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Writing a Literature Review

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A literature review is a document or section of a document that collects key sources on a topic and discusses those sources in conversation with each other (also called synthesis ). The lit review is an important genre in many disciplines, not just literature (i.e., the study of works of literature such as novels and plays). When we say “literature review” or refer to “the literature,” we are talking about the research ( scholarship ) in a given field. You will often see the terms “the research,” “the scholarship,” and “the literature” used mostly interchangeably.

Where, when, and why would I write a lit review?

There are a number of different situations where you might write a literature review, each with slightly different expectations; different disciplines, too, have field-specific expectations for what a literature review is and does. For instance, in the humanities, authors might include more overt argumentation and interpretation of source material in their literature reviews, whereas in the sciences, authors are more likely to report study designs and results in their literature reviews; these differences reflect these disciplines’ purposes and conventions in scholarship. You should always look at examples from your own discipline and talk to professors or mentors in your field to be sure you understand your discipline’s conventions, for literature reviews as well as for any other genre.

A literature review can be a part of a research paper or scholarly article, usually falling after the introduction and before the research methods sections. In these cases, the lit review just needs to cover scholarship that is important to the issue you are writing about; sometimes it will also cover key sources that informed your research methodology.

Lit reviews can also be standalone pieces, either as assignments in a class or as publications. In a class, a lit review may be assigned to help students familiarize themselves with a topic and with scholarship in their field, get an idea of the other researchers working on the topic they’re interested in, find gaps in existing research in order to propose new projects, and/or develop a theoretical framework and methodology for later research. As a publication, a lit review usually is meant to help make other scholars’ lives easier by collecting and summarizing, synthesizing, and analyzing existing research on a topic. This can be especially helpful for students or scholars getting into a new research area, or for directing an entire community of scholars toward questions that have not yet been answered.

What are the parts of a lit review?

Most lit reviews use a basic introduction-body-conclusion structure; if your lit review is part of a larger paper, the introduction and conclusion pieces may be just a few sentences while you focus most of your attention on the body. If your lit review is a standalone piece, the introduction and conclusion take up more space and give you a place to discuss your goals, research methods, and conclusions separately from where you discuss the literature itself.

Introduction:

• An introductory paragraph that explains what your working topic and thesis is
• A forecast of key topics or texts that will appear in the review
• Potentially, a description of how you found sources and how you analyzed them for inclusion and discussion in the review (more often found in published, standalone literature reviews than in lit review sections in an article or research paper)
• Summarize and synthesize: Give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: Don’t just paraphrase other researchers – add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically Evaluate: Mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: Use transition words and topic sentence to draw connections, comparisons, and contrasts.

Conclusion:

• Summarize the key findings you have taken from the literature and emphasize their significance
• Connect it back to your primary research question

How should I organize my lit review?

Lit reviews can take many different organizational patterns depending on what you are trying to accomplish with the review. Here are some examples:

• Chronological : The simplest approach is to trace the development of the topic over time, which helps familiarize the audience with the topic (for instance if you are introducing something that is not commonly known in your field). If you choose this strategy, be careful to avoid simply listing and summarizing sources in order. Try to analyze the patterns, turning points, and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred (as mentioned previously, this may not be appropriate in your discipline — check with a teacher or mentor if you’re unsure).
• Thematic : If you have found some recurring central themes that you will continue working with throughout your piece, you can organize your literature review into subsections that address different aspects of the topic. For example, if you are reviewing literature about women and religion, key themes can include the role of women in churches and the religious attitude towards women.
• Qualitative versus quantitative research
• Empirical versus theoretical scholarship
• Divide the research by sociological, historical, or cultural sources
• Theoretical : In many humanities articles, the literature review is the foundation for the theoretical framework. You can use it to discuss various theories, models, and definitions of key concepts. You can argue for the relevance of a specific theoretical approach or combine various theorical concepts to create a framework for your research.

What are some strategies or tips I can use while writing my lit review?

Any lit review is only as good as the research it discusses; make sure your sources are well-chosen and your research is thorough. Don’t be afraid to do more research if you discover a new thread as you’re writing. More info on the research process is available in our "Conducting Research" resources .

As you’re doing your research, create an annotated bibliography ( see our page on the this type of document ). Much of the information used in an annotated bibliography can be used also in a literature review, so you’ll be not only partially drafting your lit review as you research, but also developing your sense of the larger conversation going on among scholars, professionals, and any other stakeholders in your topic.

Usually you will need to synthesize research rather than just summarizing it. This means drawing connections between sources to create a picture of the scholarly conversation on a topic over time. Many student writers struggle to synthesize because they feel they don’t have anything to add to the scholars they are citing; here are some strategies to help you:

• It often helps to remember that the point of these kinds of syntheses is to show your readers how you understand your research, to help them read the rest of your paper.
• Writing teachers often say synthesis is like hosting a dinner party: imagine all your sources are together in a room, discussing your topic. What are they saying to each other?
• Look at the in-text citations in each paragraph. Are you citing just one source for each paragraph? This usually indicates summary only. When you have multiple sources cited in a paragraph, you are more likely to be synthesizing them (not always, but often
• Read more about synthesis here.

The most interesting literature reviews are often written as arguments (again, as mentioned at the beginning of the page, this is discipline-specific and doesn’t work for all situations). Often, the literature review is where you can establish your research as filling a particular gap or as relevant in a particular way. You have some chance to do this in your introduction in an article, but the literature review section gives a more extended opportunity to establish the conversation in the way you would like your readers to see it. You can choose the intellectual lineage you would like to be part of and whose definitions matter most to your thinking (mostly humanities-specific, but this goes for sciences as well). In addressing these points, you argue for your place in the conversation, which tends to make the lit review more compelling than a simple reporting of other sources.

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• What is a Literature Review? | Guide, Template, & Examples

What is a Literature Review? | Guide, Template, & Examples

Published on 22 February 2022 by Shona McCombes . Revised on 7 June 2022.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research.

There are five key steps to writing a literature review:

• Search for relevant literature
• Evaluate sources
• Identify themes, debates and gaps
• Outline the structure
• Write your literature review

A good literature review doesn’t just summarise sources – it analyses, synthesises, and critically evaluates to give a clear picture of the state of knowledge on the subject.

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Table of contents

Why write a literature review, examples of literature reviews, step 1: search for relevant literature, step 2: evaluate and select sources, step 3: identify themes, debates and gaps, step 4: outline your literature review’s structure, step 5: write your literature review, frequently asked questions about literature reviews, introduction.

• Quick Run-through
• Step 1 & 2

When you write a dissertation or thesis, you will have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

• Demonstrate your familiarity with the topic and scholarly context
• Develop a theoretical framework and methodology for your research
• Position yourself in relation to other researchers and theorists
• Show how your dissertation addresses a gap or contributes to a debate

You might also have to write a literature review as a stand-alone assignment. In this case, the purpose is to evaluate the current state of research and demonstrate your knowledge of scholarly debates around a topic.

The content will look slightly different in each case, but the process of conducting a literature review follows the same steps. We’ve written a step-by-step guide that you can follow below.

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Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

• Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
• Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
• Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
• Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research objectives and questions .

If you are writing a literature review as a stand-alone assignment, you will have to choose a focus and develop a central question to direct your search. Unlike a dissertation research question, this question has to be answerable without collecting original data. You should be able to answer it based only on a review of existing publications.

Make a list of keywords

Start by creating a list of keywords related to your research topic. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list if you discover new keywords in the process of your literature search.

• Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
• Body image, self-perception, self-esteem, mental health
• Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some databases to search for journals and articles include:

• Your university’s library catalogue
• Google Scholar
• Project Muse (humanities and social sciences)
• Medline (life sciences and biomedicine)
• EconLit (economics)
• Inspec (physics, engineering and computer science)

You can use boolean operators to help narrow down your search:

Read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

To identify the most important publications on your topic, take note of recurring citations. If the same authors, books or articles keep appearing in your reading, make sure to seek them out.

You probably won’t be able to read absolutely everything that has been written on the topic – you’ll have to evaluate which sources are most relevant to your questions.

For each publication, ask yourself:

• What question or problem is the author addressing?
• What are the key concepts and how are they defined?
• What are the key theories, models and methods? Does the research use established frameworks or take an innovative approach?
• What are the results and conclusions of the study?
• How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
• How does the publication contribute to your understanding of the topic? What are its key insights and arguments?
• What are the strengths and weaknesses of the research?

Make sure the sources you use are credible, and make sure you read any landmark studies and major theories in your field of research.

You can find out how many times an article has been cited on Google Scholar – a high citation count means the article has been influential in the field, and should certainly be included in your literature review.

The scope of your review will depend on your topic and discipline: in the sciences you usually only review recent literature, but in the humanities you might take a long historical perspective (for example, to trace how a concept has changed in meaning over time).

Remember that you can use our template to summarise and evaluate sources you’re thinking about using!

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It’s important to keep track of your sources with references to avoid plagiarism . It can be helpful to make an annotated bibliography, where you compile full reference information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

You can use our free APA Reference Generator for quick, correct, consistent citations.

To begin organising your literature review’s argument and structure, you need to understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

• Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
• Themes: what questions or concepts recur across the literature?
• Debates, conflicts and contradictions: where do sources disagree?
• Pivotal publications: are there any influential theories or studies that changed the direction of the field?
• Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

• Most research has focused on young women.
• There is an increasing interest in the visual aspects of social media.
• But there is still a lack of robust research on highly-visual platforms like Instagram and Snapchat – this is a gap that you could address in your own research.

There are various approaches to organising the body of a literature review. You should have a rough idea of your strategy before you start writing.

Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarising sources in order.

Try to analyse patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organise your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

• Look at what results have emerged in qualitative versus quantitative research
• Discuss how the topic has been approached by empirical versus theoretical scholarship
• Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text, your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

If you are writing the literature review as part of your dissertation or thesis, reiterate your central problem or research question and give a brief summary of the scholarly context. You can emphasise the timeliness of the topic (“many recent studies have focused on the problem of x”) or highlight a gap in the literature (“while there has been much research on x, few researchers have taken y into consideration”).

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, make sure to follow these tips:

• Summarise and synthesise: give an overview of the main points of each source and combine them into a coherent whole.
• Analyse and interpret: don’t just paraphrase other researchers – add your own interpretations, discussing the significance of findings in relation to the literature as a whole.
• Critically evaluate: mention the strengths and weaknesses of your sources.
• Write in well-structured paragraphs: use transitions and topic sentences to draw connections, comparisons and contrasts.

In the conclusion, you should summarise the key findings you have taken from the literature and emphasise their significance.

If the literature review is part of your dissertation or thesis, reiterate how your research addresses gaps and contributes new knowledge, or discuss how you have drawn on existing theories and methods to build a framework for your research. This can lead directly into your methodology section.

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a dissertation , thesis, research paper , or proposal .

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarise yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your  dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

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What is a literature review?

A literature review is an integrated analysis -- not just a summary-- of scholarly writings and other relevant evidence related directly to your research question.  That is, it represents a synthesis of the evidence that provides background information on your topic and shows a association between the evidence and your research question.

A literature review may be a stand alone work or the introduction to a larger research paper, depending on the assignment.  Rely heavily on the guidelines your instructor has given you.

Why is it important?

A literature review is important because it:

• Explains the background of research on a topic.
• Demonstrates why a topic is significant to a subject area.
• Discovers relationships between research studies/ideas.
• Identifies major themes, concepts, and researchers on a topic.
• Identifies critical gaps and points of disagreement.
• Discusses further research questions that logically come out of the previous studies.

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1. Choose a topic. Define your research question.

Your literature review should be guided by your central research question.  The literature represents background and research developments related to a specific research question, interpreted and analyzed by you in a synthesized way.

• Make sure your research question is not too broad or too narrow.  Is it manageable?
• Begin writing down terms that are related to your question. These will be useful for searches later.
• If you have the opportunity, discuss your topic with your professor and your class mates.

2. Decide on the scope of your review

How many studies do you need to look at? How comprehensive should it be? How many years should it cover? 

• This may depend on your assignment.  How many sources does the assignment require?

3. Select the databases you will use to conduct your searches.

Make a list of the databases you will search. 

Where to find databases:

• use the tabs on this guide
• Find other databases in the Nursing Information Resources web page
• More on the Medical Library web page
• ... and more on the Yale University Library web page

4. Conduct your searches to find the evidence. Keep track of your searches.

• Use the key words in your question, as well as synonyms for those words, as terms in your search. Use the database tutorials for help.
• Save the searches in the databases. This saves time when you want to redo, or modify, the searches. It is also helpful to use as a guide is the searches are not finding any useful results.
• Review the abstracts of research studies carefully. This will save you time.
• Use the bibliographies and references of research studies you find to locate others.
• Check with your professor, or a subject expert in the field, if you are missing any key works in the field.
• Ask your librarian for help at any time.
• Use a citation manager, such as EndNote as the repository for your citations. See the EndNote tutorials for help.

Review the literature

Some questions to help you analyze the research:

• What was the research question of the study you are reviewing? What were the authors trying to discover?
• Was the research funded by a source that could influence the findings?
• What were the research methodologies? Analyze its literature review, the samples and variables used, the results, and the conclusions.
• Does the research seem to be complete? Could it have been conducted more soundly? What further questions does it raise?
• If there are conflicting studies, why do you think that is?
• How are the authors viewed in the field? Has this study been cited? If so, how has it been analyzed?

Tips: 

• Review the abstracts carefully.  
• Keep careful notes so that you may track your thought processes during the research process.
• Create a matrix of the studies for easy analysis, and synthesis, across all of the studies.
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What is a Literature Review? How to Write It (with Examples)

A literature review is a critical analysis and synthesis of existing research on a particular topic. It provides an overview of the current state of knowledge, identifies gaps, and highlights key findings in the literature. 1 The purpose of a literature review is to situate your own research within the context of existing scholarship, demonstrating your understanding of the topic and showing how your work contributes to the ongoing conversation in the field. Learning how to write a literature review is a critical tool for successful research. Your ability to summarize and synthesize prior research pertaining to a certain topic demonstrates your grasp on the topic of study, and assists in the learning process. 

Table of Contents

• What is the purpose of literature review? 
• a. Habitat Loss and Species Extinction: 
• b. Range Shifts and Phenological Changes: 
• c. Ocean Acidification and Coral Reefs: 
• d. Adaptive Strategies and Conservation Efforts: 
• How to write a good literature review 
• Choose a Topic and Define the Research Question: 
• Decide on the Scope of Your Review: 
• Select Databases for Searches: 
• Conduct Searches and Keep Track: 
• Review the Literature: 
• Organize and Write Your Literature Review: 
• Frequently asked questions 

What is a literature review?

A well-conducted literature review demonstrates the researcher’s familiarity with the existing literature, establishes the context for their own research, and contributes to scholarly conversations on the topic. One of the purposes of a literature review is also to help researchers avoid duplicating previous work and ensure that their research is informed by and builds upon the existing body of knowledge.

What is the purpose of literature review?

A literature review serves several important purposes within academic and research contexts. Here are some key objectives and functions of a literature review: 2  

• Contextualizing the Research Problem: The literature review provides a background and context for the research problem under investigation. It helps to situate the study within the existing body of knowledge. 
• Identifying Gaps in Knowledge: By identifying gaps, contradictions, or areas requiring further research, the researcher can shape the research question and justify the significance of the study. This is crucial for ensuring that the new research contributes something novel to the field. 
• Understanding Theoretical and Conceptual Frameworks: Literature reviews help researchers gain an understanding of the theoretical and conceptual frameworks used in previous studies. This aids in the development of a theoretical framework for the current research. 
• Providing Methodological Insights: Another purpose of literature reviews is that it allows researchers to learn about the methodologies employed in previous studies. This can help in choosing appropriate research methods for the current study and avoiding pitfalls that others may have encountered. 
• Establishing Credibility: A well-conducted literature review demonstrates the researcher’s familiarity with existing scholarship, establishing their credibility and expertise in the field. It also helps in building a solid foundation for the new research. 
• Informing Hypotheses or Research Questions: The literature review guides the formulation of hypotheses or research questions by highlighting relevant findings and areas of uncertainty in existing literature. 

Literature review example

Let’s delve deeper with a literature review example: Let’s say your literature review is about the impact of climate change on biodiversity. You might format your literature review into sections such as the effects of climate change on habitat loss and species extinction, phenological changes, and marine biodiversity. Each section would then summarize and analyze relevant studies in those areas, highlighting key findings and identifying gaps in the research. The review would conclude by emphasizing the need for further research on specific aspects of the relationship between climate change and biodiversity. The following literature review template provides a glimpse into the recommended literature review structure and content, demonstrating how research findings are organized around specific themes within a broader topic. 

Literature Review on Climate Change Impacts on Biodiversity:

Climate change is a global phenomenon with far-reaching consequences, including significant impacts on biodiversity. This literature review synthesizes key findings from various studies: 

a. Habitat Loss and Species Extinction:

Climate change-induced alterations in temperature and precipitation patterns contribute to habitat loss, affecting numerous species (Thomas et al., 2004). The review discusses how these changes increase the risk of extinction, particularly for species with specific habitat requirements. 

b. Range Shifts and Phenological Changes:

Observations of range shifts and changes in the timing of biological events (phenology) are documented in response to changing climatic conditions (Parmesan & Yohe, 2003). These shifts affect ecosystems and may lead to mismatches between species and their resources. 

c. Ocean Acidification and Coral Reefs:

The review explores the impact of climate change on marine biodiversity, emphasizing ocean acidification’s threat to coral reefs (Hoegh-Guldberg et al., 2007). Changes in pH levels negatively affect coral calcification, disrupting the delicate balance of marine ecosystems. 

d. Adaptive Strategies and Conservation Efforts:

Recognizing the urgency of the situation, the literature review discusses various adaptive strategies adopted by species and conservation efforts aimed at mitigating the impacts of climate change on biodiversity (Hannah et al., 2007). It emphasizes the importance of interdisciplinary approaches for effective conservation planning. 

How to write a good literature review

Writing a literature review involves summarizing and synthesizing existing research on a particular topic. A good literature review format should include the following elements. 

Introduction: The introduction sets the stage for your literature review, providing context and introducing the main focus of your review. 

• Opening Statement: Begin with a general statement about the broader topic and its significance in the field. 
• Scope and Purpose: Clearly define the scope of your literature review. Explain the specific research question or objective you aim to address. 
• Organizational Framework: Briefly outline the structure of your literature review, indicating how you will categorize and discuss the existing research. 
• Significance of the Study: Highlight why your literature review is important and how it contributes to the understanding of the chosen topic. 
• Thesis Statement: Conclude the introduction with a concise thesis statement that outlines the main argument or perspective you will develop in the body of the literature review. 

Body: The body of the literature review is where you provide a comprehensive analysis of existing literature, grouping studies based on themes, methodologies, or other relevant criteria. 

• Organize by Theme or Concept: Group studies that share common themes, concepts, or methodologies. Discuss each theme or concept in detail, summarizing key findings and identifying gaps or areas of disagreement. 
• Critical Analysis: Evaluate the strengths and weaknesses of each study. Discuss the methodologies used, the quality of evidence, and the overall contribution of each work to the understanding of the topic. 
• Synthesis of Findings: Synthesize the information from different studies to highlight trends, patterns, or areas of consensus in the literature. 
• Identification of Gaps: Discuss any gaps or limitations in the existing research and explain how your review contributes to filling these gaps. 
• Transition between Sections: Provide smooth transitions between different themes or concepts to maintain the flow of your literature review. 

Conclusion: The conclusion of your literature review should summarize the main findings, highlight the contributions of the review, and suggest avenues for future research. 

• Summary of Key Findings: Recap the main findings from the literature and restate how they contribute to your research question or objective. 
• Contributions to the Field: Discuss the overall contribution of your literature review to the existing knowledge in the field. 
• Implications and Applications: Explore the practical implications of the findings and suggest how they might impact future research or practice. 
• Recommendations for Future Research: Identify areas that require further investigation and propose potential directions for future research in the field. 
• Final Thoughts: Conclude with a final reflection on the importance of your literature review and its relevance to the broader academic community. 

Conducting a literature review

Conducting a literature review is an essential step in research that involves reviewing and analyzing existing literature on a specific topic. It’s important to know how to do a literature review effectively, so here are the steps to follow: 1  

Choose a Topic and Define the Research Question:

• Select a topic that is relevant to your field of study. 
• Clearly define your research question or objective. Determine what specific aspect of the topic do you want to explore? 

Decide on the Scope of Your Review:

• Determine the timeframe for your literature review. Are you focusing on recent developments, or do you want a historical overview? 
• Consider the geographical scope. Is your review global, or are you focusing on a specific region? 
• Define the inclusion and exclusion criteria. What types of sources will you include? Are there specific types of studies or publications you will exclude? 

Select Databases for Searches:

• Identify relevant databases for your field. Examples include PubMed, IEEE Xplore, Scopus, Web of Science, and Google Scholar. 
• Consider searching in library catalogs, institutional repositories, and specialized databases related to your topic. 

Conduct Searches and Keep Track:

• Develop a systematic search strategy using keywords, Boolean operators (AND, OR, NOT), and other search techniques. 
• Record and document your search strategy for transparency and replicability. 
• Keep track of the articles, including publication details, abstracts, and links. Use citation management tools like EndNote, Zotero, or Mendeley to organize your references. 

Review the Literature:

• Evaluate the relevance and quality of each source. Consider the methodology, sample size, and results of studies. 
• Organize the literature by themes or key concepts. Identify patterns, trends, and gaps in the existing research. 
• Summarize key findings and arguments from each source. Compare and contrast different perspectives. 
• Identify areas where there is a consensus in the literature and where there are conflicting opinions. 
• Provide critical analysis and synthesis of the literature. What are the strengths and weaknesses of existing research? 

Organize and Write Your Literature Review:

• Literature review outline should be based on themes, chronological order, or methodological approaches. 
• Write a clear and coherent narrative that synthesizes the information gathered. 
• Use proper citations for each source and ensure consistency in your citation style (APA, MLA, Chicago, etc.). 
• Conclude your literature review by summarizing key findings, identifying gaps, and suggesting areas for future research. 

The literature review sample and detailed advice on writing and conducting a review will help you produce a well-structured report. But remember that a literature review is an ongoing process, and it may be necessary to revisit and update it as your research progresses. 

Frequently asked questions

A literature review is a critical and comprehensive analysis of existing literature (published and unpublished works) on a specific topic or research question and provides a synthesis of the current state of knowledge in a particular field. A well-conducted literature review is crucial for researchers to build upon existing knowledge, avoid duplication of efforts, and contribute to the advancement of their field. It also helps researchers situate their work within a broader context and facilitates the development of a sound theoretical and conceptual framework for their studies.

Literature review is a crucial component of research writing, providing a solid background for a research paper’s investigation. The aim is to keep professionals up to date by providing an understanding of ongoing developments within a specific field, including research methods, and experimental techniques used in that field, and present that knowledge in the form of a written report. Also, the depth and breadth of the literature review emphasizes the credibility of the scholar in his or her field.  

Before writing a literature review, it’s essential to undertake several preparatory steps to ensure that your review is well-researched, organized, and focused. This includes choosing a topic of general interest to you and doing exploratory research on that topic, writing an annotated bibliography, and noting major points, especially those that relate to the position you have taken on the topic. 

Literature reviews and academic research papers are essential components of scholarly work but serve different purposes within the academic realm. 3 A literature review aims to provide a foundation for understanding the current state of research on a particular topic, identify gaps or controversies, and lay the groundwork for future research. Therefore, it draws heavily from existing academic sources, including books, journal articles, and other scholarly publications. In contrast, an academic research paper aims to present new knowledge, contribute to the academic discourse, and advance the understanding of a specific research question. Therefore, it involves a mix of existing literature (in the introduction and literature review sections) and original data or findings obtained through research methods. 

Literature reviews are essential components of academic and research papers, and various strategies can be employed to conduct them effectively. If you want to know how to write a literature review for a research paper, here are four common approaches that are often used by researchers.  Chronological Review: This strategy involves organizing the literature based on the chronological order of publication. It helps to trace the development of a topic over time, showing how ideas, theories, and research have evolved.  Thematic Review: Thematic reviews focus on identifying and analyzing themes or topics that cut across different studies. Instead of organizing the literature chronologically, it is grouped by key themes or concepts, allowing for a comprehensive exploration of various aspects of the topic.  Methodological Review: This strategy involves organizing the literature based on the research methods employed in different studies. It helps to highlight the strengths and weaknesses of various methodologies and allows the reader to evaluate the reliability and validity of the research findings.  Theoretical Review: A theoretical review examines the literature based on the theoretical frameworks used in different studies. This approach helps to identify the key theories that have been applied to the topic and assess their contributions to the understanding of the subject.  It’s important to note that these strategies are not mutually exclusive, and a literature review may combine elements of more than one approach. The choice of strategy depends on the research question, the nature of the literature available, and the goals of the review. Additionally, other strategies, such as integrative reviews or systematic reviews, may be employed depending on the specific requirements of the research.

The literature review format can vary depending on the specific publication guidelines. However, there are some common elements and structures that are often followed. Here is a general guideline for the format of a literature review:  Introduction:   Provide an overview of the topic.  Define the scope and purpose of the literature review.  State the research question or objective.  Body:   Organize the literature by themes, concepts, or chronology.  Critically analyze and evaluate each source.  Discuss the strengths and weaknesses of the studies.  Highlight any methodological limitations or biases.  Identify patterns, connections, or contradictions in the existing research.  Conclusion:   Summarize the key points discussed in the literature review.  Highlight the research gap.  Address the research question or objective stated in the introduction.  Highlight the contributions of the review and suggest directions for future research.

Both annotated bibliographies and literature reviews involve the examination of scholarly sources. While annotated bibliographies focus on individual sources with brief annotations, literature reviews provide a more in-depth, integrated, and comprehensive analysis of existing literature on a specific topic. The key differences are as follows: 

References 

• Denney, A. S., & Tewksbury, R. (2013). How to write a literature review.  Journal of criminal justice education ,  24 (2), 218-234. 
• Pan, M. L. (2016).  Preparing literature reviews: Qualitative and quantitative approaches . Taylor & Francis. 
• Cantero, C. (2019). How to write a literature review.  San José State University Writing Center . 

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Open Access

Peer-reviewed

Research Article

Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature

Contributed equally to this work with: Srinivasan Radhakrishnan, Serkan Erbis

Affiliation Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts, United States of America

¶ ‡ These authors also contributed equally to this work.

* E-mail: [email protected]

• Srinivasan Radhakrishnan, 
• Serkan Erbis, 
• Jacqueline A. Isaacs, 
• Sagar Kamarthi

• Published: March 22, 2017
• https://doi.org/10.1371/journal.pone.0172778
• Reader Comments

27 Sep 2017: Radhakrishnan S, Erbis S, Isaacs JA, Kamarthi S (2017) Correction: Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature. PLOS ONE 12(9): e0185771. https://doi.org/10.1371/journal.pone.0185771 View correction

Systematic reviews of scientific literature are important for mapping the existing state of research and highlighting further growth channels in a field of study, but systematic reviews are inherently tedious, time consuming, and manual in nature. In recent years, keyword co-occurrence networks (KCNs) are exploited for knowledge mapping. In a KCN, each keyword is represented as a node and each co-occurrence of a pair of words is represented as a link. The number of times that a pair of words co-occurs in multiple articles constitutes the weight of the link connecting the pair. The network constructed in this manner represents cumulative knowledge of a domain and helps to uncover meaningful knowledge components and insights based on the patterns and strength of links between keywords that appear in the literature. In this work, we propose a KCN-based approach that can be implemented prior to undertaking a systematic review to guide and accelerate the review process. The novelty of this method lies in the new metrics used for statistical analysis of a KCN that differ from those typically used for KCN analysis. The approach is demonstrated through its application to nano-related Environmental, Health, and Safety (EHS) risk literature. The KCN approach identified the knowledge components, knowledge structure, and research trends that match with those discovered through a traditional systematic review of the nanoEHS field. Because KCN-based analyses can be conducted more quickly to explore a vast amount of literature, this method can provide a knowledge map and insights prior to undertaking a rigorous traditional systematic review. This two-step approach can significantly reduce the effort and time required for a traditional systematic literature review. The proposed KCN-based pre-systematic review method is universal. It can be applied to any scientific field of study to prepare a knowledge map.

Citation: Radhakrishnan S, Erbis S, Isaacs JA, Kamarthi S (2017) Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature. PLoS ONE 12(3): e0172778. https://doi.org/10.1371/journal.pone.0172778

Editor: Lidia Adriana Braunstein, Universidad Nacional de Mar del Plata, ARGENTINA

Received: July 2, 2016; Accepted: February 9, 2017; Published: March 22, 2017

Copyright: © 2017 Radhakrishnan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work was supported by NSF Scalable Nanomanufacturing Award CMMI-1120329 and in part by NSEC Award EEC-0832785. We also thank the National Institute of Standards and Technology for providing funding for this research under sponsor award number 70NANB15H028. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The structure of scientific/technical knowledge is most commonly explored using two network-based methods: co-citation and keyword co-occurrence networks [ 1 – 5 ]. While a co-citation network focuses on studying the structure of scientific communication by analyzing links between citations in the literature, a keyword co-occurrence network (KCN) focuses on understanding the knowledge components and knowledge structure of a scientific/technical field by examining the links between keywords in the literature. The present work focuses on the analysis methods based on KCNs, which have been used in theoretical and empirical studies to explore research topics and their relationships in select scientific fields [ 4 – 12 ]. These studies have demonstrated practical value and advantages of KCN-based analysis over traditional literature review approaches [ 1 ].

A KCN is created by treating each keyword as a node and each co-occurrence of a pair of words as a link between those two words (see Fig 1 ). The number of times that a pair of words co-occurs constitutes the weight of the link connecting these two keywords. The network constructed in this manner represents a weighted network.

• PPT PowerPoint slide
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The nodes indicate the keywords published in journal articles and the links represent the co-occurrence of the words; the numbers on the links indicate the weights with the thickness of the links shown proportionally to their weight.

https://doi.org/10.1371/journal.pone.0172778.g001

A few studies have explored keyword co-occurrence (or co-citation networks) as weighted networks [ 1 , 13 – 16 ]. However the metrics used to analyze the topographical structure of a network are generally limited to two measures: betweeness centrality and modularity . Betweenness centrality of a node captures the number of times the node is included in the shortest paths between all pairs of nodes in the keyword network. On the other hand, modularity represents the ability of the network to decompose into meaningful modules. In this work, the authors investigate several other analyses techniques including the study of average weight as a function of end point degree, average weighted nearest neighbor's degree as a function of degree, weighted clustering coefficient as a function of degree, and strength as a function of node degree. In addition, the authors introduce a visual analysis and a chronological analysis (as explained in visual analysis section) to overcome the biases of statistical analysis towards topical keywords and to study the evolution of network characteristics over time.

The proposed KCN-based analyses are evaluated using the nano- Environmental, Health, and Safety (nanoEHS) risk literature. This literature is selected for application of the technique, because 1) the nanoEHS risk field has emerged over the past decade, and 2) a detailed literature review of this field is available to validate the KCN-based observations and conclusions. Erbis et al., [ 17 ] have conducted a systematic review of nanoEHS risk literature using the traditional manual approach. We consider their findings to validate the results obtained from KCN-based analyses.

1.1 Theory and application

The proliferation of information in World Wide Web is accompanied by information classification and categorization issues. A user-driven categorization of information has given rise to a popular trend called as Collaborative Tagging (or Folksonomy), which allows users to categorize information using tags. The tags are keywords that facilitate information search and retrieval. Traditional classification methods, unlike collaborative tagging methods, are guided by domain experts. Jacob [ 18 ] clearly explains the difference between categorization and classification. He states that “Categorization divides the world of experience into groups or categories whose members share some perceptible similarity within a given context. That this context may vary and with it the composition of the category which is the very basis for both the flexibility and the power of cognitive categorization.” In contrast, according to Jacob [ 18 ], “Classification as process involves the orderly and systematic assignment of each entity to one and only one class within a system of mutually exclusive and non-overlapping classes; it mandates consistent application of these principles within the framework of a prescribed ordering of reality.” Supporters of tagging argue that a classification scheme is futile if the users cannot understand what the experts have defined [ 19 , 20 ]. Proponents of classification point out that tagging schemes suffer from several issues including ambiguity in the meaning of tags, proliferation of synonyms that create informational redundancy, and incursion of personal utility in tagging process [ 19 ]. These limitations may cause disintegration of information into several meaningless silos. Focusing on tags as basic dynamical entities, the process of collaborative tagging falls within the scope of semiotic dynamics [ 21 – 23 ], a new field that studies how populations of humans or agents can establish and share semiotic systems (i.e., systems of “signs” or symbols) driven by their use in communication or information management. Folksonomies exhibit dynamical aspects similar to the ones observed in human languages such as the establishment of naming conventions, competition between terms, and takeovers by neologisms [ 23 , 24 ]. It is interesting to note that the keyword selection process in scientific literature is a combination of classification and tagging schemes. Editors propose a set of thematic keywords to classify research work submitted for review and publication of articles and at the same time, authors propose a set of keywords that they think best represents their research work. The keywords appearing in research articles serve search and retrieval functions. Earlier studies report [ 25 ] that a KCN-based analysis can provide meaningful knowledge patterns when keyword selection is a hybrid between tagging and expert classification schemes. A keyword analysis conducted by Zhang et al. [ 26 ] found that the frequency rank distribution of keywords in the Proceedings of the National Academy of Sciences (PNAS) followed Zipfs law, i.e., P n ∝ n − α , where P n is frequency and n is rank. This frequency and rank relationship reveals low frequencies of most keywords and high frequency of popular keywords. In addition, the study revealed a power law scaling behavior between cumulative number of keywords and the corresponding cumulative number of distinct keywords indicating universality in scaling [ 26 ]. The existence of such scaling relationship was established in several studies related to tagging. Irrespective of the differences in the generation of user-selected tags or creation of academic keywords, they both follow the same scaling law. The study observed an exponential decay of keywords in PNAS, which is similar to that found in other high impact factor journals. They further observed that high impact-factor journals perennially published new and novel topics, while low impact-factor journals continue to publish articles on the same topics and themes for a prolonged period of time. Keyword frequencies alone fail to capture relationships between different keywords. The inability to capture keyword relationships obscures vital information on knowledge components and structure, without which it is not possible to track the evolution of a research field. To address this issue, keyword networks are generally constructed and analyzed using basic network science measures. Such analysis helps one to understand the underlying knowledge structure of a research field. For demonstration of KCN-based approach, we use the case of nanoEHS field.

2.1 Data collection

Based on previous work [ 17 ], the authors investigated literature related to nanoEHS from the Science Citation Index Expanded (SCI-EXPANDED) and Social Sciences Citation Index (SSCI) databases available through the Web of Science. These two databases provide access to more than 8,500 major scientific and technical journals and 3,000 social sciences journals across 200 disciplines. The KCNs are constructed using the Network Workbench software tool [ 27 ] to determine the most frequently occurring terms and co-occurrence patterns among them. The search terms include “nano* AND risk analysis”, “nano* AND risk assessment”, “nano* AND risk management”, and “nano* AND risk communication” (here nano* stands for any term starting with nano, e.g., nano manufacturing, nano technology, nano materials, and nano processes). A total of 850 papers were identified. These search results are refined to exclude papers related to areas other than risk analysis as well as those written in other languages. The remaining 627 papers (comprised of journal articles, conference proceedings, reviews, etc.) published between 2000 and 2013 were considered for building KCNs. Given that only four papers were published between 2000 and 2004, those four papers and that time window was excluded from the analysis; the number of papers published is too small to build a meaningful KCN for that period.

The papers published between 2005 and 2013 (623 papers) are separated into three time windows with 3 year durations: 2005–2007, 2008–2010 and 2011–2013. A separate KCN is constructed for each of the three time windows to study temporal evolution of the nanoEHS risk analysis literature between 2005 and 2013. The words and terms provided in the keyword section of the articles were first examined to eliminate redundancy, then further cleaned up data before the construction of KCNs.

2.2 Metrics

Co-occurrence networks are most suited to reveal the evolution of a system that has a finite set of entities with non-zero probability of establishing a link between them. The weighted nature of co-occurrence networks calls for network measures that are specific to weighted networks. Applying measures designed for unweighted networks to weighted networks may not yield appropriate results. A set of network measures, designed by Barrat et al. [ 28 ] for weighted networks, showed superior representation of the network’s structural characteristics. Duvuru et al. [ 25 ] statistically analyzed co-occurrence networks with metrics used for weighted networks to uncover emerging trends in academic research. This work discusses several other relevant network measures typically used for analyzing weighted networks. In general, weighted networks are represented by adjacency matrix A ij = a ij w ij , where a ij takes a value of 1 if there exists a link between node i and node j , otherwise 0. The weights are represented by w ij . The section below reviews the network measures that are relevant to the present work.

2.2.1 Degree.

2.2.2 Strength.

2.2.3 Average weight as a function of end point degree.

2.2.4 Average weighted nearest neighbor’s degree as a function of degree.

2.2.5 Weighted clustering coefficient as a function of degree.

2.3 Chronological analysis

Normally KCNs are constructed covering the entire period of interest, from the nascent stage of the field until the time of analysis. Alternatively, one can divide the lifetime of a field into regular time windows of arbitrary length (e.g., 3- or 4-year time windows), build separate KCNs for each time window, and then comparatively analyze these chronologically ordered KCNS. This approach adds a time dimension to the KCN-based analysis of the scientific literature. It sheds light on the evolution of knowledge components, knowledge structure, and research trends in the field.

2.4 Visual analysis

In general, one observes two types of keywords: topical keywords (super set keywords) and specific keywords (subset keywords). For example, “nanomaterial” is considered as a superset and “carbon nanotubes” as a subset. Topical keywords indicate a broad classification of the topics of a field, while specific keywords identify knowledge components and support search and retrieval functions. Statistical analysis on its own reveals macro characteristics, but it is likely to be biased towards topical keywords. This limitation can be overcome by visual analysis, which can give an unbiased view of all keywords. Visual analysis helps researchers identify research directions to advance a scientific field. Statistical analysis in combination with visual analysis provides richer information than any one of them independently.

The objective of the statistical analysis is to investigate the characterization of nodes, links, and network cohesion in the nanoEHS risk literature. Table 1 shows the summary of statistical analysis for the periods 2005–2007, 2008–2010, and 2011–2013. We observe an approximate doubling of nodes across each time window, which indicates rapid introduction of new keywords resulting in expansion of knowledge structure in the nanoEHS risk literature. In addition, the decrease in average degree and average strength constitutes a key characteristic of knowledge expansion. A reduction in the average degree of nodes indicates the emergence of new nodes (keywords in the field) that have not been previously found in the earlier literature. On the other hand, a reduction in the average strength indicates a reduction in co-occurrence of nodes (keyword pairs in the field). On a macroscopic level, the decreasing average degree and average strength result from the emergence of novel, nascent materials, technologies, and methods in the field. These low-degree and low-strength nodes (keywords in the field) are potential candidates for further investigation for scientists and engineers. At a microscopic level, researchers concerned with nanoEHS risk can either focus on high-degree or high-strength nodes, which represent established materials, technologies, and methods.

https://doi.org/10.1371/journal.pone.0172778.t001

Fig 2 shows the strength distribution of the KCN for periods 2005–2007, 2008–2010, and 2011–2013. The y axis in Fig 2 represents the complementary cumulative distribution function (CCDF) and the x axis represents the strength values. Both axes use logarithmic scale. The power law distribution fit is determined for the region greater than xmin , the value of which is determined by a Kolmogorov–Smirnov test. For estimated xmin , the scaling parameter α is approximated using the maximum likelihood estimation method. For a set of estimated xmin and α , the power law hypotheses is tested using the bootstrap method; the p-value s are computed to accept or reject the possibility of the underlying distribution to be a power law approximation. The strength distribution for keyword co-occurrence network can be approximated by a lognormal distribution ( μ = 1.849, σ = 0.7952385) for time period 2005–2007 and by a power law distribution for time periods 2008–2010 and 2011–2013. The scaling exponent parameter for the power law approximation are α = 2.469 for the year 2008–2010 with p -value 0.213 and α = 2.348 for the year 2011–2013 with p -value of 0.0.85. A p-value > 0.1 indicates the plausibility of a power law. S1 Supporting Information summarizes the fitting procedure, parameter estimation and goodness of fit values for all time periods. For a detailed procedure for fitting power law distribution to data one can refer to the widely cited work of Clauset et al. [ 29 ].

The solid line represents a power law fit and the dashed line represents a lognormal fit. The xmin value in each figure represents a minimum value from where the power law fit is best approximated ( xmin = 6 for 2005–2007, xmin = 5 for 2008–2010 and xmin = 11 for 2011–2013). The strength distribution for the year 2005–2007 can be approximated by a lognormal distribution ( μ = 1.849, σ = 0.7952385), while the strength distribution for the years 2008–2010 and 2011–2013 can be approximated by power law. The scaling exponent parameter for the power law approximation are α = 2.469 for the year 2008–2010 and α = 2.348 for the year 2011–2013. The x and y axis are in logarithmic scale.

https://doi.org/10.1371/journal.pone.0172778.g002

A shift in strength distribution from lognormal to power law is observed, indicating that the network topologies subsequent to 2005–2007 are scale-free networks, with high heterogeneity (i.e., fewer nodes with high strength values and a higher number of nodes with lower strength values; it translates to fewer keywords with a larger numbers of co-occurrence, instead of many keywords with smaller counts of co-occurrences). In addition, a decaying pattern for weight distribution is observed for all three time periods, which indicates a lower frequency of links with large weights and higher frequency of links with small weights. Fig 3 shows the PDF of weight distribution for all three time periods.

First period 2005–2007, second period 2008–2010, third period 2011–2013; the x and y axis are in logarithmic scale.

https://doi.org/10.1371/journal.pone.0172778.g003

Fig 4 illustrates the average strength as a function of degree for actual KCN and random network (created by distributing the weights of the actual network randomly). To determine whether the link weights are random, the average strength as a function of degree is compared for actual KCN and random networks [ 28 ]. The average strength relationship with the degree can be captured using the scaling relation, s ( k ) ~ k α . We observe α = 1 for all three time periods (2005–2007, 2008–2010, 2011–2013), indicating that both entities (strength and degree) provide the same information about the keyword co-occurrence system. A value of α > 1 would indicate that the strength of a node grows faster than its degree.

First period 2005–2007 (left figure), second period 2008–2010 (middle figure), third period 2011–2013 (right figure); the x and y axis are in logarithmic scale.

https://doi.org/10.1371/journal.pone.0172778.g004

The strength distribution and the strength vs. degree relation constitute node properties while the weight distribution constitute link properties. However these metrics alone are insufficient to measure the relationships between nodes. For assessing the relationship between different nodes, the authors use 1) average weight vs. endpoint degree, 2) average weighted nearest neighbor degree vs. degree, and 3) weighted clustering coefficient vs. degree. The endpoint degree is calculated by multiplying the degrees of the nodes on each end of the link ( k i k j ). Fig 5a shows that the average weight in the keyword network for each period increases sharply for values of k i k j > 10 3 . It indicates that the tendency of co-occurrence increases sharply for high degree keywords. However, one cannot be sure whether high degree nodes (keywords) pair up with high degree nodes or low degree nodes (i.e., several combinations of node degrees can result in the same values for k i k j , e.g., k i k j = 15*1 or k i k j = 3*5). This issue is overcome by analyzing average weighted nearest neighbor degree (see Fig 5b ). Using this measure, one can ascertain the assortative behavior of the KCN. Fig 5b shows an increase in average weighted nearest neighbor degree with increase in node degree, revealing the assortative behavior of the network (i.e., high degree keywords tend to link up with high degree keywords, while the low degree keywords tend to link up with low degree keywords). However, assortative behavior is not uniformly observed across all degrees. The value of average weighted nearest neighbor degree increases rapidly between degree 2 to degree 10; thereafter, the rate of increase declines and reaches a plateau for degrees greater than 80. This indicates absence of topological correlations for high degree keywords. This implies that the nanoEHS researchers are developing or experimenting with new methods and nanomaterials, indicating a desirable trend. To explore whether high degree nodes connect to low degree nodes, the average weighted clustering coefficient was utilized. The relationship between the average weighted cluster and degree determines whether the keywords form cohesive groups or clusters in the keyword co-occurrence system.

(a) Average weight vs. endpoint degree and (b) average weighted nearest neighbor degree vs. node degree for three time periods: 2005–2007, 2008–2010, 2011–2013.

https://doi.org/10.1371/journal.pone.0172778.g005

Fig 6 shows that keywords with a smaller degree form clusters with other smaller degree keywords, whereas keywords with a large degree connect to many keywords, and do not form clusters. In other words, hub-keywords are connected to a large set of keywords, but the members of the set themselves co-occur less frequently.

First period 2005–2007, second period 2008–2010, third period 2011–2013; the nodes with degree around 10 form clusters, whereas the nodes with degree around 100 do not form clusters, i.e., keywords with small degrees form connected communities of words, but keywords with large degrees connect with isolated keywords.

https://doi.org/10.1371/journal.pone.0172778.g006

In summary, the KCNs for nanoEHS show an increase in average weight with endpoint degree, indicating the co-occurrence of keyword pairs. The average weighted nearest neighbor degree shows the tendency of low degree nodes to attach with other low degree keywords (i.e., assortative for low degree keywords) while the high degree keywords exhibit disassortative behavior. Finally, the average weighted clustering coefficient indicates the link between a high degree keyword and a low degree keyword. The aforementioned metrics are useful to uncover macro trends pertaining to scientific trends if a keyword convention of collaborative tagging and classification are followed together. If the keyword system is based on random user based tagging alone, the strength distribution follows a Poisson distribution. Average weight as a function of endpoint degree showed no relationship, and average weighted clustering coefficient that differs from that of a scale free network failed to capture the scientific trends [ 25 ].

Table 2 displays the top twenty keywords by strength for years 2005–2007, 2008–2010, and 2011–2013. We can see the evolution of the keywords across the temporal frames. However as mentioned earlier, academic keyword article selection process is a combination of classification and tagging; this makes a difference in interpretation of what the keywords mean. From Table 2 one can see that the two keywords, nanomaterial and nanoparticle, seem to be redundant but nanoparticle is a subset of nanomaterial.

https://doi.org/10.1371/journal.pone.0172778.t002

Three KCNs, one for each of the three time periods (2005–2007, 2008–2010, and 2011–2013), are shown in Fig 7 . They map the evolution of materials, products, and methodologies in connection with nanoEHS risk analysis.

The three periods for keyword evolution are 2005–2007, 2008–2010, and 2011–2013 respectively.

https://doi.org/10.1371/journal.pone.0172778.g007

Between 2005 and 2007, hazard and toxicity risk for various types of nanoscale materials including nanoparticles, such as quantum dots, fullerenes, carbon nanotubes (e.g., single-wall carbon nanotubes, multi-wall carbon nanotubes) and nano-titanium dioxide, were the major materials investigated. In addition, different forms of nanomaterials such as nanorods, nanowires, nanopowders, and nanocrystals were also studied. In addition to characterization of the materials, the health effects of nano-enabled products such as nanomedicines, sunscreen and cosmetics were also explored. Methodologies such as data mining, probabilistic expert judgement, decision analysis and life cycle approaches were common in analysis of the EHS risk of nanomaterials between the years 2005 and 2007. During 2008–2010 time period, the toxicity of silver nanoparticle and nano-silver were studied in addition to carbon based materials such as CNTs, carbon black, bucky-balls (nC(60)). Textile products began to be investigated in addition to sunscreen and cosmetics. Monte Carlo and Markov Chain Monte Carlo simulation methods became popular techniques to study the EHS risk of nanomaterials between 2008 and 2010. Control banding and multi-criteria decision analysis methods as risk management techniques emerged as common tools to reduce the EHS risk of nanomaterials.

For the final time period between 2011 and 2013, in addition to carbon-based nanomaterials, nano-silver, and nano-titanium dioxide, gold nanoparticles were studied for the first time. Furthermore, the effect of food, food packaging, and personal care products containing nanomaterials on human health became a popular research topic. As a methodology, the Bayesian method was applied for the first time in the literature to analyze the nanoEHS risk between 2011 and 2013. Moreover, a multi-compartment modeling technique was used to analyze the EHS risk of nanomaterials during their different stages. Other modeling techniques such as non-linear and chance constraint programming approaches were also applied to make decisions under the conditions of uncertainty in EHS risk of nanomaterials. To summarize, the visual analysis clearly shows adoption of diverse methods for nanoEHS research and investigation of a variety of more nanomaterials. The focus on products show a shift from cosmetics to food and consumer products.

4. Discussion

Since 2005, there has been a rapid expansion of knowledge structure in nanoEHS risk literature (see S2 Supporting Information ). The number of keywords approximately doubled every three years. The distribution of the number of keyword co-occurrences shifted from a lognormal to power law, i.e., subsequent to 2005–2007, fewer keywords with more co-occurrence, more keywords with small count of co-occurrences. Over the years, the frequency of co-occurrences has grown faster than the growth of number of keywords. The keywords exhibit assortative behavior, i.e., high degree keywords tend to link up with high degree keywords while the low degree keywords tend to link up with low degree keywords. This assortative behavior is more pronounced for keywords that link to 10 or fewer keywords. This indicates that the nanoEHS community has been engaged in developing or experimenting with new methods and nanomaterials. Keywords with smaller degrees form clusters with smaller degree keywords whereas keywords with large degrees connect to the keywords that do not form clusters among them, i.e., keywords appear frequently in the articles with the keywords that appear rarely in the articles together.

Between 2005 and 2007, the hazard and toxicity risk of various types of nanoscale materials, new forms of nanomaterials, as well as the health effects of nano-enabled products were investigated. During this period new methodologies such as data mining, probabilistic expert judgement, decision analysis and life cycle approaches were applied for EHS risk analysis.

During the 2008–2010 time period, the toxicity of silver nanoparticles, CNTs, carbon black, bucky-balls (nC(60), nano-enabled textile products, sunscreen and cosmetics were studied more actively. During this same time frame, Monte Carlo simulation, control banding and multi-criteria decision analysis methods became popular techniques to study the EHS risk of nanomaterials.

More recently, between 2011 and 2013, the EHS risk of gold nanoparticles was studied. The effect of nanomaterial-based food, food packaging, and personal care products on human health became an active research topic. Bayesian and multi-compartment modeling techniques were employed to analyze the EHS risk of nanomaterials. Other modeling techniques such as non-linear and chance constraint programming approaches were also applied to make decisions under the conditions of uncertainty in EHS risk of nanomaterials.

5. Conclusion

In this paper, keyword co-occurrence networks are used to reveal insights into knowledge structures and their temporal dynamics of an evolving research field such as nanoEHS risk assessment. This work introduces novel analysis techniques relevant to weighted networks other than network metrics such as betweenness centrality and modularity to gain a deeper understanding of the knowledge structures. The combination of statistical analysis to uncover macro trends and visual analysis to observe micro trends serve as an effective approach to analyze trends and patterns in a literature of an emerging research field. The statistical analysis is particularly useful when the keyword system follows a combination of expert classification and collaborative tagging as opposed to random user based tagging alone.

Systematic literature reviews often focus on the results and methodologies that are presented in individual studies, and can result in detailed qualitative mapping of the body of research work. If the objective of a literature review is only to gain a macro level understanding of research subject, e.g., introduction of novel methodologies or evolution of traditional methodologies, then an in-depth comprehensive systematic literature review is time consuming. The KCN-based analysis, requiring far less time, enables macro level quantitative mapping that reveal temporal evolution of the research subject. Unlike the traditional systematic literature reviews, the KCN-based analysis will also shed light on the connections between keywords, key concepts, and key methods and methodologies through statistical measures. In the present nanoEHS KCN-based analysis, the findings on new methods, materials of interest, and product applications are aligned with what were observed through a traditional detailed literature review [ 17 ]. This observation supports the concept that KCN-based analysis can be conducted quickly to explore a vast amount of literature prior to undertaking a rigorous time-consuming systematic review. The proposed pre-systematic-review analysis can provide a structured map to conducting a literature search, as well as significantly reduce the effort required for a systematic review.

The present work demonstrates the effectiveness and usefulness of the KCN-based analysis to discover knowledge components and knowledge structure of the nanoEHS risk assessment field, however, the proposed methodology and techniques can be readily applied to any other scientific literature.

Supporting information

S1 supporting information. estimation of lower bound xmin and scaling parameter α ..

https://doi.org/10.1371/journal.pone.0172778.s001

S2 Supporting Information. Nano Environmental, Health, and Safety (NanoEHS) risk literature.

https://doi.org/10.1371/journal.pone.0172778.s002

Acknowledgments

We thank members of the NSF-funded Nanoscale Science and Engineering Center for High-rate Nanomanufacturing at Northeastern University for their technical insights. S. Abbasi, C. Bosso, M. Eckelman, L. Pourzahedi, W. Walker and P. Zhai at Northeastern contributed to discussions. This work was supported by NSF Scalable Nanomanufacturing Award CMMI-1120329 and in part by NSEC Award EEC-0832785. We also thank the National Institute of Standards and Technology for providing funding for this research under sponsor award number 70NANB15H028.

Author Contributions

• Conceptualization: JI SE SK SR.
• Data curation: SE SR.
• Formal analysis: SK SR.
• Funding acquisition: JI SK.
• Investigation: JI SE SK SR.
• Methodology: SE SR.
• Project administration: SK.
• Resources: JI SK.
• Software: SE SR.
• Supervision: JI SK.
• Validation: JI SE.
• Visualization: SE SR.
• Writing – original draft: SE SR.
• Writing – review & editing: JI SK.
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• 20. Clay Shirky. Ontology is over-rated, 2005. http://www.shirky.com/writings/ontology-overrated.html Last Accessed, December 15, 2016.
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• 27. NWB Team (2006). "Network Bench Tool." Indiana University, Northeastern University, University of Michigan.

Systematic Reviews

• Introduction
• Guidelines and procedures
• Management tools
• Define the question
• Check the topic
• Determine inclusion/exclusion criteria
• Develop a protocol
• Identify keywords

Identifying keywords

Comprehensive vs precise, using text mining to identify keywords.

• Databases and search strategies
• Grey literature
• Manage and organise
• Screen & Select
• Locate full text
• Extract data
• Examples of systematic reviews
• Accessing help This link opens in a new window
• Systematic Style Reviews Guide This link opens in a new window

It is important to find all the relevant keywords for the topic to ensure the search is comprehensive by identifying

•  different spellings, tenses and word variants of keywords
• related concepts

There are many  ways to locate these terms, including background reading, dictionaries, regular and database thesauri or subject headings and text mining tools. The process of searching will also help identify more terms.

There needs to be a balance in searching between making the search comprehensive enough to encompass everything on the topic and precise enough to only capture those results that are specifically relevant.

Both approaches have advantages and disadvantages

Increasing the comprehensiveness (or sensitivity) of a search will reduce its precision and will retrieve more non-relevant articles.

Text mining will help identify how often terms come up in the literature and help identify other related terms and subject headings that have not been considered or thought of as being useful.

Text mining is a process used to look at large amounts of text and find relationships in the results by using computer programs designed to extract and analyse this data. 

It is used to categorise information and identify trends and patterns which can be done across large documents or multiple sources (or both).

1. Mining for terms Use these tools to find alternate search terms that are related by identifying how often keywords appear and which other terms appear with them by number of occurrences.

2. Mine within the text Locate terms within blocks of text (e.g. an article) to find word patterns and frequency. More frequent words are more likely to be relevant to the topic.

3. Use visualising tools These tools create word clouds related to search terms

These are just some of the tools available for mining text that are available on the web. There is also both commercial and free software that can be downloaded and installed. The web pages linked below have lists of yet more tools.

• Systematic Review Toolbox
• EPPI Centre: Resources for automation in systematic reviews
• List of text mining software (Wikipedia)
• Top 67 software for text analysis, text mining, text analytics

Further reading:

• EPC Methods: An Exploration of the Use of Text-Mining Software in Systematic Reviews Paynter R., Bañez L. L., Berliner E., Erinoff, E., Lege-Matsuura, J., Potter, S., & Uhl, S. (2016). EPC methods: An exploration of the use of text-mining software in systematic reviews. Retrieved from: https://www.ncbi.nlm.nih.gov/books/NBK362044/
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• Next: Databases and search strategies >>
• Last Updated: Apr 26, 2024 10:38 AM
• URL: https://libguides.jcu.edu.au/systematic-review

Why Keywords Are Necessary to Build a Comprehensive Literature Review

The Importance of Preparation

The quotation, “Give me six hours to chop down a tree and I will spend four sharpening my axe,” has been attributed to Abraham Lincoln. Whether or not that attribution is valid, the message of the quotation definitely applies to the development of a comprehensive literature review . With over half a billion active websites, and academic databases with hundreds of millions of entries available for search, adequate preparation for your review is critical to not only ensure that you capture accurate and relevant data, but also that you avoid wasting your most precious resource in academic research—your time!

There’s More to This than Google

The fact that the name of the search engine company has now been adopted in the global vernacular as a verb, gives some indication of the extent to which the ease of searching for anyone and anything has influenced our lives. However, the search skills you may have developed on Google will not, in themselves, automatically make you a proficient academic researcher. The databases you will be searching will probably have their data coded and sorted differently than the rankings attributed to individual websites by Google’s ever-changing search algorithms .

The World of Boolean Search

Named after the British mathematician George Boole , the creator of Boolean logic, Boolean search involves the extension of the search keywords with “operators,” (and, or, not, and near) in order to refine the logical relationships between the search terms. In the PubMed database, for example, those Boolean operators “must be entered in uppercase letters.”

Putting quotation marks around your search term (“Boolean logic”) will limit the search to that pairing, even in general search engines such as Google. The operators will allow you to start with broader keyword pairings and then to narrow the results with each iterative search request. This enables you to track or pursue a keyword pairing that you may not have originally considered in your initial list of keywords.

Moving from Quantity to Quality

When a simple search for “Boolean logic” in Google delivers 462,000 results in 0.29 seconds, you quickly realize the challenge that is facing you. You will never be short of results (unless you pick the most obscure terms imaginable), and the success of your literature review will rest on how well you plan and refine your scientific keyword search. Remember that a literature review is a process.

While your end goal will be sufficient knowledge to establish your expertise and to validate your research question, in the early stages of the process you must come to terms with not knowing what you do not know about the subject.

On that basis, every relevant search result should be assessed before proceeding. Just as goals should be SMART (specific, measurable, attainable, realistic, and time-based), your search results can be measured using the CARS checklist:

• C –  Is this a credible resource? Anyone can put up a website and claim expertise in any field. Look for evidence of credentials.
• A –  How do you know the information is accurate? In this era of predatory and fabricated academic journals , “trust but verify,” should be applied at all times.
• R –  Are the research assumptions reasonable? Academic publishing favors the new and counterintuitive, but that should serve as a warning for cautious interpretation of any new results that relate to your topic.
• S –  Is this information supported by other sources? Citations from other journals and by other authors are one thing, but has this work ever been verified by a replication study?

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Literature Review - Finding the Resources

• The Literature
• Search Tools
• Formulating your search statement

Keyword search

More search tips - 1, more search tips - 2.

• Buliding on what you have found
• Keeping Track
• Academic Reading
• Citing Sources
• The Learning Lounge

During your literature search, especially when you search for articles in databases, you will rely very much on keyword searching. To conduct a keyword search , you need to formulate a search statement .

Below are the basic steps to develop a search statement. After going through these steps, try to build up your own search statement using this  worksheet [pdf]

Here is a diagram to help you understand:

1. Identify the keywords or the main concepts of your research topic.

• For example, for the topic Globalization of Chinese companies , the keywords are Globalization , Chinese and Companies .

2. Think of similar terms (synonyms) or phrases that might also be used to describe these concepts, to ensure that you do not miss out any relevant information. You can use a thesaurus to help you find synonyms. For example, you can first arrange the main concepts in columns. Then under each column write down similar terms or phrases that may also be used to represent that concept:  

3. Combine your search terms in a way that a database can understand. To do this, you need to use the words AND , OR , NOT (Boolean operators).

• AND combines different concepts (e.g. Globalization and Chinese listed in different columns of the table above are different concepts).
• OR combines similar concepts (e.g. Chinese and China listed in the same column above are similar concepts).
• NOT excludes the undesirable concepts

4. Make use of truncation, wildcards, parentheses and phrase searching for more productive searching. Symbols commonly used in many search tools including catalogues and databases are:

5. A search statement can then be developed

e.g. Globali?ation AND (Chinese OR China) AND (Compan* OR Corporat* OR Firm*)

Use a form (sometimes called "Quick Search", "Advanced Search" or "Form Search") to search if possible.

For example, this is more user-friendly

When necessary, in order to make the search more focused, limit the search by

• Specific search fields (e.g. journal titles, abstracts, subjects)
• Document types (e.g. scholarly articles, conferences)
• Year of publication, etc.
• << Previous: Search Techniques
• Next: Buliding on what you have found >>
• Last Updated: Apr 9, 2024 4:25 PM
• URL: https://libguides.library.cityu.edu.hk/litreview

© City University of Hong Kong | Copyright | Disclaimer

SYSTEMATIC REVIEW article

This article is part of the research topic.

Alzheimer's disease: new insights into biomechanisms and therapeutic target

Association of soluble TREM2 with Alzheimer's disease and mild cognitive impairment: A systematic review and meta-analysis Provisionally Accepted

• 1 Department of Acupuncture, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, China

The final, formatted version of the article will be published soon.

Objective: Soluble triggering receptor expressed on myeloid cells 2 (sTREM2) is a potential neuroinflammatory biomarker linked to the pathogenesis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Previous studies have produced inconsistent results regarding sTREM2 levels in various clinical stages of AD. This study aims to establish the correlation between sTREM2 levels and AD progression through a meta-analysis of sTREM2 levels in cerebrospinal fluid (CSF) and blood. Methods: Comprehensive searches were conducted in PubMed, Embase, Web of Science, and the Cochrane Library to identify observational studies reporting CSF and blood sTREM2 levels in AD patients, MCI patients, and healthy controls. A random effects meta-analysis was used to calculate the standardized mean difference (SMD) and 95% confidence intervals (CIs). Results: Thirty-six observational studies involving 3016 AD patients, 3533 MCI patients, and 4510 healthy controls were included. CSF sTREM2 levels were significantly higher in both the AD [SMD = 0.28, 95% CI (0.15, 0.41)] and MCI groups [SMD = 0.30, 95% CI (0.13, 0.47)] compared to the healthy control group. However, no significant differences in expression were detected between the AD and MCI groups [SMD = 0.09, 95% CI (-0.09, 0.26)]. Furthermore, increased plasma sTREM2 levels were associated with a higher risk of AD [SMD = 0.42, 95% CI (0.01, 0.83)]. Conclusion: CSF sTREM2 levels are positively associated with an increased risk of AD and MCI. Plasma sTREM2 levels were notably higher in the AD group than in the control group and may serve as a promising biomarker for diagnosing AD. However, sTREM2 levels are not effective for distinguishing between different disease stages of AD. Further investigations are needed to explore the longitudinal changes in sTREM2 levels, particularly plasma sTREM2 levels, during AD progression.

Keywords: Soluble TREM2, Alzheimer's disease, Mild Cognitive Impairment, Neuroinflammation, Meta-analysis

Received: 27 Mar 2024; Accepted: 03 May 2024.

Copyright: © 2024 Wang, Zhan, Zhu, Yang and Pei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Jian Pei, Department of Acupuncture, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China

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