Accelerate Research With Standard or Custom Gears

Flywheel Gears are plug-ins, or open-source extensions, that reduce data-management overhead by automating many routine, time-consuming tasks.

Open Source and Free to Use

Gears are applications that run in Flywheel, and the Gear Exchange is a collection of Gears curated and maintained by the broader Flywheel community, and housed on GitHub. Site admins can add these Gears to your site to help automate time-consuming tasks, such as metadata extraction, classification, quality assurance, BIDS conversion, and full analytic pipelines. In addition to the Gears in the Gear Exchange you can develop and share your own Gears.

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Inside Flywheel’s Gears

Utility Gears

Utility Gears automate routine functions and can be assembled into a pre-processing pipeline to save time and overhead. Utility Gears fall into several broad categories, including:

  • Conversion Gear: Converts data from one format to another
  • QA Gear: Provides quality assurance on the data
  • Classifier gear:  Automatically identifies and labels the modality of different types of data

Analysis Gears

An analysis Gear is an algorithm that has been packaged to be run and managed on your project or on a specific subject.

Benefits of Flywheel Gears

Reproducibility

Mandatory versioning and image persistence

Streamlined Provenance

Automated capture of inputs, configuration parameters, logs, and outputs

Automatic Execution

Rules-based logic automates when and how Gears and pipelines run

Configurable, Cloud-Scalable Computation

Meet time-critical workflow demands with massive parallelization

Featured Gear

BIDS-APP: C-PAC (Configurable Pipeline for the Analysis of Connectomes)

The Configurable Pipeline for the Analysis of Connectomes. C-PAC is software for performing high-throughput preprocessing and analysis of functional connectomes data using high-performance computers. C-PAC is implemented in Python using the Nipype pipelining library to efficiently combine tools from AFNI, ANTS, and FSL to achieve high quality and robust automated processing. This docker container, when built, is an application for performing participant level analyses. Future releases will include group-level analyses, when there is a BIDS standard for handling derivatives and group models.

Author

Craddock C, Sikka S, Cheung B, et al.

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Gear Library

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