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Environmental Data Science – a new journal devoted to innovative data-driven approaches to environmental problems including climate change, edited by Associate Professor Claire Monteleoni – recently published its first cluster of papers.

allows anyone to read, reproduce and re-use content and fills a gap as environmental data science research is often seen as too applied for computer science journals and too interdisciplinary for journals in the environmental sciences. The first cluster of papers published in the outlet include four application papers, a data paper, and two perspectives from authors at universities around the globe.

Monteleoni is part of the Department of Computer Science at CU ��«����Ƶ and has been working at this interface for more than a decade – including co–founding the Climate Informatics Conference in 2011.

“Data science broadly defined – AI, machine learning, statistics, and data mining – is the key to unlock insights from environmental data, and help us address major challenges, including climate change,” she said. “Environmental Data Science will highlight advances in addressing complex environmental problems using machine learning and data-driven approaches. And I am excited to be working with Cambridge University Press on this."