Omkar2118/datasets huggingface dataset:
Pirá
Full name: Pirá: A Bilingual Portuguese-English Dataset for Question-Answering about the Ocean

A large set of questions and answers about the ocean and the Brazilian coast both in Portuguese and English. Pirá is a crowdsourced question answering (QA) dataset on the ocean and the Brazilian coast designed for reading comprehension. 

The dataset contains 2261 QA sets, as well as the texts associated with them. Each QA set contains at least four elements: a question in Portuguese and in English, and an answer in Portuguese and in English. Around 90% of the QA sets also contain human evaluations.

Pirá is, to the best of our knowledge, the first QA dataset with supporting texts in Portuguese, and, perhaps more importantly, the first bilingual QA dataset that includes Portuguese as one of its languages. Pirá is also the first QA dataset in Portuguese with unanswerable questions so as to allow the study of answer triggering. Finally, it is the first QA dataset that tackles scientific knowledge about the ocean, climate change, and marine biodiversity.
Danish Airs and Grounds

Danish Airs and Grounds (DAG) is a large collection of street-level and aerial images targeting such cases. Its main challenge lies in the extreme viewing-angle difference between query and reference images with consequent changes in illumination and perspective. The dataset is larger and more diverse than current publicly available data, including more than 50 km of road in urban, suburban and rural areas. All images are associated with accurate 6-DoF metadata that allows the benchmarking of visual localization methods.

Introducing paper: Danish Airs and Grounds: A Dataset for Aerial-to-Street-Level Place Recognition and Localization
SOMOS
Full name: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis

The SOMOS dataset is a large-scale mean opinion scores (MOS) dataset consisting of solely neural text-to-speech (TTS) samples. It can be employed to train automatic MOS prediction systems focused on the assessment of modern synthesizers, and can stimulate advancements in acoustic model evaluation. It consists of 20K synthetic utterances of the LJ Speech voice, a public domain speech dataset which is a common benchmark for building neural acoustic models and vocoders. Utterances are generated from 200 TTS systems including vanilla neural acoustic models as well as models which allow prosodic variations.

Introducing paper: SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis