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462 results for “Moon”
Spherical harmonic models of the shape of the Moon (principal axis coordinate system) [LOLA]
<p>This archive contains four spherical harmonic models of the shape of the Moon in a principal axis coordinate system, truncated at different maximum spherical harmonic degrees. The highest resolution model has a maximum spherical harmonic degree of 5759, which was generated from a lunar shape model sampled at 64 pixels per degree.</p> <p>The data used to generate these models are from the LOLA instrument on the Lunar Reconaissance Orbiter, as found in the file <code>ldem_64_pa.img</code> on <a href="https://pds-geosciences.wustl.edu/lro/lro-l-lola-3-rdr-v1/lrolol_1xxx/data/lola_gdr/cylindrical/pa/">NASA's PDS website</a>. This image file was first converted to netcdf format using the <a href="https://www.generic-mapping-tools.org/">generic-mapping-tools</a> function <code>xyz2grd</code>, and the resulting gridline-registered netcdf file was read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software and expanded into spherical harmonics using the function <code>SHCoeffs.expand()</code>. The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The units of the coefficients are meters.</p> <p>The four files in this archive are</p> <ul> <li>Moon_LOLA_shape_pa_5759.bshc.gz</li> <li>Moon_LOLA_shape_pa_2879.bshc.gz</li> <li>Moon_LOLA_shape_pa_1439.bshc.gz</li> <li>Moon_LOLA_shape_pa_719.bshc.gz</li> </ul> <p>The numbers 5759, 2879, 1439, and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 64, 32, 16, and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution models were generated by truncating the spherical harmonic coefficients of the highest resolution model.</p> <p>This shape model uses the same coordinate system as most lunar gravity models. The principal axis coordinate system differs from the more common mean Earth/polar axis system by about 1 km at the equator. For a mean Earth/polar axis model, use <a href="../records/10796823">Spherical harmonic models of the shape of the Moon</a>.</p>
Spherical harmonic models of the shape of the Moon [LOLA]
<p>This archive contains four spherical harmonic models of the shape of the Moon truncated at different maximum spherical harmonic degrees. The highest resolution model has a maximum spherical harmonic degree of 5759, which was generated from a lunar shape model sampled at 64 pixels per degree in the DE421 mean Earth/polar axis coordinate frame.</p> <p>The data used to generate these models are from the LOLA instrument on the Lunar Reconaissance Orbiter, as found in the file <code>ldem_64_float.img</code> on <a href="https://pds-geosciences.wustl.edu/lro/lro-l-lola-3-rdr-v1/lrolol_1xxx/data/lola_gdr/cylindrical/float_img/">NASA's PDS website</a>. This image file was first converted to netcdf format using the <a href="https://www.generic-mapping-tools.org/">generic-mapping-tools</a> function <code>xyz2grd</code>, and the resulting pixel registed map was then converted to a gridline registration using the function <code>grdsample</code>. Following this, the resulting netcdf file was read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software and expanded into spherical harmonics using the function <code>SHCoeffs.expand()</code>. The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The units of the coefficients are meters.</p> <p>The four files in this archive are</p> <ul> <li>Moon_LOLA_shape_5759.bshc.gz</li> <li>Moon_LOLA_shape_2879.bshc.gz</li> <li>Moon_LOLA_shape_1439.bshc.gz</li> <li>Moon_LOLA_shape_719.bshc.gz</li> </ul> <p>The numbers 5759, 2879, 1439, and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 64, 32, 16, and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution models were generated by truncating the spherical harmonic coefficients of the highest resolution model.</p> <p>Note that this shape model should not be used in conjunction with most gravity models of the Moon. The gravity models use a principal axis coordinate system that differs from the mean Earth/polar axis frame by about 1 km at the equator. For a principal axis coordinate system model, use <a href="../doi/10.5281/zenodo.10796953">Spherical harmonic models of the shape of the Moon (principal axis coordinate system)</a>.</p>
Magnetic excitation moments for large moons of the giant planets
<p>Text files containing the spatially uniform (degree-1) magnetic oscillations experienced by each large moon of the giant planets as a function of frequency, also known as the excitation moments. All moments are in complex notation relative to the J2000 epoch. Used for determining the strength of induced magnetic fields from the moons from an interior conductivity structure. This dataset is compatible for use with the <a href="https://github.com/itsmoosh/MoonMag" target="_blank" rel="noopener">MoonMag</a> and <a href="https://github.com/vancesteven/PlanetProfile" target="_blank" rel="noopener">PlanetProfile frameworks</a> for calculating induced magnetic fields of target moons. Refer to the publication linked below for more information.</p> <p>All vector components are in IAU coordinates, such that at the body center, +<em>z</em> is directed along the body spin axis, +<em>x</em> is directed approximately toward the parent planet in the plane of an IAU-defined meridian feature, and +<em>y</em> is directed approximately opposite to the orbital velocity to complete the right-handed set. For the uranian moons and Triton, the IAU +<em>z</em> axes are opposite the spin axes because of their angles relative to the solar system invariable plane; the +<em>y</em> axes for these bodies are therefore directed approximately along the orbital velocity vector.</p> <p>Excitation fields are determined by evaluation of SPICE kernels over a time series at the location of the body center. Position information is inserted into a magnetospheric model for the parent planet and complex Fourier coefficients are inverted from the time series using linear least squares optimization. The magnetic field models we use for each planet are:</p> <ul> <li><strong>Jupiter</strong> - JRM33 + C2020 current sheet (Connerney et al., 2022, 2020) except Callisto, for which we use VIP4+K (Connerney et al., 1998; Khurana, 1997)</li> <li><strong>Saturn</strong> - Cassini 11+ (Cao et al., 2020)</li> <li><strong>Uranus</strong> - AH<sub>5</sub> (Herbert, 2009)</li> <li><strong>Neptune</strong> - O8 (Connerney et al., 1991)</li> </ul> <p>ASCII text files are included for all major moons of these planets.</p>
Tycho Region Database of impact craters >=21 meters on the Moon.
<p>This dataset presents the results of an innovative AI-driven lunar crater mapping project, focused exclusively on the Tycho region of the Moon at 21m/px (<strong>see Version V2 for the global catalog at 100m/px</strong>) , marking the first comprehensive application of artificial intelligence to detect, classify, and map lunar craters. Created through extensive work over a two-year period, this dataset leverages YOLOLens, a state-of-the-art deep learning model specifically optimized for high-resolution crater detection. YOLOLens, an innovative variant of the YOLO architecture, has been fine-tuned to handle the unique challenges of lunar surface imagery, delivering unparalleled accuracy in crater identification and localization. Detailed information on the model architecture and methodology can be found in relevant publications on:</p> <ol> <li>La Grassa, Riccardo, et al. <strong>"YOLOLens: A deep learning model based on super-resolution to enhance the crater detection of the planetary surfaces."</strong> Remote Sensing 15.5 (2023): 1171, https://doi.org/10.3390/rs15051171.</li> <li>La Grassa, R, et al. <strong>"LU5M812TGT: An AI-Powered global database of impact craters ≥0.4 km on the Moon"</strong>, ISPRS Journal of Photogrammetry and Remote Sensing, 2025, ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2024.11.010.<strong><br></strong></li> </ol> <p><br>The dataset preparation process involved rigorous steps in preprocessing and post-processing to enhance the quality and usability of the data. Preprocessing techniques were employed to reduce noise and enhance contrast within the complex lunar landscape, while post-processing was used to refine the accuracy of crater boundaries and dimensions detected by the model. This approach facilitated a high-confidence dataset that stands as a valuable resource for the astronomical and AI research communities.</p> <p>The area analyzed in this dataset is defined by the following coordinates: longitude [-21.0000000001998046, 44.9997999998701630] and latitude [-50.9998000002697722, 39.0000000003997229].</p> <p>This dataset, which includes over 6.8 million craters at a resolution of 21m/px, is a valuable resource for both astrophysicists and AI researchers. It provides precisely labeled crater data, including coordinates, dimensions, and classifications, serving as an essential benchmark for comparative analyses, model validation, and advancements in lunar and planetary science.</p> <p>Notably, the global catalog created using the WAC imagery at 100m/px resolution is available in <strong>version V2</strong> of this repository. This global dataset complements the Tycho region-specific data by offering a broader perspective on lunar crater distribution.</p> <p>*********************************************************************🌕🌖🌗🌘🌑🌒🌓🌔🌕 **********************************************************************</p> <p>Release of a Tycho area at 21m/px of craters catalog with more than 6.8 million craters.</p> <ul> <li><em>File source csv</em></li> <li><em>Header: Longitude, Latitude, Diameter_w, Diameter_h, Confidence.</em></li> <li><em>The coordinates are absolute in the range of [-180, +180] of Longitude and [-90, +90] of Latitude. <br></em></li> <li><em>Diameters (km)</em></li> </ul> <p> </p>
Martian crater ages and crater counting - Does the impact flux of small and large asteroids varied through time on Mars, the Earth and the Moon?
<ul> <li>The SM_mars_crater_dating.xlsx table contains all the information used to date the 49 martian impact craters considered in this study (< 600 Ma). </li> </ul> <ol> <li>CRATER ID </li> <li>CRATER NAME</li> <li>DIAM KM </li> <li>LAT </li> <li>LONG </li> <li>DEPTH RIM KM </li> <li>DEPTH SURF KM </li> <li>DEPTH FLOOR KM </li> <li>NUMBER LAYER</li> <li>MORPHO EJECTA </li> <li>PRESERVATION </li> <li>COUNT AREA KM2: counting area from ejecta banket mapping </li> <li>COUNT AREA ASCI* KM2: counting area after removal of surfaces contaminated by secondary craters </li> <li>THRESHOLD AREA KM2: minimum size of Voronoi polygon area below which all associated detected craters are considered of secondary origin</li> <li>NB SEC: number of secondary craters dentified by ASCI </li> <li>PERCENT SEC</li> <li>NB CRAT 100M: total number of craters > 100 m detected by the CDA** on the CTX global mosaic*** over the counting area</li> <li>NB PRIM 100M: number of craters identified as primaries by ASCI</li> <li>TURNOFF DIAM KM: minimum crater diameter used to fit the crater-size frequency distribution (CSFD) with an isochron</li> <li>NB CRAT FIT: number of craters used to fit the CSFD with an isochron</li> <li>AGE GA: model age based on Hartmann (2005) chronology model**** and Michael et al. (2016) fitting technique*****</li> <li>AGE MAX GA</li> <li>AGE MIN GA</li> <li>N(1): equivalent number of accumulated craters >1km per km2</li> <li>N(1) MAX</li> <li>N(1) MIN</li> </ol> <p>*ASCI: Automatic Secondary Crater Identification: A. Lagain, K. Servis, G. K. Benedix, C. Norman, S. Anderson, P. A. Bland, Model Age Derivation of Large Martian Impact Craters, Using Automatic Crater Counting Methods, Earth and Space Science 8 (2) (2021). doi:10.1029/2020EA001598.</p> <p>**CDA: Crater Detection Algorithm: G. K. Benedix, A. Lagain, K. Chai, S. Meka, S. Anderson, C. Norman, P. A. Bland, J. Paxman, M. C. Towner, T. Tan, Deriving Surface Ages on Mars Using Automated Crater Counting, Earth and Space Science 7 (3) (2020). doi:10.1029/2019EA001005.</p> <p>*** CTX global mosaic: Context Camera global mosaic: J. L. Dickson, L. A. Kerber, C. I. Fassett, B. L. Ehlmann, A Global, Blended CTX Mosaic of Mars with Vectorized Seam Mapping: A New Mosaicking Pipeline Using Principles of Non-Destructive Image Editing, in: Lunar and Planetary Science Conference (2018), p. 2480.</p> <p>**** W. K. Hartmann, Martian cratering 8: Isochron refinement and the chronology of Mars, Icarus 174 (2) (2005) 294–320. doi:10.1016/j.icarus.2004.11.023.</p> <p>***** G. G. Michael, T. Kneissl, A. Neesemann, Planetary surface dating from crater size-frequency distribution measurements: Poisson timing analysis, Icarus 277 (2016) 279–285. doi:10.1016/j.icarus.2016.05.019.</p> <ul> <li>The crater_counting.csv table contains the location and size of impact craters used to derive the ages of the 49 craters younger than 600 Ma old presented in this study. </li> </ul>
Spherical harmonic models of the shape of the Moon (principal axis coordinate system) [LDEM128]
<p>This archive contains five spherical harmonic models of the shape of the Moon in a principal axis coordinate system, truncated at different maximum spherical harmonic degrees. The highest resolution model has a maximum spherical harmonic degree of 11519, which was generated from a lunar shape model sampled at 128 pixels per degree.</p> <p>The dataset used to generate these models is the file <a href="https://doi.org/10.60903/LOLA_PA">LDEM128_PA_gridline_202405.grd</a>. As described by Neumann (2024), this shape mode is based on a combination of laser altimeter data obtained by the LOLA instrument on the Lunar Reconaissance Orbiter spacecraft and the SLDEM2015 shape model that makes use of both LOLA and Kaguya terrain camera data. The netcdf file was read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software and expanded into spherical harmonics using the function <code>SHCoeffs.expand()</code>. The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The units of the coefficients are meters.</p> <p>The five files in this archive are</p> <ul> <li>Moon_LDEM128_shape_pa_11519.sh.gz</li> <li>Moon_LDEM128_shape_pa_5759.sh.gz</li> <li>Moon_LDEM128_shape_pa_2879.sh.gz</li> <li>Moon_LDEM128_shape_pa_1439.sh.gz</li> <li>Moon_LDEM128_shape_pa_719.sh.gz</li> </ul> <p>The numbers 11519, 5759, 2879, 1439, and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 128, 64, 32, 16, and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution models were generated by truncating the spherical harmonic coefficients of the highest resolution model.</p> <p>This shape model uses the same coordinate system as most lunar gravity models. The principal axis coordinate system differs from the more common mean Earth/polar axis system by about 1 km at the equator. For a mean Earth/polar axis model, use <a href="../records/10796823">Spherical harmonic models of the shape of the Moon</a>.</p>
Spatiotemporal Dataset on Moon Jellyfish (Aurelia aurita) Incidental Observations in the Gulf of Riga and Eastern Gotland Basin, Baltic Sea
<p>This data article describes the occurrences of the moon jelly <em>Aurelia aurita </em>medusae in the Eastern Gotland basin and the Gulf of Riga (Baltic Sea) between 1998 and 2023. All data are incidental observations obtained during Latvian national monitoring cruises. </p>
Tectonic Map and Compressional Feature Map of Mare Tranquillitatis, Moon
<p>ArcGIS shapefiles of the tectonic (Compressional_Tectonism and Extensional_Tectonism) and compressional feature (Feature_Map_Classes) maps of Mare Tranquillitatis. This is complementary data for "Timing and Origin of Compressional Tectonism in Mare Tranquillitatis" published on JGR: Planets by Frueh et al. (Available on <a href="https://doi.org/10.1029/2022JE007533">https://doi.org/10.1029/2022JE007533</a>). </p> <ul> <li>Compressional_Tectonism: Polylines of wrinkle ridges, lobate scarps, and unidentified features, as well as their geodesic length, coordinates, and bearing.</li> <li>Extensional_Tectonism: Polylines of large graben and normal faults, as well as their geodesic length, coordinates, and bearing.</li> <li>Feature_Map_Classes: Compressional tectonic feature map, including their assigned erosional states.</li> </ul> <p>For a detailed description of the mapping process, features, and erosional states, we currently refer to our publication (Frueh et al., 2023).</p> <p> </p> <p>Frueh, T., Hiesinger, H., van der Bogert, C. H., Clark, J. D., Watters, T. R., & Schmedemann, N. (2023). Timing and origin of compressional tectonism in Mare Tranquillitatis. <em>Journal of Geophysical Research: Planets</em>, 128, e2022JE007533. <a href="https://doi.org/10.1029/2022JE007533">https://doi.org/10.1029/2022JE007533</a></p>
Spectral Units for Tsiolkovskiy crater (Moon, Far side)
<p>Spectral Units derived from the Moon Mineralogy Mapper (M3) data for the lunar far side Tsiolkovskiy crater.</p>
Detrended topography of the Moon
<p>These data sets represent detrended global topography of the Moon. Detrended topography is useful for photogeological analysis of subtle gently sloping features in generally smooth parts of the lunar surface. File _<strong>readme.pdf</strong> contains detailed description of the data sets. </p>
Extent of past PSRs in the north and south polar regions of the Moon
<p>Extent of perennially shadowed regions (PSRs) obtained by applying ray-tracing to LOLA shape models with various values of the maximum solar declination.</p> <p>See README.TXT for a technical description of the data files.</p> <p> </p>
Supporting Data - Double Ridge Formation over Shallow Water Sills on Jupiter's Moon Europa
<p>This archive contains data produced in support of R. Culberg, D. M. Schroeder, G. Steinbrügge, Double Ridge Formation Over Shallow Water Sills on Jupiter’s Moon Europa, <em>Nature Communications</em>, 2022. This includes the WorldView imagery in Figure 1, the reprocessed radargrams underlying the radar analysis and inversion, and outputs of all inversion and sensitivity test runs. See the README file for a complete description of the files available in this archive.</p>
gmap - qgis training material: Ingenii Basin (moon)
<p>This dataset part of the Geology and Planetary Mapping Winter School 2022 featuring Ingenii Basin as a study area.<br> Ingenii Basin is located on the lunar farside centred at 33.7°S 163.5°E within the South Pole-Aitken basin. The floor of Ingenii Basin is filled with mare materials with the basin having a diameter of 282 km.<br> We compiled a beginners – intermediate level training package for the area. The package includes the Lunar Reconnaissance Orbiter Camera (LROC) Wide Angle Camera (WAC) global mosaic (Speyerer et al., 2011) as a basemap, the Lunar Orbiter Laser Altimeter (LOLA) and SELenological and Engineering Explorer (SELENE) Kaguya merged lunar digital elevation model (DEM) (Barker et al., 2016) and spectral data in the form of a clementine Ultraviolet/Visible (UVVIS) warped color ratio mosaic (Lucey et al., 2000). The data is cut to the area of interest and a training project is set up for QGIS. </p> <p>The training package is designed as a group exercise with four adjacent tiles covering the entirety of Ingenii basin. For beginners the aim is to create a low scale map of the area where the basin rim is distinguished from the basin floor and mare unit as well as detecting smaller craters that exist in the area. These units should then be put in a stratigraphic relationship based on superposition, degradation state and embayment. For intermediate mappers this task can be extended to include the swirl features and finding potential areas for crater size frequency distribution measurement to determine absolute ages for a more detailed stratigraphy.</p> <p><br> </p> <p> </p>
Lunar Reconnaissance Orbiter Imagery for LROCNet Moon Classifier
<p><strong>Summary</strong></p> <p>We provide imagery used to train LROCNet -- our Convolutional Neural Network classifier of orbital imagery of the moon. Images are divided into train, validation, and test zip files, which contain class specific sub-folders. We have three classes: "fresh crater", "old crater", and "none". Classes are described in detail in the attached labeling guide.</p> <p><strong>Directory Contents</strong></p> <p>We include the labeling guide and training, testing, and validation data. Training data was split to avoid upload timeouts.</p> <ul> <li>LROC_Labeling_Intro_for_release.ppt: Labeling guide</li> <li>val: Validation images divided into class sub-folders <ul> <li>ejecta: "fresh crater" class</li> <li>oldcrater: "old crater" class</li> <li>none: "none" class</li> </ul> </li> <li>test: Testing images divided into class sub-folders <ul> <li>ejecta: "fresh crater" class</li> <li>oldcrater: "old crater" class</li> <li>none: "none" class</li> </ul> </li> <li>ejecta_train: Training images of "fresh crater" class</li> <li>oldcrater_train: Training images of "old crater" class</li> <li>none_train1-4: Training images of "none" class (divided into 4 just for uploading)</li> </ul> <p><strong>Data Description </strong></p> <p>We use CDR (Calibrated Data Record) browse imagery (50% resolution) from the Lunar Reconnaissance Orbiter's Narrow Angle Cameras (NACs). Data we get from the NACs are 5-km swaths, at nominal orbit, so we perform a saliency detection step to find surface features of interest. A detector developed for Mars HiRISE (Wagstaff et al.) worked well for our purposes, after updating based on LROC NAC image resolution. We use this detector to create a set of image chipouts (small 227x277 cutouts) from the larger image, sampling the lunar globe.</p> <p><strong>Class Labeling</strong></p> <p>We select classes of interest based on what is visible at the NAC resolution, consulting with scientists and performing a literature review. Initially, we have 7 classes: "fresh crater", "old crater", "overlapping craters", "irregular mare patches", "rockfalls and landfalls", "of scientific interest", and "none".</p> <p>Using the Zooniverse platform, we set up a labeling tool and labeled 5,000 images. We found that "fresh crater" make up 11% of the data, "old crater" 18%, with the vast majority "none". Due to limited examples of the other classes, we reduce our initial class set to: "fresh crater" (with impact ejecta), "old crater", and "none".</p> <p>We divide the images into train/validation/test sets making sure no image swaths span multiple sets.</p> <p><strong>Data Augmentation</strong></p> <p>Using PyTorch, we apply the following augmentation on the training set only: horizontal flip, vertical flip, rotation by 90/180/270 degrees, and brightness adjustment (0.5, 2). In addition, we use weighted sampling so that each class is weighted equally. The training set included here does not include augmentation since that was performed within PyTorch.</p> <p><strong>Acknowledgements</strong></p> <p>The author would like to thank the volunteers who provided annotations for this data set, as well as others who contributed to this work (as in the Contributor list). We would also like to thank the PDS Imaging Node for support of this work.</p> <p>The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004).</p> <p>CL#22-4763</p> <p>© 2022 California Institute of Technology. Government sponsorship acknowledged.</p>
Moon Crater Data
<p>I create a data set inspired by the <a href="https://github.com/silburt/DeepMoon">deep moon</a> toolchain. image size is 256x256 pixel the image count is 300k.</p> <p>H5 File structure:</p> <p> /image <- images (300k, 256, 256)</p> <p> /mask <- the targets (300k, 256, 256)</p> <p> /names <- name reference for additional parameter in the json file (300k)</p>
Database of Planar and Three-Dimensional Periodic Orbits and Families Near the Moon
<p>The lunarPOdatabase.zip is the digital database accompanying the paper:<br> <br> C. Franz and R. P. Russell, “Database of planar and three-dimensional periodic orbits and families near the Moon,” The Journal of the Astronautical Sciences, DOI 10.1007/s40295-022-00361-9 (accepted Nov. 2022).</p> <p>Please see the paper for details, and cite the paper as appropriate. The database is accessible and permanently archived with the following DOI <a href="https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.6411980&data=05%7C01%7C%7C1770e232817c4c99fc1b08dad0a0e3ad%7C31d7e2a5bdd8414e9e97bea998ebdfe1%7C0%7C0%7C638051686701542157%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=Spb%2FFj5YL8QKaBoojr09MCCIdloAkzLiGKHoUo3uVGE%3D&reserved=0">https://doi.org/10.5281/zenodo.6411980</a>. See accompanying license.txt and gpl-3.0.txt for license information, applying to all files included in the .zip distribution.</p> <p>The database contains over 13 million planar and three-dimensional solutions in the Earth-Moon circular restricted three body problem, grouped into 34,000 family and sub-family clusters. The database exists as human readable text files with periodic orbits organized by clusters and other dynamical characteristics. The database contains the clustered data, a README file describing the output format, an interactive GUI, and a simple MATLAB script as a basic interface with the database. The data are split into five files, one for each of the planar prograde, planar retrograde, axial prograde, axial retrograde, and x-z cases. The results (i.e. initial conditions and relevant dynamical parameters of each converged periodic orbit) are contained in a human-readable text file where each row is a new solution. The data are sorted by cluster and ordered inside the cluster to form a smooth curve. Summary files are included for both the grid search and the clustering for each run. The input parameters to the grid search software are also included with each case for reproducibility. File sizes range from approximately 1.1GB to 2.6GB, with a total uncompressed file size of 5.4GB and a total compressed file size of 1.2GB.</p> <p>It is emphasized that the GUI and other MATLAB interface files are only a preliminary capability to ease interaction with the database. They may not be stable under future releases of MATLAB. On the initial use of the GUI, we recommend to restrict the data to a single value of N (say N=1 or N=16), otherwise the number of solutions may overwhelm the system memory. If a user has difficulties using the GUI, the user is encouraged to use the MATLAB code interfaces or interface with the text files directly. The text files containing the database are the primary product provided here, with the GUI and test scripts provided as a courtesy to help ease the database's use.</p> <p>Please send questions to <a href="mailto:cfranz21@gmail.com">cfranz21@gmail.com</a> and/or <a href="mailto:ryan.russell@utexas.edu">ryan.russell@utexas.edu</a>.</p>
Spectral data associated to the publication: "Near-infrared reflectance spectroscopy of sublimating salty ice analogues. Implications for icy moons" by R. Cerubini et al. (Planetary and Space Science 211, 2022)
<p>This is the complete set of experimental NIR reflectance data collected by R. Cerubini and co-authors for the article "Near-infrared reflectance spectroscopy of sublimating salty ice analogues. Implications for icy moons" published in Planetary and Space Science 211 (2022). doi: https://doi.org/10.1016/j.pss.2021.105391.</p> <p>The article itself is published in open-access and provides the methodology for the spectral aquisitions, discussion of the errors and uncertainties, analysis of the spectra and implications for the composition of Solar System surfaces.</p> <p>The data are contained in ASCII files (columns separated by comma). The first column is the wavelength (in micrometers) and the other columns contain the reflectance data (in unit of reflectance factor). The different compositions are indicated in the filenames and correspond directly to the figures in the published paper.</p> <p> </p> <p> </p>
H'aiuru, Nilgiri Hills (Tamil Nadu). Hero-stone with snake beside sun and moon
<p>Source: Breeks, J.W. (ed. by his widow) (1873) <em>An account of the primitive tribes and monuments of the Nilagiris</em>, London.</p> <p>Former orthostat of a disrupted dolmen.<br> <br> Topmost section showing the sun, the moon and a snake.Apparently no tradition of serpent worship on the hills. According to Breeks, the snake is not a Nāga: its position beside the sun would indicate an eclipse (the sun is captured by a serpent, as maintained by some beliefs).</p>
Spherical harmonic model of the shape of Earth's Moon: MoonTopo2600p
<p><em><strong>THIS MODEL IS SUPERSEDED BY <a href="../doi/10.5281/zenodo.10796953">Spherical harmonic models of the shape of the Moon (principal axis coordinate system)</a>.</strong></em></p> <p> </p> <p><strong>MoonTopo2600p.shape</strong> is a spherical harmonic model of the shape of Earth's Moon in a principal axis coordinate system. 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>
Planmap's Deliverable 6.2- 3D geo-models based on multiple datasets of the Moon (implicit or explicit modelling)
<p>Outputs of the 3D geomodelling of the shallow-surface layered deposits on the Chang'e 3 landing site. This is based on the Yutu rover GPR channel 2B data gathered along its traverse in Sinus Iridum on the Moon.</p> <p> </p> <p>Notebooks at https://doi.org/10.5281/zenodo.4055213</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.