Omkar2118/datasets huggingface dataset:
Faster__convergence_MOEAD

Dataset with raw outputs of experiments connected to the GitHub repository:

https://github.com/yurilavinas/MOEADr/tree/ECJ

The size of the data is big, make sure to have enough space.
Webis-STEREO-21

We present the Webis-STEREO-21 dataset, a massive collection of Scientific Text Reuse in Open-access publications. It contains more than 91 million cases of reused text passages found in 4.2 million unique open-access publications. Featuring a high coverage of scientific disciplines and varieties of reuse, as well as comprehensive metadata to contextualize each case, our dataset addresses the most salient shortcomings of previous ones on scientific writing. Webis-STEREO-21 allows for tackling a wide range of research questions from different scientific backgrounds, facilitating both qualitative and quantitative analysis of the phenomenon as well as a first-time grounding on the base rate of text reuse in scientific publications.

Introducing paper: STEREO: Scientific Text Reuse in Open Access Publications
TUAC
Full name: Temple University Artifact Corpus

A new subset of the popular open source electroencephalogram (EEG) corpus – TUH EEG:
- The Temple University Artifact Corpus (TUAR) consists of high yield artifact files annotated using a five-way classification system:
1. Chewing (CHEW): An artifact resulting from the tensing and relaxing of the jaw muscles.
2. Electrode (ELEC): An artifact that encompasses various electrode related phenomena.
3. Eye Movement (EYEM): A spike-like waveform created during patient eye movement.
4. Muscle (MUSC): A common artifact with high frequency, sharp waves corresponding to patient movement.
5. Shiver (SHIV): A specific and sustained sharp wave artifact that occurs when a patient shivers.
- EEG artifacts are waveforms that are not of cerebral origin and may have been affected by several
external and physiological factors.
- These artifacts cause false alarms in seizure prediction machine learning systems.
This corpus was developed to support research and evaluation of artifact suppression technology.
NepaliNewsCorpus

# Nepali News Corpus
## Raw nepali text scrapped from several online websites.
### Total file size of concatenated text file -> 4.6 GB. 

## This raw text   corpus can be used for tasks such as casual language modelling and masked language modelling.
AFLW-19
Full name: The 19 landmark variant of AFLW.

The [original AFLW](https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/aflw/) provides at most 21 points for each face, but excluding coordinates for invisible landmarks, causing difficulties for training most of the existing baseline approaches. To make fair comparisons, the authors manually annotate the coordinates of these invisible landmarks to enable training of those baseline approaches. The new annotation does not include
two ear points because it is very difficult to decide the location of invisible ears. This causes the point number of AFLW-19 to be 19.

The original AFLW does not provide train-test partition. AFLW-19 adopts a partition with 20,000 images for training and 4,386 images for testing (AFLW-Full). In addition,  a frontal subset  (AFLW-Frontal) is proposed where all landmarks are visible (totally 1,165 images).

The new 19-point annotation file is available at the [project page](http://mmlab.ie.cuhk.edu.hk/projects/compositional.html).

Introducing paper: Unconstrained Face Alignment via Cascaded Compositional Learning
Covid Dataset

The COVID-19 CT dataset is constructed by Shenzhen Research Institute of Big Data (SRIBD), Future Network of Intelligence Institute (FNii) and CUHKSZ-JD Joint AI Lab, Chinese University of Hongkong, Shenzhen, China, which contains 368 medical findings in Chinese and 1,104 chest CT scans from the First Affiliated Hospital of Jinan University Guangzhou and the Fifth Affiliated Hospital of Sun Yat-sen University Zhuhai in China. Please see more details in our TNNLS paper - Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning.