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1,773 results for “mines”
Mining and Metallurgical Residue Database
<p><span>The dataset includes 44 relevant data attributes from 64 mining and metallurgical sites in 27 countries. </span></p>
Invasion Biology WikiProject Scientific Papers: Text Data Mining and LLM-based Information Extraction of Species, Locations, Habitats, and Ecosystems
<p>This dataset contains the abstract and full-text for publication DOIs from the Invasion Biology WikiProject (DOI: <a href="https://www.doi.org/10.5281/zenodo.12518036">10.5281/zenodo.12518036</a>). The data was retrieved using the <a href="https://ask.orkg.org/">ask.orkg.org</a> <a href="https://api.ask.orkg.org/docs#tag/Semantic-Neural-Search/operation/explore_documents_index_explore_get">API</a>. For the <a href="https://github.com/jd-coderepos/invasion-biology-IE/blob/main/scripts/ask-doi-list-fulltext-search.py">script</a> used to obtain the data, refer to the accompanying GitHub repository: <a href="https://github.com/jd-coderepos/invasion-biology-IE/" target="_blank" rel="noopener">https://github.com/jd-coderepos/invasion-biology-IE/</a>.</p> <p>The resulting CSV file includes the following fields: <code>"ASK ID"</code>, <code>"DOI"</code>, <code>"Title"</code>, <code>"Abstract"</code>, and <code>"Full-text"</code>.</p> <p>Of the 49,438 queried DOIs, the ASK database provided:</p> <ul> <li><strong>Total DOIs processed:</strong> 12,636</li> <li><strong>DOIs with neither abstract nor full-text:</strong> 36 (abstract token count was less than 10)</li> <li><strong>DOIs with abstracts but no full-text:</strong> 12,636</li> <li><strong>DOIs with both abstract and full-text:</strong> 2,834</li> </ul> <p>The second part of the dataset contains structured information extracted from the publications using the GPT-4o Large Language Model. This structured data is included in the zipped folder <code>structured-publications.zip</code>.</p> <p>The accompanying GitHub repository provides access to the code and scripts used at various stages of the information extraction (IE) process.</p> <p><strong>Theme of the Study:</strong><br>"Mining for Species, Locations, Habitats, and Ecosystems from Scientific Papers in Invasion Biology: A Large-Scale Exploratory Study with Large Language Models."</p>
An IoT-Enriched Event Log for Process Mining in Smart Factories
<p><strong>DEPRECATED - current version: </strong><a href="https://figshare.com/articles/dataset/Dataset_An_IoT-Enriched_Event_Log_for_Process_Mining_in_Smart_Factories/20130794">https://figshare.com/articles/dataset/Dataset_An_IoT-Enriched_Event_Log_for_Process_Mining_in_Smart_Factories/20130794</a></p> <p> </p> <p>Modern technologies such as the Internet of Things (IoT) are becoming increasingly important in various domains, including Business Process Management (BPM) research. One main research area in BPM is process mining, which can be used to analyze event logs, e.g., for checking the conformance of running processes. However, there are only a few IoT-based event logs available for research purposes. Some of them are artificially generated, and the problem occurs that they do not always completely reflect the actual physical properties of smart environments. In this paper, we present an IoT-enriched XES event log that is generated by a physical smart factory. For this purpose, we created the DataStream XES extension for representing IoT-data in event logs. Finally, we present some preliminary analysis and properties of the log.</p>
Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction [dataset]
<p>This dataset contains the extension of a publicly available dataset that was published initially by Ferenc et al. in their paper:</p> <p><em>“Ferenc, R.; Hegedus, P.; Gyimesi, P.; Antal, G.; Bán, D.; Gyimóthy, T. Challenging machine learning algorithms in predicting vulnerable javascript functions. 2019 IEEE/ACM 7th InternationalWorkshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE). IEEE, 2019, pp. 8–14.”</em></p> <p>The dataset contained software metrics for source code functions written in JavaScript (JS) programming language. Each function was labeled as vulnerable or clean. The authors gathered vulnerabilities from publicly available vulnerability databases.</p> <p>In our paper entitled: “<strong>Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction</strong>” and cited as:</p> <p><em>“Kalouptsoglou I, Siavvas M, Kehagias D, Chatzigeorgiou A, Ampatzoglou A. Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction. Entropy. 2022; 24(5):651. <a href="https://doi.org/10.3390/e24050651">https://doi.org/10.3390/e24050651</a>”</em></p> <p>, we presented an extended version of the dataset by extracting textual features for the labeled JS functions. In particular, we got the dataset provided by Ferenc et al. in CSV format and then we gathered all the GitHub URLs of the dataset's functions (i.e., methods). Using these URLs, we collected the source code of the corresponding JS files from GitHub. Subsequently, by utilizing the start and end line information for every function, we cut off the code of the functions. Each function was then tokenized to construct a list of tokens per function.</p> <p>To extract text features, we used a text mining technique called sequences of tokens. As a result, we created a repository with all methods' source code, the token sequences of each method, and their labels. To boost the generalizability of type-specific tokens, all comments were eliminated, as well as all integers and strings, which were replaced with two unique IDs.</p> <p>The dataset contains 12,106 JavaScript functions, from which 1,493 are considered vulnerable.</p> <p>This dataset was created and utilized during the Vulnerability Prediction Task of the Horizon2020 IoTAC Project as training and evaluation data for the construction of vulnerability prediction models. The dataset is provided in the csv format. Each row of the csv file has the following parts:</p> <ul> <li>Label: Flag with values ‘1’ for vulnerable and ‘0’ for non-vulnerable methods</li> <li>Name: The name of the JavaScript method</li> <li>Longname: The longname of the JavaScript method</li> <li>Path: The path of the file of the method in the repository</li> <li>Full_repo_path: The GitHub URL of the file of the method</li> <li>TokenX: Each next row corresponds to each token included in the method</li> </ul>
Geochemical Characterizations for Identifying Fugitive Dust Deposition and Enrichment of Surface and Subsurface Subalpine Soils from Phosphorus Mining, Eastern Ashley National Forest, Utah, 2022-2023.
Phosphorus is a non-renewable resource essential for all life. Anthropogenic alterations to the phosphorus cycle have led to widespread phosphorus pollution, and the unsustainable management of P has led to the threat of global depletion of phosphorus resources. Thus, accounting for the natural and anthropogenic flow paths of phosphorus is essential for its conservation and pollution reduction. One such source of human alteration to the phosphorus-cycle is phosphate rock mining. Mining, however, has many adverse environmental effects, including widespread fugitive dust emissions. Dust collection in the Ashley National Forest of northeastern Utah, proximate to a surface phosphorus mine, has shown phosphorus concentrations in dust more than four times that of other regional samples. Elevated phosphorus in dust near active surface mining suggests that mining emissions may alter the natural phosphorus loading of the soils in the National Forest through dust deposition; however, no research has been done to identify the abundance and range of mine-attributable phosphorus enrichment in the soils surrounding phosphate mining activities. The combined geospatial and geochemical approach of this study shows that surface soil phosphorus concentrations were found to be enriched above naturally occurring levels up to 6.5 km from mining activity (enrichment factor > 1.5), with the most significant enrichment occurring within the first 3 km (enrichment factor > 2). On average, surface phosphorus concentrations were significantly enriched by 25% within 6.5 km of phosphorus mining activity. Observed phosphorus enrichment was positively correlated with the presence of fluorapatite in the soil, which is the primary phosphorus-mineral extracted from the nearby mine. Further, bioavailable phosphorus concentrations were also higher for the soils that were enriched in phosphorus. This study shows that fugitive emissions associated with the surface mining of phosphate rock are a significan
Ptolemaida coal mines
<p>Coal mines at the Kozani-Ptolemais basin area (Greece), suppling coal to the four power plants providing almost half of the electricity requirements in Greece at the time. </p>
Large Dataset of Nigeria Covid-19 Tweets for Sentiment Analysis and Opinion Mining Tasks
<p><strong>Background</strong></p> <p>Information is essential for growth; without it, little can be accomplished. Data gathering has seen significant changes throughout the previous few centuries because of certain transitory medium. The look and style of information transference are affected by the employment of new and emerging technologies, some of which are efficient, others are reliable, and many more are quick and effective, but a few were disappointing for various reasons.</p> <p><strong>Aims</strong></p> <p>This study aims at using TextBlob and VADER analyser with historical tweets, to analyse emotional responses to the corona virus pandemic (covid-19). It shows us how much of a sociological, environmental, and economic impact it has in Nigeria, among other things. This study would be a tremendous step forward for students, researchers, and scholars who want to advance in fields like data science, machine learning, and deep learning.</p> <p><strong>Methodology</strong></p> <p>The hashtag ‘covid-19' was used to collect 1,048,575 tweets from Twitter. The tweets were pre-processed with a twitter tokenizer, and Valence Aware Dictionary for Sentiment Reasoning (VADER) and TextBlob were used for sentiment and text mining, respectively. Topic modelling was done with Latent Dirichlet Allocation (LDA). The simulated subjects, on the other hand, were visualized using Multidimensional scaling (MDS).</p> <p><strong>Results</strong></p> <p>The result of the VADER sentiment returned 39.8%, 31.3% and 28.9%, positive, neutral, and negative sentiment respectively while the result of the TextBlob sentiment returned 46.0%, 36.7% and 17.3%, neutral, positive, and negative sentiment, respectively.</p> <p><strong>Conclusion</strong></p> <p>With all of this, information from social media may be used to help organizations, governments, and nations around the world make smart and effective decisions about how to restrict and limit the negative effects of covid-19. Also know the opinion and challenges of people, then deal with problem of misinformation.</p> <p>It is concluded that with popular belief a significant number of the populace regards covid-19 as a virus that has come to stay, some believe it will eventually be conquered.</p>
S37 | LITMINEDNEURO | Neurotoxicants from literature mining PubMed
<p>This is the collection associated with list S37 LITMINEDNEURO on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S37</p> <p>LITMINEDNEURO</p> <p><strong>Neurotoxicants from literature mining PubMed</strong></p> <p>A list of chemicals associated with neurotoxicity compiled through systematic literature mining of PubMed using MeSH terms, compiled by Nancy Baker, Antony Williams (US EPA) and Emma Schymanski (LCSB), details in Schymanski et al (submitted) and on <a href="https://comptox.epa.gov/dashboard/chemical_lists/LITMINEDNEURO">CompTox list</a>. </p> <p>Updated 10/06/2019 to include entries that were registered but not yet publicly available when the previous version was released.</p>
From waste to value: Recovering critical raw materials from urban mines in the European Union and the United States
<p><strong>Submitted data was used to write an article: </strong>Jędrusiak, R., Bielowicz, B., Drobniak, A., 2023, From waste to value: Recovering critical raw materials from urban mines in the European Union and the United States, Mineral Resource Management 39 (3), 43-63. <a href="https://doi.org/10.24425/gsm.2023.147557">https://doi.org/10.24425/gsm.2023.147557</a></p> <p> </p> <p><strong>Funding acknowledgments: </strong>Agnieszka Drobniak contribution comes from the support of the Polish National Agency for Academic Exchange within the Polish Returns Programme (BPN/PPO/2021/1/00005/DEC/1), and the National Science Center, Poland (2022/01/1/ST10/00024). This research was funded by the Ministry of Science and Higher Education of Poland (subsidies no. 16.16.140.315).</p> <p> </p> <p><strong>Article Abstract: </strong>Modern human consumption, rapid urbanization and further increases in the world’s population lead to the demand for more goods and materials. However, after utilization, only some of these materials are recovered or recycled, many are discarded due to a lack of implemented recovery technologies and regulations, or due to the content of contaminants. Moreover, many of the potentially recoverable materials are deposited in landfills or shipped to less developed countries for disposal where they can cause environmental contamination. The new approach to waste management follows the hierarchy of waste prevention. First, waste is prepared for reuse and repair without the need for treatment processes, or it is recycled. If this is not possible, the waste is incinerated with energy recovery, or failing that, it is disposed of in landfills. This waste hierarchy has become one of the key factors in the transformation of a linear economy into a circular economy. Particularly noteworthy is waste containing raw materials of significant economic importance, especially those of a high supply risk due to the level of concentration in another country and import dependence. These critical raw materials (CRM) are an inherent part of our modern, technology-driven life. They are essential to national security and the economic development of every country. Their use is drastically increasing, and with it, the need to assure their reliable and unrestricted access along with lowering the environmental impact from their production and extraction. Currently, scientists and industry direct a lot of effort into finding new supplies of these materials, not only from traditional sources in nature but also from new sources like anthropogenic waste. The purpose of this study is to present the raw material potential which remains mostly unused in residues from municipal waste incineration in regions with highly developed economies – the United States and the European Union. These economies have shortages of their own raw material extraction capacity due to high levels of consumption and insufficient amounts of raw-material content in natural resources.</p>
Indicative distribution map for Ecosystem Functional Group SF2.2 Flooded mines and other voids
<p>This archive contains indicative distribution maps and profiles for <strong>SF2.2 Flooded mines and other voids</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Enhanced 3D velocity structure, seismicity relocation and basement characterization of Changning shale gas and salt mining regions in Sichuan Basin
<p>This repository contains the datasets and results of the joint inversion-based Vp/Vs model consistency constrained double difference seismic tomography carried out for the manuscript titled “Enhanced 3D velocity structure, seismicity relocation and basement characterization of Changning shale gas and salt mining regions in Sichuan Basin.” Included are the following: column descriptions of data files, catalog earthquake information (CX_event.dat), relocated events after inversion (CX_tomoDDMC.reloc), inverted Vp model (CX_Vpmodel.dat), inverted Vs model (CX_Vsmodel.dat) and inverted Vp/Vs model (CX_VpVsmodel.dat). Please consult the manual for tomoDD by Zhang and Thurber (2003) for detailed formats of these files. In addition, an averaged velocity model (MOD_averaged) computed based on the inversion results is included, and the converged model (Vp_model_reinverted) resulting from the reinversion, as well as basement structure data for Figure 14.</p>
Dataset for Multidisciplinary Uncertainty Mining - ver1
<p>This dataset contains sentences extracted from articles in various disciplines and annotated with respect to uncertainty in science. It has been produced as part of the <a href="https://project-inscim.github.io/">ANR InSciM (Modelling Uncertainty in Science) project</a>. </p> <p>The dataset is drawn from reputable scientific articles from a variety of disciplines. It consists of two distinct samples of sentences, each annotated using a different method. The first sample is obtained through uncertainty cue mapping, while the second sample is derived from manual annotation of randomly selected articles. To ensure comprehensive annotation, both samples were manually annotated using our multidimensional annotation framework.</p> <p>For a more comprehensive understanding of the construction of the dataset, including the selection of journals, sampling procedure, and the annotation methodology, see (Ningrum and Atanassova, 2023).</p> <p>This dataset provides valuable insights into the representation of uncertainty within scientific literature across different domains. Researchers and practitioners can utilize this dataset to study and analyze the different dimensions of uncertainty in scientific discourse.</p> <p>The dataset is presented as a CSV table where colons ( are used as delimiters. The columns of the table are as follows :</p> <ul> <li>source : 'db' or 'manual' referring to the method used to identify and extract the sentence;</li> <li>article_id : internal id of the article from which the sentence was extracted;</li> <li>sen_id : internal unique id of the sentence;</li> <li>cue : uncertainty cue present in the sentence;</li> <li>text : sentence text;</li> <li>journal_id : short name of the journal;</li> <li>check : 'Y' if the sentence expresses uncertainty and 'N' otherwise;</li> <li>ref, nature, context, timeline, expression : annotations of the type of uncertainty according to the annotation framework proposed by (Ningrum and Atanassova, 2023).</li> </ul> <p>It is essential to highlight the presence of duplicate data in the dataset. These duplicates arise from the detection of multiple cues in sentences during the cue mapping procedure. While one might consider omitting these duplicates, we deliberately chose to retain them. This decision allows for a more comprehensive understanding of how the cues manifest within the sentences. By analyzing the duplicate instances, we can gain valuable insights into the various ways in which the cues are expressed.</p> <p> </p> <p><strong>Bibliography</strong></p> <p>Ningrum, P. K., Atanassova, I. (2023) "Scientific Uncertainty: an Annotation Framework and Corpus Study in Different Disciplines" In 19th International Conference of the International Society for Scientometrics and Informetrics (ISSI 2023), Bloomington, Indiana, US.</p>
Uranium mobility and accumulation along the Rio Paguate, Jackpile Mine in Laguna Pueblo, NM
The mobility and accumulation of uranium (U) along the Rio Paguate, adjacent to the Jackpile Mine, in Laguna Pueblo, New Mexico was investigated using aqueous chemistry, electron microprobe, X-ray diffraction and spectroscopy analyses. Given that it is not common to identify elevated concentrations of U in surface water sources, the Rio Paguate is a unique site that concerns the Laguna Pueblo community. This study aims to better understand the solid chemistry of abandoned mine waste sediments from the Jackpile Mine and identify key hydrogeological and geochemical processes that affect the fate of U along the Rio Paguate. Solid analyses using X-ray fluorescence determined that sediments located in the Jackpile Mine contain ranges of 320 to 9200 mg kg-1 U. The presence of coffinite, a U(IV)-bearing mineral, was identified by X-ray diffraction analyses in abandoned mine waste solids exposed to several decades of weathering and oxidation. The dissolution of these U-bearing minerals from abandoned mine wastes could contribute to U mobility during rain events. The U concentration in surface waters sampled closest to mine wastes are highest during the southwestern monsoon season. Samples collected from September 2014 to August 2016 showed higher U concentrations in surface water adjacent to the Jackpile Mine (35.3 to 772 mg L-1) compared with those at a wetland 4.5 kilometers downstream of the mine (5.77 to 110 mg L-1). Sediments co-located in the stream bed and bank along the reach between the mine and wetland had low U concentrations (range 1–5 mg kg-1) compared to concentrations in wetland sediments with higher organic matter (14–15%) and U concentrations (2–21 mg kg-1). Approximately 10% of the total U in wetland sediments was amenable to complexation with 1 mM sodium bicarbonate in batch experiments; a decrease of U concentration in solution was observed over time in these experiments likely due to re-association with sediments in the reactor. The findings from this study pro
Long-term monitoring of peatlands located near oil sands mining activities surrounding Fort McMurray, Alberta, Canada (2009-Present)
Oil sands mining activities in the Fort McMurray region of Alberta, Canada, have led to increased atmospherically deposited nitrogen (N) and sulfur (S), with N steadily increasing over time and S peaking in 2009, then decreasing with the installation of scrubbers on upgrader stacks. Ecosystems (such as ombrotrophic bogs) near these mining activities see an increase to their depositional load. These peatlands are isolated from groundwater and receive inputs only from precipitation, making them uniquely susceptible to changing depositional scenarios. To evaluate the effect of oil sands development on bogs in this area, since 2009, we have collected and analyzed porewater (pH, conductivity, NH 4 + -N, NO 3 - -N, SO 4 2- -S, and total dissolved N), N and S as represented in extractions of ion exchange resin precipitation collectors (NH 4 + -N, NO 3 - -N, SO 4 2- -S), samples of new growth from the most dominant plant species (C, N, and S, with Ca, Mg, K, and P analyzed in later years), and have recorded annual growth of vegetation. For a majority of the years, we have sampled at least 3 times (June, July, and August). Some sites have burned and have been replaced by others, however, collections are on-going and data from these collections are uploaded as they are published.
Graphs and Attributes used for the attribute-structure correlation pattern mining
<p>## SCPM: An implementation of an algorithm for structural correlation pattern mining.</p> <p>The structural correlation measures how a set of attributes induces dense subgraphs in an attributed graph. A structural correlation pattern is a dense subgraph induced by a particular attribute set. Structural correlation pattern mining is useful to analyze how different attribute sets are correlated to dense subgraphs in several real-life attributed graphs.</p> <p>**Relevant Publications**</p> <p>* Arlei Silva, Wagner Meira, Jr., and Mohammed J. Zaki. Structural correlation pattern mining for large graphs. In Proceedings of the Eighth Workshop on Mining and Learning with Graphs (MLG '10).</p> <p>* Arlei Silva, Wagner Meira, Jr., and Mohammed J. Zaki. Mining Attribute-structure Correlated Patterns in Large Attributed Graphs. In Proceedings of the VLDB Endowment (PVLDB '12).</p> <p>* Arlei Silva. Structural correlation pattern mining for large graphs. M.Sc Thesis, Computer Science Department, Universidade Federal de Minas Gerais, 2011.</p> <p>* Arlei Silva, Wagner Meira Jr. Structural correlation pattern mining for large graphs. Thesis and Dissertation Contest of the Brazilian Computer Society (CTD'12).</p> <p><br> ## HOW TO</p> <p>cd to trunk and run make<br> see README in trunk</p> <p><br> ## Datasets:</p> <p>### Description:</p> <p>#### ATTRIBUTE FILE:</p> <p>Format: Lists the attributes of each vertex from the graph.</p> <p> <VERTEX_ID>,<ATTRIBUTE_ID>,<ATTRIBUTE_ID>...,<ATTRIBUTE_ID></p> <p> Example: <br> 1,A,C <br> 2,A <br> 3,A,C,D <br> 4,A,D <br> 5,A,E <br> 6,A,B,C <br> 7,A,B,E <br> 8,A,B <br> 9,A,B <br> 10,A,B,D <br> 11,A,B</p> <p>#### GRAPH FILE:</p> <p>Format: Lists the neighbors of each vertex from the graph (adjacency list). Although the graph is undirected, each edge must be included in both directions.</p> <p> <VERTEX_ID>,<NEIGHBOR_ID>,<NEIGHBOR_ID>...,<NEIGHBOR_ID> <br> <br> Example: <br> 1,4 <br> 2,3 <br> 3,2,4,5,6,7 <br> 4,1,3,5,6 <br> 5,3,4,6 <br> 6,3,4,5,7,8,9,10 <br> 7,3,6,8,11 <br> 8,6,7,9,10,11 <br> 9,6,8,10,11 <br> 10,6,8,9,11 <br> 11,7,8,9,10</p> <p>### REAL DATASETS</p> <p>Lastfm:</p> <p>attributes: attrLastFm.csv.tar.bz2</p> <p>network: graphLastFm.csv.tar.gz</p> <p>DBLP:</p> <p>attributes: newAttrDBLP.csv.tar.bz2</p> <p>network: newGraphDBLP.csv.tar.bz2</p> <p>CITESEER:</p> <p>attributes: attrCiteseer.csv.tar.bz2</p> <p>network: graphCiteseer.csv.tar.bz2</p>
Supplementary material for "Song et al., Modelling Simul. Mater. Sci. Eng., 2021: Data-mining of dislocation microstructures: concepts for coarse-graining of internal energies"
<p>This zip archive contains supplementary material in the form of datasets and jupyter notebooks that are used in the following publication:</p> <ul> <li>authors: Hengxu Song, Nina Gunkelmann, Giacomo Po, and Stefan Sandfeld</li> <li>journal: Modelling Simul. Mater. Sci. Eng.</li> <li>year: 2021</li> <li>title: Data-mining of dislocation microstructures: concepts for coarse-graining of internal energies</li> </ul>
Mining API Interactions to Analyze SoftwareRevisions for the Evolution of Energy Consumption (MSR'2021 Dataset)
<p><strong>Motivation</strong></p> <p>This repository contains the data-set used as a basis for our MSR'2021 paper <em>Mining API Interactions to Analyze Software Revisions for the Evolution of Energy Consumption</em>.</p> <p><strong>Description of the dataset</strong></p> <p>The dataset is stored in a file <em>msr_2021_dataset.csv</em> and contains the following data:</p> <ul> <li>id - an individual identifier</li> <li>sampleNr - a number identifying the group this sample relates to</li> <li>name - the name of the library examined</li> <li>className - the class name as an abbreviation</li> <li>method - the name of the executed method</li> <li>duration - duration of method execution</li> <li>durationAdjusted - duration after alignment between method trace and energy profile</li> <li>energyConsumption - computed energy consumption</li> <li>watts - recorded wattage</li> <li>`package-names` - per package uAPI profile</li> <li>uApi - the computed uAPI profile value</li> </ul> <p>The files <em>joule_anova_posthoc_result.csv</em> and <em>uAPI_anova_posthoc_result.csv</em> contain the results of the ANOVA and Tukey HSD posthoc analysis to determine accuracy and F1-score of the presented approach.</p> <p> </p> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p>
Landslides from Space - Hpakan Jade Mine Landslides, Myanmar (January 2016)
<p>Between November 2015 and January 2016 multiple landslide occurred in the Hpakan Jade Mine. In total more than 200 people died in these human made accidents.</p> <p>The pre-event acquisition is from 23rd November 2015 (Sentinel-2) and the post-event acquisition is from 22 March 2016 (Sentinel-2).<br> <br> <em>Contains modified Copernicus Sentinel data (2015-2016)</em></p>
Landslides from Space - Amyntaio Lignite Coal Mine Landslide (10th June 2017)
<p>On Saturday, 10th of June 2017 a massive landslide occurred in a lignite pit in Amyntaio, Greece. It buried 25 million tons lignite worth about 500 Million Euro and caused the permanent evacuation of Anargyroi, a village nearby.</p> <p><br> The pre-event acquisition is from 1st June 2017 (Sentinel-2) and the post-event acquisition is from 24th June 2017 (Sentinel-2). A false colour composite with near-infrared, red and green band is visualised as RGB image.<br> <br> <em>Contains modified Copernicus Sentinel data (2017)</em></p>
Landslides from Space - Kakanj Mine Waste Landslide, Bosnia (24th February 2017)
<p>On February 24<sup>th</sup> 2017 a massive mine waste landslide from an open pit coal mine occurred. It had approximately the dimension of 600 metres in width and 800 metres in length. Through this slide the stream Ribnica was dammed up and created a small lake. Because of potential dam breach 150 people of two villages had to be evacuated downstream.<br> <br> The pre-event acquisition is from 14th February 2017 (Sentinel-2) and the post-event acquisition is from 16th March 2017 (Sentinel-2).<br> <br> <em>Contains modified Copernicus Sentinel data (2017)</em></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.