Researchers spend considerable time, money and effort collecting and interrogating data. Making your data findable, accessible, interoperable and reusable (FAIR) maximises the impact of that investment, including gaining more citations for your data sets.
Why FAIR Data is Important
Using the FAIR data principles can accelerate the impact of your work as more researchers can find and reuse your data. This can result in increased collaboration with research and industry and acknowledgement of your data in other publications. It also benefits research communities, research infrastructure facilities and research organisations.
FAIR benefits researchers and research organisations in the following ways:
- gaining maximum potential from data assets
- increasing the visibility and citations of research
- improving the reproducibility and reliability of research
- staying aligned with international standards and approaches
- attracting new partnerships with researchers, business, policy and broader communities
- enabling new research questions to be answered
- establishing new innovative research approaches and tools achieving maximum impact from research.
What is FAIR Data?
FAIR provides a useful framework for thinking about sharing data in a way that will enable maximum use and reuse.
The FAIR guiding principles for scientific data management and stewardship were developed by the international research community and published in 2016 to:
- support knowledge discovery and innovation both by humans and machines
- support data and knowledge integration
- support new discoveries through the harvest and analysis of multiple datasets and outputs
- promote sharing and reuse of data
- be applied across multiple disciplines, even those that involve sensitive data
- help data and metadata to be ‘machine readable’.
The authors produced the document to provide guidelines to improve the findability, accessibility, interoperability, and reuse of digital assets. However, translating the FAIR principles into practice varies for each discipline.
The FAIR Principles
The above information was drawn partly from: European Commission Expert Group, Chaired by Simon Hodson, Turning FAIR into Reality (2018) https://doi.org/10.2777/1524
Check Your Data is FAIR
Use the ARDC’s FAIR data self-assessment tool to check the ‘FAIRness’ of a dataset. You’ll even receive tips on how to enhance its FAIRness.
Take the FAIR test.
How the ARDC Supports FAIR Data
We support FAIR data in a number of ways, including supporting and driving a number of international and national initiatives as well as finding the right resources for our users to ensure they’re always using best practice methods within their research.
We’ve also created a series of recorded webinars on FAIR that explore each of the 4 FAIR data principles in depth using practical case studies from a range of disciplines, Australian and international perspectives, and resources to support the uptake of FAIR principles.
International and national initiatives
- Australia: FAIR Access to research outputs policy statement
- Australia and international: Top 10 FAIR Data Global Sprint
- Europe: GO FAIR initiative
- US: NIH Data Commons Pilot Phase Explores Using the Cloud to Access and Share FAIR Biomedical Big Data
- US and international: Enabling FAIR Data Project
- International: FAIRmetrics working group
- FAIRsFAIR European project
- The FAIR principles as published by FORCE11
- Nature article launching the FAIR concept
- Data Sharing and Citations: New Author Guidelines Promoting Open and FAIR Data in the Earth, Space, and Environmental Sciences
- Revisiting the FAIR principles for the European Open Science Cloud
- FAIRsharing lists standards, policies and databases related to FAIR
- Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud
- Jisc report: FAIR in Practice
- SURF report: FAIR Data Advanced Use Cases
- FAIR data assessment tool
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