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1,880 results for “mars”
023488_2050_overview_mars_hirise
023488_2050_overview_mars_hirise "proposed landing sight" ;) more info may be available here: "Proposed Landing Site in Mawrth Vallis" https://www.uahirise.org/dtm/dtm.php?ID=ESP_023488_2050 This signal was analyzed by Organic. Tools used: gdal, qgis, houdini https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
036481_1835_overview_mars_hirise
036481_1835_overview_mars_hirise on what scale are we measuring "new" here ;) more info may be available here: "New Impact Site" https://www.uahirise.org/dtm/dtm.php?ID=ESP_036481_1835 This signal was analyzed by Organic. Tools used: gdal, qgis, houdini https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
Roman Museum. Premià de Mar
Remains of an octogonal roman building in Premià de Mar. Catalonia. Just a test with the RTAB-Map app WITHOUT Lidar. Just phone movement capturing features. Impressive. Captured with an iPhone Xs. Color scheme following the blueprint from https://sketchfab.com/3d-models/malbork-castle-blueprint-iphone-3d-scan-c2d55e17c9b94710942ee33b76b6e745 Source: Objaverse 1.0 / Sketchfab
Repository: Turbulent Fluxes and Evaporation/Sublimation Rates on Earth, Mars, Titan, and Exoplanets
<div>Repository: Turbulent Fluxes and Evaporation/Sublimation Rates on Earth, Mars, Titan, and Exoplanets</div> <div>Khuller & Clow (2024)</div> <div> </div> <div>Contents:</div> <div> </div> <div>1. Comparison of Khuller & Clow model, Dundas & Byrne (2010) model vs. turbulent fluxes measured by Fitzpatrick et al. (2017)</div> <div>a) Measured fluxes</div> <div>i) Measured_Abs_LE_Fitzpatrick: Absolute value of measured latent heat fluxes in W/m^2</div> <div>ii) Measured_Abs_SH_Fitzpatrick: Absolute value of measured sensible heat fluxes in W/m^2</div> <div>b) Modeled fluxes</div> <div>i) Modeled_DB_Abs_LE_Fitzpatrick: Absolute value of Dundas & Byrne (2010) modeled latent heat fluxes in W/m^2</div> <div>ii) Modeled_DB_Abs_SH_Fitzpatrick: Absolute value of Dundas & Byrne (2010) modeled sensible heat fluxes in W/m^2</div> <div>iii) Modeled_KC_Abs_LE_Fitzpatrick: Absolute value of Khuller & Clow modeled latent heat fluxes in W/m^2</div> <div>iv) Modeled_KC_Abs_SH_Fitzpatrick: Absolute value of Khuller & Clow modeled sensible heat fluxes in W/m^2</div> <div> </div> <div>2. Comparison of Khuller & Clow model, Dundas & Byrne (2010) model vs. turbulent fluxes measured by Fairall et al. (1996); Fairall et al. (2003)</div> <div>a) Measured fluxes</div> <div>i) Measured_Abs_LE_COARE: Absolute value of measured latent heat fluxes in W/m^2</div> <div>ii) Measured_Abs_SH_COARE: Absolute value of measured sensible heat fluxes in W/m^2</div> <div>b) Modeled luxes</div> <div>i) Modeled_DB_Abs_LE_COARE: Absolute value of Dundas & Byrne (2010) modeled latent heat fluxes in W/m^2</div> <div>ii) Modeled_DB_Abs_SH_COARE: Absolute value of Dundas & Byrne (2010) modeled sensible heat fluxes in W/m^2</div> <div>iii) Modeled_KC_Abs_LE_COARE: Absolute value of Khuller & Clow modeled latent heat fluxes in W/m^2</div> <div>iv) Modeled_KC_Abs_SH_COARE: Absolute value of Khuller & Clow modeled sensible heat fluxes in W/m^2</div> <div> </div> <div>3. Comparison of Khuller & Clow model, Dundas & Byrne (2010) model vs. ice sublimation rates measured by Clow et al. (1988)</div> <div>a) Measured sublimation rates</div> <div>i) Measured_dzdt_Clow: Measured sublimation rates in mm/day</div> <div> </div> <div>b) Modeled sublimation rates</div> <div>i) Modeled_DB_dzdt_Clow: Dundas & Byrne (2010) modeled sublimation rates in mm/day</div> <div>ii) Modeled_KC_dzdt_Clow: Khuller & Clow modeled sublimation rates in mm/day</div> <div> </div> <div>4. Comparison of Khuller & Clow model, Dundas & Byrne (2010) model vs. ice sublimation rate measured by Douglas & Mellon (2019)</div> <div>a) Roughness Lengths used</div> <div>i) z0_DM: Roughness lengths used in cm</div> <div> </div> <div>b) Modeled Sublimation Rates</div> <div>i) Modeled_DB_dzdt_u_001_DM: Dundas & Byrne (2010) modeled sublimation rates for u = 0.01 m/s in mm/day</div> <div>ii) Modeled_DB_dzdt_u_005_DM: Dundas & Byrne (2010) modeled sublimation rates for u = 0.05 m/s in mm/day</div> <div>iii) Modeled_DB_dzdt_u_010_DM: Dundas & Byrne (2010) modeled sublimation rates for u = 0.10 m/s in mm/day</div> <div>iv) Modeled_DB_dzdt_u_015_DM: Dundas & Byrne (2010) modeled sublimation rates for u = 0.15 m/s in mm/day</div> <div>v) Modeled_KC_dzdt_u_001_DM: Khuller & Clow modeled sublimation rates for u = 0.01 m/s in mm/day</div> <div>vi) Modeled_KC_dzdt_u_005_DM: Khuller & Clow modeled sublimation rates for u = 0.05 m/s in mm/day</div> <div>vii) Modeled_KC_dzdt_u_010_DM: Khuller & Clow modeled sublimation rates for u = 0.10 m/s in mm/day</div> <div>viii) Modeled_KC_dzdt_u_015_DM: Khuller & Clow modeled sublimation rates for u = 0.15 m/s in mm/day</div> <div> </div> <div>5. Comparison of Khuller & Clow model, Dundas & Byrne (2010) model vs. net ice sublimation inferred at Mars Phoenix landing site, by Smith et al. (2009)</div> <div>a) Parameters used</div> <div>i) z0_PHX: Roughness lengths used in cm</div> <div>ii) u_PHX: Wind speeds used in m/s</div> <div>iii) deltatheta1_PHX: Delta-surface temperatures used in K</div> <div> </div> <div>b) Modeled net sublimation</div> <div>i) Modeled_DB_deltatheta1_sensitivity_PHX: Dundas & Byrne (2010) modeled sublimation rates for varied delta-surface temperatures in cm</div> <div>ii) Modeled_DB_u_sensitivity_PHX: Dundas & Byrne (2010) modeled sublimation rates for varied wind speeds in cm</div> <div>iii) Modeled_DB_z0_sensitivity_PHX: Dundas & Byrne (2010) modeled sublimation rates for varied roughness lengths in cm</div> <div> </div> <div>6. Modeled vertical profiles of temperature and wind speed compared to Huygens probe measurements (Fulchignoni et al., 2005)</div> <div>i) Modeled_theta_Huygens_u_0_02: Temperature profile using 10-m wind speed of 0.02 m/s, in K</div> <div>ii) Modeled_theta_Huygens_u_1: Temperature profile using 10-m wind speed of 1 m/s, in K</div> <div>iii) Modeled_Z_Huygens: Altitude profile in m</div> <div> </div> <div>7. Modeled effect of various parameters on Mars ice sublimation rates</div> <div>a) Input data ranges</div> <div>i) Modeled_Mars_Theta2_warm: 3-m air temperatures for warm cases in K</div> <div>ii) Modeled_Mars_Theta2_cold: 3-m air temperatures for cold cases in K</div> <div>iii) Modeled_Mars_z0_range: Roughness lengths in cm</div> <div>iv) Modeled_Mars_P_range: Surface air pressure values in mbar</div> <div>v) Modeled_Mars_beta_range: Dimensionless gustiness parameters</div> <div> </div> <div>b) Modeled sublimation rates from Dundas & Byrne (2010) model</div> <div>i) Modeled_DB_dzdt_Mars_warm_low_wind: Sublimation rate in cm/Mars yr using 0.5 m/s wind speed at 3-m</div> <div>ii) Modeled_DB_dzdt_Mars_warm_high_wind: Sublimation rate in cm/Mars yr using 15 m/s wind speed at 3-m</div> <div>iii) Modeled_DB_dzdt_Mars_cold_low_wind: Sublimation rate in cm/Mars yr using 0.5 m/s wind speed at 3-m</div> <div>iv) Modeled_DB_dzdt_Mars_cold_high_wind: Sublimation rate in cm/Mars yr using 15 m/s wind speed at 3-m</div> <div>v) Modeled_DB_dzdt_Mars_warm_z0_var: Sublimation rate in cm/Mars yr using range of z0 values</div> <div>vi) Modeled_DB_dzdt_Mars_cold_z0_var: Sublimation rate in cm/Mars yr using range of z0 values</div> <div>vii) Modeled_DB_dzdt_Mars_warm_P_var: Sublimation rate in cm/Mars yr using range of P values</div> <div>viii) Modeled_DB_dzdt_Mars_cold_P_var: Sublimation rate in cm/Mars yr using range of P values</div> <div>xi) Modeled_DB_dzdt_Mars_warm_beta_var: Sublimation rate in cm/Mars yr using range of beta values</div> <div>x) Modeled_DB_dzdt_Mars_cold_beta_var: Sublimation rate in cm/Mars yr using range of beta values</div> <div> </div> <div>c) Modeled sublimation rates from Khuller & Clow model</div> <div>i) Modeled_KC_dzdt_Mars_warm_low_wind: Sublimation rate in cm/Mars yr using 0.5 m/s wind speed at 3-m</div> <div>ii) Modeled_KC_dzdt_Mars_warm_high_wind: Sublimation rate in cm/Mars yr using 15 m/s wind speed at 3-m</div> <div>iii) Modeled_KC_dzdt_Mars_cold_low_wind: Sublimation rate in cm/Mars yr using 0.5 m/s wind speed at 3-m</div> <div>iv) Modeled_KC_dzdt_Mars_cold_high_wind: Sublimation rate in cm/Mars yr using 15 m/s wind speed at 3-m</div> <div>v) Modeled_KC_dzdt_Mars_warm_z0_var: Sublimation rate in cm/Mars yr using range of z0 values</div> <div>vi) Modeled_KC_dzdt_Mars_cold_z0_var: Sublimation rate in cm/Mars yr using range of z0 values</div> <div>vii) Modeled_KC_dzdt_Mars_warm_P_var: Sublimation rate in cm/Mars yr using range of P values</div> <div>viii) Modeled_KC_dzdt_Mars_cold_P_var: Sublimation rate in cm/Mars yr using range of P values</div> <div>xi) Modeled_KC_dzdt_Mars_warm_beta_var: Sublimation rate in cm/Mars yr using range of beta values</div> <div>x) Modeled_KC_dzdt_Mars_cold_beta_var: Sublimation rate in cm/Mars yr using range of beta values</div> <div> </div> <div>d) Modeled latent heat fluxes from Dundas & Byrne (2010) model</div> <div>i) Modeled_DB_LE_Mars_warm_low_wind: Latent heat flux in W/m^2 using 0.5 m/s wind speed at 3-m</div> <div>ii) Modeled_DB_LE_Mars_warm_high_wind: Latent heat flux in W/m^2 using 15 m/s wind speed at 3-m</div> <div>iii) Modeled_DB_LE_Mars_cold_low_wind: Latent heat flux in W/m^2 using 0.5 m/s wind speed at 3-m</div> <div>iv) Modeled_DB_LE_Mars_cold_high_wind: Latent heat flux in W/m^2 using 15 m/s wind speed at 3-m</div> <div>v) Modeled_DB_LE_Mars_warm_z0_var: Latent heat flux in W/m^2 using range of z0 values</div> <div>vi) Modeled_DB_LE_Mars_cold_z0_var: Latent heat flux in W/m^2 using range of z0 values</div> <div>vii) Modeled_DB_LE_Mars_warm_P_var: Latent heat flux in W/m^2 using range of P values</div> <div>viii) Modeled_DB_LE_Mars_cold_P_var: Latent heat flux in W/m^2 using range of P values</div> <div>xi) Modeled_DB_LE_Mars_warm_beta_var: Latent heat flux in W/m^2 using range of beta values</div> <div>x) Modeled_DB_LE_Mars_cold_beta_var: Latent heat flux in W/m^2 using range of beta values</div> <div> </div> <div>e) Modeled latent heat fluxes from Khuller & Clow model</div> <div>i) Modeled_KC_LE_Mars_warm_low_wind: Latent heat flux in W/m^2 using 0.5 m/s wind speed at 3-m</div> <div>ii) Modeled_KC_LE_Mars_warm_high_wind: Latent heat flux in W/m^2 using 15 m/s wind speed at 3-m</div> <div>iii) Modeled_KC_LE_Mars_cold_low_wind: Latent heat flux in W/m^2 using 0.5 m/s wind speed at 3-m</div> <div>iv) Modeled_KC_LE_Mars_cold_high_wind: Latent heat flux in W/m^2 using 15 m/s wind speed at 3-m</div> <div>v) Modeled_KC_LE_Mars_warm_z0_var: Latent heat flux in W/m^2 using range of z0 values</div> <div>vi) Modeled_KC_LE_Mars_cold_z0_var: Latent heat flux in W/m^2 using range of z0 values</div> <div>vii) Modeled_KC_LE_Mars_warm_P_var: Latent heat flux in W/m^2 using range of P values</div> <div>viii) Modeled_KC_LE_Mars_cold_P_var: Latent heat flux in W/m^2 using range of P values</div> <div>xi) Modeled_KC_LE_Mars_warm_beta_var: Latent heat flux in W/m^2 using range of beta values</div> <div>x) Modeled_KC_LE_Mars_cold_beta_var: Latent heat flux in W/m^2 using range of beta values</div>
WindSightNet: Catalogue of wind speed and direction data from NASA InSight lander on Mars using seismic data
<p>Dataset associated with the publication "WindSightNet: the inter-annual variability of Martian winds retrieved from InSight's seismic data with machine learning" submitted to JGR: Planets.</p> <p>Authors:</p> <p>A. E. Stott, R. F. Garcia, N. Murdoch, D. Mimoun, M. Drilleau, C. Newman, A. Spiga, D. Banfield, M. Lemmon, S. Navarro, L. Mora-Sotomayor, C. Charalambous, W. T. Pike, P. Lognonné, W. B .Banerdt</p> <p>Files containing catalogue of winds produced from the seismic data on the NASA InSight mission using machine learning algorithm produced in above publication. Please refer to this publication for technical details.</p> <p> </p> <p>Contents:</p> <p>WindSightNet.csv - file containing wind speed and direction produced from the WindSightNet neural network based on seismic data</p> <p>TWINS.csv - comparitive wind speed and direction from TWINS wind sensor when available. </p> <p>TWINS data originally available from:</p> <p>J A Manfredi, Insight Auxiliary Payload Sensor Subsystem (APSS) Temperatures and Wind Sensor for Insight (TWINS) Archive Bundle, (2019), https://doi.org/10.17189/1518950</p> <p> </p> <p>Each file contains values for:</p> <p>Wind Speed</p> <p>Wind dir.</p> <p>Sol - number of sol of InSight mission </p> <p>UTC - Coordinated Universal Time of sample</p> <p>LTST - Local True Solar Time of sample</p> <p>L_s - Solar longitude value of sample</p> <p>Time - seconds since UNIX epoch</p> <p>Data is considered to be sampled at a rate of 0.01 Hz when there are no gaps.</p> <p> </p> <p>Example code for plotting paper figures can be found:</p> <p>https://doi.org/10.5281/zenodo.14267939</p>
Supporting material for: Geological analysis of the dome field in western Utopia Planitia, on Mars
<p>These are the suplementary materials for the named paper, included shapefiles, jupyter notebook and raster images used in the paper. </p>
Mud Flows in the Southwestern Utopia Planitia, Mars
<p>Results of a mapping campaign of putative mud-volcanic landforms in southwestern parts of Utopia basin, Mars based on CTX imagery:</p> <p>Muddy_Landforms_Curin_et_al_2021: Hills, ridges, plateaus, and complexly layered units around Adamas Labyrinthus</p> <p>Polygonal_Troughs_Curin_et_al_2021: Major polygonal troughs around Adamas Labyrinthus</p>
DEM for "The lithified aeolian dune field adjacent to the Apollinaris Sulci, Mars: Geological history and paleo-wind record"
<p>This repository contains the digital elevation model and digitally rectified graphic used in the publication titled "The lithified aeolian dune field adjacent to the Apollinaris Sulci, Mars: Geological history and paleo-wind record" by Hunt, Day, Edgett, and Chojnacki, submitted to Icarus after revision in October 2021. </p> <p>The repository contains two TIFF files, one each for the DEM and DRG, and projection and world files for each. For questions or more information contact daym@epss.ucla.edu </p>
SuperDARN data in netCDF format (2018-Mar)
<p>2018-Mar SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
Multispecies MHD study of ion escape at ancient Mars: effects of an intrinsic magnetic field and solar XUV radiation
<p>This dataset contains the simulation results in the paper "Multispecies MHD study of ion escape at ancient Mars: effects of an intrinsic magnetic field and solar XUV radiation" submitted to Journal of Geophysical Research: Space Physics.</p>
Marsquake locations and 1-D seismic models for Mars from InSight data
<p>Data used to draw the figures in the paper 'Marsquake locations and 1-D seismic models for Mars from InSight data'.</p>
Antarctic Peninsula MAR 3-hourly data
<p>Modèle Atmosphérique Régionale (MAR), was utilized Wille et al. (2022, Commun. Earth Environ.) . The files contain surface melt, runoff, surface temperature at three-hour timesteps. A description of MAR from Wille et al. (2022) follows as...</p> <p>MAR is a regional climate model specifically designed for simulating polar climate. MAR atmospheric dynamics are based on the hydrostatic approximation of the primitive equations. The exchanges between the atmospheric part of MAR and the surface are handled by the complex energy-balance snow model SISVAT, based on CROCUS that explicitly simulates 30 layers resolving the 20 first meters of snow or ice. The surface module notably represents percolation of meltwater and its retention into the snowpack. Runoff occurs when the snowpack can no longer absorb additional liquid water (i.e., snowpack water content exceeding 5% or surface liquid water over bare ice or an ice-lense layer). MARv3.11 was run at a resolution of 7.5 km and was forced by 6-hourly outputs of the latest ERA5 reanalysis between 1979 and March 2020. The first year was discarded as spin-up.</p> <p> </p>
Datasets for "Search for shallow subsurface structures in Chryse and Acidalia Planitiae on Mars"
<p>Datasets for "Search for shallow subsurface structures in Chryse and Acidalia Planitiae on Mars" Oura et al., 2022, Icarus</p> <ol> <li>CAP_SHARAD_list.csv: List of SHARAD observations used in this study</li> <li>CAP_subsurface_reflectance_revised.csv: Locations of identified subsurface reflectors</li> <li>FPB_xxxxxxxxxx.tiff/png: Radargrams and cluttergrams generated by CO-SHARPS</li> <li>HI_003222_2055_018834_2060-ALIGN-DEM.tif: HiRISE DTM used in this study</li> <li>HI_003222_2055_018834_2060-ALIGN-DRG.tif: HiRISE orthoimage used in this study</li> <li>HI_046473_2075_050877_2075-ALIGN-DEM.tif: HiRISE DTM used in this study</li> <li>HI_046473_2075_050877_2075-ALIGN-DRG.tif: HiRISE orthoimage used in this study</li> </ol> <p> </p>
CESM1-SOM Climatologies used for "Climate Sensitivity is Sensitive to Changes in Ocean Heat Transport" (published in Journal of Climate, Mar 2022)
<p>CESM1-SOM climatologies.</p> <p>Pre-industrial control run = SOM_Control.cam5.0030-0059.ann.nc</p> <p>CO2-doubling experiments:</p> <ul> <li>OHT + 30% = SOM_OHFC_P30_2XCO2_032019.cam5.0030-0059.ann.nc</li> <li>OHT + 15% = SOM_OHFC_P15_2XCO2_032019.cam5.0030-0059.ann.nc</li> <li>Control OHT = SOM_2XCO2_032019.cam5.0030-0059.ann.nc</li> <li>OHT - 15% = SOM_OHFC_M15_2XCO2_032019.cam5.0030-0059.ann.nc</li> <li>OHT - 30% = SOM_OHFC_M30_2XCO2_032019.cam5.0030-0059.ann.nc</li> </ul>
SuperDARN data in netCDF format (2016-Mar)
<p>2016-Mar SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN data in netCDF format (2014-Mar)
<p>2014-Mar SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN data in netCDF format (2013-Mar)
<p>2013-Mar SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN data in netCDF format (2012-Mar)
<p>2012-Mar SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN data in netCDF format (2011-Mar)
<p>2011-Mar SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
Mars Sample Localization Dataset
<p>Dataset for the article titled "Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulations". It contains stereo images from laboratory tests performed by a rover and real + synthetic images for sample-tube detection and pose estimation.</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.