Academic librarians are increasingly engaging in data curation by providing infrastructure (e.g., institutional repositories) and offering services (e.g., data management plan consultations) to support the management of research data on their campuses. Efforts to develop these resources may benefit from a greater understanding of disciplinary differences in research data management needs. After conducting a survey of data management practices and perspectives at our research university, we categorized faculty members into four research domains—arts and humanities, social sciences, medical sciences, and basic sciences—and analyzed variations in their patterns of survey responses. We found statistically significant differences among the four research domains for nearly every survey item, revealing important disciplinary distinctions in data management actions, attitudes, and interest in support services. Serious consideration of both the similarities and dissimilarities among disciplines will help guide academic librarians and other data curation professionals in developing a range of data-management services that can be tailored to the unique needs of different scholarly researchers.

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Disciplinary Differences in Faculty Research Data Management Practices and Perspectives

Dr. Tenopir has published over 200 journal articles, is a frequent speaker at national and international conferences, and for twenty-eight years wrote the " Online Databases " column for Library Journal. Dr. Tenopir holds a PhD degree in library and information science from the University of Illinois. he earned a bachelor's degree in mechanical engineering from Georgia Tech. Following graduation, he worked as an engineer in the aerospace, shipbuilding, and nuclear power industries. Mr. Birch earned a master's degree in computer science at UT, where he worked as a research assistant in the field of robotic software. Suzie Allard is an associate professor and Associate Director for the School of Information Sciences at the University of Tennessee. Dr. Allard's research focuses on how scientists and engineers use and communicate information, particularly data in the digital environment. Tennessee is devoted primarily to research. CICS facilitates and administers applied research in the areas of information systems and database design/evaluation, information usage, effective communication, scientific communication, science publishing, and information management. CICS works with private and public organizations, governmental agencies, and corporations to provide educational materials and research expertise in information-and communication-related projects. The work of CICS has involved dozens of researchers across disciplines and across agency borders.

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Academic libraries and research data services: Current practices and plans for the future

https://epc2012.princeton.edu/papers/120567

Ageing, productivity and wages in Austria

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

Purpose – The purpose of this paper is to demonstrate how knowledge of local research data management (RDM) practices critically informs the progressive development of research data services (RDS) after basic services have already been established. Design/methodology/approach – An online survey was distributed via e-mail to all university faculty in the fall of 2013, and was left open for just over one month. The authors sent two reminder e-mails before closing the survey. Survey data were downloaded from Qualtrics survey software and analyzed in R. Findings – In this paper, the authors reviewed a subset of survey findings that included data types, volume, and storage locations, RDM roles and responsibilities, and metadata practices. The authors found that Oregon State University (OSU) researchers are generating a wide variety of data types, and that practices vary between colleges. The authors discovered that faculty are not utilizing campus-wide storage infrastructure, and are maintaining their own stor...

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Variability in academic research data management practices: implications for data services development from a faculty survey

Objective: Cornell University’s Research Data Management Service Group (RDMSG) surveyed NSF principal investigators (PIs) at Cornell in order to understand how well-prepared researchers are to meet the new NSF data management planning requirement, to build our own understanding of the potential impact on campus services, and to identify service gaps. Methods: We administered a 43-question online survey, which included questions about the respondents’ research and research data, their interest in assistance with the creation of data management plans, and questions for each of the five general areas cited in the NSF’s Grant Proposal Guide (2011) section on data management plans. Results and Discussion: Respondents produce a wide variety of types and formats of data, although most expect to share relatively small amounts of data. Respondents are generally uncertain as to whether the data they produce conforms to disciplinary standards. The majority create no metadata; of those that do, most do not create metadata according to a particular standard. Most researchers do not express a need for advice regarding intellectual property issues. Researchers report using a variety of strategies (oncampus and commercial) for backing up and for providing access to their data sets. Conclusions: The overarching finding from our survey is that there is much uncertainty about what the new requirement means and how to meet it, and researchers welcome offers of assistance. To the extent that Cornell researchers are representative of NSF PIs, our findings reveal something about researchers’ readiness to meet the new requirement, and their attitudes towards it. Correspondence to Gail Steinhart: gss1@cornell.edu

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Prepared to Plan? A Snapshot of Researcher Readiness to Address Data Management Planning Requirements

This paper looks at leveraging DDI to enable semantic linking of social science data to other data and related resources on the Web and is organized into five use cases: (1) Linking related publications; (2) Linking people and organizations; (3) Linking geography; (4) Linking related studies; (5) Linking to licenses.

A Matched Employer-Employee Panel Data Set for Austria: 2002-2005

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Using RDF to describe and link social science data to related resources on the Web: leveraging the Data Documentation Initiative (DDI) model

This document is primarily intended for implementers of DDI-based metadata stores who are considering different technical options for housing and managing their metadata. The Data Documentation Initiative (DDI) metadata specification is expressed in the form of XML schema. With version 3, the DDI specification has become quite complex, including 21 name spaces and 846 elements. Organizations employing DDI, or considering doing so, may want to store and manage the metadata elements in relational databases, for reasons of integration with existing systems, familiarity with the concepts of relational databases (such as Structured Query Language), systems performance, and/or other reasons; select only the subset of the available DDI metadata elements that is of utility to their work, and have the flexibility of capturing metadata they need that would not fit into the DDI model. This paper discusses advantages and disadvantages of the relational database approach to managing DDI. It also describes methods for modeling DDI in relational databases and for formally defining subsets of DDI to employ in this environment.

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Representing and Utilizing DDI in Relational Databases

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Stefan Kramer

Papers

Prepared to Plan? A Snapshot of Researcher Readiness to Address Data Management Planning Requirements

A Matched Employer-Employee Panel Data Set for Austria: 2002-2005

Using RDF to describe and link social science data to related resources on the Web: leveraging the Data Documentation Initiative (DDI) model

Representing and Utilizing DDI in Relational Databases

Meeting Funders’ Data Policies: Blueprint for a Research Data Management Service Group (RDMSG)