Promises and Perils of Big Data: Philosophical Constraints on Chemical Ontologies

Abstract

Chemistry is experiencing a paradigm shift in the way it interacts with data. So-called "big data" are collected and used at unprecedented scales with the idea that algorithms can be designed to aid in chemical discovery. As data-enabled practices become ever more ubiquitous, chemists must consider the organization and curation of their data, especially as it is presented to both humans and increasingly intelligent algorithms. One of the most promising organizational schemes for big data is a construct termed an ontology. In data science, ontologies are systems that represent relations among objects and properties in a domain of discourse. As chemistry encounters larger and larger data sets, the ontologies that support chemical research will likewise increase in complexity, and the future of chemistry will be shaped by the choices made in developing big data chemical ontologies. How such ontologies will work should therefore be a subject of significant attention in the chemical community. Now is the time for chemists to ask questions about ontology design and use: How should chemical data be organized? What can be reasonably expected from an organizational structure? Is a universal ontology tenable? As some of these questions may be new to chemists, we recommend an interdisciplinary approach that draws on the long history of philosophers of science asking questions about the organization of scientific concepts, constructs, models, and theories. This Perspective presents insights from these long-standing studies and initiates new conversations between chemists and philosophers.■ CHEMISTRY'S BIG DATA ERA As with most sciences in the 21st century, chemistry is witnessing a seismic shift in data collection, storage, and use. The collection of large volumes of data (so-called "big data") for the purpose of using computer algorithms to aid in chemical discovery is becoming commonplace, and many experiments produce more raw data than any one scientist could review in a lifetime. Moreover, federal funding agencies now require extensive data management plans and that data be made open under FAIR (f̲ indable, a̲ ccessible, i̲ nteroperable, and r̲ eusable) 1 data principles. In the United States, the National Science Foundation, National Institutes of Health, and Department of Energy among others have begun requiring data management plans for proposal applications, 2−4 while similar efforts exist globally to promote robust and accessible research data management. 5,6 Along with funding agencies, researchers across the globe are pushing for accessible, usable, and reliably reproducible data in chemistry. 7−9 Many argue that this new era of data will transform modern chemistry from an Edisonian model of trial-and-error to data-driven and machine-enabled design through artificial intelligence (AI). 10,11 Chemical Data: Historical Background. Although the modern era of big data is still incipient, data hold an integral role in the history of chemistry (Figure 1). 12−14 Chemists have long prioritized documenting and sharing thei...