Difference between revisions of "Statistical cluster"

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We are aware that statistics are just one, albeit important, facet in the iMarine decision-making processes. To facilitate this, not only components propose implementation actions, but also describe the background and the anticipated impact. We are keen to enter into discussion with other iMarine partners and iMarine supporting institutions. We invite other parties (Board partners, iMarine institutions and CoP) to express their needs.
 
We are aware that statistics are just one, albeit important, facet in the iMarine decision-making processes. To facilitate this, not only components propose implementation actions, but also describe the background and the anticipated impact. We are keen to enter into discussion with other iMarine partners and iMarine supporting institutions. We invite other parties (Board partners, iMarine institutions and CoP) to express their needs.
  
== Components ==
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== The Statistical Cluster Work Plan ==
The ensemble of components constituting the statistical cluster can be summarized as:
+
  
* ICIS
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* Abstract or Executive Summary;
* [[CodelistManager]]
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* Introduction and Background (The Problems);
* Cotrix / VocBench
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* Statistical service
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* R
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* SDMX registry
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== Generic CodelistManager ==
 
  
On the left of below image you see the horizontal physical layers of the architecture. Vertically the usecases/functions.
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=== Goals and Objectives (The Outputs) ===
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[[File:CMArchitecture.png]]
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On the right you see that the codelist manager(CM) consists of n modules. All modules use the core. One module implements one or more usecases. A module would ideally be packed as a portlet, running in a portletcontainer.
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=== Resources and Constraints (The Inputs) ===
<!-- ;<font color="green">Leonardo Candela (CNR) comment: I'm a bit confused here ... why a protletcontainer? Moreover, there is no relationship between the layers and the core, eg what the core offers? I would expect to have a sort of functional specification to actually discuss this proposal.</font> -->
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'''''The different partners in the project are: iMarine, MDM (FAO) and the BoI'''''. Estimation of commonalities (for each partner as percentage of the total software stack):
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=== Strategy and Actions (from Inputs to Outputs) ===
ui 60%, service 70%, dao 80%, domain 90%.
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<!-- ;<font color="green">Leonardo Candela (CNR) comment: what project are we referring to? What are the contributions of the partners? What are the partners timeline?</font> -->
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<xh3>Project plan</xh3>
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=== Appendices (Budget, Resources, Documents, Schedule and Others) ===
* Now – December 2012:
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** The '''''iMarine project designs and develops the CM'''''.
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** '''''BoI and MDM will advise and deliver requirements'''''.
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* December 2012:
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** '''''BoI and/or MDM decide to prolong their participation or to start building their own CM'''''.
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** The '''''iMarine project will continue in any case with the by them existing CM.'''''
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The CM is an autonomous open source project, hosted on SourceForge. From the very beginning, a user interface designer is involved.
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<!-- ;<font color="green">Leonardo Candela (CNR) comment: how this plan fits with the iMarine goal?</font> -->
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<xh4>Strengths</xh4>
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* The mix of partners guarantees the right input on requirements, domain, conceptual and technical level.
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* Even a worst case scenario will result in a win-win situation for each partner.
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<xh4>Risks</xh4>
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* BoI uses Oracle, iMarine uses Postgres. It is not clear whether a database transparent approach can be agreed upon between all partners.
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* Designing a modular and agile architecture as shown above is a precondition for success and requires skilled engineers with an agile and collaborative mindset. It is not clear whether all partners have engineers with such a skillset.
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* BoI does not use currently a portletcontainer. It is not clear whether the BoI can be convinced to use the Liferay PortletContainer (as is used in the D4Science infrastructure).
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<xh4>Perspective</xh4>
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* This project can never fail, even if the BoI and/or MDM are deciding to drop out in December 2012. In December 2012, all partners will have as a starting point:
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** a well engineered domain model
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** a well engineered architecture
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* a working application to develop further on, to eventually converge from.
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* There is a good chance that there will be no drop outs in December 2012 because all partners seem to have the right mindset, need more or less the same solution and are willing to collaborate.
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* The role of BoI is informal in the iMarine project because they are not an official iMarine project partner. Their involvement is crucial because of their expertise and maturity level on the subject.
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== OpenSDMX CodelistManager ==
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==== The Overall Flow of the Statistical cluster Work Plan ====
 +
The work plan consists of a main text and appendices. The appendices may include budgets, agreements, external resources, data formats, etc. They are put into appendices at the end of the work plan, as they do not form part of the argument.
  
The further development of OpenSDMX in the iMarine project context aims to position OpenSDMX, and thus the iMarine project, as
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=== Components ===
# a supplier of services to other SDMX infrastructures, or
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# a range of services that can interpret SDMX, e.g. by offering SDMX data access and processing services.
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Where using the word D4Science, the D4Science Infrastructure is meant, which is used in the iMarine project.
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The ensemble of components constituting the statistical cluster can be summarized as:
 
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This document starts with describing premises and the SDMX Scoping, followed by the proposed functions to implement:
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* CodelistManager
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* Validation/Curation
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* Artifact Selector
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* Data Visualization
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* SDMX2RDF
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In a related context, CNR has identified the SDMX processing as an opportunity to pursue. The use cases for data-mining and transformation that could benefit from the processing services are not described here. 
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'''Premises'''
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Adopting OpenSDMX is lightweight. Clients may be reluctant to adopt D4Science. They may be exposed to D4Science capabilities through OpenSDMX, and consider to migrate services from OpenSDMX to D4Science.  Doing so, OpenSDMX can be a cost-effective enabler for D4Science, getting its clients familiar with D4Science.  Therefore these premises are defined:
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* OpenSDMX does not have a dependency with the D4Science infrastructure.
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* All OpenSDMX artefacts are portable into the D4Science infrastructure.
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* Developments are done in the context of the OpenSDMX community, directly on the OpenSDMX codebase and follow the OpenSDMX release lifecycle.
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'''Scoping'''
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The SDMX specification defines these artefacts:  datastructure (DSD), metadatastructure, categoryscheme, conceptscheme, codelist, hierarchicalcodelist, organisationscheme, agencyscheme, dataproviderscheme, dataconsumerscheme, organisationunitscheme, dataflow, metadataflow, reportingtaxonomy, provisionagreement, structureset, process, categorisation, contentconstraint, attachmentconstraint, structure, metadata, schema, data
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The artefacts written in bold are selected to be part of the iMarine project at this stage (datastructure, conceptscheme, organisationscheme,  codelist, dataflow and data).
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The artifact process can be further discussed to be taken on board or not. On the long term this one is definitely need to be taken into account because it can reflect the process of data and metadata in the system. The Bank of Italy is using their proprietary Expression Language for this purpose. Adopting either in D4Science will require at least a MOU with a large ‘SDMX’ partner. It will not be discussed here. 
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OpenSDMX is divided in 2 parts, core and plus. OpenSDMX-Core is the implementation of the SDMX REST specification with the concept of adapters. OpenSDMX-Plus contains all the functions which are additions to core, like CodelistManager, Validation, Artefact Selector, SDMX2RDF and DataVisualization.
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See the diagram below for the dependencies of the different software components and how the components relate together.
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[[File:FishFrame2Sdmx.PNG]]
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'''CodelistManager'''
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Functions distinguished for a CodelistManager are:
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* Maintenance (adding, changing or deleting codes and/or descriptions)
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* Importing from CSV/SDMX / RDF / FishFrame
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* Versioning
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* Publishing (of a new version)
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* Validity (Where when for who is it authoritative / reference / candidate)
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Possible contexts in which these functions need to be performed are
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* The codelist is already stored in an existing datastore (a datastore can be a database or a data access layer):
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** All functions are performed on this datastore (A).
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** An initial codelist is loaded from the datastore and will be copied in the CodelistManager.  The subsequent lifecycle will happen in the CodelistManager (B).
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* The codelist is a file. The file is loaded in the CodelistManager. Most of functions are performed in the CodelistManager. Additions of codes may happen by uploading new codes for the Codelist (B).
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Impact of Option A: the OpenSDMX instance does not have its own database.
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Impact of option B, the OpenSDMX instance does have its own database.
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'''Validation/Curation'''
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Vision on this has been worked out already here:
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http://opensdmxdevelopers.wikispaces.com/Curation
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The discussion on the level of validation in the context of SDMX is currently led by Eurostat. Involved parties are the Bank of Italy, Metadata Technology, Agillis and FAO. Apart from the precise outcome of these discussions, it is clear that there is a need for an infrastructure which can load/cure/validate SDMX datasets.
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'''SDMX Artefact Selector'''
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This scenario is inspired by my interpretation of the data.fao.org principle:
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* Guide the user to the data or metadata in a highly user friendly and pleasant way
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* Give the data to the user
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* So the user can go away to do whatever he wants to do with the data.
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The data.fao.org offers a simple way to find SDMX data, using the SDMX REST API. There is a need for a user interface which leads the user in a simple way to the SDMX data and metadata.
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The SDMX Artefact Selector could also be called a SDMX Registry and Repository Browser.
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'''SDMX2RDF'''
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http://publishing-statistical-data.googlecode.com/svn/trunk/specs/src/main/html/index.html
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There is an interesting group working on the transformation of the SDMX model into RDF. This work can be adopted in order to publish SDMX datasets also in RDF.
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+
 
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'''SDMX Data Visualization'''
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In order to make data visible and findable for search engines, an user interface is needed to visualize the SDMX artefacts. The first artifact to visualize is the SDMX dataset. The DSD can be used to express the data in the different languages.
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'''FishFrame2SDMX'''
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FishFrame is an upcoming standard for data collection and dissemination in the Fisheries domain, read more here: http://km.fao.org/FIGISwiki/index.php/FishFrame
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In addition, a dedicated section in this Cluster page outlines the FishFrame plans in iMarine.
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IRD is using FishFrame for data dissemination. FishFrame as a standard and intention is similar to SDMX, however only for the Fisheries domain. IRD advised that a conversion from FishFrame to SDMX makes more sense than the other way around.  Rational behind is that it is important to publish the FishFrame format according standards like SDMX, accepted outside the Fisheries community. Conversion from FishFrame to SDMX will also result in having a profound understanding how the two standards relate together. This is highly valuable knowledge for the iMarine project. Conversion from SDMX to FishFrame is not planned yet, however not excluded on the long term.
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The picture below shows the position of the FishFrame2SMX converter:  
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[[File:FishFrame2Sdmx.PNG]]
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The converter will generate SDMX codelists, datastructures and datasets.
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FishFrame does not have a dissemination protocol like the SDMX REST API.  OpenSDMX implements this protocol and the converter can be packaged as an adapter in order to publish FishFrame as SDMX artefacts through the SDMX REST API.
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[[File:OpenSdmxArtifactFishframe.PNG]]
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The above pattern can be applied one to one in D4Science.
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'''SDMX&JSON'''
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SDMX datasets can be voluminous for large amount of data. This is a pure technical problem and can be solved. The SDMX community is working on JSON, but this is yet not really being picked up. The work to be done is described here:
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* ICIS - The Data Suite offering data management, analysis and production facilities;
http://opensdmxdevelopers.wikispaces.com/Statistical+System+Architecture+Patterns#JSON
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* [[CodelistManager]] - The shared development of Cotrix, acting as a persistent storage 'agnostic' facility;
 +
* Statistical service - The iMarine Specific container and work flow organizer to bring the power of the infra to scientific users;
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* R - The interface to the tool of preference of the EA-CoP; either as an integrated tool in ICIS, or as a service in a WPS Hadoop process;
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* SDMX registry - The persistence and user orchestrator of the statistical products;
 +
* FLUX - TBD.

Revision as of 18:43, 15 March 2013

Position

The management of statistical data is a large domain, and ranges from the collection of observations on species occurrences or capture, the curation and aggregation of data, the visualization on maps, and the visual and statistical analysis of both observations and time-series. It requires import of structured data in various formats, with an emphasis on SDMX datasets. The purpose of the cluster is to produce a low-cost, versatile and reliable data-suite to cover the work-flow of data from collection to publication and to manage an appropriate set of metadata on dataset describing e.g. the provenance, ownership, and quality.

Compared to other initiatives, iMarine already offers the basic components to load, share, publish and analyze data. This makes the iMarine infrastructure an attractive option for the further development of statistical data components. In addition, the powerful services for data-processing are epected to offer substantial benefits to statistical data managers.

Many data owners in the marine statistical domain have difficulty in gaining consistent access to capture data in enough detail and with relevant metadata. There are concerns about the sheer number of datasets that have to be maintained, with multiple data streams and formats putting pressure on software developers. Concerns about the interoperability of software and the related risk of exploding support costs for software maintenance make OS development in a CoPCommunity of Practice. a potentially attractive proposition.

This cluster can build on the considerable experience that has been gained in the acquisition and management of data in previous projects. In addition, many services marshalling the data from “Sea to Shelf” are available; curation, metadata collection, transformation, mapping and repository services, to name a few. The iMarine Biodiversity partners aim is to provide a stronger, more resilient and flexible framework for Statistical Data Management. The EA-CoPCommunity of Practice. expects that services that are difficult to maintain in a single organization, such as for data mining, time-series analysis, and modeling, can be offered in a very cost-effective manner through iMarine. Collaboration in an Ecosystem Approach Community of PracticeA term coined to capture an "activity system" that includes individuals who are united in action and in the meaning that "action" has for them and for the larger collective. The communities of practice are "virtual", ''i.e.'', they are not formal structures, such as departments or project teams. Instead, these communities exist in the minds of their members, are glued together by the connections they have with each other, as well as by their specific shared problems or areas of interest. The generation of knowledge in communities of practice occurs when people participate in problem solving and share the knowledge necessary to solve the problems. can help to achieve that.

This collaboration needs to be based on reliable and free resources. Access to and maintenance of these resources is the responsability of all partners of the EA-CoPCommunity of Practice., and users of the supporting eInfrastructure will have to develop and commit to an open data policy.

An effective statistical data policy is an iMarine EA-CoPCommunity of Practice. policy; it needs to be defined and approved by the iMarine Board. After all, making clear, fair agreements on the component development of OS software and effectively enforcing these rules will increase political and CoPCommunity of Practice. support for iMarine. This proposal lists some of the components that can help achieve this.

We are aware that statistics are just one, albeit important, facet in the iMarine decision-making processes. To facilitate this, not only components propose implementation actions, but also describe the background and the anticipated impact. We are keen to enter into discussion with other iMarine partners and iMarine supporting institutions. We invite other parties (Board partners, iMarine institutions and CoPCommunity of Practice.) to express their needs.

The Statistical Cluster Work Plan

  • Abstract or Executive Summary;
  • Introduction and Background (The Problems);


Goals and Objectives (The Outputs)

Resources and Constraints (The Inputs)

Strategy and Actions (from Inputs to Outputs)

Appendices (Budget, Resources, Documents, Schedule and Others)

The Overall Flow of the Statistical cluster Work Plan

The work plan consists of a main text and appendices. The appendices may include budgets, agreements, external resources, data formats, etc. They are put into appendices at the end of the work plan, as they do not form part of the argument.

Components

The ensemble of components constituting the statistical cluster can be summarized as:

  • ICIS - The Data Suite offering data management, analysis and production facilities;
  • CodelistManager - The shared development of Cotrix, acting as a persistent storage 'agnostic' facility;
  • Statistical service - The iMarine Specific container and work flow organizer to bring the power of the infra to scientific users;
  • R - The interface to the tool of preference of the EA-CoPCommunity of Practice.; either as an integrated tool in ICIS, or as a service in a WPS Hadoop process;
  • SDMX registry - The persistence and user orchestrator of the statistical products;
  • FLUX - TBD.