
Yuting She contributed to the OHDSI/Data2Evidence repository by building and enhancing data engineering workflows focused on analytics readiness and data quality. Over three months, Yuting developed an end-to-end HANA load plugin for OMOP CDM 5.3, using Python and SQL to automate dataset extraction, schema creation, and data loading. She improved data integrity by addressing naming standards and handling NULL values, and introduced file upload and management features leveraging Supabase Storage and Node.js. Her work also integrated R-based Artemis templates and configurable CSV loading, strengthening data transformation, ingestion flexibility, and version tracking. The solutions delivered robust, maintainable pipelines for research and reporting.
Monthly summary for 2025-12 (OHDSI/Data2Evidence). Delivered two major features and several reliability improvements that strengthen data transformation, ingestion flexibility, and governance. Artemis Template Integration and Data Transformation Enhancements introduced R packages and Dockerfile updates to enable Artemis-based data transformations across the project. Data Loading Improvements introduced a configurable CSV loading option and enhanced dataset versioning with schema checks, improving reproducibility and data quality. Added get_version_info to support version tracking for datasets and deployments. Overall impact includes faster onboarding of Artemis templates, more reliable data ingestion, and traceable data versions, delivering measurable business value and stronger technical foundations.
Monthly summary for 2025-12 (OHDSI/Data2Evidence). Delivered two major features and several reliability improvements that strengthen data transformation, ingestion flexibility, and governance. Artemis Template Integration and Data Transformation Enhancements introduced R packages and Dockerfile updates to enable Artemis-based data transformations across the project. Data Loading Improvements introduced a configurable CSV loading option and enhanced dataset versioning with schema checks, improving reproducibility and data quality. Added get_version_info to support version tracking for datasets and deployments. Overall impact includes faster onboarding of Artemis templates, more reliable data ingestion, and traceable data versions, delivering measurable business value and stronger technical foundations.
November 2025 performance summary for OHDSI/Data2Evidence focused on delivering new data handling capabilities and data transformation improvements that drive downstream business value. The month emphasized feature delivery with concrete outcomes around storage-enabled asset management and data ingestion/transformation via URL-based data retrieval, improving data workflows and enablement for analytics.
November 2025 performance summary for OHDSI/Data2Evidence focused on delivering new data handling capabilities and data transformation improvements that drive downstream business value. The month emphasized feature delivery with concrete outcomes around storage-enabled asset management and data ingestion/transformation via URL-based data retrieval, improving data workflows and enablement for analytics.
September 2025 performance: OHDSI/Data2Evidence delivered an end-to-end Hana load plugin for OMOP CDM 5.3 on HANA and hardened data loading to ensure reliability and data quality. The work included Python scripts and SQL assets to download datasets, extract data, create the target schema, and load data into HANA. Key stability and data integrity fixes were implemented to address naming standards, cleanup local storage after loading, and proper handling of NULL VOCABULARY_ID values in the vocabulary and concept tables. The combined effort accelerates analytics readiness on HANA, reduces manual steps, and improves data accuracy for downstream reporting and research. Technologies include Python, SQL, ETL tooling, and HANA-specific data modeling for OMOP CDM 5.3.
September 2025 performance: OHDSI/Data2Evidence delivered an end-to-end Hana load plugin for OMOP CDM 5.3 on HANA and hardened data loading to ensure reliability and data quality. The work included Python scripts and SQL assets to download datasets, extract data, create the target schema, and load data into HANA. Key stability and data integrity fixes were implemented to address naming standards, cleanup local storage after loading, and proper handling of NULL VOCABULARY_ID values in the vocabulary and concept tables. The combined effort accelerates analytics readiness on HANA, reduces manual steps, and improves data accuracy for downstream reporting and research. Technologies include Python, SQL, ETL tooling, and HANA-specific data modeling for OMOP CDM 5.3.

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