DivShift: Exploring Domain-Specific Distribution Shifts in Large-Scale, Volunteer-Collected Biodiversity Datasets

1 code implementation • 17 Oct 2024 • Elena Sierra, Lauren E. Gillespie, Salim Soltani, Moises Exposito-Alonso, Teja Kattenborn

Here we introduce Diversity Shift (DivShift), a framework for quantifying the effects of domain-specific distribution shifts on machine learning model performance.

Deep Learning Fine-Grained Image Classification

grenepipe: A flexible, scalable, and reproducible pipeline to automate variant and frequency calling from sequence reads

1 code implementation • 28 Mar 2021 • Lucas Czech, Moises Exposito-Alonso

Processing high-throughput DNA sequencing data of individuals or populations requires stringing together independent software tools with many parameters, often leading to non-reproducible pipelines and datasets.