BIO-CODES: Enhancing AI-Readiness of Bioimaging Data with Content-Based Identifiers - project poster 2nd OSCARS AGM 2026

Zenodo (CERN European Organization for Nuclear Research)

Abstract

(Standardised) content identification is becoming an essential requirement for bioimaging data reuse and the seamless integration of bioimaging data into AI models. Transparency regarding the origin of data, distinguishing between conventionally generated and AI-generated data, is essential to maintain data integrity and facilitate informed analysis. In this context, the International Standard Content Code - ISCC is a new, open-source identification system and global ISO standard, ensuring transparency, accessibility, and widespread adoption. The BIO-CODES project seeks to enhance the AI-readiness of bioimaging data by developing and implementing content-based identifiers, using ISCC. The project addresses the increasing complexity of bioimaging data, ensuring it adheres to FAIR principles, and prepares datasets for reliable use in AI-driven analyses. By integrating globally unique references into key platforms, such as OMERO, BIO-CODES aims to advance data integrity and streamline bioimage certification processes, ensuring transparency and reproducibility.

Authors 7

1. Titusz Pan

   Institut des sciences de la communication

   Affiliation as printed

   ISCC Foundation

2. Martin Etzrodt

   Institut des sciences de la communication

   Affiliation as printed

   ISCC Foundation

3. Lemke Kira

   Institut des sciences de la communication

   Affiliation as printed

   ISCC Foundation

4. Josh Moore
5. Sebastian Posth
6. Maarten W. Paul Aachen

   Leiden University Medical Center

   Affiliation as printed

   Leiden University Medical Center

7. Sylvia E. Le Dévédec Aachen

   Leiden University

   Affiliation as printed

   Leiden University