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Codes and test datasets developed for Mapping paleolacustrine deposits with a UAV-borne multispectral camera: Implications for future drone mapping on Mars.
<p>NASA’s Ingenuity Mars Helicopter has ushered in a new era in planetary exploration by utilizing Unmanned Aerial Vehicles (UAVs) to enhance our understanding of planetary surfaces. This project evaluates the potential of UAVs for mapping Martian environments, using Lake Natron, Tanzania, as an analog for Martian paleolakes.</p> <p>During two field seasons (January and July 2023), we employed a Phantom 4 Pro drone equipped with a MicaSense RedEdge-M multispectral camera and a TerraSpec Halo VNIR-SWIR spectrometer to capture high-resolution imagery and spectral data. Almost all image processing and analysis were performed using Python scripting, except for image mosaic and Digital Elevation Model (DEM) generation.</p> <p>We benchmarked the onboard image processing capabilities using a Raspberry Pi 5 single-board computer. </p> <p>In this repository, we share all the code developed during our study. Processing steps include,<br>1. DN to radiance conversion<br>2. Panel radiance extraction<br>3. Calculate reflectance factors using DLS data<br>4. Calculate reflectance at MicaSense band<br>5. Convert radiance to reflectance using 1 point empirical line method (1p ELM)<br>6. Convert radiance to reflectance using 2 point empirical line method (2p ELM)<br>7. Atmospheric correction using 6SV method<br>8. Convert radiance to reflectance using DLS data<br>9. Calculate Band indices<br>10. Weighted Kmean clustering<br>11. Finding the optimal number of clusters using the elbow method<br>12. Cmean clustering</p> <p>We also included sample image data used in the study. Feel free to contact us for more information/data.</p>
Mars 2020 Perseverance SHERLOC WATSON camera pre-delivery characterization and calibration image data
<p>The data presented here include images acquired by the WATSON (Wide Angle Topographic Sensor for Operations and eNgineering) camera during pre-delivery characterization and calibration testing at Malin Space Science Systems (MSSS, San Diego, California, USA) in September and October 2019. They also include video documentation of the camera’s dust cover motion. WATSON is one of two imaging subsystems of the SHERLOC (Scanning Habitable Environments with Raman & Luminescence for Organics & Chemicals) instrument onboard NASA’s Mars 2020 Perseverance rover which landed in Jezero crater, Mars, in February 2021.</p> <p>These data accompany the instrument calibration and characterization report by Edgett et al. (2019) and the WATSON characteristics reported by Bhartia et al. (2021). The image data presented here are listed and described in the Appendix to Edgett et al. (2019), which is also available here with the data.</p> <p>References cited:</p> <p>Bhartia, R., L. W. Beegle, L. DeFlores, W. Abbey, J. Razzell Hollis, K. Uckert, B. Monacelli, K. S. Edgett, M. R. Kennedy, M. Sylvia, D. Aldrich, M. Anderson, S. A. Asher, Z. Bailey, K. Boyd, A. S. Burton, M. Caffrey, M. J. Calaway, R. Calvet, B. Cameron, M. A. Caplinger, B. L. Carrier, N. Chen, A. Chen, M. J. Clark, S. Clegg, P. G. Conrad, M. Cooper, K. N. Davis, B. Ehlmann, L. Facto, M. D. Fries, D. H. Garrison, D. Gasway, F. T. Ghaemi, T. G. Graff, K. P. Hand, C. Harris, J. D. Hein, N. Heinz, H. Herzog, E. Hochberg, A. Houck, W. F. Hug, E. H. Jensen, L. C. Kah, J. Kennedy, R. Krylo, J. Lam, M. Lindeman, J. McGlown, J. Michel, E. Miller, Z. Mills, M. E. Minitti, F. Mok, J. Moore, K. H. Nealson, A. Nelson, R. Newell, B. E. Nixon, D. A. Nordman, D. Nuding, S. Orellana, M. Pauken, G. Peterson, R. Pollock, H. Quinn, C. Quinto, M. A. Ravine, R. D. Reid, J. Riendeau, A. J. Ross, J. Sackos, J. A. Schaffner, M. Schwochert, M. O Shelton, R. Simon, C. L. Smith, P. Sobron, K. Steadman, A. Steele, D. Thiessen, V. D. Tran, T. Tsai, M. Tuite, E. Tung, R. Wehbe, R. Weinberg, R. H. Weiner, R. C. Wiens, K. Williford, C. Wollonciej, Y.-H. Wu, R. A. Yingst, J. Zan (2021) Perseverance’s Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) investigation, Space Science Reviews 217, 58. https://doi.org/10.1007/s11214-021-00812-z</p> <p>Edgett, K. S., M. A. Caplinger, M. A. Ravine (2019) Mars 2020 Perseverance SHERLOC WATSON Camera Pre-delivery Characterization and Calibration Report, Malin Space Science Systems, San Diego, California. https://doi.org/10.13140/RG.2.2.18447.00165</p>
Dataset for "Beating 1 Sievert: Optimal Radiation Shielding of Astronauts on a Mission to Mars" publication in Space Weather journal
<p>Datasets in .fig Matlab format and figures in .jpg format published in Space Weather journal</p> <p>effectiveDoseRF.mat contains the effective dose "response functions" and an example (how2useDoseResponceFunctions.m) of how to use them to assess GCR dose.</p>
Mars orbital images of fresh impacts from CTX
<p><strong>Mars orbital images of fresh impacts from CTX</strong></p> <p>This data set contains images obtained from observations of Mars by the Context Camera (CTX) on the Mars Reconnaissance Orbiter. Each image in this collection is a cropped region of roughly 1.8 x 1.8 km (300 x 300 pixels at 6 m/pixel). The data set consists of "positive" images centered on the location of known fresh impacts on the surface of Mars (using a fresh impact catalog [1]) and "negative" examples obtained by randomly sampling CTX images (uniformly over the surface of Mars).</p> <p><strong>Contents</strong></p> <p>The 6829 images are divided into training and validation sets to capture the configuration used to train a fresh impacts image classifier [2]. However, they can be pooled together as a single data set for other purposes (but note that augmented versions are provided only for the training data). </p> <ul> <li>training_data/: 6156 images + 5 augmented versions per image</li> <li>validation_data/: 673 images (not augmented)</li> </ul> <p>Labels are indicated in the filenames. Positive examples start with a 0, and negative examples start with a - (negative sign).</p> <p>We employed augmentation on the training examples to generate 5 augmented versions per original image. The augmentation is indicated at the end of the filename: </p> <ul> <li>AUG-HF: Horizontal flip</li> <li>AUG-VF: Vertical flip</li> <li>AUG-RO: Rotate (randomly choose 90, 180, or 270 degrees)</li> <li>AUG-CJ: Image adjustment (minor random adjustment to brightness/contrast/saturation/hue)</li> <li>AUG-BL: Blur (Gaussian filter with radius 2) </li> </ul> <p><strong>References</strong> </p> <p>[1] Daubar, I.J., McEwen, A.S., Byrne, S., Kennedy, M.R., and Ivanov, B. (2013). "The current martian cratering rate," Icarus 225, 506-516. doi:10.1016/j.icarus.2013.04.009.</p> <p>[2] Munje, M. (2021). Martian Fresh Impact Classifier (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.552336</p> <p> </p>
"Hydration of a clay-rich unit on Mars, comparison of orbital data to rover data" supplementary data
<p>MCMC results from individual DAN measurements from sols 1814 to 3069 used in this work with material classifications from Figure 11.</p>
MAR dataset
<p>MAR dataset required to run MAR (boundary conditions, vegetation, topography).</p> <p>Forcing files are only for a standard bench experiment over Antarctica forced by ERA-5 for 02/2015.</p> <p>Licenses :</p> <ul> <li>AntTOPO : <ul> <li>BedMachine_v02.nc3 : https://doi.org/10.5067/FPSU0V1MWUB6. Cite Morlighem, M. (2022). MEaSUREs BedMachine Antarctica, Version 3 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/FPSU0V1MWUB6. Date Accessed 01-26-2023.</li> <li>When using this data product in a publication, please include the following citations in addition to the data product citation provided above: Morlighem, M., E. Rignot, T. Binder, D. D. Blankenship, R. Drews, G. Eagles, O. Eisen, F. Ferraccioli, R. Forsberg, P. Fretwell, V. Goel, J. S. Greenbaum, H. Gudmundsson, J. Guo, V. Helm, C. Hofstede, I. Howat, A. Humbert, W. Jokat, N. B. Karlsson, W. Lee, K. Matsuoka, R. Millan, J. Mouginot, J. Paden, F. Pattyn, J. L. Roberts, S. Rosier, A. Ruppel, H. Seroussi, E. C. Smith, D. Steinhage, B. Sun, M. R. van den Broeke, T. van Ommen, M. van Wessem, and D. A. Young. 2020. Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet. Nature Geoscience. 13. DOI: <a href="https://doi.org/10.1038/s41561-019-0510-8">10.1038/s41561-019-0510-8</a>.</li> </ul> </li> <li>ERA-5-ANT <ul> <li><a href="https://cds.climate.copernicus.eu/#!/home">Climate Data Store Copernicus</a> -- <a href="https://apps.ecmwf.int/datasets/licences/copernicus/?utm_medium=chatbot&utm_source=cds">Copernicus license</a>; <a href="https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it">How to ancknowledge</a>;</li> <li>Acknowledgment section of the article may contain the following: Hersbach, H. et al., (2018) was downloaded from the Copernicus Climate Change Service (C3S) Climate Data Store. The results contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</li> <li>Bibliography may contain the following: Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on single levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on 14-APR-2021), 10.24381/cds.adbb2d47</li> <li>Throughout the content of the article, the dataset used maybe referred to as: Hersbach, H. et al., (2018)</li> </ul> </li> <li>ETOPO: todo</li> <li>FAO: todo</li> <li>ICEmask: todo</li> <li>SOIL: todo</li> <li>VEGE: todo</li> </ul>
Dataset for: Bedding scale correlation on Mars in western Arabia Terra
<p>Dataset for: Bedding scale correlation on Mars in western Arabia Terra</p> <p>A.M. Annex et al.</p> <p>Data Product Overview</p> <p>This repository contains all source data for the publication. Below is a description of each general data product type, software that can load the data, and a list of the file names along with the short description of the data product.</p> <p><strong>HiRISE Digital Elevation Models (DEMs).</strong></p> <p>HiRISE DEMs produced using the Ames Stereo Pipeline are in geotiff format ending with ‘*X_0_DEM-adj.tif’, the “X” prefix denotes the spatial resolution of the data product in meters. Geotiff files are able to be read by free GIS software like QGIS.</p> <p><strong>HiRISE map-projected imagery (DRGs).</strong></p> <p>Map-projected HiRISE images produced using the Ames Stereo Pipeline are in geotiff format ending with ‘*0_Y_DRG-cog.tif’, the “Y” prefix denotes the spatial resolution of the data product in centimeters. Geotiff files are able to be read by free GIS software like QGIS. The DRG files are formatted as COG-geotiffs for enhanced compression and ease of use.</p> <p><strong>3D Topography files (.ply).</strong></p> <p>Traingular Mesh versions of the HiRISE/CTX topography data used for 3D figures in “.ply” format. Meshes are greatly geometrically simplified from source files. Topography files can be loaded in a variety of open source tools like ParaView and Meshlab. Textures can be applied using embedded texture coordinates.</p> <p><strong>3D Geological Model outputs (.vtk)</strong></p> <p>VTK 3D file format files of model output over the spatial domain of each study site. VTK files can be loaded by ParaView open source software. The “block” files contain the model evaluation over a regular grid over the model extent. The “surfaces” files contain just the bedding surfaces as interpolated from the “block” files using the marching cubes algorithm.</p> <p><strong>Geological Model geologic maps (geologic_map.tif).</strong></p> <p>Geologic maps from geological models are standard geotiffs readable by conventional GIS software. The maximum value for each geologic map is the “no-data” value for the map. Geologic maps are calculated at a lower resolution than the topography data for storage efficiency.</p> <p><strong>Beds Geopackage File (.gpkg).</strong></p> <p>Geopackage vector data file containing all mapped layers and associated metadata including dip corrected bed thickness as well as WKB encoded 3D linestrings representing the sampled topography data to which the bedding orientations were fit. Geopackage files can be read using GIS software like QGIS and ArcGIS as well as the OGR/GDAL suite. A full description of each column in the file is provided below.</p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>uuid</td> <td>String</td> <td>unique identifier</td> </tr> <tr> <td>stratum_order</td> <td>Real</td> <td>0-indexed bed order</td> </tr> <tr> <td>section</td> <td>Real</td> <td>section number</td> </tr> <tr> <td>layer_id</td> <td>Real</td> <td>bed number/index</td> </tr> <tr> <td>layer_id_bk</td> <td>Real</td> <td>unused backup bed number/index</td> </tr> <tr> <td>source_raster</td> <td>String</td> <td>dem file path used</td> </tr> <tr> <td>raster</td> <td>String</td> <td>dem file name</td> </tr> <tr> <td>gsd</td> <td>Real</td> <td>ground sampling distant for dem</td> </tr> <tr> <td>wkn</td> <td>String</td> <td>well known name for dem</td> </tr> <tr> <td>rtype</td> <td>String</td> <td>raster type</td> </tr> <tr> <td>minx</td> <td>Real</td> <td>minimum x position of trace in dem crs</td> </tr> <tr> <td>miny</td> <td>Real</td> <td>minimum y position of trace in dem crs</td> </tr> <tr> <td>maxx</td> <td>Real</td> <td>maximum x position of trace in dem crs</td> </tr> <tr> <td>maxy</td> <td>Real</td> <td>maximum y position of trace in dem crs</td> </tr> <tr> <td>method</td> <td>String</td> <td>internal interpolation method</td> </tr> <tr> <td>sl</td> <td>Real</td> <td>slope in degrees</td> </tr> <tr> <td>az</td> <td>Real</td> <td>azimuth in degrees</td> </tr> <tr> <td>error</td> <td>Real</td> <td>maximum error ellipse angle</td> </tr> <tr> <td>stdr</td> <td>Real</td> <td>standard deviation of the residuals</td> </tr> <tr> <td>semr</td> <td>Real</td> <td>standard error of the residuals</td> </tr> <tr> <td>X</td> <td>Real</td> <td>mean x position in CRS</td> </tr> <tr> <td>Y</td> <td>Real</td> <td>mean y position in CRS</td> </tr> <tr> <td>Z</td> <td>Real</td> <td>mean z position in CRS</td> </tr> <tr> <td>b1</td> <td>Real</td> <td>plane coefficient 1</td> </tr> <tr> <td>b2</td> <td>Real</td> <td>plane coefficient 2</td> </tr> <tr> <td>b3</td> <td>Real</td> <td>plane coefficient 3</td> </tr> <tr> <td>b1_se</td> <td>Real</td> <td>standard error plane coefficient 1</td> </tr> <tr> <td>b2_se</td> <td>Real</td> <td>standard error plane coefficient 2</td> </tr> <tr> <td>b3_se</td> <td>Real</td> <td>standard error plane coefficient 3</td> </tr> <tr> <td>b1_ci_low</td> <td>Real</td> <td>plane coefficient 1 95% confidence interval low</td> </tr> <tr> <td>b1_ci_high</td> <td>Real</td> <td>plane coefficient 1 95% confidence interval high</td> </tr> <tr> <td>b2_ci_low</td> <td>Real</td> <td>plane coefficient 2 95% confidence interval low</td> </tr> <tr> <td>b2_ci_high</td> <td>Real</td> <td>plane coefficient 2 95% confidence interval high</td> </tr> <tr> <td>b3_ci_low</td> <td>Real</td> <td>plane coefficient 3 95% confidence interval low</td> </tr> <tr> <td>b3_ci_high</td> <td>Real</td> <td>plane coefficient 3 95% confidence interval high</td> </tr> <tr> <td>pca_ev_1</td> <td>Real</td> <td>pca explained variance ratio pc 1</td> </tr> <tr> <td>pca_ev_2</td> <td>Real</td> <td>pca explained variance ratio pc 2</td> </tr> <tr> <td>pca_ev_3</td> <td>Real</td> <td>pca explained variance ratio pc 3</td> </tr> <tr> <td>condition_number</td> <td>Real</td> <td>condition number for regression</td> </tr> <tr> <td>n</td> <td>Integer64</td> <td>number of data points used in regression</td> </tr> <tr> <td>rls</td> <td>Integer(Boolean)</td> <td>unused flag</td> </tr> <tr> <td>demeaned_regressions</td> <td>Integer(Boolean)</td> <td>centering indicator</td> </tr> <tr> <td>meansl</td> <td>Real</td> <td>mean section slope</td> </tr> <tr> <td>meanaz</td> <td>Real</td> <td>mean section azimuth</td> </tr> <tr> <td>angular_error</td> <td>Real</td> <td>angular error for section</td> </tr> <tr> <td>mB_1</td> <td>Real</td> <td>mean plane coefficient 1 for section</td> </tr> <tr> <td>mB_2</td> <td>Real</td> <td>mean plane coefficient 2 for section</td> </tr> <tr> <td>mB_3</td> <td>Real</td> <td>mean plane coefficient 3 for section</td> </tr> <tr> <td>R</td> <td>Real</td> <td>mean plane normal orientation vector magnitude</td> </tr> <tr> <td>num_valid</td> <td>Integer64</td> <td>number of valid planes in section</td> </tr> <tr> <td>meanc</td> <td>Real</td> <td>mean stratigraphic position</td> </tr> <tr> <td>medianc</td> <td>Real</td> <td>median stratigraphic position</td> </tr> <tr> <td>stdc</td> <td>Real</td> <td>standard deviation of stratigraphic index</td> </tr> <tr> <td>stec</td> <td>Real</td> <td>standard error of stratigraphic index</td> </tr> <tr> <td>was_monotonic_increasing_layer_id</td> <td>Integer(Boolean)</td> <td>monotonic layer_id after projection to stratigraphic index</td> </tr> <tr> <td>was_monotonic_increasing_meanc</td> <td>Integer(Boolean)</td> <td>monotonic meanc after projection to stratigraphic index</td> </tr> <tr> <td>was_monotonic_increasing_z</td> <td>Integer(Boolean)</td> <td>monotonic z increasing after projection to stratigraphic index</td> </tr> <tr> <td>meanc_l3sigma_std</td> <td>Real</td> <td>lower 3-sigma meanc standard deviation</td> </tr> <tr> <td>meanc_u3sigma_std</td> <td>Real</td> <td>upper 3-sigma meanc standard deviation</td> </tr> <tr> <td>meanc_l2sigma_sem</td> <td>Real</td> <td>lower 3-sigma meanc standard error</td> </tr> <tr> <td>meanc_u2sigma_sem</td> <td>Real</td> <td>upper 3-sigma meanc standard error</td> </tr> <tr> <td>thickness</td> <td>Real</td> <td>difference in meanc</td> </tr> <tr> <td>thickness_fromz</td> <td>Real</td> <td>difference in Z value</td> </tr> <tr> <td>dip_cor</td> <td>Real</td> <td>dip correction</td> </tr> <tr> <td>dc_thick</td> <td>Real</td> <td>thickness after dip correction</td> </tr> <tr> <td>dc_thick_fromz</td> <td>Real</td> <td>z thickness after dip correction</td> </tr> <tr> <td>dc_thick_dev</td> <td>Integer(Boolean)</td> <td>dc_thick <= total mean dc_thick</td> </tr> <tr> <td>dc_thick_fromz_dev</td> <td>Integer(Boolean)</td> <td>dc_thick <= total mean dc_thick_fromz</td> </tr> <tr> <td>thickness_fromz_dev</td> <td>Integer(Boolean)</td> <td>dc_thick <= total mean thickness_fromz</td> </tr> <tr> <td>dc_thick_dev_bg</td> <td>Integer(Boolean)</td> <td>dc_thick <= section mean dc_thick</td> </tr> <tr> <td>dc_thick_fromz_dev_bg</td> <td>Integer(Boolean)</td> <td>dc_thick <= section mean dc_thick_fromz</td> </tr> <tr> <td>thickness_fromz_dev_bg</td> <td>Integer(Boolean)</td> <td>dc_thick <= section mean thickness_fromz</td> </tr> <tr> <td>slr</td> <td>Real</td> <td>slope in radians</td> </tr> <tr> <td>azr</td> <td>Real</td> <td>azimuth in radians</td> </tr> <tr> <td>meanslr</td> <td>Real</td> <td>mean slope in radians</td> </tr> <tr> <td>meanazr</td> <td>Real</td> <td>mean azimuth in radians</td> </tr> <tr> <td>angular_error_r</td> <td>Real</td> <td>angular error of section in radians</td> </tr> <tr> <td>pca_ev_1_ok</td> <td>Integer(Boolean)</td> <td>pca_ev_1 < 99.5%</td> </tr> <tr> <td>pca_ev_2_3_ratio</td> <td>Real</td> <td>pca_ev_2/pca_ev_3</td> </tr> <tr> <td>pca_ev_2_3_ratio_ok</td> <td>Integer(Boolean)</td> <td>pca_ev_2_3_ratio > 15</td> </tr> <tr> <td>xyz_wkb_hex</td> <td>String</td> <td>hex encoded wkb geometry for all points used in regression</td> </tr> </tbody> </table> <p><strong>Geological Model input files (.gpkg).</strong></p> <p>Four geopackage (.gpkg) files represent the input dataset for the geological models, one per study site as specified in the name of the file. The files contain most of the columns described above in the Beds geopackage file, with the following additional columns. The final seven columns (azimuth, dip, polarity, formation, X, Y, Z) constituting the actual parameters used by the geological model (GemPy). </p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>azimuth_mean</td> <td>String</td> <td>Mean section dip azimuth </td> </tr> <tr> <td>azimuth_indi</td> <td>Real</td> <td>Individual bed azimuth</td> </tr> <tr> <td>azimuth</td> <td>Real</td> <td>Azimuth of trace used by the geological model</td> </tr> <tr> <td>dip</td> <td>Real</td> <td>Dip for the trace used by the geological mode</td> </tr> <tr> <td>polarity</td> <td>Real</td> <td>Polarity of the dip vector normal vector </td> </tr> <tr> <td>formation</td> <td>String</td> <td>String representation of layer_id required for GemPy models</td> </tr> <tr> <td>X</td> <td>Real</td> <td>X position in the CRS of the sampled point on the trace</td> </tr> <tr> <td>Y</td> <td>Real</td> <td>Y position in the CRS of the sampled point on the trace</td> </tr> <tr> <td>Z</td> <td>Real</td> <td>Z position in the CRS of the sampled point on the trace</td> </tr> </tbody> </table> <p><strong>Stratigraphic Column Files (.gpkg).</strong></p> <p>Stratigraphic columns computed from the Geological Models come in three kinds of Geopackage vector files indicated by the postfixes <code>_sc</code>, <code>rbsc</code>, and <code>rbssc</code>. File names include the wkn site name.</p> <p><strong>sc (_sc.gpkg).</strong></p> <p>Geopackage vector data file containing measured bed thicknesses from Geological Model joined with corresponding Beds Geopackage file, subsetted partially. The columns largely overlap with the the list above for the Beds Geopackage but with the following additions</p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>X</td> <td>Real</td> <td>X position of thickness measurement</td> </tr> <tr> <td>Y</td> <td>Real</td> <td>Y position of thickness measurement</td> </tr> <tr> <td>Z</td> <td>Real</td> <td>Z position of thickness measurement</td> </tr> <tr> <td>formation</td> <td>String</td> <td>Model required string representation of bed index</td> </tr> <tr> <td>bed thickness (m)</td> <td>Real</td> <td>difference of bed elevations</td> </tr> <tr> <td>azimuths</td> <td>Real</td> <td>azimuth as measured from model in degrees</td> </tr> <tr> <td>dip_degrees</td> <td>Real</td> <td>dip as measured from model in degrees</td> </tr> <tr> <td>Dip corrected bed thickness (m)</td> <td>Real</td> <td>dip corrected bed thickness in meters</td> </tr> <tr> <td>lower_point</td> <td>Real</td> <td>lower bed elevation in meters</td> </tr> <tr> <td>upper_point</td> <td>Real</td> <td>upper bed elevation in meters</td> </tr> <tr> <td>_formation</td> <td>Real</td> <td>integer number of formation string</td> </tr> <tr> <td>layer_iid</td> <td>Integer64</td> <td>integer number of layer_id</td> </tr> <tr> <td>bascom_baryte_diff_bt</td> <td>Real</td> <td>diff. in thickness from geomodel measurements</td> </tr> <tr> <td>bascom_baryte_diff_dcbt</td> <td>Real</td> <td>diff. in dip cor. thicknesses ’’</td> </tr> </tbody> </table> <p><strong>rbsc (rbsc.gpkg)</strong></p> <p>Geopackage vector file containing virtual boreholes with high resolution vertical sampling placed in a regular grid in the spatial extent of the DEM with the following columns.</p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>formation</td> <td>String</td> <td>Model required string representation of bed index</td> </tr> <tr> <td>pred_z</td> <td>Real</td> <td>Z value of bedding plane</td> </tr> <tr> <td>layer_id</td> <td>Integer64</td> <td>Bed Index from Beds Geopackage file</td> </tr> <tr> <td>section</td> <td>Real</td> <td>section number</td> </tr> <tr> <td>thickness</td> <td>Real</td> <td>thickness of the layer predicted by model</td> </tr> <tr> <td>geom</td> <td>Point</td> <td>contains X,Y,Z of point in dem CRS</td> </tr> </tbody> </table> <p><strong>rbssc (rbssc.gpkg)</strong></p> <p>Geopackage vector file containing virtual boreholes with high resolution vertical sampling placed at the centroids for each section within the spatial extent of the DEM with the following columns.</p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>formation</td> <td>String</td> <td>Model required string representation of bed index</td> </tr> <tr> <td>pred_z</td> <td>Real</td> <td>Z value of bedding plane</td> </tr> <tr> <td>layer_id</td> <td>Integer64</td> <td>Bed Index from Beds Geopackage file</td> </tr> <tr> <td>section</td> <td>Real</td> <td>section number</td> </tr> <tr> <td>thickness</td> <td>Real</td> <td>thickness of the layer predicted by model</td> </tr> <tr> <td>geom</td> <td>Point</td> <td>contains X,Y,Z of point in dem CRS</td> </tr> </tbody> </table> <p><strong>crescent_shapes.gpkg</strong></p> <p>Geopackage vector file containing the measurements of the crescent features.</p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>azimuth</td> <td>Real</td> <td>Azimuth of the crescent feature in degrees</td> </tr> </tbody> </table>
Open Soil Atlas (OSA) - Raw data from Phase I (Mar-Aug 2021, Berlin)
<p>The Open Soil Atlas citizen science project collected 77 data observations, cathegorized in a set of 10 inficators, which offer information about the quality and fertility of the soil. Area of investigation: Berlin. Data collection period: March-August 2021.</p>
Datasets for "Ice Content of Mantling Materials in Deuteronilus Mensae, Mars"
<p>These are supporting datasets for the paper titled, "Ice Content of Mantling Materials in Deuteronilus Mensae, Mars," Baker and Carter, 2023, Journal of Geophysical Research: Planets. Data Set S1 (ds01.txt) is a text file table of SHARAD radargrams analyzed in the paper. Data Sets S2 and S3 (ds02.zip and ds03.zip) are provided as ESRI shapefiles (.shp) and compressed as separate zip files. The shapefiles can be opened in most geographical information systems (GIS) software. Shapefiles consist of three individual files: main file (.shp), index file (.shx), and dBASE table (.dbf). Descriptions of each column or attribute in the text file or shapefile is provided in the readme documentation.</p>
A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR: accompanying dataset
<p>Dataset containing surface temperature and surface mass balance datasets generated from the MAR regional climate model over Greenland over two test areas using statistical downscaling tools from 6 km to 100m. The abstract of the accompanying submitted paper follows: </p> <p> </p> <p>The Greenland Ice Sheet (GrIS) has been contributing directly to sea level rise and this contribution is projected to accelerate over next decades. A crucial tool for studying the evolution surface mass loss (e.g., surface mass balance, SMB) consists of regional climate models (RCMs) which can provide current estimates and future projections of sea level rise associated with such losses. However, one of the main limitations of RCMs is the relatively coarse horizontal spatial resolution at which outputs are currently generated. Here, we report results concerning the statistical downscaling of the SMB modeled by the Modèle Atmosphérique Régional (MAR) RCM from the original spatial resolution of 6 km to 100 m building on the relationship between elevation and mass losses in Greenland. To this goal, we developed a geospatial framework that allows the parallelization of the downscaling process, a crucial aspect to increase the computational efficiency of the algorithm. The results obtained in the case of the SMB, assessed through the comparison of the modeled outputs with in-situ SMB measurements, show a considerable improvement in the case of the downscaled product with respect to the original, coarse output. In the case of the downscaled MAR product, the coefficient of determination (R<sup>2</sup>) increases from 0.868 for the original MAR output to 0.935 for the downscaled product. Moreover, the value of the slope and intercept of the linear regression fitting modeled and measured SMB values shifts from 0.865 for the original MAR to 1.015 for the downscaled product in the case of the intercept and from the value -235mm (original) to -57 mm (downscaled) in the case of the slope, considerably improving upon results previously published in the literature.</p>
Data and Software for "Gullies on Mars could have formed by melting of water ice during periods of high obliquity"
<p>Code, movies and climate model outputs for "Gullies on Mars could have formed by melting of water ice during periods of high obliquity" by Dickson et al. Science, 2023.</p>
The role of fire in the carbon dynamics of the boreal forest I. - Response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach (2003-2100).
The boreal forest contains large reserves of carbon, and across this region wildfire is a common occurrence. To improve the understanding of how wildfire influences the carbon dynamics of this region, methods were developed to incorporate the spatial and temporal effects of fire into the Terrestrial ecosystem Model (TEM). The historical role of fire on carbon dynamics of the boreal region was evaluated within the context of ecosystem responses to changing atmospheric CO2 and climate. These results show that the role of historical fire on boreal carbon dynamics resulted in a net carbon sink; however, fire plays a major role in the interannual and decadal scale variation of source/sink relationships. To estimate the effects of future fire on boreal carbondynamics, spatially and temporally explicit empirical relationships between climate andfire were quantified. Fuel moisture, monthly severity rating, and air temperature explained a significant proportion of observed variability in annual area burned. These relationships were used to estimate annual area burned for future scenarios of climate change and were coupled to TEM to evaluate the role of future fire on the carbon dynamics of the North American boreal region for the 21st Century. Simulations with TEM indicate that boreal North America is a carbon sink in response to CO2 fertilization, climate variability, and fire, but an increase in fire leads to a decrease in the sink strength. While this study highlights the importance of fire on carbon dynamics in the boreal region, there are uncertainties in the effects of fire in TEM simulations. These uncertainties are associated with sparse fire data for northern Eurasia, uncertainty in estimating carbon consumption, and difficulty in verifying assumptions about the representation of fires that occurred prior to the start of the historical fire record. Future studies should incorporate the role of dynamic vegetation to more accurately represent post-fire successional pr
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>
Transport Ratios for Plasma Waves Observed by MAVEN at Mars
<p>Transport ratios calculated for coherent plasma waves observed upstream from Mars by the Mars Atmosphere and Volatile EvolutioN (MAVEN) mission. These values were calculated by sub-sampling the ion velocity distribution measurements made by the Solar Wind Ion Analyzer (SWIA) instrument and binning them by the wave phase as estimated from the vector magnetic field measured by the Magnetometer (MAG) instrument. </p>
Spherical harmonic model of the shape of Mars: MarsTopo2600
<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>MarsTopo2600.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).</p>
Spherical harmonic model of the magnetic field of Mars from Morschhauser et al. (2014)
<p><strong>Morschhauser2014.txt.gz</strong> is a gzipped file of the magnetic potential coefficients of Mars as published by Morschhauser et al. (2014). This is the same as the file ts01.txt in the supplemental materials of this manuscript.</p>
Precipitating Solar Wind Hydrogen at Mars: Improved Calculations of the Backscatter and Albedo with MAVEN Observations
<p>These files contain the derived data products used in the paper, including the penetrating and backscatter energy spectra and directional fluxes. See Readme.txt for a description of the data that is stored in each file.</p>
Fig. 8 in Umbellula pomona sp. nov., a new sea pen from Mar del Plata Submarine Canyon (Cnidaria: Octocorallia: Pennatulacea)
Fig. 8. General aspect of the unique specimen of a juvenile-like paratype of Umbellula pomona Risaro, Williams & Lauretta sp. nov. (MACN-IN 42609, paratype C). Abbreviations: CP = central polyp; LP = lateral polyp; R = rachis; PD = peduncle; T = tentacles.
Fig. 7 in Umbellula pomona sp. nov., a new sea pen from Mar del Plata Submarine Canyon (Cnidaria: Octocorallia: Pennatulacea)
Fig. 7. Variability of sizes and ornamentations of the pinnules' sclerites of the holotype of Umbellula pomona Risaro, Williams & Lauretta sp. nov. (MACN-IN 42608).
Fig. 2. A in Umbellula pomona sp. nov., a new sea pen from Mar del Plata Submarine Canyon (Cnidaria: Octocorallia: Pennatulacea)
Fig. 2. A. General aspect of Umbellula pomona Risaro, Williams & Lauretta sp. nov. A. Holotype (MACN-IN 42608). B. Detail of the terminal cluster of paratype A (MACN-IN 42609), showing three autozooids that form the terminal cluster and amplifications of sclerites (up) and siphonozooids (down). Abbreviations: CP = central polyp; LP = lateral polyp; Pd = peduncle; R = rachis; T = tentacle; S = siphonozooids; Scl = sclerites.
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.