Guidelines for Open Research Data in Movement Sciences

Abstract

Introduction & Purpose: Open research data offer large potential for reuse, but also present complex challenges such as ownership, confidentiality, and data misuse which is especially important in human movement analysis. Although numerous datasets exist (Olugbade et al., 2023), insufficient metadata and poor compliance with the FAIR-principles (findable, accessible, interoperable, reusable) (Wilkinson et al., 2016) hinder effective data reuse. Hence, we aimed to develop guidelines for publishing data from human movement laboratories that can be adapted to similar contexts to promote FAIR data sharing.

Methods: The guidelines were developed and refined in an iterative approach involving numerous movement laboratories. Initially, a survey was conducted among Swiss movement laboratories to assess current practices in open data sharing. A workshop was then held to refine key elements of the guidelines. Based on these inputs, a draft of the guidelines was developed, refined multiple times, validated, and published (Haas et al., 2024).

Results: Choosing an appropriate license and repository is an essential step in data sharing as licenses are irrevocable and determine data use. Researchers should consider requirements from funders, institutions, and repositories. The FAIR principles and practical considerations such as file size limits, license terms, and data security can guide decisions. To enable other researchers to comprehend and make use of the deposited dataset, providing metadata is key. Metadata includes general information (e.g. data format, license) and specific information about the dataset, variables, analysis procedures, and used hard- and software.

Discussion: Data itself should at least include basic statistical measures (minimum, maximum, mean, standard deviation) of all reported outcomes. Ideally, individual participant data should be made publicly available. However, this is only possible for anonymized data and encoded data when informed consent for reuse is obtained. In general, anonymizing data is recommended to support data sharing while ensuring compliance with legal and ethical standards. When reusing data, researchers must verify its quality through thorough checks.

Conclusion: Data sharing should be considered early during project planning to ensure all necessary data is collected and ethical and legal requirements for publishing data are met. To remain effective and compliant, these guidelines need to be regularly updated and reviewed by the community.

References

Haas, M. C., Sommer, B. B., Van Rekum, S., Moerman, F., & Graf, E. S. (2024). MoveD - Open Research Data Guidelines for Movement Laboratories (Version 3). Zenodo. https://doi.org/10.5281/ZENODO.13683562

Olugbade, T., Bieńkiewicz, M., Barbareschi, G., D’amato, V., Oneto, L., Camurri, A., Holloway, C., Björkman, M., Keller, P., Clayton, M., Williams, A. C. D. C., Gold, N., Becchio, C., Bardy, B., & Bianchi-Berthouze, N. (2023). Human Movement Datasets: An Interdisciplinary Scoping Review. ACM Computing Surveys, 55(6), 1–29. https://doi.org/10.1145/3534970

Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., Da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), 160018. https://doi.org/10.1038/sdata.2016.18