Data intensive agricultural sciences : requirements based on Aginfra+ Project and high throughput phenotyping infrastructure

Data Intensive Agricultural
Sciences
Requirements Based on AgINFRA+ Project and High-
Throughput Phenotyping Infrastructure
Vincent NEGRE MAY 08, 2019

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Vincent NEGRE / Data Intensive Agricultural Sciences MAY 08, 2019
AgINFRA+ Project
Starting Date: January 2017
Duration: 36 months
Topic: H2020 EINFRA-22-2016 User-driven e-infrastructure innovation
Consortium:
➢ Agroknow, Greece (Project Coordinator)
➢ Wageningen University, Netherlands
➢ INRA, France
➢ BFR, Germany
➢ CNR, Italy
➢ UOA, Greece
➢ EGI, Netherlands
➢ Pensoft Publishers Ltd, Bulgaria

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MAY 08, 2019
The AgINFRA+ Objectives
• Demonstrate how scientific communities working on agriculture
and food topics may carry out rapid and intuitive development and
deployment of innovative applications and workflows, powered
by open e-infrastructures.
• Strengthen and illustrate the value and potential of AGINFRA+ as
a virtual research environment for the domain of agriculture and
food.
Vincent NEGRE / Data Intensive Agricultural Sciences

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MAY 08, 2019
The AgINFRA+ roadmap
• Identify the requirements of the specific scientific and technical
communities working in the targeted areas;
• Design and implement components that serve such requirements, by
exploiting, adapting and extending existing open e-infrastructures (namely,
EGI and D4Science), when required;
• Define or extend standards facilitating interoperability, reuse, and
repurposing of components in a wider context of AGINFRA+;
• Establish mechanisms for documenting and sharing data, mathematical
models, methods and components for the selected application areas

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MAY 08, 2019
The AgINFRA+ Organization
• Showcase the benefit of a VRE to 3 use cases:
• WP5 – Agro-climatic modelling (Alterra, Wageningen University)
• WP6 – Food Safety (BFR)
• WP7 – Food Security (INRA)
• 3 Technical Work Packages:
• WP2 – Semantics (Agroknow)
• WP3 – Analytics (CNR, EGI)
• WP4 – Visualization (UOA)

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MAY 08, 2019
AgINFRA+ VREs
• Modern science tend to be more than ever multidisciplinary, collaborative
and networked (Llewellyn Smith, et al., 2011).
• This trend calls for innovative, dynamic, and ubiquitous research
supporting environments (Candela et al. 2013).
• These environments are commonly referred to as either Virtual
Research Environments (Carusi & Reimer, 2010), Science Gateways
(Wilkins-Diehr, 2007), Collaboratories (Wulf, 1993), Digital Libraries
(Candela, Castelli, & Pagano, 2011) or Inhabited Information Spaces
(Snowdon, Churchill, & Frécon, 2004).
What is a VRE ?

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MAY 08, 2019
AgINFRA+ VREs
• Online (web) working environment for sciences
• Collaborative environment
• Serves the need of a research community
• Provides valuable features for the community : collaboration
support, document hosting and specific tools for data analytics,
data visualization and computation
What is a VRE ?

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MAY 08, 2019
AgINFRA+ VREs
• In the AgINFRA+ project, VREs have been deployed for each use case.
Food Safety
Agro-climatic
modeling Food Security

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AgINFRA+ VREs
• They are based on the D4Science solution developed by CNR.
• gCube technology.
gCube Application Bundles

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MAY 08, 2019
AgINFRA+ VREs
• The initial hosting infrastructure was designed, developed and put in
production back in 2007 with the support of a series of EU projects (iMarine
1 and EUBrazilOpenBio);
• Have been extended by external resources via federated access. The EGI
sites supporting the D4science infrastructure Virtual Organisation
(d4science.research-infrastructures.eu)
• https://aginfra.d4science.org/explore

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Why a VRE for Food Security ?
MAY 08, 2019
The Food Security VRE
• The aim of the Food Security VRE is to leverage Big Data opportunities in
order to sustainably maximise crop performance.
• The VRE should help plant scientists to determine which plant species and
varieties are most adapted to climate changes.
• This requires high throughput plant phenotyping, that is at the heart of plant
selection process and produces huge sets of data.

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What is High-Throughput Phenotyping ?
MAY 08, 2019
High-Throughput Phenotyping
Phenotype (traits)

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What is to measure ?
MAY 08, 2019
❖ Climate
❖ Pathogen pressure
❖ Soil
• Root biomass, distribution, …
❖ Plant structure
• Leaf area
• Biomass
• Inclination/orientation of organs
• Density of plants/stems/ears
❖ Biochemical content
• Chorophyl, water, dry matter, nitrogen,….
❖ State
• Fluoresence, skin temperature, …
Environnement
Maize Wheat AppleTree
Arabidopsis

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Phenotyping platforms
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Phenotyping facilities
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Drone Field
Phenoarch Green House

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Complex and heterogeneous data
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Various Crop Species
Various Scales
Various Data Sources
Various interactions

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❖ 20 experiments in field/greenhouse per year
• One experiment generates between 2Tbytes and 10Tbytes
• 7 millions rows in RDB + 1.5 millions of RDF triplet + 0.5 millions of
images
❖ Total data production is over 100 Tbytes/year
Some figures – PHENOME EMPHASIS (French node)
PHENOME-EMPHASIS
platforms
EPPN network

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OpenSILEX - PHIS
❖ Designed for data management in phenotyping platforms
• Management of huge, complex and heterogeneous data (millions of
images, sensor data, etc)
❖ Implement good practices of data management
• Make FAIR data
• Foster collaborations (Open and Flexible)
• Ability to understand and reproduce data processing
Phenotyping Information System

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OpenSILEX - PHIS
Architecture

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OpenSILEX - PHIS
Web Services Layer
❖ The Web Services Layer is the interface between the web user
interface and the databases
• RESTful web services developed in java
• Swagger framework
❖ Besides the specific WS, there are some new WS which are BrAPI
compliant
• The Breeding API specifies a standard interface data between crop
breading applications
• Compliant with OpenAPI specifications
• It is a shared, open API, to be used by all data providers and data
consumers who wish to participate

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❖ The Food Security VRE targets plant scientists
• https://aginfra.d4science.org/web/foodsecurity
❖ The needs of the community are:
• Deal with data complexity and data volume increasing
• Discover and access plant datasets
• Combine and integrate these datasets
• Explore, (re-)analyse, visualize
• Run workflows for predictions, knowledge discovery and decision
support
• Make data valuable (share and reuse data)

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Food Security VRE
MAY 08, 2019
- Shared Workspace
- Catalogue
Data Access
- Rstudio
- Jupyter Lab
- Galaxy
- Dataminer
Data Analytics
- Visualization tool
Data Visualization
- Vocbench
- Yam++
- Silk
Semantics
What are the functionalities ?
EGI services

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Food Security VRE
MAY 08, 2019
- Shared Workspace
- Catalogue
Data Access

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- Shared Workspace
- Catalogue
Data Access

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MAY 08, 2019
- Shared Workspace
- Catalogue
Data Access
- Rstudio
- Jupyter Lab
- Galaxy
- Dataminer
Data Analytics

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MAY 08, 2019
- Shared Workspace
- Catalogue
Data Access
- Rstudio
- Jupyter Lab
- Galaxy
- Dataminer
Data Analytics

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Food Security VRE
MAY 08, 2019
PHIS and the VRE
How do we link PHIS to the VRE ?
- Shared Workspace
- Catalogue
Data Access
- Rstudio
- Jupyter Lab
- Galaxy
- Dataminer
Data Analytics
Data Visualization
- Vocbench
- Yam++
- Silk
Semantics
OpenSilex-PHIS IS

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Food Security VRE
MAY 08, 2019
PHIS and the VRE
Algo calling BrAPI WS
Algo calling specific
WS
Data Access
- Rstudio
- Jupyter Lab
- Galaxy
- Dataminer
Data Analytics
Data Visualization
- Vocbench
- Yam++
- Silk
Semantics
OpenSilex-PHIS
- Specific REST WS
- BrAPI compliant WS

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Food Security VRE
MAY 08, 2019
PHIS and the VRE
Algo calling BrAPI WS
Algo calling specific
WS
Data Access
- Rstudio
- Jupyter Lab
- Galaxy
- Dataminer
Data Analytics
Data Visualization
- Vocbench
- Yam++
- Silk
Semantics
OpenSilex-PHIS
- Specific REST WS
- BrAPI compliant WS
Any DataBase with BrAPI
compliant WS

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MAY 08, 2019
Future Perspectives
❖ More exchange between PHIS and the VRE
• Discovery service in the VRE to find interesting PHIS data
• Run dataminer algorithms and Galaxy workflows from the VRE
directly in PHIS
❖ Evaluation of the VRE
• 2 evaluation sessions will be set up to assess the VRE features

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• Take profit of existing resources
• Reuse existing tools
• Facilitate data sharing and knowledge exchange
• Develop standards
Adding value to e-infrastructures

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MAY 08, 2019
Thank you for your attention
For more information, please contact:
pascal.neveu@inra.fr
alice.boizet@inra.fr
http://www.plus.aginfra.eu/
https://aginfra.d4science.org/
http://www.opensilex.org/
https://github.com/OpenSILEX
http://phis.inra.fr/

Data intensive agricultural sciences : requirements based on Aginfra+ Project and high throughput phenotyping infrastructure

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