diag3

diag3 is a modular diagnostic framework designed for analyzing three-dimensional data. It provides tools to validate data integrity, detect anomalies, and diagnose processing issues across domains such as medical imaging, computational geometry, and geoscience. The project emphasizes extensibility, interoperability, and reproducible workflows in research and production environments.

Origin and development: diag3 traces its lineage to earlier diagnostic utilities in the diag family and was released as an open-source project in the early 2010s. It has since evolved through community contributions, with periodic major releases that introduce new data adapters, algorithms, and visualization capabilities. The project is maintained by a community of volunteers and affiliated institutions.

Core features: The framework offers a pluggable architecture with a core engine and module plugins. It supports common 3D data formats such as NIfTI, DICOM, NRRD, VTK, and NetCDF via adapters. Validation modules check schema conformance, unit consistency, and metadata integrity. Anomaly detectors include statistical tests and machine-learning-based models, with configurable thresholds and provenance tracking. Reporting tools generate summaries, visualizations, and exportable logs.

Usage: diag3 provides both a command-line interface and a Python API. Typical workflows involve loading a dataset, running validation checks, applying anomaly detection, and exporting a report. The results can be integrated into data-processing pipelines or quality-assurance workflows, enabling automated audits of 3D data pipelines.

Reception and scope: diag3 has seen usage in academic research and clinical QA projects, valued for its modular design and emphasis on reproducibility. Criticisms include a learning curve for new users and occasional compatibility issues with niche data formats.

See also: diag, data quality, 3D data formats.