Omkar2118/datasets huggingface dataset:
TimeHetNet
Full name: Meta Dataset for Time Series with heterogeneous networks

This meta-dataset is composed of previously known datasets. 

This includes: It also uses a specific script to read and sample small tasks from specified sizes and lengths. 

DS included here are from :

PeekDB (https://github.com/RafaelDrumond/PeekDB)
Informer data-sets (https://github.com/zhouhaoyi/Informer2020)
Monash (https://zenodo.org/communities/forecasting)
UEA (http://www.timeseriesclassification.com/Downloads/Archives/Multivariate2018_arff.zip)
CNC (https://www.kaggle.com/datasets/shasun/tool-wear-detection-in-cnc-mill/download)
MINING (https://www.kaggle.com/datasets/edumagalhaes/quality-prediction-in-a-mining-process/download)
Plant_Monitoring (https://www.kaggle.com/datasets/inIT-OWL/production-plant-data-for-condition-monitoring/download)

Licenses from each db differs individually.

Introducing paper: Few-Shot Forecasting of Time-Series with Heterogeneous Channels
PeekDB

Data-set from "PEEK-An LSTM Recurrent Network for Motion Classification from Sparse Data"
Monash

Time Series Forecasting Repository containing datasets of related time series for global forecasting.
MTic
Full name: MADHAVLab Tic

Periodic Tic sounds (T0=1s) sampled at 16kHz with duration of nearly 10s.

Introducing paper: SyncNet: Using Causal Convolutions and Correlating Objective for Time Delay Estimation in Audio Signals
STEW
Full name: Simultaneous Task EEG Workload Dataset

This dataset consists of raw EEG data from 48 subjects who participated in a multitasking workload experiment utilizing the SIMKAP multitasking test. The subjects’ brain activity at rest was also recorded before the test and is included as well. The Emotiv EPOC device, with sampling frequency of 128Hz and 14 channels was used to obtain the data, with 2.5 minutes of EEG recording for each case. Subjects were also asked to rate their perceived mental workload after each stage on a rating scale of 1 to 9 and the ratings are provided in a separate file.
Age and Gender
Full name: Age and Gender Dataset

EEG signals from 60 users have been recorded whose age range lies between 6 and 55 years. Among all, there were 25 females and 35 male users. In general, all the participants were either school children or belonged to the socioeconomic cross section of the population with no medical history. The EEG recordings were acquired from all 14 electrodes operating at a sampling rate of 128 Hz. During recording, the participants were asked to comfortably sit on the chair with clear thoughts and a relaxed state.
ReferIt3D

ReferIt3D provides two large-scale and complementary visio-linguistic datasets: i) Sr3D, which contains 83.5K template-based utterances leveraging spatial relations among fine-grained object classes to localize a referred object in a scene, and ii) Nr3D which contains 41.5K natural, free-form, utterances collected by deploying a 2-player object reference game in 3D scenes. This dataset can be used for 3D visual grounding and 3D dense captioning tasks.

Introducing paper: ReferIt3D: Neural Listeners for Fine-Grained 3D Object Identification in Real-World Scenes
Southern California Seismic Network Data
Full name: Generalized Seismic Phase Detection with Deep Learning

These files are supplementary material for “Generalized Seismic Phase Detection with Deep Learning” by Ross et al. (2018), BSSA (doi.org/10.1785/0120180080). The models were trained using keras and TensorFlow, and can be used with these libraries. The training dataset contains 4.5 million seismograms evenly split between P-waves, S-waves, and pre-event noise classes. We encourage the use of this hdf5 dataset for training deep learning models, and hope that it and the model architecture in the paper can serve as a benchmark for future studies. For additional information please contact Zachary Ross (zross@caltech.edu).

Introducing paper: Generalized Seismic Phase Detection with Deep Learning
Wireless-Intelligence

Wireless-Intelligence is a database website provided for AI-based wireless communication research, in which each dataset consists of hundreds and thousands of channel samples in different forms. The data is available for free to researchers for non-commercial use.

##  What is Wireless-Intelligence?
Wireless-Intelligence is a free and open database website for AI-based wireless communication researchers. In Wireless-Intelligence, we aim to provide high-quality datasets for multiple kinds of research areas in AI-based wireless communications, including channel state information (CSI) feedback, channel estimation, positioning, and more datasets in the following updates. For each dataset, hundreds of thousands of labeled samples are provided for training and testing in different forms. We hope that the Wireless-Intelligence can help the researchers in the future.

##  Why Wireless-Intelligence?
AI-based wireless communications have attracted lots of attention from both academia and industry recently, due to its great potential of improving the performance of wireless communications systems. However, the lack of abundant high-quality wireless datasets restricts the development of AI-based wireless communications. That is why we want to establish the Wireless-Intelligence website. Here, we provide plenty of labeled wireless communication datasets for different research areas for free, so that researchers can use them to make more effective studies and comparable results.
SUES-200

Cross-view Image Dataset Across Drone and Satellite
- multi-height
- multi-scene

Introducing paper: SUES-200: A Multi-height Multi-scene Cross-view Image Benchmark Across Drone and Satellite
USC-GRAD-STDdb
Full name: Small Target Detection database

USC-GRAD-STDdb comprises 115 video segments containing more than 25,000 annotated frames of HD 720p resolution (≈1280x720) with small objects of interest from 16 (≈4x4) to 256 (≈16x16) as pixel area. The length of the videos changes from 150 up to 500 frames. The size of every object is determined through the bounding box, so that a good annotation is of utmost importance for reliable performance metrics. As it may seem obvious, the smaller the object, the harder the annotation. The annotation has been carried out with the ViTBAT tool, adjusting the boxes as much as possible to the objects of interest in each video frame. In total, more than 56,000 ground truth labels have been generated.