Slides of talk at seminar for the EuroRIs network (http://www.euroris-net.eu) of National Contact Points (NCPs) for EU funding programmes on Research Infrastructures.

1. Nikos Manouselis
Agro-Know Technologies
nikosm@agroknow.gr
Why are e-Infrastructures useful
from a small business
perspective?

2. intro

3. “The future belongs
to the companies
that turn data
into products”

4. 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

5. Our values
use open data to solve
meaningful societal challenges
create a data-powered
ecosystem that may bootstrap
agricultural & food innovation
embrace all data sources,
formats & types relevant to
agricultural research &
innovation
promote open source and
open data

6. 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

7. 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

8. • 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

9. why?

10. 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)
10

11. 11
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

12. 12
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

13. 13
There is a tremendous global
business opportunity for
companies that can leverage
open agricultural data and
expose such data into real-
world agricultural applications

14. at the core

15. • 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

16. • stats
• gene banks
• gis data
• blogs,
• journals
• open archives
• raw data
• technologies
• learning objects
• ………..
educators’
view

17. • stats
• gene banks
• gis data
• blogs,
• journals
• open archives
• raw data
• technologies
• learning objects
• ………..
researchers’
view

18. • stats
• gene banks
• gis data
• blogs,
• journals
• open archives
• raw data
• technologies
• learning objects
• ………..
practioners’
view

19. • stats
• gene banks
• gis data
• blogs,
• journals
• open archives
• raw data
• technologies
• learning objects
• ………..

20. • 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

21. Publisher
Date Catalog
Subject
ID
Author
Title
we actually share metadata

22. …sometimes, data also included

23. 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
23

24. typically look like this
24 Ternier et al., 2010

25. metadata aggregation tools
More than a harvester:
 Validation Service
 Repository Software
 Registry Service
 Harvester
25
Powered by

26. workflows with commonalities
Harvesting Validating Transforming
OAI target -
XMLs
IndexingStoring
Automatic
metadata
generation
De - duplication
service
XMLs
Triplification

27. typical problem: computing

28. typical problem: hosting

29. to curate & preserve we need

30. even when machinery exists there are
problems
• hardware maintenance
• technical support
• interoperability limitations
– no APIs for the dissemination of data across
systems
• hardware costs

31. the cloud approach
Students
Researchers
Academics

33. 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

34. what data providers need
… only a browser and internet connection

35. examples

36. CASE 1: DATA MANAGEMENT TOOL
OVER THE CLOUD

37. Educational Pathway Authoring Tool

38. Educational Pathway Authoring Tool

39. today

40. in the cloud

41. 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

42. CASE 2: GRID-POWERED MEGA
DATA POOLS

43. today

44. today

45. today

46. we create
data silos

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

50. today
Metadata aggregator for educational content
Search API
Template customization
html, css, Ajax, JS
Aggregator
Educational collection management tool
Metadata aggregator for other data types
Search API
Data management tool
Institution

51. specialise & replicate (a lot!)
Metadata aggregator for educational content
Specialised API
Template customization
html, css, Ajax, JS
Cloud
Educational collection management tool
Metadata aggregator for other data types
Specialised API
Data management tool
widget in Facebook page

52. exploitation

53. Our aim
To create data-powered
innovation ecosystems around
organisations generating,
managing & sharing digital
collections+

54. 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 ups
Innovative data-
powered start ups
VCs / angel investors
Incubators
VCs / angel investors
Incubators
Open Data
Incubator
Open Data
Incubator
Data scientists,
tech start ups,
etc.
Data scientists,
tech start ups,
etc.
54
missing component

55. • 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)
55
We want to create a new generation of domain-
focused SMEs

56. 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
56

57. OpenData
Incubator
Abstractandgeneric
Applicabletoany
knowledgedomain
Attractivetomajor
stakeholderssuchas
Europeana
OpenAgroData
Incubator
Areal-world,tangible
proof-of-conceptfor
theOpenDataIncubator
Applicabletothe
Agro-Biodiversity
knowledgedomains
Attractiveto
sustainability
incubators,investors,
andstakeholders
we believe that it can be generalised

58. summing up

60. thank you!
nikosm@agroknow.gr
http://www.agroknow.gr