Omkar2118/datasets huggingface dataset:
Continuous Defect Prediction

Continuous Defect Prediction (CDP) is a dataset of more than 11 million data rows, representing files involved in Continuous Integration (CI) builds, that synthesize the results of CI builds with data mined from software repositories. The dataset embraces 1,265 software projects, 30,022 distinct commit authors and several software process metrics that in earlier research appeared to be useful in software defect prediction. In this particular dataset the authors used TravisTorrent as the source of CI data. TravisTorrent synthesizes commit level information from the Travis CI server and GitHub open-source projects repositories.

Introducing paper: Continuous Defect Prediction: The Idea and a Related Dataset
BB-MAS
Full name: Behavioural Biometrics Multi-device and multi-Activity data from Same users

**BB-MAS** is a behavioural biometrics dataset. It consists of data collected from 117 subjects for typing (both fixed and free text), gait (walking, upstairs and downstairs) and touch on Desktop, Tablet and Phone. The dataset consists a total of about: 3.5 million keystroke events; 57.1 million data-points for accelerometer and gyroscope each; 1.7 million data-points for swipes; and enables future research to explore previously unexplored directions in inter-device and inter-modality biometrics. 

Image Source: [SU-AIS BB-MAS (SYRACUSE UNIVERSITY AND ASSURED INFORMATION SECURITY - BEHAVIORAL BIOMETRICS MULTI-DEVICE AND MULTI-ACTIVITY DATA FROM SAME USERS) DATASET](http://dx.doi.org/10.21227/rpaz-0h66)

Introducing paper: Insights from BB-MAS -- A Large Dataset for Typing, Gait and Swipes of the Same Person on Desktop, Tablet and Phone
UK-DALE

**UK-DALE** is an open-access dataset from the UK recording Domestic Appliance-Level Electricity to conduct research on disaggregation algorithms, with data describing not just the aggregate demand per building but also the `ground truth' demand of individual appliances. It was built at a sample rate of 16 kHz for the whole-house and at 1/6 Hz for individual appliances. This is the first open access UK dataset at this temporal resolution. It wAS recorded from five houses, one of which was recorded for 655 days.

Introducing paper: The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes
RAE
Full name: Rainforest Automation Energy

The Rainforest Automation Energy (RAE) dataset was create to help smart grid researchers test their algorithms which make use of smart meter data. This initial release of RAE contains 1Hz data (mains and sub-meters) from two residential houses. In addition to power data, environmental and sensor data from the house's thermostat is included. Sub-meter data from one of the houses includes heat pump and rental suite captures which is of interest to power utilities.

Introducing paper: RAE: The Rainforest Automation Energy Dataset for Smart Grid Meter Data Analysis