Data
Join our Federated Research Network
with 50% of the world's top 20 medical institutions
Clinical data, single-cell and spatial omics, and histology
Working with top academic centers to access AI-ready patient data
Unparalleled depth of data from hard-to-find cohorts
Our approach to data
Owkin works with top academic centers to curate, enrich, and generate deep, multimodal data that is AI-ready
Research-grade patient data and samples including rich modalities like spatial omics for a full heterogeneous view of the disease and potentially increased generalizability of the research results.
Privacy-enhanced federated access to 8 of top 20 oncology and general hospitals in US and Europe with the latest Standard of Care (SoC) and longitudinal data.
Complete AI-ready datasets enabled and contextualized through close relationships with PIs including 118 KOLs with a mean H-index of 50.
Challenges
Barriers to develop and validate AI across datasets
Data is distributed across several institutions, sites, and infrastructures with competing incentives
Healthcare data is private and/or proprietary
GDPR, HIPAA, EU AI Act, and country specific regulations
Data is collected for patient care, not for use in machine learning research
Different systems have different labels and structures
Heterogeneous, missing data, lacks medical context
Integrating multimodal data from different sources
Accessing data that reflects the latest SoC and new modalities like spatial omics that offer new research opportunities
Owkin’s solutions
Data services for Owkin sponsored projects
Owkin helps academic partners to upgrade their data to accelerate their AI research in real world healthcare settings. Our data services:
Make your existing data AI-ready and interoperable (enriched, harmonized, FAIRified)
We help you generate and analyze new data in cutting-edge modalities
Healthcare partnership research case studies
Accelerating research
We combine privacy-enhancing technologies, like secure data aggregation and federated learning, with critical clinical expertise and a network of academic researchers.
Federated learning software breaks medical data silos and accelerates innovation in real-world healthcare settings.
Federated learning
Connect disjointed data to build accurate models without pooling data
Federated learning powers precision medicine by unlocking access to the right quality and quantity of data to train high-performance models
With federated learning the data stays local - only models and insights travel between the servers, enhancing privacy and ensuring compliance
This technology allows research communities to benefit from access to more datasets; training on heterogenous data to generate more robust and accurate models
Spotlight
Early career researcher
Owkin collaborates with Charlotte Spencer, CRUK Clinical Research Fellow at the Francis Crick Institute, to apply the latest machine learning techniques to discover kidney cancer biomarkers.
Are you an early career researcher interested in incorporating cutting-edge AI in your research?