LabCAS is a platform for building a data commons.

The Early Detection Research Network (EDRN) Cancer Biomarker Data Commons (LabCAS) is a data commons designed to support the research community in the discovery and validation of cancer biomarkers. LabCAS serves as a comprehensive platform for the storage, management, and dissemination of a wide array of cancer biomarker-related data captured from EDRN studies.

LabCAS provides the following:

• Support for Data Delivery: Facilitate data submission from EDRN sites, following the FAIR Data Submission Guidance for EDRN.
• Metadata and Data Capture: Collect metadata and data systematically.
• Integration with Existing Repositories:Catalog data in EDRN LabCAS by linking metadata to data stored in existing NIH- and NCI-supported repositories that promote FAIR principles.
• Compliance with EDRN Data Sharing Policy: Ensure data capture aligns with the policy.
• Secure Data Sharing: Enable safe data sharing between EDRN sites.
• Linking Data to Publications: Use persistent identifiers (DOIs) to connect data with publications.
• Simplified Data Citation: Make locating, accessing, and citing original resources easy to ensure proper credit.
• Secure and Reliable Data Management: Capture, process, manage, search, and analyze scientific data securely.
• Analytical Methods Integration: Plug in various analytical methods.
• Repeatable Data Processing Pipelines: Implement consistent data processing workflows.
• Visualization and Analytical Tools: Integrate tools for data visualization and analysis.

Data Management in LabCAS:

• Public and Private Data Options: Scientific data in the EDRN Cancer Biomarkers Data Commons (LabCAS) can be made public or kept private and secure for EDRN members. Public data can be assigned a Digital Object Identifier (DOI) for linking to publications.
• Consistent Data Capture: LabCAS uses data models and metadata elements to capture data uniformly across the EDRN Cancer Biomarker Data Commons. Data can be continuously captured, linked, and integrated with various analytical methods and tools to support data-driven discoveries, resulting in a consistent data architecture.
• Hierarchical Data Organization: Data in LabCAS is structured hierarchically with collections at the top level, datasets within collections, and files within datasets.
  • Collections: Group data logically by EDRN study, protocol, or scientific publication.
  • Datasets: Subcategorize data within a collection, such as submissions for a multi-site study.
  • Files: The individual data files, stored within datasets or collections, representing the specific data elements.