Working across Scales: Shared Building-Blocks of a Sustainable Infrastructure Environment for Bio/Geodiversity Data
Notice bibliographique
Résumé
The bio- and geodiversity communities aim to build digital infrastructures capable of delivering high-quality, well-structured, and richly interlinked data at a scale that enables powerful analyses to support the conservation and management of Earth’s diversity. Informaticians, scientists, and resource managers face the challenge of globally accelerating and upscaling open data availability. Achieving this requires sustainable, resilient, and bi-directional socio-technical connections that promote interactions and feedback both ways across the data landscape. Such interactions will connect global and national repositories to local providers, data stewards, and data users in ways that are context-sensitive and adapted to partners' location, time and communities. Although communities ranging from individual local experts to the governing bodies of global platform consortia are highly motivated and productive, they struggle to manage the rapidly growing volume of data, keep pace with technical innovation, and adhere to shared standards and best practices. Persistent challenges include insufficient recognition and visibility for contributors; functional and operational limitations in infrastructure; and inadequate long-term funding. Although the importance of the life cycles of data, infrastructures, and services is well-recognized, insufficient expertise in assessing the value that they represent and generate, and integrating those benefits into global, whole-community finance strategies presents an additional challenge. However, methodologies developed by global initiatives such as the United Nations System of Environmental-Economic Accounting (SEEA; United Nations et al. 2025) and UN Biodiversity Finance (BIOFIN; Cruz-Trinidad et al. 2024) provide innovative examples for addressing these. Without mechanisms that return generated value to data providers, stewards, and infrastructure developers, the community cannot initiate the self-reinforcing cycle of social and technical capacity growth needed for global upscaling. Instead, the current resource-limited landscape threatens the robustness of community networks and the long-term sustainability of existing infrastructures. The insights from comprehensive community outreach and infrastructure reviews conducted by the U.S.-based BIOFAIR (Building an Integrated, Open, Findable, Accessible, Interoperable, and Reusable) Data Network form the foundation for a living roadmap designed to support effective solutions and sustainable models for global biodiversity data infrastructures (Kunkel et al. 2025). Informed by these findings, the International Partners for the Digital Extended Specimen (IPDES) network are discussing shared and unique strengths and gaps across partners at different levels in this ecosystem. Adding theory of change (Rice et al. 2020), as well as socioeconomic and biodiversity finance considerations to the BIOFAIR roadmap, members of IPDES developed an extended model of infrastructure sustainability and a project proposal. Key elements include: involving economists in the process of integrating data, infrastructure, and services in information-economy value chains into natural capital accounting frameworks (e.g., SEEA), and developing comprehensive finance plans for bio/geodiversity data (e.g. adapting the methodology of BIOFIN); building governance and organizational frameworks that support transparent, community-centered decision-making; engaging the global community through coordinated participation and shared infrastructures; conducting pilot implementations that apply accounting methods to key use cases; and performing risk assessments of, and establishing safeguards for, the arising feedback loops in a socioeconomic system of returning value to data providers, stewards and users. involving economists in the process of integrating data, infrastructure, and services in information-economy value chains into natural capital accounting frameworks (e.g., SEEA), and developing comprehensive finance plans for bio/geodiversity data (e.g. adapting the methodology of BIOFIN); building governance and organizational frameworks that support transparent, community-centered decision-making; engaging the global community through coordinated participation and shared infrastructures; conducting pilot implementations that apply accounting methods to key use cases; and performing risk assessments of, and establishing safeguards for, the arising feedback loops in a socioeconomic system of returning value to data providers, stewards and users. Together, these actions outline a path toward community-wide finance strategies that can support sustainable, scalable, and globally coherent biodiversity data infrastructures.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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; un appel candidat d’une seule tête enseignante, 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 ».