You are invited to take part in an evaluation of this course as part of an NIH-funded research study to assess material for teaching research data management (RDM) to librarians. Your participation in this online course and its evaluation will consist of surveys before the first module, after each of the seven modules, and at the completion of all the modules. Each survey will take less than 5 minutes and can be completed online at your computer or mobile device. The surveys for the training overall will be delivered at the beginning of the course and at the end of the course as previously mentioned, as well as 6-months after the completion of the course.
There are no known risks or discomforts associated with this evaluation. Your participation will improve educational materials in RDM for librarians. Taking part in this evaluation is completely voluntary. If you choose to begin this online training you can withdraw at any time without repercussion. If you do not wish to participate in the research study portion, you are still welcome to participate.
Your responses will be kept strictly confidential, and digital data will be stored in secure computer files at the NYU Langone Medical Center. Any report of this research that is made available to the public will not include your name or any other individual information by which you could be identified. If you have questions or want a copy or summary of this study’s results, you can contact the researcher at the email address above. If you have any questions about whether you have been treated in an illegal or unethical way, contact the NYU Langone Medical Center Institutional Research by phone at 212-263-4110 or by email at email@example.com. Please feel free to print a copy of this consent page to keep for your records.
Clicking the “Begin Activity” button below indicates that you are 18 years of age or older, and indicates your consent to participate in this online training and its associated surveys.
Distinguish between research data management needs of different categories of data
Identify the full range of data products that should be recorded for a study
Distinguish between raw, processed, and analyzed data