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462 results for “Moon”
Millimeter- to Decimeter-Scale Surface Slope and Roughness of the Moon at the Chang'e-4 Exploration Region
<p>The datasets related to the work of<em> Millimeter- to Decimeter-Scale Surface Slope and Roughness of the Moon at the Chang'e-4 Exploration Region.</em></p> <p>Cite the following references if using the DEM data. Wu, B., Li, Y., Liu, W. C., Wang, Y., Li, F., Zhao, Y., and Zhang, H. (2021), Centimeter-resolution topographic modeling and fine-scale analysis of craters and rocks at the Chang’E-4 landing site, Earth Planet. Sci. Lett., 553, 116666. <a href="https://doi.org/10.1016/j.epsl.2020.116666">https://doi.org/10.1016/j.epsl.2020.116666</a></p> <p>Guo, D., Fa, W., Wu, B., Li, Y., & Liu, Y. (2021). Millimeter- to Decimeter-Scale Surface Slope and Roughness of the Moon at the Chang’e-4 Exploration Region. <em>Geophysical Research Letters</em>, 48, e2021GL094931. <a href="https://doi.org/10.1029/2021GL094931">https://doi.org/10.1029/2021GL094931</a></p>
Full Flower Moon 2023
<p>Honorable mention in the 2023 IAU OAE Astrophotography Contest, category Still images with smartphones-mobile devices: Full Flower Moon 2023, by Joslynn Appel.</p> <p>The Flower Moon, named for the bloom of flowers in North America, casts a radiant glow over the night sky in Luzerne County, Pennsylvania, USA, in May 2023. Despite initial cloud cover, the moon's luminosity broke through around 23:00, creating an enchanting spectacle. This captivating smartphone-captured image not only commemorates the celestial event but also emphasises the significance of safeguarding our dark skies. The moon’s brilliance illuminates the intricacies of Earth’s atmosphere, creating an atmospheric drama heightened by the presence of scattered clouds. Preserving these pristine, unblemished skies is vital in retaining our connection to celestial wonders.</p> <p>Credit: Joslynn Appel/IAU OAE (<a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC BY 4.0</a>)</p> <p> </p>
Jupiter, Venus, Moon Conjunction
<p>Honorable mention in the 2023 IAU OAE Astrophotography Contest, category Still images with smartphones-mobile devices: Jupiter, Venus, Moon Conjunction, by Joslynn Appel.</p> <p>Captured with a smartphone in February 2023, over the skies of Luzerne County, Pennsylvania, USA, this photograph offers a glimpse into a conjunction, an enthralling astronomical phenomenon that occurs when two or more celestial objects are seen in close proximity in the sky from our perspective, despite the objects not being physically near to each other. In this image, the brilliance of Jupiter (top), the allure of Venus (middle), and the familiar glow of our Moon (bottom) dance together against a backdrop of delicate clouds and a treeline silhouette, making it a moment worth treasuring.</p> <p>Credit: Joslynn Appel/IAU OAE (<a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC BY 4.0</a>)</p> <p> </p>
Data for A volcanic inventory of the Moon
<p>This archive contains shapefiles for <em>A volcanic inventory of the Moon Broquet A. & Andrews-Hanna, J. C., submitted to Icarus (2023). </em>Each shapefile contains an associated .dbf, .prj, and .shx file.</p> <p>RinglikeAnomalies.shp: Shapefile providing our mapping of ring-like gravity anomalies that are associated to dikes. </p> <p>LinearAnomalies.shp: Shapefile providing our mapping of linear gravity anomalies that are associated to intrusive materials.</p>
Dataset for "A comprehensive model for pickup ion formation at the Moon"
<p>Dataset for "A comprehensive model for pickup ion formation at the Moon", submitted to J. Geophys. Res.: Planets for publication. The dataset consists of in-situ measurements of ion fluxes and densities around the Moon as observed by NASA's ARTEMIS mission, as described in the manuscript.</p>
Spherical harmonic models of the gravitational field implied by the Moon's topographic masses
<p>Provided are 12 spherical harmonic models of the gravitational field implied by the Moon's topographic masses. The models mitigate the divergence effect of spherical harmonics on the Moon's topography when compared with spectral gravity forward modelling methods. The topographic masses are expanded up to degrees 90, 180, 360 and 720, and the maximum degree of the gravitational models varies from 360 up to 2160. All models are available in the <a href="http://icgem.gfz-potsdam.de/ICGEM-Format-2011.pdf">gfc</a> format as defined by <a href="http://icgem.gfz-potsdam.de">ICGEM</a>. One of the models, <em>STU_Moon_topography_to720_gravity_to2160</em>, can also be accessed from <a href="http://icgem.gfz-potsdam.de/tom_celestial">ICGEM</a>, where it can be find under a shortened name <em>STU_MoonTopo720</em>.</p> <p>The models rely on the Runge-Krarup theorem and enable generally a more accurate evaluation of the gravitational field in the proximity to the lunar topography as compared to the models from spectral gravity forward modelling. This is because the latter ones may suffer from the divergence effect when evaluating the spherical harmonic series on or below the limit sphere encompassing all the gravitating masses (that is, also on the topography).</p> <p>The datasets have been published in Bucha, B., Hirt, C., Kuhn, M., 2019. <em>Divergence-free spherical harmonic gravity field modelling based on the Runge—Krarup theorem: a case study for the Moon</em>. Journal of Geodesy 93, 489-513, <a href="https://doi.org/10.1007/s00190-018-1177-4">https://doi.org/10.1007/s00190-018-1177-4</a></p>
Supporting material for: MoonIndex, an Open-Source Tool to Generate Spectral Indexes for the Moon from M3 Data
<p>Supplementary material for the paper called: MoonIndex, an Open-Source Tool to Generate Spectral Indexes for the Moon from M3 Data. The data without "indexes" in the name are map-projected M3 cubes, they can be used in the python library <i><strong>MoonIndex </strong></i>to obtain the spectral indexes stored in the files with "indexes" in the name.</p><p>This research was done on the framework of the EXPLORE project, that has received funding from the European Union's 2020 research and innovation program under grant agreement No 101004214. </p>
Dataset for "Guidelines for radiation-safe human activities on the Moon"
<p>Dataset for figures in "Guidelines for radiation-safe human activities on the Moon" publication in Nature Astronomy</p>
Compilation of Moon internal structure models and seismic event locations presented in Garcia et al., Space Science Review, 2019 study
<p>Compilation of of previously published internal structure model of the Moon in "named discontinuities" seismological format + 3 new models associated to the above mentioned study + previously published Moon quake location estimates by various authors.</p> <p>The study, the internal structure models and quake locations compilation were performed by the ISSI international research team on Moon Seismology and internal structure described here: http://www.issibern.ch/teams/internstructmoon/</p>
Figure 11 in Immature stages, phenology, distribution and host plants of the Andean Moon Moth Cercophana frauenfeldii Felder, 1862 (Lepidoptera: Saturniidae)
Figure 11 Larvae of C. frauenfeldii occurring on two host plants. (A) Last instar larva feeding on Cryptocarya alba (peumo). (B) Second instar larva feeding on Gomortega keule (queule) leaves.
Figure 7 in Immature stages, phenology, distribution and host plants of the Andean Moon Moth Cercophana frauenfeldii Felder, 1862 (Lepidoptera: Saturniidae)
Figure 7 Cercophana frauenfeldii cocoon.(A) Collected from rocky substrate at Laguna Torca National Reserve, Vichuquén, Chile.(B-D) Cocoons from larvae reared at the laboratory. (E-F) Details of the silk thread arrangement in the cocoon.
Figure 2 in Immature stages, phenology, distribution and host plants of the Andean Moon Moth Cercophana frauenfeldii Felder, 1862 (Lepidoptera: Saturniidae)
Figure 2 Cercophana frauenfeldii larva head. (A) Trisegmented Antenna (TrA); (B) Details of the mouthparts, Labrum (Lb) and Mandibles (Man). (C) Hypopharyngeal complex, showing Maxillary Palpi (MaP) and Spinneret (Spn); and (D) Stemmata (Ste) arrangement.
Geologic Map of Tyre Impact Crater on the Galilean Moon Europa - Digitized and Modified Versions (2024)
<p><strong>Geologic Map of Tyre Impact Crater on the Galilean Moon Europa - Digitized and Modified Versions (2024)</strong></p> <p>Files and deliverable documentation of the process of digitizing the geologic map of Tyre, Kadel et al. 2000. This may be useful if you are looking to learn how to digitize a geologic map using some form of mapping software, or to learn about the surface geology of Jupiter's icy moon Europa. </p> <p>Includes:</p> <ul> <li>read.me with supporting information</li> <li>map package</li> <li>Georeferenced PDF figures </li> <li>Shapefiles </li> </ul>
Recent Tectonic Activity in and Around the Posidonius Crater, Moon-Version 5
<p>The shapefiles and .scc files of the version 5 manuscript: "Recent tectonic activity in and around the Posidonius crater, Moon."</p>
Digital Elevation Models from Planetary Flyby Images of Mercury and the Moon with Shape and Albedo from Shading
<p>Supplemantary material to Krüll, I., Wohlfarth, K., Tenthoff, M., Wöhler, C., Galluzzi, V., Wright, J., Benkhoff, J., and Zender, J.: Shape and Albedo from Shading with Planetary Flyby Images of Mercury and the Moon, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-247, https://doi.org/10.5194/epsc2024-247, 2024.</p> <p><strong>Abstract</strong></p> <p>Surface reconstruction of planetary bodies such as the Moon and Mercury is crucial for geomorphological analysis, reflectance normalization, thermal modeling, rover landing site planning, and outreach activities. Stereo algorithms and Shape-and-Albedo-from-Shading (SAfS) are well-established methods for planetary 3D reconstruction. SAfS refines the surface slopes of a stereo Digital Elevation Model (DEM) and typically yields 3D models at image resolution. This approach is well-validated for scientifically calibrated instruments that observe the planetary body under favorable conditions. This work applied the SAfS algorithm to more challenging planetary flyby images acquired with uncalibrated off-the-shelf cameras. We investigated three scenarios: a fly-by image of the Moon captured by a GoPro during the Artemis I mission, a fly-by image of Mercury which was obtained with a monitoring camera during BepiColombo’s third flyby, and a telescope image taken in Wetter, Germany. We qualitatively and quantitatively assessed the algorithm's performance. The results of the two flyby images indicate that, despite the challenging conditions, the SAfS algorithm could reconstruct the surface up to image resolution and increase the level of detail of the input DEM. The reconstructed DEM of the telescope image is the one with the lowest resolution. All in all, our flyby-derived DEMs are accurate. They provide excellent outreach products, as demonstrated by ESA's BepiColombo flyby movie: https://www.esa.int/Science_Exploration/Space_Science/BepiColombo/BepiColombo_s_third_Mercury_flyby_the_movie</p> <p><strong>Dataset<br></strong></p> <p>We applied the SAfS algorithm to different Regions of Interest (ROIs) in the flyby and telescope images. The ROIs are marked in Artemis_Flyby_ROIs.png, Bepicolombo_Flyby3_ROIs.png and Moon_Telescope_ROIs.png, respectively. For each ROI a DEM is provided centered on the latitude and longitude (0-360, positive east) in the filename. Furthermore a Red/ Blue Stereo anaglyph of the original image was created with the SAfS DEM (for this purpose the height has been exaggerated).</p> <p> </p>
Linked collectors and determiners for: Craters of the Moon National Monument Herbarium.
Natural history specimen data linked to collectors and determiners held within, "Craters of the Moon National Monument Herbarium". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/6347031b-2348-47c6-a5ac-41216b49d17c">https://bionomia.net/dataset/6347031b-2348-47c6-a5ac-41216b49d17c</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/6347031b-2348-47c6-a5ac-41216b49d17c">https://gbif.org/dataset/6347031b-2348-47c6-a5ac-41216b49d17c</a>. Formatted as a Frictionless Data package.
Dataset for "LOROS: Laboratory Simulations of the Optical RadiOmeter composed of CHromatic Imagers (OROCHI) Experiment of the Martian Moons eXploration (MMX) Mission"
<p>This dataset hosts the image and numerical data analysed and derived in the accompanying Stabbins & Kameda article for the special issue of Progress in Earth and Planetary Science on instrumentation and preparations for the JAXA Martian Moons eXploration (MMX) mission. The paper describes and validates the performance of the Laboratory OROCHI Simulator (LOROS).</p> <p>OROCHI (Optical RadiOmeter composed of CHromatic Imagers) is a multispectral multi-view imaging system for the JAXA MMX spacecraft, that will image Phobos and Deimos across 8 visible and near-infrared spectral channels with unprecedented spatial resolution, recording data that in synergy with the other instruments of the MMX spacecraft and rover will constrain hypotheses on the origin of the Martian moons.</p> <p>LOROS is a laboratory simulator of OROCHI, constructed from commercial off-the-shelf parts.</p> <p>The dataset for the characterisation and validation of LOROS is composed of the following sub-sets:</p> <p>A. Modulation Transfer Function<br>B. Expected Reflectance of Carbonaceous Chondrite & Dark Spectralon<br>C. Radiometric Calibration<br>D. Dark Spectralon Validation</p> <div> <h2>Dataset A: Modulation Transfer Function</h2> This dataset includes the table of results of MTF measurements of the slant-edge target at 5 different random orientations in the range of ~7--10°: <div>- <code>mtf_results_07122023.csv</code></div> <br> <div>and the region-of-interest images, for each orientation and each LOROS channel, used to perform the analysis via the <a href="https://sourceforge.net/p/mtfmapper/home/Home/" target="_blank" rel="noopener">MTF Mapper software</a>:</div> <div>- <code>mtf_measurements_07122023</code></div> <br> <div>The directory tree of measurements, for the <em>n</em>th orientation, is illustrated below. Region-of-interest images are stored under <code>img</code>, and are averaged over 25 repeat images to minimise random noise, have had dark frames subtracted, and have been converted from 12-bit to 8-bit grayscale images for compatibility with the MTF Mapper software. Modulation Transfer Function (MTF) and Spatial Frequency Response (SFR) diagnostics generated by MTF Mapper are stored in the <code>results</code> directory.</div> <div> </div> <div><code>mtf_measurements_07122023</code></div> <div><code>├── mtf_knifeedge_low_07122023_*n*</code></div> <div><code>│ ├── img</code></div> <div><code>│ │ ├── 0_850_img_ave.tif</code></div> <div><code>│ │ ├── 1_475_img_ave.tif</code></div> <div><code>│ │ ├── ...</code></div> <div><code>│ ├── results</code></div> <div><code>│ │ ├── 0_850_img_ave_annotated.jpg</code></div> <div><code>│ │ ├── 0_850_img_ave_edge_mtf_values.txt</code></div> <div><code>│ │ ├── 0_850_img_ave_edge_sfr_values.txt</code></div> <div><code>│ │ ├── 1_475_img_ave_annotated.jpg</code></div> <div><code>│ │ ├── ...</code></div> <div><code>├── mtf_knifeedge_low_07122023_*n+1*</code></div> <div><code>│ ├── img</code></div> <div><code>│ │ ├── ...</code></div> <div> </div> <div>This data constitutes part of <strong>Table 1</strong> and <strong>Figure 2</strong> of the manuscript.</div> <div> <h2>Dataset B: Expected Reflectance of Carbonaceous Chondrite & Dark Spectralon</h2> This dataset includes the high-resolution ($\delta\lambda$=1 nm) reference reflectance spectra of the representative Carbonaceous Chondrite meteorite (<a href="https://westernreflectancelab.com/visor/graph/?results-selection=16136&results-item=16136&results-item=15972&results-item=231&results-item=230&graph=&form-TOTAL_FORMS=1&form-INITIAL_FORMS=0&form-MIN_NUM_FORMS=0&form-MAX_NUM_FORMS=1000&form-0-sample_name=nogoya&form-0-any_field=meteorite&form-0-id=&sort_params=-sample_name&page_selected=1&jump-to-page=" target="_blank" rel="noopener">Nogoya)</a> and the 5% reflectance Spectralon calibration target (<a href="https://www.labsphere.com/wp-content/uploads/2021/09/SpectralonStandards.pdf" target="_blank" rel="noopener">SCT5</a>):<br> <div>- <code>highres_input.csv</code></div> <br> <div>and the resampled spectra of these materials expected for OROCHI and LOROS filter wavelengths:</div> <br> <div>- <code>loros_observation.csv</code></div> <div>- <code>orochi_observation.csv</code></div> <br> <div><code>B_expected_reflectance</code></div> <div><code>├── README.md</code></div> <div><code>├── highres_input.csv</code></div> <div><code>├── loros_observation.csv</code></div> <div><code>└── orochi_observation.csv</code></div> <br> <div>This data constitutes <strong>Table 1</strong> and <strong>Figure 10</strong> of the manuscript.</div> <div> </div> <div> <div> <h2>Dataset C: Radiometric Calibration</h2> This dataset contains the image and derived data for 4 experiments with different illumination conditions for characterising the radiometric response of each of the 8 channels of LOROS.</div> <div><br> <div>This dataset contributes to <strong>Tables 2 - 4</strong> and <strong>Figures 3 - 9</strong> of the manuscript.</div> <br> <div>The final derived metrics are hosted in the spreadsheet:</div> <br> <div>- <code>measured_sensor_properties.csv</code></div> <br> <div>and image data and intermediary derived properties for each experiment are stored in the</div> <br> <div>- <code>experiments</code></div> <br> <div>directory.</div> <br> <div><code>C_radiometric_calibration</code></div> <div><code>├── README.md</code></div> <div><code>├── experiments</code></div> <div><code>│ ├── F*S5L10</code></div> <div><code>│ ├── F*S99L10</code></div> <div><code>│ ├── FGS99L2</code></div> <div><code>│ └── FGS99L10</code></div> <div><code>└── measured_sensor_properties.csv</code></div> <br> <h3><code>experiments</code> Directories</h3> In the directory of each experiment are sub-directories hosting Photon Transfer and Dark Transfer datasets, and a spreadsheet of derived metrics of these.<br> <div> </div> <div><code>C_radiometric_calibration</code></div> <div><code>├── README.md</code></div> <div><code>├── experiments</code></div> <div><code>│ ├── F*S5L10</code></div> <div><code>│ │ ├── dark_transfer_curve</code></div> <div><code>│ │ ├── photo_transfer_curve</code></div> <div><code>│ │ └── F*S5L10_derived_properties.csv</code></div> <div><code>│ └── ...</code></div> <div><code>└── measured_sensor_properties.csv</code></div> <div> </div> </div> <div> </div> <div><strong>Derived Properties</strong><br> <div> </div> <div>The spreadsheet (<code>[experiment]_derived_properties.csv</code>) collecting the properties derived from each experiment holds the following information, that has been extracted from the Photon Transfer and Dark Transfer curves as described in §4.2 of the manuscript:</div> <br> <div><code>camera # The camera number and wavelength</code></div> <div><code>k_adc # Sensitivity (e-/DN)</code></div> <div><code>full_well_e # Saturation Capacity (electrons)</code></div> <div><code>full_well_dn # Saturation Capacity (Digital Numbers)</code></div> <div><code>read_noise_e # Read Noise (electrons)</code></div> <div><code>read_noise_dn # Read Noise (Digital Numbers)</code></div> <div><code>bias_e # Offset (electrons)</code></div> <div><code>bias_dn # Offset (Digital Numbers)</code></div> <div><code>dark_current_e # Dark Current (electrons/second)</code></div> <div><code>dark_current_dn # Dark Current (Digital Numbers/second)</code></div> <div><code>DR # Dynamic Range</code></div> <div><code>lin_min # Minimum Linearity Error</code></div> <div><code>lin_max # Maximum Linearity Error</code></div> <div><code>linearity # Average Linearity Error</code></div> <div><code>snr_max # Maximum Signal-to-Noise Ratio</code></div> <div><code>t_exp_min # Minimum Exposure used in experiment (seconds)</code></div> <div><code>t_exp_max # Maximum Exposure used in experiment (seconds)</code></div> <div><code>expected_response # Expected Response (or 'Digital Flux') for OROCHI^12 at Phobos (Digital Numbers/second)</code></div> <div><code>response # Fitted Response (or 'Digital Flux') (Digital Numbers/second)</code></div> <br> <div>These values are given for each channel of LOROS, as well as the expected values for LOROS in off-the-shelf configuration (with no gain adjustment), LOROS with the gain adjustment, and OROCHI if downsampled to 12-bit resolution digital numbers.</div> <br> <div>This data constitutes <strong>Table 2</strong> of the manuscript.</div> <br> <div><strong>Dark Transfer Curve</strong></div> <br> <div>The <code>dark_transfer_curve</code> directory hosts the derived Dark Transfer Curve data (<code>derived_data</code>) and the source region-of-interest dark image pair data (<code>raw_data</code>) for each LOROS channel.</div> <br> <div><code>dark_transfer_curve</code></div> <div><code>├── derived_data</code></div> <div><code>│ ├── F*S5L10_0_850_dtc.csv</code></div> <div><code>│ ├── F*S5L10_1_475_dtc.csv</code></div> <div><code>│ ├── F*S5L10_2_400_dtc.csv</code></div> <div><code>│ ├── F*S5L10_3_550_dtc.csv</code></div> <div><code>│ ├── F*S5L10_4_725_dtc.csv</code></div> <div><code>│ ├── F*S5L10_5_950_dtc.csv</code></div> <div><code>│ ├── F*S5L10_6_650_dtc.csv</code></div> <div><code>│ └── F*S5L10_7_550_dtc.csv</code></div> <div><code>└── raw_data</code></div> <div><code>├── 0_850</code></div> <div><code>│ ├── 850_10095570us_1_calibration.tif</code></div> <div><code>│ ├── 850_10095570us_2_calibration.tif</code></div> <div><code>│ ├── 850_104us_1_calibration.tif</code></div> <div><code>│ ├── 850_104us_2_calibration.tif</code></div> <div><code>│ ├── ...</code></div> <div><code>├── 1_475</code></div> <div><code>├── 2_400</code></div> <div><code>├── 3_550</code></div> <div><code>├── 4_725</code></div> <div><code>├── 5_950</code></div> <div><code>├── 6_650</code></div> <div><code>├── 7_550</code></div> <div><code>└── camera_config.csv</code></div> <br> <div>The <code>raw_data</code> directory hosts a dark image pair for each exposure time used, and the <code>camera_config.csv</code> spreadsheet gives metadata for the system configuration, including the coordinates and dimensions of the region-of-interest for each channel.</div> <br> <div>The dark transfer curve for each experiment and each channel (<code>[experiment]_[channel]_[wavelength]_dtc</code>) gives the data derived from each raw image data, with the following values:</div> <br> <div><code>exposure # exposure duration (seconds)</code></div> <div><code>n_pix # number of pixels in the region of interest</code></div> <div><code>mean # average value of the region of interest</code></div> <div><code>std_t # total standard deviation of the region of interest</code></div> <div><code>std_rs # read+shot-noise standard deviation, copmuted from the difference of the image pair</code></div> <br> <div>This data constitutes <strong>Figures 5 and 8</strong> of the manuscript.</div> <br> <div><strong>Photon Transfer</strong></div> <br> <div>The <code>photon_transfer_curve</code> directory hosts the derived Photon Transfer Curve data (<code>derived_data</code>) and the source region-of-interest illuminated image pairs and associated dark frame image data (<code>raw_data</code>) for each LOROS channel.</div> <br> <div><code>photo_transfer_curve</code></div> <div><code>├── derived_data</code></div> <div><code>│ ├── F*S5L10_0_850_ptc.csv</code></div> <div><code>│ ├── F*S5L10_1_475_ptc.csv</code></div> <div><code>│ ├── F*S5L10_2_400_ptc.csv</code></div> <div><code>│ ├── F*S5L10_3_550_ptc.csv</code></div> <div><code>│ ├── F*S5L10_4_725_ptc.csv</code></div> <div><code>│ ├── F*S5L10_5_950_ptc.csv</code></div> <div><code>│ ├── F*S5L10_6_650_ptc.csv</code></div> <div><code>│ └── F*S5L10_7_550_ptc.csv</code></div> <div><code>└── raw_data</code></div> <div><code>├── 0_850</code></div> <div><code>│ ├── 850_104us_1_calibration.tif</code></div> <div><code>│ ├── 850_104us_2_calibration.tif</code></div> <div><code>│ ├── 850_104us_d_drk.tif</code></div> <div><code>│ ├── 850_105828us_1_calibration.tif</code></div> <div><code>│ ├── ...</code></div> <div><code>├── 1_475</code></div> <div><code>├── 2_400</code></div> <div><code>├── 3_550</code></div> <div><code>├── 4_725</code></div> <div><code>├── 5_950</code></div> <div><code>├── 6_650</code></div> <div><code>├── 7_550</code></div> <div><code>└── camera_config.csv</code></div> <br> <div>The <code>raw_data</code> directory hosts an image pair and dark frame for each exposure time used, and the <code>camera_config.csv</code> spreadsheet gives metadata for the system configuration, including the coordinates and dimensions of the region-of-interest for each channel.</div> <br> <div>The photon transfer curve for each experiment and each channel (<code>[experiment]_[channel]_[wavelength]_ptc</code>) gives the data derived from each raw image data, with the following values across the region-of-interest:</div> <br> <div><code>exposure # exposure duration (seconds)</code></div> <div><code>n_pix # number of pixels in the region of interest</code></div> <div><code>mean # average value (Digital Numbers)</code></div> <div><code>std_t # total standard deviation (Digital Numbers)</code></div> <div><code>std_rs # read+shot-noise standard deviation (Digital Numbers), computed from the difference of the image pair</code></div> <div><code>d_mean # average value of the dark (Digital Numbers)</code></div> <div><code>d_dsnu # Dark Signal Nonuniformity (Digital Numbers)</code></div> <div><code>std_s # Shot Noise (read noise removed) (Digital Numbers)</code></div> <div><code>k_adc # Sensitivity (note this the point-wise sensitivity, rather than fitted) (electrons/Digital Number)</code></div> <div><code>linearity # Linearity Error (point-wise distance to least-squares linear fit) (%)</code></div> <div><code>snr # Signal-to-Noise Ratio, derived from shot-noise (point-wise)</code></div> <div><code>snr_t # Signal-to-Noise Ratio, derived from total noise (point-wise)</code></div> <div><code>e- # Electron count, derived from sensitivity</code></div> <div><code>e-_noise # Electron shot-noise, derived from sensitivity</code></div> <br> <div>This data constitutes <strong>Figures 3, 4, 6, 7 & 9</strong> of the manuscript.</div> <br> <div><strong>Measured Sensor Properties</strong></div> <br> <div>The <code>measured_sensor_properties.csv</code> spreadsheet collects and averages the following metrics over the 4 experiments performed, to give the values for each channel, along with the expected values for LOROS in off-the-shelf configuration, gain-adjusted LOROS, and OROCHI downsampled to 12-bit resolution.</div> <br> <div><code>SNR Max</code></div> <div><code>Dynamic Range (dB)</code></div> <div><code>Dynamic Range (bits)</code></div> <div><code>Sensitivity (e-/DN)</code></div> <div><code>Saturation Capacity (e-)</code></div> <div><code>Saturation Capacity (DN)</code></div> <div><code>Read Noise (e-)</code></div> <div><code>Read Noise (DN)</code></div> <div><code>Nonlinearity (%)</code></div> <div><code>Dark Signal@30°C (e-/s)</code></div> <div><code>Dark Signal@30°C (DN/s)</code></div> <div><code>Bias (e-)</code></div> <div><code>Bias (DN)</code></div> <div><code>DSNU1288 (DN)</code></div> <div><code>DSNU1288 (e-)</code></div> <div><code>PRNU1288 (%)</code></div> <br> <div>This data constitutes <strong>Table 3</strong> of the manuscript.</div> <div> </div> <div> <h2>Dataset D: Dark Spectralon Validation</h2> This dataset contains the raw image and derived data used to demonstrate the ability of LOROS to measure the spectral reflectance of the 5% reflectance Spectralon calibration target (<a href="https://www.labsphere.com/wp-content/uploads/2021/09/SpectralonStandards.pdf" target="_blank" rel="noopener">SCT5</a>).<br> <div>The image data is hosted in the directory:</div> <br> <div>- <code>raw_data</code></div> <br> <div>and the processed data (e.g. reflectance products) are hosted in the directory:</div> <br> <div>- <code>processed_data</code></div> <br> <div><code>D_dark_spectralon_validation</code></div> <div><code>├── processed_data</code></div> <div><code>│ ├── SCT5</code></div> <div><code>│ └── SCT99</code></div> <div><code>├── raw_data</code></div> <div><code>│ ├── SCT5</code></div> <div><code>│ ├── SCT5_dark</code></div> <div><code>│ ├── SCT99</code></div> <div><code>│ └── SCT99_dark</code></div> <div><code>└── README.md</code></div> <br> <div><strong>Raw Data</strong></div> <br> <div>The raw data directory contains images captured of <code>SCT5</code> and <code>SCT99</code> (99% reflectance white Spectralon), and accompanying dark frames, hosted in the <code>SCT5_dark</code> and <code>SCT99_dark</code> frames respectively.</div> <br> <div>For each channel, 25 repeat images have been captured for the illuminated and dark frames.</div> <br> <div><strong>Processed Data</strong></div> <br> <div>The processed SCT99 and SCT5 datasets differ slightly. Both include:</div> <br> <div><code>├── img</code></div> <div><code>├── rfl</code></div> <div><code>└── rois</code></div> <br> <div>directories, with the SCT99 scene also including a <code>cal</code> directory.</div> <br> <div><code>img</code> hosts a set of <code>context</code> figures, showing the regions of interest selected, <code>fits</code> hosts the floating point mean (<code>ave</code>), standard error (<code>err</code>), standard deviation (<code>std</code>) and single-frame (<code>one</code>), all in units of Digital Number, after dark frame subtraction, flat-fielding and linearity correction. <code>uint8</code> hosts the same data rescaled to 8-bit resolution, for quick-view.</div> <br> <div><code>rfl</code> hosts the same set as <code>img</code>, after conversion to units of reflectance against the results of the SCT99 calibration (see §3.5 of the manuscript).</div> <br> <div><code>rois</code> gives plots of the mean and error of the reflectance spectrum of the region of interest, as well as the Signal-to-Noise Ratio, as well as the data for each region-of-interest (<code>roi_data</code>).</div> <br> <div><code>cal</code> also gives context figures for each channel region-of-interest, as converted to units of reflectance coefficients (1/DN/s).</div> </div> </div> </div> </div> </div>
FIG. 12 in Animal Management, preparation and sacrifice: reconstructing burial 6 at the Moon Pyramid, Teotihuacan, México
FIG. 12. — Model of the life histories of the animals interred in Burial 6 divided into four stages; acquisition, management, preparation and sacrifice and/or deposition. Arrows and letters in grey indicate the zooarchaeological and archaeological indicators of these processes.
FIG. 11 in Animal Management, preparation and sacrifice: reconstructing burial 6 at the Moon Pyramid, Teotihuacan, México
FIG. 11. — Photograph of basket excavated as a block from Burial 6. Arrow indicates the concentration of serpentine remains.
FIG. 10 in Animal Management, preparation and sacrifice: reconstructing burial 6 at the Moon Pyramid, Teotihuacan, México
FIG. 10. — Canid and felid skull preparation methods: A, Element 2194 female wolf 1-2 years old; B, Element 1960 jaguar infant.
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.