1. Pseudo Periodic Synthetic Time Series: This data set is designed for testing indexing schemes in time series databases. The data appears highly periodic, but never exactly repeats itself.
2. PMU-UD: The handwritten dataset was collected from 170 participants with a total of 5,180 numeral patterns. The dataset is named Prince Mohammad Bin Fahd University - Urdu/Arabic Database (PMU-UD).
3. 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.
4. microblogPCU: MicroblogPCU data is crawled from sina weibo microblog[http://weibo.com/]. This data can be used to study machine learning methods as well as do some social network research.
5. KEGG Metabolic Relation Network (Directed): KEGG Metabolic pathways modeled as directed relation network. Variety of graphical features presented.
6. KEGG Metabolic Reaction Network (Undirected): KEGG Metabolic pathways modeled as un-directed reaction network. Variety of graphical features presented.
7. UrbanGB, urban road accidents coordinates labelled by the urban center: Coordinates (longitude and latitude) of 360177 road accidents occurred in urban areas in Great Britain, and labelled according to the urban center where they occurred (469 possible labels).
8. Carbon Nanotubes: This dataset contains 10721 initial and calculated atomic coordinates of carbon nanotubes.
9. Localization Data for Person Activity: Data contains recordings of five people performing different activities. Each person wore four sensors (tags) while performing the same scenario five times.
10. Parking Birmingham: Data collected from car parks in Birmingham that are operated by NCP from
Birmingham City Council. UK Open Government Licence (OGL).
11. Nomao: Nomao collects data about places (name, phone, localization...) from many sources.
Deduplication consists in detecting what data refer to the same place.
Instances in the dataset compare 2 spots.
12. Skin Segmentation: The Skin Segmentation dataset is constructed over B, G, R color space. Skin and Nonskin dataset is generated using skin textures from face images of diversity of age, gender, and race people.