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Author Garijo, Daniel
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Abstract 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).
Description Affiliation: University of Southern California (Garijo, Daniel)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-03-10
Publisher Place New York
Journal AI Matters (SIGAI)
Volume Number 3
Issue Number 1
Page Count 5
Starting Page 4
Ending Page 8


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Source: ACM Digital Library