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AGINFRAPLUS DATA SCIENCE CHALLENGE INTERVIEWS
By admin
on 15 Jan 2020 12:00 AM
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During the AGINFRAplus Data Science Challenge, Agroknow managed to interview four Greek companies on the use of open data.

Particularly, the fourth questions that have been asked are:

  1. Could you please tell me what your company is dealing with? 

  1. Do you use publicly available data to power your innovation? 

  1. Do you execute some computationally demanding software algorithm? 

  1. Do you use one or more public or commercial cloud services to store and manage your data? 

The names of the companies are Synelixix, BioCos, CityCrop and Centaur Analytics while you can view their interviews in AGINFRA+ YouTube channel by clicking on their names.

 

ABOUT THE PROJECT

AGINFRA+ aims to exploit core e-infrastructures such as EGI.eu, OpenAIRE, EUDAT and D4Science, towards the evolution of the AGINFRA data infrastructure, so as to provide a sustainable channel addressing adjacent but not fully connected user communities around Agriculture and Food.

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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 ASSESSMENT

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 SECURITY

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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