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1,880 results for “mars”
AMPERE GRD & IRD Data (2022-Mar)
<p>2022-Mar AMPERE GRD and IRD data.</p><p>AMPERE IRD files contain hemispheric delta-B observations in global coordinates (geographic and AACGM). Data are blocked into 10-minute 'chunks' at 2-minute resolution for use in the fitting algorithm. Measurements flagged 'bad' in the earlier processing are removed prior to entry in IRD.</p><p>AMPERE GRD files contain hemispheric delta-B and Birkeland current fits at 2-minute resolution.</p>
AMPERE GRD & IRD Data (2021-Mar)
<p>2021-Mar AMPERE GRD and IRD data.</p><p>AMPERE IRD files contain hemispheric delta-B observations in global coordinates (geographic and AACGM). Data are blocked into 10-minute 'chunks' at 2-minute resolution for use in the fitting algorithm. Measurements flagged 'bad' in the earlier processing are removed prior to entry in IRD.</p><p>AMPERE GRD files contain hemispheric delta-B and Birkeland current fits at 2-minute resolution.</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>
Radio Science observations of Mars Express and Tianwen-1 spacecraft from the 2021 Maritan Solar Conjunction
<p>Phase scintillation dataset from the 2021 Martian solar conjunction is contained in the DATA.zip file. The Read Me pdf file contiants information useful to understanding the file naming conventions and their contents.</p>
CyZ: MARS Space Exploration Dataset.
<p>Images from NASA missions of the celestial body.</p> <p>Repository: https://github.com/decurtoidiaz/cyz</p> <p>Authors:</p> <p>J. de Curtò c@decurto.be</p> <p>I. de Zarzà z@dezarza.be</p> <p>------------------------------------------<br> File Information from CyZ-1.1<br> ------------------------------------------</p> <ul> <li>Curiosity (cyz/curiosity_cyz). <ul> <li>png (cyz/curiosity_cyz/png). <ul> <li>PNG files for the corresponding cameras.</li> </ul> </li> <li>csv (cyz/curiosity_cyz/csv). <ul> <li>CSV files. </li> </ul> </li> </ul> </li> <li>Perseverance (cyz/perseverance_cyz). <ul> <li>png (cyz/perseverance_cyz/png). <ul> <li>PNG files for the corresponding cameras. </li> </ul> </li> <li>csv (cyz/perseverance_cyz/csv). <ul> <li>CSV files. </li> </ul> </li> </ul> </li> </ul>
Data for the paper in Front. Mar. Sci. doi: 10.3389/fmars.2021.785967
<p>This archive contains data presented in the paper by the coauthors which is published in Front. Marine Sci., doi: 10.3389/fmars.2021.785967</p> <p>Filename refers to the figure number where the data set appears.</p> <p>IW_current is the MATLAB code for calculating internal wave properties under conditions of continuous stratification and vertically sheared mean current. It is used to produce Figures 11-13.</p> <p>Processed SAR image is saved in TIFF format</p>
Data of Self-Weight Consolidation Process of Water-Saturated Deltas on Mars and Earth
<p><strong>Data of the paper "Self-Weight Consolidation Process of Water-Saturated Deltas on Mars and Earth".</strong> This dataset includes five tables. <strong>Table S1</strong> is the original data of the measurements of the moisture content <em>w</em><sub>0</sub>, <strong>Table S2</strong> is the original data of the pycnometer test, which was conducted to obtain the specific gravity <em>G</em><sub>s</sub> of our samples, <strong>Table S3</strong> is the original data of the consolidation experiments, <strong>Table S4</strong> is the original data of the permeability experiments, and <strong>Table S</strong><strong>5</strong> is the martian global delta relief obtained by us based on MOLA data, which is used as the maximum thickness of a delta.</p> <p><strong>Table S1.</strong> The original data of the measurements of the moisture content <em>w</em><sub>0</sub>. The initial void ratio is calculated by equation (1). <em>A'</em> is the inner area of the consolidation container.</p> <p><strong>Table S2.</strong> The original data of the pycnometer test, which was conducted to obtain the specific gravity <em>G</em><sub>s</sub> of our samples. The specific gravity <em>G</em><sub>s </sub>can be derived from <em>m</em><sub>d</sub><em>G</em><sub>wT</sub> /(<em>m</em><sub>bw+</sub><em>m</em><sub>d+</sub><em>m</em><sub>bws</sub>), where <em>m</em><sub>d </sub>is the samples’ dry mass, <em>m</em><sub>bw </sub>is the total mass of the pycnometer and water,<em> m</em><sub>bws </sub>is the total mass of the pycnometer, water and samples, and <em>G</em><sub>wT</sub> is the specific gravity of pure water at<em> T </em>℃.</p> <p><strong>Table S3.</strong> The original data of consolidation experiments of our samples. The void ratio is calculated by equation (2).</p> <p><strong>Table S4. </strong>The original data of permeability experiments of our samples. The hydraulic conductivity <em>K </em>was calculated by equations (3) and (4). The inner area of the consolidation container is 30 cm<sup>2</sup>, the cross-sectional area<em> a' </em>of the water pipe is 0.89286 cm<sup>2</sup>, and the seepage path length <em>L</em> equals the sample initial height <em>h</em><sub>0</sub> minus the accumulated height <em>Σ</em>Δ<em>h</em><sub>i</sub>. <em>t</em>1 and <em>t</em>2<sub> </sub>are the first and the second test results of time-taken for water dropping from <em>H</em><sub>1</sub> to <em>H</em><sub>2</sub>, respectively. <em>t</em> is the average of <em>t</em>1 and <em>t</em>2. <em>T</em><em>’</em> is the temperature during the experiments. Note: we only test the <em>T</em><em>’</em> of the third group and here we used the average of <em>T’ </em>(=12.5℃) to represent the temperature of all three parallel groups during the experiments. It’s acceptable because <em>T’</em> varies slightly throughout the experiments, whose fluctuations hardly affect the order of magnitude of the hydraulic conductivity <em>K</em>. The seepage velocity <em>v</em>=<em>Q</em>/<em>A’t</em>, in which <em>Q</em> is the volume of water that seeps out of the samples.</p> <p><strong>Table S5.</strong> The delta relief of a delta. The locations of martian deltas are based on the database of Wilson et al. (2021)</p> <p> </p> <p> </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>
Wallops SuperDARN data in netCDF format (2020-Mar)
<p>2020-Mar Wallops 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>
Wallops SuperDARN data in netCDF format (2021-Mar)
<p>2021-Mar Wallops 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 Express Power Challenge dataset
<p>The Mars Express Power Challenge dataset is the official dataset of <strong>ESA's Kelvins</strong> <strong>competition for "Mars Express Power Challenge"</strong>. It consists of a training set of 3 Martian Year composed of context and electric current measurements data of ESA's Mars Express spacecraft.</p> <p>Goal of the competition was to predict the average electric current per hour. A test set of a fourth Martian year is provided for this purpose.</p> <p>Detailed information about the related competition can be found at <a href="https://kelvins.esa.int/mars-express-power-challenge/home/">https://kelvins.esa.int/mars-express-power-challenge/home/</a>.</p>
Assessment of future wind speed and wind power changes over South Greenland using the MAR regional climate model : MAR ouptuts and KATABATA weather stations timeseries
<p>Daliy MARv3.12 outputs and KATABATA weather stations timeseries used in :</p> <p>Lambin, C., Fettweis, X., Kittel, C., Fonder, M., & Ernst, D. (2022).Assessment of future wind speed and wind power changes over South Greenland using the Modèle Atmosphérique Régional regional climate model. <em>International Journal of Climatology</em>, 43(1),558–574. https://doi.org/10.1002/joc.7795574 </p> <p> </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>
Limited stability of multi-component brines on the surface of Mars
<p>This data package contains all the data used for the following publication: </p> <p>Limited stability of multi-component brines on the surface of Mars</p> <p>Vincent F. Chevrier<br> Alec Fitting<br> Edgard G. Rivera-Valentín</p> <p>The data are evaporation simulations of multicomponent mixtures of composition as measured by the Phoenix Wet Chemistry Laboratory instrument at various temperatures and for three different models (described in the manuscript). </p> <p>The organization and detailed description of the data is presented in the text file AAS35439R1_ReadMe.TXT</p>
Spherical harmonic model of the shape of Mars: MarsTopo719
<p><strong><em>THIS MODEL IS SUPERSEDED BY </em><a href="../records/10794059"><em>Spherical harmonic models of the shape of Mars</em></a></strong></p> <p> </p> <p><strong>MarsTopo719.shape</strong> is a spherical harmonic model of the shape of the planet Mars. This model makes use of 4-pi normalized spherical harmonic functions that exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The description of how this spherical harmonic model was constructed can be found in Wieczorek (2015). <strong>MarsTopo719.shape</strong> is a truncated version of <strong>MarsTopo2600.shape</strong>.</p>
Dataset for "Investigation of Mars analog candidates around Syowa Station, Antarctica"
<p>This dataset includes coordinate values of rock- and water-sampling points, water quality data, near-infrared spectrum data, subsurface hardness data, and photos obtained by the project AAS6304 "Investigation of Mars analog candidates around Syowa Station, Antarctica" during the 63rd Japanese Antarctic Research Expedition.</p> <p>Measurement and sampling points and water quality data are shown in "JARE63_AAS6304_data_sample_list.xlsx".</p> <p>"NIRScan_data.zip" is the near-infrared data obtained using NIR-S-G1, a NIR spectrometer (InnoSpectra Corporation, http://www.inno-spectra.com/en/product).</p> <p>"Aerial_photos.zip" includes photos taken from the helicopter.</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>
gmap - qgis training material: Aram Chaos (Mars)
<p>Aram Chaos is a more than 250 km large crater characterized by the presence of Chaotic Terrains forming mesas and knobs, associated with the outflow channel of Ares Vallis. The Chaotic Terrains are unconformably embayed and locally superposed by some layered hydrate minerals-bearing deposits. </p> <p> </p> <p>The training package includes a complete HRSC coverage (including images and DEMs) (Neukum et al., 2004; Jaumann et al., 2007) and selected CTX (Malin et al., 2007), HiRISE (McEwen et al., 2007), and CRISM (Murchie et al., 2007) data. The area has been divided in 8 tiles each of one ‘stand.alone’ in terms of geology but at the same time ready for collaborative mapping purposes.</p>
Colección de notas del diario La Capital de la ciudad de Mar del Plata, Buenos Aires, Argentina
<p>Se trata de un dataset de notas periodísticas del diario marplatense La Capital que cubre desde el mes de marzo de 2016 hasta junio de 2022. Contiene las siguientes variables: fecha, titulo, bajada, nota y link.</p>
Colección de notas de la Revista Puerto, Mar del Plata, Buenos Aires, Argentina
<p>Se trata de la colección completa de notas de la Revista Puerto, versión digital online, desde el año de inicio (2009) hasta el año 2020. Contiene las siguientes variables: fecha, titulo, bajada, nota, imagen, link.</p>
ScienceDex guides
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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.