Data from Start to Finish: The People, Processes, and Timespans Associated with Disseminating Data for Secondary Use
Bibliographic record
Abstract
ICPSR performs a valuable service to the research community by taking in research data and making those data amenable to secondary use. Doing so involves many people and processes to ensure that sensitive data are handled responsibly and individuals who were not involved in the original study can use the data without consulting the data producers. ICPSR staff work closely with data producers to accomplish this; however, the scope of what is involved to archive data and make them available is often opaque to researchers. This poster will describe the people, processes, and time spans involved from the time ICPSR staff begin talking with data producers about their data, through the post-release support of data.
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 | 0.113 | 0.359 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.026 | 0.027 |
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".