In the United States, many communities lack granular, “hyperlocal” data sets that monitor climate and energy conditions on a household level. These data sets can be used by policy leaders or utilities to more precisely customize lending, incentives, or engage grant programs to encourage greenhouse gas reductions for communities more immediately impacted by climate change. In response to this need to generate more granular climate data sets, the Bezos Earth Fund and BlocPower, a climate technology company, partnered with Inrupt and Howard University to generate air sensor data readings on a household level. The team sought to give research study participants transparency and insight into the data collection, and the tools to consent to and manage the data collected. By storing the data in interoperable Solid Pods, this study lays the groundwork for collecting more hyperlocal climate data to generate further insights into community-level energy or air quality concerns.
The Bezos Earth Fund, BlocPower and Howard University partnered to conduct research on the air quality of households of several neighborhoods with different income levels. However, in order for the researchers to obtain data they had to overcome several obstacles, including:
The researchers decided to provide study participants with in-home air quality sensors that would send data back to the Howard team. However, to address concerns from community members on data collection and usage, the researchers needed a platform that gave participants greater transparency and agency into how their data is used while also allowing participants to directly see the benefits from the data being collected about their homes.
In order to achieve these goals, the Bezos Earth Fund, BlocPower, and Howard University needed to:
The Bezos Earth Fund, BlocPower, and Howard University chose Solid as the basis of their data infrastructure due to its consent management and user-centric data management capabilities. These capabilities allow study participants to opt into or out of sharing their air quality data back with the research team and consenting to how the data can be used. Storing the data in Solid Pods enables growing and enriching the data set around an entity easily over time, creating the possibility of adding household or building data incrementally and in a fine-grained way. This may include information on energy usage, HVAC configuration, insulation, building measurements, or a user’s demographics. This allows researchers to work towards high-fidelity digital twins of communities with high quality data directly from residents and shared with their consent.
Participants used a Solid-enabled research study app to register a new Pod, connect it with their air quality sensor and give consent for Howard to read data for the study. The data is then used to create dashboard visualizations, which Howard researchers access through a separate Research Pod and use to draw insights from the collected data.
A web application was provided that allowed the study participant to create an account, sync their air sensor device to the account, store air sensor data and profile data in their Solid Pod, and provided consent for Howard researchers to read their air sensor data for analysis.
Through a Data Sharing section, participants could see what data was being shared with the Howard research team, and had the option to disconnect their air sensor or stop sharing their data with the Howard research team at any time. If a user decided to revoke access, they would see an alert to confirm their action and see a message outlining the implications of not sharing data with the study.
By creating a user-centric data storage approach, the Howard research team enabled collaborative, non-extractive data relationships with their research participants, engendering higher levels of trust.
When study participants can consent into or opt out of sharing this data with other parties, they become a trusted partner in the study and feel a greater sense of agency.
Creating an improved data consent and access model results in higher-quality data from study participants and paves the way to create digital twins of communities with accurate climate data, leading to greater insights.
By being able to access their own data, study participants were able to discern how their contributions to the study generated value to themselves, their neighbors and their communities.
