The OpenAQ Ethos

1. Money and staff-size do not equal impact

I don’t believe our impact has or will track with staff size or annual budget. In fact, I would argue that the smaller we can be and achieve our mission, the more awesome (e.g. efficient and sustainable) we are.

2. Support the community humbly

We play the role of cheerleader for groups fighting air inequality in their communities. We help enable activities and amplify the voices of those doing them, but we, at the end of the day, aren’t the one doing the hard work of measuring the data nor acting on it. Humbly approaching this work means doing it with appropriate attribution, transparency, and in collaboration with the community — and recognizing that what we’re doing is likely still pretty imperfect and a work in progress.

3. Done is better than perfect

Speaking of imperfect, many open data projects (including ours) have unclear boundaries of what data-types they could ingest versus what they don’t. Also, these datasets often don’t have well-defined (or any) guidelines or standards — in fact, the messier the data landscape, the more interesting and useful it can be for a group to wrangle the data in the first place. And you can spend forever and a day trying to design the perfect system and architecture. But we believe done is better than perfect. This is why we didn’t wait to launch our project until we had air quality ground monitoring data from all available countries — we started with three and grew as we could. This is why we didn’t wait to aggregate all possible data types (e.g. large volumes of low-cost sensor data, accompanying station weather data, or a lot of meta data that is out of reach). And that is why by not waiting to find massive funding or crafting schematics at our best guess of the perfect system, we’ve been able to build the only system available to date that lets you access open station-level air quality from 62 countries and counting. We’ve been able to get closer to perfect by putting something out there and getting community feedback about it as we go.

4. Build a system that improves other systems

We don’t want to aggregate and share data as an end in itself. Rather, we want to help organizations — especially governments — share their data better or at all. Our community has done this through contacting their environmental agencies to improve their APIs. Or from community members noticing errors — and eventually helping correct the way real-time air quality data is reported from a government agency. We think this is awesome because it improves air quality access to anyone utilizing that primary data source, not just those who access it through our system.

5. Don’t fall too much in love with fancy metrics

I’m a scientist. I like charts, graphs, and trend-lines. And there’s a certain sense of comfort in building a strategy that lets you systematically tick off quantitative milestones toward success. But real impact and influence often don’t work that way — we just approximate them as best we can with these artificial frameworks. For instance, I often think of that lone air quality monitor that the U.S. Embassy launched in Beijing several years ago. There’s a pretty strong narrative that connects the dots from that monitor to year-on-year improvements in annual-averaged air quality levels in Beijing since 2014. But you can’t pin that story down with a quantitative analysis showing how that monitor directly influenced the media, activist, science, and policy communities in Beijing, across China, and across the world. And if that monitor had not been launched or did not stay put because one couldn’t quantitatively define its short-term success, we would all be the worse off for it.

6. One tutorial is worth a thousand thought leaders

Joe said this to me recently. I love this phrase. Our community is awesome because they do stuff. If someone mentions, “I’d really like to see data from Country X,” that’s nice to hear. But it’s even better to point our community to a website where that data is shared. Best of all? Actually writing an adapter to ingest that data. Recently, a community member contacted me to ask if there was an open-source wrapper available that would calculate air quality index values from the raw data in our system. I said no, not yet, but it was on our Community Wishlist. He responded, “Actually, I’ll build that.” Now, that is how a grassroots, disparate open-source group gets the job done.

7. Assume the best of others

Community-building and data sharing of any sort revolve around trust. I have noticed there are usually two things lacking when groups don’t trust one another: assumed mutual good intentions and mutual respect. If those things can be remedied, so many other logistical or social obstacles can be overcome. It may sound simplistic, but the number one way to help instigate mutual assumed good intentions and respect between two groups — or between your group and another — is to be the first to articulate clearly that you assume their good intentions and to state your respect for those groups. It has helped us do concrete things like ingest more — and more accurate — data into our system, create better outcomes for our workshops, and given us opportunities to share our views on best open data practices with wider policy-relevant audiences.

8. Exist only for as long as needed

Don’t get me wrong: I adore OpenAQ. But as it stands today, I would consider it a huge win if a university, non-governmental organization, or other long-standing entity took on the project and committed to its long-term existence. It’s probably a bit more aspirational, but it’d also be wonderful if we became obsolete because countries adopted a standard real-time data sharing protocol and data-sharing mechanism. And if some organization pops up, doing identical work in a manner better than us, well, then, we should have the humility and grace to bow out. There is so much work that remains to be done in the air inequality space across the world — we’ll surely find something else worthwhile to do.



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