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Data set for the study "Interplay between climate and carbon cycle feedbacks could substantially enhance future warming"
<p>This repository contains the data necessary to reproduce the results of the paper: <br>"Interplay between climate and carbon cycle feedbacks could substantially enhance future warming" <br><a href="https://iopscience.iop.org/article/10.1088/1748-9326/adb6be" target="_blank" rel="noopener">https://iopscience.iop.org/article/10.1088/1748-9326/adb6be</a></p> <h3><strong>Data organization:</strong></h3> <p>The Zenodo repository is organized as follows inside of <code>results.zip</code>:</p> <ul> <li>Figure generation are given by "*.pynb" and "*.m" files<br><br></li> <li>Data files as NetCDF output are organized with the following structure inside of <code>data</code>:<br><br> <ul> <li><strong>Experiment/emission scenario</strong>: <code>hist-aer</code>, <code>ssp126</code>, <code>ssp434</code>, and <code>ssp245</code><br><br> <ul> <li><strong>Equilibrium climate sensitivity</strong>: <code>ecs_2.0K</code>, <code>ecs_2.5K</code>, <code>ecs_3.0K</code>, <code>ecs_3.5K</code>, <code>ecs_4.0K</code>, <code>ecs_4.5K</code>, and <code>ecs_5.0K</code><br><br> <ul> <li><strong>Experiment: </strong><code>comp</code>, <code>comp_fix_ch4</code>, <code>comp_fix_co2_ch4</code>, <code>comp_ssp_co2_ch4</code><br><br> <ul> <li><strong>Component</strong>: atmosphere (<code>atm</code>), ocean (<code>ocn</code>), land (<code>lnd</code>), sea ice (<code>sic</code>), carbon dioxide (<code>co2</code>), methane (<code>ch4</code>)<br><br></li> <li><strong>File type</strong>: for some experiments, files are divided into timeseries (<code>*_ts.nc</code>) or 2D data (<code>*.nc</code>)<br><br></li> <li>Note: <code>comp_ssp_co2_ch4</code> are the CLIMBER-X runs which used prescribed concentrations (rather than emissions) and is only available for ECS 3°C<br><br></li> <li>Note: <code>comp_fix_ch4</code> and <code>comp_fix_co2_ch4</code> is only available for ECS 2°C, 3°C, and 5°C (as shown in Fig. 4 in the manuscript)</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul>
Data set of axle loads and distances for operating passenger and freight trains for development of load models
<p>Train data for moving load models of operating trains (5,007 passenger trains and 139,182 freight trains).</p> <ul> <li>axle distances [m] and axle loads [kN] for all trains included in zip-files as txt-files</li> <li>information on line categories (DIN EN 15528), loadcases and maximum speeds for passenger trains (and assignment to passenger train numbers in previous version of publication (used for development of load model)) in xlsx-file</li> </ul>
Application Case 3: Data sets consisting of 3D scans, 3D model and the derived metadata, from two different software programs
<p>In this repository we provide 3D scan projects and the 3D models processed from them with their metadata using the example of a wood sample. The metadata was generated using our metadata generation script, which is described in the referenced publication.</p> <p>The 3D scan projects were created in different software (atos v6.2, atos 2016 and zeiss 2023). For each there is a scan project, a 3D model and the generated metadata with and without uri in this repository.</p> <p>The publication in which this application case is included: Homburg, T., Cramer, A., Raddatz, L. <em>et al.</em> Metadata schema and ontology for capturing and processing of 3D cultural heritage objects. <em>Herit Sci</em> <strong>9</strong>, 91 (2021). <a href="https://doi.org/10.1186/s40494-021-00561-w">https://doi.org/10.1186/s40494-021-00561-w</a></p> <p>Python scripts for exporting metadata can be found here: <a href="https://github.com/i3mainz/3dcap-md-gen/tree/0.1.3">GitHub - i3mainz/3dcap-md-gen</a></p>
Data Set "Systematic QM Region Construction in QM/MM Calculations Based on Uncertainty Quantification"
<p>Data set accompanying the publication "Systematic QM Region Construction in QM/MM Calculations Based on Uncertainty Quantification"</p> <p>This dataset contains:</p> <p>- PDB files of the reactant and product starting structure</p> <p>- modified AMBER95 force field file</p> <p>- AMS fragment files for the ligands and ions</p> <p>- AMS input files for all geometry optimizations and single point calculations</p>
Field investigations of salt partitioning and aqueous chemistry of freezing closed-basin lakes in Mongolia as terrestrial analogs of subsurface brine reservoirs on icy bodies [Data set]
<p>All measurement and calculation data</p> <p>Measurement and calculation data in Version 4 were uploaded in 2021-11-2</p>
PoreScript: Semi-automated Pore Size Analysis Algorithm Data Set
<p>This data set contains files related to the PoreScript semi-automatic pore size image analysis algorithm. The three MATLAB files needed for the PoreScript algorithm are named the following: </p> <p>(<a href="https://zenodo.org/api/files/47f5a723-b2de-41b6-baed-f1a6335c4b84/Jenkins_RelativeIntensityFinder_no_crop.m">Jenkins_RelativeIntensityFinder_no_crop.m</a>, <a href="https://zenodo.org/api/files/47f5a723-b2de-41b6-baed-f1a6335c4b84/Jenkins_UserInterface_no_crop.m">Jenkins_UserInterface_no_crop.m</a>, <a href="https://zenodo.org/api/files/47f5a723-b2de-41b6-baed-f1a6335c4b84/Jenkins_PoreSizeCalculator_no_crop.m">Jenkins_PoreSizeCalculator_no_crop.m</a>).</p> <p>Access the latest version of the program here:<a href="https://github.com/djenkins95/PoreScript_Update_9_26_23"> <strong>https://github.com/djenkins95/PoreScript_Update_9_26_23</strong></a></p> <p>Updated MATLAB files are more accessible to a wider range of SEM software. The updated version asks for the known length of your scale bar in pixels. There are many ways to measure the length of your scale bar. I recommend using the free software FIJI. Use the *Straight* (drawing tool to trace your scale bar), then click Analyze > Measure to determine the length in pixels. It should be noted that the length in pixels will be the same for any image taken on the same instrument, at the same magnification, and saved as the same file type (e.g., .tiff), so you can reference the length in future data sets without needed to remeasure the scale bar.</p> <p>The Zenodo repository includes the unanalyzed SEM images, analyzed images, raw pore size data, analyzed pore size data, and older .m versions.</p>
Data sets for "Pressure destabilizes oxygen vacancies in bridgmanite" by H.Fei et al.
<p>This is EPMA, XRD, and Mossbauer datasets for the article "Pressure destabilizes oxygen vacancies in bridgmanite" by H. Fei et al.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 12
<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 12 contains stitched image montages of thin sections (selected areas) through the lung of patients C04 and C07 which were acquired by scanning electron microscopy. The images show alveolae with various degree of epithelial damage.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 11
<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 11 contains a stitched image montage of a thin section (selected area) through the lung of patient C05 which was acquired by scanning electron microscopy. The image shows a lung area with a dissolved alveolar architecture and a massive type-2-cell hyperplasia.</p>
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>
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.