Research Integrity: 5 Real-World Case Studies for Easy Understanding

Dr. Nikhil A. Gokhale, Ph.D. | Mar 31, 2025

Lucas and Emily have recently joined a PhD program at the University of Chilford. They have both finished their orientation programs. Last week, they also attended a seminar on research integrity and ethics.  

Lucas: “Hey Emily, the seminar from last week was insightful, but I still do not understand a few key concepts. Moreover, I cannot relate it to any real-life situations.” 

Emily: “I recommend checking out the blog post written by Cureus Journals.”   

Considering the significant challenges researchers face in navigating research integrity, it is essential to explore real-world scenarios that highlight its applicability and significance. In today’s blog, I will discuss some real-world case studies revolving around the concept of research integrity.    

Various entities, including the Committee on Publication Ethics (COPE), the Council of Science Editors (CSE), and the European Network for Research Integrity Offices (ENRIO), provide advisory guidelines to authors, editors, peer reviewers, and other stakeholders on research integrity and ethics. Whereas research integrity primarily focuses on ensuring good practices in the execution of research, ethics mostly focus on reducing harm to humans, animals, and endangered species involved in the research process.  

The concept of research integrity is best understood through examples. To this end, I will present some real-world scenarios involving this key concept.        

Case Study #1: Are Your Experimental Results Reproducible?   

Dr. Williams asks Susan, a new PhD student, to complete an important engineering experiment. After obtaining a single set of results, Dr. Williams asks Susan to fabricate additional data points instead of diligently repeating her experiments. Susan is worried about the reproducibility of her experimental results and decides to share her concerns with Dr. Williams. Dr. Williams says that, based on his extensive experience, further experimentation seems unnecessary and that Susan should proceed to the next set of experiments. Is Dr. Williams giving the right piece of advice to Susan?    

Commentary: Regardless of how experienced principal investigators are, they should never share such advice with their PhD students. An experiment conducted just once reveals nothing about reliability, statistical significance, reproducibility, bias, or the generalizability of its results. The mentor-suggested experimental approach, therefore, is not sufficiently rigorous and can adversely affect the integrity of the scientific record. Such advice also goes against the established tenet of responsible mentorship.           

Case Study #2: Are You a Responsible Peer Reviewer? 

Dr. Zayed is conducting a peer review of a manuscript describing a new method for increasing the safety of autonomous vehicles. He quickly goes through the abstract and begins writing his review on the journal dashboard. He finishes this task in less than five minutes. Is this a good practice? 

Commentary: Researchers must conduct experiments responsibly. Similarly, peer reviewers must assess all submitted manuscripts carefully. Arriving at a decision or making recommendations to manuscript authors solely based on the abstract is an incorrect practice. Peer reviewers must always make it a point to read the entire manuscript and go through the supplemental materials, if any, before sharing their expert recommendations. This approach ensures constructive feedback, prevents the publication of flawed research, and recognizes valuable contributions to the field of study (self-driving vehicles in this case).              

Case Study #3: Image Manipulation at a Prominent Research University  

Dr. Liu is working as a postdoctoral researcher at a major university on the U.S. East Coast. Very soon, she is going to apply for a tenure-track position at another well-regarded university based in California. Dr. Liu is about to submit her manuscript to a peer-reviewed journal that is widely read by researchers from her field. One fine morning, she uses a photo-editing app to digitally alter the key findings of an image obtained using confocal microscopy. She tells herself that such steps need to be undertaken sometimes for smooth career progression. Is she doing the right thing? 

Commentary: Digitally altering confocal microscopy images to enhance certain features in a way that changes the key findings of a study violates fundamental principles of research integrity. Moreover, journals can easily detect image manipulation these days using modern research integrity tools. In the past, researchers have been banned for life from publishing in various journals owing to image or data manipulation.          

Case Study #4: Preprints and Responsible Science Communication 

Dr. Adebayo has recently posted his research on a preprint server. He is excited about the findings and wants to share them on social media. So, he hires a graphics designer and creates an attractive post highlighting the most significant findings of his study. However, he does not mention anywhere in the graphic or the description section of the social media post that the study has yet to undergo peer review and that its conclusions may change after it undergoes rigorous peer review. Is this perfectly alright for social-media-based outreach?  

Commentary: Considering how rapidly misinformation can spread on social media, researchers should exercise patience and wait for expert scrutiny before sharing their study if their audience consists primarily of laypersons. However, if they are engaging expert audiences on platforms such as ResearchGate or Mendeley, they should actively seek feedback; otherwise, it would defeat the very purpose of posting on a preprint server. Also, when discussing a study published on a preprint server, researchers must clearly state that it has not yet undergone peer review and that the conclusions of the study may change significantly or even become invalid after the review process.  

Case Study #5: Ignoring Landmark Studies During Policy-Making  

Dr. Miller is a renowned ecologist from Florida. He has been appointed to guide a committee of researchers working on an important policy related to offshore drilling. Last week, the committee members presented a slide deck clearly showing the impact of offshore drilling on marine ecosystems. However, Dr. Miller asks the committee to remove all slides discussing this relationship and focus on other topics instead. Quite interestingly, several landmark studies have clearly shown the detrimental effects of offshore drilling on marine environments. Is Dr. Miller doing the right thing?       

Commentary: Clearly, Dr. Miller is not making the best use of his scientific expertise by ignoring landmark studies during policy-making. It is quite unfortunate that he is heading a committee in charge of drafting a key policy on offshore drilling. Dr. Miller needs to do some introspection and adopt rightful measures.        

Summary 

Researchers must conduct, review, publish, share, and apply research responsibly. As evident from the five case studies discussed above, highly qualified researchers made certain incorrect decisions despite being fully capable of making the right ones. In summary, researchers must adhere to the established principles of research integrity and ethics and demonstrate responsible behavior by ensuring full compliance.   

Key Takeaways 

  • Research integrity fundamentally emphasizes ensuring good practices in the execution of research.  

  • Good researchers must show complete compliance with the established tenets of research integrity and ethics. 

  • Real-world case studies should be used to simplify difficult-to-digest concepts. 

​​​​Quiz for the Curious  

Dr. Taylor leads a research group at a major university. A technology company approaches him with a request to conduct a study demonstrating that their new AI-driven hiring system eliminates bias in recruitment. The company pays him $25,000 for the study. However, there is no preliminary evidence supporting this claim, and existing research consistently shows that AI models can inherit and even amplify biases present in training data. During the study, Dr. Taylor finds that the AI-based hiring system shows a clear preference for candidates from a certain race and geography. Despite this, he fabricates data to support the company’s claim. Which of the following statements is true? 

(A) Since Dr. Taylor accepted the funding, he is obligated to produce results in the company’s favor. 
(B) Dr. Taylor is demonstrating poor research integrity; this is a clear case of data fabrication. 
(C) Dr. Taylor may accept the grant, but he must safeguard the study’s integrity by preventing any influence from the company and transparently disclose any conflicts of interest through all appropriate channels. 
(D) Both (B) and (C). 
(E) None of the above. 

Answer: (D) 

Disclaimer: All names appearing in this blog post are completely fictitious. Any resemblance to real people is purely coincidental.