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.

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.

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

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.

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 primary goal of the Coral Reef Evaluation and Monitoring Project (CREMP) is to measure the status and trends of these communities to assist managers in understanding, protecting, and restoring the living marine resources of the Florida Keys National Marine Sanctuary. Data from the project will be used to determine (1) overall net increase or decrease in stony coral percent cover and stony coral species richness, (2) overall net change in measurable reef community parameters, (3) changes observed in individual reef communities with no overall change on a landscape scale (decreases in one location balanced by increases elsewhere) or changes that are linked to specific regions of the landscape. Each of these potential mechanisms of change will result in different spatial patterns of change. A Sanctuary-wide, rather than a single-location survey, is necessary to detect ecosystem change.

The primary goal of the Coral Reef Evaluation and Monitoring Project (CREMP) is to measure the status and trends of these communities to assist managers in understanding, protecting, and restoring the living marine resources of the Florida Keys National Marine Sanctuary. Data from the project will be used to determine (1) overall net increase or decrease in stony coral percent cover and stony coral species richness, (2) overall net change in measurable reef community parameters, (3) changes observed in individual reef communities with no overall change on a landscape scale (decreases in one location balanced by increases elsewhere) or changes that are linked to specific regions of the landscape. Each of these potential mechanisms of change will result in different spatial patterns of change. A Sanctuary-wide, rather than a single-location survey, is necessary to detect ecosystem change.

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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Csv files containing all detectable genes.

The Residential School Locations Dataset [IRS_Locations.csv] contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Indian Residential School Settlement Agreement are included in this dataset, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The dataset was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this dataset,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School.When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites.

The dataset is a set of network traffic traces in pcap/csv format captured from a single user. The traffic is classified in 5 different activities (Video, Bulk, Idle, Web, and Interactive) and the label is shown in the filename. There is also a file (mapping.csv) with the mapping of the host's IP address, the csv/pcap filename and the activity label.

Activities:

Interactive: applications that perform real-time interactions in order to provide a suitable user experience, such as editing a file in google docs and remote CLI's sessions by SSH. Bulk data transfer: applications that perform a transfer of large data volume files over the network. Some examples are SCP/FTP applications and direct downloads of large files from web servers like Mediafire, Dropbox or the university repository among others. Web browsing: contains all the generated traffic while searching and consuming different web pages. Examples of those pages are several blogs and new sites and the moodle of the university. Vídeo playback: contains traffic from applications that consume video in streaming or pseudo-streaming. The most known server used are Twitch and Youtube but the university online classroom has also been used. Idle behaviour: is composed by the background traffic generated by the user computer when the user is idle. This traffic has been captured with every application closed and with some opened pages like google docs, YouTube and several web pages, but always without user interaction.

The capture is performed in a network probe, attached to the router that forwards the user network traffic, using a SPAN port. The traffic is stored in pcap format with all the packet payload. In the csv file, every non TCP/UDP packet is filtered out, as well as every packet with no payload. The fields in the csv files are the following (one line per packet): Timestamp, protocol, payload size, IP address source and destination, UDP/TCP port source and destination. The fields are also included as a header in every csv file.

The amount of data is stated as follows:

Bulk : 19 traces, 3599 s of total duration, 8704 MBytes of pcap files Video : 23 traces, 4496 s, 1405 MBytes Web : 23 traces, 4203 s, 148 MBytes Interactive : 42 traces, 8934 s, 30.5 MBytes Idle : 52 traces, 6341 s, 0.69 MBytes

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

The primary goal of the Coral Reef Evaluation and Monitoring Project (CREMP) is to measure the status and trends of these communities to assist managers in understanding, protecting, and restoring the living marine resources of the Florida Keys National Marine Sanctuary. Data from the project will be used to determine (1) overall net increase or decrease in stony coral percent cover and stony coral species richness, (2) overall net change in measurable reef community parameters, (3) changes observed in individual reef communities with no overall change on a landscape scale (decreases in one location balanced by increases elsewhere) or changes that are linked to specific regions of the landscape. Each of these potential mechanisms of change will result in different spatial patterns of change. A Sanctuary-wide, rather than a single-location survey, is necessary to detect ecosystem change.

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.

QUT Research Data Respository Dataset Resource available for download