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830 results for “industry”
Datenaufbereitung zum State-of-the-Art und Fortschritt europäischer Gaia-X sowie Datenraum Initiativen mit einem Schwerpunkt auf Industrie 4.0 Anwendungsfälle
<p><span>In seiner Gesamtheit umfasst der Datensatz eine Sammlung von Initiativen und Projekten, die mit </span><span>Gaia-X sowie Data Spaces im Allgemeinen in Verbindung stehen</span><span>.</span> Ziel der Datensammlung war es, eine detaillierte Übersicht über bestehende Projekte zu erhalten und diese systematisch zu kategorisieren, um anschließend spezifische industrielle Anwendungsfälle herauszuarbeiten und diese zu analysieren. Der Zeitraum dieser Sammlung erstreckte sich von April 2023 bis März 2024. Anzumerken ist, dass sämtliche zur Verfügung stehenden Informationen innerhalb dieses Zeitraums in den Datensatz aufgenommen wurden. Ab März 2024 wurden keine weiteren Daten erfasst, wodurch der Datensatz den aktuellen Stand bis zu diesem Zeitpunkt widerspiegelt.</p> <p>Insgesamt wurden 211 Initiativen aus 281 Quellen zusammengetragen und ausgewertet. Diese unterteilen sich (über alle Domänen hinweg) in 102 Data Spaces und 93 Anwendungsfälle. Die 93 Anwendungsfälle beinhalten 47 Industrie 4.0 relevante Anwendungsfälle.</p>
Region-specific sourcing of lignocellulose residues as renewable feedstocks for a net-zero chemical industry
<p><span>data_1_lignocellulose_residue_grid_all_years_scenarios – datasets presenting the <em>theoretical</em>, <em>ecological</em>, and <em>available</em> potential of various lignocellulose residues on the GLOBIOM grid level (200 km </span><span>×</span><span> 200 km)</span></p> <p><span>data_2_lignocellulose_feedstock_potential_impacts_country_level_all_scenarios – datasets presenting the <em>theoretical</em>, <em>ecological</em>, and <em>available</em> potential of various lignocellulose residues on the country level and their corresponding climate-change impacts, water stress, and land-use-related biodiversity loss impacts</span></p> <p><span>LUID_CTY – shapefile for the global map with GLOBIOM grids</span></p> <p> </p>
Data Set of Industrial Metaverse Use Cases
<p>Data Set of Industrial Metaverse Use Cases</p> <p>Potential of the Industrial Metaverse – A Taxonomic Approach<br>IFIP 21st International Conference on Product Lifecycle Management (2024)</p> <p>This data set comprises the following components:</p> <ul> <li>Use Cases & Classification</li> <li>Dimensions & Characteristics</li> <li>Review Documentation</li> </ul>
Weakly Supervised Learning for Industrial Optical Inspection
<p><strong>Abstract</strong></p> <p>In the following, we present a synthetic benchmark corpus for detect detection on statistically textured surfaces.We hope that it facilitates to further develop and benchmark classification algorithms for applications of industrial optical inspection. All data is publicly available and can be downloaded from this page.</p> <p><strong>Competition at DAGM 2007 symposium</strong></p> <p>The <a href="https://www.dagm.de/">DAGM (Deutsche Arbeitsgemeinschaft für Mustererkennung e.V., German chapter of the IAPR (International Association for Pattern Recognition))</a> and the <a href="http://www.gnns.de/">GNSS (German Chapter of the European Neural Network Society)</a> offered an open competition on <em>Weakly Supervised Learning for Industrial Optical Inspection</em> held as part of the DAGM symposium in 2007.<br><br>The competition was inspired by the fact that automated optical inspection allows to reduce the cost of industrial quality control significantly. The competitors had to design a classification algorithm which:</p> <ul> <li>detects miscellaneous defects on various statistically textured backgrounds.</li> <li>learns to discern defects automatically from a weakly labelled training data.</li> <li>works on data whose exact characteristics are unknown at development time.</li> <li>adapts all parameters automatically and does not require any human intervention.</li> <li>has a moderate running time (in this competition 24 hours for training and 12 hours for the test phase).</li> <li>takes into account asymmetric costs for false positive and false negative decisions (1:20 was used for the competition).</li> </ul> <p><strong>Data description</strong></p> <p>Preview Image: <a href="../api/iiif/record:12750201:examples_small.jpg/full/!800,800/0/default.jpg" target="_blank" rel="noopener">https://zenodo.org/api/iiif/record:12750201:examples_small.jpg/full/!800,800/0/default.jpg</a></p> <p>The data is artificially generated, but similar to real world problems. The first six out of ten datasets, denoted as development datasets, are supposed to be used for algorithm development. The remaining four datasets, which are referred to as competition datasets, can be used to evaluate the performance. Researchers should consider not using or analyzing the competition datasets before the development is completed as a code of honour.<br>In the following we provide some details about the datasets:</p> <ul> <li>Each development (competition) dataset consists of 1000 (2000) 'non-defective' and of 150 (300) 'defective' images saved in grayscale 8-bit PNG format.</li> <li>Each dataset is generated by a different texture model and defect model.</li> <li>'Non-defective' images show the background texture without defects, 'defective' images have exactly one labelled defect on the background texture.</li> <li>All datasets has been randomly split into a training and testing sub-dataset of equal size.</li> <li>Weak labels are provided as ellipses roughly indicating the defective area. Technically, defective images are augmented with a separate grayscale 8-bit image in the PNG format located in a folder 'Label'. The values 0 and 255 denote background and defective area, respectively.</li> </ul> <p>All meta-data is subsumed in a separate ASCII textfile called 'Labels.txt' which is located in the 'Label' folder. The structure is as follows:<br>1 \n<br>[id of item no. 1] \t [0 if non-defective, 1 if defective] \t [filename of raw image no. 1] \t 0 \t [filename of label image no. 1 if defective, 0 otherwise] \n<br>...<br>[id of item no. N] \t [0 if non-defective, 1 if defective] \t [filename of raw image no. N] \t 0 \t [filename of label image no. N if defective, 0 otherwise] \n</p>
Industry - Chemical & Pharma., Cement, Motor systems, Excess heat recovery systems
<ul> <li>This file provides techno-economic data for energy efficiency measures applicable in Swiss industrial systems.</li> <li>Generalized indicators for measure-specific potential energy savings are not developed (except for cement industry) due to the unavailability of physical production data, ex-ante and ex-post data of the implemented measures and detailed statistics on energy use by application in Swiss industry.</li> <li>When using the data please consult and refer to the publications given in the Reference section.</li> </ul>
A set of typical relevant exposure scenarios for nanoparticles in semiconductor industry (dataset)
<p>This is an Excel database part of Deliverable 1.3 "A set of typical relevant exposure scenarios for NP’s in semiconductor industry"</p> <p>https://www.zenodo.org/record/2538388</p> <p> </p>
Consensus models to predict oral rat acute toxicity and validation on a dataset coming from the industrial context
<p>We report predictive models of acute oral systemic toxicity representing a follow-up of our previous work in the framework of the NICEATM project. It includes the update of original models through the addition of new data and an external validation of the models using a dataset relevant for the chemical industry context. A regression model for LD50 and classification model for toxicity classes according to the Global Harmonized System categories were prepared. ISIDA descriptors were used to encode molecular structures. Machine learning algorithms included Support Vector Machine (SVM), Random Forest (RF) and Naïve Bayesian. Selected individual models were combined in consensus.</p> <p>The different datasets were compared using the Generative Topographic Mapping approach. It appeared that the NICEATM datasets were lacking some relevant chemotypes for chemical industry. The new models trained on enlarged data sets have applicability domain (AD) sufficiently large to accommodate industrial compounds. The fraction of compounds inside the models’ AD increased from 58 % (NICEATM model) to 94 % (new model). Yet, the increase of training sets only slightly improved of the models’ prediction performance: RMSE values decreased from 0.56 to 0.47 and balanced accuracies increased from 0.69 to 0.71 for NICEATM and new models, respectively.</p>
Data set: Industrial IoT-driven remote path planning
<p>This compressed file contains data from three different experiments during the IIoT-REPLAN experimentation phase (Industrial IoT-drive remote path planning). IIoT-REPLAN was funded by an open call from the H2020 Fed4FIRE+ project.</p> <p>Contents:</p> <p>A. astar.csv<br> This file contains the timestamp of each movement of the Robot and the uncertainty (d) at each specific time that Switch 1 was checked. The first two columns refer to seconds while the third one is a scalar value. The total duration of the experiment is 62.33 sec and the setup of this experiment is the real time application of the Astar Algorithm with the localization being based only on the sensor of the Robot</p> <p><br> B. Dijkstra.csv <br> In this experiment the full functionality of the switching system proposed in this work is highlighted. </p> <p>C. Cloud.csv<br> In this experiment the localization algorithm and the path planning algorithm are always executed on the cloud.</p> <p>In both B,C experiments the values of each column are explained inside the Dijkstra.csv </p> <p>Also, two pictures of singlie vision-based self localization are included. </p> <p>A more detailed exposition on all of the above can be found at <br> github link : https://github.com/maravger/alphabot-ppl</p>
MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection
<p>This dataset is a sound dataset for malfunctioning industrial machine investigation and inspection (MIMII dataset). It contains the sounds generated from four types of industrial machines, i.e. valves, pumps, fans, and slide rails. Each type of machine includes seven individual product models*1, and the data for each model contains normal sounds (from 5000 seconds to 10000 seconds) and anomalous sounds (about 1000 seconds). To resemble a real-life scenario, various anomalous sounds were recorded (e.g., contamination, leakage, rotating unbalance, and rail damage). Also, the background noise recorded in multiple real factories was mixed with the machine sounds. The sounds were recorded by eight-channel microphone array with 16 kHz sampling rate and 16 bit per sample. The MIMII dataset assists benchmark for sound-based machine fault diagnosis. Users can test the performance for specific functions e.g., unsupervised anomaly detection, transfer learning, noise robustness, etc. The detail of the dataset is described in [1][2].</p> <p>This dataset is made available by Hitachi, Ltd. under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.</p> <p>A baseline sample code for anomaly detection is available on GitHub: <a href="https://github.com/MIMII-hitachi/mimii_baseline/">https://github.com/MIMII-hitachi/mimii_baseline/</a></p> <p>*1: This version "public 1.0" contains four models (model ID 00, 02, 04, and 06). The rest three models will be released in a future edition.</p> <p>[1] Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” arXiv preprint arXiv:1909.09347, 2019.</p> <p>[2] Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” in Proc. 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2019.</p>
QSPR models for bioconcentration factor (BCF): Are they able to predict data of industrial interest?
<p>This dataset is described and studied in the article </p> <p>"QSPR models for bioconcentration factor (BCF): Are they able to predict data of industrial interest?"</p> <p>published in <em>SAR and QSAR Environmental Research</em> (Taylor&Francis).</p> <p>Files description:</p> <p>SI_BCFtrainset.xlsx: a collection of 1129 chemical structures and CAS identifiers with their logBCF values extracted from various literature sources.</p> <p>SI_BCFtestset.xlsx: a collection of 204 chemical structures for which the logBCF is considered of lower reliability and used as an external test set.</p> <p>SI_FullDataset_rawdata.csv: the raw data composed of 15372 entries with the following columns: CASRN, Tissue, Duration [d], Test organism, Exposure type, Steady state, RESPONSE, RESPONSE UNIT, Media type, TakenFrom, TITLE, AUTHOR, YEAR, SOURCE, SMILES</p> <p>SI_ExcludedOutliers34.csv: 34 chemical structures that have been identified as suspicious during analysis.</p> <p> </p>
Barriers to Transitioning to a Circular Bio-based Economy: Findings from an Industrial Perspective
<p>The transition from a linear fossil-based to a circular bio-based economy represents an opportunity and a suitable pathway for achieving several sustainable development goals. However, the transition is a complex process since it requires transformative policies, purposeful innovation, access to finance, risk-taking capacity as well as new and sustainable business models and markets. Accordingly, the first step in this transition process is the identification of barriers that are hampering the transition to a sustainable circular bio-based economy. With this motivation in mind, this study reviews grey literature to identify barriers focusing on four critical sectors facing major challenges within the current linear economy and requiring a sustainable transition most urgently: construction, chemicals, plastics, and textile sectors. Employing an adapted STEEP methodology (Social, Technological, Economical, Environmental, Political), a total of 193 different barriers have been identified and clustered under six categories: cultural, technical, economic, environmental, governance, and structural. Regardless of the sector, cultural and structural barriers are identified as the most prominent; the lack of incentives for consumer behaviour change and lack of stakeholder collaboration were the most cited barriers among the literature records. From a value chain perspective, most of the barriers are related to the material processing and product manufacturing stage. Finally, potential solutions, extracted from the grey literature, are proposed to fill the gaps and overcome the identified barriers. Many of the identified barriers are common across the four investigated sectors, indicating the solutions or measures can be applicable in a wider perspective to promote the transition in the right direction.</p>
A Comprehensive Review of ANDA Submissions and Amendments Under GDUFA: FDA Guidelines for the Generic Drug Industry
<p><span>This review provides an in-depth analysis of the Food and Drug Administration's (FDA) guidance document titled <em>ANDA Submissions — Amendments to Abbreviated New Drug Applications (ANDAs) Under the Generic Drug User Fee Amendments (GDUFA)</em>, released in September 2024. The document serves as a comprehensive guide for the pharmaceutical industry, detailing the FDA's expectations regarding the classification, submission, and assessment of amendments to ANDAs and Prior Approval Supplements (PASs). The review discusses key elements of the guidance, including amendment categories (major, minor, and unsolicited), assessment timelines, the process for reclassification of amendments, and potential deficiencies in submissions. The guidance also addresses changes in classifications and assessment goals, deferred amendments, and best practices for ensuring timely FDA approval. This review aims to clarify the FDA’s current thinking on ANDA submissions under GDUFA and the practical implications for generic drug manufacturers seeking to comply with the established regulations.</span></p>
Secondary Data from Insights from Publishing Open Data in Industry-Academia Collaboration
<h1>Secondary Data from Insights from Publishing Open Data in Industry-Academia Collaboration</h1> <h2>Authors</h2> <p>Per Erik Strandberg [1], Philipp Peterseil [2], Julian Karoliny [3], Johanna Kallio [4], and Johannes Peltola [4].</p> <p>[1] Westermo Network Technologies AB (Sweden).<br>[2] Johannes Kepler University Linz (Austria)<br>[3] Silicon Austria Labs GmbH (Austria).<br>[4] VTT Technical Research Centre of Finland Ltd. (Finland).</p> <h2>Description</h2> <p>This data is to accompany a paper submitted to Elsevier's data in brief in 2024, with the title <em>Insights from Publishing Open Data in Industry-Academia Collaboration</em>.</p> <p><em>Tentative Abstract:</em> Effective data management and sharing are critical success factors in industry-academia collaboration. This paper explores the motivations and lessons learned from publishing open data sets in such collaborations. Through a survey of participants in a European research project that published 13 data sets, and an analysis of metadata from almost 281 thousand datasets in Zenodo, we collected qualitative and quantitative results on motivations, achievements, research questions, licences and file types. Through inductive reasoning and statistical analysis we found that planning the data collection is essential, and that only few datasets (2.4%) had accompanying scripts for improved reuse. We also found that authors are not well aware of the importance of licences or which licence to choose. Finally, we found that data with a synthetic origin, collected with simulations and potentially mixed with real measurements, can be very meaningful, as predicted by Gartner and illustrated by many datasets collected in our research project.</p> <h2>Secondary data from Survey</h2> <p>The file <code>survey.txt</code> contains secondary data from a survey of participants that published open data sets in the 3-year European research project InSecTT.</p> <h2>Secondary data from Zenodo</h2> <p>The file <code>secondary_data_zenodo.json</code> contains secondary data from an analysis of data sets published in Zenodo. It is accompanied with a <code>py</code>-file and a <code>ipynb</code>-file to serve as examples.</p> <h2>License</h2> <p>This data is licenced with the Creative Commons Attribution 4.0 International license. You are free to use the data if you attribute the authors. Read the license text for details.</p>
ADDRESSING TAX COMPLIANCE ISSUES FOR LOAN-BASED PAYMENT TYPES: DEVELOPMENT OF THE TAX BUFFER MECHANISM AND ITS USE IN THE FINTECH INDUSTRY
<p>In the modern world of the fintech industry, tax changes are one of the key problems, especially with fixed loans. This study examines the Tax Buffer mechanism, designed to effectively manage tax obligations that vary depending on the jurisdiction and stages of delivery of goods. The main task of the mechanism is to automatically recalculate taxes to minimize the risk of errors and reduce the burden on the accounting and legal departments of the company. The implementation of this solution allows you to reduce the number of manual operations, reduce transaction costs and improve the customer experience by eliminating the need to notify users of every change in the amount of taxes. The results of the implementation of the mechanism have shown its high efficiency: a significant reduction in the number of errors and financial disputes, as well as an increase in operational efficiency. The Tax Buffer mechanism is an important innovation that helps to increase the resilience of fintech companies to changes in tax legislation.</p>
5359 industrial VEA load profiles
Open the record for dataset details and reuse information.
GCAM input files for "Decarbonization pathways for Korea's industrial sector towards its 2050 carbon neutrality goal"
<p>GCAM input files for "Decarbonization pathways for Korea's industrial sector towards its 2050 carbon neutrality goal"</p>
Data Set of Industry Interviews on Industrial Metaverse
<p>Data Set of Industry Interviews on Industrial Metaverse</p> <p>Industrial Metaverse for Industrial Companies: An Exploratory Study<br>Smart Service Summit - Smart Services Supporting the Value Co-creation in Industrial Contexts (2024)</p> <p>This data set comprises the following components:</p> <ul> <li>Interview Questionnaire</li> <li>Interview Metadata</li> <li>Interview Data</li> <li>Interview Coding</li> <li>Interview Quotes</li> </ul>
Software Engineering Education Knowledge versus Industrial Needs
<p>Dataset of the research paper: <strong>Software Engineering Education Knowledge versus Industrial Needs</strong></p> <p><em>Contribution</em>: Determine and analyze the gap between software practitioners’ education outlined in the 2014 IEEE/ACM Software Engineering Education Knowledge (SEEK) and industrial needs pointed by Wikipedia articles referenced in Stack Overflow (SO) posts.<br> <em>Background</em>: Previous work has uncovered deficiencies in the coverage of computer fundamentals, people skills, software processes, and human-computer interaction, suggesting rebalancing.<br> <em>Research Questions</em>: 1) To what extent are developers’ needs, in terms of Wikipedia articles referenced in SO posts, covered by the SEEK knowledge units? 2) How does the popularity of Wikipedia articles relate to their SEEK coverage? 3) What areas of computing knowledge can be better covered by the SEEK knowledge units? 4) Why are Wikipedia articles covered by the SEEK knowledge units cited on SO?<br> <em>Methodology</em>: Wikipedia articles were systematically collected from SO posts. The most cited were manually mapped to the SEEK knowledge units, assessed according to their degree of coverage. Articles insufficiently covered by the SEEK were classified by hand using the 2012 ACM Computing Classification System. A sample of posts referencing sufficiently covered articles was manually analyzed. A survey was conducted on software practitioners to validate the study findings.<br> <em>Findings</em>: SEEK appears to cover sufficiently computer science fundamentals, software design and mathematical concepts, but less so areas like the World Wide Web, software engineering components, and computer graphics. Developers seek advice, best practices and explanations about software topics, and code review assistance. Future SEEK models and the computing education could dive deeper in information systems, design, testing, security, and soft skills.</p> <p>The following data files are included.</p> <ul> <li><strong>wikipedia_articles.csv</strong>: Wikipedia articles mapped to the knowledge units of the 2014 IEEE/ACM Software Engineering Education Knowledge (SEEK) and the first and second level categories of the 2012 ACM Computing Classification System (CCS).</li> <li> <p><strong>posts_analysis.csv</strong>: Stack Overflow post data and metadata.</p> </li> <li> <p><strong>posts_aggregated_codes.csv</strong>: The aggregated codes that resulted from the manual analysis of the Stack Overflow posts by grouping individual keywords assigned to the posts.</p> </li> <li> <p><strong>survey_questionnaire.csv</strong>: The final survey questionnaire.</p> </li> <li> <p><strong>survey_responses.csv</strong>: Anonymized responses of the final survey questionnaire. (E-mail addresses have been excluded for privacy reasons.)</p> </li> </ul>
Social Accounting Matrix for Lithuania, 2017 (with the shift to bioplastics in packing industry)
<p>The dataset is based on doi: 10.5281/zenodo.5077893 but includes bioplastics as the main input for the production of plastic sacks and bags.</p>
COALMOD-World 2.0 data, results, figures for: Stranded Assets in the Coal Export Industry? The Case of the Australian Galilee Basin
<p>This dataset contains all COALMOD-World 2.0 data for Hauenstein et al. (2023): New coal mines in the Australian Galilee Basin are not economically viable and are prone to become stranded assets (doi.org/10.1016/j.oneear.2023.07.005).</p> <p>With the input data files and the GAMS scenario file the model (https://github.com/chauenstein/COALMOD-World_v2.0) 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>
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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.