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July 27, 2023

Findable, Accessible, Interoperable, and Reusable (FAIR) data and code, along with a collaborative team focused on equity make this project a great example of open science.

The Lake Trophic State-US Dataset (LTS-US) is a collection of annual lake trophic state predictions for over 55,000 lakes from 1984 through 2020. Trophic state characterizes a lake's productivity, which relates to a waterbody’s color. Blue lakes (aka “Oligotrophic lakes”) have low amounts of nutrients, algae, and zooplankton. Green lakes (aka “Eutrophic lakes”) have high amounts of nutrients, algae, and zooplankton. Brown lakes (aka “Dystrophic lakes”) have high organic matter but low nutrients, algal, and zooplankton. Murky lakes (aka “Mixotrophic lakes”) have high nutrients, algae, zooplankton, and organic matter. Once we know a lake’s trophic state, limnologists can infer a lake's physical, chemical, and biological properties.

Nutrient-Color Paradigm (NCP) scheme for classifying oligotrophic, eutrophic, dystrophic, and mixotrophic lakes.
Nutrient-Color Paradigm (NCP) scheme for classifying oligotrophic, eutrophic, dystrophic, and mixotrophic lakes.

The dataset is available in a tabular format familiar to most limnologists. The project’s team constructed the dataset using open-source software, where they implemented a pipeline structure that allows for code to be run automatically and in a pre-defined order. As new data become available, certain pieces of the code can be re-run to generate an updated dataset. Future users who want to re-generate the dataset can use the code pipeline without needing to adapt the code themselves.  

The project team generated the LTS-US dataset using the following publicly accessible datasets:

Further, the project outputs were created with a spirit of FAIR and TRUST data principles in mind. The data are available on the Environmental Data Initiative Data Portal. They are accessible without the need for login or account credentials. To promote interoperability, the LTS-US maintains the unique identifiers from its source datasets. The extensive metadata and scripts enable reusability of the data. Additionally, the manuscript describing the methods is available as a preprint while it is under review.  

Aside from the project outputs, the project team is highly collaborative. It consists of a dynamic group of 20+ international researchers across academia, government, and industry. To ensure equity among co-authors, the team adheres to an open and democratized co-authorship contract. The tasks necessary to achieve co-authorship are clearly outlined and each member documented how they contributed to the various tasks. 

Data Release: 


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