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.
3,206
datasets available to search
ShareScore release 0.9.0
Dataset results
3,206 results for “property (T)”
Table of Ultracool Fundamental Properties
<p>The Table of Ultracool Fundamental Properties is a subset of The UltracoolSheet (version 2.0.0, in preparation) associated with <a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230903082S/abstract">Sanghi et al. (2023)</a>: The Hawaii Infrared Parallax Program. VI. The Fundamental Properties of 1000+ Ultracool Dwarfs and Planetary-mass Objects Using Optical to Mid-IR SEDs and Comparison to BT-Settl and ATMO 2020 Model Atmospheres.</p> <p>It is a comprehensive compilation of the astrometric, photometric, spectroscopic, age properties, and fundamental parameters (bolometric luminosities, masses, radii, surface gravities, and effective temperatures) of all objects in the above paper's sample.</p> <p>Three CSV files are provided here: (1) Ultracool_Fundamental_Properties_Table.csv (with all object information); (2) AgeValues.csv (with age values for the different age assignment categories); and (3) References.csv (with citation information). The README.txt file contains a detailed description of the contents of the three CSV files.</p> <p>When using data from the Table of Ultracool Fundamental Properties, please cite the individual papers from which the data comes. Citations codes are included for all the data in the tables, and the References table translates the citation codes into ADS bibcodes, Papers citekeys, and publication titles. For research that benefits from this compilation, please cite this Zenodo post and include the following acknowledgment:</p> <p>"This work has benefitted from The UltracoolSheet at http://bit.ly/UltracoolSheet, maintained by Will Best, Trent Dupuy, Michael Liu, Aniket Sanghi, Rob Siverd, and Zhoujian Zhang, and developed from compilations by <a href="https://ui.adsabs.harvard.edu/abs/2012ApJS..201...19D/abstract">Dupuy & Liu (2012, ApJS, 201, 19)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2013Sci...341.1492D/abstract">Dupuy & Kraus (2013, Science, 341, 1492)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2016ApJ...833...96L/abstract">Liu et al. (2016, ApJ, 833, 96)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2018ApJS..234....1B/abstract">Best et al. (2018, ApJS, 234, 1</a>), <a href="https://ui.adsabs.harvard.edu/abs/2021AJ....161...42B">Best et al. (2021, AJ, 161, 42)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230903082S/abstract">Sanghi et al. (2023)</a>, and <a href="https://ui.adsabs.harvard.edu/abs/2023AJ....166..103S/abstract">Schneider et al. (2023, AJ, 166, 103)</a>."</p>
Optical properties of marine aerosols with varying water content at wavelengths 532 and 1064 nm, modelled with a morphologically realistic aerosol model
<p>The data contain computational results obtained with the ADDA program at wavelengths 532 nm and 1064 nm, for particle sizes 0.04, 0.06, ..., 1.5 micrometers (where size = volume-equivalent dry radius), and for salt mass fractions 0.91, 0.94, 0.97, 1.00. The content of the data files is described in the README file.</p>
European Database of the Compositional Properties of Digestate and the Liquid Fraction of Digestate
<p>This Europe-wide dataset (n = 1895) contains extensive data on the physicochemical properties (pH, nitrogen, carbon, NH4, organic matter, heavy metals, etc.) of digestate and the liquid fraction of digestate. It is based on previously unpublished data from the European Biogas Association and data obtained from industrial biogas stakeholders.</p>
Properties of identified ship tracks
<p>The ship track database of is used, which used "day microphysics" images comprised of a composite of visible, near and thermal infrared channels were used to manually locate likely positions of ship tracks. Identification was assisted by examining the CDNC, calculated from the MYD06 level 2 cloud retrieval products from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. The CDNC is calculated based on the adiabatic assumption, using the cloud optical depth and cloud effective radius from the MODIS MYD06 level 2 product.</p><p>Ship locations were sourced from their automatic identification system (AIS) data, allowing an observed ship track to be linked to the generating ship. Local meteorology was gathered from the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis data, and ships mass emission rates were calculated using their specific fuel consumption and estimated drag. </p><p>Data labels:</p><ul><li>trackno: Ship track identifier (-)</li><li>sox: SO emission rate (kg s^-1)</li><li>cbh: Cloud base height (m)</li><li>blh: Boundary layer height (m)</li><li>cth: Cloud top height (m)</li><li>spd_res: Relative velocity between ship velocity and wind velocity (m s^-1)</li><li>nd_cln: Background cloud droplet number concentration (cm^-3)</li><li>nd_pol: Ship track cloud droplet number concentration (cm^-3)</li><li>cf_liq: Liquid cloud fraction (-)</li><li>lwp: Liquid water path (g m^-2)</li><li>t1000: Temperature at 1000 hPa (K)</li><li>LTS: Low tropospheric stability (K)</li><li>ctt: Cloud top temperature (K)</li><li>ctrc: Cloud top radiative cooling (W m^-2)</li><li>is_coupled: Flag for cloud coupling according to cloud base height indicator (cbh<1000 m)</li></ul>
An assessment of the (anti)androgenic properties of hexachloronaphthalene (HxCN) using a model of immature male rats (Hershberger Bioassay)
<p>The persistent organic pollutants (POPs) include polychlorinated naphthalenes (PCNs); of these, the most toxic, abundant and found in human tissues are the hexachloronaphthalenes (HxCNs). The aim of this study was to evaluate the (anti)androgenic action of HxCN using the Hershberger Bioassay (OECD 441). Castrated male Wistar rats were exposed per os to HxCN at daily doses ranging from 0.3-3.0 mg*kg b.w.-1 for 10 days. Testosterone propionate (TP) was used as the reference androgen, and flutamide (FLU) as the reference antiandrogen. Five assessor sex tissues (ASTs) were weighed: ventral prostate, seminal vesicles, levator ani-bulbocavernosus muscle (LABC), glans penis and Cowper gland. In addition to determining the absolute weight of the ASTs, a number of other tests were performed on serum hormone levels (testosterone [T], triiodothyronine 99 [T3], thyroxine [T4], LH and FSH) and the histopathology of the ASTs. </p>
Extracted Source Properties Catalog for "Monitoring the X-ray Variability of Bright X-ray Sources in M33"
<p>Supplemental data to the article "Monitoring the X-ray Variability of Bright X-ray Sources in M33" accepted for publication in ApJ. Contains all extracted source properties for the 56-source final catalog, including single-ObsID extractions and merged values. See ReadMe for column descriptions and additional comments.</p>
Selected properties of galaxy, MBHs and MBHBs populations (Izquierdo-Villalba et al. 2022)
<pre>This is a catalogue of galaxies, massive black holes (MBHs) and massive black hole binaries (MBHBs) <br>generated with L-Galaxies semi-analytical model in the version of Izquierdo-Villalba et al. 2022<br>and the dark matter merger trees extracted from the Millennium simulation (Springel et al. 2005). <br>The catalogue is created by making use of only 9 sub-volumes of the Millennium box (~2% of the whole<br>simulation, Volume = 5562144.30 Mpc3) and it contains galaxies, MBHs and MBHBs at<br>51 different redshifts (0 < z < 12.5). This catalogue is suited for studying the population<br>of MBHBs and their hosts. The properties stored in this catalogue are the following:<br> Redshift: Redshift of the galaxy/MBH/MBHB SnapNum: Snapnum of the simulation Pos: Position of the galaxy/MBH/MBHB inside the comoving box. It is an array of dimension 3. [Mpc/h] Vel: Velocity of the galaxy/MBH/MBHB inside the comoving box. It is an array of dimension 3. [km/s] Mvir: Virial mass of the dark matter sub-halo [1e10 Msun/h] Rvir: Virial radius of the dark matter sub-halo [Mpc/h] Vvir: Virial velocity of the dark matter sub-halo [km/s] Vmax: Maximum circular velocity of the dark matter halo [km/s] HotRadius: Radius of the hot gas atmosphere that surrounds the galaxy [1e10 Msun/h] ColdGas: Cold gas component of the galaxy [1e10 Msun/h] BulgeMass: Stellar mass of the bulge component [1e10 Msun/h] DiskMass: Stellar mass of the disc component [1e10 Msun/h]. The total stellar mass of the galaxy should be BulgeMass+DiskMass HotGas: Hot gas component of the galaxy [1e10 Msun/h] BlackHoleMass: Mass of the primary MBH of the galaxy [1e10 Msun/h] Lbol: Bolometric luminosity of the primary MBH of the galaxy [1e40 erg/s] fEDD: Ratio between the Lbol of the primary and the Eddington luminosity (<=1) [No dimensions] spin: Spin of the primary MBH [0,1] M_dot_acc: Accretion rate of the primary MBH of the galaxy [Msun/yr] BlackHoleMassSec: Mass of the secondary MBH (if exists) of the galaxy [1e10 Msun/h] LbolSec: Bolometric luminosity of the secondary MBH (if exists) of the galaxy [1e40 erg/s] fEDDSec: Ratio between the Lbol of the secondary MBH (if exists) and the Eddington luminosity (<=1) [No dimensions] spinSec: Spin of the primary MBH (if exists) [0,1] M_dot_acc_sec: Accretion rate of the secondary MBH (if exists) of the galaxy [Msun/yr] BinarySemiMajorAxis: Semi-major axis of the MBHB [Mpc/h] BinaryEccentricity: Eccentricity of the MBHB Sfr: Star formation rate of the galaxy [Msun/yr] BulgeSize: Size of the bulge stellar component [Mpc/h] StellarDiskRadius: Scale length of the disc stellar component [Mpc/h] GasDiskRadius: Scale length of the disc gas component [Mpc/h] The file can be read as follows: import h5py hf = h5py.File('LGal_IzquierdoVillalba2022_SubVol_0_9.h5', 'r')</pre>
Data for: Bound impurities in a one-dimensional Bose lattice gas: low-energy properties and quench-induced dynamics
<p>Dataset for <em>Bound impurities in a one-dimensional Bose lattice gas: </em><em>low-energy properties and quench-induced dynamics</em> [<a href="https://scipost.org/SciPostPhysCore.7.3.049">SciPost Phys. Core 7, 049 (2024)</a>].</p>
The LILY Database: Linking Lithology to IODP Physical, Chemical, and Magnetic Properties Data
<p>During each expedition of the International Ocean Discovery Program and its precursor, the Integrated Ocean Drilling Program (jointly referred to as IODP), vast arrays of data are collected from drill cores. These data, which are accessible from the IODP LIMS (Laboratory Information Management System) database, include physical, chemical, and magnetic properties collected semi-continuously along cores using automated track systems, as well as a variety of analyses conducted on discrete subsamples taken from the cores. In addition, the lithology of all cores is described based on visual characteristics of the surface of split cores, visual examination of smear slides and thin sections, and compositional or mineralogical information derived from geochemical analyses. We extract basic lithologic information from this complex array of descriptive information and then tie that information to all other measurements. This new database is referred to as <strong>LI</strong>MS with <strong>L</strong>itholog<strong>y</strong> (LILY). LILY currently contains over 34 million data from 89 km of core recovered on 42 expeditions conducted 2009-2019. Some uses of LILY include identifying the abundance of different lithologies, finding data from core intervals with a specific lithology, assessing the efficacy of coring systems in different lithologies, or characterizing and analyzing physical, chemical, and magnetic properties based on lithology. We illustrate the use of LILY by computing the grain density by lithology from over 24,000 moisture and density measurements and then use those grain densities, along with the large IODP bulk density dataset, to compute a new high-resolution porosity dataset with over 3.7 million new porosity estimates.</p> <h2>CONTENT DESCRIPTION:</h2> <p><strong>The main LILY database is stored in the files with the suffix DataLITH.csv.</strong> Each file contains IODP LIMS data with lithology and other metadata added. The file prefix gives the type of data. For example, AVS_DataLITH.csv contains the Automated Vane Shear (AVS) shear strength data paired with lithology and other metadata. A list of all data types is given in Supporting Information Table S1 of Childress et al. (2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>). There are a total of 23 DataLITH files.</p> <ul> <li>AVS_DataLITH.csv: automated vane shear; shear strength measurements.</li> <li>CARB_DataLITH.csv: total carbon, hydrogen, nitrogen, and sulfur, inorganic carbon (carbonate), and organic carbon measured on discrete samples.</li> <li>GE_DataLITH.csv: gas elements from gas chromatography.</li> <li>GRA_DataLITH.csv: gamma ray attenuation bulk density from the Whole-Round Multisensor Logger (WRMSL).</li> <li>ICP_DataLITH.csv: Inductively-coupled plasma data.</li> <li>IW_DataLITH.csv: interstitial water chemistry.</li> <li>JR6A_DataLITH.csv: discrete magnetic measurements from the JR6A spinner magnetometer.</li> <li>KAPPA_DataLITH.csv: Kappabridge susceptibility meter measurements.</li> <li>MAD_DataLITH.csv: moisture and density from discrete samples.</li> <li>MS_DataLITH.csv: magnetic susceptibility from the WRMSL.</li> <li>MSP_DataLITH.csv: point magnetic susceptibility from the Section Half Multisensor Core Logger (SHMSL).</li> <li>NGR_DataLITH.csv: natural gamma radiation from the Natural Gamma Radiation Logger (NGRL).</li> <li>PEN_DataLITH.csv: pocket penetrometer compressional strength measurements.</li> <li>PWB_DataLITH.csv: P-wave velocity from the bayonet system.</li> <li>PWC_DataLITH.csv: P-wave velocity from the caliper system.</li> <li>PWL_DataLITH.csv: P-wave velocity from the WRMSL.</li> <li>RGB_DataLITH.csv: Red-Green-Blue color from the Section Half Imaging Logger (SHIL).</li> <li>RSC_DataLITH.csv: reflectance spectroscopy from the SHMSL.</li> <li>SRA_DataLITH.csv: source rock analyzer measurements.</li> <li>SRM_DataLITH.csv: Superconducting Rock Magnetometer (SRM) measurements of split-core sections.</li> <li>SRMD_DataLITH.csv: SRM measurements of discrete samples.</li> <li>TCON_DataLITH.csv: thermal conductivity measured with the Teka Berlin TK04 probe.</li> <li>TOR_DataLITH.csv: Torvane shear strength measurements.</li> </ul> <p>Other compressed data folders contain multiple files used in creating the LILY database:</p> <p>RawDESC.zip: Contains 7,940 .csv files derived from the raw text content of the DESClogik Excel worksheets that was extracted, converted to comma separated value (.csv) format, and put into files with a consistent naming convention, without applying any corrections or conversions to the original text. Each file is the direct extraction of a tab from the DESC workbooks, available at <a href="https://web.iodp.tamu.edu/DESCReport/">https://web.iodp.tamu.edu/DESCReport/</a></p> <p>CoreSUMM.zip: Contains one file with Core Summary information, which includes the expedition, site, hole, core, coring type, top and bottom depths drilled, advances and recoveries, time and date of recovery, and the number of sections. These data are further paired with additional metadata (expanded core type, latitude, longitude, and water depth). Coordinates and water depth for each hole are derived from LIMS (and the JANUS database at <a href="http://www-odp.tamu.edu/database/">http://www-odp.tamu.edu/database/</a> for older expeditions).</p> <p>RawDATA.zip: Contains the raw track/discrete dataset downloaded by expedition from IODP LIMS database and placed in folders for each type of data (AVS, CARB, SRM, etc.) </p> <p>RawLITH.zip: Contains 42 .csv files, with one file for each expedition. Each file contains all lithologic description (prefix, principal and suffix, etc.) information for an entire expedition, as it was originally described. These have been transformed to a consistent format and paired with consistent identification information and additional metadata. Headers are normalized across all expeditions and SampleID information is standardized.</p> <p>CleanLITH: Contains 42 .csv files. Each file contains all lithologic description (prefix, principal and suffix) information for an entire expedition. The lithologic descriptions have been standardized to a consistent nomenclature using the dictionary given in Support Information Table S4 of Childress et al. (2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>). These data are further paired with additional metadata (e.g., degree of consolidation, expanded core type, latitude, longitude, and water depth).</p> <h2>GitHub Repository:</h2> <ul> <li>Contains a few notebooks to demonstrate how to work with the LILY database</li> <li><a href="https://github.com/IODP/LILY">IODP LILY GitHub Repository</a></li> </ul>
Database for machine learning of hydrogen storage materials properties
<p><strong>Database for machine learning of hydrogen storage materials properties</strong></p> <p>Matthew Witman<sup>a</sup>, Mark Allendorf<sup>a</sup>, Vitalie Stavila<sup>a</sup></p> <p><sup>a</sup>Sandia National Laboratories, Livermore, CA</p> <p> </p> <p><strong>Description</strong></p> <p>This ML-HydPARK dataset provides a csv file of metal hydride compositions, capacities, and thermodynamic values that can be used as target properties for building, training, and testing machine learning models. It has been parsed and cleaned from the DOE’s original publicly available HydPARK database according to the procedure in [1] to make it more suitable for immediate use with data-driven models. Generally, this removed duplicate entries, removed entries missing critical data, and attempted to fix various entries with obvious errors in the data. It is continuously updated under version control as new metal alloy hydrides are published in the open literature. Most entries contain data on the enthalpy and entropy of the hydriding reaction, as well the maximum hydrogen capacity, for which compositional machine learning models can be trained [1,2].</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The authors gratefully acknowledge research support from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Fuel Cell Technologies Office through the Hydrogen Storage Materials Advanced Research Consortium (HyMARC). This work was supported by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy<br> or the United States Government.</p> <p> </p> <p><strong>References</strong></p> <ol> <li>Witman, M.; Ling, S.; Grant, D. M.; Walker, G. S.; Agarwal, S.; Stavila, V.; Allendorf, M. D. Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning. <em>J. Phys. Chem. Lett</em>. <strong>2020</strong>, 11, 40–47.</li> <li>Witman, M.; Ek, G.; Ling, S.; Chames, J.; Agarwal, S.; Wong, J.; Allendorf, M. D.; Sahlberg, M.; Stavila, V. Data-Driven Discovery and Synthesis of High Entropy Alloy Hydrides with Targeted Thermodynamic Stability. <em>Chem. Mater</em>. <strong>2021</strong>, 33, 4067–4076.</li> </ol> <p> </p> <p><strong>Contact</strong></p> <p>Please email <a href="mailto:mwitman@sandia.gov">mwitman@sandia.gov</a> , <a href="mailto:mdallen@sandia.gov">mdallen@sandia.gov</a>, or <a href="mailto:vnstavi@sandia.gov">vnstavi@sandia.gov</a> for questions or to request addition of recent data from the literature to this dataset.</p>
Data supporting tracking changes in wetlandscape properties of the Lake Winnipeg Watershed using Landsat inundation products (1984–2020)
<p>The workbook contains time series of wetlandscape properties, climate variables, and climate oscillation indices for 1984–2020, and land cover statistics for 1992–2020 in the Lake Winnipeg Watershed. The wetlandsacpe properties were generated as part of a study by Fendereski, Ma, Mohammady, Spence, Trick, and Creed ("Tracking changes in wetlandscape properties of the Lake Winnipeg Watershed using Landsat inundation products (1984–2020)") submitted<span> </span>to the International Journal of Applied Earth Observation and Geoinformation. The use of the data is subject to citing the paper.</p>
Taxon item properties
<p>Diagram showing examples of Wikidata properties that can be used on a Wikidata item for a taxon. </p>
Robust Method for Property Prediction via Artificial Neural Networks: Incorporating Key Structural Features for Carbon Dioxide – Ionic Liquid Mixtures
<p>This Dataset comprises two sub-sets of information:</p> <ul> <li>Database and Results of the work present in the paper "Robust Method for Property Prediction via Artificial Neural Networks: Incorporating Key Structural Features for Carbon Dioxide – Ionic Liquid Mixtures" published in The Journal of Physical Chemistry B (https://doi.org/10.1021/acs.jpcb.4c04432).</li> <li>Sample of the code used, in order to reproduce any of the results presented above. This can be found in the previous version of this Dataset (v1.0 https://zenodo.org/records/11216901)</li> </ul> <p> </p> <p>Regarding the sample code, an example for all ANN Models used in this work is provided. This includes the three models used:</p> <ol> <li>One based only on Critical Properties of Ionic Liquids (CRT Model)</li> <li>One based only on Structural Properties of Ionic Liquids (STR Model)</li> <li>One combination of the previous models, taking into account both Critical and Structural Properties (COMB Model)</li> </ol> <p>In this manner, it is possible to observe the differences between the performance of the different models, either through statiscal analysis or using graphical representation. This allows for the benchmarking to be done in a more concise way.</p>
Data for: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling
<h2>Description</h2> <p>DATA REPOSITORY FOR</p> <p>Title: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and <br> multi-scale material modeling<br>By: Eva Jägle, Jithender J. Timothy, Daniel Jansen, Alisa Machner<br>Accepted by: Cement and Concrete Research</p> <p>This dataset presents the data of the paper 'Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling' submitted to and accepted by Cement and Concrete Research. The dataset follows the structure of the paper such that the calculations described therein can be reproduced.</p> <p>Data is available on three types of cement: Two ordinary Portland cements of different grinding fineness (CEM I 42.5 R und CEM I 52.5 R) and one limestone-containing blended cement (CEM II/A-LL 42.5 R). The data refer to the first 24 hours of hydration and temperature conditions of 20°C (for CEM I 42.5 R, CEM I 52.5 R, CEM II/A-LL 42.5 R) and 35°C (for CEM I 52.5 R). All data were retrieved for cement pastes with a water-to-cement ratio of 0.45.</p> <p>The dataset contains raw and processed data from quantitative X-ray diffraction, 5PL cement dissolution fitting, thermodynamic simulation with GEMS, multi-scale material modeling, ultrasonic testing and Vicat penetration tests. The data is mainly available in .xlsx files together with short descriptions in ReadMe.txt files.</p>
Effects of Periodic Normal Stress Oscillations on Frictional Properties of Simulated Natural Fault Gouges under In Situ P-T Conditions
<p>Files named by in a format of "Uxxx_xx_xxMPa_xxC" refer to the original mechanical data recorded during experiment.</p> <p>The compressed package includes the files to perform numerical modeling, modeling results and the experimental data for comparison. To replicate the numerical modeling, readers can open the COMSOL project file (".mph" file) using COMSOL software (version >5.4) then input the parameters for the boundary conditions, such as the temperature, load-point velocity, oscillation amplitude and frequency. </p>
Property listings for sale Madrid
<p>This dataset contains the property listings in Madrid as of October 31st, 2021</p> <p>From each listing, we got the following fields:</p> <p><strong>ID</strong>: the listing ID<br> <strong>Listing</strong>: It shows the listing it usually contains the type of property and the street. <br> <strong>Location</strong>: the place of the listing (city, town or neighborhood)<br> <strong>price</strong>: property price<br> <strong>old_price</strong>: previous price before the discount.<br> <strong>discount</strong>: percentage of discount<br> <strong>meters</strong>: size of the property in meters<br> <strong>sq_meter_price</strong>: price of each square meter.<br> <strong>rooms</strong>: number of rooms of the property<br> <strong>floor</strong>: the floor of the property<br> <strong>garage</strong>: It shows if the property has a garage<br> <strong>description</strong>: This field is the description of the property in Spanish.</p> <p>All the data has been scraped from idealista containing the listings as of October 31st, 2021 in Madrid. The process took about 30 hours. </p> <p>Spanish:</p> <p>El dataset contiene los inmuebles listados para su venta en la Comunidad de Madrid a fecha 31 de octubre de 2021.</p> <p>De cada una de las ofertas de venta se han recogido los siguientes campos:</p> <p><strong>ID</strong>: Es el identificador del anuncio<br> <strong>Listing</strong>: Contiene el título del anuncio que normalmente se compone del tipo de vivienda y de la calle en la que se encuentra. <br> <strong>Location</strong>: es la ubicación del inmueble que puede ser la ciudad, el pueblo o el barrio. <br> <strong>price</strong>: precio de venta del inmueble.<br> <strong>old_price</strong>: campo solo disponible en los inmuebles rebajados e indica el precio anterior del inmueble<br> <strong>discount</strong>: porcentaje de descuento del precio.<br> <strong>meters</strong>: metros cuadrados de la vivienda<br> <strong>sq_meter_price</strong>: Precio del metro cuadrado del inmueble<br> <strong>rooms</strong>: Número de habitaciones del inmueble<br> <strong>floor</strong>: Planta del inmueble<br> <strong>garage</strong>: campo que indica si el inmueble dispone de garaje, cuando su valor es nulo indica que no aparece reflejado que disponga de garaje.<br> <strong>description</strong>: Es un campo abierto donde aparece la descripción del inmueble en venta.</p> <p>Los datos han sido extraídos del portal inmobiliario idealista y el dataset contiene los inmuebles listados a fecha 31/10/2021 en la Comunidad de Madrid. Ha sido obtenido mediante un web scraper desarrollado en python en un proceso que ha durado unas 30 horas.</p>
GeoERA RESOURCE H3O-PLUS data set which contains hydraulic properties of prime aquifers and aquitards in the Dutch-Flemish-German cross-border area
<p>Dataset which contains information about hydraulic properties of harmonized hydrogeological units in the Dutch-Flemish-German cross-border region which was compiled in the GeoERA RESOURCE project under WP3 H3O-PLUS. The harmonization of the 3D geometry of the cross-border hydrogeological units in the H3O projects constituted a major step towards a common hydrogeological dataset of the Roer Valley Graben and thus the harmonization of groundwater flow models. The database that was compiled provides the characterization of these hydrogeological units with respect to their hydraulic properties, primarily their hydraulic conductivity.<br> The associated report and appendices describe the database of hydraulic properties of aquifers and aquitards based on common criteria. Attention is also given to the characterization of hydraulic properties of faults.</p>
Raw and analyzed data to manuscript "Influence of air plasma pretreatments on mechanical properties in metal-reinforced laminated wood"
<p><strong>Abstract</strong><br> The use of wood-based materials in building and construction is constantly increasing as environmental aspects and sustainability gain importance. For structural applications, however, there are many examples where hybrid material systems are needed to fulfil the specific mechanical requirements of the individual application. In particular, metal reinforcements are a common solution to enhance the mechanical properties of a wooden structural element. Metal-reinforced wood components further help to reduce cross-sectional sizes of load-bearing structures, improve the attachment of masonry or other materials, enhance the seismic safety and tremor dissipation capacity, as well as the durability of the structural elements in highly humid environments and under high permanent mechanical load. A critical factor to achieve these benefits, however, is the mechanical joint between the different material classes, namely the wood and metal parts. Currently, this joint is formed using epoxy or polyurethane (PU) adhesives, the former yielding highest mechanical strengths, whereas the latter presents a compromise between mechanical and economical constraints. Regarding sustainability and economic viability, the utilization of different adhesive systems would be preferable, whereas mechanical stabilities yielded for metal-wood joints do not permit for the use of other common adhesive systems in such structural applications.<br> This study extends previous research on the use of non-thermal air plasma pretreatments for the formation of wood-metal joints. The plasma treatments of Norway spruce (Picea abies (L.) Karst.) wood and anodized (E6/EV1) aluminum AlMgSi0.5 (6060) F22 were optimized, using water contact angle measurements to determine the effect and homogeneity of plasma treatments. The adhesive bond strengths of plasma-pretreated and untreated specimens were tested with commercial 2-component epoxy, PU, melamine-urea formaldehyde (MUF), polyvinyl acetate (PVAc), and construction adhesive glue systems. The influence of plasma treatments on the mechanical performance of the compounds was evaluated for one selected glue system via bending strength tests. The impact of the hybrid interface between metal and wood was isolated for the tests by using five-layer laminates from three wood lamellae enclosing two aluminum plates, thereby excluding the influence of congeneric wood-wood bonds. The effect of the plasma treatments is discussed based on the chemical and physical modifications of the substrates and the respective interaction mechanisms with the glue systems. </p>
First Street Foundation Property Level Flood Risk Statistics V1.3
<p>The property level flood risk statistics generated by the First Street Foundation Flood Model Version 1.3 come in CSV format. The data that is included in the CSV includes:</p> <ul> <li> <p>An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.</p> </li> <li> <p>The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.</p> </li> <li> <p>The property’s Flood Factor as well as data on economic loss.</p> </li> <li> <p>The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms in 2021, 2036, and 2051.</p> </li> <li> <p>Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for years 2021, 2036, and 2051.</p> </li> <li> <p>Information on historical events and flood adaptation, such as ID and name.</p> </li> </ul> <p>You can download a sample of the property level flood risk statistics generated by First Street's Flood Model on this page. You can purchase the property level data for areas within the contiguous United States on the First Street website <a href="https://firststreet.org/data-access/paid-access/?utm_source=Property_Statistics&utm_medium=Purchase_Data&utm_campaign=Zenodo#pricing-component">here</a>. You can find the data dictionary which breaks down the data that is available with each property-level data purchase <a href="https://firststreet.org/data-access/getting-started-with-first-street-data/data-dictionary/?utm_source=Property_Statistics&utm_medium=Data_Dictionary&utm_campaign=Zenodo">here</a>. If you are also interested in the hazard layers, you can find more information <a href="https://firststreet.org/data-access/getting-started-with-first-street-data/documentation-hazard-dictionary/?utm_source=Property_Statistics&utm_medium=Hazard_Dictionary&utm_campaign=Zenodo">here</a>.</p>
Constraining the properties of dense neutron star cores: The case of the transient low-mass X-ray binary HETE J1900.1-2455
<p>This is a basic reproduction package for the paper "Constraining the properties of dense neutron star cores: The case of the transient low-mass X-ray binary HETE J1900.1-2455" by <a href="https://doi.org/10.1093/mnras/stab2202">N. Degenaar et al. (2021)</a>. It provides reduced data products, simulated data and scripts to allow the reproduction of the work performed in this paper. It also lists software used and data archives containing the public observational data.</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.