Much Ado About Data Citation : Work done in SSHOC Task 3.4 "Making Data Findable by being Citable
Bibliographic record
Abstract
The SSHOC project is bringing together the key research data infrastructure players in SSH communities. SSHOC task “Making Data Findable by Being Citable“ (3.4) part of CLARIN-led Work Package “Lifting Technologies and Services into the SSH Cloud” is working on the use of citation in the SSH. A workshop “Data Citation in Practice” was organized June 2021 which presented solutions for efficient data citation via different perspectives from the SSH. Following this event, a study was carried out on 85 data repositories from the SSH domain investigating their approach to and facilities for data-citation (See SSHOC Deliverable D 3.5). This work was done by making extensive use of the “FAIR SSH Citation prototype”, developed in the SSHOC citation task, to harvest metadata in a normalized way from heterogeneous technologies provided by these different repositories. The presentation will provide insights on how these results can be adapted to be used by CLARIN as recommendations to improve Data Citation practice and thus foster the visibility of CLARIN resources. Another interesting complementary topic is the potential use of the citation-prototype, already integrated into the CLARIN Language Resource Switchboard, and its development for CLARIN in other complementary ways, eg. with the emerging DOG (Digital Object Gateway) SCCTC CLARIN project.
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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 | 0.059 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.051 | 0.020 |
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".