Deliverable 1.6 Data Governance Framework
Notice bibliographique
Résumé
This document contains a description of the Data Governance Framework for the INTAROS project, with an updated version of the Data Management Plan (DMP). The Data Governance Framework defines the procedures for how data management is carried out in the project, including the planning, conducting and monitoring the preparation and distribution of data collections. The DMP describes how new datasets collected or generated by partners in the project, will be managed according to guidelines for FAIR data management in Horizon 2020. Data governance in INTAROS is pragmatic and geared towards supporting partners in preparing and publishing their data collections. The planning and monitoring activities are carried out by the Data Management Theme Leader and the leaders of the four data generating work-packages in the project. Partners generating data are responsible for making their collections available in line with the recommendations of the DMP. The Data Management Theme Leader, data centre partners (AWI, CNRS, FMI, IMR, IFREMER, ONC, RADI, RIHMI-WDC) and the leader of WP5 (“Data integration and management”) (Terradue) are responsible for providing support with technical aspects of data publication and distribution. INTAROS is pan-Arctic in scope and collect in situ observations, extract parameters from satellite data and model projections in several regions and across multiple spheres (themes). The focus areas of INTAROS include Coastal Greenland, North of Svalbard, Fram Strait, the Eurasian Basin, and (5) selected sites in Siberia, Finland, Canada and Alaska. Within these areas, INTAROS partners are collecting new observations and generating high-level data products from different spheres: (1) Atmosphere, (2) Ocean, (3) Sea ice, (4) Marine ecosystems, (5) Terrestrial, (6) Glaciology, (7) Natural hazards, (8) Community-based monitoring. This makes datasets collected or generated within INTAROS relevant for a number of research projects as well as for infrastructures such as EMODNET and GEOSS. Datasets collected or generated within these spheres by the time of writing are summarised in this document, based on the deliverables from WP 2 (“Exploitation of existing observing systems”), new datasets collected in WP 3 (“Enhancement of multidisciplinary in situ observing systems”) and WP 4 (“Enhance community-based observing programs for participatory research and capacity-building”), as well as upcoming model products and derived datasets from WP6 (“Applications of iAOS towards Stakeholders”). Datasets prepared for distribution in WP 2, 3 and 4 have also been registered in the INTAROS Data Catalogue, available at https://catalog-intaros.nersc.no/. This data catalogue will be updated with new datasets collected or generated during the remainder of the INTAROS project. The DMP recommends standards for metadata and data standards that INTAROS partners should prepare their datasets in, to make it easier for other scientists and stakeholders to reuse the data. Open source tools can help scientists generate metadata and data in standard formats, such as Rosetta, GDAL (Geospatial Data Abstraction Library), NetCDF utilities, and widely used programming languages, such as Python, MATLAB and R, offer libraries that can be used to write customised format converter tools. A dataset prepared in NetCDF format can be made publicly available using data publishing tools like the Thredds Data Server (TDS). INTAROS, together with the Useful Arctic Knowledge (UAK) project has organised several user meetings and one research schools, to build competence in data management within the INTAROS consortium. Additional competence building activities are planned in INTAROS; the training material developed will be made publicly available.
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 | 0,026 | 0,054 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,006 | 0,008 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,014 | 0,011 |
| Science ouverte | 0,005 | 0,010 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,144 | 0,142 |
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 ».