Protein molecules are inherently dynamic and modulate their interactions with different molecular partners by accessing different tertiary structures under physiological conditions.Elucidating such structures remains challenging. Current momentum in deep learning and the powerful performance of generative adversarial networks (GANs) in complex domains, such as computer vision, inspires us to investigate GANs on their ability to generate physically-realistic protein tertiary structures.


Generated data, input data and saved models for the publication
Taseef Rahman, Yuanqi Du, Liang Zhao, and Amarda Shehu. Generative Adversarial Learning of Protein Tertiary Structures. Molecules, 2021.
is made available. Instructions accompany the data in a ReadMe.txt in each folder respectively for the ease of use.


The dataset is generated by performing different MiTM attacks in the synthetic electric grid in RESLab testbed at Texas A&M University, US. The testbed primarily consists of a dynamic power system simulator (Powerworld Dynamic Studio), network emulator (CORE), Snort IDS, open DNP3 master and Elasticsearch's Packetbeat index. There are raw and processed files that can be used by security enthusiasts to develop new features and also to train IDS using our feature space respectively.


This dataset is for short-term spatio-temporal PV forecasting.

This dataset consists of three two parts. The first part is the spatio-temporal PV dataset which obatined from different PV sites. The second part is the corresponding weather datasets, including temperature, wind speed, wind direction, etc. 

The dataset also contains the demo codes for showing the concept of a machine learning based PV forecasting model. 

More information will be added in the future. 


The dataset is part of the MIMIC database and specifically utilise the data corresponding to two patients with ids 221 and 230.


This data set is the result of model test trained on the basis of the Stanford earthquake dataset (stead): a global data set of seismic signals for AI, which can effectively get the seismic signal and the arrival time of seismic phase from the image, so as to prove the effectiveness of this model


Online Machine Learning for Energy-Aware Multicore Real-Time Embedded Systems Dataset is a Dataset composed of Hardware Performance Counters extracted from a Multicore Real-Time Embedded System. This Dataset encompasses every Monitorable Performance counters in a Cortex-A53 quad-core processor, totaling 54 performance counters, which are sampled periodically through a non-Intrusive Monitoring Framework implemented over Embedded Parallel Operating System (EPOS), a Real-Time Operating System.


The dataset consists of the ISFET sensor data utilized to train ML models for drift compensation.


This dataset consists of the sensor data used to develop SPICE macro model of ISFET and associated documentation.


Related to above sarch keywords following tweets were extratced b/w 15 nov 2020 to 10 jan 2021

29499  English TWEETS extracted,

4628 Japanese tweets extracted

678 Hindi tweets extracted 



YonseiStressImageDatabase is a database built for image-based stress recognition research. We designed an experimental scenario consisting of steps that cause or do not cause stress; Native Language Script Reading, Native Language Interview, Non-native Language Script Reading, Non-native Language Interview. And during the experiment, the subjects were photographed with Kinect v2. We cannot disclose the original image due to privacy issues, so we release feature maps obtained by passing through the network.



Database Structure

- YonseiStressImageDatabase

         - Subject Number (01~50)

                  - Data acquisition phase

                    (Native Language Script Reading, Native Language Interview, Non-native Language Script Reading, Non-native Language Interview)

                           - Data (*.npy, the filename is set to the time the data was acquired; YYYYMMDD_hhmmss_ms)


In the case 'Non-native_Language_Interview' data of subject 26, it was not acquired due to equipment problems.


Citing YonseiStressImageDatabase

If you use YonseiStressImageDatabase in a scientific publication, we would appreciate references to the following paper:

Now Reviewing.


Usage Policy

Copyright © 2019 AI Hub, Inc.,

AI data provided by AI Hub was built as part of a business National Information Society Agency's 'Intelligent information industry infrastructure construction project' in Korea, and the ownership of this database belongs to National Information Society Agency.

Specialized field AI data was built for artificial intelligence technology development and prototype production and can be used for research purposes in various fields such as intelligent services and chatbots.



Accurate and efficient anomaly detection is a key enabler for the cognitive management of optical networks, but traditional anomaly detection algorithms are computationally complex and do not scale well with the amount of monitoring data. Therefore, this dataset enables research on new optical spectrum anomaly detection schemes that exploit computer vision and deep unsupervised learning to perform optical network monitoring relying only on constellation diagrams of received signals.


The dataset contains a set of folders, each one representing one normal/anomalous case.

Within each folder, a number of .mat files contain the raw data collected from VPITransmissionMaker. The images folder contains the rendered constellation diagrams.

To render your own constellation diagrams, check the "generate_plots.m" file in the root folder.

More information on how to use in the GitHub repository.