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Observational marine science operates in a harsh and challenging environment. Traditionally, data collection relied on specialised research vessels, but their limited number and inability to operate in poor weather or heavy sea ice led to biased, sparse data. Instrumented moorings provide fixed-point data, and while satellites greatly improved surface observations, they only penetrate a few tens of metres in an ocean averaging over 3000 m deep.

The rise of autonomous platforms - such as drifting Argo floats and self-propelled gliders or Autonomous Underwater Vehicles (AUVs) such as the NOC built Autosub Long Range ( ALR) ‘Boaty McBoatface’ - is transforming ocean science. These platforms can collect data from beneath the ocean surface in all conditions, including under sea ice, and operate for extended periods without human presence.

With improving technology and lower costs, large-scale deployments of these platforms are becoming feasible, enabling global, daily ocean monitoring. Locally, they offer long-term presence in remote areas, surpassing the duration and scope of ship-based missions. Their use also accelerates sensor innovation, pushing the boundaries of what data can be collected. It is therefore imperative that we equip the next generation of marine researchers to be able to exploit fully the great potential that autonomous platforms offer.

This online course has been designed to provide that much-needed foundation, making use of the national facilities offered by the Marine Autonomous Robotic Systems (MARS) autonomous fleet and the British Oceanographic Data Centre (BODC), coupled to a team who have pioneered the scientific use of autonomous platforms.

It provides a grounding in the full span of knowledge required to use autonomous platforms: from knowing what platforms are available, their strengths and weaknesses, what data can be collected by them, and how one goes about assembling a mission; to what new areas of science these new platforms open up; and to the often under-estimated but vital issue of how the enormous amounts of data they can generate should be handled to make it a resource not just for one project but a valuable resource for the community for many years. It also highlights how research vessels remain a critical component of ocean observations, providing independent data to calibrate autonomous platforms and to allow observations that still require the presence of scientists, of which there will always be many.

SLOCUM-GLIDER
C-WORKER
AUTOSUB-LONG-RANGE-6000
Argo Float

 

Learning outcomes

Having successfully completed this course, candidates will be able to demonstrate knowledge and understanding of:

  • The technical and operational characteristics of a wide range of marine autonomous data gathering systems (MAS)
  • The scientific data gathering benefits and limitations of a range of marine autonomous systems (MAS) when coupled with both conventional and novel scientific sensors.
  • An overview of MAS capabilities supported by NERC within the National Marine Equipment Pool
  • How to design MAS research campaigns and deployments.
  • The full data management lifecycle for MAS at the Data Assembly Centre, including key processes, standards, and roles that ensure quality, interoperability, and effective data stewardship.
  • Practical approaches to process data sets from MAS deployments.

Having undertaken the practical elements of this course you will be able to:

  • Assess whether ocean gliders are relevant for their research
  • Design and plan a potential glider mission, considering scientific and operational constraints
  • Identify and select appropriate existing glider datasets
  • Process and analyse recovered glider data files to extract meaningful scientific information.
  • Process oceanographic data sets collected from ALR AUVs to a standard suitable for final publication. This will provide an understanding of calibration process, AUV mission design, cross correlation to other data sources (e.g. satellite data) and also how these data can be integrated into a wider oceanographic context.
  • Process oceanographic data sets collected from ALR AUVs to a standard suitable for final publication. This will provide an understanding of calibration process, AUV mission design, cross correlation to other data sources (e.g. satellite data) and also how these data can be integrated into a wider oceanographic context.
  • Explain the difference between Real-Time (RT), RT-adjusted, and Delayed-Mode (DM) data for data for each platform.
  • Identify the main types of sensors and samplers integrated in autonomous platforms (e.g. gliders, floats, ALRs) and the variables they measure (e.g. CTD, oxygen, fluorescence, pH, nitrate).
  • Evaluate criteria for selecting sensors based on scientific and operational requirements
  • Recognize the role of the NOC/OTE technology and their integration in ocean observing systems.
  • Explain the importance of sensor calibration, validation, and traceability to ensure high-quality oceanographic data
  • Understand how sensor performance impacts data processing, quality control, and downstream data management workflows

This course will be structured as three two-day modules spread over two weeks. The course will run twice in 2026.

Cohort 1 dates (Main target audience: PhD students)

Module 1: Monday 16th March and Tuesday 17th March

Module 2: Thursday 19th March and Friday 20th March

Module 3: Monday 23rd March and Tuesday 24th March

Cohort 2 dates (Main target audience: early career researchers)

Module 1: Monday 13th April and Tuesday 14th April

Module 2: Thursday 16th April and Friday 17th April

Module 3: Monday 20th April and Tuesday 21st April

Priority will be given PhD students (first) and early career researchers (second) who will be using autonomous platforms or data within the next 12 months. After those groups, PhD and ECRs with a more general interest will be offered places, and then other UKRO funding-eligible environmental scientists.

Remaining places will be offered to UK-based people working in a sector aligned with UKRI science, at a cost of £850 to match that associated with one participant at a re-run of the course. Information on total and successful numbers of applicants (including fraction that are PhD or early career) and sector will be recorded for reporting, once more fully compliant with GDPR.

Register for this course

 

Disclaimer:
The course will be widely advertised including on the UKRI and NERC email lists and NOC and Challenger Society social media channels. The application will require the minimal information required for selecting participants and tracking purposes. Name and institute will be requested but removed before participant selection, to avoid bias. Applicants will be invited to submit ethnicity, gender and age (making clear it is not compulsory), but this information will be removed before decisions and only subsequently used anonymously to assess success in attracting and delivering the course to a diversity of participants. The only requested information used to allocate places will be: whether the applicant is a PhD student, a UKRI funded early career environmental researcher, or a UKRI funding eligible researcher; whether their research requires the use of autonomous platforms or data within the next year; a short statement of their motivation for acquiring the training. Selection will be made by consensus of the full delivery team, including an independent NOC People and Skills representative. All data will be stored on the secure NOC system in adherence to GDPR guidelines.

 

Funded by

UKRI lOGO