Table of Contents
Download Cite this reportCite this
APA
Aspesi, C., Allen, N. S., Crow, R., Daugherty, S., Joseph, H., McArthur, J. T., & Shockey, N. (2019, November 1). SPARC Roadmap for Action. , https://doi.org/10.31229/osf.io/a7nk8
MLA
Aspesi, Claudio, et al. “SPARC Roadmap for Action.” , 1 Nov. 2019, https://doi.org/10.31229/osf.io/a7nk8 .
Chicago
Aspesi, Claudio, Nicole S. Allen, Raym Crow, Shawn Daugherty, Heather Joseph, Joseph T. W. McArthur, and Nick Shockey. 2019. “SPARC Roadmap for Action.” , November 1. https://doi.org/10.31229/osf.io/a7nk8
-
Executive Summary
Academic publishing is undergoing a major transition as some of its leaders are moving from a content-provision to a data analytics business. This shift is still in its early days. There are actions and strategies that institutions can consider adopting, both individually and collectively, to mitigate the potential risks posed by this trend, and to leverage potential benefits.
-
Background
Until now, commercial publishers were – at worst – seen by institutions as an annoyance for selected communities within academia. Their move into the core research and teaching missions of colleges and universities, with tools aimed at evaluating productivity and performance, means that the academic community could lose control over vast areas of its core activities.
-
What Do We Mean By Data And Data Infrastructure
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.
-
Three Categories Of Action
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.
-
Risk Mitigation
Conduct A Data Inventory
A common-sense response to the increasing volumes of data collected across campuses and the rising deployment of data analytics tools.
-
Risk Mitigation
Establish Campus Coordination Mechanisms
Coordination mechanisms to adjudicate conflicts among departments and offices will become key as increasing volumes of data are collected across campuses and data analytics tools are deployed.
-
Risk Mitigation
Revise Data Policies
It is critical for data policies to be revised to address the myriad strategic questions raised by the proliferation of data and data analytics.
-
Risk Mitigation
Revise Privacy Policies
The development of strong privacy policies is critical, and must extend beyond legal compliance.
-
Risk Mitigation
Engage in Open Procurement Practices
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.
-
Strategic Choices
Intro
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.
-
Strategic Choices
Algorithms vs. Humans
It is only a matter of time before artificial intelligence further pervades campus decision-making in ways that impact equity, privacy, and allocation of resources.
-
Strategic Choices
Quantitative vs. Qualitative Metrics
While institutions may not be ready to abandon the usage of quantitative metrics to evaluate their faculty, they should consider engaging in a genuine debate on the relative weight that they place on quantitative vs. qualitative assessment.
-
Strategic Choices
IP Exploitation vs. Knowledge Sharing
The emergence of "big data" and text and data mining has opened up new possibilities for research universities to exploit their IP in profitable ways.
-
Community Actions
Collectively Implement Strategic Practices
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.
-
Community Actions
Build or Acquire Academic Community-Controlled Infrastructure
The most direct path to ensure community control over data infrastructure is to build or acquire it.
-
Community Actions
Establish Inclusive Governance Structures
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.
-
Community Actions
Leverage Policy to Support Community Control
Another avenue to expand community’s control over data infrastructure is to advocate for favorable federal and state policies.
-
Community Actions
Realign Stakeholder Relationships
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.
-
Conclusion
The time to act is now.
-
Acknowledgements
We would like to acknowledge that the development of this publication was generously supported by grants from the Open Society Foundations and Arcadia – a charitable fund of Lisbet Rausing and Peter Baldwin.