The dataset presentes the pioneering efforts to document and categorize the health conditions of Ash Gourd (Benincasa hispida) plants in Bangladesh, focusing on five main categories: Healthy, Aphid, Downy Mildew, Leaf Curl, and Leaf Miner. This structured dataset comprises 4,460 images, divided into five categories, with each category featuring both Raw and Processed images for versatile analysis and model training. Specifically, the Healthy class includes 1,118 images (803 raw and 315 processed), the Aphid class contains 371 images (140 raw and 231 processed), Downy Mildew comprises 1,647 images (1,066 raw and 581 processed), Leaf Curl has 915 images (528 raw and 387 processed), and the Leaf Miner class totals 409 images (139 raw and 270 processed). This organized approach not only facilitates in-depth analysis but also supports the development of machine learning models for disease classification, providing valuable insights into Ash Gourd plant health.

Worldwide, eye ailments are recognized as significant contributors to nonfatal disabling conditions. In Bangladesh, 1.5% of adults suffer from blindness, while 21.6% experience low vision. Therefore, eye disease detection is crucial for preserving vision, preventing blindness, and maintaining overall health. Early detection allows for prompt intervention and treatment, preventing irreversible damage and preserving quality of life. By analyzing the dataset, researchers will be able to identify trends, develop algorithms for diagnosis, assess treatment effectiveness, and inform preventive measures.

The dataset, which focuses on mental health, was acquired via a survey given to Daffodil International University undergraduate students. A Google Forms survey was used to gather information from 500 respondents, ages 18 to 28, between May 18 and June 15, 2024. There were 143 female and 357 male responders. Four psychometric measures with international validation were used to assess mental health. A standardized questionnaire was used to gather general demographic data. The 20-item Beck Depression Inventory (BDI-II) and the 9-item Patient Health Questionnaire (PHQ-9) were used to assess depressive symptoms. The 8-item UCLA Loneliness Scale (Version 3) was used to measure loneliness in addition to the 21-item Centre for Epidemiological Studies-Depression Scale (CES-D). For future research in psychometric evaluation utilizing the previously indicated scales, the dataset, which contains descriptive statistics on the psychometric variables for the respondents, will be an invaluable resour