How to deposit research data in the University of Guelph Research Data Repositories
Bibliographic record
Abstract
This dataset provides guidance materials and templates to help you prepare your research datasets for deposit in the U of G Research Data Repositories. Please refer to the U of G Research Data Repositories LibGuide for detailed information about the U of G Research Data Repositories including additional resources for preparing datasets for deposit. The library offers a mediated data deposit service. The deposit workflow is as follows: Create your repository account. If you are a first-time depositor, complete the U of G Research Data Repositories New Depositor Intake Form. Activate your Data Repositories account by logging in with your U of G username and password. Once your account is created, contact us to set up your dataset creator access to your home department’s collection in the Data Repositories. Note: If you already have a Data Repositories account and dataset creator access, you can log in and begin a new deposit to your home department’s collection right away. Prepare your dataset. Assemble your dataset following the Dataset Deposit Checklist. Use the README file template to capture data documentation. Create a draft dataset record. Log in to the Data Repositories and create a draft dataset record following the instructions in the Dataset Submission Guide. Submit your draft dataset for review. Dataset review. Data Repositories staff will review (also referred to as curate) your dataset for alignment with the Dataset Deposit Checklist using a standard curation workflow. The curator will collaborate with you to enhance the dataset. Public release. Once ready, the dataset curator will make the dataset publicly available in the Data Repositories, with appropriate file access controls. Support: If you have any questions about preparing and depositing your dataset, please make a Publishing and Author Support Request.
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 | 0.014 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.543 | 0.611 |
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".