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5,155 results for “resolution”
Seized ecstasy pills: image dataset (full resolution jpg)
<p>According to the World Drug Report 2020, cocaine and ecstasy are the most consumed stimulant drugs, with 19 and 27 millions estimated users in 2018. Unsurprisingly, large efforts are being made to design fast and cost effective analytical methods to track and monitor the distribution networks of these synthetic drugs. Here we share two datasets of ecstasy pills seized in the north-east of Switzerland between 2010 and 2011. The first contains 621 forensic grade images of pills, while the second one consists of 486 mIR spectra. While both sets are not covering the same seizure, both provide high quality data with orthogonal information to evaluate clustering and dimension reduction methods.</p> <p> </p>
Seized ecstasy pills: infrared spectra and image datasets (low resolution jpg)
<p>According to the World Drug Report 2020, cocaine and ecstasy are the most consumed stimulant drugs, with 19 and 27 millions estimated users in 2018. Unsurprisingly, large efforts are being made to design fast and cost effective analytical methods to track and monitor the distribution networks of these synthetic drugs. Here we share two datasets of ecstasy pills seized in the north-east of Switzerland between 2010 and 2011. The first contains 621 forensic grade images of pills, while the second one consists of 486 mIR spectra. While both sets are not covering the same seizure, both provide high quality data with orthogonal information to evaluate clustering and dimension reduction methods.</p> <p>High resolution jpg files can be found here: 10.5281/zenodo.4124562</p>
Data from: Drivers of assemblage-wide calling activity in tropical anurans and the role of temporal resolution
<p>1. Temporal scale in animal communities is often associated with seasonality, despite the large variation in species activity during a diel cycle. A gap thus remains in understanding the dynamics of short-term activity in animal communities.</p> <p>2. Here we assessed calling activity of tropical anurans and addressed how species composition varied during night activity in assemblages along gradients of local and landscape environmental heterogeneity.</p> <p>3. We investigated 39 anuran assemblages in the Pantanal wetlands (Brazil) with passive acoustic monitoring during the peak of one breeding season and first determined changes in species composition between night periods (early, mid, and late) using two temporal resolutions (1-hour and 3-hour intervals). Then, we addressed the role of habitat structure (local and landscape heterogeneity variables from field-based and remote sensing metrics) and ecological context (species richness and phylogenetic relatedness) in determining changes in species composition (i) between night periods and (ii) across days.</p> <p>4. Nocturnal calling activity of anuran assemblages varied more within the 1-hour resolution than the 3-hour resolution. Differences in species composition between early and late-night periods were related to local habitat structure and phylogenetic relatedness, while a low variation in compositional changes across days was associated with low-heterogeneous landscapes. None of these relationships were observed using the coarser temporal resolution (3-hour).</p> <p>5. Our findings on the variation of calling activity in tropical anuran assemblages suggest potential trades-off mediated by fine-temporal partitioning. Local and landscape heterogeneity may provide conditions for spatial partitioning, while the relatedness among co-signaling species provides cues on the ecological overlap of species with similar requirements. These relationships suggest a role of niche dimensional complementarity on the structuring of these anuran assemblages over fine temporal scales. We argue that fine-temporal differences between species in breeding activity can influence the outcome of species interaction and that addressing temporal scaling issues can improve our understanding of dynamics of animal communities.</p>
SCYM Maize Yield Maps (Low Resolution) from Deines et al. 2021, Lobell et al. 2020
<p>Annual maize yield maps for the US Corn Belt for 1999-2018, aggregated to 10 km resolution. Yield maps are produced with the SCYM methodology at 30 m resolution using Landsat satellite data. The original high resolution yield maps are aggregated to a 10 km grid resolution to preserve privacy.</p> <p>Dataset creation is described in <a href="https://doi.org/10.1016/j.rse.2020.112174">Deines et al., 2020</a> in Remote Sensing of Environment (open access). The 2008-2018 map dataset described therein was extended back to 1999 for <a href="https://doi.org/10.1038/s43016-020-00165-w">Lobell et al. 2020</a> in Nature Food (<a href="https://rdcu.be/b9cXQ">read-only accessible version</a>). Maps for 2008-2018 use the USDA's Cropland Data Layers to identify maize pixels; maps for 1999-2007 use the Corn-Soy Data Layer produced by <a href="https://www.nature.com/articles/s41597-020-00646-4">Wang et al. 2000</a> (data + manuscript are open access).</p> <p><strong>Metadata</strong></p> <p>Yield values are provided in metric tons/hectare. </p> <p>No Data value = -999 (grid cells outside of the study area + cells with <1% coverage of SCYM 30 m yield maps)</p> <p>Map projection: EPSG:5070, CONUS Albers Equal Area</p> <p> </p> <p> </p> <p>Preferred citation(s):</p> <p>Deines, J.M., R. Patel, S. Lang, W. Dado, & D.B. Lobell. 2020. A million kernels of truth: Insights in to scalable satellite maize yield mapping and yield gap analysis from an extensive ground dataset in the US Corn Belt. <em>Remote Sensing of Environment. </em><a href="https://doi.org/10.1016/j.rse.2020.112174">https://doi.org/10.1016/j.rse.2020.112174</a></p> <p>D.B. Lobell, J.M. Deines, & S. Di Tomasso. 2020. Changes in the drought sensitivity of U.S. maize yields. <em>Nature F</em><em>o</em><em>o</em><em>d</em> 1:729-735. <a href="https://doi.org/10.1038/s43016-020-00165-w">https://doi.org/10.1038/s43016-020-00165-w</a></p> <p> </p>
WRF data for downscaling, used in Learned multi-resolution dynamical downscaling for precipitation
<p>This study uses regional climate model (RCM) simulated precipitation at low and high spatial resolution, to develop convolution neural network (CNN) based approaches, that can emulate high resolution modeled data using low resolution modeled data with cheaper computational resource than running dynamical downscaling at the high spatial resolution. Specifically, we define two types of CNNs, one that stacks variables directly and one that encodes each variable before stacking, and train each CNN type both with a conventional loss function, such as Mean Square Error (MSE), and with a conditional generative adversarial network (CGAN), for a total of four CNN variants. We compare the four new CNN-derived high resolution precipitation with precipitation generated from a bi-linear interpolater and the state-of-the-art CNN-based super-resolution (SR) technique, using the original high resolution precipitation from the RCM as ground truth. We find that SR technique produces similar results to the interpolator with smoother spatial and temporal distributions and smaller data variabilities and extremes than ground truth shows. While the new CNNs trained by MSE generate better results over some regions than the interpolator and SR technique, their predictions are still not as close as ground truth. The CNNs trained by CGAN generate more realistic and physically reasonable results. This advanced technique improves not only the data variability in time and space, and but also the extremes, such as intense and long-lasting events, based on event-feature tracking algorithm.</p>
A SAT-based Resolution of Lam's Problem (SAT instances and certificates)
<p>This repository contains SAT instances and certificates accompanying the paper "A SAT-based Resolution of Lam's Problem" appearing at <a href="https://aaai.org/Conferences/AAAI-21/">AAAI 2021</a>. This paper developed a method to generate certificates proving the nonexistence of a word of weight 19 in the code generated by a projective plane of order ten. Together with previously computed certificates this solves Lam's Problem.</p> <p>The 'a1' archive contains a certificate showing that there are exactly 66 A1 matrices up to isomorphism. Run the provided check.sh script to verify the certificate.</p> <p>The 'a2' archive contains certificates showing that there are exactly 650,370 A2 matrices up to isomorphism. Run the provided check.sh script to verify the certificates.</p> <p>The 'main' archive contains precomputed SAT instances for each of the A2 matrices up to isomorphism and partial solutions of the SAT instances. The main certificates may be generated and verified by extracting the main archive into the weight19/main directory of the MathCheck2 repository for Lam's problem (available from <a href="https://bitbucket.org/cbright/mathcheck2/src/master/">bitbucket.org/cbright/mathcheck2</a> or in the lams-problem-code.7z archive) and running the driver.sh script. The final-step/solve.sh script verifies that no partial solution can be completed to a full incidence matrix of a projective plane of order ten.</p>
Energy consumption and renewable generation data of 5 aggregators - 15 minute resolution (13 bus grid)
<p>Type: Energy consumption and renewable generation data</p> <p>Period of data collection: 19-03-2019 to 25-03-2019 (15-minute 672 periods)</p> <p>Resolution: 15 minutes</p> <p>Network: 13-bus MV grid</p> <p>Aggregator list:</p> <ul> <li>Aggregator 1: Shopping Mall; Hospital; Fire Station</li> <li>Aggregator 2: 15 houses</li> <li>Aggregator 3: 7 Office buildings</li> <li>Aggregator 4: Wind, PV</li> <li>Aggregator 5: Slow and fast-charging stations of electric vehicles</li> </ul> <p>Further data:</p> <ul> <li>Market prices 2019 summer and winter</li> <li>Wind generation curve</li> </ul> <p>Data obtained from CENERGETIC project (<a href="http://www.gecad.isep.ipp.pt/CENERGETIC/">http://www.gecad.isep.ipp.pt/CENERGETIC/</a>)</p> <p>National Funds through the FCT—Portuguese Foundation for Science and Technology, under Project PTDC/EEI-EEE/28983/2017 (CENERGETIC), CEECIND/02814/2017, UIDB/00760/2020.</p>
Temperature control on high-resolution SIMS oxygen isotopic compositions in Porites coral skeletons
<p>The dataset includes all data for "Temperature control on high-resolution SIMS oxygen isotopic compositions in Porites coral skeletons" by Zou et al. from Guangzhou Institute of Geochemistry.</p>
Summer land surface temperature from MODIS Aqua and Terra satellites for Houston in 2014 and Phoenix in 2003 at 1km resolution
<p>Satellite remote-sensing is used to collect important atmospheric and geophysical data at various spatial resolutions, providing insight into spatiotemporal surface and climate variability globally. These observations are often plagued with missing spatial and temporal information of Earth's surface due to (1) cloud cover at the time of a satellite passing and (2) infrequent passing of polar-orbiting satellites. While many methods are available to model missing data in space and time, in the case of land surface temperature (LST) from thermal infrared remote sensing, these approaches generally ignore the temporal pattern called the 'diurnal cycle' which physically constrains temperatures to peak in the early afternoon and reach a minimum at sunrise. In order to infill an LST dataset, we parameterize the diurnal cycle into a functional form with unknown spatiotemporal parameters. Using multiresolution spatial basis functions, we estimate these parameters from sparse satellite observations to reconstruct an LST field with continuous spatial and temporal distributions. These estimations may then be used to better inform scientists of spatiotemporal thermal patterns over relatively complex domains. The methodology is demonstrated using data collected by MODIS on NASA's Aqua and Terra satellites over both Houston, TX and Phoenix, AZ USA.</p>
High spatial resolution mapping identifies habitat characteristics of the invasive vine Antigonon leptopus on St. Eustatius (Lesser Antilles)
<p>On the Caribbean island of St. Eustatius, Coralita (<i>Antigonon leptopus</i>)<i> </i>is an aggressive invasive vine posing major biodiversity conservation concerns. The generation of distribution maps can address these conservation concerns by helping to elucidate the drivers of invasion. We test the use of support vector machines to map the distribution of Coralita on St. Eustatius at high spatial resolution and use this map to identify potential landscape and geomorphological factors associated with Coralita presence. This latter step was performed by comparing the actual distribution of Coralita patches to a random distribution of patches. To train the support vector machine algorithm, we used three vegetation indices and seven texture metrics derived from a 2014 WorldView-2 image. The resulting map shows that Coralita covered 3.18% of the island in 2014, corresponding to an area of 64 ha. The mapped distribution was highly accurate, with 93.2% overall accuracy (Coralita class producer's accuracy: 76.4%, user's accuracy: 86.2%). Using this classification map, we found that Coralita is not randomly distributed across the landscape, occurring significantly closer to roads and drainage channels, in areas with higher accumulated moisture, and on flatter slopes. Coralita was found more often than expected in grasslands, disturbed forest and urban areas, but was relatively rare in natural forest. These results highlight the ability of high spatial resolution data from sensors such as WorldView-2 to produce accurate invasive species, providing valuable information for predicting current and future spread risks and for early detection and removal plans.</p>
FIGURE 95 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull
FIGURE 95. FMNH PR2081, Tyrannosaurus rex. Right femur in posterior (A), lateral (B), anterior (C), and medial (D) views. Left femur in posterior (E), lateral (F), anterior (G), and medial (H) views. I, right femur, proximal view. J, Right femur, distal view. Scale = 30 cm; abbreviations in Appendix 1. Photographs by J. Weinstein.
FIGURE 39 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull
FIGURE 39. FMNH PR2081, Tyrannosaurus rex. Horizontal CT slice through braincase, 1 cm ventral to floor of endocranial cavity. Note hollow nature of parasphenoid rostrum and complex of recesses within basioccipital. Abbreviations in Appendix 1.
FIGURE 15 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull
FIGURE 15. FMNH PR2081, Tyrannosaurus rex. Sutural relationships between nasal, lacrymal, and maxilla above the antorbital fenestra. Left lateral view. See Appendix 1 for abbreviations. Photographs by J. Weinstein.
FIGURE 96 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull
FIGURE 96. FMNH PR2081, Tyrannosaurus rex. Right tibia, calcaneum, and astragalus in medial (A), posterior (B), lateral (C), and anterior (D) views. Left tibia, calcaneum, and astragalus in anterior (E), lateral (F), posterior (G), and medial (H) views. I, left tibia, proximal view. J, left astragalus and calcaneum, distal view. Scale = 30 cm; abbreviations in Appendix 1. Photographs by J. Weinstein.
FIGURE 10 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull
FIGURE 10. FMNH PR2081, Tyrannosaurus rex. Closeup of right premaxilla, showing extensive premaxillary flooring of external naris. Anterior extent of nasal under the naris cannot be determined. See Appendix 1 for abbreviations.
FIGURE 43 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull
FIGURE 43. FMNH PR2081, Tyrannosaurus rex. Medial view of left mandible focusing on dorsal tip of intramandibular joint, where the splenial, prearticular, coronoid, and supradentary converge. Abbreviations in Appendix 1.
FIGURE 92 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull
FIGURE 92. FMNH PR2081, Tyrannosaurus rex. A, Left ilium, lateral view. B, Left ilium, medial view. C, Right ilium (with sacrum attached), lateral view. Scale = 30 cm; abbreviations in Appendix 1. Photographs by J. Weinstein.
FIGURE 93 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull
FIGURE 93. FMNH PR2081, Tyrannosaurus rex. Left ischium and proximal portion of left pubis in (A) medial and (B) lateral view. Right ischium and proximal portion of right pubis in (C) lateral and (D) medial view. Scale = 30 cm; abbreviations in Appendix 1. Photographs by J. Weinstein.
FIGURE 58 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull
FIGURE 58. FMNH PR2081, Tyrannosaurus rex. Caudal vertebrae, anterior view. Scale = 1 5 cm. Photographs by J. Weinstein.
FIGURE 66 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull
FIGURE 66. FMNH PR2081, Tyrannosaurus rex. Haemal arches, anterior view. Scale = 10 cm. Photographs by J. Weinstein.
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.