BID Picture Thinking Drives Increased Biodiversity Capacity and Data Access
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.026 | 0.041 |
| Open science | 0.004 | 0.035 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".