Α short report from the RDA’s IGAD pre-meeting

The 9th Plenary Meeting of the Research Data Alliance (RDA) took place in Barcelona, Spain, from 5 to 7 April 2017. The RDA Plenary Meetings constitute a major event where more than 4000 members from 100 countries come together to discuss, develop and promote data-sharing and data-driven research infrastructure through Working and Interest Groups.rda_igad_premeeting The Interest Group on Agricultural Data (IGAD) pre-meeting took place just a couple of days before the 9th RDA plenary meeting, from 3 to 4 April 2017 and attracted more than 100 participants from all over the world.
The first day the IGAD pre-meeting included presentations related to the topics of agrisemantics, data interoperability, data sharing, as well as capacity development.… Click to read the full post

VITIS Big Data Demonstrator @ Agricultural University of Athens

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On February 20th 2017, we had the pleasure to host a workshop at the premises of the Agricultural University of Athens (AUA), co-organized together with the colleagues from the Laboratory of Viticulture.

Delegates from the Chinese Academy of Agricultural Sciences (CAAS), and more specifically from the Agricultural Information Institute and the National Science Library, were also invited to present their research activities and the Big Data infrastructures that are being implemented in CAAS, in the scope of also exchanging knowledge, experience and ideas.caas_aua

The workshop provided us with the excellent opportunity to present our use case pilot demonstrator, “VITIS”, that is being developed by Agroknow with support from the BigDataEurope Horizon 2020 project.… Click to read the full post

Can Europe lead a data revolution in agriculture and food?

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Let’s take a step to the near future.

A shared global data space for agriculture and food will propel the industry forward. Information will become available to all actors producing innovation. Analytical and decision making tools can incorporate a greater abundance of data sources. A digital economy may arise with online services and applications that use machine readable, interoperable and often publicly shared data. The necessary infrastructure components, including the technology, people, policy and business ones, may seamlessly integrate and work together.

 We believe that Europe is strategically positioned to lead such a transformation of the agriculture and food industry.Click to read the full post

Press Release: “Big Data Europe” addresses societal challenges with data technologies

Source: http://www.open-evidence.com/farmers-meet-big-data-how-data-driven-technologies-can-serve-a-traditional-sector-like-agriculture-27th-april-at-1500-cest/

European project makes big data technologies easy to use.

Big Data Europe project logoAcross society, from health to agriculture and transport, from energy to climate change and security, practitioners in every discipline recognize the potential of the enormous amounts of data being created every day. The challenge is to capture, manage and process that information to derive meaningful results and make a difference to people’s lives. The Big Data Europe project has just released the first public version of its open source platform designed to do just that. In 7 pilot studies, it is helping to solve societal challenges by putting cutting edge technology in the hands of experts in fields other than IT.Click to read the full post

Vitis Big Data Demonstrator @ Open Harvest 2016

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May was all about Agroknow’s Open Harvest! The logic behind this meeting has been explained in detail here and one of the major outcomes is the -already famous- Chania Declaration.

As promised and planned, on Thursday 19th of May, second day of the meeting and after the end of all discussions and presentations, it was time to offer all participants a first sneak preview of the use case pilot demonstrator that we have been developing in the context of the BigDataEurope Horizon 2020 project.

Vitis demo @ open harvest

In order to help participants visualize what this demonstrator will be all about and what problems could help solve, we had organized a hands-on-visit to a nearby traditional vineyard and winery.… Click to read the full post

“V” for Visualizing Vineyards and Varieties data

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In an earlier post, we explained how Variety, one of the four Vs of Big Data, applies in a specific domain as is the one of Viticulture and how all this connects with Agroknow’s work (a.k.a. our use case pilot) in the context of the BigDataEurope Horizon 2020 project.

Viticulture is one of those domains of Agriculture, and obviously not the only one, where data are present throughout the entire process, from the vineyard where various activities take place to the laboratory where analyses are being done. Different vineyards to set the experiment, different grapevine varieties or even different samples of the same variety but from different locations to study, different equipment to use, different methods and protocols to follow, all these contain more than one type of data and all these constitute variables that can differentiate the research outcomes.… Click to read the full post

EURAGRI: The EURopean AGricultural Research Initiative

Source: http://www.salon.com/2013/10/02/monsantos_big_data_taxpayer_ripoff/

euragri_logoThe European Agricultural Research Initiative (EURAGRI) is a European platform for the political and executive organizations in the fields of Food and Agricultural Policy and Research. By providing a forum for informal exchange on opinions and views on developments in science, society and industry, thus facilitating the design, implementation and planning of agri-food research, EURAGRI plays a key role in agricultural research at an EU level. From an organizational point of view, EURAGRI consists of representatives of Ministries of Agriculture and Research in various EU countries, a fact that allows EURAGRI to be in the position to advocate and work at a high level, involving EU countries as members and have an impact on the directions of agri-food research at national and EU level.… Click to read the full post

“V” for Vitis (and for Variety)

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One aspect of Agroknow’s work in the context of the BigDataEurope Horizon 2020 project (and as the project is moving forward entering its second year) is to develop a pilot addressing a specific Horizon 2020 Societal Challenge and from a domain specific perspective (in our case that of Societal Challenge 2: Food security, sustainable agriculture and forestry, marine, maritime and inland water research and the bioeconomy). Our pilot is specific to agriculture and more specifically to viticulture and the diversity of grapevine varieties. What we want to do is to explore how we can “support advanced crop data discovery, processing, combining and visualization from distributed and heterogeneous data repositories”.… Click to read the full post

Here is a BigDataEurope Webinar- Ready to join?

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One of the components of Agroknow’s work in the context of the BigDataEurope Horizon 2020 project is to identify and promote big data initiatives and existing work in the agri-food sector and at a global level and see how these could be mapped to the technical work that the project will design and deploy at a later stage. At the same time, existing efforts, work and outcomes are studied and good practices are identified, in order to be adapted and adopted in different cases. In this context, we were happy to see some month ago CGIAR, a global network of fifteen (15) agricultural research centers all over the world, show activity towards the exploitation of big data for addressing food security.… Click to read the full post

Big Data: Types of Data Used in Analytics

Data types involved in Big Data analytics are many: structured, unstructured, geographic, real-time media, natural language, time series, event, network and linked. It is necessary here to distinguish between human-generated data and device-generated data since human data is often less trustworthy, noisy and unclean.

A brief description of each type is given below.

data types

Types of data used in big data analytics

 

  1. Structured data: data stored in rows and columns, mostly numerical, where the meaning of each data item is defined. This type of data constitutes about 10% of the today’s total data and is accessible through database management systems.
Click to read the full post