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1,773 results for “mines”
Annotated Dataset for Uncertainty Mining : Gold Standard
<p> </p> <h1>Description of the dataset</h1> <p>In order to study the expression of uncertainty in scientific articles, we have put together an interdisciplinary corpus of journals in the fields of Science, Technology and Medicine (STM) and the Humanities and Social Sciences (SHS). The selection of journals in our corpus is based on the Scimago Journal and Country Rank (SJR) classification, which is based on Scopus, the largest academic database available online. We have selected journals covering various disciplines, such as medicine, biochemistry, genetics and molecular biology, computer science, social sciences, environmental sciences, psychology, arts and humanities. For each discipline, we selected the five highest-ranked journals. In addition, we have included the journals PLoS ONE and Nature, both of which are interdisciplinary and highly ranked.</p> <p>Based on the corpus of articles from different disciplines described above, we created a set of annotated sentences as follows:</p> <ul> <li>593 were pre-selected automatically, by studying the occurrences of the lists of uncertainty indices proposed by Bongelli et al. (2019), Chen et al. (2018) and Hyland (1996).</li> <li>The remaining sentences were extracted from a subset of articles, consisting of two randomly selected articles per journal. These articles were examined by two human annotators to identify sentences containing uncertainty and to annotate them.</li> <li>600 sentences not expressing scientific uncertainty were manually identified and reviewed by two annotators<br><br></li> </ul> <p>The sentences were annotated by two independent annotators following the annotation guide proposed by Ningrum and Atanassova (2024). The annotators were trained on the basis of an annotation guide and previously annotated sentences in order to guarantee the consistency of the annotations. <br>Each sentence was annotated as expressing or not expressing uncertainty (<strong>Uncertainty</strong> and <strong>No Uncertainty)</strong>.<br>Sentences expressing uncertainty were then annotated along five dimensions: Reference , Nature, Context , Timeline and Expression. <br>The annotators reached an average agreement score of 0.414 according to Cohen's Kappa test, which shows the difficulty of the task of annotating scientific uncertainty.<br>Finally, conflicting annotations were resolved by a third independent annotator.</p> <p><br>Our final corpus thus consists of a total of 1 840 sentences from 496 articles in 21 English-language journals from 8 different disciplines.<br>The columns of the table are as follows:</p> <ol> <li><strong>journal</strong>: name of the journal from where the article originates</li> <li><strong>article_title</strong>: title of the article from where the sentence is extracted</li> <li><strong>publication_year</strong>: year of publication of the article</li> <li><strong>sentence_text</strong>: text of the sentence expressing or not expressing uncertainty</li> <li><strong>uncertainty</strong>: 1 if the sentence expresses uncertainty and 0 otherwise;</li> <li><strong>ref, nature, context, timeline, expression</strong>: annotations of the type of uncertainty according to the annotation framework proposed by Ningrum and Atanassova (2023). The annotation of each dimension in this dataset are in numeric format rather than textual. The mapping betwen textual and numeric labels is presented in the Table below.</li> </ol> <table> <tbody> <tr> <td>Dimension</td> <td>1</td> <td>2</td> <td>3</td> <td>4</td> <td>5</td> </tr> <tr> <td>Reference</td> <td>Author</td> <td>Former</td> <td>Both</td> <td> </td> <td> </td> </tr> <tr> <td>Nature</td> <td>Epistemic</td> <td>Aleatory</td> <td>Both</td> <td> </td> <td> </td> </tr> <tr> <td>Context</td> <td>Background</td> <td>Methods</td> <td>Res&Disc</td> <td>Conclusion</td> <td>Others</td> </tr> <tr> <td>Timeline</td> <td>Past</td> <td>Present</td> <td>Future</td> <td> </td> <td> </td> </tr> <tr> <td>Expression</td> <td>Quantified</td> <td>Unquantified</td> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table> <p><br>This gold standard has been produced as part of the <a href="https://project-inscim.github.io/">ANR InSciM (Modelling Uncertainty in Science) project.</a> </p> <h1>References</h1> <p><br>Bongelli, R., Riccioni, I., Burro, R., & Zuczkowski, A. (2019). Writers’ uncertainty in scientific and popular biomedical articles. A comparative analysis of the British Medical Journal and Discover Magazine [Publisher: Public Library of Science]. PLoS ONE, 14 (9). <a href="https://doi.org/10.1371/journal.pone.0221933">https://doi.org/10.1371/journal.pone.0221933</a></p> <p>Chen, C., Song, M., & Heo, G. E. (2018). A scalable and adaptive method for finding semantically equivalent cue words of uncertainty. Journal of Informetrics, 12 (1), 158–180. <a href="https://doi.org/10.1016/j.joi.2017.12.004">https://doi.org/10.1016/j.joi.2017.12.004</a></p> <p><br>Hyland, K. E. (1996). Talking to the academy forms of hedging in science research articles [Publisher: SAGE Publications Inc.]. Written Communication, 13 (2), 251–281. <a href="https://doi.org/10.1177/0741088396013002004">https://doi.org/10.1177/0741088396013002004</a></p> <p>Ningrum, P. K., & Atanassova, I. (2023). Scientific Uncertainty: An Annotation Framework and Corpus Study in Different Disciplines. 19th International Conference of the International Society for Scientometrics and Informetrics (ISSI 2023). <a href="https://doi.org/10.5281/zenodo.8306035">https://doi.org/10.5281/zenodo.8306035</a></p> <p>Ningrum, P. K., & Atanassova, I. (2024). Annotation of scientific uncertainty using linguistic patterns. Scientometrics. <a href="https://doi.org/10.1007/s11192-024-05009-z">https://doi.org/10.1007/s11192-024-05009-z</a></p>
HiMAT Thesaurus for Mining Research
<p>This datasets contains the Thesaurus for HiMAT-datasets including informations related to skos-concepts and the triples of the Thesaurus' RDF representation.</p>
Historical Place Gazetteer and Historical Mine Register, Tyrol 15th, 16th century
<p>The dataset is split in two separated files in CSV format (UTF 8):</p> <p>1) Historical place gazetteer: It contains historical tyrolean names of places (core region: districts Kufstein, Schwaz; Austria) within the mining documents Hs. 37 and Hs. 1587 and their modern equivalents enriched with unique Identifiers. Furthermore, Places were localised and georeferenced as far as current scientific data and knowledge permit. A final column shows corresponding IDs between Hs. 37 and Hs. 1587.</p> <p>2) Historical mine register: The file contains historical Tyrolean names of mines and pits (core region: district Kufsten, Schwaz) within the mining documents Hs. 37 and Hs. 1587 as well as a modern standardisation of the names. The mines also received unique Identifiers.</p> <p>The registers were generated by the research team of the project “Text Mining Medieval Mining Texts” (2019-2022). Two historical mining sources were processed: “Verleihbuch der Rattenberger Bergrichter” ( Hs. 37, 1460-1463) and “Schwazer Berglehenbuch” (Hs. 1587, approx. 1515) stored by the Tyrolean Regional Archive, Innsbruck (Austria). The central research objective of T.M.M.M.T. is the extraction and representation of the legal relationships between people, claims and mines over space and time. Furthermore it deals with the semantically opening and visualisation of the montanistic network of two tyrolean mining regions.</p> <p>Citeable Transcripts are online available:<br> Hs. 37 DOI: 10.5281/zenodo.6274562<br> Hs. 1587 DOI: 10.5281/zenodo.6274928</p> <p>The research project (2019-2022) was carried out at the University of Innsbruck and funded by go!digital next generation programme of the Austrian Academy of Sciences.</p>
Endosymbiont mining from Lepidoptera genomics data
<p>Alignments and assembly files from the manuscript: 'One’s trash is someone else’s treasure: Sequence read archives from Lepidoptera genomes provide material for genome reconstruction of their endosymbionts'</p>
Map of Tigray's mineral resources (north Ethiopia) - Canadian and other international mining licences
<p>In Tigray, artisanal mining of gold in the low-lying areas with outcropping Precambrian rocks is one of the major off-farm income sources. The 17<sup>th</sup> C. Portuguese traveller Barradas had already mentioned gold production in Tembien. Rural youth seasonally migrate to inhospitable lowlands and gorges such as the largely uninhabited Weri’i River valley, to search for placer gold, washed out from weathered gold-containing quartz veins within the meta-sediments and meta-volcanics. In recent decades, large-scale gold exploration and mining of gold deposits has been carried out in various parts of Tigray by local (such as the Ezana Mining Development P.L.C.) and several foreign exploration companies particularly from Canada. Recently, The Ethiopia Cable exposed links between big Canadian mining interests and a renewed PR campaign (involving Canadian professor and lobbyist Ann Fitz-Gerald) to whitewash the Ethiopian and Eritrean governments. Earlier on, it had already been suggested that one of the reasons for the Canadian government being very late in officially addressing the atrocities in the ongoing Tigray war, might be related to the country’s mining interests in Tigray.</p> <p>Here we contextualise Tigray’s gold and base metal resources, and present a map of active and applied mineral exploration and mining licenses in Tigray. The largest exploration license areas are concessions of Canadian companies, followed by the U.S. and the United Kingdom.</p>
Mining for Framework Instantiation Pattern Interplays
<p>Software frameworks define generic application blueprints which can be instantiated into an application through application-specific instantiation actions such as overriding a method or providing an object that implements an interface. In case the framework’s documentation falls short, developers may use other instantiations of the same framework as a guide to the required instantiation actions. In this paper, we propose an automated approach to mining framework instantiation patterns from existing open-source instantiations.</p> <p>The approach leverages a graph-based representation to capture the common ways of implementing instantiation actions as well as their interplays, so called instantiation interplays. As a case study, we mined for patterns in a set of 2,028 Java projects that instantiate four of the most popular Java frameworks. We also classify the extracted interplays according to the different contexts in which they occur. We found that our approach discovers relevant practices and interplays that are not covered by previous approaches. Our results will allow developers to have a better understanding of the frameworks they instantiate.</p>
COALMOD-World 2.0 data, results, figures for: Stranded assets and early closures in global coal mining under 1.5°C
<p>This dataset contains all COALMOD-World 2.0 data for Hauenstein (2023): Stranded assets and early closures in global coal mining under 1.5°C (doi.org/10.1088/1748-9326/acb0e5) </p> <p>With the input data files and the GAMS scenario file the model (https://doi.org/10.5281/zenodo.7077678) can be run to reproduce the model results.</p> <p>Furthermore, the output.zip folder contains the results file, the R code to compile the figures, and PDFs of the figures.</p>
Exploration of historical mining site - Akersberg silver mines (St. Hanshaugen, Oslo, Norway, 04/11/2024)
<p>Exploration of the historical mining site of Akesrberg (St. Hanshaugen, Oslo, Norway, 04/11/2024)</p> <p>- Main ore minerals: pyrite, sphalerite, galena, chalcopyrite, argentite, stembergite</p> <p>- Provisional References:</p> <ul> <li>https://www.researchgate.net/publication/330971315_Akersberg_gruver_Akersberg_Silver_Mines_pages_168-174_in_Arnesen_R_Gjemte_og_glemte_steder_-_urban_utforsking_i_Oslo_og_omradet_rundt_In_Norwegian</li> <li>https://foreninger.uio.no/ngf/ngt/pdfs/NGT_71_2_121-128.pdf</li> <li>https://www.mindat.org/loc-37117.html</li> <li>https://no.wikipedia.org/wiki/Akersberg_gruver</li> </ul>
Exploration of historical mining site - Mezzano iron mines (San Bartolomeo, Cavargna Valley, Italy, 06/01/2024)
<p>Exploration of the historical mining site of Mezzano (San Bartolomeo, Cavargna Valley, Italy, 06/01/2024)</p> <p>- Main ore minerals: pyrite, chalcopyrite, siderite, aragonite</p> <p>- Provisional References:</p> <ul> <li>https://www.valcavargna.org/luoghi_di_interesse/miniere-di-mezzano/#:~:text=Le%20miniere%20di%20Mezzano&text=A%20partire%20dagli%20ultimi%20anni,Fratelli%20Campioni%20l'anno%20seguente.</li> <li>https://www.isprambiente.gov.it/it/attivita/museo/regioni/musei/miniera-di-mezzano</li> <li>https://www.valcavargna.org/tradizioni_popolari/vecchi-mestieri/siderurgia/</li> <li>https://www.research.unipd.it/handle/11577/3465257</li> <li>http://www.cmalpilepontine.it/cmvlarcer/zf/index.php/servizi-aggiuntivi/index/index/idtesto/13</li> </ul>
Vegetation of post-mining areas, Upper Silesia, Poland (Floristic composition of the plots_November_2022)
<p><span><span>The data set containing a list of plant species in the research plots along with their percentage coverage.</span></span> <span><span>The selection of plots took into account the occurrence of the dominant species (cover > 40% of the study plot area).</span></span> <span><span>The dominant species represent functional groups: monocots, forbs and legumes.</span></span></p>
Deep Reference Mining from Scholarly Literature in the Arts and Humanities - Pre-trained word embeddings
<p>Pre-trained word vectors of dimensionality 100 and 300 for the publication: Deep Reference Mining from Scholarly Literature in the Arts and Humanities, submitted to Frontiers in Digital Humanities.</p> <p>The corpus of scholarly publications from which these vectors were trained is under copyright, therefore we publish these vectors for reproducibility. Please refer to the publication's repository for further details: <a href="https://github.com/dhlab-epfl/LinkedBooksDeepReferenceParsing">https://github.com/dhlab-epfl/LinkedBooksDeepReferenceParsing</a>.</p> <p>These vectors were trained using Gensim 3.1.0. The corpus was preprocessed as follows:</p> <ol> <li>word tokenization with NLTK word_punct tokenizer.</li> <li>digits were converted into the $NUM$ token</li> <li>words less frequent than 5 times, for every document, were converted to the $UNK$ token</li> <li>vectors were trained using the function: Word2Vec(window=5, min_count=5, sg=1)</li> </ol>
Dataset for "Contrasting Effects of Organic and Mineral Nitrogen Challenge the N-Mining Hypothesis for Soil Organic Matter Priming"
<p>Dataset for the article:</p> <p>Mason-Jones, K., Schmücker, N., Kuzyakov, Y. (2018) Contrasting Effects of Organic and Mineral Nitrogen Challenge the N-Mining Hypothesis for Soil Organic Matter Priming. Soil Biology and Biochemistry 124, 38-46, https://doi.org/10.1016/j.soilbio.2018.05.024</p>
Ontology based text mining of gene-phenotype associations: application to candidate gene prediction
<p>Gene-phenotype associations play an important role in understanding<br> the disease mechanisms which is a requirement for treatment<br> development. A portion of gene-phenotype associations are observed<br> mainly experimentally and made publicly available through several<br> standard resources such as MGI. However, there is still a vast<br> amount of gene--phenotype associations buried in the biomedical<br> literature. Given the large amount of literature data, we need<br> automated text mining tools to alleviate the burden in manual<br> curation of gene-phenotype associations and to develop<br> comprehensive resources. We developed an ontology based<br> approach in combination with statistical methods to text mine<br> gene-phenotype associations from literature. Our method achieved<br> AUC values of 0.90 and 0.75 in recovering known gene-phenotype<br> associations from HPO and MGI respectively. We posit that candidate<br> genes and their relevant diseases should be expressed with similar<br> phenotypes in publications. Thus, we demonstrate the utility of our<br> approach by predicting disease candidate genes based on the semantic<br> similarities of phenotypes associated with genes and diseases. We evaluated our disease candidate prediction model on<br> the gene-disease associations from MGI. Our model achieved AUC<br> values of 0.90 and 0.87 on OMIM (human) and MGI (mouse) datasets of<br> gene-disease associations respectively. Our manual analysis on the<br> text mined data revealed that, our method can accurately extract<br> gene-phenotype associations which are not currently covered by the<br> existing public gene-phenotype resources. Overall, results indicate<br> that our method can precisely extract known as well as new<br> gene-phenotype associations from literature. This released dataset at Zenodo covers our gene-phenotype extracts from the literature. All the methods used to extract the data are available at https://github.com/bio-ontology-research-group/genepheno.</p>
Experimental datasets for sentiment analysis and emotion mining - Emotion Mining Toolkit (EMTk)
<p><strong>Description</strong></p> <p>Datasets for sentiment analysis and emotion mining, distributed with the Emotion Mining Toolkit (EMTk) Docker container (see <a href="https://collab-uniba.github.io/EMTk">https://collab-uniba.github.io/EMTk</a> for more):</p> <ul> <li>Stack Overflow - A couple of gold standards of 4,000+ posts, manually annotated for mining both emotions and polarity.</li> <li>Jira - A gold standard of ~4,000 issues, manually annotated for emotions.</li> </ul> <p><strong>Citation</strong></p> <p>Please, see the references below for the papers to cite. Do not cite this Zenodo upload directly.</p>
Annotation of Phytozome V12 protein plant sequences using the ragp pipeline for hydroxyproline-rich glycoprotein mining
<p>Hydroxyproline aware annotation of hydroxyproline-rich glycoprotein (HRGP) sequences was performed on sequence data from 62 plant proteomes obtained from Phytozome database (<a href="https://phytozome.jgi.doe.gov/pz/portal.html">https://phytozome.jgi.doe.gov/pz/portal.html</a>, version 12) using the ragp R package (<a href="https://github.com/missuse/ragp">https://github.com/missuse/ragp</a>, version 0.3.0.0001). </p> <p>In each archive a single comma separated value table (.csv) is present along with a README.txt file describing the contents of the corresponding .csv file. The archives are:</p> <p>- phytozome_V12.tar.gz - sequences from 62 plant proteomes (phytozome V12) with a total of 2797062 protein sequences.</p> <p>- phytozome_V12_phobius.tar.gz -<strong> </strong> Signal peptide prediction using Phobius (<a href="http://phobius.sbc.su.se/">http://phobius.sbc.su.se/</a>) on sequences present in phytozome_V12.tar.gz.</p> <p>- phytozome_V12_signalp.tar.gz -<strong> </strong> Signal peptide prediction using SignalP 4.1 (<a href="http://www.cbs.dtu.dk/services/SignalP-4.1/">http://www.cbs.dtu.dk/services/SignalP-4.1/</a>) on sequences present in phytozome_V12.tar.gz.</p> <p>- phytozome_V12_targetp.tar.gz - Signal peptide prediction using TargetP 1.1 (<a href="http://www.cbs.dtu.dk/services/TargetP/">http://www.cbs.dtu.dk/services/TargetP/</a>) on sequences present in phytozome_V12.tar.gz.</p> <p>- phytozome_V12_predict_hyp.tar.gz - Probability of proline hydroxylation for each proline from 266135 protein sequences which were predicted to be secreted by a majority vote (using Phobius, SignalP 4.1 and TargetP 1.1).</p> <p>- phytozome_V12_maab.tar.gz - Motif and amino acid bias (MAAB) classification of hydroxyproline-rich glycoproteins performed on 266135 protein sequences which were predicted to be secreted by a majority vote (using Phobius, SignalP 4.1 and TargetP 1.1). The number of predicted hydroxyprolines in each sequence is also indicated (based on predictions provided in phytozome_V12_predict_hyp.tar.gz).</p> <p>- phytozome_V12_scan_ag.tar.gz. - Hydroxyproline aware arabinogalactan motif scan performed on 266135 protein sequences which were predicted to be secreted by a majority vote (using Phobius, SignalP 4.1 and TargetP 1.1). Hydroxyproline predictions are provided in phytozome_V12_predict_hyp.tar.gz. </p> <p>- phytozome_V12_scan_ag_hmmscan.tar.gz - Detection of domains in a subset of protein sequences which were found to contain arabinogalactan motifs (a subset of phytozome_V12_scan_ag.tar.gz).</p> <p>The list of the 62 plant species is provided in phytozome_V12.tar.gz README.txt.</p> <p>For questions contact mdragicevic@ibiss.bg.ac.rs.</p> <p> </p> <p> </p>
Time-resolved compound repositioning predictions on a text-mined knowledge network
<p><strong>gs_positives.csv</strong>: The re-processed version of DrugCentral indications, utilized as training and testing positives in the analysis.</p> <p><strong>top_5000_predictions.csv</strong>: The top 5000 drug-disease pairs, by probability, produced by this analysis pipeline.</p> <p><strong>file_info.txt</strong>: Information about the column headings in of the two files.</p> <p> </p>
The Mining Register of Nejdek 1556/1581
<p>Transliteration of The Mining Register of Nejdek from years 1556/1581 with the database of the book records in MS Access.</p>
The Mining Register of Nejdek 1556/1581
<p>Transliteration of The Mining Register of Nejdek from years 1556/1581 with the database of the book records in MS Access.</p>
Global mining deforestation footprint data from 2000 to 2019
<p>The data in this repository is available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/<br><br>This repository includes two datasets. The first is a collection of polygons covering mines globally and the associated forest cover loss from 2000 to 2019. The polygons were derived by merging the "global-scale mining polygons version 2" (Maus et al., 2022) and mining and quarry polygon features extracted from the OpenStreetMap database (OpenStreetMap contributors, 2017). To remove double counting of areas the overlaps between the datasets were resolved by uniting intersecting features into single polygon features, i.e. keeping only the external borders of intersecting features. A random visual check was conducted, and a few small manual editing of polygons was performed where errors were identified.</p> <p>The resulting dataset is encoded as a Geopackage in the file 'global_mining_polygons.gpkg'. The GeoPackage includes a single layer with 192,584 entries called 'mining_polygons' with the following attributes:</p> <ul> <li><em>id<string></em> unique feature identifier</li> <li><em>isoa3<string></em> ISO 3166-1 alpha-3 country codes</li> <li><em>country<string></em> country names</li> <li><em>area<double></em> area of the polygon in squared kilometres</li> <li><em>geom<polygon></em> the geometry of the features in geographical coordinates WGS84</li> </ul> <p>The second dataset provides annual time series of global tree cover loss within mines from 2000 to 2019, covering all polygons in the above dataset. The area of tree cover loss for each polygon was calculated from the Global Forest Change database (Hansen et al., 2013). Each polygon also has additional string attributes with biomes derived from <em>Ecoregions 2017 <sup>© Resolve </sup></em>(Dinerstein et al., 2017) and the level of protection derived from The World Database on Protected Areas (UNEP-WCMC and IUCN, 2022).</p> <p>This dataset is encoded in CSV format in the file 'global_mining_forest_loss.csv', which includes 416,412 entries and 53 variables, such that:</p> <ul> <li><em>id<string></em> unique feature identifier</li> <li><em>id_hcluster<string></em> unique feature identifier</li> <li><em>list_of_commodities<string></em> a comma-separated list of commodities</li> <li><em>isoa3<string></em> ISO 3166-1 alpha-3 country codes</li> <li><em>country<string></em> country names</li> <li>ecoregion<em><string></em> ecoregion name</li> <li><em>biome<string></em> biome name</li> <li><em>year<numeric></em> the year</li> <li>area_forest_loss_XXX_YYY<double> the area of forest cover loss within a polygon per year in squared kilometres.</li> </ul> <p>The values of tree cover loss are disaggregated per initial percentage of tree cover (XXX) and per protection level (YYY).</p> <ul> <li>XXX can take one of: <ul> <li>000: total tree cover loss independently from the initial tree cover</li> <li>025: tree cover loss on pixels with initial tree cover between 0 and 25%</li> <li>050: tree cover loss on pixels with initial tree cover between 25 and 50%</li> <li>075: tree cover loss on pixels with initial tree cover between 50 and 75%</li> <li>100: tree cover loss on pixels with initial tree cover between 75 and 100%</li> </ul> </li> <li>YYY can take one of: <ul> <li>la: tree cover loss within strict nature reserve</li> <li>Ib: tree cover loss within wilderness area</li> <li>II: tree cover loss within national park</li> <li>III: tree cover loss within natural monument or feature</li> <li>IV: tree cover loss within habitat/species management area</li> <li>V: tree cover loss within protected landscape/seascape</li> <li>VI: tree cover loss within PA with sustainable use of natural resources</li> <li>p: tree cover loss within any type of protection, including not applicable, not assigned, or not reported</li> <li>none: when YYY is omitted, total tree cover loss within the polygon</li> </ul> </li> </ul> <p>For details about the protection levels definition see the UNEP-WCMC and IUCN (2022). The <em>id</em> can be used to link polygons to forest loss data.</p>
Gummern - Mining Waste Deposits 5.73cm DEM UAV-derived
<h2>Abstract</h2> <p>Mining Waste Deposits High Detailed Digital Elevation Model derived from Multispectral UAV DJI Mavic 3M.</p> <p>This depository 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 5.73cm DEM UAV-derived</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Mining Waste Deposits High Detailed Digital Elevation Model derived from Multispectral UAV DJI Mavic 3M</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>Mining Waste Deposits, DEM, DTM, DSM, UAV, Drone</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>See "Mining Waste Deposits Multispectral Drone Imagery" <a href="https://doi.org/10.5281/zenodo.13622458">https://doi.org/10.5281/zenodo.13622458</a></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>Hydrography</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>3.10.2023</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>3.10.2023</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>Format</p> </td> <td> <p>GeoTIFF</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>See "Mining Waste Deposits Multispectral Drone Imagery"</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.0573m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>0.05m</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 4258</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>
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
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DANDI Archive for NWB datasets
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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.