BIDSonym - a BIDSapp for the pseudo-anonymization of neuroimaging datasets
Bibliographic record
Abstract
The amount of neuroimaging data being shared increased exponentially in recent years. While thisdevelopment introduces prominent advantages concerning open, reproducible and sustainable neu-roimaging, the process of data sharing must ensure the privacy of participant data. A requirement fromboth, Ethics Review Boards and data sharing resources, datasets need to be (pseudo-) anonymized priorto sharing in order to limit participant re-identification. Depending on the dataset at hand, this processcan however become cumbersome and prone to errors. Here we introduce BIDSonym, a tool for auto-mated pseudo-anonymization of neuroimaging datasets. BIDSonym supports multiple de-identificationprocedures and operates on neuroimaging, as well as metadata files. In addition, all metadata infor-mation present in the respective files is gathered and evaluated. Its outputs furthermore allow usersto conduct a more in-depth assessment of potentially sensitive information present in a given dataset.Through its workflow and utilization of the Brain Imaging Data Structure (BIDS), BIDSonym’s appli-cation is reproducible, requires no manual intervention and is agnostic to idiosyncrasies of small andlarge scale datasets.
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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 | 0.031 | 0.098 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.052 | 0.037 |
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".