Omkar2118/datasets huggingface dataset:
PANC

Enables research on early detection of sexual predators in chats (eSPD).
It is made from the sexual predator identification dataset from PAN12 and from the dataset ChatCoder2.
It provides both full-length predator chats from PervertedJustice as well as short segments of non-predator chats. Together these can be used to evaluate eSPD systems.

Introducing paper: Early Detection of Sexual Predators in Chats
STPLS3D

Our project (STPLS3D) aims to provide a large-scale aerial photogrammetry dataset with synthetic and real annotated 3D point clouds for semantic and instance segmentation tasks.

Although various 3D datasets with different functions and scales have been proposed recently, it remains challenging for individuals to complete the whole pipeline of large-scale data collection, sanitization, and annotation (e.g., semantic and instance labels). Moreover, the created datasets usually suffer from extremely imbalanced class distribution or partial low-quality data samples. Motivated by this, we explore the procedurally synthetic 3D data generation paradigm to equip individuals with the full capability of creating large-scale annotated photogrammetry point clouds. Specifically, we introduce a synthetic aerial photogrammetry point clouds generation pipeline that takes full advantage of open geospatial data sources and off-the-shelf commercial packages. Unlike generating synthetic data in virtual games, where the simulated data usually have limited gaming environments created by artists, the proposed pipeline simulates the reconstruction process of the real environment by following the same UAV flight pattern on a wide variety of synthetic terrain shapes and building densities, which ensure similar quality, noise pattern, and diversity with real data. In addition, the precise semantic and instance annotations can be generated fully automatically, avoiding the expensive and time-consuming manual annotation process.  Based on the proposed pipeline, we present a richly-annotated synthetic 3D aerial photogrammetry point cloud dataset, termed STPLS3D, with more than 16 km^2 of landscapes and up to 18 fine-grained semantic categories. For verification purposes, we also provide a parallel dataset collected from four areas in the real environment.

Source: [https://github.com/meidachen/STPLS3D](https://github.com/meidachen/STPLS3D)
Image source: [https://github.com/meidachen/STPLS3D/blob/main/imgs/STPLS3D.png](https://github.com/meidachen/STPLS3D/blob/main/imgs/STPLS3D.png)

Introducing paper: STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset
Nico-illust
Full name: Nico-Illust

This dataset contains over 400,000 images (illustrations) from Niconico Seiga and Niconico Shunga
Restaurant-ACOS

The Restaurant-ACOS dataset is constructed based on the SemEval 2016 Restaurant dataset (Pontiki et al., 2016) and its expansion datasets (Fan et al., 2019; Xu et al., 2020).
The SemEval 2016 Restaurant dataset (Pontiki et al., 2016) was annotated with explicit and implicit aspects, categories, and sentiment. (Fan et al., 2019; Xu et al., 2020) further added the opinion annotations. We integrate their annotations to construct aspect-category-opinion-sentiment quadruples and further annotate the implicit opinions. The Restaurant-ACOS dataset contains 2286 sentences with 3658 quadruples.
It is worth noting that the Restaurant-ACOS is available for all subtasks in ABSA, including aspect-based sentiment classification, aspect-sentiment pair extraction, aspect-opinion pair extraction, aspect-opinion sentiment triple extraction, aspect-category-sentiment triple extraction, etc.

Introducing paper: Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions
Laptop-ACOS

Laptop-ACOS is a brand new Laptop dataset collected from the Amazon platform in the years 2017 and 2018 (covering ten types of laptops under six brands such as ASUS, Acer, Samsung, Lenovo, MBP, MSI, and so on). It contains 4,076 review sentences, much larger than the SemEval Laptop datasets.
For Laptop-ACOS, we annotate the four elements and their corresponding quadruples all by ourselves. We employ the aspect categories defined in the SemEval 2016 Laptop dataset. The Laptop-ACOS dataset contains 4076 sentences with 5758 quadruples. As we have mentioned, a large percentage of the quadruples contain **implicit aspects or implicit opinions** .  By comparing two datasets, it can be observed that Laptop-ACOS has a higher percentage of implicit opinions than [Restaurant-ACOS](https://paperswithcode.com/dataset/restaurant-acos) . It is worth noting that the Laptop-ACOS is available for all subtasks in ABSA, including aspect-based sentiment classification, aspect-sentiment pair extraction, aspect-opinion pair extraction, aspect-opinion sentiment triple extraction, aspect-category-sentiment triple extraction, etc.

Introducing paper: Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions
Interference suppression techniques for OPM-based MEG: Opportunities and challenges

OPM data

1. Auditory evoked field paradigm during participant movement
2. Motor-beta power changes during a finger-tapping paradigm

Introducing paper: Interference suppression techniques for OPM-based MEG: Opportunities and challenges
aethel
Full name: Automatically Extracted Theorems from Lassy

A dataset of approximately 75,000  phrases and sentences, syntactically analyzed as typelogical derivations (i.e. proofs of modal intuitionistic linear logic, or programs of the corresponding λ calculus). Analyses were obtained by transforming the dependency graphs of the Lassy-Small corpus.

Introducing paper: ÆTHEL: Automatically Extracted Typelogical Derivations for Dutch