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BlueBRIDGE receives funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No. 675680 www.bluebridge-vres.eu
Towards an e-infrastructure in
agriculture?
Donatella Castelli, CNR-ISTI
CNR-ISTI
donatella.castelli@isti.cnr.it
Euragri Workshop
Big data in agriculture: consequences for
research and research organizations
9 March 2016, Paris
Outline
• iMarine & its Virtual Research Environments
• BlueBRIDGE Virtual (Research) Environments
• Final remarks
Euragri Workshop, 9 March 2016, INRA, Paris
Terminology (1)
Euragri Workshop, 9 March 2016, INRA, Paris
e-Infrastructure
Elecronic platform operated by a responsible entity offering an
open set of basic enabling services (including access to resources)
to a Community of Practice (CoP). Through the e-Infrastructure the
CoP realises economy of scale.
Data infrastructure
“e-Infrastructure” offering services for collection, deposition,
storage, preservation, access, retrieval, analysis/mining/processing,
publication, etc.
Terminology (2)
Euragri Workshop, 9 March 2016, INRA, Paris
The Community of Practice
(the community)
The e-infrastructure
(the operational platform)
The system
(the enabling sw system)
Launch an initiative aimed at establishing and
operating an e-infrastructure contributing to the
implementation of the principles of the
Ecosystem Approach to Fisheries Management
and Conservation of Marine Living Resources
iMarine
Euragri Workshop, 9 March 2016, INRA, Paris
iMarine CoP needs
How to facitate the implementation of the an
Ecosystem Approach to fisheries management and
conservation of marine living resources?
Euragri Workshop, 9 March 2016, INRA, Paris
Interdisciplinary &
multifacets
collaboration
at the local, national,
regional and international
levels
Knowledge generation
- Access to large amount of
heterogeneous, distribuited,
across-domain data
- Rich data mining, analysis
and processing capabilities
Data Infrastructure for the
iMarine CoP
Euragri Workshop, 9 March 2016, INRA, Paris
Services
“in the style of cloud”
Leveraging others’ resources
Own resources
Third-party providers
Euragri Workshop, 9 March 2016, INRA, Paris
Leveraging others’ resources
Own resources
Third-party providers
Euragri Workshop, 9 March 2016, INRA, Paris
Hetogeneous datasets
Euragri Workshop, 9 March 2016, INRA, Paris
Semantic
Data
Biodiversity
data
Geospatial
data
Statistical
data
Documents
OAI-PMH, DC,
OpenSearch
RDF, OWL
DwC, DwC-A
ISO19139 (OGC W*S)
SDMX*
VRE as-a-Service
Euragri Workshop, 9 March 2016, INRA, Paris
e-Infrastructure
VRE
VRE
VRE
• web-based working environment
• providing access to services and resources tailored to
serve the needs of a community of practice in
accomplishing a specific goal
• open and flexible with respect to service offering and
lifetime
• providing fine-grained controlled sharing of both
intermediate and final research results
• Very low cost of creation and operation
Virtual Research Environment
VRE Definition
Euragri Workshop, 9 March 2016, INRA, Paris
3. Configure applications
2. Select applications1. Specify VRE metadata
(including policies)
4. Select data collections
Hardware setup and software
deployment completely hidden
Evolving needs of its users
completely supported
Collaborative Environment
Euragri Workshop, 9 March 2016, INRA, Paris
Share Updates
User news feed
VREs user is a member of
A single point to
Get status and updates from applications and other users they are interested in;
Get notifications about messages, jobs completion, new generated products, etc.
Workspace
A single place to
• Manage data, store and
preserve them
• Share data
Euragri Workshop, 9 March 2016, INRA, Paris
Working within a VRE (1)
The user
• Stores data in a personal workspace
• Visualizes and harmonizes data
• Saves the results for further exploitations
• Shares with his/her colleagues
Euragri Workshop, 9 March 2016, INRA, Paris
Working within a VRE (2)
The user
• Stores software in a personal workspace
• Prepares it for execution with a simple interface (one shot
process, never to repeat)
• Executes it and analyses the results
• Modifies the code in the workspace
• Shares software and/or results with his/her colleagues
Euragri Workshop, 9 March 2016, INRA, Paris
iMarine VREs
Euragri Workshop, 9 March 2016, INRA, Paris
iMarine Gateway
https://i-marine.d4science.org/
• Public VREs (used to offer an application
environment to a subset of users of a
community)
• Private VREs (used for experiments, data
access and preparation, and data analytics)
Scalable Data Mining VRE
Tabular Data
Manager
(TabMan)
Statistical
Manager
(StatMan)
Analytics
Data
preparation &
access
Monitoring the
status of the computation
Single environment
Euragri Workshop, 9 March 2016, INRA, Paris
Tabular Data Manager (TabMan)
• Manipulates Big Tabular
Datasets
• Prepares data for
analyses
• Makes data compliant
with external code lists
• Visualizes, represents
and inspects data
Euragri Workshop, 9 March 2016, INRA, Paris
TabMan imports datasets with tuna catch statistics
Yellowfin Skipjack
TabMan
Euragri Workshop, 9 March 2016, INRA, Paris
Modifying columns
TabMan
Euragri Workshop, 9 March 2016, INRA, Paris
Data Curation
b. Column splitting
using regular
expressions
c. Changing
columns types
and Codelists
compliancy
e. Denormalization:
one column per row
value
a. Duplicates
deletion
d. Produce a new
codelist
TabMan
Euragri Workshop, 9 March 2016, INRA, Paris
Adding Geometries
TabMan
Euragri Workshop, 9 March 2016, INRA, Paris
Csquare codes and FAO Ocean Areas codes
TabMan
Euragri Workshop, 9 March 2016, INRA, Paris
GIS maps published under standard formats
(WMS, WFS, WCS)
TabMan
Euragri Workshop, 9 March 2016, INRA, Paris
Tuna Atlas VRE
• Represents world wide Tuna catches in an
interactive map
• Centralize and harmonize Tuna RFMOs
Statistics with a common format
• Share the approach with other institutions
with different exploitations
FAO Tuna Atlas
• Create a Virtual Research Environment
• Centralise and harmonise catch statistics
coming from heterogeneous sources
• Modify and analyse tabular datasets
• Visualise and summarize information and
trends
D4Science support to Tuna Atlas
FIC_ITEM NAME_E ALPHA
3CODE
2494 Skipjack tuna SKJ
2496 Albacore ALB
2497 Yellowfin tuna YFT
2498 Bigeye tuna BET
2500 Black marlin BLM
2501 Striped marlin MLS
2503 Swordfish SWO
3296 Atlantic bluefin t BFT
3298 Southern bluefin t SBF
3303 Blue marlin BUM
3305 Atlantic white mar WHM
18734 Pacific bluefin tu PBF
Statistical Data Manager (StatMan)
• 100+ statistical models
• Transparent use of
cloud computing
• Automatically
generated interfaces
• Integration with R
Data access
Monitoring the
status of the computation
Statistical Manager (StatMan)
External
Computing
Facility
OGC
WPS
Interface
Data standardisation services
Statistical services
WPS
100+ Hosted algorithms
Euragri Workshop, 9 March 2016, INRA, Paris
Algorithms categories
Anomalies Detection (4)
Classification (1)
Climate (4)
Correlation Analysis (1)
Data Clustering (4)
Filtering (2)
Function Simulation (1)
Occurrences (8)
Performances Evaluation (2)
Species Simulation (6)
Training (2)
Time Series (2)
Euragri Workshop, 9 March 2016, INRA, Paris
Taxa (5)
Maps (7)
Geo Processing (14)
Bayesian Methods (5)
Obis Observations Species Data (3)
Obis Observations Trends (4)
SPECIES Procedures (2)
Vessels (3)
Databases (9)
Spread (4)
Charts (4)
Precipitations (2)
Stock Assessment (7)
Stock assessment
Length-Weight Relations: estimates Length-
Weight relation parameters for marine species,
using Bayesian methods. Developed by R. Froese, T.
Thorson and R. B. Reyes
SGVM interpolation: interpolation of vessels
trajectories. Developed by the Study Group on VMS,
involving ICES
FAO MSY: stock assessment for FAO catch data.
Developed by the Resource Use and Conservation
Division of the FAO Fisheries and Aquaculture
Department (ref. Y. Ye)
ICCAT VPA: stock assessment method for
International Commission for the Conservation
of Atlantic Tunas (ICCAT) data. Developed by
Ifremer and IRD (ref. S. Bonhommeau, J. Bard)
CMSY:estimates Maximum Sustainable Yield
from catch statistics. Prime choice for ICES as
main stock assessment tool. Developed by R.
Froese, G. Coro, N. Demirel, K. Kleisner and H. Winker
Atlantic herring
D4Science reduced time-to-market:
State-of-the-art models to estimate
Maximum Sustainable Yield
computational time reduced of 95%
in average
Catch statistics forecasting
Ecology
Atlantic cod
Coelacanth
Giant squid
AquaMaps
Neural
Networks
Neural
Networks
and MaxEnt
Geospatial data processing
Maps
comparison
NetCDF
file
Data extraction
Signal processing Periodicity detection
Maps generation
Forecasting and analysis using an online R development
environment
TabMan
Euragri Workshop, 9 March 2016, INRA, Paris
Algorithm Importer
Reduce computation time
Reduce integration time
Publish algorithm as-a-Service
Manage access policies
Provenance Management
Accounting
No software required on
desktop machines
Code is not disclosed
Euragri Workshop, 9 March 2016, INRA, Paris
Interfaces
1 - iMarine
portal
2- QGIS (via WPS Client)
3 -WPS
Euragri Workshop, 9 March 2016, INRA, Paris
Computations history and
summary
Data
importing
facilities
Computations and data
Euragri Workshop, 9 March 2016, INRA, Paris
Data exploring and
tables visualisation
facilities
Data Management
Euragri Workshop, 9 March 2016, INRA, Paris
Data collection: SmartForm VRE
Euragri Workshop, 9 March 2016, INRA, Paris
Select a Fishery survey
Define forms, controlled vocabularies, ….
Validate& Enrich
Deposit
Analyse
BioDiversityLab:
uniform access to data
Euragri Workshop, 9 March 2016, INRA, Paris
Report production: VME-DB
Euragri Workshop, 9 March 2016, INRA, Paris
The International Guidelines for the Management of Deep-Sea
Fisheries on the High Seas. VME database to assist in informed
decision making and the development of further measures to increase
sustainability and reduce impacts.
VME
record
Specific
measures
Description
(Habitat &
Biology)
RFMO
General
Measures
Meetings &
other Sources of
Information
Historical information
on fishing areas and
closed areas
Time
iMarine experience
Euragri Workshop, 9 March 2016, INRA, Paris
Research
Scientific
advice
Educa
tion
Product
innovation
BlueBRIDGE
(Sept. 2015 – Feb. 2018)
Euragri Workshop, 9 March 2016, INRA, Paris
Building Research environments fostering
Innovation, Decision making, Governance and
Education
for Blue growth
Target stakeholders
Euragri Workshop, 9 March 2016, INRA, Paris
SMEseducators
scientific
authorities
reseachers
Expected impact
Euragri Workshop, 9 March 2016, INRA, Paris
Support capacity building & Innovate current practices of
interdisciplinary communities actively involved in increasing scientific
knowledge about resource overexploitation, degraded environment
and ecosystem
More solid ground for informed advice to competent
authorities
Cost effective training & knowledge bridging between research
and innovation
Enlarged spectrum of growth opportunities
Researchers
Scientists
producing
indicators
Trainers
Enterprises
Six interrelated
detailed objectives
Euragri Workshop, 9 March 2016, INRA, Paris
Supporting the collaborative production of scientific knowledge
required for assessing the status of fish stocks and producing a global
record of stocks and fisheries
Blue
Assessment
Supporting the production of scientific knowledge for analysing socio-
economic performance in aquaculture
Blue Economy
Supporting the production of scientific knowledge for fisheries &
habitat degradation monitoring
Blue
Environment
Boosting education and knowl. bridging between research
&innovation in the area of protection and mgmt of marine resources
Blue Skill
Developing and deploying service and resource commons across
VREs to facilitate the exploitation of existing infrastructure resourcesBlue Commons
Ensuring uptake of the BlueBRIDGE tools and services, with specifc
focus on SMEs, other scientific domains & policy making contextsBlue Uptake
Specific Areas
Euragri Workshop, 9 March 2016, INRA, Paris
• Stock Assessment VRE
• Global record of Stocks and Fisheries VRE
Blue
Assessment
• Performance evaluation, benchmarking and decision making in
aquaculture VRE
• Strategic Investment analysis and Scient. Planning/Alerting VRE
Blue Economy
• Aquaculture Atlas Generation VRE
• Protected Area Impact Maps VRE
Blue
Environment
• ICES Knowledge Bridging
• IRD Knowledge Bridging
• Knowledge Bridging Programmes
Blue Skill
Supported Training Workflow
Trainers
• Trainers check the catalogue of applications and datasets
• Request integration of new applications and datasets
BB
• BB Support team checks the course specification and support the
integration
• VRE is created
Trainees
• Trainees are invited to join the VRE
• Use the VRE and interact with the trainers via VRE social facilities
• Exploit the VRE tools to perform hands-on and examination
Trainees
• May still use the VRE for accessing data and tools
• May still interact with other users
• May still perform analysis and access course materials
Euragri Workshop, 9 March 2016, INRA, Paris
Final Remarks
• The marine and agriculture sectors have many similar
needs in term of data management services
• By construction D4Science/iMarine have been built
for being able to easy accomodates other needs (e.g.
framework, standard protocols, integration of
external algorithms, VREs)
• Open to investigate collaborations and synergies
Euragri Workshop, 9 March 2016, INRA, Paris
Euragri Workshop, 9 March 2016, INRA, Paris
www.i-marine.eu
www.bluebridge-vres.eu
https://www.gcube-system.org/
www.d4science.org

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Towards an e-infrastructure in agriculture?

  • 1. BlueBRIDGE receives funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 675680 www.bluebridge-vres.eu Towards an e-infrastructure in agriculture? Donatella Castelli, CNR-ISTI CNR-ISTI donatella.castelli@isti.cnr.it Euragri Workshop Big data in agriculture: consequences for research and research organizations 9 March 2016, Paris
  • 2. Outline • iMarine & its Virtual Research Environments • BlueBRIDGE Virtual (Research) Environments • Final remarks Euragri Workshop, 9 March 2016, INRA, Paris
  • 3. Terminology (1) Euragri Workshop, 9 March 2016, INRA, Paris e-Infrastructure Elecronic platform operated by a responsible entity offering an open set of basic enabling services (including access to resources) to a Community of Practice (CoP). Through the e-Infrastructure the CoP realises economy of scale. Data infrastructure “e-Infrastructure” offering services for collection, deposition, storage, preservation, access, retrieval, analysis/mining/processing, publication, etc.
  • 4. Terminology (2) Euragri Workshop, 9 March 2016, INRA, Paris The Community of Practice (the community) The e-infrastructure (the operational platform) The system (the enabling sw system)
  • 5. Launch an initiative aimed at establishing and operating an e-infrastructure contributing to the implementation of the principles of the Ecosystem Approach to Fisheries Management and Conservation of Marine Living Resources iMarine Euragri Workshop, 9 March 2016, INRA, Paris
  • 6. iMarine CoP needs How to facitate the implementation of the an Ecosystem Approach to fisheries management and conservation of marine living resources? Euragri Workshop, 9 March 2016, INRA, Paris Interdisciplinary & multifacets collaboration at the local, national, regional and international levels Knowledge generation - Access to large amount of heterogeneous, distribuited, across-domain data - Rich data mining, analysis and processing capabilities
  • 7. Data Infrastructure for the iMarine CoP Euragri Workshop, 9 March 2016, INRA, Paris Services “in the style of cloud”
  • 8. Leveraging others’ resources Own resources Third-party providers Euragri Workshop, 9 March 2016, INRA, Paris
  • 9. Leveraging others’ resources Own resources Third-party providers Euragri Workshop, 9 March 2016, INRA, Paris
  • 10. Hetogeneous datasets Euragri Workshop, 9 March 2016, INRA, Paris Semantic Data Biodiversity data Geospatial data Statistical data Documents OAI-PMH, DC, OpenSearch RDF, OWL DwC, DwC-A ISO19139 (OGC W*S) SDMX*
  • 11. VRE as-a-Service Euragri Workshop, 9 March 2016, INRA, Paris e-Infrastructure VRE VRE VRE • web-based working environment • providing access to services and resources tailored to serve the needs of a community of practice in accomplishing a specific goal • open and flexible with respect to service offering and lifetime • providing fine-grained controlled sharing of both intermediate and final research results • Very low cost of creation and operation Virtual Research Environment
  • 12. VRE Definition Euragri Workshop, 9 March 2016, INRA, Paris 3. Configure applications 2. Select applications1. Specify VRE metadata (including policies) 4. Select data collections Hardware setup and software deployment completely hidden Evolving needs of its users completely supported
  • 13. Collaborative Environment Euragri Workshop, 9 March 2016, INRA, Paris Share Updates User news feed VREs user is a member of A single point to Get status and updates from applications and other users they are interested in; Get notifications about messages, jobs completion, new generated products, etc.
  • 14. Workspace A single place to • Manage data, store and preserve them • Share data Euragri Workshop, 9 March 2016, INRA, Paris
  • 15. Working within a VRE (1) The user • Stores data in a personal workspace • Visualizes and harmonizes data • Saves the results for further exploitations • Shares with his/her colleagues Euragri Workshop, 9 March 2016, INRA, Paris
  • 16. Working within a VRE (2) The user • Stores software in a personal workspace • Prepares it for execution with a simple interface (one shot process, never to repeat) • Executes it and analyses the results • Modifies the code in the workspace • Shares software and/or results with his/her colleagues Euragri Workshop, 9 March 2016, INRA, Paris
  • 17. iMarine VREs Euragri Workshop, 9 March 2016, INRA, Paris iMarine Gateway https://i-marine.d4science.org/ • Public VREs (used to offer an application environment to a subset of users of a community) • Private VREs (used for experiments, data access and preparation, and data analytics)
  • 18. Scalable Data Mining VRE Tabular Data Manager (TabMan) Statistical Manager (StatMan) Analytics Data preparation & access Monitoring the status of the computation Single environment Euragri Workshop, 9 March 2016, INRA, Paris
  • 19. Tabular Data Manager (TabMan) • Manipulates Big Tabular Datasets • Prepares data for analyses • Makes data compliant with external code lists • Visualizes, represents and inspects data Euragri Workshop, 9 March 2016, INRA, Paris
  • 20. TabMan imports datasets with tuna catch statistics Yellowfin Skipjack TabMan Euragri Workshop, 9 March 2016, INRA, Paris
  • 21. Modifying columns TabMan Euragri Workshop, 9 March 2016, INRA, Paris
  • 22. Data Curation b. Column splitting using regular expressions c. Changing columns types and Codelists compliancy e. Denormalization: one column per row value a. Duplicates deletion d. Produce a new codelist TabMan Euragri Workshop, 9 March 2016, INRA, Paris
  • 23. Adding Geometries TabMan Euragri Workshop, 9 March 2016, INRA, Paris
  • 24. Csquare codes and FAO Ocean Areas codes TabMan Euragri Workshop, 9 March 2016, INRA, Paris
  • 25. GIS maps published under standard formats (WMS, WFS, WCS) TabMan Euragri Workshop, 9 March 2016, INRA, Paris
  • 26. Tuna Atlas VRE • Represents world wide Tuna catches in an interactive map • Centralize and harmonize Tuna RFMOs Statistics with a common format • Share the approach with other institutions with different exploitations FAO Tuna Atlas • Create a Virtual Research Environment • Centralise and harmonise catch statistics coming from heterogeneous sources • Modify and analyse tabular datasets • Visualise and summarize information and trends D4Science support to Tuna Atlas FIC_ITEM NAME_E ALPHA 3CODE 2494 Skipjack tuna SKJ 2496 Albacore ALB 2497 Yellowfin tuna YFT 2498 Bigeye tuna BET 2500 Black marlin BLM 2501 Striped marlin MLS 2503 Swordfish SWO 3296 Atlantic bluefin t BFT 3298 Southern bluefin t SBF 3303 Blue marlin BUM 3305 Atlantic white mar WHM 18734 Pacific bluefin tu PBF
  • 27. Statistical Data Manager (StatMan) • 100+ statistical models • Transparent use of cloud computing • Automatically generated interfaces • Integration with R Data access Monitoring the status of the computation
  • 28. Statistical Manager (StatMan) External Computing Facility OGC WPS Interface Data standardisation services Statistical services WPS
  • 29. 100+ Hosted algorithms Euragri Workshop, 9 March 2016, INRA, Paris
  • 30. Algorithms categories Anomalies Detection (4) Classification (1) Climate (4) Correlation Analysis (1) Data Clustering (4) Filtering (2) Function Simulation (1) Occurrences (8) Performances Evaluation (2) Species Simulation (6) Training (2) Time Series (2) Euragri Workshop, 9 March 2016, INRA, Paris Taxa (5) Maps (7) Geo Processing (14) Bayesian Methods (5) Obis Observations Species Data (3) Obis Observations Trends (4) SPECIES Procedures (2) Vessels (3) Databases (9) Spread (4) Charts (4) Precipitations (2) Stock Assessment (7)
  • 31. Stock assessment Length-Weight Relations: estimates Length- Weight relation parameters for marine species, using Bayesian methods. Developed by R. Froese, T. Thorson and R. B. Reyes SGVM interpolation: interpolation of vessels trajectories. Developed by the Study Group on VMS, involving ICES FAO MSY: stock assessment for FAO catch data. Developed by the Resource Use and Conservation Division of the FAO Fisheries and Aquaculture Department (ref. Y. Ye) ICCAT VPA: stock assessment method for International Commission for the Conservation of Atlantic Tunas (ICCAT) data. Developed by Ifremer and IRD (ref. S. Bonhommeau, J. Bard) CMSY:estimates Maximum Sustainable Yield from catch statistics. Prime choice for ICES as main stock assessment tool. Developed by R. Froese, G. Coro, N. Demirel, K. Kleisner and H. Winker Atlantic herring D4Science reduced time-to-market: State-of-the-art models to estimate Maximum Sustainable Yield computational time reduced of 95% in average
  • 34. Geospatial data processing Maps comparison NetCDF file Data extraction Signal processing Periodicity detection Maps generation
  • 35. Forecasting and analysis using an online R development environment TabMan Euragri Workshop, 9 March 2016, INRA, Paris
  • 36. Algorithm Importer Reduce computation time Reduce integration time Publish algorithm as-a-Service Manage access policies Provenance Management Accounting No software required on desktop machines Code is not disclosed Euragri Workshop, 9 March 2016, INRA, Paris
  • 37. Interfaces 1 - iMarine portal 2- QGIS (via WPS Client) 3 -WPS Euragri Workshop, 9 March 2016, INRA, Paris
  • 38. Computations history and summary Data importing facilities Computations and data Euragri Workshop, 9 March 2016, INRA, Paris
  • 39. Data exploring and tables visualisation facilities Data Management Euragri Workshop, 9 March 2016, INRA, Paris
  • 40. Data collection: SmartForm VRE Euragri Workshop, 9 March 2016, INRA, Paris Select a Fishery survey Define forms, controlled vocabularies, …. Validate& Enrich Deposit Analyse
  • 41. BioDiversityLab: uniform access to data Euragri Workshop, 9 March 2016, INRA, Paris
  • 42. Report production: VME-DB Euragri Workshop, 9 March 2016, INRA, Paris The International Guidelines for the Management of Deep-Sea Fisheries on the High Seas. VME database to assist in informed decision making and the development of further measures to increase sustainability and reduce impacts. VME record Specific measures Description (Habitat & Biology) RFMO General Measures Meetings & other Sources of Information Historical information on fishing areas and closed areas Time
  • 43. iMarine experience Euragri Workshop, 9 March 2016, INRA, Paris Research Scientific advice Educa tion Product innovation
  • 44. BlueBRIDGE (Sept. 2015 – Feb. 2018) Euragri Workshop, 9 March 2016, INRA, Paris Building Research environments fostering Innovation, Decision making, Governance and Education for Blue growth
  • 45. Target stakeholders Euragri Workshop, 9 March 2016, INRA, Paris SMEseducators scientific authorities reseachers
  • 46. Expected impact Euragri Workshop, 9 March 2016, INRA, Paris Support capacity building & Innovate current practices of interdisciplinary communities actively involved in increasing scientific knowledge about resource overexploitation, degraded environment and ecosystem More solid ground for informed advice to competent authorities Cost effective training & knowledge bridging between research and innovation Enlarged spectrum of growth opportunities Researchers Scientists producing indicators Trainers Enterprises
  • 47. Six interrelated detailed objectives Euragri Workshop, 9 March 2016, INRA, Paris Supporting the collaborative production of scientific knowledge required for assessing the status of fish stocks and producing a global record of stocks and fisheries Blue Assessment Supporting the production of scientific knowledge for analysing socio- economic performance in aquaculture Blue Economy Supporting the production of scientific knowledge for fisheries & habitat degradation monitoring Blue Environment Boosting education and knowl. bridging between research &innovation in the area of protection and mgmt of marine resources Blue Skill Developing and deploying service and resource commons across VREs to facilitate the exploitation of existing infrastructure resourcesBlue Commons Ensuring uptake of the BlueBRIDGE tools and services, with specifc focus on SMEs, other scientific domains & policy making contextsBlue Uptake
  • 48. Specific Areas Euragri Workshop, 9 March 2016, INRA, Paris • Stock Assessment VRE • Global record of Stocks and Fisheries VRE Blue Assessment • Performance evaluation, benchmarking and decision making in aquaculture VRE • Strategic Investment analysis and Scient. Planning/Alerting VRE Blue Economy • Aquaculture Atlas Generation VRE • Protected Area Impact Maps VRE Blue Environment • ICES Knowledge Bridging • IRD Knowledge Bridging • Knowledge Bridging Programmes Blue Skill
  • 49. Supported Training Workflow Trainers • Trainers check the catalogue of applications and datasets • Request integration of new applications and datasets BB • BB Support team checks the course specification and support the integration • VRE is created Trainees • Trainees are invited to join the VRE • Use the VRE and interact with the trainers via VRE social facilities • Exploit the VRE tools to perform hands-on and examination Trainees • May still use the VRE for accessing data and tools • May still interact with other users • May still perform analysis and access course materials Euragri Workshop, 9 March 2016, INRA, Paris
  • 50. Final Remarks • The marine and agriculture sectors have many similar needs in term of data management services • By construction D4Science/iMarine have been built for being able to easy accomodates other needs (e.g. framework, standard protocols, integration of external algorithms, VREs) • Open to investigate collaborations and synergies Euragri Workshop, 9 March 2016, INRA, Paris
  • 51. Euragri Workshop, 9 March 2016, INRA, Paris www.i-marine.eu www.bluebridge-vres.eu https://www.gcube-system.org/ www.d4science.org

Editor's Notes

  1. People can contribute with: R scripts Java programs Linux programs OGC-WPS services
  2. Several formats: NetCDF, WFS, WCS, Esri-Grid, GeoTiff – CSV, RDB - DwC
  3. The App Login form Username / Password Select a Fishery survey Behind the screens User must be registered to infrastructure At infra level users are assigned surveys
  4. FishFinder - FAO Aquatic Species fact sheets DIAS - FAO Database on Introductions of Aquatic Species FIRMS - Marine Resource and Fishery fact sheets ABNJ - Deep seas SEAFO interactive mapping pilot
  5. Today important societal challenges raise questions that are not restricted to a specific sector or discipline. Nevertheless, most of the data and knowledge exchange still occurs in “silos”. BlueBRIDGE fills this gap, by simplifying sharing and re-use of knowledge produced in these sectors and facilitating collaboration among their actors on relevant societal challenges or community specific questions. In particular, BlueBRIDGE supports activities contributing to the H2020 Blue Growth Societal challenge with a strong focus on sustainablegrowth. Trasform the research data in societal data