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994 results for “2d”
Datasets for "A 2D Kaleidoscope of Electron Heat Fluxes Driven by Auroral Electron Precipitation"
<p>These three datasets are supplemental material for the Geophysical Research Letters article, A 2D Kaleidoscope of Electron Heat Fluxes Driven by Auroral Electron Precipitation.</p> <p> </p> <p>Data Set DS1. Te data plotted in Figure S1. This is the 3-beam averaged Te data described in Text S1. The first row is the heading that, after Time, lists the altitudes in meters of the data in each column. The first column is time in the format: Year-Month-Day/Hour-Minute-Second.</p> <p> </p> <p>Data Set DS2. Te data errors plotted in Figure S1. This is the 3-beam averaged Te data propagated errors described in Text S1. The first row is the heading that, after Time, lists the altitudes in meters of the data in each column. The first column is time in the format: Year-Month-Day/Hour-Minute-Second.</p> <p> </p> <p>Data Set DS3. THEMIS ASI data at the PFISR location used to calculate the heat flux plotted in Figure S1. The first row is the header. Time, the first column, is in the format: Year-Month-Day/Hour-Minute-Second. The following columns are: geographic longitude, geographic latitude, energy flux for a Gaussian distribution, mean energy for a Gaussian distribution, energy flux for a Maxwellian distribution, mean energy for a Maxwellian distribution. Units are included in the header.</p>
Figures 2a–2d in Report of thanatosis in the Central American scorpions Tityus ocelote and Ananteris platnicki (Scorpiones Buthidae)
Figures 2a–2d. Tonic immobility behavior in Tityus ocelote from Pueblo Nuevo de SarapiquÍ (2a) and Ananteris platnicki (2c) under laboratorycontrolled conditions in contrast to their normal behavior for T. ocelote (2b) and A. platnicki (2d).
Research data supporting "Self-assembled 2D free-standing Janus nanosheets with single-layer thickness"
<p>Raw research data supporting the publication Lin Y. et al., JACS, 2017, DOI: http://dx.doi.org/10.1021/jacs.7b06591</p>
Vibrational coherences in half-broadband 2D electronic spectroscopy: spectral filtering to identify excited state displacements
<p>All data presented in the figures of "Vibrational coherences in half-broadband 2D electronic spectroscopy: spectral filtering to identify excited state displacements".</p>
Fig. 8. 2D in Reconstructed masticatory biomechanics of Peligrotherium tropicalis, a non-therian mammal from the Paleocene of Argentina
Fig. 8. 2D histograms showing distribution of estimated force magnitudes for Group 1 (G1) and Group 2 (G2) muscle recruitment scenarios (on ordinate), as a function of mesiodistal location (MDL; on abscissa). A. Canis familiaris. B. Crocuta crocuta. C. Diceros bicornis. D. Didephis marsupialis. E. Equus quagga. F. Erinaceus europaeus. G. Procyon lotor. H. Puma concolor. I. Sus scrofa. J. Tayassu pecari. K. Tupaia sp. L. Ursus arctos. Abbreviations: BF, bite force; JF-W/B, working-/balancing-side joint force.
Fig. 9. 2D in Reconstructed masticatory biomechanics of Peligrotherium tropicalis, a non-therian mammal from the Paleocene of Argentina
Fig. 9. 2D histograms showing distribution of estimated force magnitudes for closed gape (CG) and open gape (OG) mandible positions (on ordinate), as a function of mesiodistal location (MDL; on abscissa). A. Canis familiaris. B. Crocuta crocuta. C. Diceros bicornis. D. Didephis marsupialis. E. Equus quagga. F. Erinaceus europaeus. G. Procyon lotor. H. Puma concolor. I. Sus scrofa. J. Tayassu pecari. K. Tupaia sp. L. Ursus arctos. Abbreviations: BF, bite force; JF-W/B, working-/balancing-side joint force.
Fig. 3. Spatial 2D in Nir Raman Scattering For The Study Of Biochemical Features Of The Human Skin Epidermis And A Skin Surface Micro-Mapping In Vitro
Fig. 3. Spatial 2D image of the human epidermis surface: A) an optical image; B) mapping scheme; C) micro–Raman signal intensity map.
The Impact of Oxygen Surface Coverage and Carbidic Carbon on the Activity and Selectivity of Two-Dimensional Molybdenum Carbide (2D-Mo2C) in Fischer–Tropsch Synthesis
<p>Datasets categorized per figure and contain data in x,y format.</p> <p>for the DFT part:</p> <p>35 elementary steps were studied. Each step is marked RX_NEB_InitialState_FinalState, and corresponds to the neb calculation for the identification of the transition state. In each file, POSCAR_00 corresponds to the initial structure and POSCAR_09 to the final structure, in both cases after geometry optimization. In each state, a file with the vibration calculation for the calculation of the Gibbs Energy is included. </p> <p>The Gibbs energies of the initial, transition and final states in table format are provided in <em>Figure 4 - panel b - Gibbs Energies_Initial_Transition_Final_states_35_elementary_reactions</em></p> <p>The calculation of Gibbs energies of the gas phase molecules in the empty unit cell, used as reference states are provided in <em>Figure 4 - Reference_state_Gases_empty_unit_cell</em></p> <p>The computations for the comparison of the different sites (HMo, Hc, atop and bridge) are provided in <em>Table S6 - Comparison_adsorption_sites</em></p> <p>The computations on the model with a partial oxygen coverage (O.67 O ML) are provided in:</p> <p><em>Figure S29 - mo2c-ctx-3x3_067OML_C_CH_CCH</em></p> <p><em>Figure S29 - mo2c-ctx-3x3_067OML_CH3_H_CH4</em></p> <p><em>Figure S29 - mo2c-ctx-3x3_067OML_CO_C_O</em></p> <p><em>Figure S29 - mo2c-ctx-3x3_067OML_CO_O_CO2</em></p>
Fig. 1. 2D in Proteomic profile of Ortleppascaris sp.: A helminth parasite of Rhinella marina in the Amazonian region
Fig. 1. 2D gel containing the somatic extract of Ortleppascaris sp. larvae. See Table 1 for details.
Combination of 1D, 2D, and 3D molecular descriptors for all the dopant-free HTMs without considering the variability introduced by fabrication methods on PSC efficiency
<p>1D, 2D, and 3D molecular descriptors for all the dopant-free HTMs <strong><span>without considering the variability introduced by fabrication methods on PSC efficiency </span></strong>along with their photovoltaic properties (PCE, JSC, and VOC)</p>
Calculated 1D, and 2D molecular descriptors for the dopant-free HTMs without PSCs classification
<p><strong><span>The calculated 1D, and 2D descriptors for the dopant-free HTMs (Without Classification of PSCs) as well as their photovoltaic properties. </span></strong></p> <p><strong><span>All 1D, and 2D descriptors were calculated using the </span></strong><strong><span><span>academically free PADEL online tool (http://www.scbdd.com/padel_desc/index/)</span></span></strong></p>
2D electrical impedance tomography dataset
<p>This is an open access electrical impedance tomography (EIT) dataset. The EIT measurements were collected from a circular body (a flat tank filled with saline) with various choices of conductive and resistive inclusions.</p> <p>The dataset consists of</p> <ul> <li>current patterns and voltage measurements of a circular tank containing different targets (archive file data_mat_files.zip containing 38 MATLAB Data files),</li> <li>photos of the tank and targets (archive file target_photos.zip containing 38 JPEG files), and</li> <li>MATLAB code for reading the data (MATLAB code file LoadData.m).</li> </ul> <p>Documentation of the dataset is available at <a href="https://arxiv.org/abs/1704.01178">arxiv.org/abs/1704.01178</a>. See also <a href="https://www.fips.fi/EIT_dataset.php">www.fips.fi/EIT_dataset.php</a>.</p> <p>The data was collected at the University of Eastern Finland.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 9. Room model generated with Autodesk 123D Catch - the 2D model
<p>Structure from motion was used for rapid modeling of a small room with all its objects. Two files were generated, a Wavefront obj and mtl (corresponding to the texture). The 3D model was post-processed with MeshLab, during which several filters were applied to clean up the model. The mesh model was also connected with the scanned model, by choosing at least 4 connection points. The 2D and 3D results are shown in Figures 9, 10. A post-processing could also be performed using the Autodesk 123D Catch web application.</p>
2D Peridynamic Displacement Fields
<p>These are databases of Peridynamic solutions for two sets of problems:</p> <p>1) Three constant strain modes (results.db)</p> <p>2) A thin pressurized crack (lfm_data.db)</p> <p>The databases contain multiple combinations of force formulations, influence functions, support radii, stabilization parameters, and smoothing parameters. Each combination is solved enough times to determine grid convergence. </p> <p>Additionally, there is a database of finite element solutions on the thin pressurized crack to serve as comparisons for accuracy and runtime (femdata.db). </p> <p>The files are SQLite3 databases written using the builtin Python library.</p> <p>See PeriFlakes: https://github.com/afqueiruga/periflakes</p>
Data for the publication "A polarization-induced 2D hole gas in undoped gallium nitride quantum wells"
<p>This upload contains the data presented in the manuscript "A polarization-induced 2D hole gas in undoped gallium nitride quantum wells", which reports the observation of a high conductivity 2DHG in an epitaxially grown GaN/AlN heterostructure. This upload contains 4 files :</p> <ul> <li>1_tabulated_raw_data.xlsx : Both the raw experimental data along with the simulation results used in the Figures 1A, 2B, 3, 4, S2 in the paper. The data is encapsulated in a Excel file, with separate sheets used for each figure.</li> <li>2_nextnano_GaN_AlN_band_diagram.in : Input file for nextnano software (<a href="https://www.nextnano.de/">https://www.nextnano.de/</a>) for generating the band diagrams in the paper.</li> <li>3_README_XRD_sim.txt : Readme file with instructions for the XRD simulation for Fig 2</li> <li>4_XRD_sim_run.py : Python script for running the XRD simulation</li> </ul>
Density-pressure isotherms of the 2D Lennard Jones fluid between the triple point temperature and the critical temperature
<p>Pressure-density isotherms of the 2D truncated-shifted Lennard-Jones fluid, with <span class="math-tex">\(r_{c} = 2.5 \sigma\)</span>:</p> <p><span class="math-tex">\(V\left(r\right) = \begin{cases} U\left(r\right) - U\left(r_{c}\right) & \text{if } 0 < r < r_{c}\\ 0 & \text{if } r \geq r_{c} \end{cases}\)</span> with <span class="math-tex">\(U\left(r\right) = 4 \varepsilon \left(\left(\frac{\sigma}{r}\right)^{12}-\left(\frac{\sigma}{r}\right)^{6}\right)\)</span></p> <p>All thermodynamic quantities are reduced with respect to the Lennard-Jones parameters <span class="math-tex">\(\sigma\)</span> and <span class="math-tex">\(\epsilon\)</span> :</p> <ul> <li>Number 2D density <span class="math-tex">\(\rho^{*} = \sigma^{2}\rho\)</span></li> <li>2D pressure <span class="math-tex">\(P^{*} = \frac{\sigma^{2}}{\varepsilon}P\)</span></li> <li>Temperature <span class="math-tex">\(T^{*} = \frac{k_{B} T}{\varepsilon}\)</span></li> </ul> <p>The temperatures of the isotherms are <span class="math-tex">\(T^{*} = 0.40\)</span>, <span class="math-tex">\(T^{*} = 0.41\)</span>, <span class="math-tex">\(T^{*} = 0.42\)</span>, <span class="math-tex">\(T^{*} = 0.43\)</span>, and <span class="math-tex">\(T^{*} = 0.44\)</span> which corresponds to the range of liquid-gas coexistence, between the triple point temperature (<span class="math-tex">\(T_{t}^{*} \approx 0.40\)</span>) and the critical temperature (<span class="math-tex">\(T_{c}^{*} \approx 0.46\)</span>). Here are reported the isotherms for the gas and liquid phases and the coexistence points.</p> <p>The liquid and gas isotherms are obtained by Molecular Dynamics with the LAMMPS software (<a href="https://lammps.sandia.gov/">https://lammps.sandia.gov/</a>). The density and temperature are imposed (Langevin thermostat) and the pressure is computed with the virial estimate. The simulations are performed for 2D systems of dimensions <span class="math-tex">\(L_{x} = 44.9 \sigma\)</span> and <span class="math-tex">\(L_{y} = 46.7 \sigma\)</span> containing between 1300 and 1900 particles in the liquid phase, and 4 to 200 particles in the gas phase. The systems are equilibrated over <span class="math-tex">\(3 \cdot 10^{7}\)</span> times steps. Then, the computation of the thermodynamic properties is performed over a variable number of time steps in order to reach a targeted accuracy (standard deviation of the pressure). The longest simulations (liquid approaching cavitation) require about <span class="math-tex">\(10^{9}\)</span> time steps of computation. The block averaging method is used to estimate the standard deviation of pressure. The data are provided in csv files named as follows : 'liq_TX.XX.txt' for the liquid at temperature <span class="math-tex">\(T^{*} = X.XX\)</span>, and 'gas_TX.XX.txt' for the gas at temperature <span class="math-tex">\(T^{*} = X.XX\)</span>. The first column is the inverse number density <span class="math-tex">\(1/\rho^{*}\)</span>, the second column is the pressure <span class="math-tex">\(P^{*}\)</span>, and the last column is the standard deviation of the pressure <span class="math-tex">\(\Delta P^{*}\)</span>.</p> <p>The coexistence points are obtained by Gibbs ensemble Monte Carlo with an in house code. The temperature is imposed and the liquid and gas densities and the coexistence pressure (virial estimate) are computed. The csv file 'coexistence.txt' contains the coexistence data in the following order: the first column is the temperature <span class="math-tex">\(T^{*}\)</span>, the second and third columns are the average and standard deviation of the inverse of the gas number density <span class="math-tex">\(1/\rho_{gas}^{*}\)</span> and <span class="math-tex">\(\Delta\left(1/\rho_{gas}^{*}\right)\)</span>, the forth and fifth columns are the average and standard deviation of the inverse of the liquid number density <span class="math-tex">\(1/\rho_{liq}^{*}\)</span> and <span class="math-tex">\(\Delta\left(1/\rho_{liq}^{*}\right)\)</span>, and the sixth and seventh columns are the average and standard deviation of the coexistence pressure <span class="math-tex">\(P^{*}\)</span> and <span class="math-tex">\(\Delta P^{*}\)</span>.</p> <p>The files 'chart_gas.pdf' and 'chart_liq.pdf' provide graphical display of the data, for the gas and liquid phases respectively.</p>
2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy
<p><strong>General</strong></p> <p>This dataset consists out of multiple Second Harmonic Generation (SHG) microscopy scans of collagen fibers in mice bones. Some mices are diseased with osteogenesis imperfecta (brittle bone).</p> <p>We used this data to investigate the segmentation of uncertain local collagen fiber orientations. The corresponding paper "2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy" is accepted at GCPR 2019.</p> <p><strong>Abstract</strong></p> <p>Collagen fiber orientations in bones, visible with Second Harmonic Generation (SHG) microscopy, represent the inner structure and its alteration due to influences like cancer. While analyses of these orientations are valuable for medical research, it is not feasible to analyze the needed large amounts of local orientations manually. Since we have uncertain borders for these local orientations only rough regions can be segmented instead of a pixel-wise segmentation. We analyze the effect of these uncertain borders on human performance by a user study. Furthermore, we compare a variety of 2D and 3D methods such as classical approaches like Fourier analysis with state-of-the-art deep neural networks for the classification of local fiber orientations. We present a general way to use pretrained 2D weights in 3D neural networks, such as Inception-ResNet-3D a 3D extension of Inception-ResNet-v2. In a 10 fold cross-validation our two stage segmentation based on Inception-ResNet-3D and transferred 2D ImageNet weights achieves a human comparable accuracy.</p> <p><strong>Links</strong></p> <p>A preprint of the paper is available at <a href="https://arxiv.org/abs/1907.12868">https://arxiv.org/abs/1907.12868</a>.</p> <p>The final publication is available at Springer via <a href="https://doi.org/10.1007/978-3-030-33676-9_26">https://doi.org/10.1007/978-3-030-33676-9_26</a></p> <p>The source code is available at <a href="https://github.com/Emprime/uncertain-fiber-segmentation">https://github.com/Emprime/uncertain-fiber-segmentation</a>.</p> <p><strong>Data description</strong></p> <p>Please read the accompanying paper for more information about the dataset. Please see the source code for more information about the usage of the data.</p> <ul> <li>shg-ce-de: contains the enhanced and denoised scans as image slices, the scans are sorted by mice (wt wildtyp, het ill mice), scan location and individual scan</li> <li>shg-masks: contains the ground truth masks for the three different classes (similar - Green, dissimilar - Red, not of interest - blue)</li> <li>shg-featues: contains the input and gt for the second stage of the proposed two stage segmentation</li> <li>shg-cross-splits: contains the 10 random splits for the 10 fold cross validation</li> <li>logs-prediction: contains the 10 tensorboard logs, weights and predictions for the 10 fold cross validations</li> </ul>
2D Dynamic Golden radial MR raw data
<p>Raw MR data set in ISMRMRD format of a 2D Golden radial acquisition of a T1MES phantom. Data acquisition is carried out continuously for 2s after a single inversion pulse. The inversion pulses make the data acquisition sensitive to T1 and allows for T1 mapping. The k-space trajectory is saved in the file based on ISMRMRD standards.</p>
Fig. 2. Landmarks and semilandmarks designated using tpsDIG for 2D in Ontogenetic changes in the craniomandibular skeleton of the abelisaurid dinosaur Majungasaurus crenatissimus from the Late Cretaceous of Madagascar
Fig. 2. Landmarks and semilandmarks designated using tpsDIG for 2D data (A) and landmark for 3D data (B). Landmark configurations aligned using generalized procrustes analysis, removing the effects of size, orientation, and position (C, D). Ontogenetic shape change visualized by generating deformation grids for 2D data (E) and warped meshes for 3D data (F).
Programmable nonlinear optical neuromorphic computing with bare 2D material MoS2
<p>This data set contains all resources for the research project "<span>Programmable nonlinear optical neuromorphic computing with bare 2D material MoS2" (published in Nature Communications (2024)).</span></p>
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.