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13 results for “Metal mines”
Open database on global coal and metal mine production
<p>See also the associated Data Descriptor published in Nature Scientific Data: <a href="https://www.nature.com/articles/s41597-023-01965-y">www.nature.com/articles/s41597-023-01965-y</a></p> <p>This data set covers global extraction of coal and metal ores on an individual mine level. It covers<br> 1171 individual mines in 80 different countries, reporting mine-level production for 80 different materials in the period 2000-2021. Furthermore, also data on mining coordinates, ownership, mineral reserves, mining waste, transportation of mining products, as well as mineral processing capacities (smelters and mineral refineries) and production is included. The data was gathered manually from more than 1900 openly available sources, such as annual or sustainability reports of mining companies. All datapoints are linked to their respective source documents. After manual screening and entry of the data, automatic cleaning, harmonization and data checking was conducted. Geoinformation was obtained either from coordinates available in company reports, or by retrieving the coordinates via Google Maps API and subsequent manual checking. For mines where no coordinates could be found, other geospatial attributes such as province, region, district or municipality were recorded, and linked to the GADM data set, available at <a href="https://www.gadm.org">www.gadm.org</a>.</p> <p>The data set, found in the "data" sub-folder, consists of 12 tables. The table “facilities” contains descriptive and spatial information of mines and processing facilities, and is available as a GeoPackage (GPKG) file. All other tables are available in comma-separated values (CSV) format. If you are working in Excel or have problems handling the GeoPackage file, it can be converted to Excel with an online tool, such as <a href="https://mygeodata.cloud/converter/gpkg-to-xlsx">https://mygeodata.cloud/converter/gpkg-to-xlsx</a>.</p> <p>A schematic depiction of the database is provided in the file database_model.pdf. A description of all variables of all tables is provided in the Excel file variables_descriptions.xlsx, and all materials for which production is reported in the database are listed in the file materials_covered.xlsx.</p> <p>For convenience, global and national coverage shares for every material and country with recorded production in the database is provided in the file coverage_table.pdf. These coverage shares were calculated by comparing the production values of this database to official production statistics reported in the UNEP IRP Global Material Flows Database, to be found under <a href="https://www.resourcepanel.org/global-material-flows-database">https://www.resourcepanel.org/global-material-flows-database</a>. For significant raw material producing countries, these coverage shares are also visualised in the file coverage_national_area_charts.pdf.</p>
Mining minerals and critical raw materials from bittern: Understanding metal ions fate in saltwork ponds
<p>Seawater represents a potential resource for raw materials extraction. Although NaCl is the most representative mineral<br> extracted other valuable compounds such as Mg, Li, Sr, Rb and B and elements at trace level (Cs, Co, In, Sc, Ga and<br> Ge) are also contained in this “liquid mine”. Most of them are considered as Critical Raw Materials by the European<br> Union. Solar saltworks, providing concentration factors of up-to 20 to 40, offer a perfect platform for the development<br> of minerals and metal recovery schemes taking benefit of the concentration and purification achieved along the evaporation<br> saltwork ponds.<br> However, the geochemistry of these elements in this environment has not been yet thoroughly evaluated. Their knowledge<br> could enable the deployment of technologies capable to achieve the recovery of valuable minerals. The high ionic<br> strengths expected (0.5–7 mol/kg) and the chemical complexity of the solutions imply that only numerical geochemical<br> codes, as PHREEQC, and the use of Pitzer model to estimate the activity coefficients of the different species in solution<br> can be adopted to provide valuable description of the systems.<br> In the present work, for the first time, PHREEQC Pitzer code database was extended to include the target minor and<br> trace elements using Trapani saltworks (Sicily, Italy) as a case study system. The model was able to predict: i) the purity<br> in halite and the major impurities contained, mainly Ca,Mgand sulphate species; ii) the fate of minor components as B,<br> Sr, Cs, Co, Ge and Ga along the evaporation ponds. The results obtained pose a fundamental step in critical raw materials<br> mining from seawater brine, for process intensification and combination with desalination.</p>
Dataset: Ishares Copper And Metals Mining ETF (ICOP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Data from: Plant traits regulated metal(loid)s in dominant herbs in an antimony mining area of the Karst Zone, China
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Modelling the origin, fate, and ecological and health impacts of heavy metals from an abandoned mercury mine in a paradise island in the Philippines
<p>This document contains supplementary tables for the article entitled "Modelling the origin, fate, and ecological and health impacts of heavy metals from an abandoned mercury mine in a paradise island in the Philippines"</p>
Data from: Do metal mines and their runoff affect plumage color? A regional scale study of streak-backed orioles in south-central Mexico
<p>Metal mining causes serious ecological disturbance, due partly to heavy metal (HM) pollution that can accumulate at mining sites themselves and be dispersed downstream as runoff. Plumage coloration is important in birds' social and ecological interactions and sensitive to environmental stressors, and several local-scale studies have found decreased carotenoid-based plumage and/or increased melanin-based plumage in wild birds exposed to HM pollution. We investigated regional-scale effects of proximity to mines and their downstream rivers as a proxy of exposure to HM-contaminated mining waste on plumage coloration in streak-backed orioles (<em>Icterus pustulatus</em>) in south-central Mexico. We measured the plumage color of museum skins using reflectance spectrometry and digital photography, then used geographic information systems to estimate each specimen's distance from the nearest mining concession and river and determine whether that river's watershed contained mines. Proximity to mines and their downstream rivers was related to ventral (but not dorsal) carotenoid-based coloration; birds collected farther from mines had more vivid yellow-orange breast plumage, and belly plumage was more vivid and redder with increasing distance from rivers with upstream mines. Breast background reflectance unexpectedly decreased with mine distance and was higher among birds whose nearest river had mines upstream. The area (but not reflectance) of melanin-based plumage was also related to mines. The area of dark back streaks decreased with mine distance, while the bib patch was smaller among birds presumably more exposed to mining waste. While some of these results are consistent with predicted effects of HM pollution on plumage, most were not straightforward, and effects differed among plumage patches and variables. Further investigation is needed to understand the direct (e.g., toxicity, oxidative stress) and/or indirect (e.g., decreased availability of carotenoid-rich food) mechanisms responsible and their individual, population, and community-level implications. </p>
Blowin' in the wind: Mapping the dispersion of Metal(loid)s from Atacama Mining
<p><strong>Raw data from elemental and mineralogical analyses of surface sediments from Alto El Loa, Antofagasta Region, Chile.</strong></p> <p><strong>Blowin' in the wind: Mapping the dispersion of Metal(loid)s from Atacama Mining</strong></p> <p><strong>Authors: Nicolás C. Zanetta-Colombo<sup>1,2</sup> *, Carlos A. Manzano<sup>3,4 </sup>, Dagmar Brombierstäudl<sup>1</sup>, Zoë L. Fleming<sup>5,6</sup>, Eugenia M. Gayo<sup>6,7</sup>, David A. Rubinos<sup>8</sup>, Óscar Jerez<sup>9</sup>, Jorge Valdés<sup>10</sup>, Manuel Prieto<sup>11,12</sup>, Marcus Nüsser<sup>1,2</sup></strong></p> <p><strong><sup>1</sup></strong><sup> </sup>Department of Geography, South Asia Institute, Heidelberg University, Heidelberg, Germany.</p> <p><strong><sup>2</sup></strong><sup> </sup>Heidelberg Center for the Environment (HCE), Heidelberg University, Heidelberg, Germany.</p> <p><strong><sup>3</sup></strong><sup> </sup>Departamento de Química, Facultad de Ciencias, Universidad de Chile, Santiago, Chile.</p> <p><strong><sup>4</sup></strong><sup> </sup>School of Public Health, San Diego State University, San Diego, CA, USA</p> <p><strong><sup>5</sup></strong><sup> </sup>Centro de Investigación en Tecnologías para la Sociedad, Universidad Del Desarrollo, Santiago, Chile.</p> <p><strong><sup>6</sup></strong><sup> </sup>Center for Climate and Resilience Research (CR)2, Chile.</p> <p><strong><sup>7</sup></strong><sup> </sup>Departamento de Geografía, Universidad de Chile, Santiago, Chile.</p> <p><strong><sup>8</sup></strong><sup> </sup>Sustainable Minerals Institute–International Centre of Excellence Chile (SMI-ICE-Chile), The University of Queensland, Australia, Las Condes, Santiago, Chile.</p> <p><strong><sup>9</sup></strong><sup> </sup>Instituto de Geología Económica Aplicada (GEA), University of Concepción, Chile. Barrio Universitario S/N, Concepción, Chile.</p> <p><strong><sup>10</sup></strong><sup> </sup>Laboratorio de Sedimentología y Paleoambientes (LASPAL), Instituto de Ciencias Naturales Alexander von Humboldt, Facultad de Ciencias del Mar y de Recursos Biológicos, Universidad de Antofagasta, Antofagasta, Chile.</p> <p><strong><sup>11 </sup></strong>Millenium Nucleus in Andean Peatlands (AndesPeat), Chile</p> <p><strong><sup>12 </sup></strong>Departamento de Ciencias Históricas y Geográficas, Universidad de Tarapacá, 18 de Septiembre 2222, Arica, Chile</p> <p> </p> <p><strong><span>Data Set 1 (ds1): </span></strong><span>Elemental concentrations (mg/kg) of metal(loid)s in surface sediment samples. The dataset also includes geographic coordinates (LONG, LAT), distance to the nearest mines (Dist_mines), distance to tailings (Dist_tailing), and buffer zone classifications for tailings (Buffer_T) and mines (Buffer_M).</span></p> <p><strong>Data set 2 (ds2)</strong>: Mineralogical composition of selected surface sediment samples. </p>
Characterization of metal-removing Mn oxides at a coal mine drainage treatment site in Glasgow, PA
<p>Dataset for: Characterization of metal-removing Mn oxides at a coal mine drainage treatment site in Glasgow, PA</p> <p>Includes the following: XRD, XAFS, FTIR, Raman, ICP-OES, EDS</p>
Water and Planetary Health Analytics (WAPHA) global metal mines database
<p class="Teaser"><span>An estimated 23 M people live on floodplains affected by potentially dangerous concentrations of toxic waste derived from past and present metal mining activity. We analyze the global dimensions of this hazard, particularly Pb, Zn, Cu and As, using a geo-referenced global database detailing all known metal mining sites, and intact/failed tailings storage facilities. We then use process-based and empirically tested modelling, to produce a global assessment of metal mining contamination in river systems, and the number of human populations, and livestock exposed. Worldwide, metal mines impact 479,200 km of river channels and 164,000 km<sup>2 </sup>of floodplains. The number of people exposed to contamination sourced from long-term discharge of mining waste into rivers is almost fifty times greater than the number directly impacted by tailings dam failures.</span></p>
Data from: Do metal mines and their runoff affect plumage color? A regional scale study of streak-backed orioles in south-central Mexico
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Water and Planetary Health Analytics (WAPHA) global metal mines database
Open the record for dataset details and reuse information.
Raw data article "Surviving adversity: exploring the presence of Lunularia cruciata (L.) Dum. on metal-polluted mining waste"
<p>This is the raw data of the article "Surviving adversity: exploring the presence of Lunularia cruciata (L.) Dum. on metal-polluted mining waste".</p>
Carbon Emissions Metals&Mining
<p>This dataset contains financial and carbon emissiona information about Metals&Mining companies. </p>
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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.