Notice bibliographique
Résumé
Since 2014, the Coastal and Ocean Information Network Atlantic (COINAtlantic) in collaboration with the Canadian node of the Ocean Biogeographic Information System (OBIS) and other academic, government and non-governmental organizations in Atlantic Canada have been rescuing species occurrence data in primary and grey literature and processing it to standards for publication through OBIS. The project has been funded in part by the Atlantic Ecosystem Initiative of Environment and Climate Change Canada and Fisheries and Oceans Canada. The project was awarded Honourable Mention in the 2016 International Data Rescue Award in the Geosciences by Elsevier and the Interdisciplinary Earth Data Alliance. COINAtlantic and OBIS share common goals of promoting and facilitating free and open access to data required for coastal and ocean management. The sharing of data and integration of datasets requires adoption of standards and use of common vocabularies. Manuals, guidelines, and cookbooks can facilitate the process. One of the deliverables of the data rescue project was the release of the first version of “Guidelines for marine species occurrence data rescue – The OBIS Canada Cookbook” in April 2017. This document includes ten recipes ranging from initial identification of sources of data to final project wrap up and lessons learned. A second deliverable was the development of a curriculum for training sessions of custodians of marine species occurrence data. Training is required at all levels in our community. Not only should data be accessible for reuse but also training information and lecture material. This course curriculum, based on the OBIS Canada Cookbook, reused some content already on-line and was tested in a workshop at the 2017 conference of the Atlantic Canada Coastal and Estuarine Research Society. (see Fig. 1). Our curriculum, as presently designed, is an intensive single day, hands on course with a focus on graduate students and early career researchers. The course has nine (9) modules which address the following topics: why we share research data including a general description of and the need for data policies and data management plans and data repositories; an introduction to OBIS and the standards used by OBIS; how to map data sets to Darwin Core terms and how to clean and reformat the data; how to standardize species lists; how to georeference observations and use of gazetteers to standardize location place names; and how to compose standardized discovery metadata. The last module is devoted to the processing of participants’ data sets under the guidance of the instructor. Future activities will include promotion of the use of the cookbook and revision of the recipes according to users’ feedback. The curriculum will be tested again with a new set of participants on an opportunistic basis and modified according to participants’ comments. A staged and edited video of the course is under consideration - the objective is to provide on-line training material. These products will augment the growing number of lesson plans and lecture material made accessible by the OBIS/GBIF community. The resources need to be promoted and reuse encouraged.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,010 | 0,003 |
| Communication savante | 0,001 | 0,007 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».