Participants were 61 children with ADHD and 60 healthy controls (boys and girls, ages 7-12). The ADHD children were diagnosed by an experienced psychiatrist to DSM-IV criteria, and have taken Ritalin for up to 6 months. None of the children in the control group had a history of psychiatric disorders, epilepsy, or any report of high-risk behaviors.



This dataset provides the magneto-inertial signals from six MIMU (2 Xsens, 2 APDM, 2 Shimmer) and orientation from 8 reflective markers (VICON) at 3 different speeds (slow, medium, fast). Marker trajectories are provided. Proprietary orientations from MIMU vendors are also included. All data are synchronized at 100 Hz.


The dataset comprises up to two weeks of activity data taken from the ankle and foot of 14 people without amputation and 17 people with lower limb amputation.  Walking speed, cadence, and lengths of strides taken at and away from the home were considered in this study.  Data collection came from two wearable sensors, one inertial measurement unit (IMU) placed on the top of the prosthetic or non-dominant foot, and one accelerometer placed on the same ankle.  Location information was derived from GPS and labeled as ‘home’, ‘away’, or ‘unknown’.  The dataset contains raw acce


This dataset includes high-resolution (1 s) power and reactive power profiles of household appliances. The dataset consists of ground truth data from a European household, laboratory measurements and few artificial created data. Specifically, the dataset includes data for TV, washing machine, toaster, iron, hairdryer, dish washer, PC, refrigerator, air-conditioner unit, range, dryer, heat pump (different modes of operation), BEV, water heater, light bulb and always-on load profiles.


Time Scale Modification (TSM) is a well-researched field; however, no effective objective measure of quality exists.  This paper details the creation, subjective evaluation, and analysis of a dataset for use in the development of an objective measure of quality for TSM. Comprised of two parts, the training component contains 88 source files processed using six TSM methods at 10 time scales, while the testing component contains 20 source files processed using three additional methods at four time scales.


Low light scenes often come with acquisition noise, which not only disturbs the viewers, but it also makes video compression harder. These type of videos are often encountered in cinema as a result of artistic perspective or the nature of a scene. Other examples include shots of wildlife (e.g. mobula rays at night in Blue Planet II), concerts and shows, surveillance camera footage and more. Inspired by all above, we are proposing a challenge on encoding low-light captured videos.

Last Updated On: 
Fri, 05/01/2020 - 09:40

This dataset was developed at the School of Electrical and Computer Engineering (ECE) at the Georgia Institute of Technology as part of the ongoing activities at the Center for Energy and Geo-Processing (CeGP) at Georgia Tech and KFUPM. LANDMASS stands for “LArge North-Sea Dataset of Migrated Aggregated Seismic Structures”. This dataset was extracted from the North Sea F3 block under the Creative Commons license (CC BY-SA 3.0).


Data from accelerometers mounted on ICE chassis.


Infrared imaging from aerial platforms can be used to detect landmines and minefields remotely and can save many lives. This dataset contains thermal images of buried and surface landmines. The images were recorded from a fixed camera for 24 hours with 15-minute intervals. DM-11 type anti-personnel landmines were used. This dataset is available for landmine detection research.


This dataset lets the users to rapidally estimate electric generator size, mass and losses for given power requirement, speed and DC bus voltage.This is accomplished through the metamodeling of a normalized optimization based machine design framework. The method is applied to a permanent magnet ac machine. This work is developed using the paper Metamodeling of Rotating Electric Machinery published in IEEE Transactions on Energy Conversion.