Special issue: Research Data Management

Guest editorial

Article Type: Guest editorial From: Program: electronic library and information systems, Volume 49, Issue 4

The last ten to 15 years has seen a growing recognition around the world of the need to manage research data better. It is true that researchers have always had a need to manage the data they create carefully, if only to ensure that they themselves can use it effectively. It is also true that research data have been shared in subject-based repositories for decades. However, at the turn of the century, it began to be realised that new forms of data-intensive science were going to create a “data deluge”. An agenda around the curation of such data emerged which encompassed outputs of e-research more generally and the digital humanities. Furthermore, the wider agenda around openness, prompted calls for more sharing of research data, to allow it to be mined by other researchers and also to ensure research integrity. For in some fields, such as psychology, a “crisis of replicability” of research has been seen to be threatening public trust in scientific results. For this and other reasons some journals have also started to require data to be made available to support published results. A final factor in the recognition of research data management (RDM) as an agenda has probably been the economic downturn. This has led to an intensification of the need for research funders to justify how the public money they give to research is spent. Better data management and more reuse justify public investment.

Through the 2000s, for these sorts of reasons, a number of policy statements at international and national level have established RDM as a priority. The prime responsibility has been seen to rest with researchers and the research institutions in which they are based. Thus universities have produced their own data policies and librarians, research administrators and IT staff and others have begun to develop support services (Pryor et al., 2014). Such services range from advice and training through to data repositories and metadata catalogues. As a relatively unfamiliar area for information professionals there is a real need for research papers that shed light on the emerging set of challenges around creating Research Data Services (RDS).

RDM is a complex agenda involving researcher self-interest, research integrity and data security, compliance to funder or publisher mandates and the ideals of open science. High level policy has spawned institutional-level policy, but the precise character of such policies naturally varies across institution. Indeed, the agreement of policy is a highly political process. Insights into how policy is developed (Higman and Pinfield, 2015) prompts us to think about the rather complex way policy emerges.

At the same time as developing policy, there is a need to investigate researchers' for requirements for support. We now have some excellent works exploring the issues at a philosophical level and through well-developed case studies, e.g. Borgman (2015). But given the diversity of research, discovering local requirements remains a major challenge. There are a number of models of how to undertake gather requirements, e.g. data curation profiling (www://datacurationprofiles.org/) or the Data Asset Framework (www.data-audit.eu/). Such methods encompass surveys or more fine-grained interview, focus group or even ethnographic work. Whitmire et al. (2015) gives us the results of a survey based analysis. Mattern et al. (2015) offers a novel methodology for exploring researchers' practices: analysis of visual narratives of the research process.

Some institutions have pioneered the development of an RDS, e.g. Edinburgh in the UK. But there cannot be just one model, given HEIs different research profiles and needs; and differing institutional structures and resources. Some institutions have put policy in place, then developed services; others have built up services, before trying to gain agreement on a policy. Activity in some institutions has been led by the library, in some by research administrators, in some by IT. Sometimes they have shared leadership. In some institutions services have been based on existing teams, in other cases a new team or even a new department has been formed to ensure a coherent offering to researchers. Some RDS focus on training or advice; others focus around an institutional research data repository. In this context, of diverse trajectories of development, case studies of how institutions have gone about designing their services are of great interest. Four in-depth case studies by Knight, Searle et al., Schmidt and Hiom et al. are published in this issue. In addition, Curdt and Hoffmeister's paper explores the development of research data solutions, from the perspective of an individual research project.

Andrew Cox

University of Sheffield, Sheffield, UK

References

Borgman, C. (2015), Big Data, Little Data, No Data: Scholarship in the Networked World, MIT Press, Cambridge, MA

Higman, R. and Pinfield, S. (2015), “Research data management and openness: the role of data sharing in developing institutional policies and practices”, Program: Electronic Library and Information Systems, Vol. 49 No. 4, pp. 364-381

Mattern, E., Jeng, W., He, D., Lyon, L. and Brenner, A. (2015), “Using participatory design and visual narrative inquiry to investigate researchers' data challenges and recommendations for library research data services”, Program: Electronic Library and Information Systems, Vol. 49 No. 4, pp. 408-423

Pryor, G., Jones, S. and Whyte, A. (2014), Delivering Research Data Management Services, Facet, London

Whitmire, A.L., Boock, M. and Sutton, S.C. (2015), “Variability in academic research data management practices: implications for data services development from a faculty survey”, Program: Electronic Library and Information Systems, Vol. 49 No. 4, pp. 382-408