Introduction

\label{introduction}
HIV vaccine studies vary in scale and complexity. Some involve a handful of animals while others include thousands of human volunteer participants and multiple immunoassays. A single funder may sponsor hundreds of studies over many years, each with its own specific goals. Centralized teams often analyze the data, which usually leads to publication of results on the planned goals. However, investigators in our design research indicated they have no easy way to get basic facts or explore ideas that are not in a paper. Labs may share individual datasets according to a funder policy but without sufficient annotation for the benefit of others. In fact, there are reasons investigators may prefer to keep data to themselves [1].
In 2010, the Global HIV Vaccine Enterprise published its second strategic plan. Calling HIV/AIDS “one of humanity’s greatest challenges,” the authors note unique properties of HIV that have resisted development of a safe and effective vaccine since its emergence in the early 1980s. Part of the plan calls for broad and rapid access across dispersed study data. The authors hoped to foster a more collaborative culture and spark diverse hypotheses through shared use of well-annotated, harmonized data [2]. Our work set out to address this need in the hopes of accelerating scientific progress.
This paper will discuss related work, needs that may differ from common visual analytics (VA) research, application design, evaluation, and the path forward.