies a unique subset of polyfunctional CD56 dim NK cells. Blood 116 , 1299–1307 (2010).

Yu, J. et al. CD94 surface density identifies a functional intermediary between the CD56 bright and CD56 dim human NK-cell subsets. Blood 115 , 274–281 (2010).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Lee, J. et al. Epigenetic modification and antibody-dependent expansion of memory-like NK cells in human cytomegalovirus-infected individuals. Immunity 42 , 431–442 (2015).

Schlums, H. et al. Cytomegalovirus infection drives adaptive epigenetic diversification of NK cells with altered signaling and effector function. Immunity 42 , 443–456 (2015).

Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184 , 3573–3587 (2021).

Tang, F. et al. A pan-cancer single-cell panorama of human natural killer cells. Cell 186 , 4235–4251 (2023).

Ruckert, T., Lareau, C. A., Mashreghi, M. F., Ludwig, L. S. & Romagnani, C. Clonal expansion and epigenetic inheritance of long-lasting NK cell memory. Nat. Immunol. 23 , 1551–1563 (2022).

Crinier, A. et al. High-dimensional single-cell analysis identifies organ-specific signatures and conserved NK cell subsets in humans and mice. Immunity 49 , 971–986 e975 (2018).

Jiang, P. et al. Systematic investigation of cytokine signaling activity at the tissue and single-cell levels. Nat. Methods 18 , 1181–1191 (2021).

Konjevic, G. M., Vuletic, A. M., Mirjacic Martinovic, K. M., Larsen, A. K. & Jurisic, V. B. The role of cytokines in the regulation of NK cells in the tumor environment. Cytokine 117 , 30–40 (2019).

Patterson, C., Hazime, K. S., Zelenay, S. & Davis, D. M. Prostaglandin E(2) impacts multiple stages of the natural killer cell antitumor immune response. Eur. J. Immunol. 54 , e2350635 (2023).

Castriconi, R. et al. Transforming growth factor beta 1 inhibits expression of NKp30 and NKG2D receptors: consequences for the NK-mediated killing of dendritic cells. Proc. Natl Acad. Sci. USA 100 , 4120–4125 (2003).

Viel, S. et al. TGF-β inhibits the activation and functions of NK cells by repressing the mTOR pathway. Sci. Signal 9 , ra19 (2016).

Angerer, P. et al. destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics 32 , 1241–1243 (2016).

Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32 , 381–386 (2014).

Ding, Y. Different developmental pathways generate functionally distinct populations of natural killer cells. Nat. Immunol. (in the press).

Jaeger, N. Diversity of group 1 innate lymphoid cells in human tissues. Nature Immunol. (in the press).

Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14 , 1083–1086 (2017).

Lambert, S. A. et al. The human transcription factors. Cell 172 , 650–665 (2018).

Bi, J. & Wang, X. Molecular regulation of NK cell maturation. Front. Immunol. 11 , 1945 (2020).

Gordon, S. M. et al. The transcription factors T-bet and Eomes control key checkpoints of natural killer cell maturation. Immunity 36 , 55–67 (2012).

Kallies, A. et al. A role for Blimp1 in the transcriptional network controlling natural killer cell maturation. Blood 117 , 1869–1879 (2011).

Collins, P. L. et al. Gene regulatory programs conferring phenotypic identities to human NK cells. Cell 176 , 348–360 (2019).

Torcellan, T. et al. Circulating NK cells establish tissue residency upon acute infection of skin and mediate accelerated effector responses to secondary infection. Immunity 57 , 124–140 (2024).

Holmes, T. D. et al. The transcription factor Bcl11b promotes both canonical and adaptive NK cell differentiation. Sci. Immunol. 6 , eabc9801 (2021).

Yang, C. et al. Heterogeneity of human bone marrow and blood natural killer cells defined by single-cell transcriptome. Nat. Commun. 10 , 3931 (2019).

Netskar, H. et al. Pan-cancer profiling of tumor-infiltrating natural killer cells through transcriptional reference mapping. Nat. Immunol . (in the press).

Smith, S. L. et al. Diversity of peripheral blood human NK cells identified by single-cell RNA sequencing. Blood Adv. 4 , 1388–1406 (2020).

Scheiter, M. et al. Proteome analysis of distinct developmental stages of human natural killer (NK) cells. Mol. Cell Proteom. 12 , 1099–1114 (2013).

Article   CAS   Google Scholar  

Melsen, J. E. et al. Single-cell transcriptomics in bone marrow delineates CD56 dim GranzymeK + subset as intermediate stage in NK cell differentiation. Front. Immunol. 13 , 1044398 (2022).

Vossen, M. T. et al. CD27 defines phenotypically and functionally different human NK cell subsets. J. Immunol. 180 , 3739–3745 (2008).

Crinier, A. et al. Single-cell profiling reveals the trajectories of natural killer cell differentiation in bone marrow and a stress signature induced by acute myeloid leukemia. Cell Mol. Immunol. 18 , 1290–1304 (2021).

Lieberman, J. & Fan, Z. Nuclear war: the granzyme A-bomb. Curr. Opin. Immunol. 15 , 553–559 (2003).

Trapani, J. A. & Sutton, V. R. Granzyme B: pro-apoptotic, antiviral and antitumor functions. Curr. Opin. Immunol. 15 , 533–543 (2003).

Guo, Y., Chen, J., Shi, L. & Fan, Z. Valosin-containing protein cleavage by granzyme K accelerates an endoplasmic reticulum stress leading to caspase-independent cytotoxicity of target tumor cells. J. Immunol. 185 , 5348–5359 (2010).

Zhao, T. et al. Granzyme K cleaves the nucleosome assembly protein SET to induce single-stranded DNA nicks of target cells. Cell Death Differ. 14 , 489–499 (2007).

Jiang, W., Chai, N. R., Maric, D. & Bielekova, B. Unexpected role for granzyme K in CD56 bright NK cell-mediated immunoregulation of multiple sclerosis. J. Immunol. 187 , 781–790 (2011).

Fellows, E., Gil-Parrado, S., Jenne, D. E. & Kurschus, F. C. Natural killer cell-derived human granzyme H induces an alternative, caspase-independent cell-death program. Blood 110 , 544–552 (2007).

Hou, Q. et al. Granzyme H induces apoptosis of target tumor cells characterized by DNA fragmentation and Bid-dependent mitochondrial damage. Mol. Immunol. 45 , 1044–1055 (2008).

Zhou, Z. et al. Granzyme A from cytotoxic lymphocytes cleaves GSDMB to trigger pyroptosis in target cells. Science 368 , eaaz7548 (2020).

Barry, K. C. et al. A natural killer-dendritic cell axis defines checkpoint therapy-responsive tumor microenvironments. Nat. Med. 24 , 1178–1191 (2018).

Bottcher, J. P. et al. NK cells stimulate recruitment of cDC1 into the tumor microenvironment promoting cancer immune control. Cell 172 , 1022–1037 (2018).

Hegewisch-Solloa, E. et al. Differential integrin adhesome expression defines human NK cell residency and developmental stage. J. Immunol. 207 , 950–965 (2021).

Zhang, Y. et al. Integrin β7 inhibits colorectal cancer pathogenesis via maintaining antitumor immunity. Cancer Immunol. Res. 9 , 967–980 (2021).

Oyer, J. L. et al. Natural killer cells stimulated with PM21 particles expand and biodistribute in vivo: clinical implications for cancer treatment. Cytotherapy 18 , 653–663 (2016).

Li, L. et al. Loss of metabolic fitness drives tumor resistance after CAR-NK cell therapy and can be overcome by cytokine engineering. Sci. Adv. 9 , eadd6997 (2023).

Demaria, O. et al. Antitumor immunity induced by antibody-based natural killer cell engager therapeutics armed with not-alpha IL-2 variant. Cell Rep. Med 3 , 100783 (2022).

Dean, I. et al. Rapid functional impairment of natural killer cells following tumor entry limits anti-tumor immunity. Nat. Commun. 15 , 683 (2024).

Vivier, E. et al. Natural killer cell therapies. Nature 626 , 727–736 (2024).

Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16 , 1289–1296 (2019).

La Manno, G. et al. RNA velocity of single cells. Nature 560 , 494–498 (2018).

Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38 , 1408–1414 (2020).

Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36 , 421–427 (2018).

Van de Sande, B. et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat. Protoc. 15 , 2247–2276 (2020).

Barten, R., Torkar, M., Haude, A., Trowsdale, J. & Wilson, M. J. Divergent and convergent evolution of NK-cell receptors. Trends Immunol. 22 , 52–57 (2001).

Stoeckius, M. et al. Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol. 19 , 224 (2018).

Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177 , 1888–1902 (2019).

Kiselev, V. Y. et al. SC3: consensus clustering of single-cell RNA-seq data. Nat. Methods 14 , 483–486 (2017).

Zappia, L. & Oshlack, A. Clustering trees: a visualization for evaluating clusterings at multiple resolutions. Gigascience 7 , giy083 (2018).

Hanzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14 , 7 (2013).

Download references

Acknowledgements

E.V.’s laboratory at CIML and Assistance-Publique des Hôpitaux de Marseille is supported by funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (TILC, grant agreement no. 694502 and MInfla-TILC, grant agreement no. 875102), the Agence Nationale de la Recherche including the PIONEER Project (ANR-17-RHUS-0007), MSDAvenir, Innate Pharma and institutional grants awarded to the CIML (INSERM, CNRS and Aix-Marseille University). D.M.D.’s laboratory is funded by the Medical Research Council (MR/W031698/1) and Wellcome (110091/Z/15/Z). L.M. is funded by Associazione Italiana contro il Cancro (AIRC), 5xmille project no. 21147. S.S. is funded by Ministero dell’Istruzione, dell’Università e della Ricerca (PRIN 2017WC8499_004) and Fondazione AIRC (AIRC 5×1000 project no. 21147). D.G.R. is supported by funding from the Medical Research Council (MRC) (MC_UU_00028) and Wellcome Trust-Academy of Medical Sciences (WT-AMS) (SBF009\1119). C.R.’s laboratory is supported by the ERC Advanced Grant ‘MEM-CLONK’ (101055157) and the Deutsche Forschungsgemeinschaft (DFG) grants SFB TRR241 B02 and RO 3565/7-1. K.J.M. was supported by the Research Council of Norway, Center of Excellence: Precision Immunotherapy Alliance (332727) and the US National Cancer Institute (P01 CA111412, P009500901). The work was supported by the European Research Council (ERC AdG ILCAdapt, 101055309 to A.D.) and by the DFG (SFB 1444/427826188 and TRR 241/375876048 to A.D., SPP1937/Di764 /9-2 to A.D.). We are grateful to the Benjamin Franklin Flow Cytometry Facility (BFFC) for support in cell sorting. B.F.F.C. is supported by DFG Instrument Grants INST 335/597-1 FUGG und INST 335/777-1 FUGG. Y.B. is supported by funding from the Swedish Research Council and Swedish Cancer Foundation. We thank the members of the CB2M (Computational Biology, Biostatistics and Modeling) group at the Marseille-Luminy Immunology Centre (CIML) for their help and support in the bioinformatics and statistical data analysis.

Author information

Authors and affiliations.

Aix Marseille Université, CNRS, INSERM, Centre d’Immunologie de Marseille-Luminy, Marseille, France

Lucas Rebuffet, Bertrand Escalière, Emilie Narni-Mancinelli & Eric Vivier

Leiden University Medical Center, Willem-Alexander Children’s Hospital, Laboratory for Pediatric Immunology, Leiden, the Netherlands

Janine E. Melsen

Leiden University Medical Center, Department of Immunology, Leiden, the Netherlands

Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK

Daniela Basurto-Lozada & Muzlifah Haniffa

Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK

T Cell Biology and Development Unit, Laboratory of Genome Integrity, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

Avinash Bhandoola & Yi Ding

Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden

Niklas K. Björkström, Karl-Johan Malmberg & Dan Sun

Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden

Yenan T. Bryceson

Division of Clinical Immunology and Transfusion Medicine, Karolinska University Hospital, Stockholm, Sweden

Sweden Broegelmann Research Laboratory, Department of Clinical Science, University of Bergen, Bergen, Norway

Department of Experimental Medicine (DIMES), University of Genoa, Genoa, Italy

Roberta Castriconi & Simona Sivori

Laboratory of Clinical and Experimental Immunology, IRCCS Istituto Giannina Gaslini, Genova, Italy

Roberta Castriconi

Department of Medicine, University of Minnesota, Minneapolis, MN, USA

Frank Cichocki & Jeffrey S. Miller

Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA

Marco Colonna

Department of Life Sciences, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, UK

Daniel M. Davis

Laboratory of Innate Immunity, Institute of Microbiology, Infectious Diseases and Immunology (I-MIDI), Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany

Andreas Diefenbach

Mucosal and Developmental Immunology, Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany

Department of Dermatology and NIHR Biomedical Research Centre, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK

Muzlifah Haniffa

Department of Immunology & Immunotherapy, The Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Amir Horowitz

Department of Oncological Sciences, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Department of Microbiology and Immunology and the Parker Institute for Cancer Immunotherapy, University of California, San Francisco, San Francisco, CA, USA

Lewis L. Lanier

Precision Immunotherapy Alliance, The University of Oslo, Oslo, Norway

Karl-Johan Malmberg

The Institute for Cancer Research, Oslo University Hospital, Oslo, Norway

Tumor Immunology Unit, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy

Lorenzo Moretta

School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland

Luke A. J. O’Neill

Institute of Medical Immunology, Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany

Chiara Romagnani

Innate Immunity, Deutsches Rheuma-Forschungszentrum Berlin (DRFZ), ein Leibniz Institut, Berlin, Germany

Berlin University Alliance, Berlin, Germany

MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge, UK

Dylan G. Ryan

IRCCS Ospedale Policlinico San Martino, Genova, Italy

Simona Sivori

Innate Pharma Research Laboratories, Innate Pharma, Marseille, France

Constance Vagne & Eric Vivier

APHM, Hôpital de la Timone, Marseille-Immunopôle, Marseille, France

Eric Vivier

Paris-Saclay Cancer Cluster, Le Kremlin-Bicêtre, France

You can also search for this author in PubMed   Google Scholar

Contributions

L.R., J.E.M., B.E., D.S. and C.V. performed the bioinformatic analysis. E.V. conceived the project with the help of all other co-authors. L.R., J.E.M., B.E., D.B.-L., A.B., N.K.B., Y.T.B., R.C., F.C., M.C., D.M.D., A.D., Y.D., M.H., A.H., L.L.L., K.-J.M., J.S.M., L.M., E.N.-M., L.A.J.O., C.R., D.G.R., S.S., D.S., C.V. and E.V. participated in the writing of the manuscript. All authors are listed alphabetically, with the exception of L.R., J.E.M. and B.E.

Corresponding author

Correspondence to Eric Vivier .

Ethics declarations

Competing interests.

E.V. and C.V. are employees of Innate Pharma. K.-J.M. is a consultant at Fate Therapeutics and Vycellix and receives research support from Fate Therapeutics, Oncopeptides for studies unrelated to this work. The other authors declare no competing interests.

Peer review

Peer review information.

Nature Immunology thanks the anonymous reviewers for their contribution to the peer review of this work. Jamie D. K. Wilson, in collaboration with the Nature Immunology team.

Additional information

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

Extended data

Extended data fig. 1 the classification of nk cells into 3 main families is robust in other blood nk cell atlas..

Based on Dataset 6. a , Uniform Manifold Approximation and Projection (UMAP) of blood NK cells from Tang et al. pan-cancer NK cells atlas. Subsets constituting less than 1% of circulating NK cells were excluded which resulted in a total of 84,343 human blood NK cells for analysis. b , UMAP of blood NK cells from Dataset 5 scored with NK1, NK2, and NK3 signatures. c , RidgePlot visualization of the scoring of the clusters defined by Tang et al. (n= 676 samples).

Extended Data Fig. 2 The classification of NK cells into 3 main families is robust in other blood NK cell samples.

Based on Dataset 4b. a , UMAP based on 2 independent samples of NK cells sorted from healthy human blood with clusters identified by unsupervised hierarchical clustering and their scoring with NK1, NK2, and NK3 signatures. b , c , Dot plot and UMAP visualization of some of the most discriminatory markers expressed at the transcriptional level by the three major subsets of human blood NK cells.

Extended Data Fig. 3 The NK cells in human blood can be divided into six subgroups.

Based on Dataset 1,2,3 and 4a a , Clustree plot of sc3 stability of clusters at different clustering resolution (from k=0.5 to k=1.4). b , Mean sc3 stability as a function of the granularity of clustering resolution. c , UMAP visualization of the plot of NK cells sorted from healthy human blood with clusters identified by unsupervised hierarchical clustering at a granularity of 0.7 (optimal resolution according to sc3 stability). d , UMAP visualization of the subpopulations of NK cells from the blood of healthy individuals with clusters identified by unsupervised hierarchical clustering after removing proliferating cells and populations representing less than 3% of total NK cells. e , UMAP visualization of NKG2C protein expression. f , Pie chart showing the proportion of each subgroup in the NK cell population in blood.

Extended Data Fig. 4 The NK cells in human blood can be divided into six subgroups.

a,c and d-f: Based on Based on Dataset 1,2,3 and 4a b: Based on Dataset 5. a , Bar graph showing the proportion of cells within each cluster in the datasets. (n= 13 samples) b , Violin plot of the scoring of all CD45 pos cells from Dataset 5 with the 13 genes characteristic of human NK cells as defined by Crinier et al. The error bars present the median +/- standard deviation. (n= 8 samples) c , Violin plot of the scoring with the 13 genes characteristic of human NK cells as defined by Crinier et al. The error bars present the median +/- standard deviation. (n= 13 samples) d, e, f , UMAP visualization of the expression of some key markers of NK1, NK2 and NK3 populations.

Extended Data Fig. 5 Markers of interest, functions and metabolism characterizing NK cell populations.

Based on Dataset 1,2,3 and 4a. a , Heatmap showing the differential expression of the genes composing three metabolic pathways of interest among NK cell subsets. The color scale is based on z-score-scaled gene expression. The z-score distribution ranges from −2 (blue) to 2 (red).

Extended Data Fig. 6 Dissection of the trajectory leading from NKint to NK1C.

Based on Dataset 1,2,3 and 4a. a , Dynamic heatmap of the evolution of the top 150 markers that evolve most along the pseudotime of the trajectory leading from the NKint subset to the NK1C subset.

Extended Data Fig. 7 Master regulators genes characteristic for each subset of NK cells in the blood and putative ontogeny of the main NK populations.

Based on Dataset 1,2,3 and 4a. a , Heatmap showing the differential expression of true transcription factors detected in NK cell subsets. The color scale is based on the z-score of the regulon activity. The z-score distribution ranges from −2 (blue) to 2 (red).

Extended Data Fig. 8 Distribution of NK1, NK2 and NK3 cell subsets in tissues.

Based on Dataset 7. a , ViolinPlot visualization of the module score of individual cells scored with signatures of NK1, NK2 and NK3 of ILC populations present in tonsil, lung and IELs and grouped by clusters (as defined in Dataset 7). The error bars present the median +/- standard deviation. (Lung: n = 4 samples, Tonsil: n = 6 samples, IEL: n = 4 samples).

Extended Data Fig. 9 Distribution of NK1, NK2 and NK3 cell subsets in the blood of cancer patients and at the tumor bed.

a: Based on dataset 1,2,3,4a. b-e: Based on dataset 6. a , Heatmap depicting accuracy of the label transfer for subset annotation tested on 20 % of the cells heldout to train the reference. b , ViolinPlot visualization of the maximum prediction score per cell in the blood NK samples of Dataset 6. Cells are grouped by their predicted identity. The error bars present the median +/- standard deviation. c , ViolinPlot visualization of the maximum prediction score per cell in the tumor-infiltrated NK samples of Dataset 6. Cells are grouped by their predicted identity. The error bars present the median +/- standard deviation. d , ViolinPlot visualization of the module score of individual blood NK cells of Dataset 6 scored with signatures of NK1, NK2 and NK3. Cells are grouped by their predicted identity. The error bars present the median +/- standard deviation. e , ViolinPlot visualization of the module score of individual tumor-infiltrated NK cells of Dataset 6 scored with signatures of NK1, NK2 and NK3. Cells are grouped by their predicted identity. The error bars present the median +/- standard deviation.

Extended Data Fig. 10 Distinct transcriptionnal phenotypes of NK1, NK2 and NK3 cell subsets in the blood of cancer patients and at the tumor bed.

Based on dataset 6. a , Heatmap showing the Spearman correlation between NK1, NK2 and NK3 populations in healthy individuals and across 22 different cancer types in both blood and tumor. The error bars present the median +/- standard deviation. (n= 676 samples).

Supplementary information

Supplementary information, reporting summary, supplementary table 1.

Differentially expressed genes.

Supplementary Table 2

Summary of cluster proportions.

Supplementary Table 3

Dataset presentation.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Rebuffet, L., Melsen, J.E., Escalière, B. et al. High-dimensional single-cell analysis of human natural killer cell heterogeneity. Nat Immunol (2024). https://doi.org/10.1038/s41590-024-01883-0

Download citation

Received : 16 January 2024

Accepted : 23 May 2024

Published : 02 July 2024

DOI : https://doi.org/10.1038/s41590-024-01883-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

Prevent plagiarism. Run a free check.

Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2023, June 22). What Is Qualitative Research? | Methods & Examples. Scribbr. Retrieved July 10, 2024, from https://www.scribbr.com/methodology/qualitative-research/

Is this article helpful?

Pritha Bhandari

Other students also liked, qualitative vs. quantitative research | differences, examples & methods, how to do thematic analysis | step-by-step guide & examples, what is your plagiarism score.