Data infrastructure can be built around predefined levels of granularity for data, for which conventions vary. The appropriate level of granularity can optimize discovery, access, interoperability, analysis, identification, citation, curation, and more. This guidance document developed by the Research Data Alliance (RDA) Data Granularity Working Group (WG), along with its Supporting Output, provides guidance on data granularity approaches, issues to consider, and priority use cases for research data infrastructure providers and other key stakeholders. We acknowledge the support provided by the RDA community and structures. This work was developed as part of the RDA TIGER ‘Research Data Alliance facilitation of Targeted International working Groups for EOSC-related Research solutions’, funded by HORIZON-INFRA-2022-EOSC-0 Grant Agreement 101094406, and we acknowledge the support provided within the project partners and resources.

This guidance will help data professionals to determine and deploy the best level of granularity for supporting data across the lifecycle. The key beneficiaries of the recommendations include: data producers (more efficient data management, greater ability to track value and use of subsets of data), data infrastructure providers (enhanced user services, clarity on the levels of data granularity on which to operate, greater ease in exchange of subsets of data (and metadata) with other systems, secondary data users (faster discovery and access of data of interest, expanded research possibilities given new ways to work with granular), and the public (benefits from faster and more sophisticated research produced). Furthermore, given the integral, cross-cutting nature of granularity across a range of issues, a prime value of this output is having leveraged and built upon existing and ongoing RDA work amongst a range of groups.

These use cases will enable the improvement of research infrastructure and the efficient creation and use of research data resources, by institutions working both individually and in partnership. Such improvements will in turn will enable further and better research and knowledge in all of the SDG areas.

Jenkyns, R., Mathiak, B., McNeill, K., Smith, G., Sun, G., Little, C., Jones, B., Elbert, D., Hellström, M., Schabinger, R., & RDA Data Granularity WG. (2024). Guidance on Data Granularity: Report of the RDA Data Granularity WG (Version 1). Research Data Alliance. https://doi.org/10.15497/RDA00126