Omkar2118/datasets huggingface dataset:
ChatEarthNet

https://zenodo.org/records/11003436

Introducing paper: ChatEarthNet: A Global-Scale Image-Text Dataset Empowering Vision-Language Geo-Foundation Models
Cambridge Law Corpus
Full name: The Cambridge Law Corpus: A Dataset for Legal AI Research

We introduce the Cambridge Law Corpus (CLC), a corpus for legal AI research. It consists of over 250 000 court cases from the UK. Most cases are from the 21st century, but the corpus includes cases as old as the 16th century. This paper presents the first release of the corpus, containing the raw text and meta-data. Together with the corpus, we provide annotations on case outcomes for 638 cases, done by legal experts. Using our annotated data, we have trained and evaluated case outcome extraction with GPT-3, GPT-4 and RoBERTa models to provide benchmarks. We include an extensive legal and ethical discussion to address the potentially sensitive nature of this material. As a consequence, the corpus will only be released for research purposes under certain restrictions.

Source: The Cambridge Law Corpus: A Dataset for Legal AI Research (https://papers.nips.cc/paper_files/paper/2023/file/819b8452be7d6af1351d4c4f9cbdbd9b-Paper-Datasets_and_Benchmarks.pdf)
Repository: https://doi.org/10.17863/CAM.100221

Introducing paper: The Cambridge Law Corpus: A Dataset for Legal AI Research
NSD
Full name: Natural Scenes Dataset

The Natural Scenes Dataset (NSD) is a large-scale fMRI dataset conducted at ultra-high-field (7T) strength at the Center of Magnetic Resonance Research (CMRR) at the University of Minnesota. The dataset consists of whole-brain, high-resolution (1.8-mm isotropic, 1.6-s sampling rate) fMRI measurements of 8 healthy adult subjects while they viewed thousands of color natural scenes over the course of 30–40 scan sessions. While viewing these images, subjects were engaged in a continuous recognition task in which they reported whether they had seen each given image at any point in the experiment. These data constitute a massive benchmark dataset for computational models of visual representation and cognition, and can support a wide range of scientific inquiry.

Source: [Natural Scenes Dataset](https://naturalscenesdataset.org/)
Operator learning using random features: a tool for scientific computing

This repository contains the datasets corresponding to the two benchmark problems appearing in the SIAM papers "The Random Feature Model for Input-Output Maps between Banach Spaces" [SIAM J. Sci. Comput., 43 (2021), pp. A3212–A3243] and the paper "Operator learning using random features: a tool for scientific computing" [to appear in SIAM Review (2024)].

The first file is the data for the viscous Burgers' equation problem. It contains Python Numpy files "train.npy" and "test.npy" that each contain both the inputs and outputs of the 1D benchmark. The other files collect the parameters that define the benchmark as explained in the papers.

The second file is for steady Darcy flow problem. It contains a MATLAB data file "data.mat" that stores the inputs and outputs of the 2D problem for both the train and test sets, as well as a file "params.mat" that stores the values of the parameters that define the benchmark.

Introducing paper: The Random Feature Model for Input-Output Maps between Banach Spaces
An operator learning perspective on parameter-to-observable maps

This repository contains the datasets corresponding to the three benchmark problems for the Fourier Neural Mappings scientific machine learning architectures. The first file is the data for the advection-diffusion problem, the second for the airfoil problem, and the third for the elliptic homogenization materials problem.

Introducing paper: An operator learning perspective on parameter-to-observable maps
MedLFQA
Full name: Medical Long-form Question Answering

MedLFQA is reconstructed by reformulating the current four biomedical long-form question-answering benchmark datasets: LiveQA, MedicationQA, HealthsearchQA, and K-QA.
MedLFQA consists of four components: question (Q), answer (A), must-have statements (MH), and nice-to-have statements (NH).
It facilitates the automatic evaluation of models' responses and provides a comprehensive understanding of how the model responds to a patient's question.
YADL
Full name: Yet Another Data LAke

Files composing the YADL data lake, for the paper "Retrieve, Merge, Predict: Augmenting Tables with Data Lakes (Experiment, Analysis & Benchmark Paper)"

Archives provided here follow the notation used for the experiments, which is different from what is reported in the paper. The four YADL versions available here are:

"binary_update" (YADL Binary)
"wordnet_full" (YADL Base)
"wordnet_vldb_10" (YADL 10k)
"wordnet_vldb_50" (YADL 50k)

Introducing paper: Retrieve, Merge, Predict: Augmenting Tables with Data Lakes