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3,688 results for “Computer”
Negative-time FTLE in the Gulf Stream computed from SSALTO/DUACS
<p>ASCII data set of negative-time finite-time Lyapunov exponent (FTLE) in the Gulf Stream calculated with velocity fields from Segment-Sol multi-missions d’ALTimétrie, Orbitographie et localisation précise/Data Unification and Altimeter Combination System (SSALTO/DUACS) absolute dynamic topography and absolute geostrophic velocity products.</p> <p>There are 7961 data sets: one per day from 7 January 1993 until 17 October 2014. Calculation of the FTLE is described in “Gulf Stream transport and mixing processes via coherent structure dynamics” by Yi Liu et al., submitted to JGR Oceans, 2017. The data can be uncompressed with standard tar commands.</p> <p>Data dimension in x and y are given in the first two lines of each set (1200x600), but are the same for each set. The grid coordinates are not included in the data set, but can be easily recreated knowing that the longitudes (x-direction) range from -89.875 (degree) to -30 (degree), and the latitudes (y-direction) range from 25.125 (degree) to 55 (degree). The data were written in a nested loop, with the y-dimension inside the x-dimension loop.</p>
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH. in Deep learning brings speed, accuracy to the life sciences.
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH.
Kaisa Helminen is CEO of Fimmic Oy, a Finnish company that created the first commercial tool integrating deep learning and computer vision for pathology research. Photograph: Sebastian Mardones / Health Capital Helsinki. in Deep learning brings speed, accuracy to the life sciences.
Kaisa Helminen is CEO of Fimmic Oy, a Finnish company that created the first commercial tool integrating deep learning and computer vision for pathology research. Photograph: Sebastian Mardones / Health Capital Helsinki.
Quantum Optimization for the Maximum Cut Problem on a Superconducting Quantum Computer
<p>The dataset includes maximum cut problem instances on 3-regular graphs, the cut number returned by the experimental runs of the greedy-enhanced quantum relax-and-round algorithm, and the estimated optimal cut number used for computing the approximation ratio from the paper <em>Benchmarking Quantum Optimization for the Maximum-Cut Problem on a Superconducting Quantum Computer</em> [<a href="https://arxiv.org/abs/2404.17579">https://arxiv.org/abs/2404.17579</a>]</p>
Enhancing Facial Emotion Recognition: A Comparative Analysis of Sobel and Laplacian Filters for Computer Vision Applications
<p><span>This project explores the efficacy of integrating Sobel and Laplacian filters to enhance the performance of Convolutional Neural Network (CNN) models for facial emotion recognition. The project was part of our final Mtech in Data Science thesis at the Institute of Defence Institute of Advanced Technology, Pune. The FER2013 dataset was utilized for the research.</span></p>
Experimental and Computational Evaluation of Lipidomic In-Source Fragmentation as a Result of Post-Ionization with Matrix-Assisted Laser Desorption/Ionization
<p>Supporting data for the manuscript 'Experimental and Computational Evaluation of Lipidomic In-Source Fragmentation as a Result of Post-Ionization with Matrix-Assisted Laser Desorption/Ionization.'</p>
SHEET project - Unibo Computer Vision Final Repository
<p> </p> <p>This version<strong> fixes the previous one, in which there were missing models weights</strong></p> <p><strong>More information regarding SHEET Project activity carried out from the UniBo group can be found at the <a href="https://github.com/ECOPOM/SHEET_project_repo">dedicated GitHub repository</a></strong>.</p> <p> </p>
Computational Strategies for Designing Bidentate Hypervalent Iodine Catalysts in Halogen Bond-Mediated Organocatalysis
<p>Computational Strategies for Designing Bidentate Hypervalent Iodine Catalysts in Halogen Bond-Mediated Organocatalysis</p> <p>Email: cristina.trujillodelvalle@manchester.ac.uk</p> <p> </p> <p>Please find the supporting information for the "Computational Strategies for Designing Bidentate Hypervalent Iodine Catalysts in Halogen Bond-Mediated Organocatalysis" paper here.</p>
Dataset I related to publication: Beyond KRAS(G12C): biochemical and computational characterization of sotorasib and adagrasib binding specificity and the critical role of H95 and Y96
<p>MD simulation data (sotorasib and adagrasib in complex with NRAS(WT) and KRAS(WT)) related to publication: "Beyond KRAS(G12C): biochemical and computational characterization of sotorasib and adagrasib binding specificity and the critical role of H95 and Y96". </p> <p>ACS Chem. Biol. 2024, XXXX, XXX, XXX-XXX</p> <p>https://pubs.acs.org/doi/10.1021/acschembio.4c00315</p> <ul> <li>The .zip files contain raw Desmond simulation trajectories of KRAS(WT) or NRAS(WT) in complex with adagrasib or sotorasib [5 replicas; each 5 us] <ul> <li>(-out.cms files and trajectories).</li> </ul> </li> </ul> <p>Related datasets: 10.5281/zenodo.10781452</p>
Dataset II related to publication: Beyond KRAS(G12C): biochemical and computational characterization of sotorasib and adagrasib binding specificity and the critical role of H95 and Y96
<p>MD simulation data (KRAS(G12C) and KRAS(G12C/Y96D)) related to publication: "Beyond KRAS(G12C): biochemical and computational characterization of sotorasib and adagrasib binding specificity and the critical role of H95 and Y96". </p> <p>ACS Chem. Biol. 2024, XXXX, XXX, XXX-XXX</p> <p>https://pubs.acs.org/doi/10.1021/acschembio.4c00315</p> <ul> <li>The .zip files contain raw Desmond simulation trajectories of KRAS(G12C) and KRAS(G12C/Y96D) with apo SII-P or in complex with sotorasib [5 replicas; each 4 us] <ul> <li>(-out.cms files and trajectories).</li> </ul> </li> </ul> <p>Related datasets: 10.5281/zenodo.10812233 </p>
Computational data for "Measurement of Coherent Vibrational Dynamics with X-ray Transient Absorption Spectroscopy Simultaneously at the Carbon K- and Chlorine L$_{2,3}$- Edges"
<p>Contains:<br><br>1. Code for and results from, time-dependent Schroedinger equation simulations for the molecular normal modes under strong electric fields inducing impulsive stimulated Raman processes.</p> <p>2. Molecular orbitals obtained from ROKS calculations for the C 1s and Cl 2p excited states (with spin-free one-electron X2C). </p>
Pre-computed Full Ankh Embeddings for VIPER
<p>Full Ankh embeddings for VIPER (Virtual Interaction Predictor for Enzyme Reactions)</p>
Rotating single star models for the Galaxy computed with MESA
<p>The model dataset described/employed in the paper "Boron depletion in Galactic early B-type stars reveals two different main sequence star populations" by Harim Jin , Norbert Langer, Daniel J. Lennon , and Charles R. Proffitt (2024).</p> <p> </p> <p><strong>single_star_models</strong></p> <p>A grid of rotating single star models for Z=0.0154 computed with MESA</p> <p>The models are named by their initial masses in log (in solar mass) and initial rotational velocities (in km/s).</p> <p>E.g., 1.080_200 is a 12 Msun with an initial velocity of 200 km/s.</p> <p>Initial masses: 5-40 Msun</p> <p>Initial rotational velocities: 0-600 km/s (or up to break-up)</p> <p> </p> <p><strong>MESA_input_data</strong></p> <p>MESA input data files to reproduce the single star models</p> <p>Works with MESA version of 10398 and MESA SDK version of x86_64-linux-20180822.</p>
Experimental and Computational Study Towards Identifying Active Sites of Supported SnOx Nanoparticles for Electrochemical CO2 Reduction Using Machine-Learned Interatomic Potentials
<p>SnOx has received great attention as an electrocatalyst for CO2 reduction reaction (CO2RR), however, it still suffers from low activity. Moreover, the atomic-level SnOx structure and the nature of the active sites are still ambiguous due to the dynamism of surface structure and difficulty in structure characterization under electrochemical conditions. Herein, we first enhance its CO2RR performance by supporting SnO2 nanoparticles on two common supports, Vulcan Carbon and TiO2 . Then, electrolysis of CO2 at various temperatures in a neutral electrolyte reveals that the application window for this catalyst is between 12 and 30 °C.<br>Furthermore, our study introduces a machine learning interatomic potential method for the atomistic simulation to investigate SnO 2 reduction and establish a correlation between SnO x structures and their CO 2 RR performance. In addition, selectivity is analyzed computationally with density functional theory simulations to identify the key differences between the binding energies of *H and *CO2−, where both are correlated with the presence of oxygen on the nanoparticle surface. This study offers in-depth insights into the rational design and application of SnOx -based electrocatalysts for CO2RR.</p>
Computational Screening of Transition Metal Complexes as Guests in the Ga4L6-12 Nanocage
<p>Optimized geometries, scripts, experimental data curated from the literature from complexes, and CSD filtering data for "Computational Screening of Putative Transition Metal Complexes as Guests in the Ga4L6-12 Nanocage." </p>
Computational Screening of Metalloporphyrins for CO2 Activation
<p><span>Electrocatalytic CO<sub>2</sub> reduction (eCO<sub>2</sub>R) to value-added chemicals offers a promising route for carbon capture and utilization. Metalloporphyrin (M-POR) is a class of catalysts for eCO<sub>2</sub>R that has drawn attention due to its tuneable electronic and structural properties. This work presents a computational screening, based on density functional theory calculations, of one of the key steps in the eCO<sub>2</sub>R: the adsorption of CO<sub>2</sub> on 110 M-PORs with varying peripheral ligands, metal centres, and oxidation states, to understand how these factors can influence CO<sub>2</sub> activation. A set of criteria was used to shortlist M-PORs that activate CO<sub>2</sub> based on their ability to lengthen the C–O bond, bend the O–C–O angle, bind CO<sub>2</sub>, and donate charge from the metal centre of the M-POR to the carbon centre of CO<sub>2</sub>. Based on defined criteria, such as binding energy, C–O bond elongation, O–C–O bending, and charge transfer, 16 systems were selected for their potential to activate CO<sub>2</sub>. These systems predominantly have the electron configuration of the metal centre in the d<sup>6</sup> and d<sup>7</sup> configurations. These systems primarily have the electron configuration of the metal centre in d<sup>6</sup> and d<sup>7</sup> configurations. Natural Bond Orbital analysis revealed the impact of electron-withdrawing groups in the system, which increases orbital splitting and, consequently, lowers the ability of the M-POR to activate CO<sub>2</sub>. Second-order perturbation theory analysis confirms that the presence of electron-donating groups in the ligand structure enhances CO<sub>2</sub> activation. This work demonstrates the interconnected effect of peripheral ligands, metal centers, and oxidation states in M-PORs on their ability to adsorb and activate CO2, thereby establishing structure-activity relationships within M-PORs.</span></p>
scHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis
<p>Analysis code for the paper "scHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis"</p>
Fostering Inclusivity and Equality in Early Childhood Education via Computational Thinking and Robotics: A Systematic Review
<p>Resources for the Systematic Literature Review (SLR) about Computational Thinking and Educational Robotics in Early Childhood Education for fostering equality and inclusion. The SLR is related to the project "COEDUIN-Alfabetización digital y STEAM en edades tempranas: propuesta co-educativa inclusiva" funded by Fundación Caja Canarias and Fundación La Caixa (ref. 2020EDU08).</p> <p>The SLR covers papers in WoS and Scopus from 2011 to 2023.</p>
Dataset: Tracking the south polar seasonal cap retreat of Mars using computer vision
<p>These CSV files contain the ellipse fit parameters used for the analysis in Acharya, P. J., Smith, I. B., & Calvin, W. M. (2024). “Tracking the South Polar Seasonal Cap Retreat of Mars Using Computer Vision.” Icarus. DOI: 10.1016/j.icarus.2024.116104. </p> <p>The SPSC ellipse is derived using a mosaic of MARCI images developed by Calvin, W. M., Cantor, B. A., & James, P. B. (2017). Interannual and seasonal changes in the south seasonal polar cap of Mars: Observations from MY 28-31 using MARCI. <em>Icarus</em>, <em>292</em>, 144-153.. Each mosaic is 1000x1000 pixels with a spatial resolution of 0.072246423 Latitude ° per pixel. </p> <p>The file names follow the convention ##.csv, where ## represents the Mars Year (MY). The following table shows what each variable presents. </p> <table> <tbody> <tr> <td><strong>Variable Name</strong></td> <td><strong>Description [Units]</strong></td> </tr> <tr> <td>Ls </td> <td> <p>Solar Lonigude [°]</p> </td> </tr> <tr> <td>Major Axis</td> <td> <p>Semi-major axis value [Latitude °]</p> </td> </tr> <tr> <td>Minor Axis</td> <td> <p>Semi-minor axis value [Latitude °]</p> </td> </tr> <tr> <td>Average Axis</td> <td> <p>Average axis (See publication for more information) [Laitutde °]</p> </td> </tr> <tr> <td>Major_Angle</td> <td> <p>Rotation of the fitted ellipse from 0E (°)</p> </td> </tr> <tr> <td>Dis_Center</td> <td> <p>Distance between the center of the ellipse and the geographical center [Latitude °]</p> </td> </tr> <tr> <td>Center X</td> <td> <p>X coordinate of the center of the ellipse [Pixels]</p> </td> </tr> <tr> <td>Center Y</td> <td> <p>Y coordinate of the center of the ellipse [Pixels]</p> </td> </tr> <tr> <td>Area</td> <td> <p>Area of the ellipse [Squared kilometers]</p> </td> </tr> <tr> <td>Circle_Radius</td> <td> <p>The radius of the circle of best fit [Latitude °]</p> </td> </tr> <tr> <td>Contour_Area</td> <td> <p>Area of the SPSC [Squared kilometers]</p> </td> </tr> </tbody> </table>
Fault-Tolerant Computing with Single Qudit Encoding in a Molecular Spin. Open data set
<div> <p>Data supporting the original figures 2, 3 and 4 (ESI) of the related manuscript.</p> </div>
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.