Dataset Card for example-preference-dataset

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/sdiazlor/example-preference-dataset/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/distilabel-internal-testing/example-generate-preference-dataset.

Context

This dataset was created only for making examples every columns has generated with random values. If you wanna create a dataset similar like this review this notebook

Content

There are five columns 'Country' = 'Bolivia', :'Argentina','Paraguay','Chile','Brazil','Peru' 'Temperature' 'Humidity' 'Pm10' 'Date'

Motivation

This dataset was created to pilot techniques for creating synthetic data from datasets containing sensitive and protected information in the local government context. Synthetic data generation replaces actual data with representative data generated from statistical models; this preserves the key data properties that allow insights to be drawn from the data while protecting the privacy of the people included in the data. We invite you to read the Understanding Synthetic Data white paper for a concise introduction to synthetic data.

This effort was a collaboration of the Urban Institute, Allegheny County’s Department of Human Services (DHS) and CountyStat, and the University of Pittsburgh’s Western Pennsylvania Regional Data Center.

Collection

The source data for this project consisted of 1) month-by-month records of services included in Allegheny County's data warehouse and 2) demographic data about the individuals who received the services. As the County’s data warehouse combines this service and client data, this data is referred to as “Integrated Services data”. Read more about the data warehouse and the kinds of services it includes here.

Preprocessing

Synthetic data are typically generated from probability distributions or models identified as being representative of the confidential data. For this dataset, a model of the Integrated Services data was used to generate multiple versions of the synthetic dataset. These different candidate datasets were evaluated to select for publication the dataset version that best balances utility and privacy. For high-level information about this evaluation, see the Synthetic Data User Guide.

For more information about the creation of the synthetic version of this data, see the technical brief for this project, which discusses the technical decision making and modeling process in more detail.

Recommended Uses

This disaggregated synthetic data allows for many analyses that are not possible with aggregate data (summary statistics). Broadly, this synthetic version of this data could be analyzed to better understand the usage of human services by people in Allegheny County, including the interplay in the usage of multiple services and demographic information about clients.

Known Limitations/Biases

Some amount of deviation from the original data is inherent to the synthetic data generation process. Specific examples of limitations (including undercounts and overcounts for the usage of different services) are given in the Synthetic Data User Guide and the technical report describing this dataset's creation.

Feedback

Please reach out to this dataset's data steward (listed below) to let us know how you are using this data and if you found it to be helpful. Please also provide any feedback on how to make this dataset more applicable to your work, any suggestions of future synthetic datasets, or any additional information that would make this more useful. Also, please copy wprdc@pitt.edu on any such feedback (as the WPRDC always loves to hear about how people use the data that they publish and how the data could be improved).

Further Documentation and Resources

1) A high-level overview of synthetic data generation as a method for protecting privacy can be found in the Understanding Synthetic Data white paper.
2) The Synthetic Data User Guide provides high-level information to help users understand the motivation, evaluation process, and limitations of the synthetic version of Allegheny County DHS's Human Services data published here.
3) Generating a Fully Synthetic Human Services Dataset: A Technical Report on Synthesis and Evaluation Methodologies describes the full technical methodology used for generating the synthetic data, evaluating the various options, and selecting the final candidate for publication.
4) The WPRDC also hosts the Allegheny County Human Services Community Profiles dataset, which provides annual updates on human-services usage, aggregated by neighborhood/municipality. That data can be explored using the County's Human Services Community Profile web site.

Create

## Overview

Create is a dataset for object detection tasks - it contains Weed Cotton annotations for 2,447 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Data generation in machine learning involves creating or manipulating data to train and evaluate machine learning models. The purpose of data generation is to provide diverse and representative examples that cover a wide range of scenarios, ensuring the model's robustness and generalization. Data augmentation techniques involve applying various transformations to existing data samples to create new ones. These transformations include: random rotations, translations, scaling, flips, and more. Augmentation helps in increasing the dataset size, introducing natural variations, and improving model performance by making it more invariant to specific transformations. The dataset contains GENERATED USA passports, which are replicas of official passports but with randomly generated details, such as name, date of birth etc. The primary intention of generating these fake passports is to demonstrate the structure and content of a typical passport document and to train the neural network to identify this type of document. Generated passports can assist in conducting research without accessing or compromising real user data that is often sensitive and subject to privacy regulations. Synthetic data generation allows researchers to develop and refine models using simulated passport data without risking privacy leaks.

Test Data Generation Tools Market Outlook

The global market size for Test Data Generation Tools was valued at USD 800 million in 2023 and is projected to reach USD 2.2 billion by 2032, growing at a CAGR of 12.1% during the forecast period. The surge in the adoption of agile and DevOps practices, along with the increasing complexity of software applications, is driving the growth of this market.

One of the primary growth factors for the Test Data Generation Tools market is the increasing need for high-quality test data in software development. As businesses shift towards more agile and DevOps methodologies, the demand for automated and efficient test data generation solutions has surged. These tools help in reducing the time required for test data creation, thereby accelerating the overall software development lifecycle. Additionally, the rise in digital transformation across various industries has necessitated the need for robust testing frameworks, further propelling the market growth.

The proliferation of big data and the growing emphasis on data privacy and security are also significant contributors to market expansion. With the introduction of stringent regulations like GDPR and CCPA, organizations are compelled to ensure that their test data is compliant with these laws. Test Data Generation Tools that offer features like data masking and data subsetting are increasingly being adopted to address these compliance requirements. Furthermore, the increasing instances of data breaches have underscored the importance of using synthetic data for testing purposes, thereby driving the demand for these tools.

Another critical growth factor is the technological advancements in artificial intelligence and machine learning. These technologies have revolutionized the field of test data generation by enabling the creation of more realistic and comprehensive test data sets. Machine learning algorithms can analyze large datasets to generate synthetic data that closely mimics real-world data, thus enhancing the effectiveness of software testing. This aspect has made AI and ML-powered test data generation tools highly sought after in the market.

Regional outlook for the Test Data Generation Tools market shows promising growth across various regions. North America is expected to hold the largest market share due to the early adoption of advanced technologies and the presence of major software companies. Europe is also anticipated to witness significant growth owing to strict regulatory requirements and increased focus on data security. The Asia Pacific region is projected to grow at the highest CAGR, driven by rapid industrialization and the growing IT sector in countries like India and China.

Synthetic Data Generation has emerged as a pivotal component in the realm of test data generation tools. This process involves creating artificial data that closely resembles real-world data, without compromising on privacy or security. The ability to generate synthetic data is particularly beneficial in scenarios where access to real data is restricted due to privacy concerns or regulatory constraints. By leveraging synthetic data, organizations can perform comprehensive testing without the risk of exposing sensitive information. This not only ensures compliance with data protection regulations but also enhances the overall quality and reliability of software applications. As the demand for privacy-compliant testing solutions grows, synthetic data generation is becoming an indispensable tool in the software development lifecycle.

Component Analysis

The Test Data Generation Tools market is segmented into software and services. The software segment is expected to dominate the market throughout the forecast period. This dominance can be attributed to the increasing adoption of automated testing tools and the growing need for robust test data management solutions. Software tools offer a wide range of functionalities, including data profiling, data masking, and data subsetting, which are essential for effective software testing. The continuous advancements in software capabilities also contribute to the growth of this segment.

In contrast, the services segment, although smaller in market share, is expected to grow at a substantial rate. Services include consulting, implementation, and support services, which are crucial for the successful deployment and management of test data generation tools. The increasing complexity of IT inf

Generate Ray

## Overview

Generate Ray is a dataset for object detection tasks - it contains 0 1 2 3 4 KH9O annotations for 279 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

1. Why build a dataset?

I wanted to run data analysis and machine learning on a large dataset to build my data science skills but I felt out of touch with the various datasets available so I thought... how about I try and build my own dataset?

2. Why gaming data?

I wondered what data should be in the dataset and settled with online digital game purchases since I am an avid gamer. Imagine getting sales data from the PlayStation Store or Xbox Microsoft Store, this is what I was aiming to replicate.

3. Scope of the dataset

I envisaged the dataset to be data created through the purchase of a digital game on either the UK PlayStation Store or Xbox Microsoft Store. Considering this, the scope of dataset varies depending on which column of data you are viewing, for example: - Date and Time: purchases were defined between a start/end date (this can be altered, see point 4) and, of course, anytime across the 24hr clock - Geographically: purchases were setup to come from any postcode in the UK - in total this is over 1,000,000 active postcodes - Purchases: the list of game titles available for purchase is 24 - Registered Banks: the list of registered banks in the UK (as of 03/2022) was 159

4. Over 42,000 rows isn't enough?

To generate the dataset, I built a function in Python. This function, when called with the number of rows you want in your dataset, will generate the dataset. For example, calling function(1000) will provide you with a dataset with 1000 rows.

Considering this, if just over 42,000 rows of data (42,892 to be exact) isn't enough, feel free to check out the code on my GitHub to run the function yourself with as many rows as you want.

Note: You can also edit the start/end dates of the function depending on which timespan you want the dataset to cover.

5. Disclaimer - this is still a work in progress!

Yes, as stated above, this dataset is still a work in progress and is therefore not 100% perfect. There is a backlog of issues that need to be resolved. Feel free to check out the backlog.

One example of this is how on various columns, the distributions of data is equal, when in fact for the dataset to be entirely random, this should not be the case. An example of this issue is the Time column. These issues will be resolved in a later update.

Last updated: 24/04/2022

The code, strainenergy_v4_1.m, was used for generating and processing the dataset for load-displacement and stress-strain. Software Matlab version 6.1 was used for running the code. The specific variables of the parameters used to generate the current dataset are as follows:• ip1: input file containing the load-displacement data• diameter: fascicle diameter• laststrainpt: an estimate of the strain at rupture, r• orderpoly: an integral value from 2-7 which represents the order of the polynomial for fitting to the data from O to q• loadat1percent: y/n; to determine the value of the load (set at 1% of the maximum load) at which the specimen became taut. ‘y’ denotes yes; ‘n’ denotes no.The logfile.txt, contains the parameters used for deriving the values of the respective mechanical properties.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

Data sets used to prepare illustrative figures for the overview article “Multiscale Modeling of Background Ozone” Overview The CMAQ model output datasets used to create illustrative figures for this overview article were generated by scientists in EPA/ORD/CEMM and EPA/OAR/OAQPS. The EPA/ORD/CEMM-generated dataset consisted of hourly CMAQ output from two simulations. The first simulation was performed for July 1 – 31 over a 12 km modeling _domain covering the Western U.S. The simulation was configured with the Integrated Source Apportionment Method (ISAM) to estimate the contributions from 9 source categories to modeled ozone. ISAM source contributions for July 17 – 31 averaged over all grid cells located in Colorado were used to generate the illustrative pie chart in the overview article. The second simulation was performed for October 1, 2013 – August 31, 2014 over a 108 km modeling _domain covering the northern hemisphere. This simulation was also configured with ISAM to estimate the contributions from non-US anthropogenic sources, natural sources, stratospheric ozone, and other sources on ozone concentrations. Ozone ISAM results from this simulation were extracted along a boundary curtain of the 12 km modeling _domain specified over the Western U.S. for the time period January 1, 2014 – July 31, 2014 and used to generate the illustrative time-height cross-sections in the overview article. The EPA/OAR/OAQPS-generated dataset consisted of hourly gridded CMAQ output for surface ozone concentrations for the year 2016. The CMAQ simulations were performed over the northern hemisphere at a horizontal resolution of 108 km. NO2 and O3 data for July 2016 was extracted from these simulations generate the vertically-integrated column densities shown in the illustrative comparison to satellite-derived column densities. CMAQ Model Data The data from the CMAQ model simulations used in this research effort are very large (several terabytes) and cannot be uploaded to ScienceHub due to size restrictions. The model simulations are stored on the /asm archival system accessible through the atmos high-performance computing (HPC) system. Due to data management policies, files on /asm are subject to expiry depending on the template of the project. Files not requested for extension after the expiry date are deleted permanently from the system. The format of the files used in this analysis and listed below is ioapi/netcdf. Documentation of this format, including definitions of the geographical projection attributes contained in the file headers, are available at https://www.cmascenter.org/ioapi/ Documentation on the CMAQ model, including a description of the output file format and output model species can be found in the CMAQ documentation on the CMAQ GitHub site at https://github.com/USEPA/CMAQ. This dataset is associated with the following publication: Hogrefe, C., B. Henderson, G. Tonnesen, R. Mathur, and R. Matichuk. Multiscale Modeling of Background Ozone: Research Needs to Inform and Improve Air Quality Management. EM Magazine. Air and Waste Management Association, Pittsburgh, PA, USA, 1-6, (2020).

Snapshot img

Synthetic Data Generation Market Size 2025-2029

The synthetic data generation market size is forecast to increase by USD 4.39 billion, at a CAGR of 61.1% between 2024 and 2029.

The market is experiencing significant growth, driven by the escalating demand for data privacy protection. With increasing concerns over data security and the potential risks associated with using real data, synthetic data is gaining traction as a viable alternative. Furthermore, the deployment of large language models is fueling market expansion, as these models can generate vast amounts of realistic and diverse data, reducing the reliance on real-world data sources. However, high costs associated with high-end generative models pose a challenge for market participants. These models require substantial computational resources and expertise to develop and implement effectively. Companies seeking to capitalize on market opportunities must navigate these challenges by investing in research and development to create more cost-effective solutions or partnering with specialists in the field. Overall, the market presents significant potential for innovation and growth, particularly in industries where data privacy is a priority and large language models can be effectively utilized.

What will be the Size of the Synthetic Data Generation Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the increasing demand for data-driven insights across various sectors. Data processing is a crucial aspect of this market, with a focus on ensuring data integrity, privacy, and security. Data privacy-preserving techniques, such as data masking and anonymization, are essential in maintaining confidentiality while enabling data sharing. Real-time data processing and data simulation are key applications of synthetic data, enabling predictive modeling and data consistency. Data management and workflow automation are integral components of synthetic data platforms, with cloud computing and model deployment facilitating scalability and flexibility. Data governance frameworks and compliance regulations play a significant role in ensuring data quality and security. Deep learning models, variational autoencoders (VAEs), and neural networks are essential tools for model training and optimization, while API integration and batch data processing streamline the data pipeline. Machine learning models and data visualization provide valuable insights, while edge computing enables data processing at the source. Data augmentation and data transformation are essential techniques for enhancing the quality and quantity of synthetic data. Data warehousing and data analytics provide a centralized platform for managing and deriving insights from large datasets. Synthetic data generation continues to unfold, with ongoing research and development in areas such as federated learning, homomorphic encryption, statistical modeling, and software development. The market's dynamic nature reflects the evolving needs of businesses and the continuous advancements in data technology.

How is this Synthetic Data Generation Industry segmented?

The synthetic data generation industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userHealthcare and life sciencesRetail and e-commerceTransportation and logisticsIT and telecommunicationBFSI and othersTypeAgent-based modellingDirect modellingApplicationAI and ML Model TrainingData privacySimulation and testingOthersProductTabular dataText dataImage and video dataOthersGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalyUKAPACChinaIndiaJapanRest of World (ROW)

By End-user Insights

The healthcare and life sciences segment is estimated to witness significant growth during the forecast period.In the rapidly evolving data landscape, the market is gaining significant traction, particularly in the healthcare and life sciences sector. With a growing emphasis on data-driven decision-making and stringent data privacy regulations, synthetic data has emerged as a viable alternative to real data for various applications. This includes data processing, data preprocessing, data cleaning, data labeling, data augmentation, and predictive modeling, among others. Medical imaging data, such as MRI scans and X-rays, are essential for diagnosis and treatment planning. However, sharing real patient data for research purposes or training machine learning algorithms can pose significant privacy risks. Synthetic data generation addresses this challenge by producing realistic medical imaging data, ensuring data privacy while enabling research and development. Moreover

Context

I am working on a Text Recognition application, but only a small part of my dataset is filled with realistic images. I thought what if I use an autoencoder to generate realistic word images from non-realistic data. so basicly it will be used as a text image augmentation.

Content

it is all word images written in the Kurdish language with Arabic script. the content of the images doesn't matter and of course, I haven't added the text itself. x_words are preprocessed and I have removed the background just to make it easier and faster for you.

Auto Generated Dataset Elephant

## Overview

Auto Generated Dataset Elephant is a dataset for instance segmentation tasks - it contains Auto Generated Dataset Elephant annotations for 261 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

Data generation in machine learning involves creating or manipulating data to train and evaluate machine learning models. The purpose of data generation is to provide diverse and representative examples that cover a wide range of scenarios, ensuring the model's robustness and generalization. The dataset contains GENERATED Vietnamese passports, which are replicas of official passports but with randomly generated details, such as name, date of birth etc. The primary intention of generating these fake passports is to demonstrate the structure and content of a typical passport document and to train the neural network to identify this type of document. Generated passports can assist in conducting research without accessing or compromising real user data that is often sensitive and subject to privacy regulations. Synthetic data generation allows researchers to develop and refine models using simulated passport data without risking privacy leaks.

Compilation of python codes for data preprocessing and VegeNet building, as well as image datasets (zip files).

Image datasets:

1. vege_original : Images of vegetables captured manually in data acquisition stage
2. vege_cropped_renamed : Images in (1) cropped to remove background areas and image labels renamed
3. non-vege images : Images of non-vegetable foods for CNN network to recognize other-than-vegetable foods
4. food_image_dataset : Complete set of vege (2) and non-vege (3) images for architecture building.
5. food_image_dataset_split : Image dataset (4) split into train and test sets
6. process : Images created when cropping (pre-processing step) to create dataset (2).

This file contains the dataset used to generate the results.

Protein-Protein, Genetic, and Chemical Interactions for Yu H (2011):Next-generation sequencing to generate interactome datasets. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Next-generation sequencing has not been applied to protein-protein interactome network mapping so far because the association between the members of each interacting pair would not be maintained in en masse sequencing. We describe a massively parallel interactome-mapping pipeline, Stitch-seq, that combines PCR stitching with next-generation sequencing and used it to generate a new human interactome dataset. Stitch-seq is applicable to various interaction assays and should help expand interactome network mapping.

To generate a representative dataset of real-world traffic in ISCX we defined a set of tasks, assuring that our dataset is rich enough in diversity and quantity. We created accounts for users Alice and Bob in order to use services like Skype, Facebook, etc. Below we provide the complete list of different types of traffic and applications considered in our dataset for each traffic type (VoIP, P2P, etc.)

We captured a regular session and a session over VPN, therefore we have a total of 14 traffic categories: VOIP, VPN-VOIP, P2P, VPN-P2P, etc. We also give a detailed description of the different types of traffic generated:

Browsing: Under this label we have HTTPS traffic generated by users while browsing or performing any task that includes the use of a browser. For instance, when we captured voice-calls using hangouts, even though browsing is not the main activity, we captured several browsing flows.

Email: The traffic samples generated using a Thunderbird client, and Alice and Bob Gmail accounts. The clients were configured to deliver mail through SMTP/S, and receive it using POP3/SSL in one client and IMAP/SSL in the other.

Chat: The chat label identifies instant-messaging applications. Under this label we have Facebook and Hangouts via web browsers, Skype, and IAM and ICQ using an application called pidgin [14].

Streaming: The streaming label identifies multimedia applications that require a continuous and steady stream of data. We captured traffic from Youtube (HTML5 and flash versions) and Vimeo services using Chrome and Firefox.

File Transfer: This label identifies traffic applications whose main purpose is to send or receive files and documents. For our dataset we captured Skype file transfers, FTP over SSH (SFTP) and FTP over SSL (FTPS) traffic sessions.

VoIP: The Voice over IP label groups all traffic generated by voice applications. Within this label we captured voice calls using Facebook, Hangouts and Skype.

TraP2P: This label is used to identify file-sharing protocols like Bittorrent. To generate this traffic we downloaded different .torrent files from a public a repository and captured traffic sessions using the uTorrent and Transmission applications.

The traffic was captured using Wireshark and tcpdump, generating a total amount of 28GB of data. For the VPN, we used an external VPN service provider and connected to it using OpenVPN (UDP mode). To generate SFTP and FTPS traffic we also used an external service provider and Filezilla as a client.

To facilitate the labeling process, when capturing the traffic all unnecessary services and applications were closed. (The only application executed was the objective of the capture, e.g., Skype voice-call, SFTP file transfer, etc.) We used a filter to capture only the packets with source or destination IP, the address of the local client (Alice or Bob).

The full research paper outlining the details of the dataset and its underlying principles:

Gerard Drapper Gil, Arash Habibi Lashkari, Mohammad Mamun, Ali A. Ghorbani, "Characterization of Encrypted and VPN Traffic Using Time-Related Features", In Proceedings of the 2nd International Conference on Information Systems Security and Privacy(ICISSP 2016) , pages 407-414, Rome, Italy.
ISCXFlowMeter has been written in Java for reading the pcap files and create the csv file based on selected features. The UNB ISCX Network Traffic (VPN-nonVPN) dataset consists of labeled network traffic, including full packet in pcap format and csv (flows generated by ISCXFlowMeter) also are publicly available for researchers.

For more information contact cic@unb.ca.

The UNB ISCX Network Traffic Dataset content
Traffic: Content
Web Browsing: Firefox and Chrome
Email: SMPTS, POP3S and IMAPS
Chat: ICQ, AIM, Skype, Facebook and Hangouts
Streaming: Vimeo and Youtube
File Transfer: Skype, FTPS and SFTP using Filezilla and an external service
VoIP: Facebook, Skype and Hangouts voice calls (1h duration)
P2P: uTorrent and Transmission (Bittorrent)
; cic@unb.ca.

features