Kwiz-Computing-Technologies-Limited/Gbif-Kenya

# Comprehensive Data Quality Assessment and Cleaning: A Case Study Using GBIF Biodiversity Data from Kenya ## Overview This repository presents a **comprehensive, reproducible framework for data quality assessment and cleaning**, demonstrated through application to biodiversity occurrence data. Using Global Biodiversity Information Facility (GBIF) data from Kenya as a case study, this framework provides transparent documentation of quality control procedures, systematic bias assessment, and gap analysis methods that can be adapted to any large-scale observational dataset. The framework emphasizes: - **Transparent data cleaning workflows** with systematic documentation of all quality filters and their impacts - **Quantitative bias assessment** across spatial, temporal, and taxonomic dimensions - **Reproducible methods** that can be applied to other datasets, regions, or research domains - **Educational design** suitable for teaching data quality principles and reproducible research practices While demonstrated using GBIF biodiversity data from Kenya, the methods, visualizations, and quality control procedures implemented here serve as a general template for assessing and improving data quality in any large observational dataset with spatial, temporal, and categorical components. ## Key Features ### Data Quality Framework - **Systematic quality tracking**: Comprehensive documentation and quantification of data quality issues at each cleaning step - **Transparent quality reporting**: Detailed breakdown of records affected by each filter with counts and percentages - **Modular quality checks**: Independent tests for coordinate validity, taxonomic completeness, temporal consistency, and duplicates - **Sensitivity analysis**: Comparison of different filtering strategies to assess robustness ### Bias Assessment Methods - **Multi-dimensional bias analysis**: Spatial, temporal, and taxonomic dimensions assessed independently - **Statistical modeling**: GLMs and GAM …