Scikick: A sidekick for workflow clarity and reproducibility during extensive data analysis
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 ease...
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| Veröffentlicht in: | PloS one 2023-07, Vol.18 (7), p.e0289171-e0289171 |
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| creator | Carlucci, Matthew Bareikis, Tadas Koncevičius, Karolis Gibas, Povilas Kriščiūnas, Algimantas Petronis, Art Oh, Gabriel |
| description | 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. |
| doi_str_mv | 10.1371/journal.pone.0289171 |
| format | Article |
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This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Carlucci et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 Carlucci et al 2023 Carlucci et al</rights><rights>2023 Carlucci et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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| subjects | Analysis Archives & records Automation Biology and life sciences Computer and Information Sciences Computer applications Configurations Data analysis Data mining Datasets Engineering and Technology Evaluation Information management Reproducibility Research and Analysis Methods Software Workflow |
| title | Scikick: A sidekick for workflow clarity and reproducibility during extensive data analysis |
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