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830 results for “Industrialization”
Data included in "Sediments near industrial ports can be hotspots of fossil carbon accumulation"
<p>Dual carbon isotope ratios of sedimentary organic carbon (SOC) from the three ports (GG, PO, and PN) in South Korea.</p>
Designing industry 4.0 implementation from the initial background and context of companies
<p>Industry 4.0 is a promising concept that allows industries to meet customers' demands with flexible and resilient processes, and highly personalised products. This concept is made up of different dimensions. For a long time, innovative digital technology has been thought of as the only dimension to succeed in digital transformation projects. Next, other dimensions have been identified such as organisation, strategy, and human resources as being key while rolling out digital technology in factories. From these findings, researchers have designed industry 4.0 theoretical models and then, built readiness models that allow for analysing the gap between the company initial situation and the theoretical model. Nevertheless, this purely deductive approach does not take into consideration a company's background and context, and eventually favours one single digital transformation model. This article aims at analysing four actual digital transformation projects and demonstrating that the digital transformation's success or failure depends on the combination of two variables related to a company's background and context. This research is based on a double approach: deductive and inductive. First, a literature review has been carried out to define industry 4.0 concept and its main dimensions and digital transformation success factors, as well as barriers, have been investigated. Second, a qualitative survey has been designed to study in-depth four actual industry digital transformation projects, their genesis as well as their execution, to analyse the key variables in succeeding or failing. 46 semi-structured interviews were carried out with projects' members. The interviews have been analysed with thematic content analysis. Then, each digital transformation project has been modelled regarding the key variables and analysed with regards to succeeding or failing. Investigated projects have consolidated the models of digital transformation. Finally, nine digital transformation models have been identified. Industry practitioners could design their digital transformation project organisation and strategy according to the right model.</p>
The effect of ethical leadership on organizational outcomes in the hospitality industry: the mediating role of trust and emotional exhaustion
<p>SPSS dataset for the paper: "The effect of ethical leadership on organizational outcomes in the hospitality industry: the mediating role of trust and emotional exhaustion".</p>
Operational data from industrial automation demonstrator EnAS
<p><strong>General description</strong></p> <p>The dataset consists of a data stream of operational data from the industrial automation demonstrator EnAS, installed in the Aalto Factory of the Future laboratory. The data includes for every register a timestamp and operational data, which consists of the state of digital sensors and actuators, as well as markers captured manually to annotate the detection of an error state in the system by the operator. </p> <p>Each row represents a snapshot of all the operational signals in the machine. The data was reported and logged by exception, i.e. a new register was logged only when a single digital signal changed its state. The data was collected without a polling period. The registers represent the natural execution time. </p> <p>The dataset was recorded between 23rd September 2020 and 2nd February 2021 by Gadidjah Ögmundsdóttir as part of her MSc thesis project. It has a total of 219893 registers.</p> <p> </p> <p><strong>Equipment</strong> </p> <p>The machine uses wireless controllers. The automated process was programmed following the IEC61499 standard in NxT Studio (software from NXT control). </p> <p>Image 1.jpg presents a plant view of the system and the component names. Image 2.jpg presents a perspective view of some components not included in Image 1. The component names appear on the column headers of the dataset.</p> <p> </p> <p><strong>Data collection process</strong></p> <p>The data was collected using a python script running a TCP IP server instance through the socket library. One controller from the system collected and concatenated all the signals and sent them to the TCP IP server, where the messages were logged in the CSV file. </p> <p>Data was collected during September and October in 2020, and January and February in 2021.</p> <p>In October 2020, the conveyor belt on section 6 broke, and was replaced by a new one in December.</p> <p>The data was typically collected on a day-to-day basis. The first recordings in October have some noise due to the mechanical adjustments on the position of sensors.</p> <p> </p> <p><strong>Process</strong> </p> <p>The solution is used to demonstrate the delivery system for small cylindrical workpieces coming in two possible colors, green or red. The pieces travel through a piece holder running over a closed loop serial conveyor system.</p> <p>There are two production variants resulting in either a correct or incorrect piece color. </p> <p>The process resulting in an incorrect piece color requires swapping the workpieces using both jacks. In contrast, the process resulting in a correct piece color, only includes handling the workpiece with the jack 2.</p> <p>Every production sequence starts moving the piece from C3 and ends in C3.</p> <p> </p> <p><strong>EnAS dataset description</strong></p> <p>Based on the equipment description, the data consists of the following columns:</p> <ul> <li>Timestamp: [YYYY-MM-DD hh:mm:ss]: Timestamp of logged data point </li> <li>C1: [0,1], a value of 0 indicates that the motor 1 is off while 1 indicates it is on</li> <li>LS1: [0,1], a value of 0 indicates that the sensor 2 is off while 1 indicates it is on</li> <li>C2: [0,1], a value of 0 indicates that the motor 2 is off while 1 indicates it is on</li> <li>LS2: [0,1], a value of 0 indicates that the sensor 2 is off while 1 indicates it is on</li> <li>C3: [0,1], a value of 0 indicates that the motor 3 is off while 1 indicates it is on</li> <li>LS3: [0,1], a value of 0 indicates that the sensor 3 is off while 1 indicates it is on</li> <li>C4: [0,1], a value of 0 indicates that the motor 4 is off while 1 indicates it is on</li> <li>LS4: [0,1], a value of 0 indicates that the sensor 4 is off while 1 indicates it is on</li> <li>C5: [0,1], a value of 0 indicates that the motor 5 is off while 1 indicates it is on</li> <li>LS5: [0,1], a value of 0 indicates that the sensor 5 is off while 1 indicates it is on</li> <li>C6: [0,1], a value of 0 indicates that the motor 6 is off while 1 indicates it is on</li> <li>LS6: [0,1], a value of 0 indicates that the sensor 6 is off while 1 indicates it is on</li> <li>TS1: [0,1], a value of 0 indicates that the Jack 1 Top sensor is off while 1 indicates it is on</li> <li>BS1: [0,1], a value of 0 indicates that the Jack 1 Bottom sensor is off while 1 indicates it is on</li> <li>ES1: [0,1], a value of 0 indicates that the Jack 1 Extended sensor is off while 1 indicates it is on</li> <li>RS1: [0,1], a value of 0 indicates that the Jack 1 Retracted sensor is off while 1 indicates it is on</li> <li>EA1: [0,1], a value of 0 indicates that the Jack 1 Extend actuator is off while 1 indicates it is on</li> <li>BA1: [0,1], a value of 0 indicates that the Jack 1 Bottom actuator is off while 1 indicates it is on</li> <li>VA1: [0,1], a value of 0 indicates that the Jack 1 gripper vacuum is off while 1 indicates it is on</li> <li>S1: [0,1], a value of 0 indicates that the sledge on Jack 1 shift is off while 1 indicates it is on</li> <li>TS2: [0,1], a value of 0 indicates that the Jack 2 Top sensor is off while 1 indicates it is on</li> <li>BS2: [0,1], a value of 0 indicates that the Jack 2 Bottom sensor is off while 1 indicates it is on</li> <li>ES2: [0,1], a value of 0 indicates that the Jack 2 Extended sensor is off while 1 indicates it is on</li> <li>RS2: [0,1], a value of 0 indicates that the Jack 2 Retracted sensor is off while 1 indicates it is on</li> <li>EA2: [0,1], a value of 0 indicates that the Jack 2 Extend actuator is off while 1 indicates it is on</li> <li>BA2: [0,1], a value of 0 indicates that the Jack 2 Bottom actuator is off while 1 indicates it is on</li> <li>VA2: [0,1], a value of 0 indicates that the Jack 2 gripper vacuum is off while 1 indicates it is on</li> <li>S2: [0,1], a value of 0 indicates that the sledge on Jack 2 shift is off while 1 indicates it is on</li> <li>ME: [0,1], 1 indicates that a motion error occurred, most likely connected to the conveyor mechanism</li> <li>HE: [0,1], 1 indicates that a handling error occurred, most likely connected to the jacks</li> <li>UE: [0,1], 1 indicates that an unknown error occurred</li> <li>PV: [1,2], 1 indicates production variant 1 which results in a correct workpiece color, and 2 indicates production variant 2 which results in an incorrect workpiece color</li> </ul>
Data and R files for the analysis of the innovative capacity and the network position of national manufacturing industries in world production
<p>Data and R files for the reproducibility of the results obtained in Kim and Ozaygen, Analysis of the innovative capacity and the network position of national manufacturing industries in world production.</p> <p>It also includes an R/Shiny application which runs at <a href="https://awekim.shinyapps.io/Manuf_shiny_R/">https://awekim.shinyapps.io/Manuf_shiny_R/ </a></p>
Supplementary dataset and tables for "Carbon-negative production of acetone and isopropanol by gas fermentation at industrial pilot scale"
<p><strong>Supplementary dataset and tables for "Carbon-negative production of acetone and isopropanol by gas fermentation at industrial pilot scale"</strong></p> <p><strong>Supplementary Dataset and Tables</strong> listing identified acetone biosynthesis genes from mining of the DJ collection and gene and part sequences used for combinatorial library and cell-free prototyping (<strong>Supplementary Tab. 1</strong>), combinatorial library combinations and results (<strong>Supplementary Tab. 2</strong>), gene KO predictions (<strong>Supplementary Tab. 3</strong>), cell-free prototyping combinations and results (<strong>Supplementary Tab. 4</strong>), proteomics results for wild-type strain plus acetone biosynthesis plasmid (<strong>Supplementary Tab. 5</strong>), proteomics results for strain Δ<em>0553</em> plus select combinatorial library plasmids (<strong>Supplementary Tab. 6</strong>), Genbank accession numbers for 272 genomes (<strong>Supplementary Tab. 7</strong>). sequences of oligonucleotides (<strong>Supplementary Tab. 8</strong>) and emission factors used to calculate the GHG emissions of acetone (<strong>Supplementary Tab. 9</strong>) and IPA (<strong>Supplementary Tab. 10</strong>) in LCA.</p>
Wood industry projects dataset
<p>Wood industry projects dataset (Horizon 2020)</p>
Example data and scripts for the paper: Team maturity and reorganization during a very large-scale agile transformation in the automotive industry
<p>Example data and scripts for the paper: Team maturity and reorganization during a very large-scale agile transformation in the automotive industry. </p>
DevOps: is there a gap between Education and Industry?
<p>This is the dataset obtained from a survey with 99 respondents about the teaching or use of DevOps technological practices in Higher-Educatoin or Industry, respectively.</p> <p>Questions are tabbled in the first line, and results of this study are published in the following Reference:</p>
Data: Survey of Implemented Mitigation Strategies and Further Needs of the United States Food Industry to Control COVID-19 in the Work Environment in Early 2021
<p>Data set related with the COVID-19 needs assessment study</p>
Benchmark Dataset (2D/3D) of an Industrial Rotary Kiln Combustion Chamber with Refuse-Derived Fuel Particles from a Light-Field-Camera
<p>Benchmark dataset for the detection of fuel particles (refuse-derived fuels - RDF) in 2D and 3D image data in a rotary kiln combustion chamber.</p> <p>Organization:<br> 01_Images: 50 Images (2D)<br> 02_Labels: Labeled ground truth image with rotary kiln, burner flame, burning particle in air, non-burning particle in air and particle on wall.<br> 03_Particle List: Lists of the coordinates of the center of gravity of particles in image coordinates.<br> 04_Point Cloud: 3D point cloud for the 50 images.<br> 05_Matlab: Code and visualization examples.<br> 06_All_Data: Images and 3D point cloud for 2010 images of a sequence containing the images with ground truth (see TXT file).</p>
Supporting Data for: Resource requirements for ecosystem conservation: A combined industrial and natural ecology approach to quantifying natural capital use in nature
<p>Data used to derive allometric equations for land area use by mammals, birds, reptiles, and insects, and data for the analysis of natural resource use at the Natural Capital Laboratory site.</p>
Study Data: Semi-Automated Prioritization of Industrial Security Findings in Agile Software Development
<p>Dataset for the study of the paper "Semi-Automated Prioritization of Industrial Security Findings in Agile Software Development"</p>
A database of Chemical Science institutions and industries in Kerala
<p>We developed this database to identify sampling units (entities related to Chemical Science education and career in Kerala) for the study 'Women’s Career Pathway in Chemical Sciences; a Multi-stage Investigation in Kerala' using two approaches:</p> <ol> <li> <p>Using search engines and visiting official databases of government departments and institutional websites.</p> </li> <li> <p>By asking faculties, researchers and students in the Chemical Science field to supplement the list generated by the first approach.</p> </li> </ol> <p>We hope that this database will be useful for researchers in the field and students who wish to pursue their careers in Chemical Sciences.</p> <p><em>All authors contributed equally to this work.</em></p> <p><em>This research is supported by the <a href="https://www.rsc.org/prizes-funding/funding/inclusion-diversity-fund/">Royal Society of Chemistry Inclusion and Diversity Fund</a>, 2020</em></p>
Intelligentization and regional industrial competitiveness
<p><span><span><span><span>Intelligentization</span></span><span>-oriented development is</span><span> a fast-developing trend of technological revolution. It </span><span><span>promot</span></span><span>es </span><span>the reconstruction of the industrial system of a region and affects its overall industrial competitiveness. </span></span><span>We collected data of 28 provinces and cities in China from 2003 to 2017 to test the influence of industrial </span><span><span>intelligentization</span></span><span> level on the industrial competitiveness of a region. </span></span></p> <p><span><span><span><span>Compared with the existing literature, the innovation of this study lies in three aspects</span></span><span><span>: </span></span></span><span>(1) </span><span><span>We </span><span>focuse on China, a developing country with the fastest development of intelligent technology in the world, to discuss the relationship between </span></span><span><span>intelligentization</span></span><span> <span>and industrial competitiveness in developing countries.</span></span><span> <span>(2) T</span></span><span><span>he research on intelligent technology mainly focuses on the micro level</span></span><span><span>, for instance</span></span><span> <span>the change of the internal structure of the industry, which belongs to the category of industrial economics. The research perspective of this study is macro level, taking regions as the research object, focusing on the change of industrial competitiveness between regions in the era of </span></span><span><span>intelligentization</span></span><span><span>, which belongs to the category of economic geography.</span></span><span> <span>(3) </span></span><span><span>According to the actual situation of the development of intelligent technology in China, we improve and construct the index system to evaluate the level of intelligentization. At the same time, the ecological niche theory is introduced for the first time in the process of evaluating industrial competitiveness</span></span><span> <span>of each region</span></span><span><span>.</span></span></span></p> <p><span><span>The result </span><span><span>reveals</span></span><span> that: </span><span><span>1) In </span></span><span>China's </span><span>provincial</span><span><span> </span></span><span><span>j</span></span><span>urisdictions</span><span><span>, the higher </span></span><span><span>the</span></span><span> </span><span><span>level of </span></span><span><span>intelligentization</span></span><span> is, </span><span><span>the lower </span></span><span>the </span><span><span>overall level of industrial competitiveness and the lower </span></span><span><span>the</span></span><span> </span><span><span>proportion of industry in the economic system</span></span><span> will be.</span><span> </span><span>In regions where the facilities are highly intelligent</span><span><span>ialized</span></span><span>,</span><span> </span><span>the </span><span><span>production sectors</span></span><span> tend to move</span><span> <span>to the less developed regions, </span></span><span>and the</span><span><span> </span></span><span><span>growth effect of technological dividends</span></span><span> is the focus</span><span><span>. 2) Compared with the </span></span><span>middle </span><span><span>region</span></span><span> and the Western </span><span><span>region</span></span><span> of China</span><span><span>, the Eastern region</span></span><span>, which is more developed</span><span><span> </span></span><span><span>with higher </span></span><span><span>intelligentization</span><span><span> </span></span></span><span><span>level</span></span><span>,</span><span><span> </span></span><span><span>ha</span></span><span>s</span><span><span> </span></span><span><span>stronger </span></span><span>ability in the research and development</span><span><span> </span></span><span><span>(</span></span><span>R&D</span><span><span>)</span></span><span> of </span><span><span>technolog</span></span><span>ies</span><span><span>, and the economic structure of the industry</span></span><span> there </span><span><span>tends to be stable</span></span><span>, manifesting </span><span><span>a strong growth</span></span><span> </span><span><span>potential.</span></span></span></p>
Design and development remote monitoring agent for energy service companies in the telecommunication industry
<p>Data set downloaded from the remote monitoring agent (RMA) over its local webpage using WiFi connection.</p>
Dataset - Literature on service robots in the hospitality industry
<p>List of fifty-nine articles retrieved from Web of Science, Google Scholar, and Scopus databases upon search inquiries with "robot," "hotel," and "hospitality" keywords. Data collection between October 2021 and March 2022. Figures of analysis in three clusters: robot*-customer relationship, robot-employee relationship, and robot-firm relationship. </p>
Dataset and code for "Modelling the potential for peat-block transplants to restore industrially contaminated Sphagnum peatlands"
<p>Code to run simulations and dataset from HYDRUS-1D simulations used in the revised manuscript titled "Modelling the potential for peat-block transplants to restore industrially contaminated Sphagnum peatlands" submitted to Ecological Engineering</p>
Data for The genetics monopolistic industry, as expected, is missing information two inches beyond their nose: in this case, transcripts
<p><strong>De novo transcriptome assembly is one of the many fundamental pieces of new research in genomics. It is, for example, the preferred method for studying non-model organisms, since it is easier and cheaper than building a genome, and reference methods are not possible without an existing genome. The transcriptomes of these organisms can thus reveal novel proteins and their isoforms that are implicated in such unique biological phenomena. This technique is also useful in cancer research as it makes possible to detect potentially significant chimeric transcripts in cancer and normal somatic tissues.</strong></p> <p><strong>Given that the genetics industry is organized in the form of a monopoly controlled by hidden lobbies who also control Academia, all the available software for de novo transcriptome assembly is being developed by academic researchers under the open-source paradigm. We report here, that as anyone could have very easily deduced from past experiences in other industries, this unethical form of organization in the industry is resulting in incompetence whose effects include missing a significant portion of the available information that could be obtained from some genomics studies. In this case, missing transcripts in transcriptome studies. We won't deep in on the consequences, but these could include overpricing, over costs, and failing to achieve the goals of some studies.</strong></p>
Data for: Industrial energy development decouples ungulate migration from the green wave
<p>The ability to freely move across the landscape to track the emergence of nutritious spring green-up (termed "green-wave surfing"), is key to the foraging strategy of migratory ungulates. Across the vast landscapes traversed by many migratory herds, habitats are being altered by development with unknown consequences for surfing. Using a unique long-term tracking dataset, we found that when energy development occurs within mule deer (<em>Odocoileus hemionus</em>) migration corridors, migrating animals become decoupled from the green wave. During the early phases of a coalbed natural gas development, deer synchronized their movements with peak green-up. But faced with increasing disturbance as development expanded, deer altered their movements by holding up at the edge of the gas field and letting the green wave pass them by. Development often modified only a small portion of the migration corridor but had far-reaching effects on behavior before and after migrating deer encountered it, thus reducing surfing along the entire route by 38.65% over the 14-year study period. Our study suggests that industrial development within migratory corridors can change the behavior of migrating ungulates and diminish the benefits of migration. Such disruptions to migratory behavior present a common mechanism whereby corridors become unprofitable and could ultimately be lost on highly developed landscapes.</p>
ScienceDex guides
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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.