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830 results for “INDUSTRY”
Engineering the thermotolerant industrial yeast Kluyveromyces marxianus for anaerobic growth ssing sterol requirements enables anaerobic growth of the thermotolerant yeast Kluyveromyces marxianus
GEO Series GSE164344. Kluyveromyces marxianus; Saccharomyces cerevisiae. 18 samples. Type: Expression profiling by high throughput sequencing.
Analysis of the Saccharomyces cerevisiae Pan-Genome Reveals a Pool of Copy Number Variants Distributed in Diverse Yeast Strains From Differing Industrial Environments.
GEO Series GSE26689. Saccharomyces bayanus; Saccharomyces cerevisiae. 98 samples. Type: Genome variation profiling by array.
Industrial antifoam agents impair ethanol fermentation and induce stress responses in yeast cells
GEO Series GSE103004. Saccharomyces cerevisiae. 9 samples. Type: Expression profiling by high throughput sequencing.
Hepatic transcriptomic profiles in zebrafish exposed to surface water with pollution gradients and different industrial discharge
GEO Series GSE92330. Danio rerio. 8 samples. Type: Expression profiling by array.
Adaptation to industrial stressors through genomic and transcriptional plasticity in a bioethanol producing fission yeast isolate [RNA-seq]
GEO Series GSE141715. Schizosaccharomyces pombe. 16 samples. Type: Expression profiling by high throughput sequencing.
RNA-Seq transcriptomic analysis reveals gene expression profiles of acetic acid bacteria under high-acidity submerged industrial fermentation process
GEO Series GSE210697. Komagataeibacter europaeus. 6 samples. Type: Expression profiling by high throughput sequencing.
Replicating dynamic humerus motion using an industrial robot dataset
<p>This dataset encompasses all of the data utilized to replicate humeral kinematics, as captured via skin marker motion capture, on a FANUC M20ia industrial robot. The activities replicated include 119 jumping jacks, 15 jug lifts, 105 jogging trials, and 15 rapid internal rotation trials. An Optotrak Certus optical tracking system was utilized to record the kinematics of the humerus as actuated by the robot (also included in this dataset) in order to compare the robotically replicated trajectories against the motion capture trajectories. This dataset is intended to accompany the "Mocap to Robot" software package that provides an algorithmic pipeline for mapping motion capture trajectories to robotically replicated motion.</p> <p>Related publication can be found at: <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242005">https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242005</a></p>
Fig. 3 in The bony fishes (Teleostei) caught by industrial trawlers off the Brazilian North coast, with insights into its conservation
Fig. 3. Species of the order Elopiformes, family Albulidae, Albula vulpes, MPEG 35222, 308 mm TL.
Industry Practices and Challenges for the Evolvability Assurance of Microservices: A Systematic Grey Literature Review
<p>This repository contains all publicly available artifacts related to a systematic grey literature review (GLR) about the evolvability assurance of microservices in industry. We selected and analyzed 295 practitioner online resources obtained via Google, Bing, and selected StackExchange communities. The study took place in 2020 from January to April.</p>
Industrial, CBD, and feral hemp: how different are their essential oil profile and antimicrobial activity?
<p>These are supplemental tables for the manuscript to be submitted to Molecules. These include:</p>
Annual Gas & Electricity demand data for Dublin Emissions Trading System (ETS) industrial buildings as of November 2019
<ul> <li>AER Reports.zip <ul> <li>Pdfs of individual Dublin buildings each containing energy data from <a href="http://epa.ie/licensing/">http://epa.ie/licensing/</a></li> </ul> </li> <li>EPA Reports.xlsx <ul> <li>Extracted energy data from AER Reports into a single spreadsheet</li> </ul> </li> </ul>
Tourism Forecast with Weather, Event, and Cross-Industry Data
<p><strong>Introduction</strong></p> <p>Substantial short-term demand fluctuations are common in the tourism industry. Therefore, tourism companies such as accommodation, transportation, catering, and leisure facilities have a vital interest in precise forecasts of the number of customers.</p> <p>We present a novel forecasting dataset for tourism, consisting of four Swiss companies, one accommodation, two transportation, and one indoor leisure businesses, all located in the same touristic region. It covers a total of ten years starting in 2007 and ending in 2016. The dataset allows using cross-series information and includes explanatory variables, such as calendar effects, event data, and weather forecast information.</p> <p>Machine learning (ML) practitioners, statisticians, and experts of tourism sectors are invited to investigate our dataset for new insights on short-term forecasting for industries in tourism.</p> <p>The Algorithmic Business Research Lab (ABIZ) of the Lucerne University of Applied Sciences and Arts, Switzerland, researches ML algorithms for businesses to support industry partners in developing business models and services based on complex algorithms as well as in the induced digital transformation. In a joint effort with Institute of Tourism (ITW) of the School of Business of the Lucerne University of Applied Sciences and Arts, Switzerland, we provide the present tourism dataset as part of a publication.</p> <p>Contacts and further information can be found at <a href="http://www.abiz.ch/.">http://www.abiz.ch/.</a></p> <p><strong>The dataset</strong></p> <p>The dataset comprises 3653 days of customer numbers of four Swiss companies in the tourism sector. There are 556 feature columns, four target columns, and two mask columns, 562 columns in total.</p> <p><strong>Target variables</strong></p> <p>The customer volume data has daily resolution and features at worst minor interruptions of a few days over a common period of ten years, starting in 2007 and ending in 2016. The missing values for one transportation and the indoor leisure company are masked, and the masks are available as indicator variables.</p> <p><strong>Feature Variables</strong></p> <p>The dataset contains both numerical and categorical,</p> <ul> <li>calendar effects, such as day of the week, weekend, and month features,</li> <li>event data, e.g., public and school holidays, free-time regional events, promotions or revisions for the facilities under exam,</li> <li>weather forecast features provided by the Federal Office of Meteorology and Climatology (MeteoSwiss), which encodes information about conditions in the locations of the four businesses and neighboring regions.</li> </ul> <p>The weather forecast data consist of information about temperature, sunshine, precipitation, and wind, forecasted up to 3 days in advance. Note that the weather forecast model is updated regularly, and therefore many features do not cover the entire period. We want to point out that there are categorical weather summary annotations created by meteorologists, which are only provided for the last year.</p> <p><strong>Dataset Download</strong></p> <p>In the published version of the dataset, feature names are replaced with pseudonyms, but descriptions are given to identify feature groups with similar meaning. The content available for download consists of </p> <ul> <li>data.csv,<br> CSV format, 11.3 MB, 3654 rows, and 562 columns with the time series data;<br> </li> <li>data-description.csv,<br> CSV format, 36 KB, 563 rows and 12 columns with feature name and short description, minimal statistics for cross-checking, and indicator variables that specify which single-company datasets a feature belongs.</li> </ul> <p> </p>
Basic data for evaluating university potential for the space industry
<p>The data contain campus recruitment data of China Aerospace Science and Technology Corporation (CASC) and China Aerospace Science and Industry Corporation (CASIC) and 41 Chinese elite universities’ enrollment of bachelor, master, and doctor degree by discipline. The data also contain the numbers of space industry related professional organizations, publication, and patent of these universities. All of these data were openly collected online.</p>
Application of the SAFT- g Mie group contribution equation of state to fluids of relevance to the oil and gas industry
<p>Calculated data for all the figures presented in publication.</p>
Sustainability Initiatives in the Hospitality Industry
<p>This dataset contains data about sustainability initiatives in the hospitality industry in the tourism destinations located in the Czech Republic. This dataset supports a paper "Sustainability Initiatives in the Hospitality Industry: Assessing the Impact on Customer Ratings" by Jitka Vávrová, Lenka Červová and Blanka Brandová. </p>
Data used in the paper_Understanding innovation in creative industries: knowledge bases and innovation performance in art restoration organisations
<p>The dataset was used in the paper: Blanca de Miguel Molina, José-Luis Hervás-Oliver & Rafael Boix Domenech (2019) Understanding innovation in creative industries: knowledge bases and innovation performance in art restoration organisations, Innovation, 21:3, 421-442, DOI: <a href="https://doi.org/10.1080/14479338.2018.1562300">10.1080/14479338.2018.1562300</a></p><p>This work was supported by the Universitat Politècnica de València, Spain (Research Project n. 2677-UPV)</p>
Dataset and code for the publication "From Unstructured Product Descriptions to Structured Data for Industry 4.0 with ChatGPT"
<p>This is the code as well as the dataset for our publication "From Unstructured Product Descriptions to Structured Data for Industry 4.0 with ChatGPT".</p><p>Please see the README.md for more information.</p>
Medical Device Industry Payments Europe 2017-2019
<p>Data on education payments from medical device companies who are members of MedTech Europe. Data is aggregated by year/company/country. Source: transparentmedtech.eu</p>
Raw landmarks related to the paper, "Evolution under intensive industrial breeding: skull size and shape comparison between historic and modern pig lineages'
<p>Raw coordinates (p x k = 82 x 3) of domestic and wild pig skulls that form the dataset for the paper, "­Evolution under intensive industrial breeding: skull size and shape comparison between historic and modern pig lineages </p>
Customer Churn Prediction for Telecommunication Industry: A Malaysian Case Study
<p>Net Promoter Score (NPS) telecommunication dataset for Month to Date (MTD) September 2019 and MTD September 2020</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.