MouseBytes: an open-access and web-based repository for cognitive data integration and sharing
Bibliographic record
Abstract
Neuroscience is undergoing an open-access revolution. Repositories in areas such as neuroimaging and genomics have provided a paradigm shift in the way we are now able to analyze, share, and re-use vast amounts of data from multiple laboratories. Unfortunately, one area that is far behind in this respect is behavioural evaluation of animal models. Thanks to a new set of automated tests based on touchscreen technology [1], there is potential for standardized outputs in animal behavioral neuroscience that are consistent with open-access sharing. However, there is currently no mechanism to provide quality control for such data or to deposit them in an open access repository for reuse, like approaches used in the imaging and genomics fields. In order to enable cognitive data, obtained using the highly translatable and automated touchscreen technology to join the open-access revolution, we have developed MouseBytes, a web-based repository that enables researchers to store, share, visualize, and analyze cognitive data. We envision that MouseBytes will be the premier platform to allow cognitive data to become open access, searchable and to be reused. Furthermore, MouseBytes will support the innovative integration of standardized behavioural with MRI and RNA-sequencing, allowing comprehensive multi-modal information in mouse models of disease.
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.039 |
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".