The document discusses research misconduct in science, highlighting issues such as plagiarism, self-plagiarism, and salami-publishing, which undermine the integrity of scientific research. It also explores the motivations behind data manipulation, the limitations of peer review, and the culture of silence that prevents scientists from addressing irregularities. Ultimately, the text argues that these systemic problems contribute to a lack of trust in scientific findings and hinder the pursuit of reliable knowledge.

1. Research misconduct:
science's self-administered
poison
Leonid Schneider,
Independent science journalist
leonid.schneider@gmail.com
Twitter: @schneiderleonid
forbetterscience.wordpress.com

2. Is bad science
individual or systemic failure?

3. $$$
Authors and institutions have little incentive to
produce reliable quality science
Paper-to-funding convertion
Funding used for research…

4. Every paper counts: quantity and quality

5. Boosting numbers:
plagiarism, self-plagiarism and salami-publishing

6. Plagiarism: the art of theft
• Re-using text written by others without proper
acknowledgments is plagiarism and misconduct
• Never present thoughts of others as your own
• It’s not enough just to cite their paper. If you need to
copy others’ text: always use quotation marks, and
give reference.
• Many journals screen for plagiarism using software
• There is no such thing as unintentional plagiarism

7. Boosting paper output:
self-plagiarism and salami-publishing
• If you re-use your own text (self-plagiarism), you
cheat readers about originality of your thoughts
• If you feel the need to re-use blocks of text: your story
lacks originality!
• Repeating old experiments in slightly modified
conditions? Where is originality in this?
• Re-use of own published data with a slightly modified
focus is salami-publishing
• Self-plagiarism and salami-publishing are commonly
used and often condoned tools of dishonest CV doping

8. The storytelling contest
Things surely changed for him
since he published in Nature…

9. Journals and funding agencies prefer
simplistic, but sensationalist “breakthrough” science
• Stem cells! Regenerative medicine! Organs from lab!
• Cancer cure!
• One-Gene-Phenotype models (Gene for autism! Gene for
schizophrenia! Gene for homosexuality!)
• Microbiome causes autism or schizophrenia or homosexuality!
• Translational/Commercial potential

10. Biological systems are very complicated,
but in biological papers simplicity rules!

11. Junior scientists are often told by their advisors:
- If you can deliver this result,
you will publish a nice paper and have a job
- If you don’t deliver this result,
you will not publish any paper and have no job
Dangerous confirmation bias:
- repeating experiment to be sure of its result’s reproducibility
is not the same as
- repeating it until the result finally fits the “expected” one
Getting there…

12. Why do scientists manipulate data?
• Motivation: to prove a pre-
conceived theory against lack
of experimental evidence
• Outcome: irreproducible
findings, pollution of scientific
literature, suffocation of
correct theories, usurpation
of a research field
• When caught: fraud scandal
and collapse of a research
field

13. Why do scientists manipulate data?
• Motivation: To scoop a
competitor lab on an
unpublished discovery they
made
• Outcome: dishonestly
acquired fame, funding and
domination of a research
field
• When caught: a careless
visionary genius, since
findings still reproducible!

14. Scientists occasionally help data to fit their
theoretical model for a publication
• Selective data acquisition, omission of critical controls
(very common)
• “Adjustments” or manipulation of data
(less common)
• Data falsification / fraud
(very rare)

15. 1. Selective data acquisition, omission of critical controls
• Cherry-picking
- discard odd samples/data which “spoil” the theory
- declare technically perfectly fine experiments as
failed if result doesn’t “fit”
• Control avoidance
- You know which control experiments would test
your theory, but you prefer not to do those

16. 2. “Adjustments” or manipulation of data
• Heavy cherry-picking
- selective deletion of entire sets of “outliers”
• Triplicating
- turning one single experiment into a triplicate
• p-hacking -
- statistics trickery to obtain significance. Most
published p-values are mysteriously just below 0.05!
• Loading controls
- gel loading controls libraries
- loading control swapping between gels
- other trickery to pretend equal gel or PCR loading

17. 3. Data falsification / fraud
• Falsification
- you think you got the experimental result right, but
just don’t have the “perfect” figure for the paper.
So you falsify one with Photoshop.
• Fraud
- you think biology is too stupid and incompetent to
follow your grand reasoning.
So you fake data against all experimental evidence to
get your theory published

18. Scientists waste time, money and their careers trying to
reproduce unreliable or manipulated results
• Poor reproducibility in combination with high competition
undermines productivity, work moral, trust and motivation
• It leads to even more data manipulation and fraud in science

19. Peer review weeds out bad science. Really?
• Data is submitted on trust as
being honest/reliable
• Peer Reviewers are scientist
colleagues, not data integrity
specialists
• Peer Reviewers only analyse
science, not its data integrity
• Peer review is not always done
diligently enough
How did this pass
peer review????

20. $$$
Traditional
peer
review
Traditional peer review is anything but transparent
Years and years of research…

21. - Journal Editors
- Decide on Quality,
Novelty, Impact
- Appoint peer
reviewers
- Make final decisions
- Peer Reviewers
- 1-4 people
- Unknown to authors
or readers
- Potential COI,
personal animosities,
lack of competence…
$$$
Too many financial and personal interests involved
Years and years of research…

22. Convincing peer reviewers (but not peer community!) is
the most important task of a scientist

23. A peer-reviewed paper is a badge of honour
• Publications are public evidence of success
• Passing peer review is a seal of scientific trustworthiness
• Often not the content counts, but where it is published (i.e,
alleged peer review quality)
• Publicly critiquing papers is seen as rude and damaging to
science

24. Individual decency in an indecent system
• Dealing with misconduct: more complicated than it sounds
• Best intentions vs the “Realpolitik” of academia

25. Climate of fear and coalition of silence
• Science is simultaneously cooperative and competitive
• Scientists’ top concern is funding, which requires collaboration
even with worst fraudsters
• Because of this, scientists rarely dispute each other publicly
• Instead, dark channels are used to damage competitors and
rare critics
$$$

26. What do you do if you spot data irregularities or
irreproducibility in a published paper?
1. Write to authors
2. Write to journal
3. Write to authors’
institution

27. Passing the buck
• Journals lack investigative authority
• Journals cannot screen lab books or interview lab members
• Journals are afraid to scare away authors
• Occasionally, institutions pass responsibility to journals anyway

28. Your paper is wrong,
professor!
See you at the
exam…
Individual criticisms are unwelcome and dangerous
• Funding concerns sabotage institutional
investigations
• Institutions often refuse to react to
anonymous hints
• Whistle-blowers are often threatened,
punished or disregarded as malicious

29. What happens if a published paper is reported
to be wrong or even to contain manipulated data?
1. Correction (rare)
2. Retraction (even rarer)
3. Nothing (most common)

30. To be continued….

31. Thanks:

32. Thank you!
Website: forbetterscience.wordpress.com
Email: leonid.schneider@gmail.com
Twitter: @schneiderleonid