‍Part one of this series explores a brief introduction to why the approach of collaborative governance is interesting and attractive in the environmental context and then defines the different types of governance models being discussed today.

Introduction

Environmental problems are considered “wicked,” consisting of different complex systems interacting with each other and necessitating a diversity of approaches for solving. They categorically call for decision-support tools like environmental data and information, alongside principles on how best to use the knowledge products. The complexity of environmental problems manifests across the requirement to monitor different environmental parameters (such as air and water quality, and biodiversity), but also to adapt those data collection efforts to the context of  existing environmental legal structures, regional politics and economic industry. Though there may be a clearly articulated goal, such as “we want to drink clean water,” getting to that goal takes people on a journey through these wickedly complex systems. Currently, the path to navigating this maze must account for other environmental factors that influence clean drinking water-- soil and air quality, an evaluation of economic alternatives, and the consideration of laws that are not related to clean water, but perhaps migratory birds, and finding the correct institutional allies to accomplish clean drinking water goals.

Acknowledging that the Open Environmental Data Project’s work rests inside this wicked problem space, we are currently exploring how alternative models of data ownership, management and data quality assurance could provide fertile ground for solving some of the problems of the environmental data maze. Our starting point is to look at a number of (old and new) frameworks for how data is collectively governed and identify when and where they’ve been applied or could be applied to the environmental problem space.

Types of different data models

As parts of the world enter into the information age -- where the sub unit is data -- there are many attempts to apply a variety of common resource-sharing structures to data. These generally fall across the following social and economic instruments: guilds, commons, collaboratives and trusts. Each approach has a slightly different purpose, but they all serve to enhance data sharing, management and stewardship practices, sometimes with a rights-based approach and a careful attention to detail on who benefits from the data. The following is a brief high-level discussion of the different types of models encountered today.

Data Guilds

A guild was a common social and economic structure in medieval Europe that allowed for collective governance over common trade industries like carpentry, masonry and steelwork. The model allowed for the communities to control the inputs (tools, raw materials, education/apprenticeship) and the outputs (houses, buildings, ships, etc.) of their trades, while receiving a fair wage for their work. There were restrictions on the tools that could be used to produce the goods in order to meet and qualify for a standard of work. In the context of data, guilds tend to be considered ways to build the labor force required for a functioning data ecosystem. Many examples lay in the venture capital market, where firms are investing in products that meet certain structural components or in the financial industry where banks are building education and training programs into constructing data analytics teams for financial product management.

Data Commons

In general, a commons can be defined as the combination of a resource, a community that gathers around that resource and a set of rules to care for the resource and its resultant community. Most often it is invoked in the context of natural resource commons - such as water, air and land - but this has evolved in the information age into “peer-to-peer” sharing networks. These networks rely on building the enabling capacities of the internet for contributive actions, or in other words, allow for a community to decide how to optimize a resource like water, air or land and define the rules for who and how they might access and use that resource. Many examples of this  commons approach in the environmental data field tend to have commons nomenclature attached to a scientific data repository with established data sharing rules and agreements among different scientific and policy institutions.

Data Collaboratives

Data collaboratives are designed to tackle a specific problem for which can only be solved, or could be better solved, with multiple data sources. The key to their functionality is carefully articulating the problem that needs a collaborative data solution. Their functionality and design rests on the desire to understand trends, and for the public reuse of the resultant data and the projects that might arise from the data. The Netherlands Center for Big Data Statistics is experimenting with combining all government collected data on community level issues, visualizing it and sharing it back with the public to create possibilities for future collaborations while the Ag Data commons by the USDA is an attempt to combine all data related to a variety of different scientific disciplines like genomics, hydrology, soils, and economic statistics, and package it for supporting more informed science and policy decisions. For further examples of what qualify or constitute as a data collaborative see Gov Labs Data Collaborative Explorer.

Data Trusts

A data trust can be defined as legal infrastructure that provides stewardship over data by a third-party entity. The core concept behind a data trust is a governance structure composed of a council of people who have a “fiduciary” role to the data entrusted to them - that role is then defined by the beneficiaries of the data and describes their rights to that data. It has mostly been applied within the context of healthcare and social media with attention to maintaining privacy and autonomy in how data can be used for commercial gain. However, the instrument could have applicability in other realms of data ecosystems where the ownership over data has implications for regulatory or stewardship purposes.

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