Crowdsourced Flow Cytometry Dataset from EVE Online’s Project Discovery for Machine Learning Applications

This dataset contains a diverse collection of pre-processed flow cytometry data assembled to support the training and evaluation of machine learning (ML) models for the gating of cell populations. The data was curated through a citizen science initiative embedded in the EVE Online video game, known as _Project Discovery_. Participants contributed to scientific research by gating bivariate plots generated from flow cytometry data, creating a crowdsourced reference set. The original flow cytometry datasets were sourced from publicly available COVID-19 and immunology-related studies on FlowRepository.org and PubMed. Data were compensated, transformed, and split into bivariate plots for analysis. This datset includes: 1) CSV files containing two-channel marker combinations per plot, 2) A SQL database capturing player-generated gating polygons in normalized coordinates, 3) Scripts and containerized environments (Singularity and Docker) for reproducible evaluation of gating accuracy and consensus scoring using the `flowMagic` pipeline, 4) Code for filtering bot inputs, evaluating user submissions, calculating F1 scores, and generating consensus gating regions. This data is especially valuable for training and benchmarking models that aim to automate the labor-intensive gating process in immunological and clinical cytometry applications.