Omkar2118/datasets huggingface dataset:
FACTIFY
Full name: a dataset on multi-modal fact verification

FACTIFY is a dataset on multi-modal fact verification. It contains images, textual claim, reference textual documenta and image. The task is to classify the claims into support, not-enough-evidence and refute categories with the help of the supporting data. We aim to combat fake news in the social media era by providing this multi-modal dataset. Factify contains 50,000 claims accompanied with 100,000 images, split into training, validation and test sets.
NICO
Full name: Non-I.I.D. Image dataset with Contexts

I.I.D. hypothesis between training and testing data is the basis of numerous image classification methods. Such property can hardly be guaranteed in practice where the Non-IIDness is common, causing in- stable performances of these models. In literature, however, the Non-I.I.D. image classification problem is largely understudied. A key reason is lacking of a well-designed dataset to support related research. In this paper, we construct and release a Non-I.I.D. image dataset called NICO, which uses contexts to create Non-IIDness consciously. Compared to other datasets, extended analyses prove NICO can support various Non-I.I.D. situations with sufficient flexibility. Meanwhile, we propose a baseline model with Con- vNet structure for General Non-I.I.D. image classification, where distribution of testing data is unknown but different from training data. The experimental results demonstrate that NICO can well support the training of ConvNet model from scratch, and a batch balancing module can help ConvNets to perform better in Non-I.I.D. settings.

Introducing paper: Towards Non-I.I.D. Image Classification: A Dataset and Baselines
Atari 100k

Atari Games for only 100k environment steps. (400k frames with frame-skip=4).

Introducing paper: Model-Based Reinforcement Learning for Atari
GOTOV
Full name: Human Physical Activity and Energy Expenditure Dataset on Older Individuals

Stylianos ParaschiakosStylianos Paraschiakos, Beekman M. (Marian), Knobbe A. (Arno), Cachucho R. (Ricardo), Slagboom P. (Eline)
Wearable sensor-based data of physical activities and indirect calorimetry for 35 (14 female, 21 male) healthy older individuals (over 60 years old). The data has been collected from different body locations and devices: 3x GeneActives accelerometers (ankle, wrist, and chest), 1x Equivital (chest) and COSMED (mask and belt on chest). The 35 individuals followed a protocol of 16 activities of daily living for approximately an hour and a half in a semi-lab environment. These include different types or paces of indoor and outdoor activities with low (lying down, sitting), mid (standing, household activities) and high (walking and cycling) levels of intensity. Additionally, some activities can be specified at different granularities. The study took place at LUMC, between February and May 2015.
SaL-Lightning

**SaL-Lightning** is a dataset for research in the field of Search as Learning. It contains detailed recordings, pre- and post-knowledge assessments of 114 participants, interaction data on real-world search behavior, as well as resource features of a user study. This data diversity has the potential to help researchers answer diverse questions tied to the entire online learning framework, from individual psychological aspects, over usability tests and data visualization over retrieval and ranking issues existing in the technology that enables this process.

Introducing paper: SaL-Lightning Dataset: Search and Eye Gaze Behavior, Resource Interactions and Knowledge Gain during Web Search
Weibo21

**Weibo21** is a benchmark of fake news dataset for multi-domain fake news detection (MFND) with domain label annotated, which consists of 4,488 fake news and 4,640 real news from 9 different domains.

Introducing paper: MDFEND: Multi-domain Fake News Detection
H2O

The **Human-to-Human-or-Object Interaction Dataset** (**H2O**) dataset is a dataset for Human-Object Interaction (HOI) detection. It consists in determining and locating the list of triplets <subject,verb,target> which describe all the simultaneous interactions in an image.

H²O is composed of the 10 301 images from [V-COCO](v-coco) dataset to which are added 3 635 images which mostly contain interactions between people.

Introducing paper: Detecting Human-to-Human-or-Object (H2O) Interactions with DIABOLO
ERD
Full name: Educational Resource Discovery

**ERD** (Educational Resource Discovery) is a corpus of 39,728 manually labeled web resources and 659 queries from NLP, Computer Vision (CV), and Statistics (STATS) for educational resource discovery.

Introducing paper: A Transfer Learning Pipeline for Educational Resource Discovery with Application in Leading Paragraph Generation
LoRa RF

This is a large-scale RF fingerprinting dataset, collected from 25 different LoRa-enabled IoT transmitting devices using USRP B210 receivers. Our dataset consists of a large number of SigMF-compliant binary files representing the I/Q time-domain samples and their corresponding FFT-based files of LoRa transmissions.

Introducing paper: Comprehensive RF Dataset Collection and Release: A Deep Learning-Based Device Fingerprinting Use Case
Turath-150K

**Turath-150K** is a database of images of the Arab world that reflect objects, activities, and scenarios commonly found there.

Broadly, the database consists of objects, activities, and scenarios commonly encountered in the Arab World (from Mauritania in the West of Africa to Iraq). More specifically, there exist 3 distinct benchmark databases; Turath-Standard, Turath-Art, and Turath-UNESCO.

Introducing paper: Turath-150K: Image Database of Arab Heritage
FS2K

**FS2K** is a high-quality Facial Sketch Synthesis (FSS). It consists of 2,104 image-sketch pairs spanning three types of sketch styles, image backgrounds, lighting conditions, skin colors, and facial attributes. FS2K differs from previous FSS datasets in difficulty, diversity, and scalability, and should thus facilitate the progress of FSS research.

Introducing paper: Facial-Sketch Synthesis: A New Challenge
KIND
Full name: Kessler Italian Named-entities Dataset

KIND is an Italian dataset for Named-Entity Recognition. It contains more than one million tokens with the annotation covering three classes: persons, locations, and organizations. Most of the dataset (around 600K tokens) contains manual gold annotations in three different domains: news, literature, and political discourses.

Introducing paper: KIND: an Italian Multi-Domain Dataset for Named Entity Recognition