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2,118 results for “Metal”
Fig 2 in Different responses of epigeic beetles to heavy metal contamination depending on functional traits at the family level
Fig 2. Mean total density ± SE of the most frequently occurring groups of beetles in three classes of contaminations along the season (circle- almost uncontaminated sites, square- moderately contaminated sites, triangle- highly contaminated sites).
The data catalog for Metallicity and alpha-abundance for 48 million stars in low-extinction regions in the Milky Way
<p>Stellar chemistry contains information on the environment in which the star was born. Therefore, measuring the chemical abundances of stars in the Milky Way, such as the overall metallicity [M/H] and the alpha-abundance [alpha/M], is essential in Galactic astronomy.</p> <p>We estimate ([M/H], [alpha/M]) for giants and dwarfs in low dust extinction region from the Gaia DR3 XP spectra by using tree-based machine-learning models trained on APOGEE DR17 (Abdurro’uf et al. 2022) and the metal-poor star sample of Li et al. (2022).</p> <p>Here, we upload the catalogues of ([M/H], [alpha/M]) for 182 million stars. The data are divided into 10 fits files. The i-th file (i=1,2,...,10) contains stars with E(B-V) value between 0.1*(i-1) and 0.1*i. Because our machine-learning models are trained on stars with low dust extinction (E(B-V)<0.1), we recommend using 48 million stars with low-dust extinction region with 0<E(B-V)<0.1 (table_light_mh_am_0p0ebv0p1.fits). The description for each column of the data is shown in column_description.png. </p> <p>The source paper of this catalog:</p> <ul> <li>Kohei Hatori "Metallicity and alpha-abundance for 48 million stars in low-extinction regions in the Milky Way" <br>https://iopscience.iop.org/article/10.3847/1538-4357/ad9686</li> </ul> <p>References:</p> <div> <div> <div> <ul> <li>Abdurro’uf, Accetta, K., Aerts, C., et al. 2022, ApJS, 259, 1026 35, doi: 10.3847/1538-4365/ac4414</li> </ul> </div> </div> </div> <ul> <li>Li, H., Aoki, W., Matsuno, T., et al. 2022, ApJ, 931, 147, doi: 10.3847/1538-4357/ac6514</li> </ul> <p> </p>
Dataset for Main Text and SI - Squeezing the threshold of metal-halide perovskite micro-crystal lasers grown by solution epitaxy by Shuyu Zhou et al.
<p>All data published in </p> <p><strong><span>Squeezing the threshold of metal-halide perovskite micro-crystal lasers grown by solution epitaxy</span></strong></p> <p><strong><span> by </span></strong><em><span>Shuyu Zhou, Viktor Rehm, Hany A. Afify, Yufei Han, Jędrzej Korczak, Andrzej Szczerbakow, Tomasz Story, Zijian Peng, Albert These, Anastasia Barabash, Andres Osvet, Christoph J. Brabec, Klaus Götz, Tobias Unruh, Felix Hilpert, Olaf Brummel, Jörg Libuda, Wolfgang Heiss</span></em></p> <p><em><span>are summarized in this rar file. </span></em></p>
Particulate atmospheric concentrations of trace metals and leachable nutrients in air at the Southeastern Mediterranean Sea (1994-1999)
<p><span>Table 1 dataset contains</span><span> aerosol concentrations of trace metals (Cd, Pb, Cu, Zn, Cr, Mn, Fe and Al)</span><span> </span><span>at the SE Mediterranean coast of Israel, </span><span>collected</span><span> between 1994 and </span><span>1999</span><span>. Total suspended particles (TSP) in air were collected</span><span> </span><span>on Whatman QM-A quartz micro</span><span>fi</span><span>bre </span><span>filters </span><span>and on Whatman 41 </span><span>fil</span><span>ters (both 20.3</span><span> cm x </span><span>25.4</span><span> </span><span>cm), by high-volume sampler</span><span> (HVS). </span><span><span> </span></span><span>The HVS was </span><span>located on the roof of the</span><span> </span><span>National Institute of Oceanography (NIO) at Tel-Shikmona</span><span>, Israel </span><span>(located on the shore, 22</span><span> </span><span>m above sea</span><span> </span><span>level) and at Maagan Michael</span><span>, Israel</span><span> (about 900m from shore, 13 m above sea level). Analyses were carried out after total digestion with HF following the procedure of ASTM (1983).</span><span> <span>Further details in Herut et al., 2001.</span></span></p> <p><span>Table 2 dataset contains leachable</span><span> inorganic</span><span> </span><span>nitrogen (NO</span><sub><span>3</span></sub><span> </span><span>+ NO<sub>2</sub></span><span>, NH</span><sub><span>4</span></sub><span>) and phosphorus (PO<sub>4</sub>)</span><span> concentrations in</span><span> aerosol</span><span> <span>(</span></span><span>total suspended particles </span><span>in air</span><span>)</span><span> </span><span>samples collected on Whatman 41 filters between</span><span> </span><span>April 1996 and January 1999</span><span>. </span><span>The atmospheric</span><span> </span><span>sampling was performed</span><span> </span><span>on the roof of the National Institute of Oceanography</span><span> </span><span>(NIO) at Tel-Shikmona (TS)</span><span>, Israel</span><span> (located on the shore and inside</span><span> </span><span>the sea, 22 m above sea level). Leaching experiments were performed to evaluate the amount of seawater leachable nitrate, ammonium, and phosphate from the TSP</span><span> using </span><span>SE</span><span> </span><span>Mediterranean </span><span>low nutrient low chlorophyll </span><span>surface seawater</span><span>. Further details in Herut et al., 2002.</span></p> <p><span>Herut, B., Nimmo<span>, M., Medway, A., Chester, R., & Krom, M. D. (2001). Dry atmospheric inputs of trace metals at the Mediterranean coast of Israel (SE Mediterranean): sources and fluxes. <em>Atmospheric Environment</em>, <em>35</em>(4), 803-813.</span><span><span>‏</span></span></span></p> <p><span>Herut, B., Collier, R., & Krom, M. D. (2002). The role of dust in supplying nitrogen and phosphorus to the Southeast Mediterranean. <em>Limnology and Oceanography</em>, <em>47</em>(3), 870-878.</span><span><span>‏</span></span></p> <p><span>Herut, B., Krom, M. D., Pan, G., & Mortimer, R. (1999). Atmospheric input of nitrogen and phosphorus to the Southeast Mediterranean: Sources, fluxes, and possible impact. <em>Limnology and Oceanography</em>, <em>44</em>(7), 1683-1692.</span><span><span>‏</span></span></p>
20240809 - Analysis (waste metals).xlsx
<p>This Excel datafile includes the times series data on material and monetary flows of the global trade of waste metals between 2000 and 2022, and the analysis (ecological network analysis and ascendency analysis). This dataset is supplementary material to the publication:</p> <p>"<strong>Zisopoulos F.K.</strong>, Fath B.D., Toboso-Chavero S., Huang H., Schraven D., Steuer B., Stefanakis A., Clark O.G., Scrieciu S., Singh S., Noll D., de Jong M. (Accepted). Inequities blocking the path to circular economies: A bio-inspired network-based approach for assessing the sustainability of the global trade of waste metals. <em>Resources, Conservation & Recycling</em>. 212(January 2025), 107958."</p> <p>F.K.Z. is grateful to the <em>Impact for Sustainability Fund</em>, a named fund at <em>Stichting Erasmus TrustFonds</em>, for funding (project number: 97090.2022.101.671/074/RB). The study falls within the Sino-Dutch project <em>“Towards Inclusive CE: Transnational Network for Wise-waste Cities (IWWCs)”</em> which is one of the projects of the <em>Erasmus Initiative Dynamics of Inclusive Prosperity</em>, and it is co-funded by the <em>Dutch Research Council</em> (NWO) and the <em>National Natural Science Foundation of China</em> (NSFC); NWO project number: 482.19.608; NSFC project number: 72061137071. H.H. acknowledges his support with a grant from Princeton University's School of Engineering and Applied Science (SEAS). D.N. acknowledges funding under the research contract 2022.05039.CEECIND/CP1734/CT0001 (<a href="https://doi.org/10.54499/2022.05039.CEECIND/CP1734/CT0001" target="_blank" rel="noopener">https://doi.org/10.54499/2022.05039.CEECIND/CP1734/CT0001</a>) through the Portuguese Foundation for Science and Technology (FCT) with MED (<a href="https://doi.org/10.54499/UIDB/05183/2020" target="_blank" rel="noopener">https://doi.org/10.54499/UIDB/05183/2020</a>) and CHANGE (<a href="https://doi.org/10.54499/LA/P/0121/2020" target="_blank" rel="noopener">https://doi.org/10.54499/LA/P/0121/2020</a>). The authors are grateful for the constructive comments of the editor and two anonymous reviewers.</p>
Dataset of the publication: Strain Switching in van der Waals Heterostructures Triggered by a Spin-Crossover Metal–Organic Framework
<p>Dataset of the publication: Strain Switching in van der Waals Heterostructures Triggered by a Spin-Crossover Metal–Organic Framework</p> <p>DOI: 10.1002/adma.202110027</p> <p>Boix-Constant, Carla; Garcia-Lopez, Victor; Navarro-Moratalla, Efren; Clemente-Leon, Miguel; Zafra, Jose Luis; Casado, Juan; Guinea, Francisco; Manas-Valero, Samuel; Coronado, Eugenio</p> <p> Adv. Mater. 34, 2110027 (2022)</p>
Dataset of the publication: Tailoring spin waves in 2D transition metal phosphorus trichalcogenides via atomic-layer substitution
<p>Dataset of the publication: Tailoring spin waves in 2D transition metal phosphorus trichalcogenides via atomic-layer substitution</p> <p>DOI: 10.1039/d2dt02482a</p> <p>A. M. Ruiz, DL. Esteras, A. Rybakov, J. J. Baldov </p> <p>Dalton Trans., 54, 44, 16816-16823 (2022)</p>
Dataset of the publication: A Novel Banana-Shaped Mixed-Metal Co/Fe Polyoxometalate Cluster
<p>Dataset of the publication: A Novel Banana-Shaped Mixed-Metal Co/Fe Polyoxometalate Cluster</p> <p>DOI: 10.1002/cplu.202400473</p> <p>J. Quirós-Huerta, J. Troya, M. Clemente-León, J. M. Clemente-Juan, E. Coronado, J. Soriano-López </p> <p>ChemPlusChem, e202400473 (2024)</p>
Dataset for Gion and Gaillard (2025) - "The Multicomponent Exchange of Metals Between Magmatic Fluids and Silicate Melts"
<p>Dataset for the publication "The Multicomponent Exchange of Metals Between Magmatic Fluids and Silicate Melts" by Austin M. Gion and Fabrice Gaillard.</p>
Numerical Calculation of the Thermodynamic Properties of Silver Erbium Alloys for Use in Metallic Magnetic Calorimeters - Data
<p>Data from simulations of the specific heat and magnetization of Ag:Er alloys. The parameter range we consider are temperatures between 1mK and 1K, external magnetic fields of up to 20mT, and erbium concentrations of up to 2000ppm.</p>
Data for "Microscopic origin of the effect of substrate metallicity on interfacial free energies"
<p>Data related to the article "Microscopic origin of the effect of substrate metallicity on interfacial free energies"</p> <p>Laura Scalfi, Benjamin Rotenberg, arXiv:2105.06799 [physics.chem-ph]</p> <p> </p>
The excess of cool supergiants from contemporary stellar evolution models defies the metallicity-independent Humphreys-Davidson limit
<p>Input files and simulation results for stellar evolution tracks computed for the paper "The excess of cool supergiants from contemporary stellar evolution models defies the metallicity-independent Humphreys-Davidson limit", as well as synthetic populations generated and catalogues of observed cool supergiants in the Magellanic Clouds used in the analysis. Version 10398 of MESA was used for the simulations. More details in the README.txt file and in the paper.</p>
Computational results for the publication "Evolution of water structures in metal-organic frameworks for improved atmospheric water harvesting"
<p>This upload contains the computationally obtained atomic coordinates for MOF-303 and MOF-333 at different water loadings.</p>
Performance of GFN1-xTB for periodic optimization of Metal-Organic Frameworks
<p>GFN-xTB optimised structures of CoRE 2014 and CoRE 2019 structures, with and without lattice optimisation.</p>
dataset for Fig 2 in NatComm "Localised structuring of metal-semiconductor cores in silica clad fibres using laser-driven thermal gradients"
<p>Infrared transmission of silicon core fiber through which gold has been laser-thermally moved to reystallize the material</p>
Scenario data for article: Environmental impacts of key metals' supply and low-carbon technologies are likely to decrease in the future
<p>This dataset contains the background scenarios for metal supply used for the publication <a href="https://doi.org/10.1111/jiec.13181">"Environmental impacts of key metals' supply and low-carbon technologies are likely to decrease in the future"</a> in the Journal of Industrial Ecology (2021).</p> <p><strong>Scenario description:</strong></p> <p>These background scenarios comprise five variables for the metals of copper, nickel, zinc, and lead for the time period of 2010-2050. These variables are:<br> V1: ore grade decline and energy requirements</p> <p>V2: market shares of primary production locations</p> <p>V3: energy efficiency improvements during smelting and refining</p> <p>V4: market shares of primary production routes</p> <p>V5: market shares of primary and secondary production.<br> <br> The associated <a href="http://doi.org/10.1111/jiec.13181">article</a> in the Journal of Industrial Ecology describes the modelling assumptions and data sources of the scenarios. It also conducts impact assessments for future metal supply and low-carbon technologies with these metal scenarios as well as additional electricity supply scenarios from the IAM of <a href="https://models.pbl.nl/image/index.php/Download#IMAGE_input_data_for_the_Prospective_Life_Cycle_Assessment_model">IMAGE</a> from <a href="https://doi.org/10.1111/jiec.12825">Mendoza Beltran et al. (2020)</a> in the background .</p> <p><strong>How to use this dataset:</strong></p> <p>The background scenarios are suitable for the life cycle inventory database of ecoinvent version 3.5 or 3.6 (allocation, cut-off by classification). They can be incorporated into ecoinvent either via the brightway-based module of <a href="https://github.com/PascalLesage/presamples">presamples</a> or using the <a href="https://github.com/LCA-ActivityBrowser">activity-browser</a> and its scenario-based calculation set-up. Thereby, they can be used as background scenarios for any other prospective LCA based on ecoinvent 3.5 or 3.6.</p> <p>Moreover, they can be combined with the electricity supply scenarios of the IAM of <a href="https://models.pbl.nl/image/index.php/Download#IMAGE_input_data_for_the_Prospective_Life_Cycle_Assessment_model">IMAGE</a> from <a href="https://doi.org/10.1111/jiec.12825">Mendoza Beltran et al. (2020)</a> using the <a href="https://github.com/LCA-ActivityBrowser/brightway-superstructure">superstructure approach</a> of the activity-browser (<a href="https://doi.org/10.1007/s11367-021-01974-2">de Koning & Steubing 2020</a>).</p> <p>Before using the dataset, please adjust the "database" columns to the name of your database, e.g. "ecoinvent3.5", and potentially also the "key" columns.</p> <p>Versions of the scenarios applicable to ecoinvent 3.7.1 or 3.8 may be added later.</p> <p><strong>License: </strong>The metal supply scenario data is licensed under the CC-BY 4.0 license.</p>
Datasets to Poly(ethylene oxide)-based Electrolytes for Solid-State Potassium Metal Batteries with Prussian Blue Positive Electrode
<p>This dataset provides the raw data to the manuscript</p> <p>"<strong>Poly(ethylene oxide)-based Electrolytes for Solid-State Potassium Metal Batteries with Prussian Blue Positive Electrode"</strong></p> <p>published in ACS Appl. Polym. Mater. (DOI: <a href="https://doi.org/10.1021/acsapm.2c00014">10.1021/acsapm.2c00014</a> ) / <a href="https://doi.org/10.1021/acsapm.2c00014">https://doi.org/10.1021/acsapm.2c00014</a></p> <p>Specifically, the following measurements are provided:</p> <p>Electrochemical cell tests of liquid and solid electrolytes ("CYCLING_" & Ratecapability test)</p> <p>Solid electrolyte characterization:</p> <p>Differential Scanning Calorimetry ("DSC_")</p> <p>Electrochemical Impedance Spectroscopy ("EIS_")</p> <p>Rheological measurements ("RHEO_")</p> <p>X-ray diffraction data ("XRD_")</p>
Fig. 2 in Accumulation Of Heavy Metals By Small Mammals The Background And Polluted Territories Of The Urals
Fig. 2. The dendrogram is obtained for element analysis (Cu+Zn+Cd) in small mammals from natural populations in the background zone (Bcg) and polluted territories (Imp). The results of cluster analysis confirmed the statistically significant differences in heavy metals total accumulation in three species of small mammals.
Visualizing Eigen/Zundel cations and their interconversion in monolayer water on metal surfaces
<p>Here lies the experimental and theoretical data for the manuscript titled " Visualizing Eigen/Zundel cations and their interconversion in monolayer water on metal surfaces" to be published in Science.</p>
Visualizing Eigen/Zundel cations and their interconversion in monolayer water on metal surfaces
<p>Here lies the tabulated data used to create the Figures for the Science manuscript abo0823 titled "Visualizing Eigen/Zundel cations and their interconversion in monolayer water on metal surfaces".</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.