| Title |
Scikick: a sidekick for workflow clarity and reproducibility during extensive data analysis / |
| Authors |
Carlucci, Matthew Robert ; Bareikis, Tadas ; Koncevičius, Karolis ; Gibas, Povilas ; Kriščiūnas, Algimantas ; Petronis, Artūras ; Oh, Gabriel |
| DOI |
10.1371/journal.pone.0289171 |
| Full Text |
|
| Is Part of |
PLoS ONE.. San Francisco : Public Library of Science. 2023, vol. 18, iss. 7, art. no. e0289171, p. [1-8].. eISSN 1932-6203 |
| Keywords [eng] |
reproducibility ; data analysis ; Scikick |
| Abstract [eng] |
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. |
| Published |
San Francisco : Public Library of Science |
| Type |
Journal article |
| Language |
English |
| Publication date |
2023 |
| CC license |
|
