General Knowledge

The MovieLens datasets, first released in 1998, describe people’s expressed preferences for movies. These preferences take the form of tuples, each the result of a person expressing a preference (a 0-5 star rating) for a movie at a particular time. These preferences were entered by way of the MovieLens web site1 — a recommender system that asks its users to give movie ratings in order to receive personalized movie recommendations. Source: The MovieLens Datasets: History and Context Image Source: http://files.grouplens.org/papers/harper-tiis2015.pdf

The Beyond the Imitation Game Benchmark (BIG-bench) is a collaborative benchmark intended to probe large language models and extrapolate their future capabilities. Big-bench include more than 200 tasks. Image source: https://arxiv.org/pdf/2206.04615.pdf

Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the evaluation of mono- and multi-lingual models in high-, medium-, and low-resource languages. Each question has four multiple-choice answers and is linked to a short passage from the FLORES-200 dataset. The human annotation procedure was carefully curated to create questions that discriminate between different levels of generalizable language comprehension and is reinforced by extensive quality checks. While all questions directly relate to the passage, the English dataset on its own proves difficult enough to challenge state-of-the-art language models. Being fully parallel, this dataset enables direct comparison of model performance across all languages. Belebele opens up new avenues for evaluating and analyzing the multilingual abilities of language models and NLP systems.

We propose the first question-answering dataset driven by STEM theorems. We annotated 800 QA pairs covering 350+ theorems spanning across Math, EE&CS, Physics and Finance. The dataset is collected by human experts with very high quality. We provide the dataset as a new benchmark to test the limit of large language models to apply theorems to solve challenging university-level questions. We provide a pipeline in the following to prompt LLMs and evaluate their outputs with WolframAlpha.

QAMPARI is an ODQA benchmark, where question answers are lists of entities, spread across many paragraphs. It was created by (a) generating questions with multiple answers from Wikipedia's knowledge graph and tables, (b) automatically pairing answers with supporting evidence in Wikipedia paragraphs, and (c) manually paraphrasing questions and validating each answer. Source: QAMPARI: : An Open-domain Question Answering Benchmark for Questions with Many Answers from Multiple Paragraphs Image Source: https://arxiv.org/pdf/2205.12665v2.pdf

DeepSportradar is a benchmark suite of computer vision tasks, datasets and benchmarks for automated sport understanding. DeepSportradar currently supports four challenging tasks related to basketball: ball 3D localization, camera calibration, player instance segmentation and player re-identification. For each of the four tasks, a detailed description of the dataset, objective, performance metrics, and the proposed baseline method are provided. Source: DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations Image Source: https://github.com/deepsportradar/instance-segmentation-challenge

The $\text{BEAR}$ dataset and its larger version, $\text{BEAR}_{\text{big}}$, are benchmarks for evaluating common factual knowledge contained in language models. This dataset was created as part of the paper "BEAR: A Unified Framework for Evaluating Relational Knowledge in Causal and Masked Language Models". For more information visit the LM Pub Quiz website. ## Citation When using the dataset or library, please cite the following paper: @misc{wilandBEARUnifiedFramework2024, title = {{{BEAR}}: {{A Unified Framework}} for {{Evaluating Relational Knowledge}} in {{Causal}} and {{Masked Language Models}}}, shorttitle = {{{BEAR}}}, author = {Wiland, Jacek and Ploner, Max and Akbik, Alan}, year = {2024}, number = {arXiv:2404.04113}, eprint = {2404.04113}, publisher = {arXiv}, url = {http://arxiv.org/abs/2404.04113}, }

fruit-SALAD is a synthetic image dataset with 10,000 generated images of fruit depictions. This combined semantic category and style benchmark comprises 100 instances each of 10 easily recognizable fruit categories and 10 easy distinguishable styles. See the paper on ArXiv. The carefully designed Style Aligned Artwork Dataset (SALAD) provides a controlled and balanced platform for the comparative analysis of similarity perception of different computational models. The SALAD framework allows the comparison of how these models perform semantic category and style recognition tasks, going beyond the level of anecdotal knowledge, making them robustly quantifiable and qualitatively interpretable. We used Stable Diffusion XL and StyleAligned to create the fruit-SALAD by carefully crafting text prompts and overseing the image generation process. The code to reproduce the fruit-SALAD_10k is available at GitHub. Please note that this dataset is available for academic research purposes only.

Genre annotations for movies The file genre2movies.csv contains genre-movie tuples based on Wikidata annotations (https://www.wikidata.org/). Data Each line in genre2movies.csv represents one genre-movie tuple. The first entry is the genre. The second entry of each line is the movie name. There are 83,670 genre-movie tuples. Joining with the Movielens 20M dataset The movies considered are from the Movielens 20M corpus: https://grouplens.org/datasets/movielens/20m/ The movie names in genre2movies.csv match the movie 'titles' in Movielens 20M. Compositions The directory "compositions" contains movies assigned to compositions of genres. The compositions are of the form: "genre A and genre B", "genre A and not genre B", "genre A and genre B and genre C", "genre A and genre B and not genre C". These assignments have been automatically generated from genre2movies.csv. We try to generate genre-compositions that are useful, e.g., for a "genre A and genre B" composition we ensure that genre B is not a subgenre of genre A, because an interesection of a superset with a subset is identical to the subset and does not form a new concept.

The sports industry is witnessing an increasing trend of utilizing multiple synchronized sensors for player data collection, enabling personalized training systems with multi-perspective real-time feedback. Badminton could benefit from these various sensors, but there is a scarcity of comprehensive badminton action datasets for analysis and training feedback. Addressing this gap, this paper introduces a multi-sensor badminton dataset for forehand clear and backhand drive strokes, based on interviews with coaches for optimal usability. The dataset covers various skill levels, including beginners, intermediates, and experts, providing resources for understanding biomechanics across skill levels. It encompasses 7,763 badminton swing data from 25 players, featuring sensor data on eye tracking, body tracking, muscle signals, and foot pressure. The dataset also includes video recordings, detailed annotations on stroke type, skill level, sound, ball landing, and hitting location, as well as survey and interview data. We validated our dataset by applying a proof-of-concept machine learning model to all annotation data, demonstrating its comprehensive applicability in advanced badminton training and research.

Dataset Description High-level explanation of dataset characteristics: This dataset includes electromyographic (EMG) signals captured using the BiTalino device. EMG signals were recorded during three conditions: rest, additional weight exercise, and squat activity. Data were collected from four young subjects aged between 20 and 24 years, including both athletes and non-athletes. Motivations and summary of its content: The collection of this data focuses on understanding muscular responses, specifically around the anterior cruciate ligament (ACL), during different types of exercise. The information obtained aims to contribute to the development of predictive models for muscular injuries and enhance the effectiveness of training and treatment programs. Potential use cases of the dataset: This dataset can be used to develop predictive models that assess the risk of muscular injuries around the ACL using EMG signal analysis. Additionally, it can be employed to optimize personalized training programs, monitor muscular recovery, and improve biomechanics in both athletes and non-athletes. ### Methodology Materials: For precise capture of EMG signals, a BiTalino device was used configured at a sampling frequency of 1 kHz. This device utilizes three electrodes to acquire EMG signals. Data acquisition: Four young volunteers, two athletes, and two non-athletes aged between 20 and 24 years were recruited. EMG signals were recorded during three tests: rest, basic squats, and squats with additional weight. Standard electrode placement procedures were followed to minimize noise and ensure measurement accuracy. Signal processing: Raw EMG data were converted to millivolts (mV) using the formula provided by BiTalino to normalize signal amplitude. Subsequently, filters were applied to remove environmental noise and highlight relevant characteristics of muscle activity. ### Results Various metrics derived from EMG signals were analyzed, including mean frequency, average peak amplitude, and average envelope amplitude. Results indicated significant differences between athletes and non-athletes in terms of muscle activation during different exercise conditions. ### Conclusions This project aims to develop predictive models based on EMG signal analysis to assess the risk of muscular injuries around the ACL. It is expected that these tools will provide a solid scientific foundation for the prevention and treatment of sports injuries, benefiting coaches, physiotherapists, and sports physicians in making informed decisions. ### Source Document where the dataset was presented: README OF THE PAPER

Sport is one of the most popular and revenue-generating forms of entertainment. Therefore, analyzing data related to this domain introduces several opportunities for Question Answering (QA) systems, such as supporting tactical decision-making. But, to develop and evaluate QA systems, researchers and developers need datasets that contain questions and their corresponding answers. In this paper, we focus on this issue. We propose QASports, the first large sports question answering dataset for extractive answer questions. QASports contains more than 1.5 million triples of questions, answers, and context about three popular sports: soccer, American football, and basketball. We describe the QASports processes of data collection and questions and answers generation. We also describe the characteristics of the QASports data. Furthermore, we analyze the sources used to obtain raw data and investigate the usability of QASports by issuing "wh-queries". Moreover, we describe scenarios for using QASports, highlighting its importance for training and evaluating QA systems.