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5,155 results for “resolution”
Data from: Ultraconserved elements improve resolution of marmot phylogeny and offer insights into biogeographic history
<p class="MsoNormal"><span>Marmots (<em>Marmota</em> spp.) comprise a lineage of large-bodied ground squirrels that diversified rapidly in the Pleistocene, when the planet quickly transitioned to a drier, colder, and highly seasonal climate—particularly at high latitudes. Fossil evidence indicates the genus spread from North America, across Beringia, and into the European Alps over the course of only a few million years, beginning in the late Pliocene. Marmots are highly adapted to survive long and severely cold winters, and this likely favored their expansion and diversification over this time period. Previous phylogenetic studies have identified two major subgenera of marmots, but the timing of important speciation events and some species relationships have been difficult to resolve. Here we use ultraconserved elements and mitogenomes, with samples from all 15 extant species, to more precisely retrace how and when marmots came to inhabit a vast Holarctic range. Our results indicate marmots arose in North America in the mid Miocene (~16.3 Mya) and dispersed across the Bering Land Bridge in the late Pliocene (~3-4 Mya); in addition, our fossil-calibrated timeline is suggestive of the rise and spread of open grasslands as being particularly important to marmot diversification. The woodchuck (<em>M. monax</em>) and the Alaska marmot (<em>M. broweri</em>) are found to be more closely related to the Eurasian species than to the other North American species. Paraphyly is evident in the bobak marmot (<em>M. bobak</em>) and the hoary marmot (<em>M. caligata</em>), and in the case of the latter the data are highly suggestive of a second, cryptic species in the Cascade Mountains of Washington.</span></p>
supplementary data for Bemelmans et al., 2023, High-resolution InSAR reveals localised pre-eruptive deformation inside the crater of Agung volcano, Indonesia.
<p>This repository contains the supplementary materials for the paper "High-resolution InSAR reveals localised pre-eruptive deformation inside the crater of Agung volcano, Indonesia." to be published in JGR: Solid Earth.</p> <p>The dataset contains files assiciated with the StaMPS time series processing. Each dataset has its own folder containing:</p> <ol> <li>*_data.csv : data file containing latitude, longitude, incidence angle, heading and LOS displacement for each acquistion (date is listed in the column name in the format yyyymmdd).</li> <li>parms.mat : parameter file used for StaMPS processing of that dataset</li> </ol> <p>The dataset also contains input and results from the GBIS modelling (/GBIS_results/). the *.inp files are the input files for each model inversion where the letter ('M','T','P','Y', or 'D') refer to the Mogi (point), McTigue (sphere), penny-shaped crack, Yang (ellipsoid), and dyke (also sill) model used for that run. folders with the same name as the *.inp file contain the inversion results (invert_*.mat), a summary table (summary_*.txt) and several figures showing the distribution and convergence of the model inversion.</p> <p>The input for the GBIS inversions is stored in /GBIS_results/INSAR_input/</p> <p>the file <a href="https://zenodo.org/api/files/f30be116-1e2f-4d52-8fcf-a54e11ab691f/matlab_functions.zip?versionId=490de786-f245-45b5-92bb-d2ae0d50be56">matlab_functions.zip </a>contains matlab functions used for data processing, visualisation, storage and conversion.</p> <p>the file <a href="https://zenodo.org/api/files/f30be116-1e2f-4d52-8fcf-a54e11ab691f/GBISv1_1_MJWB.zip?versionId=cec83c81-a0b7-4e4a-8aa8-ac81e32b8772">GBISv1_1_MJWB.zip </a>contains GBIS code adapted by the author to perform statistical analysis of the model inversion, perform region-of-interest based subsampling and store modeled results as shapefiles for further processing.</p> <p> </p>
CRLC: Cross-resolution national-scale land-cover mapping based on noisy label learning: a case study of China
<p><strong>CRLC are the 10-meter resolution land cover maps for China in 2020 achieved by a deep classification network.</strong></p> <p><strong>The maps include eight land cover classes:</strong></p> <p>1: Cropland</p> <p>2: Forest</p> <p>3: Grass/shrubland</p> <p>5: Wetland</p> <p>6: Water bodies</p> <p>8: Impervious</p> <p>9: Bareland</p> <p>10: Snow/ice</p> <p> </p> <p><strong>Reference:</strong></p> <pre>@article{liu2023cross, title={Cross-resolution national-scale land-cover mapping based on noisy label learning: A case study of China}, author={Liu, Yinhe and Zhong, Yanfei and Ma, Ailong and Zhao, Ji and Zhang, Liangpei}, journal={International Journal of Applied Earth Observation and Geoinformation}, volume={118}, pages={103265}, year={2023}, publisher={Elsevier} }</pre>
Spatial soil properties maps for Switzerland at 30 m resolution
<p>The Swiss Soil Property Map (SSPM) was developed using the quantile random forest machine learning algorithm and remotely sensed surrogate information such as terrain, climate, vegetation, and soil covariates. The SSPM dataset provides maps at 30 m resolution for different soil depths (0, 30, 60, and 100 cm) in GeoTIFF format. The mean and respective uncertainty information is provided for each map. Please note that the phosphorus spatial map is only available for the topsoil (0-20 cm) due to the unavailability of the dataset at deeper depths.</p> <table> <caption>Description of soil properties (SP) and their units</caption> <tbody> <tr> <td>SP</td> <td>Description</td> <td>units</td> </tr> <tr> <td>Sand </td> <td>Sand content</td> <td>%</td> </tr> <tr> <td>Clay</td> <td>Clay content</td> <td>%</td> </tr> <tr> <td>OC</td> <td>Organic carbon content </td> <td>%</td> </tr> <tr> <td>N</td> <td>Nitrogen content</td> <td>% </td> </tr> <tr> <td>P</td> <td>Phosphorus content</td> <td>mg/kg</td> </tr> </tbody> </table> <p>For more details / to cite this dataset please use:</p> <ul> <li><strong>Gupta, S. </strong>, Hasler, K. J., Alewell, C.: Mapping soil properties of Switzerland using remote sensing datasets and machine learning approach. Manuscript <strong>submitted</strong>, <strong>Geoderma Regional</strong>, 2023</li> </ul> <p> </p>
Data for "Tracking C-H activation with orbital resolution"
<p>These are the data displayed in the figures in the main text and in the Supplementary Materials of the manuscript entitled "Tracking C-H activation with orbital resolution".</p>
Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution (data)
<p>Data for "Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution"</p>
Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single cell resolution
<p>Supplementary Tables for article "Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single cell resolution"<br> </p>
Human STING is a proton channel (Live-cell MGAT Super-Resolution Experiment)
<p>hTERT-immortalized BJ1 cells (ATCC CRL-2522) expressing SEP-mRuby3 targeted to cis/medial Golgi (MGAT) were transduced with pXPR023 (lentiCRISPRv2) expressing an sgRNA targeting STING and selected with 0.1 µg/mL puromycin for 5 days. Cells were then transduced with blasticidin-STING-miRFP680 and selected using 10 µg/mL blasticidin HCl for 5 days. Cells were plated in 96-well glass-bottom plates (Greiner Bio-One) at 6,000 cells/well. After 48 hours, cells were incubated in Fluorobrite DMEM (Thermo Fisher Scientific, cat. #A1896701) medium supplemented with 10% FBS, 1% Pen-strep, and 1x GlutaMAX (Thermo Fisher Scientific, cat. #35050061) and stimulated with 1 µM diABZI (Invivogen, #tlrl-diabzi). All images were acquired using an LSM980 with Airyscan2 (Zeiss) with 37°C with 5% CO2 incubation. 8 z-stacks were acquired with 0.15 µm z-step. Images were acquired using a 63X 1.40 NA DIC M27 objective with Immersol 518F 37°C oil. Acquired images were Airyscan processed and then analyzed as described in the image analysis section.</p> <p>Each frame represens one timepoint imaged every 5 minutes post diABZI treatment. Channels are: SEP (super-ecliptic pHluorin), mRuby3, and STING-miRFP680.</p> <p> </p>
High-resolution figures and shape analyses files of Mengel et al.
<p>High-resolution figures and shape analyses files of Mengel et al. 2023 "The morphological diversity of dragon lacewing larvae changed more over geological time scales than anticipated" in Insects (MDPI)</p>
Model output for "A high-resolution physical-biogeochemical model for marine resource applications in the Northwest Atlantic (MOM6-COBALT-NWA12)"
<p>This dataset contains the numerical model output files that were used in the analysis presented in "A high-resolution physical-biogeochemical model for marine resource applications in the Northwest Atlantic (MOM6-COBALT-NWA12)", submitted to Geoscientific Model Development.</p>
High-resolution version for Fig. 1 in Rev. Sci. Instrum 94, 053706 (2023)
<p>Here is the same figure as Fig. 1 in Rev. Sci. Instrum 94, 053706 (2023), but with much higher resolution.</p>
KScale SE Asia LAM precipitation data (2.2 km resolution)
<p>2.2 km resolution SE Asia LAM precipitation data produced as part of the K-Scale project at the UK Met Office. The model is initialised at 20171230T0600Z and run for a 12 day integration period (T+0 to T+288).</p>
KScale Tropical Channel 2.2 km resolution Precip
<p>KScale Tropical channel 2.2 km resolution precipitation data. Model run initialised 20171230T0600Z and running from T+0 to T+288 (12 days)</p>
High Resolution Langley
<p>Each pair of CSV files corresponds to a Langley regression analysis carried out at each wavelength point from 950 nm to 2100 nm for solar radiation at the ground recorded on an individual day using a coupled solar tracker- Bruker IFS-125 M Fourier transform spectrometer as part of the MAPP campaign at the Izana atmospheric observatory, Tenerife on DD-MM-YYYY. Each file contains two columns, the first giving the wavelength in nm, the second giving either the Langley gradient or intercept result at that wavelength on that day.</p>
A dynamic modelling approach to quantify pollution contributions from critical source areas within watersheds at fine temporal resolutions
<p>The support data for <em>A dynamic modelling approach to quantify pollution contributions from critical source areas within watersheds at fine temporal resolutions</em></p>
Subset of non-redundant, high resolution multi-pass membrane protein PDB structure files from OPM
<p>A subset of OPM PDB structure files with membrane predictions curated for the MSc thesis project '<strong>Structural characterization of triplets of transmembrane α-helical segments consecutive in sequence in integral membrane proteins: a fragment-based computational pipeline'. </strong>The dataset contains PDB files downloaded from OPM on 22/02/2023. The files are alpha-helical integral membrane proteins with at least three transmembrane regions at better than 2.7 Angtrom resolution. They are non-redundant, with a sequence identity of less than 40% based on clustering with the cd-hit algorithm. </p>
Dataset for: Characterization of local deformation around hydrides in Zircaloy-4 using conventional and high angular resolution electron backscatter diffraction
<p>Datasets for:</p> <p>Characterization of local deformation around hydrides in Zircaloy-4 using conventional and high angular resolution electron backscatter diffraction</p> <p>Ruth M. Birch<sup>1,2*</sup>, James O. Douglas<sup>1</sup>, T. Ben Britton<sup>1,2</sup></p> <ol> <li>Department of Materials, Imperial College London, Exhibition Road, London, UK, SW7 2AZ</li> <li>Department of Materials Engineering, University of British Columbia, Frank Forward Building, 309-6350 Stores Road, Vancouver, BC, Canada V6T 1Z4</li> </ol> <p>---</p> <p>h5 files for all 4 examples used in the paper:</p> <ul> <li>Example 1: JustGBZrH_20kx_WD16-4_DD17_T10-4_Px100nm</li> <li>Example 2: ZrH_WD16_DD18_T10-4_px0</li> <li>Example 3: 20kx_WD16-5_DD17_T10-2_Px0-1um.</li> <li>Example 4: ZrHSpikes_18kx_WD16-5_DD17_T10-2_Px100nm<br> </li> </ul> <p>High quality figures for all figures in the paper (600 dpi)</p> <p> </p>
Dataset for Interlacing in atomic resolution scanning transmission electron microscopy
<p>Dataset for the publication: Interlacing in atomic resolution scanning transmission electron microscopy</p>
Preprocessed dataset of the Spatially Resolved Single-cell Translatomics at Molecular Resolution
<p>Here are the pre-processed image datasets of RIBOmap included in "<strong>Spatially Resolved Single-cell Translatomics at Molecular Resolution</strong>" from Zeng et al. Please refer to the README file for more detailed information. </p> <p> </p> <p><strong>Abstract</strong></p> <p>The precise control of mRNA translation is a crucial step in post-transcriptional gene regulation of cellular physiology. However, it remains a major challenge to systematically study mRNA translation at the transcriptomic scale with spatial and single-cell resolution. Here, we report the development of RIBOmap, a three-dimensional (3D) in situ profiling method to detect mRNA translation of thousands of genes simultaneously in intact cells and tissues. By applying RIBOmap to 981 genes in HeLa cells, we revealed a remarkable dependency of translation on cell-cycle stages and subcellular localization. Furthermore, we profiled single-cell translatomes of 5,413 genes in adult mouse brain tissues yielding a spatial cell atlas of 119,173 cells. The pairwise spatial mapping of single-cell translatome and transcriptome in two adjacent mouse brain slices revealed cell-type and brain-region-dependent translational regulation and suggested a translation remodeling during oligodendrocyte lineage maturation. The spatial translatome profiling detected widespread patterns of localized translation in neuronal and glial cells in intact brain tissue networks. Together, RIBOmap presents the first spatially resolved single-cell translatomics technology, accelerating our understanding of protein synthesis in the context of subcellular architecture, cell types, and tissue anatomy.</p>
Dataset: Multi-resolution 3D rendering for high-performance Web AR
<p>The input data are the following four models: (i) Middlebury TempleRing [1] (ii) Human of CoRBS dataset [2]; (iii) Anton Memorial of Harvest4D consortium [3], and; (iv) Meteora site by Meteora Project [4]. The 3D files are converted into the multiresolution format NXS of Nexus.js library [5], in its compressed format NXZ, in the compressed DRC format of Draco library [6] and in the binary gtTF format (GLB) with some optimizations using meshoptimizer library [7]. </p> <p> </p> <p> </p> <ol> <li>Nair, N.S.; Nair, M.S. Multi-View Stereo Using Graph Cuts-Based Depth Refinement. <em>IEEE Signal Process. Lett</em>. <strong>2022</strong>, <em>29</em>, 1903–1907.</li> <li>Wasenmüller, O.; Meyer, M.; Stricker, D. CoRBS: Comprehensive RGB-D Benchmark for SLAM Using Kinect V2. In Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), New York, USA, 7 – 10 March 2016, pp. 1–7.</li> <li>Harvest 4D. Available Online: <a href="https://harvest4d.org">https://harvest4d.org</a> (accessed on 23 June 2023)</li> <li>Ioannidis, C.; Tallis, I.; Pastos, I.; Boutsi, A.M.; Verykokou, S.; Soile, S.; Tokmakidis, P.; Tokmakidis, K. A WEB-BASED PLATFORM FOR MANAGEMENT AND VISUALIZATION OF GEOMETRIC DOCUMENTATION PRODUCTS OF CULTURAL HERITAGE SITES, <em>ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci</em> <strong>2021</strong>, <em>V–2–2021</em>, 113–120.</li> <li> <p>Ponchio, F.; Dellepiane, M. Fast Decompression for Web-Based View-Dependent 3D Rendering. In Proceedings of the 20th International Conference on 3D Web Technology - Web3D ’15, Heraklion, Greece, 18-21 June 2015; pp. 199–207.</p> </li> <li> <p>Draco 3D data compression. Available online: https://google.github.io/draco/ (accessed on 23 June 2023)</p> </li> <li> <p>Meshoptimizer. Available online: <a href="https://github.com/zeux/meshoptimizer/tree/master">https://github.com/zeux/meshoptimizer/tree/master</a> (accessed on 23 June 2023)</p> </li> </ol>
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.