Session 6 Introduction to Data Repositories
Bibliographic record
Abstract
Data Repositories exist as trustworthy storage and access platforms for data arising out of research activities. Some are discipline specific while others accept datasets from the gamut of disciplines. In this session we will see samples of Canadian repositories, look at their specific uses, learn why metadata is so important, learn where to find metadata schema for your specific discipline. The session will also look at the common types of data repositories, including: Subject Specific and Structured Repositories; Institutional Data Repositories; and Unstructured or General Repositories. You will be able to locate and view existing datasets in the planning phases of your own research and better prepare to manage the data assigned to you. By managing your data with deposit and preservation in mind, you will apply the data management skills you have learned this far and set yourself up to meet funder, publisher, and research lab requirements. Presenter Marjorie Mitchell is the Copyright, Scholarly Communications, and Research Data Management Librarian at UBC Okanagan. She has been presenting on Research Data Management topics since 2015.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.563 | 0.452 |
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".