Editorial · CASRAI · Research data infrastructure
Choosing Where to Put Your Data: re3data and the Landscape of Research Data Repositories
Depositing research data in a suitable repository is now an expectation of most major funders, but with thousands of repositories in existence — institutional, disciplinary, and general-purpose — choosing the right one is not straightforward. re3data, the Registry of Research Data Repositories, is the principal tool for navigating this landscape: a searchable, indexed registry of more than 3,000 research data repositories worldwide, operated under the auspices of DataCite. This article examines re3data, its metadata schema and badge system for signalling repository properties such as data access, licensing, and persistent identifier support, the main types of repository, how funders including the NIH, Wellcome, and Horizon Europe point researchers towards repository registries for repository selection, and the complementary role of FAIRsharing as a registry of databases, standards, and policies.
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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.
