Katie De Lange developed a targeted data-quality feature for the populationgenomics/production-pipelines repository, focusing on improving the consistency of genomic data processing. She implemented a conditional conversion mechanism in Python within dense_subset.py, ensuring that MatrixTable objects always include GT annotations by converting LGT fields when necessary. This approach enhanced data integrity and standardized outputs for downstream densification workflows, addressing a key compatibility requirement in genomics pipelines. Katie’s work leveraged her expertise in data processing, genomics, and the Hail framework, resulting in more maintainable and reliable data flows. During this period, her efforts were concentrated on feature development rather than bug fixes.