Omkar2118/datasets huggingface dataset:
Blizzard Challenge 2013
Full name: Blizzard Challenge 2013 - English language tasks

The English data for voice building was obtained, prepared and provided the the challenge by Lessac Technologies Inc., having originally came from the publishers Voice Factory International Inc. It comprises speech from one female professional narrator & actress, Catherine ‘Bobbie’ Byers, reading the text of a collection of classic novels. These had been divided by the publishers of the original audiobooks into a number of genres, such as “Classic Novels”, “Women’s Classics”, “Young Readers” and so on.

Introducing paper: The Blizzard Challenge 2013
OpticsBench

Image corruptions modelling primary optical aberrations.

Introducing paper: Classification robustness to common optical aberrations
dHCP
Full name: developing Human Connectome Project

The dHCP dataset contains neonatal MRI.
CVIAN

CVIAN is a cross-view dataset to support geolocalization and disaster mapping with street-view and very high resolution (VHR) satellite imagery in Florida, USA after Hurricane IAN in 2022. CVIAN contains 4,121 pairs of street-view and VHR satellite imagery, which are manually classified into 3 classes (i.e., light, medium, and heavy damage). The VHR satellite imagery was originally provided by the National Oceanic and Atmospheric Administration (NOAA) at a spatial resolution of 30cm per pixel on September 30, 2022. The street-view imagery was collected from the open-source Mapilliary platform, speicifally from a mapping campaign done by Site Tour 360 after Hurricane IAN hit the study area. CVIAN is the first of this kind dataset that can support both cross-view geolocalization and disaster mapping at the same time.
MSL
Full name: Mars Science Laboratory

This dataset contains expert-labeled telemetry anomaly data from the Mars Science Laboratory (MSL) rover, Curiosity.

Real spacecraft and curiosity rover anomalies for anomaly detection
Indications of telemetry anomalies can be found within previously mentioned ISA reports. All telemetry channels discussed in an individual ISA were reviewed to ensure that the anomaly was evident in the associated telemetry data, and specific anomalous time ranges were manually labeled for each channel. If multiple anomalous sequences and channels closely resembled each other, only one was kept for the experiment in order to create a diverse and balanced set. Anomalies were classified into two categories, point and contextual, to distinguish between anomalies that would likely be identified by properly set alarms or distance-based methods that ignore temporal information (point anomalies) and those that require more complex methodologies such as LSTMs or Hierarchical Temporal Memory (HTM) approaches to detect (contextual anomalies)

MSL:
TM Channels (27)
Total TM values (66,709)
Total anomalies (36)

Introducing paper: Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
MG-ShopDial

The MG-ShopDial dataset contains English conversations that mix different conversational goals, including search, recommendation, and question answering in the domain of e-commerce. The dataset includes 64 high-quality dialogues with a total of 2,196 utterances for scenarios of varying complexity. Intents and goals annotations are available on the utterance level. In addition to MG-ShopDial, the data collection tool Coached Conversation Collector is released. This tool supports the proposed coached human-human data collection protocol used for the creation of MG-ShopDial.

Introducing paper: MG-ShopDial: A Multi-Goal Conversational Dataset for e-Commerce
Data from the "Resistance Against Manipulative AI: key factors and possible actions" article

Data from the "Resistance Against Manipulative AI: key factors and possible actions" article

Introducing paper: Resistance Against Manipulative AI: key factors and possible actions
LMT Haptic Texture Database

While stroking a rigid tool over an object surface, vibrations induced on the tool, which represent the interaction between the tool and the surface texture, can be measured by means of an accelerometer. Such acceleration signals can be used to recognize or to classify object surface textures. The temporal and spectral properties of the acquired signals, however, heavily depend on different parameters like the applied force on the surface or the lateral velocity during the exploration. Robust features that are invariant against such scan-time parameters are currently lacking, but would enable texture classification and recognition using uncontrolled human exploratory movements. We introduce a haptic texture database which allows for a systematic analysis of feature candidates.
VedantaNY-10M

VedantaNY-10M is a curated dataset of over 750 hours of transcripts from public discourses on the Indian philosophy of Advaita Vedanta. Sourced from 612 YouTube lectures by Swami Sarvapriyananda of the Vedanta Society of New York (VSNY), the dataset contains ~10 million tokens. These lectures offer a comprehensive exposition of Advaita Vedanta, making the dataset an invaluable resource for philosophy and linguistics research.

Introducing paper: Ancient Wisdom, Modern Tools: Exploring Retrieval-Augmented LLMs for Ancient Indian Philosophy
CFEVER

- CFEVER is a Chinese Fact Extraction and VERification dataset published at AAAI 2024.
- CFEVER comprises 30,012 manually created claims based on content in Chinese Wikipedia.
- Inspired by the FEVER dataset (Thorne et al., 2018), we provide class labels (Supports, Refutes, or Not Enough Information) and evidence for each claim in the CFEVER dataset.
- Download the dataset: https://github.com/IKMLab/CFEVER-data

Introducing paper: CFEVER: A Chinese Fact Extraction and VERification Dataset
Transonic RAE 2822
Full name: Transonic Airfoil RAE2822 Dataser

Dataset of the RANS simulations over a 2D RAE2822 Airfoil at different Mach and Angle of Attack.
More than 1500 simulations spanning Mach from 0 to 0.9 and AoA from 0 to 9 degrees.
Mesh consists of 27499 nodes.

Introducing paper: Aero-Nef: Neural Fields for Rapid Aircraft Aerodynamics Simulations
MAS3K
Full name: MAS3K: An Open Dataset for Marine Animal Segmentation

MAS3K contains a total of 3,103 images, where 1,588 are for camouflaged cases, 1,322 are for common cases, and 193 are underwater images in absence of marine animals. The marine animal categories in MAS3K cover both vertebrate and invertebrate, including seven super-classes, e.g., mammals, reptile, arthropod, and marine fish, etc. Under the super-classes, MAS3K dataset has 37 sub-classes, e.g., crab, starfish, shark, and turtle, etc.
RMAS
Full name: Real-World Marine Animal Segmentation

We construct a new large-scale real-world MAS data set for conducting extensive experiments. It consists of over 3000 images with various underwater scenes and objects. Each image is annotated with an object-level mask and assigned to a category.
MSD (Mirror Segmentation Dataset)

We construct the first large-scale mirror dataset, named MSD. It includes 4, 018 pairs of images containing mirrors and their corresponding manually annotated masks.

Introducing paper: Where Is My Mirror?
PMD

We propose a large-scale benchmark here, which contains a total of 6,461 mirror images with ground truth annotations.

Introducing paper: Progressive Mirror Detection