Remote Data Processing Using Crowd-Sourced Cloud-Computing

Pandemic circumstances require creative solutions to research problems. Here we discuss processing spectromicroscopy data acquired with synchrotron spectro-microscopy on freshly formed coral skeletons. This model, however, can be used for processing any data that require decision-making and are therefore viable to human error.

Tools for processing complex and large sets of data do not always avoid human error or require consistency. To adjust our model for remote data processing to correct this error and make findings more rigorous, we introduce a model of “crowd-sourced” data processing using cloud computing. In this project, we hired a combination of ten freshmen, sophomores, and juniors to process X-PEEM synchrotron spectro-microscopy data on freshly formed coral skeletons on the Amazon Web Services cloud. These students were recruited through the Mercile J. Lee scholars’ program, whose mission is to develop the potential of academically talented individuals from underrepresented groups. Repeat data processing by MJL students led to more robust data and helped correct for individual error and bias in results.