Specifies the number of studies evaluated orselected
Steps, and targets of constructing a good review article are listed in Table 3 . To write a good review article the items in Table 3 should be implemented step by step. [ 11 – 13 ]
Steps of a systematic review
Formulation of researchable questions | Select answerable questions |
Disclosure of studies | Databases, and key words |
Evaluation of its quality | Quality criteria during selection of studies |
Synthesis | Methods interpretation, and synthesis of outcomes |
It might be helpful to divide the research question into components. The most prevalently used format for questions related to the treatment is PICO (P - Patient, Problem or Population; I-Intervention; C-appropriate Comparisons, and O-Outcome measures) procedure. For example In female patients (P) with stress urinary incontinence, comparisons (C) between transobturator, and retropubic midurethral tension-free band surgery (I) as for patients’ satisfaction (O).
In a systematic review on a focused question, methods of investigation used should be clearly specified.
Ideally, research methods, investigated databases, and key words should be described in the final report. Different databases are used dependent on the topic analyzed. In most of the clinical topics, Medline should be surveyed. However searching through Embase and CINAHL can be also appropriate.
While determining appropriate terms for surveying, PICO elements of the issue to be sought may guide the process. Since in general we are interested in more than one outcome, P, and I can be key elements. In this case we should think about synonyms of P, and I elements, and combine them with a conjunction AND.
One method which might alleviate the workload of surveying process is “methodological filter” which aims to find the best investigation method for each research question. A good example of this method can be found in PubMed interface of Medline. The Clinical Queries tool offers empirically developed filters for five different inquiries as guidelines for etiology, diagnosis, treatment, prognosis or clinical prediction.
As an indispensable component of the review process is to discriminate good, and bad quality researches from each other, and the outcomes should be based on better qualified researches, as far as possible. To achieve this goal you should know the best possible evidence for each type of question The first component of the quality is its general planning/design of the study. General planning/design of a cohort study, a case series or normal study demonstrates variations.
A hierarchy of evidence for different research questions is presented in Table 4 . However this hierarchy is only a first step. After you find good quality research articles, you won’t need to read all the rest of other articles which saves you tons of time. [ 14 ]
Determination of levels of evidence based on the type of the research question
I | Systematic review of Level II studies | Systematic review of Level II studies | Systematic review of Level II studies | Systematic review of Level II studies |
II | Randomized controlled study | Crross-sectional study in consecutive patients | Initial cohort study | Prospective cohort study |
III | One of the following: Non-randomized experimental study (ie. controlled pre-, and post-test intervention study) Comparative studies with concurrent control groups (observational study) (ie. cohort study, case-control study) | One of the following: Cross-sectional study in non-consecutive case series; diagnostic case-control study | One of the following: Untreated control group patients in a randomized controlled study, integrated cohort study | One of the following: Retrospective cohort study, case-control study (Note: these are most prevalently used types of etiological studies; for other alternatives, and interventional studies see Level III |
IV | Case series | Case series | Case series or cohort studies with patients at different stages of their disease states |
Rarely all researches arrive at the same conclusion. In this case a solution should be found. However it is risky to make a decision based on the votes of absolute majority. Indeed, a well-performed large scale study, and a weakly designed one are weighed on the same scale. Therefore, ideally a meta-analysis should be performed to solve apparent differences. Ideally, first of all, one should be focused on the largest, and higher quality study, then other studies should be compared with this basic study.
In conclusion, during writing process of a review article, the procedures to be achieved can be indicated as follows: 1) Get rid of fixed ideas, and obsessions from your head, and view the subject from a large perspective. 2) Research articles in the literature should be approached with a methodological, and critical attitude and 3) finally data should be explained in an attractive way.
Literature reviews, what is a literature review, learning more about how to do a literature review.
A literature review is a review and synthesis of existing research on a topic or research question. A literature review is meant to analyze the scholarly literature, make connections across writings and identify strengths, weaknesses, trends, and missing conversations. A literature review should address different aspects of a topic as it relates to your research question. A literature review goes beyond a description or summary of the literature you have read.
Learn how to write a review article.
What is a review article? A review article can also be called a literature review, or a review of literature. It is a survey of previously published research on a topic. It should give an overview of current thinking on the topic. And, unlike an original research article, it will not present new experimental results.
Writing a review of literature is to provide a critical evaluation of the data available from existing studies. Review articles can identify potential research areas to explore next, and sometimes they will draw new conclusions from the existing data.
To provide a comprehensive foundation on a topic.
To explain the current state of knowledge.
To identify gaps in existing studies for potential future research.
To highlight the main methodologies and research techniques.
There are some journals that only publish review articles, and others that do not accept them.
Make sure you check the aims and scope of the journal you’d like to publish in to find out if it’s the right place for your review article.
Below are 8 key items to consider when you begin writing your review article.
Make sure you have read the aims and scope for the journal you are submitting to and follow them closely. Different journals accept different types of articles and not all will accept review articles, so it’s important to check this before you start writing.
Define the scope of your review article and the research question you’ll be answering, making sure your article contributes something new to the field.
As award-winning author Angus Crake told us, you’ll also need to “define the scope of your review so that it is manageable, not too large or small; it may be necessary to focus on recent advances if the field is well established.”
When finding sources to evaluate, Angus Crake says it’s critical that you “use multiple search engines/databases so you don’t miss any important ones.”
For finding studies for a systematic review in medical sciences, read advice from NCBI .
Spend time writing an effective title, abstract and keywords. This will help maximize the visibility of your article online, making sure the right readers find your research. Your title and abstract should be clear, concise, accurate, and informative.
For more information and guidance on getting these right, read our guide to writing a good abstract and title and our researcher’s guide to search engine optimization .
Does a literature review need an introduction? Yes, always start with an overview of the topic and give some context, explaining why a review of the topic is necessary. Gather research to inform your introduction and make it broad enough to reach out to a large audience of non-specialists. This will help maximize its wider relevance and impact.
Don’t make your introduction too long. Divide the review into sections of a suitable length to allow key points to be identified more easily.
Make sure you present a critical discussion, not just a descriptive summary of the topic. If there is contradictory research in your area of focus, make sure to include an element of debate and present both sides of the argument. You can also use your review paper to resolve conflict between contradictory studies.
Angus Crake, researcher
As part of your conclusion, include making suggestions for future research on the topic. Focus on the goal to communicate what you understood and what unknowns still remains.
Always perform a final spell and grammar check of your article before submission.
You may want to ask a critical friend or colleague to give their feedback before you submit. If English is not your first language, think about using a language-polishing service.
Find out more about how Taylor & Francis Editing Services can help improve your manuscript before you submit.
Differences in... | ||
---|---|---|
Presents the viewpoint of the author | Critiques the viewpoint of other authors on a particular topic | |
New content | Assessing already published content | |
Depends on the word limit provided by the journal you submit to | Tends to be shorter than a research article, but will still need to adhere to words limit |
Complete this checklist before you submit your review article:
Have you checked the journal’s aims and scope?
Have you defined the scope of your article?
Did you use multiple search engines to find sources to evaluate?
Have you written a descriptive title and abstract using keywords?
Did you start with an overview of the topic?
Have you presented a critical discussion?
Have you included future suggestions for research in your conclusion?
Have you asked a friend to do a final spell and grammar check?
Taylor & Francis Editing Services offers a full range of pre-submission manuscript preparation services to help you improve the quality of your manuscript and submit with confidence.
How to edit your paper
Writing a scientific literature review
3 straightforward steps (with examples) + free template.
By: Derek Jansen (MBA) | Expert Reviewed By: Dr. Eunice Rautenbach | October 2019
Quality research is about building onto the existing work of others , “standing on the shoulders of giants”, as Newton put it. The literature review chapter of your dissertation, thesis or research project is where you synthesise this prior work and lay the theoretical foundation for your own research.
Long story short, this chapter is a pretty big deal, which is why you want to make sure you get it right . In this post, I’ll show you exactly how to write a literature review in three straightforward steps, so you can conquer this vital chapter (the smart way).
Before we unpack how to write the literature review chapter, we’ve got to look at the why . To put it bluntly, if you don’t understand the function and purpose of the literature review process, there’s no way you can pull it off well. So, what exactly is the purpose of the literature review?
Well, there are (at least) four core functions:
Most students understand the first point but don’t give any thought to the rest. To get the most from the literature review process, you must keep all four points front of mind as you review the literature (more on this shortly), or you’ll land up with a wonky foundation.
Okay – with the why out the way, let’s move on to the how . As mentioned above, writing your literature review is a process, which I’ll break down into three steps:
Importantly, you must complete steps one and two before you start writing up your chapter. I know it’s very tempting, but don’t try to kill two birds with one stone and write as you read. You’ll invariably end up wasting huge amounts of time re-writing and re-shaping, or you’ll just land up with a disjointed, hard-to-digest mess . Instead, you need to read first and distil the information, then plan and execute the writing.
Naturally, the first step in the literature review journey is to hunt down the existing research that’s relevant to your topic. While you probably already have a decent base of this from your research proposal , you need to expand on this substantially in the dissertation or thesis itself.
Essentially, you need to be looking for any existing literature that potentially helps you answer your research question (or develop it, if that’s not yet pinned down). There are numerous ways to find relevant literature, but I’ll cover my top four tactics here. I’d suggest combining all four methods to ensure that nothing slips past you:
Google’s academic search engine, Google Scholar , is a great starting point as it provides a good high-level view of the relevant journal articles for whatever keyword you throw at it. Most valuably, it tells you how many times each article has been cited, which gives you an idea of how credible (or at least, popular) it is. Some articles will be free to access, while others will require an account, which brings us to the next method.
Generally, universities provide students with access to an online library, which provides access to many (but not all) of the major journals.
So, if you find an article using Google Scholar that requires paid access (which is quite likely), search for that article in your university’s database – if it’s listed there, you’ll have access. Note that, generally, the search engine capabilities of these databases are poor, so make sure you search for the exact article name, or you might not find it.
At the end of every academic journal article, you’ll find a list of references. As with any academic writing, these references are the building blocks of the article, so if the article is relevant to your topic, there’s a good chance a portion of the referenced works will be too. Do a quick scan of the titles and see what seems relevant, then search for the relevant ones in your university’s database.
Similar to Method 3 above, you can leverage other students’ dissertations. All you have to do is skim through literature review chapters of existing dissertations related to your topic and you’ll find a gold mine of potential literature. Usually, your university will provide you with access to previous students’ dissertations, but you can also find a much larger selection in the following databases:
Keep in mind that dissertations and theses are not as academically sound as published, peer-reviewed journal articles (because they’re written by students, not professionals), so be sure to check the credibility of any sources you find using this method. You can do this by assessing the citation count of any given article in Google Scholar. If you need help with assessing the credibility of any article, or with finding relevant research in general, you can chat with one of our Research Specialists .
Alright – with a good base of literature firmly under your belt, it’s time to move onto the next step.
Once you’ve built a little treasure trove of articles, it’s time to get reading and start digesting the information – what does it all mean?
While I present steps one and two (hunting and digesting) as sequential, in reality, it’s more of a back-and-forth tango – you’ll read a little , then have an idea, spot a new citation, or a new potential variable, and then go back to searching for articles. This is perfectly natural – through the reading process, your thoughts will develop , new avenues might crop up, and directional adjustments might arise. This is, after all, one of the main purposes of the literature review process (i.e. to familiarise yourself with the current state of research in your field).
As you’re working through your treasure chest, it’s essential that you simultaneously start organising the information. There are three aspects to this:
I’ll discuss each of these below:
As you read each article, you should add it to your reference management software. I usually recommend Mendeley for this purpose (see the Mendeley 101 video below), but you can use whichever software you’re comfortable with. Most importantly, make sure you load EVERY article you read into your reference manager, even if it doesn’t seem very relevant at the time.
In the beginning, you might feel confident that you can remember who said what, where, and what their main arguments were. Trust me, you won’t. If you do a thorough review of the relevant literature (as you must!), you’re going to read many, many articles, and it’s simply impossible to remember who said what, when, and in what context . Also, without the bird’s eye view that a catalogue provides, you’ll miss connections between various articles, and have no view of how the research developed over time. Simply put, it’s essential to build your own catalogue of the literature.
I would suggest using Excel to build your catalogue, as it allows you to run filters, colour code and sort – all very useful when your list grows large (which it will). How you lay your spreadsheet out is up to you, but I’d suggest you have the following columns (at minimum):
If you’d like, you can try out our free catalog template here (see screenshot below).
Most importantly, as you work through the literature and build your catalogue, you need to synthesise all the information in your own mind – how does it all fit together? Look for links between the various articles and try to develop a bigger picture view of the state of the research. Some important questions to ask yourself are:
To help you develop a big-picture view and synthesise all the information, you might find mind mapping software such as Freemind useful. Alternatively, if you’re a fan of physical note-taking, investing in a large whiteboard might work for you.
Once you’re satisfied that you have digested and distilled all the relevant literature in your mind, it’s time to put pen to paper (or rather, fingers to keyboard). There are two steps here – outlining and writing:
Having spent so much time reading, it might be tempting to just start writing up without a clear structure in mind. However, it’s critically important to decide on your structure and develop a detailed outline before you write anything. Your literature review chapter needs to present a clear, logical and an easy to follow narrative – and that requires some planning. Don’t try to wing it!
Naturally, you won’t always follow the plan to the letter, but without a detailed outline, you’re more than likely going to end up with a disjointed pile of waffle , and then you’re going to spend a far greater amount of time re-writing, hacking and patching. The adage, “measure twice, cut once” is very suitable here.
In terms of structure, the first decision you’ll have to make is whether you’ll lay out your review thematically (into themes) or chronologically (by date/period). The right choice depends on your topic, research objectives and research questions, which we discuss in this article .
Once that’s decided, you need to draw up an outline of your entire chapter in bullet point format. Try to get as detailed as possible, so that you know exactly what you’ll cover where, how each section will connect to the next, and how your entire argument will develop throughout the chapter. Also, at this stage, it’s a good idea to allocate rough word count limits for each section, so that you can identify word count problems before you’ve spent weeks or months writing!
PS – check out our free literature review chapter template…
With a detailed outline at your side, it’s time to start writing up (finally!). At this stage, it’s common to feel a bit of writer’s block and find yourself procrastinating under the pressure of finally having to put something on paper. To help with this, remember that the objective of the first draft is not perfection – it’s simply to get your thoughts out of your head and onto paper, after which you can refine them. The structure might change a little, the word count allocations might shift and shuffle, and you might add or remove a section – that’s all okay. Don’t worry about all this on your first draft – just get your thoughts down on paper.
Once you’ve got a full first draft (however rough it may be), step away from it for a day or two (longer if you can) and then come back at it with fresh eyes. Pay particular attention to the flow and narrative – does it fall fit together and flow from one section to another smoothly? Now’s the time to try to improve the linkage from each section to the next, tighten up the writing to be more concise, trim down word count and sand it down into a more digestible read.
Once you’ve done that, give your writing to a friend or colleague who is not a subject matter expert and ask them if they understand the overall discussion. The best way to assess this is to ask them to explain the chapter back to you. This technique will give you a strong indication of which points were clearly communicated and which weren’t. If you’re working with Grad Coach, this is a good time to have your Research Specialist review your chapter.
Finally, tighten it up and send it off to your supervisor for comment. Some might argue that you should be sending your work to your supervisor sooner than this (indeed your university might formally require this), but in my experience, supervisors are extremely short on time (and often patience), so, the more refined your chapter is, the less time they’ll waste on addressing basic issues (which you know about already) and the more time they’ll spend on valuable feedback that will increase your mark-earning potential.
In the video below, we unpack an actual literature review so that you can see how all the core components come together in reality.
In this post, we’ve covered how to research and write up a high-quality literature review chapter. Let’s do a quick recap of the key takeaways:
This post is an extract from our bestselling short course, Literature Review Bootcamp . If you want to work smart, you don't want to miss this .
Thank you very much. This page is an eye opener and easy to comprehend.
This is awesome!
I wish I come across GradCoach earlier enough.
But all the same I’ll make use of this opportunity to the fullest.
Thank you for this good job.
Keep it up!
You’re welcome, Yinka. Thank you for the kind words. All the best writing your literature review.
Thank you for a very useful literature review session. Although I am doing most of the steps…it being my first masters an Mphil is a self study and one not sure you are on the right track. I have an amazing supervisor but one also knows they are super busy. So not wanting to bother on the minutae. Thank you.
You’re most welcome, Renee. Good luck with your literature review 🙂
This has been really helpful. Will make full use of it. 🙂
Thank you Gradcoach.
Really agreed. Admirable effort
thank you for this beautiful well explained recap.
Thank you so much for your guide of video and other instructions for the dissertation writing.
It is instrumental. It encouraged me to write a dissertation now.
Thank you the video was great – from someone that knows nothing thankyou
an amazing and very constructive way of presetting a topic, very useful, thanks for the effort,
It is timely
It is very good video of guidance for writing a research proposal and a dissertation. Since I have been watching and reading instructions, I have started my research proposal to write. I appreciate to Mr Jansen hugely.
I learn a lot from your videos. Very comprehensive and detailed.
Thank you for sharing your knowledge. As a research student, you learn better with your learning tips in research
I was really stuck in reading and gathering information but after watching these things are cleared thanks, it is so helpful.
Really helpful, Thank you for the effort in showing such information
This is super helpful thank you very much.
Thank you for this whole literature writing review.You have simplified the process.
I’m so glad I found GradCoach. Excellent information, Clear explanation, and Easy to follow, Many thanks Derek!
You’re welcome, Maithe. Good luck writing your literature review 🙂
Thank you Coach, you have greatly enriched and improved my knowledge
Great piece, so enriching and it is going to help me a great lot in my project and thesis, thanks so much
This is THE BEST site for ANYONE doing a masters or doctorate! Thank you for the sound advice and templates. You rock!
Thanks, Stephanie 🙂
This is mind blowing, the detailed explanation and simplicity is perfect.
I am doing two papers on my final year thesis, and I must stay I feel very confident to face both headlong after reading this article.
thank you so much.
if anyone is to get a paper done on time and in the best way possible, GRADCOACH is certainly the go to area!
This is very good video which is well explained with detailed explanation
Thank you excellent piece of work and great mentoring
Thanks, it was useful
Thank you very much. the video and the information were very helpful.
Good morning scholar. I’m delighted coming to know you even before the commencement of my dissertation which hopefully is expected in not more than six months from now. I would love to engage my study under your guidance from the beginning to the end. I love to know how to do good job
Thank you so much Derek for such useful information on writing up a good literature review. I am at a stage where I need to start writing my one. My proposal was accepted late last year but I honestly did not know where to start
Like the name of your YouTube implies you are GRAD (great,resource person, about dissertation). In short you are smart enough in coaching research work.
This is a very well thought out webpage. Very informative and a great read.
Very timely.
I appreciate.
Very comprehensive and eye opener for me as beginner in postgraduate study. Well explained and easy to understand. Appreciate and good reference in guiding me in my research journey. Thank you
Thank you. I requested to download the free literature review template, however, your website wouldn’t allow me to complete the request or complete a download. May I request that you email me the free template? Thank you.
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Methodology
Published on June 15, 2022 by Shaun Turney . Revised on November 20, 2023.
A systematic review is a type of review that uses repeatable methods to find, select, and synthesize all available evidence. It answers a clearly formulated research question and explicitly states the methods used to arrive at the answer.
They answered the question “What is the effectiveness of probiotics in reducing eczema symptoms and improving quality of life in patients with eczema?”
In this context, a probiotic is a health product that contains live microorganisms and is taken by mouth. Eczema is a common skin condition that causes red, itchy skin.
What is a systematic review, systematic review vs. meta-analysis, systematic review vs. literature review, systematic review vs. scoping review, when to conduct a systematic review, pros and cons of systematic reviews, step-by-step example of a systematic review, other interesting articles, frequently asked questions about systematic reviews.
A review is an overview of the research that’s already been completed on a topic.
What makes a systematic review different from other types of reviews is that the research methods are designed to reduce bias . The methods are repeatable, and the approach is formal and systematic:
Although multiple sets of guidelines exist, the Cochrane Handbook for Systematic Reviews is among the most widely used. It provides detailed guidelines on how to complete each step of the systematic review process.
Systematic reviews are most commonly used in medical and public health research, but they can also be found in other disciplines.
Systematic reviews typically answer their research question by synthesizing all available evidence and evaluating the quality of the evidence. Synthesizing means bringing together different information to tell a single, cohesive story. The synthesis can be narrative ( qualitative ), quantitative , or both.
Systematic reviews often quantitatively synthesize the evidence using a meta-analysis . A meta-analysis is a statistical analysis, not a type of review.
A meta-analysis is a technique to synthesize results from multiple studies. It’s a statistical analysis that combines the results of two or more studies, usually to estimate an effect size .
A literature review is a type of review that uses a less systematic and formal approach than a systematic review. Typically, an expert in a topic will qualitatively summarize and evaluate previous work, without using a formal, explicit method.
Although literature reviews are often less time-consuming and can be insightful or helpful, they have a higher risk of bias and are less transparent than systematic reviews.
Similar to a systematic review, a scoping review is a type of review that tries to minimize bias by using transparent and repeatable methods.
However, a scoping review isn’t a type of systematic review. The most important difference is the goal: rather than answering a specific question, a scoping review explores a topic. The researcher tries to identify the main concepts, theories, and evidence, as well as gaps in the current research.
Sometimes scoping reviews are an exploratory preparation step for a systematic review, and sometimes they are a standalone project.
Professional editors proofread and edit your paper by focusing on:
See an example
A systematic review is a good choice of review if you want to answer a question about the effectiveness of an intervention , such as a medical treatment.
To conduct a systematic review, you’ll need the following:
A systematic review has many pros .
Systematic reviews also have a few cons .
The 7 steps for conducting a systematic review are explained with an example.
Formulating the research question is probably the most important step of a systematic review. A clear research question will:
A good research question for a systematic review has four components, which you can remember with the acronym PICO :
You can rearrange these four components to write your research question:
Sometimes, you may want to include a fifth component, the type of study design . In this case, the acronym is PICOT .
Their research question was:
A protocol is a document that contains your research plan for the systematic review. This is an important step because having a plan allows you to work more efficiently and reduces bias.
Your protocol should include the following components:
If you’re a professional seeking to publish your review, it’s a good idea to bring together an advisory committee . This is a group of about six people who have experience in the topic you’re researching. They can help you make decisions about your protocol.
It’s highly recommended to register your protocol. Registering your protocol means submitting it to a database such as PROSPERO or ClinicalTrials.gov .
Searching for relevant studies is the most time-consuming step of a systematic review.
To reduce bias, it’s important to search for relevant studies very thoroughly. Your strategy will depend on your field and your research question, but sources generally fall into these four categories:
At this stage of your review, you won’t read the articles yet. Simply save any potentially relevant citations using bibliographic software, such as Scribbr’s APA or MLA Generator .
Applying the selection criteria is a three-person job. Two of you will independently read the studies and decide which to include in your review based on the selection criteria you established in your protocol . The third person’s job is to break any ties.
To increase inter-rater reliability , ensure that everyone thoroughly understands the selection criteria before you begin.
If you’re writing a systematic review as a student for an assignment, you might not have a team. In this case, you’ll have to apply the selection criteria on your own; you can mention this as a limitation in your paper’s discussion.
You should apply the selection criteria in two phases:
It’s very important to keep a meticulous record of why you included or excluded each article. When the selection process is complete, you can summarize what you did using a PRISMA flow diagram .
Next, Boyle and colleagues found the full texts for each of the remaining studies. Boyle and Tang read through the articles to decide if any more studies needed to be excluded based on the selection criteria.
When Boyle and Tang disagreed about whether a study should be excluded, they discussed it with Varigos until the three researchers came to an agreement.
Extracting the data means collecting information from the selected studies in a systematic way. There are two types of information you need to collect from each study:
You should collect this information using forms. You can find sample forms in The Registry of Methods and Tools for Evidence-Informed Decision Making and the Grading of Recommendations, Assessment, Development and Evaluations Working Group .
Extracting the data is also a three-person job. Two people should do this step independently, and the third person will resolve any disagreements.
They also collected data about possible sources of bias, such as how the study participants were randomized into the control and treatment groups.
Synthesizing the data means bringing together the information you collected into a single, cohesive story. There are two main approaches to synthesizing the data:
Generally, you should use both approaches together whenever possible. If you don’t have enough data, or the data from different studies aren’t comparable, then you can take just a narrative approach. However, you should justify why a quantitative approach wasn’t possible.
Boyle and colleagues also divided the studies into subgroups, such as studies about babies, children, and adults, and analyzed the effect sizes within each group.
The purpose of writing a systematic review article is to share the answer to your research question and explain how you arrived at this answer.
Your article should include the following sections:
To verify that your report includes everything it needs, you can use the PRISMA checklist .
Once your report is written, you can publish it in a systematic review database, such as the Cochrane Database of Systematic Reviews , and/or in a peer-reviewed journal.
In their report, Boyle and colleagues concluded that probiotics cannot be recommended for reducing eczema symptoms or improving quality of life in patients with eczema. Note Generative AI tools like ChatGPT can be useful at various stages of the writing and research process and can help you to write your systematic review. However, we strongly advise against trying to pass AI-generated text off as your own work.
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
Research bias
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 thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.
A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other academic texts , with an introduction , a main body, and a conclusion .
An annotated bibliography is a list of source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a paper .
A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.
If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.
Turney, S. (2023, November 20). Systematic Review | Definition, Example & Guide. Scribbr. Retrieved August 5, 2024, from https://www.scribbr.com/methodology/systematic-review/
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Table of Contents
As a young researcher, you might wonder how to start writing your first review article, and the extent of the information that it should contain. A review article is a comprehensive summary of the current understanding of a specific research topic and is based on previously published research. Unlike research papers, it does not contain new results, but can propose new inferences based on the combined findings of previous research.
Review articles are typically of three types: literature reviews, systematic reviews, and meta-analyses.
A literature review is a general survey of the research topic and aims to provide a reliable and unbiased account of the current understanding of the topic.
A systematic review , in contrast, is more specific and attempts to address a highly focused research question. Its presentation is more detailed, with information on the search strategy used, the eligibility criteria for inclusion of studies, the methods utilized to review the collected information, and more.
A meta-analysis is similar to a systematic review in that both are systematically conducted with a properly defined research question. However, unlike the latter, a meta-analysis compares and evaluates a defined number of similar studies. It is quantitative in nature and can help assess contrasting study findings.
Here are a few practices that can make the time-consuming process of writing a review article easier:
Writing review articles, especially systematic reviews or meta-analyses, can seem like a daunting task. However, Elsevier Author Services can guide you by providing useful tips on how to write an impressive review article that stands out and gets published!
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Overview of the review report format, the first read-through, first read considerations, spotting potential major flaws, concluding the first reading, rejection after the first reading, before starting the second read-through, doing the second read-through, the second read-through: section by section guidance, how to structure your report, on presentation and style, criticisms & confidential comments to editors, the recommendation, when recommending rejection, additional resources, step by step guide to reviewing a manuscript.
When you receive an invitation to peer review, you should be sent a copy of the paper's abstract to help you decide whether you wish to do the review. Try to respond to invitations promptly - it will prevent delays. It is also important at this stage to declare any potential Conflict of Interest.
The structure of the review report varies between journals. Some follow an informal structure, while others have a more formal approach.
" Number your comments!!! " (Jonathon Halbesleben, former Editor of Journal of Occupational and Organizational Psychology)
Informal Structure
Many journals don't provide criteria for reviews beyond asking for your 'analysis of merits'. In this case, you may wish to familiarize yourself with examples of other reviews done for the journal, which the editor should be able to provide or, as you gain experience, rely on your own evolving style.
Formal Structure
Other journals require a more formal approach. Sometimes they will ask you to address specific questions in your review via a questionnaire. Or they might want you to rate the manuscript on various attributes using a scorecard. Often you can't see these until you log in to submit your review. So when you agree to the work, it's worth checking for any journal-specific guidelines and requirements. If there are formal guidelines, let them direct the structure of your review.
In Both Cases
Whether specifically required by the reporting format or not, you should expect to compile comments to authors and possibly confidential ones to editors only.
Following the invitation to review, when you'll have received the article abstract, you should already understand the aims, key data and conclusions of the manuscript. If you don't, make a note now that you need to feedback on how to improve those sections.
The first read-through is a skim-read. It will help you form an initial impression of the paper and get a sense of whether your eventual recommendation will be to accept or reject the paper.
Keep a pen and paper handy when skim-reading.
Try to bear in mind the following questions - they'll help you form your overall impression:
While you should read the whole paper, making the right choice of what to read first can save time by flagging major problems early on.
Editors say, " Specific recommendations for remedying flaws are VERY welcome ."
Examples of possibly major flaws include:
If experimental design features prominently in the paper, first check that the methodology is sound - if not, this is likely to be a major flaw.
You might examine:
Major Flaws in Information
If methodology is less of an issue, it's often a good idea to look at the data tables, figures or images first. Especially in science research, it's all about the information gathered. If there are critical flaws in this, it's very likely the manuscript will need to be rejected. Such issues include:
If you find a major problem, note your reasoning and clear supporting evidence (including citations).
After the initial read and using your notes, including those of any major flaws you found, draft the first two paragraphs of your review - the first summarizing the research question addressed and the second the contribution of the work. If the journal has a prescribed reporting format, this draft will still help you compose your thoughts.
The First Paragraph
This should state the main question addressed by the research and summarize the goals, approaches, and conclusions of the paper. It should:
The Second Paragraph
This should provide a conceptual overview of the contribution of the research. So consider:
After drafting these two paragraphs, you should be in a position to decide whether this manuscript is seriously flawed and should be rejected (see the next section). Or whether it is publishable in principle and merits a detailed, careful read through.
Even if you are coming to the opinion that an article has serious flaws, make sure you read the whole paper. This is very important because you may find some really positive aspects that can be communicated to the author. This could help them with future submissions.
A full read-through will also make sure that any initial concerns are indeed correct and fair. After all, you need the context of the whole paper before deciding to reject. If you still intend to recommend rejection, see the section "When recommending rejection."
Once the paper has passed your first read and you've decided the article is publishable in principle, one purpose of the second, detailed read-through is to help prepare the manuscript for publication. You may still decide to recommend rejection following a second reading.
" Offer clear suggestions for how the authors can address the concerns raised. In other words, if you're going to raise a problem, provide a solution ." (Jonathon Halbesleben, Editor of Journal of Occupational and Organizational Psychology)
Preparation
To save time and simplify the review:
Now that you have completed your preparations, you're ready to spend an hour or so reading carefully through the manuscript.
As you're reading through the manuscript for a second time, you'll need to keep in mind the argument's construction, the clarity of the language and content.
With regard to the argument’s construction, you should identify:
You may also wish to consider:
Not every submission is well written. Part of your role is to make sure that the text’s meaning is clear.
Editors say, " If a manuscript has many English language and editing issues, please do not try and fix it. If it is too bad, note that in your review and it should be up to the authors to have the manuscript edited ."
If the article is difficult to understand, you should have rejected it already. However, if the language is poor but you understand the core message, see if you can suggest improvements to fix the problem:
On Grammar and Punctuation
Your primary role is judging the research content. Don't spend time polishing grammar or spelling. Editors will make sure that the text is at a high standard before publication. However, if you spot grammatical errors that affect clarity of meaning, then it's important to highlight these. Expect to suggest such amendments - it's rare for a manuscript to pass review with no corrections.
A 2010 study of nursing journals found that 79% of recommendations by reviewers were influenced by grammar and writing style (Shattel, et al., 2010).
1. The Introduction
A well-written introduction:
Originality and Topicality
Originality and topicality can only be established in the light of recent authoritative research. For example, it's impossible to argue that there is a conflict in current understanding by referencing articles that are 10 years old.
Authors may make the case that a topic hasn't been investigated in several years and that new research is required. This point is only valid if researchers can point to recent developments in data gathering techniques or to research in indirectly related fields that suggest the topic needs revisiting. Clearly, authors can only do this by referencing recent literature. Obviously, where older research is seminal or where aspects of the methodology rely upon it, then it is perfectly appropriate for authors to cite some older papers.
Editors say, "Is the report providing new information; is it novel or just confirmatory of well-known outcomes ?"
It's common for the introduction to end by stating the research aims. By this point you should already have a good impression of them - if the explicit aims come as a surprise, then the introduction needs improvement.
2. Materials and Methods
Academic research should be replicable, repeatable and robust - and follow best practice.
Replicable Research
This makes sufficient use of:
These are used to make sure observed trends are not due to chance and that the same experiment could be repeated by other researchers - and result in the same outcome. Statistical analyses will not be sound if methods are not replicable. Where research is not replicable, the paper should be recommended for rejection.
Repeatable Methods
These give enough detail so that other researchers are able to carry out the same research. For example, equipment used or sampling methods should all be described in detail so that others could follow the same steps. Where methods are not detailed enough, it's usual to ask for the methods section to be revised.
Robust Research
This has enough data points to make sure the data are reliable. If there are insufficient data, it might be appropriate to recommend revision. You should also consider whether there is any in-built bias not nullified by the control experiments.
Best Practice
During these checks you should keep in mind best practice:
If the research fails to reach relevant best practice standards, it's usual to recommend rejection. What's more, you don't then need to read any further.
3. Results and Discussion
This section should tell a coherent story - What happened? What was discovered or confirmed?
Certain patterns of good reporting need to be followed by the author:
Discussion should always, at some point, gather all the information together into a single whole. Authors should describe and discuss the overall story formed. If there are gaps or inconsistencies in the story, they should address these and suggest ways future research might confirm the findings or take the research forward.
4. Conclusions
This section is usually no more than a few paragraphs and may be presented as part of the results and discussion, or in a separate section. The conclusions should reflect upon the aims - whether they were achieved or not - and, just like the aims, should not be surprising. If the conclusions are not evidence-based, it's appropriate to ask for them to be re-written.
5. Information Gathered: Images, Graphs and Data Tables
If you find yourself looking at a piece of information from which you cannot discern a story, then you should ask for improvements in presentation. This could be an issue with titles, labels, statistical notation or image quality.
Where information is clear, you should check that:
You should also check whether images have been edited or manipulated to emphasize the story they tell. This may be appropriate but only if authors report on how the image has been edited (e.g. by highlighting certain parts of an image). Where you feel that an image has been edited or manipulated without explanation, you should highlight this in a confidential comment to the editor in your report.
6. List of References
You will need to check referencing for accuracy, adequacy and balance.
Where a cited article is central to the author's argument, you should check the accuracy and format of the reference - and bear in mind different subject areas may use citations differently. Otherwise, it's the editor’s role to exhaustively check the reference section for accuracy and format.
You should consider if the referencing is adequate:
Check for a well-balanced list of references that is:
You should be able to evaluate whether the article meets the criteria for balanced referencing without looking up every reference.
7. Plagiarism
By now you will have a deep understanding of the paper's content - and you may have some concerns about plagiarism.
Identified Concern
If you find - or already knew of - a very similar paper, this may be because the author overlooked it in their own literature search. Or it may be because it is very recent or published in a journal slightly outside their usual field.
You may feel you can advise the author how to emphasize the novel aspects of their own study, so as to better differentiate it from similar research. If so, you may ask the author to discuss their aims and results, or modify their conclusions, in light of the similar article. Of course, the research similarities may be so great that they render the work unoriginal and you have no choice but to recommend rejection.
"It's very helpful when a reviewer can point out recent similar publications on the same topic by other groups, or that the authors have already published some data elsewhere ." (Editor feedback)
Suspected Concern
If you suspect plagiarism, including self-plagiarism, but cannot recall or locate exactly what is being plagiarized, notify the editor of your suspicion and ask for guidance.
Most editors have access to software that can check for plagiarism.
Editors are not out to police every paper, but when plagiarism is discovered during peer review it can be properly addressed ahead of publication. If plagiarism is discovered only after publication, the consequences are worse for both authors and readers, because a retraction may be necessary.
For detailed guidelines see COPE's Ethical guidelines for reviewers and Wiley's Best Practice Guidelines on Publishing Ethics .
8. Search Engine Optimization (SEO)
After the detailed read-through, you will be in a position to advise whether the title, abstract and key words are optimized for search purposes. In order to be effective, good SEO terms will reflect the aims of the research.
A clear title and abstract will improve the paper's search engine rankings and will influence whether the user finds and then decides to navigate to the main article. The title should contain the relevant SEO terms early on. This has a major effect on the impact of a paper, since it helps it appear in search results. A poor abstract can then lose the reader's interest and undo the benefit of an effective title - whilst the paper's abstract may appear in search results, the potential reader may go no further.
So ask yourself, while the abstract may have seemed adequate during earlier checks, does it:
Editors say, " Does the Abstract highlight the important findings of the study ?"
If there is a formal report format, remember to follow it. This will often comprise a range of questions followed by comment sections. Try to answer all the questions. They are there because the editor felt that they are important. If you're following an informal report format you could structure your report in three sections: summary, major issues, minor issues.
Major Issues
Minor Issues
Your review should ultimately help the author improve their article. So be polite, honest and clear. You should also try to be objective and constructive, not subjective and destructive.
You should also:
Most journals give reviewers the option to provide some confidential comments to editors. Often this is where editors will want reviewers to state their recommendation - see the next section - but otherwise this area is best reserved for communicating malpractice such as suspected plagiarism, fraud, unattributed work, unethical procedures, duplicate publication, bias or other conflicts of interest.
However, this doesn't give reviewers permission to 'backstab' the author. Authors can't see this feedback and are unable to give their side of the story unless the editor asks them to. So in the spirit of fairness, write comments to editors as though authors might read them too.
Reviewers should check the preferences of individual journals as to where they want review decisions to be stated. In particular, bear in mind that some journals will not want the recommendation included in any comments to authors, as this can cause editors difficulty later - see Section 11 for more advice about working with editors.
You will normally be asked to indicate your recommendation (e.g. accept, reject, revise and resubmit, etc.) from a fixed-choice list and then to enter your comments into a separate text box.
Recommending Acceptance
If you're recommending acceptance, give details outlining why, and if there are any areas that could be improved. Don't just give a short, cursory remark such as 'great, accept'. See Improving the Manuscript
Recommending Revision
Where improvements are needed, a recommendation for major or minor revision is typical. You may also choose to state whether you opt in or out of the post-revision review too. If recommending revision, state specific changes you feel need to be made. The author can then reply to each point in turn.
Some journals offer the option to recommend rejection with the possibility of resubmission – this is most relevant where substantial, major revision is necessary.
What can reviewers do to help? " Be clear in their comments to the author (or editor) which points are absolutely critical if the paper is given an opportunity for revisio n." (Jonathon Halbesleben, Editor of Journal of Occupational and Organizational Psychology)
Recommending Rejection
If recommending rejection or major revision, state this clearly in your review (and see the next section, 'When recommending rejection').
Where manuscripts have serious flaws you should not spend any time polishing the review you've drafted or give detailed advice on presentation.
Editors say, " If a reviewer suggests a rejection, but her/his comments are not detailed or helpful, it does not help the editor in making a decision ."
In your recommendations for the author, you should:
Remember to give constructive criticism even if recommending rejection. This helps developing researchers improve their work and explains to the editor why you felt the manuscript should not be published.
" When the comments seem really positive, but the recommendation is rejection…it puts the editor in a tough position of having to reject a paper when the comments make it sound like a great paper ." (Jonathon Halbesleben, Editor of Journal of Occupational and Organizational Psychology)
Visit our Wiley Author Learning and Training Channel for expert advice on peer review.
Watch the video, Ethical considerations of Peer Review
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Scholarly or research articles are written for experts in their fields. They are often peer-reviewed or reviewed by other experts in the field prior to publication. They often have terminology or jargon that is field specific. They are generally lengthy articles. Social science and science scholarly articles have similar structures as do arts and humanities scholarly articles. Not all items in a scholarly journal are peer reviewed. For example, an editorial opinion items can be published in a scholarly journal but the article itself is not scholarly. Scholarly journals may include book reviews or other content that have not been peer reviewed.
Empirical Study: (Original or Primary) based on observation, experimentation, or study. Clinical trials, clinical case studies, and most meta-analyses are empirical studies.
Review Article: (Secondary Sources) Article that summarizes the research in a particular subject, area, or topic. They often include a summary, an literature reviews, systematic reviews, and meta-analyses.
Clinical case study (Primary or Original sources): These articles provide real cases from medical or clinical practice. They often include symptoms and diagnosis.
Clinical trials ( Health Research): Th ese articles are often based on large groups of people. They often include methods and control studies. They tend to be lengthy articles.
Opinion Piece: An opinion piece often includes personal thoughts, beliefs, or feelings or a judgement or conclusion based on facts. The goal may be to persuade or influence the reader that their position on this topic is the best.
Book review: Recent review of books in the field. They may be several pages but tend to be fairly short.
The majority of social science and physical science articles include
In the Arts and Humanities, scholarly articles tend to be less formatted than in the social sciences and sciences. In the humanities, scholars are not conducting the same kinds of research experiments, but they are still using evidence to draw logical conclusions. Common sections of these articles include:
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The Cowles Library website will be unavailable on Tuesday, Aug. 6th from 1:00 p.m.- 5:00 p.m. due to a scheduled migration to a new platform. You can reach our list of Research Databases at https://library.drake.edu/az/databases
In This Section:
Description.
A literature review, also called a review article or review of literature, surveys the existing research on a topic. The term "literature" in this context refers to published research or scholarship in a particular discipline, rather than "fiction" (like American Literature) or an individual work of literature. In general, literature reviews are most common in the sciences and social sciences.
Literature reviews may be written as standalone works, or as part of a scholarly article or research paper. In either case, the purpose of the review is to summarize and synthesize the key scholarly work that has already been done on the topic at hand. The literature review may also include some analysis and interpretation. A literature review is not a summary of every piece of scholarly research on a topic.
Literature reviews can be very helpful for newer researchers or those unfamiliar with a field by synthesizing the existing research on a given topic, providing the reader with connections and relationships among previous scholarship. Reviews can also be useful to veteran researchers by identifying potentials gaps in the research or steering future research questions toward unexplored areas. If a literature review is part of a scholarly article, it should include an explanation of how the current article adds to the conversation. (From: https://library.drake.edu/englit/criticism)
Research articles: "are empirical articles that describe one or several related studies on a specific, quantitative, testable research question....they are typically organized into four text sections: Introduction, Methods, Results, Discussion." Source: https://psych.uw.edu/storage/writing_center/litrev.pdf)
1. Identify and define the topic that you will be reviewing.
The topic, which is commonly a research question (or problem) of some kind, needs to be identified and defined as clearly as possible. You need to have an idea of what you will be reviewing in order to effectively search for references and to write a coherent summary of the research on it. At this stage it can be helpful to write down a description of the research question, area, or topic that you will be reviewing, as well as to identify any keywords that you will be using to search for relevant research.
2. Conduct a Literature Search
Use a range of keywords to search databases such as PsycINFO and any others that may contain relevant articles. You should focus on peer-reviewed, scholarly articles . In SuperSearch and most databases, you may find it helpful to select the Advanced Search mode and include "literature review" or "review of the literature" in addition to your other search terms. Published books may also be helpful, but keep in mind that peer-reviewed articles are widely considered to be the “gold standard” of scientific research. Read through titles and abstracts, select and obtain articles (that is, download, copy, or print them out), and save your searches as needed. Most of the databases you will need are linked to from the Cowles Library Psychology Research guide .
3. Read through the research that you have found and take notes.
Absorb as much information as you can. Read through the articles and books that you have found, and as you do, take notes. The notes should include anything that will be helpful in advancing your own thinking about the topic and in helping you write the literature review (such as key points, ideas, or even page numbers that index key information). Some references may turn out to be more helpful than others; you may notice patterns or striking contrasts between different sources; and some sources may refer to yet other sources of potential interest. This is often the most time-consuming part of the review process. However, it is also where you get to learn about the topic in great detail. You may want to use a Citation Manager to help you keep track of the citations you have found.
4. Organize your notes and thoughts; create an outline.
At this stage, you are close to writing the review itself. However, it is often helpful to first reflect on all the reading that you have done. What patterns stand out? Do the different sources converge on a consensus? Or not? What unresolved questions still remain? You should look over your notes (it may also be helpful to reorganize them), and as you do, to think about how you will present this research in your literature review. Are you going to summarize or critically evaluate? Are you going to use a chronological or other type of organizational structure? It can also be helpful to create an outline of how your literature review will be structured.
5. Write the literature review itself and edit and revise as needed.
The final stage involves writing. When writing, keep in mind that literature reviews are generally characterized by a summary style in which prior research is described sufficiently to explain critical findings but does not include a high level of detail (if readers want to learn about all the specific details of a study, then they can look up the references that you cite and read the original articles themselves). However, the degree of emphasis that is given to individual studies may vary (more or less detail may be warranted depending on how critical or unique a given study was). After you have written a first draft, you should read it carefully and then edit and revise as needed. You may need to repeat this process more than once. It may be helpful to have another person read through your draft(s) and provide feedback.
6. Incorporate the literature review into your research paper draft. (note: this step is only if you are using the literature review to write a research paper. Many times the literature review is an end unto itself).
After the literature review is complete, you should incorporate it into your research paper (if you are writing the review as one component of a larger paper). Depending on the stage at which your paper is at, this may involve merging your literature review into a partially complete Introduction section, writing the rest of the paper around the literature review, or other processes.
These steps were taken from: https://psychology.ucsd.edu/undergraduate-program/undergraduate-resources/academic-writing-resources/writing-research-papers/writing-lit-review.html#6.-Incorporate-the-literature-r
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A review of computer vision-based crack detection methods in civil infrastructure: progress and challenges.
2. crack detection combining traditional image processing methods and deep learning, 2.1. crack detection based on image edge detection and deep learning, 2.2. crack detection based on threshold segmentation and deep learning, 2.3. crack detection based on morphological operations and deep learning, 3. crack detection based on multimodal data fusion, 3.1. multi-sensor fusion, 3.2. multi-source data fusion, 4. crack detection based on image semantic understanding, 4.1. crack detection based on classification networks, 4.2. crack detection based on object detection networks, 4.3. crack detection based on segmentation networks.
Model | Improvement/Innovation | Backbone/Feature Extraction Architecture | Efficiency | Results |
---|---|---|---|---|
FCS-Net [ ] | Integrating ResNet-50, ASPP, and BN | ResNet-50 | - | MIoU = 74.08% |
FCN-SFW [ ] | Combining fully convolutional network (FCN) and structural forests with wavelet transform (SFW) for detecting tiny cracks | FCN | Computing time = 1.5826 s | Precision = 64.1% Recall = 87.22% F1 score = 68.28% |
AFFNet [ ] | Using ResNet101 as the backbone network, and incorporating two attention mechanism modules, namely VH-CAM and ECAUM | ResNet101 | Execution time = 52 ms | MIoU = 84.49% FWIoU = 97.07% PA = 98.36% MPA = 92.01% |
DeepLabv3+ [ ] | Replacing ordinary convolution with separable convolution; improved SE_ASSP module | Xception-65 | - | AP = 97.63% MAP = 95.58% MIoU = 81.87% |
U-Net [ ] | The parameters were optimized (the depths of the network, the choice of activation functions, the selection of loss functions, and the data augmentation) | Encoder and decoder | Analysis speed (1024 × 1024 pixels) = 0.022 s | Precision = 84.6% Recall = 72.5% F1 score = 78.1% IoU = 64% |
KTCAM-Net [ ] | Combined CAM and RCM; integrating classification network and segmentation network | DeepLabv3 | FPS = 28 | Accuracy = 97.26% Precision = 68.9% Recall = 83.7% F1 score = 75.4% MIoU = 74.3% |
ADDU-Net [ ] | Featuring asymmetric dual decoders and dual attention mechanisms | Encoder and decoder | FPS = 35 | Precision = 68.9% Recall = 83.7% F1 score = 75.4% MIoU = 74.3% |
CGTr-Net [ ] | Optimized CG-Trans, TCFF, and hybrid loss functions | CG-Trans | - | Precision = 88.8% Recall = 88.3% F1 score = 88.6% MIoU = 89.4% |
PCSN [ ] | Using Adadelta as the optimizer and categorical cross-entropy as the loss function for the network | SegNet | Inference time = 0.12 s | mAP = 83% Accuracy = 90% Recall = 50% |
DEHF-Net [ ] | Introducing dual-branch encoder unit, feature fusion scheme, edge refinement module, and multi-scale feature fusion module | Dual-branch encoder unit | - | Precision = 86.3% Recall = 92.4% Dice score = 78.7% mIoU = 81.6% |
Student model + teacher model [ ] | Proposed a semi-supervised semantic segmentation network | EfficientUNet | - | Precision = 84.98% Recall = 84.38% F1 score = 83.15% |
6. evaluation index, 7. discussion, 8. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest.
Aspect | Combining Traditional Image Processing Methods and Deep Learning | Multimodal Data Fusion |
---|---|---|
Processing speed | Moderate—traditional methods are usually fast, but deep learning models may be slower, and the overall speed depends on the complexity of the deep learning model | Slower—data fusion and processing speed can be slow, especially with large-scale multimodal data, involving significant computational and data transfer overhead |
Accuracy | High—combines the interpretability of traditional methods with the complex pattern handling of deep learning, generally resulting in high detection accuracy | Typically higher—combining different data sources (e.g., images, text, audio) provides comprehensive information, improving overall detection accuracy |
Robustness | Strong—traditional methods provide background knowledge, enhancing robustness, but deep learning’s risk of overfitting may reduce robustness | Very strong—fusion of multiple data sources enhances the model’s adaptability to different environments and conditions, better handling noise and anomalies |
Complexity | High—integrating traditional methods and deep learning involves complex design and balancing, with challenges in tuning and interpreting deep learning models | High—involves complex data preprocessing, alignment, and fusion, handling inconsistencies and complexities from multiple data sources |
Adaptability | Strong—can adapt to different types of cracks and background variations, with deep learning models learning features from data, though it requires substantial labeled data | Very strong—combines diverse data sources, adapting well to various environments and conditions, and handling complex backgrounds and variations effectively |
Interpretability | Higher—traditional methods provide clear explanations, while deep learning models often lack interpretability; combining them can improve overall interpretability | Lower—fusion models generally have lower interpretability, making it difficult to intuitively explain how different data sources influence the final results |
Data requirements | High—deep learning models require a lot of labeled data, while traditional methods are more lenient, though deep learning still demands substantial data | Very high—requires large amounts of data from various modalities, and these data need to be processed and aligned effectively for successful fusion |
Flexibility | Moderate—combining traditional methods and deep learning handles various types of cracks, but may be limited in very complex scenarios | High—handles multiple data sources and different crack information, improving performance in diverse conditions through multimodal fusion |
Real-time capability | Poor—deep learning models are often slow to train and infer, making them less suitable for real-time detection, though combining with traditional methods can help | Poor—multimodal data fusion processing is generally slow, making it less suitable for real-time applications |
Maintenance cost | Moderate to high—deep learning models require regular updates and maintenance, while traditional methods have lower maintenance costs | High—involves ongoing maintenance and updates for multiple data sources, with complex data preprocessing and fusion processes |
Noise handling | Good—traditional methods effectively handle noise under certain conditions, and deep learning models can mitigate noise effects through training | Strong—multimodal fusion can complement information from different sources, improving robustness to noise and enhancing detection accuracy |
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Method | Features | Domain | Dataset | Image Device/Source | Results | Limitations |
---|---|---|---|---|---|---|
Canny and YOLOv4 [ ] | Crack detection and measurement | Bridges | 1463 images 256 × 256 pixels | Smartphone and DJI UAV | Accuracy = 92% mAP = 92% | The Canny edge detector is affected by the threshold |
Canny and GM-ResNet [ ] | Crack detection, measurement, and classification | Road | 522 images 224 × 224 pixels | Concrete crack sub-dataset | Precision = 97.9% Recall = 98.9% F1 measure = 98.0% Accuracy in shadow conditions = 99.3% Accuracy in shadow-free conditions = 99.9% | Its detection performance for complex cracks is not yet perfect |
Sobel and ResNet50 [ ] | Crack detection | Concrete | 4500 images 100 × 100 pixels | FLIR E8 | Precision = 98.4% Recall = 88.7% F1 measure = 93.2% | - |
Sobel and BARNet [ ] | Crack detection and localization | Road | 206 images 800 × 600 pixels | CrackTree200 dataset | AIU = 19.85% ODS = 79.9% OIS = 81.4% | Hyperparameter tuning is needed to balance the penalty weights for different types of cracks |
Canny and DeepLabV3+ [ ] | Crack detection | Road | 2000 × 1500 pixels | Crack500 dataset | MIoU = 77.64% MAE = 1.55 PA = 97.38% F1 score = 63% | Detection performance deteriorating in dark environments or when interfering objects are present |
Canny and RetinaNet [ ] | Crack detection and measurement | Road | 850 images 256 × 256 pixels | SDNET 2018 dataset | Precision = 85.96% Recall = 84.48% F1 score = 85.21% | - |
Canny and Transformer [ ] | Crack detection and segmentation | Buildings | 11298 images 450 × 450 pixels | UAVs | GA = 83.5% MIoU = 76.2% Precision = 74.3% Recall = 75.2% F1 score = 74.7% | Resulting in a marginal increment in computational costs for various network backbones |
Canny and Inception-ResNet-v2 [ ] | Crack detection, measurement, and classification | High-speed railway | 4650 images 400 × 400 pixels | The track inspection vehicle | High severity level: Precision = 98.37% Recall = 93.82% F1 score = 95.99% Low severity level: Precision = 94.25% Recall = 98.39% F1 score = 96.23% | Only the average width was used to define the severity of the crack, and the influence of the length on the detection result was not considered |
Canny and Unet [ ] | Crack detection | Buildings | 165 images | - | SSIM = 14.5392 PSNR = 0.3206 RMSE = 0.0747 | Relies on a large amount of mural data for training and enhancement |
Method | Features | Domain | Dataset | Image Device/Source | Results | Limitations |
---|---|---|---|---|---|---|
Otsu and Keras classifier [ ] | Crack detection, measurement, and classification | Concrete | 4000 images 227 × 227 pixels | Open dataset available | Classifiers accuracy = 98.25%, 97.18%, 96.17% Length error = 1.5% Width error = 5% Angle of orientation error = 2% | Only accurately quantify one single crack per image |
Otsu and TL MobileNetV2 [ ] | Crack detection, measurement, and classification | Concrete | 11435 images 224 × 224 pixels | Mendeley data—crack detection | Accuracy = 99.87% Recall = 99.74% Precision = 100% F1 score = 99.87% | Dependency on image quality |
Otsu, YOLOv7, Poisson noise, and bilateral filtering [ ] | Crack detection and classification | Bridges | 500 images 640 × 640 pixels | Dataset | Training time = 35 min Inference time = 8.9 s Target correct rate = 85.97% Negative sample misclassification rate = 42.86% | It does not provide quantified information such as length and area |
Adaptive threshold and WSIS [ ] | Crack detection | Road | 320 images 3024 × 4032 pixels | Photos of cracks | Recall = 90% Precision = 52% IoU = 50% F1 score = 66% Accuracy = 98% | For some small cracks (with a width of less than 3 pixels), model can only identify the existence of small cracks, but it is difficult to depict the cracks in detail |
Adaptive threshold and U-GAT-IT [ ] | Crack detection | Road | 300 training images and237 test images | DeepCrack dataset | Recall = 79.3% Precision = 82.2% F1 score = 80.7% | Further research is needed to address the interference caused by factors such as small cracks, road shadows, and water stains |
Local thresholding and DCNN [ ] | Crack detection | Concrete | 125 images 227 × 227 pixels | Cameras | Accuracy = 93% Recall = 91% Precision = 92% F1 score = 91% | - |
Otsu and Faster R-CNN [ ] | Crack detection, localization, and quantification | Concrete | 100 images 1920 × 1080 pixels | Nikon d7200 camera and Galaxy s9 camera | AP = 95% mIoU = 83% RMSE = 2.6 pixels Length accuracy = 93% | The proposed method is useful for concrete cracks only; its applicability for the detection of other crack materials might be limited |
Adaptive Dynamic Thresholding Module (ADTM) and Mask DINO [ ] | Crack detection and segmentation | Road | 395 images 2000 × 1500 pixels | Crack500 | mIoU = 81.3% mAcc = 96.4% gAcc = 85.0% | ADTM module can only handle binary classification problems |
Dynamic Thresholding Branch and DeepCrack [ ] | Crack detection and classification | Bridges | 3648 × 5472 pixels | Crack500 | mIoU = 79.3% mAcc = 98.5% gAcc = 86.6% | Image-level thresholds lead to misclassification of the background |
Method | Features | Domain | Dataset | Image Device/Source | Results | Limitations |
---|---|---|---|---|---|---|
Morphological closing operations and Mask R-CNN [ ] | Crack detection | Tunnel | 761 images 227 × 227 pixels | MTI-200a | Balanced accuracy = 81.94% F1 score = 68.68% IoU = 52.72% | Relatively small compared to the needs of the required sample size for universal conditions |
Morphological operations and Parallel ResNet [ ] | Crack detection and measurement | Road | 206 images (CrackTree200) 800 × 600 pixels and 118 images (CFD) 320 × 480 pixels | CrackTree200 dataset and CFD dataset | CrackTree200: Precision = 94.27% Recall = 92.52% F1 = 93.08% CFD: Precision = 96.21% Recall = 95.12% F1 = 95.63% | The method was only performed on accurate static images |
Closing and CNN [ ] | Crack detection, measurement, and classification | Concrete | 3208 images 256 × 256 pixels or 128 × 128 pixels | Hand-held DSLR cameras | Relative error = 5% Accuracy > 95% Loss < 0.1 | The extraction of the cracks’ edge will have a larger influence on the results |
Dilation and TunnelURes [ ] | Crack detection, measurement, and classification | Tunnel | 6810 images image sizes vary 10441 × 2910 to 50739 × 3140 | Night 4K line-scan cameras | AUC = 0.97 PA = 0.928 IoU = 0.847 | The medial-axis skeletonization algorithm created many errors because it was susceptible to the crack intersection and the image edges where the crack’s representation changed |
Opening, closing, and U-Net [ ] | Crack detection, measurement, and classification | Concrete | 200 images 512 × 512 pixels | Canon SX510 HS camera | Precision = 96.52% Recall = 93.73% F measure = 96.12% Accuracy = 99.74% IoU = 78.12% | It can only detect the other type of cracks which have the same crack geometry as that of thermal cracks |
Morphological operations and DeepLabV3+ [ ] | Crack detection and measurement | Masonry structure | 200 images 780 × 355 pixels and 2880 × 1920 pixels | Internet, drones, and smartphones | IoU = 0.97 F1 score = 98% Accuracy = 98% | The model will not detect crack features that do not appear in the dataset (complicated cracks, tiny cracks, etc.) |
Erosion, texture analysis techniques, and InceptionV3 [ ] | Crack detection and classification | Bridges | 1706 images 256 × 256 pixels | Cameras | F1 score = 93.7% Accuracy = 94.07% | - |
U-Net, opening, and closing operations [ ] | Crack detection and segmentation | Bridges | 244 images 512 × 512 pixels | Cameras | mP = 44.57% mR = 53.13% Mf1 = 42.79% mIoU = 64.79% | The model lacks generality, and there are cases of false detection |
Sensor Type | Fusion Method | Advantages | Disadvantages | Application Scenarios |
---|---|---|---|---|
Optical sensor [ ] | Data-level fusion | High resolution, rich in details | Susceptible to light and occlusion | Surface crack detection, general environments |
Thermal sensor [ ] | Feature level fusion | Suitable for nighttime or low-light environments, detects temperature changes | Low resolution, lack of detail | Nighttime detection, heat-sensitive areas, large-area surface crack detection |
Laser sensor [ ] | Data-level fusion and feature level fusion | High-precision 3D point cloud data, accurately measures crack morphology | High equipment cost, complex data processing | Complex structures, precise measurements |
Strain sensor [ ] | Feature level fusion and decision-level fusion | High sensitivity to structural changes; durable | Requires contact with the material; installation complexity | Monitoring structural health in bridges and buildings; detecting early-stage crack development |
Ultrasonic sensor [ ] | Data-level fusion and feature level fusion | Detects internal cracks in materials, strong penetration | Affected by material and geometric shape, limited resolution | Internal cracks, metal material detection |
Optical fiber sensor [ ] | Feature level fusion | High sensitivity to changes in material properties, non-contact measurement | Affected by environmental conditions, requires calibration | Surface crack detection, structural health monitoring |
Vibration sensor [ ] | Data-level fusion | Detects structural vibration characteristics, strong adaptability | Affected by environmental vibrations, requires complex signal processing | Dynamic crack monitoring, bridges and other structures |
Multispectral satellite sensor [ ] | Data-level fusion | Rich spectral information | Limited spectral resolution, weather- and lighting-dependent, high cost | Pavement crack detection, bridge and infrastructure monitoring, building facade inspection |
High-resolution satellite sensors [ ] | Data-level fusion and feature level fusion | High spatial resolution, wide coverage, frequent revisit times, rich information content | Weather dependency, high cost, data processing complexity, limited temporal resolution | Road and pavement crack detection, bridge and infrastructure monitoring, urban building facade inspection, railway and highway crack monitoring |
Scale | Dataset/(Pixels × Pixels) | References |
---|---|---|
Image-based | 227 × 227 | [ , , , ] |
224 × 224 | [ ] | |
256 × 256 | [ ] | |
416 × 416 | [ ] | |
512 × 512 | [ ] | |
Patch-based | 128 × 128 | [ , ] |
200 × 200 | [ ] | |
224 × 224 | [ , , , , ] | |
227 × 227 | [ ] | |
256 × 256 | [ , ] | |
300 × 300 | [ , ] | |
320 × 480 | [ , ] | |
544 × 384 | [ ] | |
512 × 512 | [ , , , ] | |
584 × 384 | [ ] |
Model | Improvement/Innovation | Dataset | Backbone | Results |
---|---|---|---|---|
Faster R-CNN [ ] | Combined with drones for crack detection | 2000 images 5280 × 2970 pixels | VGG-16 | Precision = 92.03% Recall = 96.26% F1 score = 94.10% |
Faster R-CNN [ ] | Double-head structure is introduced, including an independent fully connected head and a convolution head | 1622 images 1612 × 1947 pixels | ResNet50 | AP = 47.2% |
Mask R-CNN [ ] | The morphological closing operation was incorporated into the M-R-101-FPN model to form an integrated model | 761 images 227 × 227 pixels | ResNets and VGG | Balanced accuracy = 81.94% F1 score = 68.68% IoU = 52.72% |
Mask R-CNN [ ] | PAFPN module and edge detection branch was introduced | 9680 images 1500 × 1500 pixels | ResNet-FPN | Precision = 92.03% Recall = 96.26% AP = 94.10% mAP = 90.57% Error rate = 0.57% |
Mask R-CNN [ ] | FPN structure introduces side join method and combines FPN with ResNet-101 to change RoI-Pooling layer to RoI-Align layer | 3430 images 1024 × 1024 pixels | ResNet101 | AP = 83.3% F1 score = 82.4% Average error = 2.33% mIoU = 70.1% |
YOLOv3-tiny [ ] | A structural crack detection and quantification method combined with structured light is proposed | 500 images 640 × 640 pixels | Darknet-53 | Accuracy = 94% Precision = 98% |
YOLOv4 [ ] | Some lightweight networks were used instead of the original backbone feature extraction network, and DenseNet, MobileNet, and GhostNet were selected for the lightweight networks | 800 images 416 × 416 pixels | DenseNet, MobileNet v1, MobileNet v2, MobileNet v3, and GhostNet | Precision = 93.96% Recall = 90.12% F1 score = 92% |
YOLOv4 [ ] | - | 1463 images 256 × 256 pixels | Darknet-53 | Accuracy = 92% mAP = 92% |
Datasets Name | Number of Images | Image Resolution | Manual Annotation | Scope of Applicability | Limitations |
---|---|---|---|---|---|
CrackTree200 [ ] | 206 images | 800 × 600 pixels | Pixel-level annotations for cracks | Crack classification and segmentation | With only 200 images, the dataset’s relatively small size can hinder the model’s ability to generalize across diverse conditions, potentially leading to overfitting on the specific examples provided |
Crack500 [ ] | 500 images | 2000 × 1500 pixels | Pixel-level annotations for cracks | Crack classification and segmentation | Limited number of images compared to larger datasets, which might affect the generalization of models trained on this dataset |
SDNET 2018 [ ] | 56000 images | 256 × 256 pixels | Pixel-level annotations for cracks | Crack classification and segmentation | The dataset’s focus on concrete surfaces may limit the model’s performance when applied to different types of surfaces or structures |
Mendeley data—crack detection [ ] | 40000 images | 227 × 227 pixels | Pixel-level annotations for cracks | Crack classification | The dataset might not cover all types of cracks or surface conditions, which can limit its applicability to a wide range of real-world scenarios |
DeepCrack [ ] | 2500 images | 512 × 512 pixels | Annotations for cracks | Crack segmentation | The resolution might limit the ability of models to capture very small or subtle crack features |
CFD [ ] | 118 images | 320 × 480 pixels | Pixel-level annotations for cracks | Crack segmentation | The dataset contains a limited number of data samples, which may limit the generalization ability of the model |
CrackTree260 [ ] | 260 images | 800 × 600 pixels and 960 × 720 pixels | Pixel-level labeling, bounding boxes, or other crack markers | Object detection and segmentation | Because the dataset is small, it can be easy for the model to overfit the training data, especially if you’re using a complex model |
CrackLS315 [ ] | 315 images | 512 × 512 pixels | Pixel-level segmentation mask or bounding box | Object detection and segmentation | The small size of the dataset may make the model perform poorly in complex scenarios, especially when encountering different types of cracks or uncommon crack features |
Stone331 [ ] | 331 images | 512 × 512 pixels | Pixel-level segmentation mask or bounding box | Object detection and segmentation | The relatively small number of images limits the generalization ability of the model, especially in deep learning tasks where smaller datasets tend to lead to overfitting |
Index | Index Value and Calculation Formula | Curve |
---|---|---|
True positive | - | |
False positive | - | |
True negative | - | |
False negative | - | |
Precision | PRC | |
Recall | PRC, ROC curve | |
F1 score | F1 score curve | |
Accuracy | Accuracy vs. threshold curve | |
Average precision | PRC | |
Mean average precision | - | |
IoU | IoU distribution curve, precision-recall curve with IoU thresholds |
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
Yuan, Q.; Shi, Y.; Li, M. A Review of Computer Vision-Based Crack Detection Methods in Civil Infrastructure: Progress and Challenges. Remote Sens. 2024 , 16 , 2910. https://doi.org/10.3390/rs16162910
Yuan Q, Shi Y, Li M. A Review of Computer Vision-Based Crack Detection Methods in Civil Infrastructure: Progress and Challenges. Remote Sensing . 2024; 16(16):2910. https://doi.org/10.3390/rs16162910
Yuan, Qi, Yufeng Shi, and Mingyue Li. 2024. "A Review of Computer Vision-Based Crack Detection Methods in Civil Infrastructure: Progress and Challenges" Remote Sensing 16, no. 16: 2910. https://doi.org/10.3390/rs16162910
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Each year, new MIT graduate students are tasked with the momentous decision of choosing a research group that will serve as their home for the next several years. Among many questions they face: join an established research effort, or work with a new faculty member in a growing group?
Professors Cynthia Breazeal, leading a group of over 30 students, and Ming Guo, with a lab of fewer than 10, demonstrate that excellent mentorship can thrive in a research group of any size.
Cynthia Breazeal: Flexible leadership
Cynthia Breazeal is a professor of media arts and sciences at MIT, where she founded and directs the Personal Robots group at the MIT Media Lab. She is also the MIT dean for digital learning, leading MIT Open Learning’s business and research and engagement units. Breazeal is a pioneer of social robotics and human-robot interaction, and her research group investigates social robots applied to education, pediatrics, health and wellness, and aging.
Breazeal’s focus on taking multidisciplinary approaches to her research has resulted in an inclusive and supportive lab environment. Moreover, she does not shy away from taking students with unconventional backgrounds.
One nominator joined Breazeal's lab as a design researcher without a computer science background. However, Breazeal recognized the value of their work within the context of her lab’s research directions. “I was a bit of an oddball in the group”, the nominator modestly recounts, “but had joined to help make the work in the group more human-centered.”
Throughout the student's academic journey, Breazeal offered unwavering support, whether by connecting them with experts to solve specific problems or guiding them through the academic job search process.
Over the Covid-19 pandemic, Breazeal prioritized gathering student feedback through a survey about how she could best support her research group. In response to this input, Breazeal established the Senior Research Team (SRT) within her group.
The SRT includes PhD holders such as postdocs and research scientists who provide personalized mentorship to one or two graduate students per semester. The SRT members serve as dedicated advocates and points of contact, with weekly check-ins to address questions within the lab. Additionally, SRT members meet by themselves weekly to discuss student concerns and bring up urgent issues with Breazeal directly. Lastly, students can sign up for meetings with Breazeal and participate in paper review sessions with her and co-authors.
In the nominator’s opinion, this new system was implemented because Breazeal cares about her students and her lab culture. With over 30 members in her group, Breazeal cannot provide hands-on support for everyone daily, but she still deeply cares about each person's experience in the lab. The nominator shared that Breazeal “understands as she progresses in her career, she needs to make sure that she is changing and creating new systems for her research group to continue to operate smoothly.”
Ming Guo: Emphasizing learning over achievement
Ming Guo is an associate professor in the Department of Mechanical Engineering. Guo’s group works at the interface of mechanics, physics, and cell biology, seeking to understand how physical properties and biological function affect each other in cellular systems.
A key aspect of Guo’s mentorship style is his ability to foster an environment where students feel comfortable expressing their difficulties. He actively shows empathy for his students’ lives outside of the lab, often reaching out to provide support during challenging times. When one nominator found themselves faced with significant personal difficulties, Guo made a point to check in regularly, ensuring the student had a support network of friends and labmates.
Guo champions his students both academically and personally. For instance, when a collaborating lab placed unrealistic expectations on a student’s experimental output, Guo openly praised the student’s efforts and achievements in a joint meeting, alleviating pressure and highlighting the student’s hard work.
In addition, Guo encourages vulnerable conversations about issues affecting students, such as political developments and racial inequities. During the graduate student unionization process, he fostered open discussion, showing genuine interest in understanding the challenges faced by graduate students and using these insights to better support them.
In Guo’s research group, learning and development are prioritized over achievements and goals. When students encounter challenges in their research, Guo helps them maintain perspective by validating their struggles and recognizing the skills they acquire through difficult experiments. By celebrating their progress and emphasizing the importance of the learning process, he ensures that students understand the value of their experiences beyond outcomes. This approach not only boosts their confidence, but also fosters a deeper appreciation for the scientific process and their own development as researchers.
Guo says that he feels most energized and happy when he talks to students. He looks forward to the new ideas that they present. One nominator commented on how much Guo enjoys giving feedback at group meetings: “Sometimes he isn’t convinced in the beginning, but he has cultivated our lab atmosphere to be conducive to extended discussion.”
The nominator continues, “When things do work and become really interesting, he is extremely excited with us and pushes us to share our own ideas with the wider research community.”
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We, the APA Style team, are not robots. We can all pass a CAPTCHA test , and we know our roles in a Turing test . And, like so many nonrobot human beings this year, we’ve spent a fair amount of time reading, learning, and thinking about issues related to large language models, artificial intelligence (AI), AI-generated text, and specifically ChatGPT . We’ve also been gathering opinions and feedback about the use and citation of ChatGPT. Thank you to everyone who has contributed and shared ideas, opinions, research, and feedback.
In this post, I discuss situations where students and researchers use ChatGPT to create text and to facilitate their research, not to write the full text of their paper or manuscript. We know instructors have differing opinions about how or even whether students should use ChatGPT, and we’ll be continuing to collect feedback about instructor and student questions. As always, defer to instructor guidelines when writing student papers. For more about guidelines and policies about student and author use of ChatGPT, see the last section of this post.
If you’ve used ChatGPT or other AI tools in your research, describe how you used the tool in your Method section or in a comparable section of your paper. For literature reviews or other types of essays or response or reaction papers, you might describe how you used the tool in your introduction. In your text, provide the prompt you used and then any portion of the relevant text that was generated in response.
Unfortunately, the results of a ChatGPT “chat” are not retrievable by other readers, and although nonretrievable data or quotations in APA Style papers are usually cited as personal communications , with ChatGPT-generated text there is no person communicating. Quoting ChatGPT’s text from a chat session is therefore more like sharing an algorithm’s output; thus, credit the author of the algorithm with a reference list entry and the corresponding in-text citation.
When prompted with “Is the left brain right brain divide real or a metaphor?” the ChatGPT-generated text indicated that although the two brain hemispheres are somewhat specialized, “the notation that people can be characterized as ‘left-brained’ or ‘right-brained’ is considered to be an oversimplification and a popular myth” (OpenAI, 2023).
OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat
You may also put the full text of long responses from ChatGPT in an appendix of your paper or in online supplemental materials, so readers have access to the exact text that was generated. It is particularly important to document the exact text created because ChatGPT will generate a unique response in each chat session, even if given the same prompt. If you create appendices or supplemental materials, remember that each should be called out at least once in the body of your APA Style paper.
When given a follow-up prompt of “What is a more accurate representation?” the ChatGPT-generated text indicated that “different brain regions work together to support various cognitive processes” and “the functional specialization of different regions can change in response to experience and environmental factors” (OpenAI, 2023; see Appendix A for the full transcript).
The in-text citations and references above are adapted from the reference template for software in Section 10.10 of the Publication Manual (American Psychological Association, 2020, Chapter 10). Although here we focus on ChatGPT, because these guidelines are based on the software template, they can be adapted to note the use of other large language models (e.g., Bard), algorithms, and similar software.
The reference and in-text citations for ChatGPT are formatted as follows:
Let’s break that reference down and look at the four elements (author, date, title, and source):
Author: The author of the model is OpenAI.
Date: The date is the year of the version you used. Following the template in Section 10.10, you need to include only the year, not the exact date. The version number provides the specific date information a reader might need.
Title: The name of the model is “ChatGPT,” so that serves as the title and is italicized in your reference, as shown in the template. Although OpenAI labels unique iterations (i.e., ChatGPT-3, ChatGPT-4), they are using “ChatGPT” as the general name of the model, with updates identified with version numbers.
The version number is included after the title in parentheses. The format for the version number in ChatGPT references includes the date because that is how OpenAI is labeling the versions. Different large language models or software might use different version numbering; use the version number in the format the author or publisher provides, which may be a numbering system (e.g., Version 2.0) or other methods.
Bracketed text is used in references for additional descriptions when they are needed to help a reader understand what’s being cited. References for a number of common sources, such as journal articles and books, do not include bracketed descriptions, but things outside of the typical peer-reviewed system often do. In the case of a reference for ChatGPT, provide the descriptor “Large language model” in square brackets. OpenAI describes ChatGPT-4 as a “large multimodal model,” so that description may be provided instead if you are using ChatGPT-4. Later versions and software or models from other companies may need different descriptions, based on how the publishers describe the model. The goal of the bracketed text is to briefly describe the kind of model to your reader.
Source: When the publisher name and the author name are the same, do not repeat the publisher name in the source element of the reference, and move directly to the URL. This is the case for ChatGPT. The URL for ChatGPT is https://chat.openai.com/chat . For other models or products for which you may create a reference, use the URL that links as directly as possible to the source (i.e., the page where you can access the model, not the publisher’s homepage).
You may have noticed the confidence with which ChatGPT described the ideas of brain lateralization and how the brain operates, without citing any sources. I asked for a list of sources to support those claims and ChatGPT provided five references—four of which I was able to find online. The fifth does not seem to be a real article; the digital object identifier given for that reference belongs to a different article, and I was not able to find any article with the authors, date, title, and source details that ChatGPT provided. Authors using ChatGPT or similar AI tools for research should consider making this scrutiny of the primary sources a standard process. If the sources are real, accurate, and relevant, it may be better to read those original sources to learn from that research and paraphrase or quote from those articles, as applicable, than to use the model’s interpretation of them.
We’ve also received a number of other questions about ChatGPT. Should students be allowed to use it? What guidelines should instructors create for students using AI? Does using AI-generated text constitute plagiarism? Should authors who use ChatGPT credit ChatGPT or OpenAI in their byline? What are the copyright implications ?
On these questions, researchers, editors, instructors, and others are actively debating and creating parameters and guidelines. Many of you have sent us feedback, and we encourage you to continue to do so in the comments below. We will also study the policies and procedures being established by instructors, publishers, and academic institutions, with a goal of creating guidelines that reflect the many real-world applications of AI-generated text.
For questions about manuscript byline credit, plagiarism, and related ChatGPT and AI topics, the APA Style team is seeking the recommendations of APA Journals editors. APA Style guidelines based on those recommendations will be posted on this blog and on the APA Style site later this year.
Update: APA Journals has published policies on the use of generative AI in scholarly materials .
We, the APA Style team humans, appreciate your patience as we navigate these unique challenges and new ways of thinking about how authors, researchers, and students learn, write, and work with new technologies.
American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000
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a critical review of the relevant literature and then ensuring that their research design, methods, results, and conclusions follow logically from these objectives (Maier, 2013). There exist a number of papers devoted to instruction on how to write a good review paper. Among the most . useful for scientific reviews, in my estimation, are those by
How to review a paper. A good peer review requires disciplinary expertise, a keen and critical eye, and a diplomatic and constructive approach. Credit: dmark/iStockphoto. As junior scientists develop their expertise and make names for themselves, they are increasingly likely to receive invitations to review research manuscripts.
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.
An ideal review article should be logically structured and efficiently utilise illustrations, in the form of tables and figures, to convey the key findings and relationships in the study. According to Tay , illustrations often take a secondary role in review papers when compared to primary research papers which are focused on illustrations ...
One of my favourite review-style articles 3 presents a plot bringing together data from multiple research papers (many of which directly contradict each other). This is then used to identify broad ...
Types Of Review Paper. Not all review articles are created equal. Each type has its methodology, purpose, and format, catering to different research needs and questions. Systematic Review Paper. First up is the systematic review, the crème de la crème of review types. It's known for its rigorous methodology, involving a detailed plan for ...
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 ...
Literature reviews are valuable resources for the scientific community. With research accelerating at an unprecedented speed in recent years and more and more original papers being published, review articles have become increasingly important as a means to keep up-to-date with developments in a particular area of research.
A literature review is a surveys scholarly articles, books and other sources relevant to a particular. issue, area of research, or theory, and by so doing, providing a description, summary, and ...
Writing the Review. 1Good scientific writing tells a story, so come up with a logical structure for your paper, with a beginning, middle, and end. Use appropriate headings and sequencing of ideas to make the content flow and guide readers seamlessly from start to finish.
Think about structuring your review like an inverted pyramid. Put the most important information at the top, followed by details and examples in the center, and any additional points at the very bottom. Here's how your outline might look: 1. Summary of the research and your overall impression. In your own words, summarize what the manuscript ...
Writing a literature review requires a range of skills to gather, sort, evaluate and summarise peer-reviewed published data into a relevant and informative unbiased narrative. Digital access to research papers, academic texts, review articles, reference databases and public data sets are all sources of information that are available to enrich ...
Literature reviews are in great demand in most scientific fields. Their need stems from the ever-increasing output of scientific publications .For example, compared to 1991, in 2008 three, eight, and forty times more papers were indexed in Web of Science on malaria, obesity, and biodiversity, respectively .Given such mountains of papers, scientists cannot be expected to examine in detail every ...
This paper discusses literature review as a methodology for conducting research and offers an overview of different types of reviews, as well as some guidelines to how to both conduct and evaluate a literature review paper. It also discusses common pitfalls and how to get literature reviews published. 1.
The fundamental rationale of writing a review article is to make a readable synthesis of the best literature sources on an important research inquiry or a topic. This simple definition of a review article contains the following key elements: The question (s) to be dealt with.
A literature review is a review and synthesis of existing research on a topic or research question. A literature review is meant to analyze the scholarly literature, make connections across writings and identify strengths, weaknesses, trends, and missing conversations. ... Conducting Research Literature Reviews: From Internet to Paper by Arlene ...
A review article can also be called a literature review, or a review of literature. It is a survey of previously published research on a topic. It should give an overview of current thinking on the topic. And, unlike an original research article, it will not present new experimental results. Writing a review of literature is to provide a ...
Quality research is about building onto the existing work of others, "standing on the shoulders of giants", as Newton put it.The literature review chapter of your dissertation, thesis or research project is where you synthesise this prior work and lay the theoretical foundation for your own research.. Long story short, this chapter is a pretty big deal, which is why you want to make sure ...
A systematic review is a type of review that uses repeatable methods to find, select, and synthesize all available evidence. It answers a clearly formulated research question and explicitly states the methods used to arrive at the answer. Example: Systematic review. In 2008, Dr. Robert Boyle and his colleagues published a systematic review in ...
A review article is a comprehensive summary of the current understanding of a specific research topic and is based on previously published research. Unlike research papers, it does not contain new results, but can propose new inferences based on the combined findings of previous research. Types of review articles
Briefly summarize what the paper is about and what the findings are. Try to put the findings of the paper into the context of the existing literature and current knowledge. Indicate the significance of the work and if it is novel or mainly confirmatory. Indicate the work's strengths, its quality and completeness.
WHAT IS A REVIEW PAPER? The purpose of a review paper is to succinctly review recent progress in a particular topic. Overall, the paper summarizes the current state of knowledge of the topic. It creates an understanding of the topic for the reader by discussing the findings presented in recent research papers. A review paper is not a "term ...
Review Article: (Secondary Sources) Article that summarizes the research in a particular subject, area, or topic. They often include a summary, an literature reviews, systematic reviews, and meta-analyses. Clinical case study (Primary or Original sources): These articles provide real cases from medical or clinical practice. They often include ...
After the literature review is complete, you should incorporate it into your research paper (if you are writing the review as one component of a larger paper). Depending on the stage at which your paper is at, this may involve merging your literature review into a partially complete Introduction section, writing the rest of the paper around the ...
To maximize the retrieval of papers relevant to the research, these keywords were combined with a basic set of keywords to comprehensively utilize the database; a total of 120 papers were ultimately selected for analysis and summarization over the period 2020-2024. ... This paper provides a systematic review of current research on crack ...
The purpose of this systematic review and thematic synthesis was to identify and consolidate research on the support needs of impacted Higher Education (HE) counselors that have experienced a student suicide death. When exposed to a student suicide death, counselors are often extensively involved in a postvention response. This systematic review synthesized four qualitative papers that ...
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Lastly, students can sign up for meetings with Breazeal and participate in paper review sessions with her and co-authors. In the nominator's opinion, this new system was implemented because Breazeal cares about her students and her lab culture. With over 30 members in her group, Breazeal cannot provide hands-on support for everyone daily, but ...
If you've used ChatGPT or other AI tools in your research, describe how you used the tool in your Method section or in a comparable section of your paper. For literature reviews or other types of essays or response or reaction papers, you might describe how you used the tool in your introduction.
(2022) provide reviews of the literature. Several recent papers look at the same payment pause, with a focus on distributional effects (Briones, Powell and Turner, 2023), financially distressed borrowers (Chava, Tookes and Zhang, 2023), and the flypaper effect (Katz, 2023). This paper contributes to this area in two ways.