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458 results for “Covariation”
Supplementary Dataset for "Representativeness of Eddy-Covariance Flux Footprints for Areas Surrounding AmeriFlux Sites"
<p>These datasets are supplementary to the paper "<strong>Representativeness of Eddy-Covariance Flux Footprints for Areas Surrounding AmeriFlux Sites</strong>" by Chu et al. </p> <ul> <li>Dataset S1. Summary of site-specific footprint metrics <ul> <li>filename: All_site_fpt_summary.csv</li> <li>readme: All_site_fpt_summary-README.csv</li> </ul> </li> <li>Dataset S2. All monthly footprint climatology weight maps <ul> <li>filename: monthly_footprint_climatology_weight_map.zip <ul> <li>the zip folder contains individual files of all monthly footprint weight maps</li> <li>filename: <Site-ID>_<Year>_<Month>_<DAY/NIGHT>_fpt_weight.tif</li> </ul> </li> <li>readme: README.txt </li> </ul> </li> <li>Dataset S3. All site-year footprint climatology overlapped with true-color satellite images. <ul> <li>filename: site-year_footprint_climatology_realcolor_map.zip <ul> <li>the zip folder contains individual files of footprint climatologies from all site-years</li> <li>filename: <Site-ID>_<Year>_<Spatial_Extent>_shrink_footprint_climatology.png</li> </ul> </li> <li>readme: README.txt </li> </ul> </li> <li>Dataset S4. Site-specific results and representativeness index based on the land cover type analysis. <ul> <li>filename: All_site_land_cover_dominant_summary2.csv</li> <li>readme:All_site_land_cover_dominant_summary2-README.csv</li> </ul> </li> <li>Dataset S5. Site-specific results and representativeness index based on the EVI analysis. <ul> <li>filename: All_site_Landsat_EVI_fpt_comparison2.csv</li> <li>readme: All_site_Landsat_EVI_fpt_comparison2-README.csv</li> </ul> </li> <li>Dataset S6. All available site-month EVI and time-explicit representativeness. <ul> <li>filename: All_site_Landsat_EVI_all_cutout2.csv</li> <li>readme: All_site_Landsat_EVI_all_cutout2-README.csv</li> </ul> </li> </ul>
AusEFlux: Empirical upscaling of OzFlux eddy covariance flux tower data over Australia
<p>AusEFlux (<strong>Aus</strong>tralian <strong>E</strong>mpirical <strong>Flux</strong>es) is a high resolution (500 metre) gridded estimate of Gross Primary Productivity (GPP), Ecosystem Respiration (ER), Net Ecosystem Exchange (NEE), and Evapotranspiration over the Australian continent for the period January 2003 to Present. These datasets provide a benchmark for assessment against Land Surface Model simulations, and a means for monitoring of Australia’s terrestrial carbon cycle at an unprecedented high-resolution.</p> <p><strong>Version 2.1 </strong>of AusEFlux has just been released (as of May 2025) and was created to <strong>operationalise</strong> the research datasets published in this <a href="https://doi.org/10.5194/bg-20-4109-2023">EGU Biogeosciences publication.</a> In order to operationalise these datasets, changes to the input datasets were required to align the data sources with datasets that are regularly and reliably updated, along with general improvements. The datasets provided on Zenodo have been reprojected to 5 km resolution to facilitate easier uploading and sharing, but<strong> full resolution datasets (both v1.1 and v2.1) can be accessed freely through <a href="https://thredds.nci.org.au/thredds/catalog/ub8/au/AusEFlux/catalog.html">NCI's THREDDS portal.</a></strong></p> <p><strong>Two Jupyter Notebooks</strong> have been created (one for GPP and one for NEE) that demonstrate the differences between the research datasets (v1.1) and the operational datasets (v2.1), including showing the differences in specifications and inputs. You can view/download these notebooks using the links below:</p> <p><a href="https://nbviewer.org/github/cbur24/AusEFlux/blob/master/notebooks/analysis/Compare_AusEFlux_versions_GPP.ipynb">GPP comparison between versions</a></p> <p><a href="https://nbviewer.org/github/cbur24/AusEFlux/blob/master/notebooks/analysis/Compare_AusEFlux_versions_NEE.ipynb">NEE comparisons between versions</a></p> <p>Each dataset contains three variables:</p> <ul> <li>"<flux>_median": represents the 'best-estimate' of a given flux, the units are gC/m<sup><sub>2</sub></sup>/mon<sup>-1</sup></li> <li>"<flux>_25th_percentile": represents the lower uncertainty bound, the units are gC/m<sup><sub>2</sub></sup>/mon<sup>-1</sup></li> <li>"<flux>_75th_percentile": represents the upper uncertainty bound, the units are gC/m<sup><sub>2</sub></sup>/mon<sup>-1</sup></li> </ul> <p><span><strong>Version Guide</strong>:</span></p> <p><em>v1.0:</em> DO NOT USE THIS VERSION. There was a mistake in the modelling of ecosystem respiration, so this version of the dataset should not be used. As of version 1.1, the error has been rectified.</p> <p><em>v1.1: </em>This version of the datasets are those used to inform the EGU Publication linked above. Its time range is 2003-July 2022, and its spatial resolution is 5 km on Zenodo, but the 1 km resolution datasets can be accessed through <a href="https://thredds.nci.org.au/thredds/catalog/ub8/au/AusEFlux/catalog.html">NCI's THREDDS portal</a>.</p> <p><em>v2.0: <strong>IMPORTANT NOTE:</strong> a bug in the modelling of vegetation height resulted in data artefacts in the NEE and ER fluxes over very tall mesic forests in this version. This resulted in unnaturally high ER and lower than expected NEE (less negative than would be expected). This issue has been rectified in version 2.1.</em> <strong>Version 2 datasets represent the operational version of the datasets</strong>, it includes several improvements over version 1.1. Its time-range is 2003-2024 (and will be updated annually), and its spatial resolution is 500m. A 5 km reprojected version of the dataset is included here on Zenodo, but the 500 metre datasets can be accessed through<a href="https://thredds.nci.org.au/thredds/catalog/ub8/au/AusEFlux/catalog.html"> NCI's THREDDS portal.</a></p> <p><strong>v2.1: </strong>This version is a patch to version 2.0 to remove a bug in the modelling of vegetation height. <strong>It is recommended to use this version </strong>over v2.0. 500 metre resolution datasets can be accessed through<a href="https://thredds.nci.org.au/thredds/catalog/ub8/au/AusEFlux/catalog.html"> NCI's THREDDS portal.</a></p>
Collider Bias Correction for Multiple Covariates in GWAS Using Robust Multivariable Mendelian Randomization
<p>This repository contains the data underlying the figures in paper "Collider Bias Correction for Multiple Covariates in GWAS<br>Using Robust Multivariable Mendelian Randomization".</p> <p> </p> <p> </p> <p>The file names and sheet names in the xlsx file indicate the corresponding figures of data. </p> <p><br>The underlying data of manhattan plots and QQ plots are in text file. For other figures, the underlying data are in the spreadsheet.</p> <p>In each file, column names indicate the MVMR method used to obtain the result. </p> <p>For example: </p> <p>In text files:</p> <p>The abbreviation "mPC" refers to metabolomic principle components.</p> <p>beta_no_correction: the SNP effect estimate without bias correction.</p> <p>beta_cml or beta_MVMR_cml: the standard error of SNP effect estimate after the bias correction of MVMR-cML.</p> <p>SE_UVMR_cml: the standard error of SNP effect estimate after the bias correction of UVMR-cML.</p> <p>p_value_Egger or p_value_MVMR_Egger: the p-value of SNP effect estimate after the bias correction of MVMR-Egger regression.</p> <p><br>In the spreadsheet, column names follow the same style. </p> <p>The GWAS data is also available. The column names follows the plink output file. The detailed explanations are available at https://www.cog-genomics.org/plink/2.0/formats#glm_linear</p>
Dataset related to the publication "Temporal dynamics and environmental controls of carbon dioxide and methane fluxes measured by the eddy covariance method over a boreal river"
<h2>Summary</h2> <p>Dataset related to the publication "Temporal dynamics and environmental controls of carbon dioxide and methane fluxes measured by the eddy covariance method over a boreal river" by Aki Vähä, Timo Vesala, Sofya Guseva, Anders Lindroth, Andreas Lorke, Sally MacIntyre, and Ivan Mammarella (2024), published in Biogeosciences.</p> <h2>Materials and Methods</h2> <h3>Measurement site</h3> <p>The experiment was conducted on a floating platform on the River Kitinen in northern Finland. The measurements took place from 1 June to 2 October, 2018.</p> <p>The River Kitinen is 235 km long and has a catchment area of 7672 km2. The catchment area consists mostly of managed boreal forest with Scots pine (Pinus sylvestris) and Norway spruce (Picea abies) as the main tree species, wetlands of which a large portion is drained, small streams and rivers, some low mountains and a few small settlements. The experiment site (67.37◦ N, 26.62◦ E, 173 m above sea level) was located next to the Finnish Meteorological Institute’s research and weather station in Tähtelä. At the experiment location the river is 180 m wide and forms a straight section extending approximately 600 m upstream and 1000 m downstream from the site. The direction of the river at the site is roughly north-northwest–south-southeast and it flows towards the south. The mean annual discharge, measured at the closest power plant downstream, is 103 m3 s−1. The maximum depth at the site is 7 m. The River Kitinen’s Strahler stream order at the site is 5. The floating platform was located about 70 m from the eastern river bank where the water depth was 4.5 m.</p> <h3>Eddy covariance</h3> <p>The eddy covariance system measuring water-atmosphere turbulent fluxes was mounted on a mast on the southern side of the platform. This installation consisted of an ultrasonic anemometer (uSonic-3 Scientific, METEK Meteorologische Messtechnik GmbH, Elmshorn, Germany) for measuring the wind speed in three Cartesian coordinates and the sonic temperature, an enclosed-path gas analyser (LI-7200RS, LI-COR Biosciences, Inc., Lincoln, Nebraska, USA) for measuring carbon dioxide and water vapour mole fractions, and a closed-path gas analyser (G1301-f, Picarro, Inc., Santa Clara, California, USA) for measuring methane and water vapour mole fractions. The centre of the sonic anemometer was 1.82 m above the water surface. An inclinometer (DOG2 micro-electro-mechanical system, Measurement Specialties, Inc., Hampton, Virginia, USA) was used for measuring the pitch and roll of the platform. Eddy covariance fluxes were calculated using the EddyUH software (Mammarella et al. 2016), following the state of art methodologies (Sabbatini et al. 2018, Nemitz et al. 2018).</p> <h3>Auxiliary measurements</h3> <p>Ambient air temperature and relative humidity were measured with a Rotronic HC2-S3C03 probe (Rotronic AG, Bassersdorf, Germany), mounted inside a Young model 41003 (R. M. Young Company, Traverse City, Michigan, USA) multi-plate radiation shield on the platform’s north-eastern corner. Air temperature and relative humidity were available only after 15th of June. Before that, the sonic temperature and humidity calculated from χH2O, measured with the LI-7200RS, were used instead. Atmospheric pressure and precipitation were measured at the Tähtelä weather station. Photosynthetically active radiation (PAR) in water was measured with two LI-192 sensors (LI-COR Biosciences, Inc., Lincoln, Nebraska, USA) and one LI-193 sensor (LI-COR). The sensors were hanging from wires at 0.3 m, 0.65 m and 1.0 m depths on a beam on the southern side of the platform. Measurements of water side CO2 partial pressure (pCO2) were done by using an off-axis integrated cavity output spectrometer (Ultraportable Greenhouse Gas Analyzer – UGGA), Los Gatos Research, Inc., Santa Clara, California, USA) that was connected to the headspace of an equilibrator consisting of a floating Plexiglas chamber.</p> <p>A water temperature chain was set up 100 m upstream of the platform. It consisted of five temperature loggers of the type RBR Solo (RBR Ltd. Ottawa, Ontario, Canada). The loggers were placed on a taut line mooring at depths of 0.35 m, 1.35 m, 2.35 m, 3.35 m and 4.35 m (6 June to 17 June) and 0.07 m, 1.05 m, 2.05 m, 3.05 m and 4.05 m (17 June onwards). The topmost measurement was used as the surface temperature. The water flow velocity was measured with a acoustic Doppler velocimeter (Nortek Vector, Nortek AS, Rud, Norway) which was installed on a beam on the north-western corner of the platform, facing down (Guseva et al., 2021). The depth of the measurements was 0.4 m below the surface.</p>
Nationwide geospatial dataset of environmental covariates at 1km resolution in Mexico
<p><strong>Package of 39 covariates, a combination of topographic, climatic, and vegetation derived variables with pixel sizes of 1000 m for the period of 2009 to 2014 from google earth engine (GEE) to assemble a nationwide geospatial dataset of Mexico.</strong><br> <strong>Datasets included WorldClim V1; a set of bioclimatic variables derived from the monthly temperature and rainfall <a href="https://www.zotero.org/google-docs/?tmZL4M">(Hijmans, 2005)</a>; time-series analysis of Landsat images from the Hansen Global Forest Change v1.8 (2000-2020) dataset <a href="https://www.zotero.org/google-docs/?jGj5Rn">(Hansen et al., 2013)</a>; 4-day composite dataset from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensors with fraction of photosynthetic active radiation and leaf area index at 500-m resolution <a href="https://www.zotero.org/google-docs/?t45qy3">(Myneni, Ranga et al., 2015)</a> and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Database (2000-2008) <a href="https://www.zotero.org/google-docs/?8bVVc9">(Hulley et al., 2009, 2012, 2015; Hulley & Hook, 2008, 2009, 2011; NASA JPL, 2014)</a>. All covariates were resampled to 1000 m. The resampling was done with conventional bilinear interpolation as implemented in GEE.</strong></p>
Data from: Lower-cost eddy covariance for CO2 and H2O fluxes over grassland and agroforestry
<p>This datasets contains eddy covariance fluxes calculated with EddyPro, average (co)spectra from EddyPro and meteorological data from the measurement campaigns conducted in Mariensee, Lower Saxony (Germany) in 2020 and 2021. The results were presented in the paper "Lower-cost eddy covariance for CO2 and H2O fluxes over grassland and agroforestry"; <a href="https://doi.org/10.5194/amt-2024-30">https://doi.org/10.5194/amt-2024-30</a>. </p> <p>For more information on the data see the accompanying paper. Note that the eddy covariance data are not filtered, quality checked or gap-filled in these files!</p>
soil spatial covariates at OAL-UK
<p>Dataset containing a series of soil spatial covariates (e.g. topographic attributes, soil texture, soil organic matter, etc) usable in the creation of soil digital maps for OAL-UK.</p>
Population pharmacokinetic studies of critically ill adults receiving beta-lactam antimicrobials: covariate dataset
<p>A dataset of reported covariates from a systematic review of population pharmacokinetic studies of critically ill adults receiving beta-lactam antimicrobials, including R script of statistical and graphical analyses</p>
CO2 Flux partioning in a Eddy Covariance tower of marsh ecosystem (Fuente Duque) in Doñana Biological Reserve
<p>This dataset consists of the estimated data of the CO2 flux partition: assimilation or gross primary production (GPP) and heterotrophic or ecosystem respiration (Reco), as well as the standard deviation of each estimate. These data have been estimated from the data provided by the ICTS of the Doñana Biological Reserve of the eddy covariance tower located in Fuente Duque, in the Hinojos marsh in the period between October 2020 and December 2022.</p>
Patch-level rates of nitrogen fixation and denitrification and environmental covariates from seven streams in Idaho and Michigan
We hypothesized that environmental variation at the patch scale (1 - 10’s m) would facilitate the co-occurrence of N2 fixation and denitrification through the formation of hot spots in streams. We measured rates of N2 fixation and denitrification and relative abundances of the genes nifH and nirS in patches determined by channel geomorphic units and substrate type in 4 Idaho and 3 Michigan streams encompassing a gradient of N and P concentrations. This data package includes patch-level measurements of N2 fixation and denitrification rates, relative gene abundances of nifH and nirS, and environmental covariates (nutrient concentrations, water temperature, surface and subsurface dissolved oxygen concentrations, organic matter content) that were used to explore the factors that could predict process rates and relative gene abundances across patches and streams.
Walleye spawning, ice phenology, and covariate data for Upper Midwestern Lakes: 1939-2019
The phenology of critical biological events in aquatic ecosystems are rapidly shifting due to climate change. Growing variability in phenological cues can increase the likelihood of trophic mismatches, causing recruitment failures in commercially, culturally, and recreationally important fisheries. We tested for changes in spawning phenology of regionally important walleye (Sander vitreus) populations in 194 Midwest US lakes in Minnesota, Michigan, and Wisconsin spanning 1939-2019 to investigate factors influencing walleye phenological responses to climate change and associated climate variability, including ice-off timing, lake physical characteristics, and population stocking history. Data from Wisconsin and Michigan lakes (185 and 5 out of 194 total lakes, respectively) were collected by the Wisconsin Department of Natural Resources (WDNR) and the Great Lakes Indian Fish and Wildlife Commission (GLIFWC) through standardized spring walleye mark-recapture surveys and spring tribal harvest season records. Standardized spring mark-recapture population estimates are performed shortly after ice-off, where following a marking event, a subsequent recapture sampling event is conducted using nighttime electrofishing (typically AC – WDNR, pulsed-DC – GLIFWC) of the entire shoreline including islands for small lakes and index stations for large lakes (Hansen et al. 2015) that is timed to coincide with peak walleye spawning activity (G. Hatzenbeler, WDNR, personal communication; M. Luehring, GLIFWC, personal communication; Beard et al. 1997). Data for four additional Minnesota lakes were collected by the Minnesota Department of Natural Resources (MNDNR) beginning in 1939 during annual collections of walleye eggs and broodstock (Schneider et al. 2010), where date of peak egg take was used to index peak spawning activity. For lakes where spawning location did not match the lake for which the ice-off data was collected, the spawning location either flowed into (Pike River) or wa
Mass and energy fluxes from the US-Jo2 AmeriFlux eddy covariance tower in Tromble Weir experimental watershed at the Jornada Basin LTER site, 2010-ongoing
This data package contains metadata for, and links to, 30-minute mass and energy flux data collected at an eddy covariance tower in the Tromble Weir Watershed area of the Jornada Basin in southern New Mexico, USA. These data are used to quantify the water and energy balances in a small experimental watershed, including observational studies to calculate groundwater recharge as a water balance residual, to build relations between soil moisture state and ET flux, and to quantify land-atmosphere interactions and improve our understanding of the eddy covariance method. Additionally, they have been used in modeling studies as a validation of model performance. The .csv files included in this package list and describe the data entities and variables present in data files archived at the AmeriFlux data repository (site US-Jo2; http://ameriflux.lbl.gov/sites/siteinfo/US-Jo2). The data at AmeriFlux include the 30-minute mass and energy fluxes calculated from 20 Hz data collected at the Tromble Weir Watershed tower. Instrument descriptions and detailed procedures are found in the references listed in the Methods section of this EDI package. This is an ongoing dataset that will be updated annually.
An Excel spreadsheet including eddy covariance, meteorological, and tidal data measured at Yunxiao mangrove flux tower.
<p>An Excel spreadsheet including eddy covariance, meteorological, and tidal data at Yunxiao mangrove flux tower required to reproduce key findings in Figures 4-9 in the main text (each figure corresponds to a single sheet). Contact Xudong Zhu at Xiamen University (xdzhu@xmu.edu.cn) if you have any question.</p>
Eddy covariance data processing workflow example utilizing openeddy and REddyProc R packages
<p>The example dataset is provided within the folder structure required by the workflow files (version 2025-04-27; amended on 2025-07-31) related to the R package openeddy version 0.0.0.9009. Only files needed for successful processing are included. It is shared here as part of a data processing example at <a href="https://github.com/lsigut/EC_workflow">https://github.com/lsigut/EC_workflow</a> to overcome the file size limitation of GitHub.</p>
Probabilistic classification of Fermi LAT gamma-ray sources (effect of covariate shift)
<p>Version 1:</p> <p>These are data products connected to <a href="https://arxiv.org/abs/2307.09584">https://arxiv.org/abs/2307.09584</a>, where an analysis of the effect of covariate shift on the probabilistic classification of the Fermi LAT gamma-ray sources from the 4FGL-DR3 catalog is performed.</p> <p>The files </p> <p>4FGL-DR3_6class_GMM_nmin100_prob_cat.csv<br>4FGL-DR3_6class_GMM_nmin100_weighted_prob_cat.csv</p> <p>contain probabilistic classification into 6 classes (determined in <a href="https://arxiv.org/abs/2307.09584">https://arxiv.org/abs/2301.07412</a>) with random forest and neural networks methods. The catalog in "4FGL-DR3_6class_GMM_nmin100_weighted_prob_cat.csv" is constructed including weights for associated sources used in training in order to account for the difference in the distribution of associated (training dataset) and unassociated (target dataset) sources. The catalog in "4FGL-DR3_6class_GMM_nmin100_prob_cat.csv" is constructed with unweighted training samples.</p> <p>The files</p> <p>4FGL-DR3_6class_GMM_nmin100_summary.csv<br>4FGL-DR3_6class_GMM_nmin100_weighted_summary.csv</p> <p>contain the corresponding summaries of the definition of classes and predicted numbers of sources for the RF and NN algorithms for associated sources (averaged over cases when the sources are in the testing samples) and unassociated sources.</p> <p>Detailed description of the construction of the catalogs can be found in <a href="https://arxiv.org/abs/2307.09584">https://arxiv.org/abs/2307.09584</a>.</p> <p>Version 2: update for the Fermi LAT 4FGL-DR4 catalog.</p> <p>The filenames slightly change.<br>Probabilistic catalogs with unweighted and weighted training respectively:<br>4FGL-DR4_6classes_GMM_prob_cat.csv<br>4FGL-DR4_6classes_GMM_weighted_prob_cat.csv<br><br>The corresponding summary files:<br>4FGL-DR4_6classes_GMM_summary.csv<br>4FGL-DR4_6classes_GMM_weighted_summary.csv</p> <p>Version 3: catalogs corresponding to the published version of the paper. The filenames and the format are the same as in Version 2.</p>
TEAMx-PC22 (TEAMx pre-campaing 2022) - ACINN Distributed temperature sensing, fluxes from eddy covariance measurements, and auxiliary measurements from and at the i-Box station (VF-0) Kolsass
<p><strong>Introduction</strong></p> <p>During the TEAMx-precampaign (TEAMx-PC22) in summer 2022, the Innsbruck Box (i-Box) station at the valley floor in Kolsass (CS-VF0) was extended by a vertical array with fiber-optic distributed temperature sensing (DTS). The i-Box is a testbed for studying boundary layer processes in highly complex terrain (<a href="http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-15-00246.1">Rotach et al. (2017)</a> and <a href="https://fileshare.uibk.ac.at/f/9f1101851849439483de/">i-Box WIKI</a> for further information). The mountain boundary layer is investigated using a 17 m high tower with multi-level observations of turbulence, wind speed, and temperature. DTS measurements with a spatio-temporal resolution of 0.127 m and 1 s were added to these profile measurements. The combination of DTS measurements and point observations has the capability of resolving sub-meso scale motions (<a href="https://doi.org/10.1007/s10546-021-00618-0">Pfister et al. 2021</a>) and can reveal processes within the boundary layer during the morning and evening transition (<a href="https://doi.org/10.1029/2020GL092238">Fritz et al. 2021</a>).</p> <p>The aim of TEAMx-PC22 was to test new instruments, new instrument configurations and new measurement sites to support the planning of the main TEAMx observational campaign (TOC) in 2024/2025. More details about TEAMx can be found at <a href="http://www.teamx-programme.org">http://www.teamx-programme.org</a> as well as in <a href="https://doi.org/10.15203/99106-003-1">Serafin et al. (2020)</a> and in <a href="https://doi.org/10.1175/bams-d-21-0232.1">Rotach et al. (2022)</a>.</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p><strong>1. Location</strong></p> <p>The i-Box valley-floor site is located on the almost flat floor near the town of Kolsass within the Inn Valley roughly 20 km east-north-east of Innsbruck. The site is characterized by different types of agricultural land. The 17-m high tower is a full energy-balance station and is instrumented with three vertical levels of turbulence measurements. The exact location is 47.305341°N, 11.62219°E (UTM: 698215.03 E, 5242420.95 N) at 545 m above mean sea level.</p> <p><strong>2. Temporal coverage</strong></p> <p>The TEAMx-PC22 lasted from mid-May 2022 to early October 2022. The i-Box station is running continuously, however, the provided data only covers the period when DTS data is available. The DTS array was running during the following periods:</p> <ul> <li>08.06.-14.06.2022</li> <li>28.06.-18.07.2022</li> </ul> <p><strong>3. Instrument details</strong></p> <p><em><strong>Distributed temperature sensing</strong></em></p> <p>For spatially continuous measurements of vertical temperature profiles at this tower, a DTS array was installed. Temperatures were measured with two channels at 1~Hz with a spatial resolution of 0.127~m. The used DTS instrument was an Ultima-HS (Silixa Ltd., Hertfordshire, UK) which was combined with a fibre-optic cable (900 µm outer diameter; AFL Telecommunications, Spartanburg, SC, USA) consisting of a bend-optimised optical fiber (125 µm with 50 µm core), buffered with Kevlar in a white plastic jacket. The fiber-optic cable was installed vertically towards the west of the tower such that two temperature profiles could be measured simultaneously. For the full array the approximately 450 m long fiber-optic cable was running from one DTS channel through a warm and cold reference bath towards the tower, then up and down the 17-m tower, and back through the baths towards the second channel. Accordingly, the array could be measured in both directions. For mounting at the top and bottom of the tower PVC pipes (diameter 15 cm) were used. The setup with two channels allows for sampling the array in both directions. Before entering the reference baths roughly 200 m were left on the spool slightly affecting signal-to-noise ratio. The vertical array was mapped by cooling packs. Both reference baths observed at the beginning and end of each fiber-optic cable yielded to four reference sections at two temperatures. The array was a double-ended configuration observed as two single-ended configurations which is different from the manufacturer's provided double-ended mode (<a href="https://doi.org/10.3390/s20082235">des Tombe et al. 2020</a>, <a href="https://doi.org/10.5194/essd-14-885-2022">Lapo et al. 2022</a>). Reference temperature probes were PT100 from the Ultmia-HS itself. DTS data was calibrated using the weighted-least squares approach described in <a href="https://doi.org/10.3390/s20082235">des Tombe et al. (2020)</a> and implemented in the <em>dtscalibration</em> software package (<a href="https://doi.org/10.5281/zenodo.7111585">des Tombe et al. 2022</a>) and all processing was completed using the <em>pyfocs</em> software package (<a href="https://doi.org/10.5281/zenodo.7111585">Lapo and Freundorfer 2020</a>) . As the fiber-optic cable runs through both calibration baths before and after the array creating four locations within a temperature controlled environment. Of those locations three are used for calibration (every time step) and the fourth is used for validation. A schematic of the setup is given within the files.</p> <p>After calibration a mean bias of -0.05 K and root mean squared difference of 0.22 K was determined with the validation water bath.</p> <p>Unfortunately the mounting towards the west created an artifact as the tower was partially shading the fiber-optic cable creating unphysical temperature gradients. Accordingly, data from 04.00 - 09.00 UTC should not be used for data analysis. The given data is only a single fiber of the paired vertical sections on the 17-m tower. Artifacts from the plastic ring holders are removed from the fiber.</p> <p>The DTS experiment was named the Innsbruck DTS Experiment (InnDEX22), hence, data names were chosen accordingly. But keep in mind that InnDEX22 was part of TEAMx-PC22.</p> <p><em><strong>i-Box tower</strong></em></p> <p>Full site description and all data exceeding the DTS observations can be found on <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>. Utilized and uploaded data are mainly within three categories: eddy covariance (EC) fluxes at level 1 (4 m agl) and at level 2 (8.7 m agl) and low-frequency data:</p> <ul> <li>EC flux level 1:<br>Combination of ultrasonic anemometer CSAT3 (orientation from North: 30°) and infrared gas analyzer EC150 from Campbell Scientific</li> <li>EC flux level 2:<br>Ultrasonic anemometer CSAT3 (orientation from North: 30°) from Campbell Scientific</li> <li>low-frequency data: <ul> <li>pressure: Setra 278 (Setra Systems, Inc., Boxborough, Maine, USA) at 1.4 m agl</li> <li>radiation: ventilated CGR4 pyrgeometers and CMP21 pyranometers (Kipp & Zonen, Delft, Netherlands) at 2 m agl</li> <li>temperature profile: Rotronic HC2-S3 actively ventilated at 2, 4, 8.7, and 16.9m</li> <li>wind profile: 2D ultrasonic anemometer Gill Windsonic4 at 2, 4, 6, and 12m</li> </ul> </li> </ul> <p>For the EC processing further quality criteria can be applied to assure good data quality. More information on processing of data and quality criteria is given here:</p> <ul> <li>EC fluxes processing:<br>Averaging interval of 30 min performed by the software <a href="https://www.geos.ed.ac.uk/homes/jbm/micromet/EdiRe/">EdiRe</a><br>Processing includes despiking; double-rotation of the wind components; detrending with a recursive filter and a time constant of 200 s; and applying frequency-response corrections, heat-flux corrections for humidity effects, oxygen corrections for KH20, and WPL corrections. The datafile contains several quality flags and added as description within the netcdf files; zero-plane displacement height of 0 m</li> <li>EC flux Quality Criteria (QC) flags: <ul> <li>-1: all data</li> <li>0: excluding instrument malfunction</li> <li>1: additionally skewness within range (-2 to 2) and kurtosis <8 following Vickers and Mahrt 1997</li> <li>2: additionally exclude non-stationary data</li> </ul> </li> <li>EC flux Flags: <ul> <li>0: data ok</li> <li>1: data not ok (see description of individual flag for further details)</li> </ul> </li> </ul> <p><strong>4. Data file structure</strong></p> <p><em><strong>Zip folders</strong></em></p> <p>Different data sets were generated, as measurements had different temporal resolutions or different sets of parameter. Accordingly the following data is given:</p> <ul> <li>DTS data (1 s): InnDEX22_distributed_temperature_sensing.zip</li> <li>EC flux level 1 (30 min): InnDEX22_EC_flux_lvl1.zip</li> <li>EC flux level 2 (30 min): InnDEX22_EC_flux_lvl1.zip</li> <li>Low-frequency data (1 min): InnDEX22_low_frequency_data.zip</li> </ul> <p><em><strong>File format</strong></em></p> <p>Each above mentioned folder is filled with netcdf files. One for each day. Global information contains location, instrument type etc. Parameter description is given as attributes for each parameter.</p> <p><strong>6. Contact</strong></p> <p>Contact lena.pfister(at)uibk.ac.at for any questions regarding the data set.</p> <p><em><strong>Acknowledgements</strong></em></p> <p>Special thanks to the "Institut für Meteorologie und Klimaforschung Atmosphärische Umweltforschung" (IMK-IFU), KIT-Campus Alpin, Garmisch-Partenkirchen, for lending us the DTS measurement device for TEAMx-PC22.</p> <p><strong>7. References</strong></p> <p>Fritz, A. M., Lapo, K., Freundorfer, A., Linhardt, T., & Thomas, C. K. (2021): Revealing the morning transition in the mountain boundary layer using fiber-optic distributed temperature sensing. <em>Geophysical Research Letters</em>, 48, e2020GL092238. <a href="https://doi.org/10.1029/2020GL092238">https://doi.org/10.1029/2020GL092238</a></p> <p>des Tombe, B., Schilperoort, B., Bakker, M. (2020): Estimation of Temperature and Associated Uncertainty from Fiber-Optic Raman-Spectrum Distributed Temperature Sensing. <em>Sensors</em>, 20, 2235. <a href="https://doi.org/10.3390/s20082235">https://doi.org/10.3390/s20082235</a></p> <p>des Tombe, Bas François, & Schilperoort, Bart. (2022): Dtscalibration Python package for calibrating distributed temperature sensing measurements (v1.1.2). <em>Zenodo</em>. <a href="https://doi.org/10.5281/zenodo.7111585">https://doi.org/10.5281/zenodo.7111585</a></p> <p>Pfister, L., Lapo, K., Mahrt, L., Thomas, C.K. (2021): Thermal Submesoscale Motions in the Nocturnal Stable Boundary Layer. Part 1: Detection and Mean Statistics. <em>Boundary-Layer Meteorol</em> 180, 187–202. <a href="https://doi.org/10.1007/s10546-021-00618-0">https://doi.org/10.1007/s10546-021-00618-0</a></p> <p>Lapo, K., Freundorfer, A., (2020): klapo/pyfocs v0.5: Fully-functional python package intended for atmospheric deployments of distributed temperature sensing. <em>Zenodo</em>, <a href="https://doi.org/10.5281/zenodo.7111585">https://doi.org/10.5281/zenodo.7111585</a></p> <p>Lapo, K., Freundorfer, A., Fritz, A., Schneider, J., Olesch, J., Babel, W., and Thomas, C. K. (2022): The Large eddy Observatory, Voitsumra Experiment 2019 (LOVE19) with high-resolution, spatially distributed observations of air temperature, wind speed, and wind direction from fiber-optic distributed sensing, towers, and ground-based remote sensing, <em>Earth Syst. Sci. Data</em>, 14, 885–906 <a href="https://doi.org/10.5194/essd-14-885-2022">https://doi.org/10.5194/essd-14-885-2022</a></p> <p>Serafin, S., M. W. Rotach, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. De Wekker, M. Evans, V. Grubišić, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Raudzens Bailey, J. Schmidli, G. Wohlfahrt, B. Zardi, (2020): Multi-scale transport and exchange processes in the atmosphere over mountains: Programme and experiment. <em>Innsbruck University Press</em>. <a href="https://doi.org/10.15203/99106-003-1">https://doi.org/10.15203/99106-003-1</a></p> <p>Rotach, M. W., S. Serafin, H. C. Ward, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. D. Wekker, V. Grubišic, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Bailey, J. Schmidli, C. Wittmann, G. Wohlfahrt, D. Zardi, (2022): A collaborative effort to better understand, measure, and model atmospheric exchange processes over mountains. <em>Bulletin of the American Meteorological Society</em>, 103, E1282–E1295. <a href="https://doi.org/10.1175/bams-d-21-0232.1">https://doi.org/10.1175/bams-d-21-0232.1</a></p>
Dataset for the paper "Mental Health and Burnout during Medical School: Longitudinal Evolution and Covariates" published in PLOS ONE (2024)
<p><strong>Full reference of the paper:</strong></p> <p>Carrard V, Berney S, Bourquin C, Ranjbar S, Castelao E, Schlegel K, et al. (2024) Mental health and burnout during medical school: Longitudinal evolution and covariates. PLoS ONE 19(4): e0295100. <a href="https://doi.org/10.1371/journal.pone.0295100">https://doi.org/10.1371/journal.pone.0295100</a></p>
Sexual dimorphism in subterranean amphipod crustaceans covaries with subterranean habitat type: data and R code
<p>The data and R code used for data analyses in the manuscript titled "Sexual dimorphism in <em>Niphargus </em>amphipods is predicted by surface-subterranean environmental gradient". The collection contains:</p> <ol> <li>A zipped folder "videos", where raw videos used in the study are stored.</li> <li>A zipped folder "tracking_results", where video-tracking results obtained from the raw videos are stored, along with supporting files and R code (Rscript_extract_behavior.Rmd) with custom functions (Behavior_custom_functions.Rmd) needed to analyze tracking results and retrieve final behavioral data.</li> <li>A README file with details on how the data and code is organized.</li> <li>Supplementary Material file including all raw data used in the main data analysis (SupplementaryMaterial.xlsx)</li> <li>A supporting file with data from another study used in the main data analysis (sex_ratio.xlsx, results from https://onlinelibrary.wiley.com/doi/full/10.1111/jeb.13917; https://zenodo.org/records/5175861)</li> <li>Two files containing phylogenetic trees: one complete phylogenetic tree of<em> Niphargus </em>(consensus_tree)<em> </em>and 100 randomly drawn phylogenetic trees from the stationary phase of the Bayesian analysis, pruned to focal species (100_pruned_trees), which were used in the main data analysis.</li> <li>The Rcode containing the code of the main data analysis to reproduce the reported results (RScript_data_analysis.Rmd), as well as some additional analyses not included in the manuscript.</li> </ol>
Fig. 5 in Covariation In Shapes Between The Sternum And Pelvis In Aquatic Birds With Different Locomotor Modes
Fig. 5. Covariation between dorsal projections of the sternum and pelvis estimated with the standard PLS analysis. Phylogenetic relationships are projected onto the PLS1 morphospace. Grey wireframe represents mean shape. Species designation as in fig. 4.
Fig. 6 in Covariation In Shapes Between The Sternum And Pelvis In Aquatic Birds With Different Locomotor Modes
Fig. 6. Boxplot of the ratio of sternum and pelvis centroid sizes. Box represents interquartile range with solid line inside as median, whiskers are minimal and maximal values. Locomotor categories: FP — foot-propelled divers, WP — wing-propelled divers, SSW — surface swimmers.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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