Data Curation Service Models

W. A. Collie > Uncategorized > Data Curation Service Models

Fourth paradigm service models

Research data management must not be seen as a cost sink, but rather a value stream. Research data is the raw output of the academic engine that advances science and scholarship. Data can be seen as the fundamental layer in an information hierarchy upon which information and knowledge are based (Ackoff, 1989). Therefore, we must learn to optimally manage research data as an institutional asset that will be leveraged to generate business value and marketed to a competitive advantage. 

  1. Research data collections should be marketed as distinguishing academic assets in order to secure grants, attract scholarship, seed collaboration, and attract top talent — similar to academic centers, disciplines, and library collections.
  2. Benefits of a strong framework for data governance and stewardship include: Providing guidance for timely “fourth paradigm” data challenges related to data privacy, intellectual property and ethics.
  3. Research data can also be combined with operations data and institutional assessment data in order to provide a foundation for a data-informed and data optimized campus.


The ARL SPEC Kit on Research Data Management Services (334) surveys formative efforts of ARL libraries to offer research data services in the United States.

The report offers a needed and valuable snapshot of emergent research data services but it more importantly presents significant challenges which cannot be ignored:

  • Very few are measuring effectiveness of services
  • Inneficiencies and conflicts due to lateral coordination of loosely coupled campus service units
  • Mimetic isomorphism typical of young campus services
  • Real costs currently obfuscated and ROI is not being measure


Our RDM operational networks are in a critical formative stage; strategic alignment of incentives, mission & operations must be priorities to prevent power conflict resulting from an immature and underbounded system (Alderfer, 1979; Brown, 1983) 


In addition, we must recognize that mimetic isomorphism is a dangerous phenomena that can result in young and insecure organizations becoming overly reliant on conformity in an attempt to dispel uncertainty and gain legitimacy (Boleman and Deal, 2008; DiMaggio and Powell, 1983). 


Finally, we may be relying too much on the symbolic tie to library and information centers; the investment is steep and library culture has absorbed the upfront costs. 

  • Real costs are currently obfuscated and ROI is unclear
  • We are not measuring effectiveness of service
  • We do not know what our clients perceive as value

More info

  1. Collie, Aaron. Operationalizing Digital Data Curation. Online presentation. Retrieved from: 2015
  2. Fearon, David Jr., Betsy Gunia, Sherry Lake, Barbara E. Pralle, and Andrew L. Sallans. Research Data Management Services. SPEC Kit 334. Washington, DC: Association of Research Libraries, July 2013.

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