Omkar2118/datasets huggingface dataset:
GSAP-NER
Full name: GSAP-NER 1.0

A scholarly named entity recognition dataset with focus on machine learning models and datasets.

Introducing paper: GSAP-NER: A Novel Task, Corpus, and Baseline for Scholarly Entity Extraction Focused on Machine Learning Models and Datasets
BatMobility Dataset

This is the dataset release for the ACM MobiCom 2023 paper "BatMobility: Towards Flying Without Seeing for Autonomous Drones".

* Project Website: batmobility.github.io
* Project Code: github.com/ConnectedSystemsLab/batmobility_ae

If you found this useful, please cite 

@inproceedings{sie2023batmobility,
  author    = {Sie, Emerson and Liu, Zikun and Vasisht, Deepak},
  title     = {BatMobility: Towards Flying Without Seeing for Autonomous Drones},
  booktitle = {ACM International Conference on Mobile Computing (MobiCom)},
  year      = {2023},
  doi       = {https://doi.org/10.1145/3570361.3592532},
}

Introducing paper: BatMobility: Towards Flying Without Seeing for Autonomous Drones
Multi-EuP: The Multilingual European Parliament Dataset for Analysis of Bias in Information Retrieval

The Multi-Eup is a new multilingual benchmark dataset, comprising 22K multilingual documents collected from the European Parliament, spanning 24 languages. This dataset is designed to investigate fairness in a multilingual information retrieval (IR) context to analyze both language and demographic bias in a ranking context. It boasts an authentic multilingual corpus, featuring topics translated into all 24 languages, as well as cross-lingual relevance judgments. Furthermore, it offers rich demographic information associated with its documents, facilitating the study of demographic bias.

Introducing paper: Multi-EuP: The Multilingual European Parliament Dataset for Analysis of Bias in Information Retrieval