Omkar2118/datasets huggingface dataset:
PopQA

**PopQA** is an open-domain QA dataset with 14k QA pairs with fine-grained Wikidata entity ID, Wikipedia page views, and relationship type information.

Source: [https://paperswithcode.com/paper/when-not-to-trust-language-models](https://arxiv.org/pdf/2212.10511v1.pdf)

Image Source: [https://arxiv.org/pdf/2212.10511v1.pdf](https://arxiv.org/pdf/2212.10511v1.pdf)

Introducing paper: When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories
RegDB-C*

RegDB-C* is an evaluation set that consists of algorithmically generated corruptions applied to the RegDB test-set, and especially to both the visible and the thermal data. In comparison with the RegDB-C dataset proposed by Chen et al. in "Benchmarks for Corruption Invariant Person Re-identification" paper, our dataset is used in a multimodal manner and do not consider visible data corruptions only. Used corruptions are globally the same; Noise: Gaussian, shot, impulse, and speckle; Blur: defocus, frosted glass, motion, zoom, and Gaussian; Weather: snow, frost, fog, brightness, spatter, and rain; Digital: contrast, elastic, pixel, JPEG compression, and saturate. However, corruptions are adapted to respect the thermal modality encoding, and brightness is not used to corrupt the thermal data. Five severity levels are considered per corruption.

Introducing paper: Multimodal Data Augmentation for Visual-Infrared Person ReID with Corrupted Data
SYSU-MM01-C*

SYSU-MM01-C* is an evaluation set that consists of algorithmically generated corruptions applied to the SYSU-MM01 test-set, and especially to both the visible and the thermal data. In comparison with the SYSU-MM01-C dataset proposed by Chen et al. in "Benchmarks for Corruption Invariant Person Re-identification" paper, our dataset is used in a multimodal manner and do not consider visible data corruptions only. Used corruptions are globally the same; Noise: Gaussian, shot, impulse, and speckle; Blur: defocus, frosted glass, motion, zoom, and Gaussian; Weather: snow, frost, fog, brightness, spatter, and rain; Digital: contrast, elastic, pixel, JPEG compression, and saturate. However, corruptions are adapted to respect the thermal modality encoding, and brightness is not used to corrupt the thermal data. Five severity levels are considered per corruption.

Introducing paper: Multimodal Data Augmentation for Visual-Infrared Person ReID with Corrupted Data
ThermalWORLD-C*

ThermalWORLD-C* is an evaluation set that consists of algorithmically generated corruptions applied to the ThermalWORLD test-set, and especially to both the visible and the thermal data. In comparison with the corruption approach proposed by Chen et al. in "Benchmarks for Corruption Invariant Person Re-identification" paper, our dataset is used in a multimodal manner and do not consider visible data corruptions only. Used corruptions are globally the same; Noise: Gaussian, shot, impulse, and speckle; Blur: defocus, frosted glass, motion, zoom, and Gaussian; Weather: snow, frost, fog, brightness, spatter, and rain; Digital: contrast, elastic, pixel, JPEG compression, and saturate. However, corruptions are adapted to respect the thermal modality encoding, and brightness is not used to corrupt the thermal data. Five severity levels are considered per corruption.

Introducing paper: Multimodal Data Augmentation for Visual-Infrared Person ReID with Corrupted Data
Synthetic Federated Quantum Sensing Dataset

This is the first federated quantum dataset in the literature.

Introducing paper: Quantum Federated Learning with Quantum Data
UCR Anomaly Archive

The UCR Anomaly Archive is a collection of 250 uni-variate time series collected in human medicine, biology, meteorology and industry. The collected time series contain a few natural anomalies though the majority of the anomalies are artificial . The dataset was first used in an anomaly detection contest preceding the ACM SIGKDD conference 2021.
Each of the time series contains exactly one, occasionally subtle anomaly after a given time stamp. The data before that timestamp can be considered normal.
The time series collected in the UCR Anomaly Archive can be categorized into 12 types originating from the four domains human medicine, meteorology, biology and industry. The distribution across the domains is highly imbalanced with around 64% of the times series being collected in human medicine applications, 22% in biology, 9% in industry and 5% being air temperature measurements. The time series within a single type (e.g. ECG) are not completely unique, but differ in terms of injected anomalies or a modification of the original time series through added Gaussian noise and wandering baselines. 

The downloadable archive contains, among other supplemental material, a set of slides explaining the injected anomalies with examples.

Introducing paper: Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress