The reuse of scholarly data has been improved thanks to the creation of a measurable set of principles. Information is everything in our current society. We are surrounded by data, and the growth of new information is increasing exponentially. This growth is crucial in the advancement of technology, science, etc. But inaccurate information can greatly harm the scientific community. This is where FAIR principles play a vital role. The main objective of these guidelines is to guarantee the validity and reproducibility of the published data.
What are FAIR principles?
FAIR is an acronym that defines the following principles:
FINDABLE: Persistent identifiers are necessary because they identify your data and facilitate citation. It is also important to have rich metadata because it supports findability, citation, and reuse. The more complete the metadata, the more correct the interpretation of the information will be, as the context of de data will be clearer.
ACCESSIBLE: Not all data has to be open, but if it is accessible, data should be downloadable without needing special protocols.
As open as possible, as closed as necessary
INTEROPERABLE: Data should be integrable with other applications. For that purpose, it is important to use common formats, controlled vocabulary, keywords...
REUSABLE: Make your information comprehensible by creating documentation. It should help to interpret the information correctly by explaining the content of the document, the steps followed in processing the data… and by adding any extra information needed. It also needs a clear license and provenance information on how the data was formed.
FAIR Data importance
One of the greatest challenges of science is to facilitate learning for both humans and machines. Thanks to the FAIR principles, we are able to generate data following the "Open Access" philosophy. By following this set of guidelines when generating content that is accessible to the entire community, we establish a common basis that increases the great potential of open access.
Check the "FAIRness" of your data
There is a really interesting tool to see how FAIR your data is. After completing a series of questions, you will get a score that determines the ‘FAIRness’ of the data. It is called “FAIR self assessment tool” and it is developed by the “Australian Research Data Commons”
In Orvium, we encourage our publishers to apply FAIR Principles to their publications by providing the following features:
- In our platform it is possible to set a DOI (Digital Object Identifier) to a publication, to make it findable even if the URL changes. Authors can also use an ORCID iD to identify themselves persistently.
- In Orvium all the publications are accessible to everyone, and there is no need for special protocols to download and obtain the data.
- We make use of the "Open Archives Initiative Protocol for Metadata Harvesting" (OAI-PMH), that makes the data accessible to humans and machines. It is also a tool that allows interoperability and integration of the metadata between different repositories. For example, we expose our metadata in order to integrate it with "OpenAIRE" infrastructure.
- Thanks to OAI-PMH, the metadata of the publications available in Orvium follows "Dublin Core" format, one of the most used metadata standards to guarantee the interoperability.
- All the data has a Creative Commons license rights attached, so it is clearly established the way that information can be reused.
Our aim is to build a big community based on Open Access, and these guidelines ensure a common structure, making the data useful to the scientific community.
- Libereurope: https://libereurope.eu/wp-content/uploads/2017/12/LIBER-FAIR-Data.pdf
- Openaire: https://www.openaire.eu/how-to-make-your-data-fair
- Dadun: https://unavdadun.wordpress.com/2018/11/13/principios-fair/
- Foster: https://www.fosteropenscience.eu/learning/assessing-the-fairness-of-data/#/id/5c52e8cf0d3def29462d8cb5
- Open Archives: https://www.openarchives.org/pmh/
- Cornell University: https://data.research.cornell.edu/content/writing-metadata