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242 results for “Spatial Dataset”
Datasets for the paper "High resolution spatial analyses of trace elements in coccoliths reveal new insights into element incorporation in coccolithophore calcite"
<p>complete datasets for the samples in the paper, published in Scientific Reports</p>
Co-Register of multi modal spatial dataset
Open the record for dataset details and reuse information.
Spatial Characteristics and Connectivity of Urban Floods in Eastern China: Insights from a Newly Established Dataset during 2010—2020
Open the record for dataset details and reuse information.
A spatially-explicit dataset of global wind erosion based on distributed RWEQ model
<p>This is a global wind erosion estimation dataset obtained based on the distributed RWEQ model with a resolution of 0.05°. It's the raw data of the article “Global Wind Erosion Reduction Driven by Changing Climate and Land Use” (https://doi.org/10.1029/2024EF004930). The dataset is NetCDF (Network Common Data Format) and contains 38 years between 1982 and 2019. Dimension is the year information for wind erosion, beginning in 1982 and ending in 2019.</p> <p>Coordinate system: WGS_84 (EPSG:4326)</p> <p>NoData Value=-10000</p> <p>NETCDF_VARNAME=SoilLoss</p> <p> </p> <h3><strong>Please cite this dataset via the Earth's Future journal article</strong><strong>“Global Wind Erosion Reduction Driven by Changing Climate and Land Use” </strong></h3> <h3><strong>https://doi.org/10.1029/2024EF004930</strong></h3> <p>Citation:<br>Sun, R., He, H., Jing, Y., Leng, S., Yang,G., Lü, Y., et al. (2024). Global winderosion reduction driven by changingclimate and land use. Earth's Future, 12,e2024EF004930. https://doi.org/10.1029/2024EF004930</p> <p> </p> <p>For access to the data, please contact the corresponding author.</p>
Dataset related to article"Integrating single-cell and spatial transcriptomics to elucidate the crosstalk between cancer-associated fibroblasts and cancer cells in hepatocellular carcinoma with spleen-deficiency syndrome"
<p>Most patients with hepatocellular carcinoma (HCC) in China have been diagnosed with spleen deficiency syndrome (SDS), which accelerates the progression of HCC by disrupting the tumor microenvironment (TME) homeostasis. However, the underlying mechanism remains to be explored. By integrating single-cell and spatial transcriptomics, we found that the crosstalk between CAFs and cancer cells is crucial for the tumor-promoting effect of SDS. CAFs recruited by HCC via PDGFA may lead to ECM remodeling through activation of the TGF-β pathway, thereby forming a physical barrier to block immune cell infiltration under SDS. </p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multisource data (2001-2002)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:<span>T<sub>ave</sub>, </span><span>R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%</span><span>; T<sub>max</sub>, </span><span>R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%</span><span>; T<sub>min</sub>, </span><span>R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%</span><span>).</span></p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2013-2014)
<div> <p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p> </div>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2017-2018)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2011-2012)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2009-2010)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2007-2008)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2005-2006)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2019-2020)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2003-2004)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (Samples)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2015-2016)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Dataset related to article "Spatial resolution of cellular senescence dynamics in human colorectal liver metastasis"
<p>Dataset related to article "Spatial resolution of cellular senescence dynamics in human colorectal liver metastasis"</p><p><strong>Abstract</strong></p><p>Hepatic metastasis is a clinical challenge for colorectal cancer (CRC). Senescent cancer cells accumulate in CRC favoring tumor dissemination. Whether this mechanism progresses also in metastasis is unexplored. Here, we integrated spatial transcriptomics, 3D-microscopy, and multicellular transcriptomics to study the role of cellular senescence in human colorectal liver metastasis (CRLM). We discovered two distinct senescent metastatic cancer cell (SMCC) subtypes, transcriptionally located at the opposite pole of epithelial (e) to mesenchymal (m) transition. SMCCs differ in chemotherapy susceptibility, biological program, and prognostic roles. Mechanistically, epithelial (e)SMCC initiation relies on nucleolar stress, whereby c- myc dependent oncogene hyperactivation induces ribosomal RPL11 accumulation and DNA damage response. In a 2D pre-clinical model, we demonstrated that RPL11 co-localized with HDM2, a p53-specific ubiquitin ligase, leading to senescence activation in (e)SMCCs. On the contrary, mesenchymal (m)SMCCs undergo TGFβ paracrine activation of NOX4-p15 effectors. SMCCs display opposing effects also in the immune regulation of neighboring cells, establishing an immunosuppressive environment or leading to an active immune workflow. Both SMCC signatures are predictive biomarkers whose unbalanced ratio determined the clinical outcome in CRLM and CRC patients. Altogether, we provide a comprehensive new understanding of the role of SMCCs in CRLM and highlight their potential as new therapeutic targets to limit CRLM progression.</p><p> </p>
Database for the manuscript "Improving the predictive skill of a distributed hydrological model by calibration on spatial patterns with multiple satellite datasets"
<p>******************************************************************************************************************************************************<strong>NOTICE: </strong>all datasets and tools provided in this database can and should only be used to reproduce the original experiment for which the database was created. The use of any datasets and tools in this database is subject to third party restrictions. Before copying or using this database for other purposes than reproducing the original experiment for which it was created, please ask for adequate authorisations to the author (Moctar Dembélé, mocdembele@gmail.com), who might additionaly need the authorization of the providers of the data and the tools available in this database. ******************************************************************************************************************************************************</p> <p>This database provides model outputs for the manuscript 'Improving the predictive skill of a distributed hydrological model by calibration on spatial patterns with multiple satellite datasets' by Dembélé et al.</p> <p>The content of each folder is as follows:</p> <p>-OF5 contains the model outputs for the model calibration case Q</p> <p>-OF42 contains the model outputs for the model calibration case MV-Q</p> <p>-OF46 contains the model outputs for the model calibration case MV-St</p> <p>-OF47 contains the model outputs for the model calibration case MV-Su</p> <p>-OF48 contains the model outputs for the model calibration case MV-Ea</p> <p>-OF49 contains the model outputs for the model calibration case MV</p> <p>-Input contains the data needed to setup and run the model</p> <p>-multiOFanalysis contains the results and the files of the analysis of the model outputs using the MATLAB software.</p> <p>For further information, please contact Moctar Dembélé, mocdembele@gmail.com</p> <p> </p>
Dataset related to the article "Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment"
<p>This record contains raw data related to the article "Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment"</p> <p>Abstract</p> <p>Background</p> <p>Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted electronic devices (CIED).</p> <p>Methods</p> <p>In this retrospective study, 230 patients (100 with CIED) who underwent clinically indicated CMR were used to developed and test a DL model. A novel convolutional neural network was proposed to extract the left ventricle (LV) and right (RV) ventricle endocardium and LV epicardium. In order to perform a successful segmentation, it is important the network learns to identify salient image regions even during local magnetic field inhomogeneities. The proposed network takes advantage from a spatial attention module to selectively process the most relevant information and focus on the structures of interest. To improve segmentation, especially for images with artifacts, multiple loss functions were minimized in unison. Segmentation results were assessed against manual tracings and commercial CMR analysis software cvi<sup>42</sup>(Circle Cardiovascular Imaging, Calgary, Alberta, Canada). An external dataset of 56 patients with CIED was used to assess model generalizability.</p> <p>Results</p> <p>In the internal datasets, on image with artifacts, the median Dice coefficients for end-diastolic LV cavity, LV myocardium and RV cavity, were 0.93, 0.77 and 0.87 and 0.91, 0.82, and 0.83 in end-systole, respectively. The proposed method reached higher segmentation accuracy than commercial software, with performance comparable to expert inter-observer variability (bias ± 95%LoA): LVEF 1 ± 8% vs 3 ± 9%, RVEF − 2 ± 15% vs 3 ± 21%. In the external cohort, EF well correlated with manual tracing (intraclass correlation coefficient: LVEF 0.98, RVEF 0.93). The automatic approach was significant faster than manual segmentation in providing cardiac parameters (approximately 1.5 s vs 450 s).</p> <p>Conclusions</p> <p>Experimental results show that the proposed method reached promising performance in cardiac segmentation from CMR images with susceptibility artifacts and alleviates time consuming expert physician contour segmentation.</p>
Spatial Dataset from a Nebraska Research Farm, 2021
<p>Spatial dataset for an irrigated field at the University of Nebraska-Lincoln Eastern Nebraska Research and Extension Center near Mead, Nebraska. Dataset includes maps of yield and planting density for the 2021 growing season.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.