Daset Labs builds reliable data foundations for biomedical analytics by mapping data landscapes, brokering access to restricted datasets, and providing expert data preparation services. They enable healthcare and life sciences organizations to mitigate risks in AI development and regulatory submissions through curated, cleansed, and enriched data assets.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations seeking to leverage biomedical data for healthcare and societal advancements face significant hurdles in ensuring data quality and compliance. Inadequate data preparation, including issues with mapping, access, and cleansing, can lead to AI model failures and regulatory non-compliance, particularly in the highly regulated life sciences sector.
Solution
Daset Labs provides a comprehensive suite of tools and services designed to establish robust data foundations for biomedical analytics. They specialize in mapping the existing biomedical data landscape to identify relevant resources and plan data acquisition strategies. The company facilitates access to restricted datasets through expert brokering and develops tailored approaches for data preparation, including curation, enrichment, and labeling. By addressing these critical data lifecycle stages, Daset Labs enables organizations to mitigate risks associated with AI development and regulatory submissions, ensuring the reliability and integrity of their data assets.
Target Audience
Daset Labs serves organizations within the healthcare and life sciences industries, including pharmaceutical companies, biotech firms, research institutions, and AI developers working with sensitive biomedical data.
Features
- Data landscape mapping to identify and summarize available biomedical data resources.
- Strategic planning for data acquisition, considering budget and usage restrictions.
- Expertise in brokering access to restricted and commercial biomedical datasets.
- Data preparation services including curation, cleansing, enrichment, and labeling.
- Workflow development for data aggregation and augmentation.
- Guidance on regulatory compliance for biomedical data utilization.
- Tools and methodologies to identify and mitigate data bias in AI models.