BS-HMS-Dataset is a dataset of the users' brainwave signals and the corresponding hand movement signals from a large number of volunteer participants. The dataset has two parts; (1) Neurosky based Dataset (collected over several months in 2016 from 32 volunteer participants), and (2) Emotiv based Dataset (collected from 27 volunteer participants over several months in 2019). 


There are two folders under each user; session I and sessions II. Each session folder contains four different folders; one for each activity performed by the user. Each activity folder contains .csv files; (1) EEG Data (brainwave.csv), (2) Handmovement Accelerometer Data (accelerometer.csv), and (3) Handmovement Gyroscope Data (gyroscope.csv).

A more deatailed description of the data is given in BS-HMS-Dataset-Documentation.pdf file.

Acknowledgement: This data collection was supported in part by the National Science Foundation (NSF) under grant SaTC-1527795.

Please cite: [1] Diksha Shukla, Sicong Chen, Yao Lu, Partha Pratim Kundu, Ravichandra Malapati, Sujit Poudel, Zhanpeng Jin, Vir Phoha, "Brain Signals and the Corresponding Hand Movement Signals Dataset (BS-HMS-Dataset)", IEEE Dataport, 2019. [Online]. Available: Accessed: Dec. 05, 2019.


A reliable and comprehensive public WiFi fingerprinting database for researchers to implement and compare the indoor localization’s methods.The database contains RSSI information from 6 APs conducted in different days with the support of autonomous robot.


Database Folder name "RSSI_6AP_Experiments" which includes:

+ Nexus 4 database

+ Nexus 5 database

Training & Testing data are seperated & collected in different time with different locations.

* The general format for csv data file is:

-- X & Y & The list of RSSI

-- X | Y | RSSI Vector



This dataset was used in the article "Dias-Audibert FL, Navarro LC, de Oliveira DN, Delafiori J, Melo CFOR, Guerreiro TM, Rosa FT, Petenuci DL, Watanabe MAE, Velloso LA, Rocha AR and Catharino RR (2020) Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers. Front. Bioeng. Biotechnol. 8:6. doi: 10.3389/fbioe.2020.00006", open access available at:


WGMSML-Data folder contains the mass spectra input data for the Matlab scripts which are in WGMSML-MATLAB-SourceCode folder. WGMSML-ExecutionLogsAndPlots contains logs and plots generated by the execution of the Matlab code over the input data. Main scripts are enumerated in the order of execution.






The dataset contains measurement results of Radar Cross Section of different Unmanned Aerial Vehicles at 26-40 GHz. The measurements have been performed fro quasi-monostatic case (when the transmitter and receiver are spatially co-located) in the anechoic chamber. The data shows how radio waves are scattered by different UAVs at the specified frequency range.




Some of DJI, Walkera, Parrot and Kyosho drones were measured.

The data is in ".csv" format. Each file contains the following information: frequency, theta, phi, and RCS.

The RCS signatures of the following drone models are available:

-DJI Phantom 4 Pro

-DJI F450

-DJI Mavic Pro

-Helicopter Kyosho

-Parrot AR.drone

-DJI Matrice M100

-Walkera Voyager 4

-Custom built hexacopter

-Tricopter HMF600, frame only

Polarization is mentioned in the file name:

  • HH - horizontal polarisation of the transmitter and the receiver

  • HV/VH - horizontal and vertical or vice versa

  • VV - vertical polarization of the transmitter and the receiver

In addition, 6S LiPo battery RCS is available.


 Published article can be found at:



  • Drift types (A): gradual, incremental, recurring and sudden
  • Drift perspectives (B): time and trace
  • Noise percentage (C): 0, 5, 10, 15, 20
  • Number of cases in the stream (D): 100, 500, 1000
  • Change patterns (E): baseline, cb, cd, cf, cp, IOR, IRO, lp, OIR, pl, pm, re, RIO, ROI, rp, sw


The file name follows the pattern [A]_[B]_noise[C]_[D]_[E]

An identical version of this dataset in the MXML format is available at:


We introduce a dataset concerning electric-power consumption-related features registed in seven main municipalities of Nariño, Colombia, from 2010 to 2016. The dataset consists of 4423 socio-demographic characteristics, and 6 power-consumption-referred measured values. Data were fully collected by the company Centrales Eléctricas de Nariño (CEDNEAR) according to the client consumption records.


Power consumption is obtained by manually recording the amount of

Kw/h consumed each month using the electric energy meter of the

clients. Socio-demographic data were collected from the records

available in databases of CEDENAR. Here in, to set the stratum

(categorization of socioeconomic level in Colombia) of the clients,

Colombian Law 732 of 2002 [1] was adopted. It has six level are

individually incorporated as follows: 0 = Low-low, 1 = Low, 2 = Medium-

Low, 3 = Medium, 4 = Medium-High, 5 = high.


Traditional Static Timing Analysis (STA) assumes only single input switches at a time with the side input held at non-controlling value. This introduces unnecessary pessimism or optimism which may cause degradation of performance or chip failure.  Modeling Multi-Input Switching (MIS) requires a good amount of simulations hence we provide a dataset comprising of SPICE simulations done on 2 input NAND and NOR gate.


The development of technology also influences changes in campaign patterns. Campaign activities are part of the process of Election of Regional Heads. The aim of the campaign is to mobilize public participation, which is carried out directly or through social media. Social media becomes a channel for interaction between candidates and their supporters. Interactions that occur during the campaign period can be one indicator of the success of the closeness between voters and candidates. This study aims to get the pattern of campaign interactions that occur on Twitter social media channels.


This a csv file. Please use approriate applicationThis file containing table of twitter interaction about Regional Head Election on Central Java, IndonesiaThe analysis and paper work on


This dataset is part of my PhD research on malware detection and classification using Deep Learning. It contains static analysis data: Top-1000 imported functions extracted from the 'pe_imports' elements of Cuckoo Sandbox reports. PE malware examples were downloaded from PE goodware examples were downloaded from and from Windows 7 x86 directories.



Column name: hash
Description: MD5 hash of the example
Type: 32 bytes string

Column name: GetProcAddress
Description: Most imported function (1st)
Type: 0 (Not imported) or 1 (Imported)


Column name: LookupAccountSidW
Description: Least imported function (1000th)
Type: 0 (Not imported) or 1 (Imported)

Column name: malware
Description: Class
Type: 0 (Goodware) or 1 (Malware)


We would like to thank: Cuckoo Sandbox for developing such an amazing dynamic analysis environment!
VirusShare! Because sharing is caring!
Universidade Nove de Julho for supporting this research.
Coordination for the Improvement of Higher Education Personnel (CAPES) for supporting this research.


Please refer to the dataset DOI.
Please feel free to contact me for any further information.