Ocean Datasets for Investigations
By Mae Lubetkin and Kevin Rosa
In short: Learn how to identify and use Ocean datasets as a tool for revealing the unseen or underreported dynamics of the world’s most significant bodies of water.
1. Introduction: Ocean Science, Data, Storytelling
Given our current planetary condition, many of the world’s most pressing stories are linked to the ocean. Covering 71% of Earth’s surface and interacting constantly with the atmosphere, the ocean is our greatest carbon sink and an essential indicator of climate change. Despite its critical role in maintaining a habitable planet and supporting coastal livelihoods, the ocean is often invisible to the lived experience of most individuals, particularly those far from its shores. There are so many ways to tell stories about the ocean, and countless diverse perspectives from which to understand it. Now, more than ever, we need to incorporate cross-cultural and trans-disciplinary strategies for investigative projects, particularly those concerning the ocean. This guide presents ocean datasets as a tool for revealing the unseen or underreported dynamics of the world’s most significant bodies of water.
For informed investigations and impactful storytelling, oceanographic datasets can be an essential resource for journalists, activists, and anyone interested in data-driven methods to communicate the climate crisis, environmental change, natural disasters, extractivism, and associated ocean justice issues. From bathymetric maps, subsea imagery, and 3-D habitat models, to satellite-derived and in situ real-time monitoring data – a vast amount of oceanographic media and data is publicly available.
In this Exposing the Invisible Guide, we begin with an introduction on the broader scientific history and context within which ocean data is collected, stored, and made accessible. Section two outlines the diversity of datasets, including some history, trade-offs, use cases, data collection methods, data sources, and resources to learn more about each data type that we present. Section three offers a specific application of ocean data in a case study, including: steps explaining why the data is useful for supporting this particular story; how to source and present the data; and, finally, how to bring it into a meaningful investigative report, journalistic piece, or other storytelling format. The guide concludes with a summarized approach for using ocean datasets in investigations and outlines strategies for identifying the right ocean scientists who could support you and your investigation.
Boulder resting on the seafloor offshore the Revillagigedo Archipelago in the Pacific Ocean. Credit: Ocean Exploration Trust.
1.1 Ocean Science: History and Context
Ocean science generally refers to the observation and investigation of biological, geological, physical, and chemical processes that shape and constitute global marine environments. This broad disciplinary field includes numerous sub-disciplines that focus on intricate, detailed studies of specific scientific questions concerning the ocean. Ocean scientists monitor habitat change, measure biodiversity, investigate geological phenomena, and study human impacts on ocean systems (i.e., global warming, pollution, overfishing, and extractive projects). Interactions between marine ecosystems and human activities, as well as atmospheric and coastal processes, are all carefully investigated by ocean scientists today. Despite niche specializations, there are increasingly multidisciplinary projects that involve diverse experts in order to more comprehensively understand the interconnections between oceanic processes and phenomena. Collectively, this research improves our baseline knowledge of the ocean, which can then support preservation strategies while maintaining sustainable and regenerative relationships with diverse marine ecosystems.
Although ‘contemporary’ ocean science has deep roots in European colonialism and imperial exploration, ocean knowledge systems long predate Western scientific inquiry. Indigenous and coastal communities across Oceania, as well as the Atlantic and Indian Oceans, carefully studied and navigated the seas for thousands of years. These forms of ocean science are less known or dominant on a global scale, but they nevertheless involve highly sophisticated techniques for understanding the stars, ocean swells, winds, and currents. Navigators across Oceania used interconnected and embodied forms of ocean science, on their own terms, to journey vast distances across the seas with tremendous precision. While other coastal Indigenous peoples around the world developed specific place-based systems of knowledge, including both land and marine management practices that viewed these spaces as highly linked. Most of these communities understood the ocean not as a monstrous or alien-filled void (as European explorers often depicted it), but as a vast world interconnected with our own.
Rebbilib (navigational chart) by a Marshall Islands artist in the 19th to early 20th century. These stick charts were used by Marshall Islander navigators during long ocean voyages. Credit: Gift of the Estate of Kay Sage Tanguy, 1963. Source: https://www.metmuseum.org/art/collection/search/311297
When European seafarers began worldwide exploration voyages in the 15th-16th centuries, they dismissed or actively erased these Indigenous ocean knowledge systems. European or ‘Western’ scientific models were linked to a colonial mindset that often viewed the natural world as a space to examine in order to master and own its elements, rendering them as ‘resources’. Notions of relationality were strongly opposed to the point that scientists considered their surroundings as objects to be studied rather than elements to relate to or work with. At the core, these opposing ocean scientific methods or knowledge systems reflected the specific values and worldviews of each culture, respectively.
The Challenger Expedition (1872–1876) was the first European-led systematic scientific exploration of the global oceans. In some ways, it was groundbreaking. However, it also played a key role in the broader colonial project, which aimed to map, control, and extract ‘resources’ from around the world. Today, Western or ‘contemporary’ ocean science continues to use investigatory methods that stem from European knowledge systems. Oceanographic research often occurs in the context of economic or territorial expansion and military-supported science projects. Nevertheless, these methods are beginning to open up to other forms of knowledge creation that move beyond the geopolitical interests of wealthy nations.
Map of ocean currents created by John Nelson using the “WGS 1984 Spilhaus Ocean Map in Square” projected coordinate system in ArcGIS. The Spilhaus Projection (developed by oceanographer Athelstan Spilhaus in 1942) reflects aspects of decolonial cartography by shifting focus from land-centered perspectives to an oceanic worldview. Source https://storymaps.arcgis.com/stories/756bcae18d304a1eac140f19f4d5cb3d
Thanks to the enduring activism and decolonizing work led by many Indigenous and coastal communities, there is increasing recognition of the need to reclaim ocean science by amplifying the knowledge and perspectives of those who have long understood the seas. Ocean science is just beginning this deep process of decolonization, which first seeks to acknowledge and reckon with the violent and ongoing impacts of colonization. Decolonizing ocean science requires a fundamental shift in who holds agency and sovereignty over their own ocean waters. This also relates to international waters and who is included, excluded, or undervalued throughout negotiations concerning the legal and regulatory structures governing global oceans. Today, many ocean scientific projects are co-designed and co-led by ocean knowledge holders from diverse backgrounds. Collecting ocean datasets requires a team of experts who follow cultural protocols, ensure environmental safety, and apply diverse scientific methods, all while striving for more relational practices.
1.2 Ocean Data Collection Today
Today, ocean datasets are collected by ocean scientists in collaboration with ocean engineers. These datasets are gathered from several sources to understand the global ocean and its role in maintaining Earth’s habitability and critical planetary cycles. Ocean engineers develop the tools, platforms, and instruments that are required for data collection, such as underwater vehicles, satellite-mounted sensors, and buoys. By designing technologies that can operate in diverse and sometimes extreme conditions, these engineers support and expand ocean scientific capabilities. Together, ocean scientists and engineers advance our understanding of the planet for both research and conservation. There is a considerable variety of ocean data types, tools for data collection, and associated databases to store these recorded entities. This diversity of datasets is outlined in section 2.
Like most scientific fields, funding can be secured from public governmental bodies or private sources. The ocean datasets we focus on here are publicly accessible and typically funded by governments via taxpayer contributions. This means that ocean datasets are for the people and should be accessible. Unfortunately, many public ocean datasets are stocked in complex databases and require specialized software, programming experience, or extensive knowledge to access. That said, there are plenty of datasets that can be retrieved and visualized more easily, with little to no background knowledge. There are also some ocean datasets that can be accessed with helpful tips and instructions, which is what we will focus on here. The Exposing the Invisible Toolkit is designed as a self-learning resource, we hope that this guide will support future investigations and make ocean datasets more accessible to communities and investigators around the world.
1.3 Data Gaps, Capacity Initiatives, Ocean Defenders
Conducting ocean science can be a costly endeavor. Depending on the environment, scientific goals, and technical requirements, some ocean scientific work can only be conducted by wealthy nations or private organizations. Typically, this kind of science takes place at sea or uses remote sensing techniques. For example, deep ocean exploration and research requires a ship, vehicles, or platforms to deploy down to the deep ocean, technical and computing systems aboard to process the data, and a diverse team of experts to manage these operations. In contrast, satellite remote sensing used for ocean research typically covers the entire Earth surface. Publicly funded satellite-derived ocean datasets can be useful across territorial waters, throughout international seas, and are accessible regardless of one’s nationality. Near-shore ocean science and in situ coastal monitoring efforts are more financially affordable, especially as diverse knowledge systems merge with lower-cost technologies and capacity initiatives. In this context, capacity refers to the skills, resources, and knowledge needed to effectively conduct ocean science.
As ocean science undergoes a process of decolonization, this emphasis on capacity building, development, and sharing is also strengthening. Additionally, several international ocean law and policy frameworks specifically aim to increase ocean science capacity. Ocean defenders are also central to these efforts. As groups, individuals, or organizations dedicated to protecting marine environments, defenders play a key role in advocating for capacity building within and outside scientific structures. Many defenders are fisherpeople, coastal communities, or groups directly affected by changes in climate and ocean health. Beyond advocating for sustainable and generative oceanic futures, they also fight to overcome political resistance and funding barriers. Ocean defenders, like land defenders, face challenging or dangerous obstacles while pushing for local and global ocean preservation. Ocean science and policy clearly needs collaborative approaches that bring multiple knowledge systems forward while prioritizing those most impacted by climate change, pollution, and other threats to both marine habitats and coastal livelihoods.
Ocean-defending artisanal fishers and their supporters in South Africa celebrate upon receiving the news that Shell’s permit to conduct a seismic survey on the Wild Coast had been set aside by the Makhanda High Court, in September 2022. Photo credit: Taryn Pereira. Source: https://oceandefendersproject.org/case-study/no-to-seismic-surveys/
More on capacity initiatives, knowledge gaps, and ocean defenders:
Guilhon, M., M. Vierros, H. Harden-Davies, D. Amon, S. Cambronero-Solano, C. Gaebel, K. Hassanali, V. Lopes, A. McCarthy, A. Polejack, G. Sant, J.S. Veiga, A. Sekinairai, and S. Talma. (2025). Measuring the success of ocean capacity initiatives. Oceanography 38(1).
Saba, A.O., I.O. Elegbede, J.K. Ansong, V.O. Eyo, P.E. Akpan, T.O. Sogbanmu, M.F. Akinwunmi, N. Merolyne, A.H. Mohamed, O.A. Nubi, and A.O. Lawal-Are. 2025. Building ocean science capacity in Africa: Impacts and challenges. Oceanography 38(1).
Behl, M., Cooper, S., Garza, C., Kolesar, S. E., Legg, S., Lewis, J. C., White, L., & Jones, B. (2021). Changing the culture of coastal, ocean, and marine sciences: strategies for individual and collective actions. Oceanography, 34(3), 53–60.
The Ocean Defenders Project (2025). Ocean Defenders: Protectors of our ocean environment and human rights. The Peopled Seas Initiative, Vancouver, Canada.
Belhabib, D. (2021) Ocean science and advocacy work better when decolonized. Nat Ecol Evol 5, 709–710.
Kennedy, R. and Rotjan, R. (2023). Mind the gap: comparing exploration effort with global biodiversity patterns and climate projects to determine ocean areas with greatest exploration needs. Front. Mar. Sci. (10).
Bell, K.L.C, Johannes, K.N., Kennedy, B.R.C., & Poulton, S.E. (2025) How little we’ve seen: A visual coverage estimate of the deep seafloor. Science Advances, Vol 11, Issue 19.
1.4 Ocean Datasets for Investigations and Storytelling
Ocean datasets play a crucial role in both scientific investigations and storytelling by providing evidence-based insights into the health and dynamics of our global ocean. These datasets help scientists better understand the ocean, but beyond research, they serve an important role in ocean-related investigations. Ocean datasets can support communities, journalists, and activists in raising data-backed awareness about critical marine and environmental justice issues. By sharing this data in accessible ways, oceanographic narratives can amplify the voices of coastal communities, engage the public, and inspire action in support of more regenerative ocean futures.
Numerous well-resourced journalistic and forensic organizations use ocean data to support their stories or reporting, such as Forensic Architecture, LIMINAL, Forensis, Earshot, Border Forensics, and others. In this guide, we will demonstrate how you can access datasets and conduct your own oceanic investigations. By the end, you will be able to illustrate a well-defined ocean or climate question using publicly available oceanographic datasets and media collections, which will enhance your evidence-based and visually engaging story.
2. Diversity of Data Types
The following sub-sections serve as a collection of key ocean data types, organized by how they are collected and what they reveal. Each broad data source type (i.e., the overarching technique used to gather certain kinds of ocean data) begins with a bit of history and trade-offs, and then is further broken down into specific data products. For each data product, we share use cases, data collection methods, key considerations, and some databases (often from U.S. and European agencies), followed by resources to learn more. This structure is designed to help you understand how each dataset is produced, grasp the significance of the data, and know where to go for deeper investigations or analyses.
There are many data types presented below which are organized in these four broad categories: in situ sensors; deep ocean observation, exploration, and research; mapping; and, satellite remote sensing. See Section 3 to review an example case study which demonstrates how some of these datasets may be used to support an investigative ocean and climate story.
Illustration of a range of ocean sensor platforms—ships, profiling drifters, gliders, moored buoys, and satellites. Source: https://www.ecmwf.int/en/about/media-centre/news/2021/world-meteorological-day-focuses-role-ocean-weather-and-climate
2.1 In Situ Sensors
In situ sensing refers to the direct measurement of ocean properties using instruments that are physically located within the water. Sensors for measuring temperature, salinity, pressure, currents, and of biochemical concentrations are deployed on a range of platforms with various advantages and drawbacks. While satellites provide broad spatial coverage of the ocean’s surface, in situ platforms are essential for monitoring the ocean’s interior, tracking coastal change, and measuring water properties that cannot be detected remotely.
History:
Sailors have used thermometers to measure ocean temperature since at least as early as Captain James Cook’s 1772 voyage to the Antarctic Circle (another example of colonial science forming the foreground to Western ocean science).
The Nansen bottle (1896) and later the Niskin bottle (1966) enabled the capture of water samples at specific depths, which could then be pulled up and tested for temperature and salinity on the ship.
The first bathythermograph was developed in 1938 and featured a temperature sensor on a wire which recorded a temperature profile as it was lowered into the ocean. This was used by the US Navy in WWII to improve sonar accuracy, since temperature layers alter acoustic propagation.
Today, there are thousands of advanced sensors across the world’s oceans which transmit readings in real time via satellite.
Trade-offs:
In situ sensors can only measure the ocean properties at their exact location so great consideration is taken in their placement and timing.
Powering the instruments is a constant challenge and factors into decisions about sampling frequency.
The extreme pressure in the deep ocean limits the operating depth of some sensors.
Harsh operating conditions limit the lifespan of sensors and necessitates regular maintenance/replacement, often in remote locations at high costs. This leads to less accessible areas being undersampled.
2.1.1 Moorings and Fixed Platforms
Example of a coastal data mooring. Source: Baily et al., 2019, https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2019.00180/full
About:
A range of platforms anchored in place, collecting time-series data at a fixed location.
Used in the deep ocean (e.g., the TAO array across the Pacific Ocean), the continental shelf (e.g., NOAA’s National Data Buoy Network), and at the coastline (e.g., tide gauges).
Use cases:
Long-term climate monitoring of ocean heat content and circulation.
Data inputs for forecast models, improves accuracy of ocean and weather predictions.
Early warning systems for tsunamis and hurricane storm surge. Tracking tidal heights and local sea level rise.
Water quality monitoring for pollutants, algal blooms, and hypoxia.
Data collection:
Sensor packages measure temperature, salinity, pressure, biochemistry, and more.
Some moorings have Acoustic Doppler Current Profilers (ADCPs) to measure water current velocities throughout the water column.
Key considerations:
Data today is mostly broadcasted in near-real time, but there are some platforms that require physical retrieval before the data is downloaded.
Spatial coverage is extremely limited and focused around a small set of nations.
The diversity of databases and data types can pose a challenge for accessing and working with the data.
Data sources:
Resources to learn more:
2.1.2 Drifters and Floats
Map of Argo float locations. Source: https://argo.ucsd.edu/about/status/
About:
Unanchored and unpropelled instruments that drift with the currents and take ocean measurements.
Drifters stay at the surface and provide information about surface conditions, and surface currents are calculated from their GPS trajectory.
Floats profile the water column by adjusting their buoyancy to move up and down. The Argo program is the largest and most significant, with over 4,000 Argo floats profiling the world’s oceans.
Capable of providing global coverage at lower cost than moored sensors, especially in remote open-ocean regions.
Use cases:
Drifters: Mapping near-surface currents and tracking pollutants and marine debris transport.
Floats: Measuring subsurface temperature and salinity for climate studies. Some Argo floats also have biochemical sensors.
Data collection:
Drifters: GPS-tracked, measure SST, pressure, sometimes salinity, sometimes waves.
Argo floats: Profile down to 2,000 m every 10 days, transmitting data via satellite.
Key considerations:
Drifters and floats are always moving, so you can’t get a clean timeseries for a single location like you can with moorings. Additionally, Argo floats only take one profile every 10 days in order to preserve battery life.
Argo floats don’t generally operate near the coast on the continental shelf.
Some drifters/floats lack real-time telemetry (data transmission).
Data sources:
Resources to learn more:
2.1.4 Autonomous Vehicles - ASVs and Gliders
Illustration of a glider’s sawtooth propulsion pattern. Source: https://earthzine.org/going-deep-to-go-far-how-dive-depth-impacts-seaglider-range/
About:
Autonomous Surface Vehicles (ASVs) and gliders are robotic platforms that enable long-duration, energy-efficient monitoring over vast areas.
Bridging the gap between targeted, expensive, ship-based measurements and low-cost, but uncontrolled, drifters/floats.
Use cases:
Significant overlap with the use cases for moored sensors and drifters/floats.
Targeted measurements in dangerous conditions like hurricanes.
Mapping surveys, autonomous of ship or in tandem with other vehicles.
Data collection:
Autonomous Surface Vehicles (ASVs) use solar panels, wind, or waves as a power source to supplement and recharge their batteries.
Gliders are underwater vehicles that create propulsion by adjusting their buoyancy and gliding horizontally while sinking/rising (similar to an airplane). This enables longer battery range than propellers or thrusters.
Key considerations:
Gliders and ASVs are often used in targeted studies rather than continuous global monitoring and thus have lower data availability.
Shorter mission durations than moorings or drifters/floats.
Data sources:
Resources to learn more:
2.2 Deep Ocean Observation, Exploration, and Research
Deep ocean science is typically conducted to observe long-term changes at specific seafloor sites, to explore marine habitats that are unknown to science, and to conduct applied or experimental research on focused environmental questions. A range of deep ocean data collection tools include platforms or landers, cabled observatories, and deep submergence systems—such as human-occupied vehicles (HOVs), remotely-occupied vehicles (ROVs), and autonomous underwater vehicles (AUVs).
Human-occupied vehicle (HOV) ‘Alvin’ being recovered in 2024 during an expedition to the East Pacific Rise hydrothermal vent fields. Photo credit: Mae Lubetkin
History
1872–1876: The HMS Challenger expedition, a milestone for deep ocean science but deeply tied to imperialism
Mid-20th century: Cold War military priorities further developed submersible vehicle capabilities, leading to Trieste’s 1960 Mariana Trench dive
1964 onward: HOVs expanded access to the deep ocean, e.g., Alvin (US), Nautile (France), Shinkai (Japan), and Mir (Russia)
1980s–2000s: ROVs and AUVs developed by industry (oil, mining, and defense) and scientific institutions, in parallel
2000s–present: Cabled observatories (e.g., Ocean Networks Canada, DONET in Japan), public research campaigns (e.g., NOAA, IFREMER), and oceanographic instruments expanded reach and scope.
Today: Many regions still face barriers to participation and funding in deep ocean science (as outlined in the introduction). Meanwhile, deep submergence science in wealthy nations increasingly utilizes AI, autonomous systems, 4K and 3-D imaging techniques.
Trade-offs:
Provides direct access to deep ocean environments which are inaccessible by surface vessels or remote sensing
High spatial and contextual resolution: can capture detailed imagery, samples, and detailed in situ measurements
Resource-intensive: operations usually require ships, launch/recovery teams, and specialized personnel
Limited temporal and spatial coverage: data collection is episodic, site-specific, and dependent on expedition funding, schedules, and weather conditions at-sea
High costs and technical barriers mean deep ocean science is dominated by a few well-funded institutions or nations, with limited global access
Colonial legacies persist in relation to who sets research agendas, who makes funding decisions, and who benefits from collected data
2.2.1 Deep Submergence Systems (HOVs, ROVs, AUVs)
About:
Vehicles that operate in the deep ocean water column or along the seafloor, including:
Human-occupied vehicles (HOVs): carry scientists directly, typically 1-3 observers and a pilot
Remotely operated vehicles (ROVs): tethered and piloted from a surface vessel like a ship
Autonomous underwater vehicles (AUVs): untethered and pre-programmed
These systems can operate from hours to days and are depth-rated around 4000-6000 m, but some may reach full ocean depths (11 km) and others may work well in shallower waters.
Use cases:
High-resolution visual surveys
Precise targeted sampling with environmental context and imagery at diverse environments including hydrothermal vents, methane seeps, cold-water coral habitats, and others
Biogeographic habitat mapping
Wreck exploration and infrastructure inspection
Imagery of deep ocean environments can support visual storytelling, public engagement, and education
Data collection:
Data is streamed directly to the support vessel for tethered operations
While for untethered submersibles (HOVs and AUVs) most data is retrieved when the vehicle is recovered
All physical samples are retrieved and processed upon vehicle recovery
Key considerations:
Requires experienced pilots and operational support (expensive)
AUVs need detailed mission planning (mission failure could lead to vehicle loss)
Navigation and environmental risks must be managed carefully
Data sources:
SeaDataNet - EU research vessel data, including cruise summary reports and more
EuroFleets - European initiative to compile EU research cruise data
Rolling Deck to Repository - US research vessel data, including: expedition summary, shiptrack navigation, scientific sampling event log, post-processed data
JAMSTEC Databases - Japan research vessel data, including: HOV Shinkai 6500 and ROV Kaiko mission data, cruise reports, and dive logs
Resources to learn more:
An imaging elevator equipped with two camera systems, lights, battery packs, and crates to store additional sampling tools to be used by an HOV during a dive in the same region. Source: Woods Hole Oceanographic Institution
2.2.2 Landers and Elevators
About:
Landers are relatively simple systems that descend to the seafloor and remain stationary for the duration of their deployment.
They are sometimes referred to as ‘elevators’ since they descend to the seafloor then ascend back to the surface
There are no people on landers, but they typically carry sensors, cameras, samplers, and other instruments
Depending on power supply and scientific goals, they can spend hours to sometimes months on the seafloor
Use cases:
Collecting environmental data (e.g., conductivity, temperature, pH, oxygen)
Capturing imagery of habitats or operations
Deploying baited cameras or traps to study biodiversity
Using the platform to carry additional gear or instruments to the seafloor that a deep submergence system could not transport on its own due to space limitations
Data collection:
Typically data is retrieved when the lander is recovered back on deck
Some landers will transmit data acoustically or remotely from the seafloor
The frequency that imagery or other data are collected is pre-programmed before deployment
Key considerations:
Requires careful site selection, recovery planning, and often ship time
Currents can impact the intended landing location on the seafloor, sometimes drifting the platform or lander far off-site
Limited in capabilities, not as advanced as deep submergence vehicles, but also much cheaper and easier to custom build
Data sources:
Deep ocean lander data (e.g., imagery and environmental sensor data) would be found in the same databases and repositories listed in section 2.2.1 Deep Submergence Systems (HOVs, ROVs, AUVs).
Resources to learn more:
Map of Ocean Networks Canada NEPTUNE and VENUS Observatories near Vancouver Island, Canada. Each orange square represents a node or station along the cabled observatory where instruments or sensors are mounted. Source: https://www.oceannetworks.ca/
2.2.3 Cabled Observatories
About:
Mostly permanent, wired infrastructure on the seafloor that transmit real-time power and data via fiber optic cables connected to shore stations
Similar in some ways to the data collection tools described in section 2.1 In Situ Sensors, but these networks are fixed in location and networked
People do not visit these observatories, instead they support a wide range of sensors (e.g., temperature, pressure, seismometers, hydrophones, cameras, samplers)
Can integrate with ROVs or AUV docking stations, and are also typically maintained and serviced by ROVs
Designed for continuous, high-frequency monitoring of deep ocean processes across years or decades
They connect highly diverse environments from hydrothermal vent regions to abyssal plains and continental shelves
Use cases:
Long-term and consistent monitoring of geophysical activity (e.g., earthquakes, hydrothermal vents)
Real-time data for early warning systems (e.g., tsunamis, gas releases)
To study oceanographic processes (e.g., currents, biogeochemical fluxes, and ecosystem change)
Supports public engagement and education through livestreams
Data collection:
Real-time data is livestreamed to shore stations and then available via online portals
Most are operated by national or international research infrastructures
Key considerations:
Extremely costly, high maintenance needs (ROVs are often used for annual servicing)
Site selection is key since they are fixed installations
Data sources:
Ocean Networks Canada - Oceans 3.0 Data Portal, including all datasets, dashboards, and visualizers (more info on ONC data)
US Ocean Observatories Initiative - OOI Data Portal, includes cable-linked arrays on East and West Coasts and deep Pacific
EU EMSO ERIC Data Portal, real-time and archived data, tools and research environment to investigate seafloor observatories across European margins
Resources to learn more:
Ocean Observatories Initiative - arrays, infrastructure, instruments
Ocean Networks Canada - observatories
Regional Cabled Observatories - summary by Woods Hole Oceanographic Institution
2.3 Mapping
Marine hydrography or ocean scientific mapping involves the creation of high-resolution representations of the seafloor, water column, and other associated features or phenomena (e.g., fish migrations, vents or seeps bubbling up) using vessel-based sonar, autonomous vehicles, acoustic or optical tools. It is a type of remote sensing since the mapping instrument is not on the seafloor. Unlike satellite remote sensing, which observes only the ocean surface from space, hydrographic mapping is conducted from platforms within or on the ocean surface. These mapping systems can resolve fine-scale topography (seafloor bathymetry), subsurface geologic layers, water column imaging, and habitat mapping that integrates both physical and biological data. Laser and 3-D reconstruction are other forms of high-resolution mapping.
History:
1870s–1900s: Early bathymetric charts created using lead lines, linked to colonial navigation and maritime claims as well as scientific knowledge creation
1920s–1940s: Echo sounding developed for military and commercial navigation, later repurposed for seafloor mapping
1950s–1970s: Multibeam sonar developed, enabling wider swath coverage (i.e. can map wider seafloor area, not just single points) and broader seafloor topography or bathymetry mapping
2010s–present: Autonomous vehicles, numerous specialized sonar systems, and 3-D photogrammetry advance deep mapping capabilities
Today: Mapping remains uneven globally—nations with limited funding or access to ships and processing capacity are underrepresented and do not have detailed seafloor maps of the their waters
Trade-offs:
High-resolution, fine-scale maps of seafloor and water column features
Enables geologic, biologic, and habitat-based spatial analysis
Requires significant ship time, technical expertise, and post-processing
Data coverage is patchy, most of the seafloor remains unmapped
High cost and national interests impact where mapping occurs and who benefits from the data
Bathymetric mapping using a hull-mounted multibeam sonar system. Black lines indicate the ship’s track, while the coloration represents depth differences (red is shallow, purple is deep) used for visualizing the bathymetric or topographic features of the seafloor. Source: https://www.worldofitech.com/mapping-the-ocean-floor-water-bathymetry-data/>
2.3.1 Bathymetric Mapping
About:
Measurement and charting of the depth and shape of the seafloor
Typically uses sonar-based systems (e.g., single-beam or multibeam echosounders), mounted on ships, AUVs, or towed platforms
Short-range systems (e.g., ROV-mounted sonar) provide highly detailed data over small areas (centimeters in resolution), while medium-range systems (e.g., hull-mounted multibeam on ships or AUVs) cover much larger swaths with lower resolution
Use cases:
Mapping underwater topography and geological features
Planning submersible dives and identifying hazards
Supporting infrastructure projects like cables or offshore wind farms
Creating base maps for habitat mapping or biogeographic studies (i.e. understanding what marine life lives where and how their habitats are linked to geologic features as well as currents and physical oceanographic phenomena)
Data collection:
By research vessels or autonomous vehicles using sonar systems
Key manufacturers include Kongsberg, Teledyne, R2Sonic, and Edgetech
Data is processed using specialized hydrographic software (e.g., QPS Qimera, CARIS, MB-System)
Key considerations:
Requires calibration (e.g., sound speed profiles) and correction for vessel motion
Deep ocean mapping can be slow and resource-intensive
Interpretation of raw bathymetry data requires trained analysts and geospatial tools, it is not yet fully automated
Data sources:
European Marine Observation and Data Network - EMODnet Bathymetry, multibeam datasets and other maps with a built in visualizer
NOAA National Centers for Environmental Information - NCEI data access, archive of US and global bathymetric surveys with visualizer
General Bathymetric Chart of the Oceans - GEBCO Gridded Bathymetry Data, global map interface of compiled bathymetry
Global Multi-Resolution Topography - GMRT data, global compilation of multibeam data (includes graphical map tool)
Resources to learn more:
Seabed 2030 – global initiative to map the entire seafloor by 2030
Using open-access mapping interfaces to advance deep ocean understanding (Johannes, 2025)
GeoMapApp - free map-based application for browsing, visualizing and analyzing a diverse suite of curated global and regional geoscience data sets
Plumes of bubbles emanating from the seafloor, indicating that there were methane gas seep ecosystems in this region. Sound waves reflect strongly off the gas bubbles and are visible in the water column data. Source: https://nautiluslive.org/blog/2018/08/08/more-just-bathymetry-seafloor-mapping-tool-exploration
2.3.2 Water Column Mapping
About:
Acoustic systems—usually multibeam echosounders or special water column sonars—to detect and visualize features suspended in the ocean between the surface and the seafloor
Key for detecting gas plumes, biological layers (schools of fish or migrations in the twilight zone), suspended sediments, etc.
Use cases:
Observing midwater scattering layers (biological migrations)
Detecting hydrothermal plumes amd tracking gas plumes from methane seeps
Data collection:
Most multibeam systems include water column data modes, so it is often collected in tandem with bathymetry
From ships, AUVs, and ROVs
Data must be interpreted alongside oceanographic profiles (e.g., CTD casts) and often requires manual cleaning to reduce noise
Key considerations:
Processing and interpreting water column data is a bit tedious not yet standardized
Detection is sensitive to sonar frequency, range, and sea conditions
Validation with ground-truth sampling (e.g., bottle casts, nets, sensors) is helpful
Data sources:
European Marine Observation and Data Network - EMODnet Physics, includes some water column data layers
NOAA National Centers for Environmental Information - NCEI water column sonar data
Resources to learn more:
OET - More than just Bathymetry - Seafloor Mapping as a Tool for Exploration
Migration in the Ocean Twilight Zone - often monitored with water column data
2.3.3 Seafloor Backscatter
About:
Analyzing the intensity of sound that is reflected or ‘scattered back’ from the seafloor when using sonar systems
Provides information about seafloor texture, hardness, and composition (e.g., sand, rock, mud)
Often conducted simultaneously with bathymetric mapping during ship-based or AUV surveys
Use cases:
Seafloor habitat classification or substrate mapping
Detecting anthropogenic objects or features (e.g., cables, wrecks)
Complements bathymetry for geologic or habitat models
Data collection:
Similar to bathymetry and water column mapping, backscatter data is collected using the same sonar systems and processed using similar software
Key considerations:
Requires calibration and post-processing to produce usable mosaics
Interpretation of sediment type from backscatter typically should be verified by ground-truth sampling (e.g., grabs, cores with ROVs or HOVs)
Data sources:
NOAA National Centers for Environmental Information - NCEI data access
Resources to learn more:
Sediment layers seen in the sub-bottom profiler data collected in 2021 at the New England and Corner Rise Seamounts expedition on the NOAA Ship ‘Okeanos Explorer’. Source: https://oceanexplorer.noaa.gov/technology/sub-bottom-profiler/sub-bottom-profiler.html
2.3.4 Sub-bottom Profiling
About:
Uses low-frequency acoustic pulses to penetrate below the seabed and image sediment layers or other buried geologic features
Reveals vertical structures beneath the seafloor
Typically deployed from research vessels or towed systems
Use cases:
Studying sedimentation and geological processes
Locating subseafloor gas pockets or archaeological sites
For infrastructure planning or hazard assessment (e.g., submarine landslides)
Data collection:
Chirp profilers (high resolution, shallow penetration) and boomer/sparker systems (deeper penetration) are used
Operated from vessels with sonar equipment often collected simultaneously while collecting bathymetry and other mapping data, even if the sonar systems are different
Key considerations:
Resolution and penetration are inversely related (deeper = less detail)
Can be noisy and hard to interpret without ground-truthing (e.g., sediment cores)
Data sources:
NOAA National Centers for Environmental Information - NCEI data access
European Marine Observation and Data Network - EMODnet Geology, includes sub-bottom and other forms of seafloor geological data
Resources to learn more:
NOAA - Sub-Bottom Profiler
3-D reconstructed seafloor lava flows and hydrothermal vent field from the East Pacific Rise. This 3-D model was produced using downward facing video imagery and photogrammetry techniques. Credit: Mae Lubetkin
2.3.5 Photogrammetry and 3-D Reconstruction
About:
Stitching together overlapping images or video frames from subsea camera systems often mounted on ROVs or AUVs
To create detailed mosaics or 3-D models of seafloor features
Uses optical data, offering true-color, high-resolution imagery (unlike acoustic mapping techniques described above)
Use cases:
Mapping hydrothermal vent fields, coral reefs, archaeological sites, etc.
Change detection in dynamic environments (e.g., volcanic or vent habitats, biological growth or loss)
Public engagement and educational tools
Data collection:
Collected by vehicle-mounted cameras with precise navigation and positioning
Software like Agisoft Metashape or custom photogrammetry pipelines are used for processing (which is easier now than ever before, becoming much more common in ocean sciences)
Key considerations:
Requires good lighting and water clarity
Processing is computationally intensive, and vehicle navigation data helps with plotting 3-D reconstructions onto broader bathymetric maps
Can be limited to small survey areas due to time constraints and battery limitations
Data sources:
Monterey Bay Aquarium Research Institute - MBARI Sketchfab
3-D models of seafloor sites can be found in academic papers or at individual institutions or government agencies data repositories
Resources to learn more:
Seafloor Futures (Lubetkin, 2024)
Realtime Underwater Modeling and Immersion - Ocean Exploration Trust
Underwater 3-D Reconstruction from Video or Still Imagery: Matisse and 3-D Metrics Processing and Exploitation Software (Arnaubec et al., 2023)
Seeing the Sea in 3-D - Schmidt Ocean Institute
2.4 Satellite Remote Sensing
Satellite data provides the most familiar, spatially complete picture of the ocean. This bird’s-eye perspective is invaluable for understanding large-scale phenomena like currents, sea surface temperature patterns, and phytoplankton blooms, providing visual evidence that can enhance storytelling. However, there are unique considerations since, unlike the use of satellite imagery on land, most of our understanding of the ocean does not come from the visual spectrum. In this section, we’ll introduce three of the most important types of satellite ocean data and discuss the use cases for each.
History:
1978: NASA launched Seasat, the first satellite designed for ocean research.
Significant expansion in the 1990s with missions including TOPEX/Poseidon (ocean altimetry), AVHRR (high-resolution sea surface temperature), and SeaWiFS (ocean biology).
Modern constellations are operated by NASA, NOAA, ESA, EUMETSAT, CNES, ISRO, and others.
Trade-offs:
Excellent spatial coverage that’s impossible to achieve with ships or buoys.
Very high costs, these platforms are operated by government agencies.
Only seeing the very surface of the ocean, no subsurface data.
Limited horizontal resolution (spatial detail) and temporal resolution (orbital repeat time).
Gulf of Mexico SST on a cloud-free data. Source: https://marine.rutgers.edu/cool/data/satellites/imagery/
2.4.1 Radiometry - Sea Surface Temperature (SST)
About:
Sea surface temperature (SST) is the oldest and most extensive application of satellite oceanography.
Use cases:
Tracking climate change, El Niño, and marine heat waves.
Key input for weather models (e.g., very important for hurricane forecasting).
Mapping ocean eddies, currents, and upwelling, which are critical to fisheries.
Data collection:
Two separate types of sensors measure SST: Infrared and microwave.
IR sensors have higher spatial resolution (1-4 km) and finer temporal coverage but cannot “see” through clouds, which block over 70% of the ocean at any given time.
Microwave sensors can see through most non-precipitating clouds but have a lower spatial resolution (about 25 km) and don’t work near the coastline.
Measures temperature of the top ~1 mm of the ocean
Blended products: Combine multiple sensors for better coverage (e.g., GHRSST L4)
Example SST data at different processing levels. (Merchant et al. 2019): https://www.nature.com/articles/s41597-019-0236-x
Key considerations:
Make yourself aware of the different processing levels when accessing data. Level 4 (L4) will be the easiest to work with but may not be fully accurate.
L2: Data along the original orbital track.
L3: Gridded data, sometimes averaged over time.
L4: Cloud-free, gaps are filled by various methods depending on the source.
Temporal resolution depends on the satellite orbit. There are good options that blend multiple satellites.
Data sources:
NOAA CoastWatch - Graphical Interface
Resources to learn more:
2.4.2 Radar Altimetry - Sea Surface Height (SSH)
About:
Measures ocean surface height by sending radio pulses and measuring return time.
SSH can tell us the strength of large scale currents like the Gulf Stream, as the slope of the sea surface is used to calculate the “geostrophic current”.
Use cases:
Key to understanding ocean circulation and long-term sea level rise.
Data collection:
Radar altimeters on satellites measure SSH directly (e.g., Jason-3, Sentinel-6) and then the geostrophic currents are calculated in a post-processing step.
Spatial resolution is significantly worse than SST (25+ km)
The recent SWOT satellite is a new type of altimeter with much higher resolution but has very limited coverage since there is only one currently in orbit.
Key considerations:
SSH is useful for large-scale ocean currents but not coastal tidal currents.
Similar to SST, be careful about processing level and look for re-gridded datasets.
Can generally see through clouds, so gaps are not a significant issue.
Data sources:
Copernicus MyOcean Pro and Aviso - Graphical Interfaces
Resources to learn more:
Global map of marine Chlorophyll concentration. Source: https://sos.noaa.gov/catalog/datasets/biosphere-marine-chlorophyll-concentration/
2.4.3 Optical - “Ocean Color”
About:
Ocean color sensors measure the reflectance of sunlight from the ocean surface to infer biological and chemical properties, such as algal concentration, suspended sediments, and water clarity.
Use cases:
Tracking phytoplankton blooms and changes in marine ecosystems.
Useful for monitoring water quality, including coastal sediment and oil spills.
Data collection:
Sensors measure light reflected from the ocean at different wavelengths (e.g., MODIS, VIIRS, Sentinel-3 OLCI) and then apply algorithms in order to calculate variables such as Chlorophyll-a concentration.
Key considerations:
Ocean color data is significantly affected by cloud cover, aerosols, and atmospheric correction errors.
Data sources:
NASA Worldview - Graphical Interface
Resources to learn more:
2.5 Additional databases and scientific support
The four sub-sections above (In Situ Sensors; Deep Ocean Observation, Exploration, and Research Systems; Mapping; Satellite Remote Sensing) cover the main areas of ocean scientific data types and collection methods. There are some datasets that are not discussed in this guide since they are likely less useful for investigative storytelling or require technical skills to access and interpret the data. Below are some additional databases and information on contacting scientists to support your investigation. While in section 3, we outline a case study using real data to tell an ocean story.
2.5.1 Additional databases, collections, and visualizers
Sites that either did not fit into one of the sub-sections above, or that contain information which is generated after scientific studies occur:
PANGAEA - data publisher for earth and environmental science (across disciplines)
International Seabed Authority DeepData - database hosting all data related to international deep-seabed activities, particularly those collected by contractors (i.e. nations or entities) during their exploration activities and other relevant environmental and resources-related data. Includes a dashboard and map to search for basic stats and information about what contractors have done during deep seabed mining exploration cruises.
Marine Geoscience Data System - geology and geophysical research data across collections
USGS Earthquake Hazards Program - interactive map with magnitudes and additional information (earthquakes can occur on land and in the ocean)
WoRMS – World Register of Marine Species - comprehensive taxonomic list of marine organism names
OBIS – Ocean Biodiversity Information System - global open-access data and information on marine biodiversity
Windy - animated weather maps, radar, waves and spot forecasts
2.5.2 Scientific support
All of the datasets and databases we outlined above are free and open to the public. We hope that we outlined enough context and links to user-friendly platforms to access the data so that you feel empowered to conduct your own investigations with ocean datasets. That said, some data might be more challenging to work with depending on prior experience and computing skills, among other factors. When in doubt, you can always contact an ocean scientist to ask questions or seek support. Depending on your investigation or story, you will need to contact a specific type of ocean scientist since each has their own specialty. You can start by searching for and contacting scientists at nearby universities or research institutes.
Ocean scientists and their specializations:
Physical Oceanographers - Study ocean currents, tides, waves, and ocean-atmosphere interactions. They can help explain phenomena like sea level rise or how ocean circulation affects weather and climate.
Chemical Oceanographers - Focus on the chemical composition of seawater and how it changes over time. Useful for stories involving ocean acidification, pollution, nutrient cycling, or chemical runoff impacts.
Biological Oceanographers or Marine Biologists - Study marine organisms and their interactions with the ocean environment. They are ideal sources for stories on biodiversity, fisheries, invasive species, and ecosystem health.
Geological Oceanographers or Marine Geologists - Study the structure and composition of the ocean floor. They can provide insights into underwater earthquakes, tsunamis, deep-sea mining, or the formation of underwater features.
Climate Scientists with Ocean Expertise - Examine how oceans influence and respond to climate change. They are helpful for broader climate stories that involve ocean heat content, carbon storage, or long-term trends in ocean conditions.
Marine Ecologists - Study relationships among marine organisms and their environment. They can clarify ecosystem-level impacts, like those from overfishing, coral bleaching, or marine protected areas.
Fisheries Scientists - Specialize in fish populations, fishing practices, and resource management. Helpful for reporting on commercial fishing, stock assessments, or policy/regulation issues.
Ocean Data Scientists - Work with large marine datasets and modeling, can assist with interpreting satellite data, ocean models, or big datasets.
Marine Policy Experts and Ocean Economists - Focus on the intersection of ocean science, law, and economics. Helpful for coverage of marine regulations, governance issues, or the ‘blue economy.’
Marine Technologists or Ocean Engineers - Design and use tools like underwater drones, sensors, and buoys. They can help explain how ocean data is collected and what the limitations of certain technologies might be.
3. Case Study: Gulf of Maine Ocean Warming
As ocean scientists from the Northeastern United States, we have each witnessed how rapid ocean changes are affecting the ecosystems and communities around us. For this example case study, we focus on the Gulf of Maine—a region close to home. When telling ocean stories, it is helpful to have either first-hand or personal connections to the coastal or oceanic region you are investigating.
3.1 Motivation
The Gulf of Maine is warming faster than 99% of the global ocean, making it a key site to investigate local impacts of climate change on marine environments and coastal livelihoods. Stories of changing fish stocks and stressed fisheries are already discussed in communities within the Northeastern region. Before getting into the data, we will think through the historical and ecological context of the Gulf of Maine and its fisheries.
For centuries, the regional identity has been deeply linked to the ocean. Indigenous Wabanaki peoples—including the Abenaki, Mi’kmaq, Maliseet, Passamaquoddy, and Penobscot nations—relied on these coastal waters for food as well as cultural practices and trade. They managed their coastal and marine environments with ocean knowledge developed across generations. When European colonization began, intensive cod fishing fueled transatlantic trade and early settlements. Europeans considered the cod fisheries to be so abundant that they were endless. The overfishing by settlers caused a massive collapse in cod stocks by the 1950s. Now, other important local fisheries like the American lobster are being impacted by the combination of ocean warming and historic overfishing. Harmful algal blooms have also increased in frequency which indicate that the broader Gulf ecosystems are under stress.
In the following sections, we guide you through using publicly available ocean datasets to investigate the scientific questions behind the Gulf of Maine and its warming waters. By accessing current and archival datasets you will be able to visually show the seawater temperatures going up and connect that to other environmental stories or investigations about the Gulf.
3.2 Data acquisition
In order to investigate warming in the Gulf of Maine, we will analyze surface temperatures from two different datasets: a local in situ temperature sensor and the global-average SST. With the global SST as our baseline, we’ll be able to determine how much faster the Gulf of Maine is warming compared to the rest of the world. This analysis involves database downloads, data post-processing/analysis, and data visualization. If you don’t have experience with coding and want to get started with the Python programming language, see the appendix for tips on getting setup. Otherwise, you can always consider contacting a scientist to support you with your investigation (see section 2.5.2 Scientific support).
3.2.1 Gulf of Maine buoy temperature dataset
First, we’ll go to the National Data Buoy Center (NDBC) website and look for a buoy in the Gulf of Maine with a long historical record of temperature measurements. Clicking on the “Historical Data & Climatic Summaries” link at the bottom of Station 44007’s page reveals annual text files going back to 1982.
Screenshot from the NDBC website showing potential buoys to use for Gulf of Maine case study.
The task now is to process all of this data into a more usable format. We’ll do this with a python script using the pandas data analysis library.
Loop through the years 1982-2024 and create the dataset url for each year, using the NDBC website to deduce the url structure.
Load the text data directly from each url via
pandas.read_csv()Convert the year, month, day, hour columns into a single pandas datetime column.
Combine all of the data into a single dataframe.
Save our data to a new CSV file.
An example of what the available buoy data looks like for the year 1985. The highlighted sections show the parts of the dataset that we’re interested in: the date/time and the water temperature.
3.2.2 Global mean SST dataset
Next, we want a corresponding dataset for the globally-averaged SST, in order to determine whether the Gulf of Maine is warming faster or slower than the average. The Climate Reanalyzer displays globally-averaged SST from the NOAA 1/4° Daily Optimum Interpolation Sea Surface Temperature (OISST), a long term Climate Data Record that incorporates observations from different platforms (satellites, ships, buoys and Argo floats) into a regular global grid. 1/4° refers to the grid resolution—about 25 km.
There is an option to download the underlying data from the Climate Reanalyzer website, which will save us a lot of time vs. trying to access decades of data and doing the global averaging ourselves. The data is available as a JSON file, which is a different text file format that will require a more custom approach for converting into a pandas dataframe.
Screenshot of the Climate Reanalyzer website. In the dropdown menu, we want to download the JSON data.
One key concept to note is how this data handles dates. Each year includes a list of 366 temperatures, without any explicit list of the corresponding dates. This is using the format of “day of year” and we see that the last temperature is “null” for non-leap years. When processing this data, we need to take this into account and ignore the null final value. Similar to the buoy data, we’ll re-format this data in a pandas dataframe and save to a new CSV file.
A look inside the globally-averaged SST JSON file. The data is arranged as a list of years where each year has a list of 366 temperatures.
3.3 Climatological data analysis
A standard method for analyzing climate change anomalies is to first remove the climatological “seasonal” signal from the data. This will allow us to show, for each data point, how much warmer or colder it was than the average temperature for that day of the year.
The first step is choosing which time period we’ll use for our climatology “baseline”. Here we’ve chosen 1991 to 2020 since it is fully covered by our data and matches the climatology period used by the Climate Reanalyzer website. Next, we’ll use some built-in pandas methods to get the climatological average temperature for each day and then map that to each datapoint in our timeseries. The following code snippet shows the steps used for both the buoy data and the global SST:
# Select just the data in the range of the climatology period
df_clim = df[(df.index.year >= 1991) & (df.index.year <= 2020)].copy()
# Assign the day of year (1-366) to each data point in the timeseris
df_clim["day_of_year"] = df_clim.index.dayofyear
# Take the mean for each day_of_year
df_clim = df_clim.groupby("day_of_year")["temp"].mean()
# New variable in df: the climatological temperature for that day
df["day_of_year"] = df.index.dayofyear
df["climatology_value"] = df["day_of_year"].map(df_clim)
# Temperature anomaly is observed temperature minus climatological temperature
df["anomaly"] = df["temp"] - df["climatology_value"]
Our resulting dataframe includes new columns for climatology and temperature anomaly.
3.4 Analyzing and Visualizing the results
First, we’ll simply plot the full temperature timeseries and see what we find.
The warming signal is instantly apparent in the global SST data because the seasonal signal is so small. The Gulf of Maine, however, varies by more than 15° C throughout the year so any long term changes are difficult to see in this format. Plotting the climatology signal illustrates this point (pay attention to the y-axis).
Next we’ll view our temperature anomaly data (observed temperature minus climatology). As expected, there is more noise in the buoy data since it’s taken from a single point and any given day can vary by as much as 4 °C from climatology. The globally-averaged temperature has much less variance.
For the final version of our plot, we’re incorporate 3 changes:
Fit a simple linear regression using numpy’s polyfit in order to quantify the average rate of warming for the two datasets.
Plot the monthly averages instead of the daily values in order to simplify the visual clutter.
Use the same y-axis range for the two plots for direct visual comparison.
Comparing our warming rate calculations against the published literature finds good agreement:
Gulf of Maine SST: our rate of 0.496°C/decade is within 5% of the 0.47°C/decade reported by the Gulf of Maine Research Institute. This is likely due to differences in methods—we used a single buoy and they used the OISST data averaged across the entire Gulf.
For global SST, our rate of 0.188 °C/decade is within 5% of the 0.18 °C/decade (over the past 50 years) published by Samset et al. (2023).
These final plots provide simple visual evidence of the Gulf of Maine’s rapid warming over the past 40 years. We showed the data transform from text files, to noisy timeseries, and finally to expert-validated trend lines. By removing the strong seasonal signal and focusing on the anomalies, we can clearly see the long-term warming trend in both the Gulf of Maine buoy data and the global mean SST.
Finally, note that the linear regression is useful for quantifying the recent warming in an easily understandable number but is not necessarily a predictor of future warming. The Maine Climate Science Dashboard shows the potential for human emissions to either accelerate or slow down this rapid warming.
The Maine Climate Science Dashboard combines historical water temperature measurements with different climate scenario forecasts.
4. Conclusion
Our investigation into Gulf of Maine temperatures, using readily available public datasets, highlights one local manifestation of global climate change. This rapid warming isn’t merely an abstract data point, it continues to have profound implications for the region’s biodiversity and the human communities who rely on the ocean. Marine species are highly sensitive to temperature changes, and the Gulf of Maine has been experiencing a noteworthy decline in native species and increase in warmer-water species. The next steps in this story might look to other data sources to explore: Why is the Gulf of Maine warming so quickly? and What will the region look like in the future? or How exactly are local fisheries affected by warming waters?
This case study is one example of how to find the connections between global environmental change, local ocean data, and tangible human impacts. This process offers a template for investigating similar stories in your own regions:
Start with a local observation or community concern: What are people witnessing or experiencing in your local environment?
Explore the scientific context: Consult with scientists, read relevant research, and understand the underlying environmental drivers.
Seek out publicly available data: As shown in section 2, there is a large assortment of high-quality public ocean datasets that can be used to investigate countless questions.
Connect the data back to human issues: How do the environmental changes revealed by the data affect local cultures, livelihoods, health, and economies?
The key thing to remember is that there are multiple angles to uncover and expose often-invisible impacts to the ocean. Datasets provide one lens to report on climate and environmental changes, but these stories impact communities and are thus both political and social. Just as ocean science has changed and begun to decolonize, it’s crucial to investigate and tell stories that reflect diverse experiences. Ocean data can help highlight intersecting issues—such as deep seabed mining, marine health, and colonial continuums—with evidence-based information and compelling visualizations. We hope this guide offers a practical starting point for navigating ocean science, accessing and interpreting data, and connecting your investigation to real-world consequences that are planetary in scale yet intimately local.
APPENDIX: Getting started with python
If you have not done any coding before, the initial task of setting up your coding environment can be a challenging hurdle. There are multiple options for code editors/IDEs (integrating development environment), ways of handling dependencies (the external packages you install to give you advanced functionality), and other decisions that are outside the scope of this article. Luckily, once you’ve chosen your tools, there are good resources online so here are a few recommendations and then you can seek out more detailed tutorials:
Use Visual Studio Code as your code editor (the application where you will write and run code). This is the most popular option and there is an extensive ecosystem of 3rd party plug-ins and help resources. https://code.visualstudio.com/
Use conda for package management. Your computer’s operating system may come with a version of python pre-installed but it’s not a good idea to install packages onto this global location. Instead, create separate conda “environments” for different projects. This will allow you to experiment in a safe and organized way. Here is a helpful article on the VS Code website: https://code.visualstudio.com/docs/python/environments. For example, to create a new environment that we’ll name “ocean-study” and install the “matplotlib” plotting package would look like this:
conda create -n ocean-study conda activate ocean-study conda install matplotlibNow, in VS Code, just make sure your Python Interpreter is using this environment (it will look something like
~/miniconda3/envs/ocean-study/bin/pythonand you will be able to use the matplotlib package in your code.
Finally, consider using Jupyter Notebooks for exploratory coding where you’re loading datasets and making plots. Notebooks have the file extension
.ipynband allow you to run chunks of code independently in code “cells” and view the output right below. You can also use Markdown text cells to write notes and explanations for yourself and collaborators. Instructions on using VS Code: https://code.visualstudio.com/docs/datascience/jupyter-notebooks