Dataverse North Metadata Best Practices Guide v 3.0
Bibliographic record
Abstract
One of the most useful features of the Dataverse repository software is the large number of metadata fields it provides for describing research data. This guide is intended to support both the novice and experienced user in creating metadata for datasets in a Dataverse repository. It provides official definitions of metadata fields with clarifications and tips, distinguishes between required, recommended, and optional fields, and illustrates the use of fields with examples. This version of the guide has been updated to include coverage of all available metadata fields - citation, geospatial, social science and humanities, astronomy and astrophysics, life sciences, and journal metadata. The guide was created with permission from Harvard for the use of definitions and the Texas Digital Library for basic design. Ce guide est aussi disponible en français.
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 | 0.026 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.028 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.104 | 0.202 |
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".