CDRmare Research Data Management
Within CDRmare we aim for a sustainable, responsible, and FAIR handling of research data to make valuable scientific results openly and consistently accessible. The CDRmare Data Policy forms the basis for these requirements by setting the framework for the data handling. Internal Data Management Plans (DMPs) shall ensure a transparent central overview of the decentralised planned, gathered, and finally published datasets and corresponding contact persons within the CDRmare consortia. All CDRmare researchers can receive support in their data management tasks by the individual consortia data managers and can benefit from the coordination, networking, and information provision by the central CDRmare data management.
Importance of managing data
Many research data are unique and can often only be obtained thanks to large sums of research funding. Both facts make research data extremely valuable and demand an appropriate handling and management of the data. Research data management accompanies researchers in their daily work and covers all parts of the research data life cycle – that includes the data capture/production, processing, analysis, documentation, and storage as well as archiving, publishing, citation, and (re-)use of final data results. A thorough data management creates added value for the entire research environment.
Metadata are structured descriptive information on data like research data. Metadata contain detailed characterising information about datasets in order to make data users understand how, why, when, and where the data has been collected or processed and who the responsible contact persons are. Data and metadata are closely linked, but must be considered separately from each other.
Sharing metadata, e.g. via a data management plan does not mean sharing or exchanging the research data itself, but rather providing information about planned studies, data capture, and existing data (both preliminary and published datasets). Making your metadata available at an early stage fosters synergies within projects, working groups, and collaborations.
Data publications show your research activities independently from paper publications. Research data published in suitable, trustworthy, qualified data repositories (in particular with a persistent identifier like DOI) are securely available, citable, and easily (re-)usable in the long term within your own papers and by others. The external usage of your published research data may offer new potential collaborations and co-authorships. Furthermore, via clear metadata your research results can become internationally visible on interactive data platforms like data portals (e.g. the Marine Data Portal by the DAM). Both paper and data publications improve the external visibility and impact of research missions, projects, institutions, or working groups.
CDRmare data access
All data produced and published within CDRmare shall be findable via the Marine Data Portal which is a central access point to research data from the German marine research community. Data repositories like PANGAEA and WDCC or institutional access platforms like IOWMeta are harvested by that portal. Here you find all data flagged with CDRmare in the Marine Data Portal. Additionally you can find CDRmare related datasets via data icons in the publication list.
FAIR in a nutshell
“The FAIR Guiding Principles for scientific data management and stewardship” (Wilkinson et al., Sci. Data 2016) are meant to improve the Findability, Accessibility, Interoperability, and Reuse of digital research data for humans as well as computers. They refer to three different types of entities: data (or any digital object), metadata (information about that digital object), and infrastructure.
FAIR in some more detail …
- data and metadata should easily to be found both by humans and by computer systems
- descriptive metadata should be provided complete, understandable, basic, machine-readable
- use of unique identifiers (like DOI, ORCID)
- data and metadata should be archived for the long term and should be made available in a way that they are easily accessible and can be downloaded if applicable
- no cost, open source, clear instructions for data access (e.g. for highly sensitive data only metadata access is stated like contact details or access authority)
- data should be available in a way that they can be exchanged, interpreted unambiguously, and combined in a (semi-)automated way with other datasets
- use of a common (programming) language / standard vocabulary
- qualified references between datasets that build on each other
- (meta)data should be well described to ensure that the data can be re-used for future research and can be compared with other compatible data sources
- clear and rich description of (meta)data and used standards
- information on conditions for re-use of a dataset (usage license, e.g. CC-BY)
> Data Management Team
Feel free to contact us for all matters concerning research data management: