Marine Data Science

Last update: 2019, Jan. 7

What is Marine Data Science (MDS)?

MDS is all about uncovering findings from marine data such as oceanographical data collected using both in situ methods and remote sensing. Typical parameters measured on-board relate to near-surface meteorological conditions (air temperature, wind speed, wind direction, cloudiness, etc.), sea surface conditions (e.g. sea surface temperature (SST), salinity, wave height, and wave direction) as well as subsurface water characteristics (vertical profiles of temperature, salinity, dissolved nutrients, ocean currents, or ocean bottom depth). Marine biological data comprises of spatial and temporal species occurrence and abundance data (e.g., trawl data of fish, net sampling of plankton, benthic grab samples or visual counts of mammals or sea birds), taxonomic information, trait data, sequence and high-throughput screening (HTS) data, as well as digital image data from optical sensor systems or hydroacoustic data from echo sounders.

When given a challenging question, data scientists become detectives and investigate leads and try to understand patterns within the data. It typically starts with data exploration followed by quantitative techniques drawn from mathematics, statistics, information science, and computer science in order to get a level deeper, e.g., inferential models, segmentation analysis, time series forecasting or synthetic control experiments. The overall intent is to scientifically piece together a forensic view of what the data is really saying about marine system dynamics. The data to be used, however, does not need to represent exclusively marine biotic and abiotic data. In fact, any type of data describing a climatological, terrestrial or socio-economic component that could affect the marine system could be analysed. Amongst these, regional and large-scale climatological indices, fisheries, agricultural, and demographic data are commonly used to study the extent of external forcing on the system and potential feedback mechanisms.

With the vast amount of data that are nowadays produced (~90 percent of the data in the world today has been created in the past two years1), twenty-first century marine science becomes increasingly analytical and computational across all disciplines. Since 2012, data science has become a popular buzzword when the Harvard Business Review called it “The Sexiest Job of the 21st Century”2 and is often used synonymously with statistics although it comprises of more disciplines.

Elements of data science

Data science is highly interdisciplinary and a blend of skills in three major areas:

Hence, new generations of marine scientists need to be well trained in

  • analytical and interdisciplinary thinking
  • computer programming → at least one language
  • visualizations
  • data mining, statistical modelling, machine learning, and predictive analytics

  1. Report at the IBM Consumer Products Industry

  2. Davenport, T.H. & Patil, D.J. (Oct 2012), Data Scientist: The Sexiest Job of the 21st Century, Harvard Business Review