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4,634 results for “orbit”
Terahertz Néel spin-orbit torques drive nonlinear magnon dynamics in antiferromagnetic Mn2Au
<p>Data for the publication "<strong>Terahertz Néel spin-orbit torques drive nonlinear magnon dynamics in antiferromagnetic Mn<sub>2</sub>Au"</strong>, published in <em>Nat Commun</em> <strong>14</strong>, 6038 (2023). (https://doi.org/10.1038/s41467-023-41569-z).</p> <p>A preprint (2023) can be found on arxiv (https://doi.org/10.48550/arXiv.2305.03368).</p> <p>The datasets are provided for Figures 2-4.</p> <p>Files are provided as comma-separated text files with column headers. The value delimiter is comma " , ". The decimal separator is period " . "</p>
Dataset for "Attitude and orbit coupling of planar helio-stable solar sails"
<p>Dataset and figures of the "Numerical test cases" section in the paper</p> <p>Miguel, N., Colombo, C., (2019). Attitude and orbit coupling of planar helio-stable solar sails. <em>Celestial Mechanics and Dynamical Astronomy </em>131, 59. doi:10.1007/s10569-019-9937-x.</p>
Reflectance and emission spectra of Earth-like planets orbiting red giant stars
<p>Spectra of red giant stellar hosts and reflectance/emission planetary spectra as described in Kozakis & Kaltenegger (2020). File names and content are explained in 0readme.txt. Please email theakozakis@gmail.com with any questions.</p> <p> </p>
Beating-free quantum oscillations in 2D electron gases with strong spin-orbit and Zeeman interactions
<p>Datafiles for figures in the paper:</p> <p><em>"Beating-free quantum oscillations in 2D electron gases with strong spin-orbit and Zeeman interactions"</em>, Phys. Rev. Research [<em>Accepted</em>]</p> <p>The datafiles relate to Figs. 2, 3, 10, 12, 14, 15, 16, and 17</p> <p>Figs. 1 and 13 contain schematics.</p> <p>Figs, 4, 6, and 8 are generated in a straightforwd manner using Eqs. (27), (34), and (40), respectively, using parameter values supplied in the figure captions.</p> <p>Fig. 5, is generated by Eq. (30) along with Eq. (29), using parameter values supplied in the figure captions.</p> <p>Fig. 7, is generated by Eq. (30) along with Eqs. (35) and (36), using parameter values supplied in the figure captions.</p> <p>Fig. 9, is generated by Eq. (30) along with Eqs. (41) and (42), using parameter values supplied in the figure captions.</p>
Data used to evaluate ORBITS: Optimal Repair-Based Inconsistency-Tolerant Semantics
<p>This dataset provides the input files that were used in the evaluation of the ORBITS system (Optimal Repair-Based Inconsistency-Tolerant Semantics, <a href="https://github.com/bourgaux/orbits">https://github.com/bourgaux/orbits</a>). A detailed description is available in a technical report on arXiv (<a href="https://arxiv.org/abs/2202.07980">https://arxiv.org/abs/2202.07980</a>).</p> <p><strong>Content:</strong></p> <p>Folders <em>cqapri_benchmark</em>, <em>food_inspection_benchmark</em>, and <em>physicians_benchmark</em> contain JSON files of conflict graphs and candidate queries and their causes.<br> These files are named using the following pattern: files of candidate answers and their causes are named <database>_<query>_answers_causes.json, and conflict graphs are named <database>_conflictGraph_<priority relation>.json where <priority relation> says whether the priority relation is score-structured (prio_score) or not (prio_non_score) and the probability (p<proba>) or number of scores (n<number>) used to build the priority relation.</p> <p>Folder <em>original_datasets_and_queries</em> contains the Food Inspection and Physicians datasets used to generate files from <em>food_inspection_benchmark</em> and <em>physicians_benchmark</em>.<br> Files from <em>cqapri_benchmark</em> have been generated from the CQAPri benchmark available at <a href="https://lahdak.lri.fr/CQAPri/CQAPri.php">https://lahdak.lri.fr/CQAPri/CQAPri.php</a>.<br> In all cases, we use ProvSQL (<a href="https://github.com/PierreSenellart/provsql">https://github.com/PierreSenellart/provsql</a>) to build conflict graphs and causes from the datasets.</p>
An Aligned Orbit for the Young Planet V1298 Tau b
<p>Code, data, and MCMC chains associated with the article "An Aligned Orbit for the Young Planet V1298 Tau b," by Johnson et al. 2022 (accepted to The Astronomical Journal). Pre-print available at <a href="https://arxiv.org/abs/2110.10707">arXiv</a>.</p> <p> </p>
Experimental data and scripts used for the paper "Imperfect symmetry of real annular combustors: beating thermoacoustic modes and heteroclinic orbits"
<p>The folder contains the experimental data, the scripts an the instructions to generate the figures of the paper.</p>
Dataset: Assessing MP2 frozen natural orbitals in relativistic correlated electronic structure calculations
<p>This dataset collects the unprocessed (= outputs from calculations) results discussed in the paper titled "Assessing MP2 frozen natural orbitals in relativistic correlated electronic structure calculations", by Xiang Yuan, Luvas Visscher and Andre Severo Pereira Gomes. It also contains the figures used in the manuscript.</p>
Modeling output for "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars"
<p><strong>General information:</strong></p> <p>Please contact Claire Newman (claire@aeolisresearch.com) if you have questions about this dataset.</p> <p><em><strong>Title of Dataset: </strong></em>Modeling output for "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars"</p> <p><em><strong>Author Information:</strong></em><br> Name: Claire E. Newman<br> Institution: Aeolis Research<br> Email: claire@aeolisresearch.com</p> <p>Recommended citation for this dataset: Newman C.E. (2022) "Modeling output for Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars", Dataset.</p> <p><strong>Summary taken from the paper "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars" by Kathryn M. Stack et al., accepted by JGR Planets for publication in 2022:</strong></p> <p>The orientation of Glen Torridon ridges was compared against outputs from the Mars Weather Research and Forecasting (MarsWRF) model. The MarsWRF model has been used to simulate the atmospheric circulation inside Gale crater to compare with MSL Rover Environmental Monitoring Station (REMS) wind measurements (Newman et al., 2017) and to assist in interpreting observations of dust devils (Newman et al., 2019) and aeolian changes (Baker et al., 2018, 2022). The output used in this work comes from the ‘vertical grid B’ simulations described in Newman et al. (2017), which provide the best match to observed winds and aeolian features. For Gale crater modeling, MarsWRF is run as a global model with nested higher-resolution domains that gradually increase the model’s horizontal resolution over smaller and smaller areas, finally providing output at ~400m grid spacing over the NW quadrant of the crater. The version of MarsWRF used here includes the treatment of radiative transfer in Mars’s dusty CO2 atmosphere, the seasonal CO2 cycle, subsurface-surface-atmosphere exchange of heat and momentum, and vertical mixing of heat and momentum, with surface properties (topography, roughness, albedo, etc.) based on orbital datasets and the seasonally-evolving, non-dust-storm “Mars Climate Database” dust distribution imposed (see Richardson et al., 2007 for more details).</p> <p>The model outputs minute-by-minute predictions of surface friction velocity, u*, and atmospheric density at 1.5m, ρ, for 7 sols at each of 12 periods, which are equally spaced in planetocentric solar longitude (Ls) through the martian year. For each output, the surface wind stress, τ, is found from τ=ρu_*^2. We then adjusted the contribution of each period to account for the varying number of sols Mars spends around each period over its orbit, before producing wind stress roses, which thus represent the total wind stress and direction over a non-dust-storm Mars year.</p> <p><strong>Specifically, contained in the "outputsfullcorr.nc" netCDF data file are:</strong></p> <p><strong><em>Variables:</em></strong></p> <p>PSFC: surface pressure (Pa)</p> <p>T1_5: temperature at 1.5m height above the surface (K)</p> <p>U1_5: zonal (west-to-east) wind at 1.5m height above the surface (m/s)</p> <p>V1_5: meridional (south-to-north) wind at 1.5m height above the surface (m/s)</p> <p>UST: surface friction speed (m/s)</p> <p><strong><em>Output times:</em></strong></p> <p>These variables are output from MarsWRF every minute starting at 15:10 Local True Solar Time in the first sol, with 1440 outputs per martian sol. There are 68 sols of data in total, with between 5 and 7 sols of data from each 30° Ls range, with the relative number chosen to reflect the fraction of a Mars year spent in that Ls range. In detail, the sols of the martian year used (where Ls=0 would be at the start of sol 1 and Ls=360 at the end of sol 669) are:</p> <p>Ls~0°: sols 668-669 & 01-4</p> <p>Ls~30°: sols 60-65</p> <p>Ls~60°: sols 124-130</p> <p>Ls~90°: sols 194-200</p> <p>Ls~120°: sols 254-259</p> <p>Ls~150°: sols 314-319</p> <p>Ls~180°: sols 374-378</p> <p>Ls~210°: sols 424-428</p> <p>Ls~240°: sols 474-478</p> <p>Ls~270°: sols 514-518</p> <p>Ls~300°: sols 564-568</p> <p>Ls~330°: sols 614-618</p> <p><strong><em>Output longitudes and latitudes are in file "static.nc":</em></strong></p> <p>The output is provided for a uniform grid of 19 longitudes by 15 latitudes. The longitudes and latitudes (variables XLONG and XLAT) in static.nc match those in outputsfullcorr.nc, but below are listed the longitudes and latitudes for convenience:</p> <p>Longitudes (19): 137.321, 137.3292, 137.3374, 137.3457, 137.3539, 137.3621, 137.3704, <br> 137.3786, 137.3868, 137.3951, 137.4033, 137.4115, 137.4198, 137.428, <br> 137.4362, 137.4444, 137.4527, 137.4609, 137.4691 </p> <p>Latitudes (15): -4.786012, -4.777781, -4.769551, -4.761321, -4.75309, -4.74486, -4.736629, -4.728399, -4.720168, -4.711937, -4.703707, -4.695477, -4.687246, -4.679016, -4.670785</p> <p>static.nc also provides the local topographic height used by the model at each location (variable name HGT).</p> <p><strong><em>Output data format:</em></strong></p> <p>NetCDF (Network Common Data Form) is a set of software libraries and machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data. It is also a community standard for sharing scientific data. The Unidata Program Center supports and maintains netCDF programming interfaces for <a href="https://docs.unidata.ucar.edu/netcdf-c/current/">C</a>, <a href="https://docs.unidata.ucar.edu/netcdf-cxx/current/">C++</a>, <a href="https://www.unidata.ucar.edu/software/netcdf-java/">Java</a>, and <a href="https://docs.unidata.ucar.edu/netcdf-fortran/current/">Fortran</a>. Programming interfaces are also available for Python, IDL, MATLAB, R, Ruby, and Perl.</p> <p>See: <a href="https://www.unidata.ucar.edu/software/netcdf/">https://www.unidata.ucar.edu/software/netcdf/</a></p> <p><strong>References:</strong></p> <p>Baker, M.M., Lapotre, M.G.A., Minitti, M.E., Newman, C.E., Sullivan, R., Weitz, C.M., Rubin, D.R., Vasavada, A.R., Bridges, N.T., & Lewis, K.W. (2018). The Bagnold Dunes in Southern Summer: Active Sediment Transport on Mars Observed by the Curiosity Rover. <em>Geophysical Research Letters, 45</em>(17), 8853-8863. <a href="https://doi.org/10.1029/2018GL079040">https://doi.org/10.1029/2018GL079040</a>.</p> <p>Baker, M.M., Newman, C.E., Lapotre, M.G.A., Sullivan, R., Bridges, N.T., & Lewis, K.W. (2018). Coarse Sediment Transport in the Modern Martian Environment. <em>Journal of Geophysical Research- Planets, 123</em>(6), 1380-1394. <a href="https://doi.org/10.1002/2017JE005513">https://doi.org/10.1002/2017JE005513</a>.</p> <p>Baker, M.M., Newman, C.E., Sullivan, R., Minitti, M.E., Edgett, K.S., Fey, D., Ellison, D., & Lewis, K.W. (2022). Diurnal variability in aeolian sediment transport at Gale crater, Mars, J. Geophys. Res. (Plan.), 127, e2020JE006734, https://doi.org/10.1029/2020JE006734.</p> <p>Newman, C., Gómez‐Elvira, J. G., Marín, M., Navarro, S., Torres, J., Richardson, M. I., et al. (2017). Winds measured by the Rover Environmental Monitoring Station (REMS) during the Mars Science Laboratory (MSL) rover's Bagnold Dunes Campaign and com- parison with numerical modeling using MarsWRF. <em>Icarus, 291</em>, 203–231. https://doi.org/10.1016/j.icarus.2016.12.016</p> <p>Newman, C. E., Kahanpää, H., Richardson, M. I., Martinez, G. M., Vicente‐Retortillo, A., & Lemmon, M. (2019). Convective vortex and dust devil predictions for Gale crater over 3 mars years and comparison with MSL‐REMS observations, <em>J. Geophys. Res. (Plan.</em>), 124, 3442– 3468, https://doi.org/10.1029/2019JE006082.</p> <p>Richardson, M.I., Toigo, A.D., & Newman, C.E. (2007). PlanetWRF: A general purpose, local to global numerical model for planetary atmospheric and climate dynamics<em>, J. Geophys. Res. (Plan.)</em>, 112, E09001, <a href="https://doi.org/10.1029/2006JE002825">https://doi.org/10.1029/2006JE002825</a>.</p>
Data for the article "Tuning Spin-Orbit Torques Across the Phase Transition in VO2/NiFe Heterostructure"
<p>Data for the article "Tuning Spin-Orbit Torques Across the Phase Transition in VO2/NiFe Heterostructure" (<a href="https://onlinelibrary.wiley.com/doi/full/10.1002/adfm.202111555">https://onlinelibrary.wiley.com/doi/full/10.1002/adfm.202111555</a> and <a href="http://arxiv.org/abs/2201.12984">http://arxiv.org/abs/2201.12984</a>)</p>
The Eurybates family of the Jupiter Trojan asteroids: members, orbital and spectral properties
<p>This dataset contains identified members of the Eurybates family, a collisional family of the Jupiter Trojans.</p> <p>The members were identified in Marschall et al 2022 (Implications for the collisional strength of Jupiter Trojans from the Eurybates family) using the hierarchical clustering method (HCM).</p> <p>Two tables presented in the accepted manuscript are available here in machine-readable form.</p> <p><strong>Table 3.</strong> List of all 400 Eurybates family members identified by the hierarchical clustering method (HCM) including the likely interloper (5258) Rhoeo (see discussion in Sec. 2). The table gives the asteroid number (nr.), the provisional designation (prov. des.), the absolute magnitude (H), the diameter (D), as well as the proper semi-major axis (a_prop), eccentricity (e_prop), and inclination (i_prop).</p> <p><strong>Tabel 4. </strong>Color data for Eurybates family members identified by the hierarchical clustering method (HCM) including the likely interloper (5258). The table gives the asteroid number (nr.), the provisional designation (prov. des.), the absolute magnitude (H), the diameter (D), as well as the g-i color from the Sloan Digital Sky Survey, the spectral slopes between 0.3$ to 0.9 \mu m (S), and the g-r color from the Zwicky Transient Facility Observations, ZTF.</p> <p> </p>
A Survey of Electron Conics at Jupiter Utilizing the JADE-E Data During Science Orbits 01, 03-30
<p>This dataset provides Figure 1 from the AGU <em>JGR: Space Physics</em> article of the same name as a PNG image. It also includes text files with the data to reproduce Figures 2-13 in the same AGU <em>JGR:Space Physics</em> article.</p> <p><strong>Key Points:</strong></p> <ol> <li>We surveyed the JADE-E data for science orbits 01, 03-30 and found upward, downward, and bidirectional electron conics 2.5% of the time</li> <li>We observed all electron conics to occur most often at altitudes of 0.3-0.4 R<sub>J</sub> and local times of 15-16h</li> <li>We observed all electron conic types to have energies greater than 0.7 keV below an altitude of 0.5 R<sub>J</sub> and over the main auroral region</li> </ol> <p><strong>Abstract</strong></p> <p>We present a survey of electron conics over Jupiter’s high latitude regions utilizing 22.6 hours of data from the Jovian Auroral Distribution Experiment electron (JADE-E) instrument aboard NASA’s Juno spacecraft during science orbits 01 and 03-30. We observed electron conics for about 2.5% of this time and characterized them into three types based on their direction of motion along Jupiter’s magnetic field lines: upward, downward, and bidirectional. We observed the upward electron conics most often and at energies of 0.057-80.1 keV, while we observed the downward electron conics least often and at energies of 0.073-1.2 keV. We observed bidirectional electron conics mostly around the same times and places as the upward electron conics having energies of 0.081-49.6 keV. We observed all electron conic types to occur mostly at altitudes 0.3-0.4 R<sub>J</sub> and local times 15-16h. Furthermore, we observed all electron conic types to have energies greater than 0.7 keV below an altitude of 0.5 R<sub>J</sub> and over the main auroral region.</p> <p> </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>
Text-fig. 6. Paramblypterus vratislaviensis (AGASSIZ, 1833). a: restoration of the body in lateral view, scale bar 10 mm; b: restoration of the skull in lateral view (bones that are not clearly retained on the skull are marked with dashed lines), scale bar 10 mm; c: restoration of the skull in dorsal view, scale bar 10 mm. Abbreviations: Cl – cleithrum, Dhy – dermohyal, Dpt – dermopterotic, Dsph – dermosphenotic, Ext – extrascapular, Fr – frontal, Gul – lateral gular, Ios – infraorbital superior, Ju – jugal, La – lacrymal, Md – mandible, Mx – maxilla, Na – nasal, Op – operculum, Orb – orbit, Pa – parietal, Pmx – premaxilla, Pop – preoperculum, Pt – posttemporal, Ptr – postrostral, Rbr- branchiostegal rays, Scl – supracleithrum, Soant – supraorbital anterior, Sop – suboperculum, Spi – spiracular, sr – sclerotic ring. in Actinopterygians Of The Broumov Formation (Permian) In The Czech Part Of The Intra-Sudetic Basin (The Czech Republic)
Text-fig. 6. Paramblypterus vratislaviensis (AGASSIZ, 1833). a: restoration of the body in lateral view, scale bar 10 mm; b: restoration of the skull in lateral view (bones that are not clearly retained on the skull are marked with dashed lines), scale bar 10 mm; c: restoration of the skull in dorsal view, scale bar 10 mm. Abbreviations: Cl – cleithrum, Dhy – dermohyal, Dpt – dermopterotic, Dsph – dermosphenotic, Ext – extrascapular, Fr – frontal, Gul – lateral gular, Ios – infraorbital superior, Ju – jugal, La – lacrymal, Md – mandible, Mx – maxilla, Na – nasal, Op – operculum, Orb – orbit, Pa – parietal, Pmx – premaxilla, Pop – preoperculum, Pt – posttemporal, Ptr – postrostral, Rbr- branchiostegal rays, Scl – supracleithrum, Soant – supraorbital anterior, Sop – suboperculum, Spi – spiracular, sr – sclerotic ring.
"Competition between homogeneous and inhomogeneous broadening of orbital transitions in Si:Bi", N Stavrias et al, Phys Rev B 005200
<p>Raw data for the publication, including the small-signal absorption spectrum and THz pump-probe transients of Si:Bi. </p> <p>See publication for description of purpose etc:</p> <p>"Competition between homogeneous and inhomogeneous broadening of orbital transitions in Si:Bi", N Stavrias et al, Phys Rev B 005200</p> <p>See readme file for data description</p>
Many-body quantum dynamics of spin-orbit coupled Andreev states in a Zeeman field
<p>We provide the raw data used to produce Figs.3-6-7-8-9-10-11 of our paper "Many-body quantum dynamics of spin-orbit coupled Andreev states in a Zeeman field"</p>
Data associated with "The Role of Magma Oceans in Maintaining Surface Water on Rocky Planets Orbiting M-Dwarfs"
<p>The data included here involves all simulations outlined in the manuscript "The Role of Magma Oceans in Maintaining Surface Water on Rocky Planets Orbiting M-Dwarfs", submitted to MNRAS. Files are divided between models with and without a basal magma ocean, as well as different host stars, different magma ocean saturation limits, and different fringe/sensitivity analysis checks. Files are further sub-divided based on our studied parameter space: each is for a single orbital distance within the habitable zone, and a single initial water inventory. "MO_only" files correspond to the concurrent surface magma ocean/runaway greenhouse phase, while "combined_MO_cycling" files correspond to the deep-water cycling period.</p>
The ORBIT (Object Recognition for Blind Image Training)-India Dataset
<div> <p>The ORBIT (Object Recognition for Blind Image Training) -India Dataset is a collection of 105,243 images of 76 commonly used objects, collected by 12 individuals in India who are blind or have low vision. This dataset is an "Indian subset" of the original ORBIT dataset [1, 2], which was collected in the UK and Canada. In contrast to the ORBIT dataset, which was created in a Global North, Western, and English-speaking context, the ORBIT-India dataset features images taken in a low-resource, non-English-speaking, Global South context, a home to 90% of the world’s population of people with blindness. Since it is easier for blind or low-vision individuals to gather high-quality data by recording videos, this dataset, like the ORBIT dataset, contains images (each sized 224x224) derived from 587 videos. These videos were taken by our data collectors from various parts of India using the Find My Things [3] Android app. Each data collector was asked to record eight videos of at least 10 objects of their choice. </p> </div> <div> <p>Collected between July and November 2023, this dataset represents a set of objects commonly used by people who are blind or have low vision in India, including earphones, talking watches, toothbrushes, and typical Indian household items like a belan (rolling pin), and a steel glass. These videos were taken in various settings of the data collectors' homes and workspaces using the Find My Things Android app. </p> </div> <div> <p>The image dataset is stored in the ‘Dataset’ folder, organized by folders assigned to each data collector (P1, P2, ...P12) who collected them. Each collector's folder includes sub-folders named with the object labels as provided by our data collectors. Within each object folder, there are two subfolders: ‘clean’ for images taken on clean surfaces and ‘clutter’ for images taken in cluttered environments where the objects are typically found. The annotations are saved inside a ‘Annotations’ folder containing a JSON file per video (e.g., P1--coffee mug--clean--231220_084852_coffee mug_224.json) that contains keys corresponding to all frames/images in that video (e.g., "P1--coffee mug--clean--231220_084852_coffee mug_224--000001.jpeg": {"object_not_present_issue": false, "pii_present_issue": false}, "P1--coffee mug--clean--231220_084852_coffee mug_224--000002.jpeg": {"object_not_present_issue": false, "pii_present_issue": false}, ...). The ‘object_not_present_issue’ key is True if the object is not present in the image, and the ‘pii_present_issue’ key is True, if there is a personally identifiable information (PII) present in the image. Note, all PII present in the images has been blurred to protect the identity and privacy of our data collectors. This dataset version was created by cropping images originally sized at 1080 × 1920; therefore, an unscaled version of the dataset will follow soon. </p> </div> <div> <p>This project was funded by the Engineering and Physical Sciences Research Council (EPSRC) Industrial ICASE Award with Microsoft Research UK Ltd. as the Industrial Project Partner. We would like to acknowledge and express our gratitude to our data collectors for their efforts and time invested in carefully collecting videos to build this dataset for their community. The dataset is designed for developing few-shot learning algorithms, aiming to support researchers and developers in advancing object-recognition systems. We are excited to share this dataset and would love to hear from you if and how you use this dataset. Please feel free to reach out if you have any questions, comments or suggestions. </p> </div> <div> <p>REFERENCES: </p> </div> <div> <ol> <li> <p>Daniela Massiceti, Lida Theodorou, Luisa Zintgraf, Matthew Tobias Harris, Simone Stumpf, Cecily Morrison, Edward Cutrell, and Katja Hofmann. 2021. ORBIT: A real-world few-shot dataset for teachable object recognition collected from people who are blind or low vision. DOI: <a href="https://doi.org/10.25383/city.14294597" target="_blank" rel="noreferrer noopener">https://doi.org/10.25383/city.14294597</a></p> </li> <li> <p>microsoft/ORBIT-Dataset. <a href="https://github.com/microsoft/ORBIT-Dataset" target="_blank" rel="noreferrer noopener">https://github.com/microsoft/ORBIT-Dataset</a> </p> </li> <li> <p>Linda Yilin Wen, Cecily Morrison, Martin Grayson, Rita Faia Marques, Daniela Massiceti, Camilla Longden, and Edward Cutrell. 2024. Find My Things: Personalized Accessibility through Teachable AI for People who are Blind or Low Vision. In Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems (CHI EA '24). Association for Computing Machinery, New York, NY, USA, Article 403, 1–6. <a href="https://doi.org/10.1145/3613905.3648641" target="_blank" rel="noreferrer noopener">https://doi.org/10.1145/3613905.3648641</a> </p> </li> </ol> </div>
Data for "Opposing changes in Indian Summer Monsoon Rainfall variability produced by orbital and anthropogenic forcing"
<p>The dataset for the CAM5 and LBM experiments is presented in the manuscript titled "Opposing changes in Indian summer monsoon rainfall variability produced by orbital and anthropogenic forcing. And the proxy data for the paper.</p>
Draft version of poposed images and plotting code of manuscript: Stray light correction and enhancement of nocturnal low-light image of early-morning-orbiting Fengyun-3E satellite
<p>This documentation provides a detailed description of the folder structure and image contents uploaded to the website. It aims to help users understand the purpose and organization of the files. The corresponding manuscript is titled,<strong><em> Stray light correction and enhancement of nocturnal low-light image of early-morning-orbiting Fengyun-3E satellite</em></strong>.</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.