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270 results for “craters”
Recalibration of the lunar chronology due to spatial cratering-rate variability - Data and code
<ul> <li>CR_moon.csv: Relative cratering rate shown in Fig.2. The<strong> </strong>data are provided over the full range of latitudes and longitudes, with a 1-degree bin.</li> <li>cr_lefeuvre2011.txt: Relative cratering rate proposed by Le Feuvre and Wieczorek (2011). The<strong> </strong>data are provided over the full range of latitudes and longitudes, with a 1-degree bin.</li> <li>lunar_calib_points.csv: Table summarising the lunar chronology calibration points used in this study. </li> <li>Lagain_AA_convert_age.m: Matlab code converting model ages of Plutarch and Kirkwood craters from Neukum et al. (2001) chronology into the one presented in this study. The code also computes the chronology model from Le Feuvre and Wieczorek (2011) and the one presented in this study for different locations, and compares it with the Neukum et al. (2001) chronology.</li> </ul>
Pluto and Charon Impact Crater Databases, Version 2
<p>Comma-Separated Values (CSV) files of basic impact crater data of Pluto and Charon, including latitude and longitude, diameter, and subjective confidence the features are impact craters. In the "Region Guide" files, one can find the region of each body on which the crater data reside, corresponding to a "Region" column in the database files. The Region Guide files also contain other information about the region, including surface area and the estimated completeness diameters of craters in that region. Additional metadata TXT files briefly describe the image data used to map the craters and very briefly describe the crater datasets.</p>
Gini index mean score grain size estimates for Murray formation rocks (Gale crater, Mars) from ChemCam LIBS data (sols 766-1804)
<p>This repository includes main research results from the Journal of Geophysical Research: Planets manuscript entitled <em>"Grain Size Variations in the Murray Formation: Stratigraphic Evidence for Changing Depositional Environments in Gale Crater, Mars"</em>. These datasets were also included as Supplementary tables with the manuscript. Please find the plain language summary of the manuscript and the captions for these datasets below:</p> <p><strong><em>Plain language summary:</em></strong> The lowest exposed rocks of the Murray formation in Gale crater, Mars are interpreted as ancient lake deposits based on <em>Curiosity </em>rover data. However, the duration and temporal variability of this ancient lake is still an open question. Here we characterize the vertical distribution of deposits within the entire Murray formation using new grain size information. Characterizing grain size in rocks provides information about the speed of past fluid flows, which is crucial for interpreting depositional environments. However, measuring grain size in images is rarely possible for martian rocks. Thus, we estimate grain sizes with the Gini Index Mean Score (GIMS), a grain-size proxy that uses ChemCam Laser Induced Breakdown Spectroscopy data. GIMS results indicate that the Murray formation is dominated by rocks with mud-sized grains (i.e., mudstones), suggesting mud-sized grain settled in a low energy lake environment. Mud cracks occur in some of the mudstones, indicating drying periods in a lake. Rocks with sand-sized grains (i.e., sandstones) and cross bedding occur at specific intervals, suggesting episodes with stream channels and wind-blown sand dunes. The dominance of lake deposits interspersed with stream deposits suggests that liquid water was present in Gale crater for tens of thousands to millions of years.</p> <p><strong>Table captions:</strong></p> <p><em>Table S3. </em>All Murray formation targets used in the Gini mean index score (GIMS) analysis (sols 766-1804) with summary information, general grain size estimates from MAHLI and RMI images if known, and G<sub>MEAN </sub>values with associated standard deviation errors. For targets with N/A grain sizes, grains could not be resolved in any of the images, or images were not available. Protrusions in the rocks that are not grains are likely diagenetic nodules or concretions. Targets with G<sub>MEAN</sub>=0.07 have transitional GSRs, indicated by GSR1/GSR2. Targets names are merged in the same cell for those analyses that were taken on the same rock exposure. Next to the target names, the symbol * denotes that the ChemCam target was imaged by the MAHLI, the symbol ** denotes that the dust removal tool was used before the MAHLI image was taken, and ~ signifies that a location close to the ChemCam target was imaged by the MAHLI.</p> <p><em>Table S4</em>. The mean, median, minimum and maximum G<sub>MEAN</sub> and the minimum and maximum grain size regime (GSR) for each locality in the Murray formation.</p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Crater Lake GeoTIFF
<p>An elevation model of Crater Lake, Oregon, USA</p> <p>Landform features: caldera, cinder cone, lava flow</p> <p>Resolution: 3.33 meter, 5,200 x 5,200 height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Radon (222Rn) activity in air on the crater rim of Mt. Etna Central Crater (May-October 2018)
<p><em>The dataset in the file dataset_radon.xlsx compiles radon (<sup>222</sup>Rn) activity values measured in air on the rim of Mt. Etna Central Crater with passive dosimeters during summer 2018. Passive dosimeters were installed all around the crater and in four reference sites, at two different heights above the ground (5 cm and 1 m). Geographical coordinates of installation points are given in the file. Exposition periods given in the dataset started and ended as follows: May-Oct (24/05/18-11/10/18), May-Jul (24/05/18-06/07/18) and Jul-Oct (06/07/18-11/10/18). The uncertainty for each dosimeter is given with a confidence interval of 2-σ. Dosimeters are grouped according to the sector of the rim (Nort-West, North-East, South-East and South-West + reference sites). For group mean values, the uncertainty corresponds to the standard deviation of the mean (standard deviation of the population divided by the square root of the number of elements in the population). “lost” indicates a dosimeter that was lost during the exposition, “udl” refers to a dosimeter that was under detection limit, and “damaged” corresponds to a dosimeter that was corroded by acids and could not be analysed or that was clogged in soldered dust preventing radon from entering the capsule. Note that one station (namely, that closest to the Voragine vent) was excluded from the computation of the mean value of the NE sector. </em></p> <p><em>The dataset in the file SO2_flux.pdf contains the time series of the daily bulk SO<sub>2</sub> flux measured at Mount Etna during the period 01/04/18-30/10/18.</em></p>
Camera Snapshots of Rockfalls at Dolomieu crater, Reunion Island
<p>Camera snapshots of rockfalls at Dolomieu crater, Piton de la Fournaise volcano, Reunion.</p> <p>The folders in the compressed file are structured by event date and camera.</p> <p> </p> <p>Available cameras: CBOC, DOEC, SFRC</p> <p>Snapshot interval: 0.5 s</p>
A Novel Approach to Impact Crater Mapping and Analysis on Enceladus, using Machine Learning: Supplemental data
<p>This dataset includes the crater map and equatorial crater depths and diameters presented in the paper: A Novel Approach to Impact Crater Mapping and Analysis on Enceladus, using Machine Learning.</p>
Impact Craters on Mimas, Rhea, and Iapetus (incomplete surface coverage)
<p>Comma-separated values (CSV) files of impact crater data from Iapetus, Rhea, and Mimas, based on identification of features on individual images tied to Schenk (circa 2012–2014) basemaps. Data include latitude, longitude, and diameter in units of decimal degrees (location) and kilometers (size). These data cover approximately 27%, 11%, and 73% of the surface area of each body, respectively.</p>
Phenomenological images (CL-CAM1B) of Crater Lake CALM site, Deception island, Antarctica (2023).
<p>Images acquired by a phenomenological automatic time-lapse camera (CL-CAM1B) located in Crater Lake permafrost and active layer monitoring site in Crater Lake sounders in Deception island, Antarctica, in 2023.</p> <ul> <li>File code: DEC_CL_CAM1B_2023_v100</li> <li>Location code: DEC</li> <li>Site code: CL</li> <li>Instrument code: CAM1B</li> <li>Period: 2023</li> <li>Version: 1.0.0 (jpg images as they were obtained from camera, without processing)</li> <li>Camera/manufacturer: CC5MPX by Campbell Scientific Inc.</li> <li>Resolution/Type/Format: 5Mpixels in RGB in jpg files</li> <li>Frequency: 3 images per day at 14h, 15h and 16h GMT</li> <li>Site: Close to Crater Lake CALM site, in Crater Lake sounders of Deception island, Antarctica.</li> <li>Location: </li> <li>Elevation:</li> <li>Initial dataset date/time: March 6, 2023 16:00h GMT</li> <li>Final dataset date/time: February 18, 2024 16:00h GMT</li> <li>Gaps: None</li> <li>Notes: <ul> <li>Folders: 12</li> <li>Files: 1048</li> <li>Other files: none</li> <li>Folder names structure: year_month</li> <li>Datafiles names structure: Site_camera_year_month_day_hour_minute_second.jpg</li> </ul> </li> </ul>
Database of geochemical analyses of soils and rocks related to the Bosumtwi impact crater
<p>This excel file contains geochemical analyses of soils and rocks related to the Bosumtwi impact crater and its surroudings geological units - all data included in this database have been extracted from peer-reviewed literature.</p>
Dataset for the manuscript "Attribute Recognition: A New Method for Grouping Planetary Images by Visual Characteristics, Using the Example of Mn-Rich Rocks in the Floor of Gale Crater, Mars."
<p>This dataset supports the manuscript "Attribute Recognition: A New Method for Grouping Planetary Images by Visual Characteristics, Using the Example of Mn-Rich Rocks in the Floor of Gale Crater, Mars." The dataset is contained in a single CSV file with 201 data rows (one row per NASA Curiosity rover ChemCam instrument target used in the study). The columns in this dataset include the martian solar day (sol) on which each target was imaged by ChemCam; the standoff distance from ChemCam to each target (in meters); binary columns (values are either 1 or 0, indicating presence or absence, respectively) for each of the 17 visual attributes we documented for each target image; the corresponding greyscale ChemCam RMI mosaic file location (on the Planetary Data System); and columns indicating which group each target was sorted into under each classification algorithm discussed in the text (P_{SG}: simple graph method; P_{AP}: automatic partitioning method; P_{\lambda=1.6}: community detection method with \lambda=1.6). To obtain the binary strings used for the classification algorithms, the 17 visual attribute columns can be concatenated. </p> <p>Also included is a collection of HTML files that enables easy viewing of the RMI mosaics in each cluster, using the Planetary Data System links. To use it, download the <code>.zip</code> file, unzip it, and open the <code>index.html</code> file in the browser of your choice (likely will work to simply double-click <code>index.html</code>)</p>
Solar and meteorological data collected from the Cratere Bory station (La Réunion) by the ENERGY-lab at the University of La Reunion between July 2016 and November 2021
<p>Scientific data provided by ENERGY-lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
South polar small and medium craters
<p>Dataset of small and medium craters at the lunar south pole</p>
Classification of ChemCam LIBS targets from Glen Torridon, Gale crater, Mars
<p>Point-by-point classification of all ChemCam LIBS targets in the Glen Torridon region (sols 2301 to 3007). The targets from the Greenheugh pediment are included for completeness, even though they are not studied in detail here (see Bedford et al., this issue). Columns 5 to 9 contain the geographic and stratigraphic locations of the targets. Columns 10 to 12 contain descriptions of the material that was sampled by the laser at each point, based on visual inspection of rover imagery. Column 13 indicates the quality of the data with respect to focus, based on the examination of the focus curves returned by the instrument each time an autofocus is performed (Peret et al., 2016): “poor” corresponds to a flat or noisy curve; “suboptimal” corresponds to a curve with a maximum at the edge of the range of distances scanned by the autofocus; and “uncertain” corresponds to points for which no focus curve is available, but that are likely out-of-focus given the local target topography visible in the RMI images of the target. Column 14 contains the spacecraft clock, which is unique to each row and enables identification and download of the associated spectra from the Planetary Data System (<a href="http://pds-geosciences.wustl.edu/missions/msl/">http://pds-geosciences.wustl.edu/missions/msl/</a>). Columns 15 to 42 contain the major-element oxide composition of each point, except for targets with poor focus or very high FeO<sub>T</sub> (e.g., iron meteorites), or located beyond 6 meters. Abundances are in wt%. The RMSEP (root mean squared error of prediction) reflects the model accuracy as detailed in Clegg et al. (2017). The “shots stdev” reflects the standard deviation across the laser shots (excluding the first 5). Columns 45 to 49 contain the corrected abundances for SiO<sub>2</sub>, Al<sub>2</sub>O<sub>3</sub>, Na<sub>2</sub>O and K<sub>2</sub>O, as well as the corrected sum of oxides, for targets beyond 3.5 m (Wiens et al., 2021). Column 50 contains the calculated value of the Chemical Index of Alteration. Abbreviations used: BH = Bloodstone hill; CB = Central butte; CBU = clay-bearing unit; LT = lateral traverse; MA = Mary Anning; TB = Tower butte; WB = Western butte.</p>
Modeling output for "The dynamic atmospheric and aeolian environment of Jezero crater, Mars"
<p>This dataset contains meso- and microscale numerical modeling output supporting the findings presented in the paper, "Newman et al., The dynamic atmospheric and aeolian environment of Jezero crater, Mars, Science Advances"</p>
Modeling output for "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars"
<p><strong>General information:</strong></p> <p>Please contact Claire Newman (claire@aeolisresearch.com) if you have questions about this dataset.</p> <p><em><strong>Title of Dataset: </strong></em>Modeling output for "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars"</p> <p><em><strong>Author Information:</strong></em><br> Name: Claire E. Newman<br> Institution: Aeolis Research<br> Email: claire@aeolisresearch.com</p> <p>Recommended citation for this dataset: Newman C.E. (2022) "Modeling output for Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars", Dataset.</p> <p><strong>Summary taken from the paper "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars" by Kathryn M. Stack et al., accepted by JGR Planets for publication in 2022:</strong></p> <p>The orientation of Glen Torridon ridges was compared against outputs from the Mars Weather Research and Forecasting (MarsWRF) model. The MarsWRF model has been used to simulate the atmospheric circulation inside Gale crater to compare with MSL Rover Environmental Monitoring Station (REMS) wind measurements (Newman et al., 2017) and to assist in interpreting observations of dust devils (Newman et al., 2019) and aeolian changes (Baker et al., 2018, 2022). The output used in this work comes from the ‘vertical grid B’ simulations described in Newman et al. (2017), which provide the best match to observed winds and aeolian features. For Gale crater modeling, MarsWRF is run as a global model with nested higher-resolution domains that gradually increase the model’s horizontal resolution over smaller and smaller areas, finally providing output at ~400m grid spacing over the NW quadrant of the crater. The version of MarsWRF used here includes the treatment of radiative transfer in Mars’s dusty CO2 atmosphere, the seasonal CO2 cycle, subsurface-surface-atmosphere exchange of heat and momentum, and vertical mixing of heat and momentum, with surface properties (topography, roughness, albedo, etc.) based on orbital datasets and the seasonally-evolving, non-dust-storm “Mars Climate Database” dust distribution imposed (see Richardson et al., 2007 for more details).</p> <p>The model outputs minute-by-minute predictions of surface friction velocity, u*, and atmospheric density at 1.5m, ρ, for 7 sols at each of 12 periods, which are equally spaced in planetocentric solar longitude (Ls) through the martian year. For each output, the surface wind stress, τ, is found from τ=ρu_*^2. We then adjusted the contribution of each period to account for the varying number of sols Mars spends around each period over its orbit, before producing wind stress roses, which thus represent the total wind stress and direction over a non-dust-storm Mars year.</p> <p><strong>Specifically, contained in the "outputsfullcorr.nc" netCDF data file are:</strong></p> <p><strong><em>Variables:</em></strong></p> <p>PSFC: surface pressure (Pa)</p> <p>T1_5: temperature at 1.5m height above the surface (K)</p> <p>U1_5: zonal (west-to-east) wind at 1.5m height above the surface (m/s)</p> <p>V1_5: meridional (south-to-north) wind at 1.5m height above the surface (m/s)</p> <p>UST: surface friction speed (m/s)</p> <p><strong><em>Output times:</em></strong></p> <p>These variables are output from MarsWRF every minute starting at 15:10 Local True Solar Time in the first sol, with 1440 outputs per martian sol. There are 68 sols of data in total, with between 5 and 7 sols of data from each 30° Ls range, with the relative number chosen to reflect the fraction of a Mars year spent in that Ls range. In detail, the sols of the martian year used (where Ls=0 would be at the start of sol 1 and Ls=360 at the end of sol 669) are:</p> <p>Ls~0°: sols 668-669 & 01-4</p> <p>Ls~30°: sols 60-65</p> <p>Ls~60°: sols 124-130</p> <p>Ls~90°: sols 194-200</p> <p>Ls~120°: sols 254-259</p> <p>Ls~150°: sols 314-319</p> <p>Ls~180°: sols 374-378</p> <p>Ls~210°: sols 424-428</p> <p>Ls~240°: sols 474-478</p> <p>Ls~270°: sols 514-518</p> <p>Ls~300°: sols 564-568</p> <p>Ls~330°: sols 614-618</p> <p><strong><em>Output longitudes and latitudes are in file "static.nc":</em></strong></p> <p>The output is provided for a uniform grid of 19 longitudes by 15 latitudes. The longitudes and latitudes (variables XLONG and XLAT) in static.nc match those in outputsfullcorr.nc, but below are listed the longitudes and latitudes for convenience:</p> <p>Longitudes (19): 137.321, 137.3292, 137.3374, 137.3457, 137.3539, 137.3621, 137.3704, <br> 137.3786, 137.3868, 137.3951, 137.4033, 137.4115, 137.4198, 137.428, <br> 137.4362, 137.4444, 137.4527, 137.4609, 137.4691 </p> <p>Latitudes (15): -4.786012, -4.777781, -4.769551, -4.761321, -4.75309, -4.74486, -4.736629, -4.728399, -4.720168, -4.711937, -4.703707, -4.695477, -4.687246, -4.679016, -4.670785</p> <p>static.nc also provides the local topographic height used by the model at each location (variable name HGT).</p> <p><strong><em>Output data format:</em></strong></p> <p>NetCDF (Network Common Data Form) is a set of software libraries and machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data. It is also a community standard for sharing scientific data. The Unidata Program Center supports and maintains netCDF programming interfaces for <a href="https://docs.unidata.ucar.edu/netcdf-c/current/">C</a>, <a href="https://docs.unidata.ucar.edu/netcdf-cxx/current/">C++</a>, <a href="https://www.unidata.ucar.edu/software/netcdf-java/">Java</a>, and <a href="https://docs.unidata.ucar.edu/netcdf-fortran/current/">Fortran</a>. Programming interfaces are also available for Python, IDL, MATLAB, R, Ruby, and Perl.</p> <p>See: <a href="https://www.unidata.ucar.edu/software/netcdf/">https://www.unidata.ucar.edu/software/netcdf/</a></p> <p><strong>References:</strong></p> <p>Baker, M.M., Lapotre, M.G.A., Minitti, M.E., Newman, C.E., Sullivan, R., Weitz, C.M., Rubin, D.R., Vasavada, A.R., Bridges, N.T., & Lewis, K.W. (2018). The Bagnold Dunes in Southern Summer: Active Sediment Transport on Mars Observed by the Curiosity Rover. <em>Geophysical Research Letters, 45</em>(17), 8853-8863. <a href="https://doi.org/10.1029/2018GL079040">https://doi.org/10.1029/2018GL079040</a>.</p> <p>Baker, M.M., Newman, C.E., Lapotre, M.G.A., Sullivan, R., Bridges, N.T., & Lewis, K.W. (2018). Coarse Sediment Transport in the Modern Martian Environment. <em>Journal of Geophysical Research- Planets, 123</em>(6), 1380-1394. <a href="https://doi.org/10.1002/2017JE005513">https://doi.org/10.1002/2017JE005513</a>.</p> <p>Baker, M.M., Newman, C.E., Sullivan, R., Minitti, M.E., Edgett, K.S., Fey, D., Ellison, D., & Lewis, K.W. (2022). Diurnal variability in aeolian sediment transport at Gale crater, Mars, J. Geophys. Res. (Plan.), 127, e2020JE006734, https://doi.org/10.1029/2020JE006734.</p> <p>Newman, C., Gómez‐Elvira, J. G., Marín, M., Navarro, S., Torres, J., Richardson, M. I., et al. (2017). Winds measured by the Rover Environmental Monitoring Station (REMS) during the Mars Science Laboratory (MSL) rover's Bagnold Dunes Campaign and com- parison with numerical modeling using MarsWRF. <em>Icarus, 291</em>, 203–231. https://doi.org/10.1016/j.icarus.2016.12.016</p> <p>Newman, C. E., Kahanpää, H., Richardson, M. I., Martinez, G. M., Vicente‐Retortillo, A., & Lemmon, M. (2019). Convective vortex and dust devil predictions for Gale crater over 3 mars years and comparison with MSL‐REMS observations, <em>J. Geophys. Res. (Plan.</em>), 124, 3442– 3468, https://doi.org/10.1029/2019JE006082.</p> <p>Richardson, M.I., Toigo, A.D., & Newman, C.E. (2007). PlanetWRF: A general purpose, local to global numerical model for planetary atmospheric and climate dynamics<em>, J. Geophys. Res. (Plan.)</em>, 112, E09001, <a href="https://doi.org/10.1029/2006JE002825">https://doi.org/10.1029/2006JE002825</a>.</p>
Crater Populations on the Walls of Complex Craters found near the Lunar Southern Pole
<p>Date: June 23, 2022<br> Authors: C. L. Talkington and P. E. Montalvo<br> Notes: Data sets contain crater counts on floors of 16 lunar south polar complex craters and surface slopes at all counted craters. </p> <p>Data sets in crater_data.xlsx are updated from the previous version to improve the model age calculation. The current version uses an approach using Poisson’s statistics (Michael and Neukum, 2010; Michael et al., 2016).</p> <p>ArcMap 10.7.1 was used in addition to the CraterTools Add in toolset (Kniessl et al., 2011) and DEM data from the PDS Geosciences Node (https://pds-geosciences.wustl.edu/lro/lro-l-lola-3-rdr-v1/lrolol_1xxx/data/lola_gdr/polar/jp2/) to visualize the craters.Complex craters were selected from previous studies (Cannon et al., 2020; Deutsch et al., 2020; Tye et al., 2015) as regions of interest. The counts from this work span the walls of 16 complex craters found near the lunar southern pole, while those cited refer to the floor regions (Cannon et al., 2020; Deutsch et al., 2020), or the entire crater (Tye et al., 2015). Craters were included within the analysis from definitions described by Deutsch et al., (2020) as circular features with central depressions. We considered primary craters only, and those with morphologies reflecting secondary crater populations (crater chains or clusters) were omitted. Crater sizes range from sub km range to 35 km in diameter. Their slope conditions range from very shallow to very steep. </p> <p>PDS Geoscience Node DEM used ldem_80s_20m. This DEM has a resolution of 20 m/pixel and was updated 6/2/2017.<br> Data format, .csv files:<br> - Column A: Diameter. Crater diameter [km]<br> - Column B: Slope angle of crater [deg] determined by the 500 m buffer size from the determined crater radius. </p>
Neutron and LIBS data behind figures in Gabriel et al. (2022). On an extensive late hydrologic event in Gale crater as indicated by water-rich fracture halos. JGR-Planets.
<p>This repository contains datasets that allow for the reproduction of certain figures and analysis by Gabriel et al. (2022), a peer-reviewed journal article accepted in the Journal of Geophysical Research: Planets. Below are brief descriptions of the datasets.</p> <p> </p> <p>File: TGabriel_JGR-P_DAN_Passive_NoMobility_Raw_Data_sol350-400_FigureS10.txt</p> <p>Description: These are raw neutron counts from the thermal and epithermal neutron detectors as part of the Dynamic Albedo of Neutrons instrument. Only data from rover stops for sols 350 to 400 are included. Data from rover stops allows them to be readily colocated rover localization data, which includes 'site' and 'drive' numbers that are specific to each stop.</p> <p><br> File: TGabriel_JGR-P_DAN_Passive_NoMobility_Raw_Data_sol900-1500_Figure5.txt</p> <p>Description: This is similar data to the product above, however for the sol range 900 to 1500.</p> <p> </p> <p>File: TGabriel_JGR-P_DAN_Passive_withMobility_Raw_Data_sol350-420_FigureS13.txt</p> <p>Description: This is similar data to the products above, however the dataset includes passive neutron count rates acquired while the rover was traversing, smoothed over 3 meters of lateral distance traveled. This dataset allows for the analysis of environments that may be present between rover stops, and thus not detected in 'no mobility' datasets.</p> <p> </p> <p>TGabriel_JGR-P_Kukri_CCAM_MajorOxideComposition_FigureS19TableS1.xlsx</p> <p>Description: This is the result of the Major Oxide Quantification pipeline developed by the ChemCam instrument team (sPDL Tool v2.0, 25 July 2015) as run by William Rapin. Additional H quantification in Figure S19 of Gabriel et al. (2022) is not included in this dataset, but is provided in the manuscript.</p>
Dataset for A Revision of the Formation Conditions of the Vredefort Crater.
<p>Dataset associated with Allen et al. 2022 - <em>A Revision of the Formation Conditions of the Vredefort Crater. </em>Contained are all of our input asteroid.inp files, along with their associated material.inp file (the same of which was used for all of the simulations), the full jdata.dat file for our Model A, and a movie of our three discussed models over the full time of the simulation. All simulations were created using iSALE (https://isale-code.github.io/), and some python code showing potential visualization techniques for the data can be found at https://github.com/natalieallen/iSALE_scripts. </p>
Geological Map of the Derain (H10) Quadrangle of Mercury (3 crater class version)
<p>Geological (morphostratigraphic) map recognising 3 crater degradation classes. We also have a 5 crater class version that is otherwise identical. This version is slightly revised after review for publication in J Maps (3 Aug 2022).</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.