Adherence to Data Use Agreements: Strictly follow the terms and conditions. Data use agreements are legally binding contracts that outline how you can and cannot use a particular data set. It's crucial to read and understand all the terms before you begin your research. This includes restrictions on who can access the data, what you can use it for, and whether you can share it with others. Violating these terms can have serious consequences, from losing access to the data to legal action.
Proper Attribution and Citation: Cite the data source explicitly in your dissertation and publications. Citing your data source is not just a matter of academic honesty; it's a fundamental part of good research practice. Proper attribution gives credit to the creators of the data set and allows other researchers to find and verify your work. In your dissertation and any publications that result from your research, you should include a clear and explicit citation of the data source, just as you would for a book or journal article. This demonstrates respect for the intellectual property of others and contributes to the integrity of the scientific community.
Respecting Privacy and Confidentiality: Even when a data set has been de-identified (meaning personal information has been removed), there's still a risk that individuals could be re-identified by combining the data with other publicly available information. It's your ethical responsibility to be aware of and mitigate these risks. This means handling the data with care, storing it securely, and avoiding any actions that could inadvertently compromise the privacy of the individuals who contributed to the data set.
Transparency: Be transparent about your data sources, methodology, and any data manipulations in your research. Transparency is a cornerstone of reproducible research. You should be open and honest about every step of your research process. This includes clearly stating where your data came from, explaining the specific methods you used to analyze it, and detailing any modifications or manipulations you made to the data before analysis. By being transparent, you allow other researchers to replicate your study, build on your findings, and ultimately strengthen the credibility of your work.