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ICON-LEM Ny-Ålesund low-level clouds polar night and polar day 2021/2022
<h3>Low-level clouds during the polar night and polar day simulated in ICON-LEM for Ny-Ålesund </h3> <p>This data set was created using the ICON-LEM model with ca. 600m resolution and a diagnostic tool "microphysical wrapper". It contains the meteogram output of the Ny-Ålesund column (Svalbard) and the microphysical process rates. The data was created for the polar night (Nov 2021- Feb 2022) and polar day (May - Aug 2022). Clouds are classified as low-level if their cloud top height (CTH) is below 2.5 km and the distance between any cloud with CTH above 2.5 km is at least 500 m higher. The data set was first used and described in the <em>publication: </em></p> <p>T. Kiszler, D. Ori, V. Schemann<em>. </em>(preprint) Microphysical processes involving the vapour phase dominate in simulated low-level Arctic clouds. <em>Atmospheric Physics and Chemistry, </em>https://doi.org/10.5194/egusphere-2023-2986<em><br></em></p> <p>This data is related to the repository <a href="https://github.com/TracyMcBean/Kiszler_et_al_2023_microphysics">https://github.com/TracyMcBean/Kiszler_et_al_2023_microphysics</a></p> <p><em>File description:</em></p> <p>*_PN is polar night data</p> <p>*_PD is polar day data</p> <p>LLC_<em>meteo_<yyyymm>_ICONv1</em>_v6.nc : Contains the meteogram variables (thermodynamics, surface variables, hydrometeors)</p> <p>LLC_wrapper_mass_<yyyymm>_ICONv1_v6.nc : Contains hydrometeors masses after diagnostic run of a microphysical wrapper</p> <p>LLC_wrapper_tend_<yyyymm>_ICONv1_v6.nc : Contains the mircophysical process rates showing the mass change per timestep </p> <p>low_cloud_times_v6_*.csv : Contains the date and time when a low-level cloud was detected</p>
Decoding of multisensory semantics and memories in low-level visual cortex
Open the record for dataset details and reuse information.
Single column 1D radiative transfer simulations during PS106 including low-level-stratus clouds in the central Arctic
<p>The collection of datasets published contain the input parameters and output simulations from a single column 1D radiative transfer simulations using the <strong>R</strong>apid <strong>R</strong>adiative <strong>T</strong>ransfer <strong>M</strong>odel for <strong>G</strong>eneral Circulation Model (GCM) applications (RRTMG).</p><p>The data set contains simulations for the PS106 research cruise conducted in 2017 in the Central Arctic. The simulations are based on remote sensing data which were processed with the Cloudnet algorithm to derive cloud macro - and microphyiscal products. The atmospheric profiles of temperature, pressure, and ozone are from ERA5 (European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis) and values of surface albedo from CERES (Clouds and the Earth's Radiant Energy System) SYN1deg Ed. 4.1.</p>
Mesoscale Low-Level Jet Climatology for the North and Baltic Seas
<p><strong>Mesoscale Low-Level Jet Climatology for the North and Baltic Seas</strong></p> <p>This dataset contains a mesoscale low-level jet (LLJ) climatology for the Baltic and North Seas.</p> <p>The dataset consists of many individual raster layers of LLJ characteristics zipped in the "llj_climatology.zip" file. Each layer is a netCDF4 file, which can be read directly by QGIS, Python, and many other tools. In the "figures" folder, plots showing most of the layers can be found. A more comprehensive description of the layers is given below.</p> <p>Some examples of layers contained:</p> <ul> <li>LLJ rate-of-occurrence</li> <li>LLJ height</li> <li>LLJ duration</li> <li>Wind speed and direction for at LLJ peak</li> <li>Max shear above and below the LLJ peak</li> <li>Wind speed and direction at 100, 150, 200 m</li> <li>Rotor-equivalent wind speed (REWS) for IEA 15 MW reference turbine</li> </ul> <p>Because of strong seasonality in offshore LLJ occurrences, most layers come as long-term means, including the full five years and seasonality-averaged layers. Several aggregate statistics are available for each layer, such as mean, median, and standard deviation. </p> <p>The data was created using the Weather Research and Forecasting model v4.2.1 running a five-year hindcast from 2019-06-26 to 2024-06-26. A two-domain setup was used to downscale ERA5 boundary data to 3 km horizontal grid spacing. See the associated paper for a full data generation process and validation description.</p> <p>Based on user feedback, future versions could be expanded to hold additional layers/variables, such as sector-wise Weibull parameters or time-series samples for representative points. Contact btol@dtu.dk for feedback and requests for future versions. </p> <p><strong>Full list of variables</strong></p> <ul> <li><strong>ws100, ws150, ws200</strong>: wind speed at 100, 150, and 200 meters</li> <li><strong>wd100, wd150, wd200</strong>: wind direction at 100, 150, 200 meters</li> <li><strong>rews_iea15mw</strong>: rotor-equivalent wind speed for the IEA 15 MW reference turbine</li> <li><strong>cf_iea15mw</strong>: capacity factor for the IEA 15 MW reference turbine</li> <li><strong>llj_rate</strong>: LLJ detection rate </li> <li><strong>height_of_llj_max</strong>: height of LLJ peak in meters</li> <li><strong>llj_ws_max</strong>: wind speed of LLJ peak in meters per second</li> <li><strong>llj_wind_direction</strong>: wind direction of LLJ peak in degree</li> <li><strong>llj_duration</strong>: LLJ duration in hours</li> <li><strong>llj_most_prevalent_hour</strong>: most prevalent hour-of-day during LLJ events as hour integers (0-23)</li> <li><strong>llj_most_prevalent_hour_freq</strong>: relative frequency of most prevalent hour-of-day during LLJ events</li> <li><strong>llj_most_prevalent_season</strong>: most prevalent month-of-year during LLJ events as 0-based month integers (0-11 JAN-DEC)</li> <li><strong>llj_most_prevalent_season_freq</strong>: relative frequency of most prevalent month-of-year during LLJ events </li> <li><strong>llj_max_shear_below</strong>: maximum shear between the LLJ peak and the minimum below</li> <li><strong>llj_min_shear_above</strong>: minimum (maximum negative) shear between the LLJ peak and the minimum above</li> <li><strong>height_of_max_shear_below_llj</strong>: height of maximum shear detected below the LLJ peak in meters</li> <li><strong>height_of_min_shear_above_llj</strong>: height of minimum shear detected above the LLJ peak in meters</li> <li><strong>llj_depth</strong>: the depth of the LLJ measured from "height_of_max_shear_below_llj" to "height_of_min_shear_above_llj" in meters</li> <li><strong>llj_rews_iea15mw</strong>: rotor-equivalent wind speed for the IEA 15 MW reference turbine during LLJ events</li> <li><strong>llj_cf_iea15mw</strong>: capacity factor for the IEA 15 MW reference turbine during LLJ events</li> <li><strong>llj_abs_falloff_above</strong>: absolute wind speed fall-off above the LLJ peak in meters per second</li> <li><strong>llj_rel_falloff_above</strong>: relative wind speed fall-off above the LLJ peak </li> <li><strong>llj_abs_falloff_below</strong>: absolute wind speed fall-off below the LLJ peak in meters per second</li> <li><strong>llj_rel_falloff_below</strong>: relative wind speed fall-off below the LLJ peak </li> </ul> <p><strong>Several layers exist for different aggregation and seasons for each variable. Suffixes describe the aggregation (_mean, _median, _std) and season (_DJF, _MAM, _JJA, _SON)</strong></p> <p>This work is part of the FLOW project and was supported by the European Union Horizon Europe Framework Programme (HORIZON-CL5-2021-D3-03-04) under grant agreement no. 101084205.</p>
Raw SNR data for Manuscript "GPS Interferometric Reflectometry : Using a Low Cost Antenna to Measure Water Levels"
<p>Raw GPS L1 SNR (and ancillary) data for an experiment to use a low-cost GPS antenna/receiver to measure water levels using the GNSS - Interferometric Reflectometry technique.</p> <p>The data were recorded at the RNLI lifeboat station in Sligo, Ireland (N 54<sup>o </sup>18' 17.8'', W 8<sup>o</sup> 34' 5.4'' ) using a Globalsat BU353S4 USB puck that uses a SirfStar IV receiver with patch antenna (2018 data) and a Maestro A2200A SirfStar IV module (2019 data). Both systems were mounted to a radio mast at around 16m above sea level.</p> <p>The data are stored in daily files with the naming convention sligDDD0.YY.TNR.gz where DDD is the Day of Year and YY is the year in short format (18,19). Each file is gzipped. </p> <p>The files are flat text files with fixed width columns in the following order</p> <p>1) PRN GPS satellite code</p> <p>2) Elevation (degrees)</p> <p>3) Azimuth (degrees)</p> <p>4) Seconds of Day</p> <p>5) change in elevation angle with time (degrees/second) : needed for reflector height change corrections</p> <p>6) Blank</p> <p>7) S1 SNR signal (dB-Hz)</p> <p>8) Blank reserved for S2 SNR signal</p> <p>9) Blank reserved for S5 SNR signal</p>
Root phytohormone levels of mangrove seedlings grown in soils of low and high bulk density
<p>These data contain information on root traits and concentrations of multiple hormones in root tissue of two mangrove species grown under low and high soil bulk density conditions. Inhibitors were used to assess the role of the phytohormone ethylene.</p> <h2>Description of the data and file structure</h2> <p>The root trait file contains total root length (cm), mean root length (cm), root dry weight (g), and root number data for two mangrove species (Avicennia marina, Rhizophora stylosa) grown under two different soil bulk densities: 0.2 and 1.0 g cm^-3. Treatment refers to the application of ethylene inhibitors (CoCl2: cobalt chloride, AIB: aminoisobutyric acid, control).</p> <p>The phytohormone file contains the root tissue concentrations (all in ng g^-1) for multiple hormones (ABA: abscisic acid, ACC: 1-aminocyclopropane-1-carboxylic acid, GAx: gibberellins, IAA: indole-3-acetic acid, iP: isopentenyl adenine, JA: jasmonic acid, SA: salicylic acid, tZ: cytokinin <em>trans</em>-zeatin) of two mangrove species (Avicennia marina, Rhizophora stylosa) grown under two different soil bulk densities: 0.2 and 1.0 g cm^-3. Treatment refers to the application of ethylene inhibitors (CoCl2: cobalt chloride, AIB: aminoisobutyric acid, control).</p> <p> </p> <p> </p>
Data of "Decreasing the level of hemicelluloses in sow's lactation diet affects the milk composition and post-weaning performances of low birthweight piglets"
<p>This study showed the effects of decreasing the levels of hemicelluloses in sow’s lactation diet on milk composition and on sow and piglet performances.</p> <p>Sow_performances.csv: A comma separated value file of data used to investigate the relationship between the performances of Swiss Large White sows and decreasing levels of hemicelluloses in lactation diet.</p> <p>Milk_composition.csv: A comma separated value file of data used to investigate the relationship between the milk composition of Swiss Large White sows and decreasing levels of hemicelluloses in lactation diet.</p> <p>Piglets_performances.csv: A comma separated value file of data used to investigate the relationship between the performances of Swiss Large White pigs and decreasing levels of hemicelluloses in lactation maternal diet.</p> <p>Feed_intake_piglets.csv: A comma separated value file of data used to investigate the relationship between the feed intake of Swiss Large White pigs and decreasing levels of hemicelluloses in lactation maternal diet.</p> <p>Diarrhea_postweaning.csv: A comma separated value file of data used to investigate the relationship between the post-weaning diarrhoea of Swiss Large White pigs and decreasing levels of hemicelluloses in lactation maternal diet.</p> <p>data_description.xlsx: meta data for Sow_performances.csv, Milk_composition.csv, Piglets_performances.csv, Feed_intake_piglets.csv and Diarrhea_postweaning.csv with description of variables.</p> <p> </p>
Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations
<p>This dataset contains a comprehensive set of quality-controlled remote sensing observations of low-level mixed-phase clouds collected at the high Arctic site of Ny-Ålesund, between 10 October 2021 and 31 December 2022. Cornerstones of the dataset are observations from a 35-GHz polarimetric scanning Doppler cloud radar and a 94-GHz zenith-pointing Doppler cloud radar. Radar data are complemented with thermodynamic retrievals from a microwave radiometer, liquid base height from a ceilometer and wind fields from large-eddy simulations. All data have undergone extensive quality control, especially the cloud radar data, which are accurately calibrated, matched, and corrected for gas and liquid-hydrometeor attenuation, ground clutter and range folding. This dataset is especially suited for cloud microphysical studies, and the high number of events included allows for the compiling of robust statistics. The dataset is accompanied by a data descriptor article, which is available at <a href="https://doi.org/10.5194/essd-15-5427-2023" target="_blank" rel="noopener">doi.org/10.5194/essd-15-5427-2023</a>.</p> <p> </p> <p><strong>Dataset overview</strong><br>The files include only low-level mixed-phase cloud (LLMPC) events, as well as the 2 hours preceding and following events. Each file contains an individual event, unless multiple events are less than 4 hours apart, in which case they are combined into the same file. LLMPC events are detected by requiring that ice and liquid phase coexist in a cloud layer with top below 2500 m for at least one hour. All radar variables observed in zenith (Doppler moments at 35 and 94 GHz, linear depolarization ratio (LDR) at 35 GHz), as well as microwave radiometer retrievals (temperature (T), liquid water path (LWP), integrated water vapor (IWV)), liquid base height from the ceilometer, and model data (horizontal wind speed and direction) are brought to the same time and range grids (respectively named ‘time_zen’ and ‘range_zen’ in the files). Off-zenith radar variables (reflectivity, differential reflectivity (ZDR), maximum spectral ZDR (sZDRmax), correlation coefficient (RhoHV), differential phase shift (PhiDP), and specific differential phase (KDP)) are stored on separate coordinates (named ‘time_slant’ and ‘range_slant’). All derived corrections are already applied to the data, and stored in the files, in case the user is interested in reconstructing the original data. A number of flags have been included in the files: in particular ‘MPC_detected’ indicates whether a LLMPC event was detected, and ‘liquid_attenuation_correction_flag_zen’ and ‘liquid_attenuation_correction_flag_slant’ indicate whether radar reflectivities were corrected for attenuation due to liquid hydrometeors. Liquid attenuation corrections should be especially taken into account when computing the dual-wavelength ratio (i.e., the difference between reflectivity at 35 GHz and at 94 GHz, both expressed in dBZ), and performing quantitative analyses of reflectivity fields.</p>
Supplement to "Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations"
<p>This dataset is a supplement to "Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations", available at <a href="http://doi.org/10.5281/zenodo.7803064">doi.org/10.5281/zenodo.7803064</a>. The additional variables here included are: slow edge velocity, fast edge velocity, and eddy dissipation rate (EDR). All variables are stored on the same time and range grids adopted for the main dataset. Similarly, the event selection and file structure are identical to those of the main dataset.<br><br>Slow and fast edge velocities are derived from Doppler spectra recorded by the zenith-pointing 94-GHz cloud radar. The slow (fast) edge velocity is calculated as the velocity associated with the slowest (fastest) Doppler bin above the peak noise level, belonging to a spectral cluster whose width is at least 5 Doppler bins.<br><br>The EDR is retrieved following the approach by Borque et al. (2016; <a href="http://doi.org/10.1002/2015JD024543">doi.org/10.1002/2015JD024543</a>), using as input the slow edge velocity, and model horizontal wind speed from the main dataset. EDR is retrieved in 5 minute intervals, up to a maximum range of 3 km.<br><br>The detailed documentation of the variables here included can be found in the Supporting Information to the following publication: <a href="https://doi.org/10.1029/2023GL106599" target="_blank" rel="noopener">doi.org/10.1029/2023GL106599</a>.</p>
Low-level updraft intensification in response to environmental wind profiles
<p>Supercell storms can develop a "dynamical response" whereby upward accelerations in the lower troposphere amplify as a result of rotationally induced pressure falls aloft. These upward accelerations likely modulate a supercell's ability to stretch near-surface vertical vorticity to achieve tornadogenesis. This study quantifies such a dynamical response as a function of environmental wind profiles commonly found near supercells. Self-organizing maps (SOMs) were used to identify recurring low-level wind profile patterns from 20,194 model-analyzed, near-supercell soundings. The SOM nodes with larger 0–500 m storm-relative helicity (SRH) and streamwise vorticity (ω<sub>s</sub>) corresponded to higher observed tornado probabilities. The distilled wind profiles from the SOMs were used to initialize idealized numerical simulations of updrafts. In environments with large 0–500 m SRH and large ω<sub>s</sub>, a rotationally induced pressure deficit, increased dynamic lifting, and a strengthened updraft resulted. The resulting upward-directed accelerations were an order of magnitude stronger than typical buoyant accelerations. At 500 m AGL, this dynamical response increased the vertical velocity by up to 25 m s<sup>–1</sup>, vertical vorticity by up to 0.2 s<sup>–1</sup>, and pressure deficit by up to 5 hPa. This response specifically augments the near-ground updraft (the midlevel updraft properties are almost identical across the simulations). However, dynamical responses only occurred in environments where 0–500 m SRH and ω<sub>s</sub> exceeded 110 m<sup>2</sup> s<sup>–2</sup> and 0.015 s<sup>–1</sup>, respectively. The presence vs. absence of this dynamical response may explain why environments with higher 0–500 m SRH and ω<sub>s</sub> correspond to greater tornado probabilities.</p>
Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parameterization in WRF
<p>This repository contains namelists needed to reproduce the WRF(V4.4) simulations analyzed in "Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parameterization in WRF"</p>
Precipitation, low-level jet, and geopotential height data for analyzing sources of predictability in the US northern Great Plains
<p>Dec 15, 2021</p> <p> </p> <p><strong>Precipitation, low-level jet, and geopotential height data for analyzing sources of predictability in the US northern Great Plains</strong></p> <p> </p> <p>Carlos M. Carrillo and Francisco Muñoz-Arriola</p> <p> </p> <p><strong>Motivation</strong></p> <p>The data presented here was used to investigate the uskills of precipitation in the US northern Great Plains, and it can be cited as described below. The original data for producing this data is from the Climate Forecast System (CFS) retrospective reanalysis and reforecast as well as precipitation data from the Climate Prediction Center (CPC) from the National Oceanic and Atmospheric Administration (NOAA). Also, gridded data is from the North American Regional Reanalysis (NARR) from the National Centers for Environmental Prediction (NCEP).</p> <p> </p> <p><strong>License </strong></p> <p>Creative Commons CC-BY</p> <p><strong>Disclaimer</strong></p> <p>The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.</p> <p><strong>Description of the dataset</strong></p> <p>Files are provided with the following features:</p> <p><strong>List of cases: </strong></p> <p> files.0.00.dy.txt</p> <p><strong>Low-level jet (or the GP-LLJ index)</strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/</p> <p>Master file:<strong> LLJ_pc_corr_1D_pdf_full.m</strong></p> <p>With input data</p> <p> from CFS models,</p> <p> eof1.v850.cfs.1982-2009.dy.tar</p> <p> pc1.v850.cfs.1982-2009.dy.tar</p> <p> from NARR model,</p> <p> pc1.vwnd.narr.1982-2009.tar</p> <p><strong>The geopotential height (or CGT index): </strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/</p> <p>Master file:<strong> Z200_mode_corr_1D_pdf_full.m</strong></p> <p>With input data</p> <p> xt-reco-z200.Full.123.z200.cfs.1982-2009.12-60.tar</p> <p> xt-reco-z200.Full.z200.narr.1982-2009.bin.tar</p> <p><strong>Precipitation at the US Great Plains:</strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/clim/yrcases</p> <p>Master file: <strong>prec_corr_cfs_1D_pdf_full.m</strong></p> <p>With input data:</p> <p> prec.cfs.MW.1982-2009.tar</p> <p> prec.cpc.MW.1982-2009.tar</p> <p><strong>Correlation patterns:</strong></p> <p> Precipitation: PREC.NGP.corr.txt</p> <p> LLJ: LLJ.pcs.corr.narr.pdf.txt</p> <p> Z200: Z200.pcs.corr.narr.pdf.txt</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.</p> <p><strong>Description of the dataset</strong></p> <p>Files are provided with the following features:</p> <p><strong>List of cases: </strong></p> <p> files.0.00.dy.txt</p> <p><strong>Low-level jet (or the GP-LLJ index)</strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/</p> <p>Master file:<strong> LLJ_pc_corr_1D_pdf_full.m</strong></p> <p>With input data</p> <p> from CFS models,</p> <p><strong> </strong>eof1.v850.cfs.1982-2009.dy.tar</p> <p> pc1.v850.cfs.1982-2009.dy.tar</p> <p> from NARR model,</p> <p> pc1.vwnd.narr.1982-2009.tar</p> <p><strong>The geopotential height (or CGT index): </strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/</p> <p>Master file:<strong> Z200_mode_corr_1D_pdf_full.m</strong></p> <p>With input data</p> <p> xt-reco-z200.Full.123.z200.cfs.1982-2009.12-60.tar</p> <p> xt-reco-z200.Full.z200.narr.1982-2009.bin.tar</p> <p><strong>Precipitation at the US Great Plains:</strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/clim/yrcases</p> <p>Master file: <strong>prec_corr_cfs_1D_pdf_full.m</strong></p> <p>With input data:</p> <p> prec.cfs.MW.1982-2009.tar</p> <p> prec.cpc.MW.1982-2009.tar</p> <p><strong>Correlation patterns:</strong></p> <p> Precipitation: PREC.NGP.corr.txt</p> <p> LLJ: LLJ.pcs.corr.narr.pdf.txt</p> <p> Z200: Z200.pcs.corr.narr.pdf.txt</p> <p><strong>Credit</strong></p> <p>Carlos M. Carrillo and Francisco Muñoz-Arriola, 2021: “Sources of Subseasonal Predictability of Rainfall in the Northern Great Plains”, <em>Journal of Applied Meteorology and Climatology</em>. In review.</p> <p><strong>Grant funding</strong></p> <p>This research was funded by the U.S. Geological Survey (USGS), the U.S. Department of Agriculture (USDA), the Daugherty Water for Food Global Institute (DWFI) at the University of Nebraska-Lincoln (UNL), and the UNL’s Layman Award.</p>
Dataset for "Unravelling the Origin of Ultra-Low Conductivity in SrTiO3 Thin Films: Sr Vacancies and Ti on A-Sites Cause Fermi Level Pinning"
<p>Raw data used in the paper "Unravelling the Origin of Ultra-Low Conductivity in SrTiO3 Thin Films: Sr Vacancies and Ti on A-Sites Cause Fermi Level Pinning", available at DOI: 10.1002/adfm.202202226.</p> <p>Data include impedance spectra, extracted conductivities and defect chemical mdoel calculations:</p> <p>Impdance.csv: Impedance data for differently doped STO thin films</p> <p>Fe_04_10.csv: Conductivity vs. temperature for differntly depoed Fe:STO films<br> UD_Al_Ni.csv: Conductivity vs. temperature for undoped, Al-doped and Ni-doped films<br> 0_4Fe.csv: Conductivity vs. temperature for 0.4 % Fe doped films<br> pO2.csv: Conductivity vs. pO2 at different temperatures for 0.4 % Fe doped films</p> <p>Optimal_Brouwer.csv: Brouwer diagram for STO where A site Ti counterbalances Sr vacancies<br> Buffer_Curve.csv: Variation in the ratio of Sr vacancies to A site Ti<br> Site_Equilibrium.csv: Equilibrium defect concentrations for varying standard Gibbs free energy of the site change reaction<br> Ti_Switch: Total Gibbs free energy (and defects) for varying extent of site change reaction (amount of Ti on A site)<br> ParameterSpace.csv: Conductivity as a function of Standard Gibbs free energy of site change and energy level of Sr vacancies<br> ParameterSpaceXsi.csv: Conductivity as a function of extent of site change and energy level of Sr vacancies</p> <p>Fe_Optimal_Brouwer.csv: Brouwer diagram for Fe:STO where A site Ti counterbalances Sr vacancies<br> Fe_Buffer_Curve.csv: Variation in the ratio of Sr vacancies to A site Ti<br> ParameterSpaceIronG.csv: Conductivity as a function of Fe doping and Standard Gibbs free energy of site change</p>
Text-fig. 3. a: Panoramic reconstruction of the portion of the Govone outcrop from intervals GLA10 to GLA20 in condition of low river level. b: Transported leaf assemblage in the bottom part of bed GLA20. c: Detail of the outcrop of the leaf-bearing bed GLA20 and the underlying wood-rich layer GLA19. in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results
Text-fig. 3. a: Panoramic reconstruction of the portion of the Govone outcrop from intervals GLA10 to GLA20 in condition of low river level. b: Transported leaf assemblage in the bottom part of bed GLA20. c: Detail of the outcrop of the leaf-bearing bed GLA20 and the underlying wood-rich layer GLA19.
Figure 4: Low Level view designed by pedagogic user
<p>The low level view of the Treasure Hunt metaphor is represented in the<br> ¯gure 4. In the ¯gure 4 it is possible to see how each avatar must click to<br> the object and answer to a question that the system asks. In each session<br> there are also two avatar named Guide and Helper. These avatars do not have<br> interaction with other avatars because they will give support to other avatars<br> involved in the session.<br> 83</p>
The geometry of low- and high-level perceptual spaces
<p>Processed data accompanying the manuscript 'The geometry of low- and high-level perceptual spaces' by Waraich and Victor.</p> <p>Waraich, S. A., & Victor, J. D. (2024). The Geometry of Low- and High-Level Perceptual Spaces. Journal of Neuroscience, 44(4). doi: 10.1523/JNEUROSCI.1460-23.2023</p> <p>The upload includes stimuli used for experiments, the code to create more stimuli, coordinates of perceptual spaces for all stimulus domains and subjects, and similarity judgments. </p> <p>Code for running experiments and analysis is on GitHub. Please see manuscript for the links. </p>
Fig. 2 in Low Level Genetic Diversity of Opalinid Morphotypes from the Digestive Tract of Hoplobatrachus rugulosus (Batrachia, Amphibia) in Thailand
Fig. 2. Scanning electron micrographs of opaline cells fixed in 2.5% glutaraldehyde. All opaline cells are covered with flagella throughout their body. (A) Opalinid cell with somatic ridges caused by the flagellar metachronal beating clearly defined in a spiral arrangement. (B) Fan-shaped opalinid cell with a broad anterior end and tapering posterior. (C) Uniformly elongated opalinid cell with a slightly tapering posterior end. (D) Fan-shaped opalinid cell of a similar morphotype to B but of a smaller size. Scale bar: A, C and D: 50 μm; B: 10 μm.
Fig. 1 in Low Level Genetic Diversity of Opalinid Morphotypes from the Digestive Tract of Hoplobatrachus rugulosus (Batrachia, Amphibia) in Thailand
Fig. 1. Light micrographs of opaline cells from life. (A) The opaline falx (arrow). (B) Multiple nuclei throughout the cell (arrowheads). A bend at the mid-body level is noticeable. (C) Flagella covering the body of the opaline cell (arrow). Several scores of nuclei are clearly visible (small circular clear whitish areas). (D) Prominent ridges signifying the metachronal beating of the flagella cover the opaline body in a random arrangement (arrow). (E) Flagella beating in a metachronal fashion (arrow). Opalinid cell nuclei (arrowheads). Scale bar: A and C: 100 μm; B, D and E: 50 μm.
Text-fig. 2. The relation between number of genera and localities in MN 5 of central Europe (CE) and the Iberian Peninsula (IB). a. Localities ordered from highest to lowest number of taxa; the distribution is more equitable in the Iberian Peninsula. b. Genera ordered from most common to rarest; the distribution of genera is more equitable in central Europe. The low number of common genera is an artefact of incomplete faunal lists (see text). in Generically Speaking, A Survey On Neogene Rodent Diversity At The Genus Level In The Now Database
Text-fig. 2. The relation between number of genera and localities in MN 5 of central Europe (CE) and the Iberian Peninsula (IB). a. Localities ordered from highest to lowest number of taxa; the distribution is more equitable in the Iberian Peninsula. b. Genera ordered from most common to rarest; the distribution of genera is more equitable in central Europe. The low number of common genera is an artefact of incomplete faunal lists (see text).
Microsatellite data of the paper "A putatively new ant species from the Cataglyphis cursor group displays low levels of polyandry with standard sexual reproduction"
<p>Fifty colonies of the ant Cataglyphis cursor were sampled at their nest entrance in two localities separated by 79km in the plain of Avila, west of Madrid, in July 2015 for Salobralejo (27 colonies) and in April 2014 for Castrillo de Guareña (23 colonies). Only workers near the nest entrance were collected. In Salobralejo, we also collected nine gynes at the nest entrance in three colonies. After collection, the individuals were preserved in 95% Ethanol (with 5% Tris-EDTA). From the 50 colonies sampled, we genotyped a single worker per colony for 37 colonies (Table S1). For the other 13 colonies, 114 workers were collected and genotyped to assess the colony genetic structure (86 workers from nine colonies in Salobralejo and 28 workers from four colonies in Castrillo). We also genotyped the nine gynes found in three colonies in Salobralejo. A total of 160 individuals were screened for thirteen microsatellite loci used by Eyer et al. (2023) and two supplementary loci (L76 and L3653) were amplified. The L26 locus was excluded because of amplification failures in nine individuals (18% of samples) and a highly significant Hardy-Weinberg disequilibrium (<em>P</em> < 00001 for both sites). </p> <p>The file contain the locality (Salobralejo or Castrillo), the colony number and individual identification number (code), the caste (worker or gyne). For each locus, the two alleles are provided and characterised by their size. </p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
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