A framework for linking literature-based knowledge integration and infrastructure-supported knowledge integration: Opportunities and challenges from a case study

2026-09-24

AI Summary

This study addresses the limited depth and breadth of interdisciplinary knowledge integration caused by the poor reusability of underlying data in traditional literature reviews. We propose a "dual-track integration" conceptual framework that leverages the TIB Knowledge Loom to generate machine-readable outputs, combining systematic review methodologies with knowledge gap mapping techniques to comparatively evaluate manual extraction against automated approaches for knowledge synthesis. Our analysis reveals that only 8% of the examined literature provides reusable data, while demonstrating that constructing knowledge integration frameworks linking publications to research infrastructure effectively expands integration pathways. The core contribution of this work lies in identifying that ensuring the accessibility and executability of data, code, and workflows is essential for overcoming existing bottlenecks in knowledge integration.

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.

Problem

• knowledge integration
• sustainability research
• evidence synthesis
• data reusability
• research infrastructure

Innovation

• knowledge integration
• infrastructure-supported synthesis
• machine-readable outputs
• knowledge gap map
• reusable workflows