Managing data can be a big problem. Any data that might, for example, become supplementary data in an article, needs curating. Help the user by providing facilities to capture and hold intermediate versions of the data, ad the final public version.
I should have added some more explicit ideas, from the RRS blog posts, the idea of data management (as well as a data repository):
It is essential that the Data Management elements support current, dynamic data, not just static data. You may need to capture data from instruments, process it through workflow pipelines, or simply sit and edit objects, eg correcting database entries. Data Management also needs to support the opposite: persistent data that you want to keep un-changed (or perhaps append other data to while keeping the first elements un-changed).
One important element could be the ability to check-point dynamic, changing or appending objects at various points in time (eg corresponding to an article). In support of an article, you might have a particular subset available as supplementary data, and other smaller subsets to link to graphs and tables. These checkpoints might be permanent (maybe not always), and would require careful disclosure control (for example, unknown reviewers might need access to check your results, prior to publication).
Some parts of Data Management might support laboratory notebook capabilities, keeping records with time-stamps on what you are doing, and automatically providing contextual metadata for some of the captured datasets. Some of these elements might also provide some Health and Safety support (who was doing what, where, when, with whom and for how long).
Revise
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