This dataset contains 12,064 pre-processed T1-weighted contrast-enhanced MRI brain images, categorized into four classes: Glioma, Meningioma, Pituitary, and No Tumor.The dataset is split into 80% training data and 20% testing data. Images are organized into subfolders by class.It is suitable for machine learning and deep learning research in brain tumor classification, tumor detection, and computer-aided diagnosis.

This dataset contains 12,064 pre-processed T1-weighted contrast-enhanced MRI brain images, categorized into four classes: Glioma, Meningioma, Pituitary, and No Tumor.The dataset is split into 80% training data and 20% testing data. Images are organized into subfolders by class.It is suitable for machine learning and deep learning research in brain tumor classification, tumor detection, and computer-aided diagnosis.

This dataset contains 12,064 pre-processed T1-weighted contrast-enhanced MRI brain images, categorized into four classes: Glioma, Meningioma, Pituitary, and No Tumor.The dataset is split into 80% training data and 20% testing data. Images are organized into subfolders by class.It is suitable for machine learning and deep learning research in brain tumor classification, tumor detection, and computer-aided diagnosis.

This dataset contains 12,064 pre-processed T1-weighted contrast-enhanced MRI brain images, categorized into four classes: Glioma, Meningioma, Pituitary, and No Tumor.The dataset is split into 80% training data and 20% testing data. Images are organized into subfolders by class.It is suitable for machine learning and deep learning research in brain tumor classification, tumor detection, and computer-aided diagnosis.

This dataset abstract presents findings from a comprehensive survey conducted to investigate the impact of social media on high school students in Bangladesh. With the pervasive influence of social media platforms in contemporary society, particularly among the younger demographic, understanding its effects on adolescent behavior, mental health, academic performance, and social interactions is of paramount importance.The survey was designed to gather insights into various facets of social media usage among Bangladeshi high school students, including the frequency and duration of engagement, preferred platforms, types of content consumed, and perceived benefits and drawbacks. Additionally, the survey delved into students' attitudes towards privacy, cyberbullying experiences, and the influence of social media on their self-esteem and body image.A diverse sample of high school students across different regions of Bangladesh participated in the survey, providing a nuanced perspective on the impact of social media within this demographic. The dataset encompasses quantitative responses, qualitative feedback, and demographic information, enabling a comprehensive analysis of the findings.The dataset aims to contribute to the existing body of research on the influence of social media on adolescent development, particularly within the context of Bangladesh. Researchers, educators, policymakers, and mental health professionals can utilize this dataset to gain insights into the nuanced dynamics of social media usage among Bangladeshi high school students and formulate targeted interventions to promote digital well-being and resilience in this population.