This repository contains the minimal information necessary for the analysis of pacemaker translocation quiescent periods. There is enough data provided to conduct a thorough analysis for power law behavior in pacemaker translocation activity using the powerlaw Python library, available via pip and at PyPi (https://pypi.org/project/powerlaw/). Additionally, this repository contains batch file information for the original files used in analysis and provides access to a Box link containing the original *.mcd format files of each microelectrode array (MEA) recording used to produce the data. The translocation algorithm is described in the associated manuscript.

Article abstract: Power laws are of interest to several scientific disciplines because they can provide important information about the underlying dynamics (e.g. scale invariance and self-similarity) of a given system. Because power laws are of increasing interest to the cardiac sciences as potential indicators of cardiac dysfunction, it is essential that rigorous, standardized analytical methods are employed in the evaluation of power laws. This study compares the methods currently used in the fields of condensed matter physics, geoscience, neuroscience, and cardiology in order to provide a robust analytical framework for evaluating power laws in stem cell-derived cardiomyocyte cultures. One potential power law-obeying phenomenon observed in these cultures is pacemaker translocations, or the spatial and temporal instability of the pacemaker region, in a 2D cell culture. Power law analysis of translocation data was performed using increasingly rigorous methods in order to illustrate how differences in analytical robustness can result in misleading power law interpretations. Non-robust methods concluded that pacemaker translocations adhere to a power law while robust methods convincingly demonstrated that they obey a doubly truncated power law. The results of this study highlight the importance of employing comprehensive methods during power law analysis of cardiomyocyte cultures.

Dataset was collected in the manner described in the manuscript. No post-processing (e.g. signal filtering or smoothing) was applied to the recorded field potentials. Data processing is largely limited to calculation of pacemaker (time lag) data combined with application of the algorithm for detecting pacemaker translocations. The algorithm employs a distance threshold-based method to identify when the pacemaker moves (translocates).  Refer to the original manuscript for more information.

The values given here are pacemaker translocation quiescent periods, as measured in beats.  Full recapitulation of the analysis would likely require access to MEA recordings. For 30 recordings, the file sizes equal approx. 7.5gb of data (in .mcd form; near 60gb in text form). If you want access to this data, please do not hesitate to contact the corresponding authors.  We will be happy to share the data with you in the form of a freely-accessible Box link.