Dear researchers,
Roses are red,
Making data open to share,
Think Findable, Accessible, Interoperable,
And Reusable – that’s FAIR.
As #LoveDataWeek continues, we can show our research data how much we love them by using the FAIR Principles to ensure that they are optimally available for discoverability and reuse, which will potentially increase the exposure of our research.
With Research Funders requiring the long-term preservation of the research that they fund, it is essential to ensure that data are thought about with longevity in mind. To aid this, the FAIR Principles guide researchers on how research data should be organised, so they can be easily found, accessed, usable with other data and applications, and reusable by others, to promote maximum use of their research data.
Information about the FAIR Principles can be found on our LibGuides page (see tab ‘FAIR Data’) and there is a whole host of guidance online. Here’s a summary below:
Findable: The first step is to make it possible to find data and metadata, by humans and machines/computers. Having machine-readable metadata is essential for automatic discovery of datasets.
Accessible: It is best to keep data is in a repository that provides security, appropriate metadata and includes license details. When users have found the data they require, they need to know how to access them. This may include information on authentication and authorisation. Additionally, metadata should be available, even when the data are no longer obtainable.
Interoperable: Quite often, data need to be integrated with other data, in addition to be interoperable with other workflows and applications. The format of the data should be open and accessible by other tools, and not reliant on proprietary software.
Reusable: The ultimate aim – the reuse of data. Including clear reuse license information, well-described data and metadata, documented background information, and contact details in the output record, can ensure that data are interpreted correctly and can make replication and reanalysis possible.
If you have any questions or would like further information, please do not hesitate to get in contact: rdm.lib@coventry.ac.uk.
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