  While automated experiments and high-throughput methods are becoming more
mainstream in the age of data, empowering individual researchers to capture,
collate, and contextualize their data faster and more reproducibly still
remains a challenge in science. Despite the abundance of software products to
help digitize and organize scientific information, their broader adoption in
the scientific community has been hindered by the lack of a holistic
understanding of the diverse needs of researchers and their experimental
processes. In this work, we take a user-centered approach to understand what
essential elements of design and functionality researchers (in chemical and
materials science) want in an experimental data platform to address the problem
of data capture in their experimental processes. We found that having the
capability to contextualize rich, complex experimental datasets is the primary
user requirement. We synthesize this and other key findings into design
criteria for a potential solution.
