Once data is collected, it can be released in various ways, from closed networks within an organization to platform-dependent sharing between peer organizations, publishing with closed licenses, and publishing with fully open licenses. It’s often tempting to think that making data available to the widest possible audience is the best way to maximize that data’s impact. Still, without careful consideration, there can be a myriad of unintended consequences.
This guide introduces the benefits, limitations, and recommendations for internal and external data sharing, including open data.
Related Guides & Resources
'If you want to go fast, go alone, but if you want to go far, go together,' the saying goes. That’s why collaboration is a powerful tool. Whether building internal capacity to execute a data science project or creating agreements with other value-aligned organizations to share data, partnerships can yield enormous benefits. But in reality, they can also present challenges.
Resources from The CALP Network, Principles of Digital Development and +3