Potentialités agronomiques par culture (blé, haricot, maïs, orge, tabac et tournesol).

This data product selects sample areas of digital bathymetry, chosen for their relevance to marine activities and data sources alternative to GEBCO. The approach for building the digital map of water depth is to use GEBCO as a baseline and look at a set of sample areas where GEBCO could be improved upon. Sample areas have also been selected to be representative of each continent bordering the Atlantic and expected future requirements. Data sources include GEBCO, EMODNET, USGS and CHS.

Dans un contexte marqué par de fortes mutations de l’environnement économique et social, le ROME (Répertoire Opérationnel des Métiers et des Emplois) est un outil au service de la mobilité professionnelle et du rapprochement entre offres et candidats. En décembre 2016, les référentiels de compétences du ROME évoluent afin d'améliorer la transversalité lors du rapprochement entre l'offre et la demande. Cette évolution consiste à : • réorganiser les compétences en savoir-faire et savoirs • reformuler les libellés en les simplifiant et les décontextualisant. --- Etat de la disponibilité de la donnée : - Data.gouv : donnée téléchargeable au format excel et mise à jour trimestrielle.

Data from a number of different sources have been integrated to provide new perspectives on fishing activities. Vessel Monitoring Systems (VMS) record and transmit the position and speed of fishing vessels at intervals of two hours or less. Fishing time can be calculated from the VMS data and combining this parameter with vessel logbook data, maps of fishing effort and intensity at different spatial and temporal scales can be calculated. The statistical software package “R” is used to extract the required information then re-interrogated to produce maps of fishing effort or intensity per month and year. The use of Automatic Identification System (AIS) data was not considered as combining AIS data with fisheries logbook data would pose issues namely; the ability of the AIS system to be switched off, only mandatory on vessels > 15 meters in length, cost involved to purchase data, and confidentiality.

L'État a confié à l'IGN le développement du référentiel à grande échelle (RGE). Pour ce faire, il fait appel à ses moyens propres ainsi qu’à des partenariats avec des producteurs principalement de la sphère publique. Le RGE est constitué de 5 composantes: - orthophotographique (BD ORTHO), - topographique (BD TOPO), - adresse (BD adresse), - parcellaire (BD parcellaire) - et altimétrique (RGE ALTI). Remarque : seules les BD TOPO thème hydrographique, BD ORTHO 5m, BD ORTHO 50 cm sont sous Licence Ouverte. Les autres données du RGE sont soumises aux licences IGN. ----- Etat de la disponibilité de la donnée (sous Licence Ouverte): - Data.gouv : les BD TOPO thème hydrographique, BD ORTHO 5m, BD ORTHO 50 cm (pour certains départements) sont accessibles sous format shape, xml et htlm. Mise à jour irrégulière. - PIGMA : données BD ORTHO 50 cm (2015) et BD TOPO v2.2 (2016) accessibles au format shape sous licence IGN. Aucune mise à jour. - Géocatalogue : données BD ORTHO v2 (2013) au format Tif et BD TOPO v2.1 (2015) au format shape, MIF/MID accessibles sous licence IGN. Aucune mise à jour. - IGN : renvoi direct au site IGN où les données sont téléchargeables au format JPEG et shape sous licence IGN. Mise à jour irrégulière.

La Base Adresse Nationale est une base de données qui a pour but de référencer l'intégralité des adresses du territoire français. Elle contient la position géographique de plus de 25 millions d'adresses. Elle est constituée par la collaboration entre : des acteurs nationaux tels que l'IGN, La Poste et La Poste ; des acteurs locaux tels que les collectivités, les communes, les SDIS  ; des citoyens par exemple à travers le projet OpenStreetMap et l'association OpenStreetMap France. Le projet est co-gouverné par l'Administrateur Général des Données et le Conseil National de l'Information Géographique. --- Etat de disponibilité de la donnée : Data.gouv : donnée téléchargeable en csv et mise à jour hebdomadaire. OpenStreetMap : donnée téléchargeable en csv, shape et json et mise à jour hebdomadaire. PIGMA : lien qui renvoie à la BD Adresse (Octobre 2016). Mise à jour irrégulière.

SeaDataNet gridded climatologies are based on the SeaDataNet Temperature and Salinity historical data collection v1.1. For the Atlantic Ocean there are covering 2 European sea basins: North Arctic Ocean, and North Atlantic Ocean The preparation of the products has also improved the quality, the consistency and the overall coherence of the data made available by SeaDataNet. They have been computed using DIVA software.

We took inspiration from a “Matrix of marine activities” (appropriate for each IUCN management category) extracted from IUCN paper, to achieve the first objective by computing 1 product comprising the following 12 components: Product ATLANTIC_CH02_Product_1 / MPA Atlantic network classified in IUCN classification • Traditional fishing area • Sustainable fishing area (industrial) • Leisure fishing area • Leisure activity area (diving, surfing, tourist beaches) • Shipping area (shipping trajectory, aids navigation) • Scientific activity area • Renewable energy generation facility area (ocean energy facilities, wind farms) • Aquaculture area (finfish production, shellfish production) • Shipping infrastructure area (harbours, dredging area...) • Waste discharge area • Mining area (aggregate extraction, hydrocarbon extraction) • Habitation area (urban area) Each geographic information required for the components was compiled into a layer in grid format. These grids were intersected with the MPAs layer to assign each MPA a IUCN category according to the conditional matrix illustrated below : If the MPA area contains : Habitation area (urban area) The IUCN category is :V If the MPA area contains : Mining area (aggregate extraction, hydrocarbon extraction) The IUCN category is V If the MPA area contains : Waste discharge area The IUCN category is : V If the MPA area contains : Shipping infrastructure area (harbours, dredging area...) The IUCN category is IV If the MPA area contains : Aquaculture area (finfish production, shellfish production) The IUCN category is IV If the MPA area contains : Renewable energy generation facility area (ocean energy facilities, wind farms) The IUCN category is IV If the MPA area contains : Leisure fishing area The IUCN category is IV If the MPA area contains : Sustainable fishing area (industrial) The IUCN category is IV If the MPA area contains : Shipping area (shipping trajectory, aids navigation) The IUCN category is II If the MPA area contains : Leisure activity area (diving, surfing, tourist beaches) The IUCN category is Ib If the MPA area contains : Traditional fishing area The IUCN category is Ib If the MPA area contains : Scientific activity area The IUCN category is Ia

The in-situ TAC integrates and quality control in a homogeneous manner in situ data from outside Copernicus Marine Environment Monitoring Service (CMEMS) data providers to fit the needs of internal and external users. It provides access to integrated datasets of core parameters for initialization, forcing, assimilation and validation of ocean numerical models which are used for forecasting, analysis and re-analysis of ocean physical and biogeochemical conditions. The in-situ TAC comprises a global in-situ centre and 6 regional in-situ centres (one for each EuroGOOS ROOSs). The focus of the CMEMS in-situ TAC is on parameters that are presently necessary for Copernicus Monitoring and Forecasting Centres namely temperature, salinity, sea level, current, waves, chlorophyll / fluorescence, oxygen and nutrients. The initial focus has been on observations from autonomous observatories at sea (e.g. floats, buoys, gliders, ferrybox, drifters, and ships of opportunity). The second objective was to integrate products over the past 25 to 50 years for re-analysis purposes... Gathering data from outsider organisations requires strong mutual agreements. Integrating data into ONE data base requires strong format standard definition and quality control procedures. The complexity of handling in situ observation depends not only on the wide range of sensors that have been used to acquire them but, in addition to that, the different operational behaviour of the platforms (i.e vessels allow on board human supervision, while the supervision of others should be put off until recovering or message/ping reception)°

The three digital maps provided in this product aim to assess the degree of Offshore windfarm siting suitability existing over a geographical area with a focal point where waters of France and Spain meet in Biscay Bay on 500 m depth. The maps display respectively the spatial distribution of the average and lowest windfarm siting suitability scores along with the average wind speed distribution over a time period of 10 years. They are part of a process set up to assess the fit for use quality of the currently available datasets to support a preliminary selection of potential offshore sites for wind energy development. To build these maps, GIS tools were applied to several key spatial datasets from the 5 data type domains considered in the project: Air, Marine Water, Riverbed/Seabed, Biota/Biology and Human Activities, collated during the initial stages of the project. Initially, each selected dataset was formatted and clipped to the study area extent and spatially classified according to suitability scores, to define raster layers with the variables depicting levels of current anthropogenic and environmental spatial occupation of activities, seabed depth and slope, distances to shoreline, shipping intensity, mean significant wave height, and substrate type. These pre-processed layers were employed as inputs for applying a spatial multi-criteria model using a wind farming suitability classification based on a discrete 5 grades index, ranging from Very Low up to Very High suitability. In adition to suitability maps, an average wind speed spatial distribution map for a 10 years period, at 10 m height, was obtained over the study area from the raster processing of a wind speed time series of monthly means available from daily wind analysis data. The characteristics of the datasets used in this exercise underwent an appropriateness evaluation procedure based on a comparison between their measured quality and those specified for the product. The study area, located in the Biscay Golf includes a coastal zone of Spain and France. Consequently, some zones are subject to constraints to offshore windfarm implementation due to environmental protection, visual impacts and seafoor attributes. Data gaps exist with an emphasis on fishing activity and distribution of essential habitats and species. All the spatial information made available in these maps and from the subsequent appropriateness analysis of the datasets, contributes to a clearer overview of the amount of public-access baseline knowledge currently existing for the North Atlantic basin area.