Editorial · CASRAI · Research data infrastructure
Linked Data and Knowledge Graphs in Scholarly Research: RDF, SPARQL, Wikidata and ORKG
Linked data technologies developed by the World Wide Web Consortium — principally the Resource Description Framework and the SPARQL query language — provide a principled way to connect research objects, express relationships between them, and make them queryable by machines as well as humans. Applied to scholarly research, these technologies underpin initiatives such as the Open Research Knowledge Graph at TIB Hannover, Wikidata’s role as a community-maintained scholarly knowledge base, and the widespread adoption of Schema.org’s Dataset markup to make research datasets discoverable by search engines. Understanding how linked data connects papers, datasets, authors, funders and institutions into a navigable web of scholarly knowledge helps research administrators and data infrastructure professionals appreciate the foundations of machine-readable research information systems.
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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.
