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Calculated moisture sources for the Yangtse River Valley for past, present and future climate using a Lagrangian moisture source diagnostic
<p>This dataset contains calculated moisture sources for the Yangtse River Valley (110–122°E and 27–33°N, eastern China) for past, present and future climate using a Lagrangian moisture source diagnostic. The dataset comprises gridded monthly moisture source data files and monthly time series files for a Last Glacial Maximum (LGM) simulation and a Pre-Industrial reference simulation (PRE) with CAM5.1 using prescribed sea surface temperatures, and a control simulation (CTL, 2001-2010) and a climate scenario run with representative concentration pathway 6 (RCP, 2061-2070) with the coupled NorESM-1M model. Each file covers a 10-year time period, computed with the Lagrangian moisture source diagnostic WaterSip (Sodemann et al., 2008).</p>
Data for TC Diagnostics
<p>The dataset includes intensity bin composites of column-integrated mosit static energy (MSE) spatial variance budget feedback terms for GCMs, reanalyses, and <em>CloudSat</em> for:</p> <p>Starr, J. C., A. A. Wing, S. J. Camargo, D. Kim, T. Y. Lee, and J. Moon: Using the moist static energy variance budget to evaluate tropical cyclones in climate models against reanalyses and satellite observations. <em>Journal of Climate</em>, <em>In Review.</em></p> <p><strong>Description of Files for GCMs and Reanalyses </strong></p> <p>For each of the GCMs and reanalyses used in this study, there are 4 netcdf files that are saved, 2 for intensity bin composites with maximum wind speed (Vmax) as the binning metric and 2 for minimum mean sea level pressure (MSLP). Considering each of the GCMs and reanalyses have the same file format, the AM4 model will be used as an example for what each file contains and how they are organized. Each reanalysis and GCM will have its own .tar containing the four netcdf files mentioned. </p> <div> <div>AM4_Binned_Composites_V2.nc is the Vmax-binned intensity bin composite means of all the variables. The first dimension of each of these variables within the file are "bin" which represents the bin mean value, for example the first bin value is 1.5 representing the 0-3 m/s bin, then increasing by 3 m/s from there. For the spatial composites, which are 2-dimensional variables, those have dimensions of "lat" and "lon" which range from -5 degrees to 5 degrees as the center of each spatial intensity bin composite of that variable would be 0 degrees, 0 degrees. The azimuthal mean variables have dimension "nr" which represents the radial increments. </div> <div> <div>AM4_Binned_STDEVS_of_BoxAvgs_V2.nc contains the Vmax-binned intensity bin composite standard deviations of the box averaged variables as well as the azimuthal mean feedback variables. This file is used in calculting the 5 to 95% confidence intervals for the azimuthal mean and box average plots. </div> <div> </div> <div>AM4_Binned_Composites_MSLP.nc is the minimum MSLP-binned intensity bin composite means of all the variables. This file is set up the same as the Vmax-binned file, but now the first dimension "bin" represents the bin mean value using minimum MSLP as the intensity metric. For example, the first bin of this dimension is 882.5 hPa which is the mean value of the 880-885 hPa bin. These mean values then increase by 5 hPa to the weakest bin of 1020-1025 hPa. <div> <div>AM4_Binned_STDEVS_of_BoxAvgs_MSLP.nc is set up identically to the Vmax-binned version of the standard deviation file, just now with minimum MSLP as the binning metric.</div> <div> </div> <div>These files contain all the variables that are pertinent to the MSE spatial variance budget, but also some that were not utilized in this study. The variables listed below are those that were utilized in this study.</div> <div> </div> <div>"bincounts": the number of snapshots in each intensity bin</div> <div><strong>3-D Variables (Spatial composites (bin,lat,lon)):</strong></div> <div>"hanom": anomaly of column-integrated MSE from the domain-mean column-integrated MSE</div> <div>"hanom_SEFanom": the surface enthalpy flux (SEF) feedback </div> <div>"hanom_LWanom": the longwave (LW) feedback</div> <div>"hanom_SWanom": the shortwave (SW) feedback</div> <div><strong>2-D Variables (Azimuthal mean composites (bin,nr)):</strong></div> </div> "Azmean_hSEF": Azimuthal mean SEF feedback</div> <div>"Azmean_hLW": Azimuthal mean LW feedback</div> <div>"Azmean_hSW": Azimuthal mean SW feedback</div> <div><strong>1-D Variables (Box-averaged composites (bin)):</strong></div> <div> <div> <div>"new_boxav_hvar": the box-averaged variance of column-integrated MSE</div> <div>"new_boxav_hanom_SEFanom": the box-averaged SEF feedback</div> <div>"new_boxav_hanom_LWanom": the box-averaged LW feedback</div> <div>"new_boxav_hanom_SWanom": the box-averaged SW feedback</div> <div>"new_boxav_norm_hanom_SEFanom": the normalized box-averaged SEF feedback</div> <div>"new_boxav_norm_hanom_LWanom": the normalized box-averaged LW feedback</div> <div>"new_boxav_norm_hanom_SWanom": the normalized box-averaged SW feedback</div> <div> </div> <div>To get the standard deviations of the azimuthal mean and box-averaged feedbacks of each intensity bin, the same variable names are used above in the standard deviation file.</div> </div> </div> <div><strong>Description of File for <em>CloudSat</em></strong></div> <div>This file was provided by work done in:</div> <div> </div> <div>Lee, T.-Y., and A. Wing, 2024: Satellite-based estimation on the role of cloud-radiative interaction in accelerating tropical cyclone development. <em>Journal of the Atmospheric Sciences</em>, <strong>64 (81)</strong>, 959-982, https://doi.org/https://doi.org/10.1175/JAS-D-23-0142.1.</div> <div> </div> <div> <div>CloudSat_Composite_IR_RRTMGclimlab_vi4_IR_Vmax999_000_R3.nc contains the Vmax-binned intensity bin composites of the MSE variance budget feedback variables. Each of the <em>CloudSat</em> variables are provided as radial profiles with dimensions like those in the reanalyses and GCMs of intensity bin and then radius. The variables from this file that were utilized in this study are listed below.</div> <div> </div> <div>"RadFB_LW_ALL_500": radial composite of the LW feedback</div> <div> <div> <div>"RadFB_SWDAY_ALL_500": radial composite of the SW feedback</div> <div>"RadFB_Net_ALL_500": radial composite of the total radiaitive feedback</div> <div>"RadFB_LW_CLEARSKY_500": radial composite of the clear-sky LW feedback</div> <div> <div> <div>"RadFB_SWDAY_CLEARSKY_500": radial composite of the clear-sky SW feedback</div> <div>"RadFB_Net_CLEARSKY_500": radial composite of the clear-sky total radiaitive feedback</div> </div> </div> </div> </div> </div> </div> </div>
Radar-derived storm characteristics and convective diagnostics associated with hourly maximum measured wind gusts around Australia
<p>The data in this record describes various characteristics associated with hourly measured surface wind gusts across various locations in Australia, with these characteristics and data sources described below.</p> <p>This record provides all data used in the preparation of Brown et al. (2023a), except for lightning data that can be obtained from the <a href="https://wwlln.net/">World Wide Lightning Location Network archive</a></p> <p><strong>Record contents</strong></p> <ul> <li><em>gust_observations.zip</em><br> Within this zip archive, a <em>.csv</em> file is provided for wind gust observations, along with associated storm statistics from radar, and convective diagnostics from a global reanalysis. These data are provided for each of the 20 radar domains listed in Brown et al. (2023a). The <em>.csv</em> files follow the structure: <em>gust_observations_x.csv, </em>where <em>x </em>is the identification number for each radar from the <a href="https://www.openradar.io/operational-network">Australian Unified Radar Archive</a>.<br> </li> <li><em>station_details.csv</em><br> This file provides details on the automatic weather stations that measure the wind gusts, with station identifiers (column=Station_id) consistent between <em>station_details.csv </em>and<em> gust_observations_x.csv</em>.<br> </li> <li><em>Table1.pdf</em> <br> Descriptions of convective diagnostics from reanalysis, that are provided in <em>gust_observations_x.csv</em>. This table has been extracted from the supplementary information of Brown et al. (2023a), and references in this table can be found therein.<br> </li> <li><em>radar_details.pdf</em> <br> Taken from Table 1 from Brown et al. (2023a), showing the details of radars used here for storm statistics in <em>gust_observations_x.csv</em>.<br> </li> <li><em>Fig1.jpeg</em><br> Taken from from Brown et al. (2023a), showing a map of the radar domains used here for storm statistics in <em>gust_observations_x.csv</em>.</li> </ul> <p><strong>Wind gust data</strong></p> <p>Measured wind gusts here represent a 3-second average wind speed, at a height of 10 m above ground level. We also provide some derived quantities from the gust data (see table below). Gust data is provided by the <a href="http://www.bom.gov.au/climate/data/stations/">Australian Bureau of Meteorology</a>, from 204 automatic weather stations (<em>station_details.csv)</em>, chosen to be within 100 km of a weather radar with sufficient archived data. These data are originally provided by the Bureau of Meteorology at 1-minute frequency, representing a maximum over a 1-minute interval, but are resampled in this record to hourly frequency, for comparisons with other hourly data below (see Brown et al. (2023a) for details of this resampling). Note that any reuse of this data should properly acknowledge the Australian Bureau of Meteorology.</p> <p><strong>Radar data</strong></p> <p>Radar data is obtained by the <a href="https://www.openradar.io/">Australian Unified Radar Archive</a> (AURA), produced from operational weather radar within the Australian Bureau of Meteorology network. The level1b data used here is available from the AURA dataset on the Australian NCI under a CC4-BY-NC licence from <a href="https://dx.doi.org/10.25914/5f4c85732ee80">https://dx.doi.org/10.25914/5f4c85732ee80</a>. Various properties derived from radar reflectivity and Doppler velocity data is reported here in association with the wind gust observations. These properties are only reported if there is a storm object within 10 km and 10 minutes of the gust location (see Brown et al. (2023a) for storm object definition). Radar properties are described in the table below.</p> <p><strong>Environmental data</strong></p> <p>Various convective diagnostics are associated with wind gust observations, representing the convective environment and large-scale wind profile. These diagnostics are derived from a combination of pressure-level and surface-level ERA5 data (Hersbach et al. 2020), which is provided at hourly intervals on a 0.25-degree latitude-longitude grid, hosted on the Australian NCI (<a href="http://dx.doi.org/10.25914/5fb115b82e2ba">http:// dx.doi.org/10.25914/5fb115b82e2ba</a>). Details on these convective diagnostics are provided in Brown et al. (2023a), and<strong> </strong>in <em>Table1.pdf</em> as provided in this record.</p> <p><strong>Column descriptions</strong></p> <p>The following table provides descriptions of columns of <em>gust_observations_x.csv</em></p> <table> <thead> <tr> <th scope="col">Column name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>dt_utc</td> <td>Time of the measured wind gust, from the automatic weather station data (YYYY-MM-DD HH:MM:SS UTC)</td> </tr> <tr> <td>Station_id</td> <td>Identification number of the weather station that measured the gust. See <em>station_details.csv </em>for details of each station</td> </tr> <tr> <td>Wind_gust_observed</td> <td>The measured wind gust speed (m/s)</td> </tr> <tr> <td>Peak_to_mean_wind_gust_ratio</td> <td>Ratio of the measured wind gust to the 4-hour mean at that station (with the window centred on the gust time)</td> </tr> <tr> <td>SCW</td> <td>Is the measured gust a severe convective wind event?<br> 0: Gust is either less than 25 m/s, does not have a storm object within 10 km, or has a peak-to-mean wind gust ratio less than 2.<br> 1: Gust is greater than 25 m/s, has a storm object within 10 km, and has a peak-to-mean wind gust ratio greater than 2.</td> </tr> <tr> <td>Radar_id</td> <td>Radar identification number (see <em>radar_details.pdf)</em></td> </tr> <tr> <td>Storm_speed</td> <td>Translational speed of the parent storm object (m/s). Only defined if Storm_in10km=1</td> </tr> <tr> <td>Storm_angle</td> <td>Angle of parent storm object movement. In units of degrees from N. Only defined if Storm_in10km=1</td> </tr> <tr> <td>Parent_storm_class</td> <td>The type of parent storm associated with a gust. Only defined if Storm_in10km=1. Possible types are:<br> "Non-linear"<br> "Linear"<br> "Cellular"<br> "Cell cluster"<br> "Supercellular"<br> "Embedded supercell"<br> See Brown et al. (2023a) for classification details</td> </tr> <tr> <td>Storm_in10km</td> <td>Is there a radar-derived storm object within 10 km of the gust, observed no more than 10 minutes prior to the gust?<br> 0: No<br> 1: Yes<br> See Brown et al. (2023a) for a definition of "storm object"</td> </tr> <tr> <td>Major_axis_length</td> <td>The length of the major axis of an ellipse fitted to the parent storm object (km). Only defined if Storm_in10km=1</td> </tr> <tr> <td>Minor_axis_length</td> <td>The length of the minor axis of an ellipse fitted to the parent storm object (km). Only defined if Storm_in10km=1</td> </tr> <tr> <td>Local_reflectivity_maxima</td> <td>Number of local reflectivity maxima within the parent storm object. Only defined if Storm_in10km=1</td> </tr> <tr> <td>Maximum_storm_altitude</td> <td>The maximum height of the parent storm radar reflectivity object (km). Only defined if Storm_in10km=1</td> </tr> <tr> <td>Azimuthal_shear</td> <td>Azimuthal shear of the parent storm object derived from radar data (s<sup>-1 </sup>x 1000). Only defined if Storm_in10km=1. See Brown et al (2023a) for a discussion of azimuthal shear and processing applied to this quantity here.</td> </tr> <tr> <td>ERA5_time</td> <td>Time of the ERA5 environmental data that is associated with the measured gust, corresponding to the closest previous hour (YYYY-MM-DD HH:MM:SS UTC).</td> </tr> <tr> <td>ERA5_latitude</td> <td>Location of closest ERA5 (land) grid point to measured gust, in degrees of latitude</td> </tr> <tr> <td>ERA5_longitude</td> <td>Location of closest ERA5 (land) grid point to measured gust, in degrees of longitude</td> </tr> <tr> <td>Environmental_cluster</td> <td>Event type, based on statistical clustering of environmental data (Brown et al. 2023b)<br> 0: Strong background wind cluster<br> 1: Steep lapse rate cluster<br> 2: High moisture cluster</td> </tr> <tr> <td>Umean06</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Umean01</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>U10</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WindGust10</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>S06</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>EBWD</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Umeanwindinf</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SRHE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SRH06</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DMI</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>LR_subcloud</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>LR_freezing</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>LR03</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>LR13</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WMSI</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>BDSD</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>ConvGust_wet</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>ConvGust_dry</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>GUSTEX</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DmgWind</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DmgWind_fixed</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DCAPE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WMPI</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WINDEX</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DowndraftTemp</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>ThetaeDiff</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>TEI</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WNDG</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DCP</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SCP</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SCP_fixed</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SHERB</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SHERBE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SWEAT</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MUCS6</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MLCS6</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>EffCS6</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>T_Totals</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>K_Index</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Eff_CAPE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Eff_LCL</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MLCAPE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>ML_LCL</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MUCAPE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MU_LCL</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Qmean01</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Qmean06</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> </tbody> </table> <p><strong>References</strong></p> <p>Brown, A., Dowdy, A., Lane, T. P., & Hitchcock, S. (2023b). Types of Severe Convective Wind Events in Eastern Australia. <em>Monthly Weather Review</em>, <em>151</em>(2), 419–448. https://doi.org/10.1175/MWR-D-22-0096.1</p> <p>Brown, A., A. Dowdy, T. P. Lane, & Hitchcock, S. (2023a). Long-term observational characteristics of different severe convective wind types around Australia. <em>Wea. Forecasting</em>, <a href="https://doi.org/10.1175/WAF-D-23-0069.1">https://doi.org/10.1175/WAF-D-23-0069.1</a>, in press.</p> <p>Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., et al. (2020). The ERA5 Global Reanalysis. <em>Quarterly Journal of the Royal Meteorological Society</em>, qj.3803. https://doi.org/10.1002/qj.3803</p>
Systematic Data Analysis and Diagnostic Machine Learning Reveal Differences between Compounds with Single- and Multitarget Activity
<p>The deposited files contain balanced data sets of multi-target (MT) and single-target (ST) compounds (CPDs) used for machine learning studies (https://dx.doi.org/10.1021/acs.molpharmaceut.0c00901). The first file (st_mt_data.tsv) contains 15,142 MT- and 15,081 ST-CPDs and the second (st_dt_data.tsv) 1828 DT- and 1776 ST-CPDs. For each CPD, a nonstereo_aromatic_SMILES representation, the original ChEMBL_cid, UniProt (target) IDs, and CPD category (CPD_CAT) (i.e. DT/MT/ST) is provided. DT stands for 'diverse-target' and denotes a subset of MT-CPDs (as detailed in the publication). In addition, a CPD is tagged “Y” if it continued to be present in the data set after removal of 50% randomly selected CPDs or 50% CPD nearest neighbors (NN), respectively.</p>
ECEMF Diagnostic Scenarios, version 2.0
<p>This dataset compiles diagnostic scenarios from several Integrated Assessment Models (IAM) and Energy System Models (ESM) to facilitate systematic comparison of a broad range of results across these models.</p> <p>These diagnostic scenarios were developed in the Horizon 2020 project ECEMF (https://ecemf.eu).</p> <p>Visit the ECEMF Scenario Explorer hosted by IIASA at https://ecemf.apps.ece.iiasa.ac.at/ for more information and interactive user interface to work with the scenario data.</p>
ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset
<p>ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 2 consist of two folders with 300 images in each of them as well as annotations. </p> <p>ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 1 consists of two datasets of XCA images for each of two tasks of ARCADE challenge. The first task includes in total 1200 coronary vessel tree images, which are divided into train(1000) and validation(200) groups, images for training are followed with annotations, depicting the division of a heart into 26 different regions based on the Syntax Score methodology[1]. Similarly, the second task includes a different set of 1200 images with same train-val division proportion with annotated regions containing atherosclerotic plaques. This dataset, carefully annotated by medical experts, enables scientists to actively contribute towards the advancement of an automated risk assessment system for patients with CAD. </p> <p>The dataset structure is as follows: top-level directories "syntax" and "stenosis" contain files for the two dataset objectives, namely: i) vessel branch classification according to the SYNTAX methodology; and ii) stenosis detection. Inside both directories, there are 3 subsets of the dataset, such as "train", "val", and "test". Inside each of those folders, there are 2 lower-level directories - "images", and "annotations". Inside the "images" folder there are images in ".png" format, extracted from DICOM recordings. The "annotations" folders contain single ".JSON" files, which are named in correspondence to the objective, i.e. "train.JSON", "val.JSON", and "test.JSON".</p> <p>The structure of ".JSON" contains three top-level fields: "images", "categories", and "annotations". The "images" field contains the unique "id" of the image in the dataset, its "width" and "height" in pixels, and the "file_name" sub-field, which contains specific information about the image. The "categories" field contains a unique "id" from 1 to 26, and a "name", relating it to the SYNTAX descriptions. The "annotations" field contains a unique "id" of the annotation, "image_id" value, relating it to the specific image from the "images" field, and a "category_id" relating it to the specific category from the "categories" field. The "segmentation" sub-field contains coordinates of mask edge points in "XYXY" format. Bounding box coordinates are given in the "bbox" field in the "XYWH" format, where the first 2 values represent the x and y coordinates of the left-most and top-most points in the segmentation mask. The height and width of the bounding box are determined by the difference between the right-most and bottom-most points and the first two values. Finally, the "area" field provides the total area of the bounding box, calculated as the area of a rectangle.</p> <p> </p> <p>The corresponding Dataset Article will be provided later. </p> <p>[1] Syntax score segment definitions. https://syntaxscore.org/index.php/tutorial/definitions/14-appendix-i-segment-definitions</p>
Data products and software for `X-ray diagnostics of Cassiopeia A's "Green Monster": evidence for dense shocked circumstellar plasma`
<div> <h2>Data Reproduction Package for the publication ‘X-ray diagnostics of Cassiopeia A’s “Green Monster”: evidence for dense shocked circumstellar plasma’</h2> </div> <div> <h3>Authors: Jacco Vink, Manan Agarwal, Patrick Slane, Ilse De Looze, Dan Milisavljevic, Daniel Patnaude, and Tea Temim.</h3> </div> <div> <h3>Link to paper: <a href="https://doi.org/10.3847/2041-8213/ad2fc5">https://doi.org/10.3847/2041-8213/ad2fc5</a> </h3> <p> </p> </div> <div> <h4>This package was prepared by Jacco Vink and Manan Agarwal (University of Amsterdam)</h4> </div> <div> <h3>Summary</h3> </div> <div> <p>This data reproduction package contains the data files in FITS format used to<br>generate the figures in the paper. The data files concern the revised manuscript, which incorporates changes made in response to the journal’s referee report.</p> </div> <div> <p>The paper is based on Chandra X-ray Observatory (CXO) data of Cassiopeia A taken in 2004. The raw archival data used, maintained by the Chandra Data Archive, can be retrieved using the following DOI link: <a href="https://doi.org/10.25574/cdc.209">https://doi.org/10.25574/cdc.209</a>.</p> </div> <div> <p>Additional James Webb Space Telescope (JWST) data are stored at the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute. The data used in the paper can be downloaded through DOI link <a href="https://doi.org/10.17909/szf2-bg42">https://doi.org/10.17909/szf2-bg42</a>.</p> </div> <div> <p>The data produced from the above raw data are stored in the files:</p> </div> <div> <ul> <li>green_monster_image_data.tar.gz</li> <li>spectral_files_and_models.tar.gz</li> <li>imaging_and_pca_code.tar.gz</li> <li>green_monster_pca_input_output.tar.gz</li> </ul> <p>The repository contains JWST/MIRI mosaics of Cassiopeia A which are described in detail in the paper "A JWST Survey of the Supernova Remnant Cassiopeia A", by D. Milisavljevic, T. Temim, I. De Looze, et al.; see https://arxiv.org/abs/2401.02477, to be published in ApJ letters.<br> </p> </div>
Eulerian and Lagrangian diagnostics of the dynamical properties of the water masses sampled during the Tara Pacific Expedition 2016-2018
<p>In order to provide a description of the dynamical properties of the water masses sampled, different Eulerian and Lagrangian diagnostics were calculated. </p> <p>For each of the 246 stations sampled, we proceeded as follows.</p> <p>We identified the water mass sampled at the given station. This was considered as a stadium shape with the two semi-circles centered on the starting and ending points of the transect, respectively. The radius of the stadium semi-circles was considered 0.1°, which is in accordance with previous studies25,29,30. The stadium was filled with virtual particles separated by 0.01°.</p> <p>For each virtual particle inside the stadium shape, we calculated an Eulerian or Lagrangian diagnostic (described above). The Eulerian diagnostics were extracted directly from the velocity field of the day of sampling. Concerning the Lagrangian diagnostics, these were obtained by advecting the virtual particle backward in time for an amount of time 𝞽 from the day of sampling day_S. For the Lagrangian betweenness, the advection was performed between day_S+𝞽/2 and day_S-𝞽/2, so that the advective time window was centered on the sampling day (details in25).</p> <p>For the Lagrangian diagnostics, we used the following advective times 𝞽: 5, 10, 15, 20, 30, and 60 days. The only exception is the retention time, which, by construction, was calculated only with the largest advective time, namely 𝞽=60 days.</p> <p>Once that, a given diagnostic (Eulerian or Lagrangian) was calculated for all the virtual particles filling the stadium shape, we calculated the mean value, and the 25, 50, and 75 percentiles. The percentiles were calculated in order to quantify the spatial variation of the diagnostic inside the stadium shape. Therefore, we associated each station with four values (mean, 25, 50, and 75 percentiles) of a given diagnostic.</p> <p> Furthermore, two different velocity fields were used, which are described as follows. </p> <p>Both the velocity fields were downloaded from E.U. Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/). The first velocity field used was MULTIOBS_GLO_PHY_REP_015_004 [GlobEkmanDt]. This was produced by combining the altimetry derived geostrophic velocities and modeled Ekman surface currents. It had a spatial resolution of 0.25° and a temporal resolution of one day. The second velocity field was GLOBAL_REANALYSIS_PHY_001_030 [GloryS12]. It was obtained by a NEMO model assimilating altimetry and other observations. It had a spatial resolution of 1/12° and a temporal resolution of 1 day.</p> <p>The following Eulerian diagnostics were calculated:</p> <ul> <li> <p>Absolute velocity ([Uabs], m s-1): sqrt(u2+v2), where u and v are the zonal and meridional components of the horizontal velocity field used (described below)</p> </li> <li> <p>Kinetic energy ([Ekin], m2 .s-2): 0.5*(u2+v2)</p> </li> <li> <p>Divergence ([EulerDiverg], d-1): du/dx + dv/dy</p> </li> <li> <p>Vorticity ([Vorticity], d-1): dv/dx - du/dy</p> </li> <li> <p>Okubo-Weiss ([OW], d-2): s2-vorticity2, where s2 is (du/dx-dv/dy)2 + (dv/dx+du/dy)2. If negative, it indicates that the station sampled was inside an eddy.</p> </li> </ul> <p>The following Lagrangian diagnostics were calculated:</p> <ul> <li> <p>Finite-Time Lyapunov Exponents ([Ftle], d-1): it indicates the rate of horizontal stirring, and it is a means to quantify the intensity of turbulence in a given region. FTLE are commonly used to identify Lagrangian Coherent Structures, i.e. barriers to transport. In this case, a strong FTLE value indicates a region separating water masses which were far away backward in time.</p> </li> <li> <p>Lagrangian betweenness ([betw], adimensional): this diagnostic draws inspiration from Lagrangian Flow Network Theory26. It can identify regions which act as bottlenecks for the circulation, in that they receive waters coming from different origins, and that are then spread over several different destinations. These can represent possible hotspots driving biodiversity25.</p> </li> <li> <p>Lagrangian Divergence ([LagrDiverg], d-1). This diagnostic was calculated by integrating the Eulerian divergence along the backward trajectories. If positive, it indicates a water mass that, during the previous days, was subjected to a strong divergence, thus to a possible upwelling. If negative, it indicates a strong convergence, thus possible downwelling.</p> </li> <li> <p>Retention Time ([RetentionTime], d). This diagnostic indicates how many days a water mass has spent inside an eddy in the previous period. If the water mass is outside an eddy, then its retention time is set to zero.</p> </li> </ul>
Diagnostic accuracy of a set of clinical and radiological criteria for screening of COVID-19 using RT-PCR as the reference standard - Dataset
<p>Dataset of a cohort whose summary is described below.</p> <p>Abstract</p> <p><strong>Objective:</strong> To evaluate the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of a set of clinical-radiological criteria for COVID-19 screening in patients with severe acute respiratory failure (SARF) admitted to intensive care units (ICUs), using reverse-transcriptase polymerase chain reaction (RT-PCR) as the reference standard. <strong>Method: </strong>Diagnostic accuracy study including a historical cohort of 1009 patients consecutively admitted to ICUs across six hospitals in Curitiba (Brazil) from March to September, 2020. The sample was stratified into groups by the strength of suspicion for COVID-19 (strong <em>versus</em> weak) using parameters based on three clinical and radiological (chest computed tomography) criteria. The diagnosis of COVID-19 was confirmed by RT-PCR (referent). <strong>Results:</strong> With respect to RT-PCR, the proposed criteria had 98.5% (95% confidence interval [95% CI] 97.5–99.5%) sensitivity, 70% (95% CI 65.8–74.2%) specificity, 85.5% (95% CI 83.4–87.7%) accuracy, PPV of 79.7% (95% CI 76.6–82.7%) and NPV of 97.6% (95% CI 95.9–99.2%). <strong>Conclusion: </strong>The proposed set of clinical-radiological criteria were accurate in identifying patients with strong <em>versus</em> weak suspicion for COVID-19 and had high sensitivity and considerable specificity with respect to RT-PCR. These criteria may be useful for screening COVID-19 in patients presenting with SARF.</p>
Data for the publication "Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation"
<p>Michibata et al. (2019), currently under peer-review for publication in <em>Geoscientific Model Development</em>, incorporated a diagnostic tool for warm rain microphysics into the CFMIP Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011; Swales et al., 2018), designed to evaluate model representations of aerosol–cloud–precipitation interactions at a fundamental process-level. The tool automatically generates two diagnostics related to warm rain microphysics during COSP execution in a host model. One is the contoured frequency by optical depth diagram (CFODD), which visualizes a cloud-to-rain microphysical vertical structure (Suzuki et al., 2015). The other diagnostic is a global map of warm rain fraction classified as non-precipitating clouds (< –15 dBZ<sub>e</sub>), drizzling clouds (–15 < dBZ<sub>e</sub>< 0), and precipitating clouds (0 < dBZ<sub>e</sub>).</p> <p>This repository contains the MIROC6/COSP2 input data and A-Train satellite statistics used in Michibata et al. (2019). A sample of the post-processing scripts for visualization using the GrADS software is also included in this repository.</p>
LMDZOR-INCA global model simulations diagnostics for mineral dust direct radiative effet calculations
<p>This dataset contains the diagnostic variable used to estimate the mineral dust aerosol direct radiative effect from LMDZOR-INCA global simulations using different refractive index data and different size modes and a multimodal size distribution.</p> <p>The NetCDF files provide the radiation fields shortwave all sky (solswad, topswad) and clear sky (solswad0, topswad0) (sol is for the surface and top for the top of the atmosphere) and the longwave all sky (sollwad, toplwad) and clear sky (sollwad0, toplwad0), as monthly means over global grids.</p> <p>Data are provided for the mean, minimum and maximum of the complex refractive index from Di Biagio et al. (2017) ( https://doi.org/10.5194/acp-17-1901-2017 ) and the refractive index by Volz et al. (1973) ( <a href="https://doi.org/10.1364/AO.12.000564">https://doi.org/10.1364/AO.12.000564</a> ) in the longwave spectral range and for the refractive index by Balkanski et al. (2007) ( https://doi.org/10.5194/acp-7-81-2007) corresponding to 1.5% hematite by volume in the shortwave range.</p> <p>Simulations are performed for four lognormal size distributions with mass median diameters (sigma) of 1 µm (1.8), 2.5 µm (2), 7 µm (1.9), 22 µm (2). The multimodal run is performed on the size distribution obtained as the sum of the four modes combined follwing the mass fractions of 0.6%, 4.3%, 31.5%, and 63.6% for the four modes, respectively.</p> <p>Variables for the dust atmospheric load and optical depth at 550 nm for each mode are in the mean run for each mode.</p> <p>Input mass extinction efficiency (Ext, m2/g), absorption exitinction efficiency (Abs, m2/g), single scattering albedo (w) and asymmetry factor (g) for the different radiative bands at at some wavelengths used in the MODIS sensor are provided in the 1MODE_xxum_dust_optical_data_1.5dielectric_mixture.</p>
A Gas Chromatography – Ion Mobility Spectrometry dataset for colorectal cancer diagnostic of 56 urine samples corresponding to 29 subjects.
<p><strong>Contents of the dataset</strong></p> <p>The dataset includes the set of urine samples in .mea format, which can be<br> read using the GCIMS R package.</p> <p>It also contains analytical standards in the same format, used for quality<br> control of the equipment and as a retention time alignment reference.</p> <p>If you want to preview the data, you do not need to download the full Urines.zip<br> and AnalyticalStandards.zip files, but rather use the smaller UrinesDemo.zip and<br> AnalyticalStandardsDemo.zip, with a subset of just three samples of the whole<br> dataset.</p> <p>Besides the actual measurements, you will find the annotations.csv and<br> reference_peaks.csv files, with sample annotations and some reference peaks<br> identified in the samples.</p> <p>See further details below.</p> <p><br> <strong>Sample collection</strong></p> <p>Urine samples from 29 subjects were collected at Hospital de Reus. 15 subjects<br> were diagnosed with colorectal cancer, 14 subjects were controls. The study<br> protocol was approved by the Ethics Committee of Hospital de Reus (study<br> approval no. 074/2018).</p> <p>Samples were aliquoted and frozen at -80ºC for storage.</p> <p><strong>Sample preparation</strong><br> </p> <p>Sample preparation improves urine preservation by blocking bacterial growth in<br> the urine, and favours volatile extraction. It also adds an internal standard<br> for verification of instrument variability.</p> <p><em>Stock solution preparation</em></p> <p>Dissolve 11.69 g of NaCl in about 35 mL deionized water and add 6.5 mg sodium<br> azide (NaN3). Once dissolved, add 5.50 mL 5M HCl and mark up to volume with<br> deionized water until the final volume is 50mL. The HCl 5M is used to obtain<br> an acid pH. The pH is controlled with a pH test paper. The final pH level must<br> be 2 or below. The NaCl favors the volatile extraction, and the NaN3 omits<br> the bacterial growth in the urine.</p> <p><em>Internal standard solution preparation</em><br> </p> <p>The 4-flurobenzaldehyde is located in retention time around 200 seconds and<br> can be used as an internal standard.</p> <p>Prepare a methanol stock solution using 100 ml of methanol grade for<br> preparative chromatography and 200 ml of distilled water.</p> <p>Mix 5 mL of 4-fluorobenzaldehyde with 100 mL of the methanol stock solution.</p> <p>Dilute the previous mixture in 400 mL of mili-Q water.</p> <p><br> <em>Sample preparation</em><br> </p> <p>Aliquotes were thawed before analysis. Once thawed, 300uL of the stock solution<br> were added to the urine sample, and 1.5 ml of the acidified urine sample were<br> transferred into a 20ml vial, ensuring only the supernatant of the sample<br> is transferred.</p> <p>Finally, 20 mL of the internal standard solution is added to the sample.</p> <p><strong>GC-IMS Analysis</strong></p> <p>Samples were analyzed with a GC-IMS FlavourSpec® instrument from<br> G.A.S. Dortmund (Dortmund, Germany). Samples were incubated for 15 minutes<br> at 60ºC, the flow rate of the drift gas was set at 200 ml/min, and the carrier<br> gas was set 11 ml/min. Both the drift and carrier gas were Nitrogen 5.0. The GC<br> and IMS temperature were set at 60ºC and the measurement time lasted 33 minutes.</p> <p>Besides the urines, a set of measurements of a ketone mixture was also analyzed<br> at least once per day as an analytical standard control of the equipment. The mixture<br> included 6 ketones (2-butanone, 2-pentanone, 2-hexanone, 2-heptanone,<br> 2-ocatanone and 2-nonanone). This mixture is measured in the same conditions as<br> the urine samples.</p> <p>Samples are provided in the native instrument format (.mea format), that can be<br> read with the GCIMS R package or with the instrument software.</p> <p><strong>Sample annotations</strong></p> <p>The dataset includes a CSV file with sample annotations.</p> <p>The annotations include the following information:</p> <ul> <li>Diagnostic: Either ColorectalCancer or Control</li> <li>Sex: Either Male or Female</li> <li>Sample volume (in ml)</li> <li>Fasting: Whether the sample was collected with the patient in fasting conditions</li> <li>Age in years</li> <li>Weight_kg</li> <li>Height_cm</li> <li>BMI</li> <li>Smoker: TRUE/FALSE, whether the patient smoked</li> <li>Diseases: Whether the patient suffered from ArterialHypertension, CardiacFailure, Cholesterol, Dyslipidemia, Fibromyalgia or Tuberculosis</li> <li>AnalysisDateTime: Date and time of the GC-IMS analysis of the sample</li> </ul> <p><br> <strong>Reference peaks</strong></p> <p>Some peaks were manually annotated to ease the alignment of the samples and explore<br> alignment solutions. While manual peak labelling is not generally required, we<br> attach those reference peaks as well and their locations, in case they are of<br> interest.</p> <p>These reference peaks are found at reference_peaks.csv.</p> <p> </p>
Diagnostic strategies for muscular dystrophies: a Cross-Sectional Study
<p>Datos obtenido producto de un estudio de corte transversal con el fin de establecer la prevalencia de base hospitalaria en distrofias musculares, a través de un diseño de muestreo en fases.</p>
Evaluation of COVID-19 antigen rapid diagnostic tests for self-testing in Lesotho and Zambia - Zambia data
<p>Dataset with Zambia data belonging to the publication: Evaluation of COVID-19 antigen rapid diagnostic tests for self-testing in Lesotho and Zambia - Zambia data</p>
Salinity is diagnostic of maximum potential chlorophyll and phytoplankton community structure in an Eastern Boundary Upwelling System
Coastal upwelling ecosystems associated with strong physical stirring exhibit fine-scale hydrographic and biological patchiness. Though many studies have found broad correlations between hydrographic properties (e.g., temperature and salinity) and phytoplankton biomass, we lack a detailed understanding of the underlying mechanisms and how to diagnose patchy distributions. Here, using observational data from coastal waters in the California Current System, we demonstrate that the maximum observed chlorophyll in a water parcel increases with salinity—a conservative water-mass tracer. This relationship arises from sub-euphotic zone nitrate concentrations, which also increase with salinity. Therefore, we can define maximum potential chlorophyll as a function of salinity and nitrate. We show that variations in salinity explain patterns in phytoplankton community structure and discuss how growth, grazing, and light and micronutrient limitation can generate chlorophyll values below the maximum potential. Our mechanistic explanation provides a novel framework for diagnosing biological patchiness using salinity observations.
Accuracy of EFP/AAP 2018 and CDC/AAP 2012 in partial periodontitis diagnostic protocols with 11-12 & 13-14 NHANES periodontal data
<p>Accuracy of EFP/AAP 2018 and CDC/AAP 2012 in partial periodontitis diagnostic protocols with 11-12 & 13-14 NHANES periodontal data</p>
Figures 1–13 in New records and diagnostic notes on large carpenter bees (Hymenoptera: Apidae: genus Xylocopa Latreille), from the Amazon River basin of South America
Figures 1–13. Dorsal habitus photographs of pinned, preserved specimens of females of Xylocopa (Neoxylocopa) species from the Amazon River basin, from the USNM collection. 1) X. (N.) aeneipennis. 2) X. (N.) amazonica. 3) X. (N.) aurulenta. 4) X. (N.) carbonaria. 5) X. (N.) cearensis. 6) X. (N.) fimbriata. 7) X. (N.) frontalis. 8) X. (N.) grisescens. 9) X. (N.) hirsutissima. 10) X. (N.) orthogonaspis. 11) X. (N.) similis. 12) X. (N.) suspecta. 13) X. (N.) tegulata.
Diagnostic electron microscopy of viruses with low-voltage electron microscopes. Raw image files with brief description.
<p>The zipped data container contains the raw (unprocessed) images that we have used for the preparation of our manuscript entiteled:</p> <p>"Diagnostic electron microscopy of viruses with low-voltage electron microscopes" <a href="https://doi.org/10.1369%2F0022155420929438">https://doi.org/10.1369/0022155420929438</a></p> <p>Lars Möller, Gudrun Holland, Michael Laue</p> <p>Advanced Light and Electron Microscopy (ZBS 4), Centre for Biological Threats and Special Pathogens, Robert Koch Institute, D-13353 Berlin, Germany</p> <p>The brief description of the data set comprises the abstract of the manuscript, the figures (including captions) and a description of the materials and methods used for their generation.</p>
Figure 6 in First record of Longosomatidae (Annelida: Polychaeta) from Iceland with a worldwide review of diagnostic characters of the family
Figure 6. Schematic drawings (not to scale) following Laubier et al. (1972–73), showing lateral (above) and dorsal (below) views of four species of Heterospio. Heterospio mediterranea Laubier, Picard and Ramos, 1972–73, Heterospio reducta Laubier, Picard and Ramos, 1972–73, Heterospio peruana Borowski, 1994 and Heterospio angolana Bochert and Zettler, 2009. Arrows mark position of first elongated chaetiger: black, after original description; grey, new interpretation (see text for explanations). Chaetigers numbered below lateral view of each species.
Figure 1 in First record of Longosomatidae (Annelida: Polychaeta) from Iceland with a worldwide review of diagnostic characters of the family
Figure 1. Type localities and collection localities of described and undescribed species of Heterospio, respectively, arranged in ascending order by date of description: (1) Ehlers (1874); (2) Hartman (1944); (3) Knox (1960); (4) Hartman (1965); (5) Wu and Chen (1966); (6) and (7) Laubier et al. (1972–73); (8) Uebelacker (1984); (9) and (10) Borowski (1994); (11) Bochert and Zettler (2009). The BIOICE sampling area is indicated. (*) probably represents a different species.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.