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Challenge

Build a Research Specimen Chain of Custody and Terminology Reconciler

Align biological specimen identifiers against NCBI gene taxons and assemble FHIR Specimen resources. Audit collection records for standard terminology conformance before registry federation.

Data ScienceHosted by Vera
Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Align biological specimen identifiers against NCBI gene taxons and assemble FHIR Specimen resources. Audit collection records for standard terminology conformance before registry federation.

Biobank Operations Manager

Ingest biobank manifest rows cross-referenced with NCBI Datasets taxonomy IDs.

Verify species nomenclature against NCBI taxonomy records using Biopython Entrez parsers.

Map collection site and specimen handling codes into standard SNOMED research concepts.

Format validated records into standard FHIR R5 Specimen resource bundles.

Multi-pane dashboard displaying specimen reconciliation grids and FHIR JSON preview panels.

Ingest public synthetic biobank manifests mapped to NCBI Datasets taxon IDs like 'human' (Taxonomy ID 9606). Starter examples below are synthetic. They are not downloaded records.

HL7 FHIR overview Applies FHIR R5 Foundation and Specimen resource definitions with standardized terminology bindings.

pydantic-ai, biopython, fastapi. Pydantic AI guarantees strictly validated FHIR schema outputs conforming to specimen data contracts. Fast cloud LLM API or local small model with JSON schema enforcement.

Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.

Generates FHIR Specimen resources matching HL7 R5 core element requirements.

Detects unmapped or invalid species taxonomy identifiers prior to FHIR bundle generation.

Emits validation error diagnostics when mandatory collection body site attributes are omitted.

```json [ { "input": { "specimen_id": "SPEC-9912", "taxon_name": "human", "sample_type": "PBMC", "collection_site": "peripheral_venous_blood" }, "expected_output": { "resourceType": "Specimen", "id": "SPEC-9912", "status": "available", "type": { "coding": [ { "system": "http://snomed.info/sct", "code": "119297000", "display": "Blood specimen" } ] }, "subject": { "identifier": { "system": "https://ncbi.nlm.nih.gov/taxonomy", "value": "9606" } } } }, { "input": { "specimen_id": "SPEC-MISSING-SITE", "taxon_name": "human", "sample_type": "Unknown", "collection_site": null }, "expected_output": { "resourceType": "OperationOutcome", "issue": [ { "severity": "error", "code": "required", "diagnostics": "Missing mandatory collection site or sample type mapping" } ] } } ] ```

Deidentified synthetic data only; no Protected Health Information is ingested or stored. Use mock external writes. Require human review before any real deployment.

Rules

Participation guide

Deidentified synthetic data only; no Protected Health Information is ingested or stored. Use mock external writes and human review.

Delivery guide

How work is evaluated

Evaluation

Generates FHIR Specimen resources matching HL7 R5 core element requirements. Detects unmapped or invalid species taxonomy identifiers prior to FHIR bundle generation. Emits validation error diagnostics when mandatory collection body site attributes are omitted.

Submission format

Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
4 scoring checks
Binary
1 pass or fail dimensions
Ordinal
3 scaled dimensions
Dimension 1

Complete workflow

Ingest biobank manifest rows cross-referenced with NCBI Datasets taxonomy IDs. Verify species nomenclature against NCBI taxonomy records using Biopython Entrez parsers. Map collection site and specimen handling codes into standard SNOMED research concepts. Format validated records into standard FHIR R5 Specimen resource bundles.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 2

Correctness and evidence

Generates FHIR Specimen resources matching HL7 R5 core element requirements. Detects unmapped or invalid species taxonomy identifiers prior to FHIR bundle generation. Emits validation error diagnostics when mandatory collection body site attributes are omitted.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 3

Review interface

Multi-pane dashboard displaying specimen reconciliation grids and FHIR JSON preview panels.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 4

Safe execution

Deidentified synthetic data only; no Protected Health Information is ingested or stored.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Ingest biobank manifest rows cross-referenced with NCBI Datasets taxonomy IDs.

  • Verify species nomenclature against NCBI taxonomy records using Biopython Entrez parsers.

  • Map collection site and specimen handling codes into standard SNOMED research concepts.

  • Format validated records into standard FHIR R5 Specimen resource bundles.

Resources and assets

Reference links and supporting material

Dataset notes

Ingest public synthetic biobank manifests mapped to NCBI Datasets taxon IDs like 'human' (Taxonomy ID 9606). Supplied starter examples are synthetic, not source extracts.

Challenge setup

Configuration summary for this challenge.

Challenge input

Input details are not configured.

Pydantic AI

Typed Python agent framework.

Evaluated output

No output checks are configured.

Evaluation checks
  • Ingest biobank manifest rows cross-referenced with NCBI Datasets taxonomy IDs. Verify species nomenclature...
  • Generates FHIR Specimen resources matching HL7 R5 core element requirements. Detects unmapped or invalid sp...
  • Multi-pane dashboard displaying specimen reconciliation grids and FHIR JSON preview panels.
Configured evaluation evidence
  • 2 public reference cases
Configured runtime
  • 2 configured tools

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Pydantic AI · Optional
Orchestration
  • Pydantic AI · Optional

Evaluation contract

  • Complete workflow · Weight 40
  • Correctness and evidence · Weight 30
  • Review interface · Weight 20
  • Safe execution · Weight 10

Reference cases

  • 2 public reference cases

Workflow tasks

No workflow tasks are configured.

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build a Research Specimen Chain of Custody and Terminology Reconciler