2.2 Data commons

In the previous section, we discussed the example of the USGS National Map. This is an example of a data commons, one of the most common organizing and governance structures of anti-rival goods.

A data commons is an internet based platform that stores collections of data. This platform offers users the option to upload, access, and share data. Data commons range in terms of structure, content and access. Some are completely open to the public, while some may require registration.

Data commons can also include tools and computing infrastructure to support the management and analysis of data.

What is a commons?

You may already be familiar with the term commons. Commons refers to a pool of resources—natural (such as a lake of fish) or manmade (such as a public park)—available to the surrounding community. There are many types of commons, and many ways to manage a commons.

Data commons as anti-rival

Data commons are an example of an anti-rival governance structure. They create value by combining data that would otherwise be separate. This combination has more value than the uncombined combined.

Data commons increase the value of data in several ways:

• It is more easily accessible to researchers, policymakers, and companies
• It is cheaper, as you can access data that already exists instead of gathering and creating new data
• It is easier to integrate and combine data
• Data commons are often curated for the needs of its users, making it easier to meaningfully analysis data

(These points have been adapted from a post by the Center for Translational Data Science).

Some examples of data commons:

• Open Science Data Cloud: collection of open-access scientific datasets on a variety of subjects Earth science data, biological data, social science data, and digital humanities data.
• National Cancer Institute’s  genomic data commons (GDC): database of clinical and genomic data related to cancer, including data from over 70 projects and over 85,000 cases.
• datacommons.org:  a data commons supported by Google, which synthesizes data from a variety of sources to create single graphs. Includes topics such as demographics, economics, COVID, emissions, and climate

Other forms of collective data organization

Data commons are just one example of how datasets can be organized .

Data unions, also known as data cooperatives or data trusts are a form of voluntary commons, where individuals upload data, for example their own personal data or data they collect through an app they run. This information can then be aggregated and sold. Data unions give more power to the individual, as they are able to choose which information to share, and even allow users to profit from their data being sold.

Some data unions include: Pool, Swash and Unbanx.

The Pool (www.pooldata.io) platform supports data unions to “enable ordinary people to monetize and share their data”. They have created the digital infrastructure to make it easier to upload, manage, and sell your own data.

Swash (swashapp.io) is a browser add-on that captures, pools, and sells your data on your behalf.

Unbanx (www.unbanx.me) is a mobile application that allows you to monetize your banking data.

Data unions are still a relatively new phenomenon, and many are still developing how to best capture and profit from data, while maintaining privacy and security. If you are interested in joining a data union, do your own research to find the right fit for you.