Omkar2118/datasets huggingface dataset:
GraphQuestions

GraphQuestions is a characteristic-rich dataset designed for factoid question answering. The dataset aims to provide a systematic way of constructing QA datasets with rich and explicitly specified question characteristics. Here are some key details about GraphQuestions:

1. GraphQuestions consists of a set of 5,166 factoid questions.

2. Each question is associated with logical forms and ground-truth answers.

3. The dataset is constructed based on Freebase, a large-scale knowledge base.

4. An array of question characteristics is formalized for each question.

Introducing paper: On Generating Characteristic-rich Question Sets for QA Evaluation
VoiceBank-SLR

Because there is no publicly available free dataset for
speech dereverberation, we prepared a dataset based on the
clean speech from VoiceBank-DEMAND [26] (discard the
noisy speech) and convolved them with the room impulse
response (RIR) from OpenSLR.

Introducing paper: MetricGAN-U: Unsupervised speech enhancement/ dereverberation based only on noisy/ reverberated speech
LIVE-VQC
Full name: LIVE Video Quality Challenge (VQC) Database

The great variations of videographic skills in videography, camera designs, compression and processing protocols, communication and bandwidth environments, and displays leads to an enormous variety of video impairments. Current no-reference (NR) video quality models are unable to handle this diversity of distortions. This is true in part because available video quality assessment databases contain very limited content, fixed resolutions, were captured using a small number of camera devices by a few videographers and have been subjected to a modest number of distortions. As such, these databases fail to adequately represent real world videos, which contain very different kinds of content obtained under highly diverse imaging conditions and are subject to authentic, complex and often commingled distortions that are difficult or impossible to simulate. As a result, NR video quality predictors tested on real-world video data often perform poorly. Towards advancing NR video quality prediction, we have constructed a large-scale video quality assessment database containing 585 videos of unique content , captured using 101 different devices (43 device models) by 80 different users with wide ranges of levels of complex, authentic distortions. We collected a large number of subjective video quality scores via crowdsourcing. A total of 4776 unique participants took part in the study, yielding more than 205000 opinion scores , resulting in an average of 240 recorded human opinions per video . This study is the largest video quality assessment study ever conducted along several key dimensions: number of unique contents, capture devices, distortion types and combinations of distortions, study participants, and recorded subjective scores.

Introducing paper: Large-Scale Study of Perceptual Video Quality