With the world population projected to grow significantly over the next few decades, and in the presence of additional stress caused by climate change and urbanization, securing the essential resources of food, energy and water is one of the most pressing challenges the world faces today.

There is an increasing priority placed by the U.N. and U.S. federal agencies on efforts to ensure the security of these critical resources, understand their interactions, and address common underlying agendas. At the heart of the technological challenge is "data science on environmental data".

Recognizing the need for orchestrated efforts to address the many interrelated data scientific challenges for the security of Food, Energy and Water, the goal of this Article Collection will be to introduce the emerging area of “data science for food, water, and energy (DS-FEW)” to the big data community, invite scientists and practitioners in the FEW domains to the big data community, and to provide a forum in which to collectively innovate new technology to address the challenges we face in FEW.

Topics of interest include the following three broad categories:
1. Data scientific agenda for each of the three resources;
2. Issues arising in the interactions of two or more of them;
3. Foundational agenda that are common to them.

Workshops around this theme have been held in the last 2-3 years at SIGKDD conferences, namely, DSFEW 2016, DSIFER 2017, FEED 2018, and the upcoming FEED 2019. Authors of papers presented at one of this workshop series are encouraged to submit, but the submission is open to all papers addressing the general topic.

Example topics may include:
Yield modeling, genotype phenotype analysis, genotype vs. environment (GxE) analysis, disease dispersion modeling, impact analysis of farming practices on sustainable agriculture (Food); Solar irradiance forecasting, weather forecasting for power outage prediction, utility demand and price forecasting (Energy); Weather and drought forecasting, water logging, water run-off and pollution modeling (Water). Crop genotype phenotype analysis for biofuel production (Food/Energy); Modeling water contamination by fertilizer, integrated management of land and water (Food/Water); Analysis of electricity use in irrigation, analysis of environmental impact of energy production, e.g. water temperature (Energy/Water); Assessment of environmental impact through product life-cycle, understanding impact of climate change on FEW systems (Food/Energy/Water). Big geo-spatial data management, integrated physical and statistical modeling, image and hyperspectral data analytics, multi-scale, multi-sensor data analytics, extreme event and anomaly detection (Foundations).

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