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For the sake of our future, well-being data should be FAIR
FAIR principles aim to ensure the maximum use of the digital ecosystem, data management and stewardship to extract data for research, knowledge management, downstream studies and decision-making.
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FAIR principles aim to ensure the maximum use of the digital ecosystem, data management and stewardship to extract data for research, knowledge management, downstream studies and decision-making. (Shutterstock/-)
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part from its severe impact on global health and the economy, the COVID-19 pandemic has taught us many things, including how data can be important for our well-being.
We have learned how to look at everyday data to make ourselves aware of the development and spread of the virus in our communities. We have also gotten used to hearing government officials announce updates on national COVID-19 infection rates. And at some point, we also start to question, can we trust this data?
It is still fresh in my memory how, in 2016, a researcher from the best-known university in Japan was forced to apologize for manipulating data, driving us to accept the harsh truth that not all data is accountable, including data published in high-impact journals.
Considering how vital data can be, it should be put to good use and uphold the Findable, Accessible, Interoperable and Reproducible (FAIR) principles. The FAIR principles were introduced in 2014 by a broad group of academics and private stakeholders in Leiden's "Jointly Designing a Data Fairport" and were academically published in 2016.
The principles were generated to ensure the maximum use of the digital ecosystem, data management and stewardship to extract data for research, knowledge management, downstream studies and decision-making.
One of the objectives of the FAIR principles is to produce high-quality publications by facilitating the processes of data gathering and discovery, not limited to common data but including the algorithms, methods and equations used to derive the data and results.
Lately, Indonesia has been boosting its research through a wide array of outcome-based grants and aid, prioritizing the number of published papers. However, it fails to publish its raw data and methods as part of its output.
