margins for points that have no matching value on the opposing axis (Figure 4b). These gutter plots, like the columns in Figure 2, jitter points in the unused direction to reveal density. Tooltips explain the reason and the proportion of data in each area. Again, without a place to show these data we would have to either filter them out or hide them from view, which could prevent deeper understanding of the possible lines of investigation. Like all parts of the plot, the gutters still enable brushing and range selection that reveals other points from the same subject anywhere else on the Plot.

Time axis

Comparing performance of an assay across multiple studies is a key scenario. Time series views can help discover temporal patterns in immune response or filter to data of interest, but vaccine studies present special challenges. Every study has a different schedule. There may be multiple vaccinations at different times with follow-up visits in between and after. In addition, each study has multiple treatment groups whose number and timing of events varies. Each group may also get different vaccine products or doses. Finally, each assay may be run on samples from different times than each other assay. Investigators must know the key time points of a study in order to make meaningful interpretations, but in the DataSpace they can see data from a dozen studies at once. To address these challenges we created a custom time axis in which every filtered study has a row of icons indicating when any of its treatment groups had an event (Figure 5). There is a different icon for vaccinations, follow-ups, and ”challenges,” when animals are exposed to a virus directly to determine vaccine effectiveness. Every icon can be brushed to highlight the associated data in context and to show a tooltip explaining what products and doses were given to each of the study’s groups with events at the time. Icons without associated data for the current y-axis are still shown (but desaturated) because their event timing can affect interpretation. For example, investigators know to look 1-3 weeks from a vaccination to see peak immune responses. The events can also be expanded to show a row of icons for every treatment within the studies. Modifier keys can be added to clicks to create filters like, ’day 357 for group one and day 252 for group two,’ which might be comparable in meaning although separate in time.
In analyses observed outside the DataSpace, we noted frequent comparisons of groups or studies at the moment of peak immune response. These moments may all be at different intervals from their