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319 results for “Lunar”
New observations of recently active wrinkle ridges in the lunar mare: Implications for the timing and origin of lunar tectonics
<p>Enclosed are the for the tectonically deformed lunar impact crater and recently active wrinkle ridge datasets produced in Nypaver and Thomson, 2022 (GRL). These data are presented in Figure 2 of that Manuscript and are readable in a GIS-based software (shapefile) and the LROC Quickmap interface (json). Lat/Long coordinates for all tectonically deformed craters are also presented in CSV format.</p>
Crater Populations on the Walls of Complex Craters found near the Lunar Southern Pole
<p>Date: June 23, 2022<br> Authors: C. L. Talkington and P. E. Montalvo<br> Notes: Data sets contain crater counts on floors of 16 lunar south polar complex craters and surface slopes at all counted craters. </p> <p>Data sets in crater_data.xlsx are updated from the previous version to improve the model age calculation. The current version uses an approach using Poisson’s statistics (Michael and Neukum, 2010; Michael et al., 2016).</p> <p>ArcMap 10.7.1 was used in addition to the CraterTools Add in toolset (Kniessl et al., 2011) and DEM data from the PDS Geosciences Node (https://pds-geosciences.wustl.edu/lro/lro-l-lola-3-rdr-v1/lrolol_1xxx/data/lola_gdr/polar/jp2/) to visualize the craters.Complex craters were selected from previous studies (Cannon et al., 2020; Deutsch et al., 2020; Tye et al., 2015) as regions of interest. The counts from this work span the walls of 16 complex craters found near the lunar southern pole, while those cited refer to the floor regions (Cannon et al., 2020; Deutsch et al., 2020), or the entire crater (Tye et al., 2015). Craters were included within the analysis from definitions described by Deutsch et al., (2020) as circular features with central depressions. We considered primary craters only, and those with morphologies reflecting secondary crater populations (crater chains or clusters) were omitted. Crater sizes range from sub km range to 35 km in diameter. Their slope conditions range from very shallow to very steep. </p> <p>PDS Geoscience Node DEM used ldem_80s_20m. This DEM has a resolution of 20 m/pixel and was updated 6/2/2017.<br> Data format, .csv files:<br> - Column A: Diameter. Crater diameter [km]<br> - Column B: Slope angle of crater [deg] determined by the 500 m buffer size from the determined crater radius. </p>
Far-Ultraviolet Photometric Characteristics of JSC-1A and LMS-1 Lunar Regolith Simulants: Comparative Investigations with Apollo 10084
<p>The .txt files listed here contain the data used for Figure 3 and Supplemental Figure S3 of the paper titled "Far-Ultraviolet Photometric Characteristics of JSC-1A and LMS-1 Lunar Regolith Simulants: Comparative Investigations with Apollo 10084." The experimentally derived phase curves are contained in the files which have the material name (JSC-1A, LMS-1, or Apollo 10084) followed by the wavelength alone (Lyman-a, 140 nm, 160 nm) in the file name. All files with "Hapke" in the file name are the Hapke photometric model (Hapke, 2012) fitted phase curves for the associated experimental data. Two of the JSC-1A files have either "LT38" or "GT150" included in the file name; these are the sieved grain size category data referenced in S3.</p> <p>Also included are .txt files of the data points used in Figure 4 for the JSC-1A and LMS-1 simulants. These each have "Fig4" in the file name.</p>
Compositional maps of the lunar polar regions derived from the Kaguya Spectral Profiler and the Lunar Orbiter Laser Altimeter data
<p>Compositional maps of the lunar polar regions derived from the Kaguya Spectral Profiler and the Lunar Orbiter Laser Altimeter data as described in Lemelin et al. (2022). </p> <p>This folder includes different GeoTIFF files described and shown in Lemelin et al. (2022). The files are projected in Polar Stereographic Projection, at a spatial resolution of 1000 m/pixel. A version of each of the following file is given for the north and south polar region (50-90 N/S).</p> <p>- Gridded and interpolated Spectral Profiler reflectance mosaics scaled to the LOLA dataset at 1064 nm<br> - Data counts used in the gridded and interpolated Spectral Profiler reflectance mosaics scaled to the LOLA dataset at 1064 nm<br> - Spectral Profiler FeO mosaics <br> - Spectral Profiler OMAT mosaics <br> - Spectral Profiler plagioclase mosaics <br> - Spectral Profiler olivine mosaics <br> - Spectral Profiler low-calcium pyroxene mosaics<br> - Spectral Profiler high-calcium pyroxene mosaics <br> - Nanophase iron mosaics <br> - Correlation coefficient mosaics</p>
Lunar ASP DEM Test PDS4 Bundle
<p>A pair of Ames Stereo Pipeline (ASP) generated LROC NAC Digital Elevation Models in NASA PDS4 structure.</p> <p><strong>Purpose:</strong> Created as part of a testing effort funded by the LPI. The purpose of this dataset is to provide example LROC NAC Digital Elevation Models (DEMs) generated using rapid Ames Stereo Pipeline (ASP) processing. The example DEMs were assessed for suitability for scientific analysis.</p> <p><strong>Data Set Overview: </strong>The archive contains 2 DEMs, in GeoTiff format, as a right image and a left image. The DEMs were generated using the ASP online tutorial and version of the software downloaded in March 2022 from github using the latest build (https://github.com/NeoGeographyToolkit/StereoPipeline). The DEMs cover a portion of Glushko crater's extensive ejecta ray system, Earth's Moon.</p> <p>The DEMs were generated using map-projected Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) input images that were collected as a stereo pair but have not yet been processed into a DEM using photogrammetric techniques. The base shape model for the projection is the LROC Wide Angle Camera (WAC) GLD100 topographic product. The map-projected images were run using parallel_stereo and point2dem processes in the ASP toolkit. The output is a DEM in geotiff format.</p> <p>The included test DEMs, archive structure, related documents, and xml files are formatted to best effort in pds4 format following online documentation by NASA PDS (as of Sept 2022 at https://pds.nasa.gov). Files were validated using the PDS Validate tool (downloaded Sept 2022, version v2.3.0, from https://github.com/NASA-PDS/validate). Xml templates were modified from existing related examples (Watkins 2018, Herrick and Ward 2020, Hare and Trent 2018). The provided files have been self-validated but are not validated by the Planetary Data System (PDS) and are provided for educational and training purposes only, and could contain errors or inconsistencies.</p>
FIGURA 3 in Influência do ciclo lunar no padrão de atividade de Cuniculus paca (Rodentia: Cuniculidae) em uma floresta de Mata Atlântica no Sul do Brasil
FIGURA 3: Relação entre as fases da Lua e a hora atividade de C. paca, no Parque Nacional dos Campos Gerais, PR.
FIGURA 2 in Influência do ciclo lunar no padrão de atividade de Cuniculus paca (Rodentia: Cuniculidae) em uma floresta de Mata Atlântica no Sul do Brasil
FIGURA 2: Horário de atividade de C. paca no Parque Nacional dos Campos Gerais, PR. Linhas com abas indicam horário médio de registro e intervalo de confiança de 95%.
FIGURA 1 in Influência do ciclo lunar no padrão de atividade de Cuniculus paca (Rodentia: Cuniculidae) em uma floresta de Mata Atlântica no Sul do Brasil
FIGURA 1: Localização geográfica do Parque Nacional dos Campos Gerais, PR, indicando a distribuição das armadilhas fotográficas.
Fig. 3 in Seasonal, Lunar and Tidal Influences on Habitat Use of Indo-Pacific Humpback Dolphins in Beibu Gulf, China.
Fig. 3. Map of humpback dolphin sightings based on (a) dry and wet seasons, (b) lunar phases and (c) tidal phases for the humpback dolphins in northern Beibu Gulf.
Fig. 4 in Seasonal, Lunar and Tidal Influences on Habitat Use of Indo-Pacific Humpback Dolphins in Beibu Gulf, China.
Fig. 4. The profile of distance from estuary (a) and water depth (b) of humpback dolphin sightings in northern Beibu Gulf among different seasons, lunar phases and tidal phases, and significance tests (c, d). Note: "*" indicates a significance at P <0.05, "**" indicates a significance at P <0.01.
Fig. 2 in Seasonal, Lunar and Tidal Influences on Habitat Use of Indo-Pacific Humpback Dolphins in Beibu Gulf, China.
Fig. 2. Definitions for high and low tidal phases in the Beibu Gulf, China, as used in the present study.
Synthetic Lunar Terrain: A Multimodal Open Dataset for Training and Evaluating Neuromorphic Vision Algorithms
<p><strong>Synthetic Lunar Terrain (SLT) </strong>is a dataset based on a reconstruction of a typical <strong>cratered lunar surface landscape </strong>at the <a href="https://set.adelaide.edu.au/atcsr/space-research/exterres-laboratory" target="_blank" rel="noopener">EXTERRES Laboratory</a> at University of Adelaide, Roseworthy Campus. On a surface area of <strong>3.6m x 4.8m</strong>, multiple synthetic craters with different sizes and geometries were sculpted into<strong> lunar regolith simulant</strong>. A <strong>9kW metal-halide lamp</strong> illuminated the scene, providing high contrast drop-shadows from the rims of craters and similar surface features that are characteristic for the Earth's moon.</p> <p>The purpose of this dataset is to provide multimodal recordings of visual information to develop and test algorithms on a hardware analogue of the moon rather than relying on computer simulations. In particular, comparisons between <strong>neuromorphic vision sensors</strong> like <strong>event-based cameras</strong> and imaging with <strong>conventional monocular cameras</strong> are at the core of this work. For this purpose, an event-based camera (Gen4 Prophesee with Prophesee-Sony IMX636 sensor) was mounted downward-pointing next to a optical camera (Basler a2A1920-160ucPRO with Sony IMX392 sensor) on an extendable rod which was moved above the surface in a slow and continuous sweep. In total, SLT consists of camera recordings from 21 different positions/settings, with clockwise and anti-clockwise motions under varying, extreme lighting conditions.</p> <p>The event-stream and grayscale image data can be further referenced via a detailed <strong>3D point cloud</strong> obtained by a FARO Focus S70 3D Scanner. This 3D Scan was post-processed, realigned and resampled into a 3D point cloud of <strong>~6.25M points, </strong>with a surface density of <strong>1.862 p/mm²</strong>, providing a ground-truth for the crater geometries.</p> <p>In detail, SLT contains the following:</p> <ul> <li>eventbased.zip: <ul> <li><strong>42 camera orbits</strong> in the binary <strong>EVT 3.0</strong> format (<a href="https://docs.prophesee.ai/stable/data/encoding_formats/evt3.html" target="_blank" rel="noopener">Prophesee docs</a>) </li> <li>corresponding <strong>.mp4 </strong>event-frame video rendering for visualization purposes (33.333ms accumulation time at 30FPS)</li> <li>corresponding<strong> .bias</strong> file containing settings used during recording</li> </ul> </li> <li>code.zip: <ul> <li>Standalone C++ code of the <strong>metavision EVT3-to-RAW file decoder</strong>, allowing to convert the binary EVT 3.0 format into a plaintext <strong>.csv </strong>that includes <ul> <li>the coordinates of the event-pixel,</li> <li>the polarity change,</li> <li>and the time-stamp of the event.</li> </ul> </li> <li>This code is an unmodified redistribution from the <a href="https://www.prophesee.ai/metavision-intelligence/" target="_blank" rel="noopener">Metavision SDK</a>, version 4.6.0, released by Prophesee under Apache License 2.0.</li> </ul> </li> <li> optical.zip: <ul> <li><strong>42 image sequences</strong> in <strong>.tif</strong> format (LZW, 1920x1200px, 8bit, grayscale) <ul> <li>Length of image sequences varies between about 300 to 700 images per sequence</li> </ul> </li> </ul> </li> <li>3d_scan.zip: <ul> <li><strong>SLT3d_scan.ply:</strong> 3D point cloud of the scene Stanford Polygon File Format</li> <li><strong>SLT3d_scan.xyz:</strong> 3D point cloud with plaintext x y z coordinates, white-space separated</li> </ul> </li> <li>cratermap.png: <ul> <li>Annotations of <strong>130 different surface features</strong> that have been manually identified as crater-like with approximate x,y-coordinates.</li> </ul> </li> <li>positionmap.png: <ul> <li>Illustration of the different positions from which the rod was moved over the scene (not to scale).</li> </ul> </li> <li>sample.zip: <ul> <li>A sample containing 1 event-camera orbit with the corresponding image sequence (for convenience only, to test the dataset without the need to download it's entirety)</li> </ul> </li> </ul> <p>The global coordinate frame of this dataset puts the origin at the centre of the scene. The shorter side of the terrain is roughly aligned with the x-axis, the longer side with the y-axis. The z-axis represents height/depth (compare with <strong>cratermap.png</strong>). The different conditions (compare with <strong>positionmap.png</strong>) from which the data was taken are encoded as follows:</p> <ul> <li><strong>A1, ..., A9</strong> refer to the left side of the scene (negative x)</li> <li><strong>B1, ..., B9 </strong>refer to the right side of the scene (positive x)</li> <li><strong>S1, S2, S3</strong> and <strong>S4 </strong>describe special lighting conditions and/or parameter settings</li> <li><strong>CW </strong>refers to a "clockwise" sweeping of the camera-rod, relative to the position</li> <li><strong>ACW</strong> refers to an "anti-clockwise" sweeping of the camera-rod, relative to the position</li> </ul> <p>The light from the metal-halide lamp was directed through a small opening, shining along the positive y-axis. In some of the setups, an obstacle was placed between the surface and the opening, blocking out part of the light to create a light-dark separator on the surface, emulating the <strong>terminator</strong> on the Moon between it's day and night side, resulting in highly contrastive images.</p> <p>We encourage you to consult and cite our related publication, should you find SLT useful.</p> <ul> <li>Märtens, M., Farries, K., Culton, J. and Chin, TJ. "<strong>Synthetic Lunar Terrain: A Multimodal Open Dataset for Training and Evaluating Neuromorphic Vision Algorithms</strong>", Proceedings of "<em>International Symposium on Artificial Intelligence, Robotics and Automation in Space (I-SAIRAS), 2024</em>", pp. 609-614</li> </ul> <p> </p>
Lunar rainbow and lunar Brocken spectre, by Kouji Ohnishi, Japan
<p>Third place in the 2021 IAU OAE Astrophotography Contest, category Sun/Moon haloes.</p> <p>This stunning photograph of the lunar rainbow and the lunar Brocken Spectre amidst the night sky was captured from Mount Tsubakuro located in Japan’s Hida Mountains in Nagano. Both these atmospheric occurrences are due to the moon’s light being reflected and refracted from water droplets. A lunar rainbow or a moonbow is a rare phenomenon that occurs with the right settings of a bright full moon which is less than 42° high, rain on the opposite side of the moon and a dark night sky. The Brocken Spectre is named after the highest peak of the Harz mountain range in Germany, where it was first recorded. Here it is seen from the summit as a magnified shadow of the observer cast onto the cloud surrounded by a glory consisting of concentric circles centered at the point directly opposite the bright moon in the background.</p> <p>Credit: Kouji Ohnishi/IAU OAE</p>
Data used in "The Utility of RGB Color for Discrimination of Lunar Maturity and Composition"
<p>Datasets from the paper "The Utility of RGB Color for Discrimination of Lunar Maturity and Composition", By D. T. Blewett, T. X. Choi, Y.-C. Zheng, and E. A. Cloutis, to be published in the journal <em>Earth and Space Science</em>.</p> <p>Reflectance spectra for <em>Apollo</em> lunar samples 14003, 15601, 70011, 14310, and 65015 were published by Wagner et al. (1987), <em>Icarus 69</em>, 14–28. The spectra were digitized by Amanda Hendrix and Faith Vilas (see Hendrix and Vilas (2006), <em>Astron. J. 132</em>, 1396–1404). I took the spectra that Hendrix and Vilas supplied to me and resampled them to RELAB wavelengths. The spectra are in a tab-delimited text file.</p> <p>The responsivities of the <em>Chang'E-3</em> PCAM RGB channels were published by X. Ren et al. (2014), <em>Res. Astron. Astrophys. 14</em>, 1557–1566. We digitized the RGB curves from Fig. 2 of the Ren paper. The curves are in tab-delimited text files.</p> <p> </p>
Lunar eclipses illuminate timing and climate impact of medieval volcanism
<p>This repository contains all the data and codes needed to reproduce the results and figures from the article "Lunar Eclipses Illuminate Timing and Climate Impacts of the Middle Ages" published in Nature.<br> <br> For more information, we refer the user to the readme file entitled "Guillet_et_al_Nature2023_Readme.txt".<br> <br> If you have any queries, please feel free to contact us: sebastien.guillet@unige.ch<br> <br> Thank you very much ;-)</p>
Lunar eclipses illuminate timing and climate impact of medieval volcanism
<p>This repository contains all the data and codes needed to reproduce the results and figures from the article "Lunar Eclipses Illuminate Timing and Climate Impacts of the Middle Ages" published in Nature.<br> <br> For more information, we refer the user to the readme file entitled "Guillet_et_al_Nature2023_Readme.txt".<br> <br> If you have any queries, please feel free to contact us: sebastien.guillet@unige.ch<br> <br> Thank you very much ;-)</p>
About the Lunar solid inner core and the mantle overturn
<p>Datasets for the selected models, temperature and activation enthalpy used for this study. ReadMe helps to understand the headers of the datasets files. </p>
Recalibration of the lunar chronology due to spatial cratering-rate variability - Supporting Information
<p>CR_moon.csv: Relative cratering rate shown in Fig.3 and 5.a. The<strong> </strong>data are provided over the full range of latitudes and longitudes, with a 1-degree bin.</p> <p>SI_convert_age.m: Matlab code computing model ages of Plutarch and Kirkwood craters using the chronology function presented in this study.</p>
Figure data of "Remote Detection of a Lunar Granitic Batholith at Compton-Belkovich"
<p>These are figure data for the paper "Remote Detection of a Lunar Granitic Batholith at Compton-Belkovich"</p>
Supporting Material for "Lunar Surface Model Age Derivation: Comparisons Between Automatic and Human Crater Counting Using LRO-NAC And Kaguya TC Images"
<p>Supporting Material for "Lunar Surface Model Age Derivation: Comparisons Between Automatic and Human Crater Counting Using LRO-NAC And Kaguya TC Images"</p> <p>Contents of this material</p> <ul> <li>Supplemental Text S1 and Text S2.</li> <li>Figures S1, S2, S2, S4, S5.</li> <li>Tables S1, S2</li> </ul> <p>For any questions email JHF (john.h.fairweaher@gmail.com).</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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