<p><span lang="EN-US">The eddy covariance method provides important insights about CO<sub>2</sub>, water and energy exchange-related processes on the ecosystem scale level. Data are collected quasi-continuously with sampling frequency 10 Hz at minimum, often throughout multiple years, producing large datasets. Standard data processing methods are already devised but undergo continuous refinements that should be reflected in the available software. Currently, a suite of software packages is available for computation of half-hourly products from high frequency raw data. However, software packages consolidating the further post-processing computations are not yet that common. The post-processing steps can consist of quality control, footprint modelling, computation of storage fluxes, gap-filling, flux partitioning and data aggregation. Also they can be realized in different programming languages and require various input data formats. Users would therefore often evaluate only certain aspects of the dataset which limits the amount of extractable information from obtained data and they possibly omit the features that could affect data quality or interpretation. Here we present the free R software package openeddy (<a href="https://github.com/lsigut/openeddy">https://github.com/lsigut/openeddy</a>) that provides utilities for input data handling, extended quality control checks, data aggregation and visualization and that includes a workflow (<a href="https://github.com/lsigut/EC_workflow">https://github.com/lsigut/EC_workflow</a>) that attempts to integrate all post-processing steps through incorporation of other free software packages, such as REddyProc (<a href="https://github.com/bgctw/REddyProc/">https://github.com/bgctw/REddyProc/</a>). The framework is designed for the standard set of eddy covariance fluxes, i.e. of momentum, latent and sensible heat as well as CO<sub>2</sub>. Special attention was paid to the visualization of results at different stages of processing and at different time resolutions and aggregation steps. This allows to quickly check that computations were performed as expected and it also helps to notice issues in the dataset itself. Finally, the proposed folder structure with defined post-processing steps allows to organize data in different stages of processing for improved ease of use. Produced workflow files document the whole processing chain and its possible adaptations for a given site. We believe that such a tool can be particularly useful for eddy-covariance novices, groups that cannot or do not contribute their data to regional networks for further processing or users that want to evaluate their data independently. This or similar efforts can also help to save human resources or speed up the development of new methods.</span></p>
<p><span lang="EN-US">This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the CzeCOS program, grant number LM2015061, and within Mobility CzechGlobe 2, grant number CZ.02.2.69/0.0/0.0/18_053/0016924.</span></p>
Edible Bird’s Nest: Recent Updates and Industry Insights Based On Laboratory Findings
