Omkar2118/datasets huggingface dataset:
UZLF
Full name: Leuven-Haifa High-Resolution Fundus Image Dataset for Retinal Blood Vessel Segmentation and Glaucoma Diagnosis

The Leuven-Haifa dataset contains 240 disc-centered fundus images of 224 unique patients (75 patients with normal tension glaucoma, 63 patients with high tension glaucoma, 30 patients with other eye diseases and 56 healthy controls) from the University Hospitals of Leuven. The arterioles and venules of these images were both annotated by master students in medicine and corrected by a senior annotator. All senior segmentation corrections are provided as well as the junior segmentations of the test set. An open-source toolbox for the parametrization of segmentations was developed. Diagnosis, age, sex, vascular parameters as well as a quality score are provided as metadata. Potential reuse is envisioned as the development or external validation of blood vessels segmentation algorithms or study of the vasculature in glaucoma and the development of glaucoma diagnosis algorithms. The dataset is available on the KU Leuven Research Data Repository (RDR).

Introducing paper: LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images
INSPIRE-AVR (LUNet subset)

This dataset contains 65 DFIs acquired from patients with POAG at the University of Iowa Hospitals and Clinics. DFIs were acquired using a 30° Zeiss fundus camera (Niemeijer et al 2011). The images were centered on the optic disc. The original DFIs resolution was 2392 × 2048. In order to benchmark LUNet on this dataset, the black border of the DFIs were padded to a squared resolution of 2048 × 2048 pixels and then resized to a 1444 × 1444 pixels resolution. From the resulting DFIs, 15 optic disc-centered DFIs were randomly selected to form the second external test set. No other additional metadata were provided in the open source dataset.

Introducing paper: LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images
FQ-160
Full name: Forbidden Question Dataset (160)

The forbidden question dataset they build (based on two previous works) contains 160 questions from 160 violated categories. 
In addition, they also provide the corresponding targets – which is useful for some jailbreak methods, such as GCG.

Introducing paper: JailbreakRadar: Comprehensive Assessment of Jailbreak Attacks Against LLMs
SCAND
Full name: Socially CompliAnt Navigation Dataset

Have you wondered how autonomous mobile robots should share space with humans in public spaces? Are you interested in developing autonomous mobile robots that can navigate within human crowds in a socially compliant manner? Do you want to analyze human reactions and behaviors in the presence of mobile robots of different morphologies?

If so, we introduce to you Socially CompliAnt Navigation Dataset (SCAND). Our dataset contains 25 miles and 8.7 hours of robot driven trajectories through a variety of social environments around the University of Texas at Austin campus. 

Provide:

* 138 trajectories 
* 15 days of social navigation data on 2 robots : A wheeled Clearpath Jackal and a legged Boston Dynamics Spot 
* Indoor and outdoor environments @ UT Austin campus
* 2 highly crowded football game days (including a concert at the same time!)
* Mild to heavily crowded environments
* 4 different human demonstrators

Introducing paper: Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation
ToolLens

The ToolLens dataset consists of 18,770 concise yet intentionally multifaceted queries, each associated with 1 to 3 verified tools out of a total of 464, designed to better mimic real-world user interactions.

Introducing paper: Towards Completeness-Oriented Tool Retrieval for Large Language Models
HALvest

HALvest is a textual dataset comprising 17 billion tokens in 56 languages and 13 domains.

Although HALvest is mostly in English and French, the gathered 670,861 papers are written in 56 languages across 16 domains for the unfiltered version, accounting for approximately 17 billion tokens. HALvest’s text can also serve as a valuable asset for low-resource languages, hosting documents in Basque, Catalan, or Persian to mention a few.

Introducing paper: Harvesting Textual and Structured Data from the HAL Publication Repository