1. Speaker Accent Recognition: Data set featuring single English words read by speakers from six different countries for accent detection and recognition
2. A study of Asian Religious and Biblical Texts: Mainly from Project Gutenberg, we combine Upanishads, Yoga Sutras, Buddha Sutras, Tao Te Ching and Book of Wisdom, Book of Proverbs, Book of Ecclesiastes and Book of Ecclesiasticus
3. Student Performance: Predict student performance in secondary education (high school).
4. Autism Screening Adult: Autistic Spectrum Disorder Screening Data for Adult. This dataset is related to classification and predictive tasks.
5. Sports articles for objectivity analysis: 1000 sports articles were labeled using Amazon Mechanical Turk as objective or subjective. The raw texts, extracted features, and the URLs from which the articles were retrieved are provided.
6. Drug consumption (quantified): Classify type of drug consumer by personality data
7. Communities and Crime: Communities within the United States. The data combines socio-economic data from the 1990 US Census, law enforcement data from the 1990 US LEMAS survey, and crime data from the 1995 FBI UCR.
8. Communities and Crime Unnormalized: Communities in the US. Data combines socio-economic data from the '90 Census, law enforcement data from the 1990 Law Enforcement Management and Admin Stats survey, and crime data from the 1995 FBI UCR
9. Multimodal Damage Identification for Humanitarian Computing: 5879 captioned images (image and text) from social media related to damage during natural disasters/wars, and belong to 6 classes: Fires, Floods, Natural landscape, Infrastructural, Human, Non-damage.
10. Bike Sharing Dataset: This dataset contains the hourly and daily count of rental bikes between years 2011 and 2012 in Capital bikeshare system with the corresponding weather and seasonal information.
11. Real-time Election Results: Portugal 2019: Data set of the real-time election results of the 2019 Portuguese Parliamentary Election.
12. BlogFeedback: Instances in this dataset contain features extracted from blog posts. The task associated with the data is to predict how many comments the post will receive.