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  1. Get involved in a community. First, make contact with the appropriate community for your data. In some cases, this might involve contacting the portal maintainers, or it might involve joining a consortium, as well as securing funding and setting up a data sharing plan with a specific organization.

  2. Prepare data. Before generating data, review your chosen community’s onboarding materials, which could include documentation, submission forms, webinars, one-on-one meetings, and other resources. Gather all supplemental information, and ensure you’ve met the data sharing requirements.

  3. Deposit data and add information about the data. This step includes uploading and annotating data, as well as providing any supplementary information needed to understand and curate the data. This step may also include data quality checks and metadata validation. Consortia and/or funders may have additional requirements, such as milestone reports.

  4. Determine data access controls. At Sage, we use the term data governance to refer to the practice of determining how data should be shared. This stage encompasses data licensing, as well as deciding how the data should be accessed, and by whom. Many datasets on our portals are unrestricted and open to the public, but some data require access controls, such as data use agreements and institutional review board approval.

  5. Share data. After the above steps are complete, you’re finally ready to share your data with others, whether fully open or with restrictions. Often, this step occurs some time after the above processes, once a publication embargo period lifts.

  6. Access data. Once your data is shared, it’s now accessible to others, including yourself and your colleagues. Because of the steps above, that data will be FAIR — a standard representing findable, accessible, interoperable, and reusable data. FAIR data are discoverable to users through precise metadata, understandable in terms of how the data can be used, machine-readable to enable computational analysis, and ultimately, fit for reuse.

Want to learn more about data sharing? Check out our 10 Reasons to Share Your Data article.