Reusability

Take steps to make research data and other research-relevant digital objects from public funding understandable and re-usable in the long term, including through the provision of high quality human-readable, machine-actionable, and open metadata and adequately maintained and supported bespoke algorithms, code, software, and workflows essential for re-use of data as free and open source.

Policymakers could consider to:

• Promote open licenses

• Keep data and software alive long-term through data curation: migration to new formats is necessary for this purpose, which is very complex and will only be possible for very valuable data.

• Use standards for documenting provenance (e.g. PROV Ontology (PROV-O))

• Promote the use of data management plans  

• Develop national, institutional or repository-level policies which require adherence to FAIR principles. 

Authors should make clear statement on what extent they allow access, downloading, exploitation and reproducibility of their research data.

Research data should be clearly licensed and anonymised and encrypted. Restrictions, embargos, and access rules to research data should be clearly specified.

Detailed description of data and methods to ensure that the context in which they were generated is explicit so that they can be reproduced. It is crucial to provide data with the necessary processing services and resources. 

Ensuring the long-term understandability and reusability of research data and other research-relevant digital objects from public funding involves overcoming several significant hurdles.

A major challenge is the need for discipline-specific metadata infrastructures that can be exposed according to generic methods, ensuring both specificity and broad accessibility.

Documenting provenance trails (tracking and recording the history of a dataset) is essential for the reliable reuse of data, yet current tools and practices are often inadequate.

Absence of these trails, creates difficulties when researchers and data users attempt to verify the origin, authenticity, and reliability of data before using it.

The lack of certification for data is another hurdle in building trust in data integrity and completeness. Furthermore, unclear ownership and rights issues creates challenges in issuing data use licenses, which are fundamental for lawful and open reuse.