The role of FAIR principles in high-quality research data documentation

The FAIR principles as a framework for evaluating and improving open science and research data management have gained much attention over the last years. By defining a set of properties that indicates good practice for making data findable, accessible, interoperable, and reusable (FAIR), a quality measurement is created, which can be applied to many research outputs, including research data. There are some software tools available to help with the assessment, with the F-UJI tool being the most prominent of them. It uses a set of metrics which defines tests for each of the FAIR components, and it creates an overall assessment score. The FAIR assessment is done by using aggregated metadata for a research dataset, e.g. from the data webpage URL, from a PID provider like DataCite and others, and more services like repository information by re3data. The presentation will examine differences between manually and automatically assessing FAIR principles, show the different results, and use Election Studies and COVID data studies as examples. It will highlight the role of archives in securing a high level of data and metadata quality and technically sound implementation of the FAIR principles to help researchers benefit from getting the most of their valuable research data.