Free, daily updated MAC prefix and vendor CSV database. Download now for accurate device identification.

This dataset contains >800K CSV files behind the GitTables 1M corpus.

For more information about the GitTables corpus, visit:

- our website for GitTables, or

- the main GitTables download page on Zenodo.

A csv file containing the tidal frequencies used for statistical analyses in the paper "Estimating Freshwater Flows From Tidally-Affected Hydrographic Data" by Dan Pagendam and Don Percival.

We are excited to announce that we have successfully extracted a comprehensive set of alcoholic beverage records from BevMo and compiled them into a CSV file.

This meticulously organized dataset includes key information such as product URLs, IDs, names, SKUs, GTIN14 barcodes, detailed product descriptions, availability status, pricing, currency, images, breadcrumbs, and more.

Our dataset provides an invaluable resource for anyone looking to analyze or utilize detailed BevMo product information.

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Perfect for market analysis, e-commerce insights, and competitive research.

The Facility Registry System (FRS) identifies facilities, sites, or places subject to environmental regulation or of environmental interest to EPA programs or delegated states. Using vigorous verification and data management procedures, FRS integrates facility data from program national systems, state master facility records, tribal partners, and other federal agencies and provides the Agency with a centrally managed, single source of comprehensive and authoritative information on facilities.

The benchmarking datasets used for deepBlink. The npz files contain train/valid/test splits inside and can be used directly. The files belong to the following challenges / classes:- ISBI Particle tracking challenge: microtubule, vesicle, receptor- Custom synthetic (based on http://smal.ws): particle- Custom fixed cell: smfish- Custom live cell: suntagThe csv files are to determine which image in the test splits correspond to which original image, SNR, and density.

Download Service provides pre-defined data on relationship between selected territorial elements and units of territorial registration using the ATOM technology. The service is publicly available and free-of-charge (data covers the whole territory of the Czech Republic) and enables downloading of predefined data file containing data for the whole Czech Republic. Files are created during the first day of each month with data valid to the last day of previous month. The whole dataset (7 files) is compressed (ZIP) for downloading.

Dataset

This dataset was created by Moses Moncy

Contents

Alaska DCCED Division of Corporations, Business and Professional Licensing courtesy CSV Download Link Location

The event logs in CSV format. The dataset contains both correlated and uncorrelated logs

This dataset contains spatial boundaries for the DevWA Area relating to the City of Perth Planning Scheme No.2.Please see https://perth.wa.gov.au/develop/planning-framework/planning-schemes and https://perth.wa.gov.au/develop/planning-framework/planning-policies-and-precinct-plans for more information regarding the City of Perth Planning Schemes. Show full description

This repository was created for my Master's thesis in Computational Intelligence and Internet of Things at the University of Córdoba, Spain. The purpose of this repository is to store the datasets found that were used in some of the studies that served as research material for this Master's thesis. Also, the datasets used in the experimental part of this work are included.

Below are the datasets specified, along with the details of their references, authors, and download sources.

----------- STS-Gold Dataset ----------------

The dataset consists of 2026 tweets. The file consists of 3 columns: id, polarity, and tweet. The three columns denote the unique id, polarity index of the text and the tweet text respectively.

Reference: Saif, H., Fernandez, M., He, Y., & Alani, H. (2013). Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold.

File name: sts_gold_tweet.csv

----------- Amazon Sales Dataset ----------------

This dataset is having the data of 1K+ Amazon Product's Ratings and Reviews as per their details listed on the official website of Amazon. The data was scraped in the month of January 2023 from the Official Website of Amazon.

Owner: Karkavelraja J., Postgraduate student at Puducherry Technological University (Puducherry, Puducherry, India)

Features:

• product_id - Product ID
• product_name - Name of the Product
• category - Category of the Product
• discounted_price - Discounted Price of the Product
• actual_price - Actual Price of the Product
• discount_percentage - Percentage of Discount for the Product
• rating - Rating of the Product
• rating_count - Number of people who voted for the Amazon rating
• about_product - Description about the Product
• user_id - ID of the user who wrote review for the Product
• user_name - Name of the user who wrote review for the Product
• review_id - ID of the user review
• review_title - Short review
• review_content - Long review
• img_link - Image Link of the Product
• product_link - Official Website Link of the Product

License: CC BY-NC-SA 4.0

File name: amazon.csv

----------- Rotten Tomatoes Reviews Dataset ----------------

This rating inference dataset is a sentiment classification dataset, containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. On average, these reviews consist of 21 words. The first 5331 rows contains only negative samples and the last 5331 rows contain only positive samples, thus the data should be shuffled before usage.

This data is collected from https://www.cs.cornell.edu/people/pabo/movie-review-data/ as a txt file and converted into a csv file. The file consists of 2 columns: reviews and labels (1 for fresh (good) and 0 for rotten (bad)).

Reference: Bo Pang and Lillian Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pages 115–124, Ann Arbor, Michigan, June 2005. Association for Computational Linguistics

File name: data_rt.csv

----------- Preprocessed Dataset Sentiment Analysis ----------------

Preprocessed amazon product review data of Gen3EcoDot (Alexa) scrapped entirely from amazon.in
Stemmed and lemmatized using nltk.
Sentiment labels are generated using TextBlob polarity scores.

The file consists of 4 columns: index, review (stemmed and lemmatized review using nltk), polarity (score) and division (categorical label generated using polarity score).

DOI: 10.34740/kaggle/dsv/3877817

Citation: @misc{pradeesh arumadi_2022, title={Preprocessed Dataset Sentiment Analysis}, url={https://www.kaggle.com/dsv/3877817}, DOI={10.34740/KAGGLE/DSV/3877817}, publisher={Kaggle}, author={Pradeesh Arumadi}, year={2022} }

This dataset was used in the experimental phase of my research.

File name: EcoPreprocessed.csv

----------- Amazon Earphones Reviews ----------------

This dataset consists of a 9930 Amazon reviews, star ratings, for 10 latest (as of mid-2019) bluetooth earphone devices for learning how to train Machine for sentiment analysis.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 5 columns: ReviewTitle, ReviewBody, ReviewStar, Product and division (manually added - categorical label generated using ReviewStar score)

License: U.S. Government Works

Source: www.amazon.in

File name (original): AllProductReviews.csv (contains 14337 reviews)

File name (edited - used for my research) : AllProductReviews2.csv (contains 9930 reviews)

----------- Amazon Musical Instruments Reviews ----------------

This dataset contains 7137 comments/reviews of different musical instruments coming from Amazon.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 10 columns: reviewerID, asin (ID of the product), reviewerName, helpful (helpfulness rating of the review), reviewText, overall (rating of the product), summary (summary of the review), unixReviewTime (time of the review - unix time), reviewTime (time of the review (raw) and division (manually added - categorical label generated using overall score).

Source: http://jmcauley.ucsd.edu/data/amazon/

File name (original): Musical_instruments_reviews.csv (contains 10261 reviews)

File name (edited - used for my research) : Musical_instruments_reviews2.csv (contains 7137 reviews)

The Canada Trademarks Dataset

18 Journal of Empirical Legal Studies 908 (2021), prepublication draft available at https://papers.ssrn.com/abstract=3782655, published version available at https://onlinelibrary.wiley.com/share/author/CHG3HC6GTFMMRU8UJFRR?target=10.1111/jels.12303

Dataset Selection and Arrangement (c) 2021 Jeremy Sheff

Python and Stata Scripts (c) 2021 Jeremy Sheff

Contains data licensed by Her Majesty the Queen in right of Canada, as represented by the Minister of Industry, the minister responsible for the administration of the Canadian Intellectual Property Office.

This individual-application-level dataset includes records of all applications for registered trademarks in Canada since approximately 1980, and of many preserved applications and registrations dating back to the beginning of Canada’s trademark registry in 1865, totaling over 1.6 million application records. It includes comprehensive bibliographic and lifecycle data; trademark characteristics; goods and services claims; identification of applicants, attorneys, and other interested parties (including address data); detailed prosecution history event data; and data on application, registration, and use claims in countries other than Canada. The dataset has been constructed from public records made available by the Canadian Intellectual Property Office. Both the dataset and the code used to build and analyze it are presented for public use on open-access terms.

Scripts are licensed for reuse subject to the Creative Commons Attribution License 4.0 (CC-BY-4.0), https://creativecommons.org/licenses/by/4.0/. Data files are licensed for reuse subject to the Creative Commons Attribution License 4.0 (CC-BY-4.0), https://creativecommons.org/licenses/by/4.0/, and also subject to additional conditions imposed by the Canadian Intellectual Property Office (CIPO) as described below.

Terms of Use:

As per the terms of use of CIPO's government data, all users are required to include the above-quoted attribution to CIPO in any reproductions of this dataset. They are further required to cease using any record within the datasets that has been modified by CIPO and for which CIPO has issued a notice on its website in accordance with its Terms and Conditions, and to use the datasets in compliance with applicable laws. These requirements are in addition to the terms of the CC-BY-4.0 license, which require attribution to the author (among other terms). For further information on CIPO’s terms and conditions, see https://www.ic.gc.ca/eic/site/cipointernet-internetopic.nsf/eng/wr01935.html. For further information on the CC-BY-4.0 license, see https://creativecommons.org/licenses/by/4.0/.

The following attribution statement, if included by users of this dataset, is satisfactory to the author, but the author makes no representations as to whether it may be satisfactory to CIPO:

The Canada Trademarks Dataset is (c) 2021 by Jeremy Sheff and licensed under a CC-BY-4.0 license, subject to additional terms imposed by the Canadian Intellectual Property Office. It contains data licensed by Her Majesty the Queen in right of Canada, as represented by the Minister of Industry, the minister responsible for the administration of the Canadian Intellectual Property Office. For further information, see https://creativecommons.org/licenses/by/4.0/ and https://www.ic.gc.ca/eic/site/cipointernet-internetopic.nsf/eng/wr01935.html.

Details of Repository Contents:

This repository includes a number of .zip archives which expand into folders containing either scripts for construction and analysis of the dataset or data files comprising the dataset itself. These folders are as follows:

• /csv: contains the .csv versions of the data files
• /do: contains Stata do-files used to convert the .csv files to .dta format and perform the statistical analyses set forth in the paper reporting this dataset
• /dta: contains the .dta versions of the data files
• /py: contains the python scripts used to download CIPO’s historical trademarks data via SFTP and generate the .csv data files

If users wish to construct rather than download the datafiles, the first script that they should run is /py/sftp_secure.py. This script will prompt the user to enter their IP Horizons SFTP credentials; these can be obtained by registering with CIPO at https://ised-isde.survey-sondage.ca/f/s.aspx?s=59f3b3a4-2fb5-49a4-b064-645a5e3a752d&lang=EN&ds=SFTP. The script will also prompt the user to identify a target directory for the data downloads. Because the data archives are quite large, users are advised to create a target directory in advance and ensure they have at least 70GB of available storage on the media in which the directory is located.

The sftp_secure.py script will generate a new subfolder in the user’s target directory called /XML_raw. Users should note the full path of this directory, which they will be prompted to provide when running the remaining python scripts. Each of the remaining scripts, the filenames of which begin with “iterparse”, corresponds to one of the data files in the dataset, as indicated in the script’s filename. After running one of these scripts, the user’s target directory should include a /csv subdirectory containing the data file corresponding to the script; after running all the iterparse scripts the user’s /csv directory should be identical to the /csv directory in this repository. Users are invited to modify these scripts as they see fit, subject to the terms of the licenses set forth above.

With respect to the Stata do-files, only one of them is relevant to construction of the dataset itself. This is /do/CA_TM_csv_cleanup.do, which converts the .csv versions of the data files to .dta format, and uses Stata’s labeling functionality to reduce the size of the resulting files while preserving information. The other do-files generate the analyses and graphics presented in the paper describing the dataset (Jeremy N. Sheff, The Canada Trademarks Dataset, 18 J. Empirical Leg. Studies (forthcoming 2021)), available at https://papers.ssrn.com/abstract=3782655). These do-files are also licensed for reuse subject to the terms of the CC-BY-4.0 license, and users are invited to adapt the scripts to their needs.

The python and Stata scripts included in this repository are separately maintained and updated on Github at https://github.com/jnsheff/CanadaTM.

This repository also includes a copy of the current version of CIPO's data dictionary for its historical XML trademarks archive as of the date of construction of this dataset.

The Facility Registry System (FRS) identifies facilities, sites, or places subject to environmental regulation or of environmental interest to EPA programs or delegated states. Using vigorous verification and data management procedures, FRS integrates facility data from program national systems, state master facility records, tribal partners, and other federal agencies and provides the Agency with a centrally managed, single source of comprehensive and authoritative information on facilities.

Acknowledgement
These data are a product of a research activity conducted in the context of the RAILS (Roadmaps for AI integration in the raiL Sector) project which has received funding from the Shift2Rail Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement n. 881782 Rails. The JU receives support from the European Union’s Horizon 2020 research and innovation program and the Shift2Rail JU members other than the Union.

Disclaimers
The information and views set out in this document are those of the author(s) and do not necessarily reflect the official opinion of Shift2Rail Joint Undertaking. The JU does not guarantee the accuracy of the data included in this document. Neither the JU nor any person acting on the JU’s behalf may be held responsible for the use which may be made of the information contained therein.

This "dataset" has been created for scientific purposes only - and WITHOUT ANY COMMERCIAL purposes - to study the potentials of Deep Learning and Transfer Learning approaches. We are NOT re-distributing any video or audio; our files just contain pointers and indications needed to reproduce our study. The authors DO NOT ASSUME any responsibility for the use that other researchers or users will make of these data.

General Info
The CSV files contained in this folder (and subfolders) compose the Level Crossing (LC) Warning Bell (WB) Dataset.

When using any of these data, please mention:

De Donato, L., Marrone, S., Flammini, F., Sansone, C., Vittorini, V., Nardone, R., Mazzariello, C., and Bernaudine, F., "Intelligent Detection of Warning Bells at Level Crossings through Deep Transfer Learning for Smarter Railway Maintenance", Engineering Applications of Artificial Intelligence, Elsevier, 2023

Content of the folder
This folder contains the following subfolders and files.

"Data Files" contains all the CSV files related to the data composing the LCWB Dataset:

• WB_data.csv (WB_labels.csv): representing data of the "Warning Bell (WB)" class;
• NA_data.csv (NA_labels.csv): representing data of the "No Alarm (NA)" class;
• GE_data.csv (GE_labels.csv): representing data of the "GEneric alarm (GE)" class.

"LCWB Dataset" contains all the JSON files that show how the aforementioned data have been distributed among training, validation, and test sets:

• IT_Distribution.json and UK_distribution.json respectively show how Italian (IT) WBs and British (UK) WBs have been distributed;
• The same goes for NA_Distribution.json and GE_Distribution.json, which show the distribution of NA and GE data respectively;
• DatasetDistribution.json simply incorporates the content of the aforementioned JSON files in a unique file that can be exploited to obtain exactly the same dataset we adopted in our analyses.

"Additional Files" contains some CSV files related to data we adopted to further test the deep neural network leveraged in the aforementioned manuscript:

• FR_DE_data.csv (FR_DE_labels.csv): representing data that have been used to test the generalisation performances of the network we exploited on LC WBs related to countries that were not considered in the training phase.
• Noises_data.csv (Noises_labels.csv): representing the noises that were considered to study the behaviour of the network in case of noisy data.

CSV Files Structure
Each "XX_labels.csv" file contains, for each entry, the following information:

• The identifier ("index") of the sub-class (which is not relevant in our case);
• The code-name ("mid") of the class, which is used in the "XX_data.csv" file to indicate the sub-class of a specific audio;
• The extended name of the class ("display_name").

Worth mentioning, sub-classes do not have a specific purpose in our task. They have been kept to maintain as much as possible the structure of the "class_labels_indices.csv" file provided by AudioSet. The same applies to the "XX_data.csv" files, which have roughly the same structures of "Evaluation", "Balanced train", and "Unbalanced train" AudioSet CSV files.

Indeed, each "XX_data.csv" file contains, for each entry, the following information:

• ID: the identifier of the entry;
• YTID: the YouTube identifier of the video;
• start_seconds and end_seconds: which delimit the portion of audio (extracted from YTID) which is of interest for this task;
• positive_labels: the label(s) associated with the audio.

Credits
The structure of the CSV files contained in this dataset, as well as part of their content, was inspired by the CSV files composing the AudioSet dataset which is made available by Google Inc. under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, while its ontology is available under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.

Particularly, from AudioSet, we retrieved:

• The structure of the CSV files as discussed above.
• Data contained in GE_data.csv (which is a minimal portion of data made available by AudioSet) as well as the related 19 classes (in GE_labels.csv) which we selected among the hundreds of classes included in the AudioSet ontology.

Pointers contained in "XX_data.csv" files other than GE_data.csv have been retrieved manually from scratch. Then, the related "XX_labels.csv" files have been created consequently.

More about downloading the AudioSet dataset can be found here.

Walmart products sample dataset having 1000+ records in CSV format. Download monthly dataset for walmart data and it having around 100K+ records.

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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.

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