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Plagiarism & Citation Policy

Scientific integrity is non-negotiable. All written, visual, and computational work submitted in CACoM must be your own original work or properly credited.

Violations of this policy result in automatic failure (grade = 5.0).

What Counts as Plagiarism​

Plagiarism means using another person's intellectual work — text, code, figures, or ideas — without proper acknowledgment.

Common forms include:

TypeExample
Textual plagiarismCopying or paraphrasing sentences from papers, websites, or reports without citation.
Code plagiarismUsing code snippets from GitHub or Kaggle without attribution.
Figure reuseCopying a chart or image without crediting its original source.
Data plagiarismPresenting data from another source as if you collected or processed it yourself.
Self-plagiarismReusing your own previous CACoM submission or other coursework without clear reference.
danger

Plagiarism = automatic failure.
Intent does not matter; lack of citation is enough to trigger this rule.

What Proper Citation Looks Like​

Whenever you reuse material, cite where it came from and what you did with it.

Example 1 – Text reuse​

Original: “Fetal heart rate variability reflects autonomic regulation of the fetal cardiovascular system.”

In your report:
As noted by Hoyer et al. (2020), fetal heart rate variability reflects autonomic regulation of the fetal cardiovascular system.

Example 2 – Code reuse​

# Adapted from https://github.com/physionet/ctg-analysis-tools (MIT License)
baseline = smooth_fhr(signal, window=30)

Example 3 – Figure reuse​

Figure adapted from Pardey et al. (2002), with annotations added by the authors.

Example 4 – Data reuse​

We used the open CTU-UHB dataset (Chudáček et al., 2014). Data were processed according to the original publication's description.

Reusing Previous CACoM Work​

You may build upon earlier CACoM projects only if you clearly indicate:

  • which materials you reused (data, code, or ideas),
  • which parts are new contributions, and
  • where the previous work came from, using formal citation and in-text acknowledgment.

For example:

In text / poster:
This work extends the Fullsoul Runningpad project from CACoM 2024 (Burian et al., 2024).

In bibliography:
Burian, K., Ren, C., & Strauch, A. (2024, July 30). Foot sole pressure sensing device and its usage in barefoot shoes: Fullsoul Runningpad.
Clinical Applications of Computational Medicine (CACoM), Technical University of Munich.

note

When citing previous CACoM projects, always include the year, title, authors, and course name. If the project had a written report or poster, cite it as an unpublished academic work. For numeric styles (e.g., IEEE), you may write in text:
This work extends the Fullsoul Runningpad project from CACoM 2024 [1].
and list the reference as:
[1] K. Burian, C. Ren, and A. Strauch, “Foot sole pressure sensing device and its usage in barefoot shoes: Fullsoul Runningpad,” Clinical Applications of Computational Medicine (CACoM), Technical University of Munich, Jul. 30, 2024.

Proper Referencing Standards​

Use any standard academic style (APA, IEEE, Vancouver, etc.) as long as it is:

  • consistent throughout your materials,
  • includes enough detail to locate the source (authors, title, year, DOI or URL), and
  • appears in both the text and the reference list.

For posters and slides, brief in-text citations such as (Author, Year) are sufficient.

Code and Data Licensing​

  • Respect all dataset and code licenses (MIT, GPL, CC-BY, etc.).
  • Include license files when required by the original author.
  • If your project produces reusable code, you are encouraged to release it under an open-source license — but only if all contributors agree and no restricted data are included.
  • Always check whether your dataset allows redistribution before uploading it anywhere.
  • Maintain a small references.bib or references.md file listing all sources used in your project.
  • Use citation managers (Zotero, Mendeley, etc.) to stay consistent.
  • When in doubt: cite anyway — giving credit is never wrong.
  • For internal or collaborative data, always state the origin and access path, even if not publicly shareable.
  • If you build upon someone's method or analysis pipeline, cite both the original paper and the implementation source (e.g., GitHub repo, software package).

Common Pitfalls​

caution
  • Copying text and later “adding citations” without quotation marks.
  • Copying figure layouts or designs from published posters.
  • Using ChatGPT- or Copilot-generated text that incorporates unacknowledged sources.
  • Reusing a previous CACoM repository without stating it.
  • Assuming “open data” = “no citation required.”

Quick Checklist​

  • All text written in your own words or quoted/cited properly.
  • All figures and tables have visible attributions.
  • All external code or data include source references.
  • No unacknowledged reuse of previous CACoM projects.
  • Final materials double-checked for proper citations before submission.