Sharing and depositing research data: A guide for researchers
Bibliographic record
Abstract
ENGLISH VERSION Sharing and depositing research data: A guide for researchers This repository contains R/Quarto and HTML files for the presentation on sharing and depositing research data in generalist repositories such as the Federated Research Data Repository (FRDR). To acces the source code and complete presentation files please visit the GitHub repositorty: https://alliance-rdm-gdr.github.io/CUR_Res_DepositingData/ The user can view the rendered RevealJs presentations in English or French. Note that each slide contains audio that can be played using the controls at the bottom left of the presentation. For more presentations developed by the curation team of the Federated Research Data Repository , please visit our presentations repository All scholarly material in this repository is shared under the CC BY 4.0 license. If you use this presentation in your work, please cite it using the following metadata or the citation file in the repository: Manrique-Castano, D & FRDR Curation Team. (2025). Sharing and depositing research data: A guide for researchers. (Version 2025.09). Zenodo. https://doi.org/10.5281/zenodo.16994911. Contact: curators@frdr-dfdr.ca VERSION FRANÇAISE Partage et dépôt des données de recherche : un guide à l'usage des chercheurs Ce référentiel contient des fichiers R/Quarto et HTML pour la présentation sur le partage et le dépôt des données de recherche dans les dépôts généralistes tels que Le Dépôt fédéré de données de recherche (DFDR). Pour accéder au code source et aux fichiers de présentation complets, veuillez consulter le référentiel GitHub : https://alliance-rdm-gdr.github.io/CUR_Res_DepositingData/ L'utilisateur peut consulter les présentations en anglais ou en français. Notez que chaque diapositive contient un enregistrement audio qui peut être lu à l'aide des commandes situées en bas à gauche de la présentation. Pour d'autres présentations du DFDR, veuillez consulter notre dépôt de présentations. Tout le matériel académique contenu dans ce référentiel est partagé sous la licence CC BY 4.0. Si vous utilisez cette présentation pour votre travail, veuillez la citer en utilisant les métadonnées suivantes ou le fichier de citation disponible dans le référentiel : Manrique-Castano, D & FRDR Curation Team. (2025). Dépôt des données de recherche: Guide à l'usage des chercheurs. (Version 2025.09). Zenodo. https://doi.org/10.5281/zenodo.16994911. Contact: curators@frdr-dfdr.ca
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 | 0.149 | 0.275 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.188 | 0.404 |
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".