Omkar2118/datasets huggingface dataset:
DrivAerNet
Full name: A Parametric Car Dataset for Data-driven Aerodynamic Design and Graph-Based Drag Prediction

DrivAerNet is a large-scale, high-fidelity CFD dataset of 3D industry-standard car shapes designed for data-driven aerodynamic design. It comprises 4000 high-quality 3D car meshes and their corresponding aerodynamic performance coefficients, alongside full 3D flow field information.

 It includes:

- **CFD Simulation Data**: The raw dataset, including full 3D pressure, velocity fields, and wall-shear stresses, computed using **8-16 million mesh elements** has a total size of $\sim$ **16TB**.
- **Curated CFD Simulations**: For ease of access and use, a **streamlined version of the CFD simulation data** is provided, refined to include key insights and data, reducing the size to $\sim$ **1TB**. 
- **3D Car Meshes**: A total of **4000 designs**, showcasing a variety of conventional car shapes and emphasizing the impact of minor geometric modifications on aerodynamic efficiency. The 3D meshes and aerodynamic coefficients $\sim$ **84GB**.
- 2D slices include the car's wake in the $x$-direction and the symmetry plane in the $y$-direction $\sim$ **12GB**.

Introducing paper: DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Prediction
Objaverse-XL

**Objaverse-XL** is an extensive dataset containing **over 10 million 3D objects**. This compilation includes a diverse range of 3D objects sourced from various origins:

1. **Manually Designed Objects**: These are objects created by human designers.
2. **Photogrammetry Scans**: These scans capture landmarks, everyday items, and even professional scans of historic and antique artifacts.
3. **Professional Scans**: These scans provide detailed representations of historical and valuable objects.

The dataset was developed collaboratively by researchers from institutions such as the **Allen Institute for AI**, **University of Washington**, **Columbia University**, **Stability AI**, **LAION**, and **Caltech**¹²³. It's a treasure trove for exploring and advancing 3D modeling, generation, and understanding. Whether you're interested in training models or experimenting with 3D applications, Objaverse-XL offers a rich resource for your endeavors.

(1) Objaverse-XL. https://objaverse.allenai.org/.
(2) Objaverse-XL: A Universe of 10M+ 3D Objects - Papers With Code. https://paperswithcode.com/paper/objaverse-xl-a-universe-of-10m-3d-objects.
(3) [2307.05663] Objaverse-XL: A Universe of 10M+ 3D Objects - arXiv.org. https://arxiv.org/abs/2307.05663.
(4) allenai/objaverse-xl · Datasets at Hugging Face. https://huggingface.co/datasets/allenai/objaverse-xl.
(5) Objaverse-XL: A Universe of 10M+ 3D Objects. https://papers.nips.cc/paper_files/paper/2023/hash/70364304877b5e767de4e9a2a511be0c-Abstract-Datasets_and_Benchmarks.html.

Introducing paper: Objaverse-XL: A Universe of 10M+ 3D Objects
HR-Multiwoz

HR-Multiwoz is a fully-labeled dataset of 550 conversations spanning 10 HR domains to evaluate LLM Agent. It is the first labeled open-sourced conversation dataset in the HR domain for NLP research. Please refer to HR-MultiWOZ: A Task Oriented Dialogue (TOD) Dataset for HR LLM Agent for details about the dataset construction.

Dataset Sources
Repository: xwjzds/extractive_qa_question_answering_hr
Paper: HR-MultiWOZ: A Task Oriented Dialogue (TOD) Dataset for HR LLM Agent
Leaderboard: github repo

Introducing paper: HR-MultiWOZ: A Task Oriented Dialogue (TOD) Dataset for HR LLM Agent
HR Extractive Question Answering

Dataset Card
HR-Multiwoz is a fully-labeled dataset of 5980 extractive qa spanning 10 HR domains to evaluate LLM Agent. It is the first labeled open-sourced conversation dataset in the HR domain for NLP research. Please refer to HR-MultiWOZ: A Task Oriented Dialogue (TOD) Dataset for HR LLM Agent for details about the dataset construction.

Dataset Sources
Repository: xwjzds/extractive_qa_question_answering_hr
Paper: HR-MultiWOZ: A Task Oriented Dialogue (TOD) Dataset for HR LLM Agent
Leaderboard: github repo

Introducing paper: HR-MultiWOZ: A Task Oriented Dialogue (TOD) Dataset for HR LLM Agent
AG-ReID
Full name: Aerial-Ground Person Re-identification

Person re-ID matches persons across multiple non-overlapping cameras. Despite the increasing deployment of airborne platforms in surveillance, current existing person re-ID benchmarks' focus is on ground-ground matching and very limited efforts on aerial-aerial matching. We propose a new benchmark dataset - AG-ReID, which performs person re-ID matching in a new setting: across aerial and ground cameras. Our dataset contains 21,983 images of 388 identities and 15 soft attributes for each identity. The data was collected by a UAV flying at altitudes between 15 to 45 meters and a ground-based CCTV camera on a university campus. Our dataset presents a novel elevated-viewpoint challenge for person re-ID due to the significant difference in person appearance across these cameras.

Introducing paper: Aerial-Ground Person Re-ID
AG-ReID.v2
Full name: Aerial-Ground Person Re-identification

Aerial-ground person re-identification (Re-ID) presents unique challenges in computer vision, stemming from the distinct differences in viewpoints, poses, and resolutions between high-altitude aerial and ground-based cameras. Existing research predominantly focuses on ground-to-ground matching, with aerial matching less explored due to a dearth of comprehensive datasets. To address this, we introduce AG-ReID.v2, a dataset specifically designed for person Re-ID in mixed aerial and ground scenarios. This dataset comprises 100,502 images of 1,615 unique individuals, each annotated with matching IDs and 15 soft attribute labels. Data were collected from diverse perspectives using a UAV, stationary CCTV, and smart glasses-integrated camera, providing a rich variety of intra-identity variations.

Introducing paper: AG-ReID.v2: Bridging Aerial and Ground Views for Person Re-identification
HANDAL
Full name: HANDAL: A Dataset of Real-World Manipulable Object Categories with Pose Annotations, Affordances, and Reconstructions

We present the HANDAL dataset for category-level object pose estimation and affordance prediction. Unlike previous datasets, ours is focused on robotics-ready manipulable objects that are of the proper size and shape for functional grasping by robot manipulators, such as pliers, utensils, and screwdrivers. Our annotation process is streamlined, requiring only a single off-the-shelf camera and semi-automated processing, allowing us to produce high-quality 3D annotations without crowd-sourcing. The dataset consists of 308k annotated image frames from 2.2k videos of 212 real-world objects in 17 categories. We focus on hardware and kitchen tool objects to facilitate research in practical scenarios in which a robot manipulator needs to interact with the environment beyond simple pushing or indiscriminate grasping. We outline the usefulness of our dataset for 6-DoF category-level pose+scale estimation and related tasks. We also provide 3D reconstructed meshes of all objects, and we outline some of the bottlenecks to be addressed for democratizing the collection of datasets like this one. Project website

Introducing paper: HANDAL: A Dataset of Real-World Manipulable Object Categories with Pose Annotations, Affordances, and Reconstructions
RU22Fact

Multilingual explainable fact-checking dataset on Russia-Ukraine Conflict 2022

Introducing paper: RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict