Omkar2118/datasets huggingface dataset:
SurveyBank
Full name: SurveyBank

There are 9,321 survey papers with high quality included in the SurvayBank in the domain of computer science.

Introducing paper: Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation
Brightkite

Brightkite was once a location-based social networking service provider where users shared their locations by checking-in. The friendship network was collected using their public API, and consists of 58,228 nodes and 214,078 edges. The network is originally directed but the collectors have constructed a network with undirected edges when there is a friendship in both ways. The collectors have also collected a total of 4,491,143 checkins of these users over the period of Apr. 2008 - Oct. 2010.

Introducing paper: Friendship and Mobility: User Movement In Location-Based Social Networks
Assembly101

Assembly101 is a new procedural activity dataset featuring 4321 videos of people assembling and disassembling 101 "take-apart" toy vehicles. Participants work without fixed instructions, and the sequences feature rich and natural variations in action ordering, mistakes, and corrections. Assembly101 is the first multi-view action dataset, with simultaneous static (8) and egocentric (4) recordings. Sequences are annotated with more than 100K coarse and 1M fine-grained action segments, and 18M 3D hand poses. We benchmark on three action understanding tasks: recognition, anticipation and temporal segmentation. Additionally, we propose a novel task of detecting mistakes. The unique recording format and rich set of annotations allow us to investigate generalization to new toys, cross-view transfer, long-tailed distributions, and pose vs. appearance. We envision that Assembly101 will serve as a new challenge to investigate various activity understanding problems.

Image Source: [https://assembly-101.github.io/](https://assembly-101.github.io/)

Introducing paper: Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities
GBCU
Full name: Gallbladder Cancer Ultrasound Dataset

GBCU is the first public dataset for Gallbladder Cancer identification from Ultrasound images. GBCU contains a total of 1255 (432 normal, 558 benign, and 265 malignant) annotated abdominal Ultrasound images collected from 218 patients. Of the 218 patients, 71, 100, and 47 were from the normal, benign, and malignant classes, respectively.
The sizes of the training and testing sets are 1133 and 122, respectively. To ensure generalization to unseen patients, all images of any particular patient were either in the train or the test split. We acquired data samples from patients referred to PGIMER, Chandigarh (a referral hospital in Northern India) for abdominal ultrasound examinations of suspected Gallbladder pathologies. The study was approved by the Ethics Committee of PGIMER, Chandigarh. We obtained informed written consent from the patients at the time of recruitment, and protect their privacy by fully anonymizing the data. Grayscale B-mode static images, including both sagittal and axial sections, were recorded by radiologists for each patient using a Logiq S8 machine. 

Each image is labeled as one of the three classes - normal, benign, or malignant. The ground-truth labels were biopsy-proven to assert the correctness. Additionally, bounding-box annotations for abnormal pathologies (e.g. stone, benign mural thickening, or malignancy), and the GB are provided. 

The GBCU dataset is suitable for both image classification and object detection tasks. Apart from the Gallbladder Cancer, the dataset can also be used for detection of several other pathologies.

Introducing paper: Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning