This dataset is randomly generated using the built-in function from python random.randint(). This csv file contains 2 columns, index and value. Index represents the unique row id and value represents the randomly generated value at each row.

nielsr/random-data dataset hosted on Hugging Face and contributed by the HF Datasets community

Large-Scale Random Number Dataset (5 Million Rows, 10 Features)

This dataset contains 5,000,000 samples with 10 numerical features generated using a uniform random distribution between 0 and 1.

Additionally, a hidden structure is introduced:
- Feature 2 is approximately twice Feature 1 plus small Gaussian noise.
- Other features are purely random.

📊 Dataset Details

• Rows: 5,000,000
• Columns: 10
• Format: CSV
• File Size: ~400 MB (approx.)

🎯 Intended Uses

This dataset is ideal for: - Testing and benchmarking machine learning models - Regression analysis practice - Feature engineering experiments - Random data generation research - Large-scale data processing testing (Pandas, Dask, Spark)

🏷️ 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.

📌 Notes

• All values are generated synthetically.
• No missing data.
• Safe for academic, commercial, or personal use.

Random Classes

## Overview

Random Classes is a dataset for object detection tasks - it contains Random Classes annotations for 291 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).

immortal886/random dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset includes random number generated through various methods.Method 1: shuf https://www.mankier.com/1/shufCommands used to generate dataset files: $ shuf -i 1-1000000000 -n1000000 -o random-shuf.txt$ shuf -i 1-1000000000000 -n1000000 -o random-shuf-1-1000000000000.txt$ jot -r 1000000 1 1000000000000 > random-jot-1-1000000000000.txt

• 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)

A random sample of households were invited to participate in this survey. In the dataset, you will find the respondent level data in each row with the questions in each column. The numbers represent a scale option from the survey, such as 1=Excellent, 2=Good, 3=Fair, 4=Poor. The question stem, response option, and scale information for each field can be found in the var "variable labels" and "value labels" sheets. VERY IMPORTANT NOTE: The scientific survey data were weighted, meaning that the demographic profile of respondents was compared to the demographic profile of adults in Bloomington from US Census data. Statistical adjustments were made to bring the respondent profile into balance with the population profile. This means that some records were given more "weight" and some records were given less weight. The weights that were applied are found in the field "wt". If you do not apply these weights, you will not obtain the same results as can be found in the report delivered to the Bloomington. The easiest way to replicate these results is likely to create pivot tables, and use the sum of the "wt" field rather than a count of responses.

1000 random numbers ranged from 1 to 100

This dataset is part of a series of datasets, where batteries are continuously cycled with randomly generated current profiles. Reference charging and discharging cycles are also performed after a fixed interval of randomized usage to provide reference benchmarks for battery state of health. In this dataset, four 18650 Li-ion batteries (Identified as RW9, RW10, RW11 and RW12) were continuously operated using a sequence of charging and discharging currents between -4.5A and 4.5A. This type of charging and discharging operation is referred to here as random walk (RW) operation. Each of the loading periods lasted 5 minutes, and after 1500 periods (about 5 days) a series of reference charging and discharging cycles were performed in order to provide reference benchmarks for battery state health.

Random Img Negative Examples

## Overview

Random Img Negative Examples is a dataset for object detection tasks - it contains Random Img Negative Examples annotations for 533 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).

chavinlo/random dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by ANINDYA GHOSAL

Contents

Raw random data obtained from the DC-TRNG on Cyclone IV and Cyclone V Intel FPGAs Cyclone IV (EP4CGX150DF31C7) & Cyclone V (5CEBA4F17C8) FPGAs

This dataset is part of a series of datasets, where batteries are continuously cycled with randomly generated current profiles. Reference charging and discharging cycles are also performed after a fixed interval of randomized usage to provide reference benchmarks for battery state of health. In this dataset, four 18650 Li-ion batteries (Identified as RW3, RW4, RW5 and RW6) were continuously operated by repeatedly charging them to 4.2V and then discharging them to 3.2V using a randomized sequence of discharging currents between 0.5A and 4A. This type of discharging profile is referred to here as random walk (RW) discharging. After every fifty RW cycles a series of reference charging and discharging cycles were performed in order to provide reference benchmarks for battery state health.

Dataset

This dataset was created by nazmuddhoha ansary

Contents

This dataset provides information about the number of properties, residents, and average property values for Random Lake Road cross streets in Random Lake, WI.

Random ASCII Dataset

This dataset contains random sequences of ASCII characters, with "train," "validation," and "test" splits, designed to simulate text-like structures using all printable ASCII characters. Each sequence consists of pseudo-randomly generated "words" of various lengths, separated by spaces to mimic natural language text.

  Dataset Details

Splits: Train, Validation, and Test Number of sequences: Train: 5000 sequences Validation: 5000 sequences Test: 5000… See the full description on the dataset page: https://huggingface.co/datasets/brando/random-all-ascii-dataset.

This dataset provides information about the number of properties, residents, and average property values for Knuth Road cross streets in Random Lake, WI.

The Data

This dataset is a collection of random numbers given by humans to answer the question: is there a pattern to the randomness of human choices? Could AI predict a pattern within a set of human's random choices of 20 numbers?

It is a relatively small dataset, but it is quite comprehensive.