How Discovery Systems Use DataCite Metadata: Harvester Roundtable
Bibliographic record
Abstract
In this session, we will provide an overview of some of the cutting edge tools and services available for working with DataCite metadata, including our APIs, DataCite Commons, the public data file, and beyond. Alongside this, we will hear from some of the key players who are leveraging DataCite’s 50+ million metadata records to develop innovative tools for locating research. This will be an opportunity to learn about how DataCite metadata is and can be used, to enable discovery and reuse, and the impact that rich metadata can have on the scholarly record. Speakers, chapters of the recording: Kelly Stathis (Technical Community Manager, DataCite), https://www.youtube.com/watch?v=dJgohsagG20&t=0s Maria Gould (Director of Product, DataCite), https://www.youtube.com/watch?v=dJgohsagG20&t=97s Paolo Manghi (Chief Technology Officer, OpenAIRE AMKE), https://www.youtube.com/watch?v=dJgohsagG20&t=1157s Patricia Tortosa (Editorial Content Manager, Clarivate Analytics), https://www.youtube.com/watch?v=dJgohsagG20&t=2030s Casey Meyer (Chief Technology Officer, OurResearch), https://www.youtube.com/watch?v=dJgohsagG20&t=2918s
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 | 0.035 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.044 | 0.106 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.036 | 0.033 |
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".