Big Data Research at the University of Colorado Boulder

Abstract

Background: Big data, defined as having high volume, complexity or velocity, have the potential to greatly accelerate research discovery. Such data can be challenging to work with and require research support and training to address technical and ethical challenges surrounding big data collection, analysis, and publication.&nbsp; &nbsp; Methods: The present study was conducted via a series of semi-structured interviews to assess big data methodologies employed by CU Boulder researchers across a broad sample of disciplines, with the goal of illuminating how they conduct their research; identifying challenges and needs; and providing recommendations for addressing them.&nbsp; &nbsp; Findings: Key results and conclusions from the study indicate: gaps in awareness of existing big data services provided by CU Boulder; open questions surrounding big data ethics, security and privacy issues; a need for clarity on how to attribute credit for big data research; and a preference for a variety of training options to support big data research.&nbsp; &nbsp; Recommendations: Evaluation of current access to existing research infrastructure at CU Boulder across departments and disciplines, including recommendations for how inequities could be addressed. Development of big data training curriculum, particularly for big data ethics, privacy and security, through a variety of channels (e.g., documentation and context-specific consultations for specific big data services, more general course-based curriculum).&nbsp; Consider how to address the complexity and dynamic nature of big data in the IRB process in a manner that fully and reasonably considers the ethical, security and privacy implications of a given big data research project. Creation of CU Boulder guidelines for attributing credit to the myriad contributors in big data research projects, and considering the sometimes unconventional contributions, with the goal of helping departments develop clear policies and incentives for researchers performing big data research. Development of marketing and outreach strategies to increase awareness of existing and forthcoming big data research support services at CU Boulder, in a manner that promotes equitable access to services across disciplines.&nbsp; Assessment of staffing gaps and staff-training needs in order to support big data curriculum and services. Periodic evaluation of emerging trends and needs in big data research, in order to adjust strategies and services appropriately to ensure CU Boulder is providing state-of-the-science support and infrastructure. An optimal way of addressing the complex questions above may be to establish a steering committee composed of a broad range of CU Boulder (and possibly external) stakeholders and decision makers. </p