Advancing Transparency and Accessibility: Implementing FAIR Data Principles in IPCC AR6 WGI Report
Notice bibliographique
Résumé
The Sixth Assessment Report (AR6) marked the first time that the Intergovernmental Panel on Climate Change (IPCC) recommended and implemented FAIR data principles as part of the assessment process. While the critical importance and utility of FAIR principles are widely acknowledged, their implementation is not straightforward, especially in this unique context that involves the collaboration of hundreds of scientists around the world from different disciplines, utilising diverse information sources. We describe challenges and lessons learned when sharing data and other digital resources in the context of assessing the physical science basis (Working Group I, WGI). With the primary scope of ensuring transparency and reproducibility, and providing due credit to the data creators who collaborated on the report, authors were guided and supported to encourage the public availability of the data and the associated code used in post-processing. As part of this initiative, data and code for over 200 figures have been made accessible as well as all plotted data for the Summary for Policymakers. Additionally, the assessment foundation datasets, such as climate model simulations, have been curated, along with a novel category of data — datasets constrained through expert assessment (e.g., model-based projections of global surface temperature). Moreover, an innovative digital product was produced to support and expand the assessment done in the WGI AR6, building on these datasets and synthesising key findings for Climatic Impact Drivers: the Interactive Atlas. Its formal inclusion as part of the report was possible thanks to the implementation of FAIR principles, fully compliant to best data practices. Despite successes, challenges emerged with the novel FAIR implementation because of learning by doing and real-time development, managing diverse data, requiring the introduction of new roles and workflows. For authors, adapting dataset structures to fit repository constraints and addressing metadata requirements posed difficulties, as they didn't always align with actual dataset usage or user needs. Simultaneously, repositories faced challenges adapting dataset structures to their systems, ensuring adherence to standard conventions, managing references, reviewing licences, and awaiting author feedback. In this complex landscape, the role of data curators was crucial, serving as a facilitating bridge for information and requirements exchange, providing essential support to authors and collaborating with repositories to seek solutions that effectively integrated the needs of both parties. Flexibility and simplicity proved key allies in overcoming challenges. Prioritising clarity and acknowledging the limitations of a rigid structure enabled smooth navigation of obstacles, resulting in practical and useful outcomes. The new IPCC cycle starts now. FAIR principles need to be fully integrated into the climate assessment process to adhere to the highest standards in data access and stewardship. Our recommendations include the need to integrate data management workflows and engaging authors from the outset, highlighting the significance of data-related tasks for transparency, and enhancing the tools available to support authors. Providing authors clear instructions and timelines is crucial, along with technical assistance from data science experts. Actively endorsement and support of these initiatives by the IPCC leadership is vital for their effective integration into the assessment process.
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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 | 0,359 | 0,479 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,008 | 0,010 |
| Études des sciences et des technologies | 0,005 | 0,013 |
| Communication savante | 0,034 | 0,027 |
| Science ouverte | 0,008 | 0,024 |
| Intégrité de la recherche | 0,007 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,003 |
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; l’étiquette directe de Gemma et le classifieur distillé Codex 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 ».