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3,206 results for “property (T)”
Soil properties and Q10 values along a latitudinal transect and for a 4-year laboratory incubation
<p>Soil physicochemical protection, substrates and microorganisms data were included for soils collected from seven sites along a 5,000-km-long latitudinal transect and from a 4-year laboratory incubation experiment. Q10 values data for microorganism reciprocal transplant experiments and aggregate disruption experiments were also included.</p>
Out-of-Distribution Evaluation of Active Learning Pipelines for Molecular Property Prediction Dataset
<p>Data for the paper "Out-of-Distribution Evaluation of Active Learning Pipelines for Molecular Property Prediction"</p>
Multilinear regression results for the retention of amino acids of hydrolyzed peptide fractions and their properties - 5%Prolastin
<p>The data set is a part of the PhD thesis of Nattawan Chorhirankul, Wageningen University, The Netherlands.</p>
The role of chemical properties of the material deposited in nests of white stork in shaping enzymatic activity and fungal diversity - dataset
<p>Dataset to paper: Błońska E., Jankowiak R., Lasota J., Krzemińska N., Zbyryt A., Ciach M. 2024. The role of chemical properties of the material deposited in nests of white stork in shaping enzymatic activity and fungal diversity. Environmental Science and Pollution Research 31, 2: 2583-2594. https://doi.org/10.1007/s11356-023-31383-x</p> <p>This study was financially supported by the National Science Centre, Poland (grant no. 2021/41/B/NZ8/03456).</p>
Data from "Examination of how properties of a fissioning system impact isomeric yield ratios of the fragments"
<p>Data from PRC article "Examination of how properties of a fissioning system impact isomeric yield ratios of the fragments"<br>https://doi.org/10.1103/PhysRevC.108.064602</p>
Studying gastrulation by invagination: the bending of a cell sheet by mechanical cell properties using 3D deformable cell based simulations
<p>This dataset contains the scripts and end results of invagination simulation experiments.<br>The data are the results of a 3D cell based model that was used to investigate invagination: the bending of a cell sheet, and uses cell-cell adhesion, apical constriction, cell volume conservation, collision detection and handeling.</p>
Data for a publication "Microstructure and mechanical properties of in-situ SiO2-reinforced mechanically alloyed CoCrFeNiMnX (X= 5, 20, 35 at.%) high-entropy alloys"
<p>Dataset contains data that has been used within the manuscript entitled: "Microstructure and mechanical properties of in-situ SiO2-reinforced mechanically alloyed CoCrFeNiMnX (X= 5, 20, 35 at.%) high-entropy alloys". For more information, please read the README.txt file.</p>
PollyXT and COSMO-MUSCAT data for "Investigating the link between mineral dust hematite content and intensive optical properties by means of lidar measurements and aerosol modelling"
<p>The dataset contains 4 different files: </p> <ul> <li>For the single case example on the 24 August 2021 between 2:45 to 5:27 UTC in Mndelo, Cabo Verde: <ul> <li>-Mindelo-PollyXT_CPV-20210824_0245-0527-77smooth-info.txt : contains the information of the vertically retrieved optical properties from PollyXT lidar measurements. The information contained refers to the chosen retrieval times, vertical smoothing, and reference heights</li> <li>-Mindelo-PollyXT_CPV-20210824_0245-0527-77smooth.txt : vertically retrieved optical properties per height.</li> <li>-Mindelo-model_24aug.csv : COSMO-MUSCAT vertical results of dust and mineral mass concentrations per height. The columns that end with "int mass" correspond to the integrated mass per dust layer and columns that end with numbers correspond to different size bins. For reference to the size bins see Table 1 in Gómez Maqueo Anaya et al., 2024</li> </ul> </li> <li>Mutiple case studies: <ul> <li>-Mindelo-lidar-uvvisdiff_model.csv : Twenty-two case studies with the following order: first, the mean values of the lidar-derived optical properties, along with their corresponding retrieval times and heights that define the dust plume. This is followed by the POLIPHON (Mamouri and Ansmann, 2014, 2017) data. The mean values from dust and mineral mass concentrations from the model start with the model heights where the dust plumes were calculated. At the end of the dataset rows, the times from which the modeled mean values are calculated can be found.</li> </ul> </li> </ul>
Mechanical Properties, Workability, and Experiments of Reinforced Composite Beams with Alternative Binder and Aggregate
<p><span>Arguably the most important element in the sustainability of concrete development is the discovery of an optimal sustainable binder and substitution for the increasingly depleted reserves of natural aggregates. Considerable interest has been shown in alkali-activated materials, which possess good characteristics and could be considered environmentally friendly because of their use of secondary materials in production. The aim of this study was the determination of the mechanical properties of three different mixtures based on the same locally accessible raw materials. The reference mixture contained Portland cement, the second mix contained a finely ground granulated blast furnace slag instead of cement, and the third mixture contained a portion of light artificial aggregate. The experiments focused on the testing and mutual comparison of the processability of the fresh mixture and mechanical characteristics (like compressive and flexural strength, as well as resistance to high temperatures and surface layer tear strength tests). Reinforced concrete beams without shear reinforcement and with three levels of reinforcement were also tested with a three-point bend test. The results show that, overall, the mechanical properties of all the tested mixtures were similar, but each had its own disadvantages. For example, the blast furnace slag-based mixture had a more vulnerable surface layer or a debatable loss of bulk density in the light aggregate mix at the expense of the mechanical properties. One of the main results of the research is that it was possible to technologically produce beams from the alkali-activated concrete (AAC) mixture. Then, the performed beam experiments verified the mechanism of damage, collapse, and load capacity. The obtained results are essential because they present the use of AAC not only in laboratory conditions but also for building elements. In beams without shear reinforcement, the typical tensile cracks caused by bending and shear cracks appeared under loading, where their character was affected depending on the degree of beam reinforcement and loading.</span></p>
Properties of selected alkali-activated materials for sustainable development
<p>The presented research focuses on three selected variants of alkali-activated materials, where the goal is to compare key properties from the point of view of material engineering and structural design. Tests of the mechanical properties of the examined materials are carried out and their durability is compared, namely frost resistance, resistance to chemical and de-icing substances and resistance to elevated temperature.</p>
Unique Property Reference Number (UPRN) indicator: Flood risk (UPRN_1_1)
<p>This indicator covers three main flooding risks in the UK: fluvial (rivers and streams), coastal and surface water. The indicators were computed for all the Unique Property Reference Numbers (UPRNs) in the NHS Cheshire and Merseyside Integrated Care Board region (North West England). This region is formed by the following Local Authorities: Chester and Cheshire East, Cheshire West, Halton, Knowsley, Liverpool, Sefton, St. Helens, Warrington and Wirral.</p> <p> </p>
Unique Property Reference Number (UPRN) indicator: Tree canopy coverage (UPRN_3_1)
<p>This is an indicator with the tree canopy coverage for various distances (15m, 25m, 150m, 300m and 500m) from Unique Property Refence Numbers (UPRNs) in the Office for National Statistics (ONS) UPRN directory v2024.07 (Epoch 111). This metric is based on <em>Bluesky’s National Tree Map™,</em> which is the only tree dataset to include trees in Great Britain and the Republic of Ireland that are 3 metres and taller.</p> <h3>Available files</h3> <ul> <li><strong>UPRN_3_1_tree_canopy_coverage_cm_with_coords.csv</strong>: covers the Cheshire and Merseyside region (England) and includes latitude and longitude for each UPRN.</li> <li><strong>UPRN_3_1_tree_canopy_coverage_cm.csv</strong>: covers the Cheshire and Merseyside region (England).</li> <li><strong>UPRN_3_1_tree_canopy_coverage_lothian_with_coords.csv</strong>: covers the Lothian region (Scotland) and includes latitude and longitude for each UPRN.</li> <li><strong>UPRN_3_1_tree_canopy_coverage_lothian.csv</strong>: covers the Cheshire and Merseyside region (England).</li> </ul> <p> </p> <h3>Fields</h3> <table> <tbody> <tr> <td> <p><strong>Column name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>UPRN</p> </td> <td> <p>Unique Property Reference Number as per the Office for National Statistics UPRN Directory (ONSUD) v2024.07 (Epoch 111).</p> </td> </tr> <tr> <td> <p>n_trees_15m</p> </td> <td> <p>Number of trees (above 3m) within 15m from the UPRN.</p> </td> </tr> <tr> <td> <p>tree_canopy_cover_15m</p> </td> <td> <p>Percentage of area covered by tree canopy within 15m from the UPRN. From a maximum area of approximately 707m<sup>2</sup>.</p> </td> </tr> <tr> <td> <p>n_trees_25m</p> </td> <td> <p>Number of trees (above 3m) within 25m from the UPRN.</p> </td> </tr> <tr> <td> <p>tree_canopy_cover_25m</p> </td> <td> <p>Percentage of area covered by tree canopy within 25m from the UPRN. From a maximum area of approximately 1963m<sup>2</sup>.</p> </td> </tr> <tr> <td> <p>n_trees_150m</p> </td> <td> <p>Number of trees (above 3m) within 150m from the UPRN.</p> </td> </tr> <tr> <td> <p>tree_canopy_cover_150m</p> </td> <td> <p>Percentage of area covered by tree canopy within 150m from the UPRN. From a maximum area of approximately 70686m<sup>2</sup>.</p> </td> </tr> <tr> <td> <p>n_trees_300m</p> </td> <td> <p>Number of trees (above 3m) within 300m from the UPRN.</p> </td> </tr> <tr> <td> <p>tree_canopy_cover_300m</p> </td> <td> <p>Percentage of area covered by tree canopy within 300m from the UPRN. From a maximum area of approximately 282743m<sup>2</sup>.</p> </td> </tr> <tr> <td> <p>n_trees_500m</p> </td> <td> <p>Number of trees (above 3m) within 500m from the UPRN.</p> </td> </tr> <tr> <td> <p>tree_canopy_cover_500m</p> </td> <td> <p>Percentage of area covered by tree canopy within 500m from the UPRN. From a maximum area of approximately 785398m<sup>2</sup>.</p> </td> </tr> <tr> <td> <p>latitude (only version with coordinates)</p> </td> <td> <p>Latitude of the UPRN, given in decimal degrees, where N is positive and S is negative.</p> </td> </tr> <tr> <td> <p>longitude (only version with coordinates)</p> </td> <td> <p>Longitude of the UPRN, given in decimal degrees, where E is positive and W is negative.</p> </td> </tr> </tbody> </table> <p> </p> <h3><strong>Regions</strong></h3> <p><strong>Cheshire and Merseyside (Endgland)</strong></p> <p>Includes the following local authorities:</p> <ul> <li>Cheshire East</li> <li>Cheshire West and Chester</li> <li>Halton</li> <li>Knowsley</li> <li>Liverpool</li> <li>Sefton</li> <li>St. Helens</li> <li>Warrington</li> <li>Wirral</li> </ul> <p> </p> <p><strong>Lothian (Scotland)</strong></p> <p>Includes the following local authorities:</p> <ul> <li>East Lothian</li> <li>City of Edinburgh</li> <li>Midlothian</li> <li>Westlothian</li> </ul> <p> </p>
Data for "Nanoscale modification of MOC-based composites: The influence of alumina nanosheets on microstructure and material properties"
Open the record for dataset details and reuse information.
Equation of State, Structure, and Transport Properties of Iron Hydride Melts at Planetary Interior Conditions dataset
<p>Datasets from <a href="https://doi.org/10.1029/2024JE008525">https://doi.org/10.1029/2024JE008525</a></p> <p>"Equation of State, Structure, and Transport Properties of Iron Hydride Melts at Planetary Interior Conditions"</p> <p>Emma R. Stoutenburg1, Razvan Caracas2, Natalia V. Solomatova3, and Andrew J. Campbell1</p> <p>1Department of the Geophysical Sciences, University of Chicago, Chicago, IL, USA, 2 Université Paris Cité, Institut de Physique du Globe de Paris, CNRS, 1 rue Jussieu, Paris, France, 3CNRS, Ecole Normale Supérieure de Lyon, Laboratoire de Géologie de Lyon LGLTPE UMR5276, Centre Blaise Pascal, Lyon, France</p> <p>--- </p> <p><em>Please make sure you download data from the 30 October, 2024 version. </em> </p> <p>--- </p> <p>ds01.xlsx</p> <p>Contains pressure, density, volume, and temperature of the main dataset discussed in the paper (10 and 100 GPa). Also included are the diffusivities of iron and hydrogen in units of m2/s, viscosity in units of Pa⋅s, average Fe-Fe and Fe-H coordination numbers and bond lengths, as well as the molecular hydrogen abundance and the average number of Fe in H1, H2, and H3 clusters. Nominal pressures, Pnom are provided as these data were generalized within the text; their actual pressure, Pavg, are provided as well. (v1 vs v2: viscosity columns ([visc_Pas, visc_std] were updated, as a typo was discovered in the calculation of viscosity and it was a factor of 1/3 too small; May 2024: added molecular hydrogen data; October 2024, viscosity updated to include a neglected factor of 1/2).</p> <p>ds02.xlsx</p> <p>Contains all the data used in fitting the equation of state (v1-2 vs May 2024: P<10 GPa added to equation of state fit). The first sheet includes the molar volume, temperature, pressure (P), the magnetism corrected pressure (P_magcor115), and the residuals from the equation of state fits (both Birch Murnaghan and Vinet). The second sheet includes the comparison of spin polarized and non-spin polarized pressure in the iron hydrides at ~1 GPa. The third sheet includes the comparison of spin polarized and non-spin polarized pressure in pure iron. </p> <p>FeH-EOS_XfromrhoPT.py</p> <p>added in Oct 2024 update, a simple python function that estimates the hydrogen content of an Fe-H melt with a given density, pressure, and temperature </p>
Properties for sale or rent in Spain
<p><strong>Description of the Dataset of Properties in Spain</strong></p> <p>This dataset contains <strong>538 records</strong> of properties available in Spain, with a total of <strong>19 columns </strong>that describe different aspects of each property, from its location and price to specific characteristics such as surface, number of rooms, and bathrooms, among others. The columns and their main characteristics are detailed below:</p> <p>1. <strong>Energy_Consumption</strong>: Energy consumption in kWh/m² per year. Has missing values.<br>2. <strong>Reference</strong>: Unique reference code for each property, numeric type.<br>3. <strong>Heating</strong>: Heating type. Has missing values.<br>4.<strong> Country</strong>: The country where the property is located, is always "es" (Spain).<br>5. <strong>City</strong>: City of the property.<br>6.<strong> Zone</strong>: Specific zone within the city, 492 valid records.<br>7. <strong>Energy_Class</strong>: Energy class of the property. Has missing values.<br>8. <strong>Publish_date</strong>: Date and time the property was published in `YYYY-MM-DD HH:MM:SS` format.<br>9. <strong>Sale_Price</strong>: Sale price in Euros.<br>10. <strong>Floor</strong>: The floor on which the property is located. Has missing values.<br>11. <strong>Street</strong>: Address of the property, with 507 unique streets.<br>12. <strong>Bedrooms</strong>: Number of bedrooms, presented as text (e.g., `3 bedr.`).<br>13. <strong>Elevator</strong>: Indicates whether the property has an elevator (only `Yes` or null).<br>14. <strong>Bathrooms</strong>: Number of bathrooms, with values like `1 bath` and `2 baths`.<br>15. <strong>Year_Construction</strong>: The year the property was built.<br>16. <strong>Surface</strong>: The surface area of the property in square meters.<br>17. <strong>Autonomous_Community</strong>: Autonomous Community of the property.<br>18. <strong>Contrat</strong>: Type of contract (`sale` or `rent`).<br>19. <strong>Property_Type</strong>: Type of property.</p> <p>This dataset is suitable for analyzing property characteristics in the Spanish real estate market, whether to identify price trends, surface distribution, and energy efficiency, or to assess the popularity of certain areas and types of properties.</p>
Data from: Genomic signatures of paleodrainages in a freshwater fish along the southeastern coast of Brazil: genetic structure reflects past riverine properties
Past shifts in connectivity in riverine environments (for example, sea-level changes) and the properties of current drainages can act as drivers of genetic structure and demographic processes in riverine population of fishes. However, it is unclear whether the same river properties that structure variation on recent timescales will also leave similar genomic signatures that reflect paleodrainage properties. By characterizing genetic structure in a freshwater fish species (Hollandichthys multifasciatus) from a system of basins along the Atlantic coast of Brazil we test for the effects of paleodrainages caused by sea-level changes during the Pleistocene. Given that the paleodrainage properties differ along the Brazilian coast, we also evaluate whether estimated genetic diversity within paleodrainages can be explained by past riverine properties (i.e., area and number of rivers in a paleodrainage). Our results demonstrate that genetic structure between populations is not just highly concordant with paleodrainages, but that differences in the genetic diversity among paleodrainages correspond to the joint effect of differences in the area encompassed by, and the number of rivers, within a paleodrainage. Our findings extend the influence of current riverine properties on genetic diversity to those associated with past paleodrainage properties. We discuss how these findings may explain the inconsistent support for paleodrainages in structuring divergence from different global regions and the importance of taking into account past conditions for understanding the high species diversity of freshwater fish that we currently observe in the world, and especially in the Neotropics.
Data from paper "Impact of milling machine parameters on the properties of reclaimed asphalt pavement"
<p>The dataset includes test results of road cores and samples gathered from the same locations after milling of asphalt pavement. Milling machine parameters, including moving speed, milling depth, and drum rotational speed were intentionally varied during milling. Results from four jobsites are included in the dataset. The results include binder content, penetration, softening point, coarse and fine aggregate flow coefficients, gradation of RAP, gradation of extracted RAP aggregates, as well as the calculated chunk, breakdown, and filler increase indexes. These three indexes were used to quantitatively characterize the milling operations.</p>
Porous shape-persistent rylene imine cages with tunable optoelectronic properties and delayed fluorescence
<p>A simultaneous combination of porosity and tunable optoelectronic properties, common in covalent organic frameworks, is rare in shape-persistent organic cages. Yet, organic cages offer important molecular advantages such as solubility and modularity. Herein, we report the synthesis of a series of chiral imine organic cages with three built-in rylene units by means of dynamic imine chemistry and we investigate their textural and optoelectronic properties. Thereby we demonstrate that the synthesized rylene cages can be reversibly reduced at accessible potentials, absorb from UV up to green light, are porous, and preferentially adsorb CO<sub>2</sub> over N<sub>2</sub> and CH<sub>4</sub> with a good selectivity. In addition, we discovered that the cage incorporating three perylene-3,4:9,10-bis(dicarboximide) units displays an efficient delayed fluorescence. Time-correlated single photon counting and transient absorption spectroscopy measurements suggest that the delayed fluorescence is likely a consequence of a reversible intracage charge-separation event. Rylene cages thus offer a promising platform that allows combining the porosity of processable materials and photochemical phenomena useful in diverse applications such as photocatalysis or energy storage.</p>
Dataset for "Nutritional value and sensory properties of common carp (Cyprinus carpio L.) fillets enriched with sustainable and natural feed ingredients. Food and Chemical Toxicology, 151, 112146."
<p>Dataset for "<em>Nutritional value and sensory properties of common carp (Cyprinus carpio L.) fillets enriched with sustainable and natural feed ingredients.</em><em> <strong>Food and Chemical Toxicology, 151, 112146. </strong>DOI: <a href="https://doi.org/10.1016/j.fct.2021.112197">doi.org/10.1016/j.fct.2021.112197 </a></em>"</p>
Structures for "Protonation States of Molecular Groups in the Chromophore-Binding Site Modulate Properties of the Reversibly Switchable Fluorescent Protein rsEGFP2"
<p>Files with atomic coordinates of the structures obtained in geometry optimization of molecular systems within the protein and cluster models. Supplementary materials for the paper "Protonation States of Molecular Groups in the Chromophore-Binding Site Modulate Properties of the Reversibly Switchable Fluorescent Protein rsEGFP2"</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.