Datum: A Scientific Metadata Catalog

The Challenge

Most data catalog solutions today are designed for financial, healthcare, or enterprise data, leaving the scientific community underserved. These tools prioritize integrations with business intelligence platforms but fail to support scientific file formats, research workflows, and classified environments. Scientific data professionals need a lightweight, scalable, and domain-specific cataloging system that makes research data searchable, actionable, and accessible—without the overhead of complex infrastructure.

How It Works

Datum is a minimal-infrastructure scientific data catalog designed to make research data searchable and actionable across on-premise, cloud, and classified networks. Unlike traditional catalogs, Datum works where the data lives, supporting scientific tools, file types, and environments without requiring costly conversions or external dependencies.

• Metadata Collection & Processing

    ◦ Automatically scans local, networked, and cloud storage for data.

    ◦ Extracts metadata from scientific file formats, including HDF5, GeoTIFF, NetCDF, LaTeX, Parquet, Apache Iceberg, and more.

• Search & AI-Driven Discovery

    ◦ Built-in semantic search with vector-based AI agent integration—no external licensing required.

    ◦ Users can search by data lineage, relationships, and metadata attributes.

• Minimal Infrastructure, Maximum Flexibility

    ◦ Runs as a single executable on any OS and CPU architecture.

    ◦ No reliance on external databases or indexing tools, making it perfect for edge computing and HPC clusters.

• High Security & Governance

    ◦ OIDC authentication, SCIM provisioning, and EntraID integration for enterprise security.

    ◦ Data governance tools enforce metadata requirements, embargoes, NDAs, and automatic removal of outdated data.

• Custom Plugin System

    ◦ Users can develop custom file processing, metadata extraction, or sampling plugins in any programming language.

• CLI & SDK for Automation

    ◦ Ships with a Command Line Interface (CLI) tool and a Python SDK for programmatic access.

Key Advantages

• Scientific Data First: Designed for sensor data, research datasets, and classified environments—not just business data.

• No Infrastructure Overhead: A standalone executable with no reliance on external databases or cloud dependencies.

• Seamless Integration with Scientific Workflows: Automated metadata extraction from research data without fidelity loss.

• Semantic & AI-Enabled Search: Finds data through intelligent queries, relationships, and contextual search models.

• High Security & Compliance: Designed for classified and non-classified research environments with full governance controls.

Market Applications

• Scientific Research Institutions – Organize and search massive datasets across multiple scientific disciplines.

• Classified Computing & Government Agencies – Manage secure and restricted data catalogs with built-in governance.

• High-Performance Computing (HPC) Labs – Run metadata indexing and retrieval at scale without cloud dependencies.

• Big Data & Analytics Teams – Enable data discovery and AI-enhanced search for large scientific datasets.

• Industrial R&D & Engineering Firms – Catalog sensor and experimental data for long-term access and analysis.

This software is open source and available at no cost. Download now by visiting the product's GitHub page.