Data Privacy Survey: Challenges and needs of privacy-related services for research at Utrecht University
Bibliographic record
Abstract
In the second quarter of 2022, Utrecht University (UU) Research Data Management Support (RDM Support) sent out a survey among all scientific personnel at Utrecht University, and organised one-on-one meetings with a selection of them. The aim of these efforts was to investigate 1) How UU researchers currently deal with personal data in their research, 2) What challenges they run into when handling personal data in research, and 3) How support at UU can improve their services concerning personal data in research. The survey and one-on-one meetings were part of the Data Privacy Project, an RDM Support project to improve information, tools and services surrounding personal data in research. This is a deposit of the GitHub repository which contains the reports written about the survey, the documentation, fake data, and code. The reports can be read online as well via https://utrechtuniversity.github.io/dataprivacysurvey. Changes between v1.0.1 and v1.1 Clean up the repository Add and fix repository metadata Make the report reproducible by adding fake data files and an easier option to recreate the report (tested locally) Change logo to UU logo Full Changelog: https://github.com/UtrechtUniversity/dataprivacysurvey/compare/v1.0.1...v1.1
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 | 0.116 | 0.267 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".