100+ datasets found
  1. h

    dataset-card-example

    • huggingface.co
    Updated Sep 28, 2023
    + more versions
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    Templates (2023). dataset-card-example [Dataset]. https://huggingface.co/datasets/templates/dataset-card-example
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    Dataset updated
    Sep 28, 2023
    Dataset authored and provided by
    Templates
    Description

    Dataset Card for Dataset Name

    This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

      Dataset Details
    
    
    
    
    
    
    
      Dataset Description
    

    Curated by: [More Information Needed] Funded by [optional]: [More Information Needed] Shared by [optional]: [More Information Needed] Language(s) (NLP): [More Information Needed] License: [More Information Needed]

      Dataset Sources [optional]… See the full description on the dataset page: https://huggingface.co/datasets/templates/dataset-card-example.
    
  2. d

    An example data set for exploration of Multiple Linear Regression

    • catalog.data.gov
    • data.usgs.gov
    Updated Jul 6, 2024
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    U.S. Geological Survey (2024). An example data set for exploration of Multiple Linear Regression [Dataset]. https://catalog.data.gov/dataset/an-example-data-set-for-exploration-of-multiple-linear-regression
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    Dataset updated
    Jul 6, 2024
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Description

    This data set contains example data for exploration of the theory of regression based regionalization. The 90th percentile of annual maximum streamflow is provided as an example response variable for 293 streamgages in the conterminous United States. Several explanatory variables are drawn from the GAGES-II data base in order to demonstrate how multiple linear regression is applied. Example scripts demonstrate how to collect the original streamflow data provided and how to recreate the figures from the associated Techniques and Methods chapter.

  3. f

    Collection of example datasets used for the book - R Programming -...

    • figshare.com
    txt
    Updated Dec 4, 2023
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    Kingsley Okoye; Samira Hosseini (2023). Collection of example datasets used for the book - R Programming - Statistical Data Analysis in Research [Dataset]. http://doi.org/10.6084/m9.figshare.24728073.v1
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    txtAvailable download formats
    Dataset updated
    Dec 4, 2023
    Dataset provided by
    figshare
    Authors
    Kingsley Okoye; Samira Hosseini
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This book is written for statisticians, data analysts, programmers, researchers, teachers, students, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using the R programming language. R is an open-source software and object-oriented programming language with a development environment (IDE) called RStudio for computing statistics and graphical displays through data manipulation, modelling, and calculation. R packages and supported libraries provides a wide range of functions for programming and analyzing of data. Unlike many of the existing statistical softwares, R has the added benefit of allowing the users to write more efficient codes by using command-line scripting and vectors. It has several built-in functions and libraries that are extensible and allows the users to define their own (customized) functions on how they expect the program to behave while handling the data, which can also be stored in the simple object system.For all intents and purposes, this book serves as both textbook and manual for R statistics particularly in academic research, data analytics, and computer programming targeted to help inform and guide the work of the R users or statisticians. It provides information about different types of statistical data analysis and methods, and the best scenarios for use of each case in R. It gives a hands-on step-by-step practical guide on how to identify and conduct the different parametric and non-parametric procedures. This includes a description of the different conditions or assumptions that are necessary for performing the various statistical methods or tests, and how to understand the results of the methods. The book also covers the different data formats and sources, and how to test for reliability and validity of the available datasets. Different research experiments, case scenarios and examples are explained in this book. It is the first book to provide a comprehensive description and step-by-step practical hands-on guide to carrying out the different types of statistical analysis in R particularly for research purposes with examples. Ranging from how to import and store datasets in R as Objects, how to code and call the methods or functions for manipulating the datasets or objects, factorization, and vectorization, to better reasoning, interpretation, and storage of the results for future use, and graphical visualizations and representations. Thus, congruence of Statistics and Computer programming for Research.

  4. i

    Example Dataset of Exercise Analysis and Forecasting

    • ieee-dataport.org
    Updated Jun 17, 2025
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    Chengcheng Guo (2025). Example Dataset of Exercise Analysis and Forecasting [Dataset]. https://ieee-dataport.org/documents/example-dataset-exercise-analysis-and-forecasting
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    Dataset updated
    Jun 17, 2025
    Authors
    Chengcheng Guo
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This data set is an example data set for the data set used in the experiment of the paper "A Multilevel Analysis and Hybrid Forecasting Algorithm for Long Short-term Step Data". It contains two parts of hourly step data and daily step data

  5. Dataset for Exploring case-control samples with non-targeted analysis

    • catalog.data.gov
    • datasets.ai
    Updated Nov 12, 2020
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    U.S. EPA Office of Research and Development (ORD) (2020). Dataset for Exploring case-control samples with non-targeted analysis [Dataset]. https://catalog.data.gov/dataset/dataset-for-exploring-case-control-samples-with-non-targeted-analysis
    Explore at:
    Dataset updated
    Nov 12, 2020
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    Description

    These data contain the results of GC-MS, LC-MS and immunochemistry analyses of mask sample extracts. The data include tentatively identified compounds through library searches and compound abundance. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: The data can not be accessed. Format: The dataset contains the identification of compounds found in the mask samples as well as the abundance of those compounds for individuals who participated in the trial. This dataset is associated with the following publication: Pleil, J., M. Wallace, J. McCord, M. Madden, J. Sobus, and G. Ferguson. How do cancer-sniffing dogs sort biological samples? Exploring case-control samples with non-targeted LC-Orbitrap, GC-MS, and immunochemistry methods. Journal of Breath Research. Institute of Physics Publishing, Bristol, UK, 14(1): 016006, (2019).

  6. E

    The Human Know-How Dataset

    • dtechtive.com
    • find.data.gov.scot
    pdf, zip
    Updated Apr 29, 2016
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    (2016). The Human Know-How Dataset [Dataset]. http://doi.org/10.7488/ds/1394
    Explore at:
    pdf(0.0582 MB), zip(19.67 MB), zip(0.0298 MB), zip(9.433 MB), zip(13.06 MB), zip(0.2837 MB), zip(5.372 MB), zip(69.8 MB), zip(20.43 MB), zip(5.769 MB), zip(14.86 MB), zip(19.78 MB), zip(43.28 MB), zip(62.92 MB), zip(92.88 MB), zip(90.08 MB)Available download formats
    Dataset updated
    Apr 29, 2016
    Description

    The Human Know-How Dataset describes 211,696 human activities from many different domains. These activities are decomposed into 2,609,236 entities (each with an English textual label). These entities represent over two million actions and half a million pre-requisites. Actions are interconnected both according to their dependencies (temporal/logical orders between actions) and decompositions (decomposition of complex actions into simpler ones). This dataset has been integrated with DBpedia (259,568 links). For more information see: - The project website: http://homepages.inf.ed.ac.uk/s1054760/prohow/index.htm - The data is also available on datahub: https://datahub.io/dataset/human-activities-and-instructions ---------------------------------------------------------------- * Quickstart: if you want to experiment with the most high-quality data before downloading all the datasets, download the file '9of11_knowhow_wikihow', and optionally files 'Process - Inputs', 'Process - Outputs', 'Process - Step Links' and 'wikiHow categories hierarchy'. * Data representation based on the PROHOW vocabulary: http://w3id.org/prohow# Data extracted from existing web resources is linked to the original resources using the Open Annotation specification * Data Model: an example of how the data is represented within the datasets is available in the attached Data Model PDF file. The attached example represents a simple set of instructions, but instructions in the dataset can have more complex structures. For example, instructions could have multiple methods, steps could have further sub-steps, and complex requirements could be decomposed into sub-requirements. ---------------------------------------------------------------- Statistics: * 211,696: number of instructions. From wikiHow: 167,232 (datasets 1of11_knowhow_wikihow to 9of11_knowhow_wikihow). From Snapguide: 44,464 (datasets 10of11_knowhow_snapguide to 11of11_knowhow_snapguide). * 2,609,236: number of RDF nodes within the instructions From wikiHow: 1,871,468 (datasets 1of11_knowhow_wikihow to 9of11_knowhow_wikihow). From Snapguide: 737,768 (datasets 10of11_knowhow_snapguide to 11of11_knowhow_snapguide). * 255,101: number of process inputs linked to 8,453 distinct DBpedia concepts (dataset Process - Inputs) * 4,467: number of process outputs linked to 3,439 distinct DBpedia concepts (dataset Process - Outputs) * 376,795: number of step links between 114,166 different sets of instructions (dataset Process - Step Links)

  7. m

    Dataset of development of business during the COVID-19 crisis

    • data.mendeley.com
    • narcis.nl
    Updated Nov 9, 2020
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    Tatiana N. Litvinova (2020). Dataset of development of business during the COVID-19 crisis [Dataset]. http://doi.org/10.17632/9vvrd34f8t.1
    Explore at:
    Dataset updated
    Nov 9, 2020
    Authors
    Tatiana N. Litvinova
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

  8. m

    Raw data outputs 1-18

    • bridges.monash.edu
    • researchdata.edu.au
    xlsx
    Updated May 30, 2023
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    Abbas Salavaty Hosein Abadi; Sara Alaei; Mirana Ramialison; Peter Currie (2023). Raw data outputs 1-18 [Dataset]. http://doi.org/10.26180/21259491.v1
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    xlsxAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Monash University
    Authors
    Abbas Salavaty Hosein Abadi; Sara Alaei; Mirana Ramialison; Peter Currie
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Raw data outputs 1-18 Raw data output 1. Differentially expressed genes in AML CSCs compared with GTCs as well as in TCGA AML cancer samples compared with normal ones. This data was generated based on the results of AML microarray and TCGA data analysis. Raw data output 2. Commonly and uniquely differentially expressed genes in AML CSC/GTC microarray and TCGA bulk RNA-seq datasets. This data was generated based on the results of AML microarray and TCGA data analysis. Raw data output 3. Common differentially expressed genes between training and test set samples the microarray dataset. This data was generated based on the results of AML microarray data analysis. Raw data output 4. Detailed information on the samples of the breast cancer microarray dataset (GSE52327) used in this study. Raw data output 5. Differentially expressed genes in breast CSCs compared with GTCs as well as in TCGA BRCA cancer samples compared with normal ones. Raw data output 6. Commonly and uniquely differentially expressed genes in breast cancer CSC/GTC microarray and TCGA BRCA bulk RNA-seq datasets. This data was generated based on the results of breast cancer microarray and TCGA BRCA data analysis. CSC, and GTC are abbreviations of cancer stem cell, and general tumor cell, respectively. Raw data output 7. Differential and common co-expression and protein-protein interaction of genes between CSC and GTC samples. This data was generated based on the results of AML microarray and STRING database-based protein-protein interaction data analysis. CSC, and GTC are abbreviations of cancer stem cell, and general tumor cell, respectively. Raw data output 8. Differentially expressed genes between AML dormant and active CSCs. This data was generated based on the results of AML scRNA-seq data analysis. Raw data output 9. Uniquely expressed genes in dormant or active AML CSCs. This data was generated based on the results of AML scRNA-seq data analysis. Raw data output 10. Intersections between the targeting transcription factors of AML key CSC genes and differentially expressed genes between AML CSCs vs GTCs and between dormant and active AML CSCs or the uniquely expressed genes in either class of CSCs. Raw data output 11. Targeting desirableness score of AML key CSC genes and their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 12. CSC-specific targeting desirableness score of AML key CSC genes and their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 13. The protein-protein interactions between AML key CSC genes with themselves and their targeting transcription factors. This data was generated based on the results of AML microarray and STRING database-based protein-protein interaction data analysis. Raw data output 14. The previously confirmed associations of genes having the highest targeting desirableness and CSC-specific targeting desirableness scores with AML or other cancers’ (stem) cells as well as hematopoietic stem cells. These data were generated based on a PubMed database-based literature mining. Raw data output 15. Drug score of available drugs and bioactive small molecules targeting AML key CSC genes and/or their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 16. CSC-specific drug score of available drugs and bioactive small molecules targeting AML key CSC genes and/or their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 17. Candidate drugs for experimental validation. These drugs were selected based on their respective (CSC-specific) drug scores. CSC is the abbreviation of cancer stem cell. Raw data output 18. Detailed information on the samples of the AML microarray dataset GSE30375 used in this study.

  9. h

    example-space-to-dataset-json

    • huggingface.co
    + more versions
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    Lucain Pouget, example-space-to-dataset-json [Dataset]. https://huggingface.co/datasets/Wauplin/example-space-to-dataset-json
    Explore at:
    Authors
    Lucain Pouget
    Description
  10. f

    Orange dataset table

    • figshare.com
    xlsx
    Updated Mar 4, 2022
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    Rui Simões (2022). Orange dataset table [Dataset]. http://doi.org/10.6084/m9.figshare.19146410.v1
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    xlsxAvailable download formats
    Dataset updated
    Mar 4, 2022
    Dataset provided by
    figshare
    Authors
    Rui Simões
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The complete dataset used in the analysis comprises 36 samples, each described by 11 numeric features and 1 target. The attributes considered were caspase 3/7 activity, Mitotracker red CMXRos area and intensity (3 h and 24 h incubations with both compounds), Mitosox oxidation (3 h incubation with the referred compounds) and oxidation rate, DCFDA fluorescence (3 h and 24 h incubations with either compound) and oxidation rate, and DQ BSA hydrolysis. The target of each instance corresponds to one of the 9 possible classes (4 samples per class): Control, 6.25, 12.5, 25 and 50 µM for 6-OHDA and 0.03, 0.06, 0.125 and 0.25 µM for rotenone. The dataset is balanced, it does not contain any missing values and data was standardized across features. The small number of samples prevented a full and strong statistical analysis of the results. Nevertheless, it allowed the identification of relevant hidden patterns and trends.

    Exploratory data analysis, information gain, hierarchical clustering, and supervised predictive modeling were performed using Orange Data Mining version 3.25.1 [41]. Hierarchical clustering was performed using the Euclidean distance metric and weighted linkage. Cluster maps were plotted to relate the features with higher mutual information (in rows) with instances (in columns), with the color of each cell representing the normalized level of a particular feature in a specific instance. The information is grouped both in rows and in columns by a two-way hierarchical clustering method using the Euclidean distances and average linkage. Stratified cross-validation was used to train the supervised decision tree. A set of preliminary empirical experiments were performed to choose the best parameters for each algorithm, and we verified that, within moderate variations, there were no significant changes in the outcome. The following settings were adopted for the decision tree algorithm: minimum number of samples in leaves: 2; minimum number of samples required to split an internal node: 5; stop splitting when majority reaches: 95%; criterion: gain ratio. The performance of the supervised model was assessed using accuracy, precision, recall, F-measure and area under the ROC curve (AUC) metrics.

  11. u

    Product Exchange/Bartering Data

    • cseweb.ucsd.edu
    json
    + more versions
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    UCSD CSE Research Project, Product Exchange/Bartering Data [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
    Explore at:
    jsonAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    These datasets contain peer-to-peer trades from various recommendation platforms.

    Metadata includes

    • peer-to-peer trades

    • have and want lists

    • image data (tradesy)

  12. CWL run of RNA-seq Analysis Workflow (CWLProv 0.5.0 Research Object)

    • zenodo.org
    • data.niaid.nih.gov
    • +2more
    bin, zip
    Updated Jan 24, 2020
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    Farah Zaib Khan; Farah Zaib Khan; Stian Soiland-Reyes; Stian Soiland-Reyes (2020). CWL run of RNA-seq Analysis Workflow (CWLProv 0.5.0 Research Object) [Dataset]. http://doi.org/10.17632/xnwncxpw42.1
    Explore at:
    zip, binAvailable download formats
    Dataset updated
    Jan 24, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Farah Zaib Khan; Farah Zaib Khan; Stian Soiland-Reyes; Stian Soiland-Reyes
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This workflow adapts the approach and parameter settings of Trans-Omics for precision Medicine (TOPMed). The RNA-seq pipeline originated from the Broad Institute. There are in total five steps in the workflow starting from:

    1. Read alignment using STAR which produces aligned BAM files including the Genome BAM and Transcriptome BAM.
    2. The Genome BAM file is processed using Picard MarkDuplicates. producing an updated BAM file containing information on duplicate reads (such reads can indicate biased interpretation).
    3. SAMtools index is then employed to generate an index for the BAM file, in preparation for the next step.
    4. The indexed BAM file is processed further with RNA-SeQC which takes the BAM file, human genome reference sequence and Gene Transfer Format (GTF) file as inputs to generate transcriptome-level expression quantifications and standard quality control metrics.
    5. In parallel with transcript quantification, isoform expression levels are quantified by RSEM. This step depends only on the output of the STAR tool, and additional RSEM reference sequences.

    For testing and analysis, the workflow author provided example data created by down-sampling the read files of a TOPMed public access data. Chromosome 12 was extracted from the Homo Sapien Assembly 38 reference sequence and provided by the workflow authors. The required GTF and RSEM reference data files are also provided. The workflow is well-documented with a detailed set of instructions of the steps performed to down-sample the data are also provided for transparency. The availability of example input data, use of containerization for underlying software and detailed documentation are important factors in choosing this specific CWL workflow for CWLProv evaluation.

    This dataset folder is a CWLProv Research Object that captures the Common Workflow Language execution provenance, see https://w3id.org/cwl/prov/0.5.0 or use https://pypi.org/project/cwl

    Steps to reproduce

    To build the research object again, use Python 3 on macOS. Built with:

    • Processor 2.8GHz Intel Core i7
    • Memory: 16GB
    • OS: macOS High Sierra, Version 10.13.3
    • Storage: 250GB
    1. Install cwltool

      pip3 install cwltool==1.0.20180912090223
    2. Install git lfs
      The data download with the git repository requires the installation of Git lfs:
      https://www.atlassian.com/git/tutorials/git-lfs#installing-git-lfs

    3. Get the data and make the analysis environment ready:

      git clone https://github.com/FarahZKhan/cwl_workflows.git
      cd cwl_workflows/
      git checkout CWLProvTesting
      ./topmed-workflows/TOPMed_RNAseq_pipeline/input-examples/download_examples.sh
    4. Run the following commands to create the CWLProv Research Object:

      cwltool --provenance rnaseqwf_0.6.0_linux --tmp-outdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp --tmpdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp topmed-workflows/TOPMed_RNAseq_pipeline/rnaseq_pipeline_fastq.cwl topmed-workflows/TOPMed_RNAseq_pipeline/input-examples/Dockstore.json
      
      zip -r rnaseqwf_0.5.0_mac.zip rnaseqwf_0.5.0_mac
      sha256sum rnaseqwf_0.5.0_mac.zip > rnaseqwf_0.5.0_mac_mac.zip.sha256

    The https://github.com/FarahZKhan/cwl_workflows repository is a frozen snapshot from https://github.com/heliumdatacommons/TOPMed_RNAseq_CWL commit 027e8af41b906173aafdb791351fb29efc044120

  13. h

    Llama-2-SQL-and-Code-Dataset

    • huggingface.co
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    Chris Hayduk, Llama-2-SQL-and-Code-Dataset [Dataset]. https://huggingface.co/datasets/ChrisHayduk/Llama-2-SQL-and-Code-Dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Authors
    Chris Hayduk
    Description

    Dataset Card for "Llama-2-SQL-and-Code-Dataset"

    This dataset is intended to provide LLaMA 2 improved coding and instruction following capabilities, with a specific focus on SQL generation. The dataset is in Alpaca Instruct format. Please be sure to provide the instruction and input in the prompt to the model, along with any prompt text you would like to place around those inputs. In the train split, please ignore the table column. The eval split provides example tables so that the… See the full description on the dataset page: https://huggingface.co/datasets/ChrisHayduk/Llama-2-SQL-and-Code-Dataset.

  14. u

    Pinterest Fashion Compatibility

    • cseweb.ucsd.edu
    • beta.data.urbandatacentre.ca
    json
    + more versions
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    UCSD CSE Research Project, Pinterest Fashion Compatibility [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
    Explore at:
    jsonAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    This dataset contains images (scenes) containing fashion products, which are labeled with bounding boxes and links to the corresponding products.

    Metadata includes

    • product IDs

    • bounding boxes

    Basic Statistics:

    • Scenes: 47,739

    • Products: 38,111

    • Scene-Product Pairs: 93,274

  15. i

    Example datasets for AtyImo (Spark version)

    • ieee-dataport.org
    Updated Jun 17, 2025
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    Marcos Barreto (2025). Example datasets for AtyImo (Spark version) [Dataset]. https://ieee-dataport.org/documents/example-datasets-atyimo-spark-version
    Explore at:
    Dataset updated
    Jun 17, 2025
    Authors
    Marcos Barreto
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    FEDERAL UNIVERSITY OF BAHIA (UFBA)

    ATYIMOLAB (www.atyimolab.ufba.br)

    University College London (UCL)

    Denaxas Lab (www.denaxaslab.org)

    Robespierre Pita and Clicia Pinto and Marcos Barreto and Spiros Denaxas

    /* @(#)File: $atyimo_dataset_info.txt$ @(#)Version: $v1$ @(#)Last changed: $Date: 2017/12/04 12:00:00 $ @(#)Purpose: Example data sets for the AtyImo data linkage tool @(#)Author: Robespierre Pita and Clicia Pinto and Marcos Barreto

  16. Company Datasets for Business Profiling

    • datarade.ai
    Updated Feb 23, 2017
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    Oxylabs (2017). Company Datasets for Business Profiling [Dataset]. https://datarade.ai/data-products/company-datasets-for-business-profiling-oxylabs
    Explore at:
    .json, .xml, .csv, .xlsAvailable download formats
    Dataset updated
    Feb 23, 2017
    Dataset authored and provided by
    Oxylabs
    Area covered
    Isle of Man, Tunisia, British Indian Ocean Territory, Canada, Northern Mariana Islands, Taiwan, Bangladesh, Moldova (Republic of), Andorra, Nepal
    Description

    Company Datasets for valuable business insights!

    Discover new business prospects, identify investment opportunities, track competitor performance, and streamline your sales efforts with comprehensive Company Datasets.

    These datasets are sourced from top industry providers, ensuring you have access to high-quality information:

    • Owler: Gain valuable business insights and competitive intelligence. -AngelList: Receive fresh startup data transformed into actionable insights. -CrunchBase: Access clean, parsed, and ready-to-use business data from private and public companies. -Craft.co: Make data-informed business decisions with Craft.co's company datasets. -Product Hunt: Harness the Product Hunt dataset, a leader in curating the best new products.

    We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:

    • Company name;
    • Size;
    • Founding date;
    • Location;
    • Industry;
    • Revenue;
    • Employee count;
    • Competitors.

    You can choose your preferred data delivery method, including various storage options, delivery frequency, and input/output formats.

    Receive datasets in CSV, JSON, and other formats, with storage options like AWS S3 and Google Cloud Storage. Opt for one-time, monthly, quarterly, or bi-annual data delivery.

    With Oxylabs Datasets, you can count on:

    • Fresh and accurate data collected and parsed by our expert web scraping team.
    • Time and resource savings, allowing you to focus on data analysis and achieving your business goals.
    • A customized approach tailored to your specific business needs.
    • Legal compliance in line with GDPR and CCPA standards, thanks to our membership in the Ethical Web Data Collection Initiative.

    Pricing Options:

    Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

    Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

    Experience a seamless journey with Oxylabs:

    • Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
    • Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
    • Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
    • Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

    Unlock the power of data with Oxylabs' Company Datasets and supercharge your business insights today!

  17. 1000 Empirical Time series

    • figshare.com
    • researchdata.edu.au
    png
    Updated May 30, 2023
    + more versions
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    Ben Fulcher (2023). 1000 Empirical Time series [Dataset]. http://doi.org/10.6084/m9.figshare.5436136.v10
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    pngAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Ben Fulcher
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    A diverse selection of 1000 empirical time series, along with results of an hctsa feature extraction, using v1.06 of hctsa and Matlab 2019b, computed on a server at The University of Sydney.The results of the computation are in the hctsa file, HCTSA_Empirical1000.mat for use in Matlab using v1.06 of hctsa.The same data is also provided in .csv format for the hctsa_datamatrix.csv (results of feature computation), with information about rows (time series) in hctsa_timeseries-info.csv, information about columns (features) in hctsa_features.csv (and corresponding hctsa code used to compute each feature in hctsa_masterfeatures.csv), and the data of individual time series (each line a time series, for time series described in hctsa_timeseries-info.csv) is in hctsa_timeseries-data.csv. These .csv files were produced by running >>OutputToCSV(HCTSA_Empirical1000.mat,true,true); in hctsa.The input file, INP_Empirical1000.mat, is for use with hctsa, and contains the time-series data and metadata for the 1000 time series. For example, massive feature extraction from these data on the user's machine, using hctsa, can proceed as>> TS_Init('INP_Empirical1000.mat');Some visualizations of the dataset are in CarpetPlot.png (first 1000 samples of all time series as a carpet (color) plot) and 150TS-250samples.png (conventional time-series plots of the first 250 samples of a sample of 150 time series from the dataset). More visualizations can be performed by the user using TS_PlotTimeSeries from the hctsa package.See links in references for more comprehensive documentation for performing methodological comparison using this dataset, and on how to download and use v1.06 of hctsa.

  18. i

    Sample Dataset for Testing

    • ieee-dataport.org
    Updated Apr 28, 2025
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    Alex Outman (2025). Sample Dataset for Testing [Dataset]. https://ieee-dataport.org/documents/sample-dataset-testing
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    Dataset updated
    Apr 28, 2025
    Authors
    Alex Outman
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    10

  19. Training images

    • redivis.com
    Updated Aug 17, 2022
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    Redivis Demo Organization (2022). Training images [Dataset]. https://redivis.com/datasets/yz1s-d09009dbb
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    Dataset updated
    Aug 17, 2022
    Dataset provided by
    Redivis Inc.
    Authors
    Redivis Demo Organization
    Time period covered
    Aug 8, 2022
    Description

    This is an auto-generated index table corresponding to a folder of files in this dataset with the same name. This table can be used to extract a subset of files based on their metadata, which can then be used for further analysis. You can view the contents of specific files by navigating to the "cells" tab and clicking on an individual file_kd.

  20. o

    Messy data for data cleaning exercise - Dataset - openAFRICA

    • open.africa
    Updated Oct 6, 2021
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    (2021). Messy data for data cleaning exercise - Dataset - openAFRICA [Dataset]. https://open.africa/dataset/messy-data-for-data-cleaning-exercise
    Explore at:
    Dataset updated
    Oct 6, 2021
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    A messy data for demonstrating "how to clean data using spreadsheet". This dataset was intentionally formatted to be messy, for the purpose of demonstration. It was collated from here - https://openafrica.net/dataset/historic-and-projected-rainfall-and-runoff-for-4-lake-victoria-sub-regions

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Templates (2023). dataset-card-example [Dataset]. https://huggingface.co/datasets/templates/dataset-card-example

dataset-card-example

templates/dataset-card-example

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Dataset updated
Sep 28, 2023
Dataset authored and provided by
Templates
Description

Dataset Card for Dataset Name

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

  Dataset Details







  Dataset Description

Curated by: [More Information Needed] Funded by [optional]: [More Information Needed] Shared by [optional]: [More Information Needed] Language(s) (NLP): [More Information Needed] License: [More Information Needed]

  Dataset Sources [optional]… See the full description on the dataset page: https://huggingface.co/datasets/templates/dataset-card-example.
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