1. iME4a. iMarine Tuna AtlasiME4a. iMarine Tuna Atlas
Marc Taconet/ Anton Ellenbroek / Yann Laurent
FAO Fisheries Department

2. Tuna Atlas Presentation Outline
• Collection
• Harmonization
• Aggregation
Tuna Atlas Use Case
Tuna Atlas Data Analysis and Dissemination
• Code List Management
• Mapping and mapping; COTRIX and GEO-Explorer
• Statistical data analysis
• Spatial Data Analysis; SPREAD,
• Storage and Dissemination; SDMX and FIRMS
Tuna Atlas Data Analysis and Dissemination
The results and summary
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3. The Tuna Atlas use case
the seascape
Collection
HarmonizationHarmonization
Aggregation
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4. Data Collection
• FAO compiles global tuna nominal catches and
tuna and billfishes catches (Tuna Atlas) annually
• Loaded in a database and published on-line:
– http://www.fao.org/fishery/statistics/tuna-
catches/query/en
Tuna Atlas Use Case
catches/query/en
– http://www.fao.org/figis/geoserver/tunaatlas/
• Mostly manual, data sources are heterogeneous
• How can a VRE improve the collection process?
4iMarine Event - 7 March 2014 – FAO-Rome

5. • Convert to one standard structure
• Validate against reference standards (e.g. to
the ISSCFG / ASFIS / ISO Country code list)
• Validate content (errors, gaps, formats)
Time series harmonization
Tuna Atlas Use Case
• Validate content (errors, gaps, formats)
• Reconcile with existing data (older)
• How can a VRE produce Harmonized time
series for analysis, mapping, publication
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6. Aggregation; much more than the sum of
all contributions, about more than data
• Policy framework for co-management of tuna data:
– based on Open Access / Open Data
– sharing data, tools and processes
– software development policies
– Guidelines and best practices
Tuna Atlas Use Case
– Guidelines and best practices
• Policy for governance
– Any institution can become an iMarine partner
– Everybody can be part of the development of the tools
• How can a VRE deliver flexible interoperable data
sharing; FIRMS aggregation offers leading examples
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7. Harmonization
• Code list management
• Data validation
• Shared formats (SDMX)
• Shared standard (FLUX)
Aggregation
• Fisheries
• Vessel data
• Occurrences
TA Requirements summary
Tabular Data
Policy
Guidelines
Best Practices
AnalysisAnalysis
• Time series
Sharing
• Repositories
• Open Data
• Fact sheets
• Print
Collection
• Multiple formats
• Multiple structure
• Multiple domains
• Data quality MapDisplay
• GeoCode
• Store and
synchronize
• Project and share
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Tabular Data
Management
• Time series
trends
• Forecasting
• Modelling

8. Tuna Atlas Data Analysis and Dissemination
a selection of components
Code list management
Mapping
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Spatial Data Reallocation
Analysis
Storage and dissemination

9. iMarine network of interoperable,
managed and shared resources
TA Analysis and Dissemination components
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10. • TabMan supports the validation and
harmonization of tuna atlas data
– Code list manager exposes reference data
(including from RFMO)
TA Analysis and Dissemination components
Code list management
(including from RFMO)
– Code lists are easily added in a VRE
• COTRIX will extend this support
– Manage code lists ‘outside’ iMarine
– Living apart together …. Remote but integrated
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11. Cotrix
Import Manage, Publish Code Lists
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12. TA Analysis and Dissemination components
Mapping 1 Link data: Occurrences Enrichment
Associate Environmental information to a set of
occurrence points of a species using their code lists
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13. Spatial Data Reallocation
Reallocate from FAO areas to EEZ
Change spatial resolution and precision of
capture data to better understand fisheries
• SPREAD use FIGIS geospatial data• SPREAD use FIGIS geospatial data
infrastructure and iMarine
• Spread data processing with WPS using
Terradue resources
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14. SPREAD WPS
Select an external process
Remote resource
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Parameters for the re-allocation

15. Statistical Manager; Operators
E.g to extract indicators from Tuna Atlas data
• Tabular data can be processed to e.g.:
– Spatial data reallocation => SPREAD
– (Species) name reconciliation => BiOnym
TA Analysis and Dissemination components
– Trend Analysis => Trendylyzer
– Bayesian modelling => FishBayes
• Select your algorithm and resources
– Algorithms can be predefined, or bring your own
– Resources in infra, remote, or cloud
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16. Integrating WPS with the D4Science e-Infrastructure
D4Science
Information
System
Other
D4Science
Facilities
E.g. Storage, Social, Geo
AnotherAnother
External (Cloud)
Computing
Facility
WPS
Interface
WPS
Interface
Statistical
Manager
Services
Statistical
Manager
Services
D4Science
Cloud Computing
User
System
User’s Data
101010101
Processing
WPS

17. Example: Occurrence Enrichment
Occurrences Table with
fields indication
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User-defined spatial
resolution for the projection
Layers: inputs can be i-Marine Geonetwork
UUIDs or Titles, or direct external HTTP links to
files
Supported Formats: WCS, WFS, NetCDF, ASC,
GeoTiffs
Names of the environmental features

18. Mapping; Project aggregated data
Effort indicator
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19. • SDMX one option for Tuna Atlas data storage
• Tabular data are easily exposed as Open Data (as
Chimaera will explain)
• Tabular data can be extracted and included in
iMarine infrastructure Components
Data storage, sharing and dissemination
Example options
• Tabular data can be extracted and included in
iMarine information fact-sheets such as VME-DB
• Tabular data can be displayed on maps and
stored as map-products
• Dissemination can also rely on sharing through
mail, the workspace, or download
19

20. Not a product, but an advanced solution
• A pool of tools:
– SDMX format / registry to facilitate data exchange
– An opportunity to access a rich library of integrated tools
• Time series presented as Graphs, Maps
• Code lists manager to share reference data and mapping
• Standard Mapping capacities
• R statistical capacities for advanced data analysis and processing
TA Results
• R statistical capacities for advanced data analysis and processing
• Access to remote data (write your own plugin)
– Environmental
– Biodiversity
– Fisheries
• A Collaborative managed infrastructure
– Policies, basic best practices, and generic guidelines
20FIRMS SC8 – Rome – Feb 2013