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8,547 results for “Characterization”
Dataset for a publication: "A zinc phosphate layered biodegradable Zn-0.8Mg-0.2Sr alloy: Characterization and mechanism of hopeite formation"
<p>These data are published as part of the paper: A zinc phosphate layered biodegradable Zn-0.8Mg-0.2Sr alloy: Characterization and mechanism of hopeite formation. The structure and organization of the data are outlined in the readme file. </p> <p> </p>
Application and characterization of poly(vinyl alcohol) reinforced with cellulose nanofibrils as a coating for wood – Supplementary material
<p>Supplementary material to the article "Application and characterization of poly(vinyl alcohol) reinforced with cellulose nanofibrils as a coating for wood"</p>
Dataset of "Synthesis and Characterization of Soluble Pyridinium Containing Copolyimides"
<p>Anion selective polymer membrane based on the copolyimides of ionene based on ODPA, BIS P and DAP were synthetized. Copolyimides were prepared by thermal imidization followed by quaternization. Characterisation by FTIR, NMR, SEM, EDX , TGA, DSC and EIS were performed. It was shown that content of the DAP in the membrane has significant effect on the stability of the membrane.</p>
CAMELS-LUX: Highly Resolved Hydro-Meteorological and Atmospheric Data for Physiographically Characterized Catchments around Luxembourg
<p>The CAMELS-LUX dataset encompasses hydro-meteorological time series and catchment attributes for 56 partly nested stream gauges feeding into the Luxembourgish stream network. The data is available at three temporal resolutions: daily, hourly and at a 15-minute resolution and spans the hydrological years from 2004-11-01 to 2021-10-31. The static catchment attributes cover parameters classifying the topography, geology and land use as well as climatic and hydrologic annual statistics of the 17-year time period.</p> <p>While an in depth description of the dataset as well as background information on catchments, the environment and exact calculation methods is provided in the accompanying publication in ESSD, the dataset description below isolates information on the available parameters and data structure contained in the provided files.</p> <p>Please note that the dataset might not include data corrections or validations that are subject to a date later than the date of the retrieval of the data for the processing of this dataset. This dates back to 2022 for most hydrologic time series, and to 2023 for the reanalysis data or the precipitation data. We are aware of duplicate rows in the time series file with a resolution of 15 minutes for catchment 16 as well as time stamp shifts in the precipitation data. We are working on correcting these data to update this dataset.</p> <p><strong>Data structure</strong></p> <p><strong>Time series data</strong></p> <ol> <li>Hydrologic parameters</li> <li>Precipitation parameters</li> <li>Air temperature and potential evapotranspiration parameters</li> <li>Thunderstorm relevant atmospheric parameters</li> <li> Soil Moisture parameters</li> </ol> <p><strong>Static catchment attributes</strong></p> <ol> <li>Basin IDs</li> <li>Meta catchment attributes</li> <li>Climatic catchment attributes</li> <li>Geologic catchment attributes</li> <li>Land use catchment attributes</li> <li>Topographic catchment attributes</li> </ol> <p><strong>Spatial data - shapefiles</strong></p>
Characterization of the angular-dependent emission of nitrogen-vacancy centers in nanodiamond
<p>We report on the characterization of the angular-dependent emission of single-photon emitters based on single nitrogen-vacancy (NV-) centers in nanodiamond at room temperature. A theoretical model for the calculation of the angular emission patterns of such an NV-center at a dielectric interface will be presented. For the first time, the orientation of the NV-centers in nanodiamond was determined from back focal plane images of NV-centers and by comparison of the theoretical and experimental angular emission pattern. Furthermore, the orientation of the NV-centers was also obtained from measurements of the fluorescence intensity in dependence on the polarization angle of the linearly polarized excitation laser. The results of these measurements are in good agreement. Moreover, the collection efficiency in this setup was calculated to be higher than 80% using the model of the angular emission of the NV-centers.</p>
EPR Characterization of the Heme Domain of a Self-Sufficient Cytochrome P450 (CYP116B5)
<p><strong>Description of the dataset: </strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation.</li> <li>Files are with filename extensions: .<strong>DSC</strong>, .<strong>DAT</strong>, .<strong>m</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions .<strong>DSC</strong>, .<strong>DTA.</strong></li> <li>EPR spectroscopic simulation with filename extension .<strong>m</strong>.</li> </ul> <ul> <li>CW and Pulse X-band experiments were performed on a Bruker Elexys E580 X-band spectrometer (microwave frequency 9.68 GHz) equipped with a cylindrical dielectric cavity and a helium gas-flow cryostat from Oxford Inc.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP5_20220225_CW</strong> folder includes X-band CW-EPR spectroscopic measurements, original data are in DTA/DSC/txt.</li> <li>Files in <strong>PARACAT_WP5_20220225_HYSCORE</strong> folder includes HYSCORE spectroscopic measurements, data are in .DTA, .DSC, .txt formats.</li> <li>Files in <strong>PARACAT_WP5_20220225_MATLAB</strong> folder includes computer simulations/analyses of the EPR measurements, data are in .m formats.</li> </ul> </li> </ul>
Supplemental data for characterization of alpha and beta interactions using the HeXe setup [Eur. Phys. J. C 82, 361]
<p>Repository with supplemental data to:<br> <strong>Characterization of alpha and beta interactions in liquid xenon</strong>. Jörg, F., Cichon, D., Eurin, G. <em>et al. Eur. Phys. J. C</em> <strong>82, </strong>361 (2022) <a href="https://doi.org/10.1140/epjc/s10052-022-10259-3">10.1140/epjc/s10052-022-10259-3</a><br> A pre-print of the article is available <em>on arXiv: </em><a href="http://arxiv.org/abs/2109.13735">2109.13735</a></p> <p><strong>Note: </strong>When re-using the data, please make sure to cite the article (and not only the dataset)</p> <p><br> The files contain the measured data points (as well as their statistical and systematic uncertainties) as shown in the publication.<br> All datasets are stored in the .csv format.</p> <ul> <li><strong>20210924_yields_hexe_kr83m.csv</strong><br> This file contains the normalized light and charge yields as a function of the applied field from the measurement with the <sup>83m</sup>Kr source. The data is shown in Figure 16 (dots) of the publication. Furthermore the file contains the LY ratio between the two Isomeric transitions of the <sup>83m</sup>Kr source, shown in Figure 17 of the article.</li> <li><strong>20210924_yields_hexe_rn222.csv</strong><br> This file contains the normalized light and charge yields as a function of the applied field from the measurement with the <sup>222</sup>Rn source. The data is shown in Figure 18 (blue-ish points) of the publication</li> <li><strong>20210924_drift_velocity_hexe_rn222.csv</strong><br> This file contains the measured electron drift velocity in liquid xenon at a temperature of 174.4 K in dependence of the field. The data was acquired using the <sup>222</sup>Rn source. Drift velocity is given in units of mm/µs and the datapoints are shown in Figure 20 (black dots) of the publication </li> <li><strong>20210924_drift_velocity_hexe_kr83m.csv</strong><br> This file contains the measured electron drift velocity in liquid xenon at a temperature of 174.4 K in dependence of the field. The data was acquired using the <sup>83m</sup>Kr source. Drift velocity is given in units of mm/µs and are not displayed in the publications due to visibility reasons.</li> </ul> <p><strong>Minimum working example to plot the drift velocity using the <sup>83m</sup>Kr data:</strong></p> <pre><code class="language-python"> 1 import numpy as np 2 import matplotlib.pyplot as plt 3 4 # load the data set 5 data = np.loadtxt("20220427_drift_velocity_hexe_kr83m.csv", delimiter=",") 6 7 # Plot the systematic uncertainty on the drift field 8 plt.errorbar(data[:,0], data[:,2], xerr=data[:,1], fmt="o", capsize=2, ecolor="darkgray", 9 alpha=0.7, elinewidth=3, color="black") 10 11 # Plot the actual data points 12 plt.errorbar(data[:,0], data[:,2], yerr=data[:,3], fmt="o", color="black") 13 14 # Label the axis and define the range 15 plt.ylabel("Drift Velocity [mm/µs]") 16 plt.xlabel("Drift Field [kV/cm]") 17 plt.xscale("log") 18 plt.xlim(0.006, 2) 19 plt.ylim(0, 2.4) 20 plt.show() </code></pre> <p> </p>
DATASET: characterization of the seed coat extractable phenolic profile and color in 308 common bean lines of the Spanish Diversity Panel
<p>Characterizarion of the seed coat extractable phenolic profile and color in 308 common bean lines of the Spanish Diversity Panel</p>
Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer
<p>Dataset of the paper "Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer" published in Remote Sensing [1].</p> <p>[1] Brugger P, Fuertes FC, Vahidzadeh M, Markfort CD, Porté-Agel F. Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer. <em>Remote Sensing</em>. 2019; 11(19):2247. https://doi.org/10.3390/rs11192247.</p>
Characterization of TiO2/Fe2O3 nanocomposites prepared via impregnation-calcination method
<p>The link contains XRD, SEM-EDX, UV-DRS, PL, Electrochemical measurements of the prepared TiO<sub>2</sub>/Fe<sub>2</sub>O<sub>3</sub> photocatalyst</p>
Spatial characterization of the motor and non-motor somal and axonal transcriptome in adult healthy and mutant FUS mice
<table> <tbody> <tr> <td> <p>Here we investigated the transcriptome of motor and non-motor axons and cell bodies in the context of mutant FUS-related amyotrophic lateral sclerosis (ALS). We applied Nanostring GeoMX Digital Spatial Profiler platform to profile the transcriptome of subcellular compartments in the lower motor circuitry of a mouse model ricapitulating ALS motor symptoms. This work sheds light for the first time on the transcriptomic alterations in axons and in somas which may contribute to axonal degeneration and neuromuscular junction denervation, early features of ALS.</p> </td> </tr> </tbody> </table>
PIBE project- Experimental characterization of stall noise in static and dynamic regimes using a NACA 63(3)418 airfoil
<p>Dynamic stall noise is one of the potential sources of amplitude modulations associated with wind turbine noise. This phenomenon is related to the periodic separation and reattachment of the boundary layer on the wind turbine blade suction side during its rotation. Within the framework of the PIBE project (Predicting the Impact of Wind Turbine Noise - <a href="https://www.anr-pibe.com/en">https://www.anr-pibe.com/en</a>), experiments were conducted in the anechoic wind tunnel of the École Centrale de Lyon in order to characterize stall noise on a pitching airfoil in both static and dynamic conditions.</p> <p>In version 1.0.0 of the database, <span>data from the second campaign using an instrumented NACA63(3)418 airfoil in static and dynamic conditions are provided. The static data can be found in the file static_data_NACA63418.h5 that contains:</span></p> <ol> <li>static wall pressure data : lift and pressure coefficients;</li> <li>dynamic wall pressure data : Power Spectral Density (PSD) of fluctuating wall pressure;</li> <li>far-field acoustic data : Power Spectral Density (PSD) of acoustic pressure.</li> </ol> <p><span>The structure of the file is described in Tree_structure_static_data.pdf. To read the HDF5 file, the Matlab scripts given in read_HDF5_NACA63418_static_Matlab.zip can be used.</span></p> <p><span>The dynamic data can be found in the file dynamic_data_NACA63418.h5 that contains:</span></p> <ol> <li><span>static wall pressure data : phase-averaged lift coefficients;</span></li> <li><span>dynamic wall pressure data : phase-averaged spectrograms of fluctuating wall pressure;</span></li> <li><span>far-field acoustic data : phase-averaged spectrograms of acoustic pressure.</span></li> </ol> <p><span>The structure of the file is described in Tree_structure_dynamic_data.pdf. To read the HDF5 file, the Matlab scripts given in read_HDF5_NACA63418_dynamic_Matlab.zip can be used. Only the results for a mean angle of attack of 15° and an amplitude of 15° are provided in this file.</span></p>
Supplemental catalogs for "The Sloan Digital Sky Survey Reverberation Mapping Project: Sample Characterization"
<p>We have compiled additional properties for the SDSS-RM sample in several ancillary catalogs. Below are the notes on these supplemental catalogs. There are .readme files for each additional catalog. We also include the quality assurance plots for the global spectral fits.</p> <p><strong>QA-0000-56837.ps.gz </strong>The full set of 849 quality assessment plots for the global spectral fitting. Each plot includes a top panel showing the continuum (brown) and Fe II (blue) model components; the red line is the sum of the two. The cyan diamonds are pixels masked as absorption or bad pixels. The gray brackets near the top of the panel indicate the windows used for the continuum+Fe II fit. The bottom panels present the emission line fits for five line complexes.</p> <p><strong>allqso_sdssrm.fits</strong> A FITS table of all 1214 known quasars in the 7 square degree SDSS-RM field. Only 849 of them received a fiber in the SDSS-RM spectroscopy. This table lists the basic target information of these quasars.</p> <p><strong>QSObased_Expanded_SDSSRM_107.fits</strong> The narrow MgII/FeII absorber catalog for SDSS-RM quasars, following the methodology outlined in Zhu & Ménard (2013). Each entry corresponds to one quasar. The search for narrow absorbers includes systems that have absorber redshift close to the quasar systemic redshift (|dz|<0.04). MgII absorbers blueshifted from the quasar by dz>0.04 and also redward of CIV by dz>0.02 are of high purity. MgII absorbers with |dz|<0.04 or those at wavelength blueward of CIV, or those with FeII detection but no MgII detections (likely due to bad pixels), while included in this catalog, should be treated with caution, and may contain a small fraction of false positives (mainly CIV absorbers).</p> <p>For convenience, we also provide a version of the absorber catalog organized by absorbers (<strong>Expanded_SDSSRM_107.fits</strong>), i.e., each entry corresponds to one absorber system.</p> <p><strong>rmqso32_aegis_multi_lambda.fits</strong> Multi-wavelength data compiled from Nandra et al. (2015) or 32 SDSS-RM quasars in the AEGIS field.</p> <p><strong>spitzer_seip_rm_match_1.5arcsec.fits</strong> Spitzer IRAC and MIPS data from the Spitzer Enhanced Imaging Products (SEIP) source list for 176 SDSS-RM quasars, with a matching radius of 1.5 arcseconds. This file also compiles infrared fluxes (if available) from 2MASS (Skrutskie et al. 2006).</p> <p><strong>spec_2014_BALrobust.csv</strong> List of 95 BALQSOs (including mini-BALQSOs) identified from the first-year coadded spectroscopy. This file includes BAL flags on CIV, AlIII, MgII, and FeII/FeIII. It also includes notes on individual objects.</p> <p><strong>PS1_MD07_LC_sdssrm.fits</strong> PS1 Medium Deep light curves for the SDSS-RM quasars used to compute PS1_NMAG_OK and PS1_RMS_MAG in the main catalog. Note this is the unofficial release of the PS1 MD07 data, which was approved by the PS1 collaboration. These photometric light curves may differ slightly from the final official release of the PS1 Medium Deep field data. </p>
Generated WSP: Validation of a water-sensitive paper-based method for the characterization of agricultural spray droplets
<p>Synthetic images were generated in a Python environment using the OpenCV library to replicate the distribution of droplets in WSP. The images display droplet stains represented by blue circles (255,0,0) on a yellow background (0,255,255) to enhance contrast and enable more precise analysis. The synthetic images were created in two distinct resolutions, namely 640x480 and 2560x1440 pixels, with the aim of reproducing the output of two specific digital microscopes: the Jiusion 640x480 and the Jiusion HD 2560x1440 (Shenzen, China). The resolution is chosen based on the expected practical application, ensuring that any image analysis algorithm developed can effectively process images with similar characteristics to those obtained under real conditions by these microscopes. Each pixel in this configuration corresponds to a physical size of 18.125 µm in images with a resolution of 640x480, and a size of 6.875 µm in images with a resolution of 2560x1440. Multiple patterns were created to simulate various configurations of droplet stains in WSP. The sizes of single droplet stains varied between 100 and 600 µm, with spacings of either 1000 µm or 2000 µm between drops (see attached figure). Furthermore, the same size range was utilised to generate patterns with double and overlaid droplet stains, with a consistent spacing of 2800 µm between each stain (see attached figure). The implementation of this systematic method guarantees the accurate calibration and application of image analysis algorithms in real-world situations. This allows for the representation of precise measurements and spacing that would be encountered in actual experimental conditions.</p>
Identification and characterization of the cell division protein MapZ of Streptococcus suis
<p>Supplementary data and code related to the manuscript "Identification and characterization of the cell division protein MapZ of <em>Streptococcus suis</em>".</p>
Enhanced 3D velocity structure, seismicity relocation and basement characterization of Changning shale gas and salt mining regions in Sichuan Basin
<p>This repository contains the datasets and results of the joint inversion-based Vp/Vs model consistency constrained double difference seismic tomography carried out for the manuscript titled “Enhanced 3D velocity structure, seismicity relocation and basement characterization of Changning shale gas and salt mining regions in Sichuan Basin.” Included are the following: column descriptions of data files, catalog earthquake information (CX_event.dat), relocated events after inversion (CX_tomoDDMC.reloc), inverted Vp model (CX_Vpmodel.dat), inverted Vs model (CX_Vsmodel.dat) and inverted Vp/Vs model (CX_VpVsmodel.dat). Please consult the manual for tomoDD by Zhang and Thurber (2003) for detailed formats of these files. In addition, an averaged velocity model (MOD_averaged) computed based on the inversion results is included, and the converged model (Vp_model_reinverted) resulting from the reinversion, as well as basement structure data for Figure 14.</p>
Supporting data for manuscript "Geochemical Characterization of Insoluble Particle Clusters in Ice Cores Using Two-dimensional Impurity Imaging"
<p>Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) offers micron-resolution 2D chemical imaging, which has been adapted recently to ice core analysis. The datasets are supporting information for the manuscript "Geochemical Characterization of Insoluble Particle Clusters in Ice Cores Using Two-dimensional Impurity Imaging" accepted for publication at Geochemistry, Geophysics, Geosystems (10.1029/2022GC010595). Measurements were performed at the Ca’Foscari University of Venice, considered as analytes are 23Na, 24Mg, 27Al, 29Si, 43Ca, 56Fe and 88Sr. Background and drift correction as well as image construction were performed using the software HDIP (Teledyne Photon Machines, Bozeman, MT, USA). Impurity maps are acquired as a pattern of lines, without overlap in the direction perpendicular to that of the scan, and without any further spatial interpolation. In a sample of the EGRIP Greenland ice core (from about 1256.95 m depth), maps were obtained over 3 adjacent areas. For each of the maps, for every chemical channel the intensities (in counts, after background and drift correction) are provided as a separate file, named as “ds01_Area1_Na.csv”, etc. These maps were obtained using a 20 µm square spot. This data can be used to obtain the images shown in the manuscript. For the additional map shown as Figure 9 in the manuscript, data were obtained using a LA-ICP-TOFMS for imaging a sample of the last glacial period in the EPICA Dome C (EDC) ice core, bag 1065. The maps were acquired using a 35 µm square spot, with 50% overlap between neighboring pixels to increase the spatial resolution horizontally.</p>
Electrophysiological characterization and functionality of neurons in murine cerebral organoids.
<p>Dataset includes patch-clamp recordings aim to characterize functionality of neurons belonging to murine cerebral organoids.</p> <p>Patch-clamp recordings were performed by University of Modena and Reggio Emilia unit (PI Prof. Curia Giulia).</p> <p>Organoids were generated by University of Verona unit (PI Prof. Decimo Ilaria) and transferred to Modena for electrophysiology experiments.</p>
Supplementary data: Agro-morphological and molecular characterization reveal deep insights in promising genetic diversity and marker-trait associations in Fagopyrum esculentum and F. tataricum
<p>Our study focuses on the global/European buckwheat germplasm collected as part of the ECOBREDD project. The potential of this highly diverse collection for organic buckwheat breeding was evaluated at two complementary levels: phenotypic and genetic. Here, we characterized the phenotypic and genetic diversity of a global collection of the two cultivated buckwheat species <em>Fagopyrum esculentum</em> and <em>F. tataricum</em> (190 and 51 accessions, respectively) using 37 agro-morphological traits and 24 SSR markers (Simple Sequence Repeats) (see publication and info sheet of the data).</p>
Characterization of investments profiles on the energy transition for european citizens
<ul> <li><strong>Name</strong>: Characterization of investments profiles on the energy transition for european citizens</li> <li><strong>Summary</strong>: The dataset contains: (1) surveyee consent form for the study, (2) different scenarios about the energy transition, (3) determinant factors about those scenarios, (4) socioeconomic description of the surveyee, (5) investment decisions, (6) and household characterization/description. </li> <li><strong>License</strong>: cc-BY-SA</li> <li><strong>Acknowledge</strong>: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.</li> <li><strong>Disclaimer</strong>: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European commission (Ec). EASME or the Ec are not responsible for any use that may be made of the information contained therein.</li> <li><strong>Collection Date</strong>: 22/07/2022</li> <li><strong>Publication Date</strong>: 15/10/2023</li> <li><strong>DOI</strong>: 10.5281/zenodo.4455198</li> <li><strong>Other repositories:</strong></li> <li><strong>Author</strong>: University of Deusto</li> <li><strong>Objective of collection</strong>: This data was originally collected to analyze quantitatively the decisions of everyday people in relation to their energy consumption and their reactions to specific political interventions.</li> <li><strong>Description:</strong> The dataset contains a CSV file file containing data collected from a survey about energy consumption investments. The fields that can be found for each entry are (1) Different scenarios about the energy transition and reactions to those scenarios, (money spent on energy investments, decisions about scenarios, actions taken under a blackout, etc.) (2) Determinant factors about the chosen scenarios in the previous question, which include different choices that could affect your decision about a scenario (3) socioeconomic information about the user (age, country of residence, studies), (4) estimation of the prices of various technologies related to the energy transition and (5) descriptive statistics about the household living situation (gender of user, people living in household, yearly rent, average savings per month, type of house, size of house) and also includes questions about climate change expertise. Next you can found a description of each field in the dataset <ul> <li><strong>Section 1 - Scenarios for energy transition.</strong> <ul> <li><strong>ID90.</strong> Rank in order of priority, from top to bottom, in which scenario you will be willing to live or to contribute/invest to make it possible. </li> <li><strong>ID36, ID38, ID43, ID44, ID72. </strong>Percentage of money people are willing to spend/save out of their income per scenario</li> <li><strong>ID191, ID192</strong>.. Amount of money people would spend based on an assumed case.</li> <li><strong>ID191, ID192. </strong>Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority for you and 10 stars means it is absolutely necessary for you.</li> <li><strong>[ID325, ID326, ID327, ID328, ID329, ID330, ID331, ID332, ID333, ID334, ID335, ID336, ID337, ID338, ID339, ID340, ID341, ID133, ID242]</strong>. Priority service provision in case of <em>Intermittent energy service</em>. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary.</li> <li>[<strong>ID251, ID256, ID257, ID292, ID293, ID294, ID295, ID296, ID297, ID298, ID299, ID301, ID302, ID303, ID304, ID305, ID306, ID250, ID251</strong>]. Priority service provision in case of <em>full </em><em>black-outs</em>. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary.</li> <li>[<strong>ID141, ID5, ID147</strong>]. Used for statements that best represent survey responder</li> </ul> </li> <li><strong>Section 2 - Determinants (factors).</strong> Questions used to rate (from 0 to 100) factors that may influence the decision-making process contributing to make an ideal scenario possible. <ul> <li><strong>ID100</strong> Risk profile</li> <li><strong>ID101</strong> Added value</li> <li><strong>ID102</strong> Self-Satisfaction</li> <li><strong>ID103</strong> Technical Fit</li> <li><strong>ID104</strong> Own competence</li> <li><strong>ID105</strong> Knowledge</li> <li><strong>ID106</strong> Cost-Efficiency</li> <li><strong>ID107</strong> Safety</li> <li><strong>ID108</strong> Trust</li> <li><strong>ID109</strong> Autarky</li> <li><strong>ID110</strong> Legal</li> <li><strong>ID111</strong> Climate Protection</li> <li><strong>ID112</strong> Wellbeing</li> <li><strong>ID113</strong> Coziness</li> <li><strong>ID114</strong> Rights and Duties</li> <li><strong>ID115</strong> Peer-Pressure</li> <li><strong>ID116</strong> Socialising</li> <li><strong>ID117</strong> Support</li> <li><strong>ID118</strong> Agreement</li> <li><strong>ID119</strong> Brag</li> <li><strong>ID120</strong> Fun</li> <li><strong>ID121</strong> Novelty</li> <li><strong>ID122</strong> Trends</li> <li><strong>ID123</strong> Authority</li> <li><strong>ID124</strong> Own Significance</li> <li><strong>ID125</strong> Poseur</li> <li><strong>ID2</strong> Frugality</li> <li><strong>ID3</strong> Environmental concerns</li> <li><strong>ID31</strong> Adherence</li> <li><strong>ID52</strong> Commitment</li> <li><strong>ID97</strong> Profits</li> <li><strong>ID99</strong> Credit Score</li> </ul> </li> <li><strong>Section 3 - “Socio-economic” description. </strong>Questions about the socio-economic information of the survey respondents for data stratification. The indentation represents the dependency of questions and whether this data was asked <ul> <li><strong>ID164</strong> Understanding of questions</li> <li><strong>ID300</strong> Country of residence</li> <li><strong>ID137</strong> Age</li> <li><strong>ID178</strong> Highest level of education</li> <li><strong>ID136</strong> Willingness to provide data on the investment decision (respond apply for -Investment decision section)</li> </ul> </li> <li><strong>Section 4 - Investment decision</strong>. Questions about specific prices of potential purchases-decisions related to four scenarios (respondent's lifestyle) <ul> <li>Appliances <ul> <li><strong>ID42</strong> Affordable cost of a Regular refrigerator</li> <li><strong>ID45</strong> Energy efficient refrigerator costs</li> <li><strong>ID50</strong> Willingness to purchase an energy efficient refrigerator <ul> <li><strong>ID65</strong> Why no</li> <li><strong>ID66</strong> affordable cost of an energy efficient option</li> <li><strong>ID67</strong> Years to amortize an efficient option</li> </ul> </li> </ul> </li> <li>Insulation <ul> <li><strong>ID47</strong> Affordable cost of updating to a state of the art insulation on the facade</li> <li><strong>ID56</strong> Willingness for paying/invest <ul> <li><strong>ID74</strong> Why no?</li> <li><strong>ID20</strong> affordable cost of an energy efficient option</li> <li><strong>ID34</strong> Years to amortize an energy efficient option</li> </ul> </li> </ul> </li> <li>Energy Generation <ul> <li><strong>ID68</strong> Affordable cost of a solar photovoltaic system</li> <li><strong>ID76</strong> Willingness for paying/invest <ul> <li><strong>ID84</strong> Why no?</li> <li><strong>ID132</strong> Affordable cost of a photovoltaic system</li> <li><strong>ID138</strong> Years that amortize a photovoltaic system</li> </ul> </li> </ul> </li> <li>Energy Storage <ul> <li><strong>ID142</strong> Affordable cost of an energy storage system</li> <li><strong>ID146</strong> Willingness for paying/invest <ul> <li><strong>ID181</strong> Why no? </li> <li><strong>ID182</strong> Affordable cost of an energy storage system </li> <li><strong>ID183</strong> Years that amortize an energy storage systems</li> </ul> </li> </ul> </li> <li>Heating <ul> <li><strong>ID140</strong> Affordable cost of a gas boiler</li> <li><strong>ID209</strong> Affordable cost of an energy efficient heating system</li> <li><strong>ID217</strong> Willingness for paying/invest <ul> <li><strong>ID238</strong> Why no?</li> <li><strong>ID239</strong> Affordable cost of a energy efficient option</li> <li><strong>ID241</strong> Years that amortize a heat pumps</li> </ul> </li> </ul> </li> <li>Mobility <ul> <li><strong>ID41</strong> Average kilometers traveled a typical day</li> <li><strong>ID51</strong> Usual travel option</li> <li><strong>ID264</strong> Affordable cost of a diesel or gasoline mid-range brand new car</li> <li><strong>ID265</strong> Affordable cost of a mid-range brand new electric car</li> <li><strong>ID281</strong> Willingness to buy an electric car <ul> <li><strong>ID289</strong> Why no?</li> <li><strong>ID290</strong> Affordable price of an electric car</li> <li><strong>ID291</strong> Years that amortize an electric car</li> </ul> </li> </ul> </li> </ul> </li> <li><strong>Section 5 - Household characterization</strong> <ul> <li><strong>ID127</strong> Selecting an asked value</li> <li><strong>ID189</strong> Type of living area</li> <li><strong>ID202</strong> Gender identity</li> <li><strong>ID1</strong> Those living in the house</li> <li><strong>ID32</strong> Number of inhabitants</li> <li><strong>ID220</strong> Average neat yearly income</li> <li><strong>ID229</strong> Average monthly saving</li> <li><strong>ID240</strong> Type of housing</li> <li><strong>ID249</strong> Owner / co-owner</li> <li><strong>ID255</strong> Usable area of the property (m²)</li> <li><strong>ID263</strong> Insulation level</li> <li><strong>ID270</strong> Climate zone</li> <li><strong>ID86</strong> Level of self-awareness about climate change. On scale of 0-10, where 0 is “climate change does not exist” and 10 is “I am a climate change expert/activist”</li> <li><strong>ID87</strong> Level of awareness of climate change among your peers or relatives, On a scale of 0-10, where 0 is “climate change does not exist” and 10 is “They are climate change experts/activists”</li> <li><strong>ID88</strong> Level of self-awareness about energy transition. On a scale of 0-10, where 0 is “It is the first time I hear about it” and 10 is “I am an expert or activist”</li> <li><strong>ID89</strong> Level of awareness of energy transition among your peers or relatives On a scale of 0-10, where 0 is “It is the first time they hear about it” and 10 is “They are experts or activists”</li> <li><strong>ID190</strong> feedback about survey</li> </ul> </li> </ul> </li> <li><strong>5 star</strong>: ⭐⭐⭐</li> <li><strong>Preprocessing steps:</strong> anonymization, data fusion, imputation of gaps.</li> <li><strong>Reuse:</strong> NA</li> <li><strong>Update policy:</strong> No more updates are planned</li> <li><strong>Ethics and legal aspects:</strong> Spanish electric cooperative data contains the CUPS (Meter Point Administration Number), which is personal data. A pre-processing step has been carried out to substitute the CUPS by a random value hash.</li> <li><strong>Technical aspects</strong>: </li> <li><strong>Other:</strong></li> </ul>
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.