Authors
Lauren Chenarides, Rafael Ladislau, Natasha Noy, Manish Parashar, Simon Porter, Julia Lane
Published in
Scientific data. Volume 13. Issue 1. Aug 07, 2026. Epub Aug 07, 2026.
Abstract
Scientific data is a critical input into scientific research. Yet the research data landscape is constantly changing as new datasets emerge, others are retired, or some disappear altogether. Without a systematic way to track how datasets are used across a research field, researchers have no reliable method for identifying relevant data resources or locating communities that work with them. Data-usage descriptors can substantially advance research productivity by reducing the time that researchers spend finding new and relevant datasets in their research field, and the communities that use them. This paper describes how to generate data-usage descriptors by finding how datasets are used in publications and then linking the dataset information to the publication metadata. It also shows how usage descriptors can be used to find other related datasets and their usage. It concludes by arguing that the approach represents a critical piece of foundational infrastructure that could be deployed in repositories as part of a referenceable, navigable, and contextual data framework. This article contains a reproducible workflow for constructing data-usage descriptors, based on analyzing the full text of publications in the Dimensions database. The illustrative use case is research on food security. The illustrative repository is the National Data Platform.
PMID:
42567848
Bibliographic data and abstract were imported from PubMed on 08 Aug 2026.
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