• Analysis of Multicomponent Ionic Mixtures using Blind Source Separation - a Processing Case Study Dataset 

      Maggioni, Giovanni Maria (Georgia Institute of Technology, 2019-08)
      Management and remediation of complex nuclear waste solutions require identification and quantification of multiple species. Some of the species forming the solution are unknown and they can be different from vessel to ...
    • Anatomy of a Selectively Coassembled Beta-sheet Peptide Nanofiber Dataset 

      Shao, Qing; Wong, Kong M.; Seroski, Dillon T.; Wang, Yiming; Liu, Renjie; Paravastu, Anant K.; Hudalla, Gregory A.; Hall, Carol K. (Georgia Institute of Technology, 2020-01)
      Peptide self-assembly, wherein molecule A associates with other A molecules to form fibrillar β-sheet structures, is common in nature and widely used to fabricate synthetic biomaterials. Selective coassembly of peptide ...
    • De Novo Design of Peptides that Co-assemble into β-sheet Based Nanofibrils Dataset 

      Xiao, Xingqing; Wang, Yiming; Seroski, Dillon T.; Wong, Kong M.; Liu, Renjie; Paravastu, Anant K.; Hudalla, Gregory A.; Hall, Carol K. (Georgia Institute of Technology, 2021)
      Peptides’ hierarchical co-assembly into nanostructures enables controllable fabrication of multicomponent biomaterials. In this work, we describe a novel computational and experimental approach to design pairs of ...
    • Raw Data for the Elastic Modulus of Sporopollenin 

      Qu, Zihao; Meredith, J. Carson (Georgia Institute of Technology, 2017-10-27)
      Sporopollenin, the polymer comprising the exine (outer solid shell) of pollens, is recognized as one of the most chemically- and mechanically-stable naturally-occurring organic substances. The elastic modulus of sporopollenin ...
    • Training and Validation Data for Automated Head versus Tail Classification and Cell Identification in C. elegans 

      Zhan, Mei; Crane, Matthew Muria; Lu, Hang; Ch'ng, QueeLim; Entchev, Eugeni; Caballero, Antonio; de Abreu, Diana Andrea Fernandes (Georgia Institute of Technology, 2015)
      We demonstrate the utility of a generalizable classification routine for biological image processing by developing two specific classifiers as a toolset for automation and cell identification in the model organism ...