Towards a catalogue of biodiversity databases: An ontological case study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Biodiversity informatics depends on digital access to credible information about species. Many online resources host species' data, but the lack of categorisation for these resources inhibits the growth of this entire field. To explore possible solutions, we examined the (now retired) Biodiversity Information Projects of the World (BIPW) dataset created by the Biodiversity Information Standards (TDWG); this project, which ran from 2007-2015 (officially removed from the TDWG website in 2018) was an attempt at organising the Web's biodiversity databases into an indexed list. To do this, we applied a simple classification scheme to score databases within BIPW based on nine data categories, to characterise trends and current compositions of this biodiversity e-infrastructure. Primarily, we found that of 600 databases investigated from BIPW, only 315 (~53%) were accessible at the time of this writing, underscoring the precarious nature of the biodiversity information landscape. Many of these databases are still available, but suffer accessibility issues such as link rot, thus putting the information they contain in danger of being lost. We propose that a community-driven database of biodiversity databases with an accompanying ontology could facilitate efficient discovery of relevant biodiversity databases and support smaller databases - which have the greatest risk of being lost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.034 | 0.001 |
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 it