Final published version
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Data Science of the Natural Environment
T2 - A Research Roadmap
AU - Blair, Gordon
AU - Henrys, Peter A.
AU - Leeson, Amber
AU - Watkins, John
AU - Eastoe, Emma
AU - Jarvis, Susan
AU - Young, Paul
PY - 2019/8/14
Y1 - 2019/8/14
N2 - Data science is the science of extracting meaning from potentially complex data. This is a fast moving field, drawing principles and techniques from a number of different disciplinary areas including computer science, statistics and complexity science. Data science is having a profound impact on a number of areas including commerce, health and smart cities. This paper argues that data science can have an equal if not greater impact in the area of earth and environmental sciences, offering a rich tapestry of new techniques to support both a deeper understanding of the natural environment in all its complexities, as well as the development of well-founded mitigation and adaptation strategies in the face of climate change. The paper argues that data science for the natural environment brings about new challenges for data science, particularly around complexity, spatial and temporal reasoning, and managing uncertainty. The paper also describes a case study in environmental data science which offers up insights into the promise of the area. The paper concludes with a research roadmap highlighting ten top challenges of environmental data science and also an invitation to become part of an international community working collaboratively on these problems.
AB - Data science is the science of extracting meaning from potentially complex data. This is a fast moving field, drawing principles and techniques from a number of different disciplinary areas including computer science, statistics and complexity science. Data science is having a profound impact on a number of areas including commerce, health and smart cities. This paper argues that data science can have an equal if not greater impact in the area of earth and environmental sciences, offering a rich tapestry of new techniques to support both a deeper understanding of the natural environment in all its complexities, as well as the development of well-founded mitigation and adaptation strategies in the face of climate change. The paper argues that data science for the natural environment brings about new challenges for data science, particularly around complexity, spatial and temporal reasoning, and managing uncertainty. The paper also describes a case study in environmental data science which offers up insights into the promise of the area. The paper concludes with a research roadmap highlighting ten top challenges of environmental data science and also an invitation to become part of an international community working collaboratively on these problems.
KW - Data science
KW - Earth and environmental sciences
KW - Complex systems
KW - Uncertainty
KW - Spatial and temporal reasoning
U2 - 10.3389/fenvs.2019.00121
DO - 10.3389/fenvs.2019.00121
M3 - Journal article
VL - 7
JO - Frontiers in Environmental Science
JF - Frontiers in Environmental Science
SN - 2296-665X
M1 - 121
ER -