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830 results for “INDUSTRY”
Adaptation to industrial stressors through genomic and transcriptional plasticity in a bioethanol producing fission yeast isolate
GEO Series GSE144083. Schizosaccharomyces pombe. 31 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Industrial trans fatty acids stimulate SREBP2-mediated cholesterogenesis and promote non-alcoholic fatty liver disease [Hepa1-6 cells]
GEO Series GSE136278. Mus musculus. 4 samples. Type: Expression profiling by array.
Industrial trans fatty acids stimulate SREBP2-mediated cholesterogenesis and promote non-alcoholic fatty liver disease [liver]
GEO Series GSE136271. Mus musculus. 24 samples. Type: Expression profiling by array.
DXC'11 Industrial Track Competition Data
Competition data, including nominal and faulty scenarios, for Diagnostic Problem I of the Third International Diagnostic Competition. Three file formats are provided, tab-delimited .txt files, Matlab .mat files, and tab-delimited .scn files. The scenario (.scn) files are read by the DXC framework. See the DXC'11 Industrial Track Sample Data resource page for additional documentation, including system catalogs and schematics. There were no DA entries for Diagnostic Problem II so we are withholding the data for use in a future Diagnostic Competition.
DXC'10 Industrial Track Competition Data
Competition data, including nominal and faulty scenarios, for Diagnostic Problems I and II of the Second International Diagnostic Competition. Three file formats are provided, tab-delimited .txt files, Matlab .mat files, and tab-delimited .scn files. The scenario (.scn) files are read by the DXC framework. See the DXC'10 Industrial Track Sample Data resource page for additional documentation, including system catalogs and schematics.
DXC'09 Industrial Track Competition Data
Competition data, including nominal and faulty scenarios, for Industrial Track Tier 1 and Tier 2 of the First International Diagnostic Competition. Three file formats are provided, tab-delimited .txt files, Matlab .mat files, and tab-delimited .scn files. The scenario (.scn) files are read by the DXC framework. See the DXC'09 Industrial Track Sample Data resource page for additional documentation, including system catalogs and schematics. Note that a "rematch" took place after the competition at the DX-09 Workshop. The rematch data consisted of the data for the original competition plus new data sets taken specifically for the rematch.
DXC'13 Industrial Track Sample Data
The sample scenarios provided here are competition scenarios from previous DXC competitions. They are identical to the competition data associated with previous years' projects, but also listed here for convenience. The zip files have a spreadsheet that lists the scenarios and relevant fault information. Each scenario is provided in three formats: tab-delimited .txt file, Matlab .mat file, and tab-delimited .scn file. The different formats are provided for your convenience; they have the same data. The scenario (.scn) files are the format read by the DXC framework. See the system catalogs and schematics for additional information.
DXC'11 Industrial Track Sample Data
The sample scenarios provided here are competition scenarios from Diagnostic Problems I and II of DXC'10. The zip file has a spreadsheet (and pdf) that lists the scenarios and relevant fault information. Each scenario is provided in three formats: tab-delimited .txt file, Matlab .mat file, and tab-delimited .scn file. The different formats are provided for your convenience; they have the same data. The scenario (.scn) files are the format read by the DXC framework. See the system catalogs and schematics for additional information. For those involved in last year's competition, note that DXC'10 Diagnostic Problem I competition data posted last year (dated 20101105) had some problems which were fixed in the DPI sample data set posted here. The battery "AbruptParasiticLoad" scenario (Exp. 1311) was removed as it was found to have different behavior prior to fault injection than nominal scenarios. Exp 1229_015f was too short (224 seconds) and was removed. Several runs caused an issue when a value was expected to be a real but was interpreted as an integer in the framework (1251, 1285, 1316, 1323, and 1330); this has been fixed. There are no "AbruptParasiticLoad" faults in the DXC11 DPI competition data.
DXC'10 Industrial Track Sample Data
Sample data, including nominal and faulty scenarios, for Diagnostic Problems I and II of the Second International Diagnostic Competition. Three file formats are provided, tab-delimited .txt files, Matlab .mat files, and tab-delimited .scn files. The scenario (.scn) files are read by the DXC framework. See the Second International Diagnostic Competition project page for more information.
DXC'09 Industrial Track Sample Data
Sample data, including nominal and faulty scenarios, for Tier 1 and Tier 2 of the First International Diagnostic Competition. Three file formats are provided, tab-delimited .txt files, Matlab .mat files, and tab-delimited .scn files. The scenario (.scn) files are read by the DXC framework. See the Support/Documentation section below and the First International Diagnostic Competition project page for more information.
The study of the hepatic transcription profiles of eels caged in an area affected by paper industry
GEO Series GSE53345. Anguilla anguilla. 30 samples. Type: Expression profiling by array.
Serial industrial repitching and stress
GEO Series GSE45951. Saccharomyces cerevisiae. 88 samples. Type: Expression profiling by array.
Functional genomics study of Aspergillus niger biofilms for the identification and characterization of genes of lignocellulolytic enzymes of industrial interest
GEO Series GSE85945. Aspergillus niger. 32 samples. Type: Expression profiling by array.
Lactobacillus rhamnosus GG Genomic and Phenotypic Stability in an Industrial Production Process
GEO Series GSE123727. Lacticaseibacillus rhamnosus. 40 samples. Type: Genome variation profiling by high throughput sequencing.
MLCQProjects: Towards evolving data set of industry-relevant software projects
<p>Short: This data set contains three snapshots from an evolving software project data set and a script that generates those snapshots. Projects in snapshots are annotated with features used to assess industry-relevance - documentation links, installation means, support channels and whether the project delivers functionalities or samples.</p> <p>Context: Researchers involved in mining software repositories face a challenge that many of existing data sets reflect how software was developed in the past (typically many years ago), instead of in the present. Another challenge is that data sets based on open-source software projects include projects that are not necessarily industry-relevant.</p> <p>Aim: The aim of this paper is to address the aforementioned challenges and provide both: 1) a snapshot-based evolving data set of software projects (thus reflecting their present, as well as previous states), manually enriched with data considered important for industrial relevance assessment, and 2) a method of assessing industrial relevance of software projects.</p> <p>Method: We present a systematic method of selecting data sets of software projects for the purposes of mining software repositories of potentially industry-relevant projects and a semi-systematic method of assessing the industrial relevance of those projects.</p> <p>Data set: The data set contains three snapshots (spanning over 10 months) of popular Java projects from GitHub, manually enriched with industrial relevance and maintenance-related information. The presented acquisition method is sufficient to generate further snapshots and one should be able to assess the industrial relevance of the projects using the presented assessment method. Provided data set open directions of further research, e.g, 1) evaluation of code smells or defect prediction models on industry-relevant software projects prepared by independent authors and not used to build models, 2) analysis of some social aspects of open source projects (such as tooling used for providing support) and how they evolve in time.</p>
Impacts of wood pellet industry in Southeastern USA
<p>The dataset for an interactive thalloo map showing the evidence base of the impacts of the wood pellet industry on biodiversity in southeast USA.</p>
Consumer Perception of Socially Responsible Marketing: A Study of The Food Industry in India
<p>The data indicates the consumers perception for different aspects of socially responsible marketing.</p>
Dataset for "Statistical Electromagnetics for Industrial Pharmaceutical Lyophilization"
<p>This dataset includes the underlying data for the paper entitled "Statistical Electromagnetics for Industrial Pharmaceutical Lyophilization" with the DOI of "<a href="https://doi.org/10.1093/pnasnexus/pgac052">https://doi.org/10.1093/pnasnexus/pgac052</a>"</p>
Project Data: Industrial Innovation in Transition - coded interview data
<p>Industrial innovation has changed fundamentally over the last ten years. Companies have widely adopted new tools such as open innovation, innovation networks and ecosystems, systemic innovations, public/private partnerships, crowd sourcing, social media, and demand based innovations. These new practices have improved the innovation<br> capabilities of companies and brought new challenges to the traditional innovation policy instruments. This project<br> examined widely companies’ current use of new innovation practices around Europe and the good practice experience of their use. It also evaluated existing innovation policy portfolios at national and European levels, and analyzed the differences between innovation processes and management practices in different industrial sectors. With 694 interviews of company representatives the project covered five key industrial sectors in 11 EU Member States.</p>
Sustainable Approach of Supplier Selection in Automotive Industry: A Web-based AHP-SCOR Model
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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.