Omkar2118/datasets huggingface dataset:
ARCA23K

ARCA23K is a dataset of labelled sound events created to investigate real-world label noise. It contains 23,727 audio clips originating from Freesound, and each clip belongs to one of 70 classes taken from the AudioSet ontology. The dataset was created using an entirely automated process with no manual verification of the data. For this reason, many clips are expected to be labelled incorrectly.

Introducing paper: ARCA23K: An audio dataset for investigating open-set label noise
EntityQuestions

**EntityQuestions** is a dataset of simple, entity-rich questions based on facts from Wikidata (e.g., "Where was Arve Furset born? ").

Introducing paper: Simple Entity-Centric Questions Challenge Dense Retrievers
OPV2V

**OPV2V** is a large-scale open simulated dataset for Vehicle-to-Vehicle perception. It contains over 70 interesting scenes, 11,464 frames, and 232,913 annotated 3D vehicle bounding boxes, collected from 8 towns in CARLA and a digital town of Culver City, Los Angeles.

Introducing paper: OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication
ObjectFolder

**ObjectFolder** is a dataset for multisensory object-centric perception, reasoning, and interaction. It consists of 100 virtualized objects. ObjectFolder encodes the visual, auditory, and tactile sensory data for all objects, enabling a number of multisensory object recognition tasks.

Introducing paper: ObjectFolder: A Dataset of Objects with Implicit Visual, Auditory, and Tactile Representations
Saint Gall

Saint Gall dataset contains handwritten historical manuscripts written in Latin that date back to the 9th century. It consists of 60 pages, 1 410 text lines and 11 597 words.
Konzil
Full name: Konzilsprotokolle_C

Konzil dataset was created by specialists of the University of Greifswald. It contains manuscripts written in modern German. Train sample consists of 353 lines, validation - 29 lines and test - 87 lines.
Schiller
Full name: Shiller

Schiller contains handwritten texts written in modern German. Train sample consists of 244 lines, validation - 21 lines and test - 63 lines.
Ricordi

Ricordi contains handwritten texts written in Italian. Train sample consists of 295 lines, validation - 19 lines and test - 69 lines.
Patzig

Patzig contains handwritten texts written in modern German. Train sample consists of 485 lines, validation - 38 lines and test -118 lines.
Schwerin

Schwerin contains handwritten texts written in medieval German. Train sample consists of 793 lines, validation - 68 lines and test - 196 lines.
WorldKG

The WorldKG knowledge graph is a comprehensive large-scale geospatial knowledge graph based on OpenStreetMap that provides a semantic representation of geographic entities from over 188 countries. WorldKG contains a higher number of representations of geographic entities compared to other knowledge graphs and can be used as an underlying data source for various applications such as geospatial question answering, geospatial data retrieval, and other cross-domain semantic data-driven applications.

Introducing paper: WorldKG: A World-Scale Geographic Knowledge Graph
TVRecap

**TVRecap** a story generation dataset that requires generating detailed TV show episode recaps from a brief summary and a set of documents describing the characters involved. Unlike other story generation datasets, TVRecap contains stories that are authored by professional screenwriters and that feature complex interactions among multiple characters. Generating stories in TVRecap requires drawing relevant information from the lengthy provided documents about characters based on the brief summary. In addition, by swapping the input and output, TVRecap can serve as a challenging testbed for abstractive summarization.

Introducing paper: TVStoryGen: A Dataset for Generating Stories with Character Descriptions
HYouTube

**HYouTube** is a video for Video harmonization, which aims to adjust the foreground of a composite video to make it compatible with the background. The dataset was created by adjusting the foreground of real videos to create synthetic composite videos. It is based on [Youtube-VOS](https://paperswithcode.com/dataset/youtube-vos)

Introducing paper: HYouTube: Video Harmonization Dataset
EFO-1-QA

**EFO-1-QA** is a new dataset to benchmark the combinatorial generalizability of Complex Query Answering (CQA) models by including 301 different queries types, which is 20 times larger than existing datasets.

Introducing paper: Benchmarking the Combinatorial Generalizability of Complex Query Answering on Knowledge Graphs