Lab Notebook (Electronic)

Part of the worlds-biggest-software-project initiative.

An AI-native, open-source electronic lab notebook (ELN) for structured experiment logging, protocol management, and data linking across research teams.

A modern ELN for academic, biotech, and pharma research labs. It captures experiments in structured form, manages reusable protocols, links data to inventory and instruments, and uses AI to reduce the documentation burden on bench scientists.

Why Lab Notebook (Electronic)?

Enterprise ELNs such as Benchling and IDBS E-WorkBook are custom-quoted and typically cost $50,000–$500,000+/yr, putting them out of reach for academic groups and small biotechs.
The dominant open-source option, eLabFTW, requires teams to self-host and maintain their own IT infrastructure.
Compliance-heavy platforms (SciSure, IDBS) have complex onboarding and are over-specified for many research environments.
Existing tools have limited instrument connectivity (SciNote) or are locked to a vendor's reagent ecosystem (Hivebench).
AI-assisted protocol suggestion and automatic data import from instruments are emerging trends in 2026 but are not yet standard across incumbents.

Key Features

Experiment Logging and Protocols

Structured experiment entry templates
Protocol library and management
Data and file linking to experiments
Full-text search across experiments and protocols

Collaboration and Compliance

Real-time collaboration on entries
Audit trail with user tracking and timestamps
Role-based access control
PDF export and archive
Integration with common file formats

Instruments, Inventory, and LIMS

Integration with instruments and lab equipment
LIMS connectivity for samples and data
Inventory management
Version control with diff visualization
API for third-party integrations
Mobile app for field data capture

AI-Augmented Workflows

Automated protocol suggestions from experimental goals
Integration with analysis and visualization tools
Standardized metadata support (Dublin Core)

AI-Native Advantage

AI is used to extract and structure experimental results from instrument output files directly into ELN entries, removing manual transcription errors. A protocol recommendation engine suggests validated protocols based on experiment type, reagents, and available equipment. Anomaly detection flags outliers and reproducibility failures by comparing new results against historical runs of the same protocol. A natural-language parser converts free-text bench notes into structured records, and intelligent cross-linking builds a navigable knowledge graph connecting entries to inventory, protocols, datasets, and published papers.

Tech Stack & Deployment

The project targets both self-hosted and cloud deployment, mirroring the split between eLabFTW (self-host) and SaaS incumbents. Relevant standards include FDA 21 CFR Part 11, EU Annex 11, GLP, GMP, ALCOA+ data integrity principles, FAIR Data Principles, SBOL for synthetic biology workflows, and the ISA (Investigation/Study/Assay) framework. An open API and mobile clients are part of the v1.1 scope to support third-party integration and bench-side data capture.

Market Context

The global ELN market is estimated at approximately USD 1.2 billion in 2025, projected to reach USD 2.5–3.0 billion by 2030 at a CAGR of approximately 12–15%. Enterprise platforms cost $50,000–$500,000+/yr; mid-market tools such as Labstep Pro start around $20/user/mo and SciNote Team around $290/mo. Primary buyers include principal investigators and academic group leaders, biotech and pharma R&D scientists in regulated environments, lab managers, CROs, and graduate students and postdocs.

Project Status

This project is in the research and specification phase.
Contributions, feedback, and domain expertise are welcome.

Contributing

We welcome contributions from developers, domain experts, and potential users. See CONTRIBUTING.md for guidelines.

Important: All contributions must be your own original work or clearly attributed open-source material with a compatible licence. Copyright infringement and licence violations will not be tolerated and will result in immediate removal of the offending contribution. If you are unsure whether a piece of code, text, or other material is safe to contribute, open an issue and ask before submitting.

Licence

Licence to be determined. See discussion for context.