Omkar2118/datasets huggingface dataset:
PART-OF

The **PART-OF** dataset is a dataset of relations extracted from a medical ontology. The different entities in the ontology are parts of the human body. The dataset has 16,894 nodes with 19,436 edges between them.

Source: [https://arxiv.org/pdf/1906.05939.pdf](https://arxiv.org/pdf/1906.05939.pdf)

Introducing paper: Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors
ITG
Full name: In The Groove

**In The Groove** (**ITG**) is an audio dataset where given a raw audio track, the goal is to produce a choreography step chart, similar to those used in the Dance Dance Revolution video game. It contains 133 songs choreographed by a three different authors, with 652 charts for the 133 songs.

Source: [https://arxiv.org/pdf/1703.06891.pdf](https://arxiv.org/pdf/1703.06891.pdf)
Image Source: [https://github.com/chrisdonahue/ddc](https://github.com/chrisdonahue/ddc)

Introducing paper: Dance Dance Convolution
KorSTS

**KorSTS** is a dataset for semantic textural similarity (STS) in Korean. The dataset is constructed by automatically the STS-B dataset. To ensure translation quality, two professional translators with at least seven years of experience who specialize in academic papers/books as well as business contracts post-edited a half of the dataset each and cross-checked each other’s translation afterward.
The KorSTS dataset comprises 5,749 training examples translated automatically and 2,879 evaluation examples translated manually.

Source: [https://github.com/kakaobrain/KorNLUDatasets](https://github.com/kakaobrain/KorNLUDatasets)
Image Source: [https://github.com/kakaobrain/KorNLUDatasets](https://github.com/kakaobrain/KorNLUDatasets)

Introducing paper: KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding
Detection of Traffic Anomaly
Full name: DoTA

Contains 4,677 videos with temporal, spatial, and categorical annotations.

Source: [When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos](/paper/when-where-and-what-a-new-dataset-for-anomaly)

Introducing paper: When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos
IN2LAAMA

IN2LAAMA is a set of lidar-inertial datasets collected with a Velodyne VLP-16 lidar and a Xsens MTi-3 IMU.

Source: [IN2LAAMA: INertial Lidar Localisation Autocalibration And MApping](/paper/in2laama-inertial-lidar-localisation)
TartanAir

A dataset for robot navigation task and more. The data is collected in photo-realistic simulation environments in the presence of various light conditions, weather and moving objects.

Source: [TartanAir: A Dataset to Push the Limits of Visual SLAM](/paper/tartanair-a-dataset-to-push-the-limits-of)

Introducing paper: TartanAir: A Dataset to Push the Limits of Visual SLAM
Snopes

Fact-checking (FC) articles which contains pairs (multimodal tweet and a FC-article) from snopes.com.

Source: [Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News](/paper/where-are-the-facts-searching-for-fact)

Introducing paper: Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News
DailyDialog++

Consists of (i) five relevant responses for each context and (ii) five adversarially crafted irrelevant responses for each context.

Source: [Improving Dialog Evaluation with a Multi-reference Adversarial Dataset and Large Scale Pretraining](/paper/improving-dialog-evaluation-with-a-multi)

Introducing paper: Improving Dialog Evaluation with a Multi-reference Adversarial Dataset and Large Scale Pretraining