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4,634 results for “orbit”
Analytical Framework for Precise Relative Motion in Low Earth Orbits
<p>The data sets provided here can be used to recreate the plots of the paper “Analytical Framework for Precise Relative Motion in Low Earth Orbits” available at this <a href="https://arc.aiaa.org/doi/10.2514/1.G004716">link</a>.</p> <p>That paper presents a practical and efficient analytical framework for the precise modelling of the relative motion in low Earth orbits.</p>
Orbital- and Millennial-Scale Variability in Northwest African Dust Emissions Over the Past 67,000 years — Datasets
<p><strong>Title</strong>: Orbital- and Millennial-Scale Variability in Northwest African Dust Emissions Over the Past 67,000 years — Datasets</p> <p><strong>Version</strong>: 1.0</p> <p><strong>Date of Release</strong>: December 06, 2021</p> <p><strong>Last Update</strong>: December 06, 2021</p> <p><strong>Identifier</strong>: 10.5281/zenodo.5652189</p> <p><strong>Permalink</strong>: <a href="https://doi.org/10.5281/zenodo.5652188">https://doi.org/10.5281/zenodo.5652188</a></p> <p><strong>Associated publication</strong>: Kinsley, C.W.; Bradtmiller, L.I.; McGee, D.; Galgay, M.; Stuut, J.-B.; Tjallingii, R.; Winckler, G.; deMenocal, P.B. 2021. Orbital- and Millennial-Scale Variability in Northwest African Dust Emissions Over the Past 67,000 years. Paleoceanography and Paleoclimatology. doi: <a href="https://doi.org/10.1002/essoar.10506290.1">10.1002/essoar.10506290.1</a></p> <p><strong>Link to publication preprint</strong>: <a href="https://doi.org/10.1002/essoar.10506290.1">https://doi.org/10.1002/essoar.10506290.1</a></p> <p><strong>Suggested citation</strong>: Please reference the associated publication above when using any datasets or materials in this repository.</p> <p><strong>Contact information</strong>: Christopher W. Kinsley, ckinsley@mit.edu OR cwkinsley@gmail.com</p> <p><strong>Dates of data collection and generation</strong>: August 2013 to February 2016</p> <p>---------------</p> <p><strong>DESCRIPTION OF DATA</strong></p> <p>This data repository contains the following datasets. We refer the user to the original manuscript (see above) and the text of the Supporting Information published alongside this manuscript for additional general information regarding the collection and generation of these data.</p> <p>DATA TABLES FOR ALL CORE SITES</p> <ul> <li><strong>Kinsley et al. (2021) P&P - Data Tables for OC437-7-GC-37 core - v1</strong>: This Excel workbook contains all data used in the study for the OC437-7-GC-37 core site, taken by the R/V Oceanus during the 2007 Changing Holocene Environments of the Eastern Tropical Atlantic (CHEETA) cruise. This includes the age control and age model, biogenic %s, U-Th isotopic measurements, grain size distributions and endmember modeling, and <sup>230</sup>Th-normalized flux data. All previously published data is noted as such and referenced.</li> <li> <p><strong>Kinsley et al. (2021) P&P - Data Tables for OC437-7-GC-49 core - v1</strong>: This Excel workbook contains all data used in the study for the OC437-7-GC-49 core site, taken by the R/V Oceanus during the 2007 Changing Holocene Environments of the Eastern Tropical Atlantic (CHEETA) cruise. This includes the age control and age model, biogenic %s, U-Th isotopic measurements, grain size distributions and endmember modeling, and <sup>230</sup>Th-normalized flux data. All previously published data is noted as such and referenced.</p> </li> <li> <p><strong>Kinsley et al. (2021) P&P - Data Tables for OC437-7-GC-68 core - v1</strong>: This Excel workbook contains all data used in the study for the OC437-7-GC-68 core site, taken by the R/V Oceanus during the 2007 Changing Holocene Environments of the Eastern Tropical Atlantic (CHEETA) cruise. This includes the age control and age model, biogenic %s, U-Th isotopic measurements, grain size distributions and endmember modeling, and <sup>230</sup>Th-normalized flux data. All previously published data is noted as such and referenced.</p> </li> <li> <p><strong>Kinsley et al. (2021) P&P - Data Tables for ODP 108-658C</strong><strong> core - v1</strong>: This Excel workbook contains all data used in the study for the ODP 108-658C core site, taken by the R/V JOIDES Resolution off Cap Blanc, Mauritania during Ocean Drilling Program Leg 108. This includes the age control and age model, biogenic %s, U-Th isotopic measurements, grain size distributions and endmember modeling, and <sup>230</sup>Th-normalized flux data. All previously published data is noted as such and referenced.</p> </li> </ul>
Trajectory Design for Proximity Operations: The Relative Orbital Elements' Perspective
<p>The data sets provided here can be used to recreate the plots of the paper “Trajectory Design for Proximity Operations: The Relative Orbital Elements’ Perspective” available at this <a href="https://arc.aiaa.org/doi/full/10.2514/1.G006175">link</a>.</p> <p>That paper presents how to rigorously transform back-and-forth the equations of the relative motion in the close-range regime between Hill-Clohessy-Wiltshire and Relative Orbital Elements formulations. As straightforward application, it is presented a methodology to generate piecewise constant acceleration profiles from an impulsive guidance solution, setting up a control grid that minimizes the difference between impulsive and equivalent delta-v burns corresponding to the acceleration profile.</p> <p>Applications are implementation of autonomous guidance and control policies for close-range satellite proximity operations.</p>
Spectral reflectance data of Mercury's surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015 resampled to a [55399 × 396] tabular data format.
<p>MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>This specific product is stored as a gzip compressed json, where each element is a grid cell.<br> We are in the process to publish a complete pipeline to produce this product from RAW data on https://github.com/epn-ml/MESSENGER-Mercury-Surface-Cassification-Unsupervised_DLR/ .</p> <p>Spectral reflectance data of Mercury’s surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015.<br> MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>0. Pre-filtering<br> We used the most recent dataset that had large-scale photometric corrections and thus was almost free from observation geometry effects.<br> However, extreme geometry are still present and are typically associated with high noise and some residual instrumental effects.<br> Based on our empirical tests, we filtered out observations with an emission/incidence angle ≥80∘.<br> We also calculated the median value per wavelength and per cell grid when constructing the global hyperspectral data cube and filtered out observations falling under the 2nd percentile and above 99.9th percentile to clean some residual geometry effects.<br> With this approach we create an effective noise filter while retaining enough observations to be able to analyse the entirety of the surface of the planet.</p> <p>1. Spectral resmpling<br> Unprocessed MASCS spectra could have 512 or 256 channes, depending on binning.<br> We resampled the data in the spectral dimension to a common wavelength range from 260 nm to 1052 nm with a 4 nm resolution (2 nm spectral sampling), resulting in 396 spectral channels.<br> This approach slightly oversamples the original 4.77 nm spectral resolution and removes some points from the original 200-1050 nm range.<br> The resulting data matrix is expressed in tabular form, with each row representing a single grid cell or pixel on the surface.<br> The elements of each row are the spectral reflectance values from the VIS instrument at 396 (resampled) wavelengths.</p> <p>2. Spatial resmpling<br> The whole dataset of ∼ 5 million spectra is resampled to a planet-wide rectangular grid of 1×1deg in the latitudinal band between ± 80.<br> The cell longitudinal size varies between ∼ 40 km at the equator to a minimum of ∼ 10 km at ±80∘.<br> Thus, the area spanned by each grid cell depends on the latitude. However, the same is true for the acquisition process, where higher spatial resolution is reached near the equator and lower resolution at the poles.</p>
Dataset for paper "Target selection for Near-Earth Asteroids in-orbit sample collection missions"
<p>This dataset can be used to reproduce the results of the paper titled "Target selection for Near-Earth Asteroids in-orbit sample collection missions."</p> <p>The "results" folder contains the data to reproduce the maps and the rankings of the target asteroids.</p> <p>The "trajectories" folder contains the propagation of the sample trajectories used to obtain the grids.</p>
Revealing Hidden Orbital Pseudospin Texture with Time-Reversal Dichroism in Photoelectron Angular Distributions
<p>Angle-resolved photoemission spectroscopy (ARPES) of bulk 2H-WSe2 for different crystal orientations linked to each other by time-reversal symmetry. This dataset supplements a manuscript, and was used to measure a new observable called time-reversal dichroism in photoelectron angular distributions (TRDAD), which quantifies the modulation of the photoemission intensity upon effective time-reversal operation. Experimental results are in quantitative agreement with both tight-binding model and state-of-the-art fully relativistic calculations performed using the one-step model of photoemission, unambiguously demonstrating that TRDAD reveals its orbital pseudospin texture counterpart.</p>
275 Candidates and 149 Validated Planets Orbiting Bright Stars in K2 Campaigns 0-10
<p>This dataset contains transit model posterior distributions and validation analyses for the 275 exoplanet candidates (in 233 systems) analyzed in Mayo et al. (2018), titled "275 Candidates and 149 Validated Planets Orbiting Bright Stars in K2 Campaigns 0-10".</p> <p>The dataset takes the form of 233 compressed directories each corresponding to an exoplanet system and titled after its EPIC ID. Within a given directory there are two numpy pickles named EPICXXXXXXXXX_chains.npy and EPICXXXXXXXXX_lnlikes.npy (where XXXXXXXXX is the 9 digit EPIC number) as well as n subdirectories, where n is the number of planet candidates in the system.</p> <p>The EPICXXXXXXXXX_chains.npy pickle is a representative sample of the posterior distribution of the transit model for a given exoplanet system. The pickle is a numpy array of size (j,k,l), where j is the number of walkers in the Markov chain Monte Carlo ensemble simulation that sampled the posterior distribution (note: we chose to fix j = 2*l), k is the number of walker steps reported in this dataset (the full posteriors were thinned down to between 750 and 10,000 steps), and l is the number of parameters in the transit model for the exoplanet system. The EPICXXXXXXXXX_lnlikes.npy pickle contains the associated ln(likelihood) values for each walker step in the previously described pickle. This pickle is a numpy array of size (j,k) where j and k are defined as above.</p> <p>The number of parameters will always be of the form 4 + 5*n, where n is again the number of planets in the systems. The first four parameters in the pickle are a baseline offset parameter for the normalized flux, a noise parameter to take the place of flux error bars, and two quadratic limb darkening parameters q<sub>1</sub> and q<sub>2</sub> based on Kipping et al. (2013). The next five parameters (and each subsequent set of five parameters in multi-candidate systems) refer to the reference epoch (a mid-transit time in BJD - 2454833), the period (in days), log<sub>10</sub>(R<sub>p</sub>/R<sub>*</sub>), the transit duration (T<sub>IV</sub>-T<sub>I</sub> in days), and the impact parameter. It should be noted that there is no consistent ordering of the planets in the posterior samples (for example, in a three planet system parameters 5-9 may refer to planet b, planet c, or planet d). Therefore, planetary periods should be used as reference to identify candidates. All parameters and the nature of the transit model are described in detail in Mayo et al. (2018).</p> <p>Each subdirectory contains the input and output of the validation analysis conducted via the VESPA validation package (Morton 2012, 2015). For additional details please refer to the relevant citations or the <a href="https://github.com/timothydmorton/VESPA">VESPA github repository</a>. Each subdirectory is named after the appropriate candidate listed in Mayo et al. (2018; specifically Tables 5 and 7).</p>
Data for: Terahertz orbital angular momentum modes with flexible twisted hollow core antiresonant fiber
<p>Supporting data for the published work on "Terahertz orbital angular momentum modes with flexible twisted hollow core antiresonant fiber". The data here reported are all the necessary data to reproduce the figures in the paper both measurements an simulations (except for the analytical results, which are obtained directly from the formulas included in the paper). The Info file describes each file, how they have been obtained and what they have been used for. </p>
The Nature and Orbit of the Ophiuchus Stream
<p>The *_chain.txt files contain Markov chains that model the line-of-sight<br /> velocity (RV_chains.txt), color-magnitude diagram (CMD_chains.txt), and<br /> proper motion and the extent of the Ophiuchus stellar stream (PM_chains.txt). By randomly selecting rows from these files, one can sample the corresponding probability density functions.The columns in each file are briefly described below.</p> <p>RV_chains.txt:</p> <p> column 1: line-of-sight velocity at {ell}_0=5deg,<br /> column 2: gradient in line-of-sight velocity, d(v_los)/d({ell})<br /> column 3: additional scatter in line-of-sight velocities, s</p> <p>CMD_chains.txt:<br /> column 1: age, t<br /> column 2: mass-loss parameter, eta<br /> column 3: metallicity content, Z<br /> column 4: offset in reddening with respect to the Schlegel et al. (1998)<br /> [1998ApJ...500..525S] reddening, (E(B-V)_off)<br /> column 5: distance modulus at {ell}_0=5deg,<br /> column 6: gradient distance modulus, d(DM)/d({ell})<br /> columns 7-11: uncertainty in isochrone magnitudes, {sigma}_iso_m,<br /> where m=[g,r,i,z,y,]</p> <p>PM_chains.txt:<br /> column 1: fraction of stars associated with the field population, 1-f<br /> column 2: natural logarithm of the width of the stream in the<br /> galactic latitude direction, ln({sigma}_b)<br /> column 3: natural logarithm of the width of the field population in the<br /> galactic latitude direction, ln({sigma}_p_b)<br /> column 4: A_p<br /> column 5: B_p<br /> column 6: ln({sigma}_pm)<br /> column 7: ln({sigma}_p_pm)<br /> column 8: <{mu}_{ell}><br /> column 9: d({mu}_{ell})/d({ell})<br /> column 10: <{mu}_b><br /> column 11: d({mu}_b)/d({ell})<br /> column 12: <{mu}_p_{ell}><br /> column 13: d({mu}_p_{ell})/d({ell})<br /> column 14: <{mu}_p_b><br /> column 15: d({mu}_p_b)/d({ell})<br /> column 16: {ell}_min<br /> column 17: {ell}_max<br /> column 18: A<br /> column 19: B<br /> column 20: C</p> <p> </p>
Spinning test-body orbiting around Schwarzschild black hole: circular dynamics and gravitational-wave fluxes
<p>We release gravitational wave fluxes at null-infinity from a spinning test-body in circular equatorial orbits around a Schwarzschild black hole. Four different prescriptions are used for the dynamics: the Mathisson-Papapetrou formalism under the Tulczyjew (TUL) spin-supplementary-condition (SSC), the Pirani (PIR) SSC and the Ohashi-Kyrian-Semerak (OKS) SSC, and the spinning particle limit of the effective-one-body Hamiltonian (HAM) of [Phys.~Rev.~D.90,~044018(2014)]. For more details see xxxx .</p> <p>The multipolar fluxes are given for l=2,3 m=1,2,3 at the Boyer-Lindquist radii</p> <p> r = 4 5 6 7 8 10 12 15 20 30 ,</p> <p>in cases they were not computed the data contains a "42". Note that the fluxes in these data files are assumed to contain both the +m and -m contributions, since they are identical for equatorial orbits and aligned spins. <br /> Additionally, the data files contain the key numbers describing the circular dynamics (see paper).</p> <p>Units <span class="math-tex"><em>c</em>=<em>G</em>=1.</span></p>
Data for Entanglement of Orbital Angular Momentum in Non-Sequential Double Ionization
<p>Data for publication “Entanglement of Orbital Angular Momentum in Non-Sequential Double Ionization”, available at https://arxiv.org/abs/2111.10148. A readme.txt file is included with the data.</p> <p><strong>Authors</strong></p> <p>Andrew S. Maxwell, Lars Bojer Madsen, Maciej Lewenstein</p> <p><strong>Abstract</strong></p> <p>We address orbital angular momentum (OAM) entanglement in ultrafast processes. In the strongly correlated process of non-sequential double ionization (NSDI) we demonstrate robust photoelectron entanglement. In contrast to commonly considered continuous variable entanglement measures, the discrete OAM allows for a simpler interpretation, computation, and measurement of entanglement. The logarithmic negativity reveals that the entanglement is robust to incoherent effects and an entanglement witness is used to minimize the number of measurements to detect the entanglement, while both quantities can be directly related to coherence terms between OAM channels. We quantify the entanglement for a large range of targets and field parameters to find the most entangled photoelectron pairs. This methodology provides a general way to use OAM to quantify and measure entanglement that is well-suited to attosecond processes, and we show can be exploited to enhance imaging capabilities through correlated measurements, or could be used for generation of OAM-entangled electrons.</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>
Data for "Hot-carrier transfer across a nanoparticle-molecule junction: The importance of orbital hybridization and level alignment"
<p>This upload includes the data presented and analyzed in the article "Hot-carrier transfer across a nanoparticle-molecule junction: The importance of orbital hybridization and level alignment" by Jakub Fojt, Tuomas P. Rossi, Mikael Kuisma, and Paul Erhart.</p> <p>The codes for reproducing the data are provided at <a href="https://doi.org/10.5281/zenodo.7118376">doi:10.5281/zenodo.7118376</a>.</p> <p>See <em>README.md</em> in <em>data.zip</em> for a detailed description.</p>
Mars orbital image (HiRISE) labeled data set
<p>This data set contains 3820 landmarks that were extracted from 168 HiRISE images. The landmarks were detected in HiRISE browse images. For each landmark, we cropped a square bounding box the included the full extent of the landmark plus a 30-pixel margin to left, right, top, and bottom. Each cropped image was then resized to 227x227 pixels.</p> <p><strong>Contents</strong>:</p> <ul> <li>map-proj/: Directory containing individual cropped landmark images</li> <li>labels-map-proj.txt: Class labels (ids) for each landmark image</li> <li>landmark_mp.py: Python dictionary that maps class ids to semantic names</li> </ul> <p><strong>Attribution</strong>:</p> <p>If you use this data set in your own work, please cite this DOI: 10.5281/zenodo.1048301</p> <p>Please also cite this paper, which provides additional details about the data set.</p> <p>Kiri L. Wagstaff, You Lu, Alice Stanboli, Kevin Grimes, Thamme Gowda, and Jordan Padams. "Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas." <em>Proceedings of the Thirtieth Annual Conference on Innovative Applications of Artificial Intelligence</em>, 2018.</p> <p> </p>
A Synthetic Global Spatiotemporal Sampled River Discharge Database for Different Satellite Altimetry Mission Orbits
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RRR input and output files that were used in the study reported in:</p> <ul> <li> <p>Sikder, Md. S., Bonnema, M., Emery, C. M., David, C. H., Lin, P., Pan, M., et al. (2021). A Synthetic Data Set Inspired by Satellite Altimetry and Impacts of Sampling on Global Spaceborne Discharge Characterization. <em>Water Resources Research</em>, <em>57</em>(2), e2020WR029035. <a href="https://doi.org/10.1029/2020WR029035">https://doi.org/10.1029/2020WR029035</a></p> </li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. </p> <p>Note that this dataset makes extensive use of the river network and RAPID simulations that were produced in the following study, and the paper is gratefully acknowledged here:</p> <ul> <li> <p>Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., et al. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. <em>Water Resources Research</em>, <em>55</em>(8), 6499–6516. <a href="https://doi.org/10.1029/2019WR025287">https://doi.org/10.1029/2019WR025287</a></p> </li> </ul> <p><strong>Version of record and details of this version</strong></p> <p>The version of record for this dataset (i.e. the one used in the aforementioned paper) is version V1.1 available at <a href="https://doi.org/10.5281/zenodo.4064188">https://doi.org/10.5281/zenodo.4064188</a>. This version V2.1 was produced to facilitate testing of the RRR software (<a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a>). Notable details regarding this version compared to V2.0 are as follows:</p> <ul> <li>The temporal sequence files (seq_TIM*.csv) of observations for regular temporal sampling now all have a sampling mean time of 0 second for every river reach instead of the previous value which corresponded to the cycle of observations (e.g. 259,200 seconds for a three-day regular temporal sampling). This allows to start sampling at the onset of each simulation instead of at the end of the first cycle. This change does impact the findings of the study.</li> <li>The sampled discharge files (Qout*.nc) where produced with an updated version of rrr_anl_spl_mod.py which now selects the time step at which a sample is retained using a slightly different approach. The update only impacts sampling results when the sampling time matches the river model output time step exactly, and is more accurate now. This change does impact the findings of the study.</li> </ul>
Mars orbital images of fresh impacts from CTX
<p><strong>Mars orbital images of fresh impacts from CTX</strong></p> <p>This data set contains images obtained from observations of Mars by the Context Camera (CTX) on the Mars Reconnaissance Orbiter. Each image in this collection is a cropped region of roughly 1.8 x 1.8 km (300 x 300 pixels at 6 m/pixel). The data set consists of "positive" images centered on the location of known fresh impacts on the surface of Mars (using a fresh impact catalog [1]) and "negative" examples obtained by randomly sampling CTX images (uniformly over the surface of Mars).</p> <p><strong>Contents</strong></p> <p>The 6829 images are divided into training and validation sets to capture the configuration used to train a fresh impacts image classifier [2]. However, they can be pooled together as a single data set for other purposes (but note that augmented versions are provided only for the training data). </p> <ul> <li>training_data/: 6156 images + 5 augmented versions per image</li> <li>validation_data/: 673 images (not augmented)</li> </ul> <p>Labels are indicated in the filenames. Positive examples start with a 0, and negative examples start with a - (negative sign).</p> <p>We employed augmentation on the training examples to generate 5 augmented versions per original image. The augmentation is indicated at the end of the filename: </p> <ul> <li>AUG-HF: Horizontal flip</li> <li>AUG-VF: Vertical flip</li> <li>AUG-RO: Rotate (randomly choose 90, 180, or 270 degrees)</li> <li>AUG-CJ: Image adjustment (minor random adjustment to brightness/contrast/saturation/hue)</li> <li>AUG-BL: Blur (Gaussian filter with radius 2) </li> </ul> <p><strong>References</strong> </p> <p>[1] Daubar, I.J., McEwen, A.S., Byrne, S., Kennedy, M.R., and Ivanov, B. (2013). "The current martian cratering rate," Icarus 225, 506-516. doi:10.1016/j.icarus.2013.04.009.</p> <p>[2] Munje, M. (2021). Martian Fresh Impact Classifier (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.552336</p> <p> </p>
"Hydration of a clay-rich unit on Mars, comparison of orbital data to rover data" supplementary data
<p>MCMC results from individual DAN measurements from sols 1814 to 3069 used in this work with material classifications from Figure 11.</p>
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>
Foresail-2 Orbital Lifetime Analysis
<p>The dataset has been created with the following inputs for DRAMAs CROC.</p> <table> <tbody> <tr> <td>Inputs for DRAMA</td> <td> </td> <td>Outputs for DRAMA</td> </tr> <tr> <td>Orbital elements</td> <td> </td> <td>CROC</td> </tr> <tr> <td>Inclination</td> <td>15</td> <td>°</td> <td> </td> <td>F3. Randomly tumbling satellite</td> </tr> <tr> <td>RAAN</td> <td>0</td> <td>°</td> <td> </td> <td>Average cross section for 6U</td> <td>0.170</td> <td>m^2</td> </tr> <tr> <td>Argument of perigee</td> <td>0</td> <td>°</td> <td> </td> <td>Minimum Cross Section</td> <td>0.028</td> <td>m^2</td> </tr> <tr> <td>Drag coefficient</td> <td>2.2</td> <td>N/A</td> <td> </td> <td>Maximum Cross Section</td> <td>0.268</td> <td>m^2</td> </tr> <tr> <td>SRP coefficient</td> <td>1.3</td> <td>N/A</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Orbit Predigtion Update</td> <td>08.02.2023</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>6U CubeSat with 2 folding panel + boom</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Dry mass</td> <td>14</td> <td>kg</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Main body dimensions</td> <td>0.11x0.34x0.25</td> <td>m^3</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Solar Panel X+</td> <td>0.007x0.34x0.25</td> <td>m^3</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Solar Panel X-</td> <td>0.007x0.34x0.25</td> <td>m^3</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Extended boom dimensions</td> <td>0.07x0.68x0.01</td> <td>m^3</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Magnetometer dimensions</td> <td>0.05x0.05x0.05</td> <td>m^3</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table> <p> </p> <p>OSCAR simulations have been conducted with orbits with apogee varying from 15000 km to 65000 km in 5000 km steps.<br> The perigee has been varied for every apogee height from 250 km to 400 km in 50 km steps.<br> Every simulation has been done for 4 different launch dates:</p> <table> <tbody> <tr> <td>21.03.2025 12:00:00</td> <td>Spring</td> </tr> <tr> <td>21.06.2025 12:00:00</td> <td>Summer</td> </tr> <tr> <td>21.09.2025 12:00:00</td> <td>Autumn</td> </tr> <tr> <td>21.12.2025 12:00:00</td> <td>Winter</td> </tr> </tbody> </table> <p>If not specified otherwise all settings in DRAMA have been kept to the default values.</p> <p> </p>
Dataset supporting the paper "Large Orbital Moment of Two Coupled Spin‑Half Co Ions in a Complex on Gold. ACS Nano 17, 10608 (2023)"
<p>Dataset corresponding to theoretical calculations in the paper "Large Orbital Moment of Two Coupled Spin‑Half Co Ions in a Complex on Gold" ACS Nano 17, 10608 (2023), https://pubs.acs.org/doi/10.1021/acsnano.3c01595</p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain:<br> .siesta files: STM images in WsXM format (http://www.wsxm.eu/) simulated using STMpw (https://doi.org/10.5281/zenodo.3581159).<br> CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (https://jp-minerals.org/vesta/en/).<br> .agr: grace files (https://plasma-gate.weizmann.ac.il/Grace/).</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.