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27 results for “spatial properties”
Sediment Properties Drive Spatial Variability of Potential Methane Production and Oxidation in Small Streams
<ul> <li>This dataset contains 20 data tables (Fig.2.csv, Fig.3.csv, data_PLS_stream-main-stem.csv, Fig.4_a.csv, data_PLS_subcatch.-stream-sect.csv, Fig.4_b.csv, Fig.5.csv, Fig.S1.csv, Fig.S2.csv, Fig.S3.csv, Fig.S4.csv, Fig.S5_a.csv, Fig.S5_b.csv, Fig.S6_a.csv, Fig.S6_b.csv, Fig.S6_c.csv, Fig.S6_d.csv, TableS1_data-adjustment.csv, TableS1_lit-data.csv, PMO_surface-water.csv), we separated our data tables in the respective figures/analyses presented in our paper</li> <li>We added the units to each column title of each respective data table</li> <li>Please see "Metadata.pdf" and our paper (same title as the dataset) for more information</li> </ul> <p> </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>
LAGOS-NE – Lake nutrient chemistry and geospatial data to measure spatial structure of ecosystem properties in a 17-state region of the U.S.
This dataset includes data for the lake water quality and geospatial variables that describe climate, hydrology, land use land cover, and lake characteristics that were used to study spatial structure in lake properties at the sub-continental scales (Lapierre et al. Quantifying spatial structure to improve understanding of the relationships between climate, landscape, and lake ecosystem properties, to be submitted to Ecology). All observations came from LAGOS-NELIMNO v. 1.054.1 and LAGOS-NEGEO v. 1.03 (LAke multi-scaled GeOSpatial and temporal database), an integrated database of lake ecosystems (Soranno et al. 2015). LAGOS-NE contains a complete census of lakes great than or equal to 4 ha with corresponding geospatial information for a 17-state region of the U.S., and a subset of the lakes has observational data on morphometry and chemistry. Approximately 54 different sources of data were compiled for the LAGOS-NELIMNO v. 1.054.1 dataset and were mostly generated by government agencies (state, federal, tribal) and universities. In this analysis, we compiled lake water quality data from the summer stratified season (June 15-September 15) in the most recent 10 years of data included in LAGOS-NELIMNO v. 1.054.1 (2002-2011). We report the median total nitrogen, total phosphorus, secchi depth, and chlorophyll values for each lake, which was calculated as the grand median of each yearly median value. We also include data for lake and landscape characteristics including variables related to lake morphometry, climate, hydrology, atmospheric deposition, land use and land cover.
Universal spatial properties of coral reefs
<p>Georeferenced database on the spatial properties of all individual shallow-water tropical coral reefs worldwide. The dataset was obtained by processing and analyzing the global-scale coral reef benthic data provided by the Allen Coral Atlas (ACA), a publicly available dataset of high-resolution satellite imagery and machine learning-based coral reef classifications. </p> <p>The original data, already divided into different coral provinces, was segmented to identify the individual reefs of each province using a label assignment algorithm. This allows to analyze several spatial properties of coral reefs such as the size distribution, area-perimeter relationship, fractal dimensions and shape measures.</p> <p>The dataset contains the following measures for each individual reef in each coral province:</p> <ul> <li>Area (m²)</li> <li>Perimeter (m)</li> <li>Surface fractal dimension</li> <li>Perimeter fractal dimension</li> <li>Compactness</li> <li>Diameter ratio</li> <li>Distance to nearest reef</li> <li>Longitude</li> <li>Latitude</li> <li>Geometry</li> </ul>
Recurring bleaching events disrupt the spatial properties of coral reef benthic communities across scales
<p>Marine heatwaves are causing recurring coral bleaching events on tropical reefs that are driving ecosystem change. Yet little is known about how bleaching and subsequent coral mortality impacts the spatial properties of tropical seascapes, such as patterns of organism spatial clustering and heterogeneity across scales. Changes in these spatial properties can offer insight into ecosystem recovery potential following disturbance. Here we repeatedly quantified coral reef benthic spatial properties around the circumference of an uninhabited tropical island in the central Pacific over a 9-year period that included a minor and severe marine heatwave. Benthic communities showed increased biotic homogenisation following both minor and mass bleaching, becoming more taxonomically similar with less diverse intra-island community composition. Hard coral cover, which was highly spatially clustered around the island prior to bleaching, became less spatially clustered following minor bleaching and was indiscernible from a random distribution across all scales (100–2000 m) following mass bleaching. Interestingly, the reduced degree of hard coral cover spatial clustering was already evident by the onset of mass bleaching and before any dramatic wholesale loss in island-mean coral cover occurred. Reductions in hard coral spatial clustering may therefore offer an early indication of the ecosystem becoming degraded prior to mass coral mortality. In contrast, the spatial clustering of competitive fleshy macroalgae remained unchanged through both bleaching events, while crustose coralline algae and fleshy turf algae became more spatially clustered at larger scales (200–700 m) following mass bleaching. Overall, benthic community spatial patterning became less predictable following bleaching and was no longer reflective of gradients in long-term environmental drivers that typically structure these remote reefs. Our findings provide novel insights into how climate-driven marine heatwaves can impact the spatial properties of coral reef communities over multiple scales.</p>
Quantitative modulation of a spatial enhancer through the biophysical properties of a transcription factor binding site
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Recurring bleaching events disrupt the spatial properties of coral reef benthic communities across scales
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Spatial soil properties distribution in “Hoya del río Suárez” region in Colombia.
There is a spatial soil properties distribution surface in raster format. This is the result of a study about digital soil mapping. Implementing geoestatistics (regression kriging RK) and machine learning algorithms (random forest RF, support vector machines SVM, and ensemble models), the study found the best performance for 5 soil properties (clay fraction, bulk density, total porosity, pH, and cation exchange capacity). The study was located in a region named “Hoya del río Suárez”, which is the main sugarcane-producing region in Colombia, and its land area is around 47000 hectares. Those raster surfaces were carried out in 2021, and the database used for doing this study was compiled between 2015 and 2016.
Spatial soil properties maps for Switzerland at 30 m resolution
<p>The Swiss Soil Property Map (SSPM) was developed using the quantile random forest machine learning algorithm and remotely sensed surrogate information such as terrain, climate, vegetation, and soil covariates. The SSPM dataset provides maps at 30 m resolution for different soil depths (0, 30, 60, and 100 cm) in GeoTIFF format. The mean and respective uncertainty information is provided for each map. Please note that the phosphorus spatial map is only available for the topsoil (0-20 cm) due to the unavailability of the dataset at deeper depths.</p> <table> <caption>Description of soil properties (SP) and their units</caption> <tbody> <tr> <td>SP</td> <td>Description</td> <td>units</td> </tr> <tr> <td>Sand </td> <td>Sand content</td> <td>%</td> </tr> <tr> <td>Clay</td> <td>Clay content</td> <td>%</td> </tr> <tr> <td>OC</td> <td>Organic carbon content </td> <td>%</td> </tr> <tr> <td>N</td> <td>Nitrogen content</td> <td>% </td> </tr> <tr> <td>P</td> <td>Phosphorus content</td> <td>mg/kg</td> </tr> </tbody> </table> <p>For more details / to cite this dataset please use:</p> <ul> <li><strong>Gupta, S. </strong>, Hasler, K. J., Alewell, C.: Mapping soil properties of Switzerland using remote sensing datasets and machine learning approach. Manuscript <strong>submitted</strong>, <strong>Geoderma Regional</strong>, 2023</li> </ul> <p> </p>
Influence of small-scale spatial variability of soil properties on yield formation of winter wheat
<p>This is a data set of soil properties and plant properties of winter wheat.</p> <p>The data derived from a long-term field trial for the year 2016 at the Asendorf field station 70 km north of Hanover, Germany (49 m above sea level, 52°45′48.4′′N 9°01′24.3′′E) and a field site in Triesdorf, located in Northern Bavaria (450 m a.s.l., 49°12'36.5"N 10°38'33.9"E).</p> <p>Data includes soil (OC, bulk density, texture, pH-value) and plant data (grain yield, thousand grain weight, tillers per m², spikes per m²). All methods and data will be described in an upcoming journal article in the Journal Plant and Soil (DOI:10.1007/s111104-023-06212-2).</p>
Data from: Spatial mid-domain effect overrides climate, soil properties and microbes on a cosmopolitan non-native plant across elevation
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A comparison of the temporal and spatial properties of trans-saccadic perceptual re-calibration and saccadic adaptation
<p>This Dataset was used in the paper entitled "A comparison of the temporal and spatial properties of trans-saccadic perceptual re-calibration and saccadic adaptation", published in the Journal of Vision.</p> <p>Inside the folder are three subfolders, one for Experiment 1, one for the Adjustment condition of Experiment 2 and one for the Saccade Adaptation condition of Experiment 2.</p> <p>Each subfolder contains a DATA FORMAT DESCRIPTION.txt file that details the meaning of the single files.</p>
Supplementary Information to: Increasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high–resolution visible channel: Evaluation of candidate approaches with MODIS observations
<p>This repository contains the Python programmes and datasets used for</p> <p>the research described in the following paper:<br> https://doi.org/10.5194/amt-2019-334</p> <p>It has been prepared as supplementary information to the final paper.<br> <br> Note that the actual Cloud Property Retrieval (CPP) which would be<br> required for full reproducability of the results cannot be made<br> available by the authors, that the code included here is based on Python2, and that<br> paths to the dataset have to be adapted in the code.<br> <br> The repository consists of three separate parts/directories:<br> <br> * method: Python routines for generating the cloud property retrieval<br> input based on the Meteosat and MODIS data<br> <br> * analysis: Python routines for analysing the different experiments<br> from the CPP outputs, producing the figures and calculating the<br> comparison statistics<br> <br> * datasets: the various input and output datasets used for the<br> analyses of the paper</p>
Spatial scaling properties of coral reef benthic communities
<p>The spatial structure of ecological communities on tropical coral reefs across seascapes and geographies have historically been poorly understood. Here we addressed this for the first time using spatially expansive and thematically resolved benthic community data collected around five uninhabited central Pacific oceanic islands, spanning 6° latitude and 17° longitude. Using towed-diver digital image surveys over ~140 linear km of shallow (8 – 20 m depth) tropical reef, we highlight the autocorrelated nature of coral reef seascapes. Benthic functional groups and hard coral morphologies displayed significant spatial clustering (positive autocorrelation) up to kilometre-scales around all islands, in some instances dominating entire sections of coastline. The scale and strength of these autocorrelation patterns showed differences across geographies, but patterns were more similar between islands in closer proximity and of a similar size. For example, crustose coralline algae (CCA) were clustered up to scales of 0.3 km at neighbouring Howland and Baker Islands and macroalgae were spatially clustered at scales up to ~3 km at both neighbouring Kingman Reef and Palmyra Atoll. Of all the functional groups, macroalgae had the highest levels of spatial clustering across geographies at the finest resolution of our data (100 m). There were several cases where the upper scale at which benthic community members showed evidence of spatial clustering correlated highly with the upper scales at which concurrent gradients in physical environmental drivers were spatially clustered. These correlations were stronger for surface wave energy than subsurface temperature (regardless of benthic group) and turf algae and CCA had the closest alignments in scale with wave energy across functional groups and geographies. Our findings suggest such physical drivers not only limit or promote the abundance of various benthic competitors on coral reefs, but also play a key role in governing their spatial scaling properties across seascapes.</p>
Spatial autocorrelation shapes liana distribution better than topography and host tree properties in a subtropical evergreen broadleaved forest in SW China
<p>Lianas are an important component of subtropical forests, but the mechanisms underlying their spatial distribution patterns have received relatively little attention. Here, we selected 12 most abundant liana species, constituting up to 96.9% of the total liana stems, in a 20-ha plot in a subtropical evergreen broadleaved forest at 2,472 – 2,628 m elevation in SW China. Combining data on topography (convexity, slope, aspect, and elevation) and host trees (density and size) of the plot, we addressed how liana distribution is shaped by host tree properties, topography and spatial autocorrelation by using principal coordinates of neighbor matrices (PCNM) analysis. We found that lianas had an aggregated distribution based on the Ripley's <i>K</i> function. At the community level, PCNM analysis showed that spatial autocorrelation explained 43% variance in liana spatial distribution. Host trees and topography explained 4% and 18% of the variance, but less than 1% variance after taking spatial autocorrelation into consideration. A similar trend was found at the species level. These results indicate that spatial autocorrelation might be the most important factor shaping liana spatial distribution in subtropical forest at high elevation.</p>
Dataset for "Spatial distribution and physicochemical properties of respirable volcanic ash from the 16-17 August 2006 Tungurahua eruption (Ecuador), and alveolar epithelium response in-vitro" published in GeoHealth
<p>Data Repository for:</p> <p><strong>"Spatial distribution and physicochemical properties of respirable volcanic ash from the 16-17 August 2006 Tungurahua eruption (Ecuador), and alveolar epithelium response <em>in-vitro" </em></strong>published in GeoHealth.<br> </p> <p>Julia Eychenne<sup>1,2*</sup>, Lucia Gurioli<sup>1</sup>, David Damby<sup>3</sup>, Corinne Belville², Federica Schiavi<sup>1</sup>, Geoffroy Marceau<sup>2,4</sup>, Claire Szczepaniak<sup>5</sup>, Christelle Blavignac<sup>5</sup>, Mickael Laumonier<sup>1</sup>, Emmanuel Gardés<sup>1</sup>, Jean-Luc Le Pennec<sup>6,7</sup>, Jean-Marie Nedelec<sup>8</sup>, Loïc Blanchon², Vincent Sapin<sup>2,4 </sup></p> <p><sup>1</sup> Université Clermont Auvergne, CNRS, IRD, OPGC, Laboratoire Magmas et Volcans, F-63000 Clermont-Ferrand, France</p> <p><sup>2</sup> Université Clermont Auvergne, CNRS, INSERM, Institut de Génétique Reproduction et Développement, F-63000 Clermont-Ferrand, France</p> <p><sup>3</sup> U.S. Geological Survey, California Volcano Observatory, Moffett Field, CA, USA</p> <p><sup>4</sup> Biochemistry and Molecular Genetic Department, University Hospital, F-63000 Clermont-Ferrand, France</p> <p><sup>5</sup> Université Clermont Auvergne, UCA PARTNER, Centre Imagerie Cellulaire Santé, F-63000 Clermont-Ferrand, France</p> <p><sup>6</sup> Geo-Ocean, CNRS, Ifremer, UMR6538, F-29280 Plouzané, France</p> <p><sup>7</sup> IRD Office for Indonesia & Timor Leste, Jalan Kemang Raya n°4, Jakarta 12730, Indonesia</p> <p><sup>8</sup> Université Clermont Auvergne, Clermont Auvergne INP, CNRS, ICCFn, F-63000 Clermont-Ferrand, France</p> <p><strong>This repository includes the grainsize distributions of the individual tephra fall samples, the grainsize distribution of the respirable ash sample isolated from F2, the Raman point counting data and individual spectra, the SEM images and EDX maps of the respirable ash sample, the SEM and TEM images of the <em>in-vitro</em> experiments, and the data from the LDH assays, multiplex immunoassays and RT-qPCR.</strong></p>
Data from: Invasion complexity at large spatial scales is an emergent property of interactions among landscape characteristics and invader traits
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Data from: Spatial variability in soil organic carbon in a tropical montane landscape: associations between soil organic carbon and land use, soil properties, vegetation, and topography vary across plot to landscape scales
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Spatial autocorrelation shapes liana distribution better than topography and host tree properties in a subtropical evergreen broadleaved forest in SW China
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Spatial scaling properties of coral reef benthic communities
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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.