Ten Simple Rules for Sharing Experimental and Clinical Data with the Modeling Community

Science continues to become more interdisciplinary and to involve increasingly complex data sets. Many projects in the biomedical and health related sciences adhere to the principles of FAIR data sharing, or aim to follow them. Data sharing has been proven to foster collaboration, to lead to better research outcomes, and to help ensure reproducibility of results. Data generated in biomedical and health research are specific in the sense that they are heterogeneous, often big, and highly sensitive in terms of data protection needs and contextuality. Data sharing has to respect these features, but at the same time advances in medical therapy and treatment are time-critical. Modeling and simulation of biomedical processes have become an established tool, and a global community has been developing algorithms, methodologies, and standards for applying biomedical simulation models in clinical research. However, it can be difficult for clinician scientists to follow the specific rules and recommendations for FAIR data sharing within the domain. With this paper, we aim to clarify the standard workflow for sharing experimental and clinical data with the simulation modeling community. By following these recommendations, data sharing will be improved, collaborations will become more effective, and the FAIR publication and subsequent reuse of data will become possible at the level of quality necessary in biomedical and health related sciences.