Original Work, No Plagiarism, Cite and Reference
Mary admires the NIH-funded work of her postdoctoral advisor, Henryk, who pioneers research on alternative treatments for fever due to infectious diseases. Mary is one of many co-workers who has assisted Henryk in compiling the most comprehensive database ever assembled, tracking many different infectious agents, species of animals, and different interventions and their outcomes. Henryk’s interpretation of this rich dataset suggests that some “alternative medicines” are highly effective in certain species, but have no therapeutic value in others. He is completing his analysis and interpretation, and is preparing a manuscript for submission. Mary will be a co-author because of her part in collecting data for the study.
Mary is preparing to seek an Assistant Professor position and wants to build on her postdoctoral work. She asks Henryk for permission to use the dataset to develop her own project. However, she plans to use a different methodology for analysis and interpretation of the dataset to address a different aspect of the outcomes of treatment. At that point, she will develop a career development proposal to submit to the NIH.
Henryk is unwilling to share the entire dataset prior to publishing his interpretation of these data. However, Mary has access to the database as part of her current project, and therefore she decides that it is ethical for her to look more closely at the data. Mary spends quite a lot of time looking at the data and Henryk’s analysis, and realizes that he has excluded specific datapoints that impact his interpretation. Henryk’s draft manuscript carefully justifies the exclusion of these data in the methods section so that there is no issue with data falsification.
Mary realizes that if she includes these datapoints, an entirely new understanding of therapies to treat fever could emerge. Mary is excited about her impending grant proposal, but is concerned about how to broach the discussion of her use of the data with Henryk.
Discussion Questions
1. Must Henryk share his database with Mary before publication? After publication? Must he share it with others, outside his lab, and if so, when?
2. Who owns the database at this point: Henryk? The institution? NIH? The public?
3. Why is sharing a dataset beneficial to the person who collected it? How is it potentially risky?
4. Is Henryk obligated to document how datapoints were included or excluded in the methods section of his paper?
1. Must Henryk share his database with Mary before publication? After publication? Must he share it with others, outside his lab, and if so, when?
Henryk is not obligated to share his entire database with Mary before publication. In research, it is common for data to be closely guarded until the primary investigator has the opportunity to analyze, interpret, and publish their findings. This helps protect their intellectual property and the significant effort they invested in data collection and analysis. After publication, Henryk may choose to share the dataset with others, including Mary, in accordance with the policies of his institution and any relevant funding agencies like the NIH. Sharing data after publication can promote transparency, reproducibility, and further research in the field. The timing and conditions of data sharing should be negotiated among the parties involved, keeping ethical and legal considerations in mind.
2. Who owns the database at this point: Henryk? The institution? NIH? The public?
The ownership of the database depends on the agreements and policies in place at Henryk’s institution and any funding agencies involved. Typically, the primary ownership of research data lies with the institution where the research was conducted. However, it’s important to note that the NIH may have specific data sharing policies for projects it funded. In many cases, research institutions have policies that allow the primary investigator (Henryk, in this case) to retain control of the data for a period of time, after which they may be required to share it with the public or other researchers. Ownership and sharing rights should be clarified in research contracts, grant agreements, and institutional policies.
3. Why is sharing a dataset beneficial to the person who collected it? How is it potentially risky?
Sharing a dataset can be beneficial to the person who collected it in several ways:
– **Promoting Collaboration:** Sharing data allows for collaboration with other researchers, which can lead to new insights, increased citations, and a broader impact for the research.
– **Validation and Verification:** Sharing data allows others to validate and verify the findings, increasing the credibility and reliability of the research.
– **Resource Maximization:** Sharing data can prevent duplication of efforts, as other researchers can build on the existing dataset rather than starting from scratch.
– **Ethical Responsibility:** Many funding agencies and institutions require data sharing as part of responsible research practices.
However, sharing data also comes with potential risks, including:
– **Loss of Control:** Sharing data may result in the loss of control over how the data is used and potentially misused by others.
– **Priority and Competition:** Sharing data may lead to other researchers publishing findings similar to or ahead of the original data collector, potentially impacting their career or recognition.
– **Data Privacy and Ethical Concerns:** Sharing data may involve sensitive or confidential information, and protecting privacy and ethical considerations is crucial.
– **Data Misinterpretation:** Other researchers may misinterpret or misuse the data, leading to misunderstandings or even erroneous conclusions.
Balancing the benefits and risks of data sharing requires careful consideration and adherence to ethical and legal guidelines.
4. Is Henryk obligated to document how datapoints were included or excluded in the methods section of his paper?
Yes, Henryk is obligated to document how datapoints were included or excluded in the methods section of his paper. Transparent and accurate reporting of methods is a fundamental ethical principle in scientific research. This documentation allows readers to understand how data were collected, processed, and analyzed, ensuring the research’s reproducibility and reliability. If specific datapoints were excluded from the analysis, Henryk should provide a clear and justified explanation in the methods section, detailing the criteria for exclusion and any potential impact on the results. This transparency is essential for the peer-review process, and it helps ensure the integrity of the research findings.