Increasing access to data, eg by publishing it under an open licence, can allow a wider community of people and organisations to benefit from it. But the effort required to collect and maintain the data is a key consideration in making that access sustainable.
Sharing the work of maintaining the data can help spread those costs across a wider community, while allowing users to collaborate to improve its quality.
This type of project involves developing a useful database, starting from a Fixed Schema – but this schema is likely to change over time. An Evolving Schema will allow the project to adapt to the shared needs of its contributor community.
The work of contributors will involve not just completing tasks to populate a dataset, as in a Collaborative Cataloguing project. The data maintenance will also be shared across the community.
The database is likely to contain reference data that describes an area of shared interest, rather than a series of observations that are collected in an Observation Pool.
A Shared Directory is one example of a Communal Database, but usually with a more restricted focus. A Communal Database about music would contain lists of artists, albums, tracks and musicians, as well as information about their connections.
The main difference between a Communal Database and a Knowledge Commons is that in the latter, there is much more devolved responsibility for making decisions and managing the community.
The data maintenance work might be spread across people who are not data experts and are busy with other tasks. So a good Onboarding Process and Learning Curve is important. A clear review process will be important to help maintain quality. Retrospective Reviews supported by Field Validation to capture common data errors will be important. Escalating Blocking may be required to keep the community on track.
Supporting Extendable Tooling and Bulk Changes may help to support users in the curation of the data.
If you Deliver Individual Value to contributors, whether they are individuals or organisations, then the project will deliver value from the start and not just when it has critical mass.
The changing nature of the work involved in maintaining the dataset means that Microtasks might be helpful in supporting and encouraging maintenance work.
It is important to have Clearly Defined Roles as work is shared across a distributed team. Transparent Stewardship, well-defined Published Policies and Clear Licensing will also build trust in the data and the project.