Omkar2118/datasets huggingface dataset:
MultiMed

Multilingual automatic speech recognition (ASR) in the medical domain serves as a foundational task for various downstream applications such as speech translation, spoken language understanding, and voice-activated assistants. This technology enhances patient care by enabling efficient communication across language barriers, alleviating specialized workforce shortages, and facilitating improved diagnosis and treatment, particularly during pandemics. In this work, we introduce MultiMed, the first multilingual medical ASR dataset, along with the first collection of small-to-large end-to-end medical ASR models, spanning five languages: Vietnamese, English, German, French, and Mandarin Chinese. To our best knowledge, MultiMed stands as the world's largest medical ASR dataset across all major benchmarks: total duration, number of recording conditions, number of accents, and number of speaking roles. Furthermore, we present the first multilinguality study for medical ASR, which includes reproducible empirical baselines, a monolinguality-multilinguality analysis, Attention Encoder Decoder (AED) vs Hybrid comparative study, a layer-wise ablation study for the AED, and a linguistic analysis for multilingual medical ASR. All code, data, and models are available online: [https://github.com/leduckhai/MultiMed/tree/master/MultiMed](https://github.com/leduckhai/MultiMed/tree/master/MultiMed)

Introducing paper: MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder
HCP Aging
Full name: Lifespan Human Connectome Project Aging

Lifespan HCP Release 2.0 includes cross-sectional visit 1 (V1) preprocessed structural and functional imaging data, unprocessed V1 imaging data for all included modalities (structural, high-res hippocampal T2, resting state fMRI, task fMRI, diffusion, and ASL), and non-imaging demographic and behavioral assessment data from 725 HCP-Aging (HCP-A, ages 36-100+) healthy participants (22+ TB of data).
UK Biobank Brain MRI
Full name: UK Biobank Data - Brain MRI

UK Biobank participants have generously provided a very wide range of information about their health and well-being since recruitment began in 2006. This has been added to in the following ways:  

Imaging: Brain, heart and full body MR imaging, plus full body DEXA scan of the bones and joints and an ultrasound of the carotid arteries. The goal is to image 100,000 participants, and to invite participants back for a repeat scan some years later.
FUSU

FUSU dataset covers 5 whole urban areas, 847 km^2 located in the north and south of China, with 17 land use and land cover (LULC) classes and over 170K images and 30 billion pixels of annotations, supporting segmentation, change detection and domain adaptation tasks. This data comprises 2 parts:

Bi-temporal high-resolution satellite RGB images with fine-grained annotations.

Monthly revisited Sentinel-2 and Sentinel-1 images.

Example: T1: im1/6_255.png, im1_label/6_255.png T2: im2/6_255.png, im2_label/6_255.png Sentinel: A/{8-12}/6_255_A_{8-12}.png, B/{1-12}/6_255_B_{1-12}.png, C/{1-12}/6_255_C_{1-12}.png

{A,B,C} represents the year, and {1-12} represents the month.

Shape: T1 image (512 times 512 times 3), T2 image (512 times 512 times 3), T1 label (512 times 512 times 1), T2 label (512 times 512 times 1), Sentinel image (128 times 128 times 14).

14 bands of Sentinel images include 12 bands for Sentinel-2 and 2 bands (VV/VH) for Sentinel-1. Now they are concatenated, we will separate them into single .npy file in the future for easier usage.

Introducing paper: FUSU: A Multi-temporal-source Land Use Change Segmentation Dataset for Fine-grained Urban Semantic Understanding
AVisT
Full name: A Benchmark for Visual Object Tracking in Adverse Visibility

One of the key factors behind the recent success in visual tracking is the availability of dedicated benchmarks. While being greatly benefiting to the tracking research, existing benchmarks do not pose the same difficulty as before with recent trackers achieving higher performance mainly due to (i) the introduction of more sophisticated transformers-based methods and (ii) the lack of diverse scenarios with adverse visibility such as, severe weather conditions, camouflage and imaging effects.
We introduce AVisT, a dedicated benchmark for visual tracking in diverse scenarios with adverse visibility. AVisT comprises 120 challenging sequences with 80k annotated frames, spanning 18 diverse scenarios broadly grouped into five attributes with 42 object categories. The key contribution of AVisT is diverse and challenging scenarios covering severe weather conditions such as, dense fog, heavy rain and sandstorm; obstruction effects including, fire, sun glare and splashing water; adverse imaging effects such as, low-light; target effects including, small targets and distractor objects along with camouflage. We further benchmark 17 popular and recent trackers on AVisT with detailed analysis of their tracking performance across attributes, demonstrating a big room for improvement in performance. We believe that AVisT can greatly benefit the tracking community by complementing the existing benchmarks, in developing new creative tracking solutions in order to continue pushing the boundaries of the state-of-the-art. Our dataset along with the complete tracking performance evaluation is available.
Aria Digital Twin Dataset
Full name: Aria Digital Twin Dataset

A real-world dataset, with hyper-accurate digital counterpart & comprehensive ground-truth annotation.

Dataset Content
- 200 sequences (~400 mins)
- 398 objects (324 stationary, 74 dynamic)
- 2 real indoor scenes
- Single + multi-user activities

Sensor Data per device
- 2 x outward-facing monochrome camera streams
- 1 x outward-facing RGB camera stream
- 2 x IMU streams
- 2 x Internal-facing eye tracking cameras
- Complete sensor calibrations
ARLBench Data

Around 90k different RL runs: 256 hyperparameter configurations for 10 seeds each across a total of 3 algorithms (PPO, SAC, DQN) and 22 environments.

Introducing paper: ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning
ALLO
Full name: Anomaly Localization in Lunar Orbit

ALLO is an anomaly detection and localization dataset for space stations in lunar orbit. Synthetically rendered using Blender, ALLO provides realistic images of what a robotic manipulator on a space station will encounter including possible anomalies.

Introducing paper: ALLO: A Photorealistic Dataset and Data Generation Pipeline for Anomaly Detection During Robotic Proximity Operations in Lunar Orbit
Group LAW
Full name: LAW | SuiteSparse Matrix Collection

URL: https://sparse.tamu.edu/LAW

Laboratory for Web Algorithmics (LAW), Università degli Studi di Milano
http://law.di.unimi.it/index.php

When using matrices in the LAW/ group in the collection, please follow the
citation instructions at http://law.di.unimi.it/datasets.php

If you publish results based on these graphs, please acknowledge the usage of
WebGraph and LLP by quoting the following papers:

@inproceedings{BoVWFI,
  author ="Paolo Boldi and Sebastiano Vigna",
  title = "The {W}eb{G}raph Framework {I}: {C}ompression Techniques",
  year = 2004,
  booktitle="Proc. of the Thirteenth International World Wide Web Conference (WWW 2004)",
  address="Manhattan, USA",
  pages="595--601",
  publisher="ACM Press"
}

@inproceedings{BRSLLP,
  author = "Paolo Boldi and Marco Rosa and Massimo Santini and Sebastiano Vigna",
  title = "Layered Label Propagation: A MultiResolution Coordinate-Free Ordering
  for Compressing Social Networks",
  booktitle="Proceedings of the 20th international conference on World Wide Web",
  year = 2011,
  publisher="ACM Press"
}
Calgary-Campinas Public Brain MR Dataset
Full name: Calgary-Campinas Public Brain MR Dataset

A collaborative effort between researchers at the Vascular Imaging Lab located at the University of Calgary and the Medical Image Computing Lab located at the University of Campinas (UNICAMP) originated the Calgary Campinas public brain magnetic resonance (MR) images dataset.
TaskComplexity

Dataset for task complexity classification and the complexity score prediction

Introducing paper: TaskComplexity: A Dataset for Task Complexity Classification with In-Context Learning, FLAN-T5 and GPT-4o Benchmarks
VisArgs

VisArgs is a densely annotated benchmark for visual argument understanding. It contains 1,611 images annotated with 5,112 visual premises (with regions), 5,574 commonsense premises, and reasoning trees connecting them into structured arguments. We propose three tasks for evaluating visual argument understanding: premise localization, premise identification, and conclusion.

Introducing paper: Selective Vision is the Challenge for Visual Reasoning: A Benchmark for Visual Argument Understanding