Omkar2118/datasets huggingface dataset:
riboflavin

The dataset contains 71 samples with (normalized) expression data for 4,088 genes. The response variable is the riboflavin production rate in Bacilluss subtilis. It may be used to construct a graphical model.

Introduced by Buhlmann, P., Kalisch, M., and Meier, L. (2014). High-dimensional statistics
with a view toward applications in biology. Annual Review of Statistics and Its
Application, 1(1), 255–278
LIVE Livestream

**LIVE Livestream** is a database for Video Quality Assessment (VQA), specifically designed for live streaming VQA research. The dataset is called the Laboratory for Image and Video Engineering (LIVE) Live stream Database. The LIVE Livestream Database includes 315 videos of 45 contents impaired by 6 types of distortions.

Introducing paper: Assessment of Subjective and Objective Quality of Live Streaming Sports Videos
X-CSQA

**X-CSQA** is a multilingual dataset for Commonsense reasoning research, based on [CSQA](csqa).

Introducing paper: Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning
DDPM
Full name: Deception Detection and Physiological Monitoring

The Deception Detection and Physiological Monitoring (**DDPM**) dataset captures an interview scenario in which the interviewee attempts to deceive the interviewer on selected responses. The interviewee is recorded in RGB, near-infrared, and long-wave infrared, along with cardiac pulse, blood oxygenation, and audio. After collection, data were annotated for interviewer/interviewee, curated, ground-truthed, and organized into train/test parts for a set of canonical deception detection experiments. The dataset contains almost 13 hours of recordings of 70 subjects, and over 8 million visible-light, near-infrared, and thermal video frames, along with appropriate meta, audio, and pulse oximeter data.

Introducing paper: Deception Detection and Remote Physiological Monitoring: A Dataset and Baseline Experimental Results
rSoccer

**rSoccer** is an open-source simulator for the IEEE Very Small Size Soccer and the Small Size League optimized for reinforcement learning experiments.

Introducing paper: rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot Soccer
VideoMatting108

**VideoMatting108** is a large-scale video matting and trimap generation dataset with 80 training and 28 validation foreground video clips with ground-truth alpha mattes.

Introducing paper: Attention-guided Temporally Coherent Video Object Matting
ADNI
Full name: Alzheimer's Disease NeuroImaging Initiative

Alzheimer's Disease Neuroimaging Initiative (ADNI) is a multisite study that aims to improve clinical trials for the prevention and treatment of Alzheimer’s disease (AD).[1] This cooperative study combines expertise and funding from the private and public sector to study subjects with AD, as well as those who may develop AD and controls with no signs of cognitive impairment.[2] Researchers at 63 sites in the US and Canada track the progression of AD in the human brain with neuroimaging, biochemical, and genetic biological markers.[2][3] This knowledge helps to find better clinical trials for the prevention and treatment of AD. ADNI has made a global impact,[4]
 firstly by developing a set of standardized protocols to allow the comparison of results from multiple centers,[4] and secondly by its data-sharing policy which makes available all at the data without embargo to qualified researchers worldwide.[5] To date, over 1000 scientific publications have used ADNI data.[6] A number of other initiatives related to AD and other diseases have been designed and implemented using ADNI as a model.[4] ADNI has been running since 2004 and is currently funded until 2021.[7]

Source: Wikipedia, https://en.wikipedia.org/wiki/Alzheimer%27s_Disease_Neuroimaging_Initiative
Ambiguous-HOI

Ambiguous-HOI is a challenging dataset containing ambiguous human-object interaction images for HOI detection based on HICO-DET.

Introducing paper: Detailed 2D-3D Joint Representation for Human-Object Interaction
DeepBeam

It contains 19 HDF5 files that represent a data collection campaign run on the NI mmWave Transceiver System with four SiBeam 60 GHz radio heads and on two Pi-Radio digital 60 GHz radios.

Please refer to the website deepbeam.net

Source: [https://github.com/wineslab/deepbeam](https://github.com/wineslab/deepbeam)

Introducing paper: DeepBeam: Deep Waveform Learning for Coordination-Free Beam Management in mmWave Networks
Amharic Error Corpus

Amharic Error Corpus is a manually annotated spelling error corpus for Amharic, lingua franca in Ethiopia. The corpus is designed to be used for the evaluation of spelling error detection and correction. The misspellings are tagged as non-word and real-word errors. In addition, the contextual information available in the corpus makes it useful in dealing with both types of spelling errors.

Introducing paper: Manually Annotated Spelling Error Corpus for Amharic