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.

Males of many species adjust their reproductive investment to the number of rivals present simultaneously. However, few studies have investigated whether males sum previous encounters with rivals, and the total level of competition has never been explicitly separated from social familiarity. Social familiarity can be an important component of kin recognition and has been suggested as a cue that males use to avoid harming females when competing with relatives. Previous work has succeeded in independently manipulating social familiarity and relatedness among rivals, but experimental manipulations of familiarity are confounded with manipulations of the total number of rivals that males encounter. Using the seed beetle Callosobruchus maculatus we manipulated three factors: familiarity among rival males, the number of rivals encountered simultaneously, and the total number of rivals encountered over a 48-hour period. Males produced smaller ejaculates when exposed to more rivals in total, regardless of the maximum number of rivals they encountered simultaneously. Males did not respond to familiarity. Our results demonstrate that males of this species can sum the number of rivals encountered over separate days, and therefore the confounding of familiarity with the total level of competition in previous studies should not be ignored.,Lymbery et al 2018 Full datasetContains all the data used in the statistical analyses for the associated manuscript. The file contains two spreadsheets: one containing the data and one containing a legend relating to column titles.Lymbery et al Full Dataset.xlsxLymbery et al 2018 Reduced dataset 1Contains data used in the attached manuscript following the removal of three outliers for the purposes of data distribution, as described in the associated R code. The file contains two spreadsheets: one containing the data and one containing a legend relating to column titles.Lymbery et al Reduced Dataset After 1st Round of Outlier Removal.xlsxLymbery et al 2018 Reduced dataset 2Contains the data used in the statistical analyses for the associated manuscript, after the removal of all outliers stated in the manuscript and associated R code. The file contains two spreadsheets: one containing the data and one containing a legend relating to column titles.Lymbery et al Reduced Dataset After Final Outlier Removal.xlsxLymbery et al 2018 R ScriptContains all the R code used for statistical analysis in this manuscript, with annotations to aid interpretation.,

**Correlation is significant at the 0.01 level (2-tailed).*Correlation is significant at the 0.05 level (2-tailed).'Correlation is significant at the 0.1 level (2-tailed).For each model, the two categories of sibling pairs are derived from Table 2. In each case, a possible fit (in bold) is indicated by the second correlation being less than the first.

MLR models of age at onset of T1D after removing outliers (N = 354).

Causal effect estimates using Radial MVMR with and without outlier removal with varying levels of balanced pleiotropy.

aSee Text S1.

RRegrs study for Growth Yield for original and corrected/filterred datasets: inputs training and test files, R scripts to split the datasets, plot for outlier removal.