Research misconduct (FFP)

Fabrication, falsification, or plagiarism in proposing, performing, reviewing, or reporting research, done intentionally, knowingly, or recklessly and representing a serious departure from accepted practices.

An unintentional mistake in research design, execution, analysis, or reporting, corrected when discovered and not considered misconduct.

Questionable research practice (QRP)

A poor or biased research practice (e.g., p‑hacking, HARKing, weak record‑keeping) that undermines reliability but may not meet the formal definition of misconduct.

Process in human research where participants are given understandable information about the study, risks, benefits, and rights, and voluntarily agree to take part.

Hypothesizing After Results are Known: presenting a post‑hoc hypothesis as if it were planned in advance, without disclosure.

Formal credit on a scholarly work, implying substantial contribution, involvement in writing or revising, approval of the final version, and responsibility for the content.

Trying many statistical analyses or data transformations and selectively reporting only those that yield statistically significant results, without transparency.

Ethics and Integrity in Biological Research — BIO 204: Fundamentals of Scientific Inquiry in the Biological Sciences

Q: A student realizes after submitting a poster that they accidentally swapped labels for two treatment groups in one bar graph. They immediately tell their supervisor and correct the figure. How should this be classified?

Honest error, handled appropriately. This is an honest error: an unintentional mistake discovered after the fact. The student documents and corrects it, which is the expected responsible response. There is no evidence of intent to deceive, so it is not misconduct.

Q: Which situation best reflects responsible practice?

A student who collected and analyzed data is listed as an author and also helps revise the manuscript before submission.. Option 2 reflects responsible practice: the student made a substantial contribution and helped revise the manuscript. The other options show gift authorship, inappropriate authorship, or lack of transparency about data handling.

Q: 3Rs (animal research)

Replacement, Reduction, and Refinement: guiding principles to replace animals where possible, reduce numbers used, and refine procedures to minimize pain and distress.

Why Ethics and Integrity Matter in Biology

Ethics = Core to Good Science

In biological research, your choices affect data quality, other scientists, and living organisms. Ethics and integrity are part of doing scientifically valid work, not optional add‑ons.

What You Will Learn

You will learn to distinguish honest error, questionable research practices, and misconduct; apply basic ethical principles with animals, humans, and ecosystems; and use good data and authorship practices.

Global Definitions

Agencies like the U.S. Office of Research Integrity define misconduct as fabrication, falsification, or plagiarism in proposing, doing, reviewing, or reporting research. Similar standards exist worldwide.

Ethics Frameworks

Key frameworks include the Declaration of Helsinki for human research, the 3Rs (Replacement, Reduction, Refinement) for animal work, and data/privacy rules like the EU GDPR for personal data.

Honest Error vs Questionable Practices vs Misconduct

Honest Error

Honest errors are unintentional mistakes in design, measurement, coding, or analysis. Example: mis‑labeling two tubes and later discovering the swap. They require correction and documentation, not punishment.

Questionable Research Practices

QRPs are poor practices that bias results but may not meet the legal definition of misconduct. Examples: p‑hacking, HARKing, weak record‑keeping, or over‑stating conclusions. They erode trust and reproducibility.

Fabrication, Falsification, Plagiarism

Misconduct usually means FFP: fabrication (making up data), falsification (distorting data or methods), and plagiarism (using others’ ideas or words without credit). These are serious ethical violations.

Intent and Departure from Norms

For an act to be misconduct, it must be a significant departure from accepted practice and done intentionally, knowingly, or recklessly. Honest error and good‑faith judgment calls are not misconduct.

Case Study: Data Manipulation in a Cell Culture Experiment

The Scenario

You test a drug on cancer cell lines. One replicate shows no effect, and including it removes statistical significance. A labmate suggests dropping it so the result looks clear, claiming everyone does this.

Investigate First

Check notebooks and logs: Was there contamination, wrong incubator, expired reagent, or pipetting error? Look for objective reasons why this replicate might be technically invalid.

Use Pre‑Defined Criteria

Exclusion criteria should ideally be set before seeing results. If a replicate meets a pre‑defined technical failure rule, you may exclude it, but carefully document the reason and timing.

Report Transparently

State how many replicates were run and excluded, and why. Consider showing analyses with and without the suspect replicate to demonstrate robustness and honesty.

Where It Becomes Misconduct

Dropping a replicate only because it ruins significance, without technical justification, is a questionable practice. Manipulating images or data to hide inconvenient results is falsification and misconduct.

Check Understanding: Error vs Misconduct

Choose the best answer based on the definitions you just learned.

A student realizes after submitting a poster that they accidentally swapped labels for two treatment groups in one bar graph. They immediately tell their supervisor and correct the figure. How should this be classified?

Honest error, handled appropriately
Questionable research practice
Research misconduct (falsification)
Research misconduct (plagiarism)

Show Answer

Answer: A) Honest error, handled appropriately

This is an honest error: an unintentional mistake discovered after the fact. The student documents and corrects it, which is the expected responsible response. There is no evidence of intent to deceive, so it is not misconduct.

Ethical Treatment of Animals, Humans, and Ecosystems

Animals and the 3Rs

Animal research ethics center on the 3Rs: Replacement (use alternatives when possible), Reduction (use the fewest animals for valid results), and Refinement (minimize pain and distress).

Your Role with Animals

As a student you must complete animal ethics training, follow approved protocols exactly, never add unapproved procedures, and promptly report unexpected animal distress or protocol deviations.

Humans: Core Principles

Human research relies on respect for persons, beneficence, and justice. This means informed consent, careful risk–benefit assessment, and strong privacy and data protection practices.

Field and Ecosystem Ethics

In fieldwork, obtain permits, avoid unnecessary habitat damage, use non‑destructive methods where possible, and respect local communities and Indigenous knowledge and rights.

Ethics Committees

Bodies like IACUCs, IRBs, and environmental boards review and approve studies. Your duty is to know the approved protocol, follow it, and speak up if practice no longer matches what was approved.

Thought Exercise: Applying the 3Rs and Consent

Work through these scenarios and decide what responsible science requires.

Zebrafish developmental study

You are planning to study early development in zebrafish embryos.

How could you apply Replacement? (e.g., are there in vitro or computational models for part of the question?)
How could you apply Reduction? (e.g., power analysis to choose sample size, shared control groups)
How could you apply Refinement? (e.g., improved housing, anesthesia for procedures, humane endpoints)

Write down at least one concrete action for each R.

Online survey about mental health in undergraduates

You are designing an anonymous online survey about stress and coping.

What informed consent information must participants see before starting? (purpose, voluntary nature, risks, contacts)
How will you protect privacy? (data encryption, removal of identifiers, secure storage)
When might you need extra safeguards? (e.g., questions about self‑harm, vulnerable populations)

List 3–4 specific consent and data protection steps you would implement.

Field sampling in a protected wetland

Your team will collect invertebrate samples from a protected wetland.

What permits or community permissions might you need?
How can you minimize habitat disturbance during sampling?
How will you share results with local stakeholders or conservation agencies?

Write a short plan (3–5 bullet points) that balances your research goals with ecosystem protection.

Good Data Management: From Notebook to Sharing

Organize and Protect Data

Use clear file names with dates and experiment IDs, keep raw data read‑only and separate from processed files, back up in at least two locations, and record detailed metadata in your lab notebook.

Transparent Analysis

Document every data transformation so others can reproduce it. Avoid unreported fishing for significance. Use version control for code and analysis scripts whenever possible.

Sharing and Privacy

Share non‑sensitive data in repositories with README files. For human data, de‑identify, respect consent terms, and follow laws like GDPR and your institution’s policies on personal data.

Handling Discovered Errors

If you find a data error, verify and correct it, inform your supervisor and collaborators, and update any posters, preprints, or papers. Correcting mistakes is a core part of integrity.

Practical Example: Reproducible Analysis Workflow

Here is a simple example (in R) showing how to keep data analysis transparent and reproducible.

```r

1. Load raw data (never overwrite this file)

raw <- read.csv("2026-03-18yeast-growthexp07_raw.csv")

2. Save a copy for processing

proc <- raw

3. Document cleaning steps clearly

Remove rows with missing OD600 values

proc <- subset(proc, !is.na(OD600))

Log-transform OD600 to stabilize variance

proc$log_OD600 <- log(proc$OD600)

4. Save processed data with a new filename

write.csv(proc, "2026-03-18yeast-growthexp07processedv1.csv",

row.names = FALSE)

5. Simple analysis with code kept under version control

model <- lm(log_OD600 ~ treatment, data = proc)

summary(model)

```

Key habits illustrated:

Raw data are loaded but never modified in place.
Every processing step is written in code, not done manually in a spreadsheet.
New files are saved with versioned names.
The script itself is a record of what you did, which can be stored in a version control system and shared with collaborators.

Authorship, Credit, and Collaboration

Who Deserves Authorship?

Authorship usually requires substantial contribution to the research, involvement in drafting or revising the paper, approval of the final version, and willingness to be accountable for the work.

Authorship vs Acknowledgment

Routine technical help, funding, or general supervision usually belong in the acknowledgments, not the author list. Mislabeling contributions misrepresents responsibility.

Author Order Norms

First author often leads the work and writing; last author is often the senior scientist; middle authors contribute specific pieces. Discuss order early and revisit as roles change.

Unethical Authorship Practices

Ghost authorship (leaving out a deserving contributor) and gift authorship (adding someone without real contribution) are unethical because they distort credit and accountability.

Healthy Collaboration

Create a written contribution plan, keep communication open, and use shared notebooks and version‑controlled documents so contributions are visible and disputes are less likely.

Quick Check: Authorship and Data Practices

Apply what you have learned about authorship and data management.

Which situation best reflects responsible practice?

A PI adds their friend as a coauthor to improve the paper’s chances, even though the friend did not work on the project.
A student who collected and analyzed data is listed as an author and also helps revise the manuscript before submission.
A lab technician who refilled buffers is added as a coauthor to thank them for their hard work.
To save space, the team omits mentioning that one dataset was excluded due to a calibration error.