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550 results for “copper”
Copper mineralization at Carajás mineral province - Brazil: geological, structural, and geophysical data
<p>Gridded geological, structural, and geophysical data at the Carajás mineral province. A number of known Cu occurrences are provided. This dataset is suitable for experimenting with machine learning methods.</p>
Chemical speciation of copper in a salt marsh estuary near Sapelo Island, Georgia
The concentrations of dissolved copper (Cud), copper-binding ligands, thiourea-type thiols and humic substances (HSCu) were measured in estuarine waters adjacent to Sapelo Island, Georgia, USA, on a monthly basis from April to December 2014. Here we present the seasonal cycle of copper speciation within the estuary and compare it to the development of an annually occurring bloom of ammonia oxidising archaea (AOA) Thaumarchaeota, which require copper for many enzymes. Two types of complexing ligands (L1 and L2) were found to dominate with mean complex stabilities (log K'CuL) of 14.5 and 12.8. Strong complexation resulted in lowering of the free cupric ion (Cu2+) concentration to femtomolar (fM) levels throughout the study and to sub-fM levels during the summer months. A Thaumarchaeota bloom during this period suggests that this organism manages to grow at very low Cu2+ concentrations. Correlation of the concentration of the L1 ligand class with a thiourea-type thiol and the L2 ligand class with HSCu provide convincing evidence for the identity of the ligands. Due to the stronger complex stability, 82 - 99 % of the copper was bound to L1. Thiourea-type thiols form strong Cu(I) species, suggesting that ~90% copper is present as Cu(I) in this region, upsetting the paradigm of its predominance as Cu(II). In view of the very low concentration of free copper (pCu >15 at the onset and during the bloom) and a reputedly high requirement for copper, it is likely that the Thaumarchaeota are able to access the thiol-bound copper directly.
Data for the extraction of the critical temperature of niobium films deposited on copper produced at CERN for SRF applications
<p>Each data set contains the voltage signal amplitude induced in a pickup coil by an alternating magnetic field, crossing the niobium-film-on-copper sample during its state transition from normal conducting to superconducting, the corresponding sample temperature and the estimated errors for the two measured quantities. File columns: #1 sample temperature in Kelvin, #2 estimated temperature error, #3 voltage amplitude in pickup coil in Volt, #4 estimated amplitude error.</p> <p>One data set corresponds to one sample. The critical temperature can be extracted as fit parameter by fitting the amplitude versus temperature data with a logistic function, where it corresponds to the half height of the state transition curve.</p>
The portrayal of underlings in Eastern Cālukya copper plates: textual analysis data with revised codebook
<p>This is a textual analysis dataset derived from Eastern Cālukya copperplate grants. The second version of 24 April 2024 contains a slightly revised codebook (in DOC and PDF formats with identical contents) in addition to the earlier dataset. This revision is essentially identical to that reflected in the "Revised tag" column of the dataset, except that many definitions have been made clearer and tidier, and a small number of intermediate-level categories have been added for better hierarchisation. The codes in use have not been altered.</p> <p><br>The data accompany the following forthcoming publications (title and date of publication subject to change):</p> <p>Balogh, Dániel (forthcoming 2024), 'The portrayal of underlings in Eastern Cālukya copper plates'. In: Self-Representation and Presentation of Others in Indic Epigraphical Writing, edited by Annette Schmiedchen and Dániel Balogh. Wiesbaden: Harrassowitz.</p> <div> <div>Balogh, Dániel (forthcoming 2024). ‘Textual Analysis Methodology and Royal Representation in Copperplate Grants’. In <em>Bhūtārthakathane ... Sarasvatī: Reading Poetry as a History Book</em>, edited by Marco Franceschini, Chiara Livio, and Lidia Wojtczak. Studies on the History of Śaivism. Naples: UniorPress.</div> </div> <p>The former publication introduces the method sketched out on the introductory page of this dataset and studies a particular topic through this methodology, while the latter discusses the method in more detail. An account of the technical details is in preparation by Balogh.</p> <p><br>This dataset, the underlying research and the relevant publications are results of the project DHARMA ‘The Domestication of “Hindu” Asceticism and the Religious Making of South and Southeast Asia’. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no 809994).</p>
17O-EPR determination of the structure and dynamics of copper single-metal sites in zeolites
<p><strong>Description of the dataset: </strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation and analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>spc</strong>, <strong>par</strong>, <strong>m</strong>, <strong>f34</strong>,<strong> xyz</strong>, <strong>out</strong>, <strong>in</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA</strong>,<strong> spc </strong>and<strong> par.</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong>.</li> <li>Periodic DFT computations with(out) filename extensions <strong>out</strong> and <strong>f34</strong> in ASCII format.</li> <li>Molecular cluster DFT computations with filename extensions <strong>in</strong> and <strong>out</strong> in ASCII format.</li> <li>Geometry information of cluster models is stored in <strong>xyz</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li>Q-band and X-band Pulsed-EPR spectroscopic measurements were generated by ELEXYS 580 EPR spectrophotometer equipped with SHQ cavity and ER035 M NMR gaussmeter produced by Bruker.</li> <li>Periodic DFT computations were generated using distributed parallel version of CRYSTAL17 code.</li> <li>Molecular cluster DFT computations were generated using the ORCA (v4.2.1) code.</li> <li><strong>If t</strong> <ul> <li>Files in <strong>PARACAT_WP3_20210625_01_CW</strong> folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and spc/par formats.</li> <li>Files in <strong>PARACAT_WP3_20210625_02_HYSCORE</strong> folder includes HYSCORE spectroscopic measurements; original data are in DTA/DSC formats.</li> <li>Files in <strong>PARACAT_WP3_20210625_03_ESE</strong> folder includes ESE spectroscopic measurements; original data are in DTA/DSC formats.</li> <li>Files in <strong>PARACAT_WP3_20210625_04_ENDOR</strong> folder includes ENDOR spectroscopic measurements; original data are in DTA/DSC formats.</li> <li>Files in <strong>PARACAT_WP3_20210625_05_MATLAB</strong> folder includes computer simulations/analyses of the EPR measurements; data are in m formats.</li> <li>Files in <strong>PARACAT_WP3_20210625_06_DFT </strong>folder includes periodic and cluster DFT computation inputs, outputs and geometries in ASCII format.</li> </ul> </li> </ul> <ul> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>CW</strong> – Continuous Wave EPR, <strong>ESE</strong> – Electron Spin Echo detected EPR, <strong>HYSCORE</strong> – HYperfine Sublevel CORrelation spectroscopy, <strong>ENDOR</strong> – Electron Nuclear DOuble Resonance spectroscopy, <strong>DFT </strong>– Density Functional Theory, <strong>CHA </strong>– Chabazite, zeolite topology.</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</li> <li>abbreviations: <strong>6MR, 8MR </strong>are the Cu docking sites; <strong>2Al-3NN</strong>, <strong>2Al-2NN</strong>, <strong>1Al</strong> are the different aluminium distributions analysed; <strong>1w</strong>, <strong>2w</strong>, <strong>3w, 4w</strong> indicates the number of water ligands considered in the models; <strong>eq</strong> and <strong>ax</strong> indicates equatorial and axial ligands. Periodic DFT computations with filename extension <strong>.f34</strong> include structural/symmetry information of optimized structure. Molecular cluster DFT computations with filename extension <strong>.in</strong>/<strong>.out</strong>/<strong>.xyz</strong> are inputs, outputs, and structure of cluster models.</li> </ul> </li> </ul>
Cu dataset – A copper ore labeled images dataset for segmentation training and testing
<p>This dataset is composed of 121 pairs of correlated images. Each pair contains one image of a copper ore sample acquired through reflected light microscopy (RGB, 24-bit), and the corresponding binary reference image (8-bit), in which the pixels are labeled as belonging to one of two classes: ore (0) or embedding resin (255).</p> <p>The sample came from a copper ore from Yauri Cusco (Peru) with a complex mineralogy, mainly composed of sulfides, oxides, silicates, and native copper. It was classified by size. The fraction +74-100 μm was cold mounted with epoxy resin and subsequently ground and polished.</p> <p>Correlative microscopy was employed for image acquisition. Thus, 121 fields were imaged on a reflected light microscope with a 20× (NA 0.40) objective lens and on a scanning electron microscope (SEM). In sequence, they were registered, resulting in images of 1017×753 pixels with a resolution of 0.53 µm/pixel. As matter of fact, some images (the images No. 2, 3, 24, 25, 46, 47, 69, 91, and 113) have slightly smaller sizes because they were cropped during the registration procedure to correct co-localization errors of the order of a few pixels. Finally, the images from SEM were thresholded to generate the reference images.</p> <p>Further description of this sample and its imaging procedure can be found in the work by Gomes and Paciornik (2012).</p> <p>This dataset was created for developing and testing deep learning models on semantic segmentation tasks. The paper of Filippo et al. (2021) presented a variant of the DeepLabv3+ model (Chen et al., 2018) that reached mean values of 90.56% and 92.12% for overall accuracy and F1 score, respectively, for 5 rounds of experiments (training and testing), each with a different, random initialization of network weights.</p> <p>For further questions and suggestions, please do not hesitate to contact us.</p> <p> </p> <p><strong>Contact email</strong>: ogomes@gmail.com</p> <p> </p> <p>If you use this dataset in your own work, please cite this DOI: 10.5281/zenodo.5020566</p> <p> </p> <p>Please also cite this paper, which provides additional details about the dataset:</p> <p>Michel Pedro Filippo, Otávio da Fonseca Martins Gomes, Gilson Alexandre Ostwald Pedro da Costa, Guilherme Lucio Abelha Mota. <em>Deep learning semantic segmentation of opaque and non-opaque minerals from epoxy resin in reflected light microscopy images</em>. <strong>Minerals Engineering</strong>, Volume 170, 2021, 107007, https://doi.org/10.1016/j.mineng.2021.107007.</p>
Jagjivanpur, West Bengal. Detail of the copper-plate charter of Mahendrapāla
<p>Jagjivanpur, West Bengal. Detail of the copper-plate charter of Mahendrapāla, now in the collection of the Malda Museum, Malda, West Bengal, as documented in 2007.</p>
Jagjivanpur, West Bengal. Detail of the copper-plate charter of Mahendrapāla
<p>Jagjivanpur, West Bengal. Detail of the copper-plate charter of Mahendrapāla, now in the collection of the Malda Museum, Malda, West Bengal, as documented in 2007.</p>
OB00085 Katni copper-plates of mahārāja Jayanātha, plate 3.
<p>Katni copper-plates of mahārāja Jayanātha, plate 3. The plate, one of a set of three, is not from Katni itself, but reported to have been recovered at Uchahara, the ancient Uccakalpa, in Satna district. Dated saṃvat 182 (<em>circa</em> 502 CE).</p>
OB00085 Katni copper-plates of mahārāja Jayanātha, plate 1.
<p><a href="https://siddham.network/object/ob00085/">OB00085</a> Katni copper-plates of mahārāja Jayanātha, plate 1. The plate is one of a set of three, is not from Katni itself, but reported to have been recovered at Uchahara, the ancient Uccakalpa, in Satna district. Dated saṃvat 182 (<em>circa</em> 502 CE).</p>
Copper Wire Corrosion and Meteorological Conditions in a Hangar
<p>Measurement of copper wire corrosion in a hangar (Kbely, Prague) with meteorological data (indoor and outdoor temperature, indoor and outdoor humidity, indoor and outdoor dew point, wind direction, wind speed, pressure above mean sea level). The dataset also includes measurements of pollution (SO2) from a meteorological station (Libuš, Prague) and NO2, PM10, and PM2.5 from Holešovice, Prague. The sampling period is one hour, and the measurements were collected for a duration of 390 days.</p>
Aqueous geochemical measurements and speciation calculations with concurrent copper resistance gene counts from sediment metagenomes over a seasonal cycle from 2015 to 2016 on Silver Bow Creek and Blacktail Creek near Butte, MT
<p>This dataset contains information from concurrently gathered geochemical and metagenomic samples collected from Silver Bow Creek and Blacktail Creek near Butte, MT (SBC/BC) during 2015 and 2016. SBC/BC is recovering from metal contamination related to extensive mining in the area. Full geochemical measurements, geochemical speciation calculations, and gene counts of sequences mapping to copper resistance genes using MG-RAST are included. </p>
Copper Rings Insertion Attempts Dataset
<p>The dataset consists of color images of different outcomes of the copper ring insertion task as well as the corresponding Cartesian pose of the robot end-effector and force torque data measured in the robot wrist. Data is organized into folders, representing one of four possible insertion slots from 1 to 4. In each of the folder, data is further split into 13 cases:</p> <p>- error in insertion target position ranging from -3 to 3 mm in x direction,</p> <p>- error in insertion target position ranging from -3 to 3 mm in y direction,</p> <p>- no positional error.</p> <p>Multiple attempts were made for each case.</p> <p>Each entry has unique date-time tag and comprises five files: RGB image (.jpg) and .csv files with robot reference and measured target pose in Cartesian space (positions and quaternions) and raw force-torque sensor data and force-torque data transformed to the tool frame.</p> <p>Total number of entries is 300.</p> <p>The experiments were performed with Franka Emika Panda collaborative robot. For acquisition of image data an Intel Realsense D435 RGB-D camera has been utilized. Force-torque measurements were done using ATI Nano25 sensor.</p>
Copper Rings Insertion Validation Dataset
<p>The dataset consists of color images of the fixture with inserted copper sliding rings, which is used to evaluate and validate the process of assembly of an object with low tolerances supported by multi modal exception strategy learning and ergodic control. This dataset is used to classify the insertion process into three states: OK, NotOK or NoPart. The dataset consists of two main classes: Valid Insertion, Invalid Insertion in each of the four insertion slots.</p>
Non-genetically-based intraspecific differentiation for heavy metal tolerance in the copper moss Scopelophila cataractae
<p>We used next-generation sequencing to study DNA methylation and gene expression changes in plants from four clonal populations of the metallophyte moss <em>Scopelophila cataractae</em> experimentally exposed to either Cd or Cu. For this we performed reduced representation bisulfite DNA sequencing and RNA sequencing. </p>
Data for: Comparison of Friction Extrusion Processing from Bulk and Chips of Aluminum-Copper Alloys
<p>This dataset contains measurement data, machine logs as well as microstructure and overview images for the publication "Comparison of Friction Extrusion Processing from Bulk and Chips of Aluminum-Copper Alloys".</p>
Raw Data - Photo-Responsive Doped 3D-Printed Copper Electrodes for Water Splitting: Refractory One-Pot Doping Dramatically Enhances the Performance
<p>The dataset contains raw data that complements the article:</p> <p>Photo-Responsive Doped 3D-Printed Copper Electrodes for Water Splitting: Refractory One-Pot Doping Dramatically Enhances the Performance</p> <p>Christian Iffelsberger, Daniel Rojas, and Martin Pumera<strong>*</strong></p> <p>https://doi.org/10.1021/acs.jpcc.1c10686</p> <p>Related to the MSCA Project: 888797 LoCatSpot</p>
Circular seal in a copper alloy engraved with a standing female figure, probably Lakṣmī; inscription at one side.
<p>Circular seal in a copper alloy engraved with a standing female figure, probably Lakṣmī; inscription at one side. British Museum 1897,0528.4.</p>
Metabolomics data associated with "Glial swip-10 controls systemic mitochondrial function, oxidative stress, and neuronal viability via copper ion homeostasis"
<p>Raw feature tables used for metabolomic analysis of the <em>Caenorhabditis elegans</em> mutant <em>swip-10</em>. The data were generated using liquid chromatography coupled high-resolution mass spectrometry. Two different columns were used: HILIC (+ ESI) and C18 (-ESI), coupled to a Thermo Q-Exactive Orbitrap mass spectrometer. The feature tables were generated using open-source peak peaking and alignment R packages: apLCMS and xMAanalyzer. See more details in the associated manuscript.</p>
Stress-Strain Analysis of Polycrystalline Copper with Goss Texture Using Crystal Plasticity FEM
<pre>Stress-strain analysis of single-phase polycrystalline copper with a Goss texture using a cubic representative volume element (RVE) and periodic boundary conditions, performed with Abaqus through the crystal plasticity finite element method.</pre>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.