Impact
The ASAP Collaborative Research Network (CRN), which launched in 2020, is focused on changing the way that science is done to promote breakthroughs in Parkinson’s disease (PD) research. This program encourages diverse perspectives and open discourse to support high-risk, ambitious projects, reshaping how science in the Parkinson’s field is conducted. It is designed to reinvigorate the research pipeline to identify new targets and pathways for translational studies and commercialization.
The success of the CRN hinges upon ASAP’s foundational principles of supporting collaboration, generating research-enabling resources, sharing data, and accelerating discoveries.
Collaboration
ASAP fosters connections among teams by supporting a variety of venues designed to share the latest scientific findings, tools, and technologies, enabling teamwork and idea exchange across multidisciplinary teams.
Resources
We are committed to creating resources that accelerate discovery and empower the broader scientific community. From shared datasets and protocols to open-access tools and training materials, our resources are designed to be widely accessible, reusable, and impactful. Explore how we are building a foundation for faster, more collaborative Parkinson’s research through the tools and knowledge we share.
Discoveries
From 2020 to 2025, 35 CRN teams dedicated their efforts to uncovering mechanisms driving PD pathogenesis and progression across three major focus areas.
This work led to advances in identifying novel disease mechanisms and developing preclinical tools, resources, and datasets that potentially transform PD research. Highlights of emerging discoveries across these themes showcase the initiative’s impact on understanding PD.
Open Science Policy
Through the ASAP Open Science Policy, grantees must make their research outputs accessible through community-recognized, publicly accessible repositories by the time the output is referenced in a final publication. By enabling resource and data sharing, we can improve process efficiency, reduce redundancy and costs related to tool development and data generation, enhance discussions around troubleshooting methods, and improve access to field-enabling research resources.
