Data analysis enables you to uncover hidden insights and correlations within the data, ultimately leading to the creation of knowledge. The nature of this analysis can vary significantly, depending on the heterogeneity of the data at hand. You, as the data analyst, are best equipped to determine the most appropriate methods for your specific dataset. However, it's imperative to adhere to and document the standards and methods commonly accepted within your field. You may employ a range of tools and software, including application-specific software or programming languages like Python and R [1][2][3].

Given that data analysis yields critical research outcomes, it's of utmost importance that the entire analysis workflow is thoroughly documented, published, and aligns with FAIR principles. This approach ensures the reproducibility of your results [4][5][6]. In our data-driven world, effective data analysis is paramount for informed decision-making across diverse domains. However, access to data alone is insufficient; it must be managed and analysed in a manner that guarantees it meets the criteria of being FAIR. This can be assured by providing code you used/produced for your analysis, execution environments, and data analysis workflows.