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1,880 results for “Mars”
Reconstructed high-rate SEIS data recorded during HP3 hammering from the NASA InSight mission to Mars
<p>The NASA InSight lander successfully placed a seismometer on the surface of Mars. Alongside, a hammering device was deployed that penetrated into the ground to attempt the first measurements of the planetary heat flow of Mars. The hammering of the heat probe generated repeated seismic signals that were registered by the seismometer. However, the broad frequency content of the seismic signals generated by the hammering extends beyond the Nyquist frequency governed by the seismometer's sampling rate of 100 samples per second. Here, we provide data that was reconstructed at a higher sampling rate of 2000 samples per second using a dedicated de-aliasing algorithm described in the accompanying article. This archive will be updated regularly with new data acquired on Mars. </p> <p>For a detailed data description and instructions on how to cite this dataset, please refer to the README file. </p>
LTER-Italy site Mar Piccolo of Taranto figure
<p>Geographical representation of the LTER-Italy site Mar Piccolo of Taranto (LTER_EU_IT_095) - DEIMS-ID <a href="https://deims.org/ede24c6e-9cf2-4cf8-8bf7-36ba327403b4">https://deims.org/ede24c6e-9cf2-4cf8-8bf7-36ba327403b4</a></p>
LTER-Italy site Comune Fontainemore, Riserva Naturale Mont Mars (MARS) figure
<p>Geographical representation of the LTER-Italy site Comune Fontainemore, Riserva Naturale Mont Mars (MARS) (LTER_EU_IT_075) - DEIMS-ID <a href="https://deims.org/46a11350-4bd3-4f97-874f-bfc012305633">https://deims.org/46a11350-4bd3-4f97-874f-bfc012305633</a></p>
A vertically-resolved atmospheric dust reanalysis for Mars Years 28-29 using Analysis Correction
<p>This is a dataset of meteorological variables for the atmosphere of Mars, obtained by assimilating measurements (retrievals) of atmospheric temperature and dust opacity into a 3-dimensional, time-dependent numerical model of the Martian atmospheric circulation (known as a “reanalysis”).</p> <p>The observations come from two spacecraft - the Mars Climate Sounder (MCS) instrument on board NASA’s Mars Reconnaissance Orbiter (e.g. Kleinboehl et al. 2009) and the Thermal Emission Imaging Spectrometer (THEMIS) on board NASA’s Mars Odyssey spacecraft, and cover the period from 21 September 2006 until 5 November 2009 (Mars Years 28:Ls=109.98 - 30:Ls=4.78). MCS observations include profiles of temperature and dust opacity from near the surface up to altitudes of around 80 km obtained from infrared limb-sounding (MCS version 3 retrievals, based on opacities at around 21.6 micron wavelengths), while THEMIS measurements are of column dust opacity in the infrared (centred around 9.3 micron wavelength). Further details can be found on the websites</p> <p>https://pds-geosciences.wustl.edu/missions/odyssey/themis.html,<br> https://atmos.nmsu.edu/data and services/atmospheres data/MARS/aerosols.html</p> <p>The model into which the observations are assimilated is the UK version of Laboratoire de Météorologie Dynamique Mars Global Circulation Model (LMDMGCM), a 3-dimensional, time-dependent numerical circulation model of the Martian atmosphere and near-surface environment, simulating the changing winds, temperature, pressure and dust content of the atmosphere across the whole planet. The model solves the equations of motion, mass and energy conservation using a spherical harmonic representation in the horizontal and finite difference formulation in the vertical direction, but outputs the data here on a regular longitude-latitude grid with 72 points in longitude, 36 points in latitude and 25 terrain-following sigma levels in the vertical direction (where sigma = pressure/surface pressure) on a stretched vertical grid that extends from the surface to an altitude of approximately 100 km. More details can be found in publications by Forget et al. (1999), Newman et al. (2001), Mulholland et al. (2013).</p> <p>The observations and model are linked by an assimilation scheme, based on the Analysis Correction (AC) algorithm developed by Lorenc et al. (1991) and adapted for Mars by Lewis et al. (2007). Previous reanalyses of Mars observations using this scheme include the MACDA dataset (Montabone et al. 2014) and OPENMars (Holmes et al. 2020). This new dataset, however, makes use of an extension of the AC scheme to enable assimilation of both column integrated dust opacity measurements and dust opacity profiles in the vertical direction (see Ruan et al. 2021). This new dataset therefore provides a more realistic representation of the distribution of dust loading in the Martian atmosphere than previous work, which may also result in improved representation of other meteorological variables, notably temperature.</p> <p>Data are provided as 2D and 3D fields of variables in netCDF format as generated by the numerical model on the (longitude, latitude, sigma) grid at 2-hourly intervals. Each file contains 360 time steps covering 30 Martian days or sols. The variables contained in each file are as follows:</p> <p> Variables and attributes<br> 0 lon: FLOAT(72) = FLOAT(lon)<br> 0 long_name: longitude<br> 1 units: degrees_east<br> 1 lat: FLOAT(36) = FLOAT(lat)<br> 0 long_name: latitude<br> 1 units: degrees_north<br> 2 sigma: FLOAT(25) = FLOAT(sigma)<br> 0 long_name: sigma<br> 1 units: sigma_level = p/ps<br> 3 soil: FLOAT(18) = FLOAT(soil)<br> 0 long_name: soil levels (i.e. levels below the surface to represent thermal variations)<br> 1 units: none<br> 4 time: FLOAT(360) = FLOAT(time)<br> 0 long_name: model time<br> 1 units: days since 00:00:00 (the beginning of the file)<br> 5 controle: FLOAT(100) = FLOAT(lentable)<br> 0 long_name: Table of run parameters<br> 1 description: MGCM run 5.000<br> 6 Ls: FLOAT(360) = FLOAT(time)<br> 0 Physics_diagnostic: Solar longitude (such that Ls=0 is northern Spring equinox)<br> 1 units: deg<br> 7 tsurf: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: Surface temperature<br> 1 units: K<br> 8 ps: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: surface pressure<br> 1 units: Pa<br> 9 co2ice: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: co2 ice thickness (column mass density)<br> 1 units: kg.m-2<br> 10 fluxsurf_lw: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: fluxsurf_lw (surface infrared radiative flux)<br> 1 units: W.m-2<br> 11 fluxsurf_sw: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: fluxsurf_sw (surface visible radiative flux)<br> 1 units: W.m-2<br> 12 temp: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: temperature<br> 1 units: K<br> 13 u: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: Zonal (east-west) wind<br> 1 units: m.s-1<br> 14 v: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: Meridional (north-south) wind<br> 1 units: m.s-1<br> 15 rho: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: density<br> 1 units: kg.m-3<br> 16 udrag: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: Drag velocity<br> 1 units: m/s<br> 17 udragt: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: Threshold velocity for dust lifting<br> 1 units: m/s<br> 18 aerosol: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: dust opacity considering layer thickness<br> 1 units: SI (opacity/m)<br> 19 taudustvis: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: Dust optical depth<br> 1 units: SI<br> 20 q01: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: mix. ratio<br> 1 units: kg/kg<br> 21 dqsdevtot: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: dust devil lift rate<br> 1 units: kg.m-2.s-1<br> 22 dqsstrtot: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: near surface wind stress dust lifting rate<br> 1 units: kg.m-2.s-1<br> 23 dqssedtot: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: dust sedimentation rate<br> 1 units: kg.m-2.s-1</p> <p> </p>
Vent-Proximal Deposits South of Ascraeus Mons, Mars
<p>The data repository contains the mapping shapefiles of geological units, data pertaining to the thickness measurements of the vent-proximal deposits, and Excel sheets that allow to reproduction of the spatter rampart deposits investigation south of Ascraeus Mons, Mars. The mapping and thickness measurements were conducted based on the CTX stereo-pair images P02_001774_1848, centered at 4.86°N, 254.60°E, and B01_009949_1844, centered at 4.46°N, 254.62°E, from which the digital elevation model was produced using MarsSI system (Mars System of Information) designed by Quantin-Nataf et al. (2018). To calculate vertical errors on elevations, we used an additional CTX-based DEM (B01_009949_1844 centered at 4.46°N, 254.62°E, and F02_036427_1847, centered at 4.80°N, 254.61°E).</p>
Spherical harmonic models of the shape of Mars [MOLA]
<p>This archive contains four spherical harmonic models of the shape of Mars truncated at different maximum spherical harmonic degrees. The highest resolution model has a maximum spherical harmonic degree of 5759, which was generated from a Mars shape model sampled at 64 pixels per degree.</p> <p>The data used to generate these models are from the MOLA instrument on the Mars Global Surveyor spacecraft, as found in the files <code>MEGR00N000GB.IMG</code>, <code>MEGR00N180GB.IMG</code>, <code>MEGR90N000GB.IMG</code> and <code>MEGR90N180GM.IMG</code> on <a href="https://pds-geosciences.wustl.edu/mgs/mgs-m-mola-5-megdr-l3-v1/mgsl_300x/meg064/">NASA's PDS website</a>. These four image files were first converted to netcdf format using the <a href="https://www.generic-mapping-tools.org/">generic-mapping-tools</a> function <code>xyz2grd</code>, and then turned into a single file using the function <code>grdpaste</code>. The resulting pixel registed map was then converted to a gridline registration using the function <code>grdsample</code>. Following this, the resulting netcdf file was read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software and expanded into spherical harmonics using the function <code>SHCoeffs.expand()</code>. The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The units of the coefficients are meters.</p> <p>The four files in this archive are</p> <ul> <li>Mars_MOLA_shape_5759.bshc.gz</li> <li>Mars_MOLA_shape_2879.bshc.gz</li> <li>Mars_MOLA_shape_1439.bshc.gz</li> <li>Mars_MOLA_shape_719.bshc.gz</li> </ul> <p>The numbers 5759, 2879, 1439, and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 64, 32, 16, and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution models were generated by truncating the spherical harmonic coefficients of the highest resolution model.</p> <p>These models supercede <a href="../records/3870922">Spherical harmonic model of the shape of Mars: MarsTopo2600</a> and <a href="../records/6475460">Spherical harmonic model of the shape of Mars: MarsTopo719</a>.</p>
Martian crater ages and crater counting - Does the impact flux of small and large asteroids varied through time on Mars, the Earth and the Moon?
<ul> <li>The SM_mars_crater_dating.xlsx table contains all the information used to date the 49 martian impact craters considered in this study (< 600 Ma). </li> </ul> <ol> <li>CRATER ID </li> <li>CRATER NAME</li> <li>DIAM KM </li> <li>LAT </li> <li>LONG </li> <li>DEPTH RIM KM </li> <li>DEPTH SURF KM </li> <li>DEPTH FLOOR KM </li> <li>NUMBER LAYER</li> <li>MORPHO EJECTA </li> <li>PRESERVATION </li> <li>COUNT AREA KM2: counting area from ejecta banket mapping </li> <li>COUNT AREA ASCI* KM2: counting area after removal of surfaces contaminated by secondary craters </li> <li>THRESHOLD AREA KM2: minimum size of Voronoi polygon area below which all associated detected craters are considered of secondary origin</li> <li>NB SEC: number of secondary craters dentified by ASCI </li> <li>PERCENT SEC</li> <li>NB CRAT 100M: total number of craters > 100 m detected by the CDA** on the CTX global mosaic*** over the counting area</li> <li>NB PRIM 100M: number of craters identified as primaries by ASCI</li> <li>TURNOFF DIAM KM: minimum crater diameter used to fit the crater-size frequency distribution (CSFD) with an isochron</li> <li>NB CRAT FIT: number of craters used to fit the CSFD with an isochron</li> <li>AGE GA: model age based on Hartmann (2005) chronology model**** and Michael et al. (2016) fitting technique*****</li> <li>AGE MAX GA</li> <li>AGE MIN GA</li> <li>N(1): equivalent number of accumulated craters >1km per km2</li> <li>N(1) MAX</li> <li>N(1) MIN</li> </ol> <p>*ASCI: Automatic Secondary Crater Identification: A. Lagain, K. Servis, G. K. Benedix, C. Norman, S. Anderson, P. A. Bland, Model Age Derivation of Large Martian Impact Craters, Using Automatic Crater Counting Methods, Earth and Space Science 8 (2) (2021). doi:10.1029/2020EA001598.</p> <p>**CDA: Crater Detection Algorithm: G. K. Benedix, A. Lagain, K. Chai, S. Meka, S. Anderson, C. Norman, P. A. Bland, J. Paxman, M. C. Towner, T. Tan, Deriving Surface Ages on Mars Using Automated Crater Counting, Earth and Space Science 7 (3) (2020). doi:10.1029/2019EA001005.</p> <p>*** CTX global mosaic: Context Camera global mosaic: J. L. Dickson, L. A. Kerber, C. I. Fassett, B. L. Ehlmann, A Global, Blended CTX Mosaic of Mars with Vectorized Seam Mapping: A New Mosaicking Pipeline Using Principles of Non-Destructive Image Editing, in: Lunar and Planetary Science Conference (2018), p. 2480.</p> <p>**** W. K. Hartmann, Martian cratering 8: Isochron refinement and the chronology of Mars, Icarus 174 (2) (2005) 294–320. doi:10.1016/j.icarus.2004.11.023.</p> <p>***** G. G. Michael, T. Kneissl, A. Neesemann, Planetary surface dating from crater size-frequency distribution measurements: Poisson timing analysis, Icarus 277 (2016) 279–285. doi:10.1016/j.icarus.2016.05.019.</p> <ul> <li>The crater_counting.csv table contains the location and size of impact craters used to derive the ages of the 49 craters younger than 600 Ma old presented in this study. </li> </ul>
Unit Map and Dips/Strikes of Sakarya Vallis, Gale Crater, Mars
<p>This dataset comprises the unit map derived from 3D analysis of Sakarya Vallis in Gale crater, Mars. The units are named Package 1–7. These packages have been extrapolated from morpho-stratigraphic analysis of a HiRISE scene in PRo3D. They have been further extrapolated using underlying image data. Included here is a shapefile representing the marker bed (Milliken et al. 2010) in Gale crater. The "CDD" refers to the Central Debris Deposit, identified by Hughes (2021).</p> <p>The structural data represents dip measurements along the boundaries of these packages within the feature; the "sub-package" data represent layering within the packages. For more on how dip is calculated in PRo3D, see https://pro3d.space/.</p> <p>Finally, the profiles mark the locations where topographic profiles were extracted for constructing cross-sections, as discussed in the thesis Persaud (2022).</p> <p>These data are intended to be displayed with the HiRISE ORI (https://doi.org/10.5281/zenodo.5808371) and CTX ORI mosaic (https://doi.org/10.5281/zenodo.5808357) over Sakarya Vallis, and over the basemap over the northwest of Aeolis Mons (https://doi.org/10.5281/zenodo.5808381).</p> <p>Format: SHP, SHX, DBF, PRJ, QPJ<br> Projection: Equidistant cylindrical<br> Datum: Spheroid (r = 3396.190 km)</p> <p>N.B. the PROJ4 format of the project is "+proj=eqc +lat_ts=0 +lat_0=0 +lon_0=0 +x_0=0 +y_0=0 +a=3396190 +b=3396190 +units=m +no_defs"</p>
Multi-Resolution Basemap of Northwest Aeolis Mons, Gale Crater, Mars
<p>This multi-resolution, multi-spectral basemap comprises HiRISE, CTX, MOC-NA, and CRISM imagery over the northwest of Aeolis Mons in Gale crater, Mars, to complement the HiRISE and CTX data over Sakarya Vallis (Persaud 2021, https://doi.org/10.5281/zenodo.5808371; Persaud et al. 2021, https://doi.org/10.5281/zenodo.5808357). All of the products have been georeferenced to the CTX ORI mosaic (Persaud et al. 2021) and/or each other using QGIS and GDAL; their IDs, resolutions, and sources prior to this alignment are listed below.</p> <p>The greyimage ORIs produced in this work were generated using the Ames Stereo Pipeline. The other HiRISE ORI were selected based on the data used by the USGS to make the MSL orthophoto mosaic (Calef III and Parker 2016, https://astrogeology.usgs.gov/search/map/Mars/MarsScienceLaboratory/Mosaics/MSL_Gale_Orthophoto_Mosaic_10m_v3).</p> <p>The four CRISM images were processed using the CRISM Analysis Tool (CAT) in ENVI (Morgan et al. 2009) following the workflow described in Campbell and Muller (2017), adapted from Seelos (2009). Four RGB products were assembled from one CRISM image over Sakarya Vallis using the spectral library from Viviano-Beck et al. (2014); the band assignments are listed below.</p> <p>Format: GeoTiff<br> Projection: Equidistant cylindrical<br> Datum: Spheroid (r = 3396.190 km)</p> <p>HiRISE (0.25 m/px)</p> <ul> <li>ESP_012340_1750_RED, this work</li> <li>PSP_009149_1750_RED, this work</li> <li>ESP_018854_1755_RED, this work</li> <li>ESP_019698_1750_RED, U. Arizona</li> <li>ESP_012551_1750_RED, U. Arizona</li> <li>ESP_024300_1755_RED, U. Arizona</li> <li>PSP_001488_1750_RED, U. Arizona</li> <li>PSP_009650_1755_RED, U. Arizona</li> </ul> <p>HiRISE RGB (0.50 m/px):</p> <ul> <li>ESP_061750_1750_RGB, U. Arizona (mosaicked in this work, "Mont Mercou")</li> <li>ESP_069031_1750_MRGB, U. Arizona (mosaicked in this work, "Mont Mercou")</li> <li>PSP_009149_1750_MRGB, U. Arizona (mosaicked in this work, "Mont Mercou")</li> <li>ESP_029034_1750_MRGB, U. Arizona</li> </ul> <p>MOC-NA (NASA/JPL/MSSS):</p> <ul> <li>S2200845 (2.2 m/px)</li> <li>R1100953 (6.75 m/px)</li> </ul> <p>CRISM, FRT000095ee (18 m/px):</p> <ul> <li>Hydrated mineralogy <ul> <li>Red: SINDEX2</li> <li>Green: BD2100_2</li> <li>Blue: BD1900_2</li> </ul> </li> <li>Hydrated sulphates (1) <ul> <li>Red: SINDEX2</li> <li>Green: BD1900R2</li> <li>Blue: HCPINDEX3</li> </ul> </li> <li>Mafics <ul> <li>Red: LCPINDEX3</li> <li>Green: HCPINDEX3</li> <li>Blue: OLINDEX3</li> </ul> </li> <li>Hydrated sulphates (2): SINDEX2</li> </ul> <p> </p>
Co-registered U. Arizona HiRISE DTM and ORI over Sakarya Vallis, Gale Crater, Mars
<p>Local digital terrain model (DTM) and orthorectified image (ORI) of Sakarya Vallis, west of Aeolis Mons in Gale crater, Mars. The DTM was originally processed by the University of Arizona (DTEEC_006855_1750_007501_1750_A01, https://www.uahirise.org/dtm/dtm.php?ID=PSP_006855_1750); this product is co-registered to CTX DTMs which were themselves co-registered to HRSC DTMs (Persaud et al. 2021, https://doi.org/10.5281/zenodo.5808357) using Ames Stereo Pipeline. The ORI was processed using Ames Stereo Pipeline and adjusted with GDAL.</p> <p>Format: GeoTiff<br> Projection: Equidistant cylindrical<br> Datum: Spheroid (r = 3396.190 km)<br> Bit depth: Float32<br> DTM grid-spacing: 1 m/pixel<br> ORI resolution: 0.25 m/pixel</p> <p>Stereo pairs (from University of Arizona): PSP_006855_1750_RED, PSP_007501_1750_RED</p> <ul> </ul> <p>Image ID of the ORI: PSP_007501_1750_RED</p>
CTX DTM and ORI Mosaics over Sakarya Vallis, Gale Crater, Mars
<p>Local digital terrain model (DTM) and orthorectified image (ORI) mosaics over Sakarya Vallis, west of Aeolis Mons in Gale crater, Mars. The two constituent DTMs were processed using the CASP-GO suite described in Tao et al. (2018); the ORIs were processed using Ames Stereo Pipeline. The DTMs were then co-registered to an HRSC DTM mosaic (Persaud et al. 2021, https://doi.org/10.5281/zenodo.5808354) and each other using Ames Stereo Pipeline, and then cropped and mosaicked.</p> <p>Format: GeoTiff<br> Projection: Equidistant cylindrical<br> Datum: Spheroid (r = 3396.190 km)<br> Bit depth: Float32<br> DTM grid-spacing: 18 m/pixel<br> ORI resolution: 6 m/pixel</p> <p>Stereo pairs (from Grindrod and Davis, 2018):</p> <ul> <li>P04_002675_1746_XI_05S222W, B21_017786_1746_XN_05S222W</li> <li>D02_027834_1748_XN_05S222W, G04_019698_1747_XI_05S222W</li> </ul> <p>Image IDs of the ORIs: P04_002675_1746_XI_05S222W, D02_027834_1748_XN_05S222W</p>
30-m HRSC DTM Mosaic of Gale Crater, Mars
<p>Digital terrain model (DTM) mosaic of Gale crater, Mars, processed from High-Resolution Stereo Camera (HRSC) stereo images using the modification of DLR-VICAR described by Kim and Muller (2009).</p> <p>Format: GeoTiff<br> Projection: Equidistant cylindrical<br> Datum: Spheroid (r = 3396.190 km)<br> Bit depth: Float32<br> Grid-spacing: 30 m/pixel<br> Terrain reference: 200-m MOLA and HRSC blended global DTM (Fergason et al. 2018)</p> <p>HRSC source images: H1938_0000, H1927_0000, and H1916_0000</p>
EXTREMA: Ballistic capture sets at Mars over an Earth–Mars synodic period from January 1, 2030, to February 20, 2032
<p>EXTREMA (short for Engineering Extremely Rare Events in Astrodynamics for Deep-Space Missions in Autonomy) enables self-driving spacecraft, challenging the current paradigm under which spacecraft are piloted in the interplanetary space. Deep-space guidance, navigation, and control applied in a complex scenario is the subject of EXTREMA, which wants to engineer ballistic capture in a totally autonomous fashion. EXTREMA is erected on three pillars. Pillar 1 is on autonomous navigation. Pillar 2 involves autonomous guidance and control. Pillar 3 deals with autonomous ballistic capture, the focus of this work. The project has been awarded a European Research Council (ERC) Consolidator Grant in 2019.</p> <p>In Pillar 3 it is investigated how a spacecraft can attain ballistic capture in autonomy. Ballistic capture is an event that occurs in extremely-rare occasions, and requires acquiring a proper state (position, velocity) far away from the target planet [1]. Massive numerical simulations are required to find the specific conditions that support capture [2]. On average, 1 out of 10,000 conditions explored by the algorithm grants capture [3]. The union of these points defines the capture set, which in turn is used to find the capture corridors: these are streams of orbits that can be targeted far away from the planet and that guarantee ballistic capture.</p> <p>The data set contains the initial conditions of weakly-stable, unstable, crash, moon-crash, and capture sets at Mars with initial epochs uniformly distributed from 01 JAN 2030 12:00:00.000 (UTC) to 20 FEB 2032 10:32:39.144 (UTC), covering a complete Earth–Mars synodic period of approximately 780 days. The grid of initial conditions is built to maximize the capture ratio for Mars (see Figure 10 in [3]). Initial conditions are propagated in high-fidelity. The equations of motion of the restricted n-body problem are considered. The gravitational attractions of the Sun, Mercury, Venus, Earth (B*), Mars (central body), Jupiter (B), Saturn (B), Uranus (B), and Neptune (B) are taken into account. Additionally, solar radiation pressure, Mars’ non-spherical gravity, and relativistic corrections [4] (Schwarzschild solution, geodesic precession, and Lense-Thirring precession) are also included in the model.</p> <p>For additional information about the EXTREMA project visit the page <a href="http://extrema.polimi.it">extrema.polimi.it</a>.</p> <p><strong>References</strong><br> [1] F. Topputo and E. Belbruno,'Earth–Mars transfers with ballistic capture', Celestial Mechanics and Dynamical Astronomy, Vol. 121, No. 4, 2015, pp. 329–346. DOI: <a href="http://doi.org/10.1007/s10569-015-9605-8">10.1007/s10569-015-9605-8</a>.<br> [2] F. Topputo and E. Belbruno, 'Computation of weak stability boundaries: Sun–Jupiter system', Celestial Mechanics and Dynamical Astronomy, Vol. 105, No. 1-3, 2009, pp. 3–17. DOI: <a href="http://doi.org/10.1007/s10569-009-9222-5">10.1007/s10569-009-9222-5</a><br> [3] Z.-F. Luo and F. Topputo, 'Analysis of ballistic capture in Sun–planet models', Advances in Space Research, Vol. 56, No. 6, 2015, pp. 1030–1041. DOI: <a href="http://doi.org/10.1016/j.asr.2015.05.042">10.1016/j.asr.2015.05.042</a><br> [4] C. Huang, J. C. Ries, B. D. Tapley, and M. M.Watkins, 'Relativistic effects for near-earth satellite orbit determination', Celestial Mechanics and Dynamical Astronomy, Vol. 48, No. 2, 1990, pp. 167–185. DOI: <a href="http://doi.org/10.1007/BF00049512">10.1007/BF00049512</a></p> <p>* Here B stands for barycenter.</p>
Dataset collected by the s-Nautilus profiler at "La Isleta" yacht club in the Mar Menor
<p>This dataset presents a collection of environmental measurements taken by the s-Nautilus profiler at "La Isleta" yacht club in the Mar Menor. The recorded variables include:</p> <ul> <li><strong>Time (yyyy-mm-dd hh:mm:ss)</strong>: The timestamp indicating the date and time of each measurement.</li> <li><strong>Depth (m)</strong>: The depth at which the measurements were taken.</li> <li><strong>Dissolved Oxygen (DO) </strong>: The concentration of dissolved oxygen, essential for assessing water quality and the health of the marine ecosystem.</li> <li><strong>Electrical Conductivity (EC)</strong>: A parameter indicating the ability of the water to conduct electricity, directly related to salinity and the presence of ions.</li> <li><strong>Temperature (T) (ºC)</strong>: The water temperature, a critical factor influencing many chemical and biological processes.</li> <li><strong>Battery Voltage (Batt3)</strong>: The voltage of the batteries powering the electronics of the profiler, providing insight into its autonomy.</li> </ul> <p>Measurements were taken every 6 hours during the ascent of the profiler.</p>
Mars Odyssey Neutron Spectrometer Time Series of Corrected Cateogry 1 Counting Rates, 2002-2017
<p>Ubinned time series of derived neutron data from the Mars Odyssey Neutron Spectrometer (MONS) Category 1 data, from 200 - 2017.</p>
Geological Maps in the Syrtis Major Region, Mars
<p>The dataset is an ArcGIS geodatabase for the geological maps within the Syrtis Major region illustrated in Voigt et al., 2024. The geodatabase includes contacts as line features and geologic units as point features. Related publication: J.R.C. Voigt, V.Z. Sun, C.E. Viviano,<span> </span>M. Stack (2024): Investigating Hydrated Silica in Syrtis Major, Mars: Implications for the Longevity of Water–Rock Interaction. Geophysical Research Letters. </p>
SAMI3 data in netCDF format (2019-Mar-28)
<p>SAMI3 (Sami3 is Also a Model of the Ionosphere) is a seamless, three-dimensional, physics-based model of the ionosphere (Huba et al, 2008). It is based on SAMI2, a two-dimensional model of the ionosphere (Huba et al., 2000). <br><br>SAMI3 models the plasma and chemical evolution of seven ion species (H⁺, He⁺, N⁺, O⁺, N⁺₂, NO⁺ and O⁺₂). The temperature equation is solved for three ion species (H⁺, He⁺ and O⁺) and for the electrons. Ion inertia is included in the ion momentum equation for motion along the geomagnetic field. This is important in modeling the topside ionosphere and plasmasphere where the plasma becomes collisionless. <br><br>SAMI3 includes 21 chemical reactions and radiative recombination, and uses a nonorthogonal, nonuniform, fixed grid for the magnetic latitude range +/- 89 degrees.. <br><br><b>Drivers</b><br>Neutral composition, temperature, and winds: NRLMSISE00 (Picone et al., 2002) and HWM14 (Drob et al., 2015). <br>Solar radiation: Flare Irradiance Spectral Model version 2 (FISM v2)<br>Magnetic field: Richmond apex model [Richmond, 1995]. <br>Neutral wind dynamo electric field: Determined from the solution of a 2D potential equation [Huba et at., 2008]. <br>For the SAMI3/Weimer configuration: High latitude electric field: calculated from the empirical Weimer model for the potential. <br>For the SAMI3/AMPERE configuration: High latitude electric field: calculated using the Magnetosphere-Ionosphere Coupling solver (MIX) developed by Merkin and Lyon (2010). The inputs to MIX are SAMI3's internal conductances, plus field-aligned current observations from Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), derived from the 66+ satellite Iridium NEXT constellation's engineering magnetometer data. This potential calculation is described in Chartier et al (2022). <br><br>For ease of use, SAMI3 output is remapped to a regular grid using the Earth System Modeling Framework by Hill et al (2004)</p>
SAMI3 data in netCDF format (2019-Mar-29)
<p>SAMI3 (Sami3 is Also a Model of the Ionosphere) is a seamless, three-dimensional, physics-based model of the ionosphere (Huba et al, 2008). It is based on SAMI2, a two-dimensional model of the ionosphere (Huba et al., 2000). <br><br>SAMI3 models the plasma and chemical evolution of seven ion species (H⁺, He⁺, N⁺, O⁺, N⁺₂, NO⁺ and O⁺₂). The temperature equation is solved for three ion species (H⁺, He⁺ and O⁺) and for the electrons. Ion inertia is included in the ion momentum equation for motion along the geomagnetic field. This is important in modeling the topside ionosphere and plasmasphere where the plasma becomes collisionless. <br><br>SAMI3 includes 21 chemical reactions and radiative recombination, and uses a nonorthogonal, nonuniform, fixed grid for the magnetic latitude range +/- 89 degrees.. <br><br><b>Drivers</b><br>Neutral composition, temperature, and winds: NRLMSISE00 (Picone et al., 2002) and HWM14 (Drob et al., 2015). <br>Solar radiation: Flare Irradiance Spectral Model version 2 (FISM v2)<br>Magnetic field: Richmond apex model [Richmond, 1995]. <br>Neutral wind dynamo electric field: Determined from the solution of a 2D potential equation [Huba et at., 2008]. <br>For the SAMI3/Weimer configuration: High latitude electric field: calculated from the empirical Weimer model for the potential. <br>For the SAMI3/AMPERE configuration: High latitude electric field: calculated using the Magnetosphere-Ionosphere Coupling solver (MIX) developed by Merkin and Lyon (2010). The inputs to MIX are SAMI3's internal conductances, plus field-aligned current observations from Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), derived from the 66+ satellite Iridium NEXT constellation's engineering magnetometer data. This potential calculation is described in Chartier et al (2022). <br><br>For ease of use, SAMI3 output is remapped to a regular grid using the Earth System Modeling Framework by Hill et al (2004)</p>
SAMI3 data in netCDF format (2019-Mar-25)
<p>SAMI3 (Sami3 is Also a Model of the Ionosphere) is a seamless, three-dimensional, physics-based model of the ionosphere (Huba et al, 2008). It is based on SAMI2, a two-dimensional model of the ionosphere (Huba et al., 2000). <br><br>SAMI3 models the plasma and chemical evolution of seven ion species (H⁺, He⁺, N⁺, O⁺, N⁺₂, NO⁺ and O⁺₂). The temperature equation is solved for three ion species (H⁺, He⁺ and O⁺) and for the electrons. Ion inertia is included in the ion momentum equation for motion along the geomagnetic field. This is important in modeling the topside ionosphere and plasmasphere where the plasma becomes collisionless. <br><br>SAMI3 includes 21 chemical reactions and radiative recombination, and uses a nonorthogonal, nonuniform, fixed grid for the magnetic latitude range +/- 89 degrees.. <br><br><b>Drivers</b><br>Neutral composition, temperature, and winds: NRLMSISE00 (Picone et al., 2002) and HWM14 (Drob et al., 2015). <br>Solar radiation: Flare Irradiance Spectral Model version 2 (FISM v2)<br>Magnetic field: Richmond apex model [Richmond, 1995]. <br>Neutral wind dynamo electric field: Determined from the solution of a 2D potential equation [Huba et at., 2008]. <br>For the SAMI3/Weimer configuration: High latitude electric field: calculated from the empirical Weimer model for the potential. <br>For the SAMI3/AMPERE configuration: High latitude electric field: calculated using the Magnetosphere-Ionosphere Coupling solver (MIX) developed by Merkin and Lyon (2010). The inputs to MIX are SAMI3's internal conductances, plus field-aligned current observations from Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), derived from the 66+ satellite Iridium NEXT constellation's engineering magnetometer data. This potential calculation is described in Chartier et al (2022). <br><br>For ease of use, SAMI3 output is remapped to a regular grid using the Earth System Modeling Framework by Hill et al (2004)</p>
SAMI3 data in netCDF format (2019-Mar-23)
<p>SAMI3 (Sami3 is Also a Model of the Ionosphere) is a seamless, three-dimensional, physics-based model of the ionosphere (Huba et al, 2008). It is based on SAMI2, a two-dimensional model of the ionosphere (Huba et al., 2000). <br><br>SAMI3 models the plasma and chemical evolution of seven ion species (H⁺, He⁺, N⁺, O⁺, N⁺₂, NO⁺ and O⁺₂). The temperature equation is solved for three ion species (H⁺, He⁺ and O⁺) and for the electrons. Ion inertia is included in the ion momentum equation for motion along the geomagnetic field. This is important in modeling the topside ionosphere and plasmasphere where the plasma becomes collisionless. <br><br>SAMI3 includes 21 chemical reactions and radiative recombination, and uses a nonorthogonal, nonuniform, fixed grid for the magnetic latitude range +/- 89 degrees.. <br><br><b>Drivers</b><br>Neutral composition, temperature, and winds: NRLMSISE00 (Picone et al., 2002) and HWM14 (Drob et al., 2015). <br>Solar radiation: Flare Irradiance Spectral Model version 2 (FISM v2)<br>Magnetic field: Richmond apex model [Richmond, 1995]. <br>Neutral wind dynamo electric field: Determined from the solution of a 2D potential equation [Huba et at., 2008]. <br>For the SAMI3/Weimer configuration: High latitude electric field: calculated from the empirical Weimer model for the potential. <br>For the SAMI3/AMPERE configuration: High latitude electric field: calculated using the Magnetosphere-Ionosphere Coupling solver (MIX) developed by Merkin and Lyon (2010). The inputs to MIX are SAMI3's internal conductances, plus field-aligned current observations from Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), derived from the 66+ satellite Iridium NEXT constellation's engineering magnetometer data. This potential calculation is described in Chartier et al (2022). <br><br>For ease of use, SAMI3 output is remapped to a regular grid using the Earth System Modeling Framework by Hill et al (2004)</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.