Google Summer of Code 2026

Build open source tools for neuromuscular disease research

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Why OpenMOVR?

Real healthcare impact with expert mentorship

Healthcare Impact

Support research for ALS, SMA, DMD, and other neuromuscular diseases affecting thousands worldwide.

Expert Mentorship

Work with experienced researchers, data scientists, and software engineers from the MOVR team.

Modern Tech Stack

FHIR standards, healthcare APIs, ML pipelines, and modern data processing frameworks.

2026 Project Ideas

Priority focus: Making 12 years of MOVR legacy data easily accessible to researchers worldwide

🏆 Priority Project: MOVR Legacy Datahub Library

Our flagship GSoC project - building the foundation for global neuromuscular disease research

MOVR Legacy Datahub Python Library

Intermediate
📂 GitHub Repository 📚 Developer Docs

Repository: movr-datahub-analytics | Mission: Create a pip-installable Python library that makes 12 years of MOVR legacy data (5,895+ participants, 20,152+ encounters) easily accessible to researchers, universities, and data scientists worldwide.

Vision: Democratizing Neuromuscular Disease Data

Enable any researcher to access and analyze comprehensive longitudinal data with just pip install movr-datahub

Core Features to Build:

  • Easy Data Loading: movr.load_dataset('ALS')
  • Disease-Specific Modules: ALS, DMD, SMA, BMD, LGMD, FSHD, Pompe
  • Pandas Integration: Return clean DataFrames ready for analysis
  • Longitudinal Tools: Track disease progression over time
  • Visualization Utilities: Built-in plotting functions
  • ML Framework Support: scikit-learn, PyTorch integration
  • Jupyter Examples: Comprehensive tutorial notebooks
  • Research Templates: Common analysis patterns

Example Usage:

# Install and load MOVR data
pip install movr-datahub

import movr_datahub as movr
import pandas as pd

# Load ALS patient data
als_data = movr.load_dataset('ALS', include_longitudinal=True)
print(f"Loaded {len(als_data)} ALS participants")

# Analyze disease progression
progression = movr.analyze_progression(als_data, metric='ALSFRS_R')
movr.plot_progression(progression, save='my_analysis.png')
Impact: Enable hundreds of researchers worldwide to access MOVR data without technical barriers, accelerating research into ALS, SMA, DMD and other neuromuscular diseases.
Expected Deliverables:
  • Production-ready Python package on PyPI
  • Comprehensive documentation and API reference
  • 10+ Jupyter notebook tutorials covering common research questions
  • Unit tests and integration tests
  • Example research projects and case studies
Python Pandas NumPy Matplotlib/Seaborn Jupyter PyPI Packaging Clinical Data

Supporting Projects

FHIR Data Integration Library

Intermediate

Objective: Develop a Python library for future FHIR R4 data extraction and integration (MOVR 2.0 preparation).

Key Features:

  • FHIR resource parsing and validation
  • Data quality checks and standardization
  • Integration with MOVR legacy data formats
  • Preparation for next-generation EMR integration
Python FHIR Healthcare APIs JSON/XML

Pharmacovigilance Classification System

Advanced

Objective: Build an AI-powered system to automatically classify hospitalization reasons from clinical notes, addressing the challenge of 58% "other" classifications in adverse event reporting.

Key Features:

  • Natural language processing for clinical notes
  • FDA-reportable adverse event ontology
  • Machine learning classification pipeline
  • Validation against expert annotations
Expected Outcomes: ML model, classification API, evaluation metrics, and integration with MOVR data pipeline.
Python NLP scikit-learn spaCy Clinical Ontologies

Research API and Developer Portal

Beginner

Objective: Create a comprehensive API and developer portal for researchers to access aggregated MOVR insights and integrate with their own analysis workflows.

Key Features:

  • RESTful API for data access
  • Interactive API documentation
  • Authentication and rate limiting
  • Code examples in multiple languages
Expected Outcomes: API server, documentation portal, client libraries, and usage examples.
Node.js Express OpenAPI React PostgreSQL

Data Visualization Dashboard

Intermediate

Objective: Develop an interactive web dashboard for exploring MOVR data patterns, disease progression trends, and research insights.

Key Features:

  • Interactive charts and visualizations
  • Real-time data updates
  • Export capabilities for presentations
  • Mobile-responsive design
Expected Outcomes: Web application, visualization library, deployment configuration, and user documentation.
React D3.js TypeScript Chart.js Material-UI

Clinical Trial Matching Engine

Advanced

Objective: Build a system to match MOVR participants with relevant clinical trials based on their medical history, genetic profiles, and current treatments.

Key Features:

  • Clinical trial database integration
  • Patient-trial matching algorithms
  • Privacy-preserving computation
  • Clinician notification system
Expected Outcomes: Matching engine, privacy framework, validation studies, and clinician interface.
Python Machine Learning Privacy Preserving ClinicalTrials.gov API Flask

2026 GSoC Timeline

Important dates and milestones for the program

Feb 1-19

Organization Application

OpenMOVR submits application to participate in GSoC 2026

Feb 26

Organizations Announced

Google announces accepted organizations (fingers crossed! 🤞)

Mar-Apr

Student Application Period

Students can apply for projects and interact with mentors

May 1

Student Projects Announced

Selected students and projects are revealed

May-Aug

Coding Period

12+ weeks of coding with mentor support and evaluations

Sep 8

Final Results

Project completion and final evaluations

Meet Our GSoC Team

The OpenMOVR team leading our 2026 program (accepting 1 student)

AP

Andre Daniel Paredes, PhD

GSoC Mentor & Technical Lead

Senior Director, Strategy & Informatics, Data Science

Mentorship Focus:
  • Technical architecture & Python development
  • Healthcare data standards (FHIR, clinical datasets)
  • Open source best practices & community building
  • Research integration & real-world impact

"Building tools that accelerate research for diseases that need champions"

JW

Jessica Waits

GSoC Administrator & Project Lead

Director of Clinical Operations

Administrative Role:
  • GSoC program coordination & student relations
  • Project planning & milestone management
  • Clinical research context & requirements
  • Community engagement & stakeholder relations

"Ensuring research serves patients and families first"

🎯 2026 Program Capacity

OpenMOVR can accept 1 student for GSoC 2026. We're looking for a dedicated developer passionate about healthcare impact to work on our priority MOVR Legacy Datahub Library project.

Prerequisites & Skills

What you should know before applying

🔧 Technical Skills

Required for Most Projects:

  • Proficiency in Python or JavaScript
  • Experience with Git and version control
  • Understanding of REST APIs
  • Basic knowledge of databases

Bonus Skills:

  • Healthcare data experience (FHIR, HL7)
  • Machine learning frameworks
  • Web development (React, Node.js)
  • Data visualization tools

Soft Skills

Essential Qualities:

  • Communication: Clear, proactive communication with mentors
  • Independence: Ability to research and solve problems
  • Reliability: Consistent progress and meeting deadlines
  • Curiosity: Interest in healthcare and patient impact

Healthcare Context:

  • Understanding of patient privacy importance
  • Appreciation for data quality in medical research
  • Interest in making a positive health impact

How to Apply

Steps to join our GSoC program

Application Steps

  1. Get Familiar: Review our project ideas and codebase
  2. Join Community: Introduce yourself on our communication channels
  3. Make a Contribution: Submit a small PR or fix to show your skills
  4. Write Proposal: Create a detailed project proposal
  5. Submit Application: Apply through the official GSoC website

Tips for Success

  • Start Early: Begin engaging with the community before applications open
  • Show Initiative: Propose improvements or ask thoughtful questions
  • Understand the Domain: Learn about neuromuscular diseases and healthcare data
  • Be Specific: Include detailed timelines and deliverables in your proposal
  • Communicate: Stay active in discussions and respond promptly

🚀 Ready to Apply?

Applications open through the official Google Summer of Code website.
Connect with our mentors first to discuss your project proposal.

Apply on GSoC Website → Contact Mentors View GitHub