Dataset for "Clustering analysis across different speeds reveals two distinct running techniques with no differences in running economy"

The dataset contains a single directory which includes the following files, supporting the associated publication "Clustering analysis across different speeds reveals two distinct running techniques with no differences in running economy":

- MasterDataSheet.xlsx contains demographics, anthropometrics and physiological information from each of the participants. Participants have a unique identifier, preserving their anonymity.

- MasterDataSheet_column_naming.txt describes the column names and units in MasterDataSheet.xlsx

- AllCurves_ptavgs.npy contains a python dictionary storing the average spatiotemporal (stride time and duty factor) and continuous kinematics variables (centre of mass vertical displacement normalised to leg length, trunk to pelvis, hip, knee and ankle joint angles in the sagittal plane and the pelvis segment angle in the sagittal plane) for the three stages of the incremental running test considered in the study: STG_02 (11 km/h), STG_03 (12 km/h) and STG_04 (13 km/h). The dictionary is organised as follows:

data
STG_* (running stage code)
P*** (participant code)
variables (RCOM_2, RTRUNK2PELVIS_0, RHIP_0...)

These variables are the average of all the strides in the stage.

- Clust_multispeed_avgepelvictilt.csv contains the average pelvic tilt angle during quiet standing for each participant alongside the cluster label assigned in the multispeed condition.

Scripts to replicate the results of the study can be found in the associated "clustering_runners" GitHub repository and they show how to access the data within these files.