
During November 2024, Shyi developed and integrated a new genetic dataset input for the LiLabAtVT/I2GDS2024 repository to support R Studio-based analytics workflows. Shyi curated and formatted the arg_pre_abs.csv file using CSV data management techniques, structuring gene presence and absence data across isolate IDs for downstream analysis and visualization. The work emphasized reproducibility and auditability by committing the dataset addition with clear version control. By enabling immediate use of the data within R Studio, Shyi streamlined gene-level analytics pipelines. The project demonstrated proficiency in data curation, repository management, and structured data integration, though the scope was focused on a single feature.
Monthly summary for 2024-11 focusing on key deliverables, impact, and technical accomplishments for LiLabAtVT/I2GDS2024. Key feature delivered this month: added a new genetic dataset input for R Studio analysis to streamline downstream analytics and visualizations. All changes are trackable via a single commit reference, ensuring reproducibility and auditability.
Monthly summary for 2024-11 focusing on key deliverables, impact, and technical accomplishments for LiLabAtVT/I2GDS2024. Key feature delivered this month: added a new genetic dataset input for R Studio analysis to streamline downstream analytics and visualizations. All changes are trackable via a single commit reference, ensuring reproducibility and auditability.

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