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830 results for “Industry”
Dataset: Environmental benchmarks for European Cement Industry
<p>This dataset contains the information relative to the article "Environemntal benchmarks for European cement industry".</p> <p><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.spc.2024.01.020" target="_blank" rel="noopener">Reference paper</a></p> <p><a href="https://www.researchgate.net/publication/377796848_Environmental_benchmarks_for_the_European_cement_industry" target="_blank" rel="noopener">ResearchGate link</a></p>
Survey data on attitudes towards salmon aquaculture industry in Norway, Iceland, and Tasmania (AU)
<p>The following data is from an online survey conducted in Norway, Tasmania (Australia), and Iceland. Respondents were recruited by survey companies that distributed e-mail invitations to their panels. A minimum respondent quotas was established for each region, with individuals under the age of 18 being exluded from participating in the survey. The dataset consists of a total of 2085 respondents, comprising 1183 participants from Norway, 406 from Tasmania, and 496 from Iceland. Questions were presented in their respective native language, namely Norwegian, English, and Icelandic.</p> <p>This survey data encompasses various aspects of perceptions of salmon aquaculture industry. Data was generated by the SoLic (Social License to operate for aquaculture) project (2019 - 2022), and funded by The Research Council of Norway (no. 295114). The survey was designed by the SoLic project group. </p> <p>The data and supplementary material is divided in 3 files:</p> <p>The raw survey data in .csv file format (Dataset Solic_2085 respondents.csv). The data file contains 71 variables and data from each of the 2085 respondents. Blank entries in the dataset indicate either a lack of response from the respondents or that specific questions were not applicable to certain respondents (questions exclusively posed to respondents in one country).</p> <p>Overview of survey questions and answer options (Survey.doc). The survey encompassed 28 questions related to the aquaculture industry, along with demographics, respondents’ knowledge of industry, trust in governance system, and environmental concerns. Some demographic variables were sourced from the existing panel data, while others were provided to respondents for their input.</p> <p>The codebook (Codebook.doc). The codebook provides explanations and details regarding all variables included in the survey data file. It includes coding information for each survey question, response options provided in the raw data, and further clarifies the purpose and origin of variables computed by the research group (e.g., variable on aquaculture municipality) or the survey company (e.g., weight variables for data from Norway and Iceland). When used in conjunction with the raw data, this codebook serves as a valuable guide for navigating the dataset. </p>
Historical and future water demand for households and industry for the STARS4Water river basins
<pre>This repository contains the data related to the deliverable D2.5 "Data sets on scenario narratives" prepared within the STARS4Water project ("Supporting STakeholders for Adaptive, Resilient and Sustainable Water Management").</pre> <p>The data spans historical years (2000-2020) and projections under different Shared Socioeconomic Pathways (SSP1-5) scenarios for the years 2020-2050.</p> <p>The repository contains historical and future water demand for households and industry for the STARS4Water river basins divided into two items packed in zip file:<br>1. STARS4Water_Domestic_and_Industrial_Water_Demands_historical.zip for years 2000-2020<br>2. STARS4Water_Domestic_and_Industrial_Water_Demands_projections.zip for years 2020-2050 (SSP1-SSP5)<br><br>The data in the repository was prepared based on Python scripts developed by Stephanie E. Lips and described in <em>Towards a global high </em><em>resolution water demand dataset. Effect of data quality and downscaling techniques - the case for Europe</em>, Utrecht University, 2020 as well as open source databases of WorldPop, WorldBank, UNCTADstat, EIA, Eurostat, Aquastat, UNEP an others. </p>
Dataset of "Anomaly Detection in Industrial Networks: Current State, Classification, and Key Challenges"
<p>Industrial networks are adapted to their specific requirements, especially in terms of industrial processes. To ensure sufficient security in these networks, it is necessary to set and use security policies that complement government regulations, recommendations, and relevant security standards. This paper aims to provide an in-depth analysis of the anomalies occurring within the networks and propose a structure for collecting valuable data from the experimental site based on dividing anomalies into three main categories:<br>security, operational, and service anomalies (and regular traffic recognition). We present a proof-of-concept solution/design aggregating data in industrial networks for advanced anomaly classification. Multiple data sources such as industrial communication, sensor data (additional sensors controlling device behavior), and HW status data are used as data sources. A total of three scenarios (using a physical testbed) were implemented, where we achieved an accuracy of 0.8540/0.9972 in advanced anomaly classification.</p>
Construction Industry Steel Ordering Lists (CISOL) Dataset
<p>The Construction Industry Steel Ordering Lists (CISOL) dataset comprises table-centric, real-world documents from the construction industry, annotated to facilitate the testing and training of deep learning models for table detection (TD) and table structure recognition (TSR). </p> <p>CISOL Key Features:</p> <ul> <li>Steel ordering lists from 24 construction projects carried out between 2015-2023, contributed by 10 distinct German structural engineering firms.</li> <li>Anonymized images to ensure the unrecognizability of specific project or creator information.</li> <li>A total of 3280 images, with 844 annotated following the CISOL annotation guidelines.</li> </ul> <p>CISOL is structured into two tracks:</p> <ul> <li><strong>Track A: TD-TSR </strong>version for end-to-end table detection and table structure recognition tasks.</li> <li><strong>Track B: TSR-</strong>only version for table structure recognition tasks, featuring images cropped to the actual table areas with accordingly adjusted annotations.</li> </ul> <p>The dataset is developed in accordance with the FAIR Principles, ensuring that it is Findable, Accessible, Interoperable, and Reusable. The CISOL dataset permits expansion following the established annotation guideline.</p> <p>Access to the CISOL Leaderboard will be provided at <a href="https://eval.ai/web/challenges/challenge-page/2257" target="_blank" rel="noopener">EvalAI.</a></p> <p> </p>
Dataset for the paper "Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Dataset"
<p>We present a large-scale anomaly detection dataset collected from IBM Cloud's Console over approximately 4.5 months. This high-dimensional dataset captures telemetry data from multiple data centers, specifically designed to aid researchers in developing and benchmarking anomaly detection methods in large-scale cloud environments. It contains 39,365 entries, each representing a 5-minute interval, with 117,448 features/attributes, as interval_start is used as the index. The dataset includes detailed information on request counts, HTTP response codes, and various aggregated statistics. The dataset also includes labeled anomaly events identified through IBM's internal monitoring tools, providing a comprehensive resource for real-world anomaly detection research and evaluation.</p> <p><strong>File Descriptions</strong></p> <ul> <li><code>location_downtime.csv</code> - Details planned and unplanned downtimes for IBM Cloud data centers, including start and end times in ISO 8601 format.</li> <li><code>unpivoted_data.parquet</code> - Contains raw telemetry data with 413 million+ rows, covering details like location, HTTP status codes, request types, and aggregated statistics (min, max, median response times).</li> <li><code>anomaly_windows.csv</code> - Ground truth for anomalies, listing start and end times of recorded anomalies, categorized by source (Issue Tracker, Instant Messenger, Test Log).</li> <li><code>pivoted_data_all.parquet</code> - Pivoted version of the telemetry dataset with 39,365 rows and 117,449 columns, including aggregated statistics across multiple metrics and intervals.</li> <li><code>demo/demo.[ipynb|html]</code>: This demo file provides examples of how to access data in the Parquet files, available in Jupyter Notebook (<code>.ipynb</code>) and HTML (<code>.html</code>) formats, respectively.</li> </ul> <p>Further details of the dataset can be found in <strong>Appendix B: Dataset Characteristics</strong> of the <a href="https://arxiv.org/abs/2411.09047">paper</a> titled <strong><em>"Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Dataset."</em></strong> Sample code for training anomaly detectors using this data is provided in <a href="https://doi.org/10.5281/zenodo.14598119" target="_blank" rel="noopener">this package</a>.</p> <p> </p> <p>When using the dataset, please cite it as follows:</p> <pre><code>@misc{islam2024anomaly,</code><br><code> title={Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Dataset}, </code><br><code> author={Mohammad Saiful Islam and Mohamed Sami Rakha and William Pourmajidi and Janakan Sivaloganathan and John Steinbacher and Andriy Miranskyy},</code><br><code> year={2024},</code><br><code> eprint={2411.09047},</code><br><code> archivePrefix={arXiv},</code><br><code> url={https://arxiv.org/abs/2411.09047}</code><br><code>}</code></pre> <p> </p>
Turnover of the Radio Broadcasting Industry in Europe
<p>Imputed and forecasted values of the radio broadcasting industry from the <a href="https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=sbs_na_1a_se_r2&lang=en">Annual detailed enterprise statistics for services (NACE Rev. 2 H-N and S95)</a> Eurostat folder.</p> <p>We use backcasting, forecasting, approxmation, last observation carry forward and next observation carry backwards to impute missing values, and to create realistic forecasts up to three periods. </p> <p>Compared to the Eurostat raw data we added value with <br> Increased number of observations: 65%<br> Reduced missing values: -48.1%<br> Increased non-missing subset for regression or AI: +66.67%</p>
Packaging Industry Anomaly DEtection (PIADE) Dataset
<p>PIADE dataset contains data from five industrial packaging machines:</p> <ul> <li>Machine s_1: from 2020-01-01 14:00:00 to 2021-12-31 13:00:00</li> <li>Machine s_2: from 2020-06-17 08:00:00 to 2021-12-31 07:00:00</li> <li>Machine s_3: from 2020-10-07 12:00:00 to 2022-01-01 23:00:00</li> <li>Machine s_4: from 2020-01-01 01:00:00 to 2022-01-01 23:00:00</li> <li>Machine s_5: from 2020-01-20 08:00:00 to 2022-01-01 12:00:00</li> </ul> <p>## Raw Data</p> <p>Each row represents a production interval, with the following schema:</p> <ul> <li>interval_start: start of the production interval </li> <li>equipment_ID: equipment identifier </li> <li>alarm: alarm code of the active stop reason, if it occurred </li> <li>type: idle, production, downtime, performance_loss or scheduled_downtime </li> <li>start: start of the production interval </li> <li>end: end of the production interval </li> <li>elapsed: duration of the production interval </li> <li>pi: input packages </li> <li>po: output packages </li> <li>speed: speed (packages per hour)</li> </ul> <p>There are 133 different types of alerts, and 429394 rows.<br> </p> <p>## Sequences (1h) data</p> <p>For each piece of equipment, we define sequences of length = 1 hour and we aggregate raw interval data as follows:</p> <ul> <li>'equipment_ID': machine identifier</li> <li>'#changes': changes in machine state</li> <li>'%downtime': time spent in 'downtime' state</li> <li>'%idle': time spent in 'idle' state</li> <li>'%performance_loss': time spent in 'performance loss' state</li> <li>'%production': time spent in production</li> <li>'%scheduled_downtime': time spent in scheduled downtime</li> <li>'count_sum': sum of all alarm occurrences</li> <li>'A_<XXX>': counter of alarm <XXX> occurrences</li> <li>'<state1>/<state2>': number of transitions from <state1> to <state2></li> </ul> <p> </p>
Dataset from the Survey on Industry 5.0 Concepts and Enabling Technologies, Towards an Enhanced Conservation Practice
<p>This database contains all the responses from the participants in the survey: Industry 5.0 Concepts and Enabling Technologies, Towards an Enhanced Conservation Practice.</p> <p>The main purpose of this survey was to explore how the Architecture, Engineering, Construction, Management, Operation, and Conservation (AECMO&C)<br>industry can adapt and better prepare to embrace the innovative principles and enabling technologies of Industry 5.0. This could ultimately result in<br>enhanced conservation practices for built cultural heritage.</p> <p> </p>
Dataset of paper "Bioelectrochemically-improved anaerobic digestion of fishery processing industrial wastewater"
<p>Dataset of operation of a bioelectrochemically-improved anaerobic digester (AD-BES), treating real fishery processing wastewater.<br>This dataset was used to publish the paper "Bioelectrochemically-improved anaerobic digestion of fishery processing industrial wastewater" in Journal of Water Process Engineering (DOI: 10.1016/j.jwpe.2024.105848).</p>
ncrncornell/ced2ar-nber-ces-codebook: Codebook for NBER-CES Manufacturing Industry Database
<p>Codebook for NBER-CES Manufacturing Industry Database (2009) [NAICS and SIC], by Randy A. Becker , Wayne B. Gray , Jordan Marvakov , and Eric J. Bartelsman</p> <p>Main website: <a href="https://www.nber.org/data/nberces5809.html">https://www.nber.org/data/nberces5809.html</a> (note: a newer version is available at <a href="http://www.nber.org/data/nberces.html">http://www.nber.org/data/nberces.html</a> - this codebook does not necessarily reflect the more recent version.)</p> <p>Live version of the DDI codebook at <a href="https://www2.ncrn.cornell.edu/ced2ar-web/codebooks/nber-ces/">https://www2.ncrn.cornell.edu/ced2ar-web/codebooks/nber-ces/</a></p>
Public, military and industrial units
<p>Urban Atlas based data subset for Comune di Napoli, where every element with CODE 12100 was extracted as Public, military and industrial units elements with the next information:</p> <p>gid integer area numeric perimeter numeric geom geometry(Polygon,EPSG:3035) albedo real emissivity real transmissivity real run_off_coefficient real context real fua_tunnel real</p> <p>This data is an input for local effects calculation.</p>
Monitoring Technical Debt in an Industrial Setting
<p>The dataset includes the answers on a survey with 60 software engineers (i.e., architects, developers, etc.) working for 11 software development companies located in 9 countries, to understand their needs for Technical Debt Management.</p> <p>The questionnaire is organized into an introductory section and three main ones. It begins with some demographic information (Name of Company and Role in the Company). Next, participants are asked to rate in a Likert scale (1) a group of questions based on the usefulness of Technical Debt (TD) principal indicators, and (2) a group of questions based on the usefulness of TD interest indicators, and then, they are asked (3) to consider the optimal strategy for mitigating TD. In the beginning of each Section some basic TD definitions have been provided, so as to establish a common understanding and terminology among participants.</p>
Modelling of ready biodegradability based on combined public and industrial data sources
<p>The European REACH (Registration, Evaluation, Authorization and restriction of Chemicals) Regulation, requires marketed chemicals to be evaluated for Ready Biodegradability (RB). In-silico prediction is a valid alternative to expensive and time-consuming experimental testing. However, currently available models may not be relevant to predict compounds of industrial interest, due to accuracy and applicability domain restriction issues.</p> <p>In this work we present a new and extended RB dataset (2830 compounds), issued by the merging of several public data sources. It was used to train classification models, which were externally validated and benchmarked against already-existing tools on a set of 316 compounds coming from the industrial context. New models showed good performances in terms of predictive power (BA = 0.74 – 0.79) and data coverage (83 – 91 %).</p> <p>The Generative Topographic Mapping approach was employed to compare the chemical space of the various data sources: several chemotypes and structural motifs unique to the industrial dataset were identified, highlighting for which chemical classes currently available models may have less reliable predictions.</p> <p>Finally, public and industrial data were merged into Global dataset containing 3146 compounds and including a significant subset of compounds coming from the industrial context. This is the biggest dataset reported in the literature so far which covers some chemotypes absent in the public data. Thus, predictive model developed on the Global dataset has much larger applicability domain than related models built on publicly available data. The developed model is available for the user on the Laboratory of Chemoinformatics website.</p> <p>This dataset is only the "All-Public" set, since the industrial compounds cannot be disclosed.</p> <p>This update contains additional entries from [J. Chem. Inf. Model. 52 (2012), pp. 655–669] and [J. Chem. Inf. Model. 53 (2013), pp. 867–878]</p>
Dataset of Survey Results on the Integration of Industry 4.0 in University Education (Baja California, 2024)
<p>This dataset contains the results of a survey conducted in 2024 on the integration of Industry 4.0 concepts and technologies in university education in Baja California. The survey was designed to assess the current state of adoption, challenges, and opportunities related to Industry 4.0 within academic institutions. The data includes responses from engineering students at the Autonomous University of Baja California (UABC) and the Polytechnic University of Baja California (UPBC). The insights gathered aim to inform future strategies for enhancing the implementation of Industry 4.0 in higher education curricula.</p>
Supplementary Material for "Advancing quantum technology workforce: industry insights into qualification and training needs" and "Extending the European Competence Framework for Quantum Technologies: new proficiency triangle and qualification profiles"
<p>This is a file collection as supplementary material for the paper <em>Advancing quantum technology workforce: industry insights into qualification and training needs, <a href="https://doi.org/10.1140/epjqt/s40507-024-00294-2">doi 10.1140/epjqt/s40507-024-00294-2</a>.</em> It consists of:</p> <ol> <li>Interview guide: questions and more as guideline for the interviews conducted for the industry needs analysis documented in the publication.</li> <li>Interview transcript extracts: anonymised phrases from the interviews that are given as quotes (in a shortened/liguistically smoothed out form) in the publication as well as further phrases that are refered in the results sections of the publication.</li> <li>Dataset of the follow-up survey</li> </ol> <p>The results of this study were also used to update the <a href="https://doi.org/10.5281/zenodo.10976836" target="_blank" rel="noopener">European Competence Framework for Quantum Technologies Version 2.5</a>, which is documented in <em>Extending the European Competence Framework for Quantum Technologies: new proficiency triangle and qualification profiles, <a href="https://doi.org/10.1140/epjqt/s40507-024-00302-5">doi 10.1140/epjqt/s40507-024-00302-5</a></em>. In an additional sheet, the three draft versions of qualification profile descriptions (v2.1, v2.2, v2.3) are provided.</p>
Indicative distribution map for Ecosystem Functional Group T7.4 Urban and industrial ecosystems
<p>This archive contains indicative distribution maps and profiles for <strong>T7.4 Urban and industrial ecosystems</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>
Experimental characterisation of the ULB semi-industrial furnace operating under MILD conditions with non-conventional fuels
<p>This data set contains:</p> <p>- Averaged temperature measurements data<br> - Species concentrations measurements in the exhausted gas<br> - Ensemble-averaged OH* and CH* radicals chemiluminescence spatially resolved intensity values</p> <p>The furnace is fired using a 20 kW capacity recuperative commercial flame/FLOX® burner (REKUMAT M150) of WS make. </p> <p>The following fuel mixtures were tested:</p> <p>- Fuel mixtures of CH4/H2<br> - Fuel mixtures of NH3/H2<br> - Fuel mixtures of CH3OCH3/CH4 and CH3OCH3/H2</p>
Data for: A tool based on the Industry Foundation Classes standard for dynamic data collection and automatic generation of Building Automation Control Networks
<p>This dataset shows the results obtained for a case study at TRL4 for the research paper title <em><strong>A tool based on the Industry Foundation Classes standard for dynamic data collection and automatic generation of Building Automation Control Networks</strong></em>, with DOI: https://doi.org/10.1016/j.jobe.2023.107625</p> <p>This dataset is an enhanced IFC (Industry Foundation Classes) file with the creation of the BACN (Building Automation Control Network). This IFC file includes the devices created automatically by the BACN2BIM tool (developed by CARTIF Technology Centre) for the case study validated at TRL4. The original IFC was obtained from the Institute for Automation and Applied Informatics (IAI) / Karlsruhe Institute of Technology (KIT) https://www.ifcwiki.org/images/e/e3/AC20-FZK-Haus.ifc, under an unrestricted license, as served as one of the case studies for this research.</p> <p>*Depending on the IFC viewer used, the included sensors may not be represented correctly. In this case, it is recommended to try with another IFC viewer, for example xBIM explorer https://docs.xbim.net/downloads/xbimxplorer.html or BimCollab Zoom Free https://www.bimcollab.com/en/support/downloads/</p>
Noise exposure at ultrasound-related industrial workplaces and public sites
<p>The dataset contains single measurements at different public sites and workplaces in Europe. The data has been used or obtained in the context of the project 15HLT03 “EarsII” from the EMPIR-programme.</p> <p>For each measurement metadata is available. This includes the measurement circumstances and involved machinery, a description of the measurement location and noise reduction measures, the microphone position during measurement, and the measurement procedure used to obtain the measurement data.</p> <p>A detailed description of all the quantities contained in the dataset is documented in the accompanying pdf-file.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.