Related work

\label{related-work}
The Atlas repository for HIV vaccine studies supports plotting at the level of a dataset, which is typically a single assay from a single study [4]. The Neutralizing Antibody tool in LabKey Server supports more detailed data management and analysis than the DataSpace, but for a single assay [5]. Al-Hajj et al. and Shih et al. used Tableau for analysis of similar immune system data and found evidence of the value of visual analytics [6][7].
Physicians studying medical data for insights about patients have similar VA tasks. LifeLines 2 and the work of Bernard et al. are salient examples that share design patterns with the DataSpace for cohort selection and time comparison [8][9]. This area of research is based on electronic health record data where individual cases vary widely in categorical attributes and event data, whereas subjects in vaccine studies adhere to study selection criteria and follow randomized, controlled protocols designed to enable planned analysis of multiple quantitative metrics.
Al-Hajj, Shih, Bernard, and LifeLines 2 used paired analytics, requiring a tool expert to work with a domain “user.” We aim for self-service and scale, which has led us to do more data integration in advance, provide more annotation, and focus on usability for the most common tasks.
TrialShare, ImmPort, and ImmuneSpace are most similar to the DataSpace in their goals to make studies broadly available with tools for analysis [10][11][8][12]. In some ways, they are more ambitious than the DataSpace: they support user scripts for reproducible analysis and include datasets from over a dozen immune conditions, other study types beyond vaccination, and more diverse assays. However, their built-in visualizations are mostly static, and currently work best with single studies or single assays across studies. In comparison, the DataSpace offers deeper interaction for a narrower set of tasks. While it is less flexible than code scripts, our graphical approach is meant to lower the barriers to quick insights. We believe these approaches are complementary and may benefit from each other in the future.
Many investigators in our work use a variety of general-purpose (R, JMP, Prism, Excel) and assay-specific (FlowJo) visual tools for their own data. Once an investigator identifies a cohort or idea in the DataSpace, these external tools will remain critical for deeper exploration or statistical validation.