Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Kapittelweg, Nijmegen, The Netherlands.
University of Amsterdam, Department of Brain & Cognition, Nieuwe Achtergracht, Amsterdam, The Netherlands.
PLoS Comput Biol. 2018 May 10;14(5):e1006064. doi: 10.1371/journal.pcbi.1006064. eCollection 2018 May.
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 code, reducing 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 analysis tools. Our goal is to release researchers from the constraints of specific implementation details, thereby freeing them to think about novel and creative ways to solve a given problem. Porcupine improves the overview researchers have of their processing pipelines, and facilitates both the development and communication of their work. This will reduce the threshold at which less expert users can generate reusable pipelines. With Porcupine, we bridge the gap between a conceptual and an implementational level of analysis and make it easier for researchers to create reproducible and shareable science. 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. Porcupine is free, open source, and released under the GNU General Public License v3.0.
神经影像学领域正在迅速采用更具可重复性的方法来进行数据采集和分析。数据结构和格式正在标准化，数据分析变得更加自动化。然而，随着数据分析变得更加复杂，研究人员通常不得不编写更长的分析脚本，这些脚本跨越了多种编程语言的不同工具。这使得共享或重现代码变得更加困难，从而降低了分析的可重复性。我们提出了一个工具，Porcupine，它可以直观地构建分析，并自动生成分析代码。图形表示提高了对执行分析的理解，同时保留了手动修改生成代码以满足自定义需求的灵活性。Porcupine 不仅生成分析代码，还以 Docker 镜像的形式为运行代码创建一个可共享的环境。两者结合起来，形成了一种可重复的构建、可视化和共享分析的方式。目前，Porcupine 链接到 Nipype 功能，而 Nipype 又可以访问大多数标准的神经影像学分析工具。我们的目标是使研究人员摆脱特定实现细节的限制，从而使他们能够思考解决给定问题的新颖和创造性方法。Porcupine 提高了研究人员对其处理管道的概述，并促进了他们的工作的开发和交流。这将降低不太熟练的用户生成可重复使用的管道的门槛。通过 Porcupine，我们在分析的概念和实现层面之间架起了桥梁，使研究人员更容易创建可重复和可共享的科学。我们在我们的网站上提供了广泛的示例和文档，以及适用于所有平台的安装程序文件：https://timvanmourik.github.io/Porcupine。Porcupine 是免费的、开源的，并根据 GNU 通用公共许可证 v3.0 发布。
