| Abstrakt: |
All digital objects that result from the modeling and simulation field are valid sets of research data. In general, research data are the result of intense intellectual activity that is worth communicating. This communication is an essential research practice that, whether with the aim of understanding, critiquing or further developing results, smoothly leads to collaboration, which not only involves discussions, and sharing institutional resources, but also the sharing of data and information at several stages of the research process. Data sharing is intended to improve and facilitate collaboration but quickly introduces challenges like reproducibility, reusability, interoperability, and standardization. These challenges are deeply rooted in an apparent reproducibility standard, about which there is a debate worth considering before emphasizing how the modeling and simulation workflow commonly occurs. Although that workflow is almost natural for practitioners, the sharing practices still require special attention because the principles (known as FAIR principles) that guide research practices towards data sharing also guide the requirements for machine actionable results. The FAIR principles, however, do not address the actual implementation of the data sharing process. This implementation requires careful consideration of characteristics of the sharing platforms for benefiting the most of the data sharing activity. This article serves as an invitation to integrate data sharing practices into the established routines of researchers and elaborates on the perspectives, and guidelines surrounding data sharing implementation. [ABSTRACT FROM AUTHOR] |
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