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6,381 results for “spatial”
Data and code from: Evaluating spatially explicit density estimates of unmarked wildlife detected by remote cameras.
<p>Detection data from American black bears and code used in "Evaluating spatially explicit density estimates of unmarked wildlife detected by remote cameras" published in the Journal of Applied Ecology (Evans & Rittenhouse 2018). Unmarked detection data was collected using remote cameras in northwest Connecticut in 2014, and individual detection data was determined from unique genotypes obtained from non-invasive hair snares constructed at camera sampling locations.</p> <p>EN14.rds contains detection data as an R list:</p> <p>$y (num): J (sites) x K (occasions) matrix containing detection counts</p> <p>$X (int): 2 x J matrix of site coordinates</p> <p>$xlims (num): bounding x-coordinates</p> <p>$ylims (num): bounding y-coordinates</p> <p>$M (int): upper bound for super population of individuals</p> <p>$nTraps (int): number of sampling sites (J)</p> <p>$nReps (int): number of MCMC interations</p> <p>$forest (num): vector of site-specific covariates</p> <p>$mark (int): K x I matrix storing site numbers at which individual (i) was detected on occasion k</p> <p>FullModel.R provides functions used to fit constant density models to unmarked detections incorporating covariates of detection probability.</p> <p>partialID.R provides functions and code used to estimate density from mixtures of marked and unmarked detection data</p> <p>VariableDensity.R provides functions and code used to fit variable density models to unmarked detection data incorporating spatial covariates of density.</p> <p> </p>
EXPLORATORY SPATIAL ANALYSIS OF "ACCESS" TO PHYSICAL AND DIGITAL RETAIL BANKING CHANNELS IN THE UK
<p>File built in order to explore access to banking channels in the UK (February 2019)</p> <p>The report "Exploratory Spatial Analysis of Access to Physical and Digital Retail Banking Channels in the UK" has been published by Think Forward Initiative in October 2019. You can download the full report from here: <a href="https://www.thinkforwardinitiative.com/research/exploratory-spatial-analysis-of-access-to-physical-and-digital-retail-banking-channels-in-the-uk">https://www.thinkforwardinitiative.com/research/exploratory-spatial-analysis-of-access-to-physical-and-digital-retail-banking-channels-in-the-uk</a></p> <p>Related code: <a href="https://github.com/andrasonea">andrasonea</a>/<strong><a href="https://github.com/andrasonea/TFI_AccessToBanking">TFI_AccessToBanking</a></strong></p> <p> </p>
Gridded spatial information on soil organic carbon content, density and stock in Hungary for 1992 and 2000
<p>Predictive soil organic carbon (SOC) content, density, and stock maps, along with the associated prediction uncertainty, are provided for the years 1992 and 2000, for the entire territory of Hungary. The maps refer to the topsoils (0–30 cm) with a spatial resolution of 100⨯100 m. The uncertainty associated with the SOC property maps is expressed by the lower and upper limits of the 90% prediction interval (PI), the range of values within which the true value is expected to occur 9 times out of 10. This means that there are two maps to each SOC property map, quantifying its prediction uncertainty. It should be added that all maps have been masked with open water bodies, as these areas are not relevant for soils.</p> <p><strong>For more details / to cite this dataset please use:</strong></p> <p><a href="https://doi.org/10.1038/s41597-024-04158-3">Szatmári, G., Laborczi, A., Mészáros, J., Takács, K., Benő, A., Koós, S., Bakacsi, Z., & Pásztor, L. (2024). Gridded, temporally referenced spatial information on soil organic carbon for Hungary. Scientific Data 11, 1312.</a></p> <p><strong>Custom code used for digital soil mapping and validation is available on GitHub:</strong></p> <p><a href="https://github.com/GaborSzatmari/HU-SOC-mapping" target="_blank" rel="noopener">https://github.com/GaborSzatmari/HU-SOC-mapping</a></p> <p><strong>Description of the files:</strong></p> <p>The resulting maps are shared as GeoTIFF files. The coordinate reference system is the Hungarian Unified National Projection System (HD72/EOV; EPSG: 23700) (<a href="https://epsg.io/23700" target="_blank" rel="noopener">https://epsg.io/23700</a>). The table below provides further information on the published maps. Note that the first file (00_Overview.jpg) gives an overview of the SOC property maps.</p> <table> <tbody> <tr> <td> <p><strong>SOC property maps</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> <td> <p><strong>Year</strong></p> </td> <td> <p><strong>Filename</strong></p> </td> </tr> <tr> <td> <p>SOC content map</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCc_0_30cm_1992_pred.tif</p> </td> </tr> <tr> <td> <p>SOC content, lower limit of the 90% PI</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCc_0_30cm_1992_q05.tif</p> </td> </tr> <tr> <td> <p>SOC content, upper limit of the 90% PI</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCc_0_30cm_1992_q95.tif</p> </td> </tr> <tr> <td> <p>SOC density map</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCd_0_30cm_1992_pred.tif</p> </td> </tr> <tr> <td> <p>SOC density, lower limit of the 90% PI</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCd_0_30cm_1992_q05.tif</p> </td> </tr> <tr> <td> <p>SOC density, upper limit of the 90% PI</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCd_0_30cm_1992_q95.tif</p> </td> </tr> <tr> <td> <p>SOC stock map</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCs_0_30cm_1992_pred.tif</p> </td> </tr> <tr> <td> <p>SOC stock, lower limit of the 90% PI</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCs_0_30cm_1992_q05.tif</p> </td> </tr> <tr> <td> <p>SOC stock, upper limit of the 90% PI</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCs_0_30cm_1992_q95.tif</p> </td> </tr> <tr> <td> <p>SOC content map</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCc_0_30cm_2000_pred.tif</p> </td> </tr> <tr> <td> <p>SOC content, lower limit of the 90% PI</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCc_0_30cm_2000_q05.tif</p> </td> </tr> <tr> <td> <p>SOC content, upper limit of the 90% PI</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCc_0_30cm_2000_q95.tif</p> </td> </tr> <tr> <td> <p>SOC density map</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCd_0_30cm_2000_pred.tif</p> </td> </tr> <tr> <td> <p>SOC density, lower limit of the 90% PI</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCd_0_30cm_2000_q05.tif</p> </td> </tr> <tr> <td> <p>SOC density, upper limit of the 90% PI</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCd_0_30cm_2000_q95.tif</p> </td> </tr> <tr> <td> <p>SOC stock map</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCs_0_30cm_2000_pred.tif</p> </td> </tr> <tr> <td> <p>SOC stock, lower limit of the 90% PI</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCs_0_30cm_2000_q05.tif</p> </td> </tr> <tr> <td> <p>SOC stock, upper limit of the 90% PI</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCs_0_30cm_2000_q95.tif</p> </td> </tr> </tbody> </table> <p> </p>
A proteome-wide quantitative platform for nanoscale spatially resolved extraction of membrane proteins into native nanodiscs
<p><strong>EM Quantitation:</strong></p> <p>Raw data gathered from EM images taken to determine nanodisc population size distribution.</p> <p> </p> <p><strong>NNB TGN46 analysis:</strong></p> <p>Data analysis of the Native Nanobleach experiments of TGN46 in native nanodiscs to determine population distribution of oligomeric organizations.</p> <p> </p> <p><strong>Polymer conditions:</strong></p> <p>Physiochemical characteristic and extraction conditions for all polymers in the library both commercially available and in-house.</p> <p> </p> <p><strong>Protein groups polymer screen original file:</strong></p> <p>Original output of MaxQuant data processing of polymer screen data.</p> <p> </p> <p><strong>Organelle matching:</strong></p> <p>Code used for mathcing proteins identified in the proteomics output to organelle or residence for all organellar annotations.</p> <p> </p> <p><strong>Polymer code:</strong></p> <p>Code used to process and normalize the MaxQuant output and calulate extraction efficiency across all detected proteins.</p> <p> </p> <p><strong>MAP Library Details:</strong></p> <p>Graphic and table explaining chemical details of all polymer used in the screen, both commerically available and in-house synthesized.</p> <p> </p> <p><strong>NNB TGN46:</strong></p> <p>Raw scope files for the TIRF microscopy single molecule step photobleaching experiment with TGN46.</p> <p> </p> <p><strong>Organellar Breakdown Database:</strong></p> <p>Proteins detected in the polymer screen through proteomics experiments stratified into organelle of residence.</p> <p> </p> <p><strong>Human Proteome FASTA:</strong></p> <p>The FASTA file used for proteome searching in processing the proteomics data to build the screening database.</p> <p> </p> <p><strong>Hand Curated Organellar Proteomes:</strong></p> <p>Organellar proteomes used for organellar sorting and identification of proteins detected in the screen.</p> <p> </p> <p><strong>Polymer SEC Superdex75:</strong></p> <p>Size exculsion chromatography traces for chloroSMA series of polymers. Was used to characterize length and population polydispersity.</p> <p> </p> <p><strong>Negative Stain Raw:</strong></p> <p>RAW TEM scope images of purified synaptophysin-vamp2 containing nanodiscs. Populatoin size distribution was determined.</p> <p> </p> <p><strong>FSEC Polymer CS80:</strong></p> <p>Fluoresence size exclusion chromatogram for purified synaptophysin-vamp2 containing nanodiscs to ensure population homogeneity and purity.</p> <p><strong>NMR Raw data:</strong></p> <p>NMR raw files for characterizing the in-house synthesized Chloro-SMA series and AASTY series.</p> <p> </p>
GPC/m: Global Precipitation Climatology by Machine Learning; Quasi-global, Daily, and One Degree Spatial Resolution
<p>A precipitation dataset, Global Precipitation Climatology by Machine Learning (ML), GPC/m, is released.</p> <p>This new precipitation dataset has been produced by machine learning, which is daily from 1979 to 2020 (will be to present), 1° × 1° spatial resolution. Three ML methods are used. Data is produced from outgoing longwave radiation (OLR) and atmospheric circulation from reanalysis. You can download this with DOI.</p> <p>This daily precipitation dataset has been produced by machine learning (ML) methods using satellite observations and atmospheric circulations from reanalysis. The quasi-global daily precipitation dataset has been around for 42 years from 1979 to 2020, which will be updated to the present. The spatial resolution is 1° × 1° zonally global and from 40°S to 50°N. The ML methods are supervised learning, and the reference data are estimated precipitation datasets from 2001 to the present. The input data are somewhat modified based on knowledge of the climatological background. Using the trained statistical models, we predict back to 1979, when daily precipitation data was almost unavailable globally. For now, this GPC/m precipitation dataset version is GPC/m-v1-2024. This data will be updated in the future with added value. The purpose of this dataset is a challenge to produce a climatological dataset by reducing artificial gaps as much as possible for discussion of climatology, climate variability, and climate change. This dataset is very useful for statistical analysis, such as composite analysis and correlation analysis. Disadvantages should also be understood in the description paper (Takahashi, 2024c). Also, I hope that this dataset can contribute to improving the current precipitation datasets, which are based on physical or researcher-explaining algorithms.<br><br>To facilitate analysis of the dataset, it is distributed in Network Common Data Form (netCDF) format and the Grid Analysis and Display System (GrADS) format (with control file). If you would like recently updated data, please contact the creator. If it has already been created, it can be distributed.<br><br><em>Added on September 18, 2024.</em><br>More details are in the preprint paper at this link (<a href="https://doi.org/10.48550/arXiv.2409.09639">Takahashi, 2024, https://doi.org/10.48550/arXiv.2409.09639</a>).</p> <p><em>Added on March 4, 2025.</em><br><strong>Alternative Download Options</strong><br>If you experience slow download speeds from Zenodo, alternative mirrors are available for the dataset files.<br><em><span>However, we kindly request you to download the .ctl file from Zenodo for tracking purposes.</span></em><br>Download NetCDF (.nc) or Binary (.bin) from:<br><a href="https://camo.fpark.tmu.ac.jp/gpcm.html">https://camo.fpark.tmu.ac.jp/gpcm.html</a></p>
Spatial dataset for ecological niche and spatial distribution modeling of Herichthys bartoni (Cichliformes: Cichlidae) in the Media Luna spring, Mexico
<p>Dataset for the endangered endemic cichlid <em>Herichthys bartoni</em> in the Media Luna spring, Mexico. This data includes occurrences records by species life stage (adult, juvenile and fry), in three field sessions corresponding to the summer period, in the years 1999, 2009 and 2019.</p> <p>For more information about the codes where the previous datasets could be used, visit the following repository with URL: <a href="https://doi.org/10.5281/zenodo.7603557">https://doi.org/10.5281/zenodo.7603557</a>.</p> <p>Likewise, the UC and WDp variables used to run the ecological niche and spatial distribution model, by summer period, can be found in the following repository wirh URL: <a href="https://doi.org/10.5281/zenodo.7603890">https://doi.org/10.5281/zenodo.7603890</a>.</p>
SHIFT: A DEM-Based Spatial Heterogeneity Improved Mapping of Global Geomorphic Floodplains
<h2>Description</h2> <p><strong>SHIFT</strong> (Spatial Heterogeneity Improved Floodplain by Terrain analysis) is a 90-m resolution global geomorphic floodplain map based on terrain analysis. It takes MERIT-Hydro as the terrain input and Floodplain Hydraulic Geometry (FHG) as the thresholding scheme, with the scaling parameters estimated by a stepwise framework that both respects the power law and approximates the spatial extent of hydrodynamic modeling. SHIFT effectively captures the global patterns of the geomorphic floodplains, with better regional details than existing data.</p> <h2>Data Structure</h2> <p>We provide 2 resolutions of data for different needs.</p> <ul> <li><strong>SHIFT_v3_90m</strong>: The original SHIFT data derived from MERIT-Hydro, with lakes and reservoirs removed. The resolution is 0.000833333333333 degrees under geographic coordinate system (EPSG:4326), approximately 90 meters at the equator. Pixels with value 1 are floodplains, 2 are lakes and reservoirs and 0 are non-floodplains, with empty values set as 255 (denoting pixels not within any watersheds under the threshold of 1000 km2).</li> <li><strong>SHIFT_v3_1km</strong>: The resampled SHIFT data with lakes and reservoirs marked. The resolution is 0.00833333333333 degrees under geographic coordinate system (EPSG:4326), approximately 1 km at the equator. Pixels with value 1 are floodplains, 2 are lakes and reservoirs and 0 are non-floodplains, with empty values set as 255 (denoting pixels not within any watersheds under the threshold of 1000 km2).</li> </ul> <p>Also, we provide our derived spatially-varying parameters in all Level-3 basins to support future studies. Parameters are provided in a shapefile, with 'a' denotes the proportional parameter and 'b' denotes the exponent. We aggregated MERIT-Basins based on its spatial relationship with basins from Level-3 HydroBASINS, ensuring that the centroid of a MERIT-Basin falls within the corresponding boundary. This approach accounts for slight differences in boundaries due to the use of different terrain data, preventing confusion in hydrological representation.</p> <p>For more details, please refer to:</p> <ul> <li>Zheng, K., Lin, P., and Yin, Z.: SHIFT: a spatial-heterogeneity improvement in DEM-based mapping of global geomorphic floodplains, Earth Syst. Sci. Data, 16, 3873–3891, <a href="https://doi.org/10.5194/essd-16-3873-2024" rel="noopener">https://doi.org/10.5194/essd-16-3873-2024</a>, 2024.</li> </ul> <h2>Development Log</h2> <ol> <li><strong>Changes in v3 compared to v2:</strong> <ol> <li> <p><strong>Inclusion of Missing Level-3 Basin:</strong> We have added a previously missing Level-3 basin (PFAF ID: 242) that covers an area in Eastern Europe, specifically from Warsaw to Minsk. This omission was due to a technical problem that has now been resolved. Data are now still available in two resolutions: 90-meter and 1-kilometer.</p> </li> <li> <p><strong>Updated Parameters</strong>: Along with the new boundaries, updated parameters are provided in the shapefile.</p> </li> <li> <p><strong>Re-estimated Global Floodplain Area</strong>: Based on the new data, we have re-estimated the global total floodplain area from 9.9 × 10^6 km² to 9.92 × 10^6 km². This area still represents approximately 6.6% of the total land mass.</p> </li> </ol> </li> <li><strong>Changes in v2 compared to v1:</strong> <ol> <li><strong>Parameter 'b' Estimation:</strong> We modified the technical details of parameter 'b' estimation, specifically the binning parameter, adding a constraining mechanism to handle data noise. This resulted in stabler estimates for large basins and a clearer pattern of global residual uncertainty.</li> <li><strong>Target Function for Parameter 'a':</strong> We changed our target function to balance information from both datasets, using Fleiss’s Kappa (FK) and a penalty term to reduce bias.</li> </ol> </li> </ol> <h2>Contacts</h2> <ul> <li>Kaihao Zheng, <a href="mailto:Mostaly@pku.edu.cn" target="_blank" rel="noopener">Mostaly@pku.edu.cn</a></li> <li>Peirong Lin, <a href="mailto:peironglinlin@pku.edu.cn" target="_blank" rel="noopener">peironglinlin@pku.edu.cn</a></li> </ul> <p> </p>
Spatial Analysis on Kemanggisan Community Health Center
<p>This data was collected based on the condition during COVID-19 pandemic and New Normal Era (between 2022-2023)</p>
Characterizing cell-type spatial relationships across length scales in spatially resolved omics data: data repository
<h1>CRAWDAD</h1> <p>Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we develop CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package with source code and additional documentation at https://jef.works/CRAWDAD/.</p> <p>During CRAWDAD's development, we generated simulated datasets and new cell-type annotations for human spleen data, provided here. The external datasets such as the mouse cerebellum, mouse embryo, mouse brain, and human breast cancer data used in the paper can be found in their original publication. See more information in CRAWDAD's data availability statement.</p> <h2>Simulated Datasets</h2> <ul> <li>sim.csv: the simulated data. Used in Figure 1 b-g, Supplementary Figure 1 a-c, and Supplementary Figure 9 a-b.</li> <li>ext_sim.csv: the extended simulated data. Used in Supplementary Figure 1 d-f.</li> <li>null_sim_visualization.csv: the null simulated data. Used to generate the plots Supplementary Figure 2 a-d.</li> <li>null_sim_1.csv - null_sim_10.csv: the 10 null simulated datasets. Used to quantitatively compare CRAWDAD, Squidpy’s co-occurrence implementation, and Ripley’s K Cross.</li> </ul> <h2>HuBMAP Datasets</h2> <ul> <li>pkhl.csv: annotated cell types and positions of sample HBM389.PKHL.936 from donor HBM966.VNKN.965. Used in Figure 5 a-h, Supplementary Figure 5 a, Supplementary Figure 7 a-c, and Supplementary Figure 8 c. doi:10.35079/HBM389.PKHL.936</li> <li>xxcd.csv: annotated cell types and positions of sample HBM772.XXCD.697 from donor HBM966.VNKN.965. Used in Figure 5 d-h, Supplementary Figure 5 a-c, and Supplementary Figure 7 a-c. doi:10.35079/HBM772.XXCD.697</li> <li>fsld.csv: annotated cell types and positions of sample HBM342.FSLD.938 from donor HBM245.ZWNT.288. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM342.FSLD.938</li> <li>pbvn.csv: annotated cell types and positions of sample HBM825.PBVN.284 from donor HBM245.ZWNT.288. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM825.PBVN.284</li> <li>ksfb.csv: annotated cell types and positions of sample HBM556.KSFB.592 from donor HBM298.KGNJ.374. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM556.KSFB.592</li> <li>ngpl.csv: annotated cell types and positions of sample HBM568.NGPL.345 from donor HBM298.KGNJ.374. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM568.NGPL.345</li> </ul> <h2>External Datasets</h2> <ul> <li>Mouse cerebellum: Used in Figure 2 a-e, Supplementary Figure 3 a-b, Supplementary Figure 4 a-d, and Supplementary Figure 8 a.</li> <li>Mouse embryo: Used in Figure 2 f-j, Supplementary Figure 3 c-d, Supplementary Figure 4 e-h, and Supplementary Figure 8 b.</li> <li>Human breast cancer: Used in Figure 3 a-c.</li> <li>Mouse brains: Used in Figure 4 a-e.</li> </ul>
Spatial predictions of the morpho-ecological state of Finnish palsa mires
<p>Spatial predictions of the probability of a good morpho-ecological state are provided for Finnish palsa mires as a TIFF file with a 10 m resolution, using the EUREF FIN TM35FIN coordinate system.</p> <p>These predictions were produced through spatial modeling that combined classified point data on the state of Finnish palsa mires (Ruuhijärvi et al., 2022) with high-resolution (10 m) environmental datasets. The predictions were computed for the extent of palsa mires (Tammilehto et al., 2024). Modelling was conducted in mgcv package (version 1.9.0; Wood, 2011) in R (version 4.3.2; R Core Team 2023). The predictions were developed during the preparation of the manuscript: <em>"The morpho-ecological state of palsa mires in sub-arctic Fennoscandia: insights from high-resolution spatial modelling"</em> (Leppiniemi et al., 2024, in-review).</p> <p> </p> <p>References:</p> <p>Leppiniemi, O., Karjalainen, O., Aalto, J., Yletyinen., E., Luoto, M., & Hjort, J. 2024. The morpho-ecological state of palsa mires in sub-arctic Fennoscandia: insights from high-resolution spatial modelling. (In-review).</p> <p>R Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (accessed 14 October 2024).</p> <p>Ruuhijärvi, R., Salminen, P., & Tuominen, S., 2022. Distribution range, morphological types, and state of palsa mires in Finland in the 2010s. <em>Suo</em> 73, 1–32. (In Finnish with English summary).</p> <p>Tammilehto, A., Härmä, P., Kallio, M., Törmä, M., Saikkonen, A., Tuominen, S., Impiö, M., Heikkinen, M., Kervinen, M., Jussila, T., Böttcher, K., Pääkkö, E., Kokko, A., Mäkelä, K., & Anttila, S., 2024. Ylä-Lapin luonnon kaukokartoitus – Projektin loppuraportti osa 1 – Aineistot ja menetelmät. Vantaa. (In Finnish).</p> <p>Wood, S.N., 2011. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Series B Stat. Methodol. 73, 3–36. https://doi.org/10.1111/J.1467-9868.2010.00749.X</p> <p> </p>
Spatial clustering of Neobuccinum eatoni occurrence data for potential distribution modeling
<p>The occurrence dataset for <em>Neobuccinum eatoni</em> was compiled through filtration process, starting with records from the Global Biodiversity Information Facility (GBIF) and supplemented by museum specimens and additional sources like SOMBASE, iBOL, NIWA, ANTABIF, and SCAR-AntOBIS. Further data were sourced from the National Museum of Natural History in Paris, the University of Vigo, and recent fieldwork in Antarctica, Heard Island, and Kerguelen Island. Records were meticulously screened to remove misidentified specimens, inaccurate locations, duplicates, and outdated entries, ensuring accuracy and relevance. To address spatial autocorrelation, clustering methods divided the data into distinct geographic clusters, producing a refined dataset used to model <em>N. eatoni</em>'s potential distribution with enhanced predictive reliability by reducing spatial autocorrelation effects.</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>
Deep Learning Reach-level Estimates of Mean River Depth at the Conterminous United States Spatial Scale
<p>Abstract: Estimates of riverine channel geometry play a vital role in the physical representation of stream networks in models used to predict flood and drought conditions, manage water resources, and increase our knowledge of fluvial conditions under a changing climate. A well established body of literature exists that explains the relationship between channel geometry parameters width, depth, and velocity to instantaneous river discharge using a log-log linear power-law regression. In this study, a state-of-the-art deep learning regression model is presented and compared against the power-law method to evaluate their ability to estimate cross-sectional mean river depth. Results reveal three key findings, the neural network: (1) decreases RMSE by 22% verse a CONUS scale power-law equation, (2) reduces prediction variance across Strahler stream orders, and (3) generally outperforms regional power-law equations with an average decrease in RMSE of 8.7% Lastly, a reach-level CONUS dataset of estimated mean river depth is delivered.</p> <p> </p> <p>The deep learning model was trained using the following features:</p> <ul> <li>AI - Mean aridity index of unit catchment - Trabucco and Zomer, 2019</li> <li>area - Upstream drainage area (km2) - P. Lin et al., 2020</li> <li>CLY - Mean clay content (mass percentage, %) of unit catchment - Hengl et al., 2017</li> <li>DOR - Stream segment degree of dam regulation (Scale 0. – 100.) - Grill et al., 2019</li> <li>Elev - Stream segment mean elevation - P. Lin et al., 2020</li> <li>K - Mean bedrock permeability of unit catchment surrounding stream segment - Huscroft et al., 2018</li> <li>LAI - Mean leaf area index of unit catchment - Zhu et al., 2013</li> <li>order - Strahler-Horton stream order - P. Lin et al., 2020</li> <li>P - Mean bedrock porosity of unit catchment - Huscroft et al., 2018</li> <li>QMEAN - Stream segment mean annual discharge (m3/s) - P. Lin et al., 2019</li> <li>Sin - Stream segment sinuosity - P. Lin et al., 2020</li> <li>Slp - Stream segment mean longitudinal slope - P. Lin et al., 2020</li> <li>SLT - Mean silt content (mass percentage, %) of unit catchment - Hengl et al., 2017</li> <li>SND - Mean sand content (mass percentage, %) of unit catchment - Hengl et al., 2017</li> <li>stream_wdth_va - Measured stream cross-sectional width (m) - Canova et al., 2016</li> <li>Urb - Mean urban fraction of unit catchment - Liu et al., 2018</li> </ul> <p>The deep learning model was trained using the following label:</p> <ul> <li>mean_depth_va - Measured stream mean depth (m) - Canova et al., 2016</li> </ul> <p>Predictions of mean depth were made by replacing stream_wdth_va from Canova et al., (2016) with bankfull width estimates (width_m) from P. Lin et al., (2020). Missing records from the P. Lin et al., (2020) dataset were excluded when making predictions, thus there are missing reaches in the dataset.</p>
soil spatial covariates at OAL-UK
<p>Dataset containing a series of soil spatial covariates (e.g. topographic attributes, soil texture, soil organic matter, etc) usable in the creation of soil digital maps for OAL-UK.</p>
spatial soil particle distribution at OAL-UK
<p>three raster files containing information on the spatial distribution of the soil's percentage of sand, silt and clay, respectively, at OAL-UK. The files were created following a digital soil mapping approach implemented through the Random Forest algorithm. More information on how the raster files were created can be found here: <a href="https://doi.org/10.1016/j.ecoleng.2017.04.066">https://doi.org/10.1016/j.ecoleng.2017.04.066</a> </p>
spatial soil bulk density at OAL-UK
<p>raster containing information on the spatial distribution of soil bulk density at OAL-UK. The raster was generated following a digital soil mapping approach implemented through the random forest algorithm. More detail on the map creation can be found here: <a href="https://doi.org/10.1016/j.ecoleng.2017.04.066">https://doi.org/10.1016/j.ecoleng.2017.04.066</a></p>
Setting files from: Spatially explicit paleogenomic simulations support cohabitation with limited admixture between Bronze Age Central European populations.
<p><strong>Simulated Data and Custom Scripts</strong></p> <p>This dataset release permits to simulate the expansion of populations from the Pontic Steppes to Central Europe with the version of SPLATCHE3 which is included. There are 2 main zipped folders: i) the one called "SPLATCHE3executableAndSettings" contains a "ReadMe.txt" file that contains all required information to make the simulations: the resulting ".prop" file contains proportions of genomic ancestry of the P2 layer, ancestry from P1 layer is equal to 1-(proportion from P2) ; ii) the other one called "HowToMakeFigure2" contains the R script and the tables necessary to reproduce Figure 2. See Rio J, Quilodrán CS & Currat M., Communications Biology (2021), for background.</p> <p><strong>Acknowledgments</strong></p> <p>This project was financially supported by the Swiss National Research Foundation grants n° 31003A_182577 to MC and n° P400PB_183930 to CQ, as well as the IGE3 Student Salary Award to JR.</p>
TAU-NIGENS Spatial Sound Events 2021
<p><strong>DESCRIPTION:</strong></p> <p>The <strong>TAU-NIGENS Spatial Sound Events 2021</strong> dataset contains multiple spatial sound-scene recordings, consisting of sound events of distinct categories integrated into a variety of acoustical spaces, and from multiple source directions and distances as seen from the recording position. The spatialization of all sound events is based on filtering through real spatial room impulse responses (RIRs), captured in multiple rooms of various shapes, sizes, and acoustical absorption properties. Furthermore, each scene recording is delivered in two spatial recording formats, a microphone array one (<strong>MIC</strong>), and first-order Ambisonics one (<strong>FOA</strong>). The sound events are spatialized as either stationary sound sources in the room, or moving sound sources, in which case time-variant RIRs are used. Each sound event in the sound scene is associated with a single direction-of-arrival (DoA) if static, a trajectory DoAs if moving, and a temporal onset and offset time. The isolated sound event recordings used for the synthesis of the sound scenes are obtained from the <a href="https://doi.org/10.5281/zenodo.2535878">NIGENS general sound events database</a>. These recordings serve as the development dataset for the <a href="http://dcase.community/challenge2021/task-sound-event-localization-and-detection">DCASE 2021 Sound Event Localization and Detection Task</a> of the <a href="http://dcase.community/challenge2021/">DCASE 2021 Challenge</a>.</p> <p>This dataset is the third iteration of spatialized sound event datasets based on real room responses and ambient noise from multiple spaces, with each iteration introducing more challenging conditions closer to real-life. Those iterations, including the present one, are:</p> <ul> <li><strong>TAU Spatial Sound Events 2019, </strong><a href="https://doi.org/10.5281/zenodo.2580091">development</a> and <a href="https://doi.org/10.5281/zenodo.3066124">evaluation</a> datasets.<br> 5 rooms, high direct-to-reverberant ratios (DRR), static sources only, minimum DoA separation 10°, discrete grid of DoAs, high SNR for ambient noise, maximum polyphony of 2 simultaneous events</li> <li><strong><a href="https://doi.org/10.5281/zenodo.4064792">TAU-NIGENS Spatial Sound Events 2020</a></strong>, development and evaluations datasets.<br> 13 rooms, low-to-high DRRs, static and moving sources, continuous DoAs, low-to-high SNR for ambient noise,<br> maximum polyphony of 2 simultaneous events</li> <li><strong>TAU-NIGENS Spatial Sound Events 2021</strong>.<br> Same as 2020, with the following exceptions: a more natural temporal distribution of sound events,<br> maximum polyphony of 3 target events, <strong>inclusion of additional out-of-target-classes directional interference events</strong></li> </ul> <p>The inclusion of directional interferences is the main new challenging property of the new dataset. They are spatialized in the scene in the same way as the target events, and can be either static or moving. The interfering events are sourced from the "engine", "fire", and "general" classes of the NIGENS sound event database. The interferers are considered unknown and no activity or directional labels of them are provided with the training datasets.</p> <p><strong>REPORT & REFERENCE:</strong></p> <p>If you use this dataset please cite the report on its creation, and the corresponding DCASE2020 task setup:</p> <p>Archontis Politis, Sharath Adavanne, Daniel Krause, Antoine Deleforge, Prerak Srivastava, Tuomas Virtanen (2021).<br> A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection. <br> In <em>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2021)</em>, Barcelona, Spain.</p> <p>available <a href="https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Politis_43.pdf">here</a>.</p> <p><strong>AIM:</strong></p> <p>The dataset includes a large number of mixtures of sound events with realistic spatial properties under different acoustic conditions, and hence it is suitable for training and evaluation of machine-listening models for sound event detection (SED), general sound source localization with diverse sounds or signal-of-interest localization, and joint sound-event-localization-and-detection (SELD). Additionally, the dataset can be used for evaluation of signal processing methods that do not necessarily rely on training, such as acoustic source localization methods and multiple-source acoustic tracking. The dataset allows evaluation of the performance and robustness of the aforementioned applications for diverse types of sounds, and under diverse acoustic conditions.</p> <p><strong>SPECIFICATIONS:</strong></p> <ul> <li>600 one-minute long sound scene recordings with metadata (development dataset).</li> <li>200 one-minute long sound scene recordings without metadata (evaluation dataset).</li> <li>Sampling rate 24kHz.</li> <li>About 500 sound event samples distributed over the 12 target classes (see [here](http://doi.org/10.5281/zenodo.2535878) for more details).</li> <li>About 400 sound event samples used as interference events (see [here](http://doi.org/10.5281/zenodo.2535878) for more details).</li> <li>Two 4-channel 3-dimensional recording formats: first-order Ambisonics (FOA) and tetrahedral microphone array.</li> <li>Realistic spatialization and reverberation through multichannel RIRs collected in 13 different enclosures.</li> <li>From 1184 to 6480 possible RIR positions across the different rooms.</li> <li>Both static reverberant and moving reverberant sound events.</li> <li>Three possible angular speeds for moving sources of approximately 10, 20, or 40deg/sec.</li> <li>Up to three overlapping sound events possible, temporally and spatially.</li> <li>Simultaneous directional interfering sound events with their own temporal activities, static or moving.</li> <li>Realistic spatial ambient noise collected from each room is added to the spatialized sound events, at varying signal-to-noise ratios (SNR) ranging from noiseless (30dB) to noisy (6dB) conditions.</li> </ul> <p>The IRs were collected in Finland by staff of Tampere University between 12/2017 - 06/2018, and between 11/2019 - 1/2020. The data collection received funding from the European Research Council, grant agreement <a href="https://cordis.europa.eu/project/id/637422">637422 EVERYSOUND</a>.</p> <p>More detailed information on the dataset can be found in the included README file.</p> <p><strong>EXAMPLE APPLICATION:</strong></p> <p>An implementation of a trainable model of a convolutional recurrent neural network, performing joint SELD, trained and evaluated with this dataset will be provided soon. That implementation will serve as the baseline method in the <a href="http://dcase.community/challenge2021/task-sound-event-localization-and-detection">DCASE 2021 Sound Event Localization and Detection Task</a>.</p> <p><strong>DEVELOPMENT AND EVALUATION:</strong></p> <p>The current and final version (Version 1.2) of the dataset includes the 600 development audio recordings and labels, used by the participants of Task 3 of DCASE2021 Challenge to train and validate their submitted systems, and the 200 evaluation audio recordings including their labels, used in the evaluation phase of DCASE2021.</p> <p>If researchers wish to compare their system against the submissions of DCASE2021 Challenge, they will have directly comparable results if they use the evaluation data as their testing set.</p> <p><strong>DOWNLOAD INSTRUCTIONS:</strong></p> <p>The three files, <strong><em>foa_dev.z01</em></strong>, and <strong><em>foa_dev.zip</em></strong>, correspond to audio data of the <strong>FOA </strong>recording format.<br> The three files, <strong><em>mic_dev.z01</em></strong>, and <strong><em>mic_dev.zip</em></strong>, correspond to audio data of the <strong>MIC</strong> recording format.<br> The <strong><em>metadata_dev.zip</em></strong> is the common metadata for both formats.</p> <p>The file,<strong> <em>foa_eval.zip</em></strong>, corresponds to audio data of the FOA recording format for the evaluation dataset.<br> The file, <strong><em>mic_eval.zip</em></strong>, corresponds to audio data of the MIC recording format for the evaluation dataset.<br> The <strong><em>metadata_eval.zip</em></strong> is the common metadata for both formats.</p> <p>Download the zip files corresponding to the format of interest and use your favorite compression tool to unzip these split zip files. To extract a split zip archive (named as zip, z01, z02, ...), you could use, for example, the following syntax in Linux or OSX terminal:</p> <ol> <li>Combine the split archive to a single archive: <pre>zip -s 0 split.zip --out single.zip</pre> </li> <li>Extract the single archive using unzip: <pre>unzip single.zip</pre> </li> </ol>
Vectra Polatis image of human colorectal cancer (CRC1) from: A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution.
<p>Two 4 µm thick serial sections were cut from CRC1 FFPE block using a microtome. The first slide was dewaxed and rehydrated before carrying out HIER with Antigen Retrieval Reagent-Basic (R&D Systems). The tissue was then blocked and incubated with the anti-CD3 antibody (Dako, Supplementary Table 2) followed by horseradish peroxidase (HRP) conjugated anti-rabbit antibody (Dako) and stained with 3,3' diaminobenzidine (DAB) substrate (Abcam) and haematoxylin. Areas with CD3<sup>+</sup> infiltration in the proximity of the tumour invasive margin were identified by a clinical pathologist (M. R-J.)</p> <p>The second slide was stained with a panel of six antibodies (CD8, PD1, Ki67, PDL1, CD68, GzB, Supplementary Table 2), Opal fluorophores and 4’,6-diamidino-2-phenylindole (DAPI) on a Ventana Discovery Ultra automated staining platform (Roche). Expected expression and cellular localisation of each marker as well as fluorophore brightness were used to minimise fluorescence spillage upon antibody-Opal pairing. Following a one-hour incubation at a 60°C, the slide was subjected to an automated staining protocol on an autostainer. The protocol involved deparaffinisation (EZ-Prep solution, Roche), HIER (DISC. CC1 solution, Roche) and seven sequential rounds of: one hour incubation with the primary antibody, 12 minutes incubation with the HRP-conjugated secondary antibody (DISC. Omnimap anti-Ms HRP RUO or DISC. Omnimap anti-Rb HRP RUO, Roche) and 16 minute incubation with the Opal reactive fluorophore (Akoya Biosciences). For the last round of staining, the slide was incubated with Opal TSA-DIG reagent (Akoya Biosciences) for 12 minutes followed by Opal 780 reactive fluorophore for our hour (Akoya Biosciences). A denaturation step (100°C for 8 minutes) was introduced between each staining round in order to remove the primary and secondary antibodies from the previous cycle without disrupting the fluorescent signal. The slide was counterstained with DAPI (Akoya Biosciences) and coverslipped using ProLong Gold antifade mounting media (Thermo Fisher Scientific). The Vectra Polaris automated quantitative pathology imaging system (Akoya Biosciences) was used to scan the labelled slide. Six fields of view, within the area selected by the pathologist, were scanned at 20x and 40x magnification using appropriate exposure times and loaded into inForm{Kramer, 2018 #23} for spectral unmixing and autofluorescence isolation using the spectral libraries. After spectral unmixing and merging of six 20x fields of view for a total of >5mm<sup>2</sup> ROI (Table 2), one single-tiff image was extracted for each marker and its intensity was rescaled from 0 to 1 with custom R scripts.</p>
Daily time series of spatially enhanced relative humidity for Europe at 1000 m resolution (Set 5: 2020 - 2021) derived from ERA5-Land data
<p>Overview:<br> ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br> The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:<br> 1. spatially aggregate CHELSA to the resolution of ERA5-Land<br> 2. calculate difference of ERA5-Land - aggregated CHELSA<br> 3. interpolate differences with a Gaussian filter to 30 arc seconds<br> 4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>Data provided is the daily averages of relative humidity. This set provides data for the years 2020 - 2021. For other time periods, please see further linked data sets.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>The data have been reprojected to EU LAEA.</p> <p>File naming scheme (YYYY = year; MM = month; DD = day):<br> <code>ERA5_land_rh2m_avg_daily_YYYYMMDD.tif</code></p> <p>Projection + EPSG code:<br> EU LAEA (EPSG: 3035)</p> <p>Spatial extent:<br> north: 6874000<br> south: -485000<br> west: 869000<br> east: 8712000</p> <p>Spatial resolution:<br> 1000 m</p> <p>Temporal resolution:<br> Daily</p> <p>Pixel values:<br> Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0</p> <p>Original ERA5-Land dataset license:<br> <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br> Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in Latitude-Longitude/WGS84 (EPSG: 4326) projection: <a href="http://https://doi.org/10.5281/zenodo.6344125">https://doi.org/10.5281/zenodo.6344125</a></p>
ScienceDex guides
Understand access before you commit
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.