Reproducible Computational Scientific Workflows with signac (Dice et al.).pdf (18.27 MB)
Reproducible Computational Scientific Workflows with signac (Dice et al.).pdf
poster
posted on 2019-02-26, 16:25 authored by Bradley DiceBradley Dice, Carl Simon AdorfCarl Simon Adorf, Vyas RamasubramaniVyas RamasubramaniAbstract.
Researchers in computational science are regularly posed with the
challenge of managing and analyzing large, heterogeneous, and highly
dynamic data spaces. We present signac, an open-source Python
framework that enables researchers to efficiently operate on primarily
file-based data spaces while keeping track of all relevant metadata.
The signac framework provides all components required to create
a well-defined, collectively accessible data space and to implement
reproducible workflows. The software is designed to be highly modular,
decoupling its data and workflow management components so as to minimize
the effort required for integration into existing workflows. The
serverless data management and lightweight workflow model ensure that
workflows are just as easily executed on laptops as in high-performance
computing environments. Using signac not only increases
research efficiency, it also improves reproducibility and lowers
barriers for data sharing by transparently enabling the robust tracking,
selection, and searching of data by its metadata.
Funding
CDS&E: Fast, scalable GPU-enabled software for predictive materials design & discovery
Directorate for Mathematical & Physical Sciences
Find out more...Graduate Research Fellowship Program (GRFP)
Directorate for Education & Human Resources
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