Description
Ecological research increasingly relies on integrating heterogeneous knowledge sources that are fragmented across disciplines, conceptual frameworks, and data infrastructures. This fragmentation is compounded by the fact that, despite significant advances in digital technologies, scientific results are still primarily communicated in text-based formats that machines cannot interpret without human assistance. This prevents us from taking full advantage of computational tools for knowledge organization and reuse.
These limitations are especially acute in synthesis research. Systematic reviews and meta-analyses require researchers to manually extract and organize knowledge from numerous publications, a time-consuming, error-prone process that can consume months or years of full-time work. While AI-based approaches offer new possibilities for knowledge integration, their effectiveness ultimately depends on access to structured, high-quality inputs—a foundational challenge that remains largely unaddressed.
The TIB Knowledge Loom is a novel Open Science digital library designed to address this challenge by supporting the production of machine-reusable scientific knowledge. In the Loom, scientific knowledge is curated at the level of statements that are linked to the underlying data, code, and analyses—capturing details typically absent from published articles, such as model parameters, test statistics, and uncertainty estimates. Using an ecological case study, we illustrate Loom record features and their potential to support synthesis research by strengthening reproducibility, improving data granularity, and enabling automated harvesting by downstream knowledge bases and AI systems. As the Loom is still in its early stages, we see this presentation as a valuable opportunity to gather perspectives from the synthesis research community on how the Loom could best support interdisciplinary knowledge integration and identify priorities for future development.
| Topic | Topic 1: : Methods for integrating social and ecological knowledge |
|---|