BIODIDAC is BACK – A repository of images for biology education at all levels!
Bibliographic record
Abstract
Initially launched in 1995 by two biology professors at the University of Ottawa, Antoine Morin and Jon Houseman, BIODIDAC is a fully bilingual (English and French) online database containing images in zoology, botany, human biology, and histology. In the past, nearly 8,300 registered users from 150 countries have used material from BIODIDAC, and it is estimated that around 25 million students and educators worldwide have benefited from it. Each image generally includes the name of the species presented, its common name(s), the format presented, and a detailed text description including its taxonomic classification. Overall, the repository has become a useful tool for a variety of teaching and learning objectives. Examples include the downloading of images for teaching and learning of anatomical and microscopic features and for use in assessments (i.e. labelling structures) to name a few. Unfortunately, as the applications and software used for the original version were outdated, BIODIDAC was taken offline in 2019 and unavailable to the public. Thanks to resources and services of the University of Ottawa Library and Department of Biology, a small team was formed in 2022 with the aim of updating and publishing the images and metadata as an Open Educational Resource (OER) (https://omeka.uottawa.ca/biodidac/). Participants are encouraged to bring an internet connected device to access BIODIDAC and explore the repository.
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.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.502 | 0.411 |
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".