 You know, oh Sorry, now I move that seems okay. Everybody hear me. It's all right. Okay, cool Let's see if I get this working again Here we go. Okay, cool Say my name's Eric I A little bit nervous about this talk So I feel like it was funny to mention that the first line of my speaker notes are hi My name is Eric, which is which is pretty good, but that's my cat but I work at the office for creative research, which is a I think you call us the probably a data viz studio and in Brooklyn It's I feel like that's not totally the best description of what it is that we do we're pretty pretty small group I think this this is this is my friend and and the guy who's Started the studio talking about it and saying We make visualizations online tools community platforms and public interventions that increase data literacy Facilitate understanding and promote equality our work is meant to provoke surprise and delight while deriving While driving critical thought and facilitating understanding I thought that was a pretty good description doesn't tell you exactly what it is that we do But more or less that that first part of that sentence is is the case where we're sort of all over the place We'll we build websites for science education purposes. We do data sculptures that we've put in Times Square We're working on a community a map room project right now But the project that I'm here to talk to you all about Is this thing called fuel kit? And feel kid is very much an in progress project. So I'm kind of here to I can't see that very well But that's the that's the logo It's very much an in progress project. So I'm here to sort of talk about where it came from where it's headed What are some of the ideas behind it? But the gist is is that it's an open data and open science effort That's aimed at field researchers field researchers and conservationists So that is it's it's it's targeted towards scientists, but mostly scientists that do work In the field And I'll elaborate a little more on what I what I mean by that So the the project more or less began here This is the this is the Okavango River Delta is as seen from space The Delta is is entirely inland. It's part of a drainage basin in northwest Botswana Where the river basically just comes to an end and drains out Right after passing through Angola and Namibia to the to the northwest there And although it's been designated as an Esco World Heritage Site. It's it's considered largely unprotected And for the most part generally understudied as well and there's all kinds of reasons for that but Including the geography there but But those two things are really sort of related in my mind the the difference between being unprotected and understudied Um And and really why that's a problem for conservation is that there's not there's not as much of a sort of baseline understanding of even the current conditions for wildlife in this area Um to sort of suss out whether or not any sort of efforts You know political publicity or otherwise are actually even working um So partnering with uh Local populations in the area um with funding from the National Geographic Society The Okavango Wilderness Projects has been able to send expeditions to the river with the explicit goal To sort of accomplish those things that I was talking about to sort of build the baseline understanding of the ecosystem in the area and raise awareness of of the of What's going on in this sort of amazing ecosystem in the in the middle of Africa Um, so the work started back in 2011 and Um We've been going is sort of our our part as part of the studio for every pretty much annually ever since Um, for the most part, I think you could describe this as is sort of a conventional science and documentation effort Um There's sort of an effort to record wildlife settings and things like that But you know for our part of it, uh, we wanted to bring something new to Um the way we're doing data collection in the field And that is both how we collect it and what we do and what we do with it Um, so this is this is one of our collaborators, Jacob, who works with a nonprofit called Conservify Um, Servify is a group that is looking to lower the barriers to entry for technology based conservation And and that's him working with One of the sensors that they design in the field Um, and part of what's been done with the Okavango project is to use these sort of low cost open hardware Solutions to capture a wide array of information about the area on an ongoing basis Um, and this includes all kinds of things like this is you can't see this very well again because of the light but they've Um, been working on building sort of open source hardware solutions to a lot of sort of expensive sensors like this is a water flow Sensor that that they're they're still working on and prototyping But that's one example of the kind of data that we might be able to collect in the field Um, this is a this is a weather station Many of which we can we can set up in the area to sort of Monitor what's going on even after even after we've left And with this data on on our side what we've been trying to build You know sorry With all of that data and along with things like location of team members Vehicles animal sighting social media posts We've been trying to build Sort of web representation of these expeditions that the more or less Let's folks follow the follow the the team and the effort in real time And I think one of the big things about this to me is that there's no There's not necessarily one single focus to the data collection or presentation here. There's The idea is breath so if this looks like there's a lot of things going on that's that's intentional there are a lot of things going on Social some other slides from this like this is this is one day of Of a sort of journal entry of the expedition and we've been able to aggregate You know photos from the field Individual sightings that I'll talk more about in a second But this is The idea is that this is one representation of a whole lot of data that's coming out of this project I'm even going to try to do my best to show a video of how this site actually works But this is one of those things that probably won't work Come on play Is it going to happen There we go Okay, so this is this is the representation as it stands. You can sort of see where where this area is situated in africa And this is this is the the main site that we built when you When you load up into the okavango as a as a project And you know sort of following on what I was saying that hardware philosophy was of using these low cost solutions to collect a broad variety of data You know the same way this this web presence is sort of a low cost software solution to showing a lot of data Um You know for the most part this is built on Other open technologies. We're not spending like a great deal of money to sort of produce this Um and this we'll get moving in a second I sort of took a bad video of this on my computer. I apologize, but This is sort of the idea that you can you can actually follow the team in real time like as they move as they move through the The delta area you can you can actually sort of explore all of these observations that they're making um different wildlife observations to Different sensor readings like on the weather stations you can get to the that journal section I showed earlier up there um I'm gonna move on from this if I can get out of this video sorry and One of the big ideas of this is that this is really just a front end sitting in front of this massive repository of information that we built coming out of Years and years of going going back here Um, this is just you know a snapshot of one query against the api But you know you can see that there's there's over there's almost uh Almost four million like individual recordings in here Um, and they're all kinds. They're all kinds of things like this is a very like heterogeneous dataset where we've got Like right now. This is I think these are all bad of the day Which is a sound recording of bats in the area. Um sound recording was actually a big part of this project um along with daily daily bats apparently um, but this is sort of where I I came into this projects with with all this work existing and What the question we started to ask about it is What can we what can we move on to? from all of this work that is still something that is still something that has the kind of core mission of this project, but Is something that sort of like builds on the things that we've learned and is repeatable. Um, so Um Cool, so the key things to me about into the okavango is this idea of it being a low cost platform But that I don't necessarily always mean cost, but um, this was um Cost in this context can also mean things like training team members. It can mean things like upkeep. It can mean Things like uh hiring developers to write software for For custom sensors things like that and and the platform that we were getting towards providing sort of eliminated some of the some of the pain and getting going with with with these tools and also the In addition to just the financial cost of actually using them um so This this is sort of a a set of dichotomies that they had got at me about about fieldwork where there's There's there's sort of this tendency because these Because these expeditions into areas where it might be hard to get to it might be Uh dangerous to be there. It might be You know any number of reasons The the expense associated with these expeditions can also mean that they tend to become more narrow Where you might be going there to study one particular aspect of an ecosystem because it's so hard to get there Or it's so hard to get a set of equipment there that Um, the the focus starts to narrow and the sort of general usefulness of the data you're producing narrows as well Um, so part of what I think is good about okavango is it's moving It's moving some of that where you've still got this sort of expensive to lead expedition, but Building a broader base for the kind of information that you can collect once you're out there and doing that mostly also by Introducing low cost tools in the way in the way I was talking about So so what I mean about the the way that the way that these Conservation efforts can end up being very expensive Um Can clear to me on this other project we were working on Which is we were building a set of data visualizations for the great elephant census, which was Um An effort that sort of wrapped up recently to basically go and count every single African savanna elephant in africa Um And if you're wondering how they do that, it's it's kind of the way that that might seem obvious How they would do that they actually got an airplane and actually many many airplanes and flew These sort of narrow transects up and down Covering as much area as they were allowed to or that was reasonable to you given the train I'm sort of using models based on the based on how the elephants move They can actually take those raw counts of elephants from these airplanes and produce Sort of total counts for for how many elephants they think they're they think they're our president in a given area But one of the things that kind of strikes me about this is this is like a This is like a single single purpose and very expensive way to Do this kind of work and that if you're in an airplane, you know conceivably you're seeing all kinds of things about the train You're seeing other animals. You're seeing Carcasses from poaching you're seeing Human densification and you're seeing fires. You're seeing, you know any number of things and there's there's some question about like when you mount this sort of Expedition to do this kind of work What are the tools that you're disposal? It's actually broad in the base of what you're doing while you're out there And this is to sort of like, you know, what is the what is the orthogonal data that you could be collecting and making useful in the larger group So we started on a field kit to try and solve some of these questions. We came back to the hardware again So conservafi working with conservafi They're producing they're working on producing another set of low cost sensors This is the board for the sort of base unit that we're working with right now And what this is is it's basically a piece that sort of solves the communications aspects of sensors in the field So when you're getting live data from Places where you might have not have an internet connection You need things like the ability to Store data continuously the ability to send data over a satellite uplink potentially the ability to Send data over wi-fi when it's available Um And all of that can be kind of kind of expensive one of the one of the most expensive components of systems like this can be the Satellite radio the satellite modem So the idea behind this piece of hardware is that it can act as a sort of a hub between multiple sensors to collect and communicate data So some of some of the question after that is where does this data go? So this gets to the software So we wanted to try to learn some lessons from what we did with into the archivango Which is more or less that we were Had produced this very ad hoc system over a course of years that led us to these large sort of monolithic data structures that it was difficult to Difficult to share a reason about So this was this was all stored in sort of a document database and essentially had arbitrarily Deep and arbitrarily structured Objects that were really trying to capture as much data as possible at the time but What starts to happen is that we ended up with layers upon layers of these code driven Ingest routines and this is a lot of people writing python basically To sort of take this data from a field sensor and or from a form that someone filled out on their computer later on To say oh if they Want to upload a photo of a bird they know what bird it is maybe we want to take that That bird sighting and add the taxonomy information for birds And then produce a document and it's geogas on here because actually the the root of all the documents were geogas on features And that everything we're recording had a location attached but the problem with that and if you're here for the earlier talk is that this is a It's a very side-effective process And the structure in the end of those documents is really only the sort of Imprint of these sort of hard to understand Um Nested ingest routines. So there's a couple things we want to do here. We want to kind of break this apart Move a lot of that sort of input processing into a field where the user can do something with it And also get data that we can reason about in a way that we can potentially share So if we're doing one expedition In the Okavango there might be related expeditions, particularly around Bird populations and we want to be able to share data in some sort of meaningful way And it may not be that way is is you know in the form of like publishing a paper or something like that But some sort of meaningful representation of the shared effort of going here and documenting this wildlife Um, so what we came up with is more or less breaking it out into two different pieces It's sort of strictly defined metadata structure to give us Information that we knew we were always going to have about the individual observations Um, and also bringing some sort of sense of of Typedness uh to the to the other component of the data Um, and I'll just run through this pretty quick. Um, but this is just sort of the The sort of new ingest pattern that that that we came up with It's pretty straightforward. The idea is that we a data can be Set of data can be coming from any number of Any number of sources this can be things like rock block, which is a satellite provider atulia which handles sms And we want to be able to pull the the payload data out of out of those formats We want to be able to parse a set of standard formats and also a Um A set of binary packs in the case of that that device that showed earlier And we want to be able to map those onto some set of knowable objects After the fact So just to run through what this might look like just real simply it's just basically pull something like a csv out of out of a larger objects Parse that csv And then produce some sort of user defined mapping to get an object and this is not this is not too crazy Um, but what this lets us do is use like a set of use a set of standard Um technologies that are out there to get to data that might be meaningful between projects So this is just json and json pointers. Um, you know, you can imagine storing this pretty easily having this be fairly easily editable um And in our case, we're just sort of building a lot of these in advance for the for the uh, sensor kits that we're starting with So, uh, where are we now, uh, we're We've been using a system kind of like this to to get uh, to get Um, a sort of demo of of similar functionality to what we had with the insidio commigo project running On a generic platform like this. So this was something that you're able to go and sign up for and build an expedition connect a sensor And get some output that that is sort of similar to the way we were handling it within to the okavango projects And this is in some ways like slightly less ambitious right now than what was going on and into the okavango, but The the importance of it, I guess is that um, this is not something that's bespoke. It's not something that was tailored just for this project. This is a Potential platform that we want, you know, people that we're not interacting directly with to be able to use um And i'm getting close out of time, but it's good because i'm wrapping up Um, but there are so many more problems for us to solve. Um One is that you know, it's easy to say that it's we can define these Mappings between objects and json, but there's a lot of user interface complexity behind that And there's sort of some debate about where we want to draw the line between sort of out of the box usability and how customizable it is Um, there's issues around the sort of taxonomy of those Typed objects that we're trying to produce. How do you how do you sort of decide on what a what a bird signing should look like in a Way that you can collaborate between projects a huge question The other one is just these are huge amounts of data that we're talking about the okavango project produced about four million data points So if we imagine that you know, we've got even tens of people using this system plowing the same sort of data in How do we deal with that? Those those are all good open questions that we're working on. We think we have some solutions to this I would say Stay tuned um fieldkit.org has a has a Mailing list sign up. Um, that's our website. That's conservify So there's going to be a more coming more coming out of this, but this is going to be an open source project We're working right now To get the sort of licensed stuff sorted out so that we can have it up on github But you can find that under our website And we'll email about it once that happens um But I think we want this to be A larger community effort and that we're sort of trying to leave the development on this But I think like I said originally it's one of exactly a software shop so we're sort of depending on the idea that People are going to have other resources to bring to bear on on this kind of work and are interested in it so Um on that I guess I will wrap up Thanks