European Academic Libraries Offer or Plan to Offer Research Data Services
Bibliographic record
Abstract
A Review of: Tenopir, C., Talja, S., Horstmann, W., Late, E., Hughes, D., Pollock, D., … Allard, S. (2017). Research data services in European academic research libraries. LIBER Quarterly, 27(1), 23-44. https://doi.org/10.18352/lq.10180 Abstract Objective – To investigate the current state of research data services (RDS) in European academic libraries by determining the types of RDS being currently implemented and planned by these institutions. Design – Email survey. Setting – European academic research libraries. Subjects – 333 directors of the Association of European Research Libraries (LIBER) academic member libraries. Methods – The researchers revised a survey instrument previously used for the DataONE survey of North American research libraries and conducted pilot testing with European academic library directors. The survey instrument was created using the Qualtrics software. The revised survey was distributed by email to LIBER institutions identified as academic libraries by the researchers and remained open for 6 weeks. Question topics included demographics, RDS currently offered, RDS planned, staffing considerations, and the director’s opinions on RDS. Libraries from 22 countries participated and libraries were grouped into 4 regions in order to compare regional differences. Data analysis was conducted using Excel, SPSS or R software University of Tennessee, University of Tampere, and University of Göttingen. Main Results – 119 library directors responded to more than one question beyond basic demographics, for a response rate of 35.7%. Among the libraries surveyed, more libraries offer consultative services than offered technical support for RDS, although a majority planned to offer technical services in the future. Geographically, libraries in western Europe offer more RDS compared with other regions. More libraries have reassigned or plan to reassign current staff to support RDS services, rather than hire new staff for these roles. Regardless of whether or not they currently offer RDS, library directors surveyed strongly agree that libraries need to offer RDS to remain relevant. Conclusion – The authors determine that a majority of library directors recognize that data management is increasingly important and many libraries are responding to this by implementing RDS and collaborating across their institutions and beyond to help meet these needs. Future research is suggested to track how these services develop over time, how libraries respond to the staffing challenges of RDS, and whether consultative rather than technical services continue to be primary forms of RDS offered.
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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 | 0.022 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.155 | 0.110 |
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".