Sustaining our data through data management - it's easier than you think!
Bibliographic record
Abstract
"Help! I've got these boxes of interviews and data records, and I don't know what to do with them! I want my data to be preserved." If this sounds familiar, then this presentation is one you will want to attend. We will show you an aid that will guide you through the steps necessary to curate, archive, and share your data. It is Portage, a Canadian website that is "dedicated to the shared stewardship of research data in Canada". There are many training resources available that allow you to guide yourself through the research data management process, or to obtain help from others. We will highlight the latest developments in training resources and in the DMP Assistant. You will be amazed! Join us to learn about the latest resources and how they will further your journey in ensuring that your data will last through the ages.
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 | 0.067 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.020 | 0.031 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.004 | 0.015 |
| Insufficient payload (model declined to judge) | 0.034 | 0.045 |
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".