Collaborative research is an essential practice at the Center for Sustainable Systems, and datasets are often a product of our research projects. Below we offer the repository of freely downloadable datasets for use in your work. The development and intended use of many of these datasets are described in related journal articles. Please cite these datasets when used in any publications or research products.

dataFIELD (database of Food Impacts on the Environment for Linking to Diets) was developed as part of the Wellcome Trust funded research project, "Linking Health and Environmental Outcomes to Dietary Behaviors in the United States", by CSS and researchers at Tulane University School of Public Health and Tropical Medicine. It aggregates data on the greenhouse gas emissions (GHGE) and cumulative energy demand (CED) associated with production of specific foods to facilitate linages with self-selected individual diets in the US National Health and Examination Survey (NHANES). This data represents generic “average” impact factors for the production of food commodities which, while not specific to the US, we feel is an appropriate representation of the production of food consumed in the US.

dataFIELD version 1.0 is now available for public use:


Additional documentation to familiarize users with the database structure and function is available here:


Development of the database and results on the GHGE and CED associated with individual self-selected US diets are reported in Heller et al. 2018 Environ. Res. Lett. 13 044004.

This data set contains a new monthly estimate of the water balance of the Laurentian Great Lakes, the largest freshwater system on Earth, from 1950 to 2019. The source codes and inputs to derive the new estimates are also included in this dataset. The new estimates of the water balance of the Laurentian Great Lakes were generated using the Large Lakes Statistical Water Balance Model (L2SWBM). The L2SWBM used multiple independent data sets to obtain the prior distributions and likelihood functions, which were then assimilated by a Bayesian framework to infer a feasible range of each water balance component. Detailed description of the L2SWBM is available in a paper submitted to the Scientific Data journal.


Download dataset.


CITE AS:  Do, H. X., Smith, J. P., Fry, L. M., Gronewold, A. D. (2020). Monthly water balance estimates for the Laurentian Great Lakes from 1950 to 2019 (v1.1) [Data set], University of Michigan - Deep Blue Data.