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. The steps can be followed in any order, and multiple steps may be worked on at the same time.
You will find specific advice that endorsed Decade Actions can use to secure the data legacy of the UN Ocean Decade.
Advice for additional users with a higher level of data literacy and data management experience and expertise, 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.
We recognise that Decade Actions have a vast spectrum of technical ability and resourcing, and that not every data management and data sharing scenario they may encounter can be covered in these guidelines. We encourage Decade Actions to work with the Decade Coordination Office for Ocean Data Sharing when clarification is required.
We encourage Decade Actions to work with the Decade Coordination Office for Ocean Data Sharing (DCO-ODS) and their national ocean data infrastructures when clarification or specific advice is needed.
DCO-ODS is responsible for coordinating effective knowledge and information exchange across the Ocean Decade. This coordination supports an accessible, connected, and collaborative global digital ocean ecosystem
Purpose and scope
The guidance in this page aims to support Decade Actions and other Decade Stakeholders implement the Data and Information Strategy and to address:
Accessibility and usability of digital resources:
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
Challenges in identifying and understanding the quality and provenance of existing data
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
How to Implement the Ocean Decade Data and Information Strategy
A guide for Decade Actions
This section provides practical steps to help you apply the Ocean Decade Data and Information Strategy within your Decade Action and successfully achieve its objectives.
1. Welcome to the Ocean Decade! You’ve now been endorsed.
Step 1. Make sure that all participants in the Decade Action are registered in OceanExpert. We also recommend that participants create an ORCID and link it to their OceanExpert record. This allows identification of participants in Decade Actions, even if their role or organisational affiliation changes. We also ask that all organisations connected to your Decade Action are registered in the Research Organization Registry (ROR) or a comparable registry providing persistent identifiers and organisational metadata.
To ensure accuracy and continued maintenance, these entries must be managed by an authorised representative of these organisations.
- If you are the lead or authorised representative of an organisation: Check whether your organisation already exists in ROR. If not, follow ROR guidelines to request a new record or update an existing one.
- If you are not the lead or authorised representative of an organisation: Do not create or modify records yourself without confirmation. First search the ROR registry, and if needed, raise the request with the appropriate person within the organisation.
More information on how to search, add, or update records is available here.
One benefit of this approach is to ensure we have consistency in spelling and description of an organisation across the Decade’s digital ecosystem. Please inform the Decade Coordination Office for Ocean Data Sharing (DCO-ODS) of all OceanExpert entries and ROR records for your Decade Action, or contact DCO-ODS if you are experiencing any difficulty with this step.
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 (DMP) for your project and make it available in a system such as AquaDocs or Zenodo to enable full transparency in the data sharing process.
During this planning process, be sure to consider and clearly specify in your DMP the type of data you are collecting, creating, or reusing, as this will determine the repository you can select for publishing your data (Step 3). The chosen repository should also be clearly indicated in the DMP.
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 and discuss with DCO-ODS suitable repositories to submit your (meta)data to, and allow a connection with IOC’s data discovery systems:
Step 3.2. Identify and connect with your National Oceanography Data Centre (NODC) and/or Associated Data Unit (ADU) to allow for long-term preservation of your data and to simplify connection to the global ocean digital ecosystem.
Even if they cannot host the data themselves, they should be aware of it for coordination purposes with the DCO-ODS. Well-maintained metadata catalogues are essential in this effort.
If the NODC cannot store the data, contact DCO-ODS for discussion about suitable approaches.
Step 3.3. Describe the quality of your (meta)data:
✔️ Check that data is complete, well-labelled (be clear and concise when labelling variables or files, use standardised, open vocabularies where possible) – keep in mind your dataset could be reused by someone else. Where data are not complete, attempt to include explanations for why this is the case in associated metadata and in the DMP to help others understand.
✔️ Make sure your datasets are stored and released in widely known formats using open standards (such as CSV, with clear headers, NetCDF, JSON, and others described in the link). Avoid proprietary or customised formats that require specific software or uncommon expertise to access and use effectively
✔️ Note that many Repositories have their own data standardisation and formatting requirements. Ensure these are kept in mind and included in your DMP, to ensure that submission to these repositories is possible and does not require more effort than necessary.
✔️ Prioritise the use of well-known standards for data sharing used or defined by the relevant community in order to maximise the re-use of a dataset; when unsure, contact the DCO-ODS and/or the International Oceanographic Data and Information Exchange (IODE) to get support.
Step 3.4. Review licensing of your datasets
Licensing is important when sharing data because it clearly defines how the data can be used, ensuring legal clarity, proper attribution, and promoting open, FAIR, and responsible reuse. The data owner should clearly state and communicate the license terms so that the repository and users understand their rights and obligations:
- Prioritise the use of open licenses (CC, ODC, ODbL, etc.) and communicate possible usage restrictions, and other desired permissions or limitations.
- If you work with Indigenous and local knowledge, ensure you have permission from the communities to share the data and give them correct acknowledgment using appropriate mechanisms.
- If a Decade Action needs help with deciding a standard data license to add to their data, please consult the Decade Coordination Office for Ocean Data Sharing.
4. How to store and archive your ocean data
Step 4. Work with the DCO-ODS and repositories to ensure archival of your data
Step 4.1. Connect to global systems by submitting your data to existing data infrastructure in the IODE network (National Oceanographic Data Centres; Associated Data Units; nodes of the Ocean Biodiversity Information system).
If vital (meta)data would be lost in harmonisation when submitting data to repositories, Decade Actions are also encouraged to deposit raw data in a generalist repository.
Step 4.2. Work with your data repository to assign Persistent Identifiers such as DOIs to your datasets. Here we use “Persistent Identifiers” to mean long-lasting references to objects on the internet, such as datasets. They may change the location they take users to so that the object the point to can be moved without the need for a new identifier, making them useful for applications such as citation of datasets in scientific papers or other reports.
This step will continue building up trust in your data so it can be reused and combined with other data to create new information, and be used in decision-making.
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 encourage 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
The five strategic objectives to achieve this vision and mission of the Data and Information Strategy are:
Develop an ocean digital ecosystem that encourages the sharing and equitable access of data, information and knowledge by all
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 digital ocean
How does the Implementation Plan align with 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 to connect ocean science projects, technology, and conservation projects with funders.
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.
Data repository: Online platform used to deposit completed datasets with the purpose to publish, share and/or preserve them. Personal websites and databases as well as cloud storage services (Dropbox, Google Drive, etc) are not considered repositories. (Ghent University, n.d.)
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 Plan (DMP) tools you can use to align your DMP with the Ocean Decade requirements:
- 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.
Data repository: Online platform used to deposit completed datasets with the purpose to publish, share and/or preserve them. Personal websites and databases as well as cloud storage services (Dropbox, Google Drive, etc) are not considered repositories. (Ghent University, n.d.)
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 Plan (DMP) tools you can use to align your DMP with the Ocean Decade requirements:
- 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