Omkar2118/datasets huggingface dataset:
OpinionQA

**OpinionQA** is a dataset for evaluating the alignment of LM opinions with those of 60 US demographic groups over topics ranging from abortion to automation.

Source: [Whose Opinions Do Language Models Reflect?](https://arxiv.org/pdf/2303.17548v1.pdf)

Image Source: [Whose Opinions Do Language Models Reflect?](https://arxiv.org/pdf/2303.17548v1.pdf)

Introducing paper: Whose Opinions Do Language Models Reflect?
MP-DocVQA
Full name: Multipage Document Visual Question Answering

The dataset is aimed to perform Visual Question Answering on multipage industry scanned documents. The questions and answers are reused from Single Page DocVQA (SP-DocVQA) dataset. The images also corresponds to the same in original dataset with previous and posterior pages with a limit of up to 20 pages per document.

Introducing paper: Hierarchical multimodal transformers for Multi-Page DocVQA
S&P 500 Pair Trading

A pool of real stocks from S&P 500 for recent 21 years from 01/02/2000 to 12/31/2020.
We filter stocks that have missing data throughout the whole period, resulting in 150 stocks with 5,284 trading days.

Introducing paper: Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning
CSI 300 Pair Trading

A daily emerging stock market dataset (Chinese CSI 300 dataset) including 300 stocks and 5,088 time steps from the CSMAR database. We construct our stock dataset using a pool of stocks from the CSI 300 index for the last 21 years, from 01/02/2000 to 12/31/2020. Instead of all stocks in the market, we select the stocks that used to belong to the major market index CSI 300, and filter out stocks that have missing price data over the period.

For each trading day, we use the fundamental price features as the features of stocks, including open price, close price, and volume. Additionally, we normalize price features such as open price and close price with logarithm.

The dataset randomly splits stocks into five non-overlapping sub-datasets. For each subset, the first 90% of trading days are used as train data, the following 5% as validation data, and the rest 5% as test data.

Introducing paper: Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning
NBA player performance prediction dataset

The dataset covers the 2022-23 NBA regular season (2022-10-18 to 2023-01-20) which contains 691 games in 92 game days. There are 582 active players among the 30 teams. Besides 7 basic statistics, we collected 3 tracking statistics, and 3 advanced statistics. We use tracking statistics to more accurately reflect players' movements on the court, and advanced statistics to more properly represent a player's effectiveness and contribution to the game. Together, these two types of data give us a better understanding of factors that are not visible on the scoreboard.

Introducing paper: Who You Play Affects How You Play: Predicting Sports Performance Using Graph Attention Networks With Temporal Convolution
U2OS

The archive contains original images from U2OS cells stained with Hoechst 33342 as PNG files. It also contains images (as Photoshop and GIMP files) showing hand-segmentation of the Hoechst images into regions containing single nuclei.

Introducing paper: Nuclei Segmentation In Microscope Cell Images: A Hand-Segmented Dataset And Comparison Of Algorithms
NIH3T3

The archive contains original images from NIH3T3 cells stained with Hoechst 33342 as PNG files. It also contains images (as Photoshop and GIMP files) showing hand-segmentation of the Hoechst images into regions containing single nuclei.

Introducing paper: Nuclei Segmentation In Microscope Cell Images: A Hand-Segmented Dataset And Comparison Of Algorithms
PMC-OA
Full name: PubmedCentral OpenAcess

**PMC-OA** is a large-scale dataset that contains 1.65M image-text pairs. The figures and captions from PubMed Central, 2,478,267 available papers are covered and 12,211,907 figure-caption pairs are extracted. 

Source:[PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents](https://arxiv.org/pdf/2303.07240v1.pdf)

Introducing paper: PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents
EdAcc
Full name: Edinburgh International Accents of English Corpus

**The Edinburgh International Accents of English Corpus (EdAcc)** is a new automatic speech recognition (ASR) dataset composed of 40 hours of English dyadic conversations between speakers with a diverse set of accents. EdAcc includes a wide range of first and second-language varieties of English and a linguistic background profile of each speaker.

Source: [The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR](https://arxiv.org/pdf/2303.18110v1.pdf)

Image Source: [The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR](https://arxiv.org/pdf/2303.18110v1.pdf)

Introducing paper: The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR