bdami

bdami is an open-source software framework and data standard intended to enable interoperable management and analysis of biometric, clinical, and imaging datasets across research and healthcare institutions. Developed by a collaborative community of academic and industry partners, bdami aims to unify data representation, provenance, and access controls while supporting scalable analytics.

The core of bdami is a modular data model that records subjects, samples, modalities, observations, and provenance information. The schema is designed to accommodate diverse data types, including physiological signals, genomic data, and medical images. Interoperability is achieved through a pluggable API and mappings to existing standards such as JSON-LD for data interchange and DICOM for imaging and HL7 FHIR for clinical data.

Key features include a processing engine for distributed analytics, an extensible plugin framework for models and pipelines, and built-in privacy controls such as consent management, data access governance, and audit trails. bdami emphasizes reproducibility by recording lineage information and supporting containerized workflows.

The project originated in the mid-2020s as a collaboration among universities, hospitals, and technology firms. It is released under a permissive open-source license and is maintained by a community-led governance process.

Use cases include multi-site clinical studies, longitudinal population health research, and biomedical imaging analytics. Challenges include the complexity of the data model, the need for standardized metadata, and ensuring compliance with privacy regulations. bdami continues to evolve with planned enhancements such as stronger FHIR mappings, accelerated data ingestion, and broader support for omics data.