What can be found in an data community?
In an data community on data.overheid.nl the following topics can be found:
- One central place for data within the topic. These data is visible and findable through metadata
- As much as possible government data available from different government organizations
- Shows in data what the involved organizations produce and publish
- Gives insights in the available data, quality, but also contradictions in data
- Givers insights in the involved parties, chains and network round an specific topic
- Provides direct access to the core data and figures within the subject
What does the organization of an data community look like?
Data.overheid.nl facilitates as a data broker between supply and demand of data. This makes it possible for the community to develop. In addition, at least one leader and a number of experts are linked to a community. The initiator is in fact the manager of the community, arranges the day-to-day affairs and draws up rules about the community where necessary. The experts provide the content on specific topics within the community.
Step-by-step plan setting up an data community on data.overheid.nl
Basically, data.overheid.nl follows the following four steps to start and set up a data community:
- Idea, research and direction. Determine the goal, target audience, scope and direction to make the community a success. Set up the basis for the community: datasets, themes, experts and recognisability. Create commitment with the initiators and first members, activate management for support.
- Start-up, first members and activation. Start the community with those directly involved: sets up the organization for moderation and interaction. Draw up house rules. Provide a 'warm house' with sufficient data, content, applications and a fixed group of members. Organize a highly visible community launch.
- Grow and promotion. Use the first members as community ambassodors for promotion and organic growth. Leverage existing communities, networks and social media for visibility.
- Learning and optimizing. Learn from interaction, optimize where necessary.