Omkar2118/datasets huggingface dataset:
Bochrum movement data

The linked repository holds data from a controlled single-obstacle avoidance experiment recorded in a motion laboratory. If you are using this data, please cite the following sources for the data

B. Grimme, Analysis and identification of elementary invariants as building blocks of human arm movements. PhD thesis, International Graduate School of Biosciences, Ruhr-Universität Bochum. (In German)

L.L. Raket, B. Grimme, G. Schöner, C. Igel, and B. Markussen, "Separating timing, movement conditions and individual differences in the analysis of human movement ," PLOS Computational Biology, 2016.

Introducing paper: Separating timing, movement conditions and individual differences in the analysis of human movement
The tourism forecasting competition

> The data we use include 366 monthly series, 427 quarterly series and 518 yearly series. They were supplied by both tourism bodies (such as Tourism Australia, the Hong Kong Tourism Board and Tourism New Zealand) and various academics, who had used them in previous tourism forecasting studies (please refer to the acknowledgements and details of the data sources and availability).

from [the paper](https://www.sciencedirect.com/science/article/pii/S016920701000107X)

Introducing paper: The tourism forecasting competition
HOI-SDC
Full name: Setting for Double Challenge of Human-Object Interaction Detection

In order to avoid the training process of the model being influenced by a portion of HOI classes with a very small number of instances, we remove some of the HOI classes containing a very small number of instances and HOI classes with no interaction from the training \textbf{S}et for the \textbf{D}ouble \textbf{C}hallenge. Finally, there are total 321 HOI classes, 74 object classes and 93 action classes. The training and testing set contain 37,155 and 9,666 images, respectively.

Introducing paper: FGAHOI: Fine-Grained Anchors for Human-Object Interaction Detection
HC3

The HC3 (Human ChatGPT Comparison Corpus) dataset consists of nearly 40K questions and their corresponding human/ChatGPT answers. The motivation for this dataset was to study ChatGPT's answers in contrast to human's answers. The questions range from a wide variety of domains, including open-domain, financial, medical, legal, and psychological areas.