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1,880 results for “MAR”
MAR-ERA5 European Alps (1981-2020)
<p>This deposit contains MAR simulations over the European Alps domain (7 kilometers resolution) forced by the ERA-5 reanalysis. The version of the MAR model used is v.3.10, model set-up is described in detail in Beaumet et al., 2021 (https://doi.org/10.1007/s10113-021-01830-x)<br> Contact person : Julien Beaumet (beaumetjulien@gmail.com), Martin Menegoz (martin.menegoz@univ-grenoble-alpes.fr)<br> The simulations cover the period covered by ERA5 reanalysis : 1981-2020 (1979-1980=Spin-up years)<br> Data are available at the daily frequency, with one variable (10 years of data) per file.<br> The available variables in this deposit are :<br> <strong>LWD: </strong>Surface downward longwave radiation, [W/m2]<br> <strong>LWU:</strong> Surface upward longwave radiation, [W/m2]<br> <strong>MB:</strong> Total snow water equivalent, [mm.We]<br> <strong>MBrr:</strong> Daily rainfall, [mm.We] (5)<br> <strong>MBsf:</strong> Daily snowfall, [mm.We] (5)</p> <p><strong>QQz: </strong> Near-surface specific humidity at constant height, [g/kg] (2)</p> <p><strong>SWD:</strong> Surface downward shortwave radiation, [W/m2]<br> <strong>SWU:</strong> Surface upward shortwave radiation, [W/m2]</p> <p><strong>TTmax:</strong> Near-surface maximum air temperature for the first model level above the surface (constant sigma), [C]<br> <strong>TTmin:</strong> Near-surface minimum air temperature for the first model level above the surface (constant sigma),[C]</p> <p><strong>TTz: </strong>Near-surface mean air temperature at constant-height, [C]<br> <strong>UUz:</strong> Near-surface zonal component of wind speed at constant height, [m/s]<br> <strong>VVz:</strong> Near surface Meridional component of wind speed at constant height, [m/s]<br> <strong>ZN3:</strong> Total snow height, [m]</p> <p>Other variables are available upon request (see email above).<br> AL : Surface albedo, [0-1]<br> CC: Cloud cover, [0-1]<br> CD: Low level Cloud cover, [0-1]<br> CM: Middle level Cloud cover, [0-1]<br> CU: High level Cloud cover, [0-1]<br> SP: Surface pressure, [hPa]<br> ST: Surface temperature, [C]<br> TT: Near-surface mean air temperature for the first three model level above the surface (constant sigma), [C] (1)<br> TTp: Constant pressure-level mean air temperature, [C] (4)<br> ZZ: Surface geopotential for the first three model level above the surface (constant sigma), [m]</p> <p>SHF: Surface sensible heat flux, [W/m2] LHF: Surface latent heat flux, [W/m2]</p> <p>* Latitude(LAT),longitude(LON)and surface elevation (SH) of each grid point can be read in MARgrid_EUl.nc file</p> <p>(1) For variable TT, ZZ model constant sigma level of 0.9997479<br> (2) For variables TTz, QQz constant height level at 2m<br> (3) For variables UUz, VVz constant height level at 10m<br> (4) Variables TTp, UUp, VVp available at pressure level : 925, 850, 800, 700, 600, 500, 200 hPa<br> (5) Snow height and snow water equivalent are available for three sectors which corresponds to three different vegetation type : The three vegetation type used can be readen in the file MARgrid_EUy.nc, with the variable VEG and their respectibe fraction for each grid point is given by the variable FRV. The third vegetation type (sector=3) mostly corresponds to bare soil or low crops by default, but sometimes its fraction=0, which gives unrealistic low values of snow height. In this case, using the max. value on the axis sector often gives the best results.<br> Legend of the vegetation type for the VEG variables : 0:NO_VEGETATION 1:CROPS_LOW 2:CROPS_MEDIUM 3:CROPS_HIGH 4:GRASS_LOW 5:GRASS_MEDIUM 6:GRASS_HIGH 7:BROADLEAF_LOW 8:BROADLEAF MEDIUM 9:BROADLEAF_HIGH 10:NEEDLELEAF_LOW 11:NEEDLELEAF MEDIUM 12:NEEDLELEAF_HIGH 13:City</p> <p> </p>
MAR-ERA-20C European Alps (1902-2010)
<p>This folder contains MAR simulations over the European Alps domain (7 kilometers resolution) forced by the ERA-20C reanalysis<br> The version of the MAR model used is v.3.10, model set-up is described in detail in Beaumet et al., 2021 (https://doi.org/10.1007/s10113-021-01830-x)<br> Contact person : Julien Beaumet (beaumetjulien@gmail.com), Martin Menegoz (martin.menegoz@univ-grenoble-alpes.fr)<br> The simulations cover the period covered by ERA-20C reanalysis : 1902-2010 (1901=Spin-up year)<br> Data are available at the daily frequency, with one variable per file (10 years of data per file).<br> The available variables are :</p> <p><strong>LWD: </strong>Surface downward longwave radiation, [W/m2]<br> <strong>LWU:</strong> Surface upward longwave radiation, [W/m2]<br> <strong>MB:</strong> Total snow water equivalent, [mm.We]<br> <strong>MBrr:</strong> Daily rainfall, [mm.We] (5)<br> <strong>MBsf:</strong> Daily snowfall, [mm.We] (5)</p> <p><strong>QQz: </strong> Near-surface specific humidity at constant height, [g/kg] (2)</p> <p><strong>SWD:</strong> Surface downward shortwave radiation, [W/m2]<br> <strong>SWU:</strong> Surface upward shortwave radiation, [W/m2]</p> <p><strong>TTmax:</strong> Near-surface maximum air temperature for the first model level above the surface (constant sigma), [C]<br> <strong>TTmin:</strong> Near-surface minimum air temperature for the first model level above the surface (constant sigma),[C]</p> <p><strong>TTz: </strong>Near-surface mean air temperature at constant-height, [C]<br> <strong>UUz:</strong> Near-surface zonal component of wind speed at constant height, [m/s]<br> <strong>VVz:</strong> Near surface Meridional component of wind speed at constant height, [m/s]<br> <strong>ZN3:</strong> Total snow height, [m]</p> <p>Other variables are available upon request (see email above).<br> AL : Surface albedo, [0-1]<br> CC: Cloud cover, [0-1]<br> CD: Low level Cloud cover, [0-1]<br> CM: Middle level Cloud cover, [0-1]<br> CU: High level Cloud cover, [0-1]</p> <p>LHF : Surface latent heat flux, [W/m2]</p> <p>SHF : Surface sensible heat flux, [W/m2]<br> SP: Surface pressure, [hPa]<br> ST: Surface temperature, [C]<br> TT: Near-surface mean air temperature for the first three model level above the surface (constant sigma), [C] (1)<br> TTp: Constant pressure-level mean air temperature, [C] (4)<br> ZZ: Surface geopotential for the first three model level above the surface (constant sigma), [m]</p> <p>* Latitude(LAT),longitude(LON)and surface elevation (SH) of each grid point can be read in MARgrid_EUe.nc file</p> <p>(1) For variable TT, ZZ model constant sigma level of 0.9997479<br> (2) For variables TTz, QQz constant height level are : 2m<br> (3) For variables UUz, VVz constant height level are : 10m<br> (4) Variables TTp, UUp, VVp available at pressure level : 925, 850, 800, 700, 600, 500, 200 hPa<br> (5) Snow height and snow water equivalent are available for three sectors which corresponds to three different vegetation type : The three vegetation type used can be readen in the file MARgrid_EUy.nc, with the variable VEG and their respectibe fraction for each grid point is given by the variable FRV. The third vegetation type (sector=3) mostly corresponds to bare soil or low crops by default, but sometimes its fraction=0, which gives unrealistic low values of snow height. In this case, using the max. value on the axis sector often gives the best results.<br> Legend of the vegetation type for the VEG variables : 0:NO_VEGETATION 1:CROPS_LOW 2:CROPS_MEDIUM 3:CROPS_HIGH 4:GRASS_LOW 5:GRASS_MEDIUM 6:GRASS_HIGH 7:BROADLEAF_LOW 8:BROADLEAF MEDIUM 9:BROADLEAF_HIGH 10:NEEDLELEAF_LOW 11:NEEDLELEAF MEDIUM 12:NEEDLELEAF_HIGH 13:City</p>
MAR-EC-Earth3 HIST (1961-2014) and SSP245 European Alps (2015-2100)
<p>This deposit contains MAR simulations over the European Alps domain (7 kilometers resolution) forced by the EC-EARTH3 GCM (CMIP6 version)<br> The version of the MAR model used is v.3.10, model set-up is described in detail in Beaumet et al., 2021 (https://doi.org/10.1007/s10113-021-01830-x)<br> Contact person : Julien Beaumet (beaumetjulien@gmail.com), Martin Menegoz (martin.menegoz@univ-grenoble-alpes.fr)<br> The realization used is r25i1p1f1 and simulation was done for the historial (1961-2014), SSP245 scenario (2015-2100). For information about the EC-EARTH3 simulation, contact Eduardo Moreno-Chamarro (eduardo.moreno@bsc.es).<br> Data are available at the daily frequency, with one variable per file (10 years of data per file)</p> <p><strong>CC:</strong> Cloud cover, [0-1]<br> <strong>MB:</strong> Total snow water equivalent, [mm.We]<br> <strong>MBrr:</strong> Daily rainfall, [mm.We] (5)<br> <strong>MBsf:</strong> Daily snowfall, [mm.We] (5)<br> <strong>QQz: </strong> Near-surface specific humidity at constant height, [g/kg] (2)</p> <p><strong>TTmax:</strong> Near-surface maximum air temperature for the first model level above the surface (constant sigma), [C]<br> <strong>TTmin:</strong> Near-surface minimum air temperature for the first model level above the surface (constant sigma),[C]</p> <p><strong>TTz: </strong>Near-surface mean air temperature at constant-height, [C]<br> <strong>UUz:</strong> Near-surface zonal component of wind speed at constant height, [m/s]<br> <strong>VVz:</strong> Near surface Meridional component of wind speed at constant height, [m/s]<br> <strong>ZN3:</strong> Total snow height, [m]</p> <p>Other variables are available upon request (see email above).<br> AL : Surface albedo, [0-1]<br> CD: Low level Cloud cover, [0-1]<br> CM: Middle level Cloud cover, [0-1]<br> CU: High level Cloud cover, [0-1]<br> SP: Surface pressure, [hPa]<br> ST: Surface temperature, [C]<br> TT: Near-surface mean air temperature for the first three model level above the surface (constant sigma), [C] (1)<br> TTp: Constant pressure-level mean air temperature, [C] (4)<br> ZZ: Surface geopotential for the first three model level above the surface (constant sigma), [m]</p> <p>LWD: Surface downward longwave radiation, [W/m2]<br> LWU: Surface upward longwave radiation, [W/m2]</p> <p>SWD: Surface downward shortwave radiation, [W/m2]<br> SWU: Surface upward shortwave radiation, [W/m2]</p> <p>SHF: Surface sensible heat flux, [W/m2] LHF: Surface latent heat flux, [W/m2]</p> <p>* Latitude(LAT),longitude(LON)and surface elevation (SH) of each grid point can be read in MARgrid_EUe.nc file</p> <p>(1) For variable TT, ZZ model constant sigma level of 0.9997479<br> (2) For variables TTz, QQz constant height level are : 2m<br> (3) For variables UUz, VVz constant height level are : 50m<br> (4) Variables TTp, UUp, VVp available at pressure level : 925, 850, 800, 700, 600, 500, 200 hPa<br> (5) Snow height and snow water equivalent are available for three sectors which corresponds to three different vegetation type : The three vegetation type used can be readen in the file MARgrid_EUy.nc, with the variable VEG and their respective fraction for each grid point is given by the variable FRV. The third vegetation type (sector=3) mostly corresponds to bare soil or low crops by default, but sometimes its fraction=0, which gives unrealistic low values of snow height. In this case, using the max. value on the axis sector often gives the best results.<br> Legend of the vegetation type for the VEG variables : 0:NO_VEGETATION 1:CROPS_LOW 2:CROPS_MEDIUM 3:CROPS_HIGH 4:GRASS_LOW 5:GRASS_MEDIUM 6:GRASS_HIGH 7:BROADLEAF_LOW 8:BROADLEAF MEDIUM 9:BROADLEAF_HIGH 10:NEEDLELEAF_LOW 11:NEEDLELEAF MEDIUM 12:NEEDLELEAF_HIGH 13:City<br> </p>
MAR-MPI-ESM1-2-HR SSP585 European Alps (2015-2100)
<p>This deposit contains MAR simulations over the European Alps domain (7 kilometers resolution) forced by the MPI-ESM1-2-HR GCM (CMIP6 version) for the SSP585 projection (2015 to 2100). The version of the MAR model used is v.3.10, model set-up is described in detail in Beaumet et al., 2021 (https://doi.org/10.1007/s10113-021-01830-x) Contact person : Julien Beaumet (beaumetjulien@gmail.com), Martin Menegoz (martin.menegoz@univ-grenoble-alpes.fr)<br> The realization used is r1i1p1f1.<br> Data are available at the daily frequency, with one variable per file (10 years of data per file).<br> The available variables in this deposit are :</p> <p><strong>CC:</strong> Cloud cover, [0-1]<br> <strong>MB:</strong> Total snow water equivalent, [mm.We]<br> <strong>MBrr:</strong> Daily rainfall, [mm.We] (5)<br> <strong>MBsf:</strong> Daily snowfall, [mm.We] (5)<br> <strong>QQz: </strong> Near-surface specific humidity at constant height, [g/kg] (2)</p> <p><strong>TTmax:</strong> Near-surface maximum air temperature for the first model level above the surface (constant sigma), [C]<br> <strong>TTmin:</strong> Near-surface minimum air temperature for the first model level above the surface (constant sigma),[C]</p> <p><strong>TTz: </strong>Near-surface mean air temperature at constant-height, [C]<br> <strong>UUz:</strong> Near-surface zonal component of wind speed at constant height, [m/s]<br> <strong>VVz:</strong> Near surface Meridional component of wind speed at constant height, [m/s]<br> <strong>ZN3:</strong> Total snow height, [m]</p> <p>Other variables are available upon request (see email above).<br> AL : Surface albedo, [0-1]<br> CD: Low level Cloud cover, [0-1]<br> CM: Middle level Cloud cover, [0-1]<br> CU: High level Cloud cover, [0-1]<br> SP: Surface pressure, [hPa]<br> ST: Surface temperature, [C]<br> TT: Near-surface mean air temperature for the first three model level above the surface (constant sigma), [C] (1)<br> TTp: Constant pressure-level mean air temperature, [C] (4)<br> ZZ: Surface geopotential for the first three model level above the surface (constant sigma), [m]</p> <p>LWD: Surface downward longwave radiation, [W/m2]<br> LWU: Surface upward longwave radiation, [W/m2]</p> <p>SWD: Surface downward shortwave radiation, [W/m2]<br> SWU: Surface upward shortwave radiation, [W/m2]</p> <p>SHF: Surface sensible heat flux, [W/m2] LHF: Surface latent heat flux, [W/m2]</p> <p>* Latitude(LAT),longitude(LON)and surface elevation (SH) of each grid point can be read in MARgrid_EUy.nc file</p> <p>(1) For variable TT, ZZ model constant sigma level of 0.9997479<br> (2) For variables TTz, QQz constant height level is : 2m<br> (3) For variables UUz, VVz constant height level are : 10m<br> (4) Variables TTp, UUp, VVp available at pressure level : 925, 850, 800, 700, 600, 500, 200 hPa<br> (5) Snow height and snow water equivalent are available for three sectors which corresponds to three different vegetation type : The three vegetation type used can be readen in the file MARgrid_EUy.nc, with the variable VEG and their respectibe fraction for each grid point is given by the variable FRV. The third vegetation type (sector=3) mostly corresponds to bare soil or low crops by default, but sometimes its fraction=0, which gives unrealistic low values of snow height. In this case, using the max. value on the axis sector often gives the best results.<br> Legend of the vegetation type for the VEG variables : 0:NO_VEGETATION 1:CROPS_LOW 2:CROPS_MEDIUM 3:CROPS_HIGH 4:GRASS_LOW 5:GRASS_MEDIUM 6:GRASS_HIGH 7:BROADLEAF_LOW 8:BROADLEAF MEDIUM 9:BROADLEAF_HIGH 10:NEEDLELEAF_LOW 11:NEEDLELEAF MEDIUM 12:NEEDLELEAF_HIGH 13:City<br> ~ </p>
Digital elevation model mosaic of the Hypanis region, Mars
<p><strong>This dataset accompanies the following papers:</strong></p> <p>Adler et al. (2019) Hypotheses for the origin of the Hypanis fan-shaped deposit at the edge of the Chryse escarpment, Mars: Is it a delta? Icarus, 319, 885-908. doi: https://doi.org/10.1016/j.icarus.2018.05.021</p> <p>Adler et al. (2022) Regional Geology of the Hypanis Valles System, Mars. JGR: Planets, doi: 10.1029/2021JE006994</p> <p><strong>Contents:</strong></p> <p>- DEM mosaic of the Hypanis Valles and deposit region constructed from CTX, HRSC, and MOLA elevation data (geotiff).</p> <p>- Coverage map of CTX, HRSC, and MOLA footprints used (shapefile).</p> <p>- Previews of DEM (greyscale and color) and of coverage map (png)</p> <p><strong>Description:</strong></p> <p>We constructed a regional elevation mosaic (~17 m/pixel) in Adler et al. (2019) archived here. This mosaic incorporates 10 CTX digital elevation models (DEMs) of high resolution, 3 HRSC digital elevation models of medium resolution, and 1 MOLA global DEM of low resolution. Individual CTX DEMs were generated from the stereopairs listed below. Some individual products were calibrated and formatted with the USGS Integrated Software for Imagers and Spectrometers (ISIS) and then Ames Stereo Pipeline. Other products were generated with SOCET SET. All products were controlled to MOLA shot elevation data.</p> <p><strong>Data incorporated:</strong></p> <p><strong>CTX stereopairs:</strong></p> <p><em>ID (nadir-most), ID (resolution [m/pix])</em></p> <p>P07_003631_1920, B09_013296_1920 (17.7 m/pixel)</p> <p>B17_016408_1913, G06_020443_1916 (18.5 m/pixel)</p> <p>J03_046104_1918, F05_037783_1918 (24.0 m/pixel)</p> <p>P08_004264_1912, B06_011951_1916 (17.7 m/pixel)</p> <p>G09_021788_1918, G11_022434_1918 (18.4 m/pixel)</p> <p>B17_016474_1915, B19_017186_1915 (20.2 m/pixel)</p> <p>D19_034816_1921, F01_036293_1920 (24.0 m/pixel)</p> <p>P13_006176_1918, F01_036293_1920 (18.2 m/pixel)</p> <p>D07_029845_1921, D07_029990_1921 (20.2 m/pixel)</p> <p>G21_026601_1918, P04_002774_1922 (20.2 m/pixel)</p> <p><strong>HRSC DA4:</strong></p> <p>H2134 (75 m/pixel)</p> <p>H2145 (50 m/pixel)</p> <p>H0894 (75 m/pixel)</p> <p><strong>MOLA Elevation:</strong></p> <p>128 ppd Elevation (463 m/pixel)</p> <p><strong>Funding:</strong></p> <p>The work to create individual CTX stereopair DEMs was funded by UK Space Agency (UK SA) grants ST/ K502388/1, ST/R002355/1, ST/L00643X/1, and ST/R001413/1. We thank the Science and Technology Facilities Council for supporting science relating to ExoMars Rover landing site selection activities. The work to create a mosaic using these products and others was supported by grants from the NASA Mars Odyssey Project under a subcontract to ASU administered by the Jet Propulsion Laboratory/California Institute of Technology.</p>
Supplemental data from: "From lake to river: Documenting an environmental transition across the Jura/Knockfarril Hill members boundary in the Glen Torridon region of Gale crater (Mars)."
<p>This document, uploaded on the FAIR repository Zenodo, contains large data tables pertaining to the Supplementary Online Material of the above-mentioned article.</p> <p>These tables contain the complete list of individual MAHLI and ChemCam targets investigated, detailed laminae measurements and complete ChemCam compositional data.</p>
Brightness Temperature Variances from On-Planet Views in the A1–A3 Channels by the Mars Climate Sounder
<p>Heavens, Nicholas (2022), “Brightness Temperature Variances from On-Planet Views in the A1–A3 Channels by the Mars Climate Sounder”, Zenodo, V1, doi: 10.5281</p> <p>Title: Brightness Temperature Variances from On-Planet Views in the A1–A3 Channels by the Mars Climate Sounder</p> <p>Author: Nicholas G. Heavens, Space Science Institute, Boulder, CO, USA and London, UK (nheavens@spacescience.org)</p> <p>Date: 25 March 2022 </p> <p>Overview: This dataset contains an improvement and extension of significant data analysis products related to: </p> <p>Heavens, N.G., A. Pankine, J.M. Battalio, C. Wright, D.M. Kass, A. Kleinböhl, S. Piqueux, J.T. Schofield, 2022, Mars Climate Sounder Observations of Gravity-Wave Activity throughout Mars' Lower Atmosphere, Plan. Sci. J., 3, 57, doi: 10.3847/PSJ/ac51ce. </p> <p>These fall into three broad categories: diagnoses of detrended brightness temperature variance (GW) at 595–615 cm-1 (A1), 615–645 cm-1 (A2), and 635-665 cm-1 (A3) in individual views in the nadir or off-nadir by Mars Climate Sounder on board Mars Reconnaissance Orbiter; averages and other statistics of those diagnoses in space and time; and estimated gravity wave visibility functions for nadir, off-nadir, and nadir views with baselines like off-nadir views. This document presumes the manuscript is available to the dataset user.</p> <p>The purpose of archiving this dataset is to allow for comparison with a forthcoming analysis of gravity wave activity in limb observations by Mars Climate Sounder.</p> <p>The original dataset was published as:</p> <p>Heavens, Nicholas (2022), “Brightness Temperature Variances from On-Planet Views in the A1–A3 Channels by the Mars Climate Sounder”, Mendeley Data, V2, doi: 10.17632/5k6nybdy92.2</p> <p>The extension of the dataset consists of extension of the analysis time period to the end of January 2022 (MY 36, Ls=166.87).</p> <p>The improvement consists of a flag to indicate when an on-planet observations is likely to intersect a loop structure observed in the limb, and thus be contaminated by a high altitude cloud, which results in overestimate of gravity wave activity in the tropics at night during the clear season. Averages are now included that filter out flagged observations, as well as the original averages that include the flagged observations. </p> <p>If you are using this dataset and are feeling confused or wish there were some additional information from the article in this dataset, please contact me. A complete accounts of the contents and a restatement of this description is included as <em>MCS_OP_A13_GW_Analysis_Dataset_Documentation.pdf.</em></p> <p>Acknowledgments: The archiving of this dataset is supported by NASA’s Mars Data Analysis Program (80NSSC19K1215).</p>
Mars Express thermal power dataset
<p>This dataset contains several features related to the power resources and general situation of <a href="https://www.esa.int/Science_Exploration/Space_Science/Mars_Express">ESA's Mars Express</a> spacecraft. Its purpose is the design of predictive models related to thermal power consumption. Having an efficient planning on the allocation of power is critical, as it allows to maximize the scientific gain and extend the life-time of the overall mission.</p> <p>The data has been split into two sets:</p> <ul> <li>MEX1 containing 5795 orbits from 01.01.2015 to 18.08.2019</li> <li>MEX2 containing 1507 orbits from 08.08.2019 to 31.10.2020</li> </ul> <p>MEX1 can be seen as a training set. It was used to fit a reference model (THP 2.0 - Thermal Power Model) which is available in the <strong>thp2 </strong>column. MEX2 can be seen as a test set, providing previously unseen orbits.</p> <p>All the features are aggregated for each full orbit of the spacecraft. They include:</p> <ul> <li><strong>timestamp</strong>: time [unix timestamp]</li> <li><strong>sme</strong>: the value of Sun-Mars-Earth angle [deg]</li> <li><strong>s_m_distance</strong>: Sun-Mars distance in [km]</li> <li><strong>orbital_p</strong>: length of orbital period [s]</li> <li><strong>average_power</strong>: measured average power consumption [W]</li> <li><strong>right_flag</strong>: Guidance flag, North/South flag, determines whether LVA is exposed to sun, 1 = yes, 0 = no </li> <li><strong>eclipse_l</strong>: length of the eclipse in orbit [s]</li> <li><strong>full_off</strong>: whether full off or drive off, full off = 1, only drive off = 0</li> <li><strong>average_xtx</strong>: average current drawn by transmitter component [A] (multiply by 28V to get W)</li> <li><strong>gsep_dur</strong>: time spent in GSEP [s]</li> <li><strong>lvah</strong>: Heating of the LVA (launch vehicle adapter) [combined feature]</li> <li><strong>sh</strong>: Solar heating [combined feature]</li> <li><strong>thp2</strong>: output of the reference model THP2 model [W]</li> </ul> <p><strong>lvah </strong>is a heating related to the orientation of the spacecraft and is computed by </p> <p><span class="math-tex">\(LVAH = SME \cdot \text{right_flag} \)</span></p> <p><strong>sh</strong> is another aggregated feature representing the total solar power influx. It is computed by:</p> <p><span class="math-tex">\(SH = \left( 1 - \frac{\text{eclipse_d}}{\text{orbital_p}}\right) \frac{3.846 \cdot 10^{26} W}{4 \cdot \pi \cdot (\text{s_m_distance} \cdot 1000)^2} \)</span></p> <p><strong>average_power</strong> is the target of prediction and thus of interest to estimate from other parameters.</p> <p><strong>thp2 </strong>provides a reference model that has been developed by ESA engineers to predict <strong>average_power.</strong></p>
Dynamic FRET example videos related to "Mars, a molecule archive suite for reproducible analysis and reporting of single-molecule properties from bioimages"
<p>Videos of dynamic switching between iso-I and iso-II conformations of a holiday junction at 50 mM Magnesium resulting in high and low FRET from Cy3 and Alexa647 labels positioned on the arms. Holiday junctions are surface immobilized through a biotin attachment and imaged using TIRF microscopy. The camera sensor is split using a dual view so that the acceptor emission is on the top and the donor emission is on the bottom. Videos from each position are provided as compressed zip files containing a sequence of tif files and associated metadata text file. Image sequences were collected using Micro-Manager 2.0 using ALEX or alternating laser excitation with alternating 637 and 532 pulses separated as two different channels. Beam profile images are provided for 637 and 532 excitation allowing for correction of the non-uniform beam profiles. The following 2D affine transformation matrix can be used to transform from the top acceptor emission region to the bottom donor emission region during processing.</p> <p>Affine 2D transformation from top to bottom: (m00, m01, m02, m10, m11, m12), (1.00276, 0.000208, 1.01236, 0.000267, 1.00312, 507.21025)</p> <p>A detailed image processing workflow for this dataset using Mars can be found under the example section at <a href="https://duderstadt-lab.github.io/mars-docs/">https://duderstadt-lab.github.io/mars-docs/</a> or directly at <a href="https://duderstadt-lab.github.io/mars-docs/examples/FRET_dynamic/">https://duderstadt-lab.github.io/mars-docs/examples/FRET_dynamic/</a></p>
Dataset for 'Valley Networks and the Record of Glaciation on Ancient Mars'
<p>Supplementary information (in pdf format, ~800 kb) containing the model, setup, and parameter analysis supporting the manuscript 'Valley Networks and the Record of Glaciation on Ancient Mars'. </p> <p>Includes a detailed table of parameters with references. </p>
McCulloch et al 2022 UM post-processed Mars dataset
<p>Supplementary dataset and Jupyter notebook for preproduction of UM data within figures presented in McCulloch <em>et al.,</em> 2022. </p> <p>Data is a post-processed extract of the raw dataset for each variable. Data from the raw dataset has been extracted according to the appropriate Martian month, zonally meaned and converted to a <span class="math-tex">\(\sigma\)</span>/pressure coordinate system. This process is the same as is applied to the MCD dataset, which can be seen in the Jupyter notebook.</p> <p>The notebook provides the code needed to reproduce the figures with the given data. All instructions are detailed within the notebook, including package dependencies and configuration options. Due to licensing, we are only able to provide access to the UM post-processed data, for the MCD dataset please follow the instructions within the notebook.</p>
Mars Target Encyclopedia - Labeled LPSC abstracts for four Mars missions
<p>This data set contains annotated text versions of 1635 two-page abstracts published at the Lunar and Planetary Science Conference from 1998 to 2020 of relevance to four Mars missions. The annotations were generated using named entity recognition and relation extraction provided by the MTE processing pipeline (available at https://github.com/wkiri/MTE), followed by manual review. Annotated entities include Element, Mineral, Property, and Target. Annotated relations include <strong>Contains</strong>(Target, Element | Mineral) and <strong>HasProperty</strong>(Target, Property). The extracted information (without full texts) is also available as a database (stored in .csv files) at https://pds-geosciences.wustl.edu/missions/mte/mte.htm . The complete annotated texts are provided here as a resource for further research and experimentation on information extraction methods. For more information about the Mars Target Encyclopedia and these annotations, please see:</p> <ul> <li>"<a href="https://www.hou.usra.edu/meetings/lpsc2022/pdf/1231.pdf">Targets from the Spirit Mars Exploration Rover in the Mars Target Encyclopedia</a>", Kiri L. Wagstaff, Raymond Francis, Matthew Golombek, Steven Lu, Ellen Riloff, Leslie Tamppari, Yuan Zhuang, and Thomas Stein.<br> <em>53rd Lunar and Planetary Science Conference</em>, Abstract #1231, March 2022.</li> <li>"<a href="https://www.hou.usra.edu/meetings/lpsc2021/pdf/1278.pdf">The Mars Target Encyclopedia Now Includes Mars Pathfinder and Mars Phoenix Targets</a>", Kiri L. Wagstaff, Raymond Francis, Matthew Golombek, Steven Lu, Ellen Riloff, Leslie Tamppari, and Thomas C. Stein.<br> <em>52nd Lunar and Planetary Science Conference</em>, Abstract #1278, March 2021.</li> </ul> <p>The original PDF abstracts are available at: </p> <ul> <li>For years prior to 2000: https://www.lpi.usra.edu/meetings/LPSC${two-digit-year}/pdf/${id}.pdf</li> <li>For year 2000: https://www.lpi.usra.edu/meetings/LPSC${four-digit-year}/pdf/${id}.pdf</li> <li>For years 2001-2017 (note lower-case lpsc): https://www.lpi.usra.edu/meetings/lpsc${four-digit-year}/pdf/${id}.pdf</li> <li>For years 2018-2020: https://www.hou.usra.edu/meetings/lpsc${four-digit-year}/pdf/${id}.pdf</li> </ul> <p>where ${id} is a four-digit abstract number, starting with 1001 (if available).</p> <p>The text files provided in this archive were extracted from the PDF files using the Apache Tika PDF parsing tool. They are named as ${four-digit-year}_${id}.txt. The text is provided here so that the annotations can be viewed in context. The text content remains copyright of the original abstract authors.</p> <p>The annotations (entities and relations) are provided in the format used by the brat annotation tool. They are named as ${four-digit-year}_${id}.ann. To view the annotations in a web-based graphical form, install the brat tool (http://brat.nlplab.org/). These annotations were generated using brat v1.3. The annotation files are also human-readable and can be parsed in to be used directly in code. If the .ann file is empty, then there are no relevant annotations for the associated text file.</p> <p><strong>Contents</strong>:</p> <ul> <li>mpf.zip: 591 abstracts relating to the Mars Pathfinder mission (1998-2020)</li> <li>mer-a.zip: 397 abstracts relating to the MER-A (Spirit) rover mission (2004-2020)</li> <li>mer-b.zip: 256 abstracts relating to the MER-B (Opportunity) rover mission (2005-2020)</li> <li>phx.zip: 391 abstracts relating to the Mars Phoenix Lander mission (2009-2020)</li> </ul> <p>Each directory contains a .txt and .ann file for each abstract. The .ann file is in brat standoff format (http://brat.nlplab.org/standoff.html). Additional .conf files are provided to generate color highlighting and keyboard shortcuts. These are used by the brat tool.</p> <p>Note: the same abstract may appear in more than one mission directory, if it discusses targets from more than one mission. It will have a different .ann file for each such appearance. Within each directory, a "Target" annotation is understood to refer to a target of the relevant mission.</p> <p><strong>Attribution</strong>:</p> <p>If you use this data set in your own work, please cite it as follows:</p> <p>Kiri L. Wagstaff, Raymond Francis, Matthew Golombek, Leslie Tamppari, and Steven Lu. (2022). Mars Target Encyclopedia - Labeled LPSC abstracts for four Mars missions (1.0.0.0) [Data set]. Zenodo. DOI: 10.5281/zenodo.7066107</p>
High-resolution topography and layer attitude measurements over Juventae Chasma (Valles Marineris, Mars)
<p>Stereo-derived topography over mounds in Juventae Chasma (Valles Marineris, Mars), including derived measurements (ASCII .csv and .xls). File naming in measurements follows the numbering of NASA MRO HiRiSE stereo pairs.</p> <p>Please note for all DTMs</p> <p>Format: GeoTiff<br> Projection: Equirectangular<br> Datum: Mars 2000 Sphere</p> <p>Bit depth: 32bit</p> <p>Spatial resolution: 1m/pixel</p> <p>HiRise images avalable at:https://hirise.lpl.arizona.edu/<br> (use one of the image numbers listed below in the search box)</p> <p>Stereo pairs:</p> <p>HiRISE 1 (PSP_002590_1765_RED-PSP_002946_1765_RED-DEM_cyli.tif)<br> PSP_002590_1765<br> PSP_002946_1765</p> <p><br> HiRISE 2 (PSP_006915_1760_RED-PSP_007060_1760_RED-DEM_cyli.tif)<br> PSP_006915_1760<br> PSP_007060_1760</p> <p>HiRISE 3 (ESP_015934_1760_RED-ESP_016646_1760_RED-DEM_cyli.tif)<br> ESP_016646_1760<br> ESP_015934_1760</p> <p>HiRISE 4 (ESP_020470_1755_RED-ESP_014378_1755_RED-DEM_cyli.tif)<br> ESP_020470_1755<br> ESP_014378_1755</p> <p>HiRISE 5 (PSP_002379_1755_RED-PSP_002023_1755_RED-DEM_cyli.tif)<br> PSP_002379_1755<br> PSP_002023_1755</p> <p>HiRISE 6 (ESP_016567_1755_RED-ESP_017279_1755_RED-DEM_cyli.tif)<br> ESP_016567_1755<br> ESP_017279_1755</p> <p>HiRISE 7 (PSP_003790_1755_RED-PSP_004291_1755_RED-DEM_cyli.tif)<br> PSP_003790_1755<br> PSP_004291_1755</p> <p>HiRISE 8 (ESP_016145_1775_RED-ESP_017424_1775_RED-DEM_cyli.tif)<br> ESP_016145_1775<br> ESP_017424_1775</p> <p>HiRISE 9 (PSP_008708_1780_RED-PSP_008998_1780_RED-DEM_cyli.tif)<br> PSP_008708_1780<br> PSP_008998_1780</p> <p>HiRISE 10 (ESP_011688_1760_RED-ESP_019613_1760_RED-DEM_cyli.tif)<br> ESP_019613_1760<br> ESP_011688_1760</p>
High-resolution digital topography and layer attitude measurements over Juventae Chasma (Valles Marineris, Mars)
<p>Stereo-derived topography over mounds in Juventae Chasma (Valles Marineris, Mars), including derived measurements (ASCII .csv and .xls). File naming in measurements follows the numbering of NASA MRO HiRiSE stereo pairs.</p> <p>Please note for all DTMs</p> <p>Format: GeoTiff<br> Projection: Equirectangular<br> Datum: Mars 2000 Sphere</p> <p>Bit depth: 32bit</p> <p>Spatial resolution: 1m/pixel</p> <p>HiRise images avalable at:https://hirise.lpl.arizona.edu/<br> (use one of the image numbers listed below in the search box)</p> <p>Stereo pairs:</p> <p>HiRISE 1 (PSP_002590_1765_RED-PSP_002946_1765_RED-DEM_cyli.tif)<br> PSP_002590_1765<br> PSP_002946_1765</p> <p><br> HiRISE 2 (PSP_006915_1760_RED-PSP_007060_1760_RED-DEM_cyli.tif)<br> PSP_006915_1760<br> PSP_007060_1760</p> <p>HiRISE 3 (ESP_015934_1760_RED-ESP_016646_1760_RED-DEM_cyli.tif)<br> ESP_016646_1760<br> ESP_015934_1760</p> <p>HiRISE 4 (ESP_020470_1755_RED-ESP_014378_1755_RED-DEM_cyli.tif)<br> ESP_020470_1755<br> ESP_014378_1755</p> <p>HiRISE 5 (PSP_002379_1755_RED-PSP_002023_1755_RED-DEM_cyli.tif)<br> PSP_002379_1755<br> PSP_002023_1755</p> <p>HiRISE 6 (ESP_016567_1755_RED-ESP_017279_1755_RED-DEM_cyli.tif)<br> ESP_016567_1755<br> ESP_017279_1755</p> <p>HiRISE 7 (PSP_003790_1755_RED-PSP_004291_1755_RED-DEM_cyli.tif)<br> PSP_003790_1755<br> PSP_004291_1755</p> <p>HiRISE 8 (ESP_016145_1775_RED-ESP_017424_1775_RED-DEM_cyli.tif)<br> ESP_016145_1775<br> ESP_017424_1775</p> <p>HiRISE 9 (PSP_008708_1780_RED-PSP_008998_1780_RED-DEM_cyli.tif)<br> PSP_008708_1780<br> PSP_008998_1780</p> <p>HiRISE 10 (ESP_011688_1760_RED-ESP_019613_1760_RED-DEM_cyli.tif)<br> ESP_019613_1760<br> ESP_011688_1760</p>
Mars orbital image (HiRISE) labeled data set
<p>This data set contains 3820 landmarks that were extracted from 168 HiRISE images. The landmarks were detected in HiRISE browse images. For each landmark, we cropped a square bounding box the included the full extent of the landmark plus a 30-pixel margin to left, right, top, and bottom. Each cropped image was then resized to 227x227 pixels.</p> <p><strong>Contents</strong>:</p> <ul> <li>map-proj/: Directory containing individual cropped landmark images</li> <li>labels-map-proj.txt: Class labels (ids) for each landmark image</li> <li>landmark_mp.py: Python dictionary that maps class ids to semantic names</li> </ul> <p><strong>Attribution</strong>:</p> <p>If you use this data set in your own work, please cite this DOI: 10.5281/zenodo.1048301</p> <p>Please also cite this paper, which provides additional details about the data set.</p> <p>Kiri L. Wagstaff, You Lu, Alice Stanboli, Kevin Grimes, Thamme Gowda, and Jordan Padams. "Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas." <em>Proceedings of the Thirtieth Annual Conference on Innovative Applications of Artificial Intelligence</em>, 2018.</p> <p> </p>
Mars Target Encyclopedia - LPSC abstracts labeled data set
<p>This data set contains annotated text versions of 2-page abstracts published at the Lunar and Planetary Science Conference in 2015 and 2016.</p> <p>The original PDF abstracts are available at:</p> <ul> <li>https://www.hou.usra.edu/meetings/lpsc2015/programAbstracts/view/</li> <li>https://www.hou.usra.edu/meetings/lpsc2016/programAbstracts/view/</li> </ul> <p>The text files in this archive were extracted using the Apache Tika PDF parsing tool. The text is provided here so that the annotations can be viewed. The text content remains copyright of the original abstract authors.</p> <p>The annotations (entities and relations) are provided in the format used by the brat annotation tool. To view the annotations in a web-based graphical form, install the brat tool (http://brat.nlplab.org/). These annotations were generated using brat v1.3. The annotation files are also human-readable and can be parsed in to be used directly in code.</p> <p><strong>Contents</strong>:</p> <ul> <li>lpsc15/: 62 abstracts</li> <li>lpsc16/: 55 abstracts</li> </ul> <p>Each directory contains a .txt and .ann file for each abstract. The .ann file is in brat standoff format (http://brat.nlplab.org/standoff.html).</p> <p>Additional .conf files are provided to generate color highlighting and keyboard shortcuts. These are used by the brat tool.</p> <p><strong>Attribution</strong>:</p> <p>If you use this data set in your own work, please cite this DOI:</p> <p>10.5281/zenodo.1048419</p> <p>Please also cite this paper, which provides additional details about the data set.</p> <p>Kiri L. Wagstaff, Raymond Francis, Thamme Gowda, You Lu, Ellen Riloff, Karanjeet Singh, and Nina Lanza. "Mars Target Encyclopedia: Rock and Soil Composition Extracted from the Literature." <em>Proceedings of the Thirtieth Annual Conference on Innovative Applications of Artificial Intelligence</em>, 2018.</p>
Mars surface image (Curiosity rover) labeled data set
<p>This data set consists of 6691 images spanning 24 classes that were collected by the Mars Science Laboratory (MSL, Curosity) rover by three instruments (Mastcam Right eye, Mastcam Left eye, and MAHLI). These images are the "browse" version of each original data product, not full resolution. They are roughly 256x256 pixels each.</p> <p>We divided the MSL images into train, validation, and test data sets according to their sol (Martian day) of acquisition. This strategy was chosen to model how the system will be used operationally with an image archive that grows over time. The images were collected from sols 3 to 1060 (August 2012 to July 2015). The exact train/validation/test splits are given in individual files. Full-size images can be obtained from the PDS at https://pds-imaging.jpl.nasa.gov/search/ .</p> <p><strong>Contents</strong>:</p> <ul> <li>calibrated/: Directory containing calibrated MSL images</li> <li>train-calibrated-shuffled.txt: Training labels (images in shuffled order)</li> <li>val-calibrated-shuffled.txt: Validation labels</li> <li>test-calibrated-shuffled.txt: Test labels</li> <li>msl_synset_words-indexed.txt: Mapping from class IDs to class names</li> </ul> <p><strong>Attribution</strong>:</p> <p>If you use this data set in your own work, please cite this DOI:</p> <p>10.5281/zenodo.1049137</p> <p>Please also cite this paper, which provides additional details about the data set.</p> <p>Kiri L. Wagstaff, You Lu, Alice Stanboli, Kevin Grimes, Thamme Gowda, and Jordan Padams. "Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas." <em>Proceedings of the Thirtieth Annual Conference on Innovative Applications of Artificial Intelligence</em>, 2018.</p>
Marlies van der Riet (r2229)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Marlies van der Riet<br><u>musiXplora-ID</u>: r2229<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/r2229">https://musixplora.de/mxp/r2229</a><br><u>Gender</u>: f<br><u>First Mentioned</u>: 2014<br><u>Sectors</u>: Musikforschung<br><u>Professions (Musical)</u>: Instrumentenkundlerin, Musikforscherin<br><u>Other Places of Activity</u>: Amsterdam<br><br><br><u>Medien:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>VerfasserInnen</td><td>Verfasserin</td><td>Daniël François Scheurleer, de laatste jaren van een Haags culturmecenas</td><td><a href="https://musixplora.de/mxp/5033655">5033655</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Intermittency in wind-driven surface alteration on Mars interpreted from wind streaks and measurements by InSight
<p>Shapefiles associated with the GRL publication: Intermittency in wind-driven surface alteration on Mars interpreted from wind streaks and measurements by InSight</p>
MAR-M-247 creep assessment through a modified theta projection model - Figures 2 and 5
<p>These two programs provide a way to rebuild the MAR-M-247 creep data presented in the paper:</p> <p>G. Maggiani, M.J. Roy, S. Colantoni, P.J. Withers, MAR-M-247 creep assessment through a modified theta projection model, Materialia, Volume 7, 2019, 100392, ISSN 2589-1529, https://doi.org/10.1016/j.mtla.2019.100392. http://www.sciencedirect.com/science/article/pii/S2589152919301887)<br> </p> <p>In Paper_Figure_2.m two coefficients of the paper itself are corrected and a comparison with what written in the paper and the corrected value is provided. One typo error for theta 1 at 982°C and 140 MPa where 6.9 must be 1.9. The other is for 1038°C 50 MPa theta4. In the paper it is written e^-11 while it actually should have been e^-10.</p> <p>Paper_Figure_5.m more decimal values are provided for the coefficients a, b, c and d that are used to rebuild the theta values.</p> <p> </p> <p> </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.