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David Durden

Researcher at National Ecological Observatory Network

Publications -  24
Citations -  366

David Durden is an academic researcher from National Ecological Observatory Network. The author has contributed to research in topics: Eddy covariance & Atmosphere. The author has an hindex of 8, co-authored 17 publications receiving 142 citations. Previous affiliations of David Durden include Battelle Memorial Institute.

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Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites

Housen Chu, +74 more
TL;DR: In this article, the authors evaluate the representativeness of flux footprints and evaluate potential biases as a consequence of the footprint-to-target-area mismatch, which can be used as a guide to identify site-periods suitable for specific applications.
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Connecting Land–Atmosphere Interactions to Surface Heterogeneity in CHEESEHEAD19

TL;DR: The Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019.
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eddy4R 0.2.0: a DevOps model for community-extensible processing and analysis of eddy-covariance data based on R, Git, Docker, and HDF5

TL;DR: The systematic development of portable, reproducible, and extensible EC software achieved by adopting a development and systems operation (DevOps) approach is presented and the modular extensibility of eddy4R to analyze EC data from other platforms is demonstrated.
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From NEON Field Sites to Data Portal: A Community Resource for Surface–Atmosphere Research Comes Online

TL;DR: The National Ecological Observatory Network (NEON) as discussed by the authors is a multidecadal and continental-scale observatory with sites across the United States, which was established by the National Geographic Society.
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A Robust Calibration Method for Continental-Scale Soil Water Content Measurements

TL;DR: In this article, the authors developed an approach to make consistent and comparable soil water content sensor calibrations across a continental-scale network in a production framework that incorporates a thorough accounting of uncertainties.