This repository contains the Julia code, Jupyter notebook, and data used in the study “Healthy young adults use distinct gait strategies to enhance stability when walking on mild slopes and when altering arm swing” by MacDonald et al. Instructions To run this analysis on your computer, both Julia and Jupyter Notebook 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.). In Jupyter, open the notebooks/analysis.ipynb notebook. Running all cells will reproduce the results for this paper. Description of data The data directory contains all the data used in the production of the results which were statistically tested. Each .mat file contains events and data generated in Visual3D: Events LTO/RTO (Left/right toe-off) LHS/RHS (Left/right heel-strike) HIST/HIEN (Hilly start/end) ROST/ROEN (Rocky start/end) MLST/MLEN (ML translation start/end) Data LFootPos/RFootPos (Left/right foot COM position) TrunkPos/TrunkVel/TrunkAcc (Trunk COM position, velocity, and acceleration) HeadPos/HeadVel/HeadAcc (Head COM position, velocity, and acceleration) COG (Whole-body COM/COG) The .csv files contain system state of the CAREN system produced by D-Flow software, which includes various system and software settings, most pertinent of which is the treadmill speed. The .c3d files contain the raw motion capture data from Vicon Nexus. The results of the notebooks/analysis.ipynb notebook are found in the results folder. Please see the paper for a list of the dependent variables and statistical analyses. ; This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grant RGPIN-2016-04928, NSERC Accelerator supplement RGPAS 493045-2016 and by the Ontario Ministry of Research, Innovation and Science Early Researcher Award (ERA) 16-12-206.