Seven quick tips for analysis scripts in neuroimaging

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Seven quick tips for analysis scripts in neuroimaging. / van Vliet, Marijn.

In: PLoS computational biology, 26.08.2019.

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@article{59458571bafe4182b8702bb041294dd9,
title = "Seven quick tips for analysis scripts in neuroimaging",
abstract = "Unorganized heaps of analysis code are a growing liability as data analysis pipelines are getting longer and more complicated. This is worrying, as neuroscience papers are getting retracted due to programmer error. Furthermore, analysis code is increasingly published as the push towards open science continues, so the quality of your code becomes public knowledge. In this paper, some guidelines are presented that help keep analysis code well organized, easy to understand and convenient to work with:1. Each analysis step is one script2. A script either processes a single recording, or aggregates across recordings, never both3. One master script to run the entire analysis4. Save all intermediate results5. Visualize all intermediate results6. Each parameter and filename is defined only once7. Distinguish files that are part of the official pipeline from other scriptsIn addition to discussing the reasoning behind each guideline, an example analysis pipeline is presented as a case study to see how each guideline translates into code.",
keywords = "data analysis, scripting, guidelines, programming",
author = "{van Vliet}, Marijn",
note = "Lataa lopullinen versio, kun artikkeli on ilmestynyt. Submitted as a {"}Perspective{"} piece.",
year = "2019",
month = "8",
day = "26",
language = "English",
journal = "PLoS computational biology",
issn = "1553-734X",
publisher = "Public Library of Science",

}

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TY - JOUR

T1 - Seven quick tips for analysis scripts in neuroimaging

AU - van Vliet, Marijn

N1 - Lataa lopullinen versio, kun artikkeli on ilmestynyt. Submitted as a "Perspective" piece.

PY - 2019/8/26

Y1 - 2019/8/26

N2 - Unorganized heaps of analysis code are a growing liability as data analysis pipelines are getting longer and more complicated. This is worrying, as neuroscience papers are getting retracted due to programmer error. Furthermore, analysis code is increasingly published as the push towards open science continues, so the quality of your code becomes public knowledge. In this paper, some guidelines are presented that help keep analysis code well organized, easy to understand and convenient to work with:1. Each analysis step is one script2. A script either processes a single recording, or aggregates across recordings, never both3. One master script to run the entire analysis4. Save all intermediate results5. Visualize all intermediate results6. Each parameter and filename is defined only once7. Distinguish files that are part of the official pipeline from other scriptsIn addition to discussing the reasoning behind each guideline, an example analysis pipeline is presented as a case study to see how each guideline translates into code.

AB - Unorganized heaps of analysis code are a growing liability as data analysis pipelines are getting longer and more complicated. This is worrying, as neuroscience papers are getting retracted due to programmer error. Furthermore, analysis code is increasingly published as the push towards open science continues, so the quality of your code becomes public knowledge. In this paper, some guidelines are presented that help keep analysis code well organized, easy to understand and convenient to work with:1. Each analysis step is one script2. A script either processes a single recording, or aggregates across recordings, never both3. One master script to run the entire analysis4. Save all intermediate results5. Visualize all intermediate results6. Each parameter and filename is defined only once7. Distinguish files that are part of the official pipeline from other scriptsIn addition to discussing the reasoning behind each guideline, an example analysis pipeline is presented as a case study to see how each guideline translates into code.

KW - data analysis

KW - scripting

KW - guidelines

KW - programming

M3 - Article

JO - PLoS computational biology

JF - PLoS computational biology

SN - 1553-734X

ER -

ID: 34657575