Environmental Data Science

A global leader in environmental data science, NCEAS equips researchers with the skills and tools they need to turn data into knowledge and solutions.

Environmental data science entails the theories, techniques, and tools that allow scientists to combine and integrate large amounts of data about the environment and draw meaningful insights from them. It goes hand-in-hand with synthesis science, which relies on access to existing data and the capacity to analyze them.

We develop tools, trainings, and best practices in environmental data science to enhance scientists’ ability to access, share, integrate, and analyze environmental data. Our work makes the discovery process more efficient, transparent, and reproducible, leading to results that are highly useful for natural resource decision-making.



Data Science Research

We develop state-of-the-art tools and practices to enhance scientists' capacity to harness knowledge from data, often working in partnership with other scientific institutions.


Data Science Tools

Store, share, access, and analyze data with these data science tools developed and maintained by our data scientists. Tools for computing infrastructure developers are also available.


Data Science Trainings

We train researchers in environmental data science to help increase their efficiency and productivity, enabling their science to inform solutions more quickly and effectively.


Computing Support

We provide computing and analysis support to our working groups.


Our Data Policy

NCEAS requires the scientists we support to document and publish their datasets and code. Read our full data policy.

Our own data science tools are open source, and we encourage a culture of data sharing and open access in environmental science. Through the KNB Data Repository, we make thousands of environmental datasets – generated at NCEAS and elsewhere – publicly available for free.