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7,438 results for “surface”
Utah FORGE 2024 stimulations: Microseismic event catalog from seismic surface network
<p>This microseismic catalog of the 2024 stimulations at Utah FORGE is based on the surface monitoring network consisting of 5 permanent seismic stations deployed by the University of Utah Seismograph Stations and a temporary deployment of 144 nodal geophones.<br> <br>The catalog and its compilation are described in:<br>Niemz et al. (2024). Mapping Fracture Zones with Nodal Geophone Patches: Insights from Induced Microseismicity during the 2024 Stimulations at Utah FORGE. Submitted to Seismological Research Letters.<br> <br>The local coordinate system is relative to well 16A(78)-32 (lat=38.50402147, lon=-112.8963897, elev=1650m). The magnitudes in this catalog were calibrated based on the local magnitude provided by the University of Utah Seismograph Stations. They are not authoritative.</p>
Landsat bands (cloud free), tree cover (2000, 2010), bare-ground and surface water occurrence at 250 m based on GlobalForestWatch and USGS
<p>Landsat bands (cloud free) and tree cover (2000) based on Hansen et al. (2013), global surface water occurrence based on Pekel at al. (2016), and tree cover and bare-ground cover (2010) based the USGS land cover mapping projects (University of Maryland, Department of Geographical Sciences and USGS). All layers resampled to spatial resolution 1/480 d.d. (about 250 m) using gdalwarp with "average" resampling. Antarctica is not included. Original layers are available at 30 m resolution.</p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>lcv = theme: land cover,</li> <li>bareground = variable: occurrence of bareground,</li> <li>landsat.usgs = determination method: Landsat landcover at 30 m resolution project (https://landcover.usgs.gov/glc/),</li> <li>p = probability or fraction,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2010..2010 = time reference: year 2010,</li> <li>v1.0 = version number: 1.0,</li> </ul>
A Surface-Induced Asymmetric Program Promotes Tissue Colonization by a Human Pathogen - Supplemental data Fig 4A
<p>Raw data used for Fig. 4A of the article "A Surface-Induced Asymmetric Program Promotes Tissue Colonization by a Human Pathogen" published in Cell Host & Microbe. This Western Blot dataset is composed of 4 images:</p> <ul> <li>Western Blot 1: whole cell lysate (raw image, and annotated image)</li> <li>Western Blot 2: purified pili (raw image, and annotated image)</li> </ul>
Human Bony Labyrinth: Co-Registered CT and micro-CT Images, Surface Models and Anatomical Landmarks
<p>This data set consists of 23 specimens of the human bony labyrinth. For each specimen clinical CT (0.15×0.15×0.2 mm3, voxel size) and co-registered microCT (0.06 mm isotropic voxel size) images are available. Image labels for the bony labyrinth are provided for the same image coordinates. From the image labels, 3D surface models were generated. In addition, each specimen has a descriptor file containing the coordinates of anatomical landmarks as well as a cochlear coordinate system. The data set can be used to study the morphology of the inner ear or to evaluate (semi-)automated segmentation algorithms (e.g., for the preoperative planning of surgical procedures such as cochlear implantation).</p>
Table S27: Target and identified unknown organic micropollutants detected in surface water samples taken during heavy rain events
<p>In the following table, peak intensities of detected organic micropollutants in water samples are displayed.</p> <p>This data table is part of the appendix of Chapter 4 of the PhD thesis “Novel approaches to identify drivers of chemical stress in small rivers” by Liza-Marie Beckers prepared at RWTH Aachen University and at the Helmholtz Centre for Environmental Research-UFZ. In Chapter 4, precipitation-related pollutant patterns and indicator compounds during heavy rain events were identified in the Holtemme River by nontarget screening and cluster analysis. The table contains peak heights of organic micropollutants detected in water samples taken during heavy rain events in the Holtemme River (Saxony – Anhalt, Germany). The table is structured into the following columns: Compound name, use class of compound (e.g., pharmaceutical or pesticide), distinction between target or identified unknown compounds, mass-to-charge ratio (m/z), retention time (RT), assignment to a pattern identified by cluster analysis (i.e., “Base” or “Quick”), the probability of belonging to the assigned pattern as number between 0 and 1 as well as the peak height of the compound in each sample. The samples are indicated by "B" for "bottle" and a number from 1-16. The use class “NA” indicates that now major use class for this compound could be identified.</p> <p>The sampling was triggered by combined sewer overflow at a wastewater treatment plant upstream of the sampling point. Samples were taken by an automated sampler in 30-min composite samples for 8 hours resulting in 16 samples per rain event. In total, 6 heavy rain events from May to September 2016 were sampled during this study. The table is divided into 6 subtables (i.e., Table S27 A-F). Each subtable displays compounds and their peak heights detected in samples from one heavy rain event. The different rain events are abbreviated by the sampling date:</p> <p>Table S27A displays results from the rain event samples May 29<sup>th</sup> 2016 : E2905</p> <p>Table S27B displays results from the rain event samples June 01<sup>st</sup> 2016 : E0106</p> <p>Table S27C displays results from the rain event samples June 24<sup>th</sup> 2016 : E1306</p> <p>Table S27D displays results from the rain event samples June 13<sup>th</sup> 2016 : E2406</p> <p>Table S27E displays results from the rain event samples July 13<sup>th</sup> 2016 : E1307</p> <p>Table S27F displays results from the rain event samples September 17<sup>th</sup> 2016 : E1709</p> <p>Chemical analysis of the water samples was performed by liquid chromatography (UltiMate 3000 LC system (Thermo Scientific)) coupled to high resolution mass spectrometry (Q Exactive Plus, Thermo Scientific) with a heated electrospray ionization (HESI) source. Nontarget screening was performed as it allows for a comprehensive characterization of the chemical exposure during heavy rain events. However, only annotated target compounds and unknown compounds identified by structure elucidation are presented in the table. Details on data evaluation methods are described in Chapter 4 of the PhD thesis.</p> <p>Beckers, L.M. (2019): Novel approaches to identify drivers of chemical stress in small rivers. RWTH Aachen University, Aachen.</p>
A European aerosol phenomenology – 9: LIGHT ABSORPTION PROPERTIES OF CARBONACEOUS AEROSOL PARTICLES ACROSS SURFACE EUROPE
<p>Carbonaceous aerosols (CA), composed of black carbon (BC) and organic aerosols (OA), exert an important role on the climate system through their interaction with solar radiation. Light absorption properties of CA particles are of special interest due to their important contribution to global and regional warming. Among atmospheric particulate matter (PM), BC and the absorbing components of OA (or brown carbon, BrC) are characterized by the highest absorption efficiency but their role in the current climate change, especially that of BrC, is still uncertain. Here we present the absorption properties of BC and BrC PM at 44 sites across Europe using aethalometer data collected at different types of environment (6 traffic (TR), 16 urban (UB), 7 suburban (SUB), 10 regional background (RB) and 5 mountain (M) sites). The absorption Ångström exponent (AAE) method was used to assign total measured absorption to the contributions of BC (bAbs,BC) and BrC (bAbs,BrC) to total absorption (bAbs). The results showed a clear dependence of the absorption coefficients bAbs, bAbs,BC and bAbs,BrC on station settings as follows: TR > UB > SUB > RB > M, even if significant exceptions were observed. The relative contribution of bAbs,BrC to bAbs (%AbsBrC) at 370 nm was on average lower at traffic sites (11-20%) reaching at some SUB and RB sites median annual values that accounted for more than 30% and 10% of the absorption at 370 and 660 nm, respectively. The median AAE of CA particles was correspondingly low at TR sites (1.1-1.2) where internal combustion engines dominated the CA mass concentration. Low AAE were also observed at some remote RB and M sites, likely due to the lack of proximity from BrC sources or lack of sufficiently strong secondary processes resulting in BrC. On average, AAE was lower in Western Europe (<1.3) compared to Eastern Europe (>1.3), likely due to a more extensive use of coal and biomass burning in eastern countries. The median AAE of BrC PM (AAEBrC) showed a wide range of values, from 2.5 to 6, with no clear relationship with station background or region. Assessing the seasonal variability revealed, overall, an increase of bAbs, bAbs,BC, bAbs,BrC in winter, which was attributed to meteorological conditions and more heating related emissions. Accordingly, bAbs,BrC exhibited a stronger increase than bAbs,BC, resulting in higher AAE and %AbsBrC during the winter season. The diel cycles differed between bAbs,BC and bAbs,BrC, with bAbs,BC showing the bimodal peaks during the morning and evening rush hours, whereas bAbs,BrC, together with %AbsBrC, AAE and AAEBrC, peaked at night. Decade-long trend analysis performed for a subset of stations across Europe revealed a decrease of bAbs, driven by declining bAbs,BC, whereas, overall, bAbs,BrC, %AbsBrC and AAE increased with time. This strongly implies an efficient reduction of BC mass concentrations from traffic sources in Europe and a less effective reduction of emissions from BrC sources. The observed increasing trends of AAE reflected a progressive change in the chemical composition of CA particles driven by a relative increase/decrease of BrC/BC content in CA with time.</p>
Voxel-level summary statistics of hippocampus shape, white matter microstructure, and cortical surface curvature in UK Biobank (n=33,324)
<p>This deposit hosts GWAS summary statistics of hippocampus shape (n=33,324), white matter microstructure (n=33,324), and cortical surface curvature (n=15,752) using UKB unrelated white subjects. The data was generated by using the highly efficient imaging genetics (<a href="https://github.com/Zhiwen-Owen-Jiang/heig">HEIG v1.1.0</a>) framework where only the triplets - summary statistics of low-dimensional representations (LDRs), the functional bases, and the variance-covariance matrix LDRs - are shared, which is sufficient to recover all voxel-variant pairs as well as to conduct voxel-level heritability and (cross-trait) genetic correlation analysis. Check the <a href="https://github.com/Zhiwen-Owen-Jiang/heig/wiki">tutorial</a> and the <a href="../records/13770930">example data</a> used in the tutorial. </p> <p>The shared data includes:</p> <p>1. Triplets for hippocampus shape measured by the radial distance from the medial model for each vertex. The original images contain 30,000 vertices while the shared data contains 49 LDRs. Left and right hemispheres were analyzed separately, each with 15,000 vertices.</p> <p>2. Triplets for 21 white matter tracts measured by fractional anisotropy. The original images contain 32,217 voxels and each tract contains 88 ~ 3503 voxels while the shared data contains 1,034 LDRs. Tracts were analyzed separately.</p> <p>3. Triplets for cortical surface curvature. The original images contain 59,412 vertices while the shared data contains 1,750 LDRs. The entire brain was analyzed as a whole.</p> <p>4. LD matrix and its inverse for 22 chromosomes including 460k genotyped SNPs. LD matrix and its inverse were estimated by using two separate datasets each containing 8.4k white unrelated subjects in UKB. Two regularization levels are provided: {85%, 80%} for heritability and genetic correlations within images and {75%, 70%} for cross-trait genetic correlations.</p> <p>5. LD matrix and its inverse for 22 chromosomes including 1.2 million imputed HapMap3 SNPs. LD matrix and its inverse were estimated by using two separate datasets each containing 42k white unrelated subjects in UKB. Two regularization levels are provided: {98%, 95%} for heritability and genetic correlations within images and {90%, 85%} for cross-trait genetic correlations.</p>
Arctic Gridded surface cloud fraction radiative kernels (GCF-CRKs)
<p><span>These <a name="OLE_LINK1"></a>gridded surface cloud fraction radiative kernels (GCF-CRKs) are created by integrating refined downwelling surface shortwave radiation (DSSR) estimates and a high-precision cloud fraction (CF). The DSSR is corrected by a CF-dependent model, which leveraging the correlation between the top-of-atmosphere (TOA) shortwave radiative parameters and surface radiation, combined with high-precision fused CF datasets from multiple satellite sources. </span></p> <p><span><span> </span>There are five individual files. “SFC_SW_Kernel_Arc.nc” is for CRKs of all clouds, “SFC_SW_lowcloud_Kernel_Arc.nc” is for CRKs of low-level clouds, “SFC_SW_midlowcloud_Kernel_Arc.nc” is for CRKs of mid-low-level clouds, “SFC_SW_midhighcloud_Kernel_Arc.nc” is for CRKs of mid-high-level clouds, and “SFC_SW_highcloud_Kernel_Arc.nc” is for CRKs of high-level clouds. The four cloud layers are derived from four pressure layers (surface to 700 hPa, 700-500 hPa, 500-300 hPa, and 300-50 hPa, representing low, middle-low, middle-high, and high clouds, respectively) based on the CERES-SYN stratification standard.</span></p> <p><span> </span></p> <p><span>The file format is netcdf4, and was created by Matlab. To read these files, any software supporting netcdf4 can be used. These files only involved sunlit months from Apr to Sep during 2000-2020, with the longitude from -180°~180° and the latitude from 60°N~90°N.</span></p>
Calibrated Earthquake Relocations from the TexNet Catalog (2017–2022) and Vertical Surface Deformation (2016–2022)
<p>This repository contains the relocated earthquake catalog for the Southern Delaware Basin, as presented in the manuscript titled "<em><strong>Insights into Spatiotemporal Evolution of Induced Earthquakes in the Southern Delaware Basin Using Calibrated Relocations from the TexNet Catalog (2017-2022)</strong>".</em></p> <h3>Citations:</h3> <p>Asiye Aziz Zanjani, Heather R. DeShon, Vamshi Karanam, Alexandros Savvaidis; <strong>Insights into Temporal Evolution of Induced Earthquakes in the Southern Delaware Basin Using Calibrated Relocations <em>from the TexNet Catalog (2017-2022</em>) (2025)</strong>. <em>Earth and Space Science, 12 (6), e2024EA004027. <a title="https://doi.org/10.1029/2024EA004027" href="https://doi.org/10.1029/2024EA004027"><strong>https://doi.org/10.1029/2024EA004027</strong></a></em></p> <p>The earthquake relocations were conducted using the Hypocentroidal Decomposition technique with the <strong>open-source MLOC code</strong>, achieving enhanced spatial resolution for over 5,000 events from the TexNet catalog. The relocated catalog includes critical hypocentral parameters—latitude, longitude, depth—as well as origin time, associated uncertainties, and magnitude for each event.</p> <p>This dataset is an essential resource for analyzing the spatiotemporal patterns of induced seismicity associated with anthropogenic activities, such as shallow fluid injection, in the Southern Delaware Basin following the operation of TexNet in 2017. It is suitable for use in seismic hazard assessments, modeling studies, and comparisons with other induced seismicity datasets. Additional data produced during this research includes vertical surface deformation measurements from 2016 through the end of 2022.</p> <p>The repository also contains data referenced in the manuscript’s “Data Availability Statement” and “Open Research” sections. </p> <p>List of files attached to this repository:</p> <ul> <li><strong>catalog.xls</strong>: Primary earthquake relocated catalog developed in this study</li> <li><strong>2016_2022_deformation.csv</strong>: Vertical displacement data (2016–2018) developed in this study</li> <li><strong>2019_2022_deformation.csv</strong>: Vertical displacement data (2016–2022) developed in this study</li> <li><strong>post-2017-injection.xlsx</strong>: Injection data from the Railroad Commission of Texas (<a href="https://www.rrc.texas.gov">source</a>)</li> <li><strong>Hydrofracking-post2017.xlsx</strong>: Hydrofracking well data from FracFocus (<a href="https://fracfocus.org">source</a>)</li> <li><strong>GrowClust-common.xls</strong>: 2-D GrowClust catalog for supplemental information (<a href="https://hirescatalog.texnet.beg.utexas.edu/">source</a>), https://doi.org/10.15781/76hj-ed46</li> <li><strong>TexNet-Catalog</strong>: Initial TexNet catalog's origin and phase data, https://doi.org/10.7914/SN/TX</li> </ul>
Dataset for: Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning
<p>Dataset for: Importance of satellite observations for high-resolution mapping of near-surface NO<sub>2 </sub>by machine learning</p> <p>This dataset is uploaded as a part of the article by Kim et al. (2021). The dataset is the hourly maps of near-surface nitrogen dioxide (NO<sub>2</sub>) concentrations at 100 m resolution for an Alpine domain (Switzerland and northern Italy, 6-12 °E, 42-48 °N). The dataset is provided per day (24 hours) in a netcdf (*.nc ~550MB). In this work, we have generated NO<sub>2 </sub>hourly maps for Feb. 2019 to May 2020 and, here, we upload for March 2019 only (~16 GB). If you need data for another period of time, please contact Gerrit Kuhlmann (gerrit.kuhlmann@empa.ch) or Minsu Kim (minsu.kim@empa.ch). </p>
Spectral dataset of daylights and surface properties of natural objects measured in Japan
<p>This is a spectral dataset of natural objects and daylights collected in Japan. </p> <p>We collected 359 natural objects and measured the reflectance of all objects and the transmittance of 75 leaves. We also measured daylights from dawn till dusk on four different days using a white plate placed (i) under the direct sun and (ii) under the casted shadow (in total 359 measurements). We also separately measured daylights at five different locations (including a sports ground, a space between tall buildings and a forest) with minimum time intervals to reveal the influence of surrounding environments on the spectral composition of daylights reaching the ground (in total 118 measurements).</p> <div> <div> <div> <p>If you use this dataset in your research, please cite the following publication.</p> </div> </div> </div> <div> <div> <div> </div> </div> </div> <div>Morimoto, T., Zhang, C., Fukuda, K., & Uchikawa, K. (2022). Spectral measurement of daylights and surface properties of natural objects in Japan. <em>Optics express</em>, <em>30</em>(3), 3183. https://doi.org/10.1364/OE.441063</div> <p> </p> <p>Dataset contains following Excel spread sheets and csv files:</p> <p><strong>(A) Surface properties of natural objects</strong></p> <p><strong> (A-1) Reflectance_ver1-2.xlsx and .csv</strong></p> <p><strong> (A-2) Transmittance_FrontSideUp_ver1-2.xlsx and .csv</strong></p> <p><strong> (A-2) Transmittance_BackSideUp_ver1-2.xlsx and .csv</strong></p> <p><strong>(B) Daylight measurements</strong></p> <p> <strong>(B-1) Daylight_TimeLapse_v1-2.xlsx and .csv</strong></p> <p> <strong>(B-2) Daylight_DifferentLocations_v1-2.xlsx and .csv</strong></p> <p> </p> <p>Data description</p> <p><strong>(A) Surface properties</strong></p> <p><strong>(A-1) Reflectance_ver1-2.xlsx and .csv</strong></p> <p>This file contains surface spectral reflectance data (380 - 780 nm, 5 nm step) of 359 natural objects, including 200 flowers, 113 leaves, 23 fruits, 6 vegetables, 8 barks, and 9 stones measured by a spectrophotometer (SR-2A, Topcon, Tokyo, Japan). Photos of all samples are included in the .xlsx file.</p> <p>For the analysis presented in the paper, we identified reflectance pairs that have a Pearson’s correlation coefficient across 401 spectral channels of more than 0.999 and removed one of reflectances from each pair. The column 'Used in analysis' indicates whether or not each sample is used for the analysis (TRUE indicates used and FALSE indicate not used).</p> <p>At the time of collection, we noted the scientific names of flowers, leaves and barks from a name board provided by the Tokyo Institute of Technology in which samples are collected. If not available, we used a smartphone software which automatically identifies the scientific name from an input image (<em>PictureThis - Plant Identifier</em> developed by Glority Global Group Ltd.). The names of 2 flowers and 9 stones whose name could not be identified through either method were left blank.</p> <p><strong>(A-2) Transmittance_FrontSideUp_v1-2.xlsx and .csv</strong></p> <p>This file contains surface spectral transmittance data (380 - 780 nm, 5 nm step) for 75 leaves measured by a spectrophotometer (SR-2A, Topcon, Tokyo, Japan). Photos of all samples are included in the .xlsx file.</p> <p>For this data, the transmittance was measured with the front-side of leaves up (the light was transmitted from the back side of the leaves). This is the data presented in the associated article.</p> <p><strong>(A-3) Transmittance_BackSideUp_v1-2.xlsx and .csv</strong></p> <p>Spectral transmittance data of the same leaves presented in (A-2).</p> <p>For this data, the transmittance was measured with the back-side of leaves up (the light was transmitted from the front side of the leaves).</p> <p> </p> <p><strong>(B) Daylight measurements</strong></p> <p><strong>(B-1) Daylight_TimeLapse_ver1-2.xlsx and .csv</strong></p> <p>This file contains daylight spectra from sunrise to sunset on four different days (2013/11/20, 2013/12/24, 2014/07/03 and 2014/10/27) measured by a spectrophotometer (SR-LEDW, Topcon, Tokyo, Japan) with a wavelength range from 380 nm to 780 nm with 1 nm step. We measured the reflected light from the white calibration plate placed either under a direct sunlight or under a casted shadow.</p> <p>The column 'Cloud cover' provides visual estimate of percentage of cloud cover across the sky at the time of each measurement. The column 'Red lamp' indicates whether an aircraft warning lamp at the measurement site was on (circle) or off (blank).</p> <p><strong>(B-2) Daylight_DifferentLocations_ver1-2.xlsx and .csv</strong></p> <p>This file includes daylight spectra measured at five different sites within the Suzukakedai Campus of Tokyo Institute of Technology with minimum time gap on 2014/07/08, using a spectroradiometer (IM-1000, Topcon) from 380 nm to 780 nm with 1 nm step. The instrument was oriented either towards the sun or towards the zenith sky. When the instrument was oriented to the sun, we measured spectra in two ways: (i) one using a black cylinder covering the photodetector and (ii) the other without using a cylinder.</p> <p>The column 'Cylinder' indicates whether the black cylinder was used (circle) or not (cross). The column 'Cloud cover' shows the visual estimate of percentage of cloud cover at the time of each measurement. The column 'Sun hidden in clouds' denotes whether the measurement was taken when the sun was covered by clouds (circle) or not (blank).</p>
Daily Anomalies of the Surface Atmospheric Fluxes of the Brazilian Northeast (DASAF-BNE)
<p>This dataset contains the daily anomalies of the main atmospheric fluxes throughout the Brazilian NE region. Geographically it is framed at 42.5°W - 29.75°E/20.5°S - 1.25°N, in the time range from 1979-01-01 12:00:00 to 2017-12-31 12:00:00. The DASAF-BNE dataset with a spatial resolution of 1 degree was the basis for the calculation of the daily anomalies, they were interpolated by the bilinear method to obtain a resolution of 0.25 degrees.</p>
Surface water and flooding dynamics based on seasonally continuous Landsat data (1986-2011) in a dryland river basin (monthly, seasonally, and yearly animations)
<p>The animations provided here are part of the following publication:<br> Tulbure, M.G. and M. Broich (2018). Spatiotemporal patterns and effects of climate and land use on surface water extent dynamics in a dryland region with three decades of Landsat satellite data. Science of the Total Environment. https://www.sciencedirect.com/science/article/pii/S0048969718347466</p> <p>Please refer to the above mentioned publication for a description of the data and interpretation of the patterns.</p> <p>The animations are based on statistically validated surface water and flooding extent dynamics data derived from seasonally continous Landsat TM/ETM+ and random forest models from 1986 to 2011 over Australia's Murray-Darling Basin. The overall accuracy was over 99% and producer's accuracy for water 87% +/- 3%. </p> <p>The method is described in the following publication: <br> Tulbure, M.G., M. Broich, S.V. Stehman, A. Kommareddy. (2016). Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sensing of Environment. 178: 142-157 and available here: https://www.sciencedirect.com/science/article/pii/S0034425716300621 </p>
Global Carbon Budget 2022, surface ocean fugactiy of CO2 (fCO2) and air-sea CO2 flux of individual Global ocean biogechemical models and surface ocean fCO2-based data-products
<p><strong>Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (data-products).</strong><br> There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs. </p> <p><strong>Note: </strong>These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. <strong>The best estimates of the annual global ocean carbon sink, based on the native grids of data-products and GOBMs and with the adjustments described in the Global Carbon Budget 2022 (https://doi.org/10.5194/essd-14-4811-2022, section C3), are available in the Global Carbon Budget 2022 spreadsheet.</strong></p> <p>The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 13 of the Global Carbon Budget 2022 paper (https://doi.org/10.5194/essd-14-4811-2022), the river flux adjustment needs to be added to the CO2 flux estimated from the data-products (North: 0.17 GtC yr-1, Tropics: 0.16 GtC yr-1, South: 0.32 GtC yr-1, see GCB 2022 paper, section 2.4.1). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because adjustments were applied only for global fluxes.</p> <p><strong>What is in the files?</strong></p> <p>(1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly):</p> <p><br> fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: north, tropics, south<br> fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br> sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br> area: Area per pixel, dimensions: latitude, longitude<br> area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south</p> <p>(2) The files for the GOBMs contain the following fields, for simulation A (‘contemporary simulation’, including effects of rising CO2, climate change and variability) and simulation B (‘control simulation’, constant CO2, no climate change and variability). Temporal resolution: monthly</p> <p>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br> sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br> area: Area per pixel, dimensions: latitude, longitude</p> <p><br> (3) One file ‘GCB-2022_OceanModel_RegionalBreakdown_1959-2021.nc’ with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Temporal resolution: annual.</p> <p><br> <strong>Fair data use statement:</strong><br> The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use.<br> <strong>Citation:</strong> Please cite the Global Carbon Budget 2022 (Friedlingstein et al., 2022, ESSD, https://doi.org/10.5194/essd-14-4811-2022) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2022 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720).<br> <strong>Acknowledgement:</strong> Please add the following text in the acknowledgement of your paper: “We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output.”<br> <strong>Co-authorship: </strong>An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication.</p> <p><br> Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional output</strong> from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudget.org/</p> <p> </p>
Surface Morphometrics Quantifications
<p>Quantifications, including per-mitochondrion-analysis, associated with the manuscript "Quantifying organellar ultrastructure in cryo-electron tomography using a surface morphometrics pipeline" - https://www.biorxiv.org/content/10.1101/2022.01.23.477440v3</p>
Water stable isotope, temperature and electrical conductivity dataset (snow, ice, rain, surface water, groundwater) from a high alpine catchment (2019-2021).
<p>Data collected in the Otemma forefield in Switzerland (45°56’03”N,7°24’42”) from July 2019 to October 2021.<br> Data were collected by the research teams of Bettina Schaefli<sup>2</sup> and Stuart N. Lane<sup>1</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> </ul> <p><strong>Description of the dataset</strong></p> <p>This dataset contains water stable isotope (δ<sup>2</sup>H, δ<sup>17</sup>O, δ<sup>18</sup>O), water temperature and water electrical conductivity (EC) measurements collected from the Otemma glacier catchment.</p> <p>All water isotope samples were collected directly from the source and stored in 12 mL amber glass vials with an air-tight caps. River samples were first collected with an automatic ISCO 6712 portable water sampler with 1L open plastic bottles and transferred in 12 mL vials every one to two weeks. All isotope analysis were performed using a Wavelength-Scanned Cavity Ring Down Spectrometer (Picarro 2140-I, Santa Clara, California, USA) and expressed relative to the international Vienna Standard Mean Ocean Water (VSMOW) standards.</p> <p>All EC and water temperature measurements were performed with a WTW Multi 3510 IDS logger with a IDS TetraCon® 925 probe.</p> <p>The dataset contains measurements performed at various locations within the catchment. A total of approximately 1500 measurements are provided. In the dataset each point correspond to a measurement station (column "<strong>Station</strong>") which we classified in specific class of water (column "<strong>Type</strong>") as follows :</p> <ul> <li><strong>Stream </strong>: samples collected at three locations, from the glacier snout, after a small outwash plain and 2km downstream.</li> <li><strong>Tributary </strong>: 5 hillslopes tributaries originating from small seasonal overland flow or small springs at the base of the morainic hillslope. Those tributaries were monitored weekly. In addition, a few other seasonal lateral streams were sampled in various locations (Type: Other tributaries).</li> <li><strong>Bedrock </strong>: A few exfiltrations directly leaking out of the bedrock outcrop were sampled.</li> <li><strong>Ice </strong>: Ice was sampled either as surface ice (small cores 5 cm deep), as deeper cores (5 to 8m deep) or as meltwater from supraglacial gullies. All solid ice samples were melted at ambiant air temperature in air-tight plastic bags before being transferred into 12 mL vials.</li> <li><strong>Snow </strong>: The snowpack was sampled either at the surface (0 to 5cm) or at about 20 cm depth. Where possible, meltwater leaking from the snowpack was sampled. At 3 locations in 2021, we dug snowpits from which we sampled snow at different layers with depth. All solid snow samples were melted at ambiant air temperature in air-tight plastic bags before being transferred into 12 mL vials</li> <li><strong>Rain </strong>: Rainwater was mostly sampled at our camp site at 2450 m. asl. Rainwater samples represent single rain events which are identified by dry periods of at least one day long.</li> <li><strong>Groundwater </strong>: shallow (2 to 3 meters) fully-screened groundwater wells were installed in the outwash plain and water sampled monthly in the snow-free season.</li> </ul> <p>- GPS coordinates are provided with each point (Swiss coordinate system CH1903+ / LV95<strong> (EPSG: 2056)).</strong></p> <p>- Dates are provided in local timezone (GMT+1 with daylight saving time) and in UTC date format.</p> <p>- Analyitcal error from the Picarro spectrometer is reported as 1 standard deviation.</p> <p>More information can be accessed in the corresponding publication by Müller et al. (to be published in 2023).</p> <p><strong>Data files</strong></p> <ul> <li><em>Otemma_isotope_EC_T_2019_2021.csv</em> : file containing all data with GPS coordinates</li> <li> <p><em>isotope_locations_Otemma.jpg</em> : an overview of the locations of each measurement point</p> </li> <li> <p><em>Otemma_Isotopes_2019-2020.html </em>: interactive plots of all datasets (δ<sup>2</sup>H, EC, temperature), classified by Type.</p> </li> </ul>
The effect of solvent on convectively-driven silica particle assembly: Decoupling surface tension,viscosity, and evaporation rate
<p>Dataset associated with 'The effect of solvent on convectively-driven silica particle assembly: Decoupling surface tension, viscosity, and evaporation rate’.</p> <p>The data is based on the figures below, published in the linked article (see the doi).</p> <p><strong>- Figure 1. S</strong>egmented and raw images of dip-coated films. <strong>(images, .TIF)</strong></p> <p><strong>- Figure 2. </strong>Calculated surface coverages <strong>(data, .csv)</strong></p> <p><strong>- Figure 3. </strong>Rheology on SiO<sub>2</sub>-iPrOH-Glycerol mixtures & SEM micrographs of particle films. <strong>(data, .csv; images, .TIF)</strong></p> <p><strong>- Figure 4. </strong>SEM micrographs of silica helices films. <strong>(images, .TIF)</strong></p> <p><strong>- Figure S1. </strong>Measured evaporated masses of each solvent as a function of time. <strong>(data, .csv)</strong></p> <p><strong>- Figure S2. </strong>TEM micrographs of SiO2 seeds and measured particle diameters. <strong>(data, .csv; images, .TIF)</strong></p> <p><strong>- Figure S3. </strong>TEM micrographs of SiO particles and measured particle diameters. <strong>(data, .csv; images, .TIF)</strong></p> <p><strong>- Figure S4. </strong>Calculated solvent fractions as a function of time. <strong>(data, .csv)</strong></p> <p><strong>- Figure S5.</strong> Rheology of i-PrOH-glycerol mixtures.<strong> (data, .csv)</strong></p>
17O hyperfine spectroscopy in surface chemistry and catalysis
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, Computer Simulation and Analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DTA</strong>, <strong>m</strong>, <strong>opj</strong>, <strong>out</strong>, and <strong>f34</strong>.</li> <li>Information on <strong>origin of the data</strong>: <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong> and <strong>DTA</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension<strong> m</strong></li> <li>cwEPR spectroscopic spectra with simulations with filename extension <strong>opj</strong></li> <li>Periodic DFT computations with(out) filename extensions <strong>out </strong>and <strong>f34 </strong>in ASCII format</li> <li>Molecular cluster DFT computations with filename extensions <strong>in</strong> and <strong>out</strong> in ASCII format</li> </ul> </li> <li>Are the data <strong>generated</strong> (e.g. by a machine) or <strong>collected</strong> (e.g. by means of a survey)? <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li>Q-band Pulsed-EPR spectroscopic measurements were generated by ELEXYS 580 EPR spectrophotometer equipped with SHQ cavity and ER035 M NMR gaussmeter produced by Bruker.</li> <li>Periodic DFT computations were generated using distributed parallel version of CRYSTAL17 code.</li> <li>Molecular cluster DFT computations were generated using the ORCA (v5.0.2) code.</li> </ul> </li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP4_20230309_01_ORCA</strong> folder includes molecular cluster DFT computation inputs and outputs in ASCII format.</li> <li>Files in <strong>PARACAT_WP4_20230309_02_CRYSTAL</strong> folder includes periodic DFT computation inputs and outputs in ASCII format.</li> <li>Files in <strong>PARACAT_WP4_20230309_03_CW </strong>folder includes CW-EPR spectroscopic measurements and computer simulations/analyses, original data are in DTA/DSC formats; simulations in m format; and results plotted in opj format.</li> <li>Files in <strong>PARACAT_WP4_20230309_04_Pulse</strong> folder includes subfolders of VO/ZSM-5 and Zn/ZSM-5 that contain Pulsed-EPR spectroscopic measurements and computer simulations/analyses, original data are in DTA/DSC formats; files in m format were used to process the data.</li> </ul> </li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>CW</strong> – Continuous Wave EPR, <strong>HYSCORE </strong>– HYperfine Sublevel CORrelation spectroscopy, <strong>ENDOR</strong> – Electron Nuclear DOuble Resonance, <strong>DFT</strong> – Density Functional Theory</li> <li>definitions of variables: <strong>Magnetic field, Temperature</strong></li> <li>units of measurement: <strong>Gauss (G), K</strong></li> </ul> </li> </ul>
Regional scale surface of the top of the Variscan basement in some sector of Italy - Supplementary material
<p>The dataset represent the Supplementary material of thew manuscript entitled "Map of the top of the Variscan basement in some sectors of Italy" now under revision.</p> <p>The Supplementary material consist of 9 files:</p> <ul> <li>input data: <ul> <li>dataset_CROP.csv</li> <li>deep_wells.csv</li> <li>domains.geojson</li> <li>thrusts_2.geojson</li> <li>INA_data_point.csv</li> </ul> </li> <li>output data: <ul> <li>INA_depth_1km.csv</li> <li>ONA_OA_ISA_AF_depth_5km.csv</li> <li>INA_contour.geojson</li> <li>ONA_OA_ISA_AF_contour.geojson</li> </ul> </li> </ul>
Dataset containing binominal lexemes in Harakmbut (isolate, Peru), for "The derivational use of classifiers in Western Amazonia" and "When the alienability contrast fails to surface in adnominal possession: Bound nouns in Harakmbut"
<p>This is the dataset used, amongst others, in the paper: Van linden, An. Forthcoming. When the alienability contrast fails to surface in adnominal possession: Bound nouns in Harakmbut. Special Issue “Re-assessing the explanatory potential of alienability contrasts”, guest-edited by Françoise Rose & An Van linden. <em>Linguistics – An Interdisciplinary Journal of the Language Sciences</em>. [<a href="https://doi.org/10.1515/ling-2022-0039">https://doi.org/10.1515/ling-2022-0039</a>]</p> <p>For more details, see the ReadMe file.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.