Shared datasets and timepoints can be enabled at the project level and used in different ways for cross-study alignment. Either or both may be enabled in a top-level container (project) of type Study or Dataspace, meaning that studies within that project will be able to share definitions and combined data views can be created at the project level.
Shared datasets and timepoints can be used in different ways for cross-study alignment. When enabled in a project, subfolders of type "Study" within it will be able to make use of:
First, set up the parent container, or project, which will form the source of the shared information with subfolder studies.
Once Shared Datasets and/or Shared Timepoints have been enabled, you can change the folder type from Study to Dataspace if desired. This is not necessary, but if desired, should be performed after enabling sharing.
Shared datasets must be defined in the top-level project. You may also have 'child-study-specific' datasets in child folders that will not be shared, but they must be created with dataset IDs that do not conflict with existing parent datasets.
When creating a shared dataset in the project, we recommend manually assigning a dataset id, under Advanced Settings > Dataset ID. This will prevent naming collisions in the future, especially if you plan to create folder-specific, non-shared datasets. Note that the auto-generated dataset id's follow the pattern 5001, 5002, 5003, etc. When manually assigning a dataset id, use a pattern (such as 1001, 1002, 1003, etc.) that will not collide with any auto-generated ids that may be created in the future.
When shared timepoints are enabled, the Manage tab in the top level project will include a link to Manage Shared Timepoints (or Visits). Click to use an interface similar to that for single studies to manage the timepoints or visits.
The timepoints and visits created here will be shared by all study folders in the project.
Once shared datasets and shared timepoints have been enabled, you can enable sharing of demographic data, not just the dataset definitions.
For demographics datasets, this setting is used to enable data sharing across studies. When 'No' is selected (default), each study folder 'owns' its own data rows. If the study has shared visits/timepoints, then 'Share by ParticipantId' means that data rows are shared across the project and studies will only see data rows for participants that are part of that study.
Enabling data sharing means that any individual records entered at the folder level will appear at the project level. In effect, the project level dataset become a union of the data in the child datasets. Note that inserting data directly in the project level dataset is disabled.
Create new subfolders of this project, each of type Study. You can create these 'sub studies' before or after starting to populate the shared structures at the project level.
Note that the parent container has already set the timepoint type and duration, which must match in all child studies, so you will not see those options when you Create Study.
Each of the 'sub-studies' will automatically include any definitions or timepoints you create at the project level. It is best practice to define these shared structures before you begin to add any study data or study-specific datasets.
Note that the project-level container that shares its datasets and timepoints with children sub-studies does not behave like an "ordinary" study. It is a different container type which does not follow the same rules and constraints that are enforced in regular studies. This is especially true of the uniqueness constraints that are normally associated with demographic datasets. This uniqueness constraint does not apply to datasets in the top-level project, so it is possible to have a demographics table with duplicate ParticipantIds, and similar unexpected behavior.
If the same ParticipantId occurs in multiple studies, participant groups may exhibit unexpected behavior. Participant groups do not track containers, they are merely a list of strings (ParticipantIds), and cannot distinguish the same ParticipantId in two different containers.
When viewed from the project-level study, participants may have multiple demographics datasets that report different information about the same id, there might be different dates or cohort membership for the same visit, etc.