Lisa Johnston

Developing an Approach for Data Management Education: A Report from the Data Information Literacy Project

This paper describes the initial results from the Data Information Literacy (DIL) project designed to identify the educational needs of graduate students across a variety of science disciplines and respond with effective educational interventions to meet those needs. The DIL project consists of five teams in disparate disciplines from four academic institutions in the United States. The project teams include a data librarian, a subject-specialist or information literacy librarian, and a faculty member representing a disciplinary group of students. Interviews with the students and faculty members present a detailed snapshot of graduate student needs in data management education. Following our study, educational programs addressing identified needs will be delivered in the fall of 2012 and spring of 2013. Our findings from the project interviews are analyzed here, with a preview of the training approaches that will be taken by the five teams.

MapHappy: A User-Centered Interface to Library Map Collections via a Google Maps “Mashup”

Providing users with the best possible access to the unique cartographic materials found in libraries has been a constant challenge for map librarians. In a Web 2.0 world, existing mapping APIs make it possible to extend the library OPAC past the text-based search and enable users to locate maps using a familiar interface, such as Google Maps. This article describes how librarians at the University of Minnesota developed MapHappy, a “mashup” of their existing map MARC records into a geospatial Web application providing unique access to the print maps in their collections. As expected, this project raised many questions and produced a new set of challenges and opportunities; two such problems, the issue of missing or faulty map coordinates and aligning the interface design with user expectations, are discussed. And while still in beta, a variety of future plans are considered for further development and improvement of MapHappy. This project demonstrates that the representation of library records in a geospatial format provides a more intuitive and streamlined method of identifying maps and makes accessible a wide range of data previously meaningless or invisible to users.

Approaches to Data Sharing: An Analysis of NSF Data Management Plans from a Large Research University

INTRODUCTION Sharing digital research data is increasingly common, propelled by funding requirements, journal publishers, local campus policies, or community-driven expectations of more collaborative and interdisciplinary research environments. However, it is not well understood how researchers are addressing these expectations and whether they are transitioning from individualized practices to more thoughtful and potentially public approaches to data sharing that will enable reuse of their data. METHODS The University of Minnesota Libraries conducted a local opt-in study of data management plans (DMPs) included in funded National Science Foundation (NSF) grant proposals from January 2011 through June 2014. In order to understand the current data management and sharing practices of campus researchers, we solicited, coded, and analyzed 182 DMPs, accounting for 41% of the total number of plans available. RESULTS DMPs from seven colleges and academic units were included. The College of Science of Engineering accounted for 70% of the plans in our review. While 96% of DMPs mentioned data sharing, we found a variety of approaches for how PIs shared their data, where data was shared, the intended audiences for sharing, and practices for ensuring long-term reuse. CONCLUSION DMPs are useful tools to investigate researchers’ current plans and philosophies for how research outputs might be shared. Plans and strategies for data sharing are inconsistent across this sample, and researchers need to better understand what kind of sharing constitutes public access. More intervention is needed to ensure that researchers implement the sharing provisions in their plans to the fullest extent possible. These findings will help academic libraries develop practical, targeted data services for researchers that aim to increase the impact of institutional research. External Data or Supplements: Bishoff, Carolyn; Johnston, Lisa, 2015, “Instrument used to code DMPs”, http://dx.doi.org/10.7910/DVN/5JGNMM , Harvard Dataverse. [Instrument] Johnston, Lisa R; Bishoff, Carolyn; McGrory, John; Storino, Chris; Swendsrud, Anders. (2015). Analyzed Data Management Plans (DMPs) from Successful University of Minnesota Grants from the National Science Foundation, 2011-2014 [dataset]. Retrieved from the Data Repository for the University of Minnesota, http://dx.doi.org/10.13020/D6TG6Z [Data]

Data Management Skills Needed by Structural Engineering Students: Case Study at the University of Minnesota

AbstractThe current era of e-science and big data calls on researchers to ensure the security and management of their research data. In this case study, researchers from the University of Minnesota Libraries interviewed a graduate student research team and faculty advisor to provide an understanding of the educational gaps that need to be filled to graduate data-savvy engineers. This paper presents the needed data management skills with thoughts on how the engineering discipline and academic research libraries might partner on a shared training solution.

Developing E-science and Research Services and Support at the University of Minnesota Health Sciences Libraries

ABSTRACT This article describes the development and implementation of e-science and research support services in the Health Sciences Libraries (HSL) at the University of Minnesota (UMN). A review of the broader e-science initiatives within the UMN demonstrates the needs and opportunities that the University Libraries face while building knowledge, skills, and capacity to support e-research. These experiences are being used by the University Libraries administration and HSL to apply support for the growing needs of researchers in the health sciences. Several research areas that would benefit from enhanced e-science support are described. Plans to address the growing e-research needs of health sciences researchers are also discussed.

Supplementary Data for 'How Important Are Data Curation Activities to Researchers? Gaps and Opportunities for Academic Libraries'

Data were collected from the two protocols (ratings and worksheet answers) and analyzed by institution. Blanks indicate that no data was collected at that institution for that data curation activity.

How are we Measuring Up? Evaluating Research Data Services in Academic Libraries

INTRODUCTION In the years since the emergence of federal funding agency data management and sharing requirements ( http://datasharing.sparcopen.org/data ), research data services (RDS) have expanded to dozens of academic libraries in the United States. As these services have matured, service providers have begun to assess them. Given a lack of practical guidance in the literature, we seek to begin the discussion with several case studies and an exploration of four approaches suitable to assessing these emerging services. DESCRIPTION OF PROGRAM This article examines five case studies that vary by staffing, drivers, and institutional context in order to begin a practice-oriented conversation about how to evaluate and assess research data services in academic libraries. The case studies highlight some commonly discussed challenges, including insufficient training and resources, competing demands for evaluation efforts, and the tension between evidence that can be easily gathered and that which addresses our most important questions. We explore reflective practice, formative evaluation, developmental evaluation, and evidence-based library and information practice for ideas to advance practice. NEXT STEPS Data specialists engaged in providing research data services need strategies and tools with which to make decisions about their services. These range from identifying stakeholder needs to refining existing services to determining when to extend and discontinue declining services. While the landscape of research data services is broad and diverse, there are common needs that we can address as a community. To that end, we have created a community-owned space to facilitate the exchange of knowledge and existing resources.

How Important is Data Curation? Gaps and Opportunities for Academic Libraries

INTRODUCTION Data curation may be an emerging service for academic libraries, but researchers actively “curate” their data in a number of ways—even if terminology may not always align. Building on past userneeds assessments performed via survey and focus groups, the authors sought direct input from researchers on the importance and utilization of specific data curation activities. METHODS Between October 21, 2016, and November 18, 2016, the study team held focus groups with 91 participants at six different academic institutions to determine which data curation activities were most important to researchers, which activities were currently underway for their data, and how satisfied they were with the results. RESULTS Researchers are actively engaged in a variety of data curation activities, and while they considered most data curation activities to be highly important, a majority of the sample reported dissatisfaction with the current state of data curation at their institution. DISCUSSION Our findings demonstrate specific gaps and opportunities for academic libraries to focus their data curation services to more effectively meet researcher needs. CONCLUSION Research libraries stand to benefit their users by emphasizing, investing in, and/or heavily promoting the highly valued services that may not currently be in use by many researchers.

Analyzed Data Management Plans (DMPs) from Successful University of Minnesota Grants from the National Science Foundation, 2011-2014

This data set consists of five files. The excel file (UMN_DMPReview_2011-2014_v2.xslx) represents the de-indentifed and analyzed data management plans (DMPs) from successful grants. These data are based on the review instrument, which is included both as a pdf and a png image file (UMN_DMPReviewInstrument). Included is also a text readme file (DMPReviewInstrument_Readme_v2) containing information about the study and data. Finally, there is a zipped archival copy of the excel data file that has been converted to two .csv files (Archival Version of Data).