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2,118 results for “Metal”
Metal and nutrient content from submerged aquatic macrophytes collected from Southern Coeur d'Alene Lake, Idaho (USA) in August 2018.
<p>This repository contains data and R scripts used to produce the analyses reported in the manuscript listed below. See the Readme.txt and metadata.csv files for more explanation. The .R file can be used to unbundle the .tar.gz file via the packrat library. The data and script files are contained in the .tar.gz file. </p> <p>Scofield, B.D., Fields, S.F. & Chess, D.W. Aquatic macrophytes show distinct spatial trends in contaminant metal and nutrient concentrations in Coeur d’Alene Lake, USA. <em>Environ Sci Pollut Res</em> (2023). <a href="https://doi.org/10.1007/s11356-023-27211-x">https://doi.org/10.1007/s11356-023-27211-x</a></p>
Inverse design of metal-organic frameworks for direct air capture of CO2 via deep reinforcement learning
<p>The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the ever-expanding and nearly infinite chemical space of MOFs makes it extremely challenging to identify the most optimal materials for a given application. In this work, we present a novel approach using deep reinforcement learning for the inverse design of MOFs, our motivation being designing promising materials for the important environmental application of direct air capture of CO2 (DAC). We demonstrate that the reinforcement learning framework can successfully design MOFs with critical characteristics important for DAC. Our top-performing structures populate two separate subspaces of the MOF chemical space: the subspace with high CO2 heat of adsorption and the subspace with preferential adsorption of CO2 from humid air, with few structures having both characteristics. Our model can thus serve as an essential tool for the rational design and discovery of materials for different target properties and applications.</p>
Original data for "Imaging and quantifying the chemical communication between single particles in metal alloy" article
<p>This upload contains original data and supplementary data for the publication titled "Imaging and Quantifying the Chemical Communication Between Single Particles in Metal Alloy" by L. Godeffroy, A. Makogon, S. G. Derouich, F. Kanoufi, V. Shkirskiy in the ACS Analytical Chemistry journal. The preprint of the paper is included in this upload and is also available on ChemRxiv (<a href="https://doi.org/10.26434/chemrxiv-2022-rn77b-v2">https://doi.org/10.26434/chemrxiv-2022-rn77b-v2</a>). </p> <p>The file "OpticalData.zip" contains the original optical images. Each pixel in the images has a size of 180 nm, and the time interval between frames is 1.87 seconds.</p> <p>The file "sem_identical.png" contains a scanning electron microscopy (SEM) image of the same surface area as the optical images, captured using secondary electrons.</p> <p>The file "WorkingWithData.ipynbb" is a Jupyter Lab file that demonstrates a few examples of how data can be loaded and processed in a Python environment. Python 3 was used for this purpose.</p> <p>There are also two supplementary movies:</p> <p>Movie S1: This movie provides an enlarged view (marked with a square in Movie S2) of the surface film transformation on Al6061 during immersion in 10 mM H2SO4. It includes snapshots at 17 s, 28 s, 37 s, and 84 s, along with a related SEM image of the same location.</p> <p>Movie S2: This movie presents a wide-field view of the surface film transformation on Al6061 during immersion in 10 mM H2SO4, along with a related SEM image of the same location. The square highlights the area around which the narrative in the manuscript is focused.</p>
Meshing strategies for 3D geo-electromagnetic modeling in the presence of metallic infrastructure
<p>Accompanying data to journal article</p> <blockquote> <p>Castillo-Reyes, O., Rulff, P., Schankee Um, E., Amor-Martin, A. (2023) Meshing strategies for 3D geo-electromagnetic modeling in the presence of metallic infrastructure. Accepted for publication in Computational Geosciences.</p> </blockquote>
Influence of protein corona on cytotoxicity of metal oxide nanoparticles against human keratinocyte cell line (HaCaT)
<p>The model identified, among the factors determining the cytotoxic properties of metal oxide nanoparticles against HaCaT cell lines, a number of variables related to the processes occurring on the surface of nanoparticles in a biological medium, including the ability to form protein corona.</p> <p>The selected descriptors describe both the electronic structure of the metal oxides that are the components of the nanoparticles, i.e. the ionization potential (IP_ActivM_SM_#1, IP_ActivM_SM_#2) and the initial nanoforms, i.e. the particle size (Primary size) and the percentage content of the metal oxide which is the main component of the nanoparticle (Purity_#1) ; characterize nanoparticles in the medium, i.e. the isoelectric point (PZZP_#2), stability (Stability), potential for dissolution (Dissolution), generation of reactive oxygen species (ROS production) and protein adsorption (Protein adsorption). The listed descriptors reflect the features that are discussed in the literature as potentially related to the toxic effect of nanoparticles.</p>
Identification of factors determining the process of aggregation/agglomeration of metal oxide nanoparticles in a biological medium
<p>The model allows to identify factors determining the process of aggregation/agglomeration of metal oxide nanoparticles in a biological medium and to verify the importance of ion adsorption and protein adsorption in this process. </p> <p>Model confirms the significant effect of protein adsorption on the hydrodynamic diameter of metal oxide particles in the biological medium, and does not confirm the significant effect of ion adsorption in this process. It’s an example of modeling the properties of nanoparticles, where apart from the descriptors describing the structure of nanoparticles, there are also parameters characterizing the medium.</p>
FESEM after antibacterial test on Ti- bulk metallic glass compared with Ti6Al4V
<p>Field Emission scanning electron microscopy of Ti40Zr10Cu36Pd14 and Ti6Al4V after 24 h of antibacterial test with Aggregatibacter</p>
dynamic water contact angle measurements of metallic glass coatings
<p>dynamic water contact angle measurements of Zr-Cu-Ag metallic glass coatings. The file named PBT is the non-coated sample. The other three samples were metallic glass coatings.</p>
Field Emission Scanning Electron microscopy from Zr-Cu-Ag metallic glass coatings after antibacterial test with E.Coli
<p>Field Emission Scanning Electron Microscopy Figures from metallic glass (Zr-Cu-Ag) antibacterial coatings. Coatings have the name SP in their file name. The non-coated comparison is PBT. This is after the antibacterial test with <em>E.coli</em> after 24 hours. </p>
small angle x-ray scattering from Zr-Cu-Ag metallic glass coatings
<p>small angle x-ray scattering from Zr-Cu-ag metallic glass coating to confirm whether they became amorphous or not. The coating is on PBT substrate. </p>
Archaeological sites in Taiwan during the Neolithic and Metal Age
<p>Coordinates marked by asterisks were added in the current study (see Data and methods section for details). Chinese site names are romanised following the Wade-Giles system. Site names in indigenous peoples' languages are given in romanised form followed by the Chinese name in parentheses, if available. Archaeological cultures represented by the respective site are indicated by the value "1".</p>
GAMMA: Galactic Attributes of Mass, Metallicity, and Age Dataset
<p>We introduce the <strong>GAMMA</strong> (<strong>G</strong>alactic <strong>A</strong>ttributes of <strong>M</strong>ass, <strong>M</strong>etallicity, and <strong>A</strong>ge) dataset, a comprehensive collection of galaxy data tailored for Machine Learning applications. This dataset offers detailed 2D maps and 3D cubes of 11 727 galaxies, capturing essential attributes: stellar age, metallicity, and mass.</p><p>Together with the dataset we publish our code to extract any other stellar or gaseous property from the raw simulation suite to extend the dataset beyond these initial properties, ensuring versatility for various computational tasks. Ideal for feature extraction, clustering, and regression tasks, <strong>GAMMA</strong> offers a unique lens for exploring galactic structures through computational methods and is a bridge between astrophysical simulations and the field of scientific machine learning (ML).</p><p>As a first benchmark, we apply Principal Component Analysis (PCA) on this dataset. We find that PCA effectively captures the key morphological features of galaxies with a small number of components. We achieve a dimensionality reduction by a factor of ∼200 (∼3650) for 2D images (3D cubes) with a reconstruction accuracy below 5%.</p><p>We calculate UMAP (Uniform Manifold Approximation and Projection for Dimension Reduction) on the lower dimensional PCA scores of the 2D images to visualize the image space. An interactive version of this plot can be accessed using an online <a href="https://umap-dashboard.onrender.com/">Dashboard</a> (hover over a point to see the galaxy image and the IllustrisTNG Subhalo ID).</p><p>All the code to generate this dataset and load the data structure is publicly available on <a href="https://github.com/ufuk-cakir/GAMMA">GitHub</a>, with an additional documentation page hosted on <a href="https://gamma-dataset.readthedocs.io/en/latest/">ReadTheDocs</a>.</p><p> </p>
Dataset for Quantitative description of metal center organization in single-atom catalysts
<p>Dataset for <strong>Quantitative description of metal center organization in single-atom catalysts </strong>by by K. Rossi, A. Ruiz-Ferrando, D. Faust Akl, V. Gimenez Abalos, J. Heras-Domingo, R. Graux, X. Hai, J. Lu, D. Garcia-Gasulla, N. López, J. Pérez-Ramírez, and S. Mitchell.</p> <p>The data is structured as follows:</p> <ul> <li>01_Micrographs: all micrographs employed in .tif and .png format. An <a href="https://imagej.net/">imageJ </a>macro to overlay coordinate files with images is attached.</li> <li>02_Ground_truth: contains the manually-labeled and predicted xy-coordinates of atomic positions including probabilities.</li> <li>03_All_detection_data: contains all automated predictions of atomic positions in the images of this study (uhd), and of Mitchell et. al in <em>JACS</em>, <strong>144</strong>, 8018-8029 (2022) (jacs_train, jacs_test). This folder further contains model weights and area segmentations needed to estimate the surface atomic densities.</li> <li>04_Trimetallic_analysis: Figures complementing Supplementary Figure S21.</li> </ul> <p> </p>
Extreme Metal Vocals Dataset (EMVD)
<p><strong>Extreme Metal Vocals Dataset (EMVD)</strong></p> <p>Version 1.0, October 2023</p> <p> </p> <p><strong>Created by</strong></p> <p>Modan Tailleur (1,3), Julien Pinquier (2), Laurent Millot (1), Corsin Vogel (1), Mathieu Lagrange (3)</p> <ol> <li> <p>ENS Louis-Lumière, Saint-Denis, France</p> </li> <li> <p>IRIT, Université de Toulouse, CNRS, UT3 Toulouse, France</p> </li> <li> <p>Nantes Université, Ecole Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes, France</p> </li> </ol> <p> </p> <p><strong>Publication</strong></p> <p>If using this data in an academic work, please reference the DOI and version, as well as cite the following paper, which presented the data collection procedure and the first version of the dataset:</p> <p>@misc{tailleur2024emvddatasetdatasetextreme,<br> title={EMVD dataset: a dataset of extreme vocal distortion techniques used in heavy metal}, <br> author={Modan Tailleur and Julien Pinquier and Laurent Millot and Corsin Vogel and Mathieu Lagrange},<br> year={2024},<br> eprint={2406.17732},<br> archivePrefix={arXiv},<br> primaryClass={cs.SD},<br> url={https://arxiv.org/abs/2406.17732}, <br>}</p> <p> </p> <p><strong>Description</strong></p> <p>The Extreme Metal Vocals Dataset (EMVD) comprises a collection of recordings of extreme vocal techniques performed within the realm of heavy metal music. The dataset consists of 760 audio excerpts of 1 second to 30 seconds long, totaling about 100 min of audio material, roughly composed of 60 minutes of distorted voices and 40 minutes of clear voice recordings. These vocal recordings are from 27 different singers and are provided without accompanying musical instruments or post-processing effects. The distortion taxonomy within this dataset encompasses four distinct distortion techniques and three vocal effects, all performed in different pitch ranges.</p> <p> </p> <p><strong>How to use</strong></p> <p>To get an example on how to use this dataset for deep learning applications, please follow the link to the companion website: <a href="https://github.com/modantailleur/ExtremeMetalVocalsDataset">https://github.com/modantailleur/ExtremeMetalVocalsDataset</a></p> <p> </p> <p><strong>Label Taxonomy</strong></p> <p>The label taxonomy is as follows (see our paper for further details):</p> <p>Techniques:</p> <ul> <li>Clear Voice: high, mid, low</li> <li>Black Shriek: high, mid</li> <li>Death Growl: mid, low</li> <li>Hardcore Scream: high, mid, low</li> <li>Grind Inhale</li> </ul> <p>Effects:</p> <ul> <li>Pig Squeal</li> <li>Deep Gutturals</li> <li>Tunnel Throat</li> </ul> <p> </p> <p><strong>Recording procedure</strong></p> <p>For the recording sessions, a mobile setup was selected to accommodate as many singers as possible. An SM58 microphone was employed, chosen for its prevalence as a microphone commonly used by metal singers during live performances. A closed-back headphone served for music playback and provided the singers with a monitor of their own voice if they desired to hear it during recording. An audio interface <em>Scarlett 6i6</em> by <em>Focusrite</em> was responsible for connecting the laptop, microphone, and headset.</p> <p>In some cases, singers were recorded remotely using their own equipment (a stage microphone and an audio interface) which are documented in the database. These singers were provided with a video tutorial and explanatory documents to facilitate their participation in the project, with the main author remotely guiding them. Each singer was instructed to sustain three vowels—[a] as in "cat," [i] as in "ship," and [u] as in "book"—for a duration of five seconds each. They were required to maintain a consistent pitch not only within each vowel but also across all vowels produced. After this, they were asked to perform for approximately 15 seconds using the same vocal technique, but this time with lyrics of their choosing. The lyrics had to remain the same across all technique categories. Each vocal technique was recorded across several registers (high, mid, and low) depending on their relevance to the specific technique. It's worth noting that the Grind Inhale technique, although producible in multiple registers, was recorded in only one register, as many singers deemed it potentially harmful to their voice. A musical loop was provided in the singers' headphones during each recording.</p> <p> </p> <p><strong>Grading system</strong></p> <p>Each vocalist in this study underwent a comprehensive assessment of their comfort level with each vocal technique across the various vocal registers, employing a ranking system ranging from 0 to 5. A rank of 0 signifies that they never use this technique and are not sufficiently comfortable to produce it, which ultimately results in missing data in the dataset. A rank of 3 indicates occasional use, and a rank of 5 signifies that they use it in every live performance.This dataset provides supplementary insights into the singers' practices. These include the typical microphone-to-mouth distance employed by each vocalist during recording, as well as their professional status within the field of singing. The majority of the recordings were conducted onsite, within the familiar confines of the vocalist's chosen location, whether it be their home or a professional studio, utilizing equipment provided by the authors. However, some recordings were independently done by the vocalists themselves, leveraging their personal microphones and audio interfaces. In such instances, the authors remotely guided the recording process to ensure consistency and quality. Detailed equipment specifications have been documented.</p> <p>As authors noticed that the singers auto-evaluation ranking wasn’t very effective, the main author provided grades to individual audio files created by the singers, ranging from 0 to 2. A 2 grade suggests that the technique closely represents the intended vocal technique, 1 indicates that it moderately represents the vocal technique, and 0 signifies that the technique does not adequately represent the vocal technique. Audio files rated as 0 should not be employed for deep learning applications, but they are retained within the dataset in case future re-evaluation of the audio files is desired. Notably, approximately 70\% of the dataset's audio files received grades of 2 or 1 from the authors and are thus suitable for being used in diverse applications.</p> <p> </p> <p><strong>metadata_files.csv</strong></p> <p><em>file_name : </em>the name of the audio file</p> <p><em>singer_id : </em>the id of each singer (from 1 to 27)</p> <p><em>type : </em>whether the distortion employed is a technique, an effect, or a distortion that doesn’t fit any specific category</p> <p><em>name : </em>the name of the technique or of the effect employed by the singer (‘-’ if it doesn’t fit in any category)</p> <p><em>range</em> : the range employed by the singer (‘High’, ‘Mid’, or ‘Low’)</p> <p><em>vowel</em> : the vowel employed by the singer. ‘a’ if vowel [a] as in "cat", 'i' if vowel [i] as in "ship," and 'u' if vowel [u] as in "book"</p> <p><em>authors_rank</em> : the rank given by the authors (2, 1 or 0)</p> <p><em>duration(s) </em>: duration (in seconds) of the audio file</p> <p> </p> <p><strong>metadata_singers.csv</strong></p> <p><em>singer_id</em> : the id of each singer (from 1 to 27)</p> <p><em>gender : </em>the gender of the singer (« M » if male, « F » if female)</p> <p><em>status : </em>whether the singer is professional or non-professional (« Professional », or « Non-professional »)</p> <p><em>recording : </em>whether the recording was made onsite, with the authors equipment, or if it was guided remotely (« Onsite » or « Guided »)</p> <p><em>distance_to_microphone(cm) : </em>the distance chosen by the singer to the microphone (in centimeters)</p> <p><em>microphone : </em>model of microphone that was used for the recording</p> <p><em>audio_interface : </em>audio interface used for the recordings</p> <p><em>DAW : </em>Digital Audio Workstation (DAW) used for recording the singer (Ex: ProTools, Reaper etc...)</p> <p><em>ClearVoice_High, …, TunnelThroat : </em>singer’s rank (from 0 to 5) from his auto-evalution on each technique performed in each range.<br> </p> <p><strong>split_kfolds.csv</strong></p> <p>For deep learning applications, a k-fold cross-validation with 4 folds was performed and stored in the «split_kfolds.csv » file, reserving 20% of the training data for validation.</p> <p><em>file_name : </em><em>the name of the audio file</em></p> <p><em>split0, …, split3</em> : for each split, wether the file belongs to the train subset (‘train’), the evaluation subset (‘eval’), the validation subset (‘valid’) or if it isn’t used for training (‘-’)</p> <p> </p> <p><strong>Feedback</strong></p> <p>Please help us improve EMVD by sending your feedback to:</p> <ul> <li>Modan Tailleur: <a href="mailto:modan.tailleur@gmail.com">modan.tailleur@gmail.com</a></li> </ul> <p>In case of a problem, please include as many details as possible.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>We want to thank Oriol Nieto, Geoffroy Peeters, Christophe d'Alessandro and Boris Doval for fruitful discussion. We particularly want to thank Joshua Smith for guidance for the design of the taxonomy. We also want to thank the 27 singers for bringing this dataset to life.</p>
Dataset of the paper "Improving the Stability of Photodoped Metal Oxide Nanocrystals with Electron Donating Graphene Quantum Dots"
<p>The dataset provides the data for the publication: "Improving the Stability of Photodoped Metal Oxide Nanocrystals with Electron Donating Graphene Quantum Dots"</p>
Aquatic Insect Adult Metals Dataset: Urban and Forested Watersheds in the Piedmont of NC - 2021-2022
This dataset reports concentrations of 6 target trace metals (copper, zinc, nickel, lead, chromium, and selenium) in unfiltered water, emergent aquatic adult insects (by family), biofilm mats (predominately algae), and tree roots submerged under stream water. Biological and water samples were collected from three streams in the Piedmont region of North Carolina, USA: a wastewater dominated site (Ellerbe Creek, near the USGS gage at Glen Road ), a stormwater dominated site (Ellerbe Creek, near the USGS gage on Club Blvd), and a stream draining a predominately forested watershed (New Hope Creek, near a StreamPULSE site at Hollow Rock Preserve). This data was submitted for publication in a manuscript that explores how metals are transported by aquatic emergent insects from stream ecosystems into terrestrial food webs.
Trace metal, ion, and nutrient concentrations in aeolian samples subjected to experimental freeze-thaw cycles, collected from Taylor Valley, McMurdo Dry Valleys, Antarctica (2013-2016)
This data package contains measurements of trace metal, ion, and nutrient concentrations in aeolian samples collected from several locations throughout Taylor Valley in the McMurdo Dry Valleys of Antarctica during the 2013-2014, 2014-2015, and 2015-2016 austral summers. Samples were collected by the McMurdo Dry Valleys Long Term Ecological Research Program (MCM LTER) using Big Spring Number Eight (BSNE) isokinetic wind samplers located at Explorer’s Cove, Lake Fryxell at F6, East Lake Bonney, and Taylor Glacier. Samples were then subjected to experimental freeze-thaw cycles in a controlled laboratory setting to simulate supraglacial weathering processes and then analyzed to understand how freeze-thaw cycles affect nutrient, ion, and trace metal concentrations over time.
Data for: Discovery of low-metallicity stars in the central parsec of the Milky Way
<p>Spectroscopic data from Stostad et al. (2015) and Do et al. (2015). This file contains K-band spectra from the Gemini NIFS instrument of stars in the central parsec of the Galactic center. The files are in FITS format. Additional descriptions are in in Stostad et al. (2015). </p> <p>If using this data, please cite Do et al. (2015) and Stostad et al. (2015)</p> <p>https://ui.adsabs.harvard.edu/abs/2015ApJ...808..106S/abstract<br>https://ui.adsabs.harvard.edu/abs/2015ApJ...809..143D/abstract</p> <p> </p> <p>Contributors to the creation of the spectra from this dataset include:</p> <p>Tuan Do</p> <p>Morten Stostad</p>
Supplementary files for "Effect of Alkali and Trivalent Metal Ions on the High-Pressure Phase Transition of [C2H5NH3]MI0.5MIII0.5(HCOO)3 (MI=Na, K and MIII=Cr, Al) Heterometallic Perovskites"
<p>DFT optimised structures and phonon data of [C<sub>2</sub>H<sub>5</sub>NH<sub>3</sub>] (ethylamonium, EtA) based formate perovskites EtANaCr, EtANaAl and EtAKCr. The zip-files Phonons-XXX contain the calculated force constants, the frequencies at the gamma point, the calculated density of states and the thermal properties.</p>
Nanoscale Imaging of High-Field Magnetic Hysteresis in Meteoritic Metal Using X-Ray Holography
<p>Data of magnetisation (two datasets) of the cloudy zone of Tazewell IIICD iron meteorite. Data was obtained using X-ray holography. Magnetization data is a 3D matrix containing magnetisation data in form of data[x location][y location][applied field], applied field values is provided in a separate file.</p> <p>Further details about this dataset and conditions of measurements can be found in Blukis et al., 2020 submitted to Geochemistry, Geophysics, Geosystems</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.