Lisa R Johnston
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As Data Management & Curation Lead and co-director of the University Digital Conservancy I coordinate the library's efforts around digital scholarship and research data management, access, and archiving. Prior to this I served as library liaison to the Physics, Astronomy, and Geology departments (2007-2011). My research areas of focus are scientific data curation, citation analysis, information-seeking behavior and web development of user-centered tools to access information.
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Item Measuring the Value of Data Curation: Preliminary Results from the Data Curation Network(2019-04) Hadley, Hannah; Vitale, Cynthia; Johnston, Lisa R; Kozlowski, Wendy; Lafferty-Hess, Sophia; Hunt, Shanda; Luong, Hoa; Ge, LizhaoItem Level of curation self-reported by 100 CoreTrustSeal certified repositories (2017-2019)(2021-02-10) Johnston, Lisa R; ljohnsto@umn.edu; Johnston, Lisa R; Data Curation NetworkThis dataset extracts and makes machine-actionable the responses to the "Level of curation performed" component of the CoreTrustSeal application v01 (2017-2019). The author reviewed 100 applications in pdf file format and compiled the responses into one spreadsheet for further analysis. Additionally, the CTS application instructions for v01 were parsed in order to analyze the completed applications and included here in a spreadsheet.Item The Data Curation Network: Extending the Research Data Toolkit(2020-06) Johnston, Lisa R; Coburn, Elizabeth; Kozlowski, Wendy; Hadley, HannahItem An Update on Data Curation Primers: Collaborative Resources for Data Curators(2020-03) Hudson Vitale, Cynthia; Hadley, Hannah; Herndon, Joel; Darragh, Jennifer; Lafferty-Hess, Sophia; Johnston, Lisa R; Hunt, Shanda; Blake, Mara; Fearon, Dave; Moore, Jennifer; Carlson, Jake; Borda, Susan; Kozlowski, WendyItem Collective Curation: An update from the Data Curation Network(2020-02) Johnston, Lisa R; Blake, Mara; Hadley, HannahItem Data Curation Network Update for the Texas Digital Library(2019-12) Johnston, Lisa RItem Networked Expertise for Research Data(2019-12) Johnston, Lisa R; Vitale, Cynthia; McGeary, TimItem Update for the Canadian Data Curation Forum(2019-10) Johnston, Lisa R; Vitale, CynthiaItem Data Curation Primers: Expanding the community curation toolkit(2019-09) Johnston, Lisa R; Hudson-Vitale, Cynthia; Hadley, HannahItem Community-Developed Infrastructure for Curating Research Data: DCN Update(2019-05) Coburn, Elizabeth; Hadley, Hannah; Johnston, Lisa R; Kozlowski, Wendy; Vitale, Cynthia; Herndon, Joel; Carlson, JakeItem Data Curation Network: A curator’s perspective(2019-02) Fagan, Debra; Clary, Erin; Johnston, Lisa RItem Data supporting: "Testing Our Assumptions: Preliminary Results from the Data Curation Network"(2020-06-04) Coburn, Elizabeth; Johnston, Lisa R; ecoburn@umn.edu; Coburn, Elizabeth; Data Curation Network; University LibrariesData were collected during the first year of the Data Curation Network's pilot shared data curation service. These data and analysis of these data are the basis of the findings presented in the associated manuscript.Item Data discovery and the role of academic institutional data repositories(2020-02) Johnston, Lisa RItem Extending the Research Data Toolkit: Data Curation Primers(2020) Johnston, Lisa; Hannah, Hadley; Blake, MaraSlides presented at the International Digital Curation Conference (IDCC) February 19, 2020 in Dublin, IrelandItem A Comprehensive Campus-based Approach to Address the Opportunities and Challenges Posed by Data Intensive Research(Association for Computing Machinery, 2019) Wilgenbusch, James C; Baller, Joshua A; Bates, Carla; Bollig, Evan; Johnston, Lisa R; Neuhauser, ClaudiaFaced with escalating expenses related to data storage needs and a capable set of on campus storage service providers, the University of Minnesota (UMN) developed a comprehensive framework to better address current and emerging challenges and opportunities brought by data intensive research. Elements of this framework and the process used to develop it could be applied at other research institutions to advance their efforts to address the challenges they face supporting data intensive science. While approaches may differ slightly, addressing these challenges within our respective universities is critical, and perhaps a prerequisite to building and sustaining partnerships among providers of advanced research computing and fully realizing the value of our data.Item Data Sharing Readiness in Academic Institutions(Data Curation Network, 2020-01-15) Johnston, Lisa R; Coburn, LizaIn 2017, several members of the Data Curation Network authored the Academic Research Libraries (ARL) Data Curation Spec Kit, a survey asking 124 academic research institutions in the United States and Canada to self-assess their data repository and curation services (Hudson-Vitale et al., 2017a). Three years later, institutional support for data sharing is as relevant as ever. Next month, the Association of Public Land Grant Universities (APLU) and the Association of American Universities (AAU) will convene a national summit in Washington, DC to address how public universities may increase public access to their research, particularly in light of funder and journal requirements supporting data reuse and research transparency. So, we wondered, how has the academic landscape for data repository and curation services changed? To answer this question, we used website content analysis – a method that has been used successfully in recent years for examining the broader category of academic library research data services offerings (Yoon and Schultz, 2015; Kouper, Fear, Ishida, Kollen, & WIlliams, 2017) – to better understand data repository services in academic research libraries, building on the 2017 Spec Kit results.Item Data supporting “Data Sharing Readiness in Academic Institutions” Version 1.0(2020-01-15) Johnston, Lisa R; Coburn, Liza; ljohnsto@umn.edu; Johnston, Lisa R; University LibrariesTo address how has the academic landscape for data repository and curation services changed we used website content analysis to better understand data repository services in academic research libraries, building on the 2017 ARL Spec Kit for Data Curation (Hudson-Vitale et al., 2017a). Of the 124 ARL institutions we chose to focus on academic institutions, and therefore excluded 10 civic libraries. For each of the remaining 114 ARL institutions we asked four research questions: Do they support data sharing via data repository services? How many datasets did they hold as of January 2020? What digital repository software platform was in use? How do our results compare with the 2017 ARL SPEC Kit data.Item Implementing a Cross-Institutional Staffing Model for Curating Research Data - CS3DP 2018(2018-02) Johnston, Lisa R; Carlson, Jake; Hudson-Vitale, Cynthia; Imker, Heidi; Kozlowski, Wendy; Olendorf, Robert; Stewart, Claire; Blake, Mara; Herndon, Joel; Hull, Elizabeth; McGeary, TimItem Launching the Data Curation Network(2018-02-20) Johnston, Lisa; Carlson, Jake; Hudson-Vitale, Cynthia; Imker, Heidi; Kozlowski, Wendy; Olendorf, Robert; Stewart, Claire; Blake, Mara; Herndon, Joel; Hull, Elizabeth; McGeary, Timothy MItem Implementing a Cross-Institutional Staffing Model for Curating Research Data(2018-03) Johnston, Lisa R; Carlson, Jake; Hudson-Vitale, Cynthia; Imker, Heidi; Kozlowski, Wendy; Olendorf, Robert; Stewart, Claire; Blake, Mara; Herndon, Joel; Hull, Elizabeth; McGeary, Tim