Created on 12th September 2017
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The field of neuroimaging is rapidly adopting a more reproducible approach to data acquisition and analysis. Data structures and formats are being standardised and data analyses are getting more automated. However, as data analysis becomes more complicated, researchers often have to write longer analysis scripts, spanning different tools across multiple programming languages. This makes it more difficult to share or recreate one's code, harming the reproducibility of the analysis. We present a tool, Porcupine, that constructs one's analysis visually and automatically produces analysis code. The graphical representation improves understanding of the performed analysis, while retaining the flexibility of modifying the produced code manually to custom needs. Not only does Porcupine produce the analysis code, it also creates a shareable environment for running the code, in the form of a Docker image. Together, this forms a reproducible way of constructing, visualising and sharing one's analysis. Currently, Porcupine links to Nipype functionalities, which in turn accesses most standard neuroimaging tools. With Porcupine, we intend to bridge the gap between a conceptual and an implementational level of the analysis and thus create better reproducible and shareable science. We give the researcher a better oversight of their pipeline, both while developing and communicating their work. This may also be a useful tool in the education of novice neuroimaging students, considerably cutting their learning phase. We provide a wide range of examples and documentation, as well as installer files for all platforms on our website: https://timvanmourik.github.io/Porcupine.Show more
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|Krzysztof Jacek Go...||Completed||27 Sep 2017||View review and response|