Presenter Profile
Laura S. Bruckman
Associate Professor
Case Western Reserve University, Department of Materials Science and Engineering
Laura S. Bruckman is an Associate Research Professor in the Department of Materials Science and Engineering in the Case School of Engineering, Case Western Reserve University. Her research is focused on a data science approach to materials degradation. She teaches in the Applied Data Science program at CWRU with a focus on visualization and analytics, data science research projects and communicating results to diverse stakeholders.
TALK TITLE
Study Protocols and FAIRification for Materials Data Science
KEYWORDS
FAIRification in Materials Research
TALK ABSTRACT
Typical materials science research has focused on observational experiments with changes in one variable at a time as a representative sample subset. However, this has significant problems in understanding the synergistic effects of complex materials and systems and the impact of stressors on materials as they degrade in use conditions. The more complex and interconnected system responses remain ambiguous when approached by this method. In order to understand the full parameter space of a material system and the variables that impact its design and performance, a new approach is necessary that includes the development of a comprehensive study protocol. In order to accurately utilize the process of big data analysis the overall investigation can be divided into four sections, a research plan on what questions are being asked and how are they going to be addressed, a data collection plan that includes sourcing historical data, as well as acquiring experimental data, modeling plan which addresses how the collected data will be cleaned and analyzed, FAIRifying plan to address data stewardship under the FAIR principles for data processing and sharing. FAIRification is to make data and models: findable, accessible, interoperable, and reproducible. To FAIRify scientific domains, such as materials science, requires two major steps; 1) an agreed upon standardized schema for naming data and metadata variables, and 2) an accepted form for sharing these variables and their values in a human and machine readable form. Implementation of FAIR principles has been a serious challenge because there are no established domain ontologies for materials research.