Despite the perceptibility of the effects they impart on their hosts, the most incredible capacity of viruses is in their invisibility. Invisibility is the most frightening side of the current pandemic, and invisible is also the work of the scientists striving to find a solution.This proposal presents a data visualization that aims to give visibility to those scientists working on COVID-19. Their scientific publications have been computationally analyzed and transformed into a relational structure based on lexical similarity. The result is a network of scientists whose proximity is given by their closeness in writing.An innovative visual method that hybridizes network visualizations and word clouds shows the scientists in a deep space, explorable through keywords. In such a space, individuals are situated according to their lexical similarity, and keywords are used to clarify their proximity. By zooming, the visualization reveals more information about scientists and their clusters.While a lot of visualizations during the pandemic focused on showing the spread of infection, causing anxiety among the readers, this visualization reveals the efforts of science in eradicating the virus. Making visible the enormous number of scientists working on COVID-19 research will contribute to coping more positively with the pandemic.