Agentic Metadata Template Creation for Standard Lab Equipment

Problem Statement:

Scientific laboratories heavily rely on specialized equipment from a limited number of manufacturers. These machines, such as electron microscopes, mass spectrometers, and sequencing devices, produce standardized data outputs which can be labeled by the manufacturer's detailed specifications documents and owner manuals provided alongside the machine. Once a metadata template repository exists for machine types and models, those same templates can easily be reused for the purpose of creating interoperable data.

Challenge:

Develop a system that can take as input the manufacturer-provided specification document for a specific scientific instrument and generate a robust, structured metadata template for the machine's data outputs. This system should leverage the detailed specifications to create a template that ensures data consistency and facilitates cross-laboratory data sharing.

Detailed Description:

• Science on-chain and decentralized Web Integration (Critical):

  • Participants should make every effort to integrating their tooling plugins with decentralized web technologies (e.g., IPFS, Solana, etc) to enhance data provenance, security, and accessibility. Science on-chain is one of the most important goals of this hackathon, it starts with base tooling.

  • Information should be as open as possible and only as closed as necessary. Moving science on-chain with a system default of Open is critical in designing new systems for research. While closing off information is often necessary, it should be a conscious choice made by a researcher which requires extra effort.

• Manufacturer-Specific Focus:

  • The system should be designed to handle specification documents from specific manufacturers (e.g., Thermo Fisher, Zeiss, Agilent). Mass Spectrometer and Specification Sheet as an example

  • It should recognize and adapt to the specific formats and terminologies used by these manufacturers.

• Detailed Specification Parsing:

  • The system must parse comprehensive specification documents, which often include:

    • Detailed descriptions of data output formats (e.g., file types, data structures).

    • Precise definitions of measurement parameters and units.

    • Information about machine settings and experimental conditions.

    • Calibration and quality control procedures.

    • Information regarding the software that is used to generate the data output.

  • The system should be able to handle various document formats (e.g., PDFs, technical manuals, XML schemas).

• Structured Metadata Template Generation:

  • Generate metadata templates that are:

    • Machine-readable (e.g., JSON-LD or RDF Schema are preferred).

    • Comprehensive, covering all relevant data fields.

    • Standardized, using established vocabularies and ontologies where possible.

    • Include data validation rules (e.g., data types, ranges, allowed values).

• Key Metadata Field Extraction:

  • The system should automatically identify and extract crucial metadata fields, including:

    • Instrument model and serial number.

    • Manufacturer-specific parameters and settings.

    • Data acquisition parameters (e.g., resolution, sampling rate).

    • Units of measurement (e.g., nanometers, volts, Hertz).

    • Data provenance (e.g., operator, date, time, experiment ID).

    • File format and data structure details.

    • Relevant software versioning used to create the data.

    • Mappings to relevant ontologies.

• Output:

  • A manufacturer-specific metadata template.

  • A clear, human-readable document explaining the template.

  • A validation tool to ensure data compliance.

• Potential Technologies:

  • Advanced NLP techniques for parsing technical documents.

  • Schema definition languages (JSON Schema, XML Schema).

  • Ontology mapping tools and libraries.

  • Libraries that handle specific scientific file formats.

• Evaluation Metrics:

  • Accuracy of metadata extraction from manufacturer specifications.

  • Completeness and adherence to manufacturer standards.

  • Interoperability of generated metadata.

  • User-friendliness of the generated templates.