Gregory M. Banik is the Co-Editor of the ACS Guide to Scholarly Communication. Laboratories are facing pressure to do more with less. This pressure makes it all the more important to improve efficiencies wherever possible. A discipline like chemistry has its own way of communicating—its own language. You need to avoid having different dialects to […]
Laboratories are facing pressure to do more with less. This pressure makes it all the more important to improve efficiencies wherever possible. A discipline like chemistry has its own way of communicating—its own language. You need to avoid having different dialects to ensure everyone is on the same page. A key issue here is that of data sharing and communication: data are saved across a wide range of disparate sources in various formats, and key information—such as naming and drawing of chemical structure—differs from source to source.
These inconsistencies and ambiguities cause numerous problems. As data are not standardized and simple to collate, scientists spend a great deal of time searching for relevant data, making the process highly time- and cost-inefficient. While there are initiatives aimed at bridging these gaps by standardizing nomenclature and categorization of substances in databases, these are either proprietary or unprepared for use by machine learning algorithms. As such algorithms become increasingly important in chemical analysis and have the potential to reduce costs and timelines significantly, substances must be drawn and categorized in a way these algorithms can interpret.
To do this, multiple data sources must be brought together consistently, so that databases are efficiently and quickly accessible by both machines and humans. This requires implementing a standardized guide for how researchers, academics, and industry professionals should communicate and document data and information. Communicating to humans is one thing: humans are adept at looking at a representation of chemical structure, interpreting what it means, and understanding the subtle nuances of how chemists depict this kind of information. Machines can’t do this as easily. It takes a lot of work and time to train a machine to understand ambiguity in how information is presented—so we need a different approach.
At a Glance
• Standardizing nomenclature and drawing of chemical structures across data sources improves efficiency, reduces ambiguity, and achieves better, faster performance
• The ACS Guide to Scholarly Communication provides rigorous instruction and advice on how to do this in an effective, consistent way that is suitable for machine learning applications
• Data consistency helps busy laboratories save time, reduce costs, use personnel more efficiently and productively, and do more with less.
The Solution: The ACS Guide for Scholarly Communication
The comprehensive ACS Guide to Scholarly Communication combines a diverse array of real-life examples to help students, academics, educators, librarians, and members of industry master the art of scientific communication and communicate effectively with peers, decision-makers, and the public. The ACS Guide acknowledges the rapidly changing publishing landscape via its digital-first format, which will be kept up-to-date with regular, timely updates. Rather than being a set of rules to follow in publishing research, The ACS Guide targets the heart of effective communication by rethinking how data and information are classified and documented from the very beginning.
The ACS last published such a guide in 2008, but the world has changed dramatically since then. Machines weren’t used as much a decade ago, but they’re ubiquitous now. Data is more interconnected, and machine learning is becoming critically important in data analysis, with the potential to bring exciting ‘big data’ breakthroughs. However, it’s vital that chemists record their information in a consistent, appropriate way if they wish to harness the power of machine learning. The ACS Guide to Scholarly Communication is crucial in communicating information to anyone—internally or externally, across industry, academia and all areas of science— in a precise, usable way.
By accessing and adhering to the ACS Guide to standardize their communication, laboratories can prepare to implement and fully realize the benefits of machine learning algorithms, giving them a crucial edge over their competitors and opening up new opportunities for innovation. Consistent communication of chemical data and information is key to reaching goals faster, improving productivity and efficiencies, and avoiding the pitfalls of extending timelines and rising costs. It also promises to benefit entire industries by expediting scientific discovery to benefit the whole of society.