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Neural network modelling of Baltic zooplankton abundances Citable as data publication Barth, A.; Herman, P.M.J.; (2018): Neural network modelling of Baltic zooplankton abundances. Marine Data Archive. https://doi.org/10.14284/381 Contact: Availability:  This dataset is licensed under a Creative Commons Attribution 4.0 International License.Description This data product is a series of gridded abundance maps for 40 zooplankton species from 2007 to 2013 in the Baltic Sea, based on a neural network analysis. As input data a combination of EMODnet Biology datasets were used, together with the environmental variables dissolved oxygen, salinity, temperature, chlorophyll concentration bathymetry and the distance from coast. Additionally the position (latitude and longitude) and the year are provided to the neural network. DIVAnd (n-dimensional Data-Interpolating Variational Analysis) and the neural network library Knet were used in this analysis. Scope Themes: Biology > Plankton > Zooplankton Keywords: Marine/Coastal, Zooplankton, ANE, Baltic Geographical coverage ANE, Baltic [Marine Regions] Temporal coverage 2007 - 2013 Related datasets Project EMODNETBIO III: European Marine Observation and Data Network- Biology III, more Publication Used in this dataset Yuret, D. (2016). Knet: beginning deep learning with 100 lines of Julia, in: NIPS 2016: Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain, December 5-10, 2016 . , more Barth, A. et al. (2014). divand-1.0: n-dimensional variational data analysis for ocean observations. Geosci. Model Dev. 7(1): 225-241. https://dx.doi.org/10.5194/gmd-7-225-2014, more Beckers, J.-M. et al. (2014). Approximate and efficient methods to assess error fields in spatial gridding with Data Interpolating Variational Analysis (DIVA). J. Atmos. Oceanic. Technol. 31(2): 515-530. https://dx.doi.org/10.1175/JTECH-D-13-00130.1, more Troupin, C. et al. (2012). Generation of analysis and consistent error fields using the Data Interpolating Variational Analysis (DIVA). Ocean Modelling 52-53: 90-101. https://dx.doi.org/10.1016/j.ocemod.2012.05.002, more URLs Dataset status: Completed Data type: Data products Metadatarecord created: 2019-05-10 Information last updated: 2022-06-02 | ||||
