Knowledge Infrastructures Are Growing Up: The Case for Institutional (Data) Repositories 10 Years After the Holdren Memo
Bibliographic record
Abstract
Institutional data repositories are uniquely positioned to support researchers in sharing scholarly outputs. As funding agencies develop and institute policies for research data access and sharing, institutional data repositories have emerged as a critical feature in ecosystems for data stewardship and sharing. We show that institutional data repositories can meet and exceed the requirements and recommendations of federal data policy, thereby maximizing the benefits of data sharing. We present results of a mixed-method study which explores the adoption and usage of institutional repositories to share data from 2017 to 2023. Data from two previous studies were combined with data collected in 2023 on the data sharing solutions of Association of Research Libraries member institutions in the United States and Canada. The analysis of the aggregated data indicates that data stewardship has increased in both institutional repositories and institutional data repositories with an increase in complementary infrastructure to support data sharing. We then conduct an “infrastructural inversion” (Bowker & Star, 1999) to ‘surface invisible work’ of making data repositories function well, and demonstrate that institutional data repositories have advantages for providing sustainable stewardship, curation, and sharing of research data. Finally, we show that institutional data repositories may produce additional benefits through established infrastructure, local interoperability, and control.
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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 | 0.029 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.038 | 0.069 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".