The Data and Story Library (DASL) is a collection of small and simple data sets useful for teaching basic statistical concepts. It was originally housed at the Carnegie-Mellon website, but (like many classic websites) it disappeared one day. The nice folks at Data Description, Inc. (makers of Data Desk software) have revived and updated this resource.

Data Description, Inc. Welcome to DASL – The Data And Story Library. Available in html format.

This is a classic data set for testing out image analysis. You have a data set of 25,000 images which are labelled dogs or cats. This is easy for a human to do, but can you develop an algorithm that can tell the difference?

Kaggle. Dogs vs cats. Create an algorithm to distinguish dogs from cats. Available at https://www.kaggle.com/c/dogs-vs-cats/data.

If you are interested in text mining, this is a good data set to start with. It is a bunch of text messages, each one line long, that have been classified by a human as either spam or ham (ham is a legitimate message).

Tiago A. Almeida, Jose Maria Gomez Hidalgo. SMS Spam Collection Data Set. Part of the UCI Machine Learning Repository. Available at https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection.

I normally don’t recommend articles that are stuck behind pay walls, but this is an important article. It shows how 85% of a sample of research studies using the National Inpatient Sample database failed to follow at least one of seven well documented practice recommendations of the Agency for Healthcare Research and Quality.

Rohan Khera, Suveen Angraal, Tyler Couch, John W. Welsh, Brahmajee K. Nallamothu, Saket Girotra, Paul S. Chan, Harlan M. Krumholz. Adherence to Methodological Standards in Research Using the National Inpatient Sample. JAMA. 2017;318(20):2011-2018. doi:10.1001/jama.2017.17653. Abstract available at https://jamanetwork.com/journals/jama/article-abstract/2664461.

Dirk Kruger. Get credit for your data – BMC Research Notes launches data notes. Research in Progress blog. September 29, 2017. Available at http://blogs.biomedcentral.com/bmcblog/2017/09/29/get-credit-for-your-data-bmc-research-notes-launches-data-notes/.

I’ve been looking for something like this for a while. It is a repository for data sets associated with peer-reveiwed publications. I have only glanced at it briefly, but it looks fairly easy to use with a fair number of interesting data sets/publications.

Dryad Digital Respository. Available at http://datadryad.org/.

This is yet another interesting source of data. This site specializes in databases prepared by the United States government.

Eve Ahern. The new Enigma Public. Enigma blog (June 20, 2017). Available at https://www.enigma.com/blog/the-new-enigma-public.

In contrast to the five thirty eight databases which are mostly smallish, the Kaggle data sets are, as a rule, very large. They also include a lot of text data, for natural language processing, sentiment analysis, etc.

Kaggle data. Available at https://www.kaggle.com/datasets.

This is a github repository of a lot of interesting data sets created by the Five Thirty Eight website. I presume there is a story associated with most of these data sets. The data sets look to be moderate in size for the most part and would make interesting teaching examples.

Five Thirty Eight Data. Available at https://github.com/fivethirtyeight/data.