Taking Care of Business : The Clinical Research Data Edition
Notice bibliographique
Résumé
Presentation for the Health Libraries Association of British Columbia. Take a moment to think about your researchers and the kinds of data they access and/or create. Where is this data stored and how is it organized? If they were asked to share data with another researcher would they be able to make sense of that work? If they needed to locate the data files from 5 years ago, how easy would it be to find and use datasets? What privacy and security controls have they implemented to ensure that they are appropriately safeguarding their data? Is their data governed by legislative requirements? If so, are they required to comply with additional policies and standards when using these datasets? If you are unsure about the answer to any of these questions you have come to the right place. This workshop will cover the basics of data management plans, metadata and data documentation, data privacy and security, and data sharing. It is designed to support researchers engaging in clinical research to start thinking about the steps they can take to better manage their research data. Kaitlyn Gutteridge is the Research Data Privacy and Security Officer for the ARC team. In addition, Kaitlyn serves as a member of the Compute Canada Security Council. She holds a Master of Science degree from the London School of Hygiene and Tropical Medicine where she focused her epidemiology training on multilevel modeling of chronic disease development. She previously held positions supervising the implementation of large-scale research initiatives at Simon Fraser University and the Centre for Hip Health and Mobility. Most recently, she served as the Privacy and Governance Lead at Population Data BC. In her position at Population Data BC, Kaitlyn served as the organization’s Privacy Officer and managed the negotiation, development, and execution of information sharing agreements and associated policies & procedures. Eugene Barsky is Research Data Librarian at the UBC Library. His recent peer-recognition included American Society for Engineering Education and Special Library Association awards. He published more than 20 peer-reviewed papers and presented at more than 40 conferences. Eugene is chairing the national Portage Data Discovery Expert Group, participates in building the Canadian Federated Research Data Repository (FRDR), and collaborates with Research Data Canada (RDC). Eugene is an adjunct faculty member at the iSchool at UBC, teaching courses in science librarianship and research data management, and is an active member of the Pacific Northwest data curators group.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,042 | 0,093 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,005 | 0,011 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,021 | 0,014 |
| Science ouverte | 0,006 | 0,014 |
| Intégrité de la recherche | 0,009 | 0,016 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,180 | 0,123 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».