Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
9,786
datasets available to search
ShareScore release 0.7.1
Dataset results
9,786 results for “selection”
Growth parameters and resistance to Sphaerulina musiva-induced canker are more important than wood density for increasing genetic gain from selection of Populus spp. hybrids for northern climates
<p>The data was collected from a common garden genetics trial established in 2008 in northern Alberta, Canada. The trial represents 1978 (initial number) hybrid poplar clones from 63 families and includes interspecific crosses between <em>Populus deltoides</em> (D), <em>Populus nigra</em> (N), <em>Populus balsamifera</em> (B), <em>P. maximowiczii</em> (M), and <em>P. × petrowskyana</em> (<em>P. laurifolia</em> × <em>P. nigra</em>). Female clone 24 (‘Walker’ = (<em>Populus deltoides </em>× (<em>P. laurifolia × P. nigra</em>))) and male progeny clone 2403 (‘Okanese’ = (‘Walker’ × (<em>P. laurifolia × P. nigra</em>))) were used as reference clones. The study design was a randomized complete block design, with one ramet per clone in each of four blocks. Measurements were carried out after three, eight, and 10 growing seasons on the genetics trial. Results presented in ‘HybridPoplarsTrial.csv’ file, show is the raw data, while ‘Summary data.csv’ contains the mean values for clones obtained from the four blocks. Measured and calculated traits include: DBH (diameter at breast height; 1.3 m); H (height); canker (canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)); MAI (mean annual increment), V (volume).</p> <p>Description of headings:</p> <p>Trait [unit] - Description</p> <p>DBH_Age_3 [cm] - diameter at breast height at age 3</p> <p>H_Age_3 [m] - height at age 3</p> <p>DBH_Age_8 [cm] - diameter at breast height at age 8</p> <p>H_Age_8 [m] - height at age 8</p> <p>H_Age_10 [m] - height at age 10</p> <p>DBH_Age_10 [cm] - diameter at breast height at age 10</p> <p>Canker_Age_8 - canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)</p> <p>Canker_Age_10 - canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)</p> <p>V_Age_8 [m<sup>3</sup> ha<sup>-1</sup>] - volume at age 8</p> <p>MAI_Age_8 [m<sup>3</sup> ha<sup>-1</sup> yr<sup>-1</sup>] - mean annual increment at age 8</p> <p>V_Age_10 [m<sup>3</sup> ha<sup>-1</sup>] - volume at age 10</p> <p>MAI_Age_10 [m<sup>3</sup> ha<sup>-1</sup> yr<sup>-1</sup>] - mean annual increment at age 10</p> <p>WD_Age_10 [kg m<sup>-3</sup>] - wood density at age 10</p> <p> </p>
Database on vacancies in selected non-EU countries
<p>This database is a revised version of the deliverable D3.1 of the Horizon Europe project 'Global Strategy for Skills, Migration and Development' (GS4S). For more information, please see the associated working paper: Locating Shortages in Migrants’ Origin Countries: A Big Data Approach, authored by Friedrich Poeschel. </p>
Overcoming Limitation of AlphaFold2 by Deep-mutational Scanning and Stability-Selection of Protein Sequences
<p>This repository contains the processed datasets and corresponding code used in our study. While AlphaFold2 revolutionizes protein structure prediction, its accuracy critically depends on evolutionary information from natural homologs—limiting applications for proteins with sparse sequence families. Here, we bypass this bottleneck by employing deep mutational scanning and stability-guided selection to generate artificial homologs. Fed into AlphaFold2, these synthetic sequences match the accuracy achieved on well-predicted proteins with rich natural homology, while providing highly accurate predictions for difficult targets—including orphan proteins previously deemed "unpredictable." Our approach achieves high accuracy (<3 Å RMSD for 5/8 and <2 Å RMSD for 7/8 targets after excluding intrinsically flexible regions). Thus, integrating simple, scalable molecular biology (mutagenesis/selection) with high-throughput sequencing can deliver the accuracy similar to but at a fraction of the cost and time of traditional experimental structure-determination methods. This hybrid framework could democratize high-resolution structural biology, opening avenues to determine structures of protein complexes, modified proteins, and condition-dependent conformations. </p>
Numerical weather simulation using COSMOiso in June 2019 during L-WAIVE field campaign: selected model output and post-processed data.
<p>This dataset consists of extracts from a simulation with the isotope-enabled regional numerical weather prediction model COSMOiso, which covers the timespan of the Lacustrine-Water vApor Isotope inVentory Experiment (L-WAIVE) field campaign taking place in June 2019 in the Annecy valley in the French Alps (Chazette et al. 2021).The simulation has a horizontal resolution of 0.1° (~10km) and 40 vertical levels.</p><p>This COSMOiso simulation is used in Thurnherr et al. (submitted) to compare stable water isotope measurements from various platforms. Here, we provide selected model outputs and post-processed data used in this comparison study. The post-processed data contain:</p><ol><li>COSMOiso output files for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Pressure weighted total and subcolumn averages for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Vertical cross section of selected variables at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated time series of subcolumn and total column averages at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated variables along the flight tracks from the L-WAIVE campaign (see Sodemann and Seidl, 2023).</li></ol><p>See also README files for more details on the provided data.</p><p>To access further model output and post-processed data, please contact the dataset authors.</p>
Wind Value Literature Selection for End-of-Life Valuation Excel File WP 5.1
<p>Authors from Wind Value, the Re-Wind Network and IEA Wind Task 45 carried out research on methods of processing end-of-life wind turbine blades. This included a structured literature review which selected the literature in the Excel file. Part of this work helps to estimate the value of an end-of-life wind farm contributing to Work Package 5.1 of the Wind Value project. The paper was pubished as Deeney et al. (2025) <a href="https://www.sciencedirect.com/science/article/pii/S1364032125000917?via%3Dihub">End-of-life wind turbine blades and paths to a circular economy</a>, <em>Renewable and Sustainable Energy Reviews. </em></p>
Dataset: An Analytic Hierarchy Process-Based Multicriteria Model for Component Selection in a Computational Numerical Control (CNC) Machine
<p><i><strong>"An Analytic Hierarchy Process-Based Multicriteria Model for Component Selection in a Computational Numerical Control (CNC) Machine"</strong></i></p><p><i>CHILECON 2023 - </i><a href="https://site.ieee.org/chilesur/ieee-chilecon-2023/"><i>https://site.ieee.org/chilesur/ieee-chilecon-2023/</i></a><i> </i></p><p>---</p><p>En el marco del trabajo de referencia, los autores ponemos a disposición de los lectores la base de datos utilizada para el proceso de toma de decisión multicriterio para la selección del software y del MCU de una maquina CNC. </p><p>En el repositorio podrán encontrar los datos referentes a los criterios, subcriterios, indicadores, datos, fuentes de los datos extraídos, política de decisión, cálculos de las evaluaciones de los modelos AHP aplicados y el análisis de sensibilidad de estos. Además, podrán encontrar las gráficas utilizadas en el estudio en la mejor calidad posible. </p><p>El material fue puesto a disposición de todos los interesados para fines académicos y científicos. </p><p>Atte. </p><p>Los autores. </p><p>---</p>
Selected properties of galaxy and SMBH populations (Spinoso et al. 2023)
<p>This record presents the catalogs of galaxy and Black Holes properties associated to the two runs of the modified version of the L-Galaxies Semi-Analytic Model (SAM) presented in Spinoso et al. 2023. These catalogs are aimed at providing the basic properties to study the population of Black Holes (BHs) and their host galaxies across cosmic times, obtained by running the L-Galaxies SAM over the whole Millennium-II box (see Boylan-Kolchin et al. 2009). The L-Galaxies SAM outputs summarized in these catalogs were obtained at several redshifts/snapshots, for two different runs which differ for the initial occupation fraction of BHs at the time of their formation. This initial occupation fraction is parametrized by the Gp parameter(see Spinoso et al. 2023 for details), with the two runs being characterized by Gp=1 and Gp=0.01. The catalogs are organized in two group of files, one group for each run. Each of these groups is composed by 18 different files, one per each availablle redshift, roughly corresponding to: z = 0, 0.5, 1, 1.5, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15. The two group of files can be easily distinguished by their names: indeed, the strings "Gp1" and "Gp001" are referred to the runs corresponding to the Gp=1 and Gp=0.01 values, respectively. In addition, every file includes a string of the form: "z[x.yz]" which specifies its redshift. </p> <p>The content of these catalogs is as follows: each redshift-file contains the same collection of arrays, each array being a galaxy or BH property. At each redshift, L-Galaxies outputs properties for the number 'NGAL' of galaxies identified in the Millennium-II box, at that specific redshift/snapshot. Therefore, most of the arrays have length equal to 'NGAL' (i.e. one value per each galaxy). Few of the arrays have a length of N * NGAL (i.e. N values per each galaxy). The content, units and data type of these arrays are as follows: </p> <ul> <li>"StellarMass" - Total stellar mass of each galaxy - [10^10 Msun / h] - array[NGAL]</li> <li>"Sfr" - Star formation rate of each galaxy - [Msun / yr] - array[NGAL]</li> <li>"SeedMass" - BH-seed mass. 7 values per galaxy; one value for each of the 7 possible BH-seed "flavors" modeled - [10^10 Msun / h] - array[NGAL, 7]</li> <li>"Rvir" - Virial radius of the DM halo hosting each galaxy - [Mpc / h] - array[NGAL]</li> <li>"Pos" - X, Y and Z position of each galaxy - [Mpc / h] - array[NGAL, 3]</li> <li>"Mvir" - Virial mass of the DM halo hosting each galaxy - [10^10 Msun / h] - array[NGAL]</li> <li>"Lbol" - Bolometric luminosity associated to the central AGN (==0 if the BH is not active) - [10^40 erg / s] - array[NGAL]</li> <li>"HotGas" - Mass of the hot-phase of each galaxy's gas component - [10^10 Msun / h] - array[NGAL]</li> <li>"fEDD" - Eddington ration (defined as Lbol/L_Edd, with L_Edd being the Eddington luminosity) for each AGN (==0 if the BH is not active) - [adim] - array[NGAL]</li> <li>"ColdGas" - Mass of the cold-phase of each galaxy's gas component - [10^10 Msun / h] - array[NGAL]</li> <li>"BlackHoleMass" - Mass of the central massive BH hosted by each galaxy (==0 if the galaxy does not host a central BH) - [10^10 Msun / h] - array[NGAL]</li> <li>"SeedType" - Identifier of the type of BH-seed which originated each BH (see below for details) - array[NGAL]</li> <li>"Redshift" - Redshift of each galaxy (within a single file, this is an array of identical values) - array[NGAL]</li> </ul> <p>NOTE:<br>The model presented in Spinoso et al. 2023 follows 7 different types of BH-seeds. The "SeedMass" array contains 7 mass values (one for each of these types of BH-seeds) for each galaxy in the Millennium-II box.This is the reason why the data type of "SeedMass" is [NGAL, 7]. Each of these 7 values is the sum, across the whole evolution of each galaxy, of the contributions to the total BH mass coming from each BH-seed who merged to form the final BH. In the vast majority of cases, BHs are associated to only one type of BH-seed. In those cases, 6 out of the 7 "SeedMass" values would be zero. Each element of "SeedMass" corresponds to one type of BH seed according the following scheme:<br>SeedMass[0] : total seed mass of light-seeds inherited from the GQd model (see Spinoso et al. 2023 for details)<br>SeedMass[1] : total seed mass of heavy-seeds inherited from the GQd model (see Spinoso et al. 2023 for details)<br>SeedMass[2] : un-resolved mass-growth driven by gas-accretion before the halo hosting the BH was resolved<br>SeedMass[3] : total seed mass formed as light-seeds in L-Galaxies<br>SeedMass[4] : total seed mass formed as Direct-Collapse BHs (DCBHs)<br>SeedMass[5] : total seed mass formed as intermediate-mass BH originated via Runaway Stellar Mergers (RSM)<br>SeedMass[6] : total seed mass formed as Merger-Induced Direct-Collapse BH (miDCBH)</p> <p>NOTE:<br>Similarly to "SeedMass", also the "Pos" array has more than one element per galaxy. These are the three cartesian positions of each galaxy.</p> <p>NOTE:<br>The possible values of the "SeedType" array are as follows (see Spinoso et al. 2023 for details):<br>-1 - No BH seed (the galaxy never hosted a BH)<br>1 - light seed (PopIII remnant)<br>6 - Direct-Collapse BH (DCBH)<br>7 - intermediate-mass BH originated via Runaway Stellar Mergers (RSM)<br>8 - Merger-Induced Direct-Collapse BH (miDCBH)<br>9 - mixed type: light+DCBH (the BH is the result of hierarchical mergers between light and DCBH seeds)<br>10 - mixed type: light+RSM (the BH is the result of hierarchical mergers between light and RSM seeds)</p>
Density Layers of selected Points of Interest from Open Street Map
<p>This dataset contains a raster layer of 100*100m resolution showing the density of selected amenities from Open Street Map. The amenities are selected as points of interest, where electric vehicles owners are likely to stop for a moment and recharge their vehicles. This dataset covers Europe and was obtained with the Overpass API. This dataset can be used to identify possible charging location for electric vehicles or any other purpose requiring to quantify the number of amenities in an area.</p> <p> </p> <p>This dataset shows densities of a selection of Points of Interest in Europe from Open Street Map [1].</p> <p>Included countries are:</p> <p> </p> <p><em><strong>country_codes</strong> = ['AT', 'BE', 'BG', 'HR', 'CY', 'CZ', 'DK', 'EE', 'FI', 'FR', 'DE', 'GR', 'HU', 'IE', 'IT','LV', 'LT', 'LU', 'MT', 'NL', 'PL', 'PT', 'RO', 'SK', 'SI', 'ES', 'SE', 'AL', 'AD', 'AM', 'BY', 'BA', 'FO', 'GE', 'GI', 'IS', 'IM', 'XK', 'LI', 'MK', 'MD', 'MC', 'ME', 'NO', 'SM', 'RS', 'CH', 'TR', 'UA', 'GB', 'VA']</em></p> <p> </p> <p>The requests of Points of Interest have been performed with the Overpass API [2] (free of charge).</p> <p> </p> <p>The codes included in each density are listed below :</p> <p> </p> <p><strong><em>'highway'</em></strong><em> = ['"highway"="motorway"', '"highway"="rest_area"'];</em></p> <p><strong><em>'parkings'</em></strong><em> = ['"parking"="surface"', '"parking"="multi-storey"', '"parking"="street_side"', '"parking"="underground"' , '"park_ride"' ];</em></p> <p><strong><em>'school'</em></strong><em> = ['"amenity"="college"', '"building"="college"', '"building"="university"', '"amenity"="university"', '"amenity"="school"' , '"amenity"="school"', '"amenity"="kindergarten"', '"amenity"="library"'];</em></p> <p><strong><em>'health'</em></strong><em>= ['"amenity"="clinic"', '"amenity"="dentist"', '"amenity"="school"' , '"amenity"="doctors"', '"amenity"="hospital"', '"amenity"="pharmacy"','"amenity"="veterinary"']; </em></p> <p><strong><em>'cafe'</em></strong><em>= ['"amenity"="cafe"','"amenity"="ice_cream"', '"amenity"="internet_cafe"']; </em></p> <p><strong><em>'supermarket'</em></strong><em> = ['"shop"="supermarket"', '"shop"="mall"', '"shop"= "department_store"', '"shop"= "convenience"'];</em></p> <p><strong><em>'restaurant'</em></strong><em>= ['"amenity"="restaurant"'];</em></p> <p><strong><em>'fastfood'</em></strong><em> = ['"amenity"="fast_food"'];</em></p> <p><strong><em>'sport'</em></strong><em>= ['"sport"']; </em></p> <p><strong><em>'hotel'</em></strong><em> = ['"tourism"="hotel"', '"building"="hotel"', '"tourism"="guest_house"','"tourism"="apartment"','"tourism"="hostel"','"tourism"="motel"','"tourism"="camp_site"']; </em></p> <p><strong><em>'pubs'</em></strong><em> = ['"amenity"="bar"','"amenity"="pub"', '"amenity"="biergarten"'];</em></p> <p><em>'theatre'= ['"amenity"="theatre"', '"amenity"="cinema"', '"amenity"="music_venue"', '"leisure"="stadium"' ]; </em></p> <p><strong><em>'night'</em></strong><em> = ['"amenity"="nightclub"', '"amenity"="casino"','"amenity"="gambling"','"amenity"="stripclub"']; </em></p> <p><strong><em>'socio'</em></strong><em>= ['"amenity"="arts_centre"', '"amenity"="community_centre"', '"amenity"="social_centre"', '"amenity"="music_school"', '"amenity"="language_school"']; </em></p> <p><strong><em>'shop'</em></strong><em> = ['"shop"'];</em></p> <p><strong><em>'tourism'</em></strong><em> = ['"amenity"="exhibition_centre"', '"tourism"="attraction"','"tourism"="viewpoint"','"tourism"="aquarium "','"leisure"="beach_resort "','"tourism"="gallery"','"tourism"="museum"','"tourism"="theme_park"','"tourism"="zoo"','"tourism"="artwork"'];</em></p> <p> </p> <p>The pixel values are the sum of the number of POIs of each type located in the pixel.</p> <p> </p> <p><em>Limitations of the dataset</em></p> <p>- The dataset provides densities of only a selection of points of interests, regardless of its type. The complete list of amenity codes can be found on the OSM Wiki [3].</p> <p>- Ways are only considered through their centre points.</p> <p> </p> <p>[1] “Open Street Map.” <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a> (accessed Sep. 05, 2023).</p> <p>[2] “Overpass API.” <a href="https://wiki.openstreetmap.org/wiki/Overpass_API">https://wiki.openstreetmap.org/wiki/Overpass_API</a> (accessed Sep. 05, 2023).</p> <p>[3] “Open Street Map Wiki.” <a href="https://wiki.openstreetmap.org/wiki/Key:amenity#Sustenance">https://wiki.openstreetmap.org/wiki/Key:amenity#Sustenance</a> (accessed Sep. 05, 2023).</p>
A map selection of wigeon stopover sites (core areas) based on wetland expert knowledge
<p>Stopover areas (core areas only) along the migration route of wigeons tracked with GPS transmitters were selected when they exhibited forests on more than 50% of their total surface or had less than 50% cover by water and/or wetland on the ESA’s global land cover map. We created a sample of 5,630 regions of interest (3,403 for training and 2,227 for validation), delineated with polygons assigned to land classes listed in the Table 1. We used archives of Google Earth, ESRI, and BING satellites for the photointerpretation of the land classes as described in Table 1. The classification was performed with a Sentinel-2 MultiSpectral Instrument, Level-2A image collection in Google Earth Engine (GEE) through the R-package Rgee to create a batch process applying the GEE Random forest classifier to each selected core home range. The cloudless (maximum 3%) images were selected within the period from 01/06/2021 to 30/09/2021. The optimal number of trees was estimated at 100 for an out of bag error of 14%. The overall accuracy on the validation sample was 82 %. </p>
Observed and WRF-simulated near-surface meteorological parameters on selected James Ross Island glaciers during heatwaves in summer 2022/23
<p>The files contain time series of near-surface meteorological conditions observed on Triangular Glacier and Davies Dome on James Ross Island, Antarctica and simulated time series for these glaciers based on the Weather Research and Forecasting (WRF) model output. Observations of 2-m air temperature, 2-m wind speed, net radiation and glacier surface height are available from 01 November 2022 to 16 January 2023 (net radiation is available only on Triangular Glacier). Simulated values of 2-m air temperature, 2-m wind speed, net radiation, sensible and latent heat fluxes are available from 08 November 2022 to 16 January 2023.</p>
Global Human Settlement Layer per zoom-level 18 Quadtree tile for selected countries as Spatialite database with OpenStreetMap building completeness assessment
<p>This Spatialite database contains the built-up area of the Global Human Settlement Layer (GHSL) per zoom-level 18 Quadtree tile. Additionally, it provides a comparison of the GHSL with buildings in OpenStreetMap: For each tile the built-up ratio between the building footprints and the GHSL is given and a binary completeness assessment (buildings complete, not complete) is provided for easy use. This dataset was created using the obmgapanalysis tool: https://git.gfz-potsdam.de/dynamicexposure/openbuildingmap/obmgapanalysis</p>
Data: Weak Cation Selectivity in HCN Channels Results from K+-mediated release of Na+ from selectivity filter binding sites
<p>Complementary data for the paper: Weak Cation Selectivity in HCN Channels Results from K+-mediated release of Na+ from selectivity filter binding sites.</p>
Summary of the most important features for selected ABs
<p>These data summarizes the relevant findings and the identified limitations (in terms of "Category", "Technology", "Properties", "Limitation", and "Applicability to railway"), coming from the overview of different Alternative Bearers (ABs), carried out in deliverable D21 (AB4Rail project, www.ab4rail.eu).<br> The results have provided an overview of several technologies, each of them showing specific characteristics. The heterogeneous nature of different ABs allows to provide a plethora of available communication technologies to be potentially used by the Adaptable Communication System (ACS) for different railway scenarios. All the selected ABs provide the IP interconnection feature since they are Integrated within OSI reference model.<br> In this way, it collects the planned objectives of deliverable D2.1, expressed as a technological overview of selected ABs, as possible candidates coexisting with Traditional Bearers (TBs) for supporting railway applications.</p>
Calix[6]arenes with halogen bond donor groups as selective and efficient anion transporters
<p>Dataset for the publication: <strong>Calix[6]arenes with halogen bond donor groups as selective and efficient anion transporters</strong> by A. Singh, A. Torres-Huerta, T. Vanderlinden, N. Renier, L. Martínez-Crespo, N. Tumanov, J. Wouters, K. Bartik, I. Jabin, H. Valkenier, <em>Chem. Commun.</em> <strong>2022</strong>, doi:10.1039/D2CC008472E,</p> <p>containing:</p> <ul> <li>A file with the structures of compounds <strong>1</strong>-<strong>5</strong> (PDF)</li> <li>NMR spectra for the characterisation of compounds <strong>1a</strong>, <strong>1b</strong>, <strong>1c</strong>, <strong>2</strong>, and <strong>3</strong> (Mestrenova files)</li> <li>NMR spectra for the titration experiments with compounds <strong>1</strong><strong>-5</strong> in different solvents (Mestrenova files)</li> <li>Concentrations of Host and Guests in the various titration experiments (Excel file)</li> <li>Transport data in the lucigenin assay (Excel file)</li> <li>Transport data in the HPTS assay (Excel file)</li> </ul> <p> </p> <p> </p> <div> </div>
Selected data(s) from : Five-dimensional optical data storage based on ellipse orientation and fluorescence intensity in a silver-sensitized commercial glass
<p>The data selected is based on the figures below, published in the linked article (see the doi).</p> <p>- <strong>Figure 1.</strong> (<strong>a</strong>) Femtosecond laser tight focusing in the silver-containing glass, leading to the production of fluorescent silver clusters at its periphery. (<strong>b</strong>) SLM holographic phase masks with an additional cylindrical profile leading to an elliptical pattern by DLW. (<strong>c</strong>) Oriented elliptical patterns obtained by SLM phase mask manipulation, corresponding to 2<sup>4</sup> = 16 orientation-encoded levels. <strong>(Only picture)</strong></p> <p>- <strong>Figure 2.</strong> Fabricated fluorescence calibration matrix. (<strong>a</strong>) Confocal image of all basic storage units composed by 16 intensity levels and 16 orientation levels. (<strong>b</strong>) Measured fluorescence intensity versus incident DLW intensity for the 5D decoding process. <strong>(Pictures, opj file, csv datas)</strong></p> <p><strong>- </strong> <strong>Figure 3.</strong> (<strong>a</strong>,<strong>b</strong>) are the encoded images of two Nobel laureates in 16 orientation levels and 16 intensity levels, respectively. (<strong>c</strong>) 100 × 100 entangled patterns among 16 × 16 intensity and orientation levels. (<strong>d</strong>) The fluorescence calibration matrix was fabricated for decoding (fluorescence excitation at 405 nm). <strong>(Pictures, cvs datas)</strong></p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3_2020-10-21_V01 : Figure 3</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3a_2020-10-21_V01 : Original image oritentation</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3b_2020-10-21_V01 : Original image intensity</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3c_Figure3d_2020-10-21_V01 : DLW image</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3_datas_IICT4BF_2020-10-21_V01 : Intensity image converted to 4 bit format</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3_datas_OICT4BF_T2020-10-21_V01 : Orientation image converted to 4 bit format</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3_datas_OICT4BFL_2020-10-21_V01 : Orientation image converted to 4 bit format level</li> </ol> <p><strong>- Figure 4.</strong> (<strong>a</strong>,<strong>b</strong>) Retrieved images from the initial images of Figure 3a,b, respectively. (<strong>c</strong>,<strong>d</strong>) Histograms of the level difference between original and decoded levels for the orientation direction and the fluorescence intensity, respectively. (<strong>Picture and csv datas</strong>)</p> <p>- <strong>Figure 5.</strong> (<strong>a</strong>) Confocal top-view image of one single elliptically-shaped storage unit fabricated by using type A DLW. (<strong>b</strong>) Fluorescence intensity profile along the horizontal and vertical cross section at focal plane. (<strong>c</strong>) Fluorescence intensity profile and Gaussian fitting along the z-axis (depth). (<strong>Picture, opj file, csv datas</strong>)</p> <p> </p> <p> </p>
Dataset of the publication "Halide Perovskites as Disposable Epitaxial Templates for the Phase-Selective Synthesis of Lead Sulfochloride Nanocrystals"
<p>This dataset provides the raw data associated with the publication "Halide Perovskites as Disposable Epitaxial Templates for the Phase-Selective Synthesis of Lead Sulfochloride Nanocrystals".It contains:</p> <ul> <li>A readme file meant to help the user navigate the database</li> <li>The raw data associated with all the plots and charts found in the Main Text and in the Supplementary information.</li> <li>The raw data collected during the 3D electron diffraction experiments on Pb<sub>3</sub>S<sub>2</sub>Cl<sub>2</sub> Nanocrystals. </li> <li>The CIF files of all the crystal structures refined in the work</li> <li>An atomistic model of the Pb<sub>4</sub>S<sub>3</sub>Cl<sub>2</sub>/CsPbCl<sub>3</sub> interface, which can be visualized with the freeware software Vesta. </li> </ul>
Selected data(s) from : Femtosecond direct laser writing of silver clusters in phosphate glasses for x-ray spatially-resolved dosimetry
<p>The data selected is based on the figures below, published in the linked article (see the doi).</p> <p><strong>- Figure 1.</strong> Microscopy fluorescence image of ARGOi glass sample (excitation at 365 nm) of laser-inscribed structures for the different writing irradiances at two different depths: (<strong>a</strong>) structures at 150 µm below the glass front surface, (<strong>b</strong>) structures at 550 µm below the glass front surface, and at 150 µm from the glass rear surface. <strong>(Only picture)</strong></p> <p>- <strong>Figure 2.</strong> (<strong>a</strong>) Transparent color before irradiation (ARGO glass sample), (<strong>b</strong>) yellow color after X-ray irradiation with 222 Gy (ARGO* glass sample). <strong>(Only picture)</strong></p> <p><strong>- </strong> <strong>Figure 3.</strong> (<strong>a</strong>) Absorption spectra of the ARGO and ARGO* glass sample after various X-ray doses and the difference absorption coefficient spectrum for 222 Gy vs. pristine. (<strong>b</strong>) Fit of the radiation-induced spectrum (difference between 222 Gy and pristine) considering Gaussian energy contributions for ARGO and ARGO*. (<strong>c</strong>) Absorption spectra for the GPN and GPN* glasses for X-ray doses from 5 mGy to 3 kGy [<a href="https://www.mdpi.com/2227-9040/10/3/110/htm#B39-chemosensors-10-00110">39</a>]. (<strong>d</strong>) The difference absorption coefficient spectra between different doses conditions for GPN and GPN* [<a href="https://www.mdpi.com/2227-9040/10/3/110/htm#B39-chemosensors-10-00110">39</a>]. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure3_2022-03-03_V01. <strong>Figure 3</strong></li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure3_Datas_2022-03-03_V01. Datas : <strong>wavelength, effective absorption coefficient (cm-1)</strong></li> </ol> <p>- <strong>Figure 4.</strong> Micro-luminescence of GPN* glass performed on the optically polished glass side: (<strong>a</strong>) integrated fluorescence intensity at different depths, (<strong>b</strong>) normalized spectrum evolution with depth for the 500 Gy dose [<a href="https://www.mdpi.com/2227-9040/10/3/110/htm#B39-chemosensors-10-00110">39</a>]. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure4_2022-03-03_V01. Figure 4</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure4_Datas_2022-03-03_V01. Datas</li> </ol> <p>- <strong>Figure 5.</strong> Estimated depth-dependent profiles in absolute values of the linear absorption coefficient at 405 nm. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure5_2022-03-03_V01. Figure 5</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure5_Datas_2022-03-03_V01. Datas : <strong>sample depth (mm) ; scaled linear absorption coefficient profile at 405 nm (mm-1)</strong></li> </ol> <p>- <strong>Figure 6.</strong> (<strong>a</strong>) X-ray energy spectra simulated by SpekPy for each irradiation facility, normalized by integral. (<strong>b</strong>) Geant4-simulated dose inside each sample, normalized by the surface dose; filled areas show uncertainties at 95% confidence. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure6_2022-03-03_V01. Figure 6</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure6_Datas_2022-03-03_V01. Datas : <strong>ARGO 100KV_dose ; GPN-20KV_dose ; GPN-32KV_dose</strong></li> </ol> <p>- <strong>Figure 7.</strong> Radio-photoluminescence measurement of the GPNi* glass for the inscribed structure [<a href="https://www.mdpi.com/2227-9040/10/3/110/htm#B39-chemosensors-10-00110">39</a>]. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure7_2022-03-03_V01. Figure 7</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure7_Datas_2022-03-03_V01. Datas : <strong>wavelength ; relative intensity a.u.</strong></li> </ol> <p>- <strong>Figure 8.</strong> Normalized RPL spectra excited at 325 nm: (<strong>a</strong>) for the ARGO (pristine—right axis) and ARGO* (X-ray irradiation at 222 Gy—left axis) glasses collected around 150 µm below the surface, (<strong>b</strong>,<strong>c</strong>) for the highest DLW irradiance structure for ARGOi and ARGOi* in the front- and the rear-inscribed surfaces, respectively. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure8_2022-03-03_V01. Figure 8</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure8_Datas_2022-03-03_V01. Datas : <strong>inscribed glass...</strong></li> </ol> <p>- <strong>Figure 9.</strong> (<strong>a</strong>) Differential linear absorption coefficient of the laser-inscribed structures (11 TW/cm<sup>2</sup>) for the two planes after irradiation at 222 Gy X-ray dose in the ARGOi* glass sample. (<strong>b</strong>) Average differential absorption of the inscribed structures for all DLW irradiance (as from <a href="https://www.mdpi.com/2227-9040/10/3/110/htm#fig_body_display_chemosensors-10-00110-f009">Figure 9</a>a). (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure9_2022-03-03_V01. Figure 9</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure9_Datas_2022-03-03_V01. Datas : <strong>integrated differential linear absoprtion percentage ; irradiance (TW/cm2)</strong></li> </ol> <p>- <strong>Figure 10.</strong> (<strong>a</strong>) Phase image under white light illumination of the laser inscribed structure (11 TW/cm<sup>2</sup>) before irradiation. (<strong>b</strong>) Optical path difference determined from the phase image. (<strong>c</strong>) The refractive index modification Δ<em>n</em> as a function of laser irradiance before/after 222 Gy-dose for the two planes in ARGOi, ARGOi* glass sample. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure10_2022-03-03_V01. Figure 10</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure10_Datas_2022-03-03_V01. Datas : <strong>refractive index modification ; irradiance (TW/cm2), Error bar</strong></li> </ol> <p><strong>- Figure 11.</strong> Comparison between calculated and measured Δ<em>n</em>ˆ after irradiation for a decrease in the initial value of <em>N</em><em>α</em>3 by 0.48%: (<strong>a</strong>,<strong>c</strong>) the real part Δ<em>n</em> for the front and rear surfaces, respectively; (<strong>b</strong>,<strong>d</strong>) their imaginary counterparts Δ<em>κ</em>, respectively. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure11_2022-03-03_V01. Figure 11</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure11_Datas_2022-03-03_V01. Datas : <strong>rear surface...</strong></li> </ol> <p><strong>- Figure 12.</strong> Integrated measure of the amplitude of fluorescence intensity for the different laser irradiance before and after 222 Gy-dose for the two planes in ARGOi and ARGOi* glass sample. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure12_2022-03-03_V01. Figure 12</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure12_Datas_2022-03-03_V01. Datas : <strong>integrated measure of the amplitude of fluorescence intensity ; Irradiance (TW/cm2) ; Error bar </strong></li> </ol> <p><strong>- Figure 13.</strong> (<strong>a</strong>) Composite FLIM and fluorescence intensity microscopy images of the laser-induced structure (11 TW/cm<sup>2</sup>) before and after irradiation for an emission at 425 nm from the front surface; the color-code represents the mean lifetime obtained by FAST-FLIM algorithm (color scale from 0 to 31 ns); inset: luminescence intensity only (grey-scale from 0 to 45 counts). (<strong>b</strong>) Same composite FLIM and luminescence intensity images for an emission at 510 nm. (<strong>c</strong>) Luminescence decays in arbitrary units for the emission at 425 nm of the same structure before and after irradiation for the two surfaces, and fitting curves thereof using three exponential decay functions. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure13_2022-03-03_V01. Figure 13</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure13_Datas_2022-03-03_V01. Datas : <strong>fluorescence intensity (arbitrary units) ; time (ms)</strong></li> </ol> <p>- <strong>Figure 14.</strong> Dose-dependent evolution of the amplitude ratio of extracted spectral bands for (<strong>a</strong>) the GPNi* glass sample for DLW irradiance of 13.4 TW/cm<sup>2</sup> at 160 µm below the glass surface, (<strong>b</strong>) the ARGOi and ARGOi* glass sample for DLW irradiance of 11 TW/cm<sup>2</sup> at 550 µm below the glass surface (rear surface). (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure14_2022-03-03_V01. Figure 14</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure14_Datas_2022-03-03_V01. Datas : <strong>ratio of amplitudes of spectral bands ; doses (gy)</strong>.</li> </ol>
Heat balance of selected Brayton cycle
<p>The dataset provides the heat balance of the selected Brayton cycle among the 10 cycles considered. Simulations of several cases defined by a different supercritical CO2 cycle type were performed with Ebsilon software in order to assess the net power block efficiency of the cycle and the Levelized Cost of Electricity (LCOE) of the plant. Due to its highest efficiency among the 10 envisaged Brayton cycle options, it is the Partial Cooling with Intercooling and Reheating cycle that is selected.</p> <p>The datasets could help other people design a sCO2 Brayton cycle.</p> <p>For detailed analysis, please refer to Deliverable 1.1 (Process Parameters of Solar sCO2 Brayton Cycle) to be downloaded at: <a href="https://www.compassco2.eu/wp-content/uploads/2021/02/D1.1_Process-parameters-of-solar-sCO2-Brayton-cycle.pdf">https://www.compassco2.eu/wp-content/uploads/2021/02/D1.1_Process-parameters-of-solar-sCO2-Brayton-cycle.pdf</a></p>
Occipital Nerve Stimulation Selectively Modulates Top-down Inhibitory Control
<p><strong>Objective:</strong> Here we investigate the effect of occipital nerve stimulation using low-gamma range alternating current on goal-directed and stimulus-driven attention and inhibitory training and performance. We sought to determine if stimulation modulated performance over a two-day period. <strong>Methods</strong>: We studied this effect in 47 participants recruited in one of two experiments. The goal-directed task used the stop-signal reaction time task (SSRT) during stimulation and stop-change reaction time (SCRT) in a 24-hour follow-up. Stop-signal reaction time (SSRT) and Stop-change reaction time (SCRT) were recorded in seconds, calculated using a non-integration method. SSRT/SCRT and accuracy were used as outcome measures. The stimulus-driven task used a sustained-attention reaction time task (SART), and reaction time and inhibition (NoGo) accuracy were used as outcome measures. <strong>Results</strong>: Compared to the control group, the stimulation group had improved SCRT 24 hours after combined stimulation and training. No difference in accuracy on either day were present. No difference between groups arose in the SART during training or testing. </p>
Upward, MeV-class electron beams over Jupiter's Main Aurora; Selected data for
<p>This submission provides selected ASCII data that is utilzed in a scientific study entitled: "Upward, MeV-class electron beams over Jupiter’s Main Aurora". A PDF of the manuscript is included here. The 13 authors of this study are identified in the PDF manuscirpt. The data files are labeled according to the figure numbers and panels used in the manuscript. The PDF of the paper serves to document the qualities of the data submitted. The abstract of the manuscript is as follows: </p> <p>Abstract: Jupiter’s poleward (Zone II) main aurora exhibits bi-directional electron acceleration; upward acceleration dominates but downward acceleration generates strong aurora. During Juno’s first perijove (PJ1), the upward acceleration manifested as narrow electron angular beams (within ~5 of the magnetic field) over the 30-1200 keV energy range of Juno’s Jupiter Energetic Particle Detector Investigation (JEDI). These beams can be simply connected (non-uniquely) to >10 to perhaps 100’s of MeV electrons that penetrated the radiation shielding of the camera head of the Magnetometer Investigation’s Advanced Stellar Compass (ASC). The most intense of those multiple MeV populations are shown to have been highly directional and propagating upwards. How auroral processes generate such beams is unknown. With azimuthal symmetry assumed (not demonstrated here), these beams provided >1026 s-1 of >30 keV electrons to Jupiter’s vast magnetosphere, a possibly critical and dominating source of energetic electrons to that region and ultimately to Jupiter’s radiation belts.</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.