Bibliographic record
Abstract
This is the self-supervised training dataset associated with the publication :Bilodeau, A.*, Beaupré, F.*, Chabbert, J., Bellavance, J-M, Lessard, K., Deschênes, A., Bernatchez, R., De Koninck, P., Gagné, C., Lavoie-Cardinal, F. (2025) A Self-Supervised Foundation Model for Robust and Generalizable Representation Learning in STED Microscopy. bioRxiv.The STED-FM dataset consists of 37387 images of varying size which were split into 224x224 crops. The resulting size of the dataset was 976 022 crops, all of which were used for pre-training of STED-FM. The provided datasets contain the crops.A subset of 238 683 crops each associated with one of 24 protein classes is also provided.The dataset is provided as tar files. We provide the images already preprocessed for normalization (`STED-FM-dataset-crop.tar`) and as raw values (`STED-FM-dataset-crops-raw.tar`). All files in these archives are stored as npz with keys: `image`, and `metadata`. We also provide the raw files stored as tif files (`STED-FM-dataset-crops-tiff-raw.tar`).
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.049 |
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".