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For more info on the course please follow the details in the course website or contact us through mdl@sea-eu.org
COURSE INTRO
This intensive course educates students on marine data sourcing, exploration, and valorisation, transforming data into valuable knowledge. It focuses on merging diverse datasets and using AI for data-driven modeling. The course highlights data’s importance in marine services, sustainable ocean development, and informed decision-making.
Participants will learn about various data types, acquisition platforms, and the integration of socio-economic and qualitative data. Key components include data management, quality control, and adherence to international standards like INSPIRE and FAIR, promoting open data and interoperability.
Practical, hands-on sessions are delivered in a computer lab setting, covering specialized data platforms like CMEMS and EMODnet, and providing tools for data extraction, visualization, and analysis to ensure effective data use and interpretation.
TRAINING OBJECTIVES
Integrated Assessment & Management:
– Focuses on sustainable management of coastal and marine environments.
– Covers sustainable development, blue economy, and the role of science in policy-making and societal needs.
Practical Data Skills:
– Teaches visualization, processing, and analysis of scientific data using professional software.
– Involves hands-on sessions with various types of marine data.
– Introduces software packages for oceanographic data processing.
These skills prepare students for advanced studies and careers in marine science and management.
LEARNING OUTCOMES & SKILLS
TARGETED STUDENTS
– Master’s and PhD students in marine sciences aiming to enhance their applied oceanography skills and solidify their data and scientific method applications.
– Postgraduate students from other disciplines (e.g., engineering, IT, geosciences, geography, environmental management) who rely on data in their studies.
– Professionals currently working in their fields who seek to improve their data application skills.
Additional Information
– Open to students from universities outside the SEA-EU alliance and non-university participants.
– Online practical classes available in March/April 2025 for those without ERASMUS+ funding.
– Selection is based on merit and qualifications.
– No initial programming skills required, but a relevant background from previous studies is expected.
Students joining the course do not need any initial programming skills, but are expected to have acquired the necessary backgrounds from their first degree courses.
COURSE IMPLEMENTATION
The course, organized as a SEA-EU introductory program, is primarily for students from SEA-EU universities but also open to those from other institutions. Each SEA-EU partner university appoints a coordinator to help plan and execute the course. The Board of Studies (BoS) is responsible for course content, coordination, and promotion within the SEA-EU framework.
Delivery and Structure
– Blended Approach: Combines online and in-person elements.
– Duration: 109 hours over a few months each year.
- Virtual Mobility: 13.5 hours of online lectures (March – April 2025).
- Physical Mobility: 25.5 hours of in-person or optional remote lectures and practical sessions (May 2025).
- Online Project: 70 hours of group work with mentoring, completed remotely over eight weeks (June – July 2025).
The course is open annually to all students and offers both physical and remote participation options.
COURSE ASSESSMENT
For the third run of the course this year, accreditation will be managed separately by each SEA-EU university based on an ERASMUS Learning Agreement. Some universities may offer the course without credits, and participants outside SEA-EU will not be eligible for credits. Despite this, all students will receive a certificate based on attendance and performance.
Credit Allocation: 3 ECTS
In future years, a unified accreditation system is planned. Students will be assessed throughout the course, with credits assigned for each phase and the entire course. Introductory lectures will be recorded, but online attendance is expected, with possible exemptions for reported absences.
Credits will mainly be awarded through the project mode, with individual contributions assessed. Team members must submit individual reports and participate in the final project presentation.
VIRTUAL MOBILITY
13.5.h – data concepts, foundations, remote lectures
– Marine data / introduction (different data types, importance of data ; why need of data literacy)
– Types of data: physical, biogeochemical, ecological models; observations (in situ + remote); Big Data; Artificial Intelligence
– Data sharing and FAIR principles
Open Science and Open Research Data; FAIR principles; Data archaeology
– Best practices for working with data (data organization and management, documentation, and storage and data security) (metadata standards)
Data Management; metadata; interoperability ; SeaDataNet; IOC/IODE
– Combining data from multiple sources (interoperability and interdisciplinarity)
Distributed databases; THREDDS; cloud resources and services
– Reliable oceanographic data sources (overview of oceanographic databases)
Data Mining; CMEMS and EMODnet
PHYSICAL MOBILITY
25.5h – 1 week module with physical meeting
Course Delivery Focus
The course emphasizes practical sessions, providing students with hands-on skills through problem-solving activities using real data.
Key Technical Skills Covered:
– Data retrieval and visualization
– Data analysis
Course Structure:
– Location: Dedicated computer lab with individual computing units and necessary software.
– Format: Common technical platform and open-source software to ensure accessibility.
– Initial Sessions: Introduction to essential tools for effective use throughout the course.
– Remote Option: Virtual toolkit for students to use on personal laptops.
Project Kickoff:
– Part of the week is dedicated to initiating the course project, with methodology provided to allow students to start early.
ONLINE PROJECT
70h – module to be continued remotely
Project Mode Overview
Students apply course knowledge to real-world sea-related challenges by proposing, planning, and developing data-driven solutions.
Assignment Details:
– Challenges: Address sea-related issues using data.
– Teams: Multi-disciplinary teams with mentors from different universities.
– Selection: Challenges and teams are chosen during the intensive week; student-proposed projects are allowed.
– Mentors: Each challenge has at least two mentors from different universities.
Assessment:
– Components: Project mode, heavily weighted, must be taken with the intensive practical component.
– Project Types: Individual or team projects, with distinct contributions for team members.
– Evaluation:
- Report: Assessed by mentors.
- Presentation: Assessed by an expert panel.
Key Points:
– Apply knowledge to real-world problems.
– Use data and teamwork.
– Assessed through reports and presentations.