Best Practices:

• Single institution
  • Ensure that all team members adhere to the institution's relevant policies and practices.
• Multi-institution: Using lead institution as a starting and reference point and address the following
  • Generate an inventory of all institutions' relevant policies and practices.
  • Generate a data use/sharing policy across institutions before onset of data collection.
  • Consider whether different institutions within the project have different objectives, and whether and how those differences may impact the policies and practices for the project as a whole.
  • Pick a reference set of policies and practices, and identify any deviations of the    guidelines applicable to one or more participating institutions that are more or less stringent than the reference set, and then determine whether institutions that are more lenient in those policies or practices can comply with the most stringent ones.
  • Negotiate failures to converge on the most stringent set, possibly using community-wide resources (such as this matrix!!) as additional reference points.
  • Distribute final guidelines to all team members.
  • The lead investigator at each institution should ensure adherence to guidelines at that institution.
  • Address at the outset who owns which data and how this interacts with data sharing policies, both among the participating institutions and with outsiders (as outlined along other dimensions in this matrix).
  • Ideally, all institutions should use the same cloud-based service/repository for the project, and one that is standard and appropriate for the type of data being collected (e.g., dedicated to neuroimaging  NITRC IR NIMH Data Archive [NDA]). If project- and/or institution-specific one(s) are preferable/necessary (e.g., for pilot / interim results and/or if there are special needs and/or restrictions, such as HIPAA restrictions, project-specific meta-data requirements, etc., that are not met by existing standard services or repositories), then these should also be identified at the outset, and policies/practices established for how the affected information will eventually be migrated to the project-wide platform(s).     

Things to Avoid:

• Do not assume that the lead institution is the only one responsible for determining and overseeing policies and practices related to the study.
• Do not assume that all institutions have the same policies, practices, or even objectives.
• Do not assume that each institution can operate independently.
• Do not assume that the lead investigator at each institution is solely responsible for policies and practices regarding the part of the study at that institution alone and does not need to coordinate with the other involved institutions.

Value Set Definitions: 

• Single Institution: All key members of the research team are at the same institution
• Multiple Institutions: Key members of the research team are at more than one institution

Value of Use Case Example:

Single Institution - Jordan would like to conduct a study in which they would recruit between 200 individuals from the community to examine the relationship between individual differences in facets of emotion regulation, and associated individual differences in structural and functional neural connectivity. She is also considering whether she will need a larger N, whether she needs to join or launch a multi-institution study, or whether there may be existing data that she can use.

Discussion of Use Case Example:

While in the past, Jordan may have been able to justify the use of 200 subjects, given concerns about reproducibility a power analysis may show that she needs to increase the number of subjects in order to have confidence in any reported effects. It is not inconceivable that the number may be > 1000. Therefore, she will have to consider whether she needs to either lead or participate in a multi-institution study. Alternatively, she may be able to augment or perform her study with existing publicly available data. When using the Cloud, designing a multi-institutional study can add several levels of complexity, as policies, practices, and cloud access may vary across institutions. If institutions are international or comprise a mix of types, e.g, academic, commercial, governmental, the situation may be even more complex, as different countries have different privacy laws regarding use of the Cloud. Different types of partners may also have different requirements.