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171 results for “POPs”
PopSweeper: Automatically Detecting and Resolving App-Blocking Pop-Ups to Assist Automated Mobile GUI Testing
<p>Collected data for paper: PopSweeper: Automatically Detecting and Resolving App-Blocking Pop-Ups to Assist Automated Mobile GUI Testing</p>
Pop-Machina_WP3.2_ Data from behavioural studies on incentivisation
<p>The Pop-Machina project is an EU-funded research project aiming to explore how the maker movement can contribute to cities’ transition to the circular economy. This data was used in deliverable 3.2: Incentivisation and Behavioral Insights. It consists of two files, one for a study on the endowment effect for used/broken items, the other is for a study on loyalty cards effect in various second-hand markets and their ability to incentivize donations.</p>
Ptarmigan Bay (Yukon Coast, CA) POPs analysis on permafrost soils
<p>Arctic permafrost soils are important reservoirs of contaminants on time scales ranging from days to millennia. The extensive work in the Arctic Monitoring and Assessment Program (AMAP) on Arctic contaminants highlights an acute lack of data on contaminants in Arctic soils, particularly below the permafrost table. Here we describe the work carried out on analyses of Persistent Organic Pollutants (POPs) in permafrost soils, in the Canadian study area at Ptarmigan Bay (Yukon, CA). Hexachlorobenzene, 13 congeners of Polychlorinated biphenyl (PCB) and 16 individual Polycyclic aromatic hydrocarbons (PAH) have been analysed in 95 active layer and permafrost core samples. The concentrations have been determined by using Thermo Scientific Dionex ASE 350 and Gas Chromatography - Triple Quadrupole Mass Spectrometry (Trace 1310 GC coupled with TSQ9000 TQMS, Thermo Scientific), following previously reported methods for the determination of organic contaminants in the atmospheric matrix and dry and wet deposition in the Venice Lagoon and the Antarctic atmospheric gas and particle phase (Gambaro et al., 2009; Piazza et al., 2013).</p>
Preparing Children for Anesthesia With an Educational Pop-Up Book
ClinicalTrials.gov study NCT04796077. IPD Sharing: YES. Countries: 1. Publications: 22.
Prolonged Popliteal Fossa Nerve Blockade (Prolonged Pop)
ClinicalTrials.gov study NCT02198235. IPD Sharing: NO. Countries: 1. Publications: 1.
Calistar A vs. Calistar S - Comparative Cohort Retrospective Analysis of Single Incision POP Systems
ClinicalTrials.gov study NCT03715803. IPD Sharing: NO. Countries: 1. Publications: 5.
Dynamics of leaching of POPs and additives from plastic in a Procellariiform gastric model: Diet and polymer dependent effects and implications for long-term exposure
Open the record for dataset details and reuse information.
Simulations from the CABLE-POP land surface model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the CABLE-POP land surface model are provided for two global simulations. One forced by CRU-NCEP v7 climate data for the period 1901-2015 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Landcover was fixed at 1700 values throughout the simulations. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for each individual turnover-causing process in the model were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Eighty Member Ensemble of 1.0° POP and CICE restart files on the CESM gx1v7 grid
The restart files are from Who Kim's g210.G_JRA.v14 experiment. This experiment was run with repeating "normal-year" atmospheric forcing from the JRA-55 reanalysis. Since the atmospheric forcing was repeated throughout the length of the centuries-long ocean run, the eighty sets of restart files were collected on January 1st, 00Z from eighty different years of the run. These restart files are intended to serve to initialize an 80-member CESM ensemble for users who intend to run an ensemble data assimilation experiment.
OpenFF POPS Simulation with Na+ Counterions (64:64 at 298K)
<p>128 lipid bilayer (64:64) balanced with 128 Na+ ions and hydrated with 4480 SPC/E waters. Equilibrated for 100 ns and simulated for 200 ns at 298K. Full input and output from: https://github.com/PEFrankel/OpenFF_Bilayer.</p><p>Parameters generated by OpenFF Toolkit (Mobley et al., 2018) + Interchange (https://zenodo.org/records/8229243). Forcefield line: 'openff-2.1.0.offxml' from OpenFF Sage (Boothroyd et al., 2023 / https://zenodo.org/records/7889050)</p>
Pop Up Mural Installation Hanoman Obong
Source: Objaverse 1.0 / Sketchfab
Snap and Jump: How Elastic Shells Pop Out
<p>Experimental and simulation data for the paper (<a href="https://doi.org/10.48550/arXiv.2410.08525" target="_blank" rel="noopener">preprint version</a>). This Excel file comprises 11 sheets, each corresponding to the data in Figures 2(a), 2(b)-(e), 3(a-c), 4, and S2(a,b).</p>
Data from: Pop-out search instigates beta-gated feature selectivity enhancement across V4 layers
<p>Visual search is a work-horse for investigating how attention interacts with processing of sensory information. Attentional selection has been linked to altered cortical sensory responses and feature preferences (i.e., tuning). However, attentional modulation of feature selectivity during search is largely unexplored. Here we map the spatiotemporal profile of feature selectivity during singleton search. Monkeys performed search where a pop-out feature determined the target of attention. We recorded laminar neural responses from visual area V4. We first identified "feature columns" which showed preference for individual colors. In the unattended condition, feature columns were significantly more selective in superficial relative to middle and deep layers. Attending a stimulus increased selectivity in all layers but not equally. Feature selectivity increased most in the deep layers, leading to higher selectivity in extragranular layers as compared to the middle layer. This attention-induced enhancement was rhythmically gated in phase with the beta-band local field potential. Beta power dominated both extragranular laminar compartments, but current source density analysis pointed to an origin in superficial layers, specifically. While beta-band power was present regardless of attentional state, feature selectivity was only gated by beta in the attended condition. Neither the beta oscillation nor its gating of feature selectivity varied with microsaccade production. Importantly, beta modulation of neural activity predicted response times, suggesting a direct link between attentional gating and behavioral output. Together, these findings suggest beta-range synaptic activation in V4's superficial layers rhythmically gates attentional enhancement of feature tuning in a way that affects the speed of attentional selection.</p>
Code for GMD publication - Simulated microphysical properties of winter storms from bulk-type microphysics schemes and their evaluation in WRF (v4.1.3) model during ICE-POP 2018
<p>In this repository, we include the source codes for WRF microphysics parameterization used in the GMD publication "Simulated microphysical properties of winter storms from bulk-type microphysics schemes and their evaluation in WRF (v4.1.3) model during ICE-POP 2018."</p> <p>The four 2-moment bulk microphysical parameterization codes, WDM6, WDM7, Thompson, and Morrison, are divided into 3 WDM, Thompson, and Morrison codes, and each code has been modified so that detailed microphysical processes can be checked in wrfout.</p> <p>The WDM6 and WDM7 schemes include numerical errors for ice microphysical parameterization (Kim and lim, 2021) and for cloud evaporation and melting processes (Lei et al., 2020).</p> <p>Namelist files shows the namelist.input for each case.</p> <p> </p> <p> </p>
Laboratory capability Survey - POPs in Food and Feed, CFSA
<p>POPs analysis capabilities of China National Centre for Food Safety Risk Assessment (CSFA)</p>
Laboratory capability Survey - POPs in Food and Feed, Irish State Laboratory
<p>POPs analysis capabilities at the Irish State Laboratories</p>
IKA CI Pop Music Dataset
<p><strong>IKA CI Pop Music Dataset</strong></p> <p>The <strong>IKA CI Pop Music Dataset (IKA-CI-PMD)</strong> is a dataset of music excerpts that has especially compiled to evaluate different music signal preprocessing strategies for CI listeners.<br> It contains 15 pop music excerpts with a duration of approximately 12 s each that are taken from the MedleyDB dataset (<a href="https://medleydb.weebly.com/">https://medleydb.weebly.com/</a>) curated by <a href="https://steinhardt.nyu.edu/marl/research/resources/medleydb">Rachel Bittner et. al.</a>.<br> Like MedleyDB, it is licensed MedleyDB is licensed under a <a href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.<br> The excerpts have been selected such that they contain short self-contained phrases where vocals, bass, drums and other accompaniment are likewise present.<br> They also cover a range of different pop music subgenres and songs with both male and female lead vocals.</p> <p>The signals have been modified from the original MedleyDB versions. They have been remixed to equal loudness of vocals stem and all accompaniment stems where the mixing ratios between the accompaniment stems (bass, drums, other accompaniment) was retained.<br> Furthermore, the excerpts included in the dataset are normalized to a loudness level of -27 LUFS.<br> The signals are stored in the lossless FLAC format.<br> The files contain stereo signals, where both channels are identical.</p> <p>The dataset has been compiled at the Ruhr University Bochum <a href="https://www.ruhr-uni-bochum.de/ika/index_en.html">Institute of Communication Acoustics</a> in 2022 by Johannes Gauer (<a href="mailto:mailto:johannes.gauer@rub.de">johannes.gauer@rub.de</a>) in collaboration with the fellow researchers Anil Nagathil, Benjamin Lentz, and Rainer Martin.</p>
Simulation of a POPS bilayer
<p>Simulation of a POPS lipid bilayer. The system contains 128 POPS lipids, 40 waters per lipid, and 128 sodium counter ions for the anionic PS. The lipid model by Maciejewski and Rog [1,2,3] was used. Topologies (.itp) were obtained from [3]. TIP3P water was used, and the sodium was modelled using default OPLS ion parameters. The simulation is 200 ns long. Simulation parameters are given in the .mdp file. Simulations were performed using Gromacs 5.1.4 [4]</p> <p>The trajectory (.xtc), energy file (.edr), checkpoint file (.cpt), run input file (.tpr), index file (.ndx), topology file (.top), and the final structure (.gro) are provided. </p> <p>[1] Maciejewski et al., J. Phys Chem. B 118, 2014, pp. 4571–4581, DOI: 10.1021/jp5016627 </p> <p>[2] Kulig et al., Data in Brief 5, 2015, pp. 333–336, DOI: 10.1016/j.dib.2015.09.013</p> <p>[3] Rog et al., Data in Brief 7, 2016, pp. 1171–1174, DOI: 10.1016/j.dib.2016.03.067</p> <p>[4] Abraham et al., SoftwareX 1–2, 2015, pp. 19–25, DOI: 10.1016/j.softx.2015.06.001</p> <p> </p>
GROMOS-CKP POPS simulations (versions 1 and 2) 298 K with GROMOS NH3 charges and PME
<p>GROMOS-CKP POPS simulations (298 K, starting structure from the CHARMM-GUI with the appropriate hydrogen atoms removed) performed with 1.4 nm cut-offs, PME and a long range dispersion correction. The charges for the NH3 part of the head group are taken from the standard GROMOS force field parameters. Two different simulations generated with different starting velocities are provided (the files are named v1 and v2 for these different simulations). The trajectories contain only the data from 400-500 ns of the simulations (as per the analysis provided on the nmrlipids blog) and additionally they have been processed with trjconv -skip 10 to keep the upload small.</p>
Slipids POPS simulations (versions 1 and 2) 298 K 1.0 nm cut-off with LJ-PME
<p>Slipids POPS simulations (298 K, starting structure from the CHARMM-GUI) performed with 1.0 nm cut-offs, PME and LJ-PME. Two different simulations generated with different starting velocities are provided (the files are named v1 and v2 for these different simulations). The trajectories contain only the data from 400-500 ns of the simulations (as per the analysis provided on the nmrlipids blog) and additionally they have been processed with trjconv -skip 10 to keep the upload small.</p>
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OpenNeuro
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