Replication data and code for: The role of FAIR principles in high-quality research data documentation: looking at national election studies

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 diverse 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 article examines differences between manually and automatically assessing FAIR principles, shows that there are significantly different results by using national election studies as examples. An evaluation of progress is done by comparing the automatically assessed FAIRness scores of the datasets from 2018 with those of 2024, showing that there is only a very slight yet not significant difference. Specific measures which have improved the FAIRness scores are described by the example of the Politbarometer 2022 dataset at the GESIS Data Archive. The article highlights 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.

The replication data contains the manual and automatic coded values for FAIR criteria and the complete code to re-produce the results for the article.