NASA SLS-1 and SLS-2 Rat Datasets

The decision to reanalyze research from SLS-1 and SLS-2 was made on broader methodological questions on data engineering principles and research gaps about biology in spaceflight. Our methods are intended to facilitate the data organization of older spaceflight studies to perform further studies without the impediment of spaceflight experimentation costs and time. We developed a pipeline that could be applied to other studies of similar nature to use its results to bridge differences between space life science experiments. It’s a form of systematically updating previous reviews and giving more power and purpose to older legacy data. After testing our methods with datasets from the SLS Missions, we successfully wrote code that produces a programmable data frame that creates accurate and logical plots that may be used for spaceflight data reanalysis.
The beauty of this longform data structure lies in its ability to compare multiple datasets from different experiments and tissues to generate new conclusions. Since all the measurements are stacked in one column, we can use the data frame’s simple nature to select with code which rows encompass our dataset and assign it to a variable to produce a plot. Essentially, we can choose an interesting physiological relationship that is readily available in our data frame and produce a plot to visualize how it may behave beside another dataset on a different tissue that shares the same experimental parameters. Different types of graphs, including box plots, pie charts, x/y line plots, bar graphs and more can be incorporated and automated into the code through py-matplotlib with just an input of the rows that will be used. It is exciting to be able to play around with organized and understandable datasets and visually discover interesting directions the data may take.