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1,773 results for “mines”
Gummern - Mining Waste Deposits 0.305m DEM (2023-10-02) from Pleaiades Neo
<h2>Abstract</h2> <p>Mining Waste Deposits 0.305m Digital Elevation Model derived from 2023-10-02 Panchromatic TriStereo Pleiades Neo Dataset.</p> <p>This depositry contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Mining Waste Deposits 0.305m DEM (2023-10-02) from Pleaiades Neo</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Mining Waste Deposits 0.305m Digital Elevation Model derived from 2023-10-02 Panchromatic TriStereo Pleiades Neo Dataset</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>DEM, DSM, DTM, Pleiades Neo, Minning Waste Deposits</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Gummern</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>Compare with "Mining Waste Deposits 5.73cm DEM UAV-derived" and "Mining Waste Deposits 0.495m DEM (2024-04-12) from World-View2"</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Elevation</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>20.06.2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>20.06.2024</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Raster</p> </td> </tr> <tr> <td> <p>Fromat</p> </td> <td> <p>GeoTIFF</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.30495m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>0.5m</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 25833</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Enoc Sanz Ablanedo (esana@unileon.es)</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>UNILEON</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Enoc Sanz Ablanedo (esana@unileon.es)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Gummern - Mining Waste Deposits 0.495m DEM (2024-04-12) from WorldView2
<h2>Abstract</h2> <p>Mining Waste Deposits 0.495m resolution Digital Elevation Model derived from 2024-04-12 Panchromatic Stereo WorldView2 Dataset.</p> <p>This depositry contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Mining Waste Deposits 0.495m DEM (2024-04-12) from WorldView2</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Mining Waste Deposits 0.495m resolution Digital Elevation Model derived from 2024-04-12 Panchromatic Stereo WorldView2 Dataset</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>DEM, DSM, DTM, Pleiades Neo, WorldView2, Minning Waste Deposits</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Gummern</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>Compare with "Mining Waste Deposits 5.73cm DEM UAV-derived" and "Mining Waste Deposits 0.305m DEM (2023-10-02) from Pleaiades Neo"</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Elevation</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>12.04.2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>12.04.2024</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Raster</p> </td> </tr> <tr> <td> <p>Fromat</p> </td> <td> <p>GeoTIFF</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.495m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>0.5m</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 25833</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Enoc Sanz Ablanedo (esana@unileon.es)</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>UNILEON</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Enoc Sanz Ablanedo (esana@unileon.es)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
RDF version of the data from Hagar I. Labouta et al. Meta-Analysis of Nanoparticle Cytotoxicity via Data-Mining the Literature. NanoImpact (2019)
<p>This is an RDFied version of the dataset published by Hagar I. Labouta et al. Meta-Analysis of Nanoparticle Cytotoxicity via Data-Mining the Literature. NanoImpact (2019).</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1021/acsnano.8b07562">https://doi.org/10.1021/acsnano.8b07562</a></p> <p>The Original publication authors: Hagar I. Labouta, Nasimeh Asgarian, Kristina Rinker, and David T. Cramb</p>
Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning
<p>Dataset of Javascripts used for training and testing the fingerprinting algorithms described in </p> <p>Rizzo, Valentino, Stefano Traverso, and Marco Mellia. "Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning." <em>Proceedings on Privacy Enhancing Technologies</em> 2021.1 (2021): 43-63.</p>
Data-Mining of In-Situ TEM Experiments: Towards Understanding Nanoscale Fracture
<p>Datasets for the publication in the "Computational Materials Science". This is essentially a snapshot of the gitlab repository https://gitlab.com/computational-materials-science/public/publication-data-and-code/2022-data-mining-of-in-situ-tem-experiments that might contain additional updates and scripts. A version of the manuscript can also be found at https://arxiv.org/abs/2206.11355</p>
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>
Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring (Open Pit Extraction, Valea Sesei and Roșia Poieni (Romania)).
<p>Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring in the Open Pit Extraction (mine located at Valea Sesei and Roșia Poieni (Romania)) (3D view mode).</p> <p>Accessing the GOLDENAI GUI, please refer to the following link (<strong>login required</strong>): <a href="https://next-gui.goldenai.opt-net.eu/ ">https://next-gui.goldenai.opt-net.eu/ </a></p>
Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring (Underground Extraction, Pyhäsalmi (Finland)).
<p>Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring in the Underground Extraction (mine located at Pyhäsalmi (Finland)) (2D view mode).</p> <p>Accessing the GOLDENAI GUI, please refer to the following link (<strong>login required</strong>): <a href="https://next-gui.goldenai.opt-net.eu/ ">https://next-gui.goldenai.opt-net.eu/ </a></p>
Processed data for "Model identification of neural encoding (MINE)" publication
<p>This dataset contains mouse and zebrafish data processed by MINE. These datafiles were used to generate the publication figures for the mouse cortical dataset [m<em>usall.hdf5</em>] (Figure 5) and the zebrafish whole-brain [<em>main_analysis.hdf5</em>] (Figures 6 and 7) and reticulospinal datasets [r<em>s_analysis.hdf5</em>] (Figure 6).</p> <p> </p> <p><em>Musall.hdf5 </em>contains reordered data from "Musall, S., Kaufman, M.T., Juavinett, A.L. <em>et al.</em> Single-trial neural dynamics are dominated by richly varied movements. <em>Nat Neurosci</em> <strong>22</strong>, 1677–1686 (2019)."</p> <p>The contents of each dataset are described in <em>DataContent_xxx.pdf</em></p>
Supporting data for "CoVEffect: Interactive System for Mining the Effects of SARS-CoV-2 Mutations and Variants Based on Deep Learning"
<p>This repository contains the datasets created and extracted for the paper:</p> <p>Giuseppe Serna García, Ruba Al Khalaf, Francesco Invernici, Stefano Ceri, and Anna Bernasconi. 2022.<br> "<strong>CoVEffect</strong>: Interactive System for Mining the <strong>Effects of SARS-CoV-2 Mutations and Variants</strong> Based on Deep Learning". (Available online at http://gmql.eu/coveffect)</p> <p>--------------------------------------------------------------------------------<br> LIST OF FILES WITH DESCRIPTION:<br> --------------------------------------------------------------------------------</p> <p>AdditionalFile1-effects-taxonomy:<br> Descriptions of legal values for the 'Effect' field, based on a categorized taxonomy.</p> <p>AdditionalFile2-levels-taxonomy:<br> Descriptions of legal values for the 'Level' field.</p> <p>AdditionalFile3-training_dataset_target:<br> List of target tuples (manually annotated) of 221 abstracts considered for training the model. For each abstract, target tuples follow the schema ID, DOI, title, entity, effect, level, type (mutation or variant), tuples_count (>1 when an effect/level is shared by multiple entities, #abstracts containing the same effect described in the tuple).</p> <p>AdditionalFile4-validation_dataset_target:<br> List of target tuples (manually annotated) of 50 abstracts considered for validating the prepared prediction model.<br> For each abstract, target tuples follow the schema defined for AdditionalFile3.</p> <p>AdditionalFile5-validation_dataset_highlighted:<br> Textual abstracts of the 50 manuscripts considered for validation; the text used to support the manual target annotations has been highlighted in yellow.</p> <p>AdditionalFile6-validation_dataset_prediction:<br> List of predicted annotations of 50 abstracts considered for validating the prepared prediction model. The file is split in 4 TSV, respectively for entity (a), effect (b), level (c), and whole tuple predictions (d).</p> <p>AdditionalFile7-keywords_query_list:<br> Keyword-based search run on the CORD-19 dataset to extract a relevant subset of abstracts regarding the scope of interest of CoVEffect. The Boolean logic used to combine keywords is explained in the section 'Annotations of the biology-related CORD-19 cluster'.</p> <p>AdditionalFile8-CORD-19_batch_dataset_metadata:<br> Metadata of the 7,230 papers extracted by the keyword-based query in AdditionalFile7.<br> These abstracts have been annotated by the prediction framework.</p> <p>AdditionalFile9-CORD-19_batch_dataset_prediction:<br> List of predicted annotations of 7,230 abstracts extracted from the biology-related cluster of CORD-19.</p> <p>AdditionalFile10-test_dataset_target:<br> List of target tuples (manually annotated) of 100 abstracts randomly selected from the 7,230 extracted as in AdditionalFile8.<br> For each abstract, target tuples follow the schema defined for AdditionalFile3.</p> <p>AdditionalFile11-test_dataset_prediction:<br> List of predicted annotations of 100 abstracts considered for testing the prediction model on a subset of the CORD-19 biology-related cluster. As AdditionalFile6, it is split in 4 TSV, respectively for entity (a), effect (b), level (c), and whole tuple predictions (d).</p>
Geospatial analysis of mining areas reclamation potential through Technosols in Brazil
<p>This repository contains two datasets:</p> <p>1. An update of metadata analysis with data published before 2021 resulting from the search equation "TS = (Technosol* AND (Organic carbon OR Organic matter)" in the Web of Science (WOS) database. Update from Allory 2022: https://doi.org/10.24396/ORDAR-60.</p> <p>2. A database containing geospatial datasets (inputs and outputs), R scripts, and other FOSS software files used for the geospatial analysis of land reclamation potential through Technosols in Brazil.</p>
Extensions to Mining Framework Annotation Rules
<p>Framework usage is challenging because the requirements for the correctness are often implicit. We focus on making</p> <p>such requirements more explicit by association rule mining on the data from client projects that use a framework.</p> <p>We present an extension to an existing baseline method that does this. In particular, we examine alternative rule</p> <p>quality measures used in the ranking of association rules mined, and alternatives in the selection of client projects.</p> <p>Such alternatives are novel and have not been explored in the context of the baseline method. We evaluate the alternatives</p> <p>by comparing their results to those produced by the baseline method. More concretely, we base the comparison on their</p> <p>ranking of incorrect rules, and on their measurements for the Area Under Curve metric. We conclude that some of</p> <p>the evaluated quality measures outperform the baseline for the ranking and selection of rules. We also show that the</p> <p>selection of secondary client projects, adding some clients that do not directly use the framework of interest, matters.</p>
Geochemical characterization of mineral particulate aggregates and associated biomass collected in boreholes at the Soudan Underground Mine State Park, Soudan, MN, USA.
Mineral and biological samples were collected from boreholes on the 27th level of the Soudan Underground Mine State Park, Soudan, MN, USA. These samples were characterized in order to describe the biogeochemical cycling of iron and sulfur in the crustal regions accessed by the mine's boreholes as well as the microbial communities supported by and responsible for that biogeochemical cycling. The mineral samples were characterized through X-ray diffraction and Fe XANES, the microbial biomass associated with the mineral aggregates was characterized through C XANES, and the microbial community was characterized through the assembly of metagenomes.
Tree ring, leaf mining, climate, and remote sensing data from aspen leaf miner survey sites: III - Climate, leaf mining, and NDVI data
This dataset contiains annual site-level measurements from 2004 - 2015 of growing season climate moisture index ( GS CMI; summed CMI from May - September), average site level leaf mining, and mean July - August normalized difference vegetation index (NDVI) derived from Landsat, GIMMS3g, MODIS Aqua, and MODIS Terra
New Physics Mining at the Large Hadron Collider: top pair production
<p><span class="math-tex">\(t \bar t\)</span> background events reconstructed by inclusive single-muon selection.</p> <p>Events are represented as an array of physics-motivated high-level features.</p> <p>Details are given in https://arxiv.org/abs/1811.10276</p>
New Physics Mining at the Large Hadron Collider: W -> l nu
<p><span class="math-tex">\(W \to \ell \nu\)</span> background events reconstructed by inclusive single-muon selection.</p> <p>Events are represented as an array of physics-motivated high-level features.</p> <p>Details are given in https://arxiv.org/abs/1811.10276</p>
New Physics Mining at the Large Hadron Collider: QCD multijet production
<p>QCD multijet background events reconstructed by inclusive single-muon selection.</p> <p>Events are represented as an array of physics-motivated high-level features.</p> <p>Details are given in https://arxiv.org/abs/1811.10276</p>
Databases for exploratory mode of RRE-Finder: A Genome-Mining Tool for Class-Independent RiPP Discovery
<p>RREFinder is a bioinformatic tool for the detection of RiPP Recognition Elements (RREs). See "RRE-Finder: A Genome-Mining Tool for Class-Independent RiPP Discovery".</p> <p>This database contains the required databases to run exploratory mode of the tool.</p>
Fig. 1 in Lysinimonas yzui sp. nov., isolated from cattail root soil from mine tailings
Fig. 1. Neighbour-joining phylogenetic tree based on 16S rRNA gene sequences, showing positions of N7XX-4T and related taxa within the family Microbacteriaceae. Bootstrap values of over 60% (based on 1000 replicates) are shown at branching points. Dots indicate that the corresponding branches were also recovered in the maximum parsimony tree. Bar, 0.01 substitutions per nucleotide.
Datasets for Itemset, Sequence and Tree Mining
<p>There are three different datasets included, that can be used for itemset, sequence and tree mining methods.</p> <p><strong>dense_db.zip</strong></p> <p>contains various real itemset datasets like <strong>chess</strong>, <strong>connect</strong>, <strong>mushroom</strong>, <strong>pumsb, T10I4D100K, T40I10D100K</strong> and so on, used in the papers on frequent, closed and maximal itemset mining. For example, <strong>Mohammed J. Zaki</strong> and Ching-Jui Hsiao. <strong>Efficient algorithms for mining closed itemsets and their lattice structure.</strong> <em>IEEE Transactions on Knowledge and Data Engineering</em>, 17(4):462–478, April 2005. <a href="https://doi.org/10.1109/69.846291">doi:10.1109/69.846291</a>. Or Karam Gouda and <strong>Mohammed J. Zaki</strong>. <strong>Genmax: an efficient algorithm for mining maximal frequent itemsets.</strong> <em>Data Mining and Knowledge Discovery: An International Journal</em>, 11(3):223–242, November 2005. <a href="https://doi.org/10.1007/s10618-005-0002-x">doi:10.1007/s10618-005-0002-x</a>.</p> <p> </p> <p><strong>plandata.zip</strong>: </p> <p>Planning dataset for sequence mining. It was used in the paper <strong>Mohammed J. Zaki</strong>, Neal Lesh, and Mitsunori Ogihara. <strong>PLANMINE: predicting plan failures using sequence mining.</strong> <em>Artificial Intelligence Review</em>, 14(6):421–446, December 2000. Special issue on Applications of Data Mining. <a href="https://doi.org/https://doi.org/10.1023/A:1006612804250">doi:https://doi.org/10.1023/A:1006612804250</a>.</p> <p> </p> <p><strong>cslogs.zip</strong>: </p> <p>The CSLOGS data was used for tree mining, e.g., in <strong>Mohammed J. Zaki</strong>. <strong>Efficiently mining frequent trees in a forest: algorithms and applications.</strong> <em>IEEE Transactions on Knowledge and Data Engineering</em>, 17(8):1021–1035, August 2005. Special issue on Mining Biological Data. <a href="https://doi.org/10.1109/TKDE.2005.125">doi:10.1109/TKDE.2005.125</a>.</p> <p> </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.