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


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.

Three decisions

  • Deciding to act: incentives to build/acquire RDM capacity
  • Deciding what to do: scoping out a bundle of RDM services
  • Deciding how to do it: which services will be built locally or externalized to an outside provider

The project yielded many insights into the decision-making by which our four partners acquired RDM capacity.

Pulling back the curtain on how universities acquire Research Data Management (RDM) capacity. Click To Tweet

One of the most surprising findings was that the universities we studied had developed their RDM services in anticipation of, rather than in direct response to, expressions of researcher demand for data management support, or the need to comply with external data mandates. In several cases, it was in fact library leadership that foresaw the importance of RDM as a key element of an evolving scholarly record. Based on that vision, the library took early steps to position the university to meet an emerging need.

Three components

We divide the RDM service space up into three major components:

  • Educational RDM services raise awareness of the importance of good data management both in service to open science and to meet compliance obligations, teach data management skills, and disclose internal and external RDM resources.
  • Expertise-related RDM services are human-mediated capacities geared toward solving specific data management problems encountered by researchers during the research process.
  • RDM Curation services offer the technical functionality needed to manage data sets throughout the research life cycle, especially repository services.


This helps circumscribe the kinds of services universities are looking to build or acquire in the RDM space.

Three realities

Although our findings are anchored to the project’s four case study partners, some generalizable results emerged—three important realities of research data management that we think are of interest to all universities entering the RDM space:

  • There is no “one-size fits-all” RDM solution. It must be a customized solution shaped both by local circumstances and the external environment in which the university is embedded. A corollary to this is that universities do not necessarily need to implement the full range of services within the RDM service space.
  • RDM services are shaped by multifaceted, dynamic incentives. These include compliance, evolving scholarly norms, institutional strategies, and researcher demand. Our case studies indicate that RDM is not some sort of scholarly fad, but instead is driven by real incentives that are driving university decision-making in this space. But these incentives manifest differently in different university contexts, and they can change or evolve over time. RDM services will be sustainable and valued only to the degree they can respond to these evolving incentives.
  • Data curation services are the most likely to be externalized. Infrastructure and other technical resources needed to support Curation services are expensive to implement and manage and require expertise that may not be readily available on campus. Additionally, Curation services tend to be scalable, and therefore well-suited for provision as a shared service.

In contrast, Education and Expertise services involve direct engagement with local researchers, which is more difficult to externalize and scale. We now see an extensive ecosystem of Curation providers emerging from collaborative groups, commercial entities, and government agencies. This is a particularly important development for universities just entering or planning to enter the RDM space, who can now choose from a range of relatively mature Curation service providers that were not available to earlier entrants.

These decisions, components, and realities are just the beginning of the insights about RDM capacity acquisition obtained from our case studies. To learn more about this work, please check out the report series. We found the case study approach to be a good methodology for engaging with RDM practitioners and hearing their stories. We are grateful to our case study partners for the time and effort they generously contributed to this project.