Omkar2118/datasets huggingface dataset:
DeepParliament

**DeepParliament** is a legal domain Benchmark Dataset that gathers bill documents and metadata and performs various bill status classification tasks. The dataset text covers a broad range of bills from 1986 to the present and contains richer information on parliament bill content. There are a total of 5329 documents  where 4223 are in the train and 1106 are in the test dataset. Each bill document contains many sentences in both cases, and the document’s length varies greatly. 

Source: [DeepParliament: A Legal domain Benchmark & Dataset for Parliament Bills Prediction](https://arxiv.org/pdf/2211.15424v1.pdf)

Image Source: [https://arxiv.org/pdf/2211.15424v1.pdf](https://arxiv.org/pdf/2211.15424v1.pdf)

Introducing paper: DeepParliament: A Legal domain Benchmark & Dataset for Parliament Bills Prediction
NEREL-BIO

**NEREL-BIO** is an annotation scheme and corpus of PubMed abstracts in Russian and English. It contains annotations for 700+ Russian and 100+ English abstracts. All English PubMed annotations have corresponding Russian counterparts. NEREL-BIO comprises the following specific features: annotation of nested named entities, it can be used as a benchmark for cross-domain (NEREL -> NEREL-BIO) and cross-language (English -> Russian) transfer.

Source: [NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities](https://arxiv.org/pdf/2210.11913v1.pdf)

Image Source: [https://arxiv.org/pdf/2210.11913v1.pdf](https://arxiv.org/pdf/2210.11913v1.pdf)

Introducing paper: NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities
PLABA
Full name: Plain Language Adaptation of Biomedical Abstracts

**Plain Language Adaptation of Biomedical Abstracts (PLABA)** is a dataset designed for automatic adaptation that is both document- and sentence-aligned. The dataset contains 750 adapted abstracts, totaling 7643 sentence pairs.

Source: [A Dataset for Plain Language Adaptation of Biomedical Abstracts](https://arxiv.org/pdf/2210.12242v1.pdf)

Image Source: [https://arxiv.org/pdf/2210.12242v1.pdf](https://arxiv.org/pdf/2210.12242v1.pdf)

Introducing paper: A Dataset for Plain Language Adaptation of Biomedical Abstracts
MSU BASED
Full name: MSU BASED Video Deblurring Dataset and Benchmark

Qualitative dataset with real blurred videos, created by using beam-splitter setup in lab environment
Physionet MI
Full name: Physionet EEG Motor Movement/Imagery Dataset

This data set consists of over 1500 one- and two-minute EEG recordings, obtained from 109 volunteers [2].

Subjects performed different motor/imagery tasks while 64-channel EEG were recorded using the BCI2000 system (http://www.bci2000.org) [1]. Each subject performed 14 experimental runs: two one-minute baseline runs (one with eyes open, one with eyes closed), and three two-minute runs of each of the four following tasks:
- A target appears on either the left or the right side of the screen. The subject opens and closes the corresponding fist until the target disappears. Then the subject relaxes.
- A target appears on either the left or the right side of the screen. The subject imagines opening and closing the corresponding fist until the target disappears. Then the subject relaxes.
- A target appears on either the top or the bottom of the screen. The subject opens and closes either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes.
- A target appears on either the top or the bottom of the screen. The subject imagines opening and closing either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes.

References

[1] Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N. and Wolpaw, J.R., 2004. BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Transactions on biomedical engineering, 51(6), pp.1034-1043.
[2] Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E. and PhysioBank, P., PhysioNet: components of a new research resource for complex physiologic signals Circulation 2000 Volume 101 Issue 23 pp. E215–E220.