2020/07/28 by Ha-Kyung Kwon, Kwon, Ha-Kyung, Chirranjeevi Balaji Gopal +7
Computer Science · Decision Sciences · Physics and Astronomy · #Advanced Database Systems and Queries #Computers and Society (cs.CY) #Data Stream Mining Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #Scientific Computing and Data Management #cond-mat.mtrl-sci #cs.CY
paper · pdf · doi:10.48550/arxiv.2007.14443
15 pages, 3 figures (38 pages in Supplementary Materials)
arxiv created 2020/07/28 · openalex publication_date 2020/07/28 · arxiv updated 2020/07/30 · openalex created_date 2020/08/03 · openalex updated_date 2026/07/28
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.