The given Dataset is record of different age group people either diabetic or non diabetic for theie blood glucose level reading with superficial body features like body temperature, heart rate, blood pressure etc.

The main purpose of the dataset is to understand the effect of blood glucose level on human body. 

The different superficial body parameters show sifnificant variation according to change in blood glucose level.


The use of dataset to be done for machine learning analysis or study purpose only. No medical implementations to be claimed using the given dataset.





This dataset consists of subject wise daily living activity data, which is acquired from the inbuilt accelerometer and gyroscope sensors of the smartphones.


The smartphone was mounted on the waist and front pockets of the users. All the different activities were performed in a laboratory except Running, which was performed on a Football Playground.

Smartphone used: Poco X2 and Samsung Galaxy A32s

Inbuild Sensors used: Accelerometer and Gyroscope

Ages: All subjects are Above 23 years


Smartphone used: Poco X2 and Samsung Galaxy A32s Inbuild Sensors used: Accelerometer and Gyroscope Ages: All subjects are Above 23 years Weight: All subjects are above 50 kgs No. of Subjects= 1


Packet delivery ratio data collected for the article Wireless-Sensor Network Topology Optimization in Complex Terrain: A Bayesian Approach. Published in the IEEE Internet of Things Journal. 


The cell characterization scripts and ultra low voltage flip-flop design information including 320-bit (16x20) parallel shift register design....

If you use this data, please add the citation to the following paper :


The dataset corresponds the measurement that was implemented on the control accuracy of a mixed reality application for digital twin based crane. 


Human activity recognition (HAR) has been one of the most prevailing and persuasive research topics in different fields for the past few decades. The main idea is to comprehend individuals’ regular activities by looking at bits of knowledge accumulated from people and their encompassing living environments based on sensor observations. HAR has a great impact on human-robot collaborative work, especially in industrial works. In compliance with this idea, we have organized this year’s Bento Packaging Activity Recognition Challenge.

Last Updated On: 
Sat, 07/31/2021 - 02:40
Citation Author(s): 
Sayeda Shamma Alia, Kohei Adachi, Paula Lago, Nazmun Nahid, Haru Kaneko, Sozo Inoue




The .zip archive contains a folder ‘tasks’, and a .csv file, “analysis_results.csv” which is a table with 4077 entries. The .csv table is delimeted by comma. Each subfolder of the ‘tasks’ folder represents an analysis task of a unique sample. The association between tasks and samples is shown in the analysis_results.csv table, which contains the analysis results per sample. Each row in the table represents a botnet sample and holds information such as analysis task id, file hash, URL of the server where the sample was captured from, as well as the analysis results for that sample.  For each task id, the corresponding folder contains: 1) the results of the analysis (analysis_result.json); 2) the captured traffic (capture.pcap); 3) the recorded system calls (syscalls.json) and 4) the botnet sample file (ELF binary) with the original filename. Depending on the IoT botnet sample analysed, the network traffic may include port scanning, exploitation, C2 communications and DDoS traffic.




The channel path data (signal strength, delay, angle of departure and angle of arrival, etc. of each path for each Tx-Rx pair) calculated by a ray-tracing model in an industrial warehouse, via Wireless InSite

For more details, please download the scripts and .zip to access the instructions and the data files that contain the path information.


The Internet of Things (IoT) technology has revolutionized every aspect of everyday life by making everything smarter. IoT became more popular in recent years due to its vast applications in many fields such as smart cities, agriculture, healthcare, ambient assisted living, animal tracking, etc. Localization of a sensor node refers to knowing a sensor node's geographical location in the IoT network.


This dataset consists of temporal and temperature drift characteristics of Si3N4-gate iSFET andsupplementary files