Difference between revisions of "Theme:047c3bec-1747-499b-b6d5-684cbfb81edd"
Marc.VanHoof (talk | contribs) |
|||
| (One intermediate revision by the same user not shown) | |||
| Line 5: | Line 5: | ||
|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> | |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> | ||
<references /> | <references /> | ||
| − | |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. | + | |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. |
<references /> | <references /> | ||
| − | |Important For= | + | |Important For=All stakeholders in research |
}} | }} | ||
{{Related To | {{Related To | ||
Latest revision as of 13:56, 12 October 2020
ThemesFalsification
What is this 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”.[1]
- ↑ European Science Foundation, All European Academies. The European Code of Conduct for Research Integrity. 2017.
Why is this important?
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.[1] 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.
- ↑ Springer. Data fabrication / data falsification. Available at: https://www.springer.com/gp/authors-editors/editors/data-fabrication-data-falsification/4170. Accessed 29 May, 2019.
For whom is this important?
The Embassy Editorial team, Iris Lechner contributed to this theme. Latest contribution was Oct 12, 2020
