1. Victorian Era Authorship Attribution: To create the largest authorship attribution dataset, we extracted works of 50 well-known authors. To have a non-exhaustive learning, in training there are 45 authors whereas, in the testing, it's 50
2. Northix: Northix is designed to be a schema matching benchmark problem for data integration of two entity relationship databases.
3. Health News in Twitter: The data was collected in 2015 using Twitter API. This dataset contains health news from more than 15 major health news agencies such as BBC, CNN, and NYT.
4. Farm Ads: This data was collected from text ads found on twelve websites that deal with various farm animal related topics. The binary labels are based on whether or not the content owner approves of the ad.
5. Detect Malware Types: Provide a short description of your data set (less than 200 characters).
6. DeliciousMIL: A Data Set for Multi-Label Multi-Instance Learning with Instance Labels: This dataset includes 1) 12234 documents (8251 training, 3983 test) extracted from DeliciousT140 dataset, 2) class labels for all documents, 3) labels for a subset of sentences of the test documents.
7. Amazon Commerce reviews set: The dataset is used for authorship identification in online Writeprint which is a new research field of pattern recognition.