Omkar2118/datasets huggingface dataset:
CMedia

Cmedia dataset:

This dataset consists of 200 Youtube links of pop songs (most of them are Chinese songs), together with their groundtruth files of vocal transcription. We will release 100 of them as the open set for training/validation (training set), and use the other 100 as the hidden set for test.

The training set can be downloaded here. We strongly suggest participants to use this dataset as training set (if your algorithm is data-driven), since the property of Cmedia training set is close to Cmedia hidden set.
AIOZ-GDANCE

AIOZ-GDANCE comprises 16.7 hours of whole-body motion and music audio of group dancing. The duration of each video in our dataset is ranging from 15 to 60 seconds.

Introducing paper: Music-Driven Group Choreography
3D Flow MRI data of steady flow through a 3D-printed aorta at Re = 554 and 1526 with SNR ~ 5

The research data supports Kontogiannis, A., Elgersma, S. V., Sederman, A. J. & Juniper, M. P. Bayesian inverse Navier–Stokes problems: joint flow field reconstruction and parameter learning, https://doi.org/10.48550/arXiv.2406.18464, 2024. The flow MRI experiment is described in section 5.1 of that paper and in citated papers.

The data is contained in two folders: 'highRe' corresponds to data at Reynolds number = 1526; 'lowRe' corresponds to data at Reynolds number = 554.

Each folder contains three npy files (standard binary file format for a NumPy array): flow_data.npy contains the velocity components [u,v,w] on a [115,54,77] uniform mesh; mang.npy contains the mean magnitude image (nuclear spin density) on this mesh; mask_data.npy contains a mask that identifies the fluid regions on this mesh.

The code RUNME.py is a Python file that converts these npy files to centimetres-grammes-seconds (cgs) dimensions and plots slice number 'y_slice_idx' of the data to screen.

Introducing paper: Bayesian inverse Navier-Stokes problems: joint flow field reconstruction and parameter learning
AI-DataMining

Despite the availability of vast amounts of data, legal data is often unstructured, making it difficult even for law practitioners to ingest and comprehend the same. It is important to organise the legal information in a way that is useful for practitioners and downstream automation tasks. The word ontology was used by Greek philosophers to discuss concepts of existence, being, becoming and reality. Today, scientists use this term to describe the relation between concepts, data, and entities. A great example for a working ontology was developed by Dhani and Bhatt. This ontology deals with Indian court cases on intellectual property rights (IPR) The future of legal ontologies is likely to be handled by computer experts and legal experts alike.
CHAINS
Full name: CHAINS

The CHAINS dataset is utilized to evaluate the proposed system. This dataset includes both normal speech, referred to as solo, and whispered speech, known as whsp. The dataset
comprises recordings from 36 speakers, dominantly with Irish dialects. To develop a model capable of accurately capturing the acoustic features of the Irish dialect, the normal speech portion of this dataset is used for system training, while the whispered speech portion is employed for evaluation.
Web_Manual

http://websail-fe.cs.northwestern.edu/TabEL/#Web_Manual