Add dataset analysis and reproducibility guide

.github/workflows/validate.yml

@@ -0,0 +1,26 @@
+name: Validate analysis release
+
+on:
+  push:
+  pull_request:
+
+permissions:
+  contents: read
+
+jobs:
+  validate:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+
+      - name: Check Python syntax
+        run: python -m py_compile scripts/build_scirep_analysis.py scripts/validate_release.py
+
+      - name: Validate aggregate results
+        run: python scripts/validate_release.py

.gitignore

@@ -0,0 +1,40 @@
+.DS_Store
+.venv/
+__pycache__/
+*.py[cod]
+paper/build/
+paper/tables/
+
+# The full analysis creates participant-level intermediate files. Keep them
+# local until the complete governed dataset release is public.
+data/derived/*
+!data/derived/analysis_manifest.json
+!data/derived/analysis_summary.json
+!data/derived/all_trial_feature_statistics.csv
+!data/derived/classification_summary.json
+!data/derived/cohort_adjusted_models.csv
+!data/derived/cohort_specific_effects.csv
+!data/derived/continuous_performance_validation.csv
+!data/derived/coordination_control_time_correlation.csv
+!data/derived/cycle_duration_ci.csv
+!data/derived/feature_dictionary.csv
+!data/derived/kmeans_cluster_summary.csv
+!data/derived/kmeans_thresholds.json
+!data/derived/kmeans_validation.csv
+!data/derived/metric_band_classification_comparison.csv
+!data/derived/metric_band_classification_summary.json
+!data/derived/phase_fraction_ci.csv
+!data/derived/phase_kinematics_summary.csv
+!data/derived/phase_segment_summary.csv
+!data/derived/position_smoothing_sensitivity.csv
+!data/derived/processing_sensitivity.csv
+!data/derived/trimming_rule_validation.csv
+
+# Keep a compact figure gallery in Git. The full figure set can be regenerated.
+paper/figures/*
+!paper/figures/fig1_cohort_structure.png
+!paper/figures/fig2_feature_effects.png
+!paper/figures/fig3_phase_timing.png
+!paper/figures/fig4_experience_links.png
+!paper/figures/fig21_cohort_effect_forest.png
+!paper/figures/fig23_continuous_performance_validation.png

CITATION.cff

@@ -14,9 +14,12 @@ authors:
     affiliation: University of Leeds
 identifiers:
   - type: doi
-    value: 10.5281/zenodo.20752650
+    value: 10.5281/zenodo.20752651
     description: Zenodo record for version 1.0
-repository: 'https://doi.org/10.5281/zenodo.20752650'
+  - type: doi
+    value: 10.5281/zenodo.20752650
+    description: Zenodo concept DOI for all versions
+repository: 'https://github.com/omariosc/LASK'
 repository-code: 'https://github.com/omariosc/LASK'
 url: 'https://doi.org/10.5281/zenodo.20752650'
 abstract: >-
@@ -28,8 +31,9 @@ abstract: >-
   validation set (10 trials) and a 6-DoF out-of-distribution test set (8
   trials), spanning surgeons from novice to expert. Each trial provides
   per-frame electromagnetic tool poses across roughly 91,000 frames, with
-  positions in millimetres, orientations as unit quaternions and calibrated jaw
-  angles for the 7-DoF cohorts, time-aligned to the video. Labelled keyframes
+  positions in millimetres, orientations as unit quaternions and relative
+  voltage-derived jaw-opening signals for the 7-DoF cohorts, time-aligned to
+  the video. Labelled keyframes
   supply instrument segmentation masks, tooltip and jaw keypoints, shaft-joint
   keypoints and per-component visibility flags for both instruments, alongside
   surgeon handedness and procedure experience.
@@ -68,3 +72,18 @@ references:
       name: Frontiers Media SA
     year: 2025
     doi: 10.3389/978-2-8325-5137-0
+  - type: conference-paper
+    title: Bayesian Temporal Pose Networks for Uncertainty-Calibrated Laparoscopic Tool Pose Tracking
+    authors:
+      - given-names: Omar
+        family-names: Choudhry
+      - given-names: Sharib
+        family-names: Ali
+      - given-names: Chandra Shekhar
+        family-names: Biyani
+      - given-names: Dominic
+        family-names: Jones
+    conference:
+      name: Medical Image Computing and Computer Assisted Intervention (MICCAI)
+    year: 2026
+    repository-code: https://github.com/omariosc/BTPN