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Data Processing and Ethics in Science


Josef Loidl


Even when leaving aside brazen plagiarism and data forging, there is a grey area between permissible and undue ways of collecting and presenting scientific data. Data processing, interpretation, and presentation are processes where deliberate data varnishing or unintentional biased interpretation can creep in.


This workshop aims at raising the awareness of scientific misconduct and it should provide guidelines for good scientific practice by cultivating rules of solid experimental controls, fair literature citation, and combatting self-deceit and self-plagiarism. In times when funding resources and job opportunities are limited, when competition and pressure to publish is high, we think it is particularly important to confront young scientists with these issues at an early stage of their career.

  • Psychology of data interpretation - you get the results that you want to get
  • How manipulation of microscopic and gel images is detected
  • Manipulated statistics; Benford´s law – how mathematics can detect fraud
  • Anti-plagiarism software – what can it do, how can it be used for self-monitoring?
  • How publishers deal with manipulated data
  • How universities/employers deal with scientific misconduct
  • Fair and responsible peer reviewing


Max Perutz Labs_new