exptM

ExptM is a cross-disciplinary experimental management framework designed to standardize the documentation, execution, and analysis of scientific experiments. It aims to improve reproducibility and data interoperability by providing a common data model, workflow orchestration, and integration with analysis pipelines. The project emphasizes openness, modularity, and community governance.

The core of exptM is a modular data model that represents experiments, protocols, samples, measurements, conditions, equipment, and results. Runs link specific protocol executions to observations, and the system supports versioning of definitions and audit trails. A JSON-based schema and ontological mappings enable interoperability across platforms, while a plugin system allows extensions for domain-specific instruments or analysis tools.

ExptM includes a workflow engine to orchestrate experiment steps, automatic capture of metadata, and provenance tracking to trace data back to the original protocol and conditions. It also offers an API for data ingestion, query, visualization, and integration with data repositories, ELNs, and analysis environments.

Development is led by an open-source consortium, with governance by a maintainer council and community contributions. The project publishes documentation, tutorials, and reference implementations to aid adoption. It is designed to work alongside existing lab workflows rather than replace them.

In practice, exptM has been applied in fields such as biology, materials science, and cognitive science to improve traceability and reproducibility. Critics note the learning curve and the need for careful standardization across domains. Ongoing work focuses on expanding ontologies, performance at scale, and deeper integration with laboratory instrumentation.