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91,407 results for “Effects With / Effects Of”
Data for: Ring Current Electron Precipitation During the 17 March 2013 Geomagnetic Storm: Underlying Mechanisms and Their Effect on the Atmosphere
<p>All data are included as MATLAB figure files, png files and MATLAB MAT files.</p><p>File precipitated_flux.mat contains a 4-D array of values of precipitated electron flux in [1/(s cm^2 keV)] for 289 time points from 16 March 2013 to 19 March 2013, with a 15 min time step; 100 values of energy in a range from 10 keV to 1 MeV, with a 10 keV step; on a spatial grid of 28 by 49 (P, R).</p><p>netCDF data can be opened with a variety of software tools, including Matlab, Origin or Python.</p>
Dataset: Effective T-matrix of a cylinder filled with a random 2D particulate
<p>This data is the one used in the paper "Effective T-matrix of a cylinder filled with a random 2D particulate", currently submitted to the Proceedings A of the Royal Society. A preprint version of this paper can be found at: https://arxiv.org/abs/2308.13338</p> <p>This dataset contains the numerically computed effective T-matrix of a cylinder filled with a random 2D particulate. Since the T-matrix is diagonal, only the diagonal elements T_n are computed. Values of T_n are provided for various set of parameters (frequency, particle type and volume fraction). Furthermore, for each set of parameters, T_n is computed with three different methods:</p> <p>1) The Monte Carlo method (MC), which requires computing the scattered field for several configurations of particles.</p> <p>2) The Effective Waves Method (EWM), based on results on random particulate materials. It provides a formula of the effective T-matrix with an effective wavenumber.</p> <p>3) A simplified version of the EWM when only monopole scattering is accounted for (EWM-MA). </p> <p>This dataset includes the following files: metadata_MC.csv and per each parameter one csv file with data records. The notations used in the headers of the files MCx.csv are described in the file header_notations.pdf.</p>
Data for "Let's not wing it: Effective conservation of subterranean-roosting bats"
<p>Database as both excel (.xls) and tab-delimited (.csv) associated with the publication: </p> <p>Meierhofer M.B., et al. (2023) Let’s not wing it: Effective conservation of subterranean-roosting bats. <em>Conservation biology.</em></p> <p>Please refer to the main publication for a detailed description. An explanation of the database is available in the Metadata file uploaded alongside the database. R code to reproduce the analysis pipeline is available on GitHub:</p> <p>https://github.com/StefanoMammola/Analysis_Cave_bat_conservation.git</p>
Aquamarine: Quantum-Mechanical Exploration of Conformers and Solvent Effects in Large Drug-like Molecules
<p>Open challenges in computational drug design include the understanding and accurate description of solvent effects as well as collective dispersion interactions for realistic drug-like molecules. Both interactions profoundly influence the conformational stability of drug molecules and, consequently, the determination of other important quantum-mechanical (QM) observables. In this context, we here introduce the Aquamarine (AQM) dataset -- an extensive QM dataset that contains the structural and electronic information -- of 59,786 low-and high-energy conformers of 1,653 molecules containing up to 54 non-hydrogen atoms (including C, N, O, F, P, S and Cl). To gain insights into the solvent effects, we have carried out QM calculations of structures and properties in gas phase and in an aqueous solution modeled with implicit solvent. AQM contains over 40 global (molecular) and local (atom-in-a-molecule) physicochemical properties (including ground-state and response properties) per molecular structure computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD supplemented with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By treating both molecule-solvent and dispersion interactions, the AQM dataset can help understand the impact of both interactions in structure-property and property-property relationships of realistic drug-like molecules. Therefore, we propose the AQM dataset as a benchmark for current state-of-the-art machine learning methods for property prediction as well as for the <em>de novo</em> generation of large and flexible (solvated) molecules with pharmaceutical and biological relevance.</p>
Atrial Models with Personalized Effective Refractory Period
<h1>Impact of Effective Refractory Period Personalization on Prediction of Atrial Fibrillation Vulnerability</h1> <div> </div> <div> <div><strong>Authors:</strong> Patricia Martínez Díaz, Christian Goetz, Albert Dasi, Laura Anna Unger, Annika Haas, Olaf Dössel, Armin Luik, Axel Loewe</div> <div>patricia.martinez@kit.edu / publications@ibt.kit.edu</div> <div><a href="https://doi.org/10.1093/europace/euad122.542">doi:10.1093/europace/euad122.542</a></div> <div> </div> <div>This dataset contains 7 atrial meshes, 6 left atria and 1 right atrium, derived from electroanatomical mapping and measurements of the effective refractory period (ERP), bipolar voltage (bi) and local activation times (lat). The meshes include annotations and fibers and are ready for simulations in the cardiac electrophysiology simulator <a href="https://doi.org/10.1016/j.cmpb.2021.106223">openCARP</a>. We also provide the code to reproduce 272 reentries by reading the selected parameters.par and state.roe files. The meshes were generated using <a href="https://github.com/KIT-IBT/AugmentA">AugmentA code</a> and the simulated reentries were induced following the <a href="https://doi.org/10.3389/fphys.2021.656411">PEERP protocol</a> by Azzolin et al. </div> <div> </div> <h2>Folder structure</h2> <div>The code is located in the `src` folder, the meshes in the `data` folder and the reentries in the `results` folder. </div> <div>```</div> <div>src/</div> <div> |-- run.py</div> <div> |-- induceReentry.py</div> <div> |-- getStimPoints.py</div> <div> |-- element_tag.csv</div> <div> |-- al_mk_H.par</div> <div> |-- requirements.txt</div> <div> |-- reproduceReentry.py</div> <div>data/</div> <div> |-- meshes/</div> <div> |-- P1/ </div> <div> |-- P1_with_erp_lat_bi.vtk </div> <div> |-- ERP.pts</div> <div> |-- ERP_values.txt</div> <div> |-- ablation.pts</div> <div> |-- LA_stim_points_2cm.pts</div> <div> |-- bilayer/</div> <div> <div> |-- nodal_adjustment/</div> <div> |--PARAMETER_SCENARIO.adj (e.g. Gto_continuous.adj)</div> </div> <div>.</div> <div>.</div> <div>.</div> <div> |-- P7 </div> <div>results/</div> <div> |-- MESH_SCENARIO_CV/ (e.g P1_continuous_0.3) </div> <div> |-- point_X_beat_Y</div> <div> |-- MESH_SCENARIO_CV_PERTURBATION_SET/ (e.g P1_continuous_0.7_2_1) </div> <div> |-- point_X_beat_Y </div> <div>README.md</div> <div>```</div> <div> <ul> <li>`src`: contains the source files needed to run PEERP protocol <ul> <li>`run.py` This is the main function to run the pacing protocol (not needed to run if reentries are only reproduced, check reproduceReentry.py)</li> <li>`induceReentry.py` Contains a list of pacing protocols. The PEERP protocol is included here</li> <li>`getStimPoints.py` Extract the stimulation points</li> <li>`element_tag.csv` Region tag numbering</li> <li>`al_mk_H.par` Par file with ionic scaling factors for three states; H:Healthy, M:Mild, S:Severe</li> <li>`requirements.txt` Packages to create the virtual enviroment. (This was my output of ```pip3 list> requirements.txt```)</li> <li>`reproduceReentry.py` Reentries can be reproduced given a selected folder where the .par and .roe files are stored.</li> </ul> </li> <li>`data`: contains the `meshes` folder with the bilayer meshes in openCARP (.elem, .lon and .pts) and .vtk format. Synthetic fibrotic distributions are included in the the .regele files. <ul> <li>`meshes/P1/P1_with_erp_lat_bi.vtk` Mesh with ERP, LAT and bipolar voltage data</li> <li>`meshes/P1/ERP_values.txt/` measured ERP data</li> <li>`meshes/P1/ERP.pts/` electrode coordinates where ERP data was measured</li> <li>`meshes/P1/ablation.pts/` electrode coordinates where tissue was ablated</li> <li>`meshes/P1/LA_stim_points_2cm.pts` Stimulation points for the PEERP protocol</li> <li> `meshes/P1/bilayer/LA_bilayer_with_fiber_slow_conductive.regele` Element ids corresponding to regions of low voltage (< 0.5mV)</li> <li>`meshes/P1/bilayer/LA_bilayer_with_fiber_scar.regele` Element ids corresponding to regions of low voltage (< 0.1mV)</li> <li>`meshes/P1/bilayer/LA_bilayer_with_erp_regions_um.vtk` Bilayer mesh with a discrete split where each region has a single ERP value</li> <li>`meshes/P1/bilayer/LA_bilayer_with_fiber_with_fibrosis.vtk` Bilayer mesh with fibrosis informed by low voltage areas</li> <li>`meshes/P1/bilayer/LA_bilayer_with_erp_continuous_um.vtk` Bilayer mesh with a continuous ERP distribution by interpolation of measured ERP data</li> <li>`meshes/P1/bilayer/LA_bilayer_with_erp_continuous_2ms_um.vtk` Bilayer mesh with a continuous ERP distribution by interpolation of measured ERP data with +- 2ms perturbation</li> </ul> </li> </ul> <p>We studied 7 different scenarios: </p> </div> <ol> <li>Monoregion scenario with no ERP personalization, where all nodes had the same ERP</li> <li>Control scenario with no ERP personalization, where ERP nodes of certain defined anatomical regions where modified as reported in Loewe et al. 2015 </li> <li>Regional scenario with ERP personalization, where each region had a single ERP value derived from clinical measurement</li> <li>Continuous scenario with ERP personalization, where the ERP distribution was generated by interpolation of measured ERP data</li> <li>Control scenario with fibrosis, where elements corresponding to regions of low voltage (bi<0.5 mV) where set as slow or non conducing elements</li> <li>Continuous scenario with fibrosis, where elements corresponding to regions of low voltage (bi<0.5 mV) where set as slow or non conducing elements</li> <li>Continuous scenario where ERP measurements with additional perturbation draw from a uniform distribution. The perturbations were 2,5,10 and 20 ms, and we repeated this set 5 times for P6</li> </ol> <p>In summary, we provide the following data: </p> <div> <ul> <li>7 meshes for openCARP simulations</li> <li>7 meshes in vtk format with continuous ERP distribution</li> <li>27 meshes in vtk format with continuous ERP distribution with perturbed ERP with 2,5,10 and 20ms from a random uniform distribution</li> <li>7 meshes in vtk format with regional ERP</li> <li>7 meshes in vtk format with ERP, LAT and bipolar voltage</li> <li>7 ablation set points</li> <li>7 ERP set points with their corresponding values</li> <li>209 reentries generated under 4 ERP scenarios (monoregion, control,regional,continuous) run with a conduction velocity of 0.7 0.5 and 0.3 m/s</li> <li>26 reentries generated under 2 scenarios ERP+Fibrosis (control + continuous) run with a conduction velocity 0.3 m/s</li> <li>37 reentries induced with continuous ERP for patient P3 @CV 0.3 for the sensitivity analysis </li> </ul> </div> <h2>Create a dynamic Courtemanche model</h2> <p>As we will modify the ionic parameters on a nodel basis you will need to create a dynamic Courtemanche model and then declare the variables (ionic conductances) you need to modify. In your openCARP installation folder, go to the `limpet` copy the Courtemanche.model file</p> <p>```<br>cd openCARP/physics/model/limpet<br>cp Courtemanche.model Courtemanche_nodal.model<br>vim Courtemanche_nodal.model<br>```</p> <p>Then add on top the parameters that need to be modified on a nodal-basis:</p> <p>```<br>group {<br> GK1 ;<br> Gto ;<br> GKr ;<br> GKs ;<br> GCaL ;<br> factorGKur ;<br> maxINaCa ;<br> maxIpCa ;<br>} .nodal();</p> <p>```</p> <p>Then you would need to recompile openCARP. In the terminal, go to your openCARP's top level folder:<br>```<br>cd openCARP/ <br>```</p> <p>Configure CMake with updated imp_list.txt via:<br>```<br>cmake -S. -B_build -DUPDATE_IMPLIST=ON</p> <p>```<br>Run the CMake building process:<br>```<br> cmake --build _build<br>```<br> This will generate the `.h` and `.cc` files for your dynamic model inside `physics/limpet/src/imps_src`</p> <p>**Note:** If you want to add or modify a model file after openCARP was compiled, it is possible to first clean the previous generated files during compilation by running `make clean` before recompiling openCARP.</p> <p>If you compile your own version of openCARP, then you can modify the settings.yaml file, to point to your openCARP version with the dynamic model.<br>```<br>cd .config/carputils<br>subl settings.yaml <br>```<br>Add the build name:</p> <p>```<br>CARP_EXE_DIR:<br> CPU: /Users/lm104/Documents/OpenCARP/opencarp/_build/bin<br> NODAL: /Users/lm104/Documents/OpenCARP/openCARP_nodal_adj/_build/bin<br>```<br>You can check that the new dynamic model is there by calling bench<br>```<br>bench -—list-imps<br>bench —-imp Courtemanche_nodal --imp-info<br>```</p> <p>You can find additional information about dynamic models <a href="https://opencarp.org/documentation/examples/01_ep_single_cell/04_limpet_fe">here</a>.</p> <h2>Reproduce the reentries </h2> <div>You can generate the .igb file of a specific reentry by selecting the corresponding folder in the results directory. An example is given to reproduce the reentry in P1_bi_M_LA/point_0_beat_2/reproduce_reentry.igb. Select the folder `--par_file_directory`and set `--tend` to define the duration of the simulation in miliseconds.</div> <div>_HINT: We recommend keeping the folder structure so that the other parameters, such as: mesh, scenario, state and chamber, can be read from the --par_file_directory. Otherwise, the meshes and results directories need to be modified._</div> <div>```</div> <div>cd src/</div> <div>reproduceReentry.py --par_file_directory ../results/P1_bi_M_LA/point_0_beat_2 --tend 1500</div> <div> </div> <div>```</div> <div> </div> <h3>Preparation before running the PEERP pacing protocol</h3> <div>Follow the next steps if you want to run the PEERP pacing protocol, either for the provided meshes or for your own meshes. To run the PEERP protocol in a controlled environment, it is recommended, before running the run.py, to create a virtual environment. Go to your terminal and type: </div> <div>```</div> <div>cd src/</div> <div>python3 -m venv ./myEnv</div> <div>source ./myEnv/bin/activate</div> <div>pip3 install -r requirements.txt</div> <div>```</div> <div> </div> <div>You need to add carputils to your `PATH`. You can run the code in the terminal or use and IDE to debug the code. </div> <div>Note: I am using PyCharm 2020.3. and in Settings --> Python interpreter --> show all and then in the (+) symbol, add the path to carputils there:</div> <div> </div> <div>Otherwise you can add this extra lines at the beginning of `run.py``:</div> <div>```</div> <div># Replace '/path/to/carputils' with the actual path to your carputils package</div> <div>carputils_path = '/path/to/carputils'</div> <div> </div> <div># Add the carputils path to sys.path</div> <div>sys.path.append(carputils_path)</div> <div>```</div> <h3>Run the PEERP protocol</h3> <div> </div> <div>The following example runs the PEERP from a single stimulation point. If you want to run PEERP over all the points, simply add the flag --run_all_points 1 </div> <div>```</div> <div>cd src/</div> <div>python3 run.py --giL 0.4166 --geL 1.458 --cv 0.8 --mesh monoatrial --protocol PEERP --pacing 122718 --stim_file LA_stim_points.txt --geometry LA_bilayer_with_fiber_um --cell_bcl 500 --model Courtemanche --ionic_prop_file al_mk_S.par --max_n_beats_PEERP 1 --overwrite-behaviour overwrite</div> <div>```</div> <div> </div> <h3>Running your own experiment and making your own changes</h3> <div>Extract the stimulation points on your mesh, where the PEERP protocol will be run: </div> <div>```</div> <div>python3 getStimPoints.py --mesh monoatrial --tolerance 20000 --stim_file LA_stim_points.txt --chamber LA</div> <div>```</div> <div> </div> <div>Tune conduction velocity (CV) and conductivites. The code expects the intracellular end extracellular longitudinal conductivity values as an input. We used `tuneCV` to fit CV=0.7m/s with dx=0.4mm and dt=20us</div> <div>If you want to adjust the values, run in the terminal:</div> <div>```</div> <div>tuneCV --resolution 400 --model Courtemanche --velocity 0.7 --converge True --sourceModel monodomain --surf True --dt 20</div> <div>```</div> <div>You can provide the location of the start of the activation by selecting the desired point ID:</div> <div>- Load the mesh in Paraview (or Meshalyzer)</div> <div>- click on the ? symbol</div> <div>- save the ID and change the `--pacing` argument </div> <div> </div> <div>Call `run.py` with a new mesh. The protocol starts by prepacing the mesh and then using the last beat as initial condition tu run the PEERP.</div> <div>Be aware that for a monoatrial mesh you might need to give the new id for the location of the earliest activation. Change `12345` to your desired point ID.</div> <div>```</div> <div>python3 run.py --mesh newMesh --pacing 12345 --protocol prepace --stim_file LA_stim_points.txt</div> <div>```</div> <div> </div> <div>Run the protocol with different electrical remodelling stage. You can change the .par file or select one file from the three provided: </div> <div>```</div> <div>python3 run.py --mesh newMesh --pacing 12345 --protocol PEERP --stim_file LA_stim_points.txt --args.ionic_prop 'l_mk_M.par'</div> <div>```</div> <div> </div> <div>You can also try to run a biatrial example. The biatrial mesh is also provided. You need to extract the points on the RA surface using `getStimPoints.py`, to run the RA experiment: </div> <div>```</div> <div>cd src</div> <div>python3 getStimPoints.py --mesh biatrial --tolerance 20000 --stim_file RA_stim_points.txt --chamber RA</div> <div>```</div> <div>Then run PEERP twice, one per each chamber:</div> <div> </div> <div>```</div> <div>python3 run.py --mesh biatrial --geometry LA_RA_bilayer_with_fiber --pacing 12345 --stim_file LA_endo_2cm.txt --args.ionic_prop 'l_mk_M.par'</div> <div>python3 run.py --mesh biatrial --geometry LA_RA_bilayer_with_fiber --stim_file LA_stim_points.txt --args.ionic_prop 'l_mk_M.par'</div> <p> </p> </div> <p> </p>
Effects of intercropping on the herbage production of a binary grass-legume mixture (Hedisarum coronarium L. and Lolium multiflorum Lam.) under artificial shade in Mediterranean rainfed conditions
<p>This dataset refers to the experimental raw data (csv version) collected within the trial reported in the concerned article on the following parameters:</p> <p>1. crop aboveground biomass, splitted per field, mowing, crop, treatment and replicate (crop aboveground biomass.csv)</p> <p>2. cumulated crop aboveground biomass, splitted per field, year, crop, treatment and replicate (cumulated crop aboveground biomass_year.csv)</p> <p>3. cumulated crop aboveground biomass for the two years of the growing cycle, splitted per field, crop, treatment and replicate (cumulated crop aboveground biomass_2years.csv)</p> <p>4. partial and total RYT splitted per year, field and treatment (RYT_year)</p> <p>5. partial and total RYT for the two years of the growing cycle, splitted per field and treatment (RYT_2years)</p> <p> </p> <p><br> </p>
Effect of Fe-doping on VS2 monolayer: A first-principles study
<p><span>This dataset includes the DFT results used to investigate Figures 3–7 of our study on the Effect of Fe-doping on VS2 monolayer: A first-principles study. The .dat files include band structure calculations from GGA+U (Figure 3) and GGA+U+SOC (Figure 4), magnetic anisotropy energy (MAE) data (Figure 5), dielectric constant from the optical properties (Figure 6), and absorption (Figure 7). All data were calculated using the QuantumATK code package. This dataset supports reuse and additional magnetic and optical behavior analysis in this work.</span></p>
Recommended food alternatives (healthier, eco-friendly, and cost-effective)
<p>It includes recommended food alternatives (healthier, eco-friendly, and cost-effective) for items selected from receipts. This dataset is valuable for research in consumer food science, as it captures the food choices of a small group of consumers over 21 days. It is also useful for machine learning training. All food items are linked to NAct ontology.</p> <p> </p>
The effect of dynamical states on galaxy clusters populations. I. Classification of dynamical states
<p>This repository contains three figures mentioned in "The effect of dynamical states on galaxy clusters populations. I. Classification of dynamical states" <em>(DOI to follow on publication)</em>.</p> <p>We show the contours of the X-ray surface brightness distribution (solid green lines) and the distribution of galaxies belonging to the red sequence (solid gray lines). Black crosses symbolize the positions of the X-ray peaks, black "X" marks represent the positions of the X-ray centroids, and open red circles denote the positions of the BCGs. The blue circle corresponds to the R200 of each cluster.</p>
Multiscale assessment of the effect of a stearic-palmitic sucrose ester on the crystallization of anhydrous milk fat
<p>Dataset belonging to publication 'Multiscale assessment of the effect of a stearic-palmitic sucrose ester on the crystallization of anhydrous milk fat'.</p> <p>Available via: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.foodres.2024.115243" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.foodres.2024.115243</a>.</p> <p> </p> <p>PLM = polarized light microscopy</p> <p>CryoSEM = cryo-scanning electron microscopy</p> <p>> data obtained after de-oiling fat samples with isobutanol (4x) and aceton (1x), see publication</p> <p>SAXS = small-angle X-ray scattering</p> <p>> data obtained after subtraction of intensity of empty capillary, see publication</p> <p>WAXS = wide-angle X-ray scattering</p> <p>> data obtained after subtraction of intensity of empty capillary, see publication</p> <p>USAXS = ultra-small-angle X-ray scattering</p> <p>> data obtained after subtraction of intensity of the capillary at 70°C, see publication</p> <p>DSC = differential scanning calorimetry</p> <p>> Samples are heated at 70°C for 10 min, and then crystallized following a certain protocol (see publication).</p> <p>> Samples are maintained one hour at their respective isothermal crystallization temperature.</p> <p>> Samples are rehaeted at 5°C/min to 70°C.</p> <p>SE = sucrose ester (SP30, HLB6)</p> <p>AMF = anhydrous milk fat</p> <p>AMFE = anhydrous milk fat + 0.5 wt% SE</p> <p>FC = fast cooling (20°C/min)</p> <p>SC = slow cooling (1°C/min)</p> <p> </p> <p>Project funding agency: Fonds Wetenschappelijk Onderzoek (FWO). Grant number: 1128923N. </p>
Investigating effect chains from cognitive and noise-induced short-term stress build-up to restoration in an urban or nature setting using 360° VR
<p>Dataset for demographic, psychological and physiological data obtained for RESTORE (Experiment 1 WP1). Study results are published in the article titled "Investigating effect chains from cognitive and noise-induced short-term stress build-up to restoration in an urban or nature setting using 360° VR" in the Journal of Environmental Psychology. Explanations on all variables (column names) in the datasets are given either in the second spreadsheet in each Excel file or in the csv files appended with _legend.csv (see latest version of the dataset). File 'Psychophysiological_participant_data_aggregated' is aggregated per participant (single or mean values), and the file 'Restoration_EDA_baseline-corrected_aggregated' contains EDA data aggregated per time point per restoration setting (Nature vs Urban) and prior cognitive demand condition. Methodological details on how the data was obtained and processed are given in the Open Access article.</p>
Neural correlates of expectations-induced effects of caffeine intake on executive functions
<p><strong>ABSTRACT</strong></p> <p>Placebo effects (PE) are defined as the beneficial psychophysiological outcomes of an intervention that are not attributable to its inherent properties; PE thus follow from individuals’ expectations about the effects of the intervention. The present study aims aimed at examining how expectations influence neurocognitive processes.</p> <p>We will addressed this question by contrasting three double-blinded within-subjects experimental conditions in which participants are were given decaffeinated coffee, while being told they have had received caffeinated (condition i) or decaffeinated coffee (ii), and given caffeinated coffee while being told they have had received decaffeinated coffee (iii).</p> <p>After each of these three interventions, performance and electroencephalogram will bewas recorded at rest as well as during sustained attention Rapid Visual Information Processing task (RVIP) and a Go/NoGo motor inhibitory control task.</p> <p> We first aimed to confirm previous findings for caffeine-induced enhancement on these executive components and on their associated electrophysiological indexes (attentional P3 component, response conflict N2 and inhibition P3 components (ii vs iii contrast); and then to test the hypotheses that expectations also induce these effects (i vs ii), although with a weaker amplitude (i vs iii).</p> <p>Related to the behavioral findings, wWe didn’t not confirm any of our hypotheses for behavioral improvement induced by caffeine intakeon either of the investigate tasks’ measures. Regarding the neurophysiological findingsAt the electrophysiological level, however, we confirmed that caffeine effects on increased the attentional P3 and inhibition P3 components amplitude, but not on the response conflict N2 component. Additionally, wWe dodid not confirm provide evidence that expectations do not influence any of the investigate electrophysiological indexeices. Finally, we confirm that that expectations effects are smaller compared to caffeine effects but only for the Global Field Power parameter related to the attentional P3 component.</p> <p>only for one of the investigated the attentional P3 component’s parameters, and that this effect was smaller than that of</p> <p>We conclude that Hence, previously identified caffeine effects at the behavioral level may have been overestimated and that if while expectations effects have any no influence on sustained attention and inhibitory control, they are small. XXCaffeine effects at the electrophysiological level indicate that it tends to modulate brain areas underlying attentional mechanisms in both RVIP and Go/NoGo tasks rather than being specific to inhibitory control processes.</p>
Model simulation data used in "Exploring the uncertainties in the aviation soot-cirrus effect" (Righi et al., Atmos. Chem. Phys., 2021)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2021). For details see the README.md file and Table 1 in the paper.</p>
Effects of Sinusoidal Vibrations on the Motion Response of Honeybees - datasets
<p>data sets on the effects of sinusoidal stimuli on the motion activity of honeybees. For more details please refer to </p> <p>Stefanec, M., Oberreiter, H., Becher, M. A., Haase, G., & Schmickl, T. (2021). Effects of Sinusoidal Vibrations on the Motion Response of Honeybees. <em>Frontiers in Physics</em>, <em>9</em>, 318.</p> <p>amplitude_experiments.csv contains the data of measured motion activity according to the pixel-based motion index in a certain region of interest in regards to different amplitudes at different frequencies.</p> <p>amplitude_experiments_with_velocity.csv contains the data of measured motion activity according to the pixel-based motion index in a certain region of interest in regards to different amplitudes at different frequencies as well as a post-hoc derived intensity measurement at a certain amplitude. This intensity measurement was detected by laser vibrometer on the surface of the honeycomb and represents the measurement at the point in the region of interest that had the highest intensity. This measurement could not be made during the experiments on the animals, but had to be made post-hoc, since a laser vibration measurement was only possible without animals passing through the laser point.<br> <br> frequency_experiments.csv contains the data of measured motion activity according to the pixel-based motion index in a certain region of interest in regards to different frequency stimuli.</p>
Effect of Substrates and Thermal Treatments on Metalorganic Chemical Vapor Deposition-Grown Sb2Te3 Thin Films (data)
<p>This dataset contains the raw data files connected with the figures included in the paper "<em>Effect of Substrates and Thermal Treatments on Metalorganic Chemical Vapor Deposition-Grown Sb<sub>2</sub>Te<sub>3</sub> Thin Films</em>" by <a href="https://pubs.acs.org/doi/pdf/10.1021/acs.cgd.1c00508">M. Rimoldi et al., <em>Cryst. Growth Des.</em> 2021, 21, 9, 5135–5144</a></p> <p>Note for users: </p> <p>The data in Figure 10 can be retrieved from <a href="https://zenodo.org/record/5725028#.Ybcxb73MI2w">Table 2 in the main text</a>.</p>
CFD simulation and measurements of effect of wind on non-catching rain gauge
<p>Simulation_dataset file shows the results of a CFD simulation of the measurements of a Thies laser precipitation monitor under different conditions of wind.</p> <p>Wind_tunnel_dataset shows the results of the model validation using an actual wind tunnel.</p>
Finite amplitude sound propagation effects in volume backscattering measurements for fish abundance estimation
<p>The upload contains measurement and simulation data for finite-amplitude sound propagation effects in volume backscattering measurements. The experimental data are from a trawl survey conducted in the North Sea with R/V "G. O. Sars", 6-7 November 2004, passing several times over a group of Atlantic mackerel schools. The measurements are of the relative area backscattering coefficient, relative to 38 kHz, 2000 W power setting, at</p> <p>(1) 120 kHz with 250 W transmit power setting, 200 kHz with 120 W transmit power setting<br> (2) 120 kHz with 1000 W power setting, 200 kHz with 1000 W power setting.</p> <p>A Simrad EK60 echosounder system was used, alternating between the low (1) and high (2) power settings through the measurement series.</p> <p>The corresponding simulation data are calculated using the Bergen Code numerical solver of the KZK Equation. The medium parameters input to the simulations are based on CTD data from the field survey . The transducer and amplitude data were found by laboratory measurements on echo sounders of the same type as used in the survey.</p> <p>.m files are included for both .mat data files, with details on how to read the data.</p> <p>An article describing the data has been submitted by the authors to Acta Acustica, 2022.</p>
Data and script for: "Increased birth rank of homosexual males: disentangling the older brother effect and sexual antagonism hypothesis"
<p>Data and script for Tables 2, 3, S2, S3, S4, and S5, and Figures 1, 3, 4, and S1.</p> <p>Individual dataset:</p> <p>France: <a href="https://zenodo.org/api/files/acdb78f8-397c-4bc3-8f68-ec048edbc5f5/France_data_df12.csv">France_data_df12.csv </a><br> Indonesia: <a href="https://zenodo.org/api/files/acdb78f8-397c-4bc3-8f68-ec048edbc5f5/Indonesia_data.csv">Indonesia_data.csv </a><br> Greece: <a href="https://zenodo.org/api/files/acdb78f8-397c-4bc3-8f68-ec048edbc5f5/Greek_data.csv">Greek_data.csv </a></p> <p>The file <a href="https://zenodo.org/api/files/acdb78f8-397c-4bc3-8f68-ec048edbc5f5/France_script.Rmd">France_script.Rmd </a>contains all the analyses of the french data set, including values presented Tables 2, 3, S3, S4, S5, Figures 3, 4 (output in file <a href="https://zenodo.org/api/files/acdb78f8-397c-4bc3-8f68-ec048edbc5f5/France_script.html">France_script.html</a>). Same thing for files <a href="https://zenodo.org/api/files/acdb78f8-397c-4bc3-8f68-ec048edbc5f5/Indonesia_script.Rmd">Indonesia_script.Rmd </a> and <a href="https://zenodo.org/api/files/acdb78f8-397c-4bc3-8f68-ec048edbc5f5/Greek_script.Rmd">Greek_script.Rmd</a>.</p> <p>For figure 1: <a href="https://zenodo.org/api/files/acdb78f8-397c-4bc3-8f68-ec048edbc5f5/Fig1.html">Fig1.html </a><br> For Figure S1: <a href="https://zenodo.org/api/files/acdb78f8-397c-4bc3-8f68-ec048edbc5f5/Fig_S1.html">Fig_S1.html </a><br> For Table S2: <a href="https://zenodo.org/api/files/acdb78f8-397c-4bc3-8f68-ec048edbc5f5/Table_S2_script.html">Table_S2_script.html </a><br> </p> <p> </p> <p><br> </p> <p> </p> <p> </p> <p> </p>
Dataset for: The Effect of Loading Direction on Slip and Twinning in an Irradiated Zirconium Alloy
<p><strong>This is the dataset used in the following publication: </strong></p> <p>R. Thomas, D. Lunt, M. D. Atkinson, J. Quinta da Fonseca, M. Preuss, F. Barton, J. O'Hanlon, and P. Frankel, "The Effect of Loading Direction on Slip and Twinning in an Irradiated Zirconium Alloy," in <em>Zirconium in the Nuclear Industry: 19th International Symposium</em>, ed. A. Motta and S. Yagnik (West Conshohocken, PA: ASTM International, 2021), 233-261. <a href="https://doi.org/10.1520/STP162220190027">https://doi.org/10.1520/STP162220190027</a>.</p> <p><strong>Contained in this dataset are:</strong></p> <p>A Jupyter notebook which uses the open-source DefDAP Python package (https://github.com/MechMicroMan/DefDAP) to open enclosed HRDIC, EBSD and image data for non-irradiated and 0.1 dpa proton irradiated Zircaloy-4 deformed to ~3% strain, along the rolling direction and transverse direction.</p> <p>Please use the 'develop' version of DefDAP: https://github.com/MechMicroMan/DefDAP/tree/f6b5d6ec33db9a45089fada17026432645044d2f</p> <p><strong>Publication abstract:</strong></p> <p>In this study, deformation experiments together with high-resolution digital image correlation were used to quantify the effect of proton irradiation on strain localization in Zircaloy-4 loaded along the rolling and transverse directions. Significant increases in strain heterogeneity were measured in the irradiated material compared to the nonirradiated material. This was a result of confinement of slip to channels in the irradiated material, which contain high effective shear strain values, with almost no strain in the regions between channels. The active slip systems in the material were also determined by comparing experimental slip trace angles from high-resolution digital image correlation with theoretical slip trace angles determined using grain orientation from electron backscatter diffraction. An increased amount of pyramidal and wavy basal slip, as well as tension twinning, were observed in the sample loaded along the transverse direction, compared to the sample loaded along the rolling direction, due to crystallographic texture. No significant change in slip system activity was observed as a result of 0.1 dpa proton irradiation, despite the dramatic change in slip pattern. The findings provide further insight into the role of irradiation on deformation behavior and provide quantitative data on slip system activation, for as-received and irradiated Zircaloy-4, against which to validate models.</p>
On the Effectiveness of Transfer Learning for Code Search - Replication Package
<p>This repository represents the replication package for the paper <em>On the Effectiveness of Transfer Learning for Code Search</em>.</p> <p>The paper is published in the journal <em>IEEE Transactions on Software Engineering (TSE)</em>.</p> <p>In this replication package, we provide all the data and scripts we used in our study.</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.