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A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 10
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 10 contains stitched image montages of thin sections (selected areas) through the lung of patient C08 which were acquired by transmission electron microscopy. Cells, infected with SARS-CoV-2 particles, are shown in overview (A, C) and detail (B, C).</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 14
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 14 contains stitched image montages of a thin section through the lung of patient C04 which were acquired by scanning electron microscopy. The file “Data_set_14.tif” contains a montage of the entire thin section while the other files contain selected areas of the section recorded at higher resolution.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 09
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 09 contains stitched image montages of thin sections (selected areas) through the lung of patient C03 which were acquired by scanning electron microscopy (C03_A & C) or transmission electron microscopy (C03_B). The images show accumulation of cells and debris in the alveolar cavity.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 08
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 08 contains stitched image montages of thin sections (selected areas) through the lung of patients C04 and C08 which were acquired by scanning electron microscopy (C04) or transmission electron microscopy (C08_A & B). The images show the pathological changes of the alveolar epithelium: Type-1-cells detachment from the basal membrane (C08_A & B) and type-2-cell hyperplasia (C04).</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 07
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 07 contains stitched image montages of thin sections (selected areas) through the lung of patients C04 to C06 which were acquired by scanning electron microscopy. The images show alveolae with different degree of structural modification: Intact alveolar septum (C06); alveolar septum with detached alveolar epithelium (C04); dissolved alveolar organization (C05).</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 06
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 06 contains a stitched image montage of the first and of the last semithin section from the analysis of patient C08, which was acquired by bright-field light microscopy.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 05
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 05 contains a stitched image montage of the first and of the last semithin section from the analysis of patient C07, which was acquired by bright-field light microscopy.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 15
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 15 contains stitched image montages of a thin section through the lung of patient C05 which were acquired by scanning electron microscopy. The file “Data_set_15.tif” contains a montage of the entire thin section while the other files contain selected areas of the section recorded at higher resolution.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 13
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 13 contains stitched image montages of a thin section through the lung of patient C03 which were acquired by scanning electron microscopy. The file “Data_set_13.tif” contains a montage of the entire thin section while the other files contain selected areas of the section recorded at higher resolution.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 01
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 01 contains a stitched image montage of the first and of the last semithin section from the analysis of patient C03, which was acquired by bright-field light microscopy.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 02
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 02 contains a stitched image montage of the first and of the last semithin section from the analysis of patient C04, which was acquired by bright-field light microscopy.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 03
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 03 contains a stitched image montage of the first and of the last semithin section from the analysis of patient C05, which was acquired by bright-field light microscopy.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 18
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 18 contains stitched image montages of a thin section through the lung of patient C08 which were acquired by scanning electron microscopy. The file “Data_set_18.tif” contains a montage of the entire thin section while the other files contain selected areas of the section recorded at higher resolution.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 17
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 17 contains stitched image montages of a thin section through the lung of patient C07 which were acquired by scanning electron microscopy. The file “Data_set_17.tif” contains a montage of the entire thin section while the other files contain selected areas of the section recorded at higher resolution.</p> <p> </p>
Sample data and fixed files for running 'fv3aerorad' in GSI
<p>The tarball, "fv3aerorad.tar.gz", contains the sample data and fixed files to run the regression test, 'fv3aerorad', in community Gridpoint Statistical Interpolation (GSI; https://doi.org/10.5281/zenodo.5735601).</p>
Supplementary Table 1. Raw data of egg quality parameters for 990 egg samples with ATOL (Animal Trait Ontology for Livestock) descriptors, as function of hen age, pen no. and genotype in 15 replicates.
<p>Data table of egg quality parameters</p>
CONTINUUM HYDROLOGICAL MODEL SAMPLE DATA FOR WRF 24 OCTOBER 2021 (APOLLO MEDICANE CASE)
<p>CONTINUUM HYDROLOGICAL MODEL SAMPLE DATA FOR WRF 24 OCTOBER 2021 (APOLLO MEDICANE CASE). Data are ready for publication on MyDewetra platform</p>
Data from: Passive sampling of environmental DNA in aquatic environments using 3D-printed hydroxyapatite samplers
<p>The study of environmental DNA released by aquatic organisms in their habitat offers a fast, non-invasive and sensitive approach to monitor their presence. Common eDNA sampling methods such as water filtration and DNA precipitation are time consuming, require difficult-to-handle equipment and partially integrate eDNA signals. To overcome these limitations, we created the first proof of concept of a passive, 3D-printed and easy-to-use eDNA sampler. We designed the samplers from hydroxyapatite (HAp samplers), a natural mineral with a high DNA adsorption capacity. The porous structure and shape of the samplers were designed to optimise DNA adsorption and facilitate their handling in the laboratory and in the field. Here we show that HAp samplers can efficiently collect genomic DNA in controlled set-ups, but can also collect animal eDNA under controlled and natural conditions with yields similar to conventional methods. However, we also observed large variations in the amount of DNA collected even under controlled conditions. A better understanding of the DNA-hydroxyapatite interactions on the surface of the samplers is now necessary to optimise the eDNA adsorption and to allow the development of a reliable, easy-to-use and reusable eDNA sampling tool.</p>
pyDeltaRCM sample data -- xslope simulations
<p>The model runs in this dataset were executed in support of a demonstration<br> and teaching clinic. The set of simualtions examines the effect of a basin with<br> cross-stream slope on the progradation of a delta system.<br> <br> Models were run on 02/21/2022, at the University of Texas at Austin.</p> <p>Runs were computed with pyDeltaRCM v2.1.2. See log files for complete information<br> on system and model configuration.</p> <p>Data available at Zenodo, version 1.1: 10.5281/zenodo.6301362</p> <p>Version history:<br> v1.0: 10.5281/zenodo.6226448<br> v1.1: 10.5281/zenodo.6301362<br> </p>
Sample ERA5 Climate Reanalysis Data for UW Geospatial Data Analysis Course
<p>Used for Module 09: https://uwgda-jupyterbook.readthedocs.io/en/latest/modules/09_NDarrays_xarray_ERA5/</p> <p>Generated using Copernicus Climate Change Service information [2022]<br> Original license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf</p>
ScienceDex guides
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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.