Features and functioning of Data Abstraction Assistant, a software application for data abstraction during systematic reviews

Abstract

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.

PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews

“…For example, a statement indicating that, unless otherwise specified, all data came from the primary reference for each included study would suffice. Alternatively, this could be achieved by, for example, presenting the origin of each data point in footnotes, in a column of the data table, or as a hyperlink to relevant text highlighted in reports (such as using SRDR Data Abstraction Assistant139).…”

Reproducibility of individual effect sizes in meta-analyses in psychology

“…It would be worthwhile to expand this checklist to the context of meta-analyses. Other suggestions for improvement in reporting practices include opening data, materials and workflow to use transparency as an accountability measure (e.g., by dynamic documenting through RMarkdown [31]), or the use of tools that benefit data extraction for systematic reviews [32]. Fortunately, many online initiatives promote preregistration and data sharing practices, with increasingly more journals requiring authors to share the data that would be needed by someone wishing to validate or replicate the research (e.g., PLOS ONE, Scientific Reports, the Open Science Framework, the Dataverse Project).…”

The Systematic Review Data Repository (SRDR): descriptive characteristics of publicly available data and opportunities for research

“…SRDR can serve as a valuable platform for conducting methodologic research. Examples of such research that has already been conducted using SRDR are the Data Abstraction Assistant (DAA) Trial (a randomized controlled trial that compared different data extraction approaches [26–28]) and the current study and the six other methodologic projects described in this paper [29–34]. SRDR also can serve as a source of data for meta-research (i.e., methodologic and other types of research on research [4]).…”