Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,170
datasets available to search
ShareScore release 0.9.0
Dataset results
2,170 results for “clouds”
FIGURE 8 in Anticyphon gen. nov., a new genus of Scirtidae (Coleoptera: Scirtoidea) inhabiting high altitude Andean cloud forests and páramo formation
FIGURE 8. Anticyphon oyonensis sp. nov., male genitalia. A) penis, B) tegmen, C) sternite VIII, D) sternite IX, E) tergite VIII, F) tergite IX. Scale bar = 0.5 mm.
FIGURE 4 in Anticyphon gen. nov., a new genus of Scirtidae (Coleoptera: Scirtoidea) inhabiting high altitude Andean cloud forests and páramo formation
FIGURE 4. Anticyphon paramoensis sp. nov., ventrum, SEM micrograph. Abbreviations: mf—mesoventral fossa, mpmesoventral process, sgr—subgenal ridge.
FIGURE 7 in Anticyphon gen. nov., a new genus of Scirtidae (Coleoptera: Scirtoidea) inhabiting high altitude Andean cloud forests and páramo formation
FIGURE 7. Anticyphon ecuadorensis sp. nov., male genitalia. A) penis (right parameroid broken), B) tegmen, C) sternite VIII, D) sternite IX, E) tergite VIII, F) tergite IX. Scale bar = 0.5 mm.
FIGURE 6 in Anticyphon gen. nov., a new genus of Scirtidae (Coleoptera: Scirtoidea) inhabiting high altitude Andean cloud forests and páramo formation
FIGURE 6. Anticyphon davidsoni sp. nov., male genitalia. A) male genitalia, B) penis, C) tegmen, D) sternite VIII, E) sternite IX, F) tergite VIII, G) tergite IX. Scale bar = 0.5 mm. Abbreviations: dpp—dorsal process of penis, pe—penis, pm—paramere, pmd—parameroid, s—sternite, t—tergite, tg—tegmen, trg—trigonium.
FIGURE 2 in Anticyphon gen. nov., a new genus of Scirtidae (Coleoptera: Scirtoidea) inhabiting high altitude Andean cloud forests and páramo formation
FIGURE 2. Anticyphon gen. nov., mandibles (A, D, G), maxillae (B, E, H), labia (C, F, I). A – C) A. davidsoni sp. nov., D – F) A. oyonensis sp. nov., G – I) A. paramoensis sp. nov.
FIGURE 1. Anticyphon gen. nov., habitus. A in Anticyphon gen. nov., a new genus of Scirtidae (Coleoptera: Scirtoidea) inhabiting high altitude Andean cloud forests and páramo formation
FIGURE 1. Anticyphon gen. nov., habitus. A) A. davidsoni sp. nov., B) A. ecuadorensis sp. nov., C) A. oyonensis sp. nov., female, D) A. oyonensis sp. nov., male E) A. paramoensis sp. nov., light form, F) A. paramoensis sp. nov., dark form, G) A. peruvianus sp. nov., H) A. santanderensis sp. nov. Scale bars = 1.0 mm.
Cloud-free snow cover area in the Pyrenees from MODIS
<p>This dataset contains the output of a gapfilling algorithm applied to MODIS snow products for the Pyrenees mountains as presented by Gascoin et al. (2015) and updated to the period 2000-Sep-01 to 2015-08-31 (15 hydrological years)</p> <ol> <li>Pirineos_gapfilled.tif: a multiband geotiff raster file in WGS84 UTM30N (EPSG:32630) at 500 m resolution with values 200 (snow) or 25 (no snow); <p>Corner Coordinates:<br> Upper Left ( 607750.000, 4789250.000) ( 1d40'21.72"W, 43d14'54.08"N)<br> Lower Left ( 607750.000, 4665250.000) ( 1d41'46.55"W, 42d 7'55.05"N)<br> Upper Right ( 973750.000, 4789250.000) ( 2d49'18.48"E, 43d 6'27.65"N)<br> Lower Right ( 973750.000, 4665250.000) ( 2d43' 8.76"E, 41d59'47.87"N)<br> Center ( 790750.000, 4727250.000) ( 0d32'47.24"E, 42d38'34.10"N)</p> </li> <li>Pirineos_gapfilled_dates.csv: a csv file indicating the date corresponding to each band (year, month, day)</li> <li>dem_Pirineos_UTM30_px500.tif: a geotiff raster of the elevation in WGS84 UTM30N (input of the gap-filling algorithm) with the same extent and resolution as 1.</li> <li>aspect_Pirineos_UTM30_px500.tif: a geotiff raster of the slope aspect in WGS84 UTM30N (input of the gap-filling algorithm) with the same extent and resolution as 1.</li> <li>Pirineos_gapfilled_probamap.png: a map of the mean annual number of snow days (snow cover duration) made from 1.</li> <li>Pirineos_gapfilled_scats.png: a plot of the timeseries of the daily snow cover area in km² over the Pyrenees mountain range from 2000-Sep-01 to 2015-08-31 made from 1.</li> </ol> <p><strong>Reference</strong></p> <p>Gascoin, S., Hagolle, O., Huc, M., Jarlan, L., Dejoux, J.-F., Szczypta, C., Marti, R., and Sánchez, R.: A snow cover climatology for the Pyrenees from MODIS snow products, Hydrol. Earth Syst. Sci., 19, 2337-2351, doi:10.5194/hess-19-2337-2015, 2015. http://doi.org/10.5194/hess-19-2337-2015</p> <p>Hall, D. K., V. V. Salomonson, and G. A. Riggs. 2006. MODIS/Terra Snow Cover Daily L3 Global 500m Grid, Version 5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/63NQASRDPDB0.</p> <p>Hall, D. K., V. V. Salomonson, and G. A. Riggs. 2006. MODIS/Aqua Snow Cover Daily L3 Global 500m Grid, Version 5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/ZFAEMQGSR4XD.</p>
Star Formation In Nearby Clouds (SFiNCs): X-ray And Infrared Source Catalogs And Membership. SPCM Atlas Dataset.
<p>The SPCM (SFiNCs Possible Cluster Member) Atlas dataset accompanies the article entitled ``Star Formation In Nearby Clouds (SFiNCs): X-ray And Infrared Source Catalogs And Membership,'' by Getman, Broos, Kuhn, Feigelson, Richert, Ota, Bate, and Garmire, to appear in The Astrophysical Journal Supplement Series. The paper is also available on-line on astro-ph at: https://arxiv.org/abs/1612.05282 . SPCM Atlas is a collection of 25 PDF files. Four pdf files are associated with the SFiNCs star forming region (SFR) Cep OB3b, and 21 pdf files are associated with the remaining 21 SFiNCs SFRs. Full description of SPCM Atlas is given in the Appendix B section of the article. This upload is superseded by a new version, http://doi.org/10.5281/zenodo.345398 .</p>
The MIDI Linked Data Cloud dataset
<p>The study of music is highly interdisciplinary, and thus requires the combination of datasets from multiple musical domains, such as catalog metadata (authors, song titles, dates), industrial records (labels, producers, sales), and music notation (scores). While today an abundance of music metadata exists on the Linked Open Data cloud, datasets containing interoperable symbolic descriptions of music itself, i.e. music notation with note and instrument level information, are scarce. This is the MIDI Linked Data Cloud, a dataset that represents multiple collections of digital music in the MIDI standard format as Linked Data. At the time of writing, the dataset comprises 10,215,557,355 triples of 308,443 interconnected MIDI scores, and provides Web-compatible descriptions of their MIDI events.</p>
A Novel Model Hierarchy Isolates the Limited Effect of Supercooled Liquid Cloud Optics on Infrared Radiation
<p>This dataset contains data used in and resulting from an upcoming paper. For further detail on methodology and experiments, see that paper.</p> <h2>Supercooled liquid water optics</h2> <h3>Complex refractive indices (CRIs)</h3> <ul> <li>Water_DW_300.txt</li> <li>water_RFN_240K.txt</li> <li>water_RFN_253K.txt</li> <li>water_RFN_263K.txt</li> <li>water_RFN_273K.txt</li> </ul> <p>Water_DW_300.txt is sourced from Downing & Williams 1975 (https://doi.org/10.1029/JC080i012p01656). water_RFN_240K.txt, water_RFN_253K.txt, water_RFN_263K.txt, and water_RFN_273K.txt are sourced from Rowe et al. 2020 (https://doi.org/10.1029/2020JD032624).</p> <h3>CESM lookup tables of liquid water optics</h3> <ul> <li>CESM_CRI_RFN_240K.nc</li> <li>CESM_CRI_RFN_253K.nc</li> <li>CESM_CRI_RFN_263K.nc</li> <li>CESM_CRI_RFN_273K.nc</li> </ul> <p>These optics sets were created from the corresponding Rowe et al. 2020 CRI.</p> <p> </p> <h2>SCAM output</h2> <p>History files for the four MPACE SCAM runs.</p> <ul> <li>Control: tutorial.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>240K optics: cri240K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>263K optics: cri263K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>273K optics: cri273K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> </ul> <p> </p> <h2>F1850_UVnudge1980 data</h2> <p>Data used to create graphs shown in PAPER from the F1850_UVnudge1980 experiment. For each optics set there is a mean, count (n), and standard deviation file. These statistics are calculated over the 1 year of the model run and across all 10 ensemble members for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>240K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>273K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>F1850_UVnudge1980-2018 data</h2> <p>Data used to create graphs shown in PAPER from the F1850_UVnudge1980-2018 experiment. For the variable FLDS (downwelling longwave flux at the surface), each optics set has a mean, count (n), and standard deviation file. These statistics are calculated over the 39 years of the model run and across all 3 ensemble members. </p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>B1850_UVnudge1980 data</h2> <p>Data used to create graphs shown in PAPER from the B1850_UVnudge1980 experiment. For each optics set there is a mean, count (n), and standard deviation file. These statistics are calculated over the 1 year of the model run and across all 10 ensemble members for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>F1850 data</h2> <p>Data used to create graphs shown in PAPER from the F1850 experiment. For each optics run there is a mean, count (n), and standard deviation file. These statistics are calculated over the 40 years of the model run for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.avg.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>240K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.avg.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.avg.All_data.non_filtered.nc </li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>273K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.avg.All_data.non_filtered.nc </li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.std.All_data.non_filtered.nc</li> </ul>
Cloud Forest Library
<p><strong>Cloud Forest Library</strong><br>This dataset is a point cloud collection of trees, shrubs, herbaceous plants, and other landscape elements. Each specimen is available as in .laz, .e57, .pcd, .ply, .xyz, and .3dm format. See the collection online at <a href="https://xyz.cct.lsu.edu/">xyz.cct.lsu.edu</a>.</p> <p><strong>License</strong><br>This dataset is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">Creative Commons Zero 1.0 Universal Public Domain Dedication</a> by Brendan Harmon.</p>
Supplemental dataset to "Cloud sync in response to wave-like large-scale forcings"
<p>This dataset deposits the supplemental materials for the manuscript "Cloud sync in response to wave-like large-scale forcings".</p> <p><a href="https://zenodo.org/api/records/14164798/draft/files/math_note.pdf/content" target="_blank" rel="noopener noreferrer">math_note.pdf</a> A hand-written math derivation note for equations in the appendices. </p> <p><a href="https://zenodo.org/uploads/15304862" target="_blank" rel="noopener noreferrer">movie_wL_006_T24hours.avi</a> A movie of near-surface (z=25m) water vapor mixing ratio for the wL=0.006m/s and T=24 hours experiment (the reference CM1 simulation).</p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/movie_wL_006_T18hours.avi/content" target="_blank" rel="noopener noreferrer">movie_wL_006_T18hours.avi</a> A movie of near-surface (z=25m) water vapor mixing ratio for the wL=0.006m/s and T=18 hours experiment.</p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/movie_wL_006_T12hours.avi/content" target="_blank" rel="noopener noreferrer">movie_wL_006_T12hours.avi</a> A movie of near-surface (z=25m) water vapor mixing ratio for the wL=0.006m/s and T=12 hours experiment.</p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/movie_wL_000.avi/content" target="_blank" rel="noopener noreferrer">movie_wL_000.avi</a> A movie of near-surface (z=25m) water vapor mixing ratio, without large-scale wave-like forcing.</p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/input_sounding/content" target="_blank" rel="noopener noreferrer">input_sounding</a> The initial sounding for all CM1 simulations. </p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/namelist.input/content" target="_blank" rel="noopener noreferrer">namelist.input</a> The namelist file for launching all CM1 simulations. </p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/postprocessing_CM1.zip/content" target="_blank" rel="noopener noreferrer">postprocessing_CM1.zip</a> The postprocessing code of the CM1 simulations, including intermediate output files (.mat) in data postprocessing. </p> <p><a href="https://zenodo.org/api/records/14164798/draft/files/microscopic_model.zip/content" target="_blank" rel="noopener noreferrer">microscopic_model.zip</a> The MATLAB code for the microscopic model. </p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/cm1.F/content" target="_blank" rel="noopener noreferrer">cm1.F</a> The CM1 script where the large-scale vertical velocity is programmed. You can copy it directly to your CM1/src/ path. </p> <p> </p> <p>Feel free to send an email to Dr. Hao Fu (haofu@nju.edu.cn) if you have any questions!</p> <p> </p>
CloudTracks: A Dataset for Localizing Ship Tracks in Satellite Images of Clouds
<p>The CloudTracks dataset consists of 1,780 MODIS satellite images hand-labeled for the presence of more than 12,000 ship tracks. More information about how the dataset was constructed may be found at <a href="http://github.com/stanfordmlgroup/CloudTracks">github.com/stanfordmlgroup/CloudTracks</a>. The file structure of the dataset is as follows:</p><p>CloudTracks/<br> full/<br> images/<br> (sample image name) mod2002121.1920D.png<br> jsons/<br> (sample json name) mod2002121.1920D.json</p><p>The naming convention is as follows:<br>mod2002121.1920D: the first 3 letters specify which of the sensors on the two MODIS satellites captured the image, mod for Terra and myd for Aqua. This is followed by a 4 digit year (2002) and a 3 digit day of the year (121). The following 4 digits specify the time of day (1920; 24 hour format in the UTC timezone), followed by D or N for Day or Night.</p><p>The 1,780 MODIS Terra and Aqua images were collected between 2002 and 2021 inclusive over various stratocumulus cloud regions (such as the East Pacific and East Atlantic) where ship tracks have commonly been observed. Each image has dimension 1354 x 2030 and a spatial resolution of 1km. Of the 36 bands collected by the instruments, we selected channels 1, 20, and 32 to capture useful physical properties of cloud formations.</p><p>The labels are found in the corresponding JSON files for each image. The following keys in the json are particularly important:</p><p>imagePath: the filename of the image.<br>shapes: the list of annotations corresponding to the image, where each element of the list is a dictionary corresponding to a single instance annotation. The dictionary has a key with value "shiptrack" or "uncertain" which is the label of the annotation and the corresponding value is a linestrip detailing the ship track path.</p><p>Further pre-processing details may be found at the GitHub link above. If you have any questions about the dataset, contact us at:<br><a href="mailto:mahmedch@stanford.edu">mahmedch@stanford.edu</a>, <a href="mailto:lynakim@stanford.edu">lynakim@stanford.edu</a>, <a href="mailto:jirvin16@cs.stanford.edu">jirvin16@cs.stanford.edu</a></p>
TLS Z+F Imager 5010 point clouds of hybrid poplar trees from short-rotation crops after 5, 6, and 7 growing seasons
<p>The point clouds are obtained from hybrid poplar crops installed in NE Romania, managed in short rotation (SRWCs) between 5, 6, and 7 growing seasons. The crops were planted every spring, outside the growing season, at a depth of 0.6 m in the ground with two clones: AF8 and Pannonia. Rods (2-meter-long cuttings) were used as planting material at a density of 1667 trees per ha (3 x 2 m). The scanning of the sample areas (3 x 10 trees for each variant, about 6 x 10 m) was outside the growing seasons.</p><p>The 3D model was obtained using the Z+F Imager 5010 (Zoller and Fröhlich, Wangen, Germany), phase-shift type, providing a distance estimation accuracy of ±1 mm at 25 m and a nominal range of 187 m, and the tree individualization was done in CloudCompare v.2.12 (public license). A total of six station points and eight fixed targets or remarks (200 mm spheres) for co-registration were adopted for scanning. Trees included in the survey (without leaves) were marked with a ring of adhesive tape (black with yellow, 50 mm wide) at 1.4 m height on the tree spindle to adjust the results for calibration. Individually segmented trees can be sent on request, the database has a limit of 100 files. They can be converted into different formats via the CloudCompare application.</p><p>File code: clone type _ number of growing seasons _ plot number</p>
Dataset to "Modeling Collision-Coalescence in Particle Microphysics: Numerical Convergence of Mean and Variance of Precipitation in Cloud Simulations Using University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1 " by Zmijewski, Dziekan & Pawlowska
<p>The archive contains datasets, run scripts, time series and plotting scripts used when preparing the paper: P. Zmijewski, P. Dziekan and H. Pawlowska "Modeling Collision-Coalescence in Particle Microphysics: Numerical Convergence of Mean and Variance of Precipitation in Cloud Simulations Using University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1 " submitted to Geoscientific Model Development in March 2023.</p>
A Stereo Camera Simulator for Large-Eddy Simulations of Continental Shallow Cumulus clouds based on three-dimensional Path-Tracing
<p>Dataset to produce the results of the publication: "A Stereo Camera Simulator for Large-Eddy Simulations of Continental Shallow Cumulus clouds based on three-dimensional Path-Tracing"</p><p>The dataset contains:</p><ul><li>Large-Eddy Simulation (LES) model configuration files</li><li>Selected output data of the LES experiments</li><li>Data and analysis scripts for the figures</li><li>The rendered camera images</li><li>The cloud field, cloud hulls, and reconstructed hulls</li><li>A frozen version of the open-source Blender code (version 2.90) as used in this study</li></ul><p>For the latest version of Blender, please visit:</p><p><a href="https://chat.openai.com/c/www.blender.org">www.blender.org</a></p><p>It is important to note that the method was specifically tested only on version 2.90.</p><p> </p><p>This research is supported by the German Research Foundation (DFG) under project number 430226822 (https://gepris.dfg.de/gepris/projekt/430226822). This research was supported by the U.S. Department of Energy's Atmospheric System Research, an Office of Science Biological and Environmental Research program, under grant DE-SC0022126. This work used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID bb1086. The Gauss Centre for Supercomputing e.V. (https://www.gauss-centre.eu/) is acknowledged for providing computing time on the Gauss Centre for Supercomputing (GCS) supercomputer JUWELS at the Jülich Supercomputing Centre (JSC) under projects VIRTUALLAB and RCONGM.</p>
Data for the publication "Developing a climatological simplification of aerosols to enter the cloud microphysics of a global climate model" - part 1
<p>The data is split into two datasets, for each to be smaller than 50 GB.</p>
Simulation dataset and plotting scripts used for journal article "Impact of acidity and surface modulated acid dissociation on cloud response to organic aerosol" by Sengupta et al. (2024)
<p>Simulation data underlying all figures presented in "Impact of acidity and surface modulated acid dissociation on cloud response to organic aerosol" by Sengupta et al. (2024). DOI: 10.5194/acp-24-1467-2024</p> <p>The data for each figure and the plotting scripts are included in a zip file labelled by the figure number as presented in the paper and accompanying supplement.</p>
Lugu Lake Without Cloud
<p>Honorable mention in the 2023 IAU OAE Astrophotography Contest, category Still images with smartphones-mobile devices: Lugu Lake Without Cloud, by Liu Yang.</p> <p>On the tranquil shores of Lugu Lake, the serene night sky hosts a mesmerising celestial panorama that is reflected in the calm waters below. This snapshot, taken in March 2023 in Yunnan, China, offers a breathtaking view of the galaxy's grandeur, with the Milky Way stretching majestically across the sky. The Galaxy is an intricate tapestry of stars, dust, and celestial wonders that weave a story across the canvas of the night sky. This harmonious fusion of land and sky captures a moment of tranquillity amidst patient stargazing, enhanced by a fortunate break in the cloud cover that allowed this captivating scene to unfold. The photograph receives an honourable mention in the category of Still images taken exclusively with smartphones/mobile devices.</p> <p>Credit: Liu Yang/IAU OAE (<a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC BY 4.0</a>)</p>
Stellar properties of observed stars stripped in binaries in the Magellanic Clouds - Observations and Results
<p>This Zenodo repository is one of three Zenodo repositories related to the article "Stellar properties of observed stars stripped in binaries in the Magellanic Clouds" by Y. Götberg, M.R. Drout, A.P. Ji, J.H. Groh, B.A. Ludwig, P.A. Crowther, N. Smith, A. de Koter, and S.E. de Mink. In the article, we analyze the optical spectra of ten stars and measure their stellar properties using spectral fitting. This repository contains observational data and the resulting measurements for the stellar properties of the stars analyzed in the article, along with best fit spectral models and spectral models used to estimate the wind mass loss rate of the stars. Below, we describe the content in more detail:</p> <ul> <li><strong>0_ReadMe.txt</strong>: A text file where we describe some more details regarding the content.</li> <li><strong>S2_stacked_stellar_spectra.tar.gz (4.6 MB):</strong> The spectra we use for obtaining stellar properties for the observed stars.</li> <li><strong>Table2.txt</strong>: the apparent AB magnitudes with associated errors for the stars we analyze in the paper.</li> <li><strong>Table3.txt</strong>: the stellar properties for the stars we analyze in the paper. These parameters are obtained by spectral fitting.</li> <li><strong>S5_spectra_best_fit_models.tar.gz (14 MB):</strong> The spectral energy distributions and normalized spectra for the best-fit models, recomputed such that the radius and bolometric luminosity also matches. This tarball contains best-fit spectra for all of the stars in text-files labeled with the format SED_StarX_xxx.txt (~70 MB when inflated). The stellar parameters are presented in Section 5.</li> <li><strong>S5_full_best_fit_models.tar.gz (1.9 GB):</strong> The full CMFGEN version of the best-fit models for each individual star. This tarball contains a tarball for each star, which when inflated becomes ~250-550 MB each. </li> <li><strong>S7_spectra_mdot_models (31 MB): </strong>The spectral energy distributions and normalized spectra for the models used in the mass-loss analysis that we present in Section 7.</li> <li><strong>S7_full_mdot_models (3.5 GB):</strong> The full CMFGEN version of the spectral models used for the mass-loss rate variation presented in section 7 (excluding the best-fit models, which are provided separately).</li> </ul>
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.