Omkar2118/datasets huggingface dataset:
MODA dataset
Full name: Massive Online Data Annotation Spindle Dataset

MODA is a large open-source dataset of high quality, human-scored sleep spindles (5342 spindles, from 180 subjects) that was produced by the Massive Online Data Annotation project. Sleep spindles were detected as a consensus of a number of human-expert scorers. With a median number of 5 experts scoring every EEG segment, MODA offers sleep spindle annotations of a quality unseen in previous datasets.

The dataset was described and introduced in the following publication:

Lacourse, K., Yetton, B., Mednick, S. et al. Massive online data annotation, crowdsourcing to generate high quality sleep spindle annotations from EEG data. Sci Data 7, 190 (2020). https://doi.org/10.1038/s41597-020-0533-4
SuMe
Full name: A Dataset Towards Summarizing Biomedical Mechanisms

Can language models read biomedical texts and explain the biomedical mechanisms discussed? In this work we introduce a biomedical mechanism summarization task. Biomedical studies often investigate the mechanisms behind how one entity (e.g., a protein or a chemical) affects another in a biological context. The abstracts of these publications often include a focused set of sentences that present relevant supporting statements regarding such relationships, associated experimental evidence, and a concluding sentence that summarizes the mechanism underlying the relationship. We leverage this structure and create a summarization task, where the input is a collection of sentences and the main entities in an abstract, and the output includes the relationship and a sentence that summarizes the mechanism. Using a small amount of manually labeled mechanism sentences, we train a mechanism sentence classifier to filter a large biomedical abstract collection and create a summarization dataset with 22k instances. We also introduce conclusion sentence generation as a pretraining task with 611k instances. We benchmark the performance of large bio-domain language models. We find that while the pretraining task help improves performance, the best model produces acceptable mechanism outputs in only 32% of the instances, which shows the task presents significant challenges in biomedical language understanding and summarization.

Introducing paper: SuMe: A Dataset Towards Summarizing Biomedical Mechanisms