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{{Theme
|Theme Type=Misconduct & Misbehaviors
|Has Parent Theme=Theme:28a0859b-9e52-4af4-97f0-b0f8eeac1f1c
|Title=Falsification
|Is About=Falsifcation is altering a part of the research process, often to let the results appear more sensational and relevant than they are in reality. Next to fabrication and plagiarism, falsifcation is considered as serious research misconduct. It is defined by the European Code of Conduct as “manipulating research materials, equipment or processes or changing, ommitting or suppressing data or results without justification”.<ref>European Science Foundation, All European Academies. The European Code of Conduct for Research Integrity. 2017.</ref>
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|Important Because=Falsifying data is a serious form of research misconduct. Falsified data includes omitting or adding data points, removing outliers in a dataset and manipulating images. Image manipulation is a special form of falsifcation, as it uses software to edit photos, usually of laboratory tests, to let the results appear more convincing. This concerns blots, gels, micrographs and radiological images.<ref>Springer. Data fabrication / data falsification. Available at: https://www.springer.com/gp/authors-editors/editors/data-fabrication-data-falsification/4170. Accessed 29 May, 2019.</ref>  Fabricating data is making up non-existing results, where falsifying data is to edit, add, remove or alter results and/or data sets. Falsified results, when detected, lead to sanctions for the perpetrator. Sanctions include retractions of papers, and often the end of a career in research. Falsification of data is to be discouraged and prevented.
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|Important For=All stakeholders in research
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|Related To Resource=Resource:5bbdd729-8f96-432a-a0ee-56510e343d01
|Related To Theme=Theme:5f65272f-6e95-4768-8236-bc821a97f3d8;Theme:02592695-e4f8-473c-a944-adfe0d8094c0;Theme:28a0859b-9e52-4af4-97f0-b0f8eeac1f1c;Theme:883697c8-d319-4224-991e-ce063d648efd
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{{Tags
|Has Good Practice And Misconduct=Falsification; Research Misconduct
}}