Difference between revisions of "Resource:Eb0781de-eb55-4063-9b00-cac85ca9259c"
From The Embassy of Good Science
Marc.VanHoof (talk | contribs) (Created page with "{{Resource |Resource Type=Guidelines |Title=Detecting and determining greyscales |Is About=Detecting and determining greyscales guideline |Important For=All stakeholders in re...") |
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{{Resource | {{Resource | ||
| − | |Resource Type= | + | |Resource Type=Other |
| − | |Title=Detecting and determining greyscales | + | |Title=Research Integrity - Detecting and determining greyscales |
| − | |Is About= | + | |Is About=Research integrity is the core focus area for the HEADT Centre. This includes three aspects: |
| + | |||
| + | * Plagiarism: copying of materials from other author’s works without an indication (such as quotation marks) and without a complete reference. | ||
| + | * Data falsification: creating data without actual research or manipulating data in order to achieve a particular conclusion. | ||
| + | * [https://headt.eu/page-18145 Image manipulation]: creating or altering an image in order to achieve a particular conclusion.One research goal is to develop metrics to help distinguish between the various greyscale zones that detection tools reveal which can be seen in this resource. | ||
| + | |Important Because=This research strives to understand more clearly what constitutes appropriate scholarly behavior. That is important, since research integrity decisions today depend on human effort. Part of the research is to find out on what decision makers base their findings (e.g., guidelines or standards) and whether they consider grey zone issues. If so, which grey zones, and are they appropriate? The answer to these questions varies across different disciplines, but automating misconduct detection requires clear definitions. | ||
|Important For=All stakeholders in research | |Important For=All stakeholders in research | ||
}} | }} | ||
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|Has Link=https://headt.eu/Research-Integrity | |Has Link=https://headt.eu/Research-Integrity | ||
}} | }} | ||
| − | {{Related To}} | + | {{Related To |
| + | |Related To Resource=Resource:Db4c2416-1ce5-44ca-8306-20de0d1d890e;Resource:366d47ee-4b9d-4287-8c57-88ba847480bb;Resource:5bbdd729-8f96-432a-a0ee-56510e343d01;Resource:3f8d6a6e-db25-438c-a266-dd0175fe09c0;Resource:6405cda0-87cb-42a5-8f74-eae7b933a48f;Resource:Ae1b3645-f7f2-4c55-a09d-c24935fd73db | ||
| + | |Related To Theme=Theme:65e6f304-51e2-4e41-93d3-e48518248b39;Theme:8c79e235-8481-45ea-bb57-c744dedbbb8a;Theme:A612e3c5-4f31-470f-b5bf-3751923848e8;Theme:20f32f16-72a1-46f0-b9a6-24fac05b0937 | ||
| + | }} | ||
{{Tags | {{Tags | ||
|Involves=HEADT centre Berlin | |Involves=HEADT centre Berlin | ||
|Has Location=Germany | |Has Location=Germany | ||
| + | |Has Virtue And Value=Honesty; Respect | ||
|Has Good Practice And Misconduct=Plagiarism; Falsification; Image manipulation | |Has Good Practice And Misconduct=Plagiarism; Falsification; Image manipulation | ||
}} | }} | ||
Revision as of 13:16, 17 August 2020
ResourcesOther
Research Integrity - Detecting and determining greyscales
What is this about?
Research integrity is the core focus area for the HEADT Centre. This includes three aspects:
- Plagiarism: copying of materials from other author’s works without an indication (such as quotation marks) and without a complete reference.
- Data falsification: creating data without actual research or manipulating data in order to achieve a particular conclusion.
- Image manipulation: creating or altering an image in order to achieve a particular conclusion.One research goal is to develop metrics to help distinguish between the various greyscale zones that detection tools reveal which can be seen in this resource.
Why is this important?
This research strives to understand more clearly what constitutes appropriate scholarly behavior. That is important, since research integrity decisions today depend on human effort. Part of the research is to find out on what decision makers base their findings (e.g., guidelines or standards) and whether they consider grey zone issues. If so, which grey zones, and are they appropriate? The answer to these questions varies across different disciplines, but automating misconduct detection requires clear definitions.
