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830 results for “INDUSTRY”
Research trends around the sustainability of the food industry: A Bibliometric Approach and Research Agenda
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ANALYSIS OF the risk factors of MUSCULOSKELETAL DISORDERS: A dataset on most challenging operations in the garments industry in Bangladesh and reported occupational discomforts by the workers.
<p>This dataset comprises the perceived most challenging operations in the garment industry and the occupational discomfort faced by the workers. Collected through participant surveys, the dataset encompasses demographic information and work related parameters.</p><p>The primary objective of this dataset is to identify and analyse the prevalence of MSDs in the most challenging operation in the garment industry. Researchers, ergonomists, and occupational health professionals can leverage this dataset to explore further for mitigating MSD risks in the industry.</p><p>This dataset can be used for finding the Ergonomic interventions and workplace improvements. It can facilitate specific interventions for solving work related diseases among the garment workers.</p><p>Anyone can use this dataset with proper citation.</p><p>Note: This dataset adheres to ethical considerations, and participant information is anonymized to ensure confidentiality.</p>
Subset of instances used in article "On solving the 1.5-dimensional cutting stock problem with heterogeneous slitting lines allocation in the steel industry"
<p>The dataset presented is part of the one used in the article "On solving the 1.5-dimensional cutting stock problem with heterogeneous slitting lines allocation in the steel industry" by María Sierra-Paradinas, Óscar Soto-Sánchez, Antonio Alonso-Ayuso, F. Javier Martín-Campo and Micael Gallego, in Computers & Industrial Engineering (2024), doi: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.cie.2024.110120" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.cie.2024.110120</span></a>.</p> <p>This paper proposes a mathematical optimisation model for a cutting stock problem in the steel industry. This problem appears in a Spanish company and the proposed model has been tested on real orders received by the company.</p> <p>The dataset presented here includes twelve instances used in the paper (the rest cannot be presented for confidentiality reasons). For each instance, the characteristics of the order and the solution obtained by the model are provided.</p>
A dataset for RSSI based outdoor localization using LoRaWAN in a harbor as a harsh and industrial environment
<p>Enabling precise device localization is a critical requirement for the future of industry. Leveraging signal features for location determination has emerged as a leading approach and good alternative for Global Navigation Satellite Systems (GNSS) because of their limitations (low accuracy for indoor environments, expensive chips, and high energy consumption). On this basis, to provide localization for IoT in an industry with a harsh environment, the adopted wireless networks should have a long range coverage area. LoRaWAN is one of the most common communication networks that can provide large coverage with low power consumption and low implementation cost. Between various signal features that can be used for localization, Received Signal Strength (RSS) received more attention because of their low-cost deployment. But, RSS is highly dependent and sensitive to environmental changes, such as temperature, humidity, and background noise. This sensitivity becomes more intensive in an industrial environment with a harsh and dynamic environment. In order to evaluate the environmental effects on RSS in the harsh and highly dynamic industry, we present a comprehensive repository of LoRaWAN Received Signal Strength Indicator (RSSI) measurements, collected in a harbor as a testbed featuring three LoRaWAN gateways and one mobile end node. During the data collecting process, the mobile device obtains its location via a GPS and transmits it as the LoRaWAN message. In addition, to provide more insight of the effect of dynamic environment on the RSSI, two end nodes are implemented in fixed locations. These end nodes transmit messages with fixed time intervals including their unique id. The collected dataset includes RSSI and SNR measurements recorded by multiple gateways for each transmitted packet by fixed or mobile end nodes, and timestamp. This dataset enables the development and evaluation of RSSI-based localization and allows researchers to explore the challenges and opportunities associated with localization in dynamic IoT deployments.</p>
Extra Material for the paper: Experiences from conducting rapid reviews in collaboration with practitioners --- Two industrial cases
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Supplementary Material for the paper entitled The maternity challenges in the software industry and academia: a survey with mothers from the Software Engineering field
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LoDoInd: A Benchmark Low-dose Industrial CT Dataset - 3 of 3
<h2>Summary</h2> <p>This dataset accompanies the paper "LoDoInd: Introducing A Benchmark Low-dose Industrial CT Dataset and Enhancing Denoising with 2.5D Deep Learning Techniques". We are releasing the dataset with five different dose levels, including a reference set. All datasets are pre-registered, making them immediately suitable for deep learning applications in industrial CT.</p> <h2>Description</h2> <p>The uploaded content includes reconstructed images for noise levels 5 and reference. Each level comprises 4000 slices, with each slice being 1250x1250 pixels. Due to the 50 GB space limitation per submission on Zenodo, the rest of the dataset is available through separate links listed below:</p> <ul> <li>Noise1 and Noise2 <a href="../records/10356955" target="_blank" rel="noopener">https://zenodo.org/records/10356955</a></li> <li>Noise3 and Noise4 <a href="../records/10391277" target="_blank" rel="noopener">https://zenodo.org/records/10391277</a></li> <li>Noise5 and Reference (this one) <a href="../records/10391412" target="_blank" rel="noopener">https://zenodo.org/records/10391412</a></li> </ul> <p>The scanning parameters for all noise levels are summarized in the table below:</p> <table> <tbody> <tr> <td> </td> <td>Averaged Projs</td> <td>Exposure Time/ms</td> <td>Scan Time/min</td> <td>Voltage/kV</td> <td>Current/uA</td> </tr> <tr> <td>Reference</td> <td>6</td> <td>333</td> <td>59.3</td> <td>140</td> <td>180</td> </tr> <tr> <td>Noise Level 1</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>180</td> </tr> <tr> <td>Noise Level 2</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>90</td> </tr> <tr> <td>Noise Level 3</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>45</td> </tr> <tr> <td>Noise Level 4</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>23</td> </tr> <tr> <td>Noise Level 5</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>12</td> </tr> </tbody> </table> <h2>Additional link</h2> <p>The code for supervised learning-based denoising is available <a href="https://github.com/jiayangshi/LoDoInd">code</a> .</p> <h2>Acknowledgment</h2> <p>This research was co-financed by the European Union H2020-MSCA-ITN-2020 under grant agreement no. 956172 (xCTing). </p>
LoDoInd: A Benchmark Low-dose Industrial CT Dataset - 2 of 3
<h2>Summary</h2> <p>This dataset accompanies the paper "LoDoInd: Introducing A Benchmark Low-dose Industrial CT Dataset and Enhancing Denoising with 2.5D Deep Learning Techniques". We are releasing the dataset with five different dose levels, including a reference set. All datasets are pre-registered, making them immediately suitable for deep learning applications in industrial CT.</p> <h2>Description</h2> <p>The uploaded content includes reconstructed images for noise levels 3 and 4. Each level comprises 4000 slices, with each slice being 1250x1250 pixels. Due to the 50 GB space limitation per submission on Zenodo, the rest of the dataset is available through separate links listed below:</p> <ul> <li>Noise1 and Noise2 <a href="../records/10356955" target="_blank" rel="noopener">https://zenodo.org/records/10356955</a></li> <li>Noise3 and Noise4 (this one) <a href="../records/10391277" target="_blank" rel="noopener">https://zenodo.org/records/10391277</a></li> <li>Noise5 and Reference <a href="../records/10391412" target="_blank" rel="noopener">https://zenodo.org/records/10391412</a></li> </ul> <p>The scanning parameters for all noise levels are summarized in the table below:</p> <table> <tbody> <tr> <td> </td> <td>Averaged Projs</td> <td>Exposure Time/ms</td> <td>Scan Time/min</td> <td>Voltage/kV</td> <td>Current/uA</td> </tr> <tr> <td>Reference</td> <td>6</td> <td>333</td> <td>59.3</td> <td>140</td> <td>180</td> </tr> <tr> <td>Noise Level 1</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>180</td> </tr> <tr> <td>Noise Level 2</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>90</td> </tr> <tr> <td>Noise Level 3</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>45</td> </tr> <tr> <td>Noise Level 4</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>23</td> </tr> <tr> <td>Noise Level 5</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>12</td> </tr> </tbody> </table> <h2>Additional link</h2> <p>The code for supervised learning-based denoising is available <a href="https://github.com/jiayangshi/LoDoInd">code</a> .</p> <h2>Acknowledgment</h2> <p>This research was co-financed by the European Union H2020-MSCA-ITN-2020 under grant agreement no. 956172 (xCTing). </p>
Tracking sustainable Industry 4.0 for listed companies - DataSet
<p>The DataSet contains a set of data that informs the analyses conducted for the Tracking sustainable Industry 4.0 for listed companies study. <span>This study aims to develop a replicable methodology for diagnosing sustainable Industry 4.0 status of publicly listed companies. Employing quantitative content analysis through MAXQDA software, the authors utilized a predefined keyword set and categories with lemmatization on both annual reports and Internet news. The analysis involved companies listed on the Warsaw Stock Exchange, representing about two-thirds of the total market capitalization across various sectors. Inconsistencies in communication and Internet news were observed, influenced by company characteristics. The methodology effectively addressed research questions on the incorporation of Industry 4.0 and sustainability in company reports and online communications. However, a limitation is the absence of a qualitative perspective to elucidate underlying phenomena for potential correlations. Despite this, the methodology provides valuable signals for policymakers, aiding in positioning economies globally on Industry 4.0 and sustainability. The research demonstrates high potential for replicability and traceability, applicable to other indices for sample selection, sector analyses, market exploration, and facilitating year-to-year comparisons.</span></p>
Wadkin Bursgreen Industrial Sander BGY3
Industrial Abrasive Machine (sander) by Wadkin Bursgreen, model BGY3. This machine is currently installed in the 'Green Room' of the Technical Services workshop at The Glasgow School of Art. Model created from 68 photographs, processed in Autodesk Recap Photo with 80% decimation. Source: Objaverse 1.0 / Sketchfab
Climate footprint of industry-sponsored clinical research: An analysis of a phase-1 randomized clinical study and discussion of opportunities to reduce its impact
<p>Objective: To calculate the global warming potential, in carbon dioxide (CO<sub>2</sub>) equivalent emissions, from a phase-1 clinical study Design: Retrospective analysis. Data source: Internal data held by Janssen Pharmaceuticals Studies included: Janssen-sponsored TMC114FD1HTX1002 study conducted between 2019-2021 Main outcome: measure CO<sub>2</sub> equivalents for trial activities calculated according to IPCC 2021 impact assessment methodology Results: The CO2-eq emissions generated by the trial was 17.65 tonnes. This is equivalent to the emissions generated by driving the average petrol-fueled family car 71,004km or roughly 1.8 times around the circumference of the Earth. Commuting to the clinical site by the study patients generated the most emissions (5,419kg, 31% of overall emissions), followed by trial site utilities (2,725kg, 16% of overall emissions), and Janssen site staff travel (2,560kg, 15% of overall emissions). In total, the movement of people (patient travel, Janssen site staff travel, and trial site staff travel) accounted for 8,914kg or 51% of overall trial emissions. Conclusions: Opportunities exist to reduce many of the largest contributors to the clinical trial's CO<sub>2</sub>-eq emissions. The largest contributor was patient travel (31%) and combined with sponsor (15%) and site staff (5%) travel, the movement of people was responsible for 51% of CO<sub>2</sub>-eq emissions. Decentralized trial models which seek to bring clinical trial operations closer to the patient offer opportunities to reduce patient travel. The electrification of sponsor vehicle fleets and society's transition towards electric vehicles may result in further reductions.</p>
1922 Film Industry Trade Press Corpus
<div> <p>For the first half of the twentieth century, no American industry boasted a more motley and prolific trade press than the movie business—a cutthroat landscape that set the stage for battle by ink. In 1930, Martin Quigley, publisher of <em>Exhibitors Herald</em>, conspired with Hollywood studios to eliminate all competing trade papers, yet this attempt and each one thereafter collapsed. Exploring the communities of exhibitors and creative workers that constituted key subscribers, <em>Ink-Stained Hollywood</em> tells the story of how a heterogeneous trade press triumphed by appealing to the foundational aspects of industry culture—taste, vanity, partisanship, and exclusivity. In captivating detail, Eric Hoyt chronicles the histories of well-known trade papers (<em>Variety, Motion Picture Herald</em>) alongside important yet forgotten publications (<em>Film Spectator</em>, <em>Film Mercury</em>, and <em>Camera!</em>), and challenges the canon of film periodicals, offering new interpretative frameworks for understanding print journalism's relationship with the motion picture industry and its continued impact on creative industries today.</p> <p>We selected the year 1922, with an emphasis on July 1922, for two chief reasons. First, the MHDL had already digitized a wide cross-section of trade papers from that year, including—appropriately for this book—several published outside of the United States. Second, we knew from Eric's earlier research that there was a great deal of competition within the American film industry's trade press during this period.</p> <p>In 1922, <em>Variety</em> and the Chicago-based <em>Exhibitors Herald</em>, were pursuing strategies to grow their readership and influence within the industry, emphasizing independence, integrity, and uniqueness as distinguishing factors. During the following year, <em>Exhibitors Herald</em> created the '"Herald Only' Club"—emphasizing the loyalty of subscribers who exclusively wrote into <em>Exhibitors Herald</em> and read the paper, to the exclusion of its rivals (Hartman, Rea). Given the competitive bent of the 1920s trade press, how distinct was each publication? Would the "'Herald Only' Club" have any factual grounding once the word patterns, sentences, and page structures were analyzed at scale?</p> <p>In addition to the above-mentioned trade papers, we included 16 additional unique journals. Our corpus included fan magazines (<em>Photoplay</em>, <em>Shadowland</em>,<em> </em>and <em>The Picturegoer</em>), a technical journal (<em>American Cinematographer</em>), English language trade papers published outside the U.S. (<em>Canadian Moving Picture Digest </em>and <em>The Film Renter and Moving Picture News</em>), and studio generated publicity (<em>Universal Weekly</em> and <em>Paramount Pep</em>).</p> <p>This dataset is comprised of the scans of the trade papers we analyzed, as well as a zip archive of the code we used to do the similarity analysis.</p> </div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <p>C.M. Hartman, qtd. in "'Herald Only' Club Gains Six; Veteran and Newcomer Give Reasons for Joining," <em>Exhibitors Herald</em>, March 29, 1924, 63, <a href="http://lantern.mediahist.org/catalog/exhibitorsherald18exhi_0_0073">http://lantern.mediahist.org/catalog/exhibitorsherald18exhi_0_0073</a></p> <p>George Rea letter to Exhibitors Herald, <em>Exhibitors Herald</em>, May 26, 1923, 69, <a href="http://lantern.mediahist.org/catalog/exhibitorsherald16exhi_0_0869">http://lantern.mediahist.org/catalog/exhibitorsherald16exhi_0_0869</a>.</p> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Influence of Higher Education Performance on Strategic Emerging Industries: A Path Analysis in China
<p>Raw data for article titled "Influence of Higher Education Performance on Strategic Emerging Industries: A Path Analysis in China". </p>
Types of competencies and skills for Industry 4.0
<p><strong>Types of competencies and skills for Industry 4.0</strong></p>
Dinslaken Industrie Apotheke
<p>Historical questionnaire/s 1924/1948 and index cards, partly selected enclosures regarding the history of a <br>German pharmacy, catalogued via Kalliope portal (Historischer Fragebogen 1924/1948 und Karteikarten, ggf. <br>gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient das Nachlassportal Kalliope): <br>https://kalliope-verbund.info/DE-611-BF-70963<br>[Funktion: Im Findbuch anzeigen]<br>Please note: The Kalliope catalogue entry might indicate related material in the archival folder which cannot <br>be published due to copyright or other legal restrictions (NB: Das Katalogisat bei Kalliope kann auch auf <br>Materialien - teils erheblichen Umfangs - verweisen, die aus archiv- oder urheberrechtlichen Gründen nicht <br>veröffentlicht werden dürfen).</p>
DEVELOPMENT OF THE AGRO-INDUSTRIAL COMPLEX OF RUSSIA AT THE PRESENT STAGE
<p><span>The article shows the development of agriculture in Russia at the present stage. It is agriculture that today is steadily one of the fastest growing sectors of the Russian economy. The production of certain products has been demonstrating historical records for a number of years, which has allowed Russia to become a prominent supplier to world markets. The growth of the agricultural sector has been facilitated by both natural factors (geographical location, large areas of agricultural land, vast water resources), investment inflows and improved management. The introduction of sanctions and government measures aimed at import substitution also contributed to the development of agricultural production in the country. This trend coincided with a significant increase in demand for basic foodstuffs due to population growth, primarily urban population growth, climate change, etc. It should be noted that the growth in demand contributes to the growth of long-term risks of instability in the world markets. </span></p>
Decarbonization pathways promote improvements in cement quality and reduce the environmental impact of China's cement industry
<p>The data show the results of the papre "Decarbonization pathways promote improvements in cement quality and reduce the environmental impact of China’s cement industry".</p>
Code and input data related to "Integrated decarbonization of hard-to-abate industry utilizing biomass reliefs burden on power sector"
<p>Input data and code for the submitted article: "Integrated decarbonization of hard-to-abate industry utilizing biomass reliefs burden on power sector" <br><br>by Alissa Ganter <sup>1,†</sup>, Paula Baumann <sup>1,2,†</sup>, Veis Karbassi <sup>2</sup>, Giovanni Sansavini <sup>1,*</sup></p> <p><sup>1 </sup>Reliability and Risk Engineering, Institute of Process and Energy Engineering, ETH Zurich, Leonhardstrasse 21, 8092 Zurich, Switzerland</p> <p><sup>2 </sup>School of Business and Economics, RWTH Aachen University, Kackertstraße 7, 52072 Aachen, Germany</p> <p><sup>† </sup>These authors contributed equally</p> <p><sup>*</sup>Corresponding author: sansavig@ethz.ch</p>
Industrial application of Multi-Robot Partial Destructive Disassembly Line Balancing for Multi-Product Scenarios
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Gelsenkirchen Industrie Apotheke
<p>Historical questionnaire/s 1924/1948 and index cards, partly selected enclosures regarding the history of a German pharmacy, catalogued via Kalliope portal (Historischer Fragebogen 1924/1948 und Karteikarten, ggf. gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient das Nachlassportal Kalliope):</p> <p>https://kalliope-verbund.info/ead?ead.id=DE-611-BF-70963</p> <p>Please note: The Kalliope catalogue entry might indicate related material in the archival folder which cannot be published due to copyright or other legal restrictions (NB: Das Katalogisat bei Kalliope kann auch auf Materialien - teils erheblichen Umfangs - verweisen, die aus archiv- oder urheberrechtlichen Gründen nicht veröffentlicht werden dürfen).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.