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All experimental protocols, primer sequences, and the scripts for primer design and initial data processing are available from our website [ 29].
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Details of the experimental data processing are available in the supplementary materials.
Code for data processing is available at https://github.com/rdbraatz/data-driven-prediction-of-battery-cycle-life-before-capacity-degradation.
A good survey about MapReduce for parallel data processing is available in [10].
More detailed information for data processing is available in Additional File 1.
Further details on sampling, data collection and processing are available elsewhere.
The protocols for sample extraction, data acquisition and post processing are available in the supplementary information.
Furthermore, post-processing scripts for format conversion and testing the index integrity after data processing are also available.
Perl and Matlab scripts used for data processing and analysis are available from the authors upon request.
Detailed sample processing protocols are included in Supplementary File 2. The RAW data files are available from the EDRN Catalog and Archive Service (http://cancer.jpl.nasa.gov/ecas/).nasa.gov/ecas/
Version-controlled data processing and analysis code are available from the project git repository [ http://bitbucket.org/lorainelab/soyseq]. Analysis code available in the repository includes shell scripts used to run data processing programs and R Markdown files used to perform statistical analysis.
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