Omkar2118/datasets huggingface dataset:
Metadata for all 622 UCI datasets

This dataset contains the extraction made in 2022 of all the 622 datasets that existed then at the UCI Machine Learning Repository.
It contains the index, its name, its url, the instances (number os lines), the number of attributes (columns), the year it was created, the area, such as Life, Social, etc., the web_hits at the time, the data folder url, where the data were in the internet, the dataset_file_url, the URL for the data, the dataset_file_format (format, such as data, txt, Z, etc), the names_file_url, which describe the files with the description of the attributes, the names_file_format which describe the format of the previous file, the attribute_info, which describe the information of all the attributes or columns that are in the dataset, the source, the data_set_information, the relevant_papers associated with this dataset, the papers_that_cite_this_data_set, and a final column with the number of papers that cite this dataset.
MathToF

Existing arithmetic benchmarks have a limited number of True-or-False questions. To address this gap, MathToF is created including 1,000 Chinese mathematical True-or-False questions with detailed explanations and focusing on math problems typically encountered in grades 4 to 6. It features a wide range of question types, including arithmetic, algebra, geometry, statistics, reasoning, and more, enhancing the diversity of current Chinese arithmetic datasets.

Introducing paper: Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language Models
RaTE-NER

RaTE-NER dataset is a large-scale, radiological named entity recognition (NER) dataset, including 13,235 manually annotated sentences from 1,816 reports within the MIMIC-IV database, that spans 9 imaging modalities and 23 anatomical regions, ensuring comprehensive coverage.

Additionally, we further enriched the dataset with 33,605 sentences from the 17,432 reports available on Radiopaedia, by leveraging GPT-4 and other medical knowledge libraries to capture intricacies and nuances of less common diseases and abnormalities. We manually labeled 3,529 sentences to create a test set.

Introducing paper: RaTEScore: A Metric for Radiology Report Generation
RaTE-Eval

To effectively measure the alignment between automatic evaluation metrics and radiologists' assessments in medical text generation tasks, we have established a comprehensive benchmark, RaTE-Eval, that encompasses three tasks, each with its official test set for fair comparison.

Introducing paper: RaTEScore: A Metric for Radiology Report Generation
TSS

dataset of 400 image pairs

Introducing paper: Joint Recovery of Dense Correspondence and Cosegmentation in Two Images