The Research Data Services Landscape at US and Canadian Higher Education Institutions
Bibliographic record
Abstract
While many universities have made substantial investments in research data services and are likely to continue to make further investments, obstacles such as decentralization and inefficiency, insufficient staffing, lack of technical expertise, and ambiguity about the needs of researchers continue to limit the impact of these investments. In light of these persistent challenges, Ithaka S+R revisited our 2020 inventory of data services and expanded our scope to include Canadian universities. Our findings presented here are based on a comprehensive review of data services offered at a representative sample of 120 US institutions and eight institutional members of the Canadian Association of Research Libraries (CARL). What is the optimal method to deliver research data services across disciplines? What is the best administrative home for services? Explore results from our inventory of 120 US and Canadian university research support services to learn more.
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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 | 0.024 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.038 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".