on 04 Jul 2018 12:00 AM
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On 3 July 2018 AGNFRA PLUS project published its first Book Chapter by the name “Big Data in Agricultural and Food Research: Challenges and Opportunities of an integrated Big Data e-Infrastructure”. The authors of the book chapter are Pythagoras Karampiperis, Rob Lokers, Pascal Neveu, Odile Hologne, George Kakaletris, Leonardo Candela, Matthias Filter, Nikos Manouselis Maritina Stavrakaki and Panagiotis Zervas, the publication venue is “Big Data for the Greater Good” and the publisher of the book is Springer.

The chapter focuses on the presentation of an innovative, holistic e-infrastructure solution that aims to enable researches for distinct but interconnected domains to share data, algorithms and results in a scalable and efficient fashion. It furthermore discusses on the potentially significant impact that such infrastructures can have on agriculture and food management and policy making, by applying the proposed solution in variegating agri-food related domains.

You can read the chapter here

 

ABOUT THE PROJECT

OUR COMMUNITIES

The selected AGINFRA+ Use Cases will illustrate the benefits of applying the Science as a Service approach to pressing research questions from the corresponding research communities.

AGROCLIMATIC AND ECONOMIC MODELLING

This community focuses on use cases aim to support the workflow of researchers, intermediaries and business analysts working on crop modelling, crop phenology estimation and yield forecasting, as well as related activities in the area of policy and decision support in food security, farm management advice and related activities.

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FOOD SAFETY RISK ASSEMENT

This community focuses on use cases to support scientists in the multidisciplinary field of risk assessment and emerging risk identification as there is currently a strong need to create new technology-supported solutions that facilitate the knowledge integration processes relevant for these tasks.

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FOOD SECUTIRY

This community focuses on use cases related to the high-throughput phenotyping large amount of data which need to be analyzed immediately for decision making. This aims to support phenomics researchers to select plant species and varieties which are the most adapted to specific environments and to global changes.

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