Article - Developments in research data management in academic libraries: Towards an understanding of research data service maturity.

Abstract

This article reports an international study of research data management (RDM) activities, services, and capabilities in higher education libraries. It presents the results of a survey covering higher education libraries in Australia, Canada, Germany, Ireland, the Netherlands, New Zealand, and the UK. The results indicate that libraries have provided leadership in RDM, particularly in advocacy and policy development. Service development is still limited, focused especially on advisory and consultancy services (such as data management planning support and data-related training), rather than technical services (such as provision of a data catalog, and curation of active data). Data curation skills development is underway in libraries, but skills and capabilities are not consistently in place and remain a concern. Other major challenges include resourcing, working with other support services, and achieving “buy in” from researchers and senior managers. Results are compared with previous studies in order to assess trends and relative maturity levels. The range of RDM activities explored in this study are positioned on a “landscape maturity model,” which reflects current and planned research data services and practice in academic libraries, representing a “snapshot” of current developments and a baseline for future research.

Cox, A. M., Kennan, M. A., Lyon, L. and Pinfield, S. (2017), Developments in research data management in academic libraries: Towards an understanding of research data service maturity. Journal of the Association for Information Science and Technology. doi:10.1002/asi.23781

Introducing the Data Management Plans (DMPs) Interest Group

Do you have questions about Data Management Plans such as: Why do we have them and are they really useful? What DMP tools are others using? What might the next generation of DMPs look like? What voice should researchers have in their development? How do we define best practice?

These are just a few of the questions that staff at Australian institutions are asking when evaluating DMPs, DMP tools and their effectiveness. To help facilitate the discussion, a new DMP Interest Group has been formed, facilitated by the Australian National Data Service. If DMPs keep you up at night, we warmly invite you to join in the discussion.

Why DMPs?

Significant technical and human efforts have been, and are continuing to be, directed towards the development and use of data management plans (DMPs) for research. These efforts are driven by a number of factors. An increasing number of research funders, such as Wellcome Trust, ask researchers to provide a DMP. Institutions are also looking to provide DMP tools to their researchers for a range of reasons such as risk management, to provide a useful tool, and to collect information that enables them to plan technical and human support. Some institutions, such as Curtin University, are even making DMPs mandatory for researchers under certain conditions. Others, such as the University of Colorado, sponsor a competition for the best DMP. But how useful are DMPs?

In the beginning, it’s fair to say that DMP tools were designed to capture absolutely everything to do with data management during the research lifecycle by asking a seemingly endless series of questions: what data will be created? Who will own and access the data? What facilities and equipment will be required? What metadata will be used to describe the data? Where will the data be stored? and so on. Some DMPs are huge, bewildering and somewhat terrifying documents for researchers, especially those new to data management. Nick Smale from the University of Melbourne explained at the ANDS DMP webinar in February that their initial DMP asked 90 questions and was 40 pages long! Not surprisingly, uptake was minimal. This got Nick thinking about the usefulness of DMPs in general. As he points out in his blog post, there is a surprising lack of evidence base behind DMP use and he calls for further research into the benefits of DMPs. Such research would help those looking to implement DMP tools understand how to better design those tools and encourage researchers to use them.

Next generation DMPs

While the benefits of DMPs are a work in progress, institutions are beginning to move from those long early versions of DMPs to what Victoria Stodden coins ‘DMP v2.0”. Stodden, from the University of Illinois at Urbana-Champaign, is working on an EAGER project funded by NSF. At the Research Data Alliance plenary last September, she outlined characteristics of DMPs v.2.0, including DMPs that are:

• public not private documents
• machine readable as well as human readable
• flexible living documents that can be changed through the course of a research project
• measurable (i.e. did researcher X do what they said they would do in their DMP?)
• connected to at least one other system rather than stand alone forms.

This is food for thought and moves DMP tools in a whole new direction. With such changes happening, and with more institutions launching or upgrading their DMP tools, now is the right time to launch an Interest Group to discuss this important topic. Why not join in?

Join the discussion!

Sign up for the Data Librarians Google Group or ANDS News to be notified of DMP Interest Group events.

Next DMP IG online catch-up:

See listing at the bottom of the DMP Interest Group page on the ANDS website:
http://www.ands.org.au/partners-and-communities/ands-communities/dmps-interest-group 

Chair of the Productivity Commission speaking about the inquiry into Data Availability and Use

http://www.themandarin.com.au/77139-peter-harris-rules-forcing-data-destruction-akin-burning-books

http://www.pc.gov.au/news-media/speeches/data

The final report is due at the end of the month (March 2017)

Data Curation Network releases two reports

The Data Curation Network project team has released two new reports. The first shares the results of six focus group sessions with researchers on their perceptions, use, and barriers of data curation activities. In total they engaged 91 researchers from across the planning phase institutions: U. of Minnesota, Cornell, Penn State, U of Illinois, U of Michigan, and Wash U. in St. Louis. The research revealed what curation activities are most important to researchers and where the Data Curation Network could provide the most impact. The interview instruments and assessment data are also provided.

The second report describes the results of their experimental pilot for curating the same dataset with staff from across our six institutions. They present methodology, results and concluding recommendations for how a resulting Network model will work as a distributed staffing approach to providing data curation support. Based on the findings of this pilot data curation exercise, the Data Curation Network now has a better understanding of the roles of the "local" and "DCN curators" and how data might effectively flow through the DCN once implemented beyond our six institutions. 

Read the full reports here: https://sites.google.com/site/datacurationnetwork/results

The Data Curation Network project aims to enable academic institutions to scale their data curation services by sharing staff with file format and domain specific skills so that data sets ingested by one institution might be curated by curators with that expertise at another institution. Thus they aim to provide data repositories with a wider range of curatorial expertise to handle the multi-disciplinary data types found across academic domains.

The Data Curation Network project is supported by a grant from the ALFRED P. SLOAN FOUNDATION.

To learn more, check out the website at https://sites.google.com/site/datacurationnetwork/home.