Scikick: A sidekick for workflow clarity and reproducibility during extensive data analysis - Carlucci, Matthew; Bareikis, Tadas; Koncevičius, Karolis; Gibas, Povilas; Kriščiūnas, Algimantas; Petronis, Art; Oh, Gabriel (PloS one, 2023)
Reproducibility is crucial for scientific progress, yet a clear research data analysis workflow is challenging to implement and maintain. As a result, a record of computational steps performed on the data to arrive at the key research findings is often missing. We developed Scikick, a tool that eases the configuration, execution, and presentation of scientific computational analyses. Scikick allows for workflow configurations with notebooks as the units of execution, defines a standard structure for the project, automatically tracks the defined interdependencies between the data analysis steps, and implements methods to compile all research results into a cohesive final report. Utilities provided by Scikick help turn the complicated management of transparent data analysis workflows into a standardized and feasible practice. Scikick version 0.2.1 code and documentation is available as supplementary material. The Scikick software is available on GitHub ( https://github.com/matthewcarlucci/scikick ) and is distributed with PyPi ( https://pypi.org/project/scikick/ ) under a GPL-3 license.