Linking literature and infrastructure improves research data reuse
A framework for linking literature-based knowledge integration and infrastructure-supported knowledge integration: Opportunities and challenges from a case study
Digital LibrariesDatabases
Summary
Combining knowledge from many studies helps solve big problems like climate change, but most research only shares final results, not the data or steps behind them. The authors looked at 37 studies on climate, conflict, and food security to see how often researchers share data and code openly. They found very few studies make reusable data and workflows available, which limits building on past work. Using a tool called TIB Knowledge Loom, they showed better knowledge integration is possible when data and methods are accessible and machine-readable.
What this means in practice
- •For research data managers: Improve data sharing policies by identifying gaps in current data availability practices across studies involving complex, multidisciplinary topics.
- •For software platform developers: Develop tools enabling machine-readable integration of data and workflows to enhance automated research synthesis beyond published findings.
Tested on one dataset.
Authors
Mahlet Degefu Awoke, Hadi Ghaemi, Lauren Synder, Markus Stocker, Tilman Brück
Abstract
Integrating knowledge across disciplines is central to sustainability research, yet most evidence-synthesis methods rely on findings as reported in publications, limiting verification and reuse of underlying data and workflows. We develop a conceptual framework linking literature-based and infrastructure-supported knowledge integration, using a systematic review case study to examine when integration can extend beyond reported findings. We reviewed 37 studies on climate change, violent conflict, and household food security. Literature-based synthesis enabled integration across all included studies, whereas access to reusable outputs was limited: over half provided no data availability statement, 27% reported availability upon request, but reusable data and workflows were available for only 8%. To explore infrastructure-supported integration, we used the TIB Knowledge Loom to represent studies with accessible data and code as machine-readable outputs, and produced a knowledge gap map (KGM) from manually extracted and Loom-derived data, comparing manual and infrastructure-supported synthesis. Where outputs were reusable, synthesis could be produced directly from data and workflows rather than from publications. These findings show that literature-based synthesis can be complemented by infrastructure-supported integration where outputs are accessible and usable, and that advancing knowledge integration depends not only on infrastructures but on making data, code, and workflows accessible, executable, and reusable.