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830 results for “Industrialization”
MADFORWATER: WP2: Adaptation of wastewater treatment technologies for agricultural reuse: Task2.4: Industrial wastewater treatment: Treatment of different types of wastewater by means of innovative resins: Subset1
<p>This dataset contains the data underlying the following publication: Li Wen-Tao, Cao Meng-Jie, Young Tessora, Ruffino Barbara, Dodd Michael, Li Ai-Min, Korshin Gregory. (2017).</p> <p>Application of UV absorbance and fluorescence indicators to assess the formation of biodegradable dissolved organic carbon and bromate during ozonation. Water Research 2017, 111, 154-162. <a href="http://dx.doi.org/10.1016/j.watres.2017.01.009">http://dx.doi.org/10.1016/j.watres.2017.01.009</a>.</p>
MADFORWATER: WP2: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task 2.3–Agro-industrial wastewater treatment: Subtask 2.3.1. –Treatment of olive mill wastewater (OMWW): Aerobic biological treatment in sequenced batch reactors (SBRs): Subset 1
<p>This dataset contains the data underlying the following publication: Fatma AROUS, Chadlia HAMDI, Souhir KMIHA, Nadia KHAMMASSI, Amani AYARI, Mohamed NEIFAR, Tahar MECHICHI, Atef JAOUANI (2018). Treatment of olive mill wastewater through employing sequencing batch reactor: Performance and microbial diversity assessment. 3 Biotech 2018, 8, 481. https://doi.org/10.1007/s13205-018-1486-6.</p>
The effect of particle board industry waste tar on the physical and biological durability of wood
<p>This manuscript investigated the effect of waste tar from particle board factories on some physical and biological resistance properties of Scots pine (<em>Pinus sylvestris </em>L.) and beech (<em>Fagus orientalis </em>L.) woods. Solutions were prepared by dissolving waste tar in ethanol:toluene (1v:1v) in concentrations of 5%, 10%, 15% and 20% and treated under vacuum and pressure. In addition, surface coating (SC) was applied by spreading 96% waste tar on the wood surfaces after treatment. Deep-treated and surface-coated (DT+SC) wood samples were exposed to the wood-decay fungi (<em>Corillous versicolor</em> L. and <em>Neolentinus lepideus</em> Fr.) and wood destroying house borer (<em>Hylotrupes bajulus </em>L.) larvaes. Total phenolic content, water uptake, water-repellent efficiency and surface contact angle were tested. The highest mean weight loss (45.23%) was found in the beech wood control samples exposed to <em>C. versicolor</em>. Beech samples deep-treated with a 20% concentration and surface treatment (DT+SC) yielded a mass loss of 14.03%. The <em>H. bajulus</em> larvae mortality rate was found to be 80% in the Scots pine wood samples deep-treated with 20% waste tar. The deep treatment with the waste tar solution significantly increased the surface contact angle values of the Scots pine wood, thereby significantly reducing the wettability of the wood compared to the untreated control samples. </p>
A taxonomy for improving industry-academia communication in IoT vulnerability management. Additional Material
<p>Research interview and Workshop Protocol from the paper "A taxonomy for improving industry-academia communication in IoT vulnerability management".</p>
Measurement and identification of the joint stiffness on a serial articulated industrial robot
<p>This document exemplifies elastostatic compliance calibration on an articulated industrial robot, which has been calibrated at KTH Royal Institute of Technology in 2019 using procedure outlined in the CWA-17384. All data processing is done in Matlab 2018b® using the Peter Corke’s as well as Computer Vision System toolbox for robotics. In case of questions do not hesitate to send an e-mail to <a href="mailto:theissen@kth.se">theissen@kth.se</a> to obtain data and algorithms in c++ or other formats.</p>
Augmented Reality Enhancing the Inspections of Transportation Infrastructure: Research, Education, and Industry Implementation
<p>Corresponding data set for Tran-SET Project No. 18STUNM03. Abstract of the final report is stated below for reference:</p> <p>"Transportation infrastructure needs continuous monitoring that is conducted by field inspectors regularly in the field. Currently, infrastructure inspectors climb, measure, and photograph structures annually to inform repair needs and prioritize decisions. In order to promote and accelerate early learner's expertise in decision-making capabilities during infrastructure inspections, this research project developed various software applications using augmented reality (AR) as an inspection tool for bridges and bridge management, more specifically. By objectively quantifying infrastructure field inspections, inspectors can make more accurate field assessments and managers can make better-informed decisions. This project collaborated with stakeholders, national laboratories, DOT agencies such as NCHRP and NMDOT, and local owners like the City of Albuquerque, to inform the needs of AR for field inspections. The results of this study summarized the current limitations of visual inspections from the perspective of the various owners, as well as pilot developments of AR applications and their benchmarked accuracy in comparison with visual methods. The education and training aspect of this project included teaching and exposing AR to high school students, community college students, undergraduate students, and graduate students, as well as industry (bridge inspectors). This research project’s outcome includes a webinar free to access in the NCHRP national website on this topic. The conclusion of this research is that AR can be an effective tool and that industry is interested in specific programming of AR software that matches their bridge management needs."</p>
Mapped Industrial Tree Plantations (ITP) in Caraga Region, Mindanao, Philippines for the Year 2019
<p>The Esri Shapefile, in UTM 51 WGS 1984 coordinate reference system, contains polygons of industrial tree plantation species (Falcata, Bagras, Yemane, and Mangium) in the Caraga Region, Mindanao, Philippines. The plantations were mapped through the classification of year 2019 Sentinel-2 satellite images, complemented by high-resolution satellite images available in Google Earth, as well as field surveys conducted between October 1, 2019 to September 30, 2021.</p> <p>Please refer to the Project 1 terminal report (<a href="../records/13735736" target="_blank" rel="noopener">https://zenodo.org/records/13735736</a>) for more details.</p>
Survey on Industry 4.0 implementation within 5 companies in Slovakia
<p>Dataset containing results of a survey related to Industry 4.0 implementation and usage distributed within employees of 5 selected companies in Slovakia</p>
Base de datos espacial del patrimonio industrial inmueble de la Eurociudad del Guadiana (municipios de Ayamonte, Vila Real de Santo Antonio y Castro Marim)
<div>1. Dataset language:</div> <div>Spanish</div> <div> </div> <div> </div> <div>2. Abstract:</div> <div>Estos archivos proporcionan las capas vectoriales de los registros de patrimonio industrial inmueble de la Eurociudad del Guadiana recopilados en el año 2022 y 2023 por la autora. Se ha utilizao como fuentes primarias los documentos de registros de fábricas y industrials del Archivo Distrital de Faro, del Archivo provincial de Huelva y del Archivo Municipal de Ayamonte. También se ha incorpoado datos de fuentes secundarias del Archivo de Vila Real de Santo Antonio. Asimismo, gran parte de los datos fueron recopilados mediante trabajo de campo. </div> <div> </div> <div> </div> <div>These files provide the vectorial layers of the records of the immovable industrial heritage of the Eurocity of Guadiana compiled in 2022 and 2023 by the author. We have used as primary sources the factory and industrial records documents from the District Archive of Faro, the Provincial Archive of Huelva and the Municipal Archive of Ayamonte. We have also incorporated data from secondary sources from the Archive of Vila Real de Santo Antonio. In addition, much of the data was collected through fieldwork.</div> <div> </div> <div> </div> <div> </div> <div>3. Keywords: </div> <div>Base de datos espacial; patrimonio industrial; Eurociudad del Guadiana; patrimonio cultural; fábricas; conserveras; Ayamonte; Vila Real de Santo Antonio; Castro Marim. </div> <div> </div> <div>Spatial database; industrial heritage; Eurocity of Guadiana; cultural heritage; factories; canneries; Ayamonte; Vila Real de Santo Antonio; Castro Marim. </div> <div> </div> <div> </div> <div>4. Date of data collection (fecha única o rango de fechas):</div> <div>20-05-2022 al 01-02-2023</div> <div> </div> <div>5. Publication Date:</div> <div>[ Obligatorio si es aplicable. Fecha de depósito en el repositorio | Formato DD-MM-YYYY] </div> <div> </div> <div> </div> <div>6. Grant information:</div> <div> </div> <div>Grant Agency [Organismo financiador]: Ministerio de Universidades del Gobierno de España</div> <div>Grant Number [Código del proyecto]: CAS21/00333</div> <div> </div> <div> </div> <div>7. Geographical location/s of data collection:</div> <div>Península Ibérica</div> <div> </div> <div>Iberian Peninsula</div> <div> </div> <div> </div> <div> </div> <div> <div>ACCESS INFORMATION</div> <div>------------------------</div> <div> </div> <div>1. Creative Commons License of the dataset:</div> <div>Licencia CC BY-NC-ND </div> <div> </div> <div> </div> <div>2. Dataset DOI:</div> <div>[ Obligatorio]</div> <div> </div> <div> </div> <div>3. Related publication:</div> <div>Ferreira-Lopes, P., & Pires Rosa, M. (2023). Metodología de captura y análisis de datos del patrimonio inmueble industrial de la Eurociudad del Guadiana. Ge-Conservacion, 24(1), 56-68. https://doi.org/10.37558/gec.v24i1.1205</div> <div> </div> <div>Ferreira Lopes, P., Moya Muñoz, J., & Pires Rosa, M. (2023). An in-depth look at the application of GIS for industrial heritage documentation. Conservar Património, 44, 67–81. https://doi.org/10.14568/cp28708</div> <div> </div> <div>4. Link to related datasets:</div> <div> </div> <div> </div> <div> </div> <div> </div> <div> </div> <div> </div> <div>VERSIONING AND PROVENANCE</div> <div>---------------</div> <div> </div> <div>1. Last modification date:</div> <div> 14-03-2023</div> <div> </div> <div> </div> <div>2. Were data derived from another source?:</div> <div>Sí. Los registros identificados fueron recopilatos tanto d etrabajo de campo como por diversas fuentes de consultas que están especificadas en cada uno de los registros. </div> <div> </div> <div> </div> <div>3. Additional related data not included in this dataset:</div> <div> </div> <div> </div> <div> </div> <div>METHODOLOGICAL INFORMATION</div> <div>-----------------------</div> <div>El archivo vectorial se creó en el software Quantum GIS (QGIS). </div> <div> </div> <div>Consultar: Ferreira-Lopes, P., & Pires Rosa, M. (2023). Metodología de captura y análisis de datos del patrimonio inmueble industrial de la Eurociudad del Guadiana. Ge-Conservacion, 24(1), 56-68. https://doi.org/10.37558/gec.v24i1.1205</div> <div>Ferreira Lopes, P., Moya Muñoz, J., & Pires Rosa, M. (2023). An in-depth look at the application of GIS for industrial heritage documentation. Conservar Património, 44, 67–81. https://doi.org/10.14568/cp28708</div> <div> </div> <div> </div> <div>1. Description of the methods used to collect and generate the data:</div> <div> </div> <div> </div> <div> </div> <div>2. Data processing methods:</div> <div> </div> <div> </div> <div>3. Software or instruments needed to interpret the data:</div> <div>GIS software (QGIS, ARcGIS, GvSIG, etc.)</div> <div> </div> <div>4. Information about instruments, calibration and standards:</div> <div> </div> <div> </div> <div> </div> <div>5. Environmental or experimental conditions:</div> <div> </div> <div> </div> <div> </div> <div>6. Quality-assurance procedures performed on the data:</div> <div> </div> <div> </div> <div> </div> <div> </div> <div>FILE OVERVIEW </div> <div>----------------------</div> <div>Continene 6 capas vectoriales. El archivo shapefile está compuesto por 7 archivos en formato .cpg; .prj; .sbn; .shp; .sbx; .shx; .dbf. </div> <div> </div> <div>1. Explain the file naming conversion, si es aplicable:</div> <div> </div> <div> </div> <div> </div> <div>2. File list:</div> <div> </div> <div> </div> <div> </div> <div>3. Relationship between files:</div> <div> </div> <div> </div> <div> </div> <div>4. File format:</div> <div> </div> <div> </div> <div> </div> <div>5. If the dataset includes multiple files, specify the directory structure and relationships between the files:</div> <div>Un archivo shapefile está compuesto por 7 archivos en formato .cpg; .prj; .sbn; .shp; .sbx; .shx; .dbf. </div> <p> </p> </div>
Dataset Questionnaire Social Media Marketing Activities, Brand Loyalty, Brand Trust, Brand Equity, and Industry Fashion In Indonesia
<p>The following dataset is a dataset from a study that investigated Social Media Marketing Activities, Brand Loyalty, Brand Trust, and, Brand Equity in the context of fashion industry in Indonesia.</p>
Data for an IES from a real industrial park
<p>The dataset includes historical operational data of the Integrated Energy System (IES) from an industrial park in China. Specifically, the Excel file contains the flow rate and pressure data of steam and compressed air. The pickle file includes the specific data format created for T-STGCN(<a href="https://github.com/Lirdon/TSTGCN">Lirdon/TSTGCN (github.com)</a>).</p>
High-resolution Industrial Production Energy (HIPE)
<p>The High-resolution Industrial Production Energy (HIPE) data set contains smart meter readings of ten machines and the main terminal of a power-electronics production plant over three months.</p> <p><a href="https://doi.org/10.1145/3208903.3210278" target="_blank" rel="noopener">The accompanying publication (published by ACM)</a> describes the data set and outlines open challenges and use cases with industrial energy data. This includes a dis­cussion of cha­rac­ter­is­tics of in­dus­tri­al machines and of differences to residential appliances.</p> <p>Links:</p> <ul> <li>Publication: <a href="https://doi.org/10.1145/3208903.3210278" target="_blank" rel="noopener">https://doi.org/10.1145/3208903.3210278</a></li> <li>Description: <a href="https://www.energystatusdata.kit.edu/hipe.php">https://www.energystatusdata.kit.edu/hipe.php</a></li> </ul>
Data Appendix for Lack, P., "Using Word Analysis to Track the Evolution of Emotional Well-being in Nineteenth-Century Industrializing Britain", Historical Methods (forthcoming)
<p>This file contains the data associated with the publication Lack, P., "Using Word Analysis to Track the Evolution of Emotional Well-being in Nineteenth-Century Industrializing Britain", <em>Historical Methods</em> (forthcoming). It quantifies the trend in emotional well-being expressed in a corpus of British pamphlets published between 1800 and 1900. The first page of the excel document presents this key data on the trend in emotional well-being. Sheet 1A presents summary statistics on the trend in emotional well-being and its correlation with GDP per capita and real wages. </p>
Which RESTful API Design Rules are Important and How Do They Improve Software Quality? A Delphi Study with Industry Experts
<p>The dataset of a Delphi study with 8 industry experts who reached consensus on the perceived importance and positive software quality impact of 82 RESTful API design rules from the catalogue by Mark Massé ("REST API Design Rulebook", O’Reilly Media, 2011). The replication package contains:</p> <ul> <li><strong>rules.csv:</strong> the final consensus results for the 82 rules in CSV format</li> <li><strong>rule-importance.xlsx</strong>: the detailed results and analysis for rule importance as an Excel spreadsheet</li> <li><strong>rule-sw-quality-impact.xlsx</strong>: the detailed results and analysis for rule impact on software quality as an Excel spreadsheet</li> </ul> <p>In this version, we updated the final numbers for the software quality mapping with the results from the synchronous meeting.</p>
R code and dataset to "Monetizing Spillover Effects in the Creative Industries: the Impact of Live Music Performances on Youtube Searches"
<p>Content:</p> <ol> <li>The script<strong> main_script.R</strong> includes code to run a regression discontinuity (RD) design and validation and falsification of estimated results</li> <li>The folder <strong>data</strong> contains two files: <ol> <li>bands_2016_2019.csv: a dataset of performers with additional information for each one.</li> <li>festivals_2016_2019.csv: a dataset of video search activity (as retrieved from Google Trends) for performers in file bands_2016_2019.csv</li> </ol> </li> <li>The folder <strong>source</strong> contains two additional R scripts: <ol> <li>data_preparation.R: generates the long dataset used to estimate RD effects</li> <li>status_simulation.R: randomly assigns treattment status to performers and estimates RD effects. Note this may take a long time to run. Parallel code is used: the number of cores has been set to 4. </li> </ol> </li> <li>The folder simulation_results contains simulated data after running the script status_simulation.R.</li> </ol>
FIG. 3. – Figure 3. A 30 in The versatility of bone, ivory and horn - their uses in the Sheffield cutlery industry
FIG. 3. – Figure 3. A 30cm folding knife with buffalo horn scales. Cutlers' Company collection
Dataset for "Climate and ice sheet evolutions from the last glacial maximum to the pre-industrial period with an ice sheet -- climate coupled model"
<p>This archive contains the source data of the figures presented in the manuscript "Climate and ice sheet evolutions from the last glacial maximum to the pre-industrial period with an ice sheet -- climate coupled model".</p> <p>Contact: aurelien.quiquet@lsce.ipsl.fr</p>
Fig. 2 in Industrial emissions and pesticides impact on agrobiocenosis biodiversity
Fig. 2:Ynsect species trophic categories distribution.
Fig. 1 in Industrial emissions and pesticides impact on agrobiocenosis biodiversity
Fig. 1: Insect families' distribution function of orders.
Dataset on vascular plants, Rhopalocera and Orthoptera of 35 industrial water-abstraction sites in France, including landscape and local variables
<p>Site : Site name</p> <p>X : X coordinate</p> <p>Y : Y coordinate</p> <p>Richness : Species richness taking into account individuals identified to the genus and species levels (based on data from the Vigie-Flore protocol for Flora (www.vigie-flore.fr), the STERF protocol for Rhopalocera (Manil and Henry, 2007) and the protocol of Lacoeuilhe et al. (2020) for Orthoptera)</p> <p>Richness2 : Species richness taking into account only the individuals identified to the species level</p> <p>Shannon_Diversity : Shannon index</p> <p>Abondance : For Flora, abundance is the total number of quadrats in which each species is present, and for Rhopalocera and Orthoptera, abundance is the total number of individuals</p> <p>CWM_dispersal : Community weighted mean (Garnier et al., 2004) for dispersal :</p> <ul> <li>For Flora, maximum seed-releasing height as a proxy for dispersal</li> <li>For Rhopalocera and Orthoptera, 3 classes of dispersal (1-Low dispersal, 2-Medium dispersal, 3-High dispersal)</li> </ul> <p>CWM_specialisation : Community weighted mean (Garnier et al., 2004) for specialisation :</p> <ul> <li>For Flora, Index θwb calculated using species co-occurrence data (Mobaied et al., 2015)</li> <li>For Rhopalocera, 4 classes of specialisation based on the optimal habitat of the caterpillar (1-Generalist species whose caterpillars grow in many types of habitat ; 2-Moderately generalist species whose caterpillars grow mainly in the associated habitat ; 3-Specialist species whose caterpillars grow mainly in the associated habitat ; 4-Specialist species with a very localised distribution)</li> <li>For Orthoptera, 2 classes of specialisation based on moisture preferences (0-Generalist species (mesophilic), 1-Specialist species (xerothermic and hygrophilic))</li> </ul> <p>CWM_dep_pol : Percentage of times “insects” appears as a pollen vector for a given species across various databases (Martin, 2018)</p> <p>dPC_Flora_150m : Delta Probability of Connectivty (Saura & Pascual-Hortal, 2007) calculated for Flora with dispersal distances of 150m</p> <p>dPC_Flora_500m : Delta Probability of Connectivty (Saura & Pascual-Hortal, 2007) calculated for Flora with dispersal distances of 500m</p> <p>dPC_Rhopalocera_100m : Delta Probability of Connectivty (Saura & Pascual-Hortal, 2007) calculated for Rhopalocera with dispersal distances of 100m</p> <p>dPC_Rhopalocera_300m : Delta Probability of Connectivty (Saura & Pascual-Hortal, 2007) calculated for Rhopalocera with dispersal distances of 300m</p> <p>dPC_Orthoptera_100m : Delta Probability of Connectivty (Saura & Pascual-Hortal, 2007) calculated for Orthoptera with dispersal distances of 100m</p> <p>dPC_ Orthoptera _300m : Delta Probability of Connectivty (Saura & Pascual-Hortal, 2007) calculated for Orthoptera with dispersal distances of 300m</p> <p>IFT_Herbicides_100m : Average Treatment Frequency Indice for herbicides within a radius of 100m</p> <p>IFT_Herbicides_300m : Average Treatment Frequency Indice for herbicides within a radius of 300m</p> <p>Soil : Qualitative variable, divided into 2 categories: clay vs. sandy soil</p> <p>Humidity : Semi-quantitative variable based on site habitat vegetation, divided into 3 categories: 1 (xerophilous), 2 (mesoxerophilous), 3 (meso-hygrophylous)</p> <p>Floral_dispo : Average cover of flowering plants over the 4 visits on the site (%)</p> <p>Low_herbaceous_cover : Low herbaceous cover (<20 cm) on the site (%)</p> <p>Hight_herbaceous_cover : High herbaceous cover (>40 cm) on the site (%)</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.