Table of Contents
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The purpose of this document is to build on the Landscape Analysis by offering a roadmap of potential actions that stakeholders can use to chart both individual and collective responses.
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We talk about two types of data. The first is Research Data, which refers to the data academic institutions generate through their research activities. The second is Grey Data, which refers to the vast amount of data produced by universities outside of core research activities.
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The purpose of this document is to build on the Landscape Analysis by offering a roadmap of potential actions that stakeholders can use to chart both individual and collective responses.
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These actions are designed to be concrete, practical steps that any institution can begin taking immediately.
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These actions are designed to be concrete, practical steps that any institution can begin taking immediately.
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These actions are designed to be concrete, practical steps that any institution can begin taking immediately.
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It is critical for data policies to be revised to address the myriad strategic questions raised by the proliferation of data and data analytics.
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The development of strong privacy policies is critical, and must extend beyond legal compliance.
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An important area when institutions can assert control of data is through purchasing and procurement processes. These processes should be revisited and revised to ensure that they are transparent, competitive, and fully coordinated across the institution.
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The second category of actions is more complex, since it relates to decisions that will need to be made specifically based on each individual institution’s mission, culture and values. It also involves the establishment of an explicit process to determine the position that each institution wants to take in regards to specific issues posed by the collection of data and the deployment of data analytics tools.
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A third category of actions for the community to consider focus on leveraging a strength in numbers approach, and targeting “big picture” actions institutions to regain and maintain control of their data infrastructure. This category includes a broad range of possible structural solutions to foster an open, competitive landscape for data and data analytics that is aligned with the interests of academic institutions and the communities they serve.
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Broad adoption of common terms and conditions will have a market effect that favors products and services that are in the best interests of the academic community. This includes advantaging Open Source software over “black-box” algorithms and leveling the playing field for community-owned tools to compete with commercial options whenever available.
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The most direct path to ensure community control over data infrastructure is to build or acquire it.
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It is vital for the governing bodies of infrastructure services to include representation from the communities they serve in order to ensure that management stays accountable to the community’s evolving needs.
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Another avenue to expand community’s control over data infrastructure is to advocate for favorable federal and state policies.
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These community-based actions portend several possible realignments within the academic community and its stakeholder groups that should also be considered as efforts move forward.
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The time to act is now.
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