Editorial · CASRAI · Research data infrastructure
FAIR Digital Objects: Making Research Data Machine-Actionable Beyond Metadata
FAIR principles have transformed expectations around research data sharing, but the original FAIR framework addresses metadata and findability rather than the machine-actionable operations needed to actually use data at scale. The FAIR Digital Object (FDO) framework, developed through the FDO Forum and built on the Digital Object Interface Protocol (DOIP) from the Corporation for National Research Initiatives, extends FAIR by wrapping data objects with typed, machine-executable operations. Where a standard DOI resolves to a landing page for human readers, an FDO exposes typed interfaces that allow software to retrieve, validate, and act on data without human intervention. This article examines the FDO framework, the CNRI Cordra software that implements it, the Research Data Alliance FAIR Digital Objects working group, and practical deployments in the European Open Science Cloud and biodiversity informatics.
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Identifiers for Things, Not Just Papers: IGSN and PIDINST
Persistent identifiers are familiar for articles, datasets, and people, but the physical objects of research, the rock cores, water samples, and the instruments that measure them, have long lacked stable references. The IGSN for samples and the PIDINST work for instruments extend persistent identification to the physical world, making physical research objects findable, citable, and connectable to the data they produce.
Anonymising research data: k-anonymity, differential privacy and the re-identification risk
Sharing data about people without exposing the people themselves is one of the hardest problems in research data management. This article distinguishes anonymisation from pseudonymisation, explains the privacy models researchers actually use, k-anonymity, l-diversity and differential privacy, and introduces the practical guidance from the UK Anonymisation Network (UKAN) and the ICO’s anonymisation code. It also confronts the uncomfortable reality that re-identification is often easier than it looks.
Big Data and the Vs of Data Explained for Research
Big data describes datasets so large, fast or varied that traditional tools cannot handle them. This guide explains the defining Vs, from volume and velocity to veracity and value, how distributed processing copes, and what big data means for research and FAIR data.
