Share data in a FAIR manner
The FAIR data principles are a fundamental part of open science and describe some of the central guidelines to good data management and open access to research data. FAIR is also a good way to sum up what SND has worked to achieve for many years.
The SND systems are designed to make it as easy as possible for our users to find, reuse, and share research data. In other words, we strive to make data FAIR: Findable, Accessible, Interoperable, and Reusable. For the main part, this is a matter of making data and information about data machine-readable. This becomes more important as research is more data-driven and the amount of research data increases. Working with FAIR data also requires us to follow guidelines for how humans read and understand research data.
The FAIR principles
The FAIR principles were first published in 2016. Since then, they have been adopted by for instance the European Union and many research funding organisations, universities, and research infrastructures. In Sweden, the Swedish Research Council and the National Library of Sweden have a government mandate to select criteria for how to evaluate to what extent scientific research data and publications fulfil the FAIR principles.
FAIR contains 15 principles that can be applied to research from all scientific disciplines. Below is a summary of the principles and information about how SND strives to fulfil them.
To make research data findable
- they are assigned a unique and persistent identifier
- they are described with rich and machine-readable metadata
- metadata specify the data identifier for the described data
- (meta)data are searchable and easy to find on the Internet
How does SND strive for improved findability?
SND assigns persistant identifiers of the DOI (Digital Object Identifiers) type for each version of a dataset that is made accessible in the SND research data catalogue. These identifiers make it easier to find the data for both humans and machines. If there are several versions of the data, the catalogue entry contains information about the differences between the versions. Metadata, i.e. information about data, also receive a DOI. The metadata DOI, however, isn’t visible in the catalogue. Each DOI leads to a landing page, which is a requirement from the DataCite organization. It is the membership in DataCite that allows SND to provide data with DOIs.
Research data that are shared in the SND catalogue are also findable by international users. All catalogue entries can be found via Google and metadata from studies in various specific research fields are currently findable in portals that are connected to the ARIADNE, CESSDA, and CLARIN infrastructures.
To achieve high findability, SND has certain minimum requirements on research data that are shared in the SND catalogue. This means that some information about data is mandatory, whereas other information is recommended. SND has also adopted five profiles that are adapted to describe and share data from specific research disciplines.
To make research data accessible
- (meta)data can be retrieved by their identifier, read, and accessed via a standardized communications protocol (e.g. http or ftp)
- the communications protocol is open, free, and universally implementable
- it is possible to create different user roles and mechanisms for user verification and controlled data access. The access to research data should be as open as possible and as restricted as necessary for sensitive data
- metadata are accessible even after the data are no longer available
How does SND strive for improved accessibility?
Every catalogue entry in the SND research data catalogue contains necessary information about how accessible the data are. There is information about where you can access the data, if they can be downloaded straightaway or if you must submit a request to access them. There is also information about where you can access the data if they cannot be downloaded from the SND catalogue.
In SND, we strive to make data directly downloadable as far as possible, if they aren’t restricted. If a study contains personal data or is classified, the data cannot be downloaded directly, as each request for them must be assessed before the data can be released.
If a dataset has been unpublished and is no longer accessible, it's replaced by a “tombstone page”. This page contains the metadata and information about why the data are no longer accessible.
To make research data interoperable
- (meta)data are presented with standardized, documented, and accessible semantic descriptions
- (meta)data use standardized vocabularies, terminologies, and ontologies
- (meta)data are described with references to other (meta)data, so that it is possible to understand the relations between data
How does SND strive for improved interoperability?
To make the metadata in the catalogue entries machine-readable, the information must be standardized. The SND system applies well-established controlled vocabularies, or standardized lists with words and phrases that can be used for indexing in a certain scientific field. We also use established metadata standards that are specific to various scientific disciplines. From each catalogue entry, you can export the metadata in several machine-readable formats.
SND strives to make research data accessible in as future-proof formats as possible. We deliver files in well-documented, non-proprietary (open-source code), and commonly used formats that can be read by several programs and applications. If it isn’t possible to meet all these criteria, we try to make sure that the data are accessible in formats that are common and well-established in the specific research field, so that the file contents can still be reused.
Our development of a questions and variables bank is another way in which we strive to improve the machine-readability of data. We achieve this by adding the information from questionnaires and surveys in a structured and searchable way.
To make research data reusable
- (meta)data contain multiple types of contextual information, such as scientific purpose, collection context, and what kind of equipment and software has been used
- there are clear conditions for data usage
- (meta)data are described with detailed provenance information
- (meta)data are structured and documented in accordance with applicable domain-relevant standards and formats
How does SND strive for improved reusability?
In our DORIS system, we create conditions to help researchers describe their data as detailed and understandable as possible before they are shared in the research data catalogue. The system has features to link related documents to the data description, such as code books, questionnaires, and technical reports. This information complements the machine-readable information and is often necessary for anyone who wants to reuse the material.
The catalogue entries also contain information about how data from the SND catalogue can be used. Some of the entries display a specific license for reuse of the data. It isn’t mandatory to add a license, and SND refers to the recommendations for open licenses and intellectual property from the Agency for Digital Government (DiGG) as support to select an appropriate license.
On our pages about data management you can find useful information about what researchers should think about if they want the data they share to fulfil the FAIR principles.
If you want to delve deeper into the FAIR principles, follow the links below:
- FORCE 11 – the FAIR principles from 2016: https://www.force11.org/group/fairgroup/fairprinciples
- The Swedish Research Council's criteria for FAIR research data (in Swedish): https://www.vr.se/analys/rapporter/vara-rapporter/2018-12-07-kriterier-for-fair-forskningsdata.html
- The Swedish Research Council’s coordinating work for open access to research data: https://www.vr.se/english/mandates/open-science/open-access-to-research-data.html
- The Swedish National Library’s evaluation criteria for FAIR publications (in Swedish): http://www.mynewsdesk.com/se/kungliga_biblioteket/documents/vetenskapliga-publikationer-och-fair-principerna-bedoemningskriterier-och-metod-foer-att-kunna-foelja-utvecklingen-mot-ett-oeppet-vetenskapssystem-86176
- The EU project Fostering Fair Data Practices in Europe (FAIRsFAIR): https://www.fairsfair.eu/
- The GO FAIR initiative: https://www.go-fair.org/
- Turning FAIR into reality, Final Report and Action Plan from the European Commission Expert Group on FAIR Datahttps://ec.europa.eu/info/sites/info/files/turning_fair_into_reality_1.pdf
- The fairy tale "A FAIRy tale - A fake story in a trustworthy guide to the FAIR principles for research data" from Danish e-Infrastructure Cooperation explains the principles in an entertaining and educational way: https://forskningsdata.dk/fairytale (the original pdf version can be found on https://zenodo.org/record/2248200#.YT8ZGvn7SUk)