[Editable Example] Data Use Agreement
Bibliographic record
Abstract
This resource is an editable version of Data Access Terms and Data Use Agreements in the Community Research Data Toolkit to support communities and the organizations and researchers who collaborate with them on research data management. This is a bare-bones blank template to download and edit. If you have not read the relevant chapter in the Community Research Data Toolkit Pressbook, it will not make sense. Please read that first! Data access is a crucial long-term consideration for community organizations deciding where to deposit their data. To determine how and by whom data will be accessed, it is important to consider who will authorize access, maintain the data, and be allowed to use it. Additionally, setting up systems for how access will be given or denied is essential. These considerations can be formalized into Data Access Terms and then a Data Use Agreement. Both templates are modelled off several existing data access terms used by public data repositories, including the European Genome Archive and ICPSR. Learn more about this tool in context: https://doi.org/10.71548/7mp6-fn45 This protocol is provided for general informational and educational purposes only and does not constitute legal advice. You are strongly encouraged to consult with qualified legal counsel before using or relying on this template to ensure it meets your specific needs and complies with applicable laws. McMaster University makes no representations or warranties, express or implied, regarding its accuracy, completeness, or suitability, and expressly disclaims any liability arising from its use. Any use of this template is strictly at your own risk.
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 | 0.006 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.583 | 0.482 |
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".