An Exploratory Sequential Mixed Methods Approach to Understanding Researchers’ Data Management Practices at UVM: Integrated Findings to Develop Research Data Services

This article reports on the integrated findings of an exploratory sequential mixed methods research design aimed to understand data management behaviors and challenges of faculty at the University of Vermont (UVM) in order to develop relevant research data services. The exploratory sequential mixed methods design is characterized by an initial qualitative phase of data collection and analysis, followed by a phase of quantitative data collection and analysis, with a final phase of integration or linking of data from the two separate strands of data. A joint display was used to integrate data focused on the three primary research questions: How do faculty at UVM manage their research data, in particular how do they share and preserve data in the long-term?; What challenges or barriers do UVM faculty face in effectively managing their research data?; and What institutional data management support or services are UVM faculty interested in? As a result of the analysis, this study suggests four major areas of research data services for UVM to address: infrastructure, metadata, data analysis and statistical support, and informational research data services. The implementation of these potential areas of research data services is underscored by the need for cross-campus collaboration and support

An Exploratory Sequential Mixed Methods Approach to Understanding Researchers’ Data Management Practices at UVM: Findings from the Qualitative Phase

The objective of this article is to report on the first qualitative phase of an exploratory sequential mixed methods research design focused on researcher data management practices and related institutional research data services. The aim of this study is to understand data management behaviors of faculty at the University of Vermont (UVM), a higher-research activity Research University, in order to guide the development of campus research data management services. The population of study was all faculty who received National Science Foundation (NSF) grants between 2011 and 2014 who were required to submit a data management plan (DMP); qualitative data was collected in two forms: (1) semi-structured interviews and (2) document analysis of data management plans. From a population of 47 researchers, six were included in the interview sample, representing a broad range of disciplines and NSF Directorates, and 35 data management plans were analyzed. Three major themes were identified through triangulation of qualitative data sources: data management activities, including data dissemination and data sharing; institutional research support and infrastructure barriers; and perceptions of data management plans and attitudes towards data management planning. The themes articulated in this article will be used to design a survey for the second quantitative phase of the study, which will aim to more broadly generalize data management activities at UVM across all disciplines

e-Social Science and Evidence-Based Policy Assessment : Challenges and Solutions

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The Role of the Library in the Research Enterprise

Libraries have provided services to researchers for many years. Changes in technology and new publishing models provide opportunities for libraries to be more involved in the research enterprise. Within this article, the author reviews traditional library services, briefly describes the eScience and publishing landscape as it relates to libraries, and explores possible library programs in support of research. Many of the new opportunities require new partnerships, both within the institution and externally

e-Science Infrastructure for the Social Sciences

When the term „e-Science“ became popular, it frequently was referred to as “enhanced science” or “electronic science”. More telling is the definition ‘e-Science is about global collaboration in key areas of science and the next generation of infrastructure that will enable it’ (Taylor, 2001). The question arises to what extent can the social sciences profit from recent developments in e- Science infrastructure? While computing, storage and network capacities so far were sufficient to accommodate and access social science data bases, new capacities and technologies support new types of research, e.g. linking and analysing transactional or audio-visual data. Increasingly collaborative working by researchers in distributed networks is efficiently supported and new resources are available for e-learning. Whether these new developments become transformative or just helpful will very much depend on whether their full potential is recognized and creatively integrated into new research designs by theoretically innovative scientists. Progress in e-Science was very much linked to the vision of the Grid as “a software infrastructure that enables flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions and resources’ and virtually unlimited computing capacities (Foster et al. 2000). In the Social Sciences there has been considerable progress in using modern IT- technologies for multilingual access to virtual distributed research databases across Europe and beyond (e.g. NESSTAR, CESSDA – Portal), data portals for access to statistical offices and for linking access to data, literature, project, expert and other data bases (e.g. Digital Libraries, VASCODA/SOWIPORT). Whether future developments will need GRID enabling of social science databases or can be further developed using WEB 2.0 support is currently an open question. The challenges here are seamless integration and interoperability of data bases, a requirement that is also stipulated by internationalisation and trans-disciplinary research. This goes along with the need for standards and harmonisation of data and metadata. Progress powered by e- infrastructure is, among others, dependent on regulatory frameworks and human capital well trained in both, data science and research methods. It is also dependent on sufficient critical mass of the institutional infrastructure to efficiently support a dynamic research community that wants to “take the lead without catching up”.

Digital Object Identifier (DOI) Under the Context of Research Data Librarianship

A digital object identifier (DOI) is an increasingly prominent persistent identifier in finding and accessing scholarly information. This paper intends to present an overview of global development and approaches in the field of DOI and DOI services with a slight geographical focus on Germany. At first, the initiation and components of the DOI system and the structure of a DOI name are explored. Next, the fundamental and specific characteristics of DOIs are described and DOIs for three (3) kinds of typical intellectual entities in the scholar communication are dealt with; then, a general DOI service pyramid is sketched with brief descriptions of functions of institutions at different levels. After that, approaches of the research data librarianship community in the field of RDM, especially DOI services, are elaborated. As examples, the DOI services provided in German research libraries as well as best practices of DOI services in a German library are introduced; and finally, the current practices and some issues dealing with DOIs are summarized. It is foreseeable that DOI, which is crucial to FAIR research data, will gain extensive recognition in the scientific world

An Assessment of Needed Competencies to Promote the Data Curation and Management Librarianship of Health Sciences and Science and Technology Librarians in New England

Purpose: The purpose of this study was to evaluate health sciences and science and technology librarians’ needed data curation and management (DCM) competencies to support nascent and future patron and institutional eScience research endeavors. The data from this research will be used to align a data curation and management curriculum with the educational needs of an online eScience portal community of users, and create relevant future professional development for librarians interested in data curation and eScience librarianship. Setting/Participants: The study targeted the needed data curation and data management competencies of health sciences and science and technology librarians in six U.S. states who are on a listserv of librarians interested in learning about eScience. The sample for this study was 63 librarians. Methodology: The team created the assessment tool using content analyses of digital curation and management library literature and LIS data management curricula. The survey contained 15 open-ended and closed-ended questions and was distributed to 141 librarians using Survey- Monkey (http://www.surveymonkey.com). Results/Outcomes: The team identified twenty needed competency areas related to data curation and data management. The participants identified the necessary competencies to provide data curation and data management services. Results revealed a small number of librarians engaged in DCM and infrequent data services requests. Findings suggest there is an increase in libraries pursuing strategic plans concerning data management and the library community needs to cultivate a diverse range of technical and non-technical competencies through future professional development. Librarians saw their future roles involving DCM and sought competencies in conducting data interviews with patrons and helping patrons with NSF data management requirements. The survey results indicate the greatest need for librarians is technical hands-on training in the digital description and curation of large data sets. Discussion/Conclusion: Librarians are interested in developing data curation and data management competencies to support eScience. These data indicate that future relevant professional development for librarians interested in eScience should focus on non-technical and technical DCM competencies