MISM Data and Metadata Intern

Position Summary

RENCI is seeking an intern to support the MISM platform effort by helping identify, collect, and organize metadata about relevant biomedical, infectious disease, immunology, and modeling resources. The intern will gather information about datasets, computational models, and related resources from sources such as the NIAID Data Ecosystem, biomedical model repositories, and ontology-driven standards such as the Biolink Model.

This work will support a proof of concept for constructing a knowledge graph that represents relationships among models, datasets, biological concepts, metadata standards, and ontologies. The resulting knowledge graph will be used to explore integration with CAIRNS semantic search capabilities, with the goal of improving model and data discovery across heterogeneous scientific resources.

The intern will work with the MISM technical team to:

• Identify relevant models, datasets, and metadata sources from NIAID-related resources and other biomedical data/model repositories.
• Gather structured metadata about models and datasets, including title, description, source, domain area, organism, disease area, data type, model type, inputs, outputs, assumptions, dependencies, licensing, provenance, and access information.
• Review existing metadata and ontology standards relevant to biomedical knowledge graphs, including the Biolink Model, which is designed to standardize biological knowledge graph entities and relationships. (biolink.github.io)
• Help define a practical metadata schema for representing models and datasets in the MISM platform.
• Map collected metadata to controlled vocabularies, ontologies, or knowledge graph predicates where appropriate.
• Assist with documenting how datasets, models, concepts, and ontologies relate to one another.
• Contribute to a small proof-of-concept knowledge graph that can be used to test semantic search and discovery workflows.
• Work with RENCI developers and research staff to prepare metadata for integration with CAIRNS semantic search.

By the end of the internship, the intern is expected to contribute to:

• A curated inventory of relevant models, datasets, and biomedical resources.
• A structured metadata spreadsheet, JSON, or similar machine‑readable metadata collection.
• A draft metadata schema for MISM model and dataset discovery.
• Notes comparing relevant metadata and ontology standards, including Biolink and other biomedical ontology resources.
• Example mappings between collected metadata fields and knowledge graph concepts.
• Documentation describing how the collected metadata could support knowledge graph construction and semantic search.
• A small proof‑of‑concept dataset suitable for use in a MISM/CAIRNS semantic search demonstration.

• Metadata curation for biomedical models and datasets.
• Ontology‑informed data modeling.
• Knowledge graph design for scientific discovery.
• FAIR data and model discovery principles.
• Semantic search workflows.
• Research cyberinfrastructure development in a collaborative software engineering environment.

Demonstrated possession of the competencies necessary to perform the work.

• Interest in biomedical data, computational modeling, infectious disease, immunology, or translational science.
• Familiarity with metadata standards, ontologies, controlled vocabularies, semantic web concepts, or biomedical data frameworks (e.g., FAIR principles, schema.org, Biolink Model, LinkML).
• Comfort working with structured data formats or graph‑based data models, including CSV, JSON, YAML, RDF, or knowledge graphs.
• Ability to read scientific or technical documentation or extract structured information.
• Experience with Python or other data wrangling tools.