Making RDM Measurable: A Case Study in ARCs

The FAIR data principles [1] are increasingly recognized as guiding standards in research data management (RDM). However, FAIR is not a binary state, it is a continuum [2]. This continuum applies not only to individual datasets, but also to research practices at the group, institutional, and even national levels. While many RDM efforts focus on enabling FAIRness at the level of data packages, the broader challenge lies in supporting and measuring the evolution of FAIR practices across institutions. In this contribution, we present a case study of how the Annotated Research Context (ARC) framework [2], [3] operationalizes FAIR Digital Objects (FDOs) to make FAIRness both actionable and measurable — at the level of data and beyond. ARCs offer a lightweight but powerful structure for organizing and annotating research processes, data, and workflows. Based on established metadata standards such as ISA (Investigation, Study, Assay) and Common Workflow Language (CWL), ARCs enable researchers to scaffold their projects in a way that is FAIR by design. Through iterative improvement, ARCs support an increasing level of FAIRness over time. At the technical core, each ARC is version-controlled via Git and compiled into machine-actionable formats like JSON-LD and RO-Crate [4] using schema.org and bioschemas.org vocabularies. This setup ensures compatibility with a wide range of digital infrastructure while maintaining transparency and reproducibility. A community-driven and curated set of resources [5], [6], [7], [8] and tools [9], [10], [11], [12] enables researchers to create, manage, validate, and share ARCs. The DataHUB platform [13] extends this approach by providing the environment in which ARCs can grow collaboratively —either in a centralized or federated manner. DataHUB serves as a CI/CD-powered ecosystem where ARCs can be created, refined, validated, and published. It enables automatic FAIRness assessments and supports quality monitoring through metrics such as metadata completeness, use of standard vocabularies, provenance tracking, and more. Most importantly, DataHUB makes RDM activities visible and countable. It tracks the number and quality of ARCs across various levels—individual users, research groups, institutions, or even regions. This functionality provides a novel way to quantify RDM engagement and impact, turning what was often invisible work into measurable contributions. Furthermore, the platform supports automated publication of ARCs, ensuring that FAIR Digital Objects are not only well-structured but also accessible and citable [14]. Our case study demonstrates how the ARC framework, coupled with the DataHUB infrastructure, creates a practical path for implementing FAIR principles while supporting institutional strategies for open science, compliance, and accountability. The ARC/DataHUB frameworks enable public metrics such as ARC count or contribution count, as showcased for the PLANTdataHUB on arc-rdm.org [5]. These metrics not only show that our userbase is rapidly growing but can be used on a more granular level to make institution-wide RDM efforts countable and presentable, e.g., on institution websites. This illustrates how FAIRness can be treated not as a fixed target, but as a measurable process—with concrete tools that enable researchers and institutions alike to track, improve, and showcase their progress.