This data set contains the result of applying the NIST Statistical Test Suite on accelerometer data processed for random number generator seeding. The NIST Statistical Test Suite can be downloaded from: http://csrc.nist.gov/groups/ST/toolkit/rng/documentation_software.html. The format of the output is explained in http://csrc.nist.gov/publications/nistpubs/800-22-rev1a/SP800-22rev1a.pdf.

* Repository author: Tommaso Zanotti
* email: tommaso.zanotti@unimore.it or francescomaria.puglisi@unimore.it
* Version v1.0

This repository includes MATLAB files and datasets related to the IEEE IIRW 2023 conference proceeding:
T. Zanotti et al., "Reliability Analysis of Random Telegraph Noisebased True Random Number Generators," 2023 IEEE International Integrated Reliability Workshop (IIRW), South Lake Tahoe, CA, USA, 2023, pp. 1-6, doi: 10.1109/IIRW59383.2023.10477697

The repository includes:

• The data of the bitmaps reported in Fig. 4, i.e., the results of the simulation of the ideal RTN-based TRNG circuit for different reseeding strategies.
  To load and plot the data use the "plot_bitmaps.mat" file.
• The result of the circuit simulations considering the EvolvingRTN from the HfO2 device shown in Fig. 7, for two Rgain values. Specifically, the data is contained in the following csv files:
  • "Sim_TRNG_Circuit_HfO2_3_20s_Vth_210m_no_Noise_Ibias_11n.csv" (lower Rgain)
  • "Sim_TRNG_Circuit_HfO2_3_20s_Vth_210m_no_Noise_Ibias_4_8n.csv" (higher Rgain)
• The result of the circuit simulations considering the temporary RTN from the SiO2 device shown in Fig. 8. Specifically, the data is contained in the following csv files:
  • "Sim_TRNG_Circuit_SiO2_1c_300s_Vth_180m_Noise_Ibias_1.5n.csv" (ref. Rgain)
  • "Sim_TRNG_Circuit_SiO2_1c_100s_200s_Vth_180m_Noise_Ibias_1.575n.csv" (lower Rgain)
  • "Sim_TRNG_Circuit_SiO2_1c_100s_200s_Vth_180m_Noise_Ibias_1.425n.csv" (higher Rgain)

This is a JSON format file generated by a random number generator in python. The range is 0 to 1000, and numbers are float number.This data will be used by a python script for further transformation.

The Hardware Random Number Generator (HRNG) chip market is experiencing robust growth, driven by increasing demand for secure and unpredictable random numbers across various applications. The market's expansion is fueled by the rising adoption of cryptographic applications, particularly in data security, IoT devices, and blockchain technology. These applications require high-quality, truly random numbers to ensure the integrity and confidentiality of sensitive data. Government regulations mandating stronger data security measures are further propelling market growth. Furthermore, advancements in semiconductor technology are enabling the development of smaller, more power-efficient, and cost-effective HRNG chips, broadening their accessibility and application scope. While challenges exist, such as overcoming the inherent complexities of generating truly random numbers and ensuring consistent performance across diverse operating conditions, the overall market outlook remains positive. We estimate the market size in 2025 to be $500 million, based on observed trends in related security and semiconductor markets. Considering a projected CAGR (assuming a CAGR of 15% based on industry growth in related sectors), the market is expected to exceed $1.5 billion by 2033. Leading players like ID Quantique, Qrange, and QuantumCTek are actively contributing to market innovation through continuous improvements in chip design, performance, and security features. The increasing integration of HRNG chips into diverse electronic devices, coupled with the ongoing development of quantum-resistant cryptography, presents significant opportunities for market expansion. Competitive factors include pricing strategies, technological advancements, and the ability to meet stringent security certifications. Regional growth will be influenced by factors such as government regulations, digital infrastructure development, and the rate of technological adoption in different geographical areas. The market segmentation will likely evolve as new applications emerge and technological improvements drive innovation. The forecast period of 2025-2033 represents a period of substantial growth and transformation within the HRNG chip market, driven by the accelerating demands of a increasingly interconnected and data-driven world.

Title of program: SRNG Catalogue Id: ACIE_v1_0

Nature of problem An algorithm to generate a subset of random integers from a larger set of integers has been developed to minimize both the computing time and the memory space. The algorithm deals with the whole array at the same time, generates a subset of random integers for a given percentage of the range, and the generated subset of random integers is obtained as sequentially increasing numbers. Such random integers are used to specify thepositions of randomly dispersed impurity atoms or solute atoms in many ...

Versions of this program held in the CPC repository in Mendeley Data ACIE_v1_0; SRNG; 10.1016/0010-4655(76)90065-5

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

The global Rack Random Number Generator market is projected to witness significant expansion, reaching an estimated market size of XXX million USD by 2025, with a Compound Annual Growth Rate (CAGR) of XX% during the forecast period of 2025-2033. This robust growth is primarily fueled by the escalating demand for highly secure and unpredictable random numbers across various critical sectors. The burgeoning field of quantum computing, with its inherent reliance on true randomness for complex calculations and simulations, stands as a principal driver. Furthermore, the increasing adoption of advanced cryptographic protocols in cybersecurity, financial services, and governmental applications necessitates reliable and tamper-proof random number generation. The integration of quantum-based random number generators (QRNGs) into industrial processes, particularly in areas requiring unbiased data for machine learning algorithms and statistical analysis, is also a substantial contributor to market momentum. The need for enhanced data privacy and compliance with stringent regulations surrounding data security further bolsters the market for these sophisticated devices. The market is segmented into diverse applications, with Scientific Research and Industrial Use emerging as dominant segments due to their substantial investment in advanced technologies and the critical need for high-quality randomness. Entertainment, while a smaller segment, is also showing promise with the integration of RNGs in gaming and simulation technologies. In terms of types, both Portable and Integrated Generator segments are expected to experience growth, catering to different deployment needs and cost considerations. Geographically, Asia Pacific, led by China and India, is poised to be a rapidly expanding market, driven by rapid technological adoption and significant government initiatives in quantum technology research. North America and Europe, already established markets with a strong focus on cybersecurity and R&D, will continue to be major revenue contributors, with the United States and Germany leading the way. Emerging economies in South America and the Middle East & Africa are also anticipated to witness increasing adoption rates as awareness and investment in advanced RNG solutions grow.

Analysis of sensor-based data processed for Random Number Generator seeding.

This data is purely random and created for learning purpose.

In situations where data is not readily available but needed, you'll have to resort to building up the data yourself. There are many methods you can use to acquire this data from web scraping to APIs. But sometimes, you'll end up needing to create fake or “dummy” data. Dummy data can be useful in times where you know the exact features you’ll be using and the data types included but, you just don’t have the data itself.

Features Description

• ID — a unique string of characters to identify each user.
• Gender — string data type of three choices.
• Subscriber — a binary True/False choice of their subscription status.
• Name — string data type of the first and last name of the user.
• Email —string data type of the email address of the user.
• Last Login — string data type of the last login time.
• Date of Birth — string format of year-month-day.
• Education — current education level as a string data type.
• Bio — short string descriptions of random words.
• Rating — integer type of a 1 through 5 rating of something.

Note - This Data is Purely Random (Dummy Data). if you wish, you can perform some data visualization and model building part into it.

Reference - https://towardsdatascience.com/build-a-your-own-custom-dataset-using-python-9296540a0178

The size of the Card Random Number Generator market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

Quantum Random Number Generator Appliance Market Outlook

According to our latest research, the quantum random number generator appliance market size reached USD 195.2 million globally in 2024, demonstrating a robust upward trend driven by escalating security needs and advancements in quantum technologies. The market is projected to expand at a CAGR of 31.8% from 2025 to 2033, reaching a forecasted value of USD 2.16 billion by the end of the forecast period. This remarkable growth is fueled by heightened demand for cryptographically secure random numbers in critical sectors such as BFSI, government, and telecommunications, as well as increased adoption of quantum-safe solutions to counter evolving cyber threats.

One of the primary growth factors for the quantum random number generator appliance market is the rising prevalence of sophisticated cyberattacks targeting both public and private sectors. As digital transformation accelerates globally, organizations are increasingly vulnerable to threats that can compromise sensitive data and digital infrastructure. Quantum random number generator appliances provide a fundamentally secure method of generating truly random numbers, which are essential for cryptographic protocols and secure communications. The growing awareness among enterprises regarding the limitations of classical random number generators, which are susceptible to prediction and manipulation, is further driving the adoption of quantum-based solutions. This shift is particularly notable in industries such as banking, financial services, and insurance (BFSI), where data integrity and confidentiality are paramount.

Another significant driver is the rapid evolution of quantum computing and its implications for conventional encryption techniques. As quantum computers become more powerful, traditional encryption algorithms are at risk of being rendered obsolete. Quantum random number generator appliances are emerging as a key element in the development of quantum-safe cryptographic systems, ensuring robust protection against future quantum-enabled attacks. Governments and regulatory bodies across various regions are increasingly mandating the use of advanced cryptographic methods, which is accelerating the integration of quantum random number generators into existing security frameworks. Additionally, the proliferation of Internet of Things (IoT) devices and the need for secure machine-to-machine communication are further expanding the addressable market for these appliances.

The expanding application landscape of quantum random number generator appliances is another crucial growth catalyst. Beyond cryptography, these devices are finding use in scientific research, gaming and gambling, and secure data transmission in IoT networks. For example, the gaming industry relies on high-quality random numbers to ensure fairness and unpredictability in online games and lotteries. Similarly, scientific research, particularly in fields such as simulations and statistical modeling, benefits from the enhanced randomness provided by quantum solutions. The versatility and reliability of quantum random number generator appliances are positioning them as indispensable tools across a wide array of sectors, further propelling global market growth.

From a regional perspective, North America currently dominates the quantum random number generator appliance market, accounting for the largest share in 2024. This leadership is attributed to the presence of major technology providers, strong government support for quantum research, and early adoption of advanced cybersecurity solutions across industries. Europe follows closely, driven by stringent data protection regulations and significant investments in quantum technologies. The Asia Pacific region is expected to witness the fastest growth during the forecast period, fueled by rapid digitalization, expanding IT infrastructure, and increasing awareness about quantum security. Latin America and the Middle East & Africa are also showing promising potential, albeit from a smaller base, as organizations in these regions begin to recognize the importance of quantum-safe security solutions.

Type Analysis

The quantum random number generator appliance market is segmented by type into hardware, software, and services. Hardware-based quantum random number generators have traditionally dominated the market due to their ability to generate truly random numbers by harnessing quantum mec

The Hardware Random Number Generator (HRNG) market is experiencing robust growth, driven by increasing demand for secure and reliable randomness in various applications. The market size in 2025 is estimated at $250 million, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant growth is fueled by the rising adoption of cloud computing, the Internet of Things (IoT), and blockchain technologies, all of which require high-quality, unpredictable random numbers for security and cryptographic operations. Furthermore, stringent government regulations regarding data privacy and security are driving the demand for robust HRNG solutions. Key players like Synopsys, ID Quantique, Quside, Intel, Xiphera, Shanghai XT QUANTECH, and QuantumCTek are contributing to market expansion through technological advancements and strategic partnerships. The market's growth trajectory is expected to continue its upward trend throughout the forecast period (2025-2033), propelled by factors such as the increasing sophistication of cyberattacks and the growing need for secure authentication and encryption. However, challenges like high initial investment costs for HRNG implementation and the potential for vulnerabilities in poorly designed systems could act as restraints. Nevertheless, the long-term outlook remains positive, with significant opportunities for market expansion across various sectors including finance, healthcare, automotive, and defense, where data security is paramount. The increasing integration of HRNGs into diverse devices and systems is a major contributor to this promising future.

An integrated circuit (1..1''',1a..1c) with a true random number generator (2..2'''), which comprises at least one instable physical uncloneable function (3..3''',3a,3a') for generating true random numbers (8). Hence each device of a group of devices can be provided with a unique true random generator so that each device of the group is provided with different true random numbers even when said devices are applied to identical environmental conditions. Such a random number generator may be part of a smart card as well as of a module for near field communication for example.

Description: This dataset is created solely for the purpose of practice and learning. It contains entirely fake and fabricated information, including names, phone numbers, emails, cities, ages, and other attributes. None of the information in this dataset corresponds to real individuals or entities. It serves as a resource for those who are learning data manipulation, analysis, and machine learning techniques. Please note that the data is completely fictional and should not be treated as representing any real-world scenarios or individuals.

Attributes: - phone_number: Fake phone numbers in various formats. - name: Fictitious names generated for practice purposes. - email: Imaginary email addresses created for the dataset. - city: Made-up city names to simulate geographical diversity. - age: Randomly generated ages for practice analysis. - sex: Simulated gender values (Male, Female). - married_status: Synthetic marital status information. - job: Fictional job titles for practicing data analysis. - income: Fake income values for learning data manipulation. - religion: Pretend religious affiliations for practice. - nationality: Simulated nationalities for practice purposes.

Please be aware that this dataset is not based on real data and should be used exclusively for educational purposes.

Abstract A library containing highly portable implementations of most algorithms for (pseudo) random number generation has been developed, which might be used in any area of simulation which requires random number generators. Each generator is freely configurable by the user, so the RANEXP library is particularly well-suited for applications requiring different random number generators. The algorithms are implemented in C, but are callable from Fortran application program also.

Title of program: RANEXP Catalogue Id: ACTB_v1_0

Nature of problem Any Monte Carlo simulation or statistical test requiring uniform pseudorandom numbers.

Versions of this program held in the CPC repository in Mendeley Data ACTB_v1_0; RANEXP; 10.1016/0010-4655(94)90072-8

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

The Hardware Random Number Generator (HRNG) market is booming, projected to reach $1.8 billion by 2033 with a 15% CAGR. Driven by cloud computing, IoT, and stringent security regulations, this report analyzes market size, key players (Synopsys, ID Quantique, etc.), and regional trends. Discover the opportunities and challenges in this rapidly evolving sector.

About the Dataset: Random Data with Hidden Structure

This dataset consists of 10,000,000 samples with 50 numerical features. Each feature has been randomly generated using a uniform distribution between 0 and 1. To add complexity, a hidden structure has been introduced in some of the features. Specifically, Feature 2 is related to Feature 1, making it a good candidate for regression analysis tasks. The other features remain purely random, allowing for the exploration of feature engineering and random data generation techniques.

Key Features and Structure

• Feature 1: A random number drawn from a uniform distribution between 0 and 1.
• Feature 2: A function of Feature 1, specifically Feature 2 ≈ 2 × Feature 1 + small Gaussian noise (N(0, 0.05)). This introduces a hidden linear relationship with a small amount of noise for added realism.
• Features 3 to 50: Independent random numbers generated between 0 and 1, with no relationship to each other or any other features.

This hidden structure allows you to test models on data where a simple pattern (between Feature 1 and Feature 2) exists, but with noise that can challenge more advanced models in finding the relationship.

Dataset Overview

• Rows: 10,000,000
• Columns: 50
• Format: CSV
• File Size: 5.32 GB ## Intended Uses

This dataset is versatile and can be used for various machine learning tasks, including:

• Testing and benchmarking machine learning models: Evaluate model performance on large, randomly generated datasets.
• Regression analysis practice: The relationship between Feature 1 and Feature 2 makes it ideal for testing regression models.
• Feature engineering experiments: Explore techniques for selecting, transforming, or creating new features.
• Random data generation research: Investigate methods for generating synthetic data and its applications.
• Large-scale data processing testing: Test frameworks such as Pandas, Dask, and Spark for processing large datasets.

Licensing

This dataset is made available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You are free to share and adapt the material for any purpose, even commercially, as long as proper attribution is given.

Learn more about the license here

True Random Number Generator IC Market Outlook

According to our latest research, the global True Random Number Generator IC (TRNG IC) market size reached USD 1.36 billion in 2024, driven by escalating demand for secure cryptographic solutions and the proliferation of connected devices. The market is projected to expand at a robust CAGR of 14.7% from 2025 to 2033, reaching an estimated USD 4.72 billion by 2033. This impressive growth trajectory is primarily fueled by increasing cybersecurity requirements across industries and the integration of TRNG ICs in emerging applications such as IoT, automotive, and advanced telecommunications.

The expansion of the True Random Number Generator IC market is underpinned by the rising necessity for robust data security in a digital-first world. As industries transition to cloud-based infrastructures and digital ecosystems, the need for genuine randomness in cryptographic keys becomes paramount. TRNG ICs, which generate non-deterministic random numbers based on physical phenomena, are increasingly favored over pseudo-random solutions for their superior security attributes. This shift is particularly evident in sectors like BFSI, healthcare, and IT & telecom, where data breaches and cyberattacks can have catastrophic consequences. The growing frequency and sophistication of cyber threats have compelled organizations to adopt hardware-based security measures, thus boosting demand for TRNG ICs globally.

Another significant growth driver is the exponential rise in IoT deployments and smart devices. With billions of IoT endpoints expected to be connected by 2030, each requires secure authentication and encrypted communication channels. TRNG ICs are integral to enabling secure device onboarding, firmware updates, and data transmission in IoT ecosystems. Additionally, the automotive sector, with its shift towards connected and autonomous vehicles, is increasingly embedding TRNG ICs to safeguard vehicle-to-everything (V2X) communications and prevent unauthorized access to critical vehicle systems. Consumer electronics, from smartphones to smart home devices, also represent a burgeoning market for TRNG ICs, as end-users demand higher levels of privacy and data protection.

Technological advancements and regulatory mandates are further catalyzing the adoption of True Random Number Generator ICs. Governments and industry bodies are tightening compliance requirements for data security, compelling enterprises to invest in certified hardware security modules that often incorporate TRNG ICs. Moreover, ongoing innovations in semiconductor manufacturing are enabling the integration of TRNG functionalities into smaller, more power-efficient chips, expanding their applicability across a wider range of devices. This convergence of regulatory pressure and technological progress is expected to sustain the market’s momentum over the forecast period.

Regionally, Asia Pacific is emerging as a powerhouse in the TRNG IC market, propelled by the rapid digitalization of economies such as China, India, and South Korea. North America remains a frontrunner, owing to its mature cybersecurity landscape and early adoption of advanced security technologies. Europe is also witnessing steady growth, supported by stringent data protection regulations and robust industrial automation trends. The Middle East & Africa and Latin America, while relatively nascent, are increasingly investing in digital infrastructure and security solutions, presenting untapped opportunities for market participants.

Type Analysis

The True Random Number Generator IC market is segmented by type into Discrete TRNG ICs and Integrated TRNG ICs. Discrete TRNG ICs, which are standalone chips dedicated solely to random number generation, have traditionally dominated high-security applications such as government, defense, and critical infrastructure. These ICs are valued for their isolation from other system components, reducing the risk of side-channel att

The global Hardware Random Number Generator (HRNG) Chips market is poised for substantial expansion, driven by the escalating demand for robust security solutions across diverse industries. With a projected market size estimated to reach approximately $750 million by 2025, the market is expected to experience a Compound Annual Growth Rate (CAGR) of around 18% from 2025 to 2033. This robust growth is fueled by the critical need for true randomness in applications such as cybersecurity, cryptography, secure communication, and the burgeoning fields of artificial intelligence and quantum computing. The increasing sophistication of cyber threats necessitates the deployment of highly secure and unpredictable random number generation, making HRNG chips indispensable. Furthermore, the proliferation of connected devices and the Internet of Things (IoT) further amplifies this demand, as each connected node requires secure authentication and data encryption. The automotive sector, with its increasing reliance on connected and autonomous systems, is emerging as a significant application area, as is the financial terminal industry for secure transaction processing. The market is segmented into low-speed, medium-speed, and high-speed random number chips, catering to a spectrum of performance requirements. High-speed chips are gaining traction for demanding applications like large-scale data encryption and complex simulations. Key market drivers include the growing awareness of data privacy regulations globally, the advancements in quantum technologies, and the continuous innovation in semiconductor manufacturing enabling more efficient and cost-effective HRNG chip production. However, challenges such as the relatively high initial cost of implementation for some advanced HRNG solutions and the availability of software-based pseudo-random number generators (PRNGs) might pose some restraints. Prominent players like ID Quantique, Qrange, and Quside are at the forefront of innovation, introducing advanced HRNG solutions. Geographically, Asia Pacific, particularly China and India, is expected to witness the fastest growth due to rapid industrialization, increasing adoption of advanced technologies, and a burgeoning cybersecurity market. North America and Europe remain significant markets, driven by stringent security standards and a mature technology ecosystem.

The size of the True Random Number Generator (TRNG) Chips market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

Creating a robust employee dataset for data analysis and visualization involves several key fields that capture different aspects of an employee's information. Here's a list of fields you might consider including: Employee ID: A unique identifier for each employee. Name: First name and last name of the employee. Gender: Male, female, non-binary, etc. Date of Birth: Birthdate of the employee. Email Address: Contact email of the employee. Phone Number: Contact number of the employee. Address: Home or work address of the employee. Department: The department the employee belongs to (e.g., HR, Marketing, Engineering, etc.). Job Title: The specific job title of the employee. Manager ID: ID of the employee's manager. Hire Date: Date when the employee was hired. Salary: Employee's salary or compensation. Employment Status: Full-time, part-time, contractor, etc. Employee Type: Regular, temporary, contract, etc. Education Level: Highest level of education attained by the employee. Certifications: Any relevant certifications the employee holds. Skills: Specific skills or expertise possessed by the employee. Performance Ratings: Ratings or evaluations of employee performance. Work Experience: Previous work experience of the employee. Benefits Enrollment: Information on benefits chosen by the employee (e.g., healthcare plan, retirement plan, etc.). Work Location: Physical location where the employee works. Work Hours: Regular working hours or shifts of the employee. Employee Status: Active, on leave, terminated, etc. Emergency Contact: Contact information of the employee's emergency contact person. Employee Satisfaction Survey Responses: Data from employee satisfaction surveys, if applicable.

Code Url: https://github.com/intellisenseCodez/faker-data-generator