Towards FAIR principles for research software
presentationposted on 10.03.2020, 03:58 by Paula 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