 I'm Katie Hoberling. I'm the director of policy initiatives at Open Environmental Data Project. And before I get started, I'll just give you a very quick spiel about me for some context. I'm not a data scientist, not an analyst. I don't do a lot of data management. I'm really more on the kind of policy and organizing side of things. So thinking about how open data can fit into environmental governance and policy. And most of my work is based in the US. We're kind of thinking, trying to start thinking more broadly, but I would welcome ideas or questions about how these kinds of ideas could or could not be adapted for other places. So our work at OEDP kind of sits at the intersection of three broad areas, so open data science and hardware on the one hand, community knowledge on another. And by community, I'm really thinking about local place-based kind of neighborhoods where people have shared histories and environmental action and policy. And we do the work we do because we know that environmental disasters and climate change impact everyone. But environmental racism and classism make these impacts much more amplified in communities of color and other vulnerable places. We believe that better data governance can make environmental knowledge more accessible, understandable, and usable to more kinds of people. So we're thinking here about researchers, policymakers, communities themselves, journalists, lawyers. Should I try to keep it closer to my face? Maybe that's what it is. And that these systems should and can center communities and empower them to bring about more equitable environmental action and policy. And also just to kind of level set because open can mean lots of things. Data can mean lots of things. What we mean by open is very accessible within ethical confines. So you can think about privacy and security, understandability, and interoperability. And then environmental data can take lots of different forms. It could be something very quantifiable, like air, water, or soil quality. It could be something very qualitative, like someone's lived experience. It could be about weather and climate. It could even be aerial imagery, photography, or even art. So we're thinking very, very broadly here. And what I'm going to do for the rest of my talk here today is kind of give you an overview of some exciting things that we're seeing happening in this space and making environmental data more usable and more understandable for more people, as well as a few things that we're working on ourselves at OATP. So in the US and abroad, there's lots of exciting movement happening, lots of investment opening up to environmental monitoring and data collection, as well as making that open. So yesterday, Lodax, you often talked about TOPS, the Transform to Open Science Initiative that NASA is leading. And this is actually going to be kind of a whole of government initiative in the US, where lots of agencies are getting involved. So you can. Thank you. Hello? Cool. The National Science Foundation, the EPA, all kinds of agencies are getting involved in this. There's also several $100 million that have been allocated in the last couple of years for environmental monitoring. And then also on the international scale, you've got the Creative Commons Open Climate Campaign. The UN UNICEF has released a lot of guidance in recent years about open science. So lots of exciting things happening. But we know that a lot of times data that's collected or shared is done with a very specific purpose in mind by the person who collected or is sharing that data. So we're asking the question, what if environmental data were managed and shared in ways enabled lots of different kinds of uses? And how would we use existing data sets or collect new data sets to make sense of our surroundings in varied and novel ways? And I'm going to talk about three broad kinds of environmental data to give you a sense of the kinds of needs and opportunities here. The first is regulatory and compliance data. So in the US, the way this data kind of flows is that the Environmental Protection Agency requires states to report on mostly air quality but other kinds of emissions from refineries and other kinds of corporations. So those facilities are collecting data, feeding it to states, and then it's filtering up to the EPA. And in theory, that data should be open and usable. And it often is. It's often shared. But it's rarely shared in a way that is not very often usable. It's open, but it's often shared in ways that are hard to understand, hard to integrate with other data sets. And that's because facilities are incentivized to really do the bare minimum here. They're just trying to follow the law. So oftentimes what happens is they'll share a PDF of completely unintelligible data, and that's kind of it. The photo here I have on the left is of a refinery in Richmond, California, which is actually about half an hour. From where I live, this is the Chevron refinery, and this is not like a normal occurrence. Usually this is just to kind of show you this is a fire that happened there in 2012. But this is just to kind of show you the reason why we require these kinds of things in the first place. So we're asking, how could this data better be organized and shared to support organizing, advocacy, campaigning, or litigation? And how could it support scientists and journalists in making new questions and framing them? The second kind of data I'll talk about today is community data. So we can think about air and water quality sensor networks, recorded lived experiences, and things like this app over here on the right called Smell Pittsburgh. This is an app that allows users to record their observations of odors that they smell that day and where they are. And then this picture on the left is of the Louisiana Bucket Brigade. This is an initiative that has distributed very low cost air quality sensors to people living in what's called Cancer Alley, which is an area where there's lots and lots of oil refineries and lots of pollution. So lots of initiatives going on here. But this can be really time and resource intensive, especially for people who are already overburdened or underserved. And oftentimes, it's not taken very seriously by a government or researchers. And there can be lots of different reasons for this, but generally, there's just kind of a skepticism that this data is valid. So we're asking the question, how can this data be collected and structured and shared so that it could be better integrated into decision making? And how can it be shared while respecting community values and privacy? All right. And lastly, research data. And I've got research in quotes here not because I'm trying to say research isn't real, but just because those other kinds of data could also be considered research. So this here is really mostly talking about research that's done by people in formal institutions like universities and think tanks. But you can think about individual studies in large collaborations, like what the IPCC is synthesizing, or in the US, the National Climate Assessment. This photo on the left is from the NSF Arctic Data Center, the Permafoss Discovery Gateway. And so a lot of this data is being collected and funded to collect. And there's a real opportunity here for states and cities and tribes to be using it to better inform their climate planning, especially as a lot of people are actively dealing with emergencies right now. The problem is that a lot of times this data is decontextualized. I don't know if anyone was just at the prior talk, but that was kind of a great primer for this idea of decontextualized data. So even though it might be shared openly, how are people going to understand and be able to apply it in very specific situations? So that's our first question. And how can we make it understandable and accessible to people who have various levels of data experience? So for the last part of my talk, and I think I've got five, six minutes. Sorry. Yeah. I'm going to talk about three kind of broad opportunities that we're seeing being taken advantage of in this space, a few really exciting things that we're seeing, and then some things that we're working on ourselves. And the first is shared spaces for storing and pointing to related data sets. So a lot of you are probably familiar with very centralized repositories. And these can be really important, but they are often pretty siloed by discipline. And sometimes you even have to be affiliated with an institution in order to access that data. And we believe that decentralization can support a much more diverse set of information and incorporation of data. So a few exciting things that are happening. The main STEM network is an initiative led by the Water Data Collaborative. This is kind of actually, you can kind of think of it as a social media platform for people who are doing community science projects related to water quality. So there's people on there who are from research, from utilities, from community groups, as well as local agencies. And they're all kind of talking together about the data that they're collecting. It's trying to figure out how they might integrate them better together. It's a pretty cool initiative. I encourage you to check it out if you're doing any of this kind of work. And then another thing that we're working on is something that we're calling community data hubs. And this is a very kind of nascent idea. We just got funding to start doing this, which we're really excited about. But we're kind of conceptualizing this place where communities can collect and collaboratively govern their data, share it with people in research and government, and get also feedback about what is being done with that data. So this is very early stages. We're just starting to put together a working group and that kind of thing. But if anyone's interested in these ideas, please let me know. I'd love to chat. The next is leveraging data stewards to integrate community priorities. So data stewards are increasingly being employed by universities, public agencies to support what we're calling verification. So I'm guessing most people here are aware of FAIR. This is Findability, Availability, Interoperability, and Reusability. We're trying to understand if those kinds of people could also be integrating care principles. So this was a framework, if you're not aware of this, that was kind of in response to FAIR that was trying to center indigenous and community values, so collective benefit, authority to control, responsibility, and ethics. Chief data officers as well are often being employed both in tech companies as well as government. Could they become community data integration officers? I think there's a massive switch in culture that would need to happen as well as training. But we're starting to think about what this could actually look like. So one thing that's happening that we're working on is what we're calling the Seek Commons Network. And this is a collaboration that encompasses a lot more than just this kind of training idea. But one part of this that we're really excited about is the Data Facilitators Consortium, which is trying to kind of flip the training convention on its head and take normal approaches of trying to train communities to better interact with government and understand complex and opaque processes to instead work with researchers and government to better integrate community priorities in the first place. So also very nice an idea. We're just getting started. Something that's happening and also still very much in the works, the Indigenous Traditional Ecological Knowledge Memo that the White House released a couple of years ago that was talking about both recognizing indigenous knowledge and starting to develop guidance on how agencies could actually use and apply it and work with tribes to continue working with it. All right, I think the last one, because I want to leave time for questions, and one of my favorite ones, well-designed and well-documented APIs, so application programming interfaces. I think a lot of you are probably aware of what APIs can do, but I think two of the really exciting things that we're seeing in this realm are making visualizations really accessible for people of lots of different kinds of experiences and backgrounds and being able to connect datasets from different sources. One thing that we've also heard a lot of people talk about is that if you can make the process of designing and documenting an API collaborative, that could actually be a really good place to deliberate on issues with your users and community possibly. So lots happening in this space. If you're interested in water or air quality, I would check out Water Reporter. I think actually open AQ, there's gonna be a talk later today, make sure to check that one out. The image on the right is of environmentaldata.org, so this is a platform that was designed by Create Lab at Carnegie Mellon University, and this is aggregating and visualizing data from mostly air quality sensor networks in Pittsburgh, where there's also lots of steel and other kinds of refineries. And so not just showing you where sensors are located, but making it easy enough to compare different variables from different data sets right next to each other. And you can download data from that place and kind of verify it's what you're looking for before you actually start going through it. One other really exciting thing that we're starting to get involved with in California is a Senate bill called the Refinery, Air Pollution, Transparency and Reduction Act. And this is something that actually just passed the Senate committee a couple days ago. We're thinking it's probably gonna pass, but it requires refineries who are already collecting this kind of data to actually start working with APIs that are going to make it much more understandable and usable. It does a lot of other things, but we're hoping this could be a model for other states and possibly other countries. So I think I will probably stop there. I'll put this up here as just something else that we're thinking about metadata and human readable data. I'll put my slides that are up online if you wanna take a look at any more of this later, but in the interest of time, I think I'll just stop there and see if there's any questions. And we have five minutes to switch rooms, but I also want to let you answer questions since we did start late. Can I have the next speaker come up though so we could get you started? And yes, please ask your question, but you are free to leave if you need to switch rooms as well, so. Okay, so I'll try to be as quick as I can. So first thing I just wanna say is nice to meet another Bay Area girl. I'm from Richmond, so it's nice to see a little attention on my city because people like to abuse Richmond and never try to help Richmond, so it's nice to see that. I was there in 2012 when that nasty fire was there and it was really bad. I was curious if you have heard of Amazon employees for climate justice, and if not, it might be nice to connect you because they're the tech employees that have been really kind of sticking it to Amazon corporate and pushing them to just pay attention to environmental racism and not allowing them to put all their warehouses in inner cities or in rural areas or in indigenous places that are polluting those areas, and so it might be helpful to maybe connect you with some of them if you're interested. Very much so. Yeah, I think we're starting to try to understand how labor fits into this, too. And it seems like there are third connections there, but unions aren't necessarily centering environmental justice issues and environmental justice fees aren't necessarily popping with labor, but there is a lot of shared interest and approaches there. Yeah. Oh, thanks. So yeah, I would love to connect with them, that'd be great. Okay, yeah, so we can talk later, but I just was wondering. One more question? Oh, no. All right, let's end, but please do find Katie afterwards and you now have three minutes to go to another room if you would like. Thank you. Thank you, Katie. Thank you.