Inventory of Research Data Services at United States and Canadian Universities, 2023
Bibliographic record
Abstract
The Inventory of Research Data Services at the U.S. and Canadian Universities study systematically gathered data on the research data services provided by a sample of universities in the United States and Canada. This sample included 40 Research 1 (R1) universities, 40 Research 2 (R2) universities, 40 Liberal Arts Colleges, and 8 institutional members of the Canadian Association of Research Libraries (CARL). Through a comprehensive examination of institutional websites, the study documented the types, locations, extent, and delivery methods of these services, as well as the availability of High-Performance Computing (HPC) resources and the existence of institutional repositories on each campus. Data collection was conducted using the Qualtrics platform. Carried out from March 2023 to July 2023, this inventory formed part of a collaborative research initiative aimed at coordinating research data support services across campuses. This initiative is being led by Ithaka S+R in partnership with 29 university collaborators in the United States and Canada. The findings of the inventory are publicly available in this report.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.029 | 0.083 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".