Omkar2118/datasets huggingface dataset:
MONK's Problems

There are three MONK's problems.  The domains for all MONK's problems are the same (described below).  One of the MONK's problems has noise added. For each problem, the domain has been partitioned into a train and test set.
KDD Cup 1999

This is the data set used for The Third International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with KDD-99 The Fifth International Conference on Knowledge Discovery and Data Mining. The competition task was to build a network intrusion detector, a predictive model capable of distinguishing between ``bad'' connections, called intrusions or attacks, and ``good'' normal connections. This database contains a standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment.
Arcene

ARCENE was obtained by merging three mass-spectrometry datasets to obtain enough training and test data for a benchmark. The original features indicate the abundance of proteins in human sera having a given mass value. Based on those features one must separate cancer patients from healthy patients. We added a number of distractor feature called 'probes' having no predictive power. The order of the features and patterns were randomized.
MTOP
Full name: Multilingual Task-Oriented Semantic Parsing

A multilingual task-oriented semantic parsing dataset covering 6 languages and 11 domains.

Introducing paper: MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark
Emotional Dialogue Acts

Emotional Dialogue Acts data contains dialogue act labels for existing emotion multi-modal conversational datasets.
We chose two popular multimodal emotion datasets: Multimodal EmotionLines Dataset (MELD) and Interactive Emotional dyadic MOtion CAPture database (IEMOCAP). 
EDAs reveal associations between dialogue acts and emotional states in a natural-conversational language such as Accept/Agree dialogue acts often occur with the Joy emotion, Apology with Sadness, and Thanking with Joy.

Introducing paper: EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators
Santesteban VTO

Physics-based simulated garments on top of SMPL bodies. The data is generated used a modified version of ARCSim and sequences from the CMU Motion Capture Database converted to SMPL format in SURREAL. Each simulated sequence is stored as a .pkl file that contains the following data: