To err is human, but when it comes to creating research materials, mistakes can be reduced by sharing more of our work and by using some helpful tools. These practices can make our research materials FAIRer—that is, more Findable, Accessible, …
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This project offers free activities to learn and practise reproducible data presentation. Pablo Bernabeu organises these events in the context of a Software Sustainability Institute Fellowship. Programming languages such as R and Python offer free, powerful resources for data processing, visualisation and analysis. Experience in these programs is highly valued in data-intensive disciplines. Original data has become a public good in many research fields thanks to cultural and technological advances. On the internet, we can find innumerable data sets from sources such as scientific journals and repositories (e.g., OSF), local and national governments, non-governmental organisations (e.g., data.world), etc. Activities comprise free workshops and datathons.
Dashboards for data visualisation, such as R Shiny and Tableau, allow the interactive exploration of data by means of drop-down lists and checkboxes, with no coding required from the final users. These web applications run on internet browsers, allowing for three viewing modes, catered to both analysts and the public at large: (1) private viewing (useful during analysis), (2) selective sharing (used within work groups), and (3) internet publication. Among the available platforms, R Shiny and Tableau stand out due to being relatively accessible to new users. Apps serve a broad variety of purposes. In science and beyond, these apps allow us to go the extra mile in sharing data. Alongside files and code shared in repositories, we can present the data in a website, in the form of plots or tables. This facilitates the public exploration of each section of the data (groups, participants, trials...) to anyone interested, and allows researchers to account for their proceeding in the analysis.