Browsing by keyword "data storage"
Now showing items 1-2 of 2
-
Building Research Data Services at Mount Holyoke CollegeObjective: Mount Holyoke College ranks high among liberal arts colleges in faculty research activities and has just initiated a new program in Data Science. In this context, and given the recent growth in the use of very large datasets in research, more coherent and comprehensive campus support for the management and storage of faculty research data at Mount Holyoke has become essential. This poster will describe how Library, Information, and Technology Services (LITS) at Mount Holyoke (a merged library and IT organization), strove to analyze faculty research data management and storage needs, develop policies and procedures for meeting these needs, set out support models for research data lifecycle management, and determined responsibilities for consultation and support. Methods: In 2015-2016, a working group of MHC librarians and technologists from the Research and Instructional Support (RIS) Department began exploring the need for data services at MHC. The RIS team focussed on studying data services models at other institutions, administered a survey to learn about faculty research data practices, and finally developed a proposal for expanded data services at MHC. Also in the spring of 2016, a cross-functional team was formed to meet faculty data storage and backup needs. Ten members were drawn from multiple library and IT departments. This team developed use cases and personas to begin guiding the development of policies and procedures and planning infrastructure provisioning. Additionally, metadata librarians, research and instruction librarians, and digital assets managers planned support models for metadata creation and research data management planning. Results: Gathering information from the faculty survey and interviews, along with background study of data services models elsewhere, gave LITS a better understanding of our users’ needs. These insights guided LITS in developing matrices of needs, services, and support responsibilities that allow us to better meet support requirements and future infrastructure provisioning for data storage and processing. LITS has also developed resources to support faculty in crafting research data management plans (DMPs) and creating metadata for archiving newly created data sets. LITS has recently arranged access for Mount Holyoke researchers to the Massachusetts Green High Performance Computing Center (MGHPCC) and created an MHC Data Center to provide data storage and backup on LITS maintained servers. Conclusion: The work of the Research and Instructional Support team and the LITS cross-functional team for research data support has given us a much clearer picture of how Mount Holyoke researchers are using and managing their data and has allowed us to begin plotting a path to a more coherent and robust set of services to support them in their work.
-
Usability Testing Driven Redesign of Dataverse, an Open Source Data RepositoryPurpose: This study focuses on improvements in the usability of the Dataverse data repository open source software over the course of development of the latest version, 4.0, through iterative usability testing. Subjects:Thirty current international users of Dataverse comprised of researchers, librarians, and data archivists. Method: Users were selected to participate after either volunteering or being recommended by a member of the Dataverse development team. Users participated either in person or remotely (via Skype, Google Hangout or join.me) and sessions lasted for around 45 minutes. Each session involved a user completing specific tasks in Dataverse 4.0 to validate design decisions made for workflows. Each session was recorded with Morae software in order for the data to be later analyzed. Qualitative and quantitative data were collected through observation and surveys. The System Usability Scale was used as the postsession questionnaire as a way to track the perceived usability of Dataverse 4.0 as it was developed. To identify patterns in workflow issues, affinity diagrams were used to determine which usability issues happened most frequently and when workflows were interrupted. Results: This study began in December of 2013 and concluded in February 2015 lasted throughout the development of Dataverse 4.0 therefore results varied depending on what piece of functionality or feature was being developed at the time. With iterative usability testing, the taxonomy for Dataverse 4.0 was able to come straight from users not understanding labels that had been used and suggesting labels that were more logical to them, ways to provide users with multiple entry points to editing datasets was added based off user feedback, and faceted navigation for searching dataverses, datasets, and files was improved to allow users to narrow down to only one type easily. Conclusion: Overall, Dataverse 4.0 was able to quickly be tested and designs were able to be validated when they were developed rather than waiting months for users to interact with them. Most importantly, the Dataverse development team was able to release a product that had already been through extensive user review therefore eliminating potentially large issues that could or would impact a user being able to find or add data to Dataverse.
