Literature DB >> 27795574

Blending Education and Polymer Science: Semi Automated Creation of a Thermodynamic Property Database.

Roselyne B Tchoua1, Jian Qin2, Debra J Audus3, Kyle Chard4, Ian T Foster5, Juan de Pablo6.   

Abstract

Structured databases of chemical and physical properties play a central role in the everyday research activities of scientists and engineers. In materials science, researchers and engineers turn to these databases to quickly query, compare, and aggregate various properties, thereby allowing for the development or application of new materials. The vast majority of these databases have been generated manually, through decades of labor-intensive harvesting of information from the literature; yet, while there are many examples of commonly used databases, a significant number of important properties remain locked within the tables, figures, and text of publications. The question addressed in our work is whether, and to what extent, the process of data collection can be automated. Students of the physical sciences and engineering are often confronted with the challenge of finding and applying property data from the literature, and a central aspect of their education is to develop the critical skills needed to identify such data and discern their meaning or validity. To address shortcomings associated with automated information extraction, while simultaneously preparing the next generation of scientists for their future endeavors, we developed a novel course-based approach in which students develop skills in polymer chemistry and physics and apply their knowledge by assisting with the semi-automated creation of a thermodynamic property database.

Entities:  

Keywords:  Collaborative / Cooperative Learning; Computer-based Learning; Curriculum; First-Year Undergraduate / General Public; Material Science; Physical Properties; Polymer Chemistry

Year:  2016        PMID: 27795574      PMCID: PMC5082748          DOI: 10.1021/acs.jchemed.5b01032

Source DB:  PubMed          Journal:  J Chem Educ        ISSN: 0021-9584            Impact factor:   2.979


  5 in total

1.  Python: a programming language for software integration and development.

Authors:  M F Sanner
Journal:  J Mol Graph Model       Date:  1999-02       Impact factor: 2.518

2.  From data to knowledge: chemical data management, data mining, and modeling in polymer science.

Authors:  Nico Adams; Ulrich S Schubert
Journal:  J Comb Chem       Date:  2004 Jan-Feb

3.  A Hybrid Human-Computer Approach to the Extraction of Scientific Facts from the Literature.

Authors:  Roselyne B Tchoua; Kyle Chard; Debra Audus; Jian Qin; Juan de Pablo; Ian Foster
Journal:  Procedia Comput Sci       Date:  2016-06-01

4.  ChemicalTagger: A tool for semantic text-mining in chemistry.

Authors:  Lezan Hawizy; David M Jessop; Nico Adams; Peter Murray-Rust
Journal:  J Cheminform       Date:  2011-05-16       Impact factor: 5.514

5.  Incremental Knowledge Base Construction Using DeepDive.

Authors:  Jaeho Shin; Sen Wu; Feiran Wang; Christopher De Sa; Ce Zhang; Christopher Ré
Journal:  Proceedings VLDB Endowment       Date:  2015-07
  5 in total
  2 in total

1.  Polymer Informatics: Opportunities and Challenges.

Authors:  Debra J Audus; Juan J de Pablo
Journal:  ACS Macro Lett       Date:  2017-09-15       Impact factor: 6.903

Review 2.  Opportunities and challenges of text mining in aterials research.

Authors:  Olga Kononova; Tanjin He; Haoyan Huo; Amalie Trewartha; Elsa A Olivetti; Gerbrand Ceder
Journal:  iScience       Date:  2021-02-06
  2 in total

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