The dataset utilized in this study corresponds to the year 2019 (all months) and covers an extensive region within the Mediterranean Basin. This particular year was selected to investigate seasonal variations in column-averaged dry-air mole fraction of methane (XCH₄), providing insights into methane dynamics over different climatic periods.
The FIDAL RB-SUMMIT Dataset contain operational and telemetry data collected from an RB-SUMMIT robot used in the FIDAL project. In this project, the robot is teleoperated through an immersive control system, allowing remote operation in real time.
FIDAL project website: https://fidal-he.eu/
The development of automated techniques for speech analysis-based Parkinson's disease (PD) detection has attracted a lot of interest, especially because of its possible uses in health tele-monitoring. Due to the drawbacks of the ᾳ - Synuclein Seed Amplification Assay technique, scientists are looking more closely at speech signals as a potential substitute for PD detection. In order to identify PD, this proposal describes a thorough investigation that emphasizes using both voice and unvoiced source material.
This dataset encompasses data from the MIMIC - Ⅳ, MIMIC - Ⅲ subsets, and the eICU, focusing on patients with acute pancreatitis. It includes a comprehensive range of clinical information, such as patient demographics, and laboratory test results.
This dataset contains user reviews about multiple consumer IoT devices.
No Iamge
We used official data from UNdata (last accessed November 2024), focusing on threatened species by country and year. The information was grouped into three main biodiversity categories—Vertebrates, Invertebrates, and Plants.
Using Python and Pandas, we cleaned and filtered the dataset to remove duplicates and non-country entries. For each year between 2004 and 2023, we highlighted the top 25 countries with the highest number of threatened species per category. This made the data easier to visualize and understand.
The use of technology in cricket has seen a significant increase in recent years, leading to overlapping computer vision-based research efforts. This study aims to extract front pitch view shots in cricket broadcasts by utilizing deep learning. The front pitch view (FPV) shots include ball delivery by the bowler and the stroke played by the batter. FPV shots are valuable for highlight generation, automatic commentary generation and bowling and batting techniques analysis. We classify each broadcast video frame as FPV and non-FPV using deep-learning models.
Vertical Federated Learning (VFL) enables multiple organizations to collaboratively train machine learning models without sharing raw data, particularly suited for tabular datasets with aligned sample IDs but disjoint feature spaces. Despite its growing relevance in privacy-sensitive sectors such as finance and healthcare, publicly available benchmarks for VFL on tabular data remain limited. This paper introduces and categorizes a collection of real-world tabular datasets tailored for VFL research, highlighting their feature distribution, domain applicability, and security relevance.
