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830 results for “INDUSTRY”
International comparison of cross-disciplinary integration in industry 4.0: A co-authorship analysis using academic literature databases
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Data from: Cryptic diversity of cellulose-degrading gut bacteria in industrialized humans
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HGIS data of Greater London's Industry 1865-1875
<p>Based on the First Series of the Ordnance Survey London Town Plans. Each factory is coded to indicate whether it is on both or just one of the two 19th century series of Ordnance Survey London Town Plans. I have also tried to catagorize the factories. There are some other incomplete fields or fields used in earlier versions of this database. Data used in Jim Clifford, <em>West Ham and the River Lea A Social and Environmental History of London’s Industrialized Marshland, 1839–1914</em>, UBC Press, 2017, https://www.ubcpress.ca/west-ham-and-the-river-lea</p>
MLCQ: Industry-relevant code smell data set
<p>The MLCQ data set with nearly 15000 code samples was created by software developers with professional experience who reviewed industry-relevant, contemporary Java open source projects. </p> <p>We expect that this data set should stay relevant for a longer time than data sets that base on code released years ago and, additionally, will enable researchers to investigate the relationship between developers' background and code smells' perception.</p> <p><strong>If you use this data set please cite the following paper:</strong></p> <p>Lech Madeyski and Tomasz Lewowski. MLCQ: Industry-relevant code smell data set. In <em>Evaluation and Assessment in Software Engineering (EASE2020)</em>, April 15–17, 2020, Trondheim, Norway.ACM, New York, NY, USA, 6 pages, DOI: <a href="https://doi.org/10.1145/3383219.3383264">3383219.3383264</a> URL: https://doi.org/10.1145/3383219.3383264</p> <p>Note: Pre-print should be available soon from <a href="http://madeyski.e-informatyka.pl">http://madeyski.e-informatyka.pl</a></p>
Historical data on industrial change and skills in Lithuania
<p>The data was created during a research project where the authors tried to assess the impact of radical industrial change on skills of workforce in Lithuania and other Central and Eastern European (CEE) countries between 1988 and 2008. The data at question, was predominantly used to evaluate the case of Lithuania. Currently the results of the research are in a process of being published. The research was funded by the Research Council of Lithuania (grant No. S-MOD-17-20).</p>
Load profile data of 50 industrial plants in Germany for one year
<p>This dataset holds the electric load profiles of 50 small and mid-size enterprises in Germany. The load profiles are in 15-minute time resolution for one year. The load is shown in kW as an average over 15 minutes.</p> <p>The dataset is divided into two:</p> <ul> <li>LoadProfile_20IPs_2016 shows load profiles of 20 industrial plants (IP) for the year 2016.</li> <li>LoadProfile_30IPs_2017 shows load profiles of 30 industrial plants (IP) for the year 2017.</li> </ul> <p>The IPs from the dataset for 2016 do not reappear in the dataset for 2017.</p> <p> </p> <p>The dataset LoadProfile_20IPs_2016 is evaluated in the following publication:</p> <ul> <li>Covic, N., Braeuer, F., McKenna, R., Pandzic, H., Optimizing Industrial Facilities’ Active Participation<br> in Electricity Markets under Uncertainty, 2020.</li> </ul> <p>Both datasets together are evaluated in multiple publications:</p> <ul> <li>Braeuer, F., Finck, R., McKenna, R., Comparing empirical and model-based approaches for calculating dynamic grid emission factors: An application to CO2-minimizing storage dispatch in Germany, Journal of Cleaner Production, Volume 266, 2020, 121588, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2020.121588.</li> <li>Braeuer, F., Rominger, J., McKenna, R.,Fichtner, W., Battery storage systems: An economic model-based analysis of parallel revenue streams and general implications for industry, Applied Energy, Volume 239, 2019, Pages 1424-1440, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2019.01.050.</li> </ul> <p>Enjoy.</p>
Patents and certificates of addition granted in France from 1880 to 1903 by class of industry
<p><strong>**TITRE**</strong><br> Etat des brevets d'invention et des certificats d'addition délivrés en France entre 1880 et 1903 par classes d'industrie</p> <p><strong>**VARIABLES**</strong><br> CLASS : le nom donné est le nom de la classe d'industrie qui apparaît dans la source originale.</p> <p>SUB-CLASS : le nom donné est le nom de la sous-classe d'industrie qui apparaît dans la source originale.</p> <p>TYPE : brevets d'invention ou certificats d'addition</p> <p>YEAR : année de délivrance<br> NUMBER : le nombre de brevets d'invention et de certificats d'addition délivrés.</p> <p><strong>**SOURCES**</strong></p> <p><em>La propriété industrielle : organe officiel du Bureau international de l'Union pour la protection de la propriété industrielle</em></p> <p><br> année 1880 à 1884 : 1885/12, p. 13 ; année 1885<em> </em>:<em> </em>1886/8, p. 62 ; année 1886 : 1887/9, p. 72 ; année 1887 : 1889/5, p. 73 ; année 1888 : 1889/6, p. 90 ; année 1889 : 1890/07, p. 86 ;</p> <p>année 1890 : 1891/08, p. 110 ; année 1891 : 1892/08, p. 134 ; année 1892 : 1893/07, p. 100 ; année 1893 : 1894/08, p. 102 ; année 1894 : 1896/05, p. 81; année 1895 : 1897/02, p. 32; année 1896 : 1898/06, p. 100; année 1897 : 1899/01, p. 16 ; année 1898 : 1900/03, p. 55 ; année 1899 : 1900/08, p. 140 ;</p> <p>année 1900 : 1902/02, p. 32 ; année 1901 : 1903/03, p. 52 ; année 1902 : 1904/04, p. 72 ; année 1903 : 1906/03, p. 48.</p> <p>--------------------------------------------------------------------------------------------------</p> <p><strong>**TITLE**</strong><br> Number of patents granted and certificates of addition in France between 1880 and 1903 by classes of industry</p> <p><strong>**VARIABLES**</strong></p> <p>CLASS : the name given is the one of the class of industry that appears in the original source (in French).</p> <p>SUB-CLASS : the name given is the one of the sub-class of industry that appears in the original source (in French).</p> <p>TYPE : Patent (brevets) or certificate of addition</p> <p>YEAR : year of issue<br> <br> NUMBER : the number of patents and certificates of addition granted.</p> <p> </p> <p><strong>**SOURCES**</strong><br> <em>La propriété industrielle : organe officiel du Bureau international de l'Union pour la protection de la propriété industrielle</em></p> <p>year 1880 to 1884 : 1885/12, p. 13 ; year 1885 : 1886/8, p. 62 ; year 1886 : 1887/9, p. 72 ; year 1887 : 1889/5, p. 73 ; year 1888 : 1889/6, p. 90 ; year 1889 : 1890/07, p. 86 ;</p> <p>year 1890 : 1891/08, p. 110 ; year 1891 : 1892/08, p. 134 ; year 1892 : 1893/07, p. 100 ; year 1893 : 1894/08, p. 102 ; year 1894 : 1896/05, p. 81; year 1895 : 1897/02, p. 32; year 1896 : 1898/06, p. 100; year 1897 : 1899/01, p. 16 ; year 1898 : 1900/03, p. 55 ; year 1899 : 1900/08, p. 140 ;</p> <p>year 1900 : 1902/02, p. 32 ; year 1901 : 1903/03, p. 52 ; year 1902 : 1904/04, p. 72 ; year 1903 : 1906/03, p. 48.</p>
InHARD - Industrial Human Action Recognition Dataset in the Context of Industrial Collaborative Robotics
<p><strong>Objectives</strong></p> <p>We introduce a RGB+S dataset named “Industrial Human Action Recognition Dataset” (InHARD) from a real-world setting for industrial human action recognition with over 2 million frames, collected from 16 distinct subjects. This dataset contains 13 different industrial action classes and over 4800 action samples. The introduction of this dataset should allow us the study and development of various learning techniques for the task of human actions analysis inside industrial environments involving human robot collaborations.<br> Read <strong>00-README.txt</strong> for detailed download instructions.</p> <p>More details on the dataset at <a href="https://github.com/vhavard/InHARD">https://github.com/vhavard/InHARD</a></p> <p>This work has been performed at the CESI LINEACT : <a href="https://recherche.cesi.fr/inhard-industrial-human-action-recognition-dataset/">https://recherche.cesi.fr/inhard-industrial-human-action-recognition-dataset/</a></p>
Characterization of Industrial Smoke Plumes from Remote Sensing Data
<p><strong>Characterization of Industrial Smoke Plumes from Remote Sensing Data</strong><br> </p> <p>This data set contains imaging data acquired by ESA's <a href="https://earth.esa.int/web/sentinel/missions/sentinel-2">Sentinel-2 Earth-observing satellite constellation</a> for a sample of industrial sites that were picked based on emission information provided by the <a href="https://www.eea.europa.eu/data-and-maps/data/industrial-reporting-under-the-industrial">European Pollutant Release and Transfer Register</a>. The images contain scenes of mainly industrial sites, some of which are actively emitting smoke plumes.</p> <p>This data set was created to investigate whether it would be possible to train a deep learning model to automatically identify and segment smoke plumes from remote sensing image data. Please refer to the acknowledgements section for more on information on this project.</p> <p><br> <strong>Description</strong></p> <p>Each image is provided in the GeoTIFF file format, contains a total of 13 bands and georeferencing information, and has a shape of 120 x 120 pixels (corresponding to a square area with an edge length of 1.2 km on the ground). The bands are extracted from Sentinel-2 Level-2A products, except for band 10, which has been extracted from the<br> corresponding Level-1C product (this band has not been utilized in the underlying work).</p> <p>This repository contains a total of 21,350 images. Based on manual annotation, the image sample was split into a sample of 3,750 <em>positive</em> images that contain industrial smoke plumes, and 17,600 <em>negative</em> images that do not contain smoke plumes. Furthermore, this repository contains a collection of JSON files that hold manual segmentation labels for smoke plumes present in 1,437 images. Segmentation labels were generated using <a href="http://https://labelstud.io/">label-studio</a>. Please note that polygon edge coordinates have to be scaled by a factor of 1.2 to fit the images.</p> <p><br> <strong>Content</strong></p> <p>The following tarballs are contained in this repository:</p> <ul> <li>README.md - this file</li> <li>images.tar.gz [6.0GB] - contains 21,350 GeoTIFF images</li> <li>segmentation_labels.tar.gz [350KB] - contains 1,437 JSON files</li> </ul> <p><strong>Acknowledgement</strong></p> <p>If you use this data set, please cite our publication:</p> <p><em>Mommert, M., Sigel, M., Neuhausler, M., Scheibenreif, L., Borth, D., "Characterization of Industrial Smoke Plumes from Remote Sensing Data", Tackling Climate Change with Machine Learning workshop at NeurIPS 2020.</em></p> <p>Please refer to this publication for additional information on the data set.</p> <p>The code used for this publication is available at <a href="https://github.com/HSG-AIML/IndustrialSmokePlumeDetection">github</a>.</p> <p>This data set contains modified Copernicus Sentinel data acquired in 2019, processed by ESA.</p> <p> </p> <p><strong>Responsible Author</strong></p> <p>Michael Mommert<br> University of St. Gallen, Institute of Computer Science<br> Chair Artificial Intelligence and Machine Learning<br> michael.mommert ( at ) unisg.ch</p>
Raw data for analyzing higher educational support potential for space industry
<p>The data contain campus recruitment data of China Aerospace Science and Technology Corporation (CASC) and China Aerospace Science and Industry Corporation (CASIC) and 41 Chinese elite universities’ enrollment of bachelor, master, and doctor degree by discipline. The data also contain the numbers of space industry related professional organizations, publication and, patent of these universities. All of these data were openly collected online.</p>
Replication package for "An Industrial Study on the Challenges and Effects of Diversity-based Testing in Continuous Integration"
<p>This is the replication package for the analysis done in the paper "An Industrial Study on the Challenges and Effects of Diversity-based Testing in Continuous Integration".</p> <p>The package includes: (i) CSV files with data on test case and corresponding feature coverage, as well as test execution data; (ii) CSV files including failure coverage for the executed techniques; (iii) R scripts to re-run our visual and statistical analysis when comparing techniques results; and (iv) An R Markdown file (rendered into HTML) detailing the steps of our analysis with the coresponding code.</p>
Industrial Electronics 4.0: Is It Going to Be Free and Open?
<p>The presentation addresses phenomena of free software and open hardware and it is based on twelve years of the author's experience in using free software, ten years of teaching a course that covers free software tools used in electrical engineering, about five years of experience with open hardware, and four years of teaching a reformed course in electrical measurements, which involves extensive use of free software. Teaching experiences are presented in the paper, plans for further improvements in existing courses, as well as some ideas about new courses that would cover free software and open hardware topics not covered already. Actual software tools are discussed. Also, the use of free software, open hardware, open data, and open culture in general, in research practice is presented, where open access articles and open public repositories are new means for disseminating scientific information supporting reproducibility and verifiability of the results, extending the information exchange beyond the paper form. In embedded systems, the method of building upon finds free software a useful tool, besides security and privacy advantages. Promising ongoing projects, like open instruction set architectures, are also discussed. In the time when Industry 4.0 and the fourth industrial revolution are discussed, it seems that open models of software, scientific, and intellectual production are unexpected winners, regardless indirect supporting business models. According to author's experience, free software is mature enough and offers numerous advantages to be a basic tool in education, and open culture practices require modification of curricula to address the situation already present.</p>
Data from: Estimated six percent loss of genetic variation in wild populations since the industrial revolution
Genetic variation is fundamental to population fitness and adaptation to environmental change. Human activities are driving declines in many wild populations and could have similar effects on genetic variation. Despite the importance of estimating such declines, no global estimate of the magnitude of ongoing genetic variation loss has been conducted across species. By combining studies that quantified recent changes in genetic variation across a mean of 27 generations for 91 species, we conservatively estimate a 5.4-6.5% decline in within-population genetic diversity of wild organisms since the industrial revolution. This loss has been most severe for island species, which show a 30% average decline. We identified taxonomic and geographic gaps in temporal studies that must be urgently addressed. Our results are consistent with single time-point meta-analyses, which indicated that genetic variation is likely declining. However, our results represent the first confirmation of a global decline, and provide an estimate of the magnitude of the genetic variation lost from wild populations.
Data from: Expansion of industrial plantations continues to threaten Malayan tiger habitat
Southeast Asia has some of the highest deforestation rates globally, with Malaysia being identified as a deforestation hotspot. The Malayan tiger, a critically endangered subspecies of the tiger endemic to Peninsular Malaysia, is threatened by habitat loss and fragmentation. In this study, we estimate the natural forest loss and conversion to plantations in Peninsular Malaysia and specifically in its tiger habitat between 1988 and 2012 using the Landsat data archive. We estimate a total loss of 1.35 Mha of natural forest area within Peninsular Malaysia over the entire study period, with 0.83 Mha lost within the tiger habitat. Nearly half (48%) of the natural forest loss area represents conversion to tree plantations. The annual area of new plantation establishment from natural forest conversion increased from 20 thousand ha year−1 during 1988–2000 to 34 thousand ha year−1 during 2001–2012. Large-scale industrial plantations, primarily those of oil palm, as well as recently cleared land, constitute 80% of forest converted to plantations since 1988. We conclude that industrial plantation expansion has been a persistent threat to natural forests within the Malayan tiger habitat. Expanding oil palm plantations dominate forest conversions while those for rubber are an emerging threat.
Data from: Onshore industrial wind turbine locations for the United States up to March 2014
Wind energy is a rapidly growing form of renewable energy in the United States. While summary information on the total amounts of installed capacity are available by state, a free, centralized, national, turbine-level, geospatial dataset useful for scientific research, land and resource management, and other uses did not exist. Available in multiple formats and in a web application, these public domain data provide industrial-scale onshore wind turbine locations in the United States up to March 2014, corresponding facility information, and turbine technical specifications. Wind turbine records have been collected and compiled from various public sources, digitized or position verified from aerial imagery, and quality assured and quality controlled. Technical specifications for turbines were assigned based on the wind turbine make and model as described in public literature. In some cases, turbines were not seen in imagery or turbine information did not exist or was difficult to obtain. Uncertainty associated with these is recorded in a confidence rating.
Data from: Agreements between industry and academia on publication rights: a retrospective study of protocols and publications of randomized clinical trials
Background: Little is known about publication agreements between industry and academic investigators in trial protocols and the consistency of these agreements with corresponding statements in publications. We aimed to investigate (i) the existence and types of publication agreements in trial protocols, (ii) the completeness and consistency of the reporting of these agreements in subsequent publications, and (iii) the frequency of co-authorship by industry employees. Methods and Findings: We used a retrospective cohort of randomized clinical trials (RCTs) based on archived protocols approved by six research ethics committees between 13 January 2000 and 25 November 2003. Only RCTs with industry involvement were eligible. We investigated the documentation of publication agreements in RCT protocols and statements in corresponding journal publications. Of 647 eligible RCT protocols, 456 (70.5%) mentioned an agreement regarding publication of results. Of these 456, 393 (86.2%) documented an industry partner's right to disapprove or at least review proposed manuscripts; 39 (8.6%) agreements were without constraints of publication. The remaining 24 (5.3%) protocols referred to separate agreement documents not accessible to us. Of those 432 protocols with an accessible publication agreement, 268 (62.0%) trials were published. Most agreements documented in the protocol were not reported in the subsequent publication (197/268 [73.5%]). Of 71 agreements reported in publications, 52 (73.2%) were concordant with those documented in the protocol. In 14 of 37 (37.8%) publications in which statements suggested unrestricted publication rights, at least one co-author was an industry employee. In 25 protocol-publication pairs, author statements in publications suggested no constraints, but 18 corresponding protocols documented restricting agreements. Conclusions: Publication agreements constraining academic authors' independence are common. Journal articles seldom report on publication agreements, and, if they do, statements can be discrepant with the trial protocol.
The spatiotemporal evolution and influencing factors of hotel industry in the metropolitan area: an empirical study based on China
Through the online booking platform, 10,543 big data of spatial and temporal distribution of Beijing hotel industry has been obtained in this paper. Then, the methods of GIS and the geographical detector are used to study the spatiotemporal evolution process and the influencing factors of Beijing hotel industry during 2003-2018. The results are as follows: a. During the period of 2003-2018, the hotel industry in Beijing maintained a high growth rate and had three growth peaks in 2008, 2010 and 2014. Meanwhile, major historical events, such as the Olympic Games had a significant influence on the development of the hotel industry. b. Between 2003 and 2018, the hotel industry in Beijing gradually developed from the centripetal agglomeration to aggregation + diffusion, and also from the single center to the multi-center. Besides, various hotels presented two characteristics of city orientation and scenic orientation. c. The natural geographical environment had shaped the overall pattern and characteristics of the spatial distribution of the hotel industry in Beijing, and the socio-economic factors such as commercial activities, public facilities, tourism services and traffic conditions significantly influenced the location selection of the hotel industry. Therefore, the urban center is the ideal area for the spatial layout of the hotel industry. d. Geographical detector research showed that the factors, such as administrative organs, road network density, leisure and recreational facilities, and companies have strong explanatory power for hotel location selection, which is an important reference index for hotels to select the micro location. This paper is a beneficial supplement to the existing research and has certain guiding significance for the sustainable development of Beijing hotel industry.
Industrial Kind Lettering
Inspired in the Industrial Revolution Sigs over the buildings Source: Objaverse 1.0 / Sketchfab
The Future of Bitcoin: A Quadrillion-Dollar Industry?
<h3>Research on Bitcoin's Potential Future Valuation</h3> <p><strong>Title: The Future of Bitcoin: A Quadrillion-Dollar Industry?</strong></p> <p>This research paper investigates the potential for Bitcoin (BTC) to reach a quadrillion-dollar market cap, with individual BTC prices surpassing $48,000,000. The study examines historical data, market trends, technological advancements, macroeconomic influences, and expert opinions to understand the factors driving such projections.</p> <p>Key highlights include:</p> <ol> <li> <p><strong>Historical Data and Market Trends</strong>:</p> <ul> <li>Bitcoin's price evolution from $0.0041 in 2009 to $75,830 in 2024, marked by significant events such as the Genesis block, Silk Road shutdown, and Tesla's investment.</li> <li>The dataset provides annual high and low prices, alongside major milestones.</li> </ul> </li> <li> <p><strong>Technological Advancements</strong>:</p> <ul> <li>Developments like the Lightning Network and Taproot upgrade enhance transaction speed and efficiency, boosting adoption.</li> </ul> </li> <li> <p><strong>Macroeconomic Influences</strong>:</p> <ul> <li>Bitcoin's role as a hedge against inflation and its fixed supply contrast with fiat currencies.</li> <li>Growth predictions based on M0, M1, and M2 money supply comparisons.</li> </ul> </li> <li> <p><strong>Expert Opinions and Future Projections</strong>:</p> <ul> <li>Insights from Marion Laboure of Deutsche Bank and predictions from CoinCheckup indicate a bullish trend, with potential prices reaching $173,818.51 in a year.</li> </ul> </li> </ol> <p><strong>Conclusion</strong>: The combination of technological progress, economic factors, and growing adoption points to significant growth potential for Bitcoin. This research offers valuable insights for investors and policymakers.</p> <p><strong>References</strong>:</p> <ul> <li>Investopedia, TradingView, CoinCheckup, Deutsche Bank Research, 99bitcoins, Bitcoin Magazine, Forbes India.</li> </ul>
Supplementary Data for "Exploring the Integration of Large Language Models in Industrial Test Maintenance Processes"
<p>This package contains supplementary data not directly included in the paper, including per-commit results for each prototype and the prompts used in the proof-of-concept implementations.</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.