Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
830
datasets available to search
ShareScore release 0.7.1
Dataset results
830 results for “industry”
Replication package for Transport and urban growth in the First Industrial Revolution
<p><span>Replication package for Alvarez-Palau, E. J., Bogart, D., Satchell, A.E., Shaw-Taylor, L.</span><strong><span> </span></strong><span>‘Transport and urban growth in the First Industrial Revolution’ Economic Journal<span> </span></span></p>
Selected properties and microstructure of concrete with tire rubber granulate as recycled material in construction industry
<p><span>The paper explores the use of recycled materials in the construction industry to promote sustainable development. There is a growing demand for recycling and innovative materials in engineering. The study specifically investigates the potential of tire rubber recyclate as a recycled raw material, comparing two different mixtures in an experimental program. These mixtures highlight the importance of utilizing local resources, aligning with the principles of the circular economy. The experimental program focuses on evaluation of mechanical properties in addition to specialized tests. Findings indicate that higher proportions of rubber granulate not only impact mechanical properties but also significantly affect durability when exposed to environmental factors. </span></p>
Gnuplot scripts for plotting selected leaf gas exchange and morphological data of industrial hemp
<p>Gnuplot code (scripts) for reproducing the eight figures of Sunoj et al. 2025 "Foliar gas exchange, morphology, and cannabinoid contents of three hemp varieties in southwest Texas," Agrosystems, Geosciences & Environment, 8, e70101. https://doi.org/10.1002/agg2.70101. Here is a list of the authors of the manuscript: John Sunoj V. S. (1), Xuejun Dong (1), Madhumita Joshi (1), Russell W. Jessup (2), Daniel I. Leskovar (1), and David D. Baltensperger (2). Texas A&M AgriLife Research at Uvalde, Texas, USA (1); Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA (2).</p> <p>The revised upload includes updated gnuplot scripts for reproducing Figures 2, 3, and 8, and Supplemental Figures 1-5 and Supplemental Tables 1-5 of the accepted manuscript by AGE.</p> <p>Code and data to reproduce Figure 2 (pnf_rev.eps): pn_dat_rev.txt, pnf_rev.txt</p> <p>Code and data to reproduce Figure 3 (fmf_rev.eps): fm_dat.txt, fm_rev.txt</p> <p>Code and data to reproduce Figure 8 (allom_1.eps): allom_1.txt, allom_data.csv</p> <p> </p> <p> </p> <p> </p> <p> </p>
Use of infrared spectroscopy for a sampling study of waste wood samples in a panel board industry
<p>Oral presentation at the conference 'NIR Italia Online, waiting for Slovenia 2022' (24-25 February 2021). </p> <p>NIR Italia Symposium are biennial conferences on infrared spectroscopy. Due to the pandemic situation the Italian Society for Near Infrared Spectroscopy (SISNIR), in collaboration with the InnoRenew CoE and University of Primorska, has decided to organize an online National Symposium of NIR Spectroscopy, waiting for the opportunity to meet physically next year. </p> <p>The event is an important opportunity to virtually present works, and exchange ideas, opinions and future perspectives. This presentation perfectly fits with theme of NIR spectroscopy. Indeed, the presentation is about the use of NIR spectroscopy for assessing the best sampling procedure in order to describe a really heterogeneous material as waste wood is. </p>
Data from: Drivers of global pre-industrial patterns of species turnover in planktonic foraminifera
<p>Anthropogenic climate change is altering global biogeographical patterns. However, it remains difficult to quantify how bioregions are changing because pre-industrial records of species distributions are rare. Marine microfossils, such as planktonic foraminifera, are preserved in seafloor sediments and allow the quantification of bioregions in the past. Using a recently compiled data set of pre-industrial species composition of planktonic foraminifera in 3802 worldwide seafloor sediments, we employed multivariate and statistical model-based approaches to study spatial turnover in order to 1) quantify planktonic foraminifera bioregions and 2) understand the environmental drivers of species turnover. Four latitudinally banded bioregions emerge from the global assemblage data. The polar and temperate bioregions are bi-hemispheric, supporting the idea that planktonic foraminifera species are not limited by dispersal. The equatorial bioregion shows complex longitudinal patterns and overlaps in sea surface temperature (SST) range with the tropical bioregion. Compositional-turnover models (Bayesian bootstrap generalised dissimilarity models) identify SST as the strongest driver of species turnover. The turnover rate is constant across most of the SST gradient, showing no SST threshold values with rapid shifts in species composition, but decelerates above 25°C, suggesting SST is less predictive of species composition in warmer waters. Other environmental predictors affect species turnover non-linearly, and their importance differs across regions. In the Pacific ocean, net primary productivity below 500 mgC m<sup>−2</sup> day<sup>−1</sup> drives fast compositional change. Water depth values below 3000 m (which affect calcareous microfossil preservation) increasingly drive changes in species composition among death assemblages in the Pacific and Indian oceans. Together, our results suggest that the dynamics of planktonic foraminifera bioregions are expected to be highly responsive to climate change; however, at lower latitudes, environmental drivers other than SST may affect these dynamics.</p>
SANER 2022 - Industrial Track - Investigating the Point of View of Project Management Practitioners on Technical Debt - A Preliminary Study on Stack Exchange
<p>Dataset related to the paper Investigating the Point of View of Project Management Practitioners on Technical Debt - A Preliminary Study on Stack Exchange. </p> <p> </p> <p>Saner 2022 Industrial Track</p>
Tools in industry(ROBODK)
<p>The current data set presents a simulated environment of a production line, describing common elements present around a robotic arm. The dataset focuses on segmentation and describes a total of 11 classes:</p> <ul> <li>Recipient 1 (Blue Circle) </li> <li>Recipient 2 (Yellow square</li> <li>Recipient 3 (Red rectangle) </li> <li>Recipient 4 (Brown rectangle) </li> <li>Wrench </li> <li>Screw </li> <li>Barrette </li> <li>cradle ball bearing</li> <li>Screwdriver </li> <li>Nut </li> <li>Obst1 </li> <li>Obst2 </li> <li>Background </li> </ul> <p>The data set was generated from a simulated environment in ROBODK in the modeling of a robotic arm for industrial tasks.</p>
Dataset for A MILP approach for detailed pipeline scheduling and storage management problem in the phosphate industry
<p>Case studies of a multi-product slurry pipeline scheduling and storage management problem in the phosphate industry.</p>
Varied oxygen simulations with WACCM6 (Proterozoic to pre-industrial atmosphere)
<p>The history of molecular oxygen (O<sub>2</sub>) in Earth's atmosphere is still debated; however, geological evidence supports at least two major episodes where O<sub>2</sub> increased by an order of magnitude or more: the Great Oxidation Event (GOE) and the Neoproterozoic Oxidation Event. O<sub>2 </sub>concentrations have likely fluctuated (between 10<sup>−3</sup> and 1.5 times the present atmospheric level) since the GOE ∼ 2.4 Gyr ago, resulting in a time-varying ozone (O<sub>3</sub>) layer. Using a three-dimensional (3D) chemistry climate model, we simulate changes in O<sub>3</sub> in Earth's atmosphere since the GOE and consider the implications for surface habitability, and glaciation during the Mesoproterozoic. We find lower O<sub>3</sub> columns (reduced by up to 4.68 times for a given O<sub>2</sub> level) compared to previous work; hence, higher fluxes of biologically harmful UV radiation would have reached the surface. Reduced O<sub>3</sub> leads to enhanced tropospheric production of the hydroxyl radical (OH) which then substantially reduces the lifetime of methane (CH<sub>4</sub>). We show that a CH<sub>4</sub> supported greenhouse effect during the Mesoproterozoic is highly unlikely. The reduced O<sub>3</sub> columns we simulate have important implications for astrobiological and terrestrial habitability, demonstrating the relevance of 3D chemistry-climate simulations when assessing paleoclimates and the habitability of faraway worlds.</p>
Data set for publication: A New Industry-Oriented Technique for the Wideband Characterization of Voltage Transformers
<p>This is dataset for paper published:</p> <p>G. Crotti, D. Giordano, G. D'Avanzo, P.S. Letizia, M. Luiso, "A New Industry-Oriented Technique for the Wideband Characterization of Voltage Transformers", Measurement, Volume 182, 2021, 109674, ISSN 0263-2241,<br> https://doi.org/10.1016/j.measurement.2021.109674.</p> <p> </p> <p>Excel file provides data for Figure 3, 4, 6 and 7.</p>
Digital Twins - from industrial management to healthcare practice
<p>A guest seminar offered by SCImPULSE Foundation CTO Taghi Aliyev for the University of Parma (IT) master "ARTE" https://www.masterarte-unipr.it/</p>
Towards identifying industrial crop types and associated agronomies to improve biomass production from marginal lands in Europe
<p>Background: Growing industrial crops on marginal lands has been proposed as a strategy to minimize competition for arable land and food production. In the present study, eight experimental sites in three different climatic zones in Europe (Mediterranean, Atlantic and Continental), seven advanced industrial crop species [giant reed (two clones), miscanthus (<em>M</em>. × <em>giganteus</em> and two new seed-based hybrids), saccharum (one clones), switchgrass (one variety), tall wheatgrass (one variety), industrial hemp (three varieties) and willow (eleven clones)], and six marginality factors alone or in combination (dryness, unfavorable texture, stoniness, shallow soil, topsoil acidity, heavy metal and metalloid contamination) were investigated. At each site, biophysical constraints and low-input management practices were combined with prevailing climatic conditions.</p> <p>Results: The relative yield of a site-specific low-input system compared with the site-specific control was from small to large (i.e., from -99% in industrial hemp in the Mediterranean to +210% in willow in the Continental zone), due to the genotype-by-management interaction along with climatic variation between growing seasons. Genotype selection and improved knowledge on crop response to changing environmental, site-specific biophysical constraint and input application has been detected as key to profitably grow industrial crops on marginal areas.</p> <p>Conclusions: This study may act to provide hints on how to scale-up investigated cropping systems, through low-input practices, under similar environmental and soil conditions tested at each site. However, further attention to detail on the agronomy of early plant development and management in larger multi-year and multi-location field studies with commercially scalable agronomies are needed in order to validate yield performances, and thereby to inform on the best industrial crop options.</p>
Hospitality and Tourism Industry from TR-HT
<p>Set of data used in the paper <em>The impact of ESG dimensions on firm risk of hospitality and tourism industry.</em></p> <p>The data were obtained from Thomson Reuters Eikon database (TR_Eikon), we selected those companies whose activity sector was H&T regardless of their country of origin. The data are due in "xls" format and structured in two sheets.</p> <p>- the first sheet contains the selected companies, with the next information: </p> <ol> <li>Company Name</li> <li>NAICS Sector Code</li> <li>NAICS Subsector Code</li> <li>NAICS Industry Code</li> <li>NAICS National Industry Name</li> <li>Muestra Final: an indicator variable used to remark that the respective company is used in the study.</li> </ol> <p>- The second sheet contains the next information:</p> <ol> <li>Company Id: The Company Identificator. A number differentiating each company from the rest.</li> <li>Year: The year of the correspondig register.</li> <li>P_Crisis: Pandemic Crisis. Dummy variable coded 1 if year is 2020, 0 otherwise.</li> <li>F_Crisis Financial Crisis: Dummy variable coded 1 if year is from 2008 to 2012, 0 otherwise</li> <li>Firm_Size: Firm size. Logarithm of total asses</li> <li>Leverage: Leverage. Total debt to total assets</li> <li>ROA: Return on assets. EBITDA divided by total assets</li> <li>B_Gender: Gender diversity. Number of women directors as a percentage of total directors on the board</li> <li>B_Independence: Independence. number of independent directors as a percentage of total directors on the board</li> <li>B_Size: Board size. Number of directors on the board</li> <li>Duality: CEO duality. Dummy variable taking the value 1 if the chairperson of the board is the CEO and 0 otherwise</li> <li>ESG_Score: Evironmental Social and Governance Score. A weighted average relative rating based on reported environmental, social and governance information and ranges between 0 (worst) and 100 (best). It is provided by Eikon Thomson Reuters</li> <li>SOC_Score: Social Pillar Score. A weighted average relative rating based on reported social information and ranges between 0 (worst) and 100 (best). It is provided by Eikon Thomson Reuters</li> <li>GOV_Score: Governance Pillar Score. A weighted average relative rating based on reported governance information and ranges between 0 (worst) and 100 (best). It is provided by Eikon Thomson Reuters</li> <li>ENV_Score: Environmental Pillar Score. A weighted average relative rating based on reported environmental information and ranges between 0 (worst) and 100 (best). It is provided by Eikon Thomson Reuters</li> <li>Dependent variables</li> <li>D2D: Distance of Default. Merton’s distance to default</li> <li>SD_R: Volatility of the stock returns. Standard deviation of daily stock returns</li> </ol>
TIMES-Sweden (Industrial) Heat generation technologies database
<p>This is a database containing techno-economic data for heat & power technologies, primarily focusing on technologies for heat generation in industry or district heating. The database is a compilation of information from literature, specifically tailored for use in TIMES models. Even though this specific database has been developed for TIMES-Sweden, the data can also be applied for other regions. The database is continuously updated as work progresses with the TIMES-Sweden model.</p> <p>Preferably to be used in combination with TIMES-Sweden Industry database (<a href="https://doi.org/10.5281/zenodo.4139800">10.5281/zenodo.4139800</a>), and TIMES-Sweden Fuel production technologies database (<a href="https://doi.org/10.5281/zenodo.6372926">10.5281/zenodo.6372926</a>).</p> <p>This Database is also a part of the IEA ETSAP SubRES project, with the aim to make techno-economic data more accessible. More information about ETSAP can be found here: <a href="https://iea-etsap.org/">https://iea-etsap.org/</a></p> <p>More information about TIMES-Sweden and the modelling team can be found here: <a href="http://www.ltu.se/TIMES-Sweden">http://www.ltu.se/TIMES-Sweden</a></p>
MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation for Domain Generalization Task
<p><strong>Description</strong></p> <p>This dataset is a sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task (MIMII DG). The dataset consists of normal and abnormal operating sounds of five different types of industrial machines, i.e., fans, gearboxes, bearing, slide rails, and valves. The data for each machine type includes three subsets called "sections", and each section roughly corresponds to a type of domain shift. <strong>This dataset is a subset of the dataset for <a href="https://dcase.community/challenge2022/task-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring">DCASE 2022 Challenge Task 2</a>, so the dataset is entirely the same as data included in the <a href="https://zenodo.org/record/6355122#.Ynt7rtrP2Uk">development dataset</a>. </strong>For more information, please see the pages of the <a href="https://zenodo.org/record/6355122#.Ynt7rtrP2Uk">development dataset</a> and the <a href="https://dcase.community/challenge2022/task-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring">task description</a><strong> </strong>for DCASE 2022 Challenge Task 2.</p> <p> </p> <p><strong>Baseline system</strong></p> <p>Two simple baseline systems are available on the Github repositories <a href="https://github.com/Kota-Dohi/dcase2022_task2_baseline_ae">autoencoder-based baseline</a> and <a href="https://github.com/Kota-Dohi/dcase2022_task2_baseline_mobile_net_v2">MobileNetV2-based baseline</a>. The baseline systems provide a simple entry-level approach that gives a reasonable performance in the dataset. They are good starting points, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task.</p> <p> </p> <p><strong>Conditions of use</strong></p> <p>This dataset was made by <strong>Hitachi, Ltd.</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.</p> <p> </p> <p><strong>Citation</strong></p> <p>We will publish a paper on the dataset and will announce the citation information for them, so please make sure to cite them if you use this dataset.</p> <p> </p> <p><strong>Feedback</strong></p> <p>If there is any problem, pease contact us</p> <ul> <li>Kota Dohi, <a href="mailto:kota.dohi.gr@hitachi.com">kota.dohi.gr@hitachi.com</a></li> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> </ul>
TIMES-Sweden Industry Database
<p>This is a database containing techno-economic data for industrial technologies. The database is a compilation of information from literature, specifically tailored for use in TIMES models. Even though this specific database has been developed for TIMES-Sweden, the data can also be applied for other regions. The database is continuously updated as work progresses with the TIMES-Sweden model.</p> <p>Preferably to be used in combination with TIMES-Sweden Fuel production technologies database (<a href="https://doi.org/10.5281/zenodo.6372926">10.5281/zenodo.6372926</a>), and TIMES-Sweden (Industrial) Heat generation technologies database (<a href="https://doi.org/10.5281/zenodo.6372930">10.5281/zenodo.6372930</a>).</p> <p>This Database is also a part of the ETSAP SubRES project, with the aim to make techno-ecnomic data more accessible. More information about ETSAP can be found here: <a href="https://iea-etsap.org/">https://iea-etsap.org/</a></p> <p>More information about TIMES-Sweden and the modelling team can be found here: <a href="http://www.ltu.se/TIMES-Sweden">http://www.ltu.se/TIMES-Sweden</a></p>
Companies in the Bulgarian Military-Industrial-Media-Complex
<p>Data supplement to a publication on the Bulgarian Military-Industrial-Media-Complex.</p>
Industrial Two-phase Olive Pomace Slurry-Derived Hydrochar Fuel for Energy Applications
<p>This dataset contains extended data files for the publication entitled "Industrial Two-phase Olive Pomace Slurry-Derived Hydrochar Fuel for Energy Applications."</p> <p>Data files includes: <br>- Dataset description.txt (provides abbreviations or codes of samples and their properties)<br>- Extended data.xlsx (data files for the biochemical, proximate, ultimate, HHV, and mineral characterisitics of raw material (two-phase olive pomace slurry) and hydrochars<br>- 13C-NMR data.zip (raw data files for 13C-solid nuclear magnetic resonance analysis of raw material and hydrochars)<br>- TGA-DTA data.zip (raw data for thermal gravimetric analysis of raw material and hydrochars)<br>- FTIR data.zip (raw data for fourier transform infrared analysis of raw material and hydrochars) </p> <p>Checksum numbers for enclosed data files: <br>- MD5 Checksum number for file named Extended dataset v1.0 = 6c542debe0a8b4297b8730830e7d5b58<br>- MD5 Checksum number for file named 13C-NMR data = bb25d31771d281ce1ca6518c4dc1cb41 <br>- MD5 Checksum number for file named FTIR data = 0df81ec3a25b4d130be09cef59736d30<br>- MD5 Checksum number for file named TGA-DTA data = 7205cc5a24daa94e60c82a43ae748fb2</p>
Evolución de los índices de producción industrial en la península ibérica de España y las islas Canarias
<pre>Se adjunta los datasets obtenidos de la práctica 2 de la Tipología de Datos de la Universidad UOC (Universitat Oberta de Catalunya) mediante la implementación de código Python por Jupyter Notebook mediante Anaconda.Navigator.</pre>
Dataset: Flexsteel Industries, Inc. (FLXS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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.