Abstract
This article describes a new open source scientific workflow system, the TimeStudio Project, dedicated to the behavioral and brain sciences. The program is written in MATLAB and features a graphical user interface for the dynamic pipelining of computer algorithms developed as TimeStudio plugins. TimeStudio includes both a set of general plugins (for reading data files, modifying data structures, visualizing data structures, etc.) and a set of plugins specifically developed for the analysis of event-related eyetracking data as a proof of concept. It is possible to create custom plugins to integrate new or existing MATLAB code anywhere in a workflow, making TimeStudio a flexible workbench for organizing and performing a wide range of analyses. The system also features an integrated sharing and archiving tool for TimeStudio workflows, which can be used to share workflows both during the data analysis phase and after scientific publication. TimeStudio thus facilitates the reproduction and replication of scientific studies, increases the transparency of analyses, and reduces individual researchers’ analysis workload. The project website (http://timestudioproject.com) contains the latest releases of TimeStudio, together with documentation and user forums.
Highlights
This article describes a new open source scientific workflow system, the TimeStudio Project, dedicated to the behavioral and brain sciences
In order for the reproduction process to work more smoothly, the code and data need to be prepared for cross-platform reproduction in a way that is appropriate for each individual research field (Curcin & Ghanem, 2008; McCullough et al, 2008)
We introduce the TimeStudio Project, a fully transparent system dedicated to the analysis, reproduction, and sharing of quantitative data in order to address both the practical problems and general scientific goals
Summary
This article describes a new open source scientific workflow system, the TimeStudio Project, dedicated to the behavioral and brain sciences. In April 2012, Science magazine published a policy forum article stating that custom-made analysis programs are Bblack boxes^ in the scientific work flow (Morin et al, 2012). We focus on the black boxes related to data analysis—that is, how raw data are transformed to the summary statistics representing the Bfindings^ of a study This is important, since opaque analyses hinder scientific replications. In order for the reproduction process to work more smoothly, the code and data need to be prepared for cross-platform reproduction in a way that is appropriate for each individual research field (Curcin & Ghanem, 2008; McCullough et al, 2008). There is currently no Bgold standard^ for meeting this goal (Curcin & Ghanem, 2008)
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