The data set is collected using Neurosky MindWave 2.0 Headset. It uses a single dry electrode placed at FP-1 position for the acquisition of EEG signals. The data is collected from Healthy Individuals and Epileptic Patients performing different Activities of Daily Living (ADLs) in an unconstraint environment. 


The data files are stored in a comma-separated value (.csv) format.

60 sample files of activities performed by healthy individuals and 30 sample files of activities performed by epileptic patients are present in two separate folders in the .zip file.

The sampling frequency of the headset is 512Hz and each activity is performed for a duration of 20 seconds. Every data file contains raw EEG data in a single column.  

Disclaimer: This data was collected ethically with the consent of relevant local research committees. The anonymity of subjects and confidentiality of their mental health conditions was ensured.


The dataset attached is recordings done for 5 parameters to ascertain physical soil composition. Data was collected between March 2021 and April 2021. This dataset is the raw data.


Human Activity Recognition (HAR) is the process of handling information from sensors and/or video capture devices under certain circumstances to correctly determine human activities. Nowadays, several simple and automatic HAR methods based on sensors and Artificial Intelligence platforms can be easily implemented.

In this challenge, participants are required to determine the nurse care daily activities by utilizing the accelerometer data collected from the smartphone, which is the cheapest and easy-to-implement way in real life.

Last Updated On: 
Mon, 06/07/2021 - 00:43
Citation Author(s): 
Sayeda Shamma Alia, Kohei Adachi, Paula Lago, Le Nhat Tan, Haru Kaneko, Sozo Inoue

We generated attack datasets 1 based on real data from Austin, Texas.


The Development of an Internet of Things (IoT) Network Traffic Dataset with Simulated Attack Data.

Abstract— This research focuses on the requirements for and the creation of an intrusion detection system (IDS) dataset for an Internet of Things (IoT) network domain.


This dataset is a supplementary material for paper "A Comprehensive and Reproducible Comparison of Cryptographic Primitives Execution on Android Devices"  with the measurements collected from 17 mobile devices and the code for reproducibility.


The primary data related to the collected data is located in folder Measurement and each device has the corresponding subfolder with the measurement file. The dataset consists of JSON files, each containing measurements of available devices' security primitives execution times. The data was gathered in a span of multiple 250 iterations. Each measurement was taken with a 50 repetitions interval for every primitive. We define the main components of the dataset in the following:


1)    context[] – provides the details about the device and OS including device name, model, battery-related information, Software Development Kit~(SDK) version, and basic technical specification.

2)    benchmarks[] – provides entries per primitive, such as:

i)      name – the overall identification title of the primitive, including paddung and other optional fields;

ii)     params – additional parameters unilized for the execution if any;

iii)   totalRunTimeNs – the overall time of the primitive's execution time;

iv)   metrics[] – provides entries per execution, such as:

(a)   timeNs[] – the collected/processed information of the collected data inluding entries per execution in runs[] and statistical parameters in maximumminimum, and median.

(b)  warmupIterations – number of iterations of warmup before measurements started;

(c)   repeatIterations – the number of iterations;

(d)  thermalThrottleSleepSeconds – the duration of sleep due to thermal throttling.


An example of the dataset entry:



    "context": {

        "build": {

            "device": "mooneye",

            "fingerprint": "mobvoi/mooneye/mooneye:8.0.0/OWDR.180307.020/5000261:user/release-keys",

            "model": "Ticwatch E", 

            "version": {

                "sdk": 26



        "cpuCoreCount": 2, 

        "cpuLocked": true, 

        "cpuMaxFreqHz": -1,

        "batteryCapacity, mAh": 300,

        "memTotalBytes": 514560000,

        "sustainedPerformanceModeEnabled": false


    "benchmarks": [


            "name": "benchmarkRsa4096EcbOaepSHA1AndMgf1Padding",

            "params": {},

            "className": "cz.vutbr.benchmark.AsymmetricDecryptionBenchmark",

            "totalRunTimeNs": 20873248463,

            "metrics": {

                "timeNs": {

                    "minimum": 242466000, 

                    "maximum": 284698307, 

                    "median": 245293231,

                    "runs": [







            "warmupIterations": 33,

            "repeatIterations": 1,

            "thermalThrottleSleepSeconds": 0








Note: Project group was supported by the Graduate School of Business National Research University Higher School of Economics. 


This dataset comprises sensory data of in and out miniature vehicle (mobile sink) movement in the agriculture fields. The dataset is collected from the miniature vehicle using a 9-axis Inertial Measurement Unit (IMU) sensor (MPU-9250) placed on the top of the vehicle. Though the vehicle is small but designed to handle all the hurdles of the agricultural land, such as rough and muddy surface. This dataset aims to facilitate appropriate path planning in the agricultural field for the automatic cultivation of seeds, manure spread, and nutrients insertion.


The dataset contains Multivariate Time Series (MTS) of the miniature vehicle’s in and out movement in the agricultural field. The miniature vehicle collects the sensory data of the Inertial Measurement Unit (IMU) sensor (MPU-9250) deployed on it. MPU-9250 is a 9-axis sensor used for recording the linear and angular motion of the vehicle in the jerking condition due to the uneven surface of the farmland. MPU-9250 comprises a 3-axis accelerometer, 3-axis gyroscope, and 3-axis magnetometer. These sensors are connected to a NodeMCU with an attached SD card, which stores the data. The sensory data is collected from sixteen different agricultural fields at a sampling rate of 5 Hz for 5 minutes each. Therefore, each field produces 1500 instances of tri-axial sensors (accelerometer, gyroscope, and magnetometer). Hence, the total instances we have collected is 1500 X 16 =24000.


The provided dataset computes the exact analytical bit error rate (BER) of the NOMA system in the SISO broadcast channels with the assumption of i.i.d Rayleigh fading channels. The reader has to decide on the following input: 1) Number of users. 2) Modulation orders. 3) Power assignment. 4) Pathloss. 5) Transmit signal-to-noise ratio (SNR). The output is stored in a matrix where different rows are for different users while different columns are for different transmit SNRs.


Another raw ADS-B signal dataset with labels, the dataset is captured using a BladeRF2 SDR receiver @ 1090MHz with a sample rate of 10MHz


In order to obtain the constants of our PID temperature controller, it was necessary to identify the system. The identification of the system allows us, through experimentation, to find the representation of the plant to be able to control it.

The first data with name "data_2.mat" represent the open loop test, where the sampling frequency is 100 [Hz], this data was useful to find the period of the pulse train generator, which is twice the slowest sampling time analyzed between the high pulse and low pulse of the input.