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994 results for “2d”
Animations: Turbulence sets the length scale for planetesimal formation:Local 2D simulations of streaming instability and planetesimal formation
<p>This repository includes three animations discussed in the accepted version of ''Turbulence sets the length scale for planetesimal formation:Local 2D simulations of streaming instability and planetesimal formation" to appear in the Astrophysical Journal. </p> <p>Short descriptions of the animations are as follows:</p> <p>Movie 1: Simulations Ae3L0005 and Ae3L0005. Both use $\St= 0.1$ particles, but only the larger box shows collapse and planetesimal formation. See also: <a href="https://youtu.be/gkHiluqH8HY">https://youtu.be/gkHiluqH8HY</a> </p> <p>Movie 2: The evolution of $\St= 0.1$ pebbles for all 6 different box sizes in Table 1. See also: <a href="https://youtu.be/nA87-9_trUc">https://youtu.be/nA87-9_trUc</a></p> <p>Movie 3: The evolution of $\St= 0.01$ pebbles. See also: <a href="https://youtu.be/CCywDPKVU8w">https://youtu.be/CCywDPKVU8w</a></p>
DATA of "Resolving the 2D temporal evolution of subglacial water flow with dense seismic array observations."
<p>The data set contains all data presented in the paper: <strong>Observing the subglacial hydrology network and its dynamics with a dense seismic array</strong> published in PNAS ( <a href="https://doi.org/10.1073/pnas.2023757118">https://doi.org/10.1073/pnas.2023757118</a> )</p> <p>See our online presentation of this dataset: https://meetingorganizer.copernicus.org/EGU2020/EGU2020-10710.html.</p> <p>The present data and code concerns the source location obtained with matched-field-processing analysis and the hydraulic potential calculation (Shreve, R. L. Movement of Water in Glaciers. <em>J. Glaciol.</em> <strong>11</strong>, 205–214 (1972)).</p> <p>We perform source location over 1-sec long signal segment of the vertical component only. We filter the signal within the [3-7] Hz frequency range and coherently apply the MFP each 0.1 Hz within this range. To maximize our algorithm efficiency and minimize computational costs we use a gradient-based minimization algorithm (Nelder-Mead optimization) to converge to the best match between the trial and the observed phase delays rather than an exhaustive grid-search exploration. The convergence criterion is reached when the variance of values obtained over the last 5 iterations of the optimization is smaller than 1e<sup>-2</sup> with a maximum of 3000 iterations. Our 29 different starting points used for optimization are located 250 m below the glacier surface and they uniformly cover an area of 800 x 800 m<sup>2</sup> centered on the array. We set the initial velocity to 1800 m.sec <sup>-1</sup>. The 29 punctual locations found per signal segment (1 sec) after convergence are located all in the same place if a clear global convergence exists (i.e. high MFP output) or at up to 29 different locations if up to 29 local minima exist (i.e. low MFP output).</p> <p>Timeseries of physical quantities can be found here <a href="https://doi.org/10.5281/zenodo.3701520">https://doi.org/10.5281/zenodo.3701520</a></p> <p>Spatial observations acquired during the same period can be found here <a href="https://doi.org/10.5281/zenodo.3971815">https://doi.org/10.5281/zenodo.3971815</a></p> <p> </p> <p>The RESOLVE project has been supported by a grant from LabEx OSUG@2020 (Investissement d’avenir – ANR10LABX56) and by the IDEX Université Grenoble Alpes. Most of the computations presented in this paper were performed using the GRICAD infrastructure (https://gricad.univ-grenoble-alpes.fr), which is supported by Grenoble research communities, and with the CiGri tool (https://github.com/oar-team/cigri) developed by Gricad, Grid5000 (https://www.grid5000.fr) and LIG (<a href="https://www.liglab.fr/">https://www.liglab.fr/</a>).</p> <p> </p> <p>You can find more information on the method and seismic dataset used in this paper here: <a href="https://zenodo.org/deposit/5645545">https://zenodo.org/deposit/5645545</a></p>
Example data set for 2D adaptive slice-specific z-shimming
<p>Input data and corresponding results for the scripts provided on github (https://github.com/neuroimaging-mug/R2s-mapping) for adpative slice-specific z-shimming in presence of macroscopic field variations.</p> <p>Please unzip all file in the repository path of ‘R2s-mapping’.</p>
Torre de la Sal, Cabanes, Castellón, Comunitat Valenciana, España, Europa │Proyecto de digitalización 3D, 2D
<p>Proyecto de digitalización gráfico y geomático 2D y 3D realizado por <strong>AD&D 4D </strong>cofinanciado por Generalitat Valenciana.</p> <p>Torre de la Sal se localiza en el poblado marítimo de Torre la Sal, en las inmediaciones del <strong>Parque Natural del Prat de Cabanes-Torreblanca.</strong></p> <p>La Torre de la Sal es una torre defensiva de la costa castellonense, construida entre los siglos XIV y XVI. Junto a ésta, en la <strong>Ribera de Cabanes</strong> (Castellón) existe una red de torres fortificadas: la Torre del Carmen, la Iglesia Fortificada de Albalat y las torres dels Gats y Carmelet. Estas cinco torres son un aliciente cultural y turístico, señalizadas desde Cabanes en la Ruta de las Torres.</p> <p> </p> <ul> <li>Nube de puntos georreferenciada 23 Millones Ptos <strong>Ref</strong>: <a href="https://zenodo.org/api/files/253a316b-aad0-495d-8a54-1afe8bc1b46a/2020TorreLaSalNPD23M.zip">2020TorreLaSalNPD23M.zip </a></li> <li>Modelo digital de elevaciones 2 mm. pixel <strong> Ref</strong>: <a href="https://zenodo.org/api/files/253a316b-aad0-495d-8a54-1afe8bc1b46a/2020TorreDeLaSalDEM2mm.7z">2020TorreDeLaSalDEM2mm.7z </a></li> <li>Modelo 3D Georreferenciado 46 Millones de polígonos <strong>Ref</strong>:<a href="https://zenodo.org/api/files/253a316b-aad0-495d-8a54-1afe8bc1b46a/2020TorreDeLaSalDEM2mm.7z">2020TorreDeLaSalDEM2mm.7z </a></li> </ul> <p> </p> <p>Más información: <a href="https://www.castellonarqueologico.es/yacimientos/la-plana-i/torre-de-la-sal/">Español</a> - <a href="https://www.castellonarqueologico.es/va/yacimientos/la-plana-i/torre-de-la-sal/">Valencià</a> - <a href="https://www.castellonarqueologico.es/en/yacimientos/la-plana-i/torre-de-la-sal/">English</a></p> <p> </p> <p><a href="https://www.castellonarqueologico.es/yacimientos/la-plana-i/torre-de-la-sal/3d.html">Modelo 3D</a> │Sketchfab - Online</p> <p><a href="https://www.pointbox.xyz/clouds/5f6315c09cad5d2317eeacc1">Nube de Puntos</a> (Point Cloud) │ Point Box -Online</p> <p> </p> <p>Sistema de referencia geodésico:.<strong>ETRS 89 UTM 31 (EPSG 25831)</strong></p> <p>Altitudes Ortométricas referidas al nivel medio del mar de Alicante.</p> <p>Modelo de Geóide <strong>EGM08</strong> con sobrecorreción de nivelación.</p>
STIS 1d and 2d optical/near-UV spectra of Orion targets from ULLYSES
<p>STIS 1d and 2d optical/near-UV spectra of Orion targets from ULLYSES in November-December 2020. Low-resolution spectra from 1700-10000 A. All reductions and spectral extractions were automated. Data downloaded from the MAST archive and renamed for the source name.</p> <p>CVSO 165: close binary, so extraction uncertain. Third star also in image.<br> CVSO 104: binary in image, not sure which component extracted<br> CVSO 109: close-ish faint companion in image</p>
CAM4 Homogenous Radiation 2D Fields
<p>Single-level fields from the homogenous radiation simulations included in section 6 of the JGR manuscript Needham and Randall (2021). The simulation referred to as "Control" in the manuscript has the case name "QPC4_300k_radavg-ctrl" in this repository and includes no changes from the standard CAM4 physics. The simulation refered to as "RadAvg" in the manuscript has the case name "QPC4_300k_radavg-hmg" in this repository. The only change from Control for this case is that the GCM temperature is updated using the globally averaged net radiative heating rate.</p>
Supplementary Material: Computational Study of Quasi-2D Liquid State in Free Standing Platinum, Silver, Gold, and Copper Monolayers
<p>Supplementary files for <em>Condensed Matter</em> <strong>2016</strong>, <em>1</em>(1), 1; doi:10.3390/condmat1010001; http://www.mdpi.com/ 2410-3896/1/1/1.</p> <p>Captions:</p> <p><strong>Video S1.</strong> (Pt 2400 K 5 ps) 5 ps Molecular Dynamics Movie of Pt Freestanding Monolayer at 2400 K. </p> <p><strong>Video S2.</strong> (Ag 1050K 6 ps) 6 ps Molecular Dynamics Movie of Ag Freestanding Monolayer at 1050 K.<br /> <br /> <strong>Video S3.</strong> (Au 1600K 4ps) 4 ps Molecular Dynamics Movie of Au Freestanding Monolayer at 1600 K.<br /> <br /> <strong>Video S4.</strong> (Cu 1400K 3ps) 3 ps Molecular Dynamics Movie of Cu Freestanding Monolayer at 1400 K. </p>
Generalized Approximate Message Passing Practical 2D Phase Transition Simulations Dataset
<p>This deposition contains the results from a simulation of phase transitions for various practical 2D problem suites when using the Generalised Approximate Message Passing (GAMP) reconstruction algorithm.</p> <p>The deposition consists of:</p> <ol> <li>Five HDF5 databases containing the results from the phase transition simulations (<em>gamp_practical_2d_phase_transitions_ID_[0-4]_of_5.hdf5</em>).</li> <li>The Python script which was used to create the databases (<em>gamp_practical_2d_phase_transitions.py</em>).</li> <li>A Python module with tools needed to run the simulations (<em>gamp_pt_tools.py</em>).</li> <li>MD5 and SHA256 checksums of the databases and Python scripts (<em>gamp_practical_2d_phase_transitions.MD5SUMS / gamp_practical_2d_phase_transitions.SHA256SUMS</em>).</li> </ol> <p>The HDF5 databases are licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python scripts are licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .</p> <p><strong>The files are provided as-is with no warranty as detailed in the above mentioned licenses.</strong></p>
Figure 2d. from: Three new species of Ametadoria Townsend (Diptera: Tachinidae) from Area de Conservación Guanacaste, Costa Rica - Biodiversity Data Journal 3: e5039 (10 August 2015) https://doi.org/10.3897/BDJ.3.e5039
Figure 2d. - Ametadoriakarolramosaesp. nov.; a-c: holotype male; d-f: paratype female (DHJPAR0048666)Figure 2a.Habitus, dorsalFigure 2b.Habitus, lateralFigure 2c.Head, frontalFigure 2d.Habitus, dorsalFigure 2e.Habitus, lateralFigure 2f.Head, frontal <br> Habitus, dorsal
Figure 2d. from: A new species and new records of Molophilus Curtis, 1833 (Diptera: Limoniidae) from the Western Palaearctic Region - Biodiversity Data Journal 3: e5466 (21 August 2015) https://doi.org/10.3897/BDJ.3.e5466
Figure 2d. - Molophilus variispinus Stary, 1971 Figure 2a. male habitus Figure 2b. male hypopygium, ventral (tergal) view Figure 2c. male hypopygium, ventral (tergal) view Figure 2d. aedeagal complex, lateral view <br> aedeagal complex, lateral view
Figure 2d. from: Two new species of Scymnini (Coleoptera: Coccinellidae) from Karnataka, India - Biodiversity Data Journal 3: e5296 (22 June 2015) https://doi.org/10.3897/BDJ.3.e5296
Figure 2d. - Diagnostic characters of Scymnus (Pullus) rajeshwariae sp. n.Figure 2a.Prosternal processFigure 2b.Tarsal claw in female (left) and male (right)Figure 2c.Abdominal postcoxal lineFigure 2d.Male genitalia: Tegmen, lateral viewFigure 2e.Male genitalia: Tegmen, ventral viewFigure 2f.Male genitalia: Penis <br> Male genitalia: Tegmen, lateral view
Dataset of the scientific paper "A Comparative Analysis of 2D and 3D Tasks for Virtual Reality Therapies Based on Robotic-Assisted Neurorehabilitation for Post-stroke Patients" (Front. Aging Neurosci.)
<p> There are three files with the following information:<br> - data_2d.bin, binary file with information of the different parameters of the nine subjects during 2d tasks<br> - data_3d.bin, binary file with information of the different parameters of the nine subjects during 3d tasks<br> - survey.bin, binary file with the score of the System Usability Scale (SUS) survey of each subject</p>
Generalized Approximate Message Passing Practical 2D Phase Transition Simulations Dataset 2
<p>This deposition contains the results from a simulation of phase transitions for various practical 2D and 3D problem suites when using the Generalised Approximate Message Passing (GAMP) reconstruction algorithm.</p> <p>The deposition consists of:</p> <ol> <li>Five HDF5 databases containing the results from the phase transition simulations (<em>gamp_practical_2d_phase_transitions_ID_[0-4]_of_5.hdf5</em>).</li> <li>The Python script which was used to create the databases (<em>gamp_practical_2d_phase_transitions.py</em>).</li> <li>A Python module with tools needed to run the simulations (<em>gamp_pt_tools.py</em>).</li> <li>MD5 and SHA256 checksums of the databases and Python scripts (<em>gamp_practical_2d_phase_transitions.MD5SUMS / gamp_practical_2d_phase_transitions.SHA256SUMS</em>).</li> </ol> <p>The HDF5 databases are licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python scripts are licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .</p> <p><strong>The files are provided as-is with no warranty as detailed in the above mentioned licenses.</strong></p>
2D Cartesian MR raw data
<p>2D Cartesian acquisition of the brain including raw MR data in ISMRMRD format and image data in dicom and nifti format.</p>
2D Halide Perovskite (PEA2PbBr4, PEA2PbI4) CL and EDX dataset
<p>1) Dataset of hyperspectral cathodoluminescence (CL) maps for three samples: PEA2PbBr4, PEA2PbI4 and lateral heterostructures PEA2PbBr4-I4.</p><p>Hyperspectral data is stored in the <a href="http://hyperspy.org/hyperspy-doc/current/user_guide/io.html#hspy-format">"hspy"</a> HyperSpy HDF5 specification, and can be loaded and analysed using Python (see <a href="http://hyperspy.org/hyperspy-doc/current/index.html">HyperSpy documentation</a>). Each hspy file contains comprehensive measurement metadata accessible in the "original_metadata" attribute in Python.</p><p>2) Dataset of hyperspectral energy-dispersive X-ray (EDX) spectroscopy for PEA2PbBr4-I4. Also in "hspy" format.</p><p> </p>
Large Spots DeepMIB project, synthetic dataset for testing 2D semantic segmentation
<p>A complete DeepMIB project with a synthetic dataset generated for quick tests of semantic segmentation approaches.<br>The dataset includes a trained DeepLabV3-Resnet18 network for detection of large spots on a black background. </p><p>The network can be opened by loading "2D_LargeSpots_2cl_DeepLabV3.mibCfg" file by</p><ul><li><i>MIB->Menu->Tools->Deep learning segmentation->Options tab->Config files->Load </i></li><li>Drag and drop of the config file into DeepMIB window</li></ul><p>Microscopy Image Browser: <a href="https://mib.helsinki.fi">https://mib.helsinki.fi</a></p>
Data and Software for "Determining the orientation of a magnetic reconnection X line and implications for a 2D coordinate system"
<p>Supporting information for "Determining the orientation of a magnetic reconnection X line and implications for a 2D coordinate system", by Denton et al. Includes a copy of the paper and previous relevant papers, the simulation data used in the paper, and the reconstruction code used in the paper. See the readme files.</p>
Hyperspectral photoluminescence and reflectance microscopy of 2D materials
<h2>Description of Uploaded Raw Data and Programs for Recreating Figures</h2><h3>Raw Data</h3><p>The raw data in this dataset is primarily in ".sif" binary format, which is used in the creation of Figures 2, 3, 4, and Supplementary Information (SI) Figure 2 in the paper. The ".sif" files contain spectrum data. The data for Figure 3 also includes focal data provided as .png and intensity line-cuts in .csv files.</p><p>A Python program, "load_sif.py", is included in the dataset to read and process these ".sif" files.</p><h3>Software and Programs</h3><p>The figures in the paper were generated using Python programs, which are included in the dataset. These programs are:</p><p>for Figure 2:<i> RClf_calibration.py </i></p><p>for Figure 3: <i>knife_edge_measurement.py </i>and <i>plot_intensity_profile_imageJ.py </i></p><p>for Figure 4 as well as SI Figure 1: <i>PL_linefocus_2color.py, PL_fit_image.py, PL_line_fit.py, RC_linefocus_2color.py </i>and<i> RC_line.py </i></p><p>for SI Figure 2: <i>BG_spectum_PL.py </i>and<i> Ref_spectum_RC.py </i></p><h3>Steps to Recreate Figures</h3><p>Download the zipped folder for each figure. The Python programs are using the ".sif", ".png", and ".csv" files from the downloaded folder.</p><p>Please ensure you have the appropriate software to run these Python programs and handle the provided file formats.</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>
Abb. 2d-2f in Ergänzende Daten zur Noctuiden-Fauna der Kapverden (Cabo Verde) (Lepidoptera, Nolidae, Erebidae, Noctuidae)
Abb. 2d-2f: (2d) Fogo. Blick vom Kraterrand in die Caldera; (2e) Maio. Blick vom Kalkgipfel des Penoso, mit 436 m die höchste Erhebung der Insel; (2f) Maio. Die Praia Preta nahe der Inselhauptstadt Vila do Maio mit einem dichten Teppich von Mittagsblumen (Mesobryanthemum).
ScienceDex guides
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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.