posted on 2020-03-10, 03:58authored byPaula Andrea Martinez
The FAIR Guiding Principles, published in 2016, aim to improve the findability, accessibility,
interoperability and reusability of digital research objects for both humans and machines.
The FAIR principles are also directly relevant to research software. In this position paper
“Towards FAIR principles for research software”, we summarised and developed a basis for
community discussion. At the start, we discussed what makes software different from data
concerning the application of the FAIR principles, and which desired characteristics of
research software go beyond FAIR. Then, we presented an analysis of where the existing
principles can directly apply to software, and where they need to be adapted or
reinterpreted. Our next step after the position paper is to prompt for community-agreed
identifiers for FAIR research software.
Acknowledgments
To all the authors of Towards FAIR principles for research software
https://doi.org/10.3233/DS-190026, and the numerous people who contributed to the
discussions around FAIR research software at different occasions preceding the work on this
paper.
References
Lamprecht, Anna-Lena, et al. (2019) Towards FAIR principles for research software. Data
Science. https://doi.org/10.3233/DS-190026
ABOUT THE AUTHOR(S)
Dr Paula Andrea Martinez is leading the National Training Program for the Characterisation
Community in Australia since 2019. She works for the National Image Facility (NIF). Last year
she worked at ELIXIR Europe coordinating the Bioinformatics and Data Science training
program in Belgium and collaborated with multiple ELIXIR nodes in the development of
Software best practices. Her career, spanning Sweden, Australia and Belgium nurtured her
experience in Bioinformatics and Research Software development for complex and dataintensive science. She started a career in Computer Science, later on, interested in research
methods development and now outreach and advocacy in data and software best practices