Best Practices:

• Utilize available campus resources on cloud-based computing/data science.
• Determine to what extent you, the researcher, will need to provide system administration for the Cloud as opposed to having someone else take this on.
• Look for existing tooling that meet the needs and skill level of the researcher (i.e., look at recent papers).
• Participate in training courses for neuroscience data in the cCoud.
• Look within funding agencies for training or informational researcher resources on cloud-based computing.
   

Things to Avoid:

• “Hidden” costs and risks: educate yourself! Remember that things you may not be used to paying for (e.g., CPUs you own but do not use, may cost you in a cloud-based environment).
• Reinventing the wheel: take advantage of what is already available.
• Don't go it alone.
• Don't be afraid to ask the Cloud provider for help/support/money, including coverage of any recurring fees.
• Focusing only on the short term: think about how your future needs, both for this study and future studies, and whether you need to invest in additional training now.

Value Set Definitions: 

• Low: Researcher has basic familiarity with neuroimaging tools and workflows in a local environment, but little or no experience with cloud-based computing. Researchers who would label themselves as low on this dimension should consider carefully whether they have the time and resources needed to develop the necessary expertise in order to use cloud-based resources for their projects. Even though the research group may not have the necessary expertise now, the group will want to think about whether they need to develop this expertise for their future research efforts.
• Medium: Researcher has good computational and data skills but only modest cloud-based computing experience.
• High: Researcher has computational and data skills; has cloud-based computing experience.
   

Value of Use Case Example:

Medium - Jordan did a postdoctoral fellowship in the lab of a mentor who was regularly engaged in larger scale studies that sometimes utilized cloud-based computing and engaged in broad data sharing. Jordan learned to do some analyses in the Cloud while a postdoctoral fellow, but was not responsible for setting up any of the infrastructure and was not responsible for setting up data sharing on any projects.

Discussion of Use Case Example:

The researcher is fortunate that she has some experience with working in the Cloud, but probably not enough to avoid common mistakes without additional training and access to those with more expertise. Therefore, it is critical that Jordan increase her level of training and familiarize herself with best practices and what is available as whether or not she uses the Cloud now, she may need to do so in the future.