Bibliographic record
Abstract
A Review of: Johnston, L. R., Carlson, J., Hudson-Vitale, C., Imker, H., Kozlowski, W., Olendorf, R., & Stewart, C. (2018). How important are data curation activities to researchers? Gaps and opportunities for academic libraries. Journal of Librarianship and Scholarly Communication, 6(1), 1-24. https://doi.org/10.7710/2162-3309.2198 Abstract Objective – To identify the data curation activities most valued by researchers at universities. Design – Focus group and survey instrument. Setting – Six R1: Doctoral Universities in the United States of America that are part of a Data Curation Network (DCN) project to design a shared data curation service. Subjects – 91 researchers, librarians, and support staff. Methods – The authors used focus group methodology to collect data about valued data curation activities, current practices, and satisfaction with existing services or activities. Six focus groups were conducted at participants’ places of employment. Participants reviewed a list of 35 possible data curation activities, including documentation, data visualization, and rights management. A card-swapping exercise enabled subjects to rank the most important issues on a scale of 1-5, with “most important” activities becoming the subject of a facilitated discussion. In a short paper-based survey, participants also noted whether a data curation practice is in place at their institution, and their satisfaction with the practice. Main Results – Twelve data curation activities were identified as “highly rated” services that academic institutions could focus on providing to researchers. Documentation, Secure Storage, Quality Assurance, and Persistent Identifier were the data curation activities that the majority of participants rated as “most important.” Participants identified the data curation practices in place at their institutions, including documentation (80%), secure storage (75%), chain of custody (64%), metadata (63%), file inventory or manifest (58%), data visualization (58%), versioning (56%), file format transformations (55%), and quality assurance (52%). Participants reported low levels of satisfaction with their institutions’ data curation activities. Conclusion – Academic libraries have an opportunity to develop or improve existing data curation services by focusing on the twelve data curation activities that researchers, staff, and librarians value but that could be implemented in a more satisfactory way. The authors conclude that their organization, the Data Curation Network, has an opportunity to improve data curation services or to offer new or expanded services.
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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 | 0.151 | 0.489 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.021 | 0.031 |
| Open science | 0.009 | 0.022 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.208 | 0.143 |
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".