These datasets contain bulk BTE simulation results for GaAs, InP, GaSb and InAs as a function of electric field at 300 K.


To read the data we suggest to

1. un-zip the data

2. read it with e.g. the pandas library for Python or any *csv reader


This is a large Chinese taxonomic knowledge base, which is translated from Probase by the neural network.

It has 11,292,493 IsA pairs with an accuracy of 86.6%.



This is a large Chinese commonsense knowledge base, which is translated from ConceptNet 5.6, with around 2 million triples and an accuracy of 89.6%.


This dataset presents the results obtained for Ingestion and Reporting layers of a Big Data architecture for processing performance management (PM) files in a mobile network. Flume was used in the Ingestion layer. Flume collected PM files from a virtual machine that replicates PM files from a 5G network element (gNodeB). Flume transferred PM files to High Distributed File System (HDFS) in XML format. Hive was used in the Reporting layer. Hive queries the raw data from HDFS. Hive queries a view from HDFS.



The current maturity of autonomous underwater vehicles (AUVs) has made their deployment practical and cost-effective, such that many scientific, industrial and military applications now include AUV operations. However, the logistical difficulties and high costs of operating at-sea are still critical limiting factors in further technology development, the benchmarking of new techniques and the reproducibility of research results. To overcome this problem, we present a freely available dataset suitable to test control, navigation, sensor processing algorithms and others tasks.


This repository contains the AURORA dataset, a multi sensor dataset for robotic ocean exploration.

It is accompanied by the report "AURORA, A multi sensor dataset for robotic ocean exploration", by Marco Bernardi, Brett Hosking, Chiara Petrioli, Brian J. Bett, Daniel Jones, Veerle Huvenne, Rachel Marlow, Maaten Furlong, Steve McPhail and Andrea Munafo.

Exemplar python code is provided at


The dataset provided in this repository includes data collected during cruise James Cook 125 (JC125) of the National Oceanography Centre, using the Autonomous Underwater Vehicle Autosub 6000. It is composed of two AUV missions: M86 and M86.

  • M86 contains a sample of multi-beam echosounder data in .all format. It also contains CTD and navigation data in .csv format.

  • M87 contains a sample of the camera and side-scan sonar data. The camera data contains 8 of 45320 images of the original dataset. The camera data are provided in .raw format (pixels are ordered in Bayer format). The size of each image is of size 2448x2048. The side-scan sonar folder contains a one ping sample of side-scan data provided in .xtf format.

  • The AUV navigation file is provided as part of the data available in each mission in .csv form.


The dataset is approximately 200GB in size. A smaller sample is provided at and contains a sample of about 200MB.

Each individual group of data (CTD, multibeam, side scan sonar, vertical camera) for each mission (M86, M87) is also available to be downloaded as a separate file. 



This dataset is a set of eighteen directed networks that represents message exchanges among Twitter accounts during eighteen crisis events. The dataset comprises 645,339 anonymized unique user IDs and 1,396,709 edges that are labeled with respect to Plutchik's basic emotions (anger, fear, sadness, disgust, joy, trust, anticipation, and surprise) or "neutral" (if a tweet conveys no emotion).


This paper concerns static output feedback stabilization of polytopic discrete LTI systems. The previous related studies were mainly based on LMI approaches which are naturally conservative. In this paper, a novel design algorithm is presented that iteratively partitions a primary design space to subspaces. Then, by assessing stabilizability status of each generated subspace, the algorithm determines the total stabilizable parts and removes the undesired parts of the design space.


EmoSurv is a dataset containing keystroke data along with emotion labels. Timing and frequency data is recorded while participants are typing free and fixed texts before and after being induced specific emotions. These emotions are: Anger, Happiness, Calmness, Sadness, and Neutral state.

First, data is collected while the participant is in a neutral state. Then, the participant watches an eliciting video. Once the emotion is induced in the participant, he types another fixed and free text.


The dataset contains 4 .csv files:

  • File 1: Fixed Text Typing Dataset which is collected while a participants it typing a fixed text and it  includes the following features: User Id, Emotion Index, Index, Key Code, key Down, key Up, D1U1, D1U2, D1D2, U1D2, U1U2, D1U3, D1D3, and Answer.

  • File 2: Free Text Typing Dataset which is collected while a participants it typing a free text and it  includes the following features:  User Id, Emotion Index, Index, Key Code, key Down, key Up, D1U1, D1U2, D1D2, U1D2, U1U2, D1U3, D1D3, and Answer.

  • File 3: Frequency Dataset which includes frequency related features like User ID, textIndex, EmotionIndex, DelFreq, LeftFreq, and TotTime.

  • File 4: Participants Information dataset which includes demographics information like UserID, TypeWith, TypistType, PCTimeAverage, AgeRange, gender, status, degree, and country.


  • UserID: each participant is allocated the same ID in the 4 files.

  • Emotion Index: H (for Happy), S (for Sad), A (for Angry), C (for Calm), and N (for Neutral state).

  • Key Code: the key pressed by the participant.

  • Key Down: is the exact timestamp of the key down event. 

  • Key Up: is the exact timestamp of the key up event.

  • TextIndex: the type of text typed being either FI (for Fixed text) or FR (for Free text)

  • D1U1 (DT1): Time between first key down and first key up 1

  • D1U2 (Dig2): Time between first key down and second key up 2

  • D1D2 (Dig1): Time between first key down and second key down 2

  • U1D2 (FT1 / FT2): Time between first key up and second key down 2

  • U1U2 (Dig3): Time between first key up and second key up 2

  • D1U3 (Trig2): Time between first key down and third key up 3

  • D1D3 (Trig1): Time between first key down and third key down 3

  • Answer: Takes “R” (as right answer) if the participant answered correctly the accuracy question and “W” (as wrong answer) if he incorrectly answered it. (The accuracy question is a MCQ related to the video that the participant has watched)

  • DelFreq: Relative frequency of delete key NA

  • LeftFreq: Relative frequency of backspace key NA

  • Typing speed: Number of key pressed in each task the time spent from the first key pressed to the last key released (in the same task). 

  • TypeWith: specifies if the participant types using one hand or two hands

  • TypistType: specifies whether the participant uses one finger, two fingers, or is a touch typist (multiple fingers) to type a text.

  • PCTimeAverage: is the average time a user spends on his/her computer per day.

  • AgeRange: 16-19, 20-29, 30-39, >= 40years old. 

  • Gender: Male, or female

  • Status: Student, or professional

  • Degree: College/University, or High school. 

  • Country: Place of residence.

The figure attached in Documentation section represents how the timing features are calculated.


Grant of License

We grant You a non-exclusive, non-transferable, revocable license to use the EmoSurv  Dataset solely for Your non-commercial, educational, and research purposes only, but without any right to copy or reproduce, publish or otherwise make available to the public or communicate to the public, sell, rent or lend the whole or any constituent part of the Emosurv Dataset thereof. 



A high level of monitoring is necessary for the safety and product quality of the electrical fused magnesia furnace (EFMF). In this paper, a monitoring method based on latent subspace for EFMF is proposed to fully mine the effective information of multi-source heterogeneous data in the process. By minimizing the distance of different types of data in the subspace, the corresponding projection matrix is obtained. Then the data is projected into the obtained subspace to estimate whether fault occurs.In summary, the main contributions of this paper are threefold.


experimental data