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2,113 results for “High resolution”
Open data for the article "Low-resistivity, high-resolution W-C electrical contacts fabricated by direct-write focused electron beam induced deposition"
<p>Open data for the article "Low-resistivity, high-resolution W-C electrical contacts fabricated by direct-write focused electron beam induced deposition", which will be published in Open Research Europe</p>
Global daily high spatial resolution XCO2 product during 2019-2021
<p>Validation results are in preparation. The full dataset will be uploaded after the acceptance of our paper.</p>
HiP-RI: High-resolution spatial assessment of precipitation using in-situ and remote sensing data in the Cordillera Blanca, Peru
<p>The HiP-RI product was obtained from CHIRP, PERSIANN and GPM datasets, also vegetation products (NDVI-BOKU), topography (DEM SRTM) and data from 38 meteorological stations (2012-2020) were used to estimate precipitation in the Cordillera Blanca, northern sector of the Peruvian Andes. The observed data underwent quality control. A Gaussian filter, resampling and temporal homogenization at monthly scale were applied to the raster data. Subsequently, a linear regression model was built with the different datasets that served as predictors for precipitation spatialization. This allowed obtaining the best R2 values between the in situ data and those estimated with the model (HiP-RI). The results obtained were satisfactory with R2 values higher than 0.60 and an RMSE = 54%.</p>
NUUR-Texture500: A Diverse Dataset of High Resolution Homogeneous Textures
<p>"NUUR-Texture500" is a dataset of diverse texture images created to facilitate research on texture analysis and synthesis. The textures in this dataset are spatially homogeneous, ranging from regular to stochastic, typically containing repeated elements with random variations in position, shape, orientation and color. For a detailed description of the dataset construction and contents, readers should refer to the following paper:</p> <p>Jue Lin, Gaurav Sharma, Thrasyvoulos N. Pappas, "Towards Universal Texture Synthesis by Combining Texton Broadcasting with Noise Injection in StyleGAN-2", Journal of e-Prime - Advances in Electrical Engineering, Electronics and Energy (3) (2023), https://doi.org/10.1016/j.prime.2022.100092</p> <p>Permission to copy and use this dataset for noncommercial use is hereby granted provided this notice is retained in all copies and the dataset distribution and the paper mentioned below are clearly cited.</p> <p>Contacts:<br> Jue Lin: jue.lin@u.northwestern.edu<br> Gaurav Sharma: gaurav.sharma@rochester.edu<br> Thrasyvoulos N. Pappas: pappas@ece.northwestern.edu</p> <p>Disclaimer: </p> <p>The dataset is provided "as is" with ABSOLUTELY NO WARRANTY expressed or implied. Use at your own risk.</p> <p>Acknowledgment: </p> <p>The NUUR-Texture500 texture images are curated from a number of publicly accessible sources. We acknowledge and thank the original sites for their contributions:<br> Flickr www.flickr.com<br> NeedPix www.needpix.com<br> Pexels www.pexels.com<br> PickUpImage www.pickupimage.com<br> Pixabay www.pixabay.com<br> PublicDomainPictures www.publicdomainpictures.net<br> RawPixel www.rawpixel.com<br> Unsplash www.unsplash.com<br> WikimediaCommons commons.wikimedia.org</p> <p> </p>
High resolution shotgun metagenomics: the more data, the better?
<p>This data archive contains results generated using a high resolution shotgun metagenomics (HRSM) bioinformatic pipeline (ShotgunMG - https://jtremblay.github.io/shotgunmg.html) for the following projects:</p> <p>Human gut microbiome dataset: PRJNA588513</p> <p>Antarctic soil dataset: PRJNA513362</p> <p>Agricultural soil dataset: PRJNA513362</p> <p>Mock communities: PRJNA873699</p> <p>These analyses were performed in the context of evaluating if shallow shotgun metagenomic sequencing is an adequate approach to analyze SM sequencing data using a HRSM pipeline.</p> <p>Briefly, these PRJNA projects were analyzed using identical bioinformatic procedures, but using various initial raw sequencing data loads.</p> <p>Notable end results in each archive includes: de novo co-assembly (fasta files), contigs and genes abubance matrices. Beta-diversity (Bray-Curtis dissimilarity matrices), alpha diversity (richness, chao1, Simpson and Shannon indexes matrices), taxonomic summaries from the kingdom to up to the species level and Metagenome Assembled Genomes (MAGs).</p>
High-resolution hard X-ray tomography and histology of a rat jaw for stem cell-mediated distraction osteogenesis
<p>Histology and microtomography of a rat jaw after distraction. These datasets appear in "<em>Combining high-resolution hard X-ray tomography and histology for stem cell-mediated distraction osteogenesis</em>" Applied Sciences 12(12) (2022) 6268.</p> <p>Micromography (hdr/img files) has pixel size of 10.0228 µm. Histology (.tif file) has pixel size of 0.243094 µm.</p> <p>Slice to volume registration scripts can be found at https://github.com/grodgers1/SliceToVolume.</p>
High-resolution maps of material stock, population and employment in Austria from 1985 to 2018
<p>Global societal material stocks such as buildings and infrastructure accumulated rapidly within recent decades, along with population growth. Material stocks constitute the physical basis of most socio-economic activities and services, such as mobility, housing, health, or education. The dynamics of stock growth, and its relation to the population that demands those services, is an essential indicator for long-term societal resource use and patterns of emissions. The creation of societal material stock creates path dependencies for future resource use, with an important impact on how the transformation towards sustainable societies can succeed.</p> <p>This dataset features detailed maps of material stock and population, as well as the distribution of jobs, for Austria on a 30m grid. The data is based on recent maps of material stock and building volume (compare to Haberl et al. 2021, doi: 10.1021/acs.est.0c05642, data: https://zenodo.org/record/4522892), recent and historic census data, and a time series of Landsat TM, ETM+, and OLI Earth Observation data.</p> <p><strong>Temporal extent</strong></p> <p>The data contains annual maps from 1985 to 2018.</p> <p><strong>Data format and units</strong></p> <p>Per Austrian federal state, the data come in tiles of 30x30km. The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Please consider the generation of image pyramids before using *.vrt files.</p> <p>All image data has 34 bands, where band 1 is data for 1985, and band 34 is data for 2018.</p> <p>The dataset features</p> <ul> <li>population (Scaled by 100 to reduce data storage size. Divide by 100 to get people per cell)</li> <li>jobs (Scaled by 100 to reduce data storage size. Divide by 100 to get jobs per cell)</li> <li>mass (in tons) of … <ul> <li>total material stock <ul> <li>material stock in buildings <ul> <li>in commercial and industrial buildings</li> <li>in multi-family residential buildings</li> <li>in high-rise buildings</li> <li>in single-family residential buildings</li> <li>in lightweight buildings</li> </ul> </li> <li>material stock in road infrastructure</li> <li>material stock in rail infrastructure</li> <li>material stock in other infrastructure</li> </ul> </li> </ul> </li> </ul> <p><strong>Further information</strong></p> <p>For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website </a>to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p> <p><strong>Funding</strong></p> <p>This research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).</p> <p> </p>
Global spatiotemporal continuous daily high-resolution total column carbon monoxide for TROPOMI
<p>A novel framework is developed to recover missing data in global TROPOMI TCCO product over land from Jun. 01 2018 to May. 31 2021 by fusing multisource data. Validation results show that the accuracy of recovered results is satisfactory and close to that of TROPOMI, with the R of 0.885 against NDACC and 0.918 against TCCON. Furthermore, the recovered results achieve a small (distinctly) better performance than those of MOPITT (CAMS). The spatial pattern of the recovered TCCO is consistent with that of the MOPITT TCCO and can specify much finer spatial details by comparison with CAMS.</p>
Ind. Ch. 59 Grant of Mrgesvaravarman, Year 8, seal High resolution TIFF
<p>Ind. Ch. 59 Grant of Mrgesvaravarman, Year 8, seal. High resolution TIFF</p>
Ind. Ch. 59 Grant of Mrgesvaravarman, Year 8 High resolution TIFF
<p>Ind. Ch. 59 Grant of Mṛgeśvaravarman, Year 8. High resolution TIFF</p>
Ind. Ch. 60 Grant of the time of Ravivarman, seal High resolution TIFF
<p><a href="https://siddham.network/object/ob01055/">OB01055</a> Ind. Ch. 60 Grant of the time of Ravivarman, seal High resolution TIFF</p>
Figure 6 in High-resolution stratigraphy of the Changhsingian (Late Permian) successions of NW Iran and the Transcaucasus based on lithological features, conodonts and ammonoids
Figure 6. Characteristic Changhsingian conodonts from the Julfa region (scale bars equal to 100 µm); all specimens stored in the collection of the Ferdowsi University, Mashhad. (A) Clarkina orientalis (Barskov and Koroleva, 1970); FUM#1J192.1; upper Julfa beds (Vedioceras beds), Ali Bashi 1 section. (B) Clarkina subcarinata Sweet, 1973; FUM#4J142.8; Zal Member (Ali Bashi Formation), Ali Bashi 4 section. (C) Clarkina changxingensis Wang and Wang, 1981; FUM#4J153.1; Zal Member (Ali Bashi Formation), Ali Bashi 4 section. (D) Clarkina bachmanni Kozur, 2004; FUM#AJ185.23; Paratirolites Limestone (Ali Bashi Formation), Aras Valley section. (E) Clarkina nodosa Kozur, 2004; FUM#G249.16; Paratirolites Limestone (Ali Bashi Formation), Ali Bashi M section. (F) Clarkina yini Mei, 1998; FUM#AJ192.4; Paratirolites Limestone (Ali Bashi Formation), Aras Valley section. (G) Clarkina abadehensis Kozur, 2004; FUM#1J248.9; Paratirolites Limestone (Ali Bashi Formation), Ali Bashi 1 section. (H) Clarkina hauschkei Kozur, 2004, FUM#1J249D.9; Paratirolites Limestone (Ali Bashi Formation), Ali Bashi 1 section. (I) Hindeodus eurypyge Nicoll, Metcalfe and Wang, 2002, FUM#1J255.7 (cusp broken); Zal Member (Ali Bashi Formation), Ali Bashi 1 section. (J) Hindeodus typicalis Sweet, 1970, FUM#G233.5; Paratirolites Limestone (Ali Bashi Formation), Ali Bashi M section. (K) Hindeodus typicalis Sweet, 1970, FUM#4J200.56; Paratirolites Limestone (Ali Bashi Formation), Ali Bashi 4 section. (L) Hindeodus julfensis Sweet, 1973, FUM#1J198.4; Zal Member (Ali Bashi Formation), Ali Bashi 4 section. (M) Hindeodus praeparvus Kozur, 1996, FUM#G274.6 (cusp broken); Aras Member (Elikah Formation), Ali Bashi M section. (N) Hindeodus changxingensis Wang, 1995, FUM#4J201.6 (cusp broken); Aras Member (Elikah Formation), Ali Bashi 4 section. (O) Merrillina ultima Kozur, 2004, FUM#AJ204.13; Aras Member (Elikah Formation), Aras Valley section. (P) Hindeodus parvus Kozur and Pjatakova, 1976, FUM#4J213.1; Elikah Formation; Ali Bashi 4 section.
Figure 5 in High-resolution stratigraphy of the Changhsingian (Late Permian) successions of NW Iran and the Transcaucasus based on lithological features, conodonts and ammonoids
Figure 5. The correlation of the conodont schemes by Kozur (2005, 2007), Shen and Mei (2010) and own results with the ammonoid stratigraphy by Shevyrev (1965) and own results.
Figure 7 in High-resolution stratigraphy of the Changhsingian (Late Permian) successions of NW Iran and the Transcaucasus based on lithological features, conodonts and ammonoids
Figure 7. Characteristic Changhsingian ammonoids from the Julfa region (scale bars equal to 5 mm); all specimens stored in the collection of the Museum für Naturkunde, Berlin. (A) Phisonites triangulus Shevyrev, 1965 from the Aras Valley section, specimen MB.C.22703; × 1.0. (B) Iranites transcaucasius (Shevyrev, 1965) from the Aras Valley section, specimen MB.C.22704; × 1.0. (C) Dzhulfites nodosus Shevyrev, 1965 from the Aras Valley section, specimen MB.C.22705; × 1.0. (D) Shevyrevites nodosus Shevyrev, 1965 from the Aras Valley section, specimen MB.C.22706; × 1.0. (E) Paratirolites trapezoidalis Shevyrev, 1965 from the Ali Bashi 4 section, specimen MB.C.22707; × 0.75. (F) Stoyanowites dieneri (Stoyanow, 1910) from the Aras Valley section, specimen MB.C.22708; × 1.0. (G) Paratirolites vediensis Shevyrev, 1965 from the Ali Bashi N section, specimen MB.C.22709; × 0.75. (H) Abichites stoyanowi (Kiparisova, 1947) from the Ali Bashi N section, specimen MB.C.22710; × 1.25. (I) Arasella minuta (Zakharov, 1983) from the Ali Bashi N section, specimen MB.C.22711; × 1.25.
High-Resolution Vector-borne Disease Infection Risk Mapping with Area-to-Point Kriging and Species Distribution Modeling - Datasets
<p>Datasets and notebooks used in the publication High-Resolution Vector-borne Disease Infection Risk Mapping with Area-to-Point Kriging and Species Distribution Modeling</p>
Data and code for high-resolution climate-resilient corridor mapping in southwestern Costa Rica (beta)
<p>High-resolution mapping and validation of potential climate-resilient corridors in southwestern Costa Rica. A fully documented, complete version of this repository will be archived with a DOI upon manuscript publication.</p>
Figure 2 in Detection of the Trp-2027-Cys Mutation in Fluazifop-P-butyl-resistant Itchgrass (RottboelliO cochinchinensis) using High-Resolution Melting Analysis (HRMA)
Figure 2. Agarose gel (1,8%) showing polymerase chain reaction products (89 bp) of the chloroplastic acetyl-coenzyme A carboxylase gene carboxyl-transferase domain targeted by HRMA primers RottF and RottR.
Figure 5 in Detection of the Trp-2027-Cys Mutation in Fluazifop-P-butyl-resistant Itchgrass (RottboelliO cochinchinensis) using High-Resolution Melting Analysis (HRMA)
Figure 5. High-resolution melting analysis (HRMA) for detection of the Trp-2027-Cys mutation in Rottboellia cochinchinensis carboxyl-transferase domain of the acetylcoenzyme A carboxylase gene conferring resistance to fluazifop-P-butyl. (B) Normalized plot and (C) difference plot using susceptible (wild type) as the reference genotype.
Figure 4 in Detection of the Trp-2027-Cys Mutation in Fluazifop-P-butyl-resistant Itchgrass (RottboelliO cochinchinensis) using High-Resolution Melting Analysis (HRMA)
Figure 4. High-resolution melting analysis (HRMA) for detection of mutation Trp-2027-Cys in Rottboellia cochinchinensis carboxyl-transferase domain of the acetyl-coenzyme A carboxylase gene conferring resistance to fluazifop-P-butyl. Three genotypes are included: wild type (homozygous TGG, susceptible), mutant homozygous (TGC, resistant), and artificial mutant heterozygous (TGG/TGC, possibly resistant). (A) Representative profiles of the melting curves (derivative melt curves), (B) normalized plot, and (C) difference plot using susceptible (wild type) as the reference genotype.
Figure 1 in Detection of the Trp-2027-Cys Mutation in Fluazifop-P-butyl-resistant Itchgrass (RottboelliO cochinchinensis) using High-Resolution Melting Analysis (HRMA)
Figure 1. Sequence alignment showing the single-nucleotide change (G/C) within the chloroplastic acetyl-coenzyme A carboxylase gene carboxyl-transferase domain fragments from Rottboellia cochinchinensis susceptible (G) and resistant (C) biotypes. The sequences of the HRMA primers are colored in red. Position numbers (Alopecurus myosuroides full ACCase sequence, GenBank AJ310767, numbering) are given above the nucleotide sequences. Conserved nucleotides are indicated by dots.
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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.