The Data Curation Profiles Toolkit: The Profile Template

The Data Curation Profile is composed of the following sections and sub-sections, each of which is defined below. The information used to populate the profile will largely come from the information gathered through the use of the Interview Worksheet and the Interviewer’s Manual. The following table provides a broad overview of how the interview modules correspond to the sections of the Data Curation Profile template

Opportunities and Barriers for Librarians in Exploring Data: Observations from the Data Curation Profile Workshops

Setting and Objective: The Purdue University Libraries offered a series of professional development workshops in 2011-2012 to provide librarians with a broad understanding of issues in data curation and to train them in the use of the Data Curation Profiles Toolkit (DCP Toolkit). An additional goal of the workshop was to develop a better understanding of the experiences, attitudes, and needs of librarians as they explore new roles. Design and Methods: Workshop participants were asked to complete three surveys: one before the workshop, one right afterwards, and one delivered three months after they had attended the workshop. Participants’ responses to the survey questions that pertained to demographic information, confidence levels, and levels of engagement before and after the workshop were reviewed and analyzed. Results: The results of the survey indicated that participants’ confidence levels in multiple areas increased after the workshop, but that their levels of engagement remained relatively stagnant. An analysis of the free text comments made in the survey revealed multiple issues in librarians’ efforts to increase their engagement in working with data including time, staffing, and organizational support from their library. Conclusions: The challenges encountered by librarians seeking to engage in data management and curation issues are found at the individual level (acquiring skills and confidence) and at the organizational level (creating a supportive environment). Both levels will need to be addressed by libraries seeking to develop data services

Data Management and Sharing from the Perspective of Graduate Students: An Examination of Culture and Practice at the Water Quality Field Station

Libraries are actively seeking to identify and respond to the data management and curation needs of researchers. One important perspective in this area that is often overlooked is that of graduate students. This case study uses the Data Curation Profile Toolkit to interview six graduate students working for Agronomy researchers at the Water Quality Field Station (WQFS) research lab to understand the students’ practices with data, the challenges they face, and their attitudes towards managing and sharing data. Though a small study, this research could provide new insights for libraries creating data services, particularly in regards to graduate students

Units of Evidence for Analyzing Subdisciplinary Difference in Data Practice Studies

Digital libraries (DLs) are adapting to accommodate research data and related services. The complexities of this new content spans the elements of DL development, and there are questions concerning data selection, service development, and how best to align these with local, institutional initiatives for cyberinfrastructure, data-intensive research, and data stewardship. Small science disciplines are of particular relevance due to the prevalence of this mode of research in the academy, and the anticipated magnitude of data production. To support data acquisition into DLs – and subsequent data reuse – there is a need for new knowledge on the range and complexities inherent in practice-data-curation arrangements for small science research. We present a flexible methodological approach crafted to generate data units to analyze these relationships and facilitate crossdisciplinary comparisons.Library Services (LG-06-07-0032-07) and National Science Foundation (OCI-0830976).is peer reviewe

Assessing Perceived Usability of the Data Curation Profile Toolkit Using the Technology Acceptance Model

The Data Curation Profiles Toolkit (DCPT) emerged out of a Purdue University Libraries’ 2004 initiative to engage in multidisciplinary research. It is a tool developed to assist librarians and other information professionals to conduct data interviews and identify the needs of researchers when managing, sharing, or curating their data. The DCPT has been widely adopted and applied in various contexts but its usability as a tool has not been formally assessed. To address this need, we have conducted a survey of users of the DCPT. The survey included quantitative measures of potential influencing factors of using the DCPT and its perceived usability (its usefulness as a tool and its ease of use). Open-ended questions about users’ experiences with the DCPT were also included to better understand the strengths and weaknesses of the tool, as well as areas that could be improved. Factor analysis of the quantitative results and subsequent regression models revealed several underlying factors that affect the perceived usability of the DCPT. Responses to the open-ended questions revealed several themes of users’ concerns: the amount of time required to use the DCPT, the structure and format of the DCPT, alignment of the DCPT with particular contexts, and the use of the DCPT to engage faculty and the library community. By correlating themes identified from the open-ended questions with the analysis of quantitative data, this paper provides the first empirical assessment of the DCPT that could help further improve the toolkit’s usability based on user needs and expectations. The methodology used in the study could readily be applied to assess and improve the utility of other tools used by data and information professional

The Data Curation Profiles Toolkit: User Guide

A Data Curation Profile is a tool that can be used to provide a foundational base of information about a particular set of data that may be curated by an academic library or other institution. This user guide provides a background of a Data Curation Profile, their purpose, components, and one may develop their own Data Curation Profile

‘DIY’ Research Data Management Training Kit for Librarians

Abstract – This paper discusses extended professional development training in research data management for librarians piloted at the University of Edinburgh. This is framed by the evolving research data management Roadmap at the University, national and international initiatives in managing research data by bodies such as Jisc and LIBER, and the subsequent need to ‘up skill ’ information professionals in the emerging area of academic research data management. This knowledge-transfer exercise includes independent study based on the research data MANTRA course and reflective writing, face to face sessions with different speakers giving short presentations followed by discussion, and group exercises. The resultant training ‘kit ’ was released in Spring 2013 with an open licence for other institutions, particularly those without local research data management expertise, to utilise for ‘DIY ’ RDM training

Using Data Curation Profiles (DCPs) as a means of raising data management awareness

While one can discuss data management plans in a general sense, there is no single solution for managing the diverse data generated by various disciplines and projects. Therefore one possible solution is to determine best practices for individual data management plans guided by a more general Data Curation Profile (DCP). The DCPs were created at Purdue University and the University of Illinois Urbana-Champaign through a grant from the Institute of Museum and Library Services. Using a DCP, librarians and/or researchers explore various data management issues. Once a profile has been completed, not only will the librarian have a richer understanding of the kind and quantity of data that might have to be curated and archived, but the researcher will have a better understanding of their data preferences related to sharing and intellectual property, regardless of where the data ultimately resides. Current applications of the DCP at Purdue will be discussed. [ACS Abstract