IntegriLAB: a blockchain-enabled electronic lab notebook for reproducible neuroimaging research

Area of Science:

• Data-intensive scientific research
• Neuroimaging analysis workflows
• Scientific reproducibility and data integrity

Background:

• Managing complex research data, scripts, and documentation is challenging, especially in neuroimaging.
• Existing Electronic Lab Notebooks (ELNs) often lack interoperability, authorship certification, and immutable audit trails.
• Fragmented tools and poor integration hinder reproducibility in scientific research.

Purpose of the Study:

• To propose a novel Electronic Lab Notebook (ELN) framework, IntegriLAB, for end-to-end research process tracking.
• To address the limitations of current ELNs in terms of interoperability, authorship certification, and auditability.
• To enhance trust and reproducibility in data-intensive scientific fields.

Main Methods:

• Developed a centralized, web-based ELN framework (IntegriLAB) integrating data, document, and code repositories.
• Integrated existing tools like DataLad, Overleaf, and GitHub for a proof-of-concept case study.
• Utilized LabTrace, a green blockchain technology, for cryptographically signing and immutably recording research activities.

Main Results:

• IntegriLAB successfully consolidates diverse research components into a unified system.
• The framework enables real-time monitoring of the research cycle while maintaining familiar user workflows.
• LabTrace provides immutable, cryptographically signed records, ensuring data integrity and verifiable authorship.

Conclusions:

• IntegriLAB advances research reproducibility by unifying project management and secure data handling.
• The blockchain-based verification system fosters trust and strengthens collaborative research practices.
• This novel ELN framework offers a scalable solution for managing complex, data-intensive research projects.