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Systematic literature reviews (SLRs) are important because they provide a rigorous and systematic approach to identifying, evaluating, and synthesizing all available research studies on a particular research question or topic. By conducting an SLR, researchers can obtain a comprehensive and unbiased overview of the existing evidence, which can be used to inform future research, practice, and policy. SLRs are designed to be transparent and rigorous, using well-defined search and selection criteria, and a reproducible methodology. SLRs are therefore considered the gold standard for synthesizing evidence, and they are increasingly being used in many fields of research to guide decision-making and policy development. But wait ????!!! There’s more.

One common problem that researchers may face when conducting systematic literature reviews (SLRs) is struggling with extracting data from the included studies. Data extraction is a critical step in the SLR process, as it involves systematically identifying and recording key information from each study, which is then used to inform the synthesis and analysis of the evidence.

You can find many blogs on the internet which teach you how to extract data from SLR (Systematic Literature Review) but then what is the difference between us and them? The difference is only that we are going to teach you to extract data for SLR but in a lesser time. We will give you the names and also the uses of five tools which can help you to efficiently extract data for SLR in a lesser time. So, Let’s get started.

1. Covidence: Covidence is a web-based platform designed to support the entire systematic review process, including study selection and data extraction. The platform allows multiple reviewers to work on the review simultaneously and offers customizable data extraction forms.

Wait!!! It’s just the start. Now, we will know more about this tool. We will be discussing how Covidence can be used to organize and extract data from literature sources. Sounds great right? So, let’s go to the ride then.

Covidence is a web-based software that can be used to organize and extract data from literature sources in the following ways:

• Importing and organizing literature sources: Researchers can import literature sources from databases like PubMed, Embase, and Cochrane Library into Covidence. Covidence automatically detects and removes any duplicates and allows researchers to organize their sources into different categories, such as included, excluded, and undecided.

• Screening studies for relevance: Covidence provides a user-friendly interface for screening studies for relevance to the research question. Researchers can use customizable screening forms to assess the eligibility of each study, and they can mark studies as included, excluded, or undecided. Multiple reviewers can work together to screen studies, and Covidence keeps track of each reviewer's decisions.

• Extracting data from included studies: Once studies have been included in the review, Covidence can be used to extract relevant data from each study. Researchers can create customizable data extraction forms that capture important information such as study characteristics, participant demographics, and outcomes. Covidence also includes a data extraction tool that makes it easy to extract data from PDFs and other documents.

• Managing and resolving conflicts: Covidence includes features to help manage conflicts that arise during the screening and data extraction process. For example, reviewers can assign tasks to one another and use the built-in messaging features to communicate about conflicts or disagreements.

• Synthesizing and reporting findings: Covidence can be used to generate summary tables and graphs that summarize the data extracted from included studies. Researchers can also use Covidence to synthesize findings across studies and generate a report that summarizes the key findings of the review.

We are still not over yet with this tool. We have one last question to discuss which we think will add a lot of value to this blog. We are going to provide some tips for using Covidence to streamline data extraction which will be helpful to streamline the whole blog in a way. So, here we go.

Here are some tips for using Covidence to streamline data extraction:

• Customize your data extraction forms: Covidence allows you to create customizable data extraction forms that capture the information you need from each study. Take the time to create a form that is tailored to your research question and includes all the relevant fields. This will help to streamline the data extraction process and ensure that you are capturing all the information you need.

• Use the data extraction tool: Covidence includes a data extraction tool that can be used to extract data directly from PDFs and other documents. This tool can save you a lot of time and effort, especially when dealing with large numbers of studies.

• Assign tasks to team members: If you are working with a team, use Covidence's task assignment feature to assign data extraction tasks to individual team members. This will help to ensure that everyone is working efficiently and that no studies are missed.

• Use the tagging feature: Covidence includes a tagging feature that allows you to tag data extraction fields with keywords or phrases. This can be useful when you want to group studies by a particular characteristic or when you want to search for studies with specific features.

• Use the data export feature: Once you have completed data extraction, use Covidence's data export feature to export your data to a spreadsheet or statistical software. This will allow you to perform further analysis and synthesize your findings across studies.

2. DistillerSR: DistillerSR is a web-based platform that supports the entire systematic review process, including screening, data extraction, and data analysis. It offers a range of features, including customizable data extraction forms, automatic reference management, and real-time collaboration.

Now the same question we are going to answer for this tool but in a different format. But why the same question? Because it’s up to you to choose which tool suits you the most and saves a lot of time. So, we are going to discuss the special features of DistillerSR to extract data from literature sources. So, let’s begin then.

DistillerSR is a powerful tool for systematic review management and offers several special features for data extraction from literature sources. Here are some of the key features of DistillerSR for data extraction:

• Customizable data extraction forms: DistillerSR allows researchers to create customizable data extraction forms that capture the information they need from each study. These forms can include fields for study characteristics, participant demographics, and outcomes of interest. The forms can be easily modified and customized to suit the needs of the research question.

• Automated data extraction: DistillerSR includes a feature for automated data extraction, which can extract data from full-text articles, tables, and figures using natural language processing (NLP). This feature can save researchers significant time and effort, especially when dealing with large numbers of studies.

• Manual data extraction: DistillerSR also allows for manual data extraction, which can be useful for capturing information that cannot be extracted using NLP or for validating the results of automated data extraction. Researchers can extract data from full-text articles or from scanned copies of articles.

• Data validation and quality control: DistillerSR includes a range of features for data validation and quality control, including duplicate checking, error reporting, and inter-rater reliability testing. These features help to ensure the accuracy and consistency of the extracted data.

• Collaboration tools: DistillerSR includes several collaboration tools that facilitate teamwork and communication among reviewers. Researchers can assign tasks to team members, leave comments and notes on studies, and communicate with one another through the platform.

• Data visualization: DistillerSR provides several tools for data visualization, including summary tables and graphs, forest plots, and funnel plots. These tools can help researchers to synthesize findings across studies and to identify patterns or trends in the data.

Here, our final tip comes. When everyone is asking for tips, then why should we stay behind? Hence, we are going to provide some tips for using DistillerSR to streamline data extraction. So, its our time to give you some tips and we hope, you will make good use of the tips.

Here are some tips for using DistillerSR to streamline data extraction:

• Use automated data extraction when possible: DistillerSR's automated data extraction feature can save you a lot of time and effort, especially when dealing with large numbers of studies. Be sure to review the results of automated data extraction to ensure that the extracted data is accurate.

• Use manual data extraction for validation: Manual data extraction can be useful for validating the results of automated data extraction or for capturing information that cannot be extracted using NLP. Use manual data extraction selectively and prioritize fields that are not captured through automated extraction.

• Use duplicate checking and error reporting: DistillerSR includes features for duplicate checking and error reporting, which can help to ensure the accuracy and consistency of the extracted data. Use these features regularly to catch any errors or inconsistencies in the data.

• Use inter-rater reliability testing: If you are working with a team, use DistillerSR's inter-rater reliability testing feature to test the consistency of data extraction between reviewers. This will help to ensure that all reviewers are extracting data in a consistent and accurate manner.

• Utilize collaboration tools: DistillerSR includes several collaboration tools, such as task assignments and commenting features. Use these tools to facilitate teamwork and communication among reviewers, and to ensure that everyone is working efficiently.

• Use data visualization tools: DistillerSR provides several tools for data visualization, including summary tables and graphs, forest plots, and funnel plots. These tools can help you to synthesize findings across studies and to identify patterns or trends in the data.

3. EPPI-Reviewer: EPPI-Reviewer is a web-based platform that supports the entire systematic review process, including study selection and data extraction. It offers customizable data extraction forms, automatic reference management, and real-time collaboration.

Now, an important question comes regarding this tool which is the special features of EPPI-Reviewer which is helpful for extracting data easily. Believe me, after reading this, you will know where you were wasting most of your time and how this tool can help you to recoup the wasted time. So, let’s begin.

EPPI-Reviewer is a comprehensive systematic review software tool that offers many advanced features to facilitate the systematic review process. While other systematic review software tools may have similar features, EPPI-Reviewer does have a few unique features that distinguish it from other tools:

• Inclusion/exclusion screening: EPPI-Reviewer includes an advanced screening feature that allows reviewers to screen studies based on their inclusion or exclusion criteria. This feature uses machine learning algorithms to suggest which studies should be included or excluded based on the reviewer's decisions, thereby saving time and effort.

• Real-time collaboration: EPPI-Reviewer allows multiple reviewers to work on the same review project in real-time, with changes made by one reviewer automatically updated and visible to all other reviewers. This feature is particularly useful for large review projects with multiple reviewers.

• Citation-by-citation review: EPPI-Reviewer offers a unique citation-by-citation review feature that allows reviewers to focus on specific sections of an article, such as the abstract, methods, or results section, instead of having to read the entire article. This feature can save time and make the review process more efficient.

• Customizable extraction forms: EPPI-Reviewer allows reviewers to create customizable extraction forms with conditional logic, which allows for a more flexible and streamlined data extraction process. This feature can be especially helpful for reviews with complex data extraction requirements.

• Statistical analysis: EPPI-Reviewer offers advanced statistical analysis tools that can be used to analyze and visualize data extracted from studies. These tools include meta-analysis, network meta-analysis, and sensitivity analysis.

Now, finally the most important question regarding this tool comes. Ok, first guess what is it and tell us in the comments. The reason we are asking this question is to test your observation and critical thinking skill. So, the question is very easy, how to use the EPPI-Reviewer tool? So, let’s know this answer also.

EPPI-Reviewer is a web-based systematic review software tool that can be used to facilitate various stages of a systematic review. Here are the general steps for using EPPI-Reviewer:

• Create a review project: To get started, you will need to create a review project in EPPI-Reviewer. This involves setting up the review question, inclusion and exclusion criteria, and search terms.

• Conduct a literature search: Next, you will need to conduct a literature search using one or more databases, such as PubMed or Scopus. You can then import the search results into EPPI-Reviewer.

• Screen studies for inclusion: EPPI-Reviewer allows you to screen studies for inclusion based on your inclusion and exclusion criteria. You can either screen studies manually or use EPPI-Reviewer's machine learning algorithms to suggest which studies should be included or excluded based on your decisions.

• Extract data from studies: Once you have identified the studies that meet your inclusion criteria, you can extract data from them using EPPI-Reviewer's customizable data extraction forms. EPPI-Reviewer allows you to extract data using natural language processing (NLP) or manual data extraction.

• Analyze and synthesize data: EPPI-Reviewer offers several data analysis and synthesis tools, such as meta-analysis and network meta-analysis, that can be used to analyze and synthesize data from the included studies.

• Collaborate with team members: EPPI-Reviewer offers several collaboration tools that allow you to work with team members, such as assigning tasks, leaving comments and notes on studies, and communicating through the platform.

• Write up the review: Once you have analyzed and synthesized the data, you can use EPPI-Reviewer to write up the review. EPPI-Reviewer offers several tools for organizing and structuring the review, such as tables and graphs.  

4. Rayyan: Rayyan is a web-based platform designed to support the study selection process of systematic reviews. It offers a range of features, including customizable data extraction forms, automatic reference management, and real-time collaboration.

Now, I think you know what will be the next question? The special features of Rayyan. Now, it will not only save your precious time but also it can help you to extract authentic data from systematic literature reviews. So, here we go riding business class the Rayyan train.

Rayyan is a web-based systematic review software tool that offers several features to facilitate the systematic review process. Some of its special features include:

• Automated screening: Rayyan offers a machine learning-based algorithm that can help reviewers automate the screening process. This feature can save time and effort and help reviewers identify relevant studies more efficiently.

• Collaborative screening: Rayyan allows multiple reviewers to screen studies collaboratively. Reviewers can easily share studies with their team members and work together to make decisions about inclusion and exclusion.

• Dual review: Rayyan allows for dual review of studies, where two reviewers independently assess studies for inclusion and exclusion. This feature can improve the accuracy and consistency of the review process.

• Quality assessment: Rayyan includes a quality assessment tool that can be used to assess the quality of studies. The tool includes pre-defined criteria and a customizable scoring system.

• Full-text review: Rayyan includes a full-text review feature that allows reviewers to read and extract data from full-text articles within the platform. This feature can save time and streamline the review process.

• Exporting and sharing: Rayyan allows reviewers to export their data and results in various formats, such as CSV, Excel, or PDF. This feature can facilitate sharing and collaboration among reviewers and stakeholders.

Now, I hope you have enjoyed riding the Rayyan train but if you really liked riding the train, then you should learn how to operate the train to ride whenever you want. So, we need to learn how to use Rayyan? So, let’s become the captain sorry the operator of the train so that we get so much time to do some other work.

Rayyan is a web-based application that can be used for systematic review management. Here are the steps to use Rayyan:

• Sign up for an account: Go to the Rayyan website and sign up for a free account.

• Create a new project: Once you've signed up and logged in, click on the "New Project" button to create a new project.Give a name and a description to your project.

• Import articles: You can import articles into Rayyan from various sources such as PubMed, Scopus, and Google Scholar. Click on "Import" and choose the source from which you want to import articles.

• Screen articles: Once you've imported articles into Rayyan, you can start screening them. Click on "Screening" and choose the type of screening you want to do (title/abstract or full-text). You can also create custom screening questions and assign them to team members.

• Collaborate: Rayyan allows you to collaborate with other team members on your project. You can add team members to your project and assign them roles such as reviewer or screener.

• Extract data: Once you've screened articles, you can extract data from them using Rayyan's data extraction feature. Click on "Extraction" and choose the data extraction form that you want to use. You can also create custom data extraction forms.

• Analyze data: Once you've extracted data from articles, you can analyze it using Rayyan's analysis features. You can create tables and charts to visualize your data and export your data to various formats such as Excel and CSV.

• Write your review: Finally, you can write your review using Rayyan's review writing feature. Click on "Write" and start writing your review. Rayyan provides a template that you can use to structure your review.

5. Excel or Google Sheets: Many researchers use spreadsheet programs such as Excel or Google Sheets to extract and manage data for SLRs. While these programs do not offer the same level of automation and collaboration as specialized platforms, they can be a cost-effective and flexible option. 

Now, we are going to discuss something different which no blogs has covered before. I hope you haven’t guessed about it. We are going to discuss how spreadsheets can be used to organize and extract data from literature sources. If you didn’t know, spreadsheets are an excellent way to organize and also extract data. So, let’s begin.

Spreadsheets are a useful tool for organizing and extracting data from literature sources during a systematic literature review (SLR). Here are some ways that spreadsheets can be used for this purpose:

• Creating a database: Researchers can create a spreadsheet to serve as a database of all the studies that meet the inclusion criteria for the SLR. This can include key information about each study, such as the author, title, publication date, study design, sample size, and outcome measures.

• Managing data extraction: Researchers can use spreadsheets to manage the data extraction process. They can create a table or form that lists all the variables that need to be extracted from each study, along with instructions for how to extract the data. This can help to ensure that all relevant data is extracted in a consistent and systematic way.

• Analysis and synthesis: Researchers can use spreadsheets to compile and synthesize the extracted data from the included studies. For example, they can use spreadsheet functions to calculate summary statistics, such as means, medians, or effect sizes, and to perform subgroup analyses based on study characteristics.

• Quality assessment: Researchers can use spreadsheets to manage the quality assessment process for each included study. They can create a checklist of quality criteria, along with instructions for how to assess each criterion and use the spreadsheet to record the results of the quality assessment.

Now another question is needed to the discussed. Do you know how to create an efficient spreadsheet for data extraction? If you don’t, then you also don’t know how much time you are wasting. So, let’s know the answer of this question.

Creating an efficient spreadsheet for data extraction during a systematic literature review (SLR) requires careful planning and attention to detail. Here are some steps that researchers can follow to create an efficient spreadsheet for data extraction:

• Identify the key variables: Researchers should identify the key variables or data points that need to be extracted from each study. This can include information about the study design, sample size, outcome measures, and any relevant statistical results.

• Standardize the data extraction form: Researchers should create a standardized data extraction form or template that lists all the key variables and provides clear instructions on how to extract the data. This can help to ensure that the data is extracted consistently and systematically across all included studies.

• Use drop-down menus and data validation: To improve the accuracy and consistency of the data extraction, researchers can use drop-down menus and data validation to ensure that the data is entered in a consistent format. For example, they can use drop-down menus to specify the type of study design or use data validation to ensure that the sample size is entered as a numeric value.

• Use conditional formatting: Conditional formatting can be used to highlight or flag data that meets certain criteria or conditions. For example, researchers can use conditional formatting to highlight studies with a high risk of bias or to flag studies with missing data.

• Test the data extraction form: Researchers should test the data extraction form to ensure that it captures all the relevant data and that the instructions are clear and easy to follow. They can do this by conducting a pilot extraction on a subset of the included studies and using feedback from the pilot to refine and improve the data extraction form.

• Collaborate with others: Researchers should collaborate with other members of their research team, such as a second reviewer or a data analyst, to ensure the accuracy and completeness of the data extraction process. They can use the spreadsheet to track the progress of the data extraction and to communicate any issues or questions that arise during the process.

Now, finally we came to the last question. Its our job to help you streamline your work so we are going to give you some tips for using spreadsheets to streamline data extraction. Your time will be greatly reduced.

Here are some tips for using spreadsheets to streamline data extraction during a systematic literature review:

• Use a standardized data extraction form: Creating a standardized form for data extraction can help to ensure that data is consistently and accurately collected across all studies. This form can be created in a spreadsheet and should include fields for each data point that needs to be extracted, along with clear instructions for how to extract the data.

• Use conditional formatting to flag potential issues: Conditional formatting can be used to highlight data points that meet certain criteria or that may require further investigation. For example, researchers can use conditional formatting to flag studies with a high risk of bias or to highlight missing data.

• Use drop-down lists and data validation: Using drop-down lists and data validation can help to improve the accuracy and consistency of the data extraction. Drop-down lists can be used to standardize the values for certain data points, such as study design or outcome measures. Data validation can be used to ensure that the data entered into a field meets specific criteria, such as being a numeric value or falling within a certain range.

• Use formulas to streamline calculations: If calculations need to be performed on the extracted data, researchers can use formulas in the spreadsheet to automate these calculations. This can cut down on time and error-prone work.

• Collaborate with other researchers: If multiple researchers are involved in the data extraction process, using a shared spreadsheet can facilitate collaboration and help to ensure that the data is consistent across all reviewers. The spreadsheet can be used to track progress, communicate any issues, and facilitate discussion and decision-making.

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