Enhancing AI-Readiness of Bioimaging Data with Content-Based Identifiers

The BIO-CODES project addresses the growing complexity of bioimaging data by developing and implementing content-based identifiers using the International Standard Content Code (ISCC). Our mission is to enhance the AI-readiness of bioimaging data while ensuring adherence to FAIR principles and maintaining data integrity for reliable use in AI-driven analyses.

• 🏷️ Standardized Content Identification: Implement ISCC (ISO 24138) for bioimaging data
• 🤖 AI-Ready Data: Prepare bioimaging datasets for seamless AI integration
• 🔍 Data Integrity: Ensure transparency and verification of bioimaging data
• 🔄 FAIR Compliance: Make bioimaging data Findable, Accessible, Interoperable, and Reusable
• 🌐 Platform Integration: Integrate unique identifiers into key platforms like OMERO

Current bioimaging data faces several critical challenges:

• Non-FAIR Compliance: Much bioimaging data doesn't follow FAIR principles
• Lack of Robust Identification: Current methods insufficient for generative AI models
• Data Integrity Risks: Difficulty to connect research output with original bioimaging data
• Reproducibility Issues: Limited transparency affects scientific reproducibility

BIO-CODES implements the ISO 24138 International Standard Content Code (ISCC) for bioimaging

The ISCC is a content-derived, multi-component identifier that:

• Generates unique codes directly from digital content
• Uses cryptographic and similarity hash algorithms
• Supports data integrity verification and similarity detection
• Enables decentralized content identification
• Is completely open-source and transparent

• Meta-Code: Encodes metadata similarity
• Content-Code: Captures perceptual/structural content similarity for images
• Data-Code: Encodes raw data similarity
• Instance-Code: Functions like a checksum for exact data identification

• Leiden University
• ISCC Foundation

• OMERO: Primary integration platform for bioimaging data management
• Imaging Core Facilities: Testing with routine proprietary formats
• Vendor Collaboration: Engaging with equipment manufacturers

• Data Deduplication: Identify duplicate images across datasets
• Database Synchronization: Maintain consistency across platforms
• Provenance Tracking: Trace data origin and modifications
• AI Model Validation: Ensure training data integrity
• Quality Assurance: Verify image authenticity and completeness

The BIO-CODES project will:

• Enhance Collaboration: Standardized identifiers improve data sharing
• Improve AI Reliability: Better data quality leads to more trustworthy AI models
• Increase Reproducibility: Clear data provenance supports scientific validation
• Enable Automation: Streamlined workflows for AI-driven research

• Project Website: OSCARS Project - BIO-CODES
• ISCC Standard: ISO 24138:2024
• ISCC Foundation: https://iscc.codes/
• OMERO Platform: https://www.openmicroscopy.org/omero/

bioimaging AI-readiness ISCC ISO-24138 data-integrity FAIR-principles content-identification digital-assets reproducibility life-sciences

This project embraces open-source principles. Individual repositories may have specific license terms - please check each repository for details.

We welcome contributions from the scientific community, developers, and institutions interested in advancing bioimaging data standards. Please check individual repository contribution guidelines.

For more information about the BIO-CODES project, please visit our project page or reach out through the OSCARS project channels.

The BIO-CODES project is part of the OSCARS initiative, working to enhance the AI-readiness and FAIR compliance of bioimaging data through innovative content-based identification systems.