A simple kit to use computational notebooks for more openness, reproducibility, and productivity in research.
The ubiquitous use of computational work for data generation, processing, and modeling increased the importance of digital documentation in improving research quality and impact. Computational notebooks are files that contain descriptive text, as well as code and its outputs, in a single, dynamic, a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-09-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010356 |
| _version_ | 1828386142560452608 |
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| author | Ludmilla Figueiredo Cédric Scherer Juliano Sarmento Cabral |
| author_facet | Ludmilla Figueiredo Cédric Scherer Juliano Sarmento Cabral |
| author_sort | Ludmilla Figueiredo |
| collection | DOAJ |
| description | The ubiquitous use of computational work for data generation, processing, and modeling increased the importance of digital documentation in improving research quality and impact. Computational notebooks are files that contain descriptive text, as well as code and its outputs, in a single, dynamic, and visually appealing file that is easier to understand by nonspecialists. Traditionally used by data scientists when producing reports and informing decision-making, the use of this tool in research publication is not common, despite its potential to increase research impact and quality. For a single study, the content of such documentation partially overlaps with that of classical lab notebooks and that of the scientific manuscript reporting the study. Therefore, to minimize the amount of work required to manage all the files related to these contents and optimize their production, we present a starter kit to facilitate the implementation of computational notebooks in the research process, including publication. The kit contains the template of a computational notebook integrated into a research project that employs R, Python, or Julia. Using examples of ecological studies, we show how computational notebooks also foster the implementation of principles of Open Science, such as reproducibility and traceability. The kit is designed for beginners, but at the end we present practices that can be gradually implemented to develop a fully digital research workflow. Our hope is that such minimalist yet effective starter kit will encourage researchers to adopt this practice in their workflow, regardless of their computational background. |
| first_indexed | 2024-12-10T05:32:06Z |
| format | Article |
| id | doaj.art-be7b0589d543417f8469acf74fc118ff |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-10T05:32:06Z |
| publishDate | 2022-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-be7b0589d543417f8469acf74fc118ff2022-12-22T02:00:30ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-09-01189e101035610.1371/journal.pcbi.1010356A simple kit to use computational notebooks for more openness, reproducibility, and productivity in research.Ludmilla FigueiredoCédric SchererJuliano Sarmento CabralThe ubiquitous use of computational work for data generation, processing, and modeling increased the importance of digital documentation in improving research quality and impact. Computational notebooks are files that contain descriptive text, as well as code and its outputs, in a single, dynamic, and visually appealing file that is easier to understand by nonspecialists. Traditionally used by data scientists when producing reports and informing decision-making, the use of this tool in research publication is not common, despite its potential to increase research impact and quality. For a single study, the content of such documentation partially overlaps with that of classical lab notebooks and that of the scientific manuscript reporting the study. Therefore, to minimize the amount of work required to manage all the files related to these contents and optimize their production, we present a starter kit to facilitate the implementation of computational notebooks in the research process, including publication. The kit contains the template of a computational notebook integrated into a research project that employs R, Python, or Julia. Using examples of ecological studies, we show how computational notebooks also foster the implementation of principles of Open Science, such as reproducibility and traceability. The kit is designed for beginners, but at the end we present practices that can be gradually implemented to develop a fully digital research workflow. Our hope is that such minimalist yet effective starter kit will encourage researchers to adopt this practice in their workflow, regardless of their computational background.https://doi.org/10.1371/journal.pcbi.1010356 |
| spellingShingle | Ludmilla Figueiredo Cédric Scherer Juliano Sarmento Cabral A simple kit to use computational notebooks for more openness, reproducibility, and productivity in research. PLoS Computational Biology |
| title | A simple kit to use computational notebooks for more openness, reproducibility, and productivity in research. |
| title_full | A simple kit to use computational notebooks for more openness, reproducibility, and productivity in research. |
| title_fullStr | A simple kit to use computational notebooks for more openness, reproducibility, and productivity in research. |
| title_full_unstemmed | A simple kit to use computational notebooks for more openness, reproducibility, and productivity in research. |
| title_short | A simple kit to use computational notebooks for more openness, reproducibility, and productivity in research. |
| title_sort | simple kit to use computational notebooks for more openness reproducibility and productivity in research |
| url | https://doi.org/10.1371/journal.pcbi.1010356 |
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