Data Science Opportunities to Transform Chemical Sciences and Engineering: Proceedings of a Workshop—in Brief (2018) / Chapter Skim
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Data Science: Opportunities to Transform Chemical Sciences and Engineering: Proceedings of a Workshop - in Brief
Data Science: Opportunities to Transform Chemical Sciences and Engineering: Proceedings of a Workshop - in Brief
Pages 1-12
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Accordingly, the Chemical Sciences Roundtable of the National Academies of Sciences, Engineering, and Medicine, in collaboration with the National Academies Board on Mathematical Sciences and Analytics, held a workshop to explore opportunities to use data science to transform chemical sciences and engineering on February 27–28, 2018, in Washington, DC. Stakeholders from academia, government, and industry convened to discuss the challenges and opportunities to integrate data science into chemical sciences and engineering practice and data science training for the future chemical sciences and engineering workforce.
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He emphasized the need for chemical sciences curricula to include training in data science, statistics, machine learning, and computer programming so that graduates will be better prepared to overcome the challenges in the future. Changes are already happening in policy and training workshops; for example, the National Science Foundation (NSF)
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Riley noted that this is an example of a more advanced machine learning model that surpasses other methods. Riley concluded his presentation with four take-away messages: (1)
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Ferguson noted that increased insight will come from increased data sharing and that the chemical sciences community needs to facilitate the cultural shift that encourages scientists to publish their codes, provide more details about their methods, and make their databases more accessible. Bligaard added that machine learning models can help to reduce complexity by identifying which signals are real and which can be ignored and thus improve physical and chemical models that describe the desired system.
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García-Muñoz, however, cautioned against assuming that big data are necessarily equivalent to informative data. Moderator Bruce Garrett, director of the Chemical Sciences, Geosciences, and Biosciences Division in the Office of Basic Energy Sciences at the US Department of Energy (DOE)
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Competing demands on campuses can prove to be problematic, so Ewing emphasized the need for faculty to present a clear argument to administrators about how such programs could be valuable for the universities. BREAKOUT GROUP DISCUSSIONS Participants spent the afternoon of the first day of the workshop in small groups discussing data analytic methods, data quality assessment, and data management and translation in the chemical sciences and chemical engineering communities.
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They included instituting and describing instrument calibration procedures, using standard materials and samples, recording metadata and history associated with the measurements, recording data provenance and data ownership, describing the domain for which data will be useful, building relationships with new data sources, and replicating experiments. Higdon continued that the chemical sciences community could benefit from thinking more about standards for metadata and ways to interconnect databases to improve data accessibility and could leverage advances in computer science and statistics.
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He stated that improved metadata management is the most urgent need for the chemical sciences community and continued that data integration can be challenging in academia, where instruments and methods are constantly evolving. As such, data management has to be flexible enough to accommodate new types of data.
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and distinguished two possible outcomes of data science education: data literacy and data acumen. He defined data literacy as overcoming data and math phobias and developing basic data visualization skills and an understanding of the data life cycle so that one can identify obvious problems.
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. The Midwest Big Data Hub also participates in regional and national meetings to encourage data science education and workforce development, with a specific interest in broadening participation, and offers data science training opportunities.
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A participant said that data science or statistical training is only encouraged, not required, by the ACS to obtain a bachelor's degree in chemistry. The ACS is in the process of revising the guidelines for curricular approval to be more outcomes based, so input from the chemical sciences community would be helpful.
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Planning committee members were Michelle Chang, University of California, Berkeley; Leo Chiang, The Dow Chemical Company; Bruce Garrett, DOE; Carlos Gonzalez, National Institute of Standards and Technology; John Gregoire, California Institute of Technology; and Angela Wilson, NSF. ABOUT THE CHEMICAL SCIENCES ROUNDTABLE The Chemical Sciences Roundtable provides a neutral forum to advance understanding of issues in the chemical sciences and technologies that affect government, industry, academic, national laboratory, and nonprofit sectors, and the interactions among them; and to furnish a vehicle for education, the exchange of information, and the discussion of issues and trends that affect the chemical sciences.
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