Data and Code for: Interlimb coordination in Parkinson's Disease is affected by a visuospatial dual task
Bibliographic record
Abstract
This results of this research are fully reproducible using the source code, Jupyter notebooks, and raw data used to produce the results for the above titled project. Raw data included in this repository: raw motion capture trials, as .c3d files system output from CAREN control software, D-Flow (includes treadmill speed, etc) OpenSim models Instructions To run this analysis on your computer, both Julia and Jupyter (notebook or lab) must be installed. A version of Julia appropriate for your OS can be downloaded from the Julia website, and Jupyter can be installed from within Julia (in the REPL) with ] add IJulia Alternate instructions for installing Jupyter can be found on the IJulia github or the Jupyter homepage (not recommended). From within the main repository directory, start Julia and then start Jupyter in the Julia REPL using IJulia notebook(;dir=pwd()) or if using a system Jupyter installation, start Jupyter from your favorite available shell (e.g. Powershell on Windows, bash on any *nix variant, etc.). The primary analysis is found in the Analysis notebook. Two other notebooks related to this research are also included.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.450 | 0.261 |
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".