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6,467 results for “process”
Modeling Flocculation Processes of Cohesive Sediment Through Moment Methodology
<p>The data were used in the paper "Modeling Flocculation Processes of Cohesive Sediment Through Moment Methodology" which was submitted to "<em>Water Resources Research</em>". In this paper, a new mechanistic Lagrangian-type flocculation dynamic model was proposed by solving the population balance equation with moment methodology and similarity transformation, and the influence of particles' surface heterogeneities (<em>F</em><sub>2a</sub>) was incorporated. Furthermore, the roles of shear rate, sediment concentration, particle size and surface heterogeneity were investigated theoretically.</p>
Data for: Historical and contemporary processes drive global phylogenetic structure across geographical scales: Insights from bat communities
<p><strong>Aim</strong>: Patterns of evolutionary relatedness among co-occurring species are driven by scale-dependent contemporary and historical processes. Yet, we still lack a detailed understanding of how these drivers impact the phylogenetic structure of biological communities. Here, we focused on bats – one of the most speciose and vagile groups of mammals – and test the predictions of three general biogeographical hypotheses that are particularly relevant to understanding how paleoclimatic stability, local diversification rates, and geographical scales shaped their present-day phylogenetic community structure.</p> <p><strong>Location</strong>: Worldwide, across restrictive geographical extents: global, east-west hemispheres, biogeographical realms, tectonic plates, biomes, and ecoregions.</p> <p><strong>Time period</strong>: Last Glacial Maximum (~22,000 years ago) to the present.</p> <p><strong>Major taxa studied</strong>: Bats (Chiroptera)</p> <p><strong>Methods</strong>: We estimated bat phylogenetic community structure across restrictive geographical extents and modelled it as a function of paleoclimatic stability, and in situ net diversification rates.</p> <p><strong>Results</strong>: Limiting geographical extents from larger to smaller scales strongly changed the phylogenetic structure of bat communities. The magnitude of these effects is less noticeable in the western hemisphere, where frequent among-realm biota interchange could have been maintained through bats' adaptive traits. Highly phylogenetically related bat communities are generally more common in regions that changed less in climate since the last glacial maximum, supporting the expectation that stable climates allow for increased phylogenetic clustering. Finally, increased in situ net diversification rates are associated with greater phylogenetic clustering in bat communities.</p> <p><strong>Main conclusions</strong>: We show that the worldwide phylogenetic structure of bat assemblages varies as a function of geographical extents, dispersal barriers, paleoclimatic stability and in situ diversification. The integrative framework used in our study, which can be applied to other taxonomic groups, has proven useful to not only explain the evolutionary dynamics of community assembly but could also help tackle questions related to scale dependence in community ecology and biogeography.</p>
Extraction of clinical phenotypes for Alzheimer disease dementia from clinical notes using natural language processing
<p><strong>Objectives</strong></p> <p>There is much interest in utilizing clinical data for developing prediction models for Alzheimer disease (AD) risk, progression, and outcomes. Existing studies have mostly utilized curated research registries, image analysis, and structured Electronic Health Record (EHR) data. However, much critical information resides in relatively inaccessible unstructured clinical notes within the EHR.</p> <p><strong>Materials and Methods</strong></p> <p>We developed a natural language processing (NLP)-based pipeline to extract AD-related clinical phenotypes, documenting strategies for success and assessing the utility of mining unstructured clinical notes. We evaluated the pipeline against gold-standard manual annotations performed by two clinical dementia experts for AD-related clinical phenotypes including medical comorbidities, biomarkers, neurobehavioral test scores, behavioral indicators of cognitive decline, family history, and neuroimaging findings.</p> <p><strong>Results</strong></p> <p>Documentation rates for each phenotype varied in the structured versus unstructured EHR. Inter-annotator agreement was high (Cohen's kappa = 0.72–1) and positively correlated with the NLP-based phenotype extraction pipeline's performance (average F1-score = 0.65-0.99) for each phenotype.</p> <p><strong>Discussion</strong></p> <p>We developed an automated NLP-based pipeline to extract informative phenotypes that may improve the performance of eventual machine-learning predictive models for AD. In the process, we examined documentation practices for each phenotype relevant to the care of AD patients and identified factors for success.</p> <p><strong>Conclusion</strong></p> <p>Success of our NLP-based phenotype extraction pipeline depended on domain-specific knowledge and focus on a specific clinical domain instead of maximizing generalizability. </p>
Piezomagnetic Fields Generated by the Gas Injection Process in Hutubi Ultralarge Underground Gas Storage System (China)
<p>All data used in paper "Piezomagnetic Fields Generated by the Gas Injection Process in Hutubi Ultralarge Underground Gas Storage System (China)".</p>
Supplementary data for: "Towards automatic generation of control structures for Process Flow Diagrams (PFDs) with Artificial Intelligence"
<p>Uploaded on 20. February 2023</p> <p>This is the supplementary data for the publication</p> <p>"Towards automatic generation of control structures for Process Flow Diagrams (PFDs) with Artificial Intelligence" (2023) by Edwin Hirtreiter, Lukas Schulze Balhorn, Artur M. Schweidtmann</p> <p>Corresponding author: A. M. Schweidtmann, E-mail: a.schweidtmann@tudelft.nl<br> Delft University of Technology, Department of Chemical Engineering, Process Intelligence Group, Van der Maasweg 9, 2629 HZ Delft, The Netherlands</p> <p>The folder contains json files with the training (train), test (test), and augmented training (train_augm) data files. The json files contain syntetically generated SFILES. </p> <p>The pre-print of the manuscript is accessible at https://doi.org/10.48550/arXiv.2211.05583</p>
M81 post-processed results from Lightning SED fitting
<p>The post-processed results from the Lightning SED fitting code as fit by the authors for the spatially resolved map of M81. The results are for the MCMC sampler (m81_map_mcmc.fits.gz) and the MPFIT minimizer (m81_map_mpfit.fits.gz). Fitting of this example was performed on a 32-core node of the Pinnacle cluster at the Arkansas High Performance Computing Center.</p>
Earthquake catalogs for: A specific earthquake processing workflow for studying long-lived explosive volcanic eruptions with application to the 2008 Okmok eruption
<p>Repository for the seismic catalogs from Garza-Giron et al. (2023a,b). These include the catalog with absolute locations using NonLinLoc (Lomax et al., 2001; Lomax and Curtis, 2001), and the relocated catalogs using hypoDD (Waldhauser and Ellsworth, 2000) and GrowClust (Trugman and Shearer, 2017).</p> <p>The header of the CSV files is as follows:</p> <p><strong>Date</strong> (year/month/day), <strong>Time</strong> (hr:min:sec:msec), <strong>Latitude</strong> (decimal degrees), <strong>Longitude</strong> (decimal degrees), <strong>Depth</strong> (km), <strong>Magnitude</strong> (Ml calculated for this study), <strong>Event_type</strong> (VT:vulcano-tectonic;LP:long-period), <strong>Number of stations</strong> where the event was detected, <strong>ID</strong></p> <p>References:</p> <div>Garza‐Giron, R., Brodsky, E. E., Spica, Z. J., Haney, M. M., & Webley, P. W. (2023a). A specific earthquake processing workflow for studying long‐lived, explosive volcanic eruptions with application to the 2008 Okmok Volcano, Alaska, eruption. <em>Journal of Geophysical Research: Solid Earth</em>, e2022JB025882.</div> <div> </div> <div> <div>Garza‐Girón, R., Brodsky, E. E., Spica, Z. J., Haney, M. M., & Webley, P. W. (2023b). Earthquakes record cycles of opening and closing in the enhanced seismic catalog of the 2008 Okmok Volcano, Alaska, eruption. <em>Journal of Geophysical Research: Solid Earth</em>, <em>128</em>(7), e2023JB026893.</div> <div> </div> </div> <p>Lomax A, Curtis A (2001) Fast, probabilistic earthquake location in 3-D models using oct-tree importance sampling. Geophys Res Abstracts, 3:955.</p> <p>Lomax, A., Zollo, A., Capuano, P., and Virieux, J. (2001). Precise, absolute earthquake location under Somma‐Vesuvius volcano using a new 3D velocity model.Geophysical Journal International,146, 313–331.</p> <p>Trugman, D. T., and Shearer, P. M. (2017). GrowClust: A hierarchical clustering algorithm for relative earthquake relocation, with application to the Spanish Springs and Sheldon, Nevada, earthquake sequences. Seismological Research Letters, 88(2A), 379-391.</p> <p>Waldhauser, F., and Ellsworth, W. L. (2000). A double-difference earthquake location algorithm: Method and application to the northern Hayward fault, California. Bulletin of the Seismological Society of America, 90(6), 1353-1368.</p>
IMPROVER: the new probabilistic post processing system at the UK Met Office: BAMS paper Data
<p>© Crown Copyright, Met Office</p> <p>This is the data associated with the figures in the IMPROVER BAMS paper 2023: <a href="https://doi.org/10.1175/BAMS-D-21-0273.1">https://doi.org/10.1175/BAMS-D-21-0273.1</a>.</p> <p>Gridded data is in CF-NetCDF with reasonably self explanatory metadata, other data such as for Figure 9's wind speed calibration is in CSV.</p>
Tissue microarray data and processing scripts for The molecular consequences of androgen activity in the human breast
<p>This repository contains raw and processed data from the CODEX imaging dataset in this publication.</p> <p>The RAW data tables provide the resulting nuclei and membrane staining signals obtained from the nuclei segmentation described in the Methods.</p> <p>The processed data file provides the clustered and annotated version described in Methods.</p> <p>The repository also contains two scripts describing the processing of snRNA-seq and snATAC-seq data.</p>
The turnover of plant-frugivore interactions along plant range expansion: consequences for natural colonisation processes
<p><span>Plant-animal mutualisms such as seed dispersal are key interactions for sustaining plant range shifts. Whether the organisation of interactions with seed dispersers is reconfigured along the expansion landscape template, and its effects accelerating or slowing colonisation, remain elusive. Here we analyse plant-frugivore interactions in a scenario of rapid population expansion of a Mediterranean juniper. We combined complex network analyses with intensive field surveys, sampling interactions between individual plants and frugivores by DNA-Barcoding and phototrapping over two seasons. We assess the role of intrinsic and extrinsic intraspecific variability in shaping interactions and we estimate the contribution of individual plants to seed rain. The whole interaction network was highly structured, with a distinct set of modules including individual plants and frugivore species arranged concordantly along the expansion gradient. The modular configuration found was partially shaped by individual neighbourhood context (density and fecundity) and phenotypic traits (cone size). Interaction reconfiguration resulted in a higher and uneven contribution to seed dispersal rain by individuals of the expansion boundaries, providing signals of the colonisation local-history. Our study provides novel insights into the key role of mutualistic interactions in colonisation scenarios by promoting fast plant expansion processes.</span></p>
A Note on Aerosol Processing by Droplet Collision-Coalescence
<p>Simulation results for the above-mentioned publication. The files *_out.nc contain spectral data, *_out_time.nc time series. The prefixes small, medium, and large refer to the initial aerosol size distribution.</p>
GMAW & WAAM Process monitoring using XARION Eta300 Ultra laser microphone
<p>Repository contains extra wide bandwidth acoustic process monitoring data of a stable and unstable gas metal arc welding (GMAW) process variant Fronius "cold metal transfer" (CMT) of G3Si1 steel wire, which is used for additive manufacturing. Additional Video acquisition of the welding process is provided.</p>
Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Heart Electromechanics Model Using Gaussian Processes Emulators - Training Datasets
<p>This database contains all training datasets for the Gaussian processes emulators (GPEs) trained in the study entitled "Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Electromechanics Model Using Gaussian Processes Emulators", submitted to PLOS Computational Biology.</p> <p>Every folder contains two csv files:</p> <p>- parameters.csv: the rows are the samples and the columns represent the parameters that were varied in the analysis</p> <p>- outputs.csv: the rows are the samples and the columns represent the values for the output features simulated for each sample</p> <p>In ventricular_cell_model, there are four folders:</p> <p>- ionic: ToR-ORd model samples used to train GPEs to predict the ventricular calcium transient features</p> <p>- contraction_isometric_stretch1.0: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isometric contractions with no strain (or stretch 1.0).</p> <p>- contraction_isometric_stretch1.1: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isometric contractions with 0.1 strain (or stretch 1.1).</p> <p>- contraction_isotonic: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isotonic.</p> <p>The folder atrial_contraction_model follows the same structure, but the ionic model was Courtemanche, used to represent an atrial rather than ventricular calcium transient.</p> <p>The folder tissue_electrophysiology contains the training dataset for the GPEs to predict total atrial and ventricular activation times with an Eikonal model.</p> <p>The folder passive_mechanics contains the training dataset for the GPEs to predict inflated volumes and mean atrial and ventricular fiber strains for a passive inflation.</p> <p>The folder CircAdapt contains the training dataset for the GPEs to predict four-chamber pressure and volume features with the CircAdapt ODE model.</p> <p>Finally, the folder fourchamber contains the samples generated with a 3D-0D four-chamber electromechanics model to predict pressure and volume biomarkers for cardiac function.</p> <p>The details about the model can be found in the original publication.</p>
Textual Process Descriptions and Corresponding BPMN Models
<p>This dataset provides an archive with 24 textual process descriptions in different granularities, each with 8-11 BPMN models, that represent possible solutions for modelling the description. Each BPMN model has an assigned score of 0..5 which describes how good a human expert deemed the solution.</p>
Dataset and codes of the article "Neural correlates of hierarchical predictive processes in autistic adults"
<p>Data and code related to the article "Neural correlates of hierarchical predictive processes in autistic adults" by Laurie-Anne Sapey-Triomphe, Lauren Pattyn, Veith Weilnhammer, Philipp Sterzer and Johan Wagemans (Nature Communications):</p> <p>- Behavioral dataset of the 26 neurotypical participants (NT_behavioral_data.zip) and of the 26 autistic participants (ASD_behavioral_data.zip)</p> <p>- Source data of the graphics appearing in the article (Source data.xls)</p> <p>- Matlab codes used to run the experiment (Codes_to_run_experiment.zip)</p> <p>- Matlab codes to perform the main behavioral analyses (Codes_behavioral_analyses.zip) and to analyze the behavioral data with the HGF models (Codes_comput_model_analyses.zip)</p> <p>- Matlab codes to preprocess (Codes_fMRI_preprocessing.zip) and run the main fMRI analyses (Codes_fMRI_analyses.zip)</p>
Business process models for a ride fulfilment process in a ride-hailing company enabled by an autonomous driving system
<p>The repository contains the business process models of a ride fulfilment process in a ride-hailing company enabled by an autonomous driving system. The models are depicted using BPMN.2.0 language and are extended with annotations for usage in the <a href="https://dpotool.cs.ut.ee/">DPO tool</a>. The models, which contain "Pleak" as a part of the title, are created using PE-BPMN language and some of them are annotated for usage in the <a href="https://pleak.io/">Pleak</a> tool set. </p> <p>The repository also contains two .csv files which contain the results of the simple disclosure analysis ("<em>6_RideFulfillment_PK_SecSharing_combined-Simple-Disclosure-results.csv</em>") and the results of the leak-when analysis ("<em>7_Pleak_RideFulfillment_PK_SecrSharing_BPMN_Leak_When-results.csv</em>") in the ride fulfilment process. Both analyses have been conducted using the <a href="https://pleak.io/">Pleak</a> tool set and the business process models with respective names from this repository.</p>
Advanced photochemical processes for the manufacture of nanopowders: an evaluation of long-term pilot plant operation
<p>Dataset for the publication:</p> <p>Advanced photochemical processes for the manufacture of nanopowders: an evaluation of long-term pilot plant operation</p> <p>React. Chem. Eng., 2022, 7, 968–977</p> <p>DOI: 10.1039/d1re00374g</p>
Multi-fidelity Gaussian Process Emulation for Atmospheric Radiative Transfer Models
<p>This repository contains several datasets of spectral atmospheric transfer functions (i.e. path radiance, transmittances, spherical albedo) simulated with MODTRAN6 atmospheric radiative transfer model. The simulations are stored in hdf5 files using the Atmospheric Look-up table Generator (ALG) toolbox (<a href="https://doi.org/10.5194/gmd-13-1945-2020">https://doi.org/10.5194/gmd-13-1945-2020</a>). Each dataset has an associated .xml file that includes the configuration of ALG/MODTRAN6 executions. All datasets include the input atmospheric/geometric variables that are summarized in the following table. Each dataset file has a random distribution (based on latin hypercube sampling) these input variables with varying number of points (e.g. train500.h5 contains 500 samples). The <em>reference </em>dataset contains 10000 samples and was used as reference for evaluating Gaussian Processes emulators.</p> <table> <tbody><tr> <th>Input Variables</th> <th>Units</th> <th>Min</th> <th>Max</th> </tr> </tbody><tbody> <tr> <td>O3 column concentration</td> <td>atm-cm</td> <td>0.25</td> <td>0.45</td> </tr> <tr> <td>Columnar Water Vapor</td> <td>g/cm2</td> <td>0.2</td> <td>4</td> </tr> <tr> <td>Aerosol Optical Thickness</td> <td>-</td> <td>0.04</td> <td>0.6</td> </tr> <tr> <td>Asymmetry parameter</td> <td>-</td> <td>0.5</td> <td>0.85</td> </tr> <tr> <td>Angstrom exponent</td> <td>-</td> <td>0.1</td> <td>2</td> </tr> <tr> <td>Single Scattering Albedo</td> <td>-</td> <td>0.8</td> <td>1</td> </tr> <tr> <td>Surface elevation</td> <td>km</td> <td>0</td> <td>2.5</td> </tr> <tr> <td>Solar Zenith Angle</td> <td>deg</td> <td>0</td> <td>70</td> </tr> <tr> <td>Relative Zenith Angle</td> <td>deg</td> <td>0</td> <td>180</td> </tr> </tbody> </table> <p> </p>
i-Box (Innsbruck Box) – processed eddy-covariance data: 2-min statistics
<p><strong>Abstract</strong></p> <p>The dataset contains eddy-covariance data from five i-Box stations in the Austrian Inn Valley, which have been processed to 2-min statistics. The i-Box is a long-term measurement platform, including a small network of eddy-covariance stations in the lower Inn Valley, to study boundary-layer processes in mountainous terrain. More information about the i-Box can be found at <a href="https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en">https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en</a> and in Rotach et al. (2017).</p> <p> </p> <p><strong>Data description</strong></p> <p><em>Station locations</em></p> <p>The present dataset contains processed data from five i-Box stations located in the Austrian Inn Valley. The Inn Valley is an approximately southwest-northeast oriented valley in the western part of Austria, with a depth of about 2000 m and a width of about 2 km at the valley floor. The locations of the sites are shown in the overview figure i-Box_sites.pdf.</p> <ul> <li> <p>VF0 is located at the almost flat valley floor. The site is surrounded by grassland and agricultural fields. (47.305°N, 11.622°E, 545 m MSL)</p> </li> <li> <p>SF8 is located at the foot of the north sidewall next to a steep embankment between an agricultural field and a concrete parking lot. (47.326°N, 11.652°E, 575 m MSL)</p> </li> <li> <p>SF1 is located on an almost flat plateau running along the northern valley sidewall. The site is mainly surrounded by grassland and agricultural fields. (47.317°N, 11.616°E, 829 m MSL)</p> </li> <li> <p>NF10 is located on an approximately 10 deg slope on the south sidewall, covered by grassland. (47.300°N, 11.673°E, 930 m MSL)</p> </li> <li> <p>NF27 is located on a steep, grass-covered slope on the south sidewall, with a slope angle of about 25 deg. (47.288°N, 11.631°E, 1009 m MSL)</p> </li> </ul> <p>Further information about station locations can be found in Rotach et al. (2017) and Lehner et al. (2021).</p> <p><em>Temporal coverage</em></p> <p>The dataset contains processed data between 2014 and 2020. Some instruments were replaced and new instruments were added during this period. Data gaps occur as a result of instrument malfunctions and maintenance.</p> <p><em>Instrumentation</em></p> <p>Each station is equipped with at least one sonic anemometer and a gas analyzer. The instrumentation usually consists of a CSAT3 sonic anemometer (Campbell Scientific, USA) and KH20 Krypton hygrometer (Campbell Scientific) or an EC150 open-path infrared gas analyzer (Campbell Scientific). In 2020, several of the instruments were replaced with an Irgason (Campbell Scientific), which combines an open-path infrared gas analyzer with a sonic anemometer. Pressure, air temperature, and humidity used for calculating flux corrections are measured with Setra 278 sensors (Setra Systems, USA) and Rotronic HC2A-S temperature and humidity probes (Rotronic, Switzerland).</p> <ul> <li> <p>VF0: CSAT3 and EC150 at 4.0 m, CSAT3 at 8.7 m, CSAT3 and KH20 (until July 2020) or Irgason (since July 2020) at 16.9 m</p> </li> <li> <p>SF8: CSAT3 at 6.1, CSAT3 and KH20 (until September 2020) or Irgason (since September 2020) at 11.2 m</p> </li> <li> <p>SF1: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 6.8 m</p> </li> <li> <p>NF10: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 5.7 m</p> </li> <li> <p>NF27: CSAT3 at 1.5 (since September 2017), CSAT3 and KH20 (until November 2016) or Irgason (since September 2017) 6.8 m</p> </li> </ul> <p>Further information about the instrumentation can be found in Rotach et al. (2017), Lehner et al. (2021), and in the ACINN database:</p> <ul> <li> <p>VF0: <a href="https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html">https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html</a></p> </li> <li> <p>SF8: <a href="https://acinn-data.uibk.ac.at/pages/i-box-terfens.html">https://acinn-data.uibk.ac.at/pages/i-box-terfens.html</a></p> </li> <li> <p>SF1: <a href="https://acinn-data.uibk.ac.at/pages/i-box-eggen.html">https://acinn-data.uibk.ac.at/pages/i-box-eggen.html</a></p> </li> <li> <p>NF10: <a href="https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html">https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html</a></p> </li> <li> <p>NF27: <a href="https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html">https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html</a></p> </li> </ul> <p><em>Data processing</em></p> <p>Raw 20-Hz data were quality controlled and rotated into a streamline coordinate system using double rotation before block averaging the data to 2-min statistics, without previous filtering. Flux corrections were applied to the turbulence statistics, including a frequency response correction (Aubinet et al. 2012) with spectral models following Moore (1986), Højstrup (1981), and Kaimal et al. (1972); a sonic heat-flux correction of the vertical heat flux and the temperature variance (Schotanus et al. 1983); a WPL correction of the vertical moisture flux (Webb et al. 1980); and an Oxygen correction of the vertical moisture flux for data from Krypton hygrometers (van Dijk et al. 2003).</p> <p>The quality control procedures include the removal of data during periods of instrument malfunction as indicated by the instruments’ quality flags, a despiking, the removal of data points exceeding 30 m s<sup>-1</sup> for the horizontal wind components, 10 m s<sup>-1</sup> for the vertical wind velocity, and 50 g m<sup>3</sup> for water vapor density, and the removal of sonic temperature data outside the range -20 – 40°C. The removed data are replaced with random values drawn from a Gaussian distribution, with its mean and standard deviation calculated over a 30-s data window.</p> <p>Quality flags are based on the criteria described in Stiperski and Rotach (2016):</p> <ul> <li> <p>-1: More than 10% of the raw data within the averaging period are replaced during the quality control.</p> </li> <li> <p>0: More than 90% of the raw data fulfill the quality control criteria.</p> </li> <li> <p>1: In addition to fulfilling the quality control criteria, the skewness is within the range -2–2 and the kurtosis is less than 8.</p> </li> <li> <p>2: In addition to the above criteria, the stationarity test by Foken and Wichura (1996) is below 30% and the uncertainty is less than 50% based on Stiperski and Rotach (2016) and Wyngaard (1973)</p> </li> </ul> <p><em>Data files</em></p> <ul> <li> <p>i-Box_sites.pdf contains a map of the i-Box stations.</p> </li> <li> <p>list_variables.pdf contains a list of variable names with a short description.</p> </li> <li> <p>SITENAME_2min.zip contains the processed turbulence statistics, split into yearly files. There is more than one file per year if the instrumentation changed during the year or because of memory restrictions during the processing.</p> </li> </ul> <p><em>Acknowledgments</em></p> <p>Data processing was performed in the framework of the TExSMBL (Turbulent Exchange in the Stable Mountain Boundary Layer) project funded by the Austrian Science Fund (FWF) under grant V 791-N. Data were processed on the LEO HPC infrastructure of the University of Innsbruck.</p> <p><em>References</em></p> <p>Aubinet M, Vesala T, D P (eds) (2012) Eddy Covariance. A practical guide to measurements and data analysis. Springer, Dordrecht, DOI 10.1007/978-94-007-2351-1</p> <p>Højstrup J (1981) A simple model for the adjustment of velocity spectra in unstable conditions downstream of an abrupt change in roughness and heat flux. Boundary-Layer Meteorol 21:341–356, DOI 10.1007/bf00119278</p> <p>Kaimal JC, Wyngaard JC, Izumi Y, Coté OR (1972) Spectral characteristics of surface-layer turbulence. Q J R M Soc 98:563–589, DOI 10.1002/qj.49709841707</p> <p>Lehner M, Rotach MW, Sfyri E, Obleitner F (2021) Spatial and temporal variations in near-surface energy fluxes in an Alpine valley under synoptically undisturbed and clear-sky conditions. Q J R M Soc 147:2173–2196, DOI 10.1002/qj.4016</p> <p>Moore CJ (1986) Frequency response corrections for eddy correlation systems. Boundary-Layer Meteorol 37:17–35, DOI 10.1007/BF00122754</p> <p>Rotach MW, Stiperski I, Fuhrer O, Goger B, Gohm A, Obleitner F, Rau G, Sfyri E, Vergeiner J (2017) Investigating exchange processes over complex topography—the Innsbruck Box (i-Box). Bull Amer Meteorol Soc 98:787–805, DOI 10.1175/BAMS-D-15-00246.1</p> <p>Schotanus P, Nieuwstadt FTM, de Bruijn HAR (1983) Temperature measurement with a sonic anemometer and its application to heat and moisture fluxes. Boundary-Layer Meteorol 26:81–93, DOI 10.1007/BF00164332</p> <p>Stiperski, I. and Rotach, M.W. (2016) On the measurement of turbulence over complex mountainous terrain. Boundary-Layer Meteorology, 159, 97–121. DOI 10.1007/s10546-015-0103-z.</p> <p>Van Dijk A, Kohsiek W, de Bruin HAR (2003) Oxygen sensitivity of Krypton and Lyman-α hygrometers. J Atmos Ocean Technol 20:143–151, DOI 10.1175/1520-0426(2003)020¡0143:OSOKAL¿2.0.CO;2</p> <p>Webb EK, Pearman GI, R L (1980) Correction of flux measurements for density effects due to heat and water vapour transfer. Q J R M Soc 106:85–100, DOI 10.1002/qj.49710644707</p> <p>Wyngaard, J.C. (1973). On surface layer turbulence. In D.A. Haugen (Ed.), Workshop on Micrometeorology, American Meteorological Society, pp. 101–150.</p>
i-Box (Innsbruck Box) – processed eddy-covariance data: 1-min statistics
<p><strong>Abstract</strong></p> <p>The dataset contains eddy-covariance data from five i-Box stations in the Austrian Inn Valley, which have been processed to 1-min statistics. The i-Box is a long-term measurement platform, including a small network of eddy-covariance stations in the lower Inn Valley, to study boundary-layer processes in mountainous terrain. More information about the i-Box can be found at <a href="https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en">https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en</a> and in Rotach et al. (2017).</p> <p> </p> <p><strong>Data description</strong></p> <p><em>Station locations</em></p> <p>The present dataset contains processed data from five i-Box stations located in the Austrian Inn Valley. The Inn Valley is an approximately southwest-northeast oriented valley in the western part of Austria, with a depth of about 2000 m and a width of about 2 km at the valley floor. The locations of the sites are shown in the overview figure i-Box_sites.pdf.</p> <ul> <li> <p>VF0 is located at the almost flat valley floor. The site is surrounded by grassland and agricultural fields. (47.305°N, 11.622°E, 545 m MSL)</p> </li> <li> <p>SF8 is located at the foot of the north sidewall next to a steep embankment between an agricultural field and a concrete parking lot. (47.326°N, 11.652°E, 575 m MSL)</p> </li> <li> <p>SF1 is located on an almost flat plateau running along the northern valley sidewall. The site is mainly surrounded by grassland and agricultural fields. (47.317°N, 11.616°E, 829 m MSL)</p> </li> <li> <p>NF10 is located on an approximately 10 deg slope on the south sidewall, covered by grassland. (47.300°N, 11.673°E, 930 m MSL)</p> </li> <li> <p>NF27 is located on a steep, grass-covered slope on the south sidewall, with a slope angle of about 25 deg. (47.288°N, 11.631°E, 1009 m MSL)</p> </li> </ul> <p>Further information about station locations can be found in Rotach et al. (2017) and Lehner et al. (2021).</p> <p><em>Temporal coverage</em></p> <p>The dataset contains processed data between 2014 and 2020. Some instruments were replaced and new instruments were added during this period. Data gaps occur as a result of instrument malfunctions and maintenance.</p> <p><em>Instrumentation</em></p> <p>Each station is equipped with at least one sonic anemometer and a gas analyzer. The instrumentation usually consists of a CSAT3 sonic anemometer (Campbell Scientific, USA) and KH20 Krypton hygrometer (Campbell Scientific) or an EC150 open-path infrared gas analyzer (Campbell Scientific). In 2020, several of the instruments were replaced with an Irgason (Campbell Scientific), which combines an open-path infrared gas analyzer with a sonic anemometer. Pressure, air temperature, and humidity used for calculating flux corrections are measured with Setra 278 sensors (Setra Systems, USA) and Rotronic HC2A-S temperature and humidity probes (Rotronic, Switzerland).</p> <ul> <li> <p>VF0: CSAT3 and EC150 at 4.0 m, CSAT3 at 8.7 m, CSAT3 and KH20 (until July 2020) or Irgason (since July 2020) at 16.9 m</p> </li> <li> <p>SF8: CSAT3 at 6.1, CSAT3 and KH20 (until September 2020) or Irgason (since September 2020) at 11.2 m</p> </li> <li> <p>SF1: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 6.8 m</p> </li> <li> <p>NF10: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 5.7 m</p> </li> <li> <p>NF27: CSAT3 at 1.5 (since September 2017), CSAT3 and KH20 (until November 2016) or Irgason (since September 2017) 6.8 m</p> </li> </ul> <p>Further information about the instrumentation can be found in Rotach et al. (2017), Lehner et al. (2021), and in the ACINN database:</p> <ul> <li> <p>VF0: <a href="https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html">https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html</a></p> </li> <li> <p>SF8: <a href="https://acinn-data.uibk.ac.at/pages/i-box-terfens.html">https://acinn-data.uibk.ac.at/pages/i-box-terfens.html</a></p> </li> <li> <p>SF1: <a href="https://acinn-data.uibk.ac.at/pages/i-box-eggen.html">https://acinn-data.uibk.ac.at/pages/i-box-eggen.html</a></p> </li> <li> <p>NF10: <a href="https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html">https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html</a></p> </li> <li> <p>NF27: <a href="https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html">https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html</a></p> </li> </ul> <p><em>Data processing</em></p> <p>Raw 20-Hz data were quality controlled and rotated into a streamline coordinate system using double rotation before block averaging the data to 1-min statistics, without previous filtering. Flux corrections were applied to the turbulence statistics, including a frequency response correction (Aubinet et al. 2012) with spectral models following Moore (1986), Højstrup (1981), and Kaimal et al. (1972); a sonic heat-flux correction of the vertical heat flux and the temperature variance (Schotanus et al. 1983); a WPL correction of the vertical moisture flux (Webb et al. 1980); and an Oxygen correction of the vertical moisture flux for data from Krypton hygrometers (van Dijk et al. 2003).</p> <p>The quality control procedures include the removal of data during periods of instrument malfunction as indicated by the instruments’ quality flags, a despiking, the removal of data points exceeding 30 m s<sup>-1</sup> for the horizontal wind components, 10 m s<sup>-1</sup> for the vertical wind velocity, and 50 g m<sup>3</sup> for water vapor density, and the removal of sonic temperature data outside the range -20 – 40°C. The removed data are replaced with random values drawn from a Gaussian distribution, with its mean and standard deviation calculated over a 30-s data window.</p> <p>Quality flags are based on the criteria described in Stiperski and Rotach (2016):</p> <ul> <li> <p>-1: More than 10% of the raw data within the averaging period are replaced during the quality control.</p> </li> <li> <p>0: More than 90% of the raw data fulfill the quality control criteria.</p> </li> <li> <p>1: In addition to fulfilling the quality control criteria, the skewness is within the range -2–2 and the kurtosis is less than 8.</p> </li> <li> <p>2: In addition to the above criteria, the stationarity test by Foken and Wichura (1996) is below 30% and the uncertainty is less than 50% based on Stiperski and Rotach (2016) and Wyngaard (1973)</p> </li> </ul> <p><em>Data files</em></p> <ul> <li> <p>i-Box_sites.pdf contains a map of the i-Box stations.</p> </li> <li> <p>list_variables.pdf contains a list of variable names with a short description.</p> </li> <li> <p>SITENAME_1min.zip contains the processed turbulence statistics, split into yearly files. There is more than one file per year if the instrumentation changed during the year or because of memory restrictions during the processing.</p> </li> </ul> <p><em>Acknowledgments</em></p> <p>Data processing was performed in the framework of the TExSMBL (Turbulent Exchange in the Stable Mountain Boundary Layer) project funded by the Austrian Science Fund (FWF) under grant V 791-N. Data were processed on the LEO HPC infrastructure of the University of Innsbruck.</p> <p><em>References</em></p> <p>Aubinet M, Vesala T, D P (eds) (2012) Eddy Covariance. A practical guide to measurements and data analysis. Springer, Dordrecht, DOI 10.1007/978-94-007-2351-1</p> <p>Højstrup J (1981) A simple model for the adjustment of velocity spectra in unstable conditions downstream of an abrupt change in roughness and heat flux. Boundary-Layer Meteorol 21:341–356, DOI 10.1007/bf00119278</p> <p>Kaimal JC, Wyngaard JC, Izumi Y, Coté OR (1972) Spectral characteristics of surface-layer turbulence. Q J R M Soc 98:563–589, DOI 10.1002/qj.49709841707</p> <p>Lehner M, Rotach MW, Sfyri E, Obleitner F (2021) Spatial and temporal variations in near-surface energy fluxes in an Alpine valley under synoptically undisturbed and clear-sky conditions. Q J R M Soc 147:2173–2196, DOI 10.1002/qj.4016</p> <p>Moore CJ (1986) Frequency response corrections for eddy correlation systems. Boundary-Layer Meteorol 37:17–35, DOI 10.1007/BF00122754</p> <p>Rotach MW, Stiperski I, Fuhrer O, Goger B, Gohm A, Obleitner F, Rau G, Sfyri E, Vergeiner J (2017) Investigating exchange processes over complex topography—the Innsbruck Box (i-Box). Bull Amer Meteorol Soc 98:787–805, DOI 10.1175/BAMS-D-15-00246.1</p> <p>Schotanus P, Nieuwstadt FTM, de Bruijn HAR (1983) Temperature measurement with a sonic anemometer and its application to heat and moisture fluxes. Boundary-Layer Meteorol 26:81–93, DOI 10.1007/BF00164332</p> <p>Stiperski, I. and Rotach, M.W. (2016) On the measurement of turbulence over complex mountainous terrain. Boundary-Layer Meteorology, 159, 97–121. DOI 10.1007/s10546-015-0103-z.</p> <p>Van Dijk A, Kohsiek W, de Bruin HAR (2003) Oxygen sensitivity of Krypton and Lyman-α hygrometers. J Atmos Ocean Technol 20:143–151, DOI 10.1175/1520-0426(2003)020¡0143:OSOKAL¿2.0.CO;2</p> <p>Webb EK, Pearman GI, R L (1980) Correction of flux measurements for density effects due to heat and water vapour transfer. Q J R M Soc 106:85–100, DOI 10.1002/qj.49710644707</p> <p>Wyngaard, J.C. (1973). On surface layer turbulence. In D.A. Haugen (Ed.), Workshop on Micrometeorology, American Meteorological Society, pp. 101–150.</p>
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