Intellectual Capital and Scientific Collaboration
Saturday, November 2, 2013, 1:30pm
Assessing Library Capacity to Manage Research Data: An Intellectual Capital Perspective
Sheila Corrall and Mary Anne Kennan
Can libraries cope with e-science? Our current study explores whether university libraries have the capacity to engage with the management of research data sets as an extension of their established responsibility for stewardship of scholarly knowledge resources. We are using an intellectual capital lens to focus on the intangible assets of academic libraries. Several authors have argued that intellectual capital theory can assist libraries in developing new measures of performance (Huotari & Iivonen, 2005; Kostagiolas & Asonitis, 2009; Town, 2011). Existing research has examined the knowledge and skills (human capital) requirements for data management, but no studies have systematically investigated the structural and relational capital that libraries can bring to the task of data stewardship. Our objective is to gain a broader and deeper understanding of the factors helping or hindering the efforts of librarians in the data management arena, and to empower library practitioners by providing an evaluation framework supported by practical examples, which libraries can employ to assess their own capacity to engage in this emerging area of professional practice.
Advances in network technologies, the growth of e-science and digital humanities, and policy developments related to open science, have created opportunities for libraries to extend their roles in scholarly communication and information resources to the management of research data. Lewis (2010, p. 145) has argued that such data represent “an integral part of the global research knowledge base” and managing these resources “should be a natural extension of the university library’s current role,” confirming Garritano and Carlson’s (2009) assertion that data are “simply another information resource.” Research funding agencies, such as the National Science Foundation (NSF, 2007) and the European Commission (HLG, 2010) similarly envisage librarians taking on new roles as data librarians and data scientists. Data curation evidently fits with established library functions, notably resource description and repository management (Corrall, 2012). However, some stakeholders have questioned whether librarians have the domain knowledge and technical skills needed for the task (Gold, 2007; Henty, 2008). Others have described relevant professional knowledge and skills found in libraries, and have also pointed to other potential assets such as their outreach and liaison work, in addition to existing systems and procedures that can be adapted or extended to deal with data (Gabridge, 2009; Garritano & Carlson, 2009; Walters, 2009).
A significant body of literature on library engagement with research data has emerged within the last five years. State-of-the-art surveys have been conducted in North America (Soehner, Steeves, & Ward, 2010; Tenopir, Birch, & Allard, 2012; Tenopir, Sandusky, Birch, & Allard, 2013), and internationally (Auckland, 2012; Corrall, Kennan, & Afzal, 2013; Shearer & Argáez, 2010), which have also assessed library preparedness for new roles, but concentrating on skills and knowledge gaps, with only limited discussion of the relevant structural features and collaborative relationships identified by practitioners in the field. However, there is now a growing number of published institutional case studies documenting the pioneering efforts of libraries around the world in providing data management support and services to meet research needs. These individual reports offer more rounded accounts than larger-scale investigations, yielding valuable additional insights into factors supporting or constraining practitioner adoption of new practices, but the findings have not yet been effectively synthesized.
The present study was designed to fill this research gap and breaks new ground by examining academic library capacity for managing research data from an intellectual capital perspective. The study adopted the categorization of intellectual assets provided by the Organisation for Economic Co-operation and Development (OECD, 2008) as an analytical framework. The OECD classification was chosen because of its international standing (making it an appropriate choice for investigating library practices in different countries), and more specifically because the descriptors set out in the 2008 synthesis report resonated strongly with the data collected by the research team and offered a predetermined set of concepts and keywords that could be used to inform and focus the analysis and synthesis of the evidence.
The present investigation is based on secondary data analysis (Heaton, 1998), drawing on data collected by the authors for prior studies as part of an ongoing program of work investigating library support for research. The new conceptual focus on the intellectual assets that academic libraries bring to data management is enabling us to analyze the data sets from a different perspective, and to focus intensively on aspects of particular interest in greater depth than previously. Three data sources are being used: • Questionnaire responses to a survey of library support for research distributed in Australia, New Zealand, the United Kingdom, and Ireland during 2012 (n=140). • Published literature on research data management reviewed before and after the questionnaire survey, which includes case studies of 20 institutions in 4 countries. • Web-based documentation identified during an exploratory study of organizational structures adopted by the libraries of 24 leading research universities in the UK.
Preliminary analysis indicates that libraries have important structural and relational assets that should be taken into account alongside their widely recognized human assets when evaluating their capacity to engage with research data. Established and emergent structures, including subject liaisons, coordinating roles, working groups, specialist positions, and fund management, are being used to progress new forms of research support. Existing systems and procedures are also being extended and adapted to deal with research data.