CSV

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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.

The dataset includes BIM-IoT integration data such as Revit model, images, and IoT time-series data. The abstract is as follows: Automatic indoor environmental quality (IEQ) monitoring plays a pivotal role in the management of green building operations. Traditional monitoring methods that integrate Building Information Modeling (BIM) and the Internet of Things (IoT) are unable to perform automatic detection. This study addresses the limitation by introducing a BIM-AIoT based ‘LabMonitor’ approach for real-time IEQ monitoring and prediction.

This dataset provides detailed information and customer reviews for restaurants listed on Zomato in Bangalore, with a focus on The Nest - The Den Bengaluru, located on ITPL Main Road, Whitefield. It includes key attributes such as location, contact details, rating, cuisines offered, average cost, and detailed user-generated reviews. The dataset is ideal for sentiment analysis, customer feedback mining, restaurant recommendation systems, and hospitality service quality studies.

This dataset provides detailed information and customer reviews for restaurants listed on Zomato in Bangalore, with a focus on The Nest - The Den Bengaluru, located on ITPL Main Road, Whitefield. It includes key attributes such as location, contact details, rating, cuisines offered, average cost, and detailed user-generated reviews. The dataset is ideal for sentiment analysis, customer feedback mining, restaurant recommendation systems, and hospitality service quality studies.

This dataset provides detailed information and customer reviews for restaurants listed on Zomato in Bangalore, with a focus on The Nest - The Den Bengaluru, located on ITPL Main Road, Whitefield. It includes key attributes such as location, contact details, rating, cuisines offered, average cost, and detailed user-generated reviews. The dataset is ideal for sentiment analysis, customer feedback mining, restaurant recommendation systems, and hospitality service quality studies.

This dataset is a unified compilation of the Electricity Transformer Temperature (ETT) datasets: ETTh1, ETTh2, ETTm1, and ETTm2. It includes both hourly and minute-level temperature and load data collected from power transformers, which are vital for developing and benchmarking time-series forecasting models. The dataset contains features such as high and medium voltage transformer temperatures (HUFL, HULL, MUFL, MULL) and the operational temperature (OT), which serves as the primary prediction target.

This dataset comprises 30 CSV files featuring text-based narratives developed as part of the MOVING (MOuntain Valorisation through INterconnectedness and Green Growth) Horizon 2020 project, which explores 454 value chains across 23 rural regions in 16 European countries. Additionally, it includes 30 JSON files that annotate the keywords within these narratives, linking them to their corresponding Wikidata entries and QIDs.

This dataset comprises 30 CSV files featuring text-based narratives developed as part of the MOVING (MOuntain Valorisation through INterconnectedness and Green Growth) Horizon 2020 project, which explores 454 value chains across 23 rural regions in 16 European countries. Additionally, it includes 30 JSON files that annotate the keywords within these narratives, linking them to their corresponding Wikidata entries and QIDs.