Robert Olendorf

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 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.

Big Data in Libraries

The term Big Data is somewhat loose. Roughly defined, it refers to any data that exceeds the users ability to analyze it in one of three dimensions (the three Vs): Volume, Velocity and Variety. Laney [1, 2] Each of these has different challenges. Huge volumes of data require the ability to store and retrieve the data efficiently. High velocity data requires the ability to ingest the data as it is created, essentially very fast internet connections. Highly variable data can be difficult to organize and process due to its unpredictability and unstructured nature. Bieraugel [3] Also, multiple data streams can be combined to answer a variety of question. All forms of big data can require high performance computing and specialized software to analyze. Given the fuzziness of defining big data,