Open Climate community call: April 27, 2021
Over the next six months, a series of “OpenClimate community calls” will be hosted around different topics related to the connection and intersection between the climate crisis/climate action and the open movement. We’ll do short, easily digestible write-ups for each call, but if you’re interested in going more in-depth on the topics, we encourage you to 1) join the calls, 2) watch the call recordings, 3) follow along with call notes, or 4) suggest a topic that you’d like to present about or host.
The second call of this series focused on how open data works for decision-makers? Sub-questions included:
- How is using open data different in different contexts (kinds of work, geographies, etc)?
- Why are people going to other kinds of data? Are there other competing alternatives to open data? Are there practicality issues or tradeoffs with open data?
- What kinds of impact does open data create? How are end-users using it in the workflows?
- What challenges do you run into when talking with other people in your space about open data?
For our second call, we had Angela Eaton (Director of Data Inclusion at Open Environmental Data Project) and Anna Grijalva (UNDP Accelerator Lab Ecuador). The focus of this call was data-driven solutions for climate action.
Presentation by Angela Eaton from the Open Environmental Data Project
Angela opened the presentation by sharing with us thought-provoking questions in relationship with community data collection and gathering. She signaled the need for giving community ideals and nature an equal weight in decision making. Then she offered insights into what community and participatory data gathering might mean, pointing out that “environmental data gathering means different things for different people”, but “community science occurs in many forms and spaces, and it might be designed for cultural meaning”.
She went on to describe ways in which communities can come together and have different practices to create knowledge. This knowledge is always related to specific spaces, and it could help with creating resilience. She shared the example of local indigenous communities from what is currently known as the San Francisco Bay area, and how the traditional knowledge that these communities have has led to higher focus on community values in practice, higher participation rates and increased scientific value — towards collective land management.
She also pointed out the importance of creating trust within a community and between communities, industry and government. She mentioned how pseudo-participation in community data gathering and collection can hurt overall willingness, and the importance to view “trust as a non-renewable resource”. It is important that communities and individuals feel that they are the authors of open data, rather than the subjects.
Angela ended her presentation by posing some questions. One of her friends took photos with his cellphone of San Francisco as it was experiencing the 2020 wildfires with what Angella calls an “imperfect data collection devices”. She asked us: how does editing an image in a cell phone reflect memory and experience? What does self-selection mean for data collection, and how different experiences can affect data? Which images (a cellphone image or a satellite image) has a higher community value? Which one would you trust the most?
Presentation by Anna Grijalva from the Accelerator Labs UNDP in Ecuador on “Deforestation & Cows”
Anna Grijalva then shared her experience, introducing us to the great “Data Powered Positive Deviance” project by the Accelerator Labs UNDP in Ecuador with a reflection on “Deforestation & Cows”. The Accelerator Labs are, in Anna’s words, “a bet”, with an understanding that solutions to climate change and sustainability challenges don’t come from a desk, but rather from the practitioners on the ground.
The project that is being implemented in several countries is called Data Powered Positive Deviance and it looks at the data ecosystem around deforestation and development in depth. In doing so, there are several questions that the projects are tackling, such as who the partners are in collecting, analyzing and making data usable: Who holds the data? Who has access? What’s the frequency in which data is collected?
In the case of the project in Ecuador, they are looking at different data sets of sustainable cattle ranching in the Amazon forest in Ecuador. Some of the data sources are public data (i.e. not openly licensed but published somewhere) and open source data (i.e. something published that gives access to the raw files).
The positive deviance is a score given to the farmers who deforest less than their peers; positive deviance is given to those farmers who perform “above their peers”. These values don’t remain static but rather they evolve over time. Some of the farmers were consistently “positive deviances”, some weren’t, but improved their performance over time, and some of them were positive deviances and then stopped being sustainable.
This data allows the Accelerator Labs to learn about these experiences of sustainable cattle ranching and understand the changes in behaviour and how farms can work in more sustainable ways.
Community discussion
Then the floor was opened for discussion. Emilio Velis, facilitator of this call, repeated the prompt for this call: “How does open data work for decision-makers? Do they need more data (amount, sources)?”. Angela pointed out that “there’s a lot of types of information and knowledge, but not everything fits into columns and rows. An interesting thing in terms of equity and inclusion is to figure out how to capture that knowledge into a data system and how to make use of that for the public greater good, to add a community voice”.
From the macro data to the smallest, principal policy maker
One of our community members identified how both speakers showed satellite images of the California wildfires and of the Amazon forest, to then describe more local experiences. Is there a connection there?
Anna took the question and went deep to explain what she has been seeing with this experiment. On one hand, she responded to the dimension between the macro/micro data level. How is the macro data level really relevant for the local context? That’s why implementing mixed methods is so important, to run interviews with the people in the ground (in her example, farmers) and uncheck whatever does not apply that has been found with the “macro level data”.
As a way of example, Anna shared that most of these farmers don’t perceive the remaining vegetation as forests. When they went to the ground, farmers asked the researchers: “what forests are you talking about?”. But this is not only attributable to the farmers: Anna shared that Ecuadors’ national definition of forests “as something that has wild animals” also impacts how the community sees the forest. In this case, these forests and remaining forests don’t have wild animals. The question then is: does the macro data make sense in the context?
She then continued to explain what she felt was the second dimension of the question to refer to the farmer as the “principal policy maker”. How can the macro data be useful for the community and how can they use it in a relevant way? Since farmers are the ones raising cattle in a sustainable way, it is necessary to break the idea that “we create data for policy makers with policy makers being seen as the local or national government or the authorities”. The way that the Accelerator Labs UNDP in Ecuador is thinking of policy makers is the smallest possible policy maker, “we’re thinking of a policy maker as everyone that has a say or an action” in the issue at stake. In her example, when talking about cattle and deforestation, farmers are the policy makers.
This also changes the nature of the relationship when collecting and analyzing data. It breaks the extractivist relationship, but also brings into question: if the farmer is the main policy maker for the information that is being collected, do they feel comfortable with fancy or digital applications? Do they feel comfortable using the data? It forces us to think about sharing data differently instead of sharing data in a way that only 2% of the population can use.
How can the open movement help the environmental data movement?
Another participant asked about the relationship between the open movement and this type of project. How can the open movement be helpful?
Anna pointed out as an example that these farmers are the ones that receive the most pressure — on one hand to be sustainable but also to provide food. They need to be seen as policy makers. And in doing so, ask what are the best ways to create and share information. For example, for file formats that are inaccessible, it is worth asking: “how do you make that useful for the farmer?”. It is necessary to ask the farmer: “how can this be useful for you?”, and co-design the way you’re going to share the work with the farmer. Sometimes who has access and can use these data are not necessarily the farmers.
Angela stressed the importance of avoiding pseudo-participation in data collection, production and representation. The more open the data is, the more transparency there is, and the more that levels the conversation among the partners.
Moving from legal and technical openness
Finally, there was a question about “legal & technical openness”, but what other classes of openness would be good to see in the future?
Angela said that data collection and production needs to be pulled out from being a scientific endeavour made in a highly-technical way. She stressed that if that’s the only way anyone can make sense of this data, then the open movement has failed. It is not just about open as in licensing, but also open in how we interact with the data and where we do that. It is important to create transparency between groups and ensure data is useful for all groups.
She also added that when data are closed, then a lot of time is wasted in discussing the legitimacy or accuracy about the dataset. She highlighted that one of the benefits from the data being truly open is that if you feel there’s a data gap, you can contribute to make sure that data gap doesn’t exist.
Anna closed with a reflection pointing that it is important to be honest about the limitations of the data. As she put it, there can always be better data and better methods, but being more honest on the limitations will make sharing better.
Join us on June 29, 2021
If you are interested in following up with the conversation, please join us for the next call on June 29th! We’ll be exploring questions around “what are the challenges of translating the open movement work into environmental research of the climate crisis?”. Luis Felipe will be facilitating a conversation between Myanna Lahsen, National Institute for Space Research, Brazil and Sílvio Carlos, Socioenvironmental Institute, Brazil.
