Open science is prompting wide efforts to make data from research available for broader use. However, sharing data is complicated by important protections on the data (e.g., protections of privacy and intellectual property). The spectrum of options existing between data needing to be fully open access and data that simply cannot be shared at all is quite limited. This paper puts forth a generalized remote secure enclave as a socio-technical framework consisting of policies, human processes, and technologies that work hand in hand to enable controlled access and use of restricted data. Based on experience in implementing the enclave for computational, analytical access to a massive collection of in-copyright texts, we discuss the synergies and trade-offs that exist between software components and policy and process components in striking the right balance between safety for the data, ease of use, and efficiency.

Text data mining and analysis has emerged as a viable research method for scholars, following the growth of mass digitization, digital publishing, and scholarly interest in data re-use. Yet the texts that comprise datasets for analysis are frequently protected by copyright or other intellectual property rights that limit their access and use. This article discusses the role of libraries at the intersection of data mining and intellectual property, asserting that academic libraries are vital partners in enabling scholars to effectively incorporate text data mining into their research. We report on activities leading up to an IMLS-funded National Forum of stakeholders and discuss preliminary findings from a systematic literature review, as well as initial results of interviews with forum stakeholders. Emerging themes suggest the need for a multi-pronged distributed approach that includes a public campaign for building awareness and advocacy, development of best practice guides for library support services and training, and international efforts toward data standardization and copyright harmonization.

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