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Background Conducting systematic reviews (“reviews”) requires a great deal of effort and resources. Making data extracted during reviews available publicly could offer many benefits, including reducing unnecessary duplication of effort, standardizing data, supporting analyses to address secondary research questions, and facilitating methodologic research. Funded by the US Agency for Healthcare Research and Quality (AHRQ), the Systematic Review Data Repository (SRDR) is a free, web-based, open-source, data management and archival platform for reviews. Our specific objectives in this paper are to describe (1) the current extent of usage of SRDR and (2) the characteristics of all projects with publicly available data on the SRDR website. Methods We examined all projects with data made publicly available through SRDR as of November 12, 2019. We extracted information about the characteristics of these projects. Two investigators extracted and verified the data. Results SRDR has had 2552 individual user accounts belonging to users from 80 countries. Since SRDR’s launch in 2012, data have been made available publicly for 152 of the 735 projects in SRDR (21%), at a rate of 24.5 projects per year, on average. Most projects are in clinical fields (144/152 projects; 95%); most have evaluated interventions (therapeutic or preventive) (109/152; 72%). The most frequent health areas addressed are mental and behavioral disorders (31/152; 20%) and diseases of the eye and ocular adnexa (23/152; 15%). Two-thirds of the projects (104/152; 67%) were funded by AHRQ, and one-sixth (23/152; 15%) are Cochrane reviews. The 152 projects each address a median of 3 research questions (IQR 1–5) and include a median of 70 studies (IQR 20–130). Conclusions Until we arrive at a future in which the systematic review and broader research communities are comfortable with the accuracy of automated data extraction, re-use of data extracted by humans has the potential to help reduce redundancy and costs. The 152 projects with publicly available data through SRDR, and the more than 15,000 studies therein, are freely available to researchers and the general public who might be working on similar reviews or updates of reviews or who want access to the data for decision-making, meta-research, or other purposes.

INTRODUCTION: During systematic reviews, data abstraction is labor- and time-intensive and error-prone. Existing data abstraction systems do not track specific locations and contexts of abstracted information. To address this limitation, we developed a software application, the Data Abstraction Assistant (DAA), and surveyed early users about their experience using DAA. FEATURES OF DAA: We designed DAA to encompass three essential features: (1) a platform for indicating the source of abstracted information; (2) compatibility with a variety of data abstraction systems; and (3) user-friendliness. HOW DAA FUNCTIONS: DAA: (1) converts source documents from PDF to HTML format (to enable tracking of source of abstracted information); (2) transmits the HTML to the data abstraction system; and (3) displays the HTML in an area adjacent to the data abstraction form in the data abstraction system. The data abstractor can mark locations on the HTML that DAA associates with items on the data abstraction form. EXPERIENCES OF EARLY USERS OF DAA: When we surveyed 52 early users of DAA, 83% reported that using DAA was either very or somewhat easy; 71% are very or somewhat likely to use DAA in the future; and 87% are very or somewhat likely to recommend that others use DAA in the future. DISCUSSION: DAA, a user-friendly software for linking abstracted data with their exact source, is likely to be a very useful tool in the toolbox of systematic reviewers. DAA facilitates verification of abstracted data and provides an audit trail that is crucial for reproducible research.systematic reviews; data abstraction; software; data tracking; data exchange

Objectives Data Abstraction Assistant (DAA) is a software for linking items abstracted into a data collection form for a systematic review to their locations in a study report. We conducted a randomized cross-over trial that compared DAA-facilitated single-data abstraction plus verification (“DAA verification”), single data abstraction plus verification (“regular verification”), and independent dual data abstraction plus adjudication (“independent abstraction”). Study Design and Setting This study is an online randomized cross-over trial with 26 pairs of data abstractors. Each pair abstracted data from six articles, two per approach. Outcomes were the proportion of errors and time taken. Results Overall proportion of errors was 17% for DAA verification, 16% for regular verification, and 15% for independent abstraction. DAA verification was associated with higher odds of errors when compared with regular verification (adjusted odds ratio [OR] = 1.08; 95% confidence interval [CI]: 0.99–1.17) or independent abstraction (adjusted OR = 1.12; 95% CI: 1.03–1.22). For each article, DAA verification took 20 minutes (95% CI: 1–40) longer than regular verification, but 46 minutes (95% CI: 26 to 66) shorter than independent abstraction. Conclusion Independent abstraction may only be necessary for complex data items. DAA provides an audit trail that is crucial for reproducible research.

ᅟ This is a response to a Letter. Data abstraction is a time-consuming and error-prone systematic review task. Shokraneh and Adams categorize available techniques for tracking data during data abstraction into three methods: simple annotation, descriptive addressing, and Cartesian coordinate system. While we agree with the categorization of the techniques, we disagree with the authors’ statement that descriptive addressing is a PDF-independent method, i.e., any sort of descriptive addressing must reference a specific version of PDF file and not just any PDF of said report. Different versions of PDFs of the same report might place text and tables on different locations of the same page and/or on different pages. Consequently, it is our opinion that any kind of source location information should be accompanied by the source or linked by an intermediary service such as the Data Abstraction Assistant (DAA).

Background Data abstraction, a critical systematic review step, is time-consuming and prone to errors. Current standards for approaches to data abstraction rest on a weak evidence base. We developed the Data Abstraction Assistant (DAA), a novel software application designed to facilitate the abstraction process by allowing users to (1) view study article PDFs juxtaposed to electronic data abstraction forms linked to a data abstraction system, (2) highlight (or “pin”) the location of the text in the PDF, and (3) copy relevant text from the PDF into the form. We describe the design of a randomized controlled trial (RCT) that compares the relative effectiveness of (A) DAA-facilitated single abstraction plus verification by a second person, (B) traditional (non-DAA-facilitated) single abstraction plus verification by a second person, and (C) traditional independent dual abstraction plus adjudication to ascertain the accuracy and efficiency of abstraction. Methods This is an online, randomized, three-arm, crossover trial. We will enroll 24 pairs of abstractors (i.e., sample size is 48 participants), each pair comprising one less and one more experienced abstractor. Pairs will be randomized to abstract data from six articles, two under each of the three approaches. Abstractors will complete pre-tested data abstraction forms using the Systematic Review Data Repository (SRDR), an online data abstraction system. The primary outcomes are (1) proportion of data items abstracted that constitute an error (compared with an answer key) and (2) total time taken to complete abstraction (by two abstractors in the pair, including verification and/or adjudication). Discussion The DAA trial uses a practical design to test a novel software application as a tool to help improve the accuracy and efficiency of the data abstraction process during systematic reviews. Findings from the DAA trial will provide much-needed evidence to strengthen current recommendations for data abstraction approaches. Trial registration The trial is registered at National Information Center on Health Services Research and Health Care Technology (NICHSR) under Registration # HSRP20152269: https://wwwcf.nlm.nih.gov/hsr_project/view_hsrproj_record.cfm?NLMUNIQUE_ID=20152269&SEARCH_FOR=Tianjing%20Li. All items from the World Health Organization Trial Registration Data Set are covered at various locations in this protocol. Protocol version and date: This is version 2.0 of the protocol, dated September 6, 2016. As needed, we will communicate any protocol amendments to the Institutional Review Boards (IRBs) of Johns Hopkins Bloomberg School of Public Health (JHBSPH) and Brown University. We also will make appropriate as-needed modifications to the NICHSR website in a timely fashion. Electronic supplementary material The online version of this article (doi:10.1186/s13643-016-0373-7) contains supplementary material, which is available to authorized users.