Inauguration Event

Friday, 15th October 2021 at 1:00pm CEST


All times are in Central European Summer Time (CEST)
1:00pm Introduction to the Marine Data Literacy Course
(Aldo Drago, Aleksandra Dudkowska, Sally Close, Ralph Schneider, Alfredo Izquierdo Gonzalez, Jadranka Šepić)
1:15pm Interventions from the SEA-EU partner universities
  • University of Cádiz (Laura Howard)
  • University of Malta (Godfrey Baldacchino)
  • University of Kiel (Ralph Schneider)
  • Université de Bretagne Occidentale in Brest (Yves-Marie Paulet)
  • University of Gdańsk (Arnold Kłonczyński)
  • University of Split (Đurđica Miletić)
1:40pm Interventions from the course Faculty
  • University of Gdańsk (Aleksandra Cupiał)
  • University of Split (Hrvoje Kalinić)
1:50pm Interventions from the students
  • University of Malta (Rami Kalfouni)
  • University of Kiel (Stella Buchwald)
2:00pm Closure

Schedule for the 10 intro lectures

Lecture 1 Monday 18th October 2021 5:00pm-6:30pm (CEST)
Lecture 2 Wednesday 20th October 2021 5:00pm-6:00pm (CEST)
Lecture 3 Friday 22nd October 2021 5:00pm-6:00pm (CEST)
Lecture 4 Monday 25th October 2021 5:00pm-6:30pm (CEST)
Lecture 5 Wednesday 27th October 2021 5:00pm-6:30pm (CEST)
Lecture 6 Friday 29th October 2021 5:00pm-6:00pm (CEST)
Lecture 7 Monday 8th November 2021 5:00pm-6:30pm (CET)
Lecture 8 Wednesday 10th November 2021 5:00pm-6:30pm (CET)
Lecture 9 Monday 15th November 2021 5:00pm-6:30pm (CET)
Lecture 10 Wednesday 17th November 2021 5:00pm-6:30pm (CET)
Intro to practical sessions Friday 19th November 2021 5:00pm-6:30pm (CET)

Schedule for 8 practicals

All times are in Central European Time (CET)
Practical session 1 Tuesday 23rd November 2021 4:00pm-7:00pm
Practical session 2 Friday 26th November 2021 4:00pm-7:00pm
Preparatory session I for Project Mode Tuesday 30th November 2021 4:00pm-5:30pm
Practical session 3 Wednesday 1st December 2021 4:00pm-7:00pm
Practical session 4 Friday 3rd December 2021 4:00pm-7:00pm
Practical session 5 Tuesday 7th December 2021 4:00pm-7:00pm
Practical session 6 Friday 10th December 2021 4:00pm-7:00pm
Practical session 7 Monday 12th December 2021 4:00pm-7:00pm
Practical session 8 Wednesday 14th December 2021 4:00pm-7:00pm
Preparatory session II for Project Mode Thursday 16th December 2021 4:00pm-5:30pm


Introductory lectures banner
University Title Duration Key points of the lecture Resources
University of Malta
Aldo Drago
Introduction to Marine Data 1.5 hrs
  • Why do we need marine data?
  • How do we measure and model the sea?
  • Different types of marine data: physical, biogeochemical, ecological; models; observations (in situ + remote)
  • What is operational oceanography?
  • Technologies used to produce marine data
  • Temporal and spatial constraints in data collection and interpretation; how to measure and interpret data avoiding pitfalls
  • The value addition chain of data; downstream services and data product delivery
University of Cadiz
Alfredo Izquierdo
Reliable oceanographic data sources:  Met-ocean data sets: climate, reanalysis, forecast and in situ data 1 hr
  • Met-ocean data: types and characteristics
  • Types of products PHYS CMEMS
  • Types of products INSITU CMEMS
  • "Data lexicon": Quality information document and product user manual
Join lecture
IODE / EMODnet Peter Pissierssens
Jelle Rondelez
Pier-Luigi Buttigieg
Jan-Bart Calewaert
Concepts of Ocean Data Management 1 hr
  • Introduction to IODE and UN Ocean Decade and its data chapter
  • Introduction to the FAIR Guiding Principles for scientific data management and stewardship
  • Best practices and standards for management and analysis of marine data
  • Introduction to EMODnet
University of Malta
Adam Gauci
Online Data Portals 1.5 hrs
  • What oceanographic data is freely available? How can this be accessed?
  • Demonstration of professional online data interfaces to visualise in near-real-time ocean data products
  • Portals from where numeric data derived from in-situ measurements, remote sensing, and forecasting models, can be downloaded
  • Demonstration of visualisation software including Panoply, the Sentinel Application Toolbox (SNAP), and QGIS, that can be used to process downloaded data
  • Simple data processing techniques to added-value products (such as colour composites, vegetation and water quality indices, etc…) 
Université de Bretagne Occidentale
Sally Close
Accessing and transforming data 1.5 hrs
  • Brief introduction to web scraping and API
  • Brief introduction to data wrangling
  • Interacting with data servers part 1 (openDAP)
  • Introduction to cloud storage and cloud computing
  • Interacting with data servers part 2 (Zarr over HTTP)
  • Introduction to Pangeo, Binder, Google Colab
University of Cadiz
Jesús Gómez-Enri
Reliable oceanographic data sources:  Ocean Remote Sensing: Data source, downloading and software (SNAP) 1 hr
  • Data source for the blue ocean (SST, sea level, waves, etc.)
  • Data source of the green ocean (chlorophyll-a concentration)
  • First steps with Sentinel Data Hub for data downloading
  • First steps with Sentinel Application Platform (SNAP)
University of Malta
Joel Azzopardi
Applying AI to Oceanography 1.5 hrs
  • Introduction
  • What is Artificial Intelligence? Demystifying the hype surrounding AI
  • AI needs huge amounts of data to operate, and oceanographic data is readily available
  • Overview of different AI applications used in oceanography
CAU Kiel
Peer Kröger
Managing and Processing (Big) Scientific Data 1.5 hrs Managing (Big) Scientific Data
  • IT tools and systems
  • Relational Database Management Systems
    • Data model
    • Basic properties of transactions in RDMS
  • Geo-Information systems
    • Spatial, temporal, and spatio-temporal data models
    • NoSQL Databases
    • Data models
    • Cap-theorem (consistency and availability)
Processing (Big) Scientific Data
  • Data Preprocessing
  • Overview on Distributed Data Processing
    • Hadoop, map-reduce
    • Spark
    • Flink
University of Split
Hrvoje Kalinić
Introduction to learning algorithms, neural networks and clustering 1.5 hrs
  • What is unsupervised learning?
  • Distinction between clustering and classification
  • Cluster properties and basic approaches to cluster analysis
  • Examples of some algorithm and applications
  • What is supervised learning?
  • Distinction between classification and regression problem
  • Learning algorithms and why are the introduced
  • Learning as an optimization problem. Gradient descent. Local optimum
  • Linear separability and complexity of neural network architecture
  • Simple (shallow) neural network implementation
University of Gdansk
Aleksandra Dudkowska
Applying AI to Oceanography: case studies 1.5 hrs Case studies and demonstration of the different AI/ML models used in Oceanography


Practicals banner
University Title Duration Highlights on Practical
University of Malta
Adam Gauci & Aldo Drago
1. Model and satellite CMEMS Sea Surface Temperature data 3 hrs The Copernicus Marine Environment Monitoring Service (CMEMS) is an EU information service based on satellite earth observation and in-situ data. CMEMS provides state-of-the-art analyses and forecasts of oceanographic parameters which offer an unprecedented capability to observe, understand, and anticipate marine environment events. CMEMS offers unique access to oceanographic products through an online catalogue.  In this project the students will be shown how to:
  • Log in the Marine Copernicus Portal.
  • Download daily averaged sea surface temperature (SST) data from a high-resolution model that is available for ten consecutive days.
  • Download satellite observed SST data (L4) generated over the same domain and ten-day period.
  • Load and visualise the model and satellite netcdf files in Panoply.
  • Identify a grid cell in the model and satellite datasets, and extract the SST time series over the ten days being considered.
  • Generate a time series plot that overlays the SST predicted by the model and observed by the satellite over the ten-day period being considered.
  • Generate a scatter plot to visualize the correlation between the model and the satellite.
  • Compute the Mean Square Error between the model and satellite SST.
University of Split
Frano Matić & Jadranka Šepić
2. Sea-level time series: detecting processes, stationarity and trends 3 hrs Students will learn how to detect stationarity and trends in oceanographic time series. The analysis will be focused on sea-level time series.
  • Students will be introduced to various open access sea-level data repositories and given information on how to choose the best data for their research
  • Students will download sea-level data from the selected repository
  • Students will plot the sea-level data and visually assess it
  • Students will then be familiarized with different ocean processes detectable in sea-level time series and learn how to recognize and extract these processes by filtering procedures
  • Students will be introduced to basic statistical parameters, the normal distribution, Student's t-test and linear regression
  • Students will estimate stationarity and trends of selected component of downloaded sea-level time series and asses the significance of estimated trends
Students will estimate trends for different time windows and evaluate stationarity of trends
Université de Bretagne Occidentale
Sally Close
3. From in situ observations to gridded data 3 hrs Practical session to enable students manipulate in situ observations and transform the input data into a gridded product. Through this practical, they will also discover how data availability affects the accuracy of the gridded output, and see how this problem can: (1) particularly affect certain undersampled regions of the ocean, and (2) limit the time scales that can reasonably be resolved.

Argo data will be used as an example. The students will aim to create weekly, monthly, annual and multiannual gridded products of temperature / salinity for different regions of the ocean with varying levels of data coverage.

A set of instructions will be provided so that the students can achieve their objective using a “no-programming” option: students who do not have any experience in programming can complete the practical by firstly using the Coriolis web interface to select and download the data, and then by using Ocean Data View to visualise and transform the data
University of Gdansk
Aleksandra Cupiał, Wojciech Brodziński & Gabriela Gic-Grusza
4. From data formats to practical use of water column depth data 3 hrs This session will include two sub-sessions.
Firstly, students will learn how to manipulate different data formats (txt, geoTiff, NCDF). During this task, emphasis will be put on the importance of selecting subsets of data (both in temporal and spatial scale) and its influence on final analysis.
Secondly, students will learn the basic aspects of spatial data interpolation and discover how different methods of interpolation impact on data accuracy of the gridded output. In this part, water column depth data will be used and some aspects of the modelling of bottom topography will be emphasized.
Sets of instructions will be provided so that the students can achieve the same objective using Jupyter Notebook / Python programming.
CAU Kiel
Julia Gottschalk
5a. Marine data visualization and analysis with Ocean Data View (ODV) 1.5 hrs
  • Visualize ocean data in water column plots, section plots/transects, surface plots and scatter plots
  • Visualize multiple datasets with overlays; Getting acquainted with quality flags of marine data products
  • Calculate iso-surface variables and derived variables
  • Learning the pro and cons of different map projections and colorscaling; Gridding (incl. DIVA)
  • Vector plotting
CAU Kiel
Julia Gottschalk
5b. Marine Biogeochemistry: monitoring programs, observational platforms and associated data products 1.5 hrs
  • History and challenges of ocean biogeochemistry monitoring programs, including logistics, inter-calibration and quality control
  • Value and purpose of various monitoring programs of marine biogeochemistry: from the Baltic Sea to the Southern Ocean, from a global to regional scale, from time-series to globally gridded datasets
  • Key concepts of marine nutrient and element cycles (e.g. from GEOTRACES, etc.) and anthropogenic impacts on the marine carbon and carbonate systems (e.g., from GLODAPv2) revealed by these datasets
University of Cadiz
Jesús Gómez-Enri
6. Oil Spill Detection from space with SENTINEL-1 3 hrs Major oil spills from sinking super-tankers, or other marine accidents like the Deepwater Horizon (DWH) oil rig explosion and collapse, are thankfully very rare these days. However, smaller oil spills from shipping are unfortunately still common. Most ships have some type of fuel on board and if they are involved in an accident, there is a risk of that fuel leaking into the sea. More worryingly, some ships still deliberately release oil into the sea. In some areas even reaching the oil can be as hard as cleaning it up. Mud flats, swamps, marsh-lands and deltas can be particularly difficult.
University of Split
Žarko Kovač
7. Primary production time series analysis and model parameter estimation 3 hrs
  • How is primary production measured?
  • How is primary production modelled?
  • How to optimize model performance and extract parameter values from data?
  • How to fill data gaps using models?
University of Cadiz
Tomás Fernández Montblanc
8a. Reliable oceanographic data sources: Introduction to sea state and wind wave characterization 1.5 hrs
  • Why wave data is needed?
  • Which are the main wave characteristics and why?
  • Typologies of wave data
  • Advantages/disadvantages and synergies between wave data measurements and model outputs
  • Where can publicly available wave data be found?
  • How to characterize the wave climate?
University of Cadiz
Tomás Fernández Montblanc
8b. Wave Climate Characterization 1.5 hrs Determine the extreme wave climate in a coastal region
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