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Tools and techniques for computational reproducibility.

Piccolo SR, Frampton MB - Gigascience (2016)

Bottom Line: When those steps have been described in sufficient detail that others can retrace the steps and obtain similar results, the research is said to be reproducible.However, in practice, computational findings often cannot be reproduced because of complexities in how software is packaged, installed, and executed-and because of limitations associated with how scientists document analysis steps.No single strategy is sufficient for every scenario; thus we emphasize that it is often useful to combine approaches.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Brigham Young University, Provo, UT, 84602, USA. stephen_piccolo@byu.edu.

ABSTRACT
When reporting research findings, scientists document the steps they followed so that others can verify and build upon the research. When those steps have been described in sufficient detail that others can retrace the steps and obtain similar results, the research is said to be reproducible. Computers play a vital role in many research disciplines and present both opportunities and challenges for reproducibility. Computers can be programmed to execute analysis tasks, and those programs can be repeated and shared with others. The deterministic nature of most computer programs means that the same analysis tasks, applied to the same data, will often produce the same outputs. However, in practice, computational findings often cannot be reproduced because of complexities in how software is packaged, installed, and executed-and because of limitations associated with how scientists document analysis steps. Many tools and techniques are available to help overcome these challenges; here we describe seven such strategies. With a broad scientific audience in mind, we describe the strengths and limitations of each approach, as well as the circumstances under which each might be applied. No single strategy is sufficient for every scenario; thus we emphasize that it is often useful to combine approaches.

No MeSH data available.


Example of a Jupyter notebook. This example contains code (in the Python programming language) for generating random numbers and plotting them in a graph within a Jupyter notebook. Importantly, the code and output object (graph) are contained within the same document. See Additional file 3 for an executable version of the notebook
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Fig3: Example of a Jupyter notebook. This example contains code (in the Python programming language) for generating random numbers and plotting them in a graph within a Jupyter notebook. Importantly, the code and output object (graph) are contained within the same document. See Additional file 3 for an executable version of the notebook

Mentions: Jupyter notebooks and knitr reports can be saved in various output formats, including hypertext markup language (HTML) and portable document format (PDF; see examples in Figs. 3 and 4; Additional files 3 and 4). Increasingly, scientists include such documents as supplementary materials to journal manuscripts, enabling others to repeat analysis steps and recreate manuscript figures [70–73].Fig. 3


Tools and techniques for computational reproducibility.

Piccolo SR, Frampton MB - Gigascience (2016)

Example of a Jupyter notebook. This example contains code (in the Python programming language) for generating random numbers and plotting them in a graph within a Jupyter notebook. Importantly, the code and output object (graph) are contained within the same document. See Additional file 3 for an executable version of the notebook
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4940747&req=5

Fig3: Example of a Jupyter notebook. This example contains code (in the Python programming language) for generating random numbers and plotting them in a graph within a Jupyter notebook. Importantly, the code and output object (graph) are contained within the same document. See Additional file 3 for an executable version of the notebook
Mentions: Jupyter notebooks and knitr reports can be saved in various output formats, including hypertext markup language (HTML) and portable document format (PDF; see examples in Figs. 3 and 4; Additional files 3 and 4). Increasingly, scientists include such documents as supplementary materials to journal manuscripts, enabling others to repeat analysis steps and recreate manuscript figures [70–73].Fig. 3

Bottom Line: When those steps have been described in sufficient detail that others can retrace the steps and obtain similar results, the research is said to be reproducible.However, in practice, computational findings often cannot be reproduced because of complexities in how software is packaged, installed, and executed-and because of limitations associated with how scientists document analysis steps.No single strategy is sufficient for every scenario; thus we emphasize that it is often useful to combine approaches.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Brigham Young University, Provo, UT, 84602, USA. stephen_piccolo@byu.edu.

ABSTRACT
When reporting research findings, scientists document the steps they followed so that others can verify and build upon the research. When those steps have been described in sufficient detail that others can retrace the steps and obtain similar results, the research is said to be reproducible. Computers play a vital role in many research disciplines and present both opportunities and challenges for reproducibility. Computers can be programmed to execute analysis tasks, and those programs can be repeated and shared with others. The deterministic nature of most computer programs means that the same analysis tasks, applied to the same data, will often produce the same outputs. However, in practice, computational findings often cannot be reproduced because of complexities in how software is packaged, installed, and executed-and because of limitations associated with how scientists document analysis steps. Many tools and techniques are available to help overcome these challenges; here we describe seven such strategies. With a broad scientific audience in mind, we describe the strengths and limitations of each approach, as well as the circumstances under which each might be applied. No single strategy is sufficient for every scenario; thus we emphasize that it is often useful to combine approaches.

No MeSH data available.