BID Picture Thinking Drives Increased Biodiversity Capacity and Data Access
Notice bibliographique
Résumé
Open science and data accessibility are widely recognized as essential to addressing today’s global challenges. Regarding biodiversity loss, greater data accessibility not only improves understanding of threats and their underlying causes, it also helps inform actionable mechanisms to address them. The Global Biodiversity Information Facility (GBIF) has at the core of its mission, the mobilization of the data, skills, and technologies needed to enable access to comprehensive biodiversity information, demonstrated by the sharing of more than three billion occurrence records through its infrastructure. However, the map of occurrence data accessible through GBIF continues to show notable imbalances in the distribution of data, which often reflect gaps in capacity for data sharing. This disparity underscores the need to strengthen the ability of all to participate in and benefit from biodiversity data sharing, as emphasized in Targets 20 and 21 of the Kunming-Montreal Global Biodiversity Framework. GBIF’s approach to capacity development responds directly to this challenge by focusing on strengthening both the capacity to mobilize biodiversity data through GBIF and the capacity to use GBIF-mediated data, areas where GBIF is best positioned to have an impact. This approach is exemplified by GBIF’s European Union-funded Biodiversity Information for Development (BID) programme, which focuses on developing capacities from individual to regional levels, within sub-Saharan Africa, Latin America and the Caribbean, and the Pacific. The BID programme aims to empower communities of practice to actively engage in data mobilization activities aligned with user needs identified regionally or nationally. GBIF’s approach to capacity development is an ongoing learning journey, as illustrated by the continuous refinement of the BID programme since its launch in 2015. From addressing the steep learning curve in the mobilization of standardized data to exploring mechanisms for cross-regional knowledge-sharing, GBIF has tailored and implemented a broad range of solutions. To date, the BID programme has contributed to increased data accessibility, addressing knowledge gaps, and building the capacity of data holders and users. Many institutions began publishing data for the first time through the programme, and several countries, such as Suriname, Zimbabwe, and Tonga, recognized the value of maintaining national biodiversity information facilities. Moreover, outputs of the BID programme, including training material and published biodiversity data, have been reused countless times, both within and outside of the original BID target regions. With renewed support in 2024, the BID programme continues to offer opportunities for engagement in calls for proposals, the development of training material and workshops reflecting the latest practices in data mobilization and use. GBIF also seeks to align and expand these activities through collaboration with other related initiatives and by actively seeking feedback from the community. By fostering collaboration across multiple scales and promoting knowledge sharing mechanisms, GBIF’s capacity development ultimately seeks to empower the global community to address biodiversity challenges collectively.
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 | 0,041 | 0,071 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,009 | 0,030 |
| Communication savante | 0,026 | 0,041 |
| Science ouverte | 0,004 | 0,035 |
| Intégrité de la recherche | 0,007 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,027 | 0,004 |
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 ».