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830 results for “INDUSTRY”
FTIR-Plastics: a Fourier Transform Infrared Spectroscopy dataset for the six most prevalent industrial plastic polymers.
<p><span><span>Two datasets are presented: FTIR-Plastics-C4 and FTIR-Plastics-C8, comprising 6,000 spectra obtained through Fourier Transform Infrared Spectroscopy (FTIR) applied to the six most used synthetic polymers: Polyethylene Terephthalate (PET), High-Density Polyethylene (HDPE), Polyvinyl Chloride (PVC), Low-Density Polyethylene (LDPE), Polypropylene (PP), and Polystyrene (PS). The key feature of the datasets lies in the FTIR analysis, which reports the percentage transmittance as the intensity measure as a function of the wavelength of an Infrared light source, expressed as wavenumber (with units in cm</span></span><sup><span><span>-1</span></span></sup><span><span>). FTIR analysis was performed using a Jasco FTIR PRO 4x spectrophotometer with a wavenumber resolution setting of 8 cm</span></span><sup><span><span>-1</span></span></sup><span><span> for FTIR-Plastics-C8 and 4 cm</span></span><sup><span><span>-1</span></span></sup><span><span> for FTIR-Plastics-C4, both employing a configuration of 32 scans and a range from 4000 to 400 cm</span></span><sup><span><span>-1</span></span></sup><span><span>. The datasets are presented in CSV (comma-separated values) format, including the following information (per each column):</span></span></p> <ul> <li> <p><span><span><strong>IDE</strong></span></span><span><span>: unique identifier of the sample.</span></span></p> </li> <li> <p><span><span><strong>Polymer: </strong></span></span><span><span>type of synthetic polymer (PET, HDPE, PVC, LDPE, PP, or PS).</span></span></p> </li> <li> <p><span><span><strong>Technique: </strong></span></span><span><span>Type of technique used (FTIR).</span></span></p> </li> <li> <p><span><span><strong>Sample: </strong></span></span><span><span>polymer sample number.</span></span></p> </li> <li> <p><span><span><strong>BR</strong></span></span><span><span>: scanning configuration (32).</span></span></p> </li> <li> <p><span><span><strong>RST</strong></span></span><span><span>: resolution configuration (8 or 4 cm</span></span><sup><span><span>-1</span></span></sup><span><span>).</span></span></p> </li> <li> <p><span><span><strong>Data (x) y Data(y): </strong></span></span><span><span>1884 pairs of columns for FTIR-Plastics-C8 and 3751 pairs of columns for FTIR-Plastics-C4, representing values on the "x" axis (wavenumber) and the "y" axis values associated with molecular vibration intensities, indicating the transmittance (%), which differentiates each polymer.</span></span></p> </li> </ul> <p><span><span>Additionally, the files generated by the Jasco spectrophotometer for each polymer are provided, which were standardized by adding a header with the following structure:</span></span></p> <ul> <li> <p><span><span>TITLE SAMPLE NAME: referring to the name of the analyzed polymer.</span></span></p> </li> <li> <p><span><span>DATA TYPE: specifying the characterization technique.</span></span></p> </li> <li> <p><span><span>MEASUREMENT INFORMATION: equipment used for data collection.</span></span></p> </li> <li> <p><span><span>MODEL NAME: name of the equipment used.</span></span></p> </li> <li> <p><span><span>SERIAL No: serial number assigned to the equipment used.</span></span></p> </li> <li> <p><span><span>ACCESSORY: complementary device integrated into the equipment.</span></span></p> </li> <li> <p><span><span>LIGHT SOURCE: standardized light source related to the DLATGS detector.</span></span></p> </li> <li> <p><span><span>RESOLUTION: parameters are used to distinguish the wavenumber in the analyzed materials.</span></span></p> </li> <li> <p><span><span>XUNIT/HORIZONTAL AXIS: referring to the unit’s title assigned on the x-axis.</span></span></p> </li> <li> <p><span><span>YUNITS/VERTICAL AXIS: referring to the unit’s title designated on the y-axis.</span></span></p> </li> <li> <p><span><span>FIRSTX: initial value set for the x-axis.</span></span></p> </li> <li> <p><span><span>FIRSTY: initial value set for the y-axis.</span></span></p> </li> <li> <p><span><span>LASTX: final value set for the x-axis.</span></span></p> </li> <li> <p><span><span>LASTY: final value set for the y-axis.</span></span></p> </li> <li> <p><span><span>NPOINTS: total data points in the file.</span></span></p> </li> </ul> <p><span><span>Data collection was carried out meticulously, following specific steps to ensure the accuracy and reliability of the results. The calibration certificates issued by the supplier (calibration_certificate.pdf) corresponding to the equipment used in the experiments and data collection that give rise to these databases are attached.</span></span></p>
Influence of Green Human Resource Practices on Environment Sustainability of Cement Industry in Pakistan
<p>Global warming aligns firms green human resource management (HRM) practices, categories with environmental sustainability. Researchers believes that environment friendly practices, employees’ empowerment, awareness towards green environment promotes firms’ businesses. Aim of the current study is analysis of the GHRM practices on environment sustainability of cement industry in Pakistan. The first hypothesis was Green recruitment and selection practice have significant influence on environmental sustainability of cement industry in Pakistan. The second hypothesis was green performance management practice have significant influence on environmental sustainability of cement industry in Pakistan. There were two independent variables (green recruitment and selection and green performance management and appraisal) one dependent variable environment sustainability were tested. SPSS and2nd generation statistical software Smart PLS 3.2.9 were used for the measurement model and structural model. Both hypotheses show significance level i.e., green recruitment and selection GRaS>ES (β = .235, t = 2.385, p < .003) with f2 of .035 and green performance appraisal GPA>ES (β = .258, t = 3.345, p < .002) with f2 of .025 were significant. The current study suggests that employees’ recruitments and appraisals in private sector should be performed on the GHRM practices to achieve the environment sustainable goals.</p>
Machine Learning Tools for Peptide Bioactivity Evaluation Implications for Cell Culture Media Optimization and the Broader Cultivated Meat Industry
Open the record for dataset details and reuse information.
Annexes from the paper A Hybrid Methodology for the Facility Re-Layout Problem (FRLP) of Food Production Systems in Industry 5.0 Context
<p>In the attached dataset we present all the data collected and obtained in the case study of our model to improve the redistribution of food production plants. The importance of the attached data lies in the fact that they consist of real data collected in a small snack production plant located in the northern part of Ecuador, and were used to validate the methodology proposed in the article entitled: "A Hybrid Methodology for the Facility Re-Layout Problem (FRLP) of Food Production Systems in Industry 5.0 Context", which is currently in the publication stage.</p>
Output data from the optimization of an industrial waste heat recovery system
<p>Output data from the multi objective optimization of a ORC-District Heating recovery system for industrial waste heat exploitation</p> <p> </p>
SAT Competition 2007 Industrial Track Benchmarks
<p>These are the 234 CNF files in DIMACS format from the industrial track of the SAT competition 2007.</p> <p> </p>
Distributed SAT Competition 2002 Industrial Track Benchmarks
<p>These are the 183 CNF files in DIMACS format from the industrial track of the SAT competition 2002 which were distributed after the competition.</p>
Distributed SAT Competition 2004 Industrial Track Benchmarks
<p>These are the 324 CNF files in DIMACS format from the industrial track of the SAT competition 2004 which were distributed after the competition.</p> <p> </p>
Distributed SAT Competition 2005 Industrial Track Benchmarks
<p>These are 176 CNF files in DIMACS format from the industrial track of the SAT competition 2005 which were distributed after the competition. Some benchmarks were only available on webpages of submitters. See also <a href="../records/6528885">https://zenodo.org/records/6528885</a></p> <p> </p>
Distributed SAT Competition 2003 Industrial Track Benchmarks
<p>These are the 100 CNF files in DIMACS format from the industrial track of the SAT competition 2003 which were distributed after the competition.</p>
PROPOSITION D'UN CADRE REFERENTIEL D'EVALUATION DU NIVEAU DE MATURITE LOGISTIQUE POUR LES ENTREPRISES DE LA XYLO-INDUSTRIE DE LA SOUS-REGION CEMAC
<p><strong><u><span>RESUME </span></u></strong></p> <p><span>Notre communication s’intéresse à l’évaluation du niveau de maturité logistique dans les entreprises de la xylo-industrie implantée dans les pays de la sous-région CEMAC en contexte d’intensification de la stratégie d’industrialisation de cette filière. Ayant constaté l’absence d’un outil ou de cadre d’évaluation adaptée à ce contexte, il est question dans cette recherche de proposer aux dirigeants des entreprises de la sous-région CEMAC, un cadre référentiel adapté à la xylo-industrie qui pourrait les aider à évaluer en permanence la fonction logistique en vue d’une amélioration continue gage de compétitivité sur le marché international des bois tropicaux débités. </span></p> <p><strong><em><span> </span></em></strong></p> <p><strong><em><span>Mots-clés</span></em></strong><em><span> : Logistique ; Supply Chain Management ; PME ; xylo-industrie ; CEMAC. </span></em></p>
Determination of Optimal Thermal Comfort Conditions in the Iron Smelting Industry Work Environment Using Firefly Algorithm
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
IS Development Strategy in the Leather Bag Craft Industry: A SWOT Analysis Perspective
<p>This material has presented on 2nd International Conference on Advance Research in Social and Economic Science in October 25, 2023.</p>
Growth Dynamics and System Models for the Restaurant Industry: Data and Analysis from Taiwanese Chains
<p><span>This dataset includes raw and curated data, system dynamics models, and feedback loop diagrams used in the study of growth dynamics in the restaurant industry, focusing on Taiwanese chains. The data supports the findings presented in the paper "The Growth Dynamics of the Restaurant Industry from Single Store to Chain Store in Taiwan: A Systems Thinking Perspective.”</span></p>
IMAD-DS: A Dataset for Industrial Multi-Sensor Anomaly Detection Under Domain Shift Conditions
<p>IMAD-DS is a dataset developed for multi-rate multi-sensor anomaly detection (AD) in industrial environments, that considers varying operational and environmental conditions known as domain shifts.</p> <p><strong>Dataset Overview:</strong></p> <p>This dataset includes data from two scaled industrial machines: a robotic arm and a brushless motor.</p> <p>It includes both normal and abnormal data recorded under various operating conditions to account for domain shifts. These shifts are categorized into:</p> <p>Robotic Arm: The robotic arm is a scaled version of a robotic arm used to move silicon wafers in a factory. Anomalies are created by removing bolts at the nodes of the arm, resulting in an imbalance in the machine.<br>Brushless Motor: The brushless motor is a scaled representation of an industrial brushless motor. Two anomalies are introduced: first, a magnet is moved closer to the motor load, causing oscillations by interacting with two symmetrical magnets on the load; second, a belt that rotates in unison with the motor shaft is tightened, creating mechanical stress.</p> <p>The following domain shifts are included in the dataset:</p> <p>Operational Domain Shifts: Variations caused by changes in machine conditions (e.g., load changes for the robotic arm and speed changes for the brushless motor).</p> <p>Environmental Domain Shifts: Variations due to changes in background noise levels.</p> <p>Combinations of operating and environmental conditions divide each machine's dataset into two subsets: the <em>source domain</em> and the <em>target domain</em>. The source domain has a large number of training examples. The target domain, instead, has limited training data. This discrepancy highlights a common issue in the industry where sufficient training data is often unavailable for the target domain, as machine data is collected under controlled environments that do not fully represent the deployment environments.</p> <p> </p> <p><strong>Data Collection and Processing:</strong></p> <p>Data is collected using the STEVAL-STWINBX1 IoT Sensor Industrial Node. The sensor used to record the dataset are the following.</p> <p>· Analog Microphone (16 kHz)</p> <p>· 3-axis Accelerometer (6.7 kHz)</p> <p>· 3-axis Gyroscope (6.7 kHz)</p> <p>Recordings are conducted in an anechoic chamber to control acoustic conditions precisely</p> <p><strong>Data Format:</strong><strong><br></strong>Files are already divided into train and test sets. Inside each folder, each sensor's data is stored in a separate '.parquet' file.</p> <p>Sensor files related to the <em>same</em> segment of machine data share a unique ID. The mapping of each machine data segment to the sensor files is given in .csv files inside the train and test folders. Those .csv files also contain metadata denoting the operational and environmental conditions of a specific segment.</p> <p> </p> <p> </p> <p> </p>
IVD-SEG:Standardized Datasets for Industrial Vision Defect Segmentation
<h1> </h1> <h1>Code[<a href="https://github.com/KLIVIS/IVD-SEG">Github</a>]</h1> <h1>abstract</h1> <p>We introduce IVD-SEG, a dataset encompassing defect images from 43 different industrial products, totaling 5686 images, spanning tasks that include binary and multiclass segmentation. Within IVD-SEG, we meticulously propose 12 sub-datasets, including two newly developed datasets by our team. Serving as a large-scale standardized industrial defect image dataset, all images are unified to a 256 × 256 size, accompanied by semantic segmentation annotations. This standardization facilitates users unfamiliar with industrial product defects to utilize the dataset, allowing them to focus on exploring algorithmic performance on the IVD-SEG dataset. To our knowledge, the dataset we propose is currently the most comprehensive and voluminous compilation of industrial defect images, thereby contributing to the advancement of relevant research in industrial image analysis. Simultaneously, our dataset supports research and education in various fields, including computer vision and machine learning. We conducted benchmark tests on IVD-SEG using several baseline methods, including representative CNN and ViT networks.<br><br></p>
Modeling Languages for Digital Twins - A Survey Among the German Automotive Industry
<p>This repository contains the replication package for the paper _Modeling Languages for Digital Twins: A Survey Among the German Automotive Industry_ by Jérôme Pfeiffer, Dominik Fuchß, Thomas Kühn, Robin Liebhart, Dirk Neumann, Christer Neimöck, Christian Seiler, Anne Koziolek, and Andreas Wortmann. <br>The paper has been submitted to the practice track of [MODELS 2024](https://conf.researchr.org/track/models-2024/models-2024-technical-track#Practice-Track).</p> <h3>Data</h3> <p>This replication package contains all information from the survey:<br>- `results.csv`: A csv version of all data exported from LimeSurvey (German). Personal information from the participants has been removed. This file can be imported to reproduce the extraction results described in our paper.<br>- `survey_german.pdf`: The pdf version of the original survey in German. <br>- `survey_german.md`: A markdown version of the original survey in German. <br>- `survey_english.md`: A markdown version of the survey translated into English. </p> <h3>Selection of participants and distribution</h3> <p>With both versions, the survey can be executed again with a different target audience in English or German. In our case we wanted to reach as much participants from diverse work areas as possible, where we invited the participants by email via an internal mailing list of 189 members of the SofDCar project. To improve the response rate, we implemented two deadline extensions from the initial one-month-long time frame with 2 weeks of additional response time. Together with the deadline extension, we sent a mail to inform and remind the members of the consortium of the survey.</p> <h3>Data extraction</h3> <p>In total, we had 96 participants, of which 43 completed the questionnaire. For incomplete survey responses, we took only the available answers and did not include the missing answers in our data analysis. For data analysis we utilized the commercial Tool IBM SPSS and custom python scripts.</p> <h2>Research Questions </h2> <p>- RQ1: How is the DT understood in the automotive industry?<br> - RQ1.1: For which phases of automotive development are DTs<br>important?<br> - RQ1.2: What are desired properties of DTs?<br> - RQ1.3: What are desired purposes of using DTs?<br> - RQ1.4: How do these purposes change in relation to different phases of automotive development?<br>- RQ2: Which modeling languages and modeling tools are currently employed in the automotive industry?<br> - RQ2.1: Which kinds of models are important during automotive development?<br> - RQ2.2: How important are which models in the phases of automotive development?<br> - RQ2.3: Which tools are used to create and maintain these models?</p>
Dataset for Publication: Industrially Relevant Conditions in Lab-Scale Analysis for Alkaline Water Electrolysis
<p>The provided data contains the calculations and plots of the manuscript 'Industrially Relevant Conditions in Lab-Scale Analysis for Alkaline Water Electrolysis'. All experimental procedures and an in-depths analysis can be found <a href="https://chemistry-europe.onlinelibrary.wiley.com/doi/full/10.1002/celc.202300432">there</a>. (DOI: <a href="https://doi.org/10.1002/celc.202300432">10.1002/celc.202300432 )</a>. The data is available in .opju files (origin plots), .xlsx files (excel sheets for calculations) and in the .csv format. </p>
Industrially relevant characterisation of a Ni mesh anode in alkaline water electrolysis
<p>A dataset on the characterisation of a 1 cm² Ni mesh anode in alkaline water electrolysis is provided herein. Setup wise, a three electrode beaker cell setup was used and industrially relevant conditions (<em>e.g.</em> 80°C & 30 wt.% KOH) were applied. Further details on the setup can be found in a previous <a href="https://doi.org/10.1002/celc.202300432">publication.</a></p>
Material Suplementar - Is secure software development education necessary in the software industry? Answers from professionals of a technology hub in Brazil
<p><span>Context: The education and training of information security professionals is essential to ensure the protection of data and systems, as well as the privacy and security of sensitive information. Problem: This work aims to explore the context of a local technology hub to answer the following research question: Is secure software development education necessary in the software industry? Solution: To answer this question, an exploratory study was performed to understand the need for secure software development education from the point of view of software practitioners in a technology hub. Method: A questionnaire was prepared and sent to professionals of a Brazilian technology hub. Answers were analyzed by using qualitative research methods. Results: We obtained thirty eight answers. According to the results obtained, the majority of participants consider information security education important for the development of secure software. However, there is still a lack of information security education. Contributions: It is concluded that companies should invest more in adequate and comprehensive training on the topic, in addition to encouraging and rewarding professionals who prioritize software security in their projects. It is essential to disseminate a culture of information security throughout the organization, from senior management to development professionals, to make everyone aware of the importance of information security and their responsibility in maintaining it. Finally, it should be noted that developers have a crucial role in ensuring software security, being responsible for seeking knowledge and improving their skills in secure development through training, reading and practice</span><span>. (Paper accepted in the Brazilian Symposium of Software Engineering)</span></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.