Ocean data: from vision to action
A guide to implementing the Ocean Decade Data and Information Strategy
What to expect
This page aims to provide practical advice on how to implement the Ocean Decade Data and Information Strategy’s vision of a trusted, inclusive, and interconnected digital ecosystem. This digital ecosystem will make it easier to share, discover, access, and (re)use data across geographies and disciplines.
You will find specific advice for endorsed Decade Actions.
Advice for additional users, such as Decade coordination bodies, National Oceanography Data Centres and Associated Data Units, and Capacity Development stakeholders, will be added on this page at a later stage.
Purpose and scope
The purpose of the Data and Information Strategy Implementation Plan is to orient stakeholders in the Ocean Decade to collaboratively address:
Accessibility and usability of digital resources:
There are still some difficulties in submitting, finding, accessing, and using existing data
Lack of awareness of different types of data and information available and how to access them
There are challenges in identifying and understanding the quality and provenance of existing data
There is limited interoperability of data, which hinders their use and application
Data and knowledge gaps:
Significant gaps in ocean data remain, both by variable or data type, and in terms of spatial and temporal coverage, because the data do not exist or because they are currently held in hidden, inaccessible databases
Underappreciation of Indigenous and traditional knowledge held by diverse communities about the ocean, most of which – especially at local scale – has no digital presence
Resourcing and capacity:
Data management planning and resourcing are not prioritised at the outset or are of insufficient quantity and quality, leading to loss or poor management of data and reduction of the impact of the investment
There are resource and capacity gaps to collect, curate, manage and share ocean data and information digitally, including a lack of trained data management personnel
Value and awareness:
General lack of understanding of the socio-economic value of data and the benefits of open data sharing in a time of planetary climate crisis
The Ocean Decade Data and Information Strategy
To support the Ocean Decade’s work, the Data Coordination Group developed the Ocean Decade Data and Information Strategy to enable the exchange of interoperable, reliable, accessible, and timely ocean data and other digital resources. Its implementation represents a huge opportunity to transform the way ocean data and information are produced, shared, managed, and used globally and equitably.
We encourage communities and individuals working towards the vision of a digital ocean ecosystem in support of the Ocean Decade to establish and maintain meaningful, open cooperation across regional, economic, sectoral, cultural, disciplinary, and other divides.
Our vision is that by 2030, we will have: a trusted, inclusive, and interconnected ocean data and information ecosystem that is actively used for decision making to support sustainable ocean management.
Our mission to achieve the stated vision by 2030 is: to catalyse a solution-oriented, global digital transformation for the digital ecosystem we need to overcome the Decade Challenges.
How to use the Ocean Decade Data and Information Strategy: A guide for Decade Actions
This strategy should not only guide the efforts of all Decade Actions and related Decade bodies, but it should extend well beyond 2030. The wider and longer-term ambition of this strategy is thus to help align the digital efforts of the wider ocean community, including all stakeholders concerned with or dealing with data, information, software, or other digital assets for sustainable ocean management.
1. Welcome to the Ocean Decade! You’ve now been endorsed.
Step 1. Once you’ve been endorsed by the Ocean Decade, make sure that all participants in the Decade Action are registered in OceanExpert. Participants may also wish to create an ORCID and link it to their OceanExpert record. We also ask that all organisations connected to your Decade Action are registered in the Research Organization Registry. Please inform the Decade Coordination Office for Ocean Data Sharing of all OceanExpert entries and ROR records for your Decade Action, or let the Decade Coordination Office for Ocean Data Sharing (DCO-ODS) know if you are experiencing any difficulty with this step.
✅ Completing this step contributes to achieving Objective 1: Develop an ocean digital ecosystem that encourages sharing and equitable access of multidisciplinary data, information and knowledge by all
2. Do you collect, create or use data or information in your Decade Action?
If you don’t collect, create or use data or information in your Decade Action: You don’t need to go further, the Decade’s Data and Information Strategy is targeted to Decade Actions that collect, create or use data and information.
If you do collect, create or use data or information in your Decade Action, please follow Step 2: Make sure to have a data management plan for your project and make it available in a system such as AquaDocs or Zenodo.
✅ Completing this step contributes to achieving Objective 3: Build trust in data and information shared across the ocean digital ecosystem
3. How to manage your ocean data
Step 3. Once you have your Data Management Plan (DMP), contact the Decade Coordination Office for Ocean Data Sharing (DCO-ODS) to:
Step 3.1. Identify the type of data you’re collecting and the suitable repositories for it:
✅ Completing this step contributes to achieving Objective 2: Improve data discovery and usability across the ocean digital ecosystem
Step 3.2. Identify and connect with your National Oceanography Data Centre (NODC) and/or Associated Data Unit (ADU)
Step 3.3. Describe the quality of your data:
✔️ Check that data is complete, well-labelled (be clear and concise when labelling variables or files) – keep in mind your dataset could be reused by someone else.
✅ Completing this step contributes to achieving Objective 3: Build trust in data and information shared across the ocean digital ecosystem
✔️ Make sure your datasets are in a standardised and understandable format (such as CSV, with clean headers). Keep your data in common, open formats (CSV, NetCDF, JSON, etc.).
More information about file formats here: What data formats are available? – Ocean Observatories Initiative; Ocean Data Formats and codes – NOAA; Delivery formats – British Oceanographic Data Centre
✅ Completing this step contributes to achieving Objective 2: Improve data discovery and usability across the ocean digital ecosystem
✔️ Prioritise the use of community standards for data sharing; when unsure, contact the DCO-ODS and/or the International Oceanographic Data and Information Exchange (IODE) Programme to get support.
✅ Completing this step contributes to achieving Objective 3: Build trust in data and information shared across the ocean digital ecosystem
Step 3.4. Review licensing of your datasets
✅ Completing this step contributes to achieving Objective 3: Build trust in data and information shared across the ocean digital ecosystem
4. How to store and archive your ocean data
Step 4. Work with the DCO-ODS and repositories to ensure archival of your data
✅ Completing this step contributes to achieving Objective 2: Improve data discovery and usability across the ocean digital ecosystem
Step 4.1. Connect to global systems
Step 4.2. Can be reused and combined with other data to create new information, and be used in decision making: for instance, assign Persistent Identifiers such as DOIs to your datasets to ensure long-term access
What is the Decade’s Data and Information Strategy?
With the increasing number of initiatives that aim to collect data with the help of new sensors, autonomous platforms, and diverse techniques to measure and monitor the ocean, the data landscape is becoming more complex. To address this, common standards, increased interoperability, and enhanced partnerships are essential – priorities that the Ocean Decade is actively driving forward.
The Ocean Decade Data and Information Strategy
To support the Ocean Decade’s work, the Data Coordination Group developed the Data and Information Strategy to enable the exchange of interoperable, reliable, accessible, and timely ocean data and other digital resources. Its implementation represents a huge opportunity to transform the way ocean data and information are produced, shared, managed, and used globally and equitably.
We encourage communities and individuals working towards the vision of a digital ocean ecosystem in support of the Ocean Decade to establish and maintain meaningful, open cooperation across regional, economic, sectoral, cultural, disciplinary, and other divides.
The Vision of the Data and Information Strategy’s is that by 2030, we will have:
A trusted, inclusive, and interconnected ocean data and information ecosystem that is actively used for decision making to support sustainable ocean management.
The Strategy’s mission to achieve the stated vision by 2030 is:
To catalyse a solution-oriented, global digital transformation for the digital ecosystem we need to overcome the Decade Challenges.
Objectives
Develop an ocean digital ecosystem that encourages the sharing and equitable access of data, information and knowledge
Improve data discovery and usability across the ocean digital ecosystem
Build trust in data and information shared across the ocean digital ecosystem
Prioritise digital solutions that support decisions for sustainable ocean management
Expand, empower, and mobilise global communities to advance and maintain the ocean digital
How does this fit other information?
The Data and Information Strategy’s Implementation Plan is part of a suite of documents published by the IOC and Ocean Decade, laying out the ambition, strategy, implementation, and detail of a digital ecosystem for the ocean.
Click here for the list of ocean data documents produced as part of the Ocean Decade
Resourcing
The Ocean Decade offers for its Decade Actions and bodies different opportunities for capacity development such as the Ocean Matcher platform.
Ocean Matcher is an innovative web-based platform that uses cutting-edge technology and semantic matching to connect funders with high-impact ocean projects. It was developed through a collaboration between leading philanthropic and science organisations committed to advancing the goals of the Ocean Decade.
How to find more help?
The Decade Coordination Office for Ocean Data Sharing (DCO-ODS) supports Decade Actions in data and information management and sharing, promoting sustained digital interoperability between the data, information, and digital knowledge generated by initiatives across the Decade.
The DCO-ODS approach is to:
Connect Decade Actions and National Decade Committees (NDCs) with national and regional ocean data facilities
Develop guidance on thematic ocean data sharing practices and approaches (data management planning, data attribution, and citation)
Grow and support a global community of practice in ocean data management through training, webinars and capacity development opportunities
The DCO-ODS offers different tools to support Decade Actions:
Are you in need of support with data-sharing, finding data, or data management? Explore the Ocean Decade Data Resources Toolkit!
Can’t find a solution to your question? Go to the Ocean Decade Data Sharing Helpdesk to ask our data experts.
Best practice: A method which consistently and provably outperforms other methods with comparable objectives against known benchmarks. Claims that a method is a best practice should be verified by an independent third-party with all testing results and documentation publicly available.
Capacity building: A process during which some capacity of an entity is established or extended (e.g. establishing new supercomputing centres to provide more computational power).
Capacity development: A process during which some capacity of an entity is enhanced, often resulting in indirect capacity gains for other entities (e.g. the optimisation of a supercomputing centre’s operations, while computational power remains the same, human and electrical capacity is freed for other purposes).
CARE principles: Collective benefit / Authority to control / Responsibility / Ethics: A set of data principles which, together with their sub-principles, provide guidance on how data and digital systems can address tension that Indigenous communities feel between: 1) protecting Indigenous rights and interests in Indigenous data (including traditional knowledges) and 2) supporting open data, machine learning, broad data sharing and big data initiatives.
Co-design: A collaborative process during which those tasked with designing an entity engage non-designers (especially end-users) as participants in a facilitated design process. The goal of the co-design process is to reduce assumptions about user requirements and ensure these are implemented as early and as deeply as possible.
Data: A set of values, symbols or signs (recorded on any type of medium) that represent one or more properties of an entity or that entity in its totality. For example, the numbers generated by a sensor, values derived from a model or analysis, text entered into a survey, symbolic inscriptions in physical objects or the raw text of a document. Note: this range of this term includes “metadata”, which is data about data. “Subject data” is used (in contrast to “metadata”) to refer to data about an entity of interest, i.e. the “subject” which data is about.
Database: A data repository in which data has been organised and – to varying degrees – normalised according to a defined schema to optimise access and retrieval.
Data literacy: The capacity of individuals to effectively discover, acquire, examine, evaluate, understand, create, (re)use and otherwise handle data. Data literacy is closely related to digital literacy; however, it is concerned with an individual’s ability to work with data themselves rather than the tools which use them.
Digital ecosystem: A distributed, adaptive, open socio-technical system with properties of self-organisation, perpetuation and scalability parallel to natural ecosystems, comprising: 1) interlinked technological elements which store, process, share or otherwise handle data and 2) the human communities which operate and govern them. The World Wide Web is an example of a digital ecosystem, as would be a private network of servers. Depending on the technical compatibility and governance of their parts, digital ecosystems may have varying degrees of fragmentation. As fragmentation is reduced (e.g. by data federation), digital ecosystems may mature into data fabrics, lakehouses, data spaces or similar multi-system architectures.
FAIR principles: Findability / Accessibility / Interoperability / Reusability: A set of data principles which, together with their sub-principles, provide guidance on how data and digital systems can behave in a more collaborative way in networked systems.
Interoperability (of data): A property of a volume of data, realised when it is validly processed with other data across multiple, independent systems. Highly interoperable data is rendered in formats, with semantic markup, and with other attributes which allow independent systems to understand and process it with minimal resource usage, human intervention or transformation.
Open data: Data which is accessible, exploitable, editable and (re)shareable by anyone for any purpose and is associated with an open license.
Persistent identifier: A long-lasting (usually at least a decade) reference to a resource. Contemporary persistent identifiers (PIDs) are Web actionable, stored as URLs or parts of URLs, but they need not be.
Quality: An assertion directed at some entity which expresses how good or bad that entity is in relation to some rubric or set of criteria.
Quality assurance: A process during which an agent uses a defined rubric or system to evaluate some entity. Entities that pass the quality control processes embedded within a quality assurance process are deemed fit for onward processing or delivery to other agents.
Quality control: A process during which an entity’s compliance to a standard or ideal state is assessed. This process may also include the acceptance or rejection of entities based on their compliance with or exceeding of criteria derived from the standard or ideal state.
Unique identifier: An identifier which has a very high probability of being unique.
Examples of existing Data Management Plans (DMP) tooling to leverage in aligning your DMP to the Ocean Decade:
- Implementation of a Data Management Quality Management Framework at the Marine Institute, Ireland
- NASA EarthData – Data Management Guidance for ESD-funded Researchers
- DMP Tool – an open tool managed by the California Digital Library, a division of the University of California Office of the President.
- The DMPs of Horizon Europe’s MARine COastal BiOdiversity Long-term Observations (MARCO-BOLO)
- Guidelines for a Data Management Plan – Intergovernmental Oceanographic Commission Manuals and Guides #99
- IODE online, self-paced Data Management Course
- NOAA Data Management Handbook
- Data Resources Toolkit
-
Are you in need of support with data-sharing, finding data, or data management? Explore the Ocean Decade Data Resources Toolkit!
- Data Sharing Helpdesk
-
Can’t find a solution to your question? Go to the Ocean Decade Data Sharing Helpdesk to ask our data experts.
- Glossary
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Best practice: A method which consistently and provably outperforms other methods with comparable objectives against known benchmarks. Claims that a method is a best practice should be verified by an independent third-party with all testing results and documentation publicly available.
Capacity building: A process during which some capacity of an entity is established or extended (e.g. establishing new supercomputing centres to provide more computational power).
Capacity development: A process during which some capacity of an entity is enhanced, often resulting in indirect capacity gains for other entities (e.g. the optimisation of a supercomputing centre’s operations, while computational power remains the same, human and electrical capacity is freed for other purposes).
CARE principles: Collective benefit / Authority to control / Responsibility / Ethics: A set of data principles which, together with their sub-principles, provide guidance on how data and digital systems can address tension that Indigenous communities feel between: 1) protecting Indigenous rights and interests in Indigenous data (including traditional knowledges) and 2) supporting open data, machine learning, broad data sharing and big data initiatives.
Co-design: A collaborative process during which those tasked with designing an entity engage non-designers (especially end-users) as participants in a facilitated design process. The goal of the co-design process is to reduce assumptions about user requirements and ensure these are implemented as early and as deeply as possible.
Data: A set of values, symbols or signs (recorded on any type of medium) that represent one or more properties of an entity or that entity in its totality. For example, the numbers generated by a sensor, values derived from a model or analysis, text entered into a survey, symbolic inscriptions in physical objects or the raw text of a document. Note: this range of this term includes “metadata”, which is data about data. “Subject data” is used (in contrast to “metadata”) to refer to data about an entity of interest, i.e. the “subject” which data is about.
Database: A data repository in which data has been organised and – to varying degrees – normalised according to a defined schema to optimise access and retrieval.
Data literacy: The capacity of individuals to effectively discover, acquire, examine, evaluate, understand, create, (re)use and otherwise handle data. Data literacy is closely related to digital literacy; however, it is concerned with an individual’s ability to work with data themselves rather than the tools which use them.
Digital ecosystem: A distributed, adaptive, open socio-technical system with properties of self-organisation, perpetuation and scalability parallel to natural ecosystems, comprising: 1) interlinked technological elements which store, process, share or otherwise handle data and 2) the human communities which operate and govern them. The World Wide Web is an example of a digital ecosystem, as would be a private network of servers. Depending on the technical compatibility and governance of their parts, digital ecosystems may have varying degrees of fragmentation. As fragmentation is reduced (e.g. by data federation), digital ecosystems may mature into data fabrics, lakehouses, data spaces or similar multi-system architectures.
FAIR principles: Findability / Accessibility / Interoperability / Reusability: A set of data principles which, together with their sub-principles, provide guidance on how data and digital systems can behave in a more collaborative way in networked systems.
Interoperability (of data): A property of a volume of data, realised when it is validly processed with other data across multiple, independent systems. Highly interoperable data is rendered in formats, with semantic markup, and with other attributes which allow independent systems to understand and process it with minimal resource usage, human intervention or transformation.
Open data: Data which is accessible, exploitable, editable and (re)shareable by anyone for any purpose and is associated with an open license.
Persistent identifier: A long-lasting (usually at least a decade) reference to a resource. Contemporary persistent identifiers (PIDs) are Web actionable, stored as URLs or parts of URLs, but they need not be.
Quality: An assertion directed at some entity which expresses how good or bad that entity is in relation to some rubric or set of criteria.
Quality assurance: A process during which an agent uses a defined rubric or system to evaluate some entity. Entities that pass the quality control processes embedded within a quality assurance process are deemed fit for onward processing or delivery to other agents.
Quality control: A process during which an entity’s compliance to a standard or ideal state is assessed. This process may also include the acceptance or rejection of entities based on their compliance with or exceeding of criteria derived from the standard or ideal state.
Unique identifier: An identifier which has a very high probability of being unique.
- Further reading
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Examples of existing Data Management Plans (DMP) tooling to leverage in aligning your DMP to the Ocean Decade:
- Implementation of a Data Management Quality Management Framework at the Marine Institute, Ireland
- NASA EarthData – Data Management Guidance for ESD-funded Researchers
- DMP Tool – an open tool managed by the California Digital Library, a division of the University of California Office of the President.
- The DMPs of Horizon Europe’s MARine COastal BiOdiversity Long-term Observations (MARCO-BOLO)
- Guidelines for a Data Management Plan – Intergovernmental Oceanographic Commission Manuals and Guides #99
- IODE online, self-paced Data Management Course
- NOAA Data Management Handbook