Schema-driven framework for data submission, integration, and dissemination in the Pan-Canadian Genome Library
Bibliographic record
Abstract
The Pan-Canadian Genome Library (PCGL) will serve as a central repository for harmonized genomic and clinical datasets across Canada. It will enable researchers to conduct cross-study comparisons, enhance data-driven insights, and foster collaboration in precision medicine initiatives. Here, we present a modular, extensible schema framework for PCGL which is designed to support the submission, integration, and dissemination of heterogeneous clinical and genomic data from diverse domains, such as cancer, infectious diseases, and rare diseases. The framework consists of three schema types: Base, Extension, and Custom schemas. The Base schema, maintained by the Data Coordination Center (DCC) administrators, defines a set of common, standardized, and ontology-driven fields to ensure consistency and interoperability across datasets. This Base schema powers the Research Portal to provide a unified platform for researchers to explore, query and analyze harmonized datasets, so as to foster reproducible research within the PCGL ecosystem. For studies collecting additional attributes for their data and metadata, Extensions are collaboratively developed by DCC admins and data submitters to capture specialized elements. The Base and Extension schemas are then merged to create Custom schemas, which are tailored to individual studies. These Custom schemas are registered in a centralized Schema Registry for version control, validation, and governance. The Submission Portal which is built upon the registered Custom schemas, will support study-specific data submission and validation. This ensures that all submitted data not only aligns with PCGL standards through rigorous quality and compliance validations, but also enhances data diversity by incorporating unique attributes specific to each study. Our framework balances the need to enable comprehensive data submission with minimal burden on data submitters to ensure that the submission process remains efficient. While data submitted with the Extension schema is not searchable through Research Portal, both Base and Extension data remain fully downloadable to support further analysis and automated data curation at later stages. A key challenge lies in developing harmonization tools to assist data submitters in converting their datasets to the PCGL data format. Future work will focus on refining these tools and streamlining the mapping process between external data models and the PCGL schema, while also supporting interoperability with external standards such as FHIR, OMOP, and Phenopackets, to further enhance collaborative research across Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.021 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".