Omkar2118/datasets huggingface dataset:
GerMS-AT
Full name: GERMS-AT: A Sexism/Misogyny Dataset of Forum Comments from an Austrian Online Newspaper

This dataset contains 7984 user comments from an Austrian online newspaper. The comments have been annotated by 4 or more out of 11 annotators as to how strong sexism/mysogyny is present in the comment. It was used in the GermEval 2024 Shared Task 1: GerMS-Detect to evaluate data-driven approaches to automatically detect sexism in user comments.

The dataset was introduced and described in Brigitte Krenn, Johann Petrak, Marina Kubina, and Christian Burger. 2024. Germs-at: A sexism/misogyny dataset of forum comments from an Austrian online newspaper. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 7728–7739.

The data can be downloaded here: https://huggingface.co/datasets/ofai/GerMS-AT
Machine Learning for Analyzing Atomic Force Microscopy (AFM) Images Generated from Polymer Blends

Dataset used in "Machine Learning for Analyzing Atomic Force Microscopy (AFM) Images Generated from Polymer Blends". Contains AFM images in raw ".ibw" format.

Introducing paper: Machine Learning for Analyzing Atomic Force Microscopy (AFM) Images Generated from Polymer Blends
CASIA-CXR

Medical report generation (MRG), which aims to automatically generate a textual description of a specific medical image (e.g., a chest X-ray), has recently received increasing research interest. Building on the success of image captioning, MRG has become achievable. However, generating language-specific radiology reports poses a challenge for data-driven models due to their reliance on paired image-report chest X-ray datasets, which are labor-intensive, time-consuming, and costly. In this paper, we introduce a chest X-ray benchmark dataset, namely CASIA-CXR, consisting of high-resolution chest radiographs accompanied by narrative reports originally written in French. To the best of our knowledge, this is the first public chest radiograph dataset with medical reports in this particular language. Importantly, we propose a simple yet effective multimodal encoder-decoder contextually-guided framework for medical report generation in French. We validated our framework through intra-language and cross-language contextual analysis, supplemented by expert evaluation performed by radiologists. The dataset is freely available at: https://www.casia-cxr.net/.
Data on COVID-19 by Our World in Data

COVID-19 dataset for the world.

Dong, E., Du, H., & Gardner, L. (2020). "An interactive web-based dashboard to track COVID-19 in real time." The Lancet Infectious Diseases, 20(5), 533-534.
mdCATH
Full name: mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics

This dataset comprises all-atom systems for 5,398 CATH domains, modeled with a state-of-the-art classical force field, and simulated in five replicates each at five temperatures from 320 K to 450 K.

Introducing paper: mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics
SCI
Full name: Self-Contradictory Instructions

Large multimodal models (LMMs) excel in adhering to human instructions. However, self-contradictory instructions may arise due to the increasing trend of multimodal interaction and context length, which is challenging for language beginners and vulnerable populations. We introduce the Self-Contradictory Instructions benchmark to evaluate the capability of LMMs in recognizing conflicting commands. It comprises 20,000 conflicts, evenly distributed between language and vision paradigms. It is constructed by a novel automatic dataset creation framework, which expedites the process and enables us to encompass a wide range of instruction forms. Our comprehensive evaluation reveals current LMMs consistently struggle to identify multimodal instruction discordance due to a lack of self-awareness. Hence, we propose the Cognitive Awakening Prompting to inject cognition from external, largely enhancing dissonance detection.

Introducing paper: Dissecting Dissonance: Benchmarking Large Multimodal Models Against Self-Contradictory Instructions