Nikos Manouselis Agro-Know Technologies

nikosm@agroknow.gr

Why are e-Infrastructures useful from a small business

perspective?

intro

“The future belongs to the companies

that turn data into products”

We help organizations and people to address societal and

environmental challenges using solutions that are

informed and enhanced by high-quality data

We develop and put in real practice end-to-end, modular

solutions that transform data into meaningful knowledge

and services

Our values

use open data to solve meaningful societal challengescreate a data-powered ecosystem that may bootstrap agricultural & food innovationembrace all data sources, formats & types relevant to agricultural research & innovationpromote open source and open data

Our vision

To add value to the rich information available in the

wide spectrum of agricultural and biodiversity sciences

To make it universally accessible, useful and meaningful, through

innovative tools, services and applications

Unorganized Content in local and remote sites

Widgets

Authoring services

Data Discovery Services

Analytics services

Agro-Know Data Platform

Ingestion Translation Publication

Harvesting BlossomCultivation

Organized and structured Content in local and remote

DBs

Educational

Bibliographic

Other

Enrichment

Aggregate data from diverse sources

Works with different type

of data

Prepare data for

meaningful services

Educational

Bibliographic

data aggregation & sharing hub

• Value Generation Methods & Tools– Green Learning Network (GLN) Data Pool– Agricultural Bibliography Network (ABN) Data Pool

• Data Sharing Tools– OER & educational pathways– digital libraries & repositories– digitized specimens & observations– learning management systems

• Discovery Spaces– Landing pages, Micro-sites, Web portals, Apps

• Innovation Methods & Tools– Creativity Accelerator, Training curricula, Open Data Incubator

product families

why?

Resilience, flexibility and policies that favor R&D investment in staple food

research and efficient input use will be the pillars on which future food security

depends.

- FAO Report(http://www.fao.org/docrep/014/i2280e/i2280e10.pdf)

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Key facts about agricultural trends

Agriculture is about to experience a “growth shock” in order to cover the exponentially increasing food needs of the global population

• All demographic and food demand projections suggest that, by 2050, the planet will face severe food crises due to our inability to meet agricultural demand – by 2050:

• 9.3 billion global population, 34% higher than today• 70% of the world’s population will be urban, compared to

49% today• food production (net of food used for biofuels) must

increase by 70%

• According to these projections, and in order to achieve the forecasted food levels by 2050, a total investment of USD 83 billion per annum will be required

• A large part of this investment will need to be focused on R&D

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Open Data in Agriculture

One of the most promising routes to agriculture modernisation is the provision of Open Data to all interested parties

• In an era of Big Data, one of the most promising routes to achieve R&D excellence in agriculture is Open Data, and in particular:– provisioning, – maintaining,– enriching with relevant metadata and– making openly available a vast amount of open agricultural data

• The use and wide dissemination of these data sets is strongly advocated by a number of global and national policy makers such as:– The New Alliance for Food Security and Nutrition G-8 initiative– FAO of the UN– DEFRA & DFID in UK– USDA & USAID in the US

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There is a tremendous global business opportunity for

companies that can leverage open agricultural data and expose such data into real-

world agricultural applications

at the core

• publications, theses, reports, other grey literature• educational material and content, courseware• primary data, such as measurements & observations

– structured, e.g. datasets as tables– digitized, e.g. images, videos

• secondary data, such as processed elaborations– e.g. dendrograms, pie charts, models

• provenance information, incl. authors, their organizations and projects

• experimental protocols & methods• social data, tags, ratings, etc.• …

research(+) content

• stats

• gene banks

• gis data

• blogs,

• journals

• open archives

• raw data

• technologies

• learning objects

• ………..

educators’ view

• stats

• gene banks

• gis data

• blogs,

• journals

• open archives

• raw data

• technologies

• learning objects

• ………..

researchers’ view

• stats

• gene banks

• gis data

• blogs,

• journals

• open archives

• raw data

• technologies

• learning objects

• ………..

practioners’ view

• stats

• gene banks

• gis data

• blogs,

• journals

• open archives

• raw data

• technologies

• learning objects

• ………..

• aim is:promoting data sharing and

consumption related to any research activity aimed at improving productivity and quality of crops

ICT for computing, connectivity, storage, instrumentation

research data infrastructures

Publisher

Date Catalog

SubjectID

AuthorTitle

we actually share metadata

…sometimes, data also included

metadata aggregations

• concerns viewing merged collections of metadata records from different sources

• useful: when access to specific supersets or subsets of networked collections– records actually stored at aggregator– or queries distributed at virtually aggregated

collections

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typically look like this

24 Ternier et al., 2010

metadata aggregation tools

More than a harvester:

Validation Service Repository Software Registry Service Harvester

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Powered by

workflows with commonalities

Harvesting Validating Transforming

OAI target - XMLs

IndexingStoring

Automatic metadata generation

De - duplication service

XMLs

Triplification

typical problem: computing

typical problem: hosting

to curate & preserve we need

even when machinery exists there are problems

• hardware maintenance• technical support• interoperability limitations

– no APIs for the dissemination of data across systems

• hardware costs

the cloud approach

Students

Researchers

Academics

Storage and Processing Monitoring/Management/Allocation layer

Virtualization of Infrastructure Layer

Virtual Machines

Virtualization of Infrastructure LayerVirtualized Infrastractures Management LayerGUI tools and APIs

Cloud provider A Cloud provider B Cloud provider B

what can be hosted on the cloud

• Data storage & management tools– APIs for content dissemination in large networks

• Processing & visualisation tools• Metadata aggregation infra• Search engines and apps for institutions or

communities

what data providers need

… only a browser and internet connection

examples

CASE 1: DATA MANAGEMENT TOOL OVER THE CLOUD

Educational Pathway Authoring Tool

Educational Pathway Authoring Tool

today

in the cloud

comparing costs for hosting data management tool at own site and cloud

Cloud•cloud hosting = 20 euros/month•set up effort = 1hr•back up included

•Total for 5 years = 1200 euros

Hosting at institution•1 server+monitor+ups = 1200 euros•set up > 1 day effort or 100 euros•hardware maintenance effort = difficult to be defined but significant

•Total for 5 years = 1300 +personnel for hardware maintenance+ costs of unexpected HW breakdowns e.g. supplier, hard disk

Costs of software support could be the same for both cases

Costs of software support could be the same for both cases

After 5 years the HW should be renewed/upgraded

After 5 years the HW should be renewed/upgraded

CASE 2: GRID-POWERED MEGA DATA POOLS

today

today

today

we create data silos

CASE 3: SETTING UP SEARCH SERVICE/PORTAL OVER THE CLOUD

today

Metadata aggregator for educational content

Search API

Template customizationhtml, css, Ajax, JS

Agg

rega

tor

Educational collection management tool

Metadata aggregator for other data types

Search API

Data management tool

Inst

itutio

n

specialise & replicate (a lot!)

Metadata aggregator for educational content

Specialised API

Template customizationhtml, css, Ajax, JS

Clo

ud

Educational collection management tool

Metadata aggregator for other data types

Specialised API

Data management tool

widget in Facebook page

exploitation

Our aim

To create data-powered innovation ecosystems around

organisations generating, managing & sharing digital

collections+

Need: to cover a specific gap in a data-powered innovation ecosystem

Open data providers (cultural institutions,

public sector etc)

Open data providers (cultural institutions,

public sector etc)

Creative start ups & industry

Creative start ups & industry

Innovative data-powered start upsInnovative data-

powered start upsVCs / angel investors

IncubatorsVCs / angel investors

IncubatorsOpen DataIncubatorOpen DataIncubator

Data scientists, tech start ups,

etc.

Data scientists, tech start ups,

etc.

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missing component

• We work in focused efforts that will bring together and support three different groups of start-ups:

– Start-ups that process agro data (data science powered)

– Start-ups that build apps on agro data (agro data consumers, agro apps producers)

– Start-ups that develop innovative agro/ food products (agro apps consumers)

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We want to create a new generation of domain-focused SMEs

Open Agro Data Incubation programme

Open Agro Data Hackathon

Open Agro Data Hackathon

Open Agro Data Boot camp

Open Agro Data Boot camp

Open Agro Data Meet Ups

Open Agro Data Investor Days

Open Agro Data Investor Days

Open Agro Data Introductory

Course

Open Agro Data Introductory

Course

We believe that a community-powered comprehensive, end-to-end, modular approach can greatly facilitate the process of attracting, selecting

and incubating data-powered start-ups in the knowledge domain of agriculture

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summing up

thank you!nikosm@agroknow.gr

http://www.agroknow.gr