The Access & Reuse stage of the research data lifecycle is pivotal for enabling the discovery and application of existing data. Ensuring data quality, which includes verifying its origin and completeness, is essential for effective reuse. Additionally, proper citation of datasets is mandatory, ensuring that all elements, such as authorship, publication date, and persistent identifiers, are accurately recorded. This practice maintains scholarly integrity and enhances the visibility and impact of research data.
The Registry of Research Data Repositories (Re3data) can be used in two ways; either to find open science data sets that can be reused or to find the best repository for publishing data. Re3data is a global registry of research data repositories that covers research data repositories from different academic disciplines. It includes repositories that enable permanent storage of and access to data sets to researchers, funding bodies, publishers, and scholarly institutions. Re3data promotes a culture of sharing, increased access and better visibility of research data.
Although the essential requirement for reusing a data set is that it complies with the FAIR Data Principles, that alignment does not guarantee data quality. According to the Research Data Management: Information Platform for Max Planck Institute Researchers, data quality is a context-dependent and multidimensional concept, which refers to reusing other scientist data and keeping the quality potential of the data they produce. Data quality applies to datasets and their documentation (metadata) and presentation at the level of individual data (e.g. tables), which is essential for reuse. In particular, it is important to check for the quality of data origin, that is, discover how individual values/data points for a given variable (or field) recorded, captured, labelled, gathered, computed, or represent and be cautious of any missing data.
Further information on data quality:
Citing research data is mandatory, just as citing any other material used in research, such as journal articles, books or conference proceedings. Basic elements of data citation, based on recommendations by Taylor & Francis Author Services, Digital Curation Centre Guide on How to Cite Datasets and Link to Publications, and Columbia University Instructions on Citing data Sources are:
The way in which these elements are combined and sequenced depends on the chosen or designated citation style (e.g.. APA, MLA, Harvard, etc.). Examples of various citation styles can be found here.