• DRUCKEN
  •  Login

    Benutzeranmeldung

    Bitte geben Sie Ihren Benutzernamen und Ihr Passwort ein, um sich an der Website anzumelden.
    Anmelden

Of Hiding Data in Analogue and Digital Times

Of Hiding Data in Analogue and Digital Times

Of Hiding Data in Analogue and Digital Times

Author(s): Anne Frühbis-Krüger, Xenia Bogomolec

Publication: Bunsen-Magazin, Issue 2 2021, Seiten: 109-111

Publisher: Deutsche Bunsen-Gesellschaft für physikalische Chemie e.V., Frankfurt

Language: Englisch

 

 

Abstract

The future of scientific research is increasingly digital. As technologies such as machine learning and laboratory automation continue to advance rapidly, research organizations around the world are investing in digitalization initiatives aimed at reducing operational costs, gaining workflow efficiencies, enhancing innovation, finding new opportunities, reducing waste and even improving safety. Electronic lab notebooks (ELNs) have become standard in many research settings. Synthetic chemists are increasingly turning to predictive retrosynthesis tools to help them more quickly identify pathways to create new molecules. Machine learning algorithms are supporting traditional innovation approaches by predicting everything from bioactivity of drug candidates to likely properties of new polymeric materials. Artificial intelligence (AI) was even responsible for identification of some of the therapeutics being used to treat COVID-19 patients. Though much progress has been made in the past few years, even the most enthusiastic advocates of these technologies acknowledge that the applications for which they are being broadly implemented today only begin to scratch the surface of the long-term potential impact of digitalization on research productivity.

Cite this: Frühbis-Krüger, Anne, Bogomolec, Xenia (2021): Of Hiding Data in Analogue and Digital Times. Bunsen-Magazin 2021, 2: 109-111. Frankfurt am Main: Deutsche Bunsen-Gesellschaft für physikalische Chemie e.V.