Omkar2118/datasets huggingface dataset:
zbMATH Open dataset 2021

zbMATH Open contains over 4 million bibliographic entries with reviews or abstracts drawn from more than 3.000 journals and book series and more than 190.000 books.

Introducing paper: zbMATH Open: API Solutions and Research Challenges
Text-to-3D House Model
Full name: Text--to--3D House Model

The dataset contains 2,000 houses, 13,478 rooms and 873 (some rooms have same textures so this number is smaller than the total number of rooms.) texture images with corresponding natural language descriptions. These descriptions are firstly generated from some pre-defined templates and then refined by human workers. The average length of the description is 173.73 and there are 193 unique words. In our experiments, we use 1,600 pairs for training while 400 for testing in the building layout generation. For texture synthesis, we use 503 data for training and 370 data for testing.

Introducing paper: Intelligent Home 3D: Automatic 3D-House Design from Linguistic Descriptions Only
EchoCP

**EchoCP** is an echocardiography dataset in cTTE targeting PFO (Patent foramen ovale) diagnosis. EchoCP consists of 30 patients with both rest and Valsalva maneuver videos which covers various PFO grades.

Patent foramen ovale (PFO) is a potential separation between the septum, primum and septum secundum located in the anterosuperior portion of the atrial septum. PFO is one of the main factors causing cryptogenic stroke which is the fifth leading cause of death in the United States. For PFO diagnosis, contrast transthoracic echocardiography (cTTE) is preferred as being a more robust method compared with others.

Introducing paper: EchoCP: An Echocardiography Dataset in Contrast Transthoracic Echocardiography for Patent Foramen Ovale Diagnosis
WikiPII

WikiPII, an automatically labeled dataset composed of Wikipedia biography pages, annotated for personal information extraction.

Introducing paper: A Privacy-Preserving Approach to Extraction of Personal Information through Automatic Annotation and Federated Learning
InFashAI
Full name: Inclusive Fashion AI

AI algorithms, and in particular Machine Learning (ML) algorithms, learn from data tasks that have been traditionally done by humans such as: image classification, facial recognition, linguistic translation etc. To have a good generalization capability, AI algorithms must learn from sufficiently representative data, which is unfortunately not often the case. This results in a hyper-specialization of AI and its inability to perform well on new data whose distribution is too far from the one of the training set. It raises ethical questions which will undoubtedly have direct or indirect consequences on society. However, and despite biases they can entail, AI technologies are revolutionizing virtually every industry, and are forcing players in those industries to reinvent their businesses.

Like many industries, the fashion industy is being hit hard by exponential advances in AI. AI technologies are now able to describe a dress model, extract its attributes (color, style, type of sleeve, etc.), predict future fashion trends and even offer personalized clothing styles. Several publicly large datasets of fashion images made it possible.

However, the lack of diversity in available datasets is palpable. For example, images of African fashion are almost absent from these datasets. It raises problems in terms of ability to generalize algorithms trained on those datasets to African styles for instance, and therefore it limits the adoption of AI technologies within the African fashion industry.

Therefore, for an inclusive AI in the field of fashion, and to ensure that African fashion can benefit from the potentials of AI, Ai4Innov initiated the Inclusive Fashion AI project (InFashAI) which aims to create datasets that are much more representative of the diversity that exists in the world of fashion. We will first focus on building up a large volume of data on African fashion. This dataset will be progressively open source and, we hope, will be the backbone for AI tools adapted to African fashion.

Introducing paper: Neural Fashion Image Captioning : Accounting for Data Diversity
PNPB dataset
Full name: Pig Novelty Preference Behavior dataset

The dataset consists of a total of 20 videos, each of which is 5.5 minutes long in duration. The videos are captured at a resolution of 1024x1024 and at 30 frames per second. Each video contains only one pig performing the Novel Object Recognition task.

It contains annotations for the following tasks:

*Action Recognition*: Time intervals for object investigations made by the pig were manually annotated.

*Keypoint Detection*: The base of the tail, the tip of the nose, the crown of the head, and a bounding box for the whole pig were annotated for 668 frames.

Introducing paper: Vision-based Behavioral Recognition of Novelty Preference in Pigs
Place Pulse 2.0

Place Pulse is a crowdsourcing effort that aims to map which areas of a city are perceived as safer, livelier, wealthier, more active, beautiful and friendly. By asking users to select images from a pair, Place Pulse collected more than 1.5 million reports that evaluate more than 100,000 images from 56 cities.

Source: https://figshare.com/articles/dataset/Place_Pulse/11859993

Image source: https://www.media.mit.edu/projects/place-pulse-1/overview/

Introducing paper: Deep Learning the City : Quantifying Urban Perception At A Global Scale