In this presentation, DEMETER representative, Dr Raul Palma discusses the practicalities of realizing data spaces - purpose, challenges and opportunities. This presentation was delivered at the SmartAgriHubs final event in September 2022. DEMETER is developing the Agriculture Information Model (AIM) which is a data model and ontology that describes all data needed by DEMETER applications and the usage of which ensures semantic interoperability between data and various components.

1. From Semantic
Interoperability
towards Data Spaces
Dr. Raul Palma
Head of Data Analytics and Semantics Department
Poznan Supercomputing and Networking Center
Realising Data Spaces – Purpose, Challenges
and Opportunities
28/09/2022

2. Place
Pilot
icon
Interoperability challenges in AgTech sector
Source: Accenture
The rapid advances of IoT technologies, AI and Big Data, among
others, have boosted the adoption of smart farming practices.
This, however, has led to an explosion of data, generated by a wide
range of different systems and platforms that rarely interoperate.
Some of the key challenges hampering the seamless exchange and
integration of the data produced or collected by those systems
include:
Availability of data in different formats and represented
according to different models
Insufficient interoperability mechanisms that enable the
connection of existing agri-food data models

3. Place
Pilot
icon
DEMETER functional
architecture overview
Mechanisms to
integrate and
deploy solutions
the common language to
exchange data.

4. Place
Pilot
icon
DEMETER Agriculture
Information Model - AIM
AIM follows a modular approach in a layered architecture:
realized as a suite of ontologies and associated JSON-LD contexts enabling both the specification of
formal semantics, and a simple adoption and implementation by tech providers, plus a set of SHACL
shapes enabling validation of data at the semantic level.
implemented in line with best practices, reusing existing standards and well-scoped models
establishes alignments between base models to enable their interoperability and the integration of
existing data
AIM aims to establish the basis of a common agricultural data space, enable the interoperation of
different systems, and the analysis of data produced by those systems in an integrated manner

5. Place
Pilot
icon
Layers of the
DEMETER
Agriculture
Information
Model (AIM)

6. Place
Pilot
icon Types of data represented via AIM
AIM represents a wide variety of data types that are generic or are specific to the agrifood domain, such as :
• Farm data (e.g., field data, field status, soil data, Crops/treatment/fertilisation data, farm input data,
energy consumption data, ...)
• Earth Observation Data (e.g., satellite data, remote sensing imagery, soil maps, vegetation indices,
such as NDVI, EVI, NDRE, NDMI)
• Meteorological data (e.g., temperature, humidity, wind speed/direction, solar radiation, pressure,
etc.)
• Agricultural machinery data (e.g., engine data, fuel consumption, emissions, exhaust gas, NOx-
conversion, exhaust temperatures, ...)
• Representation of data quality metrics
• Field Operations data (irrigation, fertilisation, soil tillage)
• Livestock data
• Traceability data (transport)
• Financial farm data, benchmarking data and KPIs
• Farmer information

7. Place
Pilot
icon Semantic Interoperability via AIM
AIM provides the basis to enable a semantic interoperability data space: it defines the data
elements (concepts, properties and relations) relevant to agri applications, including the
semantics associated to the information exchanged.
AIM establish (semantic) mapping to the various standards/ontologies
FIWARE
Saref4Agri
INSPIRE and FOODIE
ADAPT
AGROVOC
EO standards
ISO standards
Units Ontologies

8. Place
Pilot
icon
Reuse large repository of Linked Data related to agriculture with
over 1 billion triples
DEMETER
Data
Pipelines
AIM

9. Place
Pilot
icon
From common semantic data
models to standard APIs
q The semantic data models provide the common language
(lingua franca) to represent data, with explicit semantics, so
that different components can understand and validate it
q The data pipelines allow the harmonization of data
according to those models in order to enable an integrated
view over different data source
q However, different components normally implement
different APIs that expose or consume the data
q -> need for standardized APIs

10. Place
Pilot
icon Key characteristics of a data space - EU view
A secure and privacy-preserving IT infrastructure to pool, access, process, use
and share data.
A data governance mechanism, comprising a set of admin and contractual
rules that determine the rights to access, process, use and share data in a
trustful, transparent manner and in compliance with existing legislation.
Data holders are in control of who can have access to their data, for which
purpose and under which conditions it can be used.
Presence of (vast amounts of) data that are made available on a voluntary
basis and that can be reused against remuneration or for free, depending on
the data holder’s decision.
Participation by an open number of organisations/ individuals in full respect of
competition rules and ensuring non-discriminatory access for all participants
Doris Marquardt, AGRI/F2 Joël Bacquet, CNECT/E4 6 September 2022
IT infrastructrue
data governance
access control
big data
open participation

11. Place
Pilot
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Leveraging previous experiences to build
data space for agriculture
Doris Marquardt, AGRI/F2 Joël Bacquet, CNECT/E4 6 September 2022

12. Place
Pilot
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Preparatory actions for Data Spaces -
Agriculture Data Space
Step 1: Preparatory action: Co-ordination and Support Action (CSA)
à selected proposal for agriculture: AgriDataSpace - start 1st October 2022.
• Expected outcomes:
• Inventory of existing platforms
• Proposed design approach based on scenarios
• Governance scheme
• Blue-print for the data space
• Interim results used for the development of the implementation action
• Input to the work of the data space support centre
Step 2: First implementation project
à Call in 2024

13. Place
Pilot
icon AgriDataSpace
q Understanding and mapping the data sharing landscape (e.g., European Code of Conduct)
q Develop building blocks for profitable and responsible data space in agriculture
(governance models, business models, legislative framework)
q Develop a conceptual reference architecture for a common data space framework iand a
reference technology canvas
q analyse existing data models, vocabularies (e.g., AIM)
q look into the best data interoperability solutions (technical & API, semantic etc.)
q Engage stakeholders in various activities for evaluation and validation
q Develop a roadmap towards implementation of the EU Agriculture Data Space

14. Place
Pilot
icon AgriDataSpace

15. Place
Pilot
icon All Data for Green Deal (AD4GD)
Aims to co-create and shape the EU Green Deal Data Space as an open hub for FAIR data and standards-
based services that support the key EU priorities
Main focus on interoperability concepts that bridge the semantic and technology gaps
Enable the combination and integration of data from different sources
Demonstrate and validate the approach in three pilots, involving intl. organizations, scientists and
researchers, citizens, decision makers and solution providers
Building blocks for Data Spaces, IDSA

16. For more information visit:
www.h2020-demeter.eu
or Email us at:
info@h2020-demeter.eu
rpalma@man.poznan.pl