DataMaze: An open source methods and data repository for Morris water maze experiments
Bibliographic record
Abstract
A core limitation of behavioural neuroscience is the use of a given assay with very different apparatus specifications (e.g., size) and task parameters (e.g., retention intervals). Such procedural variability largely contributes to the outcome discrepancy across various studies and labs, thereby hindering research progress and the replicability of the outcomes (Crabbe et al., 1999). There is an immediate need for informative and inclusive quantitative benchmarks to design experiments, consider and analyze available data, and interpret the results accordingly, thereby reinforcing the FAIR principles. We took the first step in this direction by establishing a comprehensive database, starting with the Morris water maze (DataMaze). DataMaze contains and consolidates specifications and task parameters from thousands of papers (∼5000) and includes data along with related experimental manipulations. To reinforce this effort, we introduce easy-to-calculate indices that will help compare different studies in terms of parameters and outcomes, both prospectively and retrospectively. We also showcase the use of DataMaze for secondary data analysis. The ultimate aim of DataMaze is to transform research practices and reporting standards in behavioural neuroscience, such that researchers quantitatively utilize virtually all published information in evaluating their experiments, analyzing data, and interpreting results. DataMaze will remain a live and accessible platform that will grow retrospectively by incorporating information from additional studies and prospectively by accepting new and richer entries (e.g., individual subject-level data) from researchers.
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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.010 | 0.019 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".