KAAP 400 - Lecture 3

What is Responsible Conduct of Research (RCR)?

RCR is conducting research with integrity, honesty, transparency, and accountability. Key aspects include human and animal research ethics, data management and integrity, mentorship and collaboration, publication and authorship, and conflicts of interest.

What are examples of research fraud and scandals?

• He Jiankui: In 2018, claimed to have created genetically edited babies (“CRISPR babies”). He was later sentenced to 3 years in prison for illegal medical practices, including forging ethical approvals and misleading doctors.

• Bharat Aggarwal: Published research claiming curcumin could kill cancer cells. His work faced a 2012 investigation for alleged data falsification, and by 2022, 30 papers had been retracted, with others corrected or under concern. These cases demonstrate the serious consequences of research misconduct, falsification, and failure to follow ethical procedures.

What is an example of research misconduct involving data fabrication, plagiarism, and poor mentoring?

Ranga Dias, a physicist at the University of Rochester, claimed to have discovered a room-temperature superconductor, which would have been a major scientific breakthrough. He was accused of falsifying data and plagiarizing grant applications and his PhD thesis. Graduate students raised concerns about the research, and co-authors later requested retractions after data-falsification claims emerged. He had also been recognized on TIME’s 2021 TIME100 list.

How many scientists fabricate or falsify research?

About 2% of scientists admitted to fabricating, falsifying, or modifying data/results at least once, while about 14% reported that their colleagues had questionable research practices. The estimated true rate may be around 5%.

What was the Theranos research scandal?

Theranos, founded by Elizabeth Holmes, claimed it could perform hundreds of blood tests accurately using a single drop of blood, despite the technology not being properly validated or peer-reviewed. The company falsified data and misrepresented results to investors, partners, and patients, including through a partnership with Walgreens. Holmes was sentenced to 11+ years in prison for defrauding investors through knowingly false claims about the technology.

What does the COVID-19 research example teach us about questionable research?

During COVID-19, some studies published in major journals, including The Lancet and New England Journal of Medicine, were later retracted. One retracted study claimed hydroxychloroquine could harm COVID-19 patients, which contributed to halted clinical trials and confusion about treatments. A BMJ Public Health study was also retracted after improperly attempting to link COVID-19 vaccines to deaths. Almost 700 COVID-19 papers have been retracted, demonstrating the importance of careful research, peer review, and scientific integrity.

What are predatory journals, and why are they a problem?

Predatory journals are “pay-for-play” publications that often lack rigorous peer review, editing, and quality control, offering guaranteed acceptance primarily for financial gain rather than advancing scientific knowledge. They may also misrepresent their editorial boards. Jeffrey Beall coined the term “predatory journals” and created Beall’s List of problematic publishers. Most researchers, however, maintain high research standards.

What are examples of scientific dishonesty and questionable research practices?

Falsification is manipulating research materials, equipment, processes, or data to produce desired outcomes. Fabrication is making up data or results that were never collected, such as fictional participants or measurements. Other questionable practices include selective reporting, “p-hacking” (trying multiple analyses to obtain significant results), hypothesizing after results are known, and improper data sharing or authorship practices.

Why are data management and integrity important in research?

Accurate and transparent data management supports reproducibility, reliability, and credibility. Poor data management can cause errors, misinterpretation, and ethical breaches. Researchers should record data exactly as collected, take detailed notes, document methods/protocols/calibration/equipment settings, keep data organized and labeled, and use secure storage with multiple backups.

What are the key ethics principles for authorship and publication?

Authors should make significant contributions to study design, data collection, analysis, or manuscript writing. Researchers should avoid gift authorship for minimal contributions and properly acknowledge others who do not qualify as authors. Routine tasks like data entry or routine lab work do not automatically qualify someone for authorship. Authorship should be discussed at the start of the project and revisited as roles change.

What is the ethical problem in this scenario, what are the consequences, and how could the data be reused ethically?

The ethical problem is duplicate/redundant publication and failure to properly acknowledge previously published work. The researcher is presenting previously published tables and figures as new data. Consequences may include corrections or retractions, damage to credibility, and research/institutional penalties. The researcher could ethically reuse the data by clearly citing the original paper, identifying what is previously published, and presenting a genuinely new analysis or research question.

What are plagiarism and self-plagiarism, and what are their consequences?

Plagiarism is copying someone else’s words or ideas without proper credit, including paraphrasing without citation. Self-plagiarism is reusing your own previously published work without proper citation or disclosure, misleading readers about its originality. Consequences include manuscript rejection, disciplinary action, reputational damage, and loss of credibility.

What is a conflict of interest in research, and how should it be managed?

A conflict of interest occurs when a researcher’s personal, financial, or professional interests could compromise—or appear to compromise—their objectivity. For example, owning stock in a company whose supplement they are studying could create bias in analyzing or reporting results. Researchers should disclose conflicts, use independent oversight/review, and clearly communicate funding sources and potential conflicts to participants and readers.

What are the ethical considerations for using AI in research?

Researchers should clearly document AI use, methods, analyses, and technologies to ensure transparency and reproducibility. Journal policies vary: some prohibit AI-generated content, while others allow it with full disclosure. Researchers must follow the specific journal’s AI policies when using AI in their work.

What are the core principles for responsible use of AI in research?

Responsible AI use requires accountability (researchers remain responsible for research integrity), verification (check AI outputs for errors and hallucinations), transparency and disclosure (document how and when AI is used), data security (do not use public AI tools with confidential/identifiable data without safeguards or IRB approval), bias mitigation (evaluate potential errors and bias), and originality (use AI for tasks like brainstorming, formatting, and repetitive work—not to replace critical thinking, analysis, or original scientific reasoning).

What ethical, procedural, and methodological issues exist in this scenario, how could they affect the study, and what should researchers do?

The researchers may have ethical and methodological concerns because Spanish-speaking participants and those with limited transportation may not have equal access to the study. Moving forward without addressing these barriers could create unfair participant selection, reduce participation, and affect the study’s results. Researchers should provide materials and procedures in appropriate languages, address transportation barriers, and ensure recruitment and participation are fair and accessible, while following the approved research procedures and IRB requirements.

What is cultural competence in research, and why is it important?

Cultural competence means designing and conducting research that respects and responds to participants’ cultural, social, and demographic diversity. Ignoring cultural factors can cause biased results and reduced generalizability. Researchers should consider language barriers, cultural norms, and other cultural factors when designing protocols, collecting data, and interpreting results.

Which actions are considered research misconduct?

• No: Accidentally entering one incorrect data point.

• Yes: Adjusting outlier measurements to fit a hypothesis (data falsification).

• Yes: Sharing identifiable participant data without consent.

• Yes: Failing to disclose funding or financial conflicts of interest.

• Yes: Using information from a manuscript under peer review for personal research before publication.

• Yes: Publishing the same dataset in two journals while presenting each as entirely new findings.

• Yes: Sharing identifiable participant data publicly without removing identifiers.

• No: Re-analyzing published data for a different hypothesis while clearly citing the original source.