Spatial and temporal flux data coverage have improved significantly in recent years, due to standardization, automation and management of data collection, and better handling of the generated data. With more stations and networks, larger data streams from each station, and smaller operating budgets, modern tools are required to effectively and efficiently handle the entire process. These tools should produce standardized verifiable datasets, and provide a way to cross-share the standardized data with external collaborators to leverage available funding, and promote data analyses and publications. In 2015, new open-path and enclosed flux measurement systems1 were developed, based on established gas analyzer models2,3, with the goal of improving stability in the presence of contamination over older models4, refining temperature control and compensation5,6, providing more accurate gas concentration measurements1, and synchronizing analyzer and anemometer data streams in a very careful manner7. In late 2017, the new open-path system was further refined to simplify hardware configuration, to significantly reduce power consumption and cost, and to prevent or considerably minimize flow distortion8 in the anemometer to increase data coverage. Additionally, all new systems incorporate complete automated on-site flux calculations using EddyPro® Software9 run by a weatherized remotely-accessible microcomputer to provide standardized traceable data sets for fluxes and supporting variables. This presentation will describe details and results from the latest field tests of the new flux systems, in comparison to older models and control reference instruments. References: 1 Burba G., W. Miller, I. Begashaw, G. Fratini, F. Griessbaum, J. Kathilankal, L. Xu, D. Franz, E. Joseph, E. Larmanou, S. Miller, D. Papale, S. Sabbatini, T. Sachs, R. Sakai, D. McDermitt, 2017. Comparison of CO2 Concentrations, Co-spectra and Flux Measurements between Latest Standardized Automated CO2/H2O Flux Systems and Older Gas Analysers. 10th ICDC Conference, Switzerland: 21-25/08 2 Metzger, S., G. Burba, S. Burns, P. Blanken, J. Li, H. Luo, R. Zulueta, 2016. Optimization of an enclosed gas analyzer sampling system for measuring eddy covariance fluxes of H2O and CO2. AMT, 9: 1341-1359 3 Burba, G., 2013. Eddy Covariance Method for Scientific, Industrial, Agricultural and Regulatory Applications. LI-COR Biosciences: 331 pp. 4 Fratini, G., McDermitt, D.K. and Papale, D., 2014. Eddy-covariance flux errors due to biases in gas concentration measurements: origins, quantification and correction. Biogeosciences, 11(4), pp.1037-1051. 5 McDermitt, D., J. Welles, and R. Eckles, 1993. Effects of temperature, pressure, and water vapor on gas phase infrared absorption by CO2. LI-COR, Inc. Lincoln, NE. 6 Welles, J. and D. McDermitt, 2005. Measuring carbon dioxide in the atmosphere. In: Hatfield J. and J. Baker (Eds.) Micrometeorology in Agricultural Systems. ASA-CSSA-SSSA, Madison, W