
Codefair is your personal assistant when it comes to making your research software reusable and especially complying with the Findable, Accessible, Interoperable, Reusable (FAIR) Principles for Research Software. Whether you are developing artificial intelligence (AI)/machine learning (ML) models with Python, data visualization tools with Jupyter notebook, or data analysis code with R, Codefair is here to assist you. By communicating with you through GitHub issues and submitting pull requests, Codefair will make sure that your software follows best coding practices, provides metadata in standard format, includes a license file, is archived on Zenodo, and much more. With Codefair by your side, you're not just developing software but you're advocating for better software practices. Learn more on the app's website codefair.io.
| 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 |
