AI buzzwords explained: scientific workflows

The reproducibility of scientific experiments is crucial for corroborating, consolidating and reusing new scientific discoveries. However, the constant pressure for publishing results (Fanelli, 2010) has removed reproducibility from the agenda of many researchers: in a recent survey published in Nature (with more than 1500 scientists) over 70% of the participants recognize to have failed to reproduce the work from another colleague at some point in time (Baker, 2016). Analyses from psychology and cancer biology show reproducibility rates below 40% and 10% respectively (Collaboration, 2015) (Begley & Lee, 2012). As a consequence, retractions of publications have occurred in the last years in several disciplines (Marcus & Oransky, 2014) (Rockoff, 2015), and the general public is now skeptical about scientific studies on topics like pesticides, depression drugs or flu pandemics (American, 2010).