Research software refers to any software created during the research process or for a research purpose. Research software comes in various formats, such as Python scripts, desktop software, or web applications, and is developed for various purposes, including data visualization, data analysis, computational modeling, or artificial intelligence (AI) development. Research software has become a critical element of scientific research, and making it reusable is therefore critical to enable the reproducibility of research results, prevent duplicate efforts, and ultimately increase the pace of discoveries. In this webinar, we will introduce the FAIR (Findable, Accessible, Interoperable, Reusable) principles for Research Software (or FAIR4RS principles), which are adaptations of the FAIR data principles but tailored specifically for research software. These principles provide a set of aspirational instructions for optimizing the reusability of research software. We will then present the FAIR Biomedical Research Software (FAIR-BioRS) guidelines, which are a set of minimal, actionable, step-by-step instructions we have established to easily make biomedical research software compliant with the FAIR4RS Principles. We will also introduce a new task force started under the Research Software Alliance (ReSA) that aims to build upon the work of the FAIR-BioRS guidelines to establish actionable guidelines for making any research software FAIR. Finally, we will provide simple instructions that anyone can follow to start making their software FAIR and also present a tool called Codefair that helps to make research software FAIR without breaking a sweat.

Presenter

• Bhavesh Patel (FAIR Data Innovations Hub, California Medical Innovations Institute)

Presenter Bio

Bhavesh Patel is a Research Professor at the California Medical Innovations Institute (CalMI2), a nonprofit located in San Diego, CA. He completed his Ph.D. in Mechanical Engineering at UC Berkeley where he specialized in computational modeling. He joined CalMI2 right after graduating in 2015 where he has been developing computational models for biomedical applications. Since 2019, he is also leading the FAIR Data Innovations Hub, a division at CalMI2 where he and his team are developing various standards, guidelines, and computer tools that make it easier for biomedical researchers to make their data, software, and other research outcomes FAIR.