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321 results for “Venus”
Predicted times of bow Shock crossings at Venus from the ESA/Venus Express mission, using spacecraft ephemerides and magnetic field data, with a predictor-corrector algorithm
<p><strong>CHARACTERISTICS</strong><br> Planet: <strong>Venus</strong><br> Radius: <strong>R<sub>V</sub> = 6051.8 km</strong> (volumetric mean planetary radius)<br> Spacecraft: <strong>ESA/Venus Express</strong><br> Spacecraft coordinates system: <strong>Venus Solar Orbital (VSO)</strong> equivalent to <em>Sun-State </em>coordinate system:</p> <ul> <li>+<em>X<sub>VSO</sub></em> points towards the Sun from the planet’s centre,</li> <li>+<em>Z<sub>VSO</sub></em> towards Venus’ North pole and perpendicular to the orbital plane defined as the <em>X<sub>VSO</sub></em>–<em>Y<sub>VSO</sub></em> plane passing through the centre of Venus,</li> <li><em>Y<sub>VSO</sub></em> completes the orthogonal system.</li> </ul> <p>Time span: <strong>01/04/2006 to 25/11/2014</strong><br> Total number N of candidate bow shock crossings in the database: <strong>N = 4950</strong><br> Number of quasi-parallel bow shock crossings: <strong>N<sub>||</sub> = 844</strong><br> Number of quasi-perpendicular bow shock crossings: <strong>N<sub><span class="math-tex">\(\perp\)</span></sub> = 4106</strong></p> <p><strong>ORIGINAL DATASETS USED</strong><br> The original Venus Express/MAG data repository on which these algorithms were applied is available on ESA's Planetary Science Archive system (PSA) at: https://archives.esac.esa.int/psa/ftp/VENUS-EXPRESS/MAG/. For this study, 1-Hz magnetic field data was used.</p> <p><strong>METHOD</strong><br> To construct this database from the original datasets above, the predictor and predictor-corrector algorithms used are described for the Mars case in:<br> Simon Wedlund, C., Volwerk, M., Beth, A., Mazelle, C., Möstl, C., Halekas, J., Gruesbeck, J. and Rojas-Castillo, D., (2021), A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN mission, <em>Journal of Geophysical Research</em>, <strong>127</strong>, e2021JA029942. <a href="https://doi.org/10.1029/2021JA029942">https://doi.org/10.1029/2021JA029942</a></p> <p>They consist of two consecutive steps: </p> <ol> <li>Predictor geometric algorithm based on 2D or 3D existing fits for prediction of the Venus bow shock position. The original fits were taken from 2D conic fits in the plane <span class="math-tex">\(\left(X_\text{VSO}, \sqrt{Y_\text{VSO}^2+Z_\text{VSO}^2}\right)\)</span>performed on the datasets of <strong>Persson et al. (2023)</strong>, Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express, <em>Zenodo</em> (<a href="http://doi.org/10.5281/zenodo.7679677">https://doi.org/10.5281/zenodo.7679677</a>).</li> <li>Corrector algorithm based on magnetic field measurements.</li> </ol> <p>We also provide the angle between the average Interplanetary Magnetic Field (IMF) vector upstream of the shock and the shock normal, noted <span class="math-tex"><em>θ</em><sub><em>B</em><em>n</em></sub></span> (ThetaBn). Assuming a locally smooth shock surface, this gives a first indication of the geometry of the shock, so that:</p> <ul> <li><span class="math-tex">45<sup>∘</sup><<em>θ</em><sub><em>B</em><em>n</em></sub><135<sup>∘</sup></span>: quasi-perpendicular shock condition</li> <li><span class="math-tex"><em>θ</em><sub><em>B</em><em>n</em></sub>≤45<sup>∘</sup> and <em>θ</em><sub><em>B</em><em>n</em></sub><span class="math-tex">\(\geq\)</span>135<sup>∘</sup></span>: quasi-parallel shock condition</li> </ul> <p>Uncertainty on these angles is estimated to be ± 5º. </p> <p>For details, see <strong>Simon Wedlund et al. (2022)</strong> above, §2.3 pp. 10-12.</p> <p><strong>VARIABLES DESCRIPTION</strong></p> <p>This database contains the following ASCII variables:</p> <ul> <li>Bow shock times in Venus Express' database (1-s resolution): <em>T</em><sub>bs</sub></li> <li>Venus Solar Orbital coordinates of the shock, in units of Venus radius <em>R</em><sub>V </sub>(<em>R</em><sub>V</sub> = 6051.8 km):<br> <em>X<sub>VSO</sub></em>,<sub> </sub><em>Y<sub>VSO</sub></em>, <em>Z<sub>VSO</sub></em> and Euclidean distance <span class="math-tex">\(R_{VSO} = \sqrt{X_{VSO}^2 + Y_{VSO}^2 + Z_{VSO}^2}\)</span> (in <em>R<sub>V</sub></em>)</li> <li>Solar Zenith angle in degrees: <em>SZA</em> = <span class="math-tex">\(\tan^{-1}{Y_{VSO}^2+Z_{VSO}^2 \over X_{VSO}^2}\)</span> (in º) </li> <li>Angle between average B-field direction and shock normal assuming a smooth shock surface <span class="math-tex">\(\theta_{Bn}\)</span> (ThetaBn, in º, calculated with atan2(norm(cross(<strong>B</strong>,<strong>ñ</strong>),dot(<strong>B</strong>,<strong>ñ</strong>)), with <strong>B</strong> the magnetic field vector and <strong>ñ</strong> the vector normal to the shock surface): <ul> <li>45 < ThetaBn < 135 deg: quasi-<span class="math-tex">\(\perp\)</span> shock</li> <li>ThetaBn <span class="math-tex">\(\leq\)</span> 45 deg & ThetaBn <span class="math-tex">\(\geq\)</span> 135 deg: quasi-|| shock</li> </ul> </li> <li>Interplanetary Magnetic Field (IMF) upstream average vector in VSO coordinates, <em>B<sub>x</sub></em>, <em>B<sub>y</sub></em>, <em>B<sub>z</sub></em> (in nT).</li> <li>Flag for direction of crossing: <ul> <li>flag = 0: magnetosheath <span class="math-tex">\(\longrightarrow\)</span> solar wind (2447 events)</li> <li>flag = 1: solar wind <span class="math-tex">\(\longrightarrow\)</span> magnetosheath (2503 events)</li> </ul> </li> </ul> <p><strong>WARNING</strong></p> <ol> <li>This version of the database is currently in a preliminary stage of application and, as such, is not fully tested. Solar wind upstream magnetic field values (IMF) are given only as a first approximation for each orbit segment. See point 2 for caveats. For carefully manually picked shock crossings, the user is referred to the database of:<br> <strong>Persson et al. (2023)</strong>, Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express, <em>Zenodo</em> (<a href="http://doi.org/10.5281/zenodo.7679677">https://doi.org/10.5281/zenodo.7679677</a>)</li> <li>This database is based on an automatic statistical geometrical estimate, further refined by constraints on magnetic fields. This is aimed at giving a first approximation of the shock area times in the Venus Express data. It is particularly suited to statistical studies and region identification in the Venus Express datasets. As such, this database should be used as a <em>first indicator</em> of the shock location, and <em>with</em> <em>caution</em>: it <strong>CANNOT</strong>, and <strong>WILL NOT </strong>substitute, especially in case studies, for a careful analysis of the full magnetometer and plasma bow shock signatures. Moreover, the algorithm is optimised for detecting the first disturbance observed in the magnetic field immediately ahead of the shock's foot (in the foreshock area), and not for the detection of other structures in the shock, such as the shock ramp. The "shock" location is therefore given here with typical uncertainties of about 0.040 R<sub>V</sub> (with R<sub>V</sub> = 6051.8 km, i.e., about 250 km in the radial direction). Finally, for multiple shock crossings, the algorithm chooses the first occurrence of the shock starting from the undisturbed solar wind.</li> </ol> <p>Current formatting optimised for MATLAB.</p> <p><strong>ACKNOWLEDGEMENTS</strong><br> C. Simon Wedlund and M. Volwerk thank the Austrian Science Fund (FWF) project P32035-N36. </p> <p><strong>LICENSE AND RIGHTS</strong><br> This database is shared under a Creative Commons CC-BY-4.0 license.</p> <p>Version 1 (c) Cyril Simon Wedlund @ Space Research Institute of Graz (IWF), <br> Austrian Academy of Sciences, 2022-10-05<br> Contact email: cyril.simon.wedlund@gmail.com</p>
Earth - Venus Low-Thrust Optimal Transfers / Database A
<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. </p> <p>This database was generated with a perturbation size of 0.2 and contains 429,316 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format:</p> <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.</p>
Earth - Venus Low-Thrust Optimal Transfers / Database F
<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. </p> <p>This database was generated with a perturbation size of (5.0, 1.0, 1.0, 0.0, 0.0, 0.01) and contains 557,395 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format:</p> <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.</p>
Earth - Venus Low-Thrust Optimal Transfers / Database E
<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. </p> <p>This database was generated with a perturbation size of 20.0 and contains 409,076 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format:</p> <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.</p>
Earth - Venus Low-Thrust Optimal Transfers / Database D
<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. </p> <p>This database was generated with a perturbation size of 5.0 and contains 265,603 trajectories with 128 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format:</p> <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.</p>
Earth - Venus Low-Thrust Optimal Transfers / Database C
<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. </p> <p>This database was generated with a perturbation size of 0.4 and contains 764,479 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format:</p> <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.</p>
Earth - Venus Low-Thrust Optimal Transfers / Database B
<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. </p> <p>This database was generated with a perturbation size of 0.4 and contains 382,193 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format:</p> <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.</p>
Earth - Venus Low-Thrust Optimal Transfers / Database G
<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. </p> <p>This database was generated with a perturbation size of (5.0, 1.0, 1.0, 0.0, 0.0, 0.01) and contains 999,985 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format:</p> <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.</p>
Global map of elastic thickness on Venus
<p>This map is Figure 14 from Anderson, F. S., and S. E. Smrekar (2006), Global mapping of crustal and lithospheric thickness on Venus, J. Geophys. Res., 111, E08006, doi:10.1029/2004JE002395. The location labels have been removed. Estimates of elastic thickness have an error of ±10 to 15 km. Caveats in Anderson and Smrekar (2006) should be carefully understood prior to use. No value of elastic thickness was obtained in areas in white. </p>
BepiColombo magnetic field data (MPO-MAG) from the two Venus flybys
<p> </p> <p>Vector magnetic field data from the first two Venus flybys (highres and 1-sec res).</p> <p>*Data will be properly archived on ESA's PSA, once the data has been finalized and/or cleaned*</p> <p>Reference frame: VSO</p> <p>Trajectory information is also included. </p>
Divergent Evolution of Earth and Venus—Transparent Version
<p>The theory of planetary heat pipes now tells us Venus and Earth may follow separate ways even though they shared a similar beginning. In the context of exoplanet studies, this means rocky planets with a hot surface—or with a thick atmosphere that acts like a blanket—may not exhibit plate tectonics. Also read: <a href="https://doi.org/10.1029/2022GL100987">https://doi.org/10.1029/2022GL100987</a></p>
Simulation Data for the article 'The Influence of Cloud Condensation Nucleus (CCN) Coagulation on the Venus Cloud Structure'
<p>This dataset contains the NetCDF output files from simulations using PlanetCARMA in support of the work published in the manuscript, "The Influence of Cloud Condensation Nucleus (CCN) Coagulation on the Venus Cloud Structure." A summary of the included NetCDF data is found in the README file that is part of the data object. The submission version of this dataset contains only those simulations that provided data that were discussed in the accepted final manuscript. However, additional simulations were carried out in the course of the work, and are described in the manuscript. Upon request, the authors will revise this data repository by adding such data products from among that list as may be requested by others.</p>
Venus visco-elastic Love numbers
<p>Supporting software and data for the manuscript <strong>Constraining the Venus interior structure with future VERITAS measurements of the gravitational atmospheric loading </strong></p> <p>https://doi.org/10.3847/PSJ/acc73c</p> <p> </p> <p>See README.pdf for additional details. </p>
Striped venus clam (Chamelea gallina) abundance, size, and biomass off Bevano River mouth (2019)
<p>This dataset provides the abundance (ind. m<sup>-2</sup>) of the striped venus clams, <em>Chamelea gallina</em> (Linnaeus, 1758), at 71 random sampling points (Fig. 3) from 0.5 to 8 m depth along the coast (5 km) off the NATURA 2000 site IT4070009 "Ortazzo, Ortazzino e Foce del Torrente Bevano", sampled from 22 May to 4 July, 2019. Where available, the mean and standard deviation of shell length (i.e. the maximum distance between anterior and posterior margins) of <em>Chamelea gallina </em>determined to the nearest 0.01 mm using a manual calliper, and the wet biomass per square metre (g m<sup>-2</sup>), estimated on the basis of the mean shell length, by length–weight relationship (according to <a href="https://doi.org/10.1080/24750263.2019.1668066">Petetta et al., 2019</a>), were provided. Depth, sediment grain size, and organic matter at each point are also provided. </p> <p>The dataset is provided in three formats: </p> <ul> <li>Microsoft Excel XLSX file, including 3 sheets (Dataset, Fields and units, Parameters) </li> <li>CSV files (UTF-8), 3 files corresponding to the 3 sheets of the Excel file </li> <li>ESRI Shapefile (UTF-8, geometry point, EPSG:4326 - WGS 84)</li> </ul> <p> </p> <p>The dataset includes 71 records, one for each sampling point, and 17 fields, which are described in the Excel sheet/CSV file “Fields and units” (see also Table 6). The Excel sheet/CSV file provides details and coefficients of the length–weight relationship (log W= log a + b log L; where: W=wet weight (g), L=length (mm), Log base=10) used to estimate the wet biomass from the mean lengths and abundances (the calculation formulas are present in the Excel sheet).</p> <p>Finally, ESRI Shapefile provides users with direct upload in any Geographic Information System (GIS). Nevertheless, due to this file format limitations, dataset field names have been truncated and/or renamed to fit 10 characters.</p> <p>Fields in the dataset (NA=not available).</p> <table> <thead> <tr> <th scope="col"> <p><strong>Field</strong></p> </th> <th scope="col"> <p><strong>Darwin Core term</strong></p> </th> <th scope="col"> <p><strong>Unit</strong></p> </th> <th scope="col"> <p><strong>Precision</strong></p> </th> <th scope="col"> <p><strong>Note</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>locationID</p> </td> <td> <p>locationID</p> </td> <td> <p>NA</p> </td> <td> <p>NA</p> </td> <td> <p>Sampling location identifier (ID) specific to the data set</p> </td> </tr> <tr> <td> <p>samplingDate</p> </td> <td> <p>eventDate</p> </td> <td> <p>YYYY-MM-DD</p> </td> <td> <p>NA</p> </td> <td> <p>Conforms to ISO 8601-1:2019</p> </td> </tr> <tr> <td> <p>samplingTime</p> </td> <td> <p>eventTime</p> </td> <td> <p>HH:MM</p> </td> <td> <p>± 10 min</p> </td> <td> <p>Central European Summer Time CEST (UTC+2) conforms to ISO 8601-1:2019</p> </td> </tr> <tr> <td> <p>decimalLatitude</p> </td> <td> <p>decimalLatitude</p> </td> <td> <p>decimal degrees</p> </td> <td> <p>± 0.00001</p> </td> <td> <p>WGS84 (EPSG: 4326) - WAAS/EGNOS enabled GPS position</p> </td> </tr> <tr> <td> <p>decimalLongitude</p> </td> <td> <p>decimalLongitude</p> </td> <td> <p>decimal degrees</p> </td> <td> <p>± 0.00001</p> </td> <td> <p>WGS84 (EPSG: 4326) - WAAS/EGNOS enabled GPS position</p> </td> </tr> <tr> <td> <p>Depth</p> </td> <td> <p>maximumDepthInMeters</p> </td> <td> <p>m</p> </td> <td> <p>± 0.1</p> </td> <td> <p>Mean Lower Low Water - measured with echosounder or depth gauge corrected by tide gauge of Porto Corsini (RA)</p> </td> </tr> <tr> <td> <p>SamplingGear</p> </td> <td> <p>NA</p> </td> <td> <p>NA</p> </td> <td> <p>NA</p> </td> <td> <p>Van Veen grab operated from boat or bailer manually operated by diver inside a cylindrical frame</p> </td> </tr> <tr> <td> <p>SamplingArea</p> </td> <td> <p>NA</p> </td> <td> <p>m2</p> </td> <td> <p>± 0.001</p> </td> <td> <p>Sampler size</p> </td> </tr> <tr> <td> <p>Mud</p> </td> <td> <p>NA</p> </td> <td> <p>% dry mass</p> </td> <td> <p>± 0.1%</p> </td> <td> <p>Sediment particles <63 µ wet sieved recovered on Whatman filter paper and then dried at 80°C for 24 hours before weighing at ± 0.00001 g</p> </td> </tr> <tr> <td> <p>FineSand</p> </td> <td> <p>NA</p> </td> <td> <p>% dry mass</p> </td> <td> <p>± 0.1%</p> </td> <td> <p>Sediment particles 250-63 µ wet sieved recovered on Whatman filter paper and then dried at 80°C for 24 hours before weighing at ± 0.00001 g</p> </td> </tr> <tr> <td> <p>MediumSand</p> </td> <td> <p>NA</p> </td> <td> <p>% dry mass</p> </td> <td> <p>± 0.1%</p> </td> <td> <p>Sediment particles >250 µ wet sieved recovered on Whatman filter paper and then dried at 80°C for 24 hours before weighing at ± 0.00001 g</p> </td> </tr> <tr> <td> <p>OrganicMatter</p> </td> <td> <p>NA</p> </td> <td> <p>% dry mass</p> </td> <td> <p>± 0.1%</p> </td> <td> <p>Sediment organic matter content obtained by Loss of weight on Ignition (LOI%) at 450°C 8h and weighted at ± 0.00001 g</p> </td> </tr> <tr> <td> <p>Individuals</p> </td> <td> <p>NA</p> </td> <td> <p>ind. sample-1</p> </td> <td> <p>± 1</p> </td> <td> <p>Individuals of <em>Chamelea gallina </em>retrieved in each sample, preserved in alcohol sorted and classified under microscope</p> </td> </tr> <tr> <td> <p>Abundance</p> </td> <td> <p>NA</p> </td> <td> <p>ind. m-2</p> </td> <td> <p>± 10</p> </td> <td> <p>Abundance of <em>Chamelea gallina</em> per square meter estimated on the basis of the sampling area</p> </td> </tr> <tr> <td> <p>MeanLength</p> </td> <td> <p>NA</p> </td> <td> <p>mm</p> </td> <td> <p>± 0.01</p> </td> <td> <p>Mean shell length (i.e. the maximum distance between anterior and posterior margins) of <em>Chamelea gallina</em> determined to the nearest 0.01 mm using a manual calliper</p> </td> </tr> <tr> <td> <p>SDLength</p> </td> <td> <p>NA</p> </td> <td> <p>mm</p> </td> <td> <p>± 0.01</p> </td> <td> <p>Standar deviation of mean shell length of <em>Chamelea gallina</em></p> </td> </tr> <tr> <td> <p>WetMass</p> </td> <td> <p>NA</p> </td> <td> <p>g m-2</p> </td> <td> <p>± 1</p> </td> <td> <p>Wet biomass per square meter of <em>Chamelea gallina</em> estimated on the basis of the mean shell length, by length–weight relationship (according to <a href="https://doi.org/10.1080/24750263.2019.1668066">Petetta et al., 2019</a> DOI:10.1080/24750263.2019.1668066), and abundnce</p> </td> </tr> </tbody> </table> <p> </p> <p>This dataset comes from the project "Characterization of the mouth area of the Bevano River and identification of strategies for the conservation and enhancement of nursery areas for protected species of commercial interest", carried out by the Interdepartmental Research Center for Environmental Sciences (CIRSA) of the Alma Mater Studiorum University of Bologna. The project was financed by the Emilia-Romagna Region (call FLAG Costa dell'Emilia-Romagna 2018) with funds from the European Union (FEAMP 2014/2020, Action 2.A.a, "Marine and lagoon habitats - Studies and research"), and took place from January to August 2019 (<a href="https://doi.org/10.5281/zenodo.4016598">Abbiati et al., 2019</a>). Finally, this dataset has been revised and completed within the project “Ecosystem for Sustainable Transition in Emilia-Romagna” (ECOSISTER, Code: ECS_00000033 - CUP: B33D21019790006).</p> <p> </p>
Venus coronae topographic (a)symmetry classification (from Gülcher et al., 2023, JGR Planets)
<p>This is a PDF file of the coronae classification that accompanies the manuscript "<strong>Tectono-magmatic evolution of asymmetric coronae on Venus: Topographic classification and 3D thermo-mechanical modeling</strong>" by Gülcher et al. (2023) in <i>Journal of Geophysical Research: Planets</i>, 128, e2023JE007978, <a href="https://doi.org/10.1029/2023JE007978">https://doi.org/10.1029/2023JE007978</a><i> </i><br><br>This database consists of the 150 largest coronae (those with a diameter equal to or larger than 300 km) in the publicly available Venusian coronae nomenclature database (USGS Planetary Nomenclature, (<a href="https://planetarynames.wr.usgs.gov/Page/VENUS/target"><i>https://planetarynames.wr.usgs.gov/Page/VENUS/target</i></a>) and the database of Stofan et al. (1992, <i>JGR, </i><a href="https://doi.org/10.1029/92je01314">https://doi.org/10.1029/92je01314</a>) combined, and five additional smaller coronae. The (a)symmetry of these coronae is defined based on the topographic features (e.g., troughs, rims, rises) and their variability across the coronae. For further information on this classification, please see the main paper. The global distribution of this classification is illustrated in Figure 1 in the main paper and Figure S1 in the Supplementary Information SI1. </p>
[Dataset] Spatial and temporal variability of the 365-nm albedo of Venus observed by the camera on board Venus Express
<p>This is the derived data, presented in a publication entitled "Spatial and temporal variability of the 365-nm albedo of Venus observed by the camera on board Venus Express" (JGR:Planet, doi: 10.1029/2019JE006271). See the paper for details. See 'Readme.txt' for the file descriptions.</p>
Spherical harmonic model of the planet Venus: VenusTopo719
<p><strong>VenusTopo719.shape</strong> is a spherical harmonic model of the shape of the planet Venus. This model makes use of 4-pi normalized spherical harmonic functions that exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The description of how this spherical harmonic model was constructed can be found in Wieczorek (2015).</p>
Jupiter, Venus, Moon Conjunction
<p>Honorable mention in the 2023 IAU OAE Astrophotography Contest, category Still images with smartphones-mobile devices: Jupiter, Venus, Moon Conjunction, by Joslynn Appel.</p> <p>Captured with a smartphone in February 2023, over the skies of Luzerne County, Pennsylvania, USA, this photograph offers a glimpse into a conjunction, an enthralling astronomical phenomenon that occurs when two or more celestial objects are seen in close proximity in the sky from our perspective, despite the objects not being physically near to each other. In this image, the brilliance of Jupiter (top), the allure of Venus (middle), and the familiar glow of our Moon (bottom) dance together against a backdrop of delicate clouds and a treeline silhouette, making it a moment worth treasuring.</p> <p>Credit: Joslynn Appel/IAU OAE (<a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC BY 4.0</a>)</p> <p> </p>
Phases of Venus
<p>First place winner in the 2023 IAU OAE Astrophotography Contest, category Still images of phases of Venus: Phases of Venus, by Stephane Gonzales</p> <p>This is a mesmerising series of images of Venus captured from Surgères, Charente-Maritime, France, over a period of six months in 2015. The phases appear similar to the phases we see of our own Moon and occur for similar reasons. Only half of Venus is illuminated by the Sun and, from Earth, we can sometimes only see part of that illuminated half, depending on the relative positions of the Sun, Earth and Venus. Both Mercury and Venus exhibit phases because their orbit is between the Sun and the orbit of Earth. Depending on the position of Venus relative to the Sun and Earth, Venus goes through its phases over a period of time. This sequence of images beautifully showcases the transition from the ‘gibbous’ to the slender crescents. The use of infrared filters helped to capture Venus's dense perpetual cloud cover during daylight in sharp detail, providing a glimpse into the mysterious nature of the planet’s atmosphere.</p> <p>Credit: Stephane Gonzales/IAU OAE (<a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC BY 4.0</a>)</p> <p> </p>
Venus and Mercury Trails
<p>Honorable mention in the 2023 IAU OAE Astrophotography Contest, category Still images of phases of Venus: Venus and Mercury Trails, by Marcella Giulia Pace.</p> <p>In this composite image, both Mercury (left) and Venus (right) can be seen heading into the sunset. The phases of each are beautifully captured as they descend. Not all planets or moons in the Solar System show phases as viewed from Earth. This phenomenon occurs because the orbits of Venus and Mercury are positioned between Earth’s orbit and the Sun, sometimes allowing us to see only part of the illuminated portion of each planet. These phases are similar to the phases we see of our own Moon.</p> <p>Credit: Marcella Giulia Pace (<a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC BY 4.0</a>)</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.