As works are integrated to form new compositions, contributors are recorded and given credit.
We view digital products as composites. Over time, a given work can have numerous predecessors, those works from which it is derived. Works may also incorporate components in which they depend upon for their operation. Moreover, each of these predecessors and components may themselves have further dependencies. Even when a work is the result of a solitary creator, it still relies upon a community that provides baseline knowledge and shared conventions of expression.
To build a collaborative ecosystem, recording contributions is essential. Therefore, a work’s registration within our cooperative not only lists its creators, but also names its dependencies, recursively registering them as needed. Dependencies include predecessors from which the current work is derived as well as components which are incorporated. Even though there are no obligations to do so, registration of existing public domain works is encouraged, so that credit can be given. Moreover, a given registration could also include references to related materials or other sources of inspiration suitable for public acknowledgment.
Collectively, these registrations form an acknowledgement graph. For any registered work, we can generate a manifest that lists its dependencies and corresponding creators. Conversely, for any creator, we would be able to list those works to which they have contributed, as well as works that depend upon their creations. This acknowledgment graph could be queryable so that any stakeholder would be able to learn more about the people who are responsible for a given work, opening up new opportunities for recognition and collaboration.
Aggregate usage statistics can also be generated. As purchase records are combined with this acknowledgement graph, it is possible to compute how many copies of a component or predecessor were licensed. These usage statistics can also be generated for registered public domain works or referenced papers. To protect the anonymity of buyers, these statistics would be aggregates, perhaps subtotaled by region or other customer public profile attribute.