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2,170 results for “clouds”
Datasets for Yue et al. (2022), Atmospheric Measurement Techniques: "Evaluating the Consistency and Continuity of Pixel-Scale Cloud Property Data Records From Aqua and SNPP"
<p>Pixel scaled collocated AIRS, Aqua MODIS, SNPP CrIS, and SNPP VIIRS cloud retrieval products. These datasets are used to generate analyses and results presented in Yue et al. (2022, AMT): "Evaluating the Consistency and Continuity of Pixel-Scale Cloud Property Data Records From Aqua and SNPP"</p>
TRIPEx-pol dataset containing LV2 cloud radar data and polarimetric radar data
<p>This dataset contains all data used for the publication von Terzi et al. 2022, ACP: "Ice microphysical processes in the dendritic growth layer: A statistical analysis combining multi-frequency and polarimetric Doppler cloud radar observations". This dataset combines the observations from vertically pointing X-, Ka- and W-Band radars and observations from a polarimetric W-Band radar pointing at 30° elevation.The dataset also contains variables derived from Doppler spectra observations: The spectral Edge velocity derived from Ka-Band vertically pointing radar and the maximum of the spectral ZDR (sZDRmax) from the polarimetric W-Band radar at 30° elevation. The data where averaged within periods when the polarimetric W-Band radar was measuring at 30° elevation. This corresponds to approximately 5 minute periods. The non-averaged LV2 data from the vertically pointing X-, Ka- and W-band radars is further available at: 10.5281/zenodo.5025636</p>
WHUL8-CDb, a block-level labelled cloud detection dataset collected from Landsat 8
<p>WHUL8-CDb was collected for the training of weakly-supervised cloud detection methods. It includes 500 globally distributed Landsat 8 Level-1C images (Fig. 1) collected following the distribution of the L8 Biome dataset as it comes to biomes and dominant land cover types. The motivation is that clouds are similar with each other in different regions, while the backgrounds are very different. In order to increase the diversity of training samples, we selected images with more background pixels by setting the cloud percentage from 0 to 50 when searching images. To handle all cloud situations, 5 images full of clouds were collected and included in WHUL8-CDb.</p> <p>The WHUL8-CDb dataset canbe downloaded on the following links:</p> <p>First part (1~11) : <a href="http://doi.org/10.5281/zenodo.6420035">https://doi.org/10.5281/zenodo.6420035</a></p> <p>Second part (12~22) : <a href="http://doi.org/10.5281/zenodo.6467663">https://doi.org/10.5281/zenodo.6467663</a></p> <p>Third part (23~33): <a href="http://doi.org/10.5281/zenodo.6510533">https://doi.org/10.5281/zenodo.6510533</a></p> <p>Fourth part (34~44): <a href="http://doi.org/10.5281/zenodo.6510679">https://doi.org/10.5281/zenodo.6510679</a></p> <p>Fifth part (45~55): <a href="http://doi.org/10.5281/zenodo.6510681">https://doi.org/10.5281/zenodo.6510681</a></p> <p>Sixth part (56~65): <a href="http://doi.org/10.5281/zenodo.6510681">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.6510683">10.5281/zenodo.6510683</a></p>
Cloud-free Chinese Gaofen-1 WFV near-infrared surface reflectance over Huailai remote sensing test site throughout 2020
<p>Land surface reflectance product form the starting point for many application regions such as land cover mapping and the generation of biophysical essential climate variables (ECV). Therefore, ensuring the quality of surface reflectance products is necessary to maintain the integrity of the research outcoming of these application areas. However, ground validation of surface reflectance satellite products is challenging, because ground “truth” on a coarse grid scale based on sparse ground measurements is subject to uncertainty due to spatial heterogeneity. In order to quantify the influence of spatial heterogeneity on the uncertainty of surface reflectance ground “truth” in different sampling cases, we generated the high-resolution (16 m) near-infrared surface reflectance over Huailai remote sensing test site based on Chinese Gaofen-1 WFV Band4 data.</p> <p> </p> <p>All cloud-free GF-1 WFV images throughout the year 2020 were extracted. And there are 25 images in total, with at least one image for each month. The WFV Band4 data covering the whole Huailai test station have been processed into Analysis Ready Data (ARD) system, which aims to simplify and reduce the users’ burden by providing pre-processing such as geometric alignment, radiometric recalibration, and atmospheric correction (Zhong et al., 2021). The geometric normalization of the GF-1 WFV data was realized with the procedure developed by Shan et al. (2014). And the radiometric normalization was finished through cross-calibrating with the Landsat TM/OLI with the method proposed by Yang et al. (2015). The 25 images have been layer stacked into one file according to their acquisition time.</p> <p> </p> <p> </p> <p>Reference:</p> <p>Shan, X. J., P. Tang, and C. M. Hu (2014), An automatic geometric precision correction system based on hierarchical registration for HJ-1 A/B CCD images, Int J Remote Sens, 35(20), 7154-7178.</p> <p>Yang, A., B. Zhong et al. (2015), Cross-calibration of GF-1/WFV over a desert site using Landsat-8/OLI imagery and ZY-3/TLC data, Remote Sens., 7, 10763–10787.</p> <p>Zhong, B., A. Yang, Q. Liu, S. Wu, X. Shan, and X Mu (2021), Analysis ready data of the chinese gaofen satellite data, Remote Sens., 13, 9, 1709.</p>
Six new species of Pristimantis (Anura: Strabomantidae) from Llanganates National Park and Sangay National Park in Amazonian cloud forests of Ecuador
<p><strong>Fig. S1. Phylogenetic relationships of <em>Pristimantis</em> species showing <em>Pristimantis anaiae</em> sp. nov., <em>Pristimantis glendae</em> sp. nov., <em>Pristimantis kunam</em> sp. nov., <em>Pristimantis resistencia</em> sp. nov., <em>Pristimantis venegasi</em> sp. nov. and <em>Pristimantis tamia</em> sp. nov relations.</strong></p> <p>Maximum likelihood tree obtained for genes 16S, 12S, RAG1 and ND1. Support values are on the corresponding branches: aLRT values above the slash and bootstrap below. The phylogeny was derived from an analysis of 4155 bp for of mitochondrial (gene fragments 12S and 16S) and nuclear (gene fragments RAG1) DNA sequences for 235 terminals. For each specimen, museum number or, in unavailable, GenBank accession number is shown, as well as its locality. Outgroup is not shown. Abbreviations: CCS = confirmed candidate species, UCS = unconfirmed candidate species, ECU = Ecuador.</p> <p> </p> <p><strong>Figure S3. Phylogenetic relationships, based on mitochondrial genes only, showing <em>Pristimantis </em>new species relations.</strong> </p> <p>Maximum likelihood tree obtained for mitochondrial genes 12S, 16S and ND1. Support values are on the corresponding branches: aLRT values above the slash and bootstrap below. The phylogeny was derived from an analysis of 3573 bp for 235 samples of mitochondrial genes DNA sequences. For each specimen, museum number or, in unavailable, GenBank accession number is shown, as well as its locality. Outgroup is not shown. Abbreviations: CCS = confirmed candidate species, UCS = unconfirmed candidate species, ECU = Ecuador. In red, the inconsistencies found.</p>
Processed microphysical profiles of convective cloud scenes from satellite over ATTO
<p class="MsoNormal">We present a new approach of analyzing and interpreting vertical profiles of cloud microstructure obtained by satellite remote sensing. The method is based on a spectral bin microphysics adiabatic parcel model and aims to elucidate the effects of aerosols on the evolution of convective clouds and related microphysical processes, including the activation of cloud condensation nuclei (CCN), the growth of cloud droplets, and the formation of precipitation. Characteristic features in the vertical profiles of effective radius (<em><span>r</span></em><sub>e</sub>) and temperature (<em>T</em>) reveal different microphysical zones in convective clouds related to the change increase of <em><span>r</span></em><sub>e</sub> with decreasing <em>T</em>. The classification of the different microphysical zones includes the (1) condensational growth of droplets, (2) growth by coalescence, (3) rainout, (4) secondary droplet activation zone (<em>SAZ</em>), (5) mixed-phase of ice particles and water droplets and (6) glaciation of the cloud. The detection of the <em>SAZ</em> is introduced here for the first time. This method allows us to identify the activation of aerosol particles above cloud base and their role in the invigoration of deep convective clouds.</p>
Data: Clouds drive differences in future surface melt over the Antarctic ice shelves (Kittel et al., 2022)
<p>Outputs used in:</p> <p><em>Kittel, C., Amory, C., Hofer, S., Agosta, C., Jourdain, N. C., Gilbert, E., Le Toumelin, L., Gallée, H., and Fettweis, X.: Clouds drive differences in future surface melt over the Antarctic ice shelves, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-263, accepted, 2021.</em></p> <ul> <li>MAR outputs with summer values of melt, surface energy budget components, and cloud properties over the Antarctic ice sheet (1980--2100)</li> <li>Grid file used in MAR simulations</li> </ul> <p>If you need other variables or output frequencies from MAR, write me (c2kittel@gmail.com) and I will be glad to help you. I will also be happy to share the scripts I have developed to analyse the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR outputs.<br> <br> Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below that contained both informations related to MAR. In order to document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact C. Kittel to add their works in the list of MAR-related publications. </p> <p>"We thank C. Kittel and the MAR team which make available the model outputs, as well agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR simulations. "</p> <p>You should also refer to and cite the following paper:</p> <p><em>Kittel, C., Amory, C., Hofer, S., Agosta, C., Jourdain, N. C., Gilbert, E., Le Toumelin, L., Gallée, H., and Fettweis, X.: Clouds drive differences in future surface melt over the Antarctic ice shelves, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-263, accepted, 2021.</em></p>
Postprocessed trajectory output for ice cloud microphysics - ICON and CLaMS-Ice models
<p>Postprocessed output from a trajectory module implemented in ICON v 2.3.0. The trajectories track density, temperature, pressure, specific humidity, cloud ice mass and number mixing ratios, cloud liquid mass and number mixing ratios, graupel mass and number mixing ratios, and ice sedimentation mass and number mixing ratios both into and out of the parcel. They are initiated over the Sichuan basin and allowed to flow for 51 hours westward during which the cross India into the Arabian Sea. This trajectory output is also used to run an offline microphysics box model, CLaMS-Ice. qih-Nih* files contain histograms of ice mass mixing ratio (qi) and ice crystal number concentration (Ni); het-hom-pre* files contain process tendencies from heterogeneous nucleation, homogeneous nucleation, and preexisting ice in CLaMS-Ice; qippmvNi-TRHi* files contain qi and Ni versus a range of cirrus temperatures and a range of supersaturations with respect to ice; qi_ppmv_abs* and Ni_abs* files contain probability distributions of qi and Ni differences over absolute time; and qi_ppmv_norm* and Ni_norm* files contain probability distributions of qi and Ni differences over normalized time. Suffixes in all cases indicate the cloud microphysical setup that the trajectory values were used to run with 1M = one-moment scheme, 2M = two-moment scheme, Tf = temperature fluctuation parameterization in CLaMS-Ice simulations, and noSHflux = no pseudo-mixing tendency included in CLaMS-Ice simulations.</p>
Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: LIDAR Point Cloud of a Portion of UTEP Campus
<p>This Autodesk ReCap file is a combination of numerous individual LiDAR scans captured using a Leica Terrestial LiDAR system. The scan includes some black and white and some color scans. The area of campus generally focuses on the southwestern portion of campus including the Interdisciplinary Research Building, the Mining Minds roundabout, the Sun Bowl 2 Parking Lot, the University Bookstore, and the Sun Bowl Parking Garage, and roads including University Ave. and Sun Bowl Drive.</p>
The Role of Cloud-Cloud Interactions in the Lifecycle of Shallow Cumulus Clouds
<ul> <li>Supporting datasets for the paper "The Role of Cloud-Cloud Interactions in the Lifecycle of Shallow Cumulus<br> Clouds", which was submitted to the Journal of Atmospheric Sciences. </li> <li>Those are a subset of post-processed datasets from WRF-LES simulation and Lagrangian tracker (Feng et al., 2018). See manuscript for more details.</li> </ul> <p>Reference: </p> <p>Feng, Z., L. R. Leung, R. A. Houze, S. Hagos, J. Hardin, Q. Yang, B. Han, and J. Fan, 2018:634<br> Structure and Evolution of Mesoscale Convective Systems: Sensitivity to Cloud Microphysics635<br> in Convection-Permitting Simulations Over the United States. Journal of Advances in Modeling636<br> Earth Systems, 10 (7), 1470–1494, https://doi.org/10.1029/2018MS001305.637<br> 30</p> <p> </p>
Subdaily meteorological measurements of temperature, direction of the movement of the clouds, and cloud cover in the Late Maunder Minimum by Louis Morin in Paris
<p>Supplementary dataset of the publication: Pliemon, T., Foelsche, U., Rohr, C., and Pfister, C.: Subdaily meteorological measurements of temperature, direction of the movement of the clouds, and cloud cover in the Late Maunder Minimum by Louis Morin in Paris, Clim. Past, 2022</p> <p>For more details see the file.</p> <p> </p>
WHUS2-CR, a thin cloud removal dataset for Sentinel-2 images
<p>WHUS2-CR is a thin cloud removal dataset for Sentinel-2A images. WHUS2-CR contains 36 paired cloud and corresponding clear Sentinel-2A images evenly distributed over the world.</p> <p><strong>Because the max storage limitation of one dataset is 50 GB, 5 files can not be uploaded on this dataset. They can be found on : <a href="https://doi.org/10.5281/zenodo.5616753">https://doi.org/10.5281/zenodo.5616753</a>. (The reported results in ref [1] are in CRMSS-result.rar, CRMSS-10-4.rar and CRMSS-4-4.rar, respectively.)</strong></p> <p><strong>The dataset reproducing code and model source code for ref [2] are on :</strong> <a href="https://github.com/Neooolee/WHUS2-CR">https://github.com/Neooolee/WHUS2-CR</a></p> <p>If you use this dataset for your research, please cite us accordingly:</p> <p>#Reference: </p> <p>[1] J. Li, Z. W, Z. Hu, J. Z, M. Li, L. Mo and M. Molinier, “Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion,” ISPRS J. Photogramm. Remote Sens., vol. 166, pp. 373–389, Aug. 2020, <a href="http://doi.org/10.1016/j.isprsjprs.2020.06.021">http://doi.org/10.1016/j.isprsjprs.2020.06.021</a>.</p> <p><br> [2] J. Li, Z. Wu, Z. Hu, Z. Li, Y. Wang, and M. Molinier, “Deep learning based thin cloud removal fusing vegetation red edge and short wave infrared spectral information for Sentinel-2A imagery,” Remote Sens., vol. 13, no. 1, p. 157, Jan. 2021, <a href="http://doi.org/10.3390/rs13010157">http://doi.org/10.3390/rs13010157</a>.</p> <p>The training and testing image list used in reference [2] is (The training and testing small patches are listed in <a href="https://zenodo.org/api/files/bae57b05-f2cc-4729-b97c-eec7c215e9e4/filenos.xlsx">filenos.xlsx</a>):</p> <p>Training set</p> <table> <tbody> <tr> <td>1</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160403T030602_N0201_R075_T50TMK_20160403T031209</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160413T031632_N0201_R075_T50TMK_20160413T031626</td> </tr> <tr> <td>2</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20181111T053041_N0207_R105_T43RGM_20181111T083104</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20181121T053121_N0207_R105_T43RGM_20181121T091419</td> </tr> <tr> <td>3</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160925T104022_N0204_R008_T32ULB_20160925T104115</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160915T104022_N0204_R008_T32ULB_20160915T104018</td> </tr> <tr> <td>4</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160528T153912_N0202_R011_T18TWL_20160528T154746</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160518T155142_N0202_R011_T18TWL_20160518T155138</td> </tr> <tr> <td>5</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20181208T170701_N0207_R069_T14QMG_20181208T202913</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20181218T170711_N0207_R069_T14QMG_20181218T203015</td> </tr> <tr> <td>6</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20180809T190911_N0206_R056_T10UFB_20180810T002400</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20180819T190911_N0206_R056_T10UFB_20180820T002955</td> </tr> <tr> <td>7</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190306T132231_N0207_R038_T22KHV_20190306T164115</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190224T132231_N0207_R038_T22KHV_20190224T164104</td> </tr> <tr> <td>8</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20181012T084901_N0206_R107_T37VCC_20181012T110218</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20181022T085011_N0206_R107_T37VCC_20181022T110901</td> </tr> <tr> <td>9</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190619T023251_N0207_R103_T50JKP_20190619T071925</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190629T023251_N0207_R103_T50JKP_20190629T053618</td> </tr> <tr> <td>10</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190901T032541_N0208_R018_T47NPF_20190901T070148</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190911T032541_N0208_R018_T47NPF_20190911T084555</td> </tr> <tr> <td>11</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160419T083012_N0201_R021_T36RUU_20160419T083954</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160409T083012_N0201_R021_T36RUU_20160409T084024</td> </tr> <tr> <td>12</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190218T143751_N0207_R096_T19HCC_20190218T175945</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190208T143751_N0207_R096_T19HCC_20190208T180253</td> </tr> <tr> <td>13</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20180609T061631_N0206_R034_T42TWL_20180609T081837</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20180530T061631_N0206_R034_T42TWL_20180530T082050</td> </tr> <tr> <td>14</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191111T025941_N0208_R032_T49QGF_20191111T055938</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191101T025841_N0208_R032_T49QGF_20191101T054434</td> </tr> <tr> <td>15</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190818T103031_N0208_R108_T31SEA_20190818T124651</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190808T103031_N0208_R108_T31SEA_20190808T124427</td> </tr> <tr> <td>16</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191202T105421_N0208_R051_T29PPP_20191202T112025</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191212T105441_N0208_R051_T29PPP_20191212T111831</td> </tr> <tr> <td>17</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190919T074611_N0208_R135_T35JPL_20190919T105208</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190929T074711_N0208_R135_T35JPL_20190929T100745</td> </tr> <tr> <td>18</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190725T142801_N0208_R053_T20LMR_20190725T175149</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190804T142801_N0208_R053_T20LMR_20190804T175038</td> </tr> <tr> <td>19</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191101T043931_N0208_R033_T46TDK_20191101T074915</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191022T043831_N0208_R033_T46TDK_20191022T063301</td> </tr> <tr> <td>20</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160509T065022_N0202_R020_T41UNV_20160509T065018</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160519T064632_N0202_R020_T41UNV_20160519T064833</td> </tr> <tr> <td>21</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191019T012631_N0208_R131_T53LKF_20191019T030531</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191029T012721_N0208_R131_T53LKF_20191029T040003</td> </tr> <tr> <td>22</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190503T071621_N0207_R006_T38PMB_20190503T092340</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190423T071621_N0207_R006_T38PMB_20190423T093049</td> </tr> <tr> <td>23</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190724T011701_N0208_R031_T56VLM_20190724T031136</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190714T011701_N0208_R031_T56VLM_20190714T031656</td> </tr> <tr> <td>24</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20181020T012651_N0206_R074_T54TXN_20181020T032526</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20181010T012651_N0206_R074_T54TXN_20181010T055606</td> </tr> <tr> <td>25</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20170224T162331_N0204_R040_T16REV_20170224T162512</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20170214T162351_N0204_R040_T16REV_20170214T163022</td> </tr> <tr> <td>26</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190613T032541_N0207_R018_T49UFT_20190613T062257</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190623T032541_N0207_R018_T49UFT_20190623T061953</td> </tr> <tr> <td>27</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190208T011721_N0207_R088_T53KLP_20190208T024521</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190129T011721_N0207_R088_T53KLP_20190129T024501</td> </tr> <tr> <td>28</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190530T184921_N0207_R113_T12VVN_20190530T222535</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190520T184921_N0207_R113_T12VVN_20190520T222900</td> </tr> </tbody> </table> <p>Testing set</p> <table> <tbody> <tr> <td>1</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20150826T084006_N0204_R064_T37UCQ_20150826T084003</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20150905T083736_N0204_R064_T37UCQ_20150905T084002</td> </tr> <tr> <td>2</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191101T000241_N0208_R030_T56HLH_20191101T012241</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191111T000241_N0208_R030_T56HLH_20191111T012137</td> </tr> <tr> <td>3</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190711T174911_N0208_R141_T13TEE_20190711T212846</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190701T174911_N0207_R141_T13TEE_20190701T212910</td> </tr> <tr> <td>4</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190201T093221_N0207_R136_T32PRR_20190201T113425</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190211T093121_N0207_R136_T32PRR_20190211T103706</td> </tr> <tr> <td>5</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190314T021601_N0207_R003_T52SCF_20190314T055026</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190304T021601_N0207_R003_T52SCF_20190304T042035</td> </tr> <tr> <td>6</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20180804T045701_N0206_R119_T46VDH_20180804T065907</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20180725T045701_N0206_R119_T46VDH_20180725T065359</td> </tr> <tr> <td>7</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190707T213531_N0207_R086_T05VPJ_20190707T231819</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190627T213531_N0207_R086_T05VPJ_20190628T010801</td> </tr> <tr> <td>8</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190605T125311_N0207_R052_T24MXV_20190605T160555</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190615T125311_N0207_R052_T24MXV_20190615T142536</td> </tr> </tbody> </table>
The Large Magellanic Cloud Revealed in Gravitational Waves with LISA: Population Release
<p>The Large Magellanic Cloud (LMC)’s binary populations for study by the <em>Laser Interferometer Space Antenna (LISA)</em> as generated by Keim et al. in a paper submitted to MNRAS (Keim, M. A., Korol, V., Rossi, E. M. The Large Magellanic Cloud Revealed in Gravitational Waves with LISA. <em>Monthly Notices of the Royal Astronomical Society</em>, 2022, submitted). The files include all current double white dwarfs in the LISA band (‘LISABand’), all which will be detectable with a S/N>7 after 4 yrs (‘Detect’), and all which are detached/non-accreting, i.e. sure LISA sources (‘Detached’). This release represents a 2.7*10^9 stellar mass LMC, and includes distribution models based on observation (‘M1’) and simulation (‘M3’). For more information, please refer to Keim et al. (2022). We request that researchers utilising any of these populations cite Keim et al. (2022).</p> <p>The data columns are as follows:</p> <p>Column 1 = Right Ascension (Degrees)</p> <p>Column 2 = Declination (Degrees)</p> <p>Column 3 = Age (Myr, since formation of Main Sequence Pair)</p> <p>Column 4 = Mass of White Dwarf One (Msun)</p> <p>Column 5 = Mass of White Dwarf Two (Msun)</p> <p>Column 6 = Radius of White Dwarf One (Rsun)</p> <p>Column 7 = Radius of White Dwarf Two (Rsun)</p> <p>Column 8 = Orbital Radius (Rsun)</p> <p>Column 9 = Frequency (Hz)</p> <p>Column 10 = Chirp (Hz^2)</p> <p>Column 11 = Latitude (Radians)</p> <p>Column 12 = Longitude (Radians)</p> <p>Column 13 = Amplitude (Defined with a prefactor of 2)</p> <p>Column 14 = Inclination (Radians)</p> <p>Column 15 = Polarization (Radians)</p> <p>Column 16 = Orbital Phase (Radians)</p> <p>Column 17 = Distance (kpc)</p> <p>Column 18 = Mass Transfer (1= Yes, i.e. Roche Lobe Overfill, 0= No)</p>
Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test [DATA]
<p>Data and metadata for the publication "Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test", published in Earth and Space Science.</p>
Some original, intermediate, and result data in the papar entited "Highway marking extraction and degradation analysis by using MLS point clouds"
<p>Some original, intermediate, and result data in the papar entited "Highway marking extraction and degradation analysis by using MLS point clouds"</p>
Seeing shapes in clouds: the fallacy of deriving ecological hypotheses from statistical distributions
<p>The explanations behind observations of global patterning in species diversity pre-date the field of ecology itself. The generation of new species-area theories, in particular, far outpaces their falsification, resulting in a centuries-old accumulation in species diversity theories. We use historical assessment and new data analysis to argue that one of the earliest recognized and most consistent patterns in species diversity is not strictly an ecological phenomenon and, when ecological mechanism is invoked, the range of potential mechanisms is too numerous for tractable hypothesis falsification. We provide a historical parallel in that the normal distribution once was treated as a pattern assuming a biological mechanism rather than a statistical distribution that can be generated by biological and non-biological forces. Similarly, power law distributions are ubiquitous in aggregated data, such as the species-area relationship. That nearly identical broad-scale aggregation patterns are observed for both ecological and non-ecological data as a function of area suggest that these broad-scale patterns reflect a statistical distribution that, in itself, cannot be used to discern between or among ecological and non-ecological mechanisms. We argue that by seeking processes in such a ubiquitous pattern, ecologists may read ecological mechanism into statistical patterns, and we suggest that falsifying broad-scale diversity distribution hypotheses should be a greater priority than generating or parameterizing new ones.</p>
Importance of high-latitude sources for ice-nucleating particles in cold-air outbreaks and the implications for cloud-phase feedback
<p>This dataset contains FLEXPART results and codes for data analysis and visualization used in the MRes research project. </p> <p>Numpy arrays for FLEXPART data are in "data_flexpart.zip" with longitude and latitude coordination files.</p> <p>Codes for data analysis and visualization are in "ipynb.zip". Codes were written in Python language. Codes are categorized by figures in the article. </p>
Shadowcasting from 3D point clouds
<p>This video shows a time-lapse of shadows modelled by using a 3D point cloud of an urban garden, Villa Revedin Bolasco. </p>
Data used in "Observation of secondary ice production in clouds at low temperatures"
<p>To support open research, the data used in Korolev et al 2022 "Observation of secondary ice production in clouds at low temperatures" submitted to Atmospheric Chemistry and Physics has been provided here. <br> This in-situ data was collected by Environment and Climate Change Canada (ECCC) in collaboration with the National Research Council (NRC) on the NRC Convair-580 research aircraft on March 25 2017 between 11:00 and 12:00 UTC.<br> Radar data included was collected and processed by N. Cuong and M. Wolde of NRC Canada. <br> </p>
Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
<p>Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.</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.