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

The Data Curation Profiles Toolkit: Interview Worksheet

This worksheet is designed to elicit the information necessary to develop a data curation profile about data from a particular research project. In your responses to this worksheet and to the interview questions, please limit your focus to the data associated with the particular research project you have selected. This worksheet is meant to be filled out as a part of the interview and your responses to the questions in each module will serve to guide the conversation. The interviewer will then ask you some follow up questions about your responses to gather additional details and to better understand your priorities and needs

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

Data Information Literacy: Developing Data Information Literacy Programs

Researchers are under increasing pressure to manage, organize, describe and document their data in ways that enable others to discover, understand and reuse their work. However, the knowledge and skills needed to be successful in these tasks are not often a part of a student\u27s education in college or graduate school. Librarians have an opportunity to address this gap in student\u27s education through developing data literacy programming, but developing effective data literacy programs can seem daunting. This session will introduce students to a model for creating data literacy programming developed as a part of the Data Information Literacy project. We will begin by reviewing the findings from interviews conducted with faculty and students at four universities. We will then walk through the DIL model step by step. Finally, participants will work through case studies to explore potential opportunities and generate possible approaches to offering data literacy programs. Jake Carlson is Research Data Services Manager, University of Michigan

The Data Curation Profiles Toolkit: Interviewer\u27s Manual

The Interviewer’s Manual provides the framework for the interview. It contains text and questions to be read to the participating researcher over the course of the interview. It is meant to be used in conjunction with the Interview Worksheet. The Interview Worksheet should be given to the interviewee to fill out over the course of the interview. Some of the questions you will ask will be in response to the answers given by the researcher in the Interview Worksheet. To aid in the readability of this document during the interview the font has been enlarged. The instructions to the interviewer are colored in red and are in italics. The explanatory text that is meant to be read to the interviewee is in “quotes”

How Do Researchers Define Their Data Lifecycle and What Can We Learn from Their Definitions?

This poster presents a comparison of the data lifecycles of 32 researchers as articulated by researchers themselves. Similarities and differences between the stages within these data lifecycles are noted and implications for data service providers are discussed. A critical element of providing data services is developing a thorough understanding of the nature of the data being produced by researchers. Data lifecycle models are being developed by organizations providing data services as a means to communicate with researchers and other stakeholders who would make use of these services. The test of an effective data lifecycle model is its ability to resonate and connect with the researcher. In other words, does the researcher see her data set (and by extension her needs for her data) represented in the data lifecycle model provided by service organization? Conducting a side by side comparison of the 32 data lifecycle tables presented in DCPs demonstrates the complexity and challenge of developing and communicating data services in ways that resonate across or even within fields of research. An array of terminologies and classifications are employed, the number of stages varies widely and activities such as processing data may be carried out over multiple stages. The results described in this poster demonstrate some limitations of data lifecycle models and emphasize the importance of building strong communication channels with individual researchers. Published DCPs are available through the Data Curation Profiles Directory: http://docs.lib.purdue.edu/dcp/

Data Services in Libraries: Past, Present and Future

Jake Carlson, MLIS, MA, is Director of Research Data Services, University of Michigan Library. He presented an overview and history of data services in libraries, including challenges for the future

Agronomy / Biofuels - Purdue University

The study of a species of switch grass, Miscanthus, and its potential use as a biofuel is addressed in this data curation profile. Specifically, the graduate student studies how Miscanthus cycles nutrients, nitrogen, and carbon and achieves high yields compared to other perennial crops. Although the she recognizes the importance of sharing her data to the greater research community, she is unsure of the resources available to make sharing possible. For her data to be most useful to others it would need to be connected to the article that contains her methodology in generating and working with the data

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

Developing Data Literacies for Graduate Students in the Social Sciences

What competencies in working with data do graduate students in the Social Sciences need to acquire before they graduate? What roles can librarians and other information professionals play in teaching these competencies to graduate students? This paper will report on preliminary findings from an investigation into the data management competencies and skill gaps of graduate students in the social sciences. Building from the work of the Data Information Literacy (DIL) project (http://datainfolit.org), this study uses an interview-based approach to discern how competencies in working with data are understood and valued by graduate students and their faculty advisors. The DIL project identified and employed 12 data competencies as starting points for interviews and for developing educational programming on data literacies for graduate students. As the original DIL project focused on students in five different STEM fields, this extended study into the social sciences (DIL-SS) will allow for comparisons of perceptions and practices between these disciplines. In addition, DIL-SS presents an opportunity to further develop the 12 DIL competencies and test their relevance to educational needs in the social sciences. Our findings will inform the work of librarians and others involved in offering data management education and consulting services in academic settings