Data governance, political strategy, and longevity in the Salton Sea Community Science Project

Open Environmental Data Project
7 min readOct 10, 2023

This is the seventh post for the Community Data Hubs Documentation series. This series will document the thought and conversation trajectories within the process of creating the building blocks of our Community Data Hubs model and OEDP’s broader data stewardship work. The first of these blogs will document the progress of the Community Data Hubs Advisory Group, which is working alongside OEDP to tackle conceptual questions related to the model, including social and technical infrastructures, stewardship, and community data.

This post documents the fifth meeting of the Community Data Hubs Advisory Group on July 19, 2023. For this meeting, we asked the group to read a brief account of the Salton Sea Community Science Program. This served as a springboard for an open-ended conversation regarding various components of data stewardship.

Our conversation centered on several major questions — specific to this case, but applicable to broader questions around data stewardship:

  1. In what ways does broader political context shape the opportunities and risks associated with data collection?
  2. What data governance procedures best serve community science programs?
  3. What kinds of infrastructure, technical and social, might help to secure longevity for community science programs?
  4. How can we advance the goals of open science through data sharing while maintaining a commitment to community data sovereignty?

In what ways does broader political context shape the opportunities and risks associated with data collection?

The High Country News (HCN) article indicates that data collected by the Salton Sea Community Science Program has relevance to policymakers. Advisory Group members were hungry for more specificity: Is this data meant to confirm a generally known truth (i.e., that the Salton Sea is shrinking) or challenge a dominant narrative? Who is contesting the reality of the situation ? Group members noted, alongside the article, that the broader reality of this transformation is sensorially apparent: water is visibly lower from year to year and newly exposed lake beds produce a strong odor. Given this, what is the role of water testing? Does it hope to name the pollutants at play or map exposure pathways? Answering these questions can help to specify the kinds of data people hope to accumulate, the intended audience for that data, and the final forms in which they might be presented. One group member noted, for instance, that if the strategic goal is to add scientific heft to existing community observations, the project can accumulate data on a fairly small scale while still having a great deal of relevance to the community and to policymakers.

At the end of the conversation, we returned to the theme of political context; one group member suggested that different strategic positions of data stewards might warrant different governance models. For instance, researchers and community members may decide they want to accumulate data that is legible as “neutral” to other parties, creating the scientific record upon which subsequent activism can be based. In other cases, community groups and affiliated researchers may choose a “data activism approach,” where the parameters of a research project flow directly out of and in support of community demands. This group member noted that simple recommendations might go a long way in empowering either project; for instance, presenting your data during the public comment period of a county or municipal meeting puts that data on the public record. Perhaps these kinds of simple recommendations could populate an eventual “resource library” as part of the CDH model.

What data governance procedures would best serve this particular community science program?

Group members acknowledged that issues of data governance — especially data ownership — were largely absent from the HCN piece. This raises some concerns as several were aware of community science initiatives in which unclear governance procedures ultimately led to harm of one kind or another befalling the community in question.

One group member pointed to an example in Tonawanda, New York, where community members ultimately had to sue the University of Buffalo when affiliated researchers retained ownership over community data as their collaborative project fell apart. Another group member offered the example of an Indigenous community that sought control over the data collection surrounding a nearby industrial facility out of a fear that, otherwise, the data might be used by the opposing corporate entity to exonerate its pollution. Intersecting with questions related to data-sharing below, group members noted that even if and when data is accumulated in good faith, no community science program has a monopoly on the interpretation of its data. There are inherent risks to the research process which must be considered and built into governance procedures, and will remain even in the best case.

Group members asked if a licensing agreement might be useful in limiting risk. Could a license specify that data could not be used by corporate entities? Group members agreed that enforcement would be challenging. This brought to the fore broader questions of institution-building within the community science movement. Whereas we often focus on the complex social and technical questions surrounding each local iteration of a community science program, a broader union or network connecting these initiatives could take on legal or legislative campaigns that shape the (inter)national regulatory context.

While group members generally agreed that questions of ownership are central to data governance, one member wondered if it always makes sense to raise them at the outset, depending on the scale of the project and its associated risks. Respecting the steep financial and time constraints communities affected by pollution are often under, this group member advised that we must triage questions of data governance alongside policy timelines and the community’s own sense of urgency.

What kinds of infrastructure, technical and social, might help to secure longevity for community science programs?

Conversations regarding longevity sprung out of those on data ownership, as clear data governance procedures can help to specify what happens to data if/when a community project ends, be it because it has achieved its goals or because community capacity has shifted. Data governance, at its best, plans for the “afterlives” of data.

Group members agreed that in many cases of environmental injustice, longevity is essential to accumulating strong, conclusive data. There are several technical components to achieving longevity, from matters of data storage to sampling consistency. However, group members emphasized the importance of a community’s social infrastructure in securing the long term viability of a community science project. Endurance requires budgetary planning, some institutional consistency, and programs that cultivate personal, not just practical, relationships between community members. Several group members noted that the community science fellowships they were aware of did not offer funds to support these kinds activities, despite being fundamental to good data collection. One group member wondered if we could imagine “fundraising fellows” that could be paired with community science initiatives. Another group member noted that the best community science projects cultivate “community champions,” members of the affected population who accumulate a fluency with the relevant regulatory ecosystem, develop media skills, and sustain relationships among community members.

How can we advance the goals of open science through data sharing while maintaining a commitment to community data sovereignty?

Group members had a range of questions regarding how the Salton Sea Community Science program could optimize its procedures for sharing data with other community members, policymakers, and researchers. One group member wondered what incentive structures we might create to encourage that “extra-mile” of data sharing: Could we deposit the data somewhere that will be accessible to other scientists, perhaps in a metric that is standardized for the sake of comparability? One group member suggested that officials at the California State Water Resources Control Board might be able to consult with community science programs like the Salton Sea’s to help them conform their water testing data to statewide standards.

“Openness,” however, must be negotiated alongside risk and questions of data sovereignty. For example, one group member noted that while the community science program sought to distribute its findings to other community members via public fliers, they might encounter various challenges in the outreach process, stemming from the large undocumented population nearby. Those folks include many agricultural workers whose primary toxic exposure may be on the job, rather than through the runoff the community science program is seeking to document and mitigate. Moreover, group members noted that researchers’ desire for broad and deep data sometimes comes into tension with community ownership. This tension is heightened in contexts where researchers seek to collect data that overlaps with Indigenous or Traditional Ecological Knowledge (ITEK), which communities may not seek to share at all or only in ways resonant with their existing cultural/legal frameworks. Group members indicated that ITEK may not be at play in this particular example, but also raised questions about who should adjudicate when such concerns are relevant. Having explored these ambiguities, however, one group member noted that an overabundance of caution might prevent community science programs from sharing actionable data that could significantly improve the health outcomes of community members.

Insights and questions to revisit during the CDH Model Co-Design Process:

  1. Rather than a concretized set of rules or techniques, our Community Data Hubs model will include a modular set of approaches and best practices from which community science projects can pick and choose to fit their particular circumstances, strategic goals, and potential risks. Our CDH model will guide the decision making process as community members and researchers build a data stewardship plan, rather than offer a transportable abstract framework.
  2. The CDH might benefit from a compilation of resources with practical guides on how to, for instance, get your data on the public record by presenting at a meeting of a local governing body.
  3. Local community science initiatives would benefit greatly from the existence of a broader umbrella organization. This network could take on the (inter)national regulatory or legal issues that shape, but exceed, the local context in which community science programs operate, either through lobbying or targeted lawsuits.



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