[Good Practice Data Linkage]. / Gute Praxis Datenlinkage (GPD).

Individual data linkage of different data sources for research purposes is being increasingly used in Germany in recent years. However, generally accepted methodological guidance is missing. The aim of this article is to define such methodological standards for research projects. Another aim is to provide readers with a checklist for critical appraisal of research proposals and articles. Since 2016, an expert panel of members of different German scientific societies have worked together and developed 7 guidelines with a total of 27 practical recommendations. These recommendations include (1) research aims, questions, data sources and resources, (2) infrastructure and data flow, (3) data privacy, (4) ethics, (5) key variables and type of linkage, (6) data validation/quality assurance and (7) long-term use for future research questions. The authors provide a rationale for each recommendation. Future revisions will include any new developments in science and data privacy.

Good Practice Data Linkage (GPD): A Translation of the German Version.

The data linkage of different data sources for research purposes is being increasingly used in recent years. However, generally accepted methodological guidance is missing. The aim of this article is to provide methodological guidelines and recommendations for research projects that have been consented to across different German research societies. Another aim is to endow readers with a checklist for the critical appraisal of research proposals and articles. This Good Practice Data Linkage (GPD) was already published in German in 2019, but the aspects mentioned can easily be transferred to an international context, especially for other European Union (EU) member states. Therefore, it is now also published in English. Since 2016, an expert panel of members of different German scientific societies have worked together and developed seven guidelines with a total of 27 practical recommendations. These recommendations include (1) the research objectives, research questions, data sources, and resources; (2) the data infrastructure and data flow; (3) data protection; (4) ethics; (5) the key variables and linkage methods; (6) data validation/quality assurance; and (7) the long-term use of data for questions still to be determined. The authors provide a rationale for each recommendation. Future revisions will include new developments in science and updates of data privacy regulations.