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The data dataset corresponds to the coding of the matrix for recording the variable information. The tables are attached according to the univariate and bivariate analysis of the data.

Speech Emotion Recognition (SER) is a rapidly evolving field of research aimed at identifying and categorizing emotional states through the analysis of speech signals. As SER holds significant socio-cultural and commercial importance, researchers are increasingly leveraging machine learning and deep learning techniques to drive advancements in this domain. A high-quality dataset is an essential resource for SER studies in any language. Despite Urdu being the 10th most spoken language globally, there is a significant lack of robust SER datasets, creating a research gap. Existing Urdu SER datasets are often limited by their small size, narrow emotional range, and repetitive content, reducing their applicability in real-world scenarios. To address this gap, the Urdu Speech Emotion Corpus (UrSEC) was developed. This comprehensive dataset includes 3500 Urdu speech signals sourced from 10 professional actors, with an equal representation of male and female speakers from diverse age groups. The dataset encompasses seven emotional states: Angry, Fear, Boredom, Disgust, Happy, Neutral, and Sad. The speech samples were curated from a wide collection of Pakistani Urdu drama serials and telefilms available on YouTube, ensuring diversity and natural delivery. Unlike conventional datasets, which rely on predefined dialogs recorded in controlled environments, UrSEC features unique and contextually varied utterances, making it more realistic and applicable for practical applications. To ensure balance and consistency, the dataset contains 500 samples per emotional class, with 50 samples contributed by each actor for each emotion. Additionally, an accompanying Excel file provides detailed metadata for each recording, including the file name, duration, format, sample rate, actor details, emotional state, and corresponding Urdu dialog. This metadata enables researchers to efficiently organize and utilize the dataset for their specific needs. The UrSEC dataset underwent rigorous validation, integrating expert evaluation and model-based validation to ensure its reliability, accuracy, and overall suitability for advancing research and development in Urdu Speech Emotion Recognition.

This retrospective study between January 2017 and January 2024 is significant as it examines 217 DMD boys diagnosed before the age of two in a country without a DMD screening program.

Catana dataset 20241209

Ács AR, Ion MC, Miok K, Laza AV, Pitic A, Robnik-Šikonja M, Pârvulescu L (2024) Threats assessment of the endemic Idle Crayfish (Austropotamobius bihariensis Pârvulescu, 2019): Lessons from long-term monitoring. Aquatic Conservation: Marine and Freshwater Ecosystems 34, e70033 (https://doi.org/10.1002/aqc.70033)

Macrofaunal data from cores along the length of a flood-tidal sand delta in the mouth of the warm-temperate Knysna estuarine bay in the Western Cape, South Africa

Follow the methodology of the research for replication. The blue colour bar in the data is the otimised values. Please feel free to use the sorting feature in excel to sort it according to your needs.

Raw Data: Correlation Between Evidence And Disposition Time In Misdemeanor Criminal Cases

Raw data in the manuscript by Ceron-Cordoba JF, et al to "Determination of Spray Drying Parameters for Lactobacillus reuteri and Lactobacillus plantarum to include them in Ice Cream Formulated with Sweet Whey"

The structural files of proteins and small molecular compounds involved in molecular docking.