Scientific Transparency

Saving Science – Starting with Neuroscience

SciTran is a software project that has grown out of the Project on Scientific Transparency at Stanford University. At the heart of SciTran is a RESTful API, designed to enable and foster reproducible research through data management and processing.

The SciTran API delivers efficient and robust organization, archiving, and sharing of scientific data. We have developed an ecosystem system for neuroimaging data, but our goal is to build a system that is flexible enough to accommodate all types of scientific data – from paper-and-pencil tests to genomics.

All SciTran components are open-source software, released under the MIT license, and hosted on GitHub. Feel free to try it out or to contribute.

Our friends at Flywheel, a substantial contributor to our effort, have built a complete data management and processing solution, including a modern web-based user interface, on top of the SciTran components. Flywheel offers commercial support for installation and maintenance of their system; such support is clearly beyond the scope of SciTran itself.

The SciTran codebase continues to be developed and maintained by the original group at Stanford's VISTA Lab, in close collaboration with Flywheel. Meanwhile, the VISTA Lab group has transitioned its main focus from developing SciTran core functionality to creating scientific applications that run within the SciTran framework, with a particular focus on reproducible methods in neuroscience research.

The SciTran software has been adopted by the Stanford Center for Reproducible Neuroscience, led by Russ Poldrack and Chris Gorgolewski. The center has supported members of the SciTran team for collaborating on the OpenfMRI platform.



The following actively-developed SciTran components are available:

Core   RESTful HTTP API, written in Python and backed by MongoDB
Reaper   Instrument integration, mainly focused on DICOM
Client   MATLAB and Python APIs for SciTran Core
Organizer   Data Organizer desktop app
Apps   Reproducible scientific applications


Brian Wandell
Project Lead

Gunnar Schaefer
Engineering Lead

Michael Perry
Software Engineer

Renzo Frigato
Software Engineer

Robert Dougherty
Scientific Advisor

Funding Sources