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Access control is on Projects, Datasets (incl. Dataset layers, Analysis Results), and Scripts/Algorithms.. (Note, there is no such thing as a 'private' resource. Scripts and data are always available at least to the project team and later to the public.)
Versioning is on Data Layers (including Analysis Results, Networks, Gene Lists, Workstream *maybe* (TBD) Workstream Steps) and Scripts/Algorithms. A Dataset inherits the versions of its layers, e.g. when a layer is revised the version of the Dataset is (conceptually) incremented. (Note, a likely design decision is to delegate document versioning to Google Apps and version control systems.)
Following applies to Projects, Workstreams, and Datasets. and Datasets as detailed in this page: http://pixeltheoryinc.com/clients/sage/platform/Profile.html
Commenting can be done on Datasets, Dataset Layers, and Scripts/AlgorithmsScripts, Projects, Workstreams.
Study size of a Dataset is the size of the union of sample sets for its Data Layers.A Dataset has number of subjects involved in the study. (Note this is not equivalent to the number of samples, which may be more.)
A Dataset may have a single "study area / disease" attribute. (This is TBD.)
Answered Questions:
- are data-sets associated with projects? if so, is the association optional?
Yes and yes.
Can a ds be associated with multiple projects?
Yes. Idea is there is a global listing of datasets which may be public, or restricted to certain groups / individuals. When you browse the datasets tab you are browsing this global, Sage-curated and approved library of datasets.
We have also heard the need for project teams to upload their own data sets, which would be limited in scope to the project. Idea is to give users the ability to quickly start working with data without being gated by curation. Users could then "publish" the data from the project to the global library, which requires the same curation process as any other data (although hopefully the project teams get it in reasonable shape to begin with.)
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