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423 results for “spatial use”
Annual time series of global VIIRS nighttime lights for 2000-2024 at 500-m spatial resolution extrapolated using logistic regression
<p>The <a href="https://eogdata.mines.edu/products/vnl/"><strong>Annual Visible Night Light (VNL) V2</strong></a> (VIIRS) images at 500-m spatial resolution for the period 2012 to 2024 (Elvidge et al., 2021) have been used to extrapolate the values backwards for years 2000–2011. This was done by fitting a logistic regression (per pixel) and then predicting the values for the previous years (see nightlights_stack_500m.R). After consistent time-series have been produced, I also derived the difference between year 2024 and year 2000 (nightlights.difference_viirs.v21_m_500m_s_2000_2024_go_epsg4326_v20230318.tif): this shows average rate of change for the 25 years period. Use with caution: extrapolation of values can lead to artifacts. For most of the land surface, however, it appears that the growth of night lights follows exponential growth function and hence nights in the past can be represented accurately by fitting decay / logistic regression function.</p> <p>Original values from the Annual VNL V2 product have been converted from 0–200 to 0–2000 scale and are available as Cloud-Optimized GeoTIFFs.</p> <p>Principal components (PC1, PC2, PC3, PC4) were derived using SAGA GIS (sums-of-squares-and-cross-products matrix) method. The first PC1 usually matches the long-term mean value, PC2 matches the 1st derivation in values. File "nightlights_dmsp.v10_m_1km_s_19920101_20241231_go_epsg4326_v20251006.tif" contains 33 years 1992 to 2024, but at 1 km resolution.</p> <p>To cite the Annual VNL V2, please use:</p> <ul> <li>Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F. C., & Taneja, J. (2021). <a href="https://doi.org/10.3390/rs13050922">Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019</a>. Remote Sensing, 13(5), 922. https://doi.org/10.3390/rs13050922</li> </ul> <p>Historic night light images (1 km resolution) are also available from <a href="https://doi.org/10.6084/m9.figshare.9828827.v10">Figshare</a>:</p> <ul> <li>Li, X., Zhou, Y., Zhao, M., & Zhao, X. (2020). <a href="https://doi.org/10.1038/s41597-020-0510-y">A harmonized global nighttime light dataset 1992–2018</a>. Scientific data, 7(1), 168. https://doi.org/10.1038/s41597-020-0510-y</li> </ul>
Data files for figures in "Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability" by Bird et al.
<p>The data files for figures in <i>Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability</i> by Bird, Bodeker and Clem. The files required to create each figure in the paper and in the supplementary material are described in a readme.txt file which is also provided below:</p><p><strong>Figure 1</strong></p><p>The background image was obtained from the 'NaturalEarthFeature' function of the python Cartopy library (Figure1_background.png). The data required to generate the plots shown in Figure 1 are provided in the Figure1.nc file:</p><ul><li>The latitudes and longitudes for the 10,000 training sites are provided in Training_location_latitudes and Training_location_longitudes variables. </li><li>The latitudes and longitudes for the 8 sites used to demonstrate the ability of the CNN to generalise spatially are provided in the Validation_location_latitudes and Validation_location_longitudes variables. </li><li>The block maxima at each of the 8 sites are provided in the Location_1year_block_maxima variable.</li><li>The GEV fits at 0°C are provided in the GEV_fit_at_0.0C variable.</li><li>The GEV fits at 1.5°C are provided in the GEV_fit_at_1.5C variable.</li></ul><p><strong>Figure 2</strong></p><ul><li>The 1-in-100 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named Figure2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-100 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named Figure2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure 3</strong></p><ul><li>The cumulative distribution functions (CDFs) shown in the lower four panels are provided as text files listing the ARI in years and the daily total precipitation depth in mm. These files are named CDF_<lat>_<long>.dat where <lat> is the latitude and <long> is the longitude. Files for each region are zipped into .7z files named Figure3_<region>.7z where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The latitudes and longitudes for the upper panels can be inferred from the file names for each region.</li></ul><p><strong>Figure 4</strong></p><p>The data for each panel are provided in a netCDF file named Figure4_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 5</strong></p><p>The data are provided as text files named Figure5_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the sensitivities at ARIs of 10, 20, 50, 100, and 200 years.</p><p><strong>Figure 6</strong></p><p>The data are provided as text files named Figure6_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the precipitation depths and the sensitivities at ARIs of 10, 20, 50, 100, and 200 years. </p><p><strong>Figure 7</strong></p><p>The data for each panel are provided in a netCDF file named Figure7_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 8</strong></p><p>The data are provided as text files named Figure8_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the global surface temperature anomaly (°C) and the average negative log likelihood.</p><p><strong>Figure S1 and Figure S3</strong></p><p>The data are provided as text files named Figure_S1_and_S3_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes its contents.</p><p><strong>Figure S2</strong></p><ul><li>The 1-in-20 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named FigureS2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-20 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named FigureS2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure S4</strong></p><ul><li>The block maxima for each site are provided in text files named FigureS4_blockmaxima_siteA.txt and FigureS4_blockmaxima_siteB.txt. A header at the top of each column described the column contents. </li><li>The GEV-derived curves for each site are provided in text files named FigureS4_gevcurves_siteA.txt and FigureS4_gevcurves_siteB.txt. A header at the top of each column described the column contents. </li></ul><p><strong>Figure S5</strong></p><p>There are no data associated with this figure. This figure was made using Microsoft Powerpoint.</p><p><strong>Figure S6</strong></p><p>The data are provided as text files named FigureS6_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes the files contents.</p>
Model output used in the manuscript "Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency"
<p>This *.zip file contains the model output from seasonal variability experiments using the NPZD-DOP GEOMAR biogeochemical model (<a href="https://doi.org/10.1016/j.pocean.2010.05.002" target="_blank" rel="noopener">Kriest et al., 2010</a>) coupled with the MITgcm 2.8deg ocean circulation via the transport matrix method (<a href="https://doi.org/10.1016/j.ocemod.2004.04.002" target="_blank" rel="noopener">Khatiwala et al., 2005</a>; <a href="https://doi.org/10.1029/2007GB002923" target="_blank" rel="noopener">Khatiwala, 2007</a>; <a href="https://doi.org/10.5281/zenodo.1246300" target="_blank" rel="noopener">Khatiwala, 2018</a>).</p> <p>These model outputs are presented and discussed in the Preprint "<em>Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency</em>", published by Geophysical Research Letters (<a href="https://doi.org/10.1029/2023GL107050" target="_blank" rel="noopener">de Melo Viríssimo et al., 2024</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original model. For this matter, we also refer you to <a href="https://doi.org/10.1029/2021GB007101" target="_blank" rel="noopener">de Melo Viríssimo et al. (2022)</a>.</p> <p>All files uploaded were generated from simulations run by the authors, except: the grid file, the salinity field, and the temperature field, which came with the model; and the density fields, who were computed from the MITgcm 2.8deg transport matrix by Dr Rafaelle Bernadello, using a TEOS-10 Matlab routine (<a href="http://www.teos-10.org/">http://www.teos-10.org/</a>).</p> <p>For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.</p>
Dataset used for the analysis described in "Spatial patterns and controls on wind erosion in the Great Basin"
<p>This data set contains AERO model outputs and associated Bureau of Land Management Assessment, Inventory, and Monitoring calculated values for functional plant group cover estimates for monitoring plots across the Great Basin. Versrion 2 (V2) includes MLRA number and sampling year column ("sample_yr") that were omitted in previous version.</p>
Investigating dynamics between energy use and socio-demographic characteristics in spatial modeling of residential energy consumption
<p>Files represent datasets (2017 Residential Building Stock Assessment and American Community Survey 2012-2017 5-year estimate) and R-code associated with the analysis. </p>
Using RS-DAT to study continental-scale phenology at high-spatial resolution
This repository includes the notebooks employed to calculate and analyze a gridded phenological model, as computed from a set of daily meteorological variables.
Global monthly sectoral water withdrawal and allocation datasets (QUAlloc, water use and allocation model) at 10 km spatial resolution
<p>Output data of water withdrawals and water allocation per water source from the sectoral water use and allocation model (QUAlloc).</p> <p>Dataset properties:</p> <ul> <li>spatial resolution: 10 km (global-scale)</li> <li>temporal resolution: monthly time-step</li> <li>period: 1980 - 2019</li> <li>units: m3/month</li> </ul> <p>Output datasets:<br> <data_type>_<sector_name>_allocated_to_<source_type>_monthlyTot_1980_2019.nc</p> <ul> <li><data_type><br> <ul> <li>"withdrawal": refers to the water that is withdrawn at a water source level to satisfy the demands within an allocation zone</li> <li>"demand": refers to the withdrawn water that is supplied to each location (cell) where there are demands to satisfy</li> </ul> </li> <li><sector_name> <ul> <li>"domestic"</li> <li>"irrigation"</li> <li>"livestock"</li> <li>"manufacture"</li> <li>"thermoelectric"</li> </ul> </li> <li><source_type> <ul> <li>"renewable_surfacewater": refers to water obtained from the surface water system components (e.g., direct runoff, base flow, interflow, etc.)</li> <li>"renewable_groundwater": refers to water obtained from aquifers that are recharged by percolation from the upper soil layers</li> <li>"nonrenewable_groundwater": refers to water obtained from aquifers not replenished on a human time scale</li> </ul> </li> </ul> <p>The sectoral water use and allocation model used, QUAlloc, can be found at: https://github.com/SustainableWaterSystems/QUAlloc.</p>
Predicted times, spatial coordinates of bow shock crossings and shock geometry at Mars from the NASA/MAVEN mission, using spacecraft ephemerides and magnetic field data, with a predictor-corrector algorithm
<p><strong>CHARACTERISTICS</strong><br>Planet: <strong>Mars</strong><br>Radius: <strong>R<sub>M</sub> = 3389.5 km</strong> (volumetric mean planetary radius)<br>Spacecraft: <strong>NASA/Mars Atmosphere and Volatile Evolution (MAVEN)</strong><br>Spacecraft coordinates system: <strong>Mars Solar Orbital (MSO)</strong> equivalent to <em>Sun-State </em>coordinate system:</p> <ul> <li>+<em>X<sub>MSO</sub></em> points towards the Sun from the planet’s centre,</li> <li>+<em>Z<sub>MSO</sub></em> towards Mars’ North pole and perpendicular to the orbital plane defined as the <em>X<sub>MSO</sub></em>–<em>Y<sub>MSO</sub></em> plane passing through the centre of Mars,</li> <li><em>Y<sub>MSO</sub></em> completes the orthogonal system.</li> </ul> <p>Time span: <strong>01/11/2014 to 30/04/2024</strong> (Mars Years MY32 to MY36 included, part of MY37).<br>Total number N of candidate bow shock crossings in the database: <strong>N = 20107</strong></p> <p><strong>ORIGINAL DATASETS USED</strong><br>The original MAVEN/MAG data repository on which these algorithms were applied is available on NASA's Planetary Data System (PDS) at <a href="https://doi.org/10.17189/1414178">https://doi.org/10.17189/1414178</a>. For this study, 1-Hz magnetic field data was used.</p> <p><strong>METHOD</strong><br>To construct this database from the original datasets above, the predictor and predictor-corrector algorithms used are described in:<br>Simon Wedlund, C., Volwerk, M., Beth, A., Mazelle, C., Möstl, C., Halekas, J., Gruesbeck, J. and Rojas-Castillo, D., (2022), A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN mission, <em>Journal of Geophysical Research</em>, <strong>127</strong>, 1-33, e2021JA029942, <a href="https://doi. org/10.1029/2021JA029942">https://doi. org/10.1029/2021JA029942</a>. </p> <p>Also available at: <a href="https://doi.org/10.1002/essoar.10507942.1">https://doi.org/10.1002/essoar.10507942.1 </a> and as arXiv e-print: <a href="https://doi.org/10.48550/arXiv.2109.04366">https://doi.org/10.48550/arXiv.2109.04366</a></p> <p>These algorithms consist of two consecutive steps: </p> <ol> <li>Predictor geometric algorithm based on J. Gruesbeck's 3D model (<a href="https://doi.org/10.1029/2018JA025366">Gruesbeck et al. 2018</a>) for prediction of Mars bow shock position</li> <li>Corrector algorithm based on magnetic field measurements (magnitude and fluctuations).</li> </ol> <p><strong>REMARK ON VERSIONS</strong><br>From Version 3 onwards, we also provide the angle between the average Interplanetary Magnetic Field (IMF) vector upstream of the shock and the shock normal, noted \(\theta_{Bn}\)(ThetaBn). Assuming a smooth shock surface and the 3D model of Gruesbeck et al. (2018, all points), this gives a first indication of the geometry of the shock, so that:</p> <ul> <li>45<sup>∘</sup><<em>θ</em><sub><em>B</em><em>n</em></sub><135<sup>∘</sup>: quasi-perpendicular shock condition</li> <li><em>θ</em><sub><em>B</em><em>n</em></sub>≤45<sup>∘</sup> and <em>θ</em><sub><em>B</em><em>n</em></sub>≥135<sup>∘</sup>: quasi-parallel shock condition</li> </ul> <p>Uncertainty on these angles is estimated to be ± 5º. </p> <p>From Version 4 onwards, we also added the solar longitude Ls (in degrees).</p> <p>For details, see Simon Wedlund et al. (2022) above, §2.3 pp. 10-12. Note that due to minor adjustments in the code, some of the ThetaBn angles calculated here for the examples of Fig. 6 in Simon Wedlund et al. (2022) may slightly differ from the values quoted in the paper.</p> <p><strong>VARIABLES DESCRIPTION</strong><br>This database contains the following ASCII variables:</p> <ul> <li>Bow shock times in MAVEN's database (1-s resolution): <em>T</em><sub>bs</sub></li> <li>Mars Solar Orbital coordinates of the shock, in units of Mars radius <em>R</em><sub><em>M</em> </sub>(<em>R<sub>M</sub></em> = 3389.5 km):<br><em>X<sub>MSO</sub></em>,<sub> </sub><em>Y<sub>MSO</sub></em>, <em>Z<sub>MSO</sub></em> and Euclidean distance \(R_{MSO} = \sqrt{X_{MSO}^2 + Y_{MSO}^2 + Z_{MSO}^2}\) (in <em>R<sub>M</sub></em>)</li> <li>Solar Zenith angle in degrees: <em>SZA</em> = \(\tan^{-1}{Y_{MSO}^2+Z_{MSO}^2 \over X_{MSO}^2}\) (in º) </li> <li>Angle between average B-field direction and shock normal assuming a smooth shock surface \(\theta_{Bn}\) (ThetaBn, in º) <ul> <li>45 < ThetaBn < 135 deg: quasi-⊥ shock</li> <li>ThetaBn ≤45 deg & ThetaBn ≥ 135 deg: quasi-|| shock</li> </ul> </li> <li>Solar longitude Ls, in degrees.</li> <li>Flag for crossing: <ul> <li>sheath \(\longrightarrow\) solar wind, flag = 0.</li> <li>solar wind \(\longrightarrow\) sheath, flag = 1.</li> </ul> </li> </ul> <p><strong>WARNING</strong><br>This database is based on an automatic statistical geometrical estimate, further refined by constraints on magnetic field. It is aimed at giving a first approximation of the shock area times in the MAVEN data. It is particularly suited to statistical studies and region identification in the MAVEN datasets. As such, this database should be used as a <em>first indicator</em> of the shock location, and <em>with</em> <em>caution</em>: it <strong>CANNOT</strong>, and <strong>WILL NOT </strong>substitute, especially in case studies, for a careful analysis of the full magnetometer and plasma suite bow shock signatures. Moreover, the algorithm is optimised for detecting the first disturbance observed in the magnetic field immediately ahead of the shock's foot (in the foreshock area), and not for the detection of other structures in the shock, such as the shock ramp. The "shock" location is therefore given here with typical uncertainties of about 0.075 R<sub>M</sub> (with R<sub>M</sub> = 3389.5 km, i.e., about 250 km in the radial direction). Finally, for multiple shock crossings, the algorithm chooses the first occurrence of the shock starting from the undisturbed solar wind.</p> <p>Current formatting optimised for MATLAB.</p> <p><strong>ACKNOWLEDGEMENTS</strong><br>C. Simon Wedlund and M. Volwerk thank the Austrian Science Fund (FWF) project P32035-N36. C. Möstl thanks the Austrian Science Fund FWF projects P31659-N27, P31521-N27. A. Beth thanks the Swedish National Space Agency (SNSA) and its support with the grant 108/18. This database was notably used to add to the Helio4Cast database which monitors solar wind parameters in the solar system (<a href="https://doi.org/10.6084/m9.figshare.6356420">https://doi.org/10.6084/m9.figshare.6356420</a>). Helio4Cast is available at <a href="http://www.helioforecast.space/icmecat">www.helioforecast.space/icmeca</a>t and <a href="http://www.helioforecast.space/sircat">www.helioforecast.space/sircat</a>. </p> <p><strong>LICENSE AND RIGHTS</strong><br>This database is shared under a Creative Commons CC-BY-4.0 license.</p> <p>Version 1 (c) Cyril Simon Wedlund @ Space Research Institute of Graz (IWF), <br> Austrian Academy of Sciences (ÖAW), 2021-09-08<br>Version 2 (c) CSW @ ÖAW/IWF, 2021-11-30 -- Addition of R_MSO and SZA<br>Version 3 (c) CSW @ ÖAW/IWF, 2022-02-09 -- Addition of ThetaBn<br>Version 4 (c) CSW @ ÖAW/IWF, 2025-03-20 -- Addition of Ls, Bx, By, Bz and Bt.</p> <p> </p> <p><br>Contact email: cyril.simon.wedlund@gmail.com</p>
Quantifying Dissolution Dynamics in Porous Media Using a Spatial Flow Focusing Profile
<p>The diverse range of patterns in porous media formed by dissolution processes depends on the relative magnitude of flow, transport, and chemical reactions at pore surfaces. However, distinguishing between regimes often relies solely on qualitative, visual comparisons of emergent structures. Here, we propose a quantitative measure capable of identifying different regimes using the concept of the spatial flow focusing profile, which segments the medium into cross sections along the flow direction to calculate the flow focusing index for each section. We employ this measure in numerical simulations of a dissolving porous medium using a pore-network model. We obtain a morphological phase diagram of dissolution patterns, which we characterize using the flow focusing profile. In particular, we demonstrate that analyzing the temporal changes in the profile allows one to quantitatively distinguish between wormholing and channeling. The transition between them is shown to be affected by the heterogeneity of the system.</p>
Dataset for "Scaling of ultrashort-pulsed laser structuring processes for electromobility applications using a spatial light modulator"
<p>The dataset represents the experimental data for publication "<span>Scaling of ultrashort-pulsed laser structuring processes for electromobility applications using a spatial light modulator</span>"</p>
Spatial models of topsoil properties in Romania using digital soil mapping techniques
<p>The database includes the selected spatial models of soil variables for the Romanian territory derived by digital soil mapping techniques, accepted for publication in:</p> <p>Cristian Valeriu Patriche, Bogdan Roșca, Radu Gabriel Pîrnău, Ionuț Vasiliniuc, <em>Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning</em><strong>, PLOS ONE</strong>, 2023</p> <p>The database includes the selected spatial models of soil variables for the Romanian territory derived by digital soil mapping techniques. The file names indicate the soil variable and the method used for interpolation (RK – regression - kriging, EML – ensemble machine learning, GWR_OK – Geographically Weighted Regression – Ordinary kriging).</p> <p>The raster data is classified and saved in tif format with a resolution of 100 x 100 m. The spatial reference is Stereographic projection 1970 (Pulkovo_1942_Adj_58_Stereo_70).</p> <p>The soil variables are classified as follows:</p> <table> <tbody> <tr> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Classes</strong></p> </td> </tr> <tr> <td> <p><strong>1</strong></p> </td> <td> <p><strong>2</strong></p> </td> <td> <p><strong>3</strong></p> </td> <td> <p><strong>4</strong></p> </td> <td> <p><strong>5</strong></p> </td> <td> <p><strong>5</strong></p> </td> <td> <p><strong>7</strong></p> </td> </tr> <tr> <td> <p><em>pH</em></p> </td> <td> <p>≤ 5</p> <p>(strongly acid)</p> </td> <td> <p>5.1 – 5.8 (moderately acid)</p> </td> <td> <p>5.9 – 6.8</p> <p>(weakly acid)</p> </td> <td> <p>6.9 – 7.2</p> <p>(neutral)</p> </td> <td> <p>7.3 – 8.4</p> <p>(weakly alkaline)</p> </td> <td> <p>8.5 – 8.8 (moderately alkaline)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>EC (mS m<sup>-1</sup>)</em></p> </td> <td> <p>≤ 12.75</p> </td> <td> <p>12.76 – 16.49</p> </td> <td> <p>16.50 – 20.04</p> </td> <td> <p>20.05 – 24.18</p> </td> <td> <p>24.19 – 29.11</p> </td> <td> <p>29.12 – 35.23</p> </td> <td> <p>≤ 35.24</p> </td> </tr> <tr> <td> <p><em>OC (g kg<sup>-1</sup>)</em></p> </td> <td> <p>< 7.5</p> <p>(very low)</p> </td> <td> <p>7.5 – 17.4</p> <p>(low)</p> </td> <td> <p>17.4 – 37.8 (moderate)</p> </td> <td> <p>37.8 – 61.0</p> <p>(high)</p> </td> <td> <p>> 61</p> <p>(very high)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>CaCO<sub>3</sub></em></p> <p><em>(g kg<sup>-1</sup>)</em></p> </td> <td> <p>0</p> <p>(no carbonates)</p> </td> <td> <p>1 – 10</p> <p>(low)</p> </td> <td> <p>11 – 40</p> <p>(medium 1)</p> </td> <td> <p>41 – 80</p> <p>(medium 2)</p> </td> <td> <p>81 – 107</p> <p>(medium 3)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>P (mg kg<sup>-1</sup>)</em></p> </td> <td> <p>< 4</p> <p>(extremely low)</p> </td> <td> <p>4 – 8</p> <p>(very low)</p> </td> <td> <p>8 – 18</p> <p>(low)</p> </td> <td> <p>18 – 36</p> <p>(medium)</p> </td> <td> <p>36 – 72</p> <p>(high)</p> </td> <td> <p>> 72</p> <p>(very high)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>N (g kg<sup>-1</sup>)</em></p> </td> <td> <p>≤ 1</p> <p>(very low)</p> </td> <td> <p>1.1 – 1.4</p> <p>(low)</p> </td> <td> <p>1.5 – 2.0</p> <p>(medium 1)</p> </td> <td> <p>2.1 – 2.7</p> <p>(medium 2)</p> </td> <td> <p>2.8 – 6.0</p> <p>(high)</p> </td> <td> <p>> 6</p> <p>(very high)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>K (mg kg<sup>-1</sup>)</em></p> </td> <td> <p>≤ 40 *</p> <p>(extremely low)</p> </td> <td> <p>41 – 65 *</p> <p>(very low)</p> </td> <td> <p>66 – 130</p> <p>(low)</p> </td> <td> <p>131 – 200 (medium)</p> </td> <td> <p>201 – 300</p> <p>(high)</p> </td> <td> <p>> 300</p> <p> (very high)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>Clay (%)</em></p> </td> <td> <p>≤ 25</p> <p> (low 1)</p> </td> <td> <p>26 – 32</p> <p>(low 2)</p> </td> <td> <p>33 – 40</p> <p>(medium 1)</p> </td> <td> <p>41 – 45</p> <p>(medium 2)</p> </td> <td> <p>≥ 46</p> <p> (high)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>Silt (%)</em></p> </td> <td> <p>< 25</p> <p>(medium 1)</p> </td> <td> <p>25 – 32</p> <p>(medium 2)</p> </td> <td> <p>33 – 40</p> <p>(high 1)</p> </td> <td> <p>41 – 50</p> <p>(high 2)</p> </td> <td> <p>> 50</p> <p>(high 3)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>Sand (%)</em></p> </td> <td> <p>< 15</p> <p>(low 1)</p> </td> <td> <p>15 – 25</p> <p>(low 2)</p> </td> <td> <p>26 – 35</p> <p>(low 3)</p> </td> <td> <p>36 – 56</p> <p>(medium)</p> </td> <td> <p>> 56</p> <p>(high)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p>* classes not present on the Romanian territory</p> <p> </p> <p> </p> <p> </p>
Data Used for "Mapping Tribes: Ottoman Spatial Thinking in Iraq and Arabia, c. 1910"
<p>This repository contains data used for our article, "Mapping Tribes: Ottoman Spatial Thinking in Iraq and Arabia, c. 1910". The README.md file explains the contents. This data can also be found at https://github.com/opengulf/ottoman-map.</p>
Data from: Identifying priority areas for spatial management of mixed fisheries using ensemble of multi-species distribution models. Panzeri D. et al., 2023, Fish and Fisheries
<p>Panzeri D.<sup>1</sup>, Russo T., Arneri E., Carlucci R., Cossarini G., Isajlović I., Krstulović Šifner S., Manfredi C., Masnadi F., Reale M., Scarcella G., Solidoro C., Spedicato M.T., Vrgoč N., W. Zupa, Libralato S<sup>2</sup>.</p> <p><sup>1 </sup>dpanzeri@ogs.it<br> <sup>2 </sup>slibralato@ogs.it</p> <p>Spatial fisheries management is widely used to reduce overfishing, rebuild stocks, and protect biodiversity. However, the effectiveness and optimization of spatial measures depend on accurately identifying ecologically meaningful areas, which can be difficult in mixed fisheries. To apply a method generally to a range of target species, we developed an ensemble of species distribution models (e-SDM) that combines general additive models, generalized linear mixed models, random forest, and gradient-boosting machine methods in a training and testing protocol. The e-SDM was used to integrate density indices from two scientific bottom trawl surveys with the geopositional data, relevant oceanographic variables from the three-dimensional physical-biogeochemical operational model, and fishing effort from the vessel monitoring system. The determined best distributions for juveniles and adults are used to determine hot spots of aggregation based on single or multiple target species. We applied e-SDM to juvenile and adult stages of 10 marine demersal species representing 60% of the total demersal landings in the central areas of the Mediterranean Sea. Using the e-SDM results, hot spots of aggregation and grounds potentially more selective were identified for each species and for the target species group of otter trawl and beam trawl fisheries. The results confirm the ecological appropriateness of existing fishery restriction areas and support the identification of locations for new spatial management measures.</p> <p>Data (csv) for Panzeri et al. 2023</p> <p>1. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Ensemble_density_F&F_D.Panzeri_et_al_2023.csv: CSV file with density values (column pred) in terms of number of individuals (log N/km2) for each species (column sp) and life stage (column age) for each grid cell (X = longitude and Y = latitude).</a> </p> <p>2. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Getis_hotspot_F&F_D.Panzeri_et_al_2023.csv: CSV file with Getis ord Gi* values (column Gi) derived from the previous file 1, developed for each species and life stage for each grid cell (X = longitude and Y = latitude).</a></p> <p>3. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Multispecies_HotSpot_F&F_D.Panzeri_et_al_2023.csv: Frequency map expressed as the number of species for each grid cell (column freq) that has the hotspot (previous file 2) above the third quartile.</a></p> <p> </p> <p> </p>
Effects of food supplementation and helminth removal on space use and spatial overlap in wild bank vole populations
<p>Animal space use and spatial overlap can have important consequences for population-level processes such as social interactions and pathogen transmission. Identifying how environmental variability and inter-individual variation affect spatial patterns and in turn influence interactions in animal populations is a priority for the study of animal behavior and disease ecology. Environmental food availability and macroparasite infection are common drivers of variation, but there are few experimental studies investigating how they affect spatial patterns of wildlife. Bank voles (<em>Clethrionomys glareolus</em>) are a tractable study system to investigate spatial patterns of wildlife and are amenable to experimental manipulations. We conducted a replicated, factorial field experiment in which we provided supplementary food and removed helminths in vole populations in natural forest habitats and monitored vole space use and spatial overlap using capture-mark-recapture methods. Using network analysis, we quantified vole space use and spatial overlap. We compared the effects of food supplementation and helminth removal and investigated the impact of season, sex, and reproductive status on space use and spatial overlap. We found that food supplementation decreased vole space use while helminth removal increased space use. Space use also varied by sex, reproductive status, and season. Spatial overlap was similar between treatments despite up to three-fold differences in population size. By quantifying the spatial effects of food availability and macroparasite infection on wildlife populations, we demonstrate the potential for space use and population density to trade off and maintain consistent spatial overlap in wildlife populations. This has important implications for spatial processes in wildlife including pathogen transmission.</p>
Comparison of spatial transcriptomics technologies used for tumor cryosections
<p>This repository contains data from a spatial transcriptomics (ST) analysis of brain tumor cryosections (medulloblastoma with extensive nodularity, MBEN). It is associated with the preprint by Rademacher, Huseynov, Bortolomeazzi et al. 2024, <em>bioRxiv</em>, <a href="https://doi.org/10.1101/2024.04.03.586404">https://doi.org/10.1101/2024.04.03.586404</a>, that has a full description of the work. In the study four imaging-based ST methods – RNAscope HiPlex, Molecular Cartography, MERFISH/Merscope, and Xenium – as well as sequencing-based ST (Visium) and single cell RNA sequencing of dissociated nuclei (snRNA-seq) were compared. The files provided here are described in readme.txt and include the transcript count matrices acquired on the Visium platform as well as Seurat objects of the data and analysis results for the comparison of the different technologies. The data for snRNA-seq, RNAscope HiPlex and Molecular Cartography included in the Seurat objects are based on the primary data acquired in a previous study (Ghasemi et al. 2024, Nat Commun, <a href="https://doi.org/10.1038/s41467-023-44117-x">https://doi.org/10.1038/s41467-023-44117-x</a>). </p>
SMART: Spatial transcriptomics deconvolution using marker-gene-assisted topic model
<p>Source code and simulated datasets used in manuscript "SMART: Spatial transcriptomics deconvolution using marker-gene-assisted topic model"</p>
reference genome used for scRNA-seq mapping with CellRanger in the method spatial-scERA
<p>The modified <em>Drosophila </em>melanogaster (dm6) reference genome used for the mapping with CellRanger in the method paper about spatial-scERA</p> <p>The genome is composed of the original genome from EnsembleMetazo website (BDGP6.46.110). An addition of 26 chromosomes (one for the plasmid construct and 25 for the tested enhancer sequences) is also present to allow for the mapping of mRNAs comming from our constructs. </p>
Spatially mapping the baseline and bisphenol-A exposed Daphnia magna lipidome using desorption electrospray ionisa-tion - mass spectrometry
<p>Data from desorption electrospray ionisation - mass spectrometry of <em>Daphnia magna</em> tissue section from control and daphnids exposed to 5 ppm of bisphenol A over their 7<sup>th</sup> adult instar, specifically four sampling points; 8, 24, 48 and 72 h after the 6<sup>th</sup> brood, as well as data acquired from a blank DESI slide.</p> <p>Data is provided in the form of *imzML files with the associated *.ibd of the same name.</p>
Augmented Objects as Portals into Virtual Worlds: Using Audio to Create Immersive Experiences in Extended Realities - UMBRELLA AUDIO SPATIALIZATION DEMO
<p><strong>Technical demonstration</strong></p> <p>The results of the projection mapping system in the project are clear from the <a href="https://blog.zhdk.ch/immersivearts/dreaming-of-time-and-space/">main documentation video</a>; however, the impact of the spatial audio system in particular, is best experienced from directly underneath the umbrellas, where one can best appreciate the various levels of mixed reality. Unfortunately, it is difficult to document these effects within the artistic context of the project, and as such, we include a brief set of examples to better demonstrate the 6 degree of freedom sound spatialization capabilities of the umbrella system.</p> <p><em><strong>NOTE:</strong></em> The audio in the following examples is recorded from a fixed perspective (initially underneath the umbrella) and rendered binaurally. Unfortunately, the ambisonic microphone used does not capture directionality very well when the source (in this case, the umbrella speakers) is less than ~1 meter away, and in retrospect, a single channel of pink noise was not a wise choice as a source material, as it appears to cause additional phasing issues. Additionally, the effectiveness of binaural audio varies from listener to listener, so <em>the perceived effect in the video is not as strong as when experienced in person</em>; nonetheless, it is possible to get the basic idea of the spatialization algorithm in action from these examples.</p> <p>PLEASE WEAR HEADPHONES IN ORDER TO EXPERIENCE THE 3D EFFECT.</p> <p>In addition to the view of the entire scene from an outside perspective, several other views of the underlying software are displayed throughout the video, including:</p> <ul> <li> <p>A radar view of the scene (umbrella and sound source) as seen by the space manager software, where the:</p> <ul> <li> <p>Blue circle = umbrella</p> </li> <li> <p>Cyan triangle, yellow square = sound source</p> </li> </ul> </li> <li> <p>A view of elements of the spatialization software running on the umbrella, specifically the:</p> <ul> <li> <p>Relative gain calculations and current output levels of each speaker</p> </li> <li> <p>Results of supporting calculations (e.g. sound location after transformation from the global to local coordinate system, and scaling factors used to attenuate the overall volume of the sound as the distance from the umbrella to the sound changes)</p> </li> </ul> </li> </ul> <p><strong>Demo #1</strong></p> <p>Stationary umbrella with a moving virtual sound source (anchored to a rigid body)</p> <p><strong>Demo #2</strong></p> <p>Rotating umbrella with a stationary sound source (anchored to a rigid body)</p> <p><strong>Demo #3</strong></p> <p>Moving umbrella with a fixed sound source (anchored to a rigid body)</p> <p><strong>Demo #4</strong></p> <p>Moving umbrella with a fixed sound source (anchored to a virtual point in space, located above the microphone); as the umbrella approaches the source, the sound first fades into the room, then collapses into the umbrella, as show in Figure 7 ("Fading between umbrella and room with distance") in the main paper</p>
Data used for developing a parameterization for spatial distribution of solar irradiance over rugged terrain
<p>This dataset includes data and results produced while developing a parameterization for the spatial distribution of solar radiation over mountainous terrain. The method is designed for applications in Earth System Models.</p> <p>The four items included in this dataset are the following:</p> <p><strong>GFDL_preproc_dems</strong> includes digital elevations maps derived from the Shuttle Radar Topography Mission (SRTM) for three sample domains.</p> <p><strong>single-time-step </strong>Includes atmospheric optical properties used as input for Monte Carlo simulations.</p> <p><strong>gmd_2021_grids_light_*</strong> Include maps of terrain parameters derived from SRTM elevation data, and the partition of the domains in homogeneous tiles (sub-grid units) for different number and types of of land units.</p> <p><strong>rmc_simul_res</strong> results of the Monte Carlo simulations, consisting of maps of simulated solar radiation for different domains and solar angles.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.