Development of a data-driven scientific methodology: From articles to chemometric data products

Information and data science algorithms were combined to predict the outcome of an experiment in chemical engineering. Using the Scientific Method workflow, we started the journey with the formulation of a specific question. At the research stage, the common process of querying and reading articles on scientific databases was substituted by a systematic review with a built-in recursive data mining method. This procedure identifies a specific community of knowledge with the key concepts and experiments that are necessary to address the formulated question. A small subset of relevant articles from a very specific topic among thousands of papers was identified while assuring the loss of the least amount of information through the process. The secondary dataset was bigger than a common individual study. The process revealed the main ideas currently under study and identified optimal synthesis conditions to produce a chemical substance. Once the research step was finished, the experimental information was compiled and prepared for metaanalysis using a supervised learning algorithm. This is a hypothesis generation stage whereby the secondary dataset was transformed into experimental knowledge about a particular chemical reaction. Finally, the predicted sets of optimal conditions to produce the desired chemical compound were validated in the laboratory.

Automated summary

This study employs information and data science algorithms to predict the outcome of a chemical engineering experiment. By using a systematic review and data mining method, a specific knowledge community is identified, and optimal synthesis conditions for producing the desired compound are determined.