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1,670 results for “forcing”
Data deposit accompanying Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields
<p>Dataset accompanying the paper: <em>"Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields"</em>. Contains the training sets curated during active learning as well as .xyz files used for creating the Figures. <br> <br> The paper highlights that the computational efficiency of ML force fields not only results in decreased computational costs for routine catalytic investigations but also facilitates more comprehensive exploration of catalytic pathways.</p> <p><strong>Published in NPJ Computational Materials</strong>: <a href="https://www.nature.com/articles/s41524-023-01124-2">https://www.nature.com/articles/s41524-023-01124-2</a><br> Formerly on Arxiv: <a href="https://arxiv.org/abs/2301.09931">https://arxiv.org/abs/2301.09931</a></p>
ACCESS-OM2 1° resolution global repeat decade full forcing interannual simulation data for 1972-2018
<p>This data set contains the <strong>full forcing</strong> interannual simulation output from the global ocean-sea ice model ACCESS-OM2 in the 1° horizontal configuration over the period 1972-2018.</p> <p>This simulation was branched off from the repeat decade forcing spin-up and alongside the control simulation (see light blue and black lines in Fig. 1c in the publication linked below).</p> <p>The control simulation output can be found here: https://zenodo.org/record/8339578 The output here as well as in the control simulation is saved in sets of ten years (output200, output201, ...) in the ocean/ and ice/ folders as netcdf files.</p> <p>The last output folder contains the data for 2012-2018 with the last four years of this output folder (output204) are again the 1972-1975 period and should be omitted from any analysis.</p> <p>For more information on the spin-up and the model configuration, see the Methods section and Fig. 1 in Huguenin, M.F., Holmes, R.M. & England, M.H. Drivers and distribution of global ocean heat uptake over the last half century. <em>Nat Commun</em> 13, 4921 (2022). https://doi.org/10.1038/s41467-022-32540-51</p> <p> </p>
Dataset for "Droplet collection efficiencies inferred from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions"
<p>This dataset in includes MODIS-CloudSat CFODD reference data, the updated Warm Rain Diagnostics implemented in COSPv2.0, RANSAC regression analysis, and figure production scripts associated with the manuscript “Droplet collection efficiencies estimated from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions”<br> Authors: Beall, Charlotte, M.; Ma, Po-Lun; Christensen, Matthew W.; Mülmenstädt, Johannes; Varble, Adam; Suzuki, Kentaroh; Michibata, Takuro<br> Journal: Atmospheric Chemistry & Physics (submitted, 2023)</p>
Dataset for DOI: 10.1109/LRA.2019.2927936. "Multi-DoF Force Characterization of Soft Actuators"
<p>This dataset contains the raw measurement data and MATLAB scripts for the following publication: S. Joshi and J. Paik, "Multi-DoF Force Characterization of Soft Actuators," in <em>IEEE Robotics and Automation Letters</em>, vol. 4, no. 4, pp. 3679-3686, Oct. 2019. doi: 10.1109/LRA.2019.2927936</p> <p><br> The .csv files contain measured values of soft actuator pressure, displacement and force output. The MATLAB scripts help to extract and plot this raw data.</p>
Forcing vine regrowth in vitis vinifera cv. touriga nacional at Douro region (datasets)
<p>Datasets related to the trials performed during 2018 and 2019 in Douro Superior sub-region on 'Touriga Nacional’ grape, at Quinta do Ataide on Tou, in the course of the VISCA project. This data was used to the prepare the article with the DOI 10.5281/zenodo.3568194 (Forcing vine regrowth in vitis vinifera cv. touriga nacional at Douro region). For each year (2018 and 2019), it includes data on : fertility; harvested quantity; Maturation; ProDrawn leaf water potential quantity.</p> <p>The same data was also used for the article "EFEITO DA INTERVENÇÃO EM VERDE CROP FROCING NA CASTA TOURIGA NACIONAL", DOI 10.5281/zenodo.3686023</p> <p>These datasets are provided as part of the open access policy of VISCA project which is participating to the Open Research Data Pilot (ORDP) initiated in H2020, in particular for data used in scientific publications issued during the lifetime of the project.</p>
CICE gx3 JRA55 Forcing Data - 2020.03.20
<p>This file contains the gx3 JRA55 forcing data for CICE. </p> <p>The latest information about CICE forcing data and files can be found at the GitHub Resource Index: <a href="https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index">https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index</a> under the "Input Data" link.</p> <p>These data are provided by the CICE Consortium (<a href="https://github.com/CICE-Consortium">https://github.com/CICE-Consortium</a>).</p>
CICE gx1 WOA Forcing Data - 2020.03.20
<p>This file contains the gx1 WOA forcing data for CICE. </p> <p>The latest information about CICE forcing data and files can be found at the GitHub Resource Index: <a href="https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index">https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index</a> under the "Input Data" link.</p> <p>These data are provided by the CICE Consortium (<a href="https://github.com/CICE-Consortium">https://github.com/CICE-Consortium</a>).</p>
CICE gx3 NCAR_bulk Forcing Data - 2020.03.20
<p>This file contains the gx3 NCAR bulk forcing data for CICE. </p> <p>The latest information about CICE forcing data and files can be found at the GitHub Resource Index: <a href="https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index">https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index</a> under the "Input Data" link.</p> <p>These data are provided by the CICE Consortium (<a href="https://github.com/CICE-Consortium">https://github.com/CICE-Consortium</a>).</p>
CICE gx3 WW3 Forcing Data - 2020.03.20
<p>This file contains the gx3 Wave Watch 3 forcing files for CICE. </p> <p>The latest information about CICE forcing data and files can be found at the GitHub Resource Index: <a href="https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index">https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index</a> under the "Input Data" link.</p> <p>These data are provided by the CICE Consortium (<a href="https://github.com/CICE-Consortium">https://github.com/CICE-Consortium</a>).</p>
Configuration files for model stations presented in the manuscript "Sensitivity of shelf sea marine ecosystems to temporal resolution meteorological forcing"
<p>This repository contains configuration files for running GOTM-FABM-ERSEM at stations L4 and CCS to produce results presented in the manuscript "Sensitivity of shelf sea marine ecosystems to meteorological forcing" in addition to meteorology files for running the sensitivity analysis presented in the manuscript. Ncfiles containing model results for all scenarios presented in the manuscript are also included within the zip files for both stations</p> <p><br> GOTM code is freely available from: <br> https://github.com/gotm-model/code</p> <p><br> FABM code is freely available from:<br> https://github.com/fabm-model/fabm.git</p> <p><br> ERSEM code is freely available from:</p> <p><a href="https://www.pml.ac.uk/Modelling_at_PML/Access_Code">https://www.pml.ac.uk/Modelling_at_PML/Access_Code</a><br> </p> <p>Instructions for compiling GOTM-FABM-ERSEM can be found in the ERSEM git repository after registering for the code using the link above. </p> <p>Versions/commits for the model code used to create results presented in this manuscript are:</p> <p>GOTM: commit 38e5d5b77adc7b3b5364aed7d7e4921b04b1781f </p> <p>FABM: commit 69da88c87ec59a51d1e2143c1f76111526ed6498 </p> <p>ERSEM: Version 19.04</p> <p> </p> <p> </p>
Pure POPC Membrane Simulation Using Charmm-Drude Force Field with OpenMM
<p>400 ns MD simulation of pure POPC membrane using Charmm-Drude polarizable force field. The system contains 72 POPC lipids and 2809 SWM4 water molecules.</p> <p>The simulation have been performed using OpenMM 7.4.1</p> <p>Before running the Drude simulation, the system has been equilibriated using Charmm36 force field for 200 ns. The last frame of that simulation was used to generate Drude polarizable model. The first 100 ns of the Drude simulation has been discarded from this dataset.</p> <p>This dataset does not contain the water molecules.</p> <p><strong>Please note that</strong> the trajectories might need to be realigned.</p>
Data from: Proximate and ultimate drivers of variation in bite force in the insular lizards Podarcis melisellensis and Podarcis sicula
<p>Bite force is a key performance trait in lizards since biting is involved in many ecologically relevant tasks, including foraging, fighting, and mating. Several factors have been previously suggested to impact bite force in lizards, such as head morphology (proximate factors), or diet, intraspecific competition, and habitat characteristics (ultimate factors). However, these have been generally investigated separately and mostly at the interspecific level. We tested which factors drive variation in bite force at the population level and to what extent. Our study includes 20 populations of two closely-related lacertid species, <i>Podarcis melisellensis </i>and <i>Podarcis sicula</i>, which inhabit islands in the Adriatic. We found that lizards with more forceful bites have relatively wider and taller heads, and consume more hard prey and plant material. Island isolation correlates with bite force, likely by driving the resource availability. Bite force is only poorly explained by proxies of intraspecific competition. The linear distance from a large island and the proportion of difficult-to-reduce food items consumed are the ultimate factors that explain most of the variation in bite force. Our findings suggest that the way in which morphological variation affects bite force is species-specific, likely reflecting the different selective pressures operating on the two species.</p>
stream discharge and forcing data
<p>Files in this dataset list the stream discharge and forcing data for our study "Assessing streamflow sensitivity to precipitation variability in karst-influenced catchments with unclosed water balance". </p>
Understanding context and forces for choosing organizational structures for continuous delivery
<p>Vídeo de fallback para apresentação no WTDSoft 2020 - X Workshop de Teses e Dissertações.</p> <p>Abstract:</p> <p>Nesta pesquisa, pretendemos entender as estruturas organizacionais adotadas por organizações produtoras de software para gerenciar equipes técnicas de TI em um contexto de entrega contı́nua. Seguindo as diretrizes da Grounded Theory, entrevistamos 46 profissionais de TI para investigar como as organizações que buscam a entrega contı́nua organizam suas equipes de desenvolvimento e operações. Entre nossos resultados, descobrimos quatro estruturas organizacionais: (1) departamentos em silos, (2) DevOps clássico, (3) equipes multifuncionais e (4) times de plataforma. Depois de descobrir essas estruturas e suas propriedades, descrevemos, neste artigo, nossos planos para entender melhor quais propriedades e forças contextuais levam uma organização a adotar uma estrutura organizacional em detrimento das demais.</p>
Dataset for Pore-scale investigation of forced imbibition in porous media
<p>Dataset for the manuscript titled" Pore-scale investigation of forced imbibition in porous media"</p>
Dataset From: Oscillatory structural forces between charged interfaces in solutions of oppositely charged polyelectrolytes
<p>The dataset for the publication "Oscillatory structural forces between charged interfaces in solutions of oppositely charged polyelectrolytes". DOI: 10.1039/d0sm01257b.</p> <p>Files containing data have .dat extension and are in text format.</p>
HTTPS Brute-force dataset with extended network flows
<p>We are publishing a dataset we created for designing a brute-force detector of attacks in HTTPS. The dataset consists of extended network flows that we captured with flow exporter <a href="https://github.com/CESNET/ipfixprobe">Ipifixprobe</a>. Apart from traditional fields like source and destination IP addresses and ports, each flow contains information (size, direction, inter-packet time, TCP flags) about up to the first 100 packets. The sizes of packets are taken from the transport layer (TCP, UPD); packets with zero payload (e.g., TCP ACKs) are ignored.</p> <p>We publish three files:</p> <ul> <li><em>flows.csv</em>, which contains raw flow data.</li> <li><em>aggregated_flows.csv</em>, which contains aggregated flows</li> <li><em>samples.csv</em>, which contains samples with extracted features. This data can be used for training a machine-learning classification model.</li> </ul> <p> </p> <p>All IP addresses, source ports, TLS SNIs are sha256-hashed. Column <em>CLASS</em> is 0 for benign samples and 1 for brute-force samples.</p> <p><br> <strong>Brute-force data</strong><br> The brute-force data were generated with three popular attack tools - Ncrack, Thc-hydra, and Patator. Attacks were performed against these applications:</p> <ul> <li> WordPress</li> <li> Joomla </li> <li> MediaWiki</li> <li> Ghost</li> <li> Grafana</li> <li> Discourse</li> <li> PhpBB</li> <li> OpenCart</li> <li> Redmine</li> <li> Nginx</li> <li> Apache</li> </ul> <p>The <em>SCENARIO</em> columns indicate which tool and application were used to generate the sample.</p> <p><strong>Benign data</strong><br> Bening data consists of eight captures from a backbone network. The <em>SCENARIO</em> column indicates individual captures.</p> <p> </p> <p> </p> <p> </p>
Climate Forced Hydropower Simulations Using NASA NEX-GDDP
<p>* This update includes the corrected values for all Peruvian Hydropower Plants included in the original dataset. </p> <p>This dataset includes the results of simulations of future hydropower usable capacity for power plants in Brazil, Colombia, and Peru. These simulations have been forced using NASA's Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset, which includes maximum temperature, minimum temperature, and precipitation simulations from 21 Global Climate Models (GCM) and three scenarios. The scenarios include a retrospective run (1950-2005) and two projection runs for Representative Concentration Pathways (RCP) 4.5 and 8.5. There is a folder for each country that includes a power plant characteristics file, with a list of all the power plants and the characteristics used for the analysis (installed capacity, effective height, reservoir specifications, etc.). Additionally, there is a folder including the dates for the usable capacity files. Each file inside the usable capacity folder is labeled "power_", followed by the power plant name (e.g. "tres_irmaos"), and the scenario (e.g. "rcp45_2006_2099"). </p> <p>This work is based on the future publication: Caceres, A.L., Jaramillo, P., Matthews, H.S., Samaras, C. & Nijssen B. "Hydropower under climate uncertainty: characterizing the usable capacity of Brazilian, Colombian and Peruvian power plants under climate scenarios".</p>
PermaRisk Projekt: Soil Parameters and Climate Forcing
<p>This dataset contains soil parameters and climate forcing data required to simulate the ground thermal dynamics for regions in Alaska (Prudhoe Bay), Canada (Churchill), and Russia (Lena-River Delta) using the permafrost model CryoGrid (https://github.com/CryoGrid). The soil data were compiled in the frame of the Project: Simulating erosion processes in permafrost landscapes under a warming climate – a risk assessment for ecosystems and infrastructure within the Arctic - PermaRisk funded by the German Federal Ministry of Education and Research (BMBF).</p>
Dataset From: Structural and double layer forces between silica surfaces in suspensions of negatively charged nanoparticles
<p>The dataset for the publication "Structural and double layer forces between silica surfaces in suspensions of negatively charged nanoparticles". DOI: 10.1021/acs.langmuir.0c02917.</p> <p>Files containing data have .dat extension and are in text format</p>
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OpenNeuro
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