The Michael J. Fox Foundation for Parkinson’s Research (MJFF) supports work on several high-profile Parkinson’s disease (PD) datasets, including from major international efforts like the Global Parkinson’s Genetics Program (GP2) and the Parkinson’s Progression Markers Initiative (PPMI). These datasets offer unprecedented opportunities for Parkinson’s researchers, and MJFF wants to maximize their impact.
That’s why MJFF developed the Data Community of Practice — an inclusive, collaborative platform open to researchers across all stages of their careers, regardless of their research background, formal training or funding status. Think of it as a digital commons based around a forum or message board where researchers can ask questions, share insights, engage in new collaborations, and problem solve together. It’s like a Stack Overflow or Reddit specifically for Parkinson’s data – be it data generated by MJFF or other research stakeholders.
How Can This Data Community Help Researchers?
In the Data Community of Practice, you’ll find conversations that tackle the practical challenges of working with PD data. Whether it’s technical troubleshooting, analysis approaches or starting new collaborations with peers, there’s a wealth of knowledge shared through the forum’s discussions and a slew of resources that accompany those conversations. You can make new posts or reply to preexisting threads; search ongoing conversations; and interact with colleagues who have shared their research interests in our member directory.
Here are just a few ways the community can help your research.
Interpreting Complex PD Datasets
Working with complex data can sometimes be confusing, especially when you’re unsure about which data to use or how to interpret it. The Data Community of Practice provides resources like clarification threads to connect with others who can help you make sense of the datasets, ensuring you’re using the right features for your research.
Finding the Right Data, Faster
Knowing what datasets can most effectively answer your research question of interest is a recurring challenge. In response to this, community members group-authored the guide, Getting Started with Parkinson’s Disease Data. This resource can help you figure out which best meets your needs, providing guidance on how to start working with four datasets: PPMI, GP2, AMP-PD, and Fox Insight. It includes study descriptions, data features, how to access data, intended data uses, data set strengths and limitations, links to pre-existing documentation, plus tips and other considerations for handling these data.
Leveraging Preexisting Code to Prepare Data for Analysis
A major hurdle in research is the need for custom code solutions, but building everything from scratch can be time-consuming. That’s why community members have collaborated on and shared ready-to-use code for working with PD data, ranging from highly reproducible project analysis templates to PPMI-specific data processing and analysis pipelines. Have a workflow or pipeline you’re looking to share, need another pair of eyes to review your code or looking for help using GitHub more broadly? The community can help, saving you time and energy on your next project.
Find Collaborators and Receive Training
Finding collaborators to round out a project team’s expertise can often delay a project from moving forward. The community directory offers the ability to understand members’ research interests and the ‘Find A Collaborator’ section enables you to make posts seeking colleagues to join in on your work.
You are also able to join a task force and contribute to resource generation, such as one focused on data modalities and methods or propose your own. Perhaps you are looking to act as a mentor for up-and-coming colleagues or looking for a mentor for yourself or for one of your students? We encourage you to join the Mentorship and Training Task force, make a post, or indicate your willingness to mentor in your profile.
The right mentor or mentee can be one of the most rewarding parts of a research career, but it can also be challenging. That’s why community members set up the Mentorship & Training Task Force to help researchers pair with others who can provide guidance, support and expertise. Whether you’re looking for a mentor to help you navigate your project or want to guide someone else in their research, this task force can help you make meaningful connections.
Who Contributes to the Data Community?
The Data Community of Practice is for any researcher who is working with PD-related datasets (funded or not) and wants to collaborate with others with shared research interests. That includes colleagues with a focus in other diseases who want to engage in cross-disease collaboration by taking advantage of the amazing data available in PD research.
Members of the community come from all over the world—you can join hundreds of member researchers from more than 30 countries, a broad mix of academics in the PD, AD, and MS space; industry researchers; and students ranging from high school to post-doctoral fellows. What unites everyone is a shared goal to make it easier for all of us to use PD data more effectively, learn from one another, and ultimately, improve outcomes for people living with Parkinson’s disease.
What Does the Community Look Like in Action?
The community is already making a difference, with members collaborating actively.
For instance, if you’re trying to work with imaging data, you can explore the matching MRI sessions thread to get tips on ensuring data consistency. Want to learn more about the seed amplification assay? Join the ongoing conversation or connect with one of the authors providing an overview of that data. If genetics is more your focus, the community has you covered with guides on how to approach analyses in admixed populations, multi-omic data analyses, or conducting a GWAS.
Beyond being a place to publicize tools and resources or find new collaborators, the community provides you the opportunity to ask (or answer) questions not previously posed. Your curiosity, expertise, and engagement are what makes the community successful and ultimately helps us all accelerate progress towards shared research aims. Share your questions, invite colleagues, and join us –the MJFF Data Community of Practice is here to help.