Treffer: Data Functionalization for Gas Chromatography in Python

Title:
Data Functionalization for Gas Chromatography in Python
Language:
English
Authors:
Green, Michael, Chen, Xiaobo (ORCID 0000-0002-7127-4813)
Source:
Journal of Chemical Education. Apr 2020 97(4):1172-1175.
Availability:
Division of Chemical Education, Inc. and ACS Publications Division of the American Chemical Society. 1155 Sixteenth Street NW, Washington, DC 20036. Tel: 800-227-5558; Tel: 202-872-4600; e-mail: eic@jce.acs.org; Web site: http://pubs.acs.org/jchemeduc
Peer Reviewed:
Y
Page Count:
4
Publication Date:
2020
Sponsoring Agency:
National Science Foundation (NSF)
Contract Number:
DMR1609061
Document Type:
Fachzeitschrift Journal Articles<br />Reports - Evaluative
Education Level:
Higher Education
Postsecondary Education
Geographic Terms:
DOI:
10.1021/acs.jchemed.9b00818?ref=pdf
ISSN:
0021-9584
Entry Date:
2020
Accession Number:
EJ1250849
Database:
ERIC

Weitere Informationen

For undergraduate students to be prepared for graduate school and industry, it is imperative that they understand how to merge the theoretical insights gleaned through their undergraduate education with the raw data sets acquired through materials analysis. Thus, the ability to implement data analysis is a vital skill that students should develop. Furthermore, students should be fluent in methodologies that can translate to domains beyond their undergraduate curriculum. In this technology report, we demonstrate data functionalization in the Python programming language via data derived from gas chromatography. The programming approach to data analysis is designed to be flexible in order to allow students to take the lessons learned herein and apply them to novel systems outside of the experiment and outside of the academy.

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