sampling-bias

An interactive framework to visualize and analyze your AutoML process in real-time.

Official Code for "Non-Probability Sampling Network for Stochastic Human Trajectory Prediction (CVPR 2022)"

Faster, better, smarter ecological niche modeling and species distribution modeling

Portfolio-grade audit of a student mental health & academic pressure survey. Measures coverage and sample imbalance, runs validity checks, highlights measurement and selection bias risks, and converts messy open-text “stress causes” into a transparent taxonomy. Ships a Markdown report, figures, and a Streamlit dashboard.

Longform data analysis article arguing every “dataset” is actually three: Observed (captured rows), Missing (what should exist but doesn’t), and Excluded (what filters/joins/dropna removed). Includes dataset accounting, join-loss and missingness audits, segmentation checks, and practical templates to prevent biased KPIs and wrong conclusions.

R code used for the analyses of the paper: Spatial conservation prioritisation in data-poor countries: a quantitative sensitivity analysis using different taxa

Pipelines to evaluate Breast Cancer Purity Score and to correct sampling bias

Efficient Multistream Classification using Direct DensIty Ratio Estimation

A global, taxon-stratified, high-resolution sampling-effort dataset from GBIF for bias-aware ecological modelling — reproducible R-based workflow for generating global sampling-effort rasters from GBIF occurrence records across spatial, temporal, and taxonomic scales. For more details, see El-Gabbas (2026), Diversity and distributions (accepted).

This project presents an exploratory and statistical analysis of amphibian occurrence records in Brazil using data from the Global Biodiversity Information Facility (GBIF).

🚀📐 Representación gráfica de distribución de muestreos aleatorios.

Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inference

Simulate the impact of sampling bias in the derivation of effect sizes

📊 Evaluate survey quality and bias through coverage, representativeness, and measurement risk audits for reliable insights and data validity checks.