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1,255 results for “High-resolution”
Comprehensive performance comparison of high-resolution array platforms for genome-wide Copy Number Variation (CNV) analysis in humans [Agilent021365]
GEO Series GSE96898. Homo sapiens. 2 samples. Type: Genome variation profiling by genome tiling array.
A cost-effective and flexible workflow for high-resolution spatial transcriptomics in fixed tissue
GEO Series GSE292893. Homo sapiens; Mus musculus. 52 samples. Type: Expression profiling by high throughput sequencing.
High-resolution analysis of cell-state transitions in yeast suggests widespread transcriptional tuning by alternative starts
GEO Series GSE137711. Saccharomyces cerevisiae. 288 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
KSHV chromatin looping facilitates effective gene expression: high-resolution mapping of K-Rta binding sites in the KSHV genome [ChIP-seq]
GEO Series GSE99943. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
High-resolution map of copy number variations in motor cortex of Control and Sporadic Amyotrphic Lateral Sclerosis patients by using a customized exon-centric comparative genomic hybridization array.
GEO Series GSE107375. Homo sapiens. 40 samples. Type: Genome variation profiling by array.
Targeted high-resolution chromosome conformation capture at genome-wide scale in mouse erythroid cells
GEO Series GSE160229. Mus musculus. 3 samples. Type: Other.
High-resolution genome-wide dissection of transcriptional regulatory activity and function in human cells
GEO Series GSE104001. Homo sapiens. 11 samples. Type: Other; Expression profiling by high throughput sequencing.
GOES-16 cloud-motion wind and ASCAT ocean surface wind data for the article "Evolution of an atmospheric Kármán vortex street from high-resolution satellite winds: Guadalupe Island case study"
<p>This repository contains GOES-16 cloud-motion winds and ASCAT ocean surface winds derived for and analysed in the article "Evolution of an atmospheric Kármán vortex street from high-resolution satellite winds: Guadalupe Island case study".</p> <p> </p> <p><strong>GOES-16 Local Cloud-Motion Vectors</strong></p> <p>Data in two ASCII text files: <em>raw5x5g16b2_2018d129_1437z_2232z_north.txt</em> and <em>raw5x5g16b2_2018d129_1437z_2232z_south.txt</em>, with the former containing data for the upper half and the latter for the lower half of the study domain between ~26<sup>o</sup>N and ~29.5<sup>o</sup>N. Both files include 96 records, each record corresponding to a specific 5-minute time interval between 14:37 UTC and 22:32 UTC on 9 May 2018—9 May is day of year 129. The start and end times are given at the beginning of each record in YYYYDDDHHMM format, where Y is year, D is day of year, H is hour, and M is minute. For example, the first record contains data between 14:37 UTC and 14:42 UTC, as indicated by the start and end times of 20181291437 and 20181291442. Then follows the four column headers LAT LON SPD DIR, corresponding to latitude (degree), longitude (degree), wind speed (m/s), and wind direction (meteorological convention, degree north), respectively—note that no cloud-top height/pressure value was calculated for the wind vectors. Each subsequent line is a single GOES-16 local cloud-motion vector, derived from 5x5-pixel band 2 (0.64 micron visible red band) image templates, which represent an area of ~2.5x2.5 km<sup>2</sup> at the subsatellite point.</p> <p> </p> <p><strong>MODIS–GOES-16 3D Cloud-Motion Vectors</strong></p> <p>Data in two netCDF files: <em>MOD.A2018129.1810-75_ABI_CONUS_band_02_goes16.nc</em> and <em>MYD.A2018129.2120-75_ABI_CONUS_band_02_goes16.nc</em>, which correspond to the MODIS Terra and MODIS Aqua overpasses, respectively. These joint MODIS–GOES-16 wind retrievals were derived using ~8x8 km<sup>2</sup> red band image templates sampled every 2 km. The data files are self-explanatory, but the variables "lat", "lon", "V_3D", and "H_3D" provide the latitude (degree), longitude (degree), the [east-west, north-south] wind components (m/s), and the geometric stereo height (m) for each wind retrieval.</p> <p> </p> <p><strong>ASCAT Ocean Surface Wind Vectors</strong></p> <p>Data in two netCDF files: <em>ascat_20180509_030000_metopa_59945_srv_o_063_ovw_new.nc</em> and <em>ascat_20180509_040000_metopb_29259_srv_o_063_ovw_new.nc</em>, which correspond to the MetOp-A and MetOp-B overpasses, respectively. These ASCAT ocean surface retrievals are stress-equivalent winds at 10 m height, given on a 6.25-km grid. The data files are self-explanatory, but the variables "lat", "lon", "wind_speed", and "wind_dir" provide the latitude (degree), longitude (degree), the wind speed (m/s), and the wind direction (oceanographic convention, degree north) for each wind retrieval. <em>Note that wind direction follows the oceanographic convention and refers to the direction towards which the wind blows (equivalent to meteorological wind direction minus 180<sup>o</sup>)!</em></p>
Evaluation of a customized variable-resolution global model and its application for high-resolution weather forecasts in East Asia
<p>The data for shallow water test for the paper "Evaluation of a customized variable-resolution global model and its application for high-resolution weather forecasts in East Asia" are in the two sw[2,5]_GlobalIntegrals.tar.bz2 files.</p> <p>The data of MPAS model output is stored as Demo data of the <a href="https://cpas.earth/">CPAS cloud platform</a> with Jupyter visualization tools and user interface. Please see "How-to-visualize-MPAS-model-data.pdf" on how to use it.</p>
High-resolution X-ray diffraction dataset for the coiled-coil oligomerisation domain of human Arc
<p>0.95-Å resolution diffraction dataset for the crystal structure of human Arc coiled-coil dimerisation domain (PDB entry 6YTU). Data were collected on beamline P13 at EMBL/DESY (Hamburg, Germany). </p>
Malaria in High-Resolution: Modelling and Mapping Plasmodium falciparum Parasite Rate using Very-High-Resolution Satellite Derived Indicators in Sub-Saharan African Cities
<p>Metadata, results and supplementary material of the following <a href="https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-020-00232-2#Sec28">article</a>.</p> <p>The Out of Bag error of the Kampala Land use product is 18,57%.</p> <p>The Out of Bag error of the Dar es Salaam land use product is 16%.</p> <p>More information on the LC products can be found in <a href="https://zenodo.org/record/3711903#.YGGo0tLiuzU">here</a> and <a href="https://zenodo.org/record/3711905#.YGGtUNLiuzU">here</a>.</p> <p>This research is funded by the Belgian Science Policy through the <a href="http://react.ulb.be/">REACT</a> project.</p> <p> </p>
Antarctic Circumpolar Current transport through Drake Passage: What can we learn from comparing high-resolution model results to observations?
<p>Datasets for the Journal of Geophysical Research: Oceans publication: "Antarctic Circumpolar Current transport through Drake Passage: What can we learn from comparing high-resolution model results to observations?"</p> <p>[Abstract] Uncertainty exists in the time-mean total transport of the Antarctic Circumpolar Current (ACC), the world’s strongest ocean current. The two most recent observational programs in Drake Passage, DRAKE and cDrake, yielded transports of 141 and 173.3 Sv, respectively. In this paper, we use a realistic 1/12° global ocean simulation to interpret these observational estimates and reconcile their differences. We first show that the modeled ACC transport in the upper 1000 m is in excellent agreement with repeat shipboard acoustic Doppler current profiler (SADCP) transects and that the exponentially decaying transport profile in the model is consistent with the profile derived from repeat hydrographic data. By further comparing the model results to the cDrake and DRAKE observations, we argue that the modeled 157.3 Sv transport, i.e. approximately the average of the cDrake and DRAKE estimates, is actually representative of the time-mean ACC transport through the Drake Passage. The cDrake experiment overestimates the barotropic contribution in part because the array undersampled the deep recirculation southwest of the Shackleton Fracture Zone, whereas the surface geostrophic currents used in the DRAKE estimate yielded a weaker near-surface transport than implied by the SADCP data. We also find that the modeled baroclinic and barotropic transports are not correlated, thus monitoring either baroclinic or barotropic transport alone may be insufficient to assess the temporal variability of the total ACC transport.</p>
A new perspective on evaluating high-resolution urban climate simulation with urban canopy parameters
<p>A new perspective on evaluating high-resolution urban climate simulation with urban canopy parameters</p>
Replication data for Convolutional neural networks with hierarchical context transfer for high-resolution spatiotemporal predictions
<p>Data represents number of posts from Instagram in area for six large cities (New York, London, Moscow, Vienna, Tokyo, and Saint Petersburg) covering 2017 and 2018. We aggregated data by hours and split each city using hierarchical area split by 10x10 grid with three levels. The size of the cell on the micro-level equals to 50 meters. Thus, we get 8760 examples for each year. Cells with zero number of posts in an hour are not listed.</p> <p>Data stored in JSON files with the following format:<br> {"2018-12-13 04:00:00":<br> {"(4, 4)":<br> {"sum":1,<br> "data":<br> {"(9, 6)":<br> {"sum":1,<br> "data":{"(2, 4)":1}}}}}}</p>
Data from: Long-term test–retest reliability of striatal and extrastriatal dopamine D2/3 receptor binding: study with [11C]raclopride and high-resolution PET
We measured the long-term test–retest reliability of [11C]raclopride binding in striatal subregions, the thalamus and the cortex using the bolus-plus-infusion method and a high-resolution positron emission scanner. Seven healthy male volunteers underwent two positron emission tomography (PET) [11C]raclopride assessments, with a 5-week retest interval. D2/3 receptor availability was quantified as binding potential using the simplified reference tissue model. Absolute variability (VAR) and intraclass correlation coefficient (ICC) values indicated very good reproducibility for the striatum and were 4.5%/0.82, 3.9%/0.83, and 3.9%/0.82, for the caudate nucleus, putamen, and ventral striatum, respectively. Thalamic reliability was also very good, with VAR of 3.7% and ICC of 0.92. Test-retest data for cortical areas showed good to moderate reproducibility (6.1% to 13.1%). Our results are in line with previous test–retest studies of [11C]raclopride binding in the striatum. A novel finding is the relatively low variability of [11C]raclopride binding, providing suggestive evidence that extrastriatal D2/3 binding can be studied in vivo with [11C]raclopride PET to be verified in future studies.
Long time-series (1980-2020) high-resolution (1km) and multi-depth soil organic carbon dataset in China
<p>unit: kg C m-2 (soil oganic carbon density)</p><p>0100: denote 0-100 cm</p><p>020: denote 0-20 cm</p><p>Example 1980: 1980-1984 (five years mean soc)</p><p> </p><p> </p><p> </p>
High-Resolution Satellite-Derived River Network of the Yellow River Basin
<p>This repository contains continuous monthly river networks of the Yellow River Basin at 10 m resolution.</p> <p>This repository supports the paper published below. Please refer to the following citation when using the datasets and codes.</p> <p>"Li, P., Zhang, Y., Liang, C., Wang, H., & Li, Z. (2024). High spatiotemporal resolution river networks mapping on catchment scale using satellite remote sensing imagery and DEM data. <strong><em>Geophysical Research Letters</em></strong>, 51, e2023GL107956. https://doi.org/10.1029/<br>2023GL107956"</p> <p>If you have any questions on it, please do not hesitate to contact me (pengli@ouc.edu.cn).</p>
Model output for "A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"
<p>This dataset contains the numerical model output files used in the analysis described in "A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"</p>
High-resolution traffic flow data in Glasgow
<p><strong>Description of the dataset</strong></p> <p>This dataset offers a long-term traffic flow data at an intra-city scale with high spatio-temporal granularity. The dataset covers the Glasgow City Council area for four consecutive years spanning the COVID-19 pandemic, from October 2019 to September 2023, providing comprehensive temporal and spatial coverage. </p> <p>The code used to produce the indicators is available at: <a href="https://github.com/YueLi-0816/TrafficFlowData">https://github.com/YueLi-0816/TrafficFlowData</a>.</p> <p>This work is funded by the China Scholarship Council (CSC) from the Ministry of Education of P.R. China, the ESRC’s ongoing support for the Urban Big Data Centre (UBDC), and the Royal Society International Exchange Scheme.</p> <p><strong>Contents</strong></p> <p>Sensor status metadata - status.csv</p> <table> <tbody> <tr> <td> <p><span>Column name</span></p> </td> <td><span>Description</span></td> </tr> <tr> <td> <p><span>id</span></p> </td> <td><span>Unique ID for each sensor, e.g., GA0601_T.</span></td> </tr> <tr> <td> <p><span>latitude</span></p> </td> <td><span>The Latitude in decimal degrees of WGS84 coordinates, e.g., 55.86238129.</span></td> </tr> <tr> <td> <p><span>longitude</span></p> </td> <td><span>The Longitude in decimal degrees of WGS84 coordinates, e.g., -4.26570708.</span></td> </tr> <tr> <td> <p><span>step 1</span></p> </td> <td><span>The status of sensors at the current step is 1 if retained and 0 if removed.</span></td> </tr> <tr> <td> <p><span>step 2</span></p> </td> <td><span>The status of sensors at the current step is 1 if retained and 0 if removed.</span></td> </tr> <tr> <td> <p><span>step 3</span></p> </td> <td><span>The status of sensors at the current step is 1 if retained and 0 if removed.</span></td> </tr> <tr> <td> <p><span>step 4</span></p> </td> <td><span>The status of sensors at the current step is 1 if retained and 0 if removed.</span></td> </tr> <tr> <td> <p><span>step 5.1</span></p> </td> <td><span>The status of sensors at the current step is 1 if retained and 0 if removed.</span></td> </tr> <tr> <td> <p><span>step 5.2</span></p> </td> <td><span>The status of sensors at the current step is 1 if retained and 0 if removed.</span></td> </tr> </tbody> </table> <p>Sensor location metadata - locations.csv</p> <table> <tbody> <tr> <td>Column name</td> <td>Description</td> </tr> <tr> <td>id</td> <td>Unique ID for each sensor, e.g., GA0601_T.</td> </tr> <tr> <td>latitude</td> <td>The Latitude in decimal degrees of WGS84 coordinates, e.g., 55.86238129.</td> </tr> <tr> <td>longitude</td> <td>The Longitude in decimal degrees of WGS84 coordinates, e.g., -4.26570708.</td> </tr> </tbody> </table> <p> </p> <p>Traffic flows metadata</p> <table> <tbody> <tr> <td>File name</td> <td>Description</td> </tr> <tr> <td>[sensor_id].csv</td> <td>Traffic flows. [sensor_id] refers to the ‘id’ from the locations.csv</td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td>Column name</td> <td>Description</td> </tr> <tr> <td>date</td> <td>The date the data is collected (YYYY-MM-DD), e.g., 2021-11-04.</td> </tr> <tr> <td>time</td> <td>The hours of the day the data is collected range from 0 to 23, 0 = [0,1), 23 = [23,24).</td> </tr> <tr> <td>flow</td> <td>Number of vehicles that pass the sensor location during the one-hour interval. </td> </tr> </tbody> </table>
High-Resolution TURBINE fMRI Dataset 3
<p>Isotropic 0.67 mm visual cortex-slab TURBINE raw dataset 3 (in ISMRMRD format) for "Ultra-High Resolution fMRI at 7T using Radial-Cartesian TURBINE sampling" published in Magnetic Resonance in Medicine.</p> <p> </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.