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128 results for “shadows”
Sidescan Sonar Substrate, Depth, and Shadow Image-Label-Pairs
<p>Substrate, depth, and shadow image-label pairs used to train side scan sonar segmentation models v1.0 implemented in PINGMapper v2.0.</p><p> </p><p>Images were labeled with <a href="https://github.com/Doodleverse/dash_doodler">Doodler</a> and <a href="https://www.makesense.ai/">Make Sense.</a></p>
Panel data used in the paper ""The shadow price of irrigation water in major groundwater depleting countries"
<p>The panel data are used in an econometric analysis estimating Cobb-Douglas production functions that are subsequently used to calculate the shadow price (current marginal value) or irrigation water in 11 major groundwater depleting countries.</p>
Simulated Self-user Shadowing for Mobile Phone Antennas at 28 GHz and at 60 GHz
<p>The purpose of this dataset is to supplement the data presented in our conference publication "Self-user shadowing effects of millimeter-wave mobile phone antennas in a browsing mode" at EuCAP 2019 (see <a href="https://ieeexplore.ieee.org/document/8739947">https://ieeexplore.ieee.org/document/8739947</a>).</p> <p>This dataset contains the 3-D surface meshes of the two numeric human body models used in the above publication. One body model holds the mobile phone with one hand (vertically, "OneHand") and the other body model with both hands (horizontally, "TwoHand"). The body models were initially exported from MakeHuman (<a href="http://www.makehumancommunity.org">http://www.makehumancommunity.org</a>), the actual body postures were then created with Blender 3D Creation Suite (<a href="https://www.blender.org">https://www.blender.org</a>), and these final body models were exported in OBJ format (a generic geometry definition file format). Then these models were imported into CST Studio Suite (<a href="http://www.cst.com">http://www.cst.com</a>) in order to simulate the 3-D realised-gain patterns of the antenna. The material properties of the human body model used in the simlations are described in detail in the above publication. Also the dual-polarised mobile-phone antenna design with one vertical feed port and one horizontal feed port is described in detail within the above publication (see Fig. 3) and is not part of this dataset. (Note that "port #1" in Fig. 3 of the publication denotes the vertical antenna port for the <em>one-hand</em> case, while "port #1" denotes the horizontally antenna port in the <em>two-hand</em> case.)</p> <p>This dataset also contains the simulated 3-D polarimetric, directional, complex-valued (real, imaginary) realised-gain patterns, seperately for 28 GHz and for 60 GHz, in 1-degree resolution in both phi and theta directions. The patterns are seperately given for the vertical ("VPolPatch")and the horizontal feed port ("HPolPatch"). The 2-D pattern cuts presented in the above publication (in Figs. 5-11) are subsets of the 3-D patterns in this dataset.</p> <p>The format of the eight ascii files {xxGHzStandingyyHandzzPolPatch.txt} is a follows:<br> 1st column: Theta angle in degrees<br> 2nd column: Phi angle in degrees<br> 3rd column: real part of Gain, theta component, in dBi<br> 4th column: imaginary part of Gain, theta component, in dBi<br> 5th column: real part of Gain, phi component, in dBi<br> 6th column: imaginary part of Gain, phi component, in dBi<br> where xx is "28" or "60" (GHz), yy is "One" or "Two" (-hand grip), and zz is "H" or "V" (-pol. antenna port), as described above.</p> <p>The spherical coordinate system is used in accordance to the IEEE-standard spherical coordinate system. The underlying Cartesian coordinate system is shown in the two attached preview (PNG) image files for both human body models, where the z-axis (theta=0 degrees) points to the directions of the head of the human, the x-axis (phi=0 degrees) towards the left side of the human, and the y-axis toward the back of the human.<br> </p>
Dataset for "A new method for identifying weather-induced power system stress using shadow prices"
<p>These are data accompanying "A new method for identifying weather-induced power system stress using shadow prices". They consist of</p> <ul> <li>solved network files (generated with <a href="https://github.com/PyPSA/pypsa-eur/">PyPSA-Eur</a>, here v0.6.1), used for the analysis,</li> <li>necessary data to reproduce the figures in the paper and supplementary material.</li> </ul> <p>The optimised network files are of the form `workflow_data/results/stressful-weather/optimum/{weather_year}_181_90m_c1.25_Co2L0.0-1H.nc` (for weather_years in {1980,...,2019}). Unsolved ones can be found in `workflow_data/networks/...`.</p> <p>The filenames in `plot_data/` indicate which figure the data are associated to (e.g. `plot_data/fig_1_hourly_costs.csv` contains the hourly electricity costs during the winter of all networks and is necessary for Figure 1). We also added weather data for all system-defining events (mean surface level pressure, 10m wind speed anomaly, 2m temperature anomaly) in .nc files.</p> <p>Find more information about how to use these data and how they were generated in the README of the GitHub repository: <a href="https://github.com/koen-vg/stressful-weather/tree/v0">https://github.com/koen-vg/stressful-weather/tree/v0</a>.</p>
Figs 50-66 in From the shadows of the past: Moricand senior and junior, two 19th century naturalists from Geneva, with their newly described taxa and molluscan types
Figs 50-66. Unionidae, Etheriidae, and Streptaxidae. (50-53) Unionidae. (50-53) Monocondylaea costulata (J. Moricand, 1858), syntype, MHNG-INVE-91234 (D = 35.0). (54-57) Etheriidae. (54-57) Bartlettia stefanensis (J. Moricand, 1856), syntype, MHNG-INVE-91237 (D = 75.7). (58-66) Streptaxidae. (58-63) Steptartemon comboides (d'Orbigny, 1835), (58-60) syntype of Helix (Cochlodonta) comboides brasiliensis S. Moricand, 1836, MHNG-INVE-68684 (D = 9.01), (61-63) syntype of Helix (Cochlodonta) comboides edentula S. Moricand, 1836, MHNG-INVE-68683 (D = 6.30). (64-66) Streptartemon streptodon (S. Moricand, 1851), syntype, MHNG-INVE-68696 (D = 8.86). ►
Figs 14-31 in From the shadows of the past: Moricand senior and junior, two 19th century naturalists from Geneva, with their newly described taxa and molluscan types
Figs 14-31. Planorbidae, Unionidae, and Helicinidae. (48-53) Planorbidae. (14) Drepanotrema cimex (S. Moricand, 1838), syntype, MHNG-INVE-86802 (D = 6.03). (15-16) Drepanotrema depressissimus (S. Moricand, syntype, MHNG-INVE-86940 (D = 8.99). (17-19) Uncancylus concentricus (d'Orbigny, 1835), syntype of Ancylus barilensis S. Moricand, 1846, MHNG-INVE-87402 (D = 7.49). (20-23) Unionidae. (20-23) Monocondylaea franciscana (S. Moricand, 1838), holotype, MHNG-INVE-91235 (D = 39.5). (24-31) Helicinidae. (24-27) Helicina caracolla (S. Moricand, 1836), syntype, MHNG- INVE-91246 (D = 15.2). (28-31) Helicina haematostoma (S. Moricand, 1838), syntype, MHNG-INVE-91253 (D = 8.75). ►
Figs 4-6 in From the shadows of the past: Moricand senior and junior, two 19th century naturalists from Geneva, with their newly described taxa and molluscan types
Figs 4-6. Inventories of the Moricand collection. (4) Titles of front covers in both books. (5) Part of text dealing with terrestrial mollusc species. (6) First page of 'Coquilles terrestres et fluviatiles', showing progress through time.
Figs 1-3 in From the shadows of the past: Moricand senior and junior, two 19th century naturalists from Geneva, with their newly described taxa and molluscan types
Figs 1-3. Portraits. (1) S. Moricand, at unknown but probably young age (coll. MHNG). (2) J.S. Blanchet (after García Polo, 2015). (3) A. Brot (modified from Campos, 2013: 255).
Figs 7-13 in From the shadows of the past: Moricand senior and junior, two 19th century naturalists from Geneva, with their newly described taxa and molluscan types
Figs 7-13. Thiaridae and Pleuroceridae. (7-9) Thiaridae. (7-9) Aylacostoma crenocarina (S. Moricand, 1841), (7) syntype of Melanopsis crenocarina melanostoma S. Moricand, 1841, MHNG-INVE-91242 (H = 38.8), (8) syntype of Melanopsis crenocarina bilineata S. Moricand, 1841, MHNG-INVE-91240 (H = 36.2), (9) syntype of Melanopsis crenocarina leucostoma S. Moricand, 1841, MHNG-INVE-91241 (H = 42.9). (10-13) Pleuroceridae. (10) Doryssa ventricosa (J. Moricand, 1856), syntype, MHNG-INVE-91243 (H = 37.6). (11) Doryssa brasiliensis (S. Moricand, 1838), syntype, MHNG-INVE-91238 (H = 41.1). (12) Doryssa macapa (J. Moricand, 1856), syntype, MHNG-INVE-91239 (H = 37.1). (13) Doryssa cingulata (J. Moricand, 1860), syntype, MHNG-INVE-91245 (H = 33.5). ►
Figs 80-88 in From the shadows of the past: Moricand senior and junior, two 19th century naturalists from Geneva, with their newly described taxa and molluscan types
Figs 80-88. Streptaxidae, Euconulidae, and Charopidae. (80-82) Streptaxidae. (80-82) Streptartemon cryptodon (S. Moricand, 1851), syntype, MHNG-INVE-68687 (D = 3.68). (83-85) Euconulidae. (83-85) Pseudoguppya semenlini (S. Moricand, 1846), syntype, MHNG-INVE-70933 (D = 2.26). (86-88) Charopidae. (86-88) Lilloiconcha pleurophora (S. Moricand, 1846), syntype, MHNG-INVE-69077 (D = 2.15).
Figs 32-49 in From the shadows of the past: Moricand senior and junior, two 19th century naturalists from Geneva, with their newly described taxa and molluscan types
Figs 32-49. Ampullariidae, Planorbidae, Unionidae, Polygyridae, and Megalomastomidae. (32-33) Ampullariidae. (32-33) Pomacea decussata (S. Moricand, 1836), syntype, MHNG-INVE-33485 (H = 29.4). (34-36) Planorbidae. (34-36) Biomphalaria glabrata (Say, 1818), syntype of Planorbis dentifer J. Moricand, 1853, MHNG-INVE-86932 (D = 12.7). (37-40) Unionidae. (37-40) Monocondylaea reticulata (J. Moricand, 1858), syntype, MHNG-INVE-91236 (D = 41.8). (41-46) Polygyridae. (41-43) Practicolella (Practicolella) berlandieriana (S. Moricand, 1834), syntype, MHNG-INVE-37027 (H = 8.98). (44-46) Polygyra (Linisia) texasiana texasiana (S. Moricand, 1833), syntype, MHNG-INVE-72781 (D = 10.4). (47-49) Megalomastomidae. (47-49) Aperostoma blanchetiana (S. Moricand, 1836), syntype, MHNG-INVE-91233 (D = 30.3). ►
Research data for `Quantifying information scrambling via Classical Shadow Tomography on Programmable Quantum Simulators'
<p>Research data associated with the paper `Quantifying information scrambling via Classical Shadow Tomography on Programmable Quantum Simulators'. Contains raw data obtained from simulations run on the IBM quantum device ibm_lagos.</p>
KappaSet: Sentinel-2 KappaZeta Cloud and Cloud Shadow Masks
<p><strong>General information</strong></p> <p>The dataset consists of 9251 labelled sub-tiles from 1038 Sentinel-2 (S2) Level-1C (L1C) products distributed over the globe. In terms of seasonal distribution, S2 products can be divided into the following groups:</p> <ul> <li> <p>Winter products: 29 austral and 142 boreal S2 products</p> </li> <li> <p>Sprint products: 45 austral and 257 boreal S2 products</p> </li> <li> <p>Summer products: 30 austral and 293 boreal S2 products</p> </li> <li> <p>Autumn products: 29 austral and 213 boreal S2 products</p> </li> </ul> <p>Each S2 product was oversampled at 10 m resolution for 512 x 512 pixels sub-tiles. From each S2 product, the most challenging ~5 sub-tiles per product were selected for labelling. Each selected L1C S2 product represents different clouds, such as cumulus, stratus, or cirrus, which are spread over various geographical locations around the world. The classification pixel-wise map consists of the following categories:</p> <ul> <li> <p>0 – UNDEFINED: pixels that the labeler is not sure which class they belong to;</p> </li> <li> <p>1 – CLEAR: pixels without clouds or cloud shadows;</p> </li> <li> <p>2 – CLOUD SHADOW: pixels with cloud shadows;</p> </li> <li> <p>3 – SEMI TRANSPARENT CLOUD: pixels with thin clouds through which the land is visible; include cirrus clouds that are on the high cloud level (5-15km).</p> </li> <li> <p>4 – CLOUD: pixels with cloud; include stratus and cumulus clouds that are on the low cloud level (from 0-0.2km to 2km).</p> </li> <li> <p>5 – MISSING: missing or invalid pixels.</p> </li> </ul> <p>The dataset was labelled using Computer Vision Annotation Tool (CVAT) and Segments.ai. With the possibility of integrating an active learning process in Segments.ai, the labelling was performed semi-automatically. The distribution of the dataset is presented in the Figure below. Color represents the season from which the product was chosen.</p> <p>The dataset limitations must be considered: the data mostly covers terrestrial regions (around 91%) and includes some water areas (around 9%); only around 7% of the dataset contains snow. Current sub-tiles do not have georeferencing. </p> <p><strong>Contributions and Acknowledgements</strong></p> <p>The data were annotated by Olga Wold, Mariana Rohtsalu, Nikita Murin, Joosep Truupõld and Fariha Harun. The data verification and Software Development were performed by Indrek Sünter, Heido Trofimov, Anton Kostiukhin, Marharyta Domnich, Mihkel Järveoja, Olga Wold and Tetiana Shtym. The methodology was developed by Kaupo Voormansik, Indrek Sünter, Marharyta Domnich and Tetiana Shtym.</p> <p>The data were collected, processed, and checked as a part of “KappaMask: AI-based Cloudmask Processor for Sentinel-2” project. We thank Segments.ai team for providing a wonderful annotation tool that was actively used to prepare the dataset. In the end, we thank European Space Agency (ESA) for supporting, advising, and funding the project.</p> <p>The project was funded by <em><strong>European Space Agency,</strong></em> Contract No. 4000132124/20/I-DT.</p>
Leak-resilient enzyme-free nucleic acid dynamical systems through shadow cancellation
<p>DNA strand displacement (DSD) emerged as a prominent reaction motif for engineering nucleic acid-based computational devices with programmable behaviors. However, strand displacement circuits are susceptible to background noise that disrupts the circuit behavior, commonly known as leaks. The side effects of leaks are particularly severe in circuits with complex dynamical elements (e.g., feedback loops), as their leaks amplify nonlinearly, disrupting the circuit function. Shadow cancellation is a dynamic leak-elimination strategy originally proposed to control the leak growth in such circuits. However, the kinetic restrictions of the proposed method introduce a significant design overhead, making it less accessible. In this work, we use domain-level DSD simulations to examine the method's capabilities, the inner workings of its components, and, most importantly, robustness to practical deviations in its design requirements. First, we show that the method could stabilize the dynamics of several leak-affected catalytic and autocatalytic dynamical systems of practical importance. Then, through several probing experiments, we show that its design restrictions could be significantly relaxed without impacting the circuit function through simple adjustments to the circuit parameters. Finally, we discuss several ideas to tackle the practical challenges in applying the method to arbitrary DSD circuits, paving the way for future experimental work.</p>
Wind shadows from U.S. east coast offshore wind energy lease areas.
<p>Georeferenced data layers describing whole wind farm wakes (wind shadows) for use in planning and development along the U.S. east coast based on WRF simulations performed using the accompanying namelist. Full details of the analysis are provided in: Pryor and Barthelmie: Wind shadows impact planning of large offshore wind farms</p> <p> </p> <p>This work is supported by the U.S. Department of Energy (DoE) (DE-SC0016605). The research used computing resources from the National Science Foundation: Extreme Science and Engineering Discovery Environment (XSEDE) (allocation award to SCP is TG-ATM170024) and National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.</p>
Fig. 9 in The research history of the Middle Triassic fishes of Monte San Giorgio: getting out of the shadow of aquatic reptiles
Fig. 9 Prosantichthys (= Pholidophoridae gen. et sp. indet. by BÜrgin, 1999a, 1999b), plate and counterplate, PIMUZ T 3928
Fig. 7 Eoeugnathus megalepis BROUGH 1939 in The research history of the Middle Triassic fishes of Monte San Giorgio: getting out of the shadow of aquatic reptiles
Fig. 7 Eoeugnathus megalepis BROUGH 1939, composite reconstruction by the author based mainly on PIMUZ T 1356
Fig. 5 Various actinopterygian fishes from Monte San Giorgio, A in The research history of the Middle Triassic fishes of Monte San Giorgio: getting out of the shadow of aquatic reptiles
Fig. 5 Various actinopterygian fishes from Monte San Giorgio, A, large-sized fishes (30–100 cm), B medium-sized fishes (10–29 cm), C small sized fishes (2–9 cm); reconstructions by the author. 1—Birgeria stensiöi, 2—Saurichthys curionii, 3—Colobodus bassanii, 4—Ptycholepis barboi, 5—Bobasatrania ceresiensis, 6—Pholidopleurus ticinensis, 7—Ticinolepis crassidens, 8—Ctenognathichthys bellotti, 9—Meridensia meridensis, 10—Peltopleurus lissocephalus, 11—Aetheodontus besanensis, 12—Placopleurus minus, 13—Habroichthys minimus, 14—Luganoia lepidosteoides, 15—Peltoperleidus macrodontus, 16—Ophiopsidae indet. Scale bars A and B = 10 cm, C = 1 cm
Fig. 1 in The research history of the Middle Triassic fishes of Monte San Giorgio: getting out of the shadow of aquatic reptiles
Fig. 1 Title paper of the publication by Erik Andersson (1916) on some Triassic fish remains from Monte San Giorgio
Fig. 3 in The research history of the Middle Triassic fishes of Monte San Giorgio: getting out of the shadow of aquatic reptiles
Fig. 3 Emil Kuhn's figure 1 from Kuhn (1946a), showing the large Acrodus fossil from Monte San Giorgio
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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.