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99
datasets available to search
ShareScore release 0.9.0
Dataset results
99 results for “High spatial resolution”
High-resolution spatial RNA profiling of tissues with immuno-labeling based LCM
GEO Series GSE158396. Homo sapiens; Mus musculus. 13 samples. Type: Expression profiling by high throughput sequencing.
High-Resolution Spatial Transcriptomics Reveals Stroma Damage in Human Ovarian Tissue Response to Cryopreservation
GEO Series GSE267323. Homo sapiens. 6 samples. Type: Other.
High resolution spatial trancrioptomics combined with deep learning-based image segmentation enables single-cell spatial profiling of archival FFPE kidney tissue from patients with idiopathic nephroti
GEO Series GSE300895. Homo sapiens. 3 samples. Type: Other.
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>
High Resolution Spatial Mapping of Microbiome-Host Interactions via in situ Polyadenylation and Spatial RNA Sequencing [Ileum, with in situ Polyadenylation]
GEO Series GSE277197. Mus musculus. 1 samples. Type: Other.
High Resolution Spatial Mapping of Microbiome-Host Interactions via in situ Polyadenylation and Spatial RNA Sequencing [Ileum, without in situ Polyadenylation]
GEO Series GSE277196. Mus musculus. 1 samples. Type: Other.
High Resolution Spatial Mapping of Microbiome-Host Interactions via in situ Polyadenylation and Spatial RNA Sequencing
GEO Series GSE276866. Mus musculus. 8 samples. Type: Other.
High Resolution Spatial Mapping of Microbiome-Host Interactions via in situ Polyadenylation and Spatial RNA Sequencing [Spatial transcriptomics]
GEO Series GSE316608. Mus musculus. 3 samples. Type: Other.
High Resolution Spatial Mapping of Microbiome-Host Interactions via in situ Polyadenylation and Spatial RNA Sequencing (Stereoseq)
GEO Series GSE308507. Mus musculus. 5 samples. Type: Other.
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.