Part IV: Design in open environmental data and hardware products

Designing for an unknown user profile

While a significant number of data harmonization products and projects exist, there are a lack of analysis tools for using the data, and of metrics for how the resultant data is being used by other actors. This is a problem in advocating for the necessity of these data products because we are unable to see their impact beyond educational purposes. More so, this makes it difficult to track examples of where data has been used or integrated in decision making towards policy, regulatory or enforcement activities. Additionally, and sometimes by design, acknowledging that open datasets are used to come to a public decision for which the decision-makers could be held liable, proves to be too risky for incorporation.

Creating a digital infrastructure to “find the story” can be difficult. Currently, much of the way we are taught to design data infrastructure is through brainstorming the “end users”. When trying to build a data infrastructure that allows users to explore and contribute to the story, design decisions that are made along the way do not always acknowledge the many points at which the user interacts with the product and the unpredictability of the intentions of the users. For example, big data can be incredibly valuable, but the value is derived from the quality of the questions that are asked of the dataset. If a user is faced with all the data in the world about the ocean, but only in a daily series format, and they want to know something about the minute trends during low and high tide, then the rigidity of the time series suddenly renders the dataset useless. The fluidity of social, cultural and physical environments come up against the rigidity of technical data infrastructure. Because of this challenge there are limited opportunities or scaffolding available to users of open environmental data to build, identify and create stories from the data.

Data infrastructure and power

Human bias, and the desire to retain power, are entrenched in social and therefore technical systems and replicated in the case of environmental data. Designing openness and transparency into an open environmental data ecosystem while prioritizing privacy and ownership of data requires rethinking power structures in data and more broadly in science. This shift in control and ownership of data, information and process is complicated by a number of human factors including both unintentional and/or unacknowledged, and intentional bias. From the angle of government, being open to solutions beyond the administrative rigor around data and tool use might alleviate challenges around the use of open data and the resultant innovation ecosystem. Yet new data sources have the potential to implicate government employees in negligence or lead to new results and insights which could shift the scope of an employee's work. In addition, principles that are designed to guide responsible use of open data ecosystems (and others), such as FAIR (findable, accessible, interoperable, reusable) and CARE (collective benefits, authority to control, responsibility, ethics), and the inability to be responsive to widespread use of these principles, stymies constructive innovation and advancement leading to the maintenance of the status quo. These social implications are the results of human bias which can prevent the construction of open data ecosystems or significantly reduce the scope of their impact.

Hardware design

The surge of interest in open hardware over the last decade has led to an assortment and array of parent projects and new, many times localized, versions of those projects, but has not yet grappled with the infrastructural needs of supporting these projects. Projects have to be responsive during the initial design stages to the multi-geographic needs that require attention to a variety of criteria such as cross-national/entity agreements, collaborative (or at least coordinated) documentation systems, models for localizing bill of materials, protocols and standardization of QA/QC, and a clear sense of what the business model and ability to redistribute product will look like. Problematically, many times the technical challenges are solved in isolation from the eventual users. This isolation of solution-finding creates products that are technically competent but perhaps not culturally or socially engaging.

We acknowledge there are many actors and stakeholders in this space that are actively working towards remediating these problems and if we haven’t already, we’d be interested in hearing from you. Please tweet @OpenEnviroData about your project or send us a note at info@openenvironmentaldata.org.

Next up: Part V: Tools and processes for incorporating and managing environmental data

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