World Ocean Atlas 2018 (WOA18) is a set of objectively analyzed (one degree grid and quarter degree grid) climatological fields of in situ temperature, salinity, dissolved oxygen, Apparent Oxygen Utilization (AOU), percent oxygen saturation, phosphate, silicate, and nitrate at standard depth levels for annual, seasonal, and monthly compositing periods for the World Ocean. Quarter degree fields are for temperature and salinity only. It also includes associated statistical fields of observed oceanographic profile data interpolated to standard depth levels on quarter degree, one degree, and five degree grids. Temperature and salinity fields are available for six decades (1955-1964, 1965-1974, 1975-1984, 1985-1994, 1995-2004, and 2005-2017) an average of all decades representing the period 1955-2017, as well as a thirty year "climate normal" period 1981-2010. Oxygen fields (as well as AOU and percent oxygen saturation) are available using all quality controlled data 1960-2017, nutrient fields using all quality controlled data from the entire sampling period 1878-2017. This accession is a product generated by the National Centers for Environmental Information's (NCEI) Ocean Climate Laboratory Team. The analyses are derived from the NCEI World Ocean Database 2018.

Cartographie historique des écluses à poissons sur l'île de Ré.

This data set presents the resulting assessment grid (based on the EEA reference grid) with the classification of chemical status of the transitional, coastal and marine waters in the context of the Water Framework Directive (WFD) and the Marine Strategy Framework Directive (MSFD), providing a mapping of contamination 'problem areas' and 'non-problem areas' based on measurements of biological effects. This classification has been performed using the CHASE+ tool, with classifications of the of contaminant status of indicators of biological effects. The status is evaluated in five classes, where NPAhigh and NPAgood are recognised as ‘non-problem areas’ and PAmoderate, PApoor and PAbad are recognised as ‘problem areas’. Monitoring biological effects is restricted to a few indicators (e.g. imposex) and data coverage is currently limited. Biological effects have thus been addressed in only 134 assessment units, mostly in the Baltic Sea, the North Sea and the North-East Atlantic Ocean. This data set underpins the findings and cartographic representations published in the EEA report “Contaminants in Europe’s seas” (No 25/2018). See the mentioned report for further information.

Ce jeu de données permet d'accéder aux 9 millions d'entreprises et 10 millions d'établissements actifs du répertoire Sirene de l'Insee qui enregistre quotidiennement leur état civil : quelle que soit leur forme juridique ; quel que soit leur secteur d'activité (industriels, commerçants, artisans, professions libérales, agriculteurs, collectivités territoriales, banques, assurances, associations...) ; situés en France métropolitaine, ainsi qu'en Guadeloupe, Martinique, Guyane, La Réunion, Mayotte, Saint-Barthélemy, Saint-Martin et Saint-Pierre-et-Miquelon. Les organismes publics ou privés et les entreprises étrangères qui ont une représentation ou une activité en France y sont également répertoriés. Le répertoire Sirene est ainsi la principale source exhaustive sur l'ensemble des entreprises et des établissements actifs. --- Etat de disponibilité de la donnée: - Data.gouv : répertoire SIREN téléchargeable au format csv et mise à jour quotidienne. - PIGMA : donnée de 2009 au format shape. - Géocatalogue : métadonnée au format csv du 01/01/2016.

Combination MPAs and monitoring stations for biodiversity elements

This vector dataset represents the benthic broad habitat types in Europe Seas potentially affected by the ship wakes. When navigating, the propellers of ships generate a turbulent mixing of the water that can produce sediment re-suspension in soft bottoms of shallow areas. This can increase the turbidity in those areas, affecting the seafloor organisms (especially those that are directly dependent on light, such as aquatic plants). It may also contribute to an increase of the the eutrophication level of the ecosystem (since the turbid waters may become warmer, which may turn into a reduction of dissolved oxygen in water). Finally, turbidity can produce an increase in the inputs of contaminants and microbial pathogens, since those can become attached to the suspended solids. The dataset has been prepared in the context of the development of the first European Maritime Transport Environmental Report (https://www.eea.europa.eu/publications/maritime-transport).

Le document présente l'Observatoire des Espaces Naturels, Agricoles, Forestiers et Urbains (NAFU), dispositif où l'Etat et la Région s'unissent pour donner les moyens de prévoir et d'agir

Auteur(s): Sourgens Carole , Projet urbain de bâtiments (de logements?) s'insérant dans le réseau dense et complexe du quartier historique St-Pierre à Bordeaux

Ce jeu de données donne les intercommunalités sur le département de la Gironde.

This dataset comprises the global frequency, classification and distribution of marine heat waves (MHWs) from 1996-2020, in Chauhan et al. 2023 (https://doi.org/10.3389/fmars.2023.1177571). The classification was done based on their attributes and using different baselines. Daily SST values were extracted from the NOAA-OISST v2 high-resolution (0.25°) dataset from 1982-2020. MHWs were detected using the method presented by Hobday et al. 2016 (https://doi.org/10.1016/j.pocean.2015.12.014), and by using the 95th percentile of the accumulated temperature distribution to flag the extreme events. A shifting baseline of 8-year rolling period was selected between the years 1982-1996, since this period shows relatively stable maximum values of temperature across different ocean regions. The shifting baseline aims to account for the decadal changes of westerly winds, temperatures and ocean gyres circulations. The classification was done using the KMeans clustering algorithm to identify the relevant features of MHWs and classify them into separate groups based on feature similarities. This algorithm takes MHW features, namely duration, maximum intensity, rate onset and rate decline, as input vectors and applies clustering in the 4-dimensional feature space where each data point represents an MHW event. Note that all the MHWs features are standardized because unequal variances can put more weight on variables with smaller variances. This record comprehends the geospatial datasets of: Average number of MHW days per year (i.e., the sum of all MHW days divided by the total number of years, 1996-2020). Average cumulative intensity per year (i.e., the sum of cumulative intensity divided by the total number of years, 1996-2020). Total number of MHW events across the different periods averaged on the total number of years (1989-2020). The period 1982-1988 was only used as an initial baseline without calculating MHWs. Spatial distribution of three MHW categories: moderate MHWs, abrupt and Intense MHWs and extreme MHWs; displaying the total number of MHW days normalized by the number of years considered (i.e., 1989-2020). Distribution of Extreme MHWs across the different periods (A) 1989-1996, (B) 1997-2004, (C) 2005-2012, (D) 2013-2020. The relative frequency (γ) is a ratio of extreme MHWs in a specific period and all extreme MHWs in the same cluster for all periods.