Raising the visibility of protected data: A pilot data catalog project

Sharing research data that is protected for legal, regulatory, or contractual reasons can be challenging and current mechanisms for doing so may act as barriers to researchers and discourage data sharing. Additionally, the infrastructure commonly used for open data repositories does not easily support responsible sharing of protected data. This chapter presents a case study of an academic university library’s work to configure the existing institutional data repository to function as a data catalog. By engaging in this project, university librarians strive to enhance visibility and access to protected datasets produced at the institution and cultivate a data sharing culture

Training researchers how to manage data to produce better results, enable reuse, and provide for long-term access

The existing academic research workforce is ill equipped to manage research data using increasingly complex computing technologies available to them. Despite the availability of ever more powerful desktops, mobile technologies, and high performance cloud computing and storage, universities are failing to provide graduate students with adequate data management skills for research in academia or industry. The challenge for mid- and late-career faculty is even greater, given that it is much more difficult to change established research practices in the midst of ongoing projects. This skills gap puts at risk billions of research dollars, the integrity of vast quantities of research data, and the quality of life for millions of people. Providing faculty and students with the skills they need to collect, manage, and share their data effectively is a challenge many academic libraries are taking on. Though libraries may provide some technological solutions, our most valuable contributions lay in expertise and trust. We have the resources to fill this skills gap using our information management expertise, teaching skills, knowledge of the scholarly ecosystem, ability to facilitate conversation across departmental and disciplinary boundaries, and a uniquely holistic understanding of the scholarly record. At IUPUI, data management training is the foundation of our data services. This perspective is informed by the recognition that many graduate programs are not sufficiently preparing students to manage research data in this digital age. Before we can expect academic researchers to share, preserve, and curate their data, they must understand the value and importance of data management. This chapter will describe our initial foray into data management training, the lessons learned, and the next phase of our educational efforts. In developing the program, we drew upon best practices in instructional design and information literacy, literature on the lab experience in science, and data management expertise from various research communities. Focusing on teaching practical techniques for responsible data management, we use the data management plan as a tool for teaching as well as for research. The initial training offered at IUPUI has reached a diverse audience, many of whom were not identified as stakeholders when developing the curriculum. Development of the lab, assessment results, and modifications made to subsequent iterations will be described as a working example of an evolving data literacy program

Surveying North American Academic Library Websites for Instructional Outreach and Delivery Reveals a Broad Range of Approaches Employed

A review of: Yang, S. Q., & Chou, M. (2014). Promoting and teaching information literacy on the Internet: Surveying the web sites of 264 academic libraries in North America. Journal of Web Librarianship, 8(1), 88-104. http://dx.doi.org/10.1080/19322909.2014.85558

Improving data management in academic research: Assessment results for a pilot lab

Common practices for data collection, storage, organization, documentation, sharing, re-use, and preservation are often suboptimal. Issues often arising from common data practices include data loss, corruption, poor data integrity, and an inability to demonstrate the provenance (i.e., the origin) of the data. Ineffective data management can result in data that are unusable for re-use and re-analysis. However, effective data management practices exist to support data integrity, interoperability, and re-use. These practices maximize the value and potential impact of any particular dataset. In light of the gap between common practice and known effective strategies, we developed an intensive lab curriculum to train students and research support staff in implementing these strategies. This lab addresses the lack of formal data management training available on our campus and targets key processes in the data life cycle, promoting strategies that facilitate generation of quality data appropriate for re-use

Practical data management: Enabling graduate students and staff to function as ethical actors

Graduate students and staff are the unrecognized daily stewards for much of the research data collected on academic campuses. Often, they are performing these vital tasks without understanding their role as part of the research life cycle and scholarly communication process. Providing data management training is vital to ensuring the integrity of valuable research data. This poster will describe a series of instructional tools designed to provide a skills base for responsible data management

Library and Information Science Research Literature is Chiefly Descriptive and Relies Heavily on Survey and Content Analysis Methods

evidence summaryObjective – To compare the research articles produced by library and information science (LIS) practitioners, LIS academics, and collaborations between practitioners and academics. Design – Content analysis. Setting – English-language LIS literature from 2008 through 2012. Subjects – Research articles published in 13 library and information science journals. Methods – Using a purposive sample of 769 articles from selected journals, the authors used content analysis to characterize the mix of authorship models, author status (practitioner, academic, or student), topic, research approach and methods, and data analysis techniques used by LIS practitioners and academics. Main Results – The authors screened 1,778 articles, 769 (43%) of which were determined to be research articles. Of these, 438 (57%) were written solely by practitioners, 110 (14%) collaboratively by practitioners and academics, 205 (27%) solely by academics, and 16 (2%) by others. The majority of the articles were descriptive (74%) and gathered quantitative data (69%). The range of topics was more varied; the most popular topics were libraries and librarianship (19%), library users/information seeking (13%), medical information/research (13%), and reference services (12%). Pearson’s chi-squared tests detected significant differences in research and statistical approaches by authorship groups. Conclusion – Further examination of practitioner research is a worthwhile effort as is establishing new funding to support practitioner and academic collaborations. The use of purposive sampling limits the generalizability of the results, particularly to international and non-English LIS literature. Future studies could explore motivators for practitioner-academic collaborations as well as the skills necessary for successful collaboration. Additional support for practitioner research could include mentorship for early career librarians to facilitate more rapid maturation of collaborative research skills and increase the methodological quality of published research

Exploring the Disconnect Between Information Literacy Skills and Self-Estimates of Ability in First-Year Community College Students

evidence summaryA review of: Gross, M., & Latham, D. (2012). What’s skill got to do with it?: Information literacy skills and self-views of ability among first-year college students. Journal of the American Society for Information Science and Technology, 63(3), 574-583. doi: 10.1002/asi.2168

Building Data Services From the Ground Up: Strategies and Resources

There is a scarcity of practical guidance for developing data services in an academic library. Data services, like many areas of research, require the expertise and resources of teams spanning many disciplines. While library professionals are embedded into the teaching activities of our institutions, fewer of us are embedded in research activities occurring across the full life cycle. The significant challenges of managing, preserving, and sharing data for reuse demand that we take a more active role. Providing support for funder data management plans is just one option in the data services landscape. Awareness of the institutional and library culture in which we operate places an emphasis on the importance of relationships. Understanding the various cultures in which our researchers operate is crucial for delivering data services that are relevant and utilized. The goal of this article is to guide data specialists through this landscape by providing key resources and strategies for developing locally relevant services and by pointing to active communities of librarians and researchers tackling the challenges associated with digital research data