How Libraries Help Make Your Data Management as Easy as Pie
Bibliographic record
Abstract
Academic libraries at Association of Research Libraries (ARL) & Carnegie R1 universities in the U.S. and Canada provide leadership to deliver comprehensive integrated Web-based data management services for faculty, graduate students, and researchers. Data management makes data more findable, usable, and reproducible; supports an ethical, responsible research environment; and meets funder and journal data-sharing requirements. Since the White House Office of Science and Technology Policy’s 2013 memorandum requiring federal agencies to increase public access to the results of federally funded research, many funders and journals have mandated data planning and sharing. Developing high quality data management plans take time and require training on essential elements and accepted practices. As a result, data management services are in demand by faculty, graduate students, and researchers. To that end, academic libraries have been developing a rich array of data management services that includes support for drafting and reviewing data management plans; sharing best practices related to data sharing, storage, and security; recommending data curation strategies; and more. This poster discusses findings from a survey of 145 ARL and Carnegie R1 library websites in the United States and Canada related to the work libraries are leading to provide user-centered, web-based data management services. We share key data points; identify trends in the development of library-based data management services; and note recommendations for libraries to prepare for future growth in data management services as technology and research continues to evolve and expand.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.044 | 0.041 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.088 | 0.145 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".