Abstract

Atmospheric simulationchambers continue to be indispensable toolsfor research in the atmospheric sciences. Insights from chamber studiesare integrated into atmospheric chemical transport models, which areused for science-informed policy decisions. However, a centralizeddata management and access ... Show moreAtmospheric simulationchambers continue to be indispensable toolsfor research in the atmospheric sciences. Insights from chamber studiesare integrated into atmospheric chemical transport models, which areused for science-informed policy decisions. However, a centralizeddata management and access infrastructure for their scientific productshad not been available in the United States and many parts of theworld. ICARUS (Integrated Chamber Atmospheric data Repository forUnified Science) is an open access, searchable, web-based infrastructurefor storing, sharing, discovering, and utilizing atmospheric chamberdata [https://icarus.ucdavis.edu]. ICARUS has two parts: a data intake portal and a search and discoveryportal. Data in ICARUS are curated, uniform, interactive, indexedon popular search engines, mirrored by other repositories, version-tracked,vocabulary-controlled, and citable. ICARUS hosts both legacy dataand new data in compliance with open access data mandates. Targeteddata discovery is available based on key experimental parameters,including organic reactants and mixtures that are managed using thePubChem chemical database, oxidant information, nitrogen oxide (NOx)content, alkylperoxy radical (RO2) fate, seed particleinformation, environmental conditions, and reaction categories. Adiscipline-specific repository such as ICARUS with high amounts ofmetadata works to support the evaluation and revision of atmosphericmodel mechanisms, intercomparison of data and models, and the developmentof new model frameworks that can have more predictive power in thecurrent and future atmosphere. The open accessibility and interactivenature of ICARUS data may also be useful for teaching, data mining,and training machine learning models. Show less