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91,407 results for “Effects With / Effects Of”
MINIATURA 6 Housing decisions, behavioral aspects of choices, price expectations and anchoring effect - Polsh case study
<p>The data was created as a result of a survey conducted in accordance with the guidelines: - the survey questionnaire consisted of approximately 30 questions and a form, - the surveyed population was defined as 1,000 households living in a large Polish city (over 450,000 inhabitants), quota selection based on the number of city inhabitants, - CAWI method (online), - completion date: 1 week. The survey was parameterized. Part of the sample is a control trial, part is an experimental trial.</p><p>Dane powstały w wyniku przeprowadzonej ankiety zgodnie z wytycznymi: - kwestionariusz badania składał się z ok. 30 pytań oraz metryczki, - badana zbiorowość określono na 1000 gospodarstw domowych zamieszkałych w dużym mieście Polski (powyżej 450 tys. ludności), dobór kwotowy na podstawie liczby mieszkańców miast, - badanie metodą CAWI (on-line), - termin realizacji 1 tydzień. Ankieta byłą sparametryzowana. Część próby stanowi próba kontrolna, część próba eksperymentalna. </p>
Data from: Grand Theft Empathy? Evidence for the absence of effects of violent video games on empathy for pain and emotional reactivity to violence
<p><strong>Abstract:</strong></p><p>Influential accounts claim that violent video games (VVG) decrease players' emotional empathy by desensitizing them to both virtual and real-life violence. However, scientific evidence for this claim is inconclusive and controversially debated. To assess the causal effect of VVGs on the behavioral and neural correlates of empathy and emotional reactivity to violence, we conducted a prospective experimental study using functional magnetic resonance imaging (fMRI). We recruited eighty-nine male participants without prior VVG experience. Over the course of two weeks, participants played either a highly violent video game, or a non-violent version of the same game. Before and after this period, participants completed an fMRI experiment with paradigms measuring their empathy for pain and emotional reactivity to violent images. Applying a Bayesian analysis approach throughout enabled us to find substantial evidence for the absence of an effect of VVGs on the behavioral and neural correlates of empathy. Moreover, participants in the VVG group were not desensitized to images of real-world violence. These results imply that short and controlled exposure to VVGs does not numb empathy nor the responses to real-world violence. We discuss the implications of our findings regarding the potential and limitations of experimental research on the causal effects of VVGs. While VVGs might not have a discernible effect on the investigated subpopulation within our carefully controlled experimental setting, our results cannot preclude that effects could be found in special vulnerable subpopulations, or in settings with higher ecological validity.<br> </p><p><strong>Dataset:</strong><br>This dataset contains the fMRI data collected for the study in the BIDS-format (https://bids.neuroimaging.io/)</p><ul><li>functional neuroimaging (*_bold.nii.gz) data of 89 human participants, collected during two tasks:<ul><li>Empathy-for-Pain paradigm (Session 1 & 2)</li><li>Emotional Reactivity paradigm (Session 2)</li></ul></li><li>associated event files (*_events.tsv) containing event onsets, durations, and behavioral covariates</li><li>metadata</li></ul><p>FMRI bold timeseries are fully preprocessed, as described in the manuscript.</p><p>Additional data, such as behavioral data in a simpler format, can be accessed on https://osf.io/yx423/</p><p> </p>
Raw experimental data for `Tailoring the Rotational Memory Effect in Multimode Fibers`
<p>Raw data for the article [**Tailoring the Rotational Memory Effect in Multimode Fibers**](https://arxiv.org/abs/2310.19337)</p><p>Measurement of transmission matrices and rotational memory effect for 4 segments of 50 micron core graded index multimode fibers with a numerical aperture of 0.2.</p>
Effect of chitosan-based surfaces on biofilms formed by Cobetia marina
<p>The characterization (roughness and water contact angle) of poly (lactic acid) surfaces coated with chitosan of different molecular weights and concentrations obtained from the <i>Loligo opalescens</i> pen was performed. The antifouling activity of these surfaces against <i>Cobetia marina</i> biofilm formation was evaluated, as well as the mechanism of action of this type of chitosan.</p>
Effect of salinity on flows of dense colloidal suspensions - Additional Data
<p>Additional dataset for the article "Effect of salinity on flows of dense colloidal suspensions".</p>
Data from: Effects of dispersal and geomorphology on riparian seedbanks and vegetation in a boreal stream
<p>SiteData: information that describes 20 riparian zones along Svartån, a boreal free-flowing stream, indicated per LocationID (column A). Coordinates are given in SWEREF 99 TM (column B and C) and degrees of longitude and latitude (column D and E). RPD refers to River Process Domain and takes one of three forms: lake, rapid or slow-flowing. Side of stream indicates plot placement when looking towards downstream. Data collection is described in the paper linked to below. </p> <p> </p> <p>LitterData: information that describes species lists of litter seedbanks from 20 riparian sites. Litter samples were taken in an unstandardised manner at each location. LocationID refers to locations as described in file SiteData, RPD refers to River Process Domain and takes one of three forms: lake, rapid or slow-flowing.</p> <p> </p> <p>SeedData: information that describes the soil seedbank composition from 20 riparian sites. LocationID refers to locations as described in file SiteData, RPD refers to River Process Domain and takes one of three forms: lake, rapid or slow-flowing. Layer refers to samples that are taken from from layer 0-1 cm in the soil, 1-5 cm or from 5-10 cm deep. Data collection is described in the paper linked to below. </p> <p> </p> <p>VegetationData: information that describes vegetation composition from 20 riparian sites. LocationID refers to locations as described in file SiteData, RPD refers to River Process Domain and takes one of three forms: lake, rapid or slow-flowing. Abundance is indicated following the categories in Table 1. Data collection is described in the paper linked to below. </p> <p> </p> <p>Table 1. Vegetation cover classes.</p> <table> <tbody> <tr> <td> <p><strong>Code</strong></p> </td> <td> <p><strong>Cover (%)</strong></p> </td> </tr> <tr> <td> <p>0</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p><1</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>1-3</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>3-5</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>5-15</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>15-25</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>25-50</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>50-75</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p>75-100</p> </td> </tr> </tbody> </table> <p> </p> <p>For more information, help or collaboration, please contact Jacqueline.Hoppenreijs@kau.se. If you use the data here in your work or research, please cite the publication appropriately.</p>
Dataset 1 for Publication: Separation-dependent near-field effects in Mie scattering spectra of two optically trapped aerosol droplets
<p>Dataset for Publication: ASCII files of Mie spectra for each experimentally analysed run, calibrated wavelength files, and brightfield images at each interdroplet separation.</p>
Interior Delta Effects Telemetry Data
<p>This is the telemetry data for an acoustic tagging study that investigated the effect of water management actions on fish survival in the Interior Sacramento Delta, California, USA.</p> <p> </p> <p> </p>
Dataset: The effects of class balance on the training energy consumption of logistic regression models
<p>Two synthetic datasets for binary classification, generated with the Random Radial Basis Function generator from WEKA. They are the same shape and size (104.952 instances, 185 attributes), but the "balanced" dataset has 52,13% of its instances belonging to class c0, while the "unbalanced" one only has 4,04% of its instances belonging to class c0. Therefore, this set of datasets is primarily meant to study how class balance influences the behaviour of a machine learning model.</p>
Probiotics reshape the coral microbiome in situ without detectable off-targeted effects in the surrounding environment.
<p>The R code scripts and Supplementary data files from the paper: "Probiotics reshape the coral microbiome in situ without detectable off-targeted effects in the surrounding environment," accepted in Communications Biology. All R code and data necessary to reproduce the published results are available. </p>
On effective spectral wideband models for clear sky atmospheric emissivity and transmissivity
<p><strong>Overview</strong></p> <p>The HDF5 file contains primary measurement data and secondary processing data that was used to assess clear sky effective emissivity and transmissivity estimates and generate the results in the associated manuscript (accepted and forthcoming).</p> <p>Data is indexed by solar time and provided per site for years 2010 through 2015. Sample Python code is provided to reconstruct training and validation sets by concatenating all 'tra' or 'val' samples across sites. Results can be explored by modifying choice of filters and constructing new training and validation sets.</p> <p><strong>Data usage</strong></p> <p>The usage of the data presented here is intended for research and development purposes only and implies explicit reference to the paper:<br><em>Matsunobu, L. M., & Coimbra, C. F. M. (2024). On effective spectral wideband models for clear sky atmospheric emissivity and transmissivity. Journal of Geophysical Research: Atmospheres, 129, e2023JD039798. https://doi.org/10.1029/2023JD039798</em></p> <p><strong>Data description</strong></p> <p>Column names and descriptions are as follows:<br>- dlw_m: measured downwelling longwave [W/m^2]<br>- ghi_m: measured global horizontal irradiance [W/m^2]<br>- dni_m: measured direct normal irradiance [W/m^2]<br>- dhi_m: measured diffuse horizontal irradiance [W/m^2]<br>- rh_m: measured relative humidity [%]<br>- pa_m: measured atmospheric pressure [hPa]<br>- t_m: measured temperature [K]<br>- sza: solar zenith angle [deg]<br>- ghi_c: clear sky global horizontal irradiance [W/m^2]<br>- dni_c: clear sky direct normal irradiance [W/m^2]<br>- dhi_c: clear sky diffuse horizontal irradiance [W/m^2]<br>- cs1: clear sky filter 1<br>- cs2: clear sky filter 2<br>- site_elev: station elevation [m]<br>- clr_pct: fraction of samples identified as clear for the given site and day<br>- clr_num: number of samples identified as clear for the given site and day<br>- pw_hpa: water vapor partial pressure [hPa]<br>- alt_correction: altitude correction<br>- tra: indicate if sample is included in training set<br>- val: indicate if sample is included in validation set<br>- sqrt_pw: square root of non-dimensional water vapor partial pressure<br>- e_sky: effective clear sky emissivity</p> <p>The last two columns, 'sqrt_pw' and 'e_sky' represent the input and target for linear regression, i.e. e_sky = c_1 + (c_2 * sqrt_pw).<br>Altitude corrected sky emissivity, or expected emissivity for a station at sea-level, is found by e_sky - alt_correction.</p> <p><strong>Sample code (Python v3.8)</strong></p> <pre>import pandas as pd site = "GWC" # or other station code df = pd.read_hdf("data.h5", key=site) # import single site</pre> <p>Training and validation sets can be reconstructed as below. Linear regression on 'sqrt_pw' to predict 'e_sky' - 'alt_correction' in the resultant training set will reproduce results in the associated manuscript.</p> <pre>training = [] validation = [] surfrad_sites = ['BON', 'DRA', 'FPK', 'GWC', 'PSU', 'SXF', 'TBL'] for site in surfrad_sites: # loop through sites df = pd.read_hdf("data.h5", key=site) df["site"] = site # add site name training.append(df.loc[df.tra]) # append samples marked as training validation.append(df.loc[df.val]) # append samples marked as validation # join respective set samples across sites training = pd.concat(training, ignore_index=False) validation = pd.concat(validation, ignore_index=False)</pre> <p>Reproduce regression results</p> <pre>from sklearn.linear_model import LinearRegression c1 = 0.6 # set intercept (c1 constant) x = training.sqrt_pw.to_numpy().reshape(-1, 1) y = training.e_sky - training.alt_correction - c1 # adjust for altitude and c1 y = y.to_numpy().reshape(-1, 1) model = LinearRegression(fit_intercept=False) model.fit(x, y) c2 = model.coef_[0][0] print(f"c1={c1:.3f}, c2={c2:.3f}") # output: c1=0.600, c2=1.652</pre>
Effect of Surfactants on 1,2-Dichloroethane-in-Water Droplet Impacts at Electrified Liquid-Liquid Interface
<p>The data set for the submited publication "Effect of Surfactants on 1,2-Dichloroethane-in-Water Droplet Impacts at Electrified Liquid-Liquid Interface". </p>
Data from: The effect of probe density coverage on the detection of oenological tannins in quartz crystal microbalance with dissipation monitoring (QCM-D) experiments
<p>Polyphenols, crucial compounds in grapes, musts, and wines, influence grape ripening, must fermentation, and final wine quality. Current detection methods for polyphenols are expensive, time-consuming, and reliant on specialized laboratories and personnel. This study proposes the use of a functionalized acoustic sensor to address these limitations and efficiently detect oenological polyphenols.</p> <p>The method employs a quartz crystal microbalance with dissipation monitoring (QCM-D) combined with a gelatin-based probe layer to detect the target analyte. The sensor is functionalized by optimizing probe coverage density, accomplished through the use of 12-mercaptododecanoic acid (12-MCA) for probe immobilization onto the gold sensor surface, along with dithiothreitol (DTT) as a reducing and competitive binding agent. Varying concentrations of 12-MCA and DTT allow for control over probe density, with QCM-D measurements demonstrating effective adjustment, ranging from 0.2 × 10^13 to 2 × 10^13 molecules cm^−2. The study also explores the interaction between the probe and tannins, confirming the ability of the sensor to detect them. Notably, lower probe coverage yields higher detection signals when normalized to probe immobilization signals. Additionally, significant alterations in the mechanical properties of the functionalization layer occur after interaction with samples.</p> <p>Combining QCM-D with gelatin functionalization presents promising applications in the wine industry. This approach enables real-time monitoring, requires minimal sample preparation, and offers high sensitivity for quality control purposes.</p>
Supplementary data for "Effect of Uncertainty in Water Vapor Continuum Absorption on CO2 Forcing, Longwave Feedback, and Climate Sensitivity"
<h3>This dataset is supplementary to the article "Effect of Uncertainty in Water Vapor Continuum Absorption on CO2 Forcing, Longwave Feedback, and Climate Sensitivity".</h3> <h3>spectral_olr.nc</h3> <p>This file contains the spectral outgoing longwave radiation (OLR) calculated using the line-by-line radiative transfer model ARTS and the radiative-convective equilibrium model konrad. It contains spectral OLR for surface temperatures from 270K to 330K for different strengths of the water vapor continuum absorption.</p> <h3>opacity_emission_level.py</h3> <p>This file also contains the spectrally resolved optical depth and the emission level of outgoing longwave radiation for the considered absorption species (H2O lines, H2O continuum, H2O self continuum, H2O foreign continuum, CO2, N2, and O2).</p> <h3>continuum_reference_conditions.nc</h3> <p>This file contains the reference continuum absorption coefficients that were used to calculate the adjustment to the foreign continuum for the single-constraint experiment.</p> <h3>continuum_all_profiles.nc</h3> <p>This file contains the reference continuum absorption coefficients that were used to calculate the adjustment to the foreign continuum for the general-constraint experiment.</p> <h3>modified_continuum_input_files_single_constraint.zip and modified_continuum_input_files_general_constraint.zip</h3> <p>These files contain the modified continuum data files used for the implementation of the MT_CKD continuum model in the line-by-line model ARTS for the single-constraint and general-constraint experiments, respectively.</p> <h3>tau_column.nc and tau_profile.nc</h3> <p>These files contain separately for each absorption species the vertically integrated opacity spectra, and the opacity profiles at two selected wavenumbers.</p> <p> </p>
Dataset for "Effect of the atomic structure of complexions on the active disconnection mode during shear-coupled grain boundary motion"
<p>This repository contains the data of the simulations and theoretical<br>calculations of the paper "Effect of the atomic structure of complexions on the active disconnection mode during shear-coupled grain boundary motion".</p>
Supplementary data: Winter cover cropping: Effect on soybean and synergistic implications on soil microbiome
<p>Supplementary data: (i) Agronomic and quality data of soybean (2 varieties) grown in 2 years (2020 & 2021) in two management systems (organic & low-input) with different cover crops; (ii) Soil microbiome analysis of the soybean field trials.</p>
BIONANO-MSCA4U. Biosynthesis of AgNP from Pseudomonas N5.12 and antimicrobial effect
<p>This dataset presents the collected data corresponding to characterization of the biosynthetized AgNP with <em>Pseudomonas</em> N5.12. Listing of all the different data collected, produced, and published are available open-access for the EU-funded <strong>MSCA4Ukraine project (ID:101101923)</strong> combined in different format (in .cvs, .xlsx, .txt and .pdf versions).</p> <p><em>Abbreviation of sample names: S1-S5 – different ratio of bacterial supernatant and 1 mM AgNO3 solution: <code>S1</code> (5:1), <code>S2</code> (4:2), <code>S3</code> (3:3), <code>S4</code> (2:4), <code>S5</code> (1:5). The next letter indicates the pH of the medium (<code>7</code> or <code>9</code>) at which the samples were synthesized.</em></p>
The Effects of Plant-Microbe-Environment Interactions on Mineral Weathering Patterns in a Granular Basalt
<p>Data used in the Milici et al. <em>Geobiology </em>article "The Effects of Plant-Microbe-Environment Interactions on Mineral Weathering in Granular Basalt". The data result from a greenhouse experiment in which 14 genotypes of Alfalfa <em>(Medicago</em> sativa) were grown in an unweathered granular basaltic tephra, exposed to an early successional soil microbial community, and replicated across three different soil moisture treatments. This experiment seeks to identify the roles of vascular plants and soil microbes on mineral weathering. Please see the article for full project description. </p> <p>General File Descriptions:</p> <p>"AllPerformanceGeochem.csv" contains both the performance and geochemistry data associated with each plant grown in the experiment and is used for the majority of the analyses.</p> <p>"FullCensusTimeSeries.csv" contains the growth and survival data for the plants across the entire 3 month duration of the experiment and is used only to calculate survival rate and growth rate.</p> <p>"pottingsoilmass.csv" contains the data for alfalfa grown in potting soil and is used to compare how much the basalt limited plant growth relative to a potting soil control. </p> <p>These data are cleaned and formatted for analysis via the code in the github repository linked to this data repository. </p> <p> </p>
Resources for Mitigating Chemotherapy Side Effects through Targeted Gamma-Ray Delivery and CNNs
<p>This repository includes datasets and code used in the study "Mitigating Chemotherapy Side Effects through Targeted Gamma-Ray Delivery and Convolutional Neural Networks." The resources comprise:<br>- Binding Affinity Data: Used for simulations.<br>- Brain Tumor MRI and Chest CT Scan Datasets: Used for model training.<br>- Lightweight Deep CNN: Code for building and testing models.</p>
Disentangling the effects of eutrophication and natural variability on macrobenthic communities across French coastal lagoons
<p>We present here the raw data and scripts to reproduce the results presented in the preprint "Disentangling the effects of eutrophication and natural variability on macrobenthic communities across French coastal lagoons" available on BioRxiv. Before using the scripts and associated data, we recommend reading the "readme" word document also available, which details the information available in the different data sheets. </p> <p>Preprint abstract : </p> <p>Coastal lagoons are transitional ecosystems that host a unique diversity of species and support many ecosystem services. Owing to their position at the interface between land and sea, they are also subject to increasing human impacts, which alter their ecological functioning. Because coastal lagoons are naturally highly variable in their environmental conditions, disentangling the effects of anthropogenic disturbances like eutrophication from those of natural variability is a challenging, yet necessary issue to address. Here, we analyze a dataset composed of macrobenthic invertebrate abundances and environmental variables (hydro-morphology, water, sediment and macrophytes) gathered across 29 Mediterranean coastal lagoons located in France, to characterize the main drivers of community composition and structure. Using correlograms, linear models and variance partitioning, we found that lagoon hydro-morphology (connection to the sea and lagoon surface), which affects the level of environmental variability (salinity and temperature), as well as lagoon-scale benthic habitat diversity (using macrophyte morphotypes) seemed to regulate macrofauna distribution, while eutrophication and associated stressors like low dissolved oxygen, acted upon the existing communities, mainly by reducing species richness and diversity. Furthermore, M-AMBI, a multivariate index composed of species richness, Shannon diversity and AMBI (AZTI's Marine Biotic Index) and currently used to evaluate the ecological state of French coastal lagoons, was more sensitive to eutrophication (18%) than to natural variability (9%), with nonetheless 49% of its variability explained jointly by both. To improve the robustness of benthic indicators like M-AMBI and increase the effectiveness of lagoon benthic habitat management, we call for a revision of the ecological groups at the base of the AMBI index and of the current lagoon typology which could be inspired by the lagoon-sea connection levels used in this study. </p>
ScienceDex guides
Understand access before you commit
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.