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126 results for “Industrial dataset”
Dataset for "Architectural Security Weaknesses in Industrial Control Systems: An Empirical Study Based on Security Advisories' Vulnerability Reports"
<p>Supplementary artifacts to "<em>Architectural Security Weaknesses in Industrial Control Systems (ICS): An Empirical Study based on Disclosed Software Vulnerabilities</em>"</p> <p>Published in the Proceedings from the 2019 IEEE International Conference on Software Architecture (ICSA)</p> <p>Package Contains:</p> <p>- Raw output showing Components, CAWEs, and CVEs per report</p> <p>- Frequency Data (# of reports) for those concerns</p> <p>- ICS Component - Term Dictionary </p> <p>- HTML versions of reports studied in paper</p>
GECCO Industrial Challenge 2017 Dataset: A water quality dataset for the 'Monitoring of drinking-water quality' competition at the Genetic and Evolutionary Computation Conference 2017, Berlin, Germany.
<p>Dataset of the 'Industrial Challenge: Monitoring of drinking-water quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2017, Berlin, Germany</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of water quality data</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>M. Friese, J. Stork, A. Fischbach, M. Rebolledo, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided and prepared by:</p> <p>Thüringer Fernwasserversorgung,</p> <p>IMProvT research project (S. Moritz)</p> <p><br> </p> <p>Industrial Challenge: Monitoring of drinking-water quality</p> <p> </p> <p>Description:</p> <p>Water covers 71% of the Earth's surface and is vital to all known forms of life. The provision of safe and clean drinking water to protect public health is a natural aim. Performing regular monitoring of the water-quality is essential to achieve this aim.</p> <p>Goal of the GECCO 2017 Industrial Challenge is to analyze drinking-water data and to develop a highly efficient algorithm that most accurately recognizes diverse kinds of changes in the quality of our drinking-water.</p> <p> </p> <p>Submission deadline:</p> <p>June 30, 2017</p> <p>Official webpage:</p> <p><a href="http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/">http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/</a></p>
Dataset for the paper "Ripa M, Di Felice LJ, Giampietro M (2021). The energy metabolism of post-industrial economies. A framework to account for externalization across scales. Energy, 214." https://doi.org/10.1016/j.energy.2020.118943
<p>This repository contains the data needed to reproduce the results in: </p> <p>Ripa M, Di Felice LJ, Giampietro M (2021). The energy metabolism of post-industrial economies. A framework to account for externalization across scales. Energy, 214." https://doi.org/10.1016/j.energy.2020.118943</p> <p>The dataset was also used for a case study in "Di Felice L., Dunlop T., Giampietro M., Kovacic Z., Renner A., Ripa M., Velasco-Fernández R. – Report on the Quality Check of the Robustness of the Narrative behind Energy Directives. MAGIC (H2020–GA 689669) Project Deliverable 5.4, 30 November 2018". (link: https://magic-nexus.eu/documents/d54-report-narratives-behind-energy-directives).</p> <p>Sources of data are specified in the dataset (under tab "References")</p>
GECCO Industrial Challenge 2019 Dataset: A water quality dataset for the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition at the Genetic and Evolutionary Computation Conference 2019, Prague, Czech Republic.
<p>Dataset of the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 13th-17th 2019, Prague, Czech Republic</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>1. Original train dataset of water quality data provided to participants (identical to gecco2019_train_water_quality.csv)</p> <p>2. Call for Participation</p> <p>3. Rules and Description of the Challenge</p> <p>4. Resource Package provided to participants</p> <p>5. The complete dataset, consisting of train, test and validation merged together (gecco2019_all_water_quality.csv)</p> <p>6. The test dataset, which was used for creating the leaderboard on the server (gecco2019_test_water_quality.csv)</p> <p>7. The train dataset, which participants had available for training their models (gecco2019_train_water_quality.csv)</p> <p>8. The validation dataset, which was used for the end results for the challenge (gecco2019_valid_water_quality.csv)</p> <p> </p> <p>The challenge required the participants to submit a program for event detection. A training dataset was available to the participants (gecco2019_train_water_quality.csv). During the challenge the participants were able to upload a version of their program to out online platform, where this version was scored against the testing dataset (gecco2019_test_water_quality.csv), thus an intermediate leaderboard was available. To avoid overfitting against this dataset, at the end of the challenge, the end result was created from scoring with the validation dataset (gecco2019_valid_water_quality.csv). </p> <p>Train, Test, Validation dataset are from the same measuring station and are in chronological order. So the timestamps from the test dataset begin directly after the train timestamps, while the validation timestamps begin directly after the test timestamps. </p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, S. Moritz, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p> </p> <p>Internet of Things: Online Event Detection for Drinking Water Quality Control</p> <p> </p> <p>Description:</p> <p>For the 8th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2018 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.</p> <p><br> Competition Opens: End of January/Start of February 2019<br> Final Submission: 30 June 2019</p> <p>Official webpage:</p> <p><a href="https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php">https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php</a></p> <p> </p>
Industrial Benchmark Dataset for Customer Escalation Prediction
<p>This is a real-world industrial benchmark dataset from a major medical device manufacturer for the prediction of customer escalations. The dataset contains features derived from IoT (machine log) and enterprise data including labels for escalation from a fleet of thousands of customers of high-end medical devices. </p> <p>The dataset accompanies the publication "System Design for a Data-driven and Explainable Customer Sentiment Monitor" (submitted). We provide an anonymized version of data collected over a period of two years.</p> <p>The dataset should fuel the research and development of new machine learning algorithms to better cope with real-world data challenges including sparse and noisy labels, and concept drifts. Additional challenges is the optimal fusion of enterprise and log based features for the prediction task. Thereby, interpretability of designed prediction models should be ensured in order to have practical relevancy. </p> <p><strong>Supporting software</strong></p> <p>Kindly use the corresponding <a href="https://github.com/annguy/customer-sentiment-monitor">GitHub repository</a> (https://github.com/annguy/customer-sentiment-monitor) to design and benchmark your algorithms. </p> <p> </p> <p><strong>Citation and Contact</strong><br> </p> <p>If you use this dataset please cite the following publication:</p> <p><br> </p> <pre><code>@ARTICLE{9520354, author={Nguyen, An and Foerstel, Stefan and Kittler, Thomas and Kurzyukov, Andrey and Schwinn, Leo and Zanca, Dario and Hipp, Tobias and Jun, Sun Da and Schrapp, Michael and Rothgang, Eva and Eskofier, Bjoern}, journal={IEEE Access}, title={System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data}, year={2021}, volume={9}, number={}, pages={117140-117152}, doi={10.1109/ACCESS.2021.3106791}}</code></pre> <p> </p> <p>If you would like to get in touch, please contact an.nguyen@fau.de.<br> </p>
Dataset used in article "A 2-dimensional guillotine cutting stock problem with variable-sized stock for the honeycomb cardboard industry"
<p>The dataset presented is part of the one used in the article "A 2-dimensional guillotine cutting stock problem with variable-sized stock for the honeycomb cardboard industry" by P. Terán-Viadero, A. Alonso-Ayuso and F. Javier Martín-Campo, published in International Journal of Production Research (2023), doi: <a href="https://doi.org/10.1080/00207543.2023.2279129">10.1080/00207543.2023.2279129</a>. </p><p>In the paper mentioned above, two mathematical optimisation models are proposed for the Cutting Stock Problem in the honeycomb cardboard sector. This problem appears in a Spanish company and the models proposed have been tested with real orders received by the company, achieving a reduction of up to 50% in the leftover generated. </p><p>The dataset presented here includes six of the twenty cases used in the paper (the rest cannot be presented for confidentiality reasons). For each case, the characteristics of the order and the solution obtained by the two models are provided for the different scenarios analysed in the paper.</p><p> </p><p>*Version 1.1 contains the same data but renamed according to the instances name in the final version of the article.<br>*Version 1.2 adds the PDF with the accepted version of the article publised in International Journal of Production Research (2023), doi: <a href="https://doi.org/10.1080/00207543.2023.2279129">10.1080/00207543.2023.2279129</a>. </p>
Dataset for the uncertainty assessment of confocal measurements of industrial samples
<p>These original measurement data relate to the publication: J. Paredes, G. Kortaberria: Towards task-specific uncertainty assessment for imaging confocal microscopes, <a href="https://www.euspen.eu/knowledge-base/ICE23191.pdf">ICE23191.pdf (euspen.eu)</a>. Please refer to this open access publication for a detailed description of the measurement setup and procedure.</p><p>All data are in ASCII-format. Each file contains 2 columns, where they are the measured <i>x/y</i> and <i>z</i>-coordinates of the 2D profile extracted from the surface. All the coordinates are recorded in micrometers. The 0/90 at the end of the file names represent the orientation of the extracted profile being<i> x</i> and <i>y</i> direction respectively.</p><p>The topoghraphy files contain 3 columns, they are the measured<i> x</i>, <i>y</i>, and <i>z</i>-coordinates of the surface.</p><p><strong>Acknowledgement</strong></p><p>This project 20IND07 TracOptic has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme. Funder name: European Metrology Programme for Innovation and Research (EMPIR).</p>
Dataset of the manuscript: Efficient removal of nanoplastics from industrial wastewater through synergetic electrophoretic deposition and particle-stabilized foam formation
<p>This dataset is based on the data underlying the figures shown in the manuscript titled Efficient removal of nanoplastics from industrial wastewater through synergetic electrophoretic deposition and particle-stabilized foam formation. A readme file is uploaded to decribe the content of all data folders. All data are sorted according to their appearance in the figures of the main manuscript.</p>
Dataset for A MILP approach for detailed pipeline scheduling and storage management problem in the phosphate industry
<p>Case studies of a multi-product slurry pipeline scheduling and storage management problem in the phosphate industry.</p>
MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation for Domain Generalization Task
<p><strong>Description</strong></p> <p>This dataset is a sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task (MIMII DG). The dataset consists of normal and abnormal operating sounds of five different types of industrial machines, i.e., fans, gearboxes, bearing, slide rails, and valves. The data for each machine type includes three subsets called "sections", and each section roughly corresponds to a type of domain shift. <strong>This dataset is a subset of the dataset for <a href="https://dcase.community/challenge2022/task-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring">DCASE 2022 Challenge Task 2</a>, so the dataset is entirely the same as data included in the <a href="https://zenodo.org/record/6355122#.Ynt7rtrP2Uk">development dataset</a>. </strong>For more information, please see the pages of the <a href="https://zenodo.org/record/6355122#.Ynt7rtrP2Uk">development dataset</a> and the <a href="https://dcase.community/challenge2022/task-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring">task description</a><strong> </strong>for DCASE 2022 Challenge Task 2.</p> <p> </p> <p><strong>Baseline system</strong></p> <p>Two simple baseline systems are available on the Github repositories <a href="https://github.com/Kota-Dohi/dcase2022_task2_baseline_ae">autoencoder-based baseline</a> and <a href="https://github.com/Kota-Dohi/dcase2022_task2_baseline_mobile_net_v2">MobileNetV2-based baseline</a>. The baseline systems provide a simple entry-level approach that gives a reasonable performance in the dataset. They are good starting points, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task.</p> <p> </p> <p><strong>Conditions of use</strong></p> <p>This dataset was made by <strong>Hitachi, Ltd.</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.</p> <p> </p> <p><strong>Citation</strong></p> <p>We will publish a paper on the dataset and will announce the citation information for them, so please make sure to cite them if you use this dataset.</p> <p> </p> <p><strong>Feedback</strong></p> <p>If there is any problem, pease contact us</p> <ul> <li>Kota Dohi, <a href="mailto:kota.dohi.gr@hitachi.com">kota.dohi.gr@hitachi.com</a></li> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> </ul>
Dataset: Flexsteel Industries, Inc. (FLXS) 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.
Dataset: Forward Industries, Inc. (FORD) 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.
Dataset: FGI Industries Ltd. (FGI) 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.
Dataset: FGI Industries Ltd. (FGIWW) 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.
Dataset: Fidelity Crypto Industry and Digital Payments ETF (FDIG) 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.
Dataset: iShares Environmental Infrastructure and Industrials ETF (EFRA) 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.
Dataset: Deswell Industries, Inc. (DSWL) 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.
Dataset: Cavco Industries, Inc. (CVCO) 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.
Dataset: CSW Industrials, Inc. (CSWI) 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.
Dataset: CEA Industries Inc. (CEADW) 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.
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.