FAIR enough? Can we (already) benefit from applying the FAIR data principles to software?

The FAIR Guiding Principles were published to improve the reuse of scholarly data by making it findable, accessible, interoperable and reusable. The intent of the authors was that ”the principles apply not only to ‘data’ in the conventional sense, but also to the algorithms, tools, and workflows that led to that data”. Certainly, each of the four foundational principles make sense for research software: but can we apply them in such a way as to be useful? Can the training and guidance that has been developed to support FAIR data be repurposed easily for software? Can this provide an incentive that improves research software publishing more generally? How does FAIR for software relate to software management plans? This talk will discuss how the FAIR principles can be put into action for research software, why this is important and useful for research, and what this means for the transparency, reproducibility, and reusability of research software