*.csv (zip)

This dataset is used to develop an algorithm for evaluating machining quality. When machining a workpiece in a milling process, vibration signals can be recorded by a 3-axis accelerometer, which is attached on the spindle of a CNC milling machine. To evaluate machining quality, the vibration signals can be segmented and extracted the corresponding features, in the time, frequency, and time-frequency domains. After serving with the features, a model can be developed to estimate the machining quality, such as the roughness of a workpiece.


It contains the four biomarkers which we have selected for the instrument, in the first column we have the recordings for heart, in second we have recordings for temperature, third is for muscle activity and last column is for oxygen levels.


This dataset is used to develop an algorithm for automatic segmenting the collected signals. When machining a workpiece in a milling process, vibration signals can be recorded by a 3-axis accelerometer, which is attached on the spindle of a CNC milling machine. To segment the recorded signals, a moving window (0.5 sec) is applied to sample the vibration signals and manually labeled the corresponding modes, i.e. dry run or milling, of each window. To verify the algorithm, 3 types of operations are provided and recorded in csv format. 


This dataset is associated with an IEEE journal submission titled: "Prediction of larynx function using multichannel surface EMG classification" by the associated authors. The dataset consists of surface electromyography (sEMG) signals recorded from 10 study participants (5 control, 5 laryngectomees), each undertaking 3 recording sessions.

During each session the following were recorded:


Route planning also known as pathfinding is one of the key elements in logistics, mobile robotics and other applications, where engineers face many conflicting objectives. However, most of the current route planning algorithms consider only up to three objectives. In this paper, we propose a scalable many-objective benchmark problem covering most of the important features for routing applications based on real-world data. We define five objective functions representing distance, traveling time, delays caused by accidents, and two route specific features such as curvature and elevation.


The dataset consists of echo data collected at the Matre research station (61°N) of the Institute of Marine Research (IMR), Norway. Six square sea cages (12 × 12 m and 15 m depth; approximately 2000 m^3) were used. The fish's vertical distribution and density were observed continuously by a PC-based echo integration system (CageEye MK IV, software version 1.1.1., CageEye AS, Steinkjer, Norway) connected to an upward facing transducer which multiplexes between 50 kHz (42° acoustic beam angle) and 200 kHz (14° beam angle).


Radio-frequency noise mapping data collected from Downtown, Back Bay and North End neighborhoods within Boston, MA, USA in 2018 and 2019.


The diversity of video delivery pipeline poses a grand challenge to the evaluation of adaptive bitrate (ABR) streaming algorithms and objective quality-of-experience (QoE) models.

Here we introduce so-far the largest subject-rated database of its kind, namely WaterlooSQoE-IV, consisting of 1350 adaptive streaming videos created from diverse source contents, video encoders, network traces, ABR algorithms, and viewing devices.

We collect human opinions for each video with a series of carefully designed subjective experiments.


This dataset includes PV power production measured on the SolarTech Lab, Politecnico di Milano, Italy. Data are freely available for scientific research purpose and further data validation.

In particular, the dataset is composed of the following variables and specifics, with a time resolution of 1 minute: