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The case for data science in experimental chemistry: examples and recommendations.
Junko Yano1, Kelly J Gaffney2,3, John Gregoire4
1Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. JYano@lbl.gov.
Nature Reviews. Chemistry
|April 28, 2023
Summary
Data science offers new solutions for experimental chemistry, enhancing experiment design and understanding. Integrating data science and chemistry through co-design principles is key to advancing research.
Area of Science:
- Physical Sciences
- Chemistry
- Data Science
Background:
- The physical sciences community is leveraging data science for experimental chemistry.
- Challenges exist in fully exploiting these data science opportunities.
Purpose of the Study:
- Focus on experimental co-design in chemistry.
- Highlight how data science is transforming experimental conduct.
- Outline opportunities for integrating data science and experimental chemistry.
Main Methods:
- Reviewing current applications of data science in experimental chemistry.
- Identifying key areas for co-design integration.
- Proposing recommendations for future collaboration and development.
Main Results:
- Data science is changing experimental design, conduct, and result interpretation.
- Experimental co-design presents significant opportunities for advancing chemistry.
- Specific recommendations are provided for enhancing integration.
Conclusions:
- Stronger collaboration between chemists and data scientists is crucial.
- Development of chemistry-specific data science methods is needed.
- Integrating algorithms, software, hardware, and diverse data sources will advance chemistry research through co-design.
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