This discussion-focused workshop examines how the FAIR (Findable, Accessible, Interoperable, Reusable) principles are and can be applied to eScience research objects beyond data. Invited speakers will present the idea of FAIR and its application to objects such as software, workflows, machine learning models, and executable notebooks, and where FAIR is going. Invited talks will be followed by a panel discussion guided by questions suggested by the attendees. From the talks, questions and discussions, we plan a white paper to be written after the workshop, with workshop speakers and attendees as authors.
This workshop explores innovations and experiences around developing portable, general, reproducible workflows while paying attention to providing open data with verifiable authenticity while protecting privacy, where needed. We are looking for community discussion and participation on the above topics plus the following. First, component packaging via containers and virtual machines, automation scripting, deployment, portability builds, and system support for these and other relevant activities. Second, provenance collection, exploration, and tracking are key for a well-documented scientific output. Third, issues with managing large data sets and workflow intermediate data, particularly those intended to manage publicly accessed data for use and reuse are encouraged. Finally, new techniques and technologies that address portability and reproducibility requirements, such as those required for peer reviewed publication, are also requested.