Best Practices for Scientific Computing

Wilson, Greg; Aruliah, D. A.; Brown, C. Titus; Hong, Neil Chue; Davis, M. Ryleigh; Guy, Richard; Haddock, Steven H. D.; Huff, Kathryn D.; Mitchell, Ian M.; Plumbley, Mark D.; Waugh, B. M.; White, Ethan; Wilson, P.A.

doi:10.1371/journal.pbio.1001745

Cited by 576 publications

(586 citation statements)

References 51 publications

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“…Previous to this evolution, most team members with any coding experience-not necessarily in R-had learned just enough to accomplish whatever task had been before them using their own unique conventions. Given the com plexity of the OHI project, we needed to learn to code collabora tively and incorporate best 50,51 or good enough practices 12,52 into our coding, so that our methods could be co developed and vetted by multiple team members. Using a version control system not only improved our file and data management, but allowed individuals to feel less inhibited about their coding contributions, since files could always be reverted back to previous versions if there were problems.…”

Section: Improving Reproducibility and Collaborationmentioning

confidence: 99%

Our path to better science in less time using open data science tools

Best²,

et al. 2017

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Section: Improving Reproducibility and Collaborationmentioning

confidence: 99%

Our path to better science in less time using open data science tools

Best²,

et al. 2017

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“…A continuous integration framework using the Travis CI service (https://travis-ci.org/) 117 was added so that software builds and unit tests can be run automatically upon pull requests and 118 other software updates. Continuous integration provides a safeguard against code updates that 119 break existing functionality by providing a report that shows which tests passed or failed for each 120 build (Wilson et al, 2014). The effectiveness of continuous integration depends on having 121 thorough unit test coverage of the PlantCV code base.…”

mentioning

confidence: 99%

PlantCV v2.0: Image analysis software for high-throughput plant phenotyping

Gehan

Abbasi

Berry

et al. 2017

Preprint

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32Systems for collecting image data in conjunction with computer vision techniques are a powerful 33 tool for increasing the temporal resolution at which plant phenotypes can be measured non-34 destructively. Computational tools that are flexible and extendable are needed to address the 35 diversity of plant phenotyping problems. We previously described the Plant Computer Vision 36 (PlantCV) software package, which is an image processing toolkit for plant phenotyping 37

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“…From limited comparative experiments one can find in the literature, it is not possible to claim which exhibits higher quality or "better software" [98](Ch.15). However, we can argue that writing open-source code promotes some aspects of what are "good programming practices" [125], while also promoting the inclusion of more extensive and complete documentation, modularization, and version control that are shown to improve the productivity of scientific programming [98,125].…”

Section: Challengesmentioning

confidence: 99%

Automatic music transcription: challenges and future directions

et al. 2013

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. Abstract Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects. Permanent repository link

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Best Practices for Scientific Computing

Abstract: We describe a set of best practices for scientific software development, based on research and experience, that will improve scientists' productivity and the reliability of their software.

Cited by 576 publications

References 51 publications

Our path to better science in less time using open data science tools

Our path to better science in less time using open data science tools

PlantCV v2.0: Image analysis software for high-throughput plant phenotyping

Automatic music transcription: challenges and future directions

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