Welcome to the GitHub organization of MRI Lab Graz! Here, we provide open-source tools and pipelines to facilitate the use of the Brain Imaging Data Structure (BIDS) format in neuroimaging research. Our tools are designed to streamline data conversion, organization, analysis, and sharing. Below, you'll find an overview of the repositories and their functionalities.

• Description: Automates the conversion of raw DICOM data to the BIDS format.
• Features:
  • Handles multi-session and multi-subject datasets.
  • Customizable for different scanner outputs.
  • Integrates quality control steps.

• Description: Pre-built LimeSurvey templates for generating and managing participant codes automatically.
• Features:
  • Consistent participant ID creation.
  • Ensures anonymity and integrity of participant data.

• Description: Scripts and pipelines for preprocessing and statistical analysis of functional data in the BIDS format.
• Features:
  • Automated model generation (models.json).
  • Integration with SPM and other tools.
  • User-friendly setup for students and researchers.

• Description: Utilities to work with datasets under version control using Datalad.
• Features:
  • Automates dataset versioning and sharing.
  • Facilitates collaborative workflows.
  • Ensures reproducibility of data processing.

• Tools: Various smaller scripts and pipelines for:
  • Quality control of imaging data.
  • Conversion of physiological recordings to BIDS.
  • Custom utilities for managing BIDS-compliant datasets.

We welcome contributions from the community! Whether it’s fixing a bug, improving documentation, or adding new features, feel free to submit pull requests or open issues in the respective repositories.

For questions, feedback, or collaboration inquiries, please reach out to us at karl.koschutnig@uni-graz.at or open an issue in this repository.

The MRI Lab Graz is dedicated to advancing neuroimaging research through accessible, reproducible, and efficient tools. By adhering to the BIDS standard, we aim to enhance collaboration and data sharing within the scientific community.

Thank you for visiting our organization! 🌟