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Abstract
Abstract: Open Science practices have become well established in recent years. In this position paper, we argue that Open Data in particular holds great potential for empirical research in sports science, and sport and exercise psychology in particular, since it fosters the reintegration of scientific knowledge as primary research data in subsequent research life cycles. On that account, the sports science community has to develop a unified position on research data management, which supports the implementation of Open Science practices and standards. To this end, in this article we first define Open Science and research data management (RDM) and describe them in the context of sports science. We then present examples of existing, relevant RDM solutions, with a particular focus on sport and exercise psychology and neighboring disciplines. Finally, we derive perspectives for the development of a sustainable RDM structure and present current developments within the German sports science community.
Highlights
Perspektiven und Potentiale von Open Data für die Sportwissenschaft: Das „Was“, das „Warum“ und das „Wie“ Zusammenfassung: Open Science-Praktiken haben sich in den letzten Jahren in vielen Wissenschaftsdisziplinen etabliert
Crüwell and colleagues (2019) introduced the different elements of Open Science in their review article and concluded by stating: Open Science practices are a collection of behaviors that improve the quality and value of psychological research
Open Science has become well established with a substantial number of scientists having acquired related knowledge and funding bodies, publishers as well as academic institutions standardly demanding the implementation of Open Science practices by researchers
Summary
Different perspectives come into play when considering the conceptual and technical aspects of RDM. Researchers demand explicit guidelines on Open Science standards and practices, together with simple and automated RDM processes These user demands, in turn, are being met by new developments on the data science side, of which we can mention only a small selection here: On a conceptual level, the Canonical Workflow Framework for Research (CWFR) emerged. As a digital infrastructure for the production, curation, publishing, and reuse of machine-actionable scientific knowledge, it applies the FAIR principles to the scientific knowledge published in articles in order to enable the efficient reintegration of scientific knowledge as primary research data in research life cycles For such Research Knowledge Graphs, the technical challenges primarily consist in the efficient production of machineactionable scientific knowledge, quality assurance, and service usability. Other journals do not separate data from manuscripts and instead focus on the technical accessibility of datasets to reviewers and automated quality measurement
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