Posts tagged under: Research Data Management

Libraries and RDM: Three decisions, three components, three realities

Brian Lavoie

libraries-rdm

New data mandates, open science advocacy, and replication of research results have focused attention on data management practices during the research process. This, in turn, has led to the development of services, infrastructure, and other resources to support Research Data Management (RDM) needs at research universities.

But how are research universities addressing the challenge of managing research data throughout the research life cycle?

The Realities of Research Data Management is a four-part series from OCLC Research that looks at the context, influences, and choices that research universities face in acquiring RDM capacity. We launched this project to pull back the curtain a bit on how universities work through the process of acquiring RDM capacity. Our findings are derived from detailed case studies of four research universities:

  • University of Edinburgh (UK)
  • University of Illinois at Urbana–Champaign (US)
  • Monash University (Australia)
  • Wageningen University & Research (Netherlands)

Our focus is on three major decision points that universities face in acquiring RDM capacity.

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Bringing order to the chaos of digital data

digital-chaos

530 million songs. 90 years of high-definition video. 250,000 Libraries of Congress. That’s how much data we produce every day—2.5 exabytes according to Northeastern University. I guess that’s not surprising, given the amount of activity that goes on in social media, websites, email messages and texting.

Much of that data, though, is personal and ephemeral. Videos, photos, tweets and stories that can be passed along and deleted without any thought or care about accuracy or archiving.

But in the scholarly community, a similar and perhaps more significant explosion of digital data is occurring. Here the stakes may be much higher. Without trusted stewardship, data from research will not be effectively collected and preserved for reuse. And when this happens, research innovation and advancement slows significantly.

This is new territory in many ways. Data have been collected and preserved for thousands of years, but never at the volume we see today, nor with some of the deliberate (and in some cases, legally mandated) intentions for reuse.

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