Data Governance: GKC as an Analytic Tool for Shared Knowledge and Information Resources

Module developed by Michael Madison from the University of Pittsburgh’s School of Law

This module guides learners through a series of steps that help them understand the ways data, datasets, and other collections of information are entrenched within social contexts. These contexts often have a systematic character, meaning that data producers, stewards, and users create and participate in structured systems of rules and social norms–known as governance–that guide how data is used appropriately.

Participatory governance of data by members of a community is called a “knowledge commons,” an approach that focuses first on the production and collection of data, including the people responsible and their motivations; second, on the threats and opportunities associated with storing, analyzing, and using the data; and third, on the rules and standards that specify who may interact with the data and how. Using this approach helps those who produce, steward, and use data understand how to design better governance for more effective data use.

Our approach introduces learners to seeing the principal elements of that character by using techniques derived from research in knowledge commons, which studies systems governing shared knowledge and information resources.



This exercise will ask you to apply the knowledge commons approach of understanding governance to a Zooniverse project. Using the key concepts of the knowledge commons field, learners will identify and practice a short series of steps giving them the power to explore and use data in ways that align their own goals and broader community and social interests, and the power to see when others are doing so, or failing to do so.

To complete this exercise, you will need a computer with an internet connection. We expect that this exercise will take you about 30 minutes to complete.

Exercise Download


Use knowledge commons techniques to explore data governance in additional contexts.


We recommend that you explore a different but likely familiar context: Wikipedia. Wikipedia is a prominent example of how the internet has opened up new possibilities for producing, preserving, distributing, and accessing knowledge.  

For this exploration, treat the textual information on a Wikipedia page as a dataset in the context of Wikipedia as a knowledge commons. Pick a Wikipedia entry that is between 3 and 5 pages long (not too short and not too long) and that is not subject to any posted reservations about its completeness or bias. Then determine:

  • How was it produced and why?
  • Who produced it, who manages it, and who uses it?
  • Do key concerns arise from:
    • Privacy questions? 
    • Data security or integrity questions? 
    • From a diversity of uses, known or unknown?
  • What rules and standards exist within the setting?

We also recommend exploring different data portals. We suggest looking at datasets in the Western Pennsylvania Regional Data Center and applying the same approach as above.


  1. Data and datasets are characteristically shared resources, in that data are produced collaboratively, or are used by people other than their producers, or both.
  2. Shared data are often caught up in social “dilemmas,” which are conflicts between goals of individual producers or users, on the one hand, and broader social or collective goals, on the other.
  3. Governance of shared data can be designed in ways that enhance the value of the data (or detract from it). Good governance includes community rules, social norms, and laws. The best approaches, such as the participatory approach known as “knowledge commons,” acknowledge and address social dilemmas while maintaining productive data collection and use.

This work is licensed under a Creative Commons Attribution 4.0 International License.