openICPSR: Public access data sharing at ICPSR
Bibliographic record
Abstract
There exists a growing desire, and growing requirements for scientific research data collected by federal funds to be shared publicly and without charge. Agencies such as the NSF and NIH require data management plans as part of research proposals and the Office of Science and Technology Policy (OSTP) is requiring federal agencies to develop plans to increase public access to results of federally funded scientific research. To be effectively shared, data must be described and documented, discoverable online, and accessible, both today and into the future. Data must be curated. Data curation requires data sharing entities are sustainable. Sustainability requires funding. In early 2014, ICPSR launched a fee-for-deposit service that provides free access to data and documentation to the public and is sustained by deposit fees. openICPSR is a research data-sharing service for the social and behavioral sciences. openICPSR data are: widely and immediately accessible at no cost to data users, safely stored by a trusted repository dedicated to long-term data stewardship, and protected against confidentiality and privacy concerns. This session will demonstrate the openICPSR system and discuss how researchers can take advantage of this new means of archiving data to comply with federal data sharing and preservation standards.
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 | 0.069 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.009 | 0.034 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.129 | 0.126 |
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".