Data Retriever

We handle the data so you can focus on the science

Many publicly available datasets do not adhere to any agreed-upon standards in format, data structure or method of access. As a result acquiring and utilizing available datasets can be a time consuming and error prone process. The Data Retriever automates the tasks of finding, downloading, and cleaning up publicly available data files, and then stores them in a local database. The automation of this process reduces the time for a user to get most large datasets up and running by hours, and in some cases days. Small datasets can be downloaded and installed in seconds and large datasets in minutes. The program also cleans up known issues with the datasets and automatically restructures them into standard formats before inserting the data into your choice of database management systems (Microsoft Access, MySQL, PostgreSQL, and SQLite, on Windows, Mac and Linux).

“Thanks to the Data Retriever I went from idea to results in 30 minutes, and to a submitted manuscript in two months.” – Jean Philippe Gibert


If you use the Data Retriever, please use the following citation:
Morris, Benjamin D., and Ethan P. White. 2013. “The EcoData Retriever: Improving Access to Existing Ecological Data.” PLoS ONE 8 (6) (jun): 65848. doi:10.1371/journal.pone.0065848.

Publications using the Data Retriever

Gibert, J.P. & J.P. DeLong. 2014. Temperature alters food web body-size structure. Biology Letters 10: 20140473.

Locey, K.J. and D.J. McGlinn. 2013. Efficient algorithms for sampling feasible sets of macroecological patterns. PeerJ PrePrints 1:e78v1

Locey, K.J. and E.P. White. 2013. How species richness and total abundance constrain the distribution of abundance. Ecology Letters. 16:1177-1185.

Coyle, J.R., A.H. Hurlbert, and E.P. White. 2013. Opposing mechanisms drive richness patterns of core and transient bird species. The American Naturalist 181: E83-90.

White, E.P., K.M. Thibault, and X. Xiao. 2012. Characterizing species-abundance distributions across taxa and ecosystems using a simple maximum entropy model. Ecology.


The Data Retriever was written by Ben Morris, Ethan White, and Henry Senyondo. This work was funded by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative through Grant GBMF4563 to Ethan White, and by the National Science Foundation as part of a CAREER award to Ethan White.