Self-assembled Peptide-p-electron Supramolular Polymers: FAIR Data and Student Training
Broad dissemination. All computational codes have been hosted on GitHub (https://github.com/), simulation trajectories on the Materials Data Facility (https://materialsdatafacility.org/) and Zenodo(http://zenodo.org), and a project webpage provides information about the team and research outcomes (https://sites.krieger.jhu.edu/dmref/).
Graduate mentoring. Graduate researchers Sayak Panda, Jessie Dibble and Taein Lee at JHU, Kirill Shmilovich and Nick Herringer at UChicago, Yifan Yao at UIUC, and postdoc Hyun-June Jang at JHU interact with all project personnel, receive mentorship and training in multi-disciplinary research. Kirill Shmilovich’s training in data science enabled him to secure a competitive Summer 2021 internship within the AI division of Bosch.
Undergraduate and high school research. At Chicago, undergraduates Melody Leung and Rohan Kapoor were trained in molecular modeling calculations and machine learning techniques. At JHU, undergraduate Anna Stouffer became familiar with multi-step manual peptide synthesis techniques and basic spectroscopic methods to characterize peptide nanomaterials (see image at right).
Example of sharing of simulation and experimental data via Zenodo.org including minting of a permanent DOI and sharing under FAIR (findable-accessible-interoperable-reusable) principles