Over the past several decades, the scientific process has relied more and more on computational analysis of data to produce digital artifacts. Fields like molecular biology, neuroscience, linguistics, and astrophysics, to name a few, have been revolutionized by this trend to the point that computational workflows are ubiquitous. Although most of these workflows are very similar at a high level - collecting data, analyzing it with code, and publishing the resulting figures - implementation details differ widely.
While there exist standards such as the FAIR Guiding Principles for organizing and sharing data, there are not widely adopted standards for reliably regenerating analyses from said data, especially across compute environments. This talk presents an open framework for archived, reproducible, and transparent science (ARTS) that aims to do exactly this - by packaging data, code, and figures in containers and uploading it to a persistent, trusted, and accessible archive.