WP2: Access to marine biological data | Emodnet Biology

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WP2: Access to marine biological data


+          MBA, IEO, IMR, Aarhus University, SYKE, MARIS, NIMRD, IPMA, SMHI, ICES, Deltares, IH Cantabria, Ifremer, NIOZ, OGS, ILVO, UkrSCES



The main objective for WP2 is covered in Task 1: Maintain and improve a common method of access to data held in repositories. The data covered by this proposal will primarily include the following groups, macroalgae, angiosperms, benthos, birds, fish, mammals, phytoplankton and zooplankton in European seas, more specifically defined in six regions: Arctic, Atlantic, Baltic Sea, Black Sea, Mediterranean Sea and North Sea, including their coastal and estuarine zones. Data from other regions is also covered within the proposal, even though it is not the main focus of the work proposed.

WP2 will continue to use the standards, vocabularies and data formats from the previous phases, thereby providing consistency and ensuring interoperability for providers and users:

  • The OBIS-ENV Darwin Core (DwC) format, not only allowing the inclusion of presence/absence data of marine biodiversity data, but also the storage of additional measurements or facts sampled alongside with the biological sampling
  • The World Register of Marine Species (WoRMS), the authoritative and comprehensive list of names of marine organisms worldwide
  • The Marine Regions Gazetteer, a standard list of marine georeferenced place names and areas
  • The BODC controlled vocabularies, lists of standardised terms that cover a broad spectrum of disciplines of relevance to the oceanographic and wider community

All of the above allow interoperability with other systems - e.g. the Ocean Biodiversity Information System (OBIS) and the Global Biodiversity Information Facility (GBIF) - and remove ambiguities when interpreting the data.


Methodology & activities

The data management activities will focus on an even further automatisation of the data flow, compared to what is accomplished in Phase III. This will be suitable for all partners and sub-contractor that already generate DarwinCore files in a (semi-)automated way based on their local structured and maintained databases. All partners/sub-contractor are required to adhere to the DarwinCore EventCore format, which was introduced in the previous phase. Where partners/sub-contractor have absence data available for datasets that were submitted in previous phases, they will be encouraged to update their data with this additional information, which has a high value for the creation of data products in relation to time-evolution and migration.

The online training course developed in the framework of the data training for data grant partners in Phase III will be kept available online through the OceanTeacher platform and – whenever relevant and needed – updates will be made. All partners and sub-contractor have access to this information and they are urged to take the necessary time to get familiar with the content of this course, especially when they are new to the project, or new staff members have joined. Another aim for this online course is to improve the data literacy of not only data providers but also users.

When providing new data or updates to existing data through IPT, each partner/sub-contractor will use the online available tools to quality check the data they provide, and – to the highest extent possible – implement the agreed standards, including the linkage with the BODC controlled vocabularies for the Extended Measurements or Facts (eMoF) data they provide. Partners and sub-contractor providing their data through web-services or other channels will be advised by the Data Management Team on best practices for quality control, standardisation and the use of controlled vocabularies.

Based on the results of the questionnaire to identify historical data within the Consortium, a selection of these resources will be made available to volunteers for digitisation. Within the feasibility for recognition of specific ecological traits and/or sampling devices/methodologies in text, dictionaries will drive such term recognition process. The dictionaries will be created semi-automatically, based on existing collections of terms (e.g. control vocabularies).


Output (Deliverables)

  • D 2.1. Inventory of possible historical data resources within the consortium (M6)
  • D 2.2. Technical implementation of data flows for the new project partners/sub-contractor (M6)
  • D 2.3. Report on efforts undertaken in rescuing historical data through citizen science (M18)
  • D 2.4. At least 3 linkages with databases/initiatives outside of the original Consortium, resulting in extra data/information available via the Portal (M20)
  • D2.5:  Feasibility study for recognition of specific ecological traits and/or sampling devices/methodologies in text (M22)
  • D 2.6. Report on the standardisation and integration of the proposed new and updated datasets (M24)


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