What is it about?
With research data booming, we need better ways to find and use restricted access data (such as patient records and official government statistical data). Existing standards don't offer a solution that balances accessibility and privacy. We propose the DataSet-Variable (DSV), a novel ontology that combines existing standards, and is able to describe not only dataset-level, but also variable-level metadata. By also following the FAIR Guiding Principles, this approach allows researchers to discover and assess confidential data without compromising privacy. In a case study, DSV effectively supported knowledge discovery and data exploration for diverse datasets. By facilitating interoperability and discovery while respecting privacy, DSV unlocks the potential of hidden data for research.
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Why is it important?
This work helps in facilitating the discovery and reuse of data that cannot be found open and freely available on the web, such as medical records, proprietary data or national statistical data.
Perspectives
We propose an ontology that combines and contribute to existing standards (CSV on the Web and RDF Data Cube). The ontology is easy to use, and proposes a user-friendly but effective way of describing restricted access data on the web.
Margherita Martorana
Vrije Universiteit Amsterdam
Read the Original
This page is a summary of: Advancing data sharing and reusability for restricted access data on the Web: introducing the DataSet-Variable Ontology, December 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3587259.3627559.
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