R code used for each data set to perform negative binomial regression, calculate overdispersion statistic, generate summary statistics, remove outliers

Identification of errors or anomalous values, collectively considered outliers, assists in exploring data or through removing outliers improves statistical analysis. In biomechanics, outlier detection methods have explored the ‘shape’ of the entire cycles, although exploring fewer points using a ‘moving-window’ may be advantageous. Hence, the aim was to develop a moving-window method for detecting trials with outliers in intra-participant time-series data. Outliers were detected through two stages for the strides (mean 38 cycles) from treadmill running. Cycles were removed in stage 1 for one-dimensional (spatial) outliers at each time point using the median absolute deviation, and in stage 2 for two-dimensional (spatial–temporal) outliers using a moving window standard deviation. Significance levels of the t-statistic were used for scaling. Fewer cycles were removed with smaller scaling and smaller window size, requiring more stringent scaling at stage 1 (mean 3.5 cycles removed for 0.0001 scaling) than at stage 2 (mean 2.6 cycles removed for 0.01 scaling with a window size of 1). Settings in the supplied Matlab code should be customised to each data set, and outliers assessed to justify whether to retain or remove those cycles. The method is effective in identifying trials with outliers in intra-participant time series data.

Measurement Configuration Dataset

This is the anonymous reviewing version; the source code repository will be added after the review.

This dataset provides reproduction data for performance measurement configuration at source code level in Java. The measurement data can be obtained using the precision-experiments repository https://anonymous.4open.science/r/precision-experiments-C613/ (Examining Different Repetition Counts) yourself. These data conatained here are the data we obtained from execution on i7-4770 CPU @ 3.40GHz.

The analysis was tested on Ubuntu 20.04 and gnuplot 5.2.8. It will not work with older gnuplot versions.

To execute the analysis, extract the data by

tar -xvf basic-parameter-comparison.tar tar -xvf parallel-sequential-comparison.tar

and afterwards build the precision-experiments repo and execute the analysis by

cd precision-experiments/precision-analysis/ ../gradlew fatJar cd scripts/configuration-analysis/ ./executeCompleteAnalysis.sh ../../../../basic-parameter-comparison ../../../../parallel-sequential-comparison

Afterwards, the following files will be present:

precision-experiments/precision-analysis/scripts/configuration-analysis/repetitionHeatmaps/heatmap_all_en.pdf (Heatmaps for different repetition counts)

precision-experiments/precision-analysis/scripts/configuration-analysis/repetitionHeatmaps/heatmap_outlierRemoval_en.pdf (Heatmap with and without outlier removal for 1000 repetitions)

precision-experiments/precision-analysis/scripts/configuration-analysis/histogram_outliers_en.pdf (Histogram of the outliers)

precision-experiments/precision-analysis/scripts/configuration-analysis/heatmap_parallel_en.pdf (Heatmap with sequential and parallel execution)

With the rapid increase of large-scale datasets, biomedical data visualization is facing challenges. The data may be large, have different orders of magnitude, contain extreme values, and the data distribution is not clear. Here we present an R package ggbreak that allows users to create broken axes using ggplot2 syntax. It can effectively use the plotting area to deal with large datasets (especially for long sequential data), data with different magnitudes, and contain outliers. The ggbreak package increases the available visual space for a better presentation of the data and detailed annotation, thus improves our ability to interpret the data. The ggbreak package is fully compatible with ggplot2 and it is easy to superpose additional layers and applies scale and theme to adjust the plot using the ggplot2 syntax. The ggbreak package is open-source software released under the Artistic-2.0 license, and it is freely available on CRAN (https://CRAN.R-project.org/package=ggbreak) and Github (https://github.com/YuLab-SMU/ggbreak).