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

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Vincent Negre presentation at EGI conference 2019 on Virtual Research Environment (VRE) developed for the Plant Phenotyping Research community.

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

  1. 1. Data Intensive Agricultural Sciences Requirements Based on AgINFRA+ Project and High- Throughput Phenotyping Infrastructure Vincent NEGRE MAY 08, 2019
  2. 2. .02 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
  3. 3. .03 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
  4. 4. .04 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 Vincent NEGRE / Data Intensive Agricultural Sciences
  5. 5. .05 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) Vincent NEGRE / Data Intensive Agricultural Sciences
  6. 6. .06 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 ? Vincent NEGRE / Data Intensive Agricultural Sciences
  7. 7. .07 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 ? Vincent NEGRE / Data Intensive Agricultural Sciences
  8. 8. .08 MAY 08, 2019 AgINFRA+ VREs • In the AgINFRA+ project, VREs have been deployed for each use case. Vincent NEGRE / Data Intensive Agricultural Sciences Food Safety Agro-climatic modeling Food Security
  9. 9. .09 MAY 08, 2019 AgINFRA+ VREs • They are based on the D4Science solution developed by CNR. • gCube technology. Vincent NEGRE / Data Intensive Agricultural Sciences gCube Application Bundles
  10. 10. .010 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 Vincent NEGRE / Data Intensive Agricultural Sciences
  11. 11. .011 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. Vincent NEGRE / Data Intensive Agricultural Sciences
  12. 12. .012 What is High-Throughput Phenotyping ? MAY 08, 2019 High-Throughput Phenotyping Vincent NEGRE / Data Intensive Agricultural Sciences Phenotype (traits)
  13. 13. .013 What is to measure ? MAY 08, 2019 High-Throughput Phenotyping Vincent NEGRE / Data Intensive Agricultural Sciences ❖ 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
  14. 14. .014 Phenotyping platforms MAY 08, 2019 High-Throughput Phenotyping Vincent NEGRE / Data Intensive Agricultural Sciences
  15. 15. .015 Phenotyping facilities MAY 08, 2019 High-Throughput Phenotyping Vincent NEGRE / Data Intensive Agricultural Sciences Drone Field Phenoarch Green House
  16. 16. .016 Complex and heterogeneous data MAY 08, 2019 High-Throughput Phenotyping Vincent NEGRE / Data Intensive Agricultural Sciences Various Crop Species Various Scales Various Data Sources Various interactions
  17. 17. .017 MAY 08, 2019 High-Throughput Phenotyping ❖ 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) Vincent NEGRE / Data Intensive Agricultural Sciences PHENOME-EMPHASIS platforms EPPN network
  18. 18. .018 MAY 08, 2019 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 Vincent NEGRE / Data Intensive Agricultural Sciences
  19. 19. .019 MAY 08, 2019 OpenSILEX - PHIS Architecture Vincent NEGRE / Data Intensive Agricultural Sciences
  20. 20. .020 MAY 08, 2019 OpenSILEX - PHIS Web Services Layer Vincent NEGRE / Data Intensive Agricultural Sciences ❖ 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
  21. 21. .021 MAY 08, 2019 The Food Security VRE ❖ 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) Vincent NEGRE / Data Intensive Agricultural Sciences
  22. 22. .022 Food Security VRE MAY 08, 2019 The Food Security VRE - Shared Workspace - Catalogue Data Access - Rstudio - Jupyter Lab - Galaxy - Dataminer Data Analytics - Visualization tool Data Visualization - Vocbench - Yam++ - Silk Semantics Vincent NEGRE / Data Intensive Agricultural Sciences What are the functionalities ? EGI services
  23. 23. .023 Food Security VRE MAY 08, 2019 The Food Security VRE - Shared Workspace - Catalogue Data Access Vincent NEGRE / Data Intensive Agricultural Sciences
  24. 24. .024 MAY 08, 2019 The Food Security VRE - Shared Workspace - Catalogue Data Access Vincent NEGRE / Data Intensive Agricultural Sciences
  25. 25. .025 MAY 08, 2019 The Food Security VRE - Shared Workspace - Catalogue Data Access - Rstudio - Jupyter Lab - Galaxy - Dataminer Data Analytics Vincent NEGRE / Data Intensive Agricultural Sciences
  26. 26. .026 MAY 08, 2019 The Food Security VRE - Shared Workspace - Catalogue Data Access - Rstudio - Jupyter Lab - Galaxy - Dataminer Data Analytics Vincent NEGRE / Data Intensive Agricultural Sciences
  27. 27. .027 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 - Visualization tool Data Visualization - Vocbench - Yam++ - Silk Semantics Vincent NEGRE / Data Intensive Agricultural Sciences OpenSilex-PHIS IS
  28. 28. .028 Food Security VRE MAY 08, 2019 PHIS and the VRE How do we link PHIS to the VRE ? Algo calling BrAPI WS Algo calling specific WS Data Access - Rstudio - Jupyter Lab - Galaxy - Dataminer Data Analytics - Visualization tool Data Visualization - Vocbench - Yam++ - Silk Semantics Vincent NEGRE / Data Intensive Agricultural Sciences OpenSilex-PHIS - Specific REST WS - BrAPI compliant WS
  29. 29. .029 Food Security VRE MAY 08, 2019 PHIS and the VRE How do we link PHIS to the VRE ? Algo calling BrAPI WS Algo calling specific WS Data Access - Rstudio - Jupyter Lab - Galaxy - Dataminer Data Analytics - Visualization tool Data Visualization - Vocbench - Yam++ - Silk Semantics Vincent NEGRE / Data Intensive Agricultural Sciences OpenSilex-PHIS - Specific REST WS - BrAPI compliant WS Any DataBase with BrAPI compliant WS
  30. 30. .030 MAY 08, 2019 Future Perspectives Vincent NEGRE / Data Intensive Agricultural Sciences ❖ 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
  31. 31. .031 • Take profit of existing resources • Reuse existing tools • Facilitate data sharing and knowledge exchange • Develop standards Adding value to e-infrastructures
  32. 32. .032 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/ Vincent NEGRE / Data Intensive Agricultural Sciences

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