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Collaboration across institutional boundaries is widespread and increasingtoday. It depends on federations sharing data that often have governance rulesor external regulations restricting their use. However, the handling of datagovernance rules (aka. data-use policies) remains manual, time-consuming anderror-prone, limiting the rate at which collaborations can form and respond tochallenges and opportunities, inhibiting citizen science and reducing dataproviders' trust in compliance. Using an automated system to facilitatecompliance handling reduces substantially the time needed for such non-missionwork, thereby accelerating collaboration and improving productivity. We presenta framework, Dr.Aid, that helps individuals, organisations and federationscomply with data rules, using automation to track which rules are applicable asdata is passed between processes and as derived data is generated. It encodesdata-governance rules using a formal language and performs reasoning onmulti-input-multi-output data-flow graphs in decentralised contexts. We testits power and utility by working with users performing cyclone tracking andearthquake modelling to support mitigation and emergency response. We querystandard provenance traces to detach Dr.Aid from details of the tools andsystems they are using, as these inevitably vary across members of a federationand through time. We evaluate the model in three aspects by encoding real-lifedata-use policies from diverse fields, showing its capability for real-worldusage and its advantages compared with traditional frameworks. We argue thatthis approach will lead to more agile, more productive and more trustworthycollaborations and show that the approach can be adopted incrementally. This,in-turn, will allow more appropriate data policies to emerge opening up newforms of collaboration.

Dr.Aid: Supporting Data-governance Rule Compliance for Decentralized Collaboration in an Automated Way