## Appendix A An Algorithm to (Semi-)Automatically Remove Outliers from OBR Strain Data  
[PERSON]'s [56] and [PERSON] [57] tests are very well-known mathematical algorithms to identify outliers in data. Other methods to find outliers are, e.g., a simple minimum/maximum-value check, determining the quartiles, or other quantiles, of the data points around the value in question and checking for a maximum allowed distance from these quartiles/quantiles, or combinations of all those [58]. However, these methods depend either on normally-distributed data points or are not reliable for step-like structures, which have been observed in the experiments described above. While, e.g., the program described in [58] and other similar programs are successful in detecting outliers in a dataset, the program parameters usually must be tweaked to get good outlier detection. This may be feasible if just a couple of measurements have to be checked; doing so for, e.g., 250 measurements per experiment will become strenuous fast. A reasonable simple algorithm was devised that can easily be implemented in a software tool, to search and substitute outliers in the case when the functional relation of the strain data is not known beforehand and step structures may occur. The following five assumptions are made to devise the presented algorithm:  
1. The data have a limited number of outliers. 2. Equally-spaced points in the x-direction are assumed. 3. The data are section-wise \"flat\". 4. Three \"flat points\" are considered as one section. 5. If data are missing between two consecutive flat sections, a linear interpolation is assumed between these. Assumption 3 means that the measurement fluctuations and value changes due to (continuous) strain gradients are lower than a given threshold within a section. In the herein presented work, a threshold of 0.03% strain was chosen as the maximum allowed change in strain between two consecutive points. This is a variation of a minimum/maximum-value check, albeit it is a local check in this case. Steep strain gradients exceed this value, and Assumption 4 and 5 take care of these cases. Steep strain gradients occur between two consecutive flat sections. The algorithm checks if the data points are on the (linear) line between such sections, plus/minus an allowed error. The allowed error was chosen to be 0.05% strain. The number of points that constitute a flat section (three) is chosen to keep the software implementation simple. If three outliers in a row fulfill by chance Assumption 3, these are considered as correct data and will not be substituted. However, the user must take a look at the corrected data anyway (see below) and would recognize such artifacts easily. A higher number is possible; a lower number would lead to more artifacts. Assumption 5 determines how outliers are substituted. The assumed linear relationship is most debatable, but every physical reality can be used here. In the case of no further information, as usual in science, [PERSON]'s principle entia non sunt multiplicanda praeter necessitatem (often falsely attributed to [PERSON] [59]) applies. The following measures are taken to reduce the chance of falsely dismissing correct data that have the characteristics of an outlier. Get a first dataset by evaluating record \(i-1\) with record \(i-2\) as the reference. This is the same as measurement number \(i-1\). Get the second dataset by evaluating record \(i\) with record \(i-1\) as the reference. This is measurement number \(i\) and the one that is to be checked for outliers. The third dataset is obtained by evaluating the same record \(i\) with the next to last record \(i-2\) as the reference. To make sure that an outlier that appears in the dataset of interest (the second) is not falsely substituted, the algorithm checks if the third dataset has a data point at the same position as the added up values of the first and second datasets. Since the reference is different for the second dataset, real outliers should only with a very small chance appear at the same location.