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FAIR software is a topic of growing importance in the research software landscape. There have been efforts to describe the how the FAIR principles apply to research software and work in this direction is still ongoing. Even though the definition of the FAIR software principles is still in flux, recommendations are available to improve software in accordance to the spirit of the FAIR principles. In this session we would like to introduce howfairis: a Python package to analyse software's compliance with the FAIR software recommendations. We will describe how howfairis analyses your code to measure its level of compliance with the FAIR software recommendations. We will show how our Github Action can test your software automatically. We will also show how to add a badge to your GitHub repository showing to the world how FAIR your software is! Given that the FAIR principles for software are still evolving, howfairis will also evolve to match new developments in the area. We would like new users to give us feedback and to contribute to the development of this tool!
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 4 | |
| downloads | 4 |

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