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)”
Data from: Can the soil seed bank of Rumex obtusifolius in productive grasslands be explained by management and soil properties?
<p><em>Rumex obtusifolius</em> is a problematic weed in temperate grasslands worldwide as it decreases yield and nutritional value of forage. Because the species can recruit from the seed bank, we determined the effect of management and soil properties on the soil seed bank of <em>R. obtusifolius</em> in intensively managed, permanent grasslands in Switzerland (CH), Slovenia (SI), and United Kingdom (UK). Following a paired case-control design, soil cores were taken from the topsoil of grassland with a high density of <em>R. obtusifolius</em> plants (cases) and from nearby parcels with very low R. obtusifolius density (controls). Data on grassland management, soil nutrients, pH, soil texture, and density of R. obtusifolius plants were also collected. Seeds in the soil were germinated under optimal conditions in a glasshouse. The number of germinated seeds of R. obtusifolius in case parcels was 866 ±152 m<sup>-2</sup> (CH, mean ±SE), 628 ±183 m<sup>-2</sup> (SI), and 752 ±183 m<sup>-2</sup> (UK), with no significant difference among countries. Densities in individual case parcels ranged from 0 up to approximately 3000 seeds m<sup>-2</sup> (each country). Control parcels had significantly fewer seeds, with a mean of 51 ±18, 75 ±52, and 98 ±52 seeds m<sup>-2</sup> in CH, SI, and UK, respectively, and a range between 0 and up to 1000 seeds m-2. Across countries, variables explaining variation in the soil seed bank of <em>R. obtusifolius</em> in case parcels were soil pH (negative relation), silt content (negative), land-use intensity (negative), and aboveground <em>R. obtusifolius</em> plant density (positive). Because a large soil seed bank can sustain grassland infestation with <em>R. obtusifolius</em>, management strategies to control the species should target the reduction in the density of mature plants, prevention of the species' seed production and dispersal, as well as the regulation of the soil pH to a range optimal for forage production.</p>
Supporting dataset for "Surface energy dynamics and canopy structural properties in intact and disturbed forests in the Southern Amazon"
<p>Supporting dataset for the manuscript “Surface energy dynamics and canopy structural properties in intact and disturbed forests in the Southern Amazon", currently under review in the Journal of Geophysical Research: Biogeosciences.</p>
Comparing effects of food mechanical properties on oral processing behaviors in two sympatric lemur species - Dataset
<p>Dataset for maximal random slope mixed models from manuscript "Comparing effects of food mechanical properties on oral processing behaviors in two sympatric lemur species". Column headers are described in the article and the key tab of the excel. </p>
Evaluation of Fresh and Hardened Properties of 3D-Printed Engineered Cementitious Composites (ECC) Designed for Sustainable and Resilient Infrastructure Systems
<p>3D concrete printing is a cutting-edge construction technique that has the potential to revolutionize the construction industry due to cost-saving in terms of labor and formwork costs, efficiency in construction, lower safety-related risks, and a higher degree of automation. However, several issues still make its adoption relatively slower on a large scale. Engineered cementitious composites (ECC), a class of ultra-high-strength concrete, can be a potential solution to some problems, such as reinforcement and durability. The preliminary phase of a Tran SET project focused on the design of 3D printable ECC, considering the concrete mix design proportions and their potential effects on fresh and hardened properties to achieve an optimized printable ECC mix. The type and content of various concrete ingredients such as cement, admixtures, aggregates, and fibers have considerable influence on the several properties in the fresh and hardened state. The replacement levels for cement were 0%, 50%, and 75% for mineral admixture, 10% for silica fume, and 0.4% for nano-clay by cement mass. Locally available fine aggregates were also used at 25% and 40% by mass of binder. In addition, the influence of fiber types and contents was also investigated. Two types of fibers, polyethylene (PE) and polyvinyl alcohol (PVA) fibers, were used at different levels, such as 0%, 1%, 1.5%, and 2% of the total volume of the mix. Moreover, four mixes, including FA50-MC, FA40SF10-MC, S50-MC, and FA40MK10-MC, were evaluated in terms of mechanical performance (flexural strength and direct tensile strength). The flowability of the ECC mixes was reduced with the incorporation of slag, metakaolin, higher aggregate content, and PE fibers. The 2% of both PVA and PE fibers had a reduction in compressive strength as compared to the low fiber content. Moreover, the PE-ECC exhibited superior tensile ductility as compared to the PVA-ECC due to the more fiber bridging by the PE fibers at the crack interface.</p>
Database of physicochemical and optical properties of black carbon fractal aggregates
<p>In order to estimate the climate impact of highly absorbing black carbon (BC) aerosols, it is necessary to know their optical properties. The Lorentz-Mie theory, often used to calculate the optical properties of BC under the spherical morphological assumption, produces discrepancies when compared to measurements. In light of this, researchers are currently investigating the possibility of computing the optical properties of BC using a realistic fractal aggregate morphology. To determine the optical properties of such BC fractal aggregates, the Multiple Sphere T-Matrix method (MSTM) is used, which can take more than 24 hours for a single simulation depending on the aggregate properties. This study provides a highly accurate benchmark machine-learning algorithm that can be used to generate the optical properties of BC fractal aggregate in a fraction of a second. The machine learning algorithm was trained over an extensive database of physicochemical and optical properties of BC fractal aggregates. The extensive training data helped develop an ML algorithm that can accurately predict the optical properties of BC fractal aggregates with an average deviation of less than one percent from their actual values. Specifically, the ML algorithm provides the option to generate the optical properties in the visible spectrum using either kernel ridge regression (KRR) or artificial neural networks (ANN) for a BC fractal aggregate of desired physicochemical properties like size, morphology, and organic coating. The dataset of physicochemical and optical properties of BC fractal aggregates are provided here. The developed ML algorithm for predicting the optical properties of BC fractal aggregates (https://github.com/jaikrishnap/Machine-learning-for-prediction-of-BCFAs) is highly useful for real-world applications due to its wide parameter range, high accuracy, and low computational cost.</p> <p><strong>Contents</strong></p> <ul> <li>database_optical_properties_black_carbon_fractal_aggregtates.csv, data file, comma-separated values</li> <li>database_header.txt, metadata, text</li> </ul> <p><strong>Citation for the database: </strong></p> <p>B., Romshoo, T., Müller, B., Patil, J., Michels, T., Kloft, M., and Pöhlker, M.: Database of physicochemical and optical properties of black<br> carbon fractal aggregates, Dataset, https://doi.org/10.5281/zenodo.7523058, 2023.</p>
DIB_Dataset for the psychometric properties of the construction of a verbal aptitude test instrument to assess prospective high school students' majors
<p>The dataset consists of raw data from the response to the construct resulting from the development of a verbal aptitude test instrument and the results of data analysis using the Rasch model analysis approach.</p>
Data sets and machine learning models for: Predicting critical properties and acentric factor of fluids using multi-task machine learning
<p>The experimental data sets, data splits, additional features, QM calculations, model predictions, and final machine learning models for the manuscript "Predicting Critical Properties and Acentric Factor of Fluids Using Multi-Task Machine Learning". <strong>Citation should refer directly to the manuscript:</strong></p> <ul> <li> <p>Biswas, S.; Chung, Y.; Ramirez, J.; Wu, H.; Green, W. H. Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning. <em>Journal of Chemical Information and Modeling.</em> <strong>2023</strong> <em>63</em> (15), 4574-4588. DOI: <a href="https://doi.org/10.1021/acs.jcim.3c00546">10.1021/acs.jcim.3c00546</a></p> </li> </ul> <p>To use the machine learning models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/crit_prop">https://github.com/yunsiechung/chemprop/tree/crit_prop</a>. </p> <p>Detailed information can be found in README.md file.</p> <p> </p> <p><strong>Details on the properties considered</strong></p> <p>The data set includes the following 8 properties:</p> <ul> <li>Tc: critical temperature, in K</li> <li>Pc: critical pressure, in bar</li> <li>rhoc: critical density, in mol/L</li> <li>omega: acentric factor, unitless</li> <li>Tb: boiling point, in K</li> <li>Tm: melting point, in K</li> <li>dHvap: enthalpy of vaporization at boiling point, in kJ/mol</li> <li>dHfus: enthalpy of fusion at melting point, in kJ/mol</li> </ul> <p><strong>Details on the files</strong></p> <p>1. Data sets under CritProp_v1.1.0:</p> <ul> <li>all_data: includes the data sets used in this work. All data points are listed for each chemical compound as well as its corresponding data source. The details of the data sources can be found in the README.md file. The distribution of the data set is included in each folder. <ul> <li>estimated_data_for_pretraining: contains the estimated data from Yaws' handbook that are used to pre-train our machine learning (ML) model.</li> <li>experimental_data: contains the experimental data (references 1 - 15) used to fine-tune our final ML model.</li> </ul> </li> <li>additional_features: includes the additional features tested for the ML model. The Abraham features are generated for all data (references 1 - 15) while the acsf, qm, and rdkit features are only generated for the data from references 1 - 9. <ul> <li>abraham: Abraham solute parameters (E, S, A, B, L). Molecular features.</li> <li>acsf: ACSF (atom-centered symmetry functions). Atomic features that are coverted from the 3D coordinates of the compound</li> <li>qm_atom: QM (quantum chemical) atomic feature. </li> <li>qm_mol: QM molecular feature.</li> <li>rdkit: Selected RDKit 2D molecular features.</li> </ul> </li> <li>data_splits_and_model_predictions: contains the training and test sets used to evaluate the model. It also contains the predicted values from our final ML model for each test set. <ul> <li>random and scaffold splits: training and test sets that include the data from references 1 - 9.</li> <li>external test set: a test set that includes the data from only references 10 - 15.</li> </ul> </li> </ul> <p>2. Machine learning (ML) model files:</p> <ul> <li>CritProp_ML_model_files_with_abraham_feat.zip: contains the Chemprop ML model files that are trained using Abraham features as additional molecular features. This gives the best results.</li> <li>CritProp_ML_model_files_without_additional_feat.zip: contains the Chemprop ML model files that are trained without any additional features. This gives the second best results.</li> </ul> <p>To use these ML models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/crit_prop">https://github.com/yunsiechung/chemprop/tree/crit_prop</a></p> <p>3. QM (quantum chemical) calculations:</p> <ul> <li>QM_calculations.zip: contains the results of the QM calculations that are performed to compute QM features.</li> </ul> <p> </p> <p> </p>
Ocean Gateways and Ocean Circulation Dynamics: Unveiling the Deep Water-Mass properties in the Western Equatorial Pacific and Eastern Indian Ocean since the middle
<p>File contains a dataset related to census counts and stable isotopes that we used to write our manuscript.</p>
Characterisation of electro- and thermophysical properties of materials used in 3D printing
<p>A complex of materials’ properties was measured: electrical conductivity, thermal expansion, thermal conductivity, the density of the materials, and the technical density of construction with porosity.</p>
Data from: Insight into the structural and magnetotransport properties of epitaxial alpha-Fe2O3/Pt(111) heterostructures: The role of the reversed layer sequence
<p>We report on the chemical structure and spin Hall magnetoresistance (SMR) in epitaxial α-Fe<sub>2</sub>O<sub>3</sub>(hematite)(0001)/Pt(111) bilayers with hematite thicknesses of 6 nm and 15 nm grown by molecular beam epitaxy on a MgO(111) substrate. Unlike previous studies that involved Pt overlayers on hematite, the present hematite films were grown on a stable Pt buffer layer and displayed structural changes as a function of thickness. These structural differences (the presence of a ferrimagnetic phase in the thinner film) significantly affected the magnetotransport properties of the bilayers. We observed a sign change of the SMR from positive to negative when the thickness of hematite increased from 6 nm to 15 nm. For α-Fe<sub>2</sub>O<sub>3</sub>(15 nm)/Pt, we demonstrated room-temperature switching of the Néel order with rectangular, nondecaying switching characteristics. Such structures open the way to extending magnetotransport studies to more complex systems with double asymmetric metal/hematite/Pt interfaces.</p>
Data from: Quantum computation of frequency-domain molecular response properties using a three-qubit iToffoli gate
<p>The quantum computation of molecular response properties on near-term quantum hardware is a topic of substantial interest. Computing these properties directly in the frequency domain is desirable, but the circuits require large depth if the typical hardware gate set consisting of single- and two-qubit gates is used. Here, we report the application of a high-fidelity multipartite gate, the iToffoli gate, to the computation of frequency-domain response properties of diatomic molecules. The iToffoli gate enables a ~50% reduction in circuit depth and ~40% reduction in circuit execution time compared to the traditional gate set. We show that the molecular properties obtained with the iToffoli gate exhibit comparable or better agreement with theory than those obtained with the native CZ gates. Our work is among the first demonstrations of the practical usage of a native multi-qubit gate in quantum simulation, with diverse potential applications to near-term quantum computation.</p>
Dataset and scripts for publication "Property design of extruded magnesium-gadolinium alloys through machine learning"
<p>Data and scripts accompanying publication "Property design of extruded magnesium-gadolinium alloys through machine learning"</p>
Research Data Quality Assurance – an intellectual property perspective
<p>The presentation summarizes the intellectual property issues, regarding the second Demonstrator in the FAIR Data Spaces project, Research Data Quality Assurance. It will especially outline the specific copyright law paragraphs, which may be and are important for this demonstrator and the participants in the project.</p>
Observed properties of Starlink satellites
<p>Tabulated measurements of Starlink satellites observed by the Multi-site All-Sky CAmeRA (MASCARA) instrument. This was created by Peter Breslin during his master thesis at Leiden University. Thesis title: 'Mega-constellation satellites: Assessing their interference on ground-based astronomy'.</p>
The circadian clock of the bacterium B. subtilis evokes properties of complex, multicellular circadian systems
<p>Circadian clocks are pervasive throughout nature, yet only recently has this adaptive regulatory program been described in non-photosynthetic bacteria. Here, we describe an inherent complexity in the <em>Bacillus</em> <em>subtilis</em> circadian clock. We find that <em>B. subtilis</em> entrains to blue and red light and that circadian entrainment is separable from masking through fluence titration and frequency demultiplication protocols. We identify circadian rhythmicity in constant light, consistent with Aschoff's Rule, and entrainment aftereffects, both of which are properties described for eukaryotic circadian clocks. We report that circadian rhythms occur in wild isolates of this prokaryote, thus establishing them as a general property of this species, and that its circadian system responds to the environment in a complex fashion that is consistent with multicellular eukaryotic circadian systems.</p>
Magnetic properties of siliciclastic cave sediments from the Ciur Izbuc Cave
<p>Magnetic properties of siliciclastic cave sediments from the Ciur Izbuc Cave (Apuseni Mts., Romania: 46.85171° N, 22.40014° E, 540 m). Further details can be found in Moldovan et al. (2016), Fossil invertebrates records in cave sediments and paleoenvironmental assessments: a study of four cave sites from Romanian Carpathians, Biogeosciences, 13, 483-487, https://doi.org/10.5194/bg-13-483-2016.</p>
Magnetic properties of siliciclastic cave sediments from the Leșu Cave
<p>Magnetic properties of siliciclastic cave sediments from the Leșu Cave (Peștera cu Apă din valea Leșu) (Apuseni Mts., Romania: 46.824332° N, 22.556533° E, 721 m). Further details can be found in Moldovan et al. (2016), Fossil invertebrates records in cave sediments and paleoenvironmental assessments: a study of four cave sites from Romanian Carpathians, Biogeosciences, 13, 483-487, https://doi.org/10.5194/bg-13-483-2016.</p>
Soil water retention curves and soil physicochemical properties
<p class="MsoPlainText"><span>To identify the contribution of soil organic and inorganic fractions to soil water retention, we compiled the data of soil water retention curves and soil physicochemical properties. Data includes site information (Table 1), soil pH, soil C, cation exchange capacity (CEC), exchangeable basic cations, particle size distribution, soil texture class, contents of short-range-order minerals (Table 2), and soil water retention at different pressures, saturated hydraulic conductivity, and termite nest percent in soil profile (Table 3).</span></p>
Influence of small-scale spatial variability of soil properties on yield formation of winter wheat
<p>This is a data set of soil properties and plant properties of winter wheat.</p> <p>The data derived from a long-term field trial for the year 2016 at the Asendorf field station 70 km north of Hanover, Germany (49 m above sea level, 52°45′48.4′′N 9°01′24.3′′E) and a field site in Triesdorf, located in Northern Bavaria (450 m a.s.l., 49°12'36.5"N 10°38'33.9"E).</p> <p>Data includes soil (OC, bulk density, texture, pH-value) and plant data (grain yield, thousand grain weight, tillers per m², spikes per m²). All methods and data will be described in an upcoming journal article in the Journal Plant and Soil (DOI:10.1007/s111104-023-06212-2).</p>
Modeling the thermodynamic properties of saturated lactones in non-ideal mixtures with the SAFT-γ Mie approach. JCED 2023
<p>All computational data in the publication.</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.