DSW Metrics
The DSW tool has in-built metrics to provide a more thorough understanding of how well the plan will produce FAIR (Findable, Accessible, Interoperable, Reusable) data. In addition, it provides two further metrics on Openness and DMP Practice.
These metrics are there to help researchers improve on the FAIRness of their data and improve on their data management and stewardship practices. The metrics are not a score of how ‘good’ the research or data management plan is.
In addition to overall scores, the metrics are also broken down by chapter, making it easier to identify where improvements could be made.
When completing the questionnaire, if one of more of the metrics are low, it is worth investigating why this might be and changing the way data are managed to improve the overall metrics. However, sometimes for reasons outside of a researcher’s control, a questionnaire may have a low metric that cannot be improved, for example, there are no suitable controlled vocabularies to tag data leading to a low Reusability metric or for privacy reasons data cannot be made publicly accessible leading to a low Openness metric. However, the aim should be to make data as FAIR, open and as well managed as possible.
Findability metric
The Findability metric describes how easily data can be located. The score associated with an answer will be higher if it makes it easier for humans or for computers to locate your dataset, for example, if it ends up in an index or has a unique resolvable identifier.
Accessibility metric
The Accessibility metric describes how well access to the data is described and how easy it is to implement. The score associated with an answer will be higher if it makes it easier for humans and computers to get to the data. This is determined by, for example, the protocol for accessing the data or for authenticating users, and also by the guaranteed longevity of the repository. Note that this is different from the Openness metric.
Interoperability metric
The Interoperability metric describes how well the data interoperates with other data. The score associated with an answer will be higher if it makes it easier for humans and computers to couple the data with other data and 'understand' relationships. This is influenced by the use of standard ontologies for different fields and proper descriptions of the relations. It is also influenced by proper standard metadata that is agreed by the community.
Reusability metric
The Reusability metric describes how well the data is suitable for reuse in other contexts. The score associated with an answer will be higher if it makes it easier for humans and computers to reuse the data. This is influenced largely by proper description of how the data was obtained, and by the conditions that are put on the reuse (license and, for personally identifying information, consent).
Openness metric
The Openness metric describes how open the data are. Note that this is different from the Accessibility metric. A score associated with an answer will be high if the data is openly accessible, and low if restrictions apply to access and re-use.
DMP Practice metric
The DMP Practice metric describes how data meets best practice. A score associated with an answer will be high if a practice would be considered preferable over alternatives, i.e. generally a good idea.