description Project Discovery: Flow Cytometry Overview
*Project Discovery: Flow Cytometry* is a 2020 citizen-science minigame integrated into the massively multiplayer online game *EVE Online*. It is distinguished by its crowdsourcing model, where players analyze in-game representations of flow cytometry data to identify and mark distinct cell populations. This player-generated data was utilized by researchers to refine machine learning models for COVID-19 research and immunology. It is designed for gamers looking to contribute to real-world science through gameplay.
help Project Discovery: Flow Cytometry FAQ
What was Project Discovery: Flow Cytometry in EVE Online?
Project Discovery: Flow Cytometry was a citizen-science minigame integrated directly into the EVE Online massively multiplayer game. Launched in 2020, the minigame tasked EVE players with analyzing real-world flow cytometry data to help scientists identify different populations of cells.
How did EVE Online players contribute to COVID-19 research via Project Discovery?
Players contributed by participating in the Flow Cytometry phase of Project Discovery, where they examined cell populations to help researchers understand human immune responses. Specifically, the data processed by the EVE Online community was used to aid in COVID-19 research and help scientists identify how human cells react to the virus.
Did players receive in-game rewards for playing Project Discovery: Flow Cytometry?
Yes, EVE Online players who accurately marked the cell populations in the Flow Cytometry minigame were rewarded with in-game currency and exclusive cosmetic items. Participants could earn Analysis Karma and various rewards, which could be exchanged for unique SKINs (Ship Kin Identifications) and specialized apparel.
Who were the real-world scientific partners for EVE Online's Flow Cytometry project?
The 2020 Flow Cytometry phase of Project Discovery was a collaboration between CCP Games and researchers at BC Cancer, located in Canada. The partnership allowed EVE Online players to process massive amounts of biological data, significantly speeding up the analysis required for human immunology research.
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