FAIR principles for climate services information systems
Notice bibliographique
Résumé
Ongoing climate change is increasingly impacting ecosystems and living conditions. To understand climate change effects on all scales ranging from regional to global and to develop appropriate response strategies, reliable, easily accessible climate location information is crucial. The United Nations framework of climate change policy emphasizes the role of open data as an essential component to enable efficient implementation of appropriate climate change strategies. Data offered at the various portals and climate services needs to be Findable, Accessible, Interoperable, and Reusable (FAIR). This is particularly important when several communities need to work together in order to develop the most effective response strategies. These communities not only involve climate scientists and meteorologists, but also climate impact analysts, hydrologists, agronomists, urban planners, ecologists, and many more. Screening the web for available data, it becomes apparent that there is no shortage of portal solutions built upon climate data archives. Portal solutions have turned out in the past of often being targeted towards a specific, and sometimes rather small, number of users from within a single community. Cross-community integration and thus enhanced reusability and interoperability was not in focus. Due to recent ongoing international domain crossing efforts, FAIR principles are increasingly respected also for the portal architectures of the information systems itself. For example, the Open Geospatial Consortium (OGC) develops standards and best practices that enable FAIR principles across communities. FAIR principles across communities require a set of essential ingredients to work effectively. These ingredients include metadata models that allow discovery (Findable), interfaces to access the data (Accessible), data models that are well documented (Interoperable) and can be efficiently consumed by others (Reusable). Because data volumes are continuously growing and therefore require new approaches for efficient data processing, OGC has extended the ‘Reusable’ component in FAIR. ‘Reusable’ now includes mechanisms for executing applications close to the physical location of the data. What was previously a data provisioning system now needs to be extended to support processing capacities up to the level where user-defined applications can be deployed and executed. In a sense, for data to be FAIR, it needs to be accompanied by equally FAIR services. This presentation is showing current realisations of leading climate services information systems that implement the extended FAIR principle. The presentation will sort out roles and capabilities of standardized web APIs that can be assembled in line with data and processing environments for interoperable climate data across communities in the most efficient way. Once paired with OGC’s new “Applications-to-the-Data” architecture and strong metadata models, the web APIs enable effective integration of climate data with data from other disciplines within state-of-the art cloud environments that feature not only reusability of data, but also of applications, data processes, and scientific workflows.
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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 | 0,027 | 0,056 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,006 | 0,005 |
| Études des sciences et des technologies | 0,007 | 0,012 |
| Communication savante | 0,025 | 0,026 |
| Science ouverte | 0,007 | 0,013 |
| Intégrité de la recherche | 0,013 | 0,013 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,050 | 0,022 |
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 ».