WEBVTT 1 00:00:00.000 --> 00:00:03.860 Great. So, introduce myself first. I'm Sharon Pittman. 2 00:00:04.020 --> 00:00:06.880 I manage the Inspiring South Australia Project, 3 00:00:07.080 --> 00:00:09.060 which is based at the South Australian Museum. 4 00:00:09.940 --> 00:00:11.860 And that's a program that's Australia-wide 5 00:00:11.860 --> 00:00:14.680 about engaging the community in science 6 00:00:14.680 --> 00:00:17.680 and improving science literacy generally. 7 00:00:19.480 --> 00:00:22.520 Welcome to the Adelaide Festival of Ideas. 8 00:00:22.540 --> 00:00:25.560 And today's session is Discovering Citizen Science 9 00:00:25.560 --> 00:00:27.620 with Dr Phil Roteman, 10 00:00:27.620 --> 00:00:29.820 who I've known for about 10 years. 11 00:00:31.380 --> 00:00:36.380 Phil has run the Discovery Circle for Citizen Science initiative 12 00:00:36.380 --> 00:00:40.020 at the University of South Australia for a number of years now. 13 00:00:40.200 --> 00:00:43.320 He did his PhD in citizen science. 14 00:00:44.520 --> 00:00:49.200 And he runs quite a few innovative citizen science projects, 15 00:00:49.600 --> 00:00:51.200 especially in South Australia. 16 00:00:52.400 --> 00:00:54.960 He's engaged many thousands of people 17 00:00:54.960 --> 00:00:57.520 in activities like tracking, 18 00:00:57.620 --> 00:01:00.220 pet cats, which you'll no doubt hear more about, 19 00:01:00.860 --> 00:01:03.200 counting koalas, recording bat calls, 20 00:01:04.400 --> 00:01:07.520 bio-blitz events that you might have heard about 21 00:01:07.520 --> 00:01:09.960 around Adelaide and South Australia, 22 00:01:10.920 --> 00:01:14.640 goannas, all sorts of citizen science projects 23 00:01:14.640 --> 00:01:18.720 that engage the community in various ways. 24 00:01:19.540 --> 00:01:21.340 Phil has also set up 25 00:01:21.340 --> 00:01:24.800 the Australian Citizen Science Association in South Australia 26 00:01:24.800 --> 00:01:27.600 and is the past chair of the Australian Citizen Science Association. 27 00:01:30.080 --> 00:01:33.760 So really you could say he's Mr Citizen Science, 28 00:01:34.960 --> 00:01:36.460 for this date at least. 29 00:01:37.220 --> 00:01:43.020 And I'm sure there'll be a lot of questions at the end 30 00:01:43.020 --> 00:01:45.680 because Phil, of all the people in this state, 31 00:01:45.780 --> 00:01:46.940 will be the one to answer them. 32 00:01:47.380 --> 00:01:50.440 So let's welcome Phil and enjoy. 33 00:01:50.660 --> 00:01:51.220 Thank you. 34 00:01:53.300 --> 00:01:55.340 Okay, I'm going to do a few things today. 35 00:01:55.460 --> 00:01:57.580 First of all, I'll give you a really brief introduction to my work. 36 00:01:57.580 --> 00:01:57.800 I'm a citizen science professor myself. 37 00:01:58.200 --> 00:01:59.700 And then I'll give you a little presentation 38 00:01:59.700 --> 00:02:01.840 that I call Citizen Science by Threes. 39 00:02:02.220 --> 00:02:04.600 And I give it by threes because I've done some research 40 00:02:04.600 --> 00:02:06.800 and I've found that if you group things in threes, 41 00:02:07.600 --> 00:02:09.940 they're more memorable, they're funnier, 42 00:02:10.580 --> 00:02:12.360 and the speaker appears more knowledgeable. 43 00:02:12.660 --> 00:02:14.200 And I thought that would be a good thing. 44 00:02:14.280 --> 00:02:15.140 So please remember that. 45 00:02:15.860 --> 00:02:18.420 Then if I've got time, I'll talk about some useful projects 46 00:02:18.420 --> 00:02:21.040 that are around in citizen science that might interest you 47 00:02:21.040 --> 00:02:23.180 and how to find projects in citizen science. 48 00:02:23.540 --> 00:02:26.660 So I've been involved in this CAPER for a number of years, 49 00:02:26.720 --> 00:02:27.560 as Sharon mentioned, and I've been involved in this project 50 00:02:27.580 --> 00:02:29.480 since first working with Professor Chris Daniels. 51 00:02:29.480 --> 00:02:32.980 We ran a series of operations where we were looking at the interactions 52 00:02:32.980 --> 00:02:35.300 between people and wildlife working with the ABC. 53 00:02:35.640 --> 00:02:36.960 And these were really popular projects. 54 00:02:37.220 --> 00:02:39.340 We asked people to fill in online surveys. 55 00:02:39.640 --> 00:02:41.440 We had thousands of people participating 56 00:02:41.440 --> 00:02:44.880 and tell us about their experiences with blue tongues, possums, 57 00:02:44.940 --> 00:02:46.540 magpies and spiders. 58 00:02:47.240 --> 00:02:50.760 And then in 2012, we ran a really neat project 59 00:02:50.760 --> 00:02:52.240 called The Great Koala Count. 60 00:02:52.460 --> 00:02:54.820 And it's a good introduction to citizen science. 61 00:02:55.200 --> 00:02:56.960 We had a small group of scientists. 62 00:02:57.580 --> 00:03:00.020 We wanted to study koalas, particularly in the Adelaide Hills. 63 00:03:00.500 --> 00:03:02.900 And we thought, well, we'd been working with the community 64 00:03:02.900 --> 00:03:04.300 on this series of operations, 65 00:03:04.720 --> 00:03:07.800 and how are we going to study this large number of koalas 66 00:03:07.800 --> 00:03:09.920 over a really dispersed area in the Adelaide Hills? 67 00:03:10.300 --> 00:03:11.620 Let's get the citizens involved. 68 00:03:12.200 --> 00:03:14.480 So we had a smartphone app developed 69 00:03:14.480 --> 00:03:17.800 so that we could ask the citizens to go out looking for koalas 70 00:03:17.800 --> 00:03:20.640 and when they saw one, take a photograph of the koala 71 00:03:20.640 --> 00:03:23.780 through the app, and that sends the information to us 72 00:03:23.780 --> 00:03:26.520 and gives us two really important bits of information. 73 00:03:26.520 --> 00:03:29.320 Firstly, there's a photograph of the koala, 74 00:03:29.420 --> 00:03:32.420 so we can verify that the person actually saw a koala, 75 00:03:32.560 --> 00:03:34.500 which is more important than you might think. 76 00:03:34.600 --> 00:03:36.360 It's really important from a scientific perspective 77 00:03:36.360 --> 00:03:40.500 that we know someone has observed what they've reported. 78 00:03:41.020 --> 00:03:44.140 With koalas, they're fairly easy to identify, so that's not a problem. 79 00:03:44.400 --> 00:03:48.120 But we did get a few jokers sending in things like photos of koala street signs 80 00:03:48.120 --> 00:03:51.420 and photos of cats, and someone sent in a photo of their foot. 81 00:03:51.620 --> 00:03:53.160 So we could delete those ones, 82 00:03:53.300 --> 00:03:55.420 and we knew we had good quality data left. 83 00:03:55.420 --> 00:04:00.020 And the smartphones also gave us the location of the koala, 84 00:04:00.160 --> 00:04:03.820 so we could start to build a map of where koalas were across the Adelaide Hills 85 00:04:03.820 --> 00:04:06.040 and start to model the koala population. 86 00:04:06.520 --> 00:04:10.720 So that's the idea of getting the public out and doing part of the science. 87 00:04:10.860 --> 00:04:12.140 That's what citizen science is. 88 00:04:12.360 --> 00:04:13.940 And in that particular project, 89 00:04:14.120 --> 00:04:19.340 we had 500 people go out on one day and collect 1,500 koala observations. 90 00:04:19.760 --> 00:04:20.940 So it's pretty impressive. 91 00:04:21.280 --> 00:04:23.800 And that was a Wednesday, I think it was, the middle of the week. 92 00:04:23.940 --> 00:04:25.400 And also it was about three weeks 93 00:04:25.420 --> 00:04:26.660 and it was about 37 degrees on that day. 94 00:04:26.760 --> 00:04:28.780 So we had a lot of people going out in the morning and in the evening. 95 00:04:28.940 --> 00:04:31.900 So that's the kind of thing that citizen science can do. 96 00:04:32.420 --> 00:04:35.080 And since then, I've set up this initiative called the Discovery Circle, 97 00:04:35.260 --> 00:04:38.360 where I've been running all sorts of citizen science projects. 98 00:04:38.400 --> 00:04:41.160 So the BioBlitz events and Cat Tracker and things like that, 99 00:04:41.760 --> 00:04:42.880 another koala count, 100 00:04:42.960 --> 00:04:46.640 and even getting people to monitor the food they're producing in their gardens. 101 00:04:47.020 --> 00:04:50.360 But there's a lot more to citizen science than the projects that I've been running. 102 00:04:50.460 --> 00:04:53.000 There's now health science projects and town planning projects 103 00:04:53.000 --> 00:04:54.940 and a lot of astronomy projects. 104 00:04:55.420 --> 00:04:56.680 And I'll talk about some of those today. 105 00:04:57.140 --> 00:05:01.120 I must acknowledge that the Discovery Circle's been funded by the university, 106 00:05:01.380 --> 00:05:03.300 but also by the Department for Environment and Water, 107 00:05:03.520 --> 00:05:06.700 the NRM Board, the City of Marion and the City of Salisbury. 108 00:05:06.760 --> 00:05:09.260 So they have all supported this initiative over the last five years. 109 00:05:09.620 --> 00:05:12.600 And this particular presentation has been supported 110 00:05:12.600 --> 00:05:14.800 by the Office for the Ageing at the Department of Health. 111 00:05:15.060 --> 00:05:16.940 So, citizen science by threes. 112 00:05:17.160 --> 00:05:20.160 Here's a definition of citizen science that I often use. 113 00:05:20.420 --> 00:05:23.480 It's where professional researchers engage with the public 114 00:05:23.480 --> 00:05:25.100 to collect or analyse data, 115 00:05:25.420 --> 00:05:28.000 within a cooperative framework of research and education. 116 00:05:28.700 --> 00:05:29.520 What does it mean? 117 00:05:29.700 --> 00:05:33.320 It means the public and the scientists working together to do research. 118 00:05:33.660 --> 00:05:35.760 It doesn't mean that we're researching the public, 119 00:05:35.880 --> 00:05:39.220 but actually getting you involved in doing some of the scientific research. 120 00:05:39.620 --> 00:05:42.020 And there are three important words there. 121 00:05:42.240 --> 00:05:43.420 The first one is engage. 122 00:05:43.780 --> 00:05:45.040 We've got to engage the public. 123 00:05:45.140 --> 00:05:47.480 We've got to get people like you involved in these projects. 124 00:05:48.040 --> 00:05:51.080 And the sheer numbers of people that do get involved in the projects 125 00:05:51.080 --> 00:05:52.900 show that they are engaging. 126 00:05:53.060 --> 00:05:55.300 They wouldn't keep coming back and doing more projects 127 00:05:55.420 --> 00:05:56.560 if they weren't engaging and interesting. 128 00:05:57.200 --> 00:05:58.500 The second one is research. 129 00:05:58.760 --> 00:06:01.480 And there has to be research involved for it to be citizen science. 130 00:06:01.940 --> 00:06:04.440 We're not talking about running engagement projects, 131 00:06:04.560 --> 00:06:06.460 which can have a great number of benefits. 132 00:06:06.720 --> 00:06:09.380 But we're talking about actually contributing to scientific research. 133 00:06:09.980 --> 00:06:11.280 And the third one is education. 134 00:06:11.560 --> 00:06:12.640 And I think this is really important, 135 00:06:12.800 --> 00:06:16.540 because all good citizen science projects have an educational component. 136 00:06:16.880 --> 00:06:19.320 People learn how to get involved in the project. 137 00:06:19.380 --> 00:06:21.180 They learn about the topic of interest, 138 00:06:21.300 --> 00:06:22.820 the taxa that's being studied, 139 00:06:22.880 --> 00:06:24.860 or the scientific phenomenon that's being studied. 140 00:06:25.420 --> 00:06:27.600 And they learn about the process of science 141 00:06:27.600 --> 00:06:29.280 by getting involved in the project. 142 00:06:29.440 --> 00:06:32.060 So there's research, there's education, and there's engagement. 143 00:06:32.320 --> 00:06:33.820 Citizen science by threes. 144 00:06:34.180 --> 00:06:36.680 Here's three examples of citizen science projects 145 00:06:36.680 --> 00:06:39.580 that I like to use to further demonstrate what it's about. 146 00:06:40.300 --> 00:06:44.060 The Christmas bird count started in 1900 in North America. 147 00:06:44.520 --> 00:06:46.140 It was started by a guy who thought, 148 00:06:46.260 --> 00:06:49.820 wouldn't it be great if people were observing birds 149 00:06:50.420 --> 00:06:51.540 rather than shooting them? 150 00:06:51.800 --> 00:06:55.200 And in 1900 he got his friends and colleagues and family out, 151 00:06:55.420 --> 00:06:59.200 and he had 27 people count birds with him around Christmas time. 152 00:06:59.520 --> 00:07:00.780 It's still running today. 153 00:07:00.980 --> 00:07:04.440 So last year was the 117th Christmas bird count. 154 00:07:04.600 --> 00:07:06.220 And typically these days, 155 00:07:06.300 --> 00:07:10.100 over 70,000 people participate and collect bird data 156 00:07:10.100 --> 00:07:12.760 just after Christmas each year in North America. 157 00:07:13.080 --> 00:07:15.620 And that has created a huge data set, 158 00:07:15.660 --> 00:07:17.500 an amazing number of bird observations. 159 00:07:17.720 --> 00:07:20.340 It has led to all sorts of scientific research 160 00:07:20.340 --> 00:07:22.140 and supported all sorts of scientific research 161 00:07:22.140 --> 00:07:24.160 into the changing bird distributions 162 00:07:24.160 --> 00:07:26.640 and bird biology and all sorts of things. 163 00:07:26.940 --> 00:07:29.940 It's also engaged these thousands of people 164 00:07:29.940 --> 00:07:32.200 and has huge educational outputs. 165 00:07:32.460 --> 00:07:34.140 So the people who get involved, 166 00:07:34.260 --> 00:07:37.440 they have experienced birders mentoring younger people 167 00:07:37.440 --> 00:07:39.320 to go out and get involved in the project, 168 00:07:39.380 --> 00:07:41.880 or inexperienced birders to learn how to go birding 169 00:07:41.880 --> 00:07:44.880 and learn how to collect data and learn how to submit that data 170 00:07:44.880 --> 00:07:47.580 and understand what you're seeing when you look at the maps 171 00:07:47.580 --> 00:07:49.400 that are produced from the project. 172 00:07:49.780 --> 00:07:53.260 So it has a great outcome in terms of education. 173 00:07:53.980 --> 00:07:56.620 Coco-RAS is probably the worst-named 174 00:07:56.620 --> 00:07:58.200 citizen science project in the world. 175 00:07:59.700 --> 00:08:00.740 Is this being recorded? 176 00:08:02.120 --> 00:08:06.220 Coco-RAS is the Community Collaborative Rain, Hail and Snow Network. 177 00:08:06.400 --> 00:08:09.300 And it's different because it's a meteorological project. 178 00:08:09.660 --> 00:08:12.760 It's asking people to collect weather observations 179 00:08:12.760 --> 00:08:13.600 from their gardens. 180 00:08:14.740 --> 00:08:18.760 Participants are given a rain gauge and a hail pad. 181 00:08:19.080 --> 00:08:21.380 I think you probably all know what a rain gauge is. 182 00:08:21.440 --> 00:08:23.180 Rain falls in it, you measure how much rain has fallen. 183 00:08:23.260 --> 00:08:27.220 A hail pad is a bit like a bit of styrofoam covered in alfoil. 184 00:08:27.340 --> 00:08:30.800 And when it hails, you can measure the size of the indentations 185 00:08:30.800 --> 00:08:34.380 and report that, and bureaus of meteorology 186 00:08:34.380 --> 00:08:36.420 are trying to understand more about hail 187 00:08:36.420 --> 00:08:39.560 and when it hails, how it hails, where it hails, why it hails. 188 00:08:40.680 --> 00:08:42.840 And they're very interested in this data, 189 00:08:42.980 --> 00:08:45.040 and coincidentally so are insurance companies 190 00:08:45.040 --> 00:08:46.840 interested in studying more about hail. 191 00:08:47.820 --> 00:08:51.120 Galaxy Zoo is a completely different project, 192 00:08:51.160 --> 00:08:52.220 again, for a couple of reasons. 193 00:08:53.260 --> 00:08:54.180 One, it's an astronomy project, 194 00:08:54.480 --> 00:08:57.860 so looking into space rather than looking at something on Earth. 195 00:08:58.100 --> 00:09:03.140 And two, it's getting citizens to analyse data rather than collect data. 196 00:09:03.860 --> 00:09:07.220 And Galaxy Zoo was set up because the Sloan telescope 197 00:09:07.220 --> 00:09:09.400 was taking millions of photographs of galaxies 198 00:09:10.260 --> 00:09:12.320 and astronomers didn't have time to look at them all. 199 00:09:12.720 --> 00:09:14.880 So they set up a very neat system 200 00:09:14.880 --> 00:09:17.800 where the photographs of galaxies go automatically online 201 00:09:17.800 --> 00:09:19.560 onto a website, Galaxy Zoo, 202 00:09:19.560 --> 00:09:24.360 and it asks the public to have a look at those photos 203 00:09:24.360 --> 00:09:25.640 and classify the galaxies. 204 00:09:25.860 --> 00:09:29.100 So right-spiralling galaxy, left-spiralling galaxy, and so on. 205 00:09:29.560 --> 00:09:30.320 Elliptical galaxy. 206 00:09:30.560 --> 00:09:34.420 And it gives instructions on how to do that 207 00:09:34.420 --> 00:09:36.260 and examples of what these galaxies look like. 208 00:09:36.560 --> 00:09:39.000 It's not difficult to do, and if you give it a try, 209 00:09:39.520 --> 00:09:41.240 you start to pick it up pretty quickly 210 00:09:41.240 --> 00:09:44.300 and you can move through classifying galaxies very quickly. 211 00:09:45.380 --> 00:09:46.720 They've had over a million participants 212 00:09:46.720 --> 00:09:49.540 who have classified over 100 million galaxies. 213 00:09:49.560 --> 00:09:51.820 And led to all sorts of discoveries about 214 00:09:51.820 --> 00:09:53.980 even things like the creation of the universe. 215 00:09:54.160 --> 00:09:56.200 So this sort of research helps with that. 216 00:09:56.980 --> 00:09:58.680 It's led to scientific publications 217 00:09:58.680 --> 00:10:00.680 and it's been really engaging. 218 00:10:00.980 --> 00:10:05.580 There's a great story about the guitarist from the band Queen. 219 00:10:05.960 --> 00:10:08.240 Does anyone know the guitarist from the band Queen? 220 00:10:08.760 --> 00:10:11.020 Brian May, thank you. Dr Brian May. 221 00:10:11.220 --> 00:10:14.520 Because Brian May actually has a PhD in astrophysics. 222 00:10:14.960 --> 00:10:17.400 And Brian May loves Galaxy Zoo. 223 00:10:17.680 --> 00:10:19.540 And so Brian May put it on the website. 224 00:10:19.560 --> 00:10:21.840 He wrote on social media how much he loves Galaxy Zoo one time. 225 00:10:22.180 --> 00:10:25.980 And a Dutch schoolteacher who doesn't care too much for astronomy 226 00:10:26.560 --> 00:10:29.640 but loves the band Queen saw Brian May's tweet 227 00:10:29.640 --> 00:10:32.420 and thought, well, if Galaxy Zoo is good enough for Brian May, 228 00:10:32.820 --> 00:10:35.240 Galaxy Zoo is good enough for me, I'll give it a go. 229 00:10:35.520 --> 00:10:36.680 Her name was Hannie. 230 00:10:37.040 --> 00:10:40.200 So Hannie started classifying galaxies through Galaxy Zoo. 231 00:10:40.580 --> 00:10:43.340 I think of it like... Is it Tinder where you do that? 232 00:10:43.500 --> 00:10:44.500 What's that app where you do... 233 00:10:44.500 --> 00:10:46.500 It's like right spiralling galaxy, left spiralling galaxy, 234 00:10:46.700 --> 00:10:48.660 right spiralling galaxy, left spiralling... elliptical galaxy. 235 00:10:49.560 --> 00:10:51.520 Actually, I should have mentioned that they have a very clever system 236 00:10:52.020 --> 00:10:54.900 of having multiple people classify each galaxy. 237 00:10:55.200 --> 00:10:57.260 And if all of the people say the same thing, 238 00:10:57.440 --> 00:11:00.000 they have a really high level of confidence with that classification. 239 00:11:00.480 --> 00:11:03.680 If some people say right spiralling and one person says elliptical, 240 00:11:03.820 --> 00:11:06.620 then they flag that one and the astronomers have a look at it. 241 00:11:06.720 --> 00:11:08.900 So it is very clever ways built into the system 242 00:11:08.900 --> 00:11:10.300 to make sure they're getting good quality data. 243 00:11:11.280 --> 00:11:14.740 And so Hannie was working her way through Galaxy Zoo 244 00:11:14.740 --> 00:11:17.920 and she came across something that didn't fit the descriptions. 245 00:11:18.300 --> 00:11:19.320 Didn't fit the descriptions. 246 00:11:19.320 --> 00:11:20.000 Didn't fit the descriptions of... 247 00:11:22.020 --> 00:11:23.200 that were given online. 248 00:11:23.860 --> 00:11:27.020 And so she pressed the I don't know button. 249 00:11:27.360 --> 00:11:28.600 I don't know what this is. 250 00:11:28.820 --> 00:11:31.700 And so that flagged to astronomers they should also have a look at that one. 251 00:11:32.440 --> 00:11:34.620 And when they had a look, they hadn't seen it before either. 252 00:11:35.040 --> 00:11:37.580 And actually they discovered a new astronomical object. 253 00:11:38.080 --> 00:11:39.640 And they named it after Hannie. 254 00:11:39.760 --> 00:11:41.560 They named it after the person who discovered it, 255 00:11:41.600 --> 00:11:42.940 which is great for citizen scientists. 256 00:11:43.280 --> 00:11:45.420 They named it Hannie's Vorwerp. 257 00:11:45.920 --> 00:11:47.460 And a Vorwerp is Dutch. 258 00:11:47.600 --> 00:11:48.680 Hannie was a Dutch school teacher. 259 00:11:49.320 --> 00:11:51.940 Vorwerp is Dutch for unknown object. 260 00:11:52.220 --> 00:11:53.400 So Hannie's object. 261 00:11:55.620 --> 00:11:58.040 Now going on with citizen science by threes. 262 00:11:58.040 --> 00:11:58.980 That was three examples. 263 00:12:00.020 --> 00:12:02.140 Here are four triangles. 264 00:12:02.560 --> 00:12:04.740 I hope you forgive me for having four of them. 265 00:12:04.800 --> 00:12:06.640 But they are triangles so they are three-sided. 266 00:12:07.040 --> 00:12:10.540 And this is a good way to think about citizen science 267 00:12:10.540 --> 00:12:14.000 and how it compares to regular science done by scientists. 268 00:12:14.380 --> 00:12:17.240 And if you think about the projects across the top run by scientists 269 00:12:17.240 --> 00:12:19.300 and the projects across the bottom run by citizen scientists, 270 00:12:19.780 --> 00:12:22.840 the first triangle on the left, number of participants, 271 00:12:23.040 --> 00:12:25.700 if you've got scientists involved, you've got the pointy end. 272 00:12:25.800 --> 00:12:27.740 You've just got a few people involved in the project, 273 00:12:27.980 --> 00:12:30.200 limited by the number of scientists who are involved 274 00:12:30.200 --> 00:12:31.500 and the funding you have and so on. 275 00:12:32.000 --> 00:12:35.320 But if you get citizens involved, you can get the big end of the triangle. 276 00:12:35.640 --> 00:12:39.200 You can get lots more people involved to be either collecting information 277 00:12:39.200 --> 00:12:41.020 or analysing information for you. 278 00:12:41.480 --> 00:12:44.520 In terms of training, scientists typically have a lot more training, 279 00:12:44.620 --> 00:12:46.340 so they get the big end of the triangle. 280 00:12:46.600 --> 00:12:48.780 But you can, through citizen science projects, 281 00:12:48.780 --> 00:12:50.540 give people training so you can upskill them 282 00:12:50.540 --> 00:12:53.140 and move them up through this sort of diagram. 283 00:12:53.560 --> 00:12:57.380 In terms of scale, scientists are often limited by scale 284 00:12:57.380 --> 00:12:58.820 because they get the pointy end of the triangle. 285 00:12:58.940 --> 00:13:02.700 There's few scientists involved, but you can cover much larger distances 286 00:13:02.700 --> 00:13:07.240 or look through much more data or go for a lot longer period of time 287 00:13:07.240 --> 00:13:08.320 if you get citizens involved. 288 00:13:08.840 --> 00:13:13.400 In terms of complexity, typically scientists can do more complex things 289 00:13:13.400 --> 00:13:14.380 because they've got the training, 290 00:13:14.500 --> 00:13:17.140 whereas citizen scientists are limited by their training. 291 00:13:17.940 --> 00:13:20.440 Within that, there are a few different types of projects. 292 00:13:20.900 --> 00:13:22.460 So there's large-scale projects, 293 00:13:22.720 --> 00:13:25.600 and these are ones where we're typically getting lots of people involved. 294 00:13:25.840 --> 00:13:26.960 They don't need much training. 295 00:13:27.140 --> 00:13:30.160 There's a larger scale involved, and it's not particularly complex. 296 00:13:30.560 --> 00:13:32.880 And the Great Koala Count is a good example of that. 297 00:13:33.400 --> 00:13:36.400 Koalas aren't particularly difficult to identify when they're seen, 298 00:13:36.520 --> 00:13:39.220 and so getting a lot of people involved worked really well 299 00:13:39.220 --> 00:13:42.900 to get a lot of koala sightings to do the data analysis we wanted to do. 300 00:13:43.420 --> 00:13:45.100 Then there are more focused projects. 301 00:13:45.560 --> 00:13:47.120 This is where we have fewer people involved, 302 00:13:47.140 --> 00:13:47.460 and we've got fewer participants. 303 00:13:48.120 --> 00:13:50.560 There's some training involved, or they have some skills. 304 00:13:50.940 --> 00:13:54.120 There's a smaller scale, and there's more complex tasks. 305 00:13:54.600 --> 00:13:59.100 A good example is a project I've been running with a PhD student here in Adelaide 306 00:13:59.100 --> 00:14:00.600 called Birding the Birbs. 307 00:14:00.860 --> 00:14:04.920 And we've got experienced birders involved, so the skill level is high, 308 00:14:05.100 --> 00:14:09.420 and they've been going out to 165 sites around Adelaide four times 309 00:14:09.420 --> 00:14:10.880 and doing 20-minute bird surveys. 310 00:14:11.240 --> 00:14:13.360 They've observed 20,000 birds, 311 00:14:13.540 --> 00:14:15.820 and we're analysing the relationship 312 00:14:15.820 --> 00:14:19.260 between the bird species distributions and the urban form. 313 00:14:19.540 --> 00:14:22.960 So how the habitat we have in our gardens, 314 00:14:23.080 --> 00:14:25.500 how dense the housing is, and all sorts of other factors. 315 00:14:25.780 --> 00:14:27.760 And then we're projecting that forward to look at, 316 00:14:27.800 --> 00:14:29.980 well, with the Adelaide 30-year plan, for example, 317 00:14:30.120 --> 00:14:34.120 what will happen to bird species in the city over that period of time? 318 00:14:34.500 --> 00:14:37.240 Now, that project required experienced birders. 319 00:14:37.280 --> 00:14:38.600 We didn't have time to train people, 320 00:14:38.700 --> 00:14:41.160 because this PhD project had to be done in... 321 00:14:41.160 --> 00:14:43.400 Well, we hoped it would be done in three or four years, 322 00:14:43.440 --> 00:14:45.800 but it's a PhD project, so it's going on a little bit longer. 323 00:14:45.800 --> 00:14:49.240 But we didn't have time to train people, 324 00:14:49.360 --> 00:14:51.180 so we made sure we had experienced birders involved. 325 00:14:51.420 --> 00:14:52.560 So it's a more focused project. 326 00:14:53.020 --> 00:14:54.540 And then we have the no-go zone. 327 00:14:55.180 --> 00:14:56.980 Citizen science isn't good for everything. 328 00:14:57.340 --> 00:14:58.580 You can't do all sorts of science, 329 00:14:58.620 --> 00:15:00.980 every scientific project through citizen science, 330 00:15:01.080 --> 00:15:04.880 but there are many projects where it's advantageous to get people involved. 331 00:15:05.280 --> 00:15:07.580 And that's from a scientific perspective, 332 00:15:07.820 --> 00:15:09.780 being advantageous to get people involved, 333 00:15:10.020 --> 00:15:11.860 but it's also great for the people. 334 00:15:12.380 --> 00:15:15.200 And I've done lots of surveys after projects and said, 335 00:15:15.200 --> 00:15:17.140 well, why did you get involved and what did you get out of it? 336 00:15:17.580 --> 00:15:19.880 And I've looked at projects around the world 337 00:15:19.880 --> 00:15:22.080 and found that there are three key things 338 00:15:22.080 --> 00:15:23.420 that people get out of citizen science. 339 00:15:24.160 --> 00:15:26.020 One is personal development. 340 00:15:26.220 --> 00:15:27.940 So people gain new knowledge 341 00:15:27.940 --> 00:15:30.420 by learning about whatever it is they're studying, 342 00:15:30.500 --> 00:15:32.200 go out and researching or looking at online. 343 00:15:32.680 --> 00:15:35.060 They get new skills, how to identify things 344 00:15:35.060 --> 00:15:37.700 or how to classify things or how to find things. 345 00:15:39.120 --> 00:15:41.980 It's great for physical health and mental health, 346 00:15:42.020 --> 00:15:43.240 great for health overall. 347 00:15:43.240 --> 00:15:45.760 So physical health, the koala camp, 348 00:15:45.940 --> 00:15:48.000 people going out for walks, looking for koalas. 349 00:15:48.000 --> 00:15:51.180 There's an obvious physical benefit to going out, 350 00:15:51.200 --> 00:15:52.020 getting out in nature. 351 00:15:52.240 --> 00:15:54.640 And getting out in nature has other benefits as well. 352 00:15:55.400 --> 00:15:58.540 In terms of mental health, getting out into nature is beneficial. 353 00:15:58.840 --> 00:16:01.000 So it's much more beneficial to go for a walk in nature 354 00:16:01.000 --> 00:16:03.520 than it is to go for a walk in the city, for example. 355 00:16:03.700 --> 00:16:05.120 So these projects are getting people out. 356 00:16:05.660 --> 00:16:08.260 BioBlitz events, where we have teams of scientists 357 00:16:08.260 --> 00:16:10.420 converge on an urban park 358 00:16:10.420 --> 00:16:13.220 and take the community on bird surveys and batches 359 00:16:13.240 --> 00:16:16.520 and water surveys and all sorts. 360 00:16:16.660 --> 00:16:18.180 That's getting people out into nature. 361 00:16:18.340 --> 00:16:19.760 So they have mental health benefits 362 00:16:19.760 --> 00:16:21.100 as well as physical health benefits. 363 00:16:21.380 --> 00:16:24.280 And often the projects get people together to do things. 364 00:16:24.360 --> 00:16:25.620 There's social connectivity involved. 365 00:16:26.000 --> 00:16:28.320 There's some great stories from citizen science projects 366 00:16:28.320 --> 00:16:30.300 where people have been struggling 367 00:16:30.300 --> 00:16:32.040 to connect with their local communities. 368 00:16:32.240 --> 00:16:34.440 And they've got involved in citizen science projects 369 00:16:34.440 --> 00:16:38.740 and become involved in teams that do research with scientists 370 00:16:38.740 --> 00:16:41.080 and that's helped them to connect with the community 371 00:16:41.080 --> 00:16:42.580 and been great for their mental health. 372 00:16:43.240 --> 00:16:44.180 So there's a lot of benefits there. 373 00:16:45.080 --> 00:16:48.980 And often people report that what they get out of citizen science 374 00:16:48.980 --> 00:16:51.480 is that they feel like they're making a contribution. 375 00:16:52.100 --> 00:16:54.000 And I find this one particularly interesting. 376 00:16:54.640 --> 00:16:56.820 It's important to all of us that we're valued. 377 00:16:57.300 --> 00:16:59.500 But I find this one interesting because there's different reasons 378 00:16:59.500 --> 00:17:01.500 that people feel they're making a contribution. 379 00:17:01.780 --> 00:17:05.260 And people say it's either because they're contributing to science. 380 00:17:05.280 --> 00:17:09.160 Some people like to feel that they're part of a scientific endeavour. 381 00:17:09.560 --> 00:17:12.520 Or they're contributing to nature if it's a nature-based project. 382 00:17:12.520 --> 00:17:14.440 Or they're doing something for society. 383 00:17:15.280 --> 00:17:18.620 And often people will say on top of that that it's fun. 384 00:17:18.940 --> 00:17:21.000 So when we're setting up citizen science projects 385 00:17:21.000 --> 00:17:22.280 we also need to consider that. 386 00:17:22.420 --> 00:17:24.260 We want to make it enjoyable for contributors. 387 00:17:24.660 --> 00:17:25.720 It's a three-way process. 388 00:17:26.140 --> 00:17:28.700 So here's a scientist on the left, has an idea, 389 00:17:28.960 --> 00:17:30.640 gives some information to the community. 390 00:17:30.780 --> 00:17:33.980 Community does something, gives something back to the scientist, 391 00:17:34.280 --> 00:17:36.800 and then the scientists give the results back to the community. 392 00:17:36.960 --> 00:17:38.580 Really important to be a three-way process. 393 00:17:39.640 --> 00:17:41.940 Sometimes that idea comes from the community too. 394 00:17:42.520 --> 00:17:45.080 A lot of pollution projects, water pollution, air pollution, 395 00:17:45.480 --> 00:17:48.120 noise pollution projects have been started 396 00:17:48.120 --> 00:17:51.140 where a member of the community has a concern about one of those things 397 00:17:51.140 --> 00:17:55.020 and then gets a scientist involved to run a citizen science project. 398 00:17:55.400 --> 00:17:59.140 One example is a project that I've been working on in South Australia 399 00:17:59.140 --> 00:18:00.060 about cats. 400 00:18:00.780 --> 00:18:04.120 And this is where I've got some questions about cats and cat management. 401 00:18:04.300 --> 00:18:07.180 And the community often have questions about cats too. 402 00:18:07.240 --> 00:18:10.980 So cat owners want to know more about their cats and where they roam. 403 00:18:11.380 --> 00:18:12.280 So, um, 404 00:18:12.520 --> 00:18:15.320 I run a project in South Australia called Cat Tracker. 405 00:18:15.920 --> 00:18:20.580 And we ran this project because we wanted to get an idea 406 00:18:20.580 --> 00:18:22.080 of where cats go when they go outside. 407 00:18:22.740 --> 00:18:25.100 There's been some previous research before this project. 408 00:18:25.280 --> 00:18:28.840 For example, a colleague in the US tracked 11 cats 409 00:18:29.360 --> 00:18:32.440 and he found that nine of them, sorry, ten of them, 410 00:18:32.460 --> 00:18:36.760 stayed very close to home, but one of them went into a local park. 411 00:18:37.000 --> 00:18:39.780 But it's such a small sample with only 11 cats tracked 412 00:18:39.780 --> 00:18:41.760 that we can't really extrapolate from that and say, well, 413 00:18:41.760 --> 00:18:46.680 9% of cats go into parks and 91% of cats stay close to home. 414 00:18:47.100 --> 00:18:49.020 If you want to start making some of those generalisations 415 00:18:49.020 --> 00:18:50.740 and understanding what's driving this behaviour, 416 00:18:51.060 --> 00:18:52.500 you need a much larger sample. 417 00:18:52.820 --> 00:18:55.460 And that's where we thought that citizen science might be really useful 418 00:18:55.460 --> 00:18:57.960 to get lots of people involved to track their cats. 419 00:18:58.500 --> 00:19:00.000 From the cat owner perspective, 420 00:19:01.620 --> 00:19:05.640 previous research had said that cat owners don't know where their cats go 421 00:19:05.640 --> 00:19:07.820 when they go outside the cat flap or over the back fence, 422 00:19:07.960 --> 00:19:09.960 and they're really interested to find out. 423 00:19:10.060 --> 00:19:11.600 And this might be a really good way 424 00:19:11.600 --> 00:19:14.240 to engage cat owners in thinking about how they look after their cats. 425 00:19:14.620 --> 00:19:17.880 So it was really interesting from both the scientific perspective 426 00:19:17.880 --> 00:19:18.860 and from the owner perspective. 427 00:19:19.660 --> 00:19:24.520 So we came up with a system where we lend people little GPS units 428 00:19:24.520 --> 00:19:27.580 to put on their cats to find out where their cats go. 429 00:19:27.700 --> 00:19:31.780 And actually, I brought my cat along today to demonstrate. 430 00:19:32.260 --> 00:19:35.760 I was hoping they wouldn't have someone stationed on the door 431 00:19:35.760 --> 00:19:37.900 to make sure live animals aren't coming into the... 432 00:19:38.540 --> 00:19:40.220 But Sheffield's a very good cat. 433 00:19:41.860 --> 00:19:42.560 Aren't you, Sheffy? 434 00:19:43.720 --> 00:19:44.560 This is Sheffield. 435 00:19:47.920 --> 00:19:49.820 He's very quiet. Good for presentations. 436 00:19:51.160 --> 00:19:53.240 And this is the little GPS unit we put on them. 437 00:19:53.320 --> 00:19:54.680 So we post these out to cat owners. 438 00:19:54.880 --> 00:19:57.780 They put them on their own cat for a week, being a citizen scientist, 439 00:19:58.020 --> 00:20:02.380 and then post it back to us to find out where the cat went. 440 00:20:02.520 --> 00:20:05.820 And we can then put the map of those cat tracks online. 441 00:20:06.760 --> 00:20:07.680 And here's an example. 442 00:20:08.060 --> 00:20:09.840 This is the first cat we tracked, Snowy. 443 00:20:10.140 --> 00:20:11.580 And Snowy had just gone... 444 00:20:11.580 --> 00:20:16.760 pretty much into the neighbours' houses and into nearby houses 445 00:20:16.760 --> 00:20:19.540 and had a home range of about half a hectare. 446 00:20:20.280 --> 00:20:21.400 And we tracked another cat. 447 00:20:21.480 --> 00:20:23.140 This is down near Hallett Cove. 448 00:20:23.620 --> 00:20:26.320 Does anyone live down near Hallett Cove there along Lonsdale Road? 449 00:20:27.120 --> 00:20:31.320 So this cat's spending a lot of time in the fields down by Hallett Cove there. 450 00:20:31.420 --> 00:20:34.220 And this one had a home range of about 25 hectares. 451 00:20:34.540 --> 00:20:37.300 So this is the information we're giving back to cat owners, these maps. 452 00:20:37.460 --> 00:20:38.020 We put them online. 453 00:20:38.180 --> 00:20:39.780 You can actually go to the Discovery Circle website 454 00:20:39.780 --> 00:20:41.560 and look through the maps of a cat. 455 00:20:41.580 --> 00:20:44.560 We have about 428 cats. 456 00:20:45.300 --> 00:20:46.540 That's Sheffield's home range. 457 00:20:51.540 --> 00:20:53.080 That's my office at Mawson Lakes. 458 00:20:55.280 --> 00:20:56.360 It's a little bit embarrassing. 459 00:20:56.460 --> 00:20:59.280 One night I decided to put that online on the Discovery Circle website 460 00:20:59.280 --> 00:21:02.660 as one of the cats, and it's our most popular web page. 461 00:21:03.940 --> 00:21:08.000 I even gave him a new taxonomic definition. 462 00:21:08.120 --> 00:21:10.440 I called him Felis Catus Inanimator. 463 00:21:11.680 --> 00:21:12.620 It was a late night. 464 00:21:13.440 --> 00:21:15.940 But we got lots of variation in cat home ranges. 465 00:21:16.060 --> 00:21:20.080 So here's one, two, three, four, five, six, seven cats around Walkleys Heights. 466 00:21:20.180 --> 00:21:22.180 And you can see that there's quite a lot of variation 467 00:21:22.180 --> 00:21:24.420 in the distances cats are going from their homes. 468 00:21:24.740 --> 00:21:27.600 There's a couple of those cats live in the same houses. 469 00:21:27.660 --> 00:21:29.460 And you can see that some go a long way and some don't. 470 00:21:29.480 --> 00:21:32.760 And cat owners were really engaged and interested in these results. 471 00:21:33.440 --> 00:21:35.140 We put them all online so you can... 472 00:21:35.140 --> 00:21:36.320 Yeah, that's the Discovery Circle website. 473 00:21:36.400 --> 00:21:38.460 You can go through and you can click on the cats there, 474 00:21:39.480 --> 00:21:41.480 including the wonderfully relaxed Casper. 475 00:21:42.260 --> 00:21:43.840 Find out where Casper went. 476 00:21:44.580 --> 00:21:47.820 But we collected over 60,000 data points. 477 00:21:48.140 --> 00:21:50.960 The median home range for cats was a hectare, 478 00:21:50.980 --> 00:21:53.940 so half cats went further and half cats went less than that. 479 00:21:54.680 --> 00:21:58.440 The largest one we tracked in South Australia was a 31-hectare home range, 480 00:21:58.620 --> 00:22:02.020 and the smallest was tiny, just basically a cat staying at home. 481 00:22:02.700 --> 00:22:04.020 Here's one that interested us. 482 00:22:04.080 --> 00:22:07.420 When I first plugged this one in, I was confused for a moment. 483 00:22:07.460 --> 00:22:08.880 Any ideas what happened to this cat? 484 00:22:09.520 --> 00:22:10.000 Yeah. 485 00:22:10.000 --> 00:22:13.260 This cat was going to its owner's work every day. 486 00:22:14.900 --> 00:22:17.340 Unfortunately, I had to delete this from all further data analysis. 487 00:22:17.480 --> 00:22:18.880 It wasn't really a useful cat track. 488 00:22:18.960 --> 00:22:21.360 It was interesting, but maybe the most interesting thing 489 00:22:21.360 --> 00:22:23.220 is this person takes a different route to work each day. 490 00:22:25.340 --> 00:22:27.160 So I've tracked cats in South Australia, 491 00:22:27.280 --> 00:22:29.360 and I've had colleagues tracking cats in New Zealand, 492 00:22:29.520 --> 00:22:33.140 the UK and the US as part of the same project, 493 00:22:33.680 --> 00:22:35.440 and we've been using all the same methods. 494 00:22:35.560 --> 00:22:37.020 And here's one that was tracked in New Zealand. 495 00:22:37.820 --> 00:22:39.540 And this one, Penny, 496 00:22:40.660 --> 00:22:43.260 lives just on the outskirts of Wellington in an urban area, 497 00:22:43.400 --> 00:22:45.420 but you can see Penny went missing for the week, basically, 498 00:22:45.440 --> 00:22:47.120 when she was wearing the cat tracker. 499 00:22:47.620 --> 00:22:51.420 And when we analysed that, we came up with a 214-hectare home range. 500 00:22:51.580 --> 00:22:53.360 So this cat's covering a lot of distance. 501 00:22:54.000 --> 00:22:56.020 We just tracked one in Tasmania recently 502 00:22:56.020 --> 00:22:58.660 that had about a 115-hectare home range. 503 00:22:58.780 --> 00:23:02.260 So cats can indeed go a long way when they get out of home. 504 00:23:03.180 --> 00:23:06.220 We found that 88% of the cats had a larger home range at night, 505 00:23:06.360 --> 00:23:08.060 and we found that wandering cats, 506 00:23:08.340 --> 00:23:09.980 so these are cats that are going further, 507 00:23:10.000 --> 00:23:11.620 further than one hectare. 508 00:23:12.260 --> 00:23:13.440 They're crossing more roads, 509 00:23:13.620 --> 00:23:15.300 they're showing more signs of being in fights, 510 00:23:15.540 --> 00:23:16.900 they're seen with more prey, 511 00:23:17.100 --> 00:23:19.980 and these are cats that are typically spending less time inside. 512 00:23:20.560 --> 00:23:22.760 And when we gave cat owners back their cat tracks 513 00:23:22.760 --> 00:23:26.400 and this sort of information, a report on all of our findings, 514 00:23:26.740 --> 00:23:28.820 we did find that a lot of them changed their attitudes 515 00:23:28.820 --> 00:23:30.960 and behaviours towards cat management. 516 00:23:31.200 --> 00:23:34.400 So they became more accepting of the need to keep cats indoors, 517 00:23:34.600 --> 00:23:37.540 and they actually started keeping their cats indoors more. 518 00:23:37.780 --> 00:23:39.340 And we're just about to publish a paper on that, 519 00:23:40.000 --> 00:23:40.420 so you need to get out there. 520 00:23:41.080 --> 00:23:43.920 We also found the curious case of the clandestine cats. 521 00:23:44.700 --> 00:23:48.380 And this happened when I received a cat track. 522 00:23:48.940 --> 00:23:53.580 I've just noticed that my daughter's been feeding Sheffield again. 523 00:23:54.960 --> 00:23:56.620 I don't know how he lives on this stuff. 524 00:23:56.820 --> 00:23:57.700 Not a lot of protein in it. 525 00:23:59.260 --> 00:24:00.540 The curious case, sorry, 526 00:24:00.620 --> 00:24:02.300 the curious case of the clandestine cats. 527 00:24:03.380 --> 00:24:08.160 A cat owner sent back the cat tracks, the GPS unit, with a note. 528 00:24:08.300 --> 00:24:09.900 And actually, we used to get lots of notes. 529 00:24:10.000 --> 00:24:12.180 We'd get notes back from cat owners that would say, 530 00:24:12.240 --> 00:24:13.240 thank you for tracking the cat. 531 00:24:13.340 --> 00:24:14.680 And sometimes they were actually from the cat 532 00:24:14.680 --> 00:24:16.440 with a little paw print in the bottom corner and stuff, 533 00:24:16.520 --> 00:24:17.780 saying, thank you for tracking me. 534 00:24:17.860 --> 00:24:19.200 I'm really looking forward to seeing where I've gone. 535 00:24:21.060 --> 00:24:24.220 But this one, this particular one, said, really sorry. 536 00:24:24.580 --> 00:24:26.140 I've completely wasted your time. 537 00:24:26.540 --> 00:24:29.540 It's been really wet this week, and our cat hasn't gone anywhere. 538 00:24:31.020 --> 00:24:35.040 And I was sort of not really excited about plugging that GPS unit 539 00:24:35.040 --> 00:24:36.180 into the computer to see where it went. 540 00:24:36.200 --> 00:24:37.460 I expected to see nothing. 541 00:24:38.080 --> 00:24:39.300 And when I plugged it in, 542 00:24:39.300 --> 00:24:42.180 I saw the largest cat track I'd seen to that point. 543 00:24:42.200 --> 00:24:42.680 It was huge. 544 00:24:43.160 --> 00:24:44.840 And I thought, well, there's this real mismatch 545 00:24:44.840 --> 00:24:47.620 between what the cat owner's told me and what I'm seeing on the screen. 546 00:24:48.300 --> 00:24:52.860 And it didn't make a lot of sense until I separated the cat track 547 00:24:52.860 --> 00:24:55.140 into day range and night range. 548 00:24:55.620 --> 00:24:58.120 And I could see then really clearly what had happened. 549 00:24:58.180 --> 00:24:59.100 The day range was nothing. 550 00:24:59.180 --> 00:25:00.680 The cat hadn't gone anywhere during the day. 551 00:25:00.780 --> 00:25:01.860 It was all at night. 552 00:25:02.140 --> 00:25:05.260 And so the cat owner has seized the cat at home during the day, 553 00:25:05.300 --> 00:25:06.880 on the couch, doing nothing. 554 00:25:07.080 --> 00:25:08.700 And then when the cat owner goes to bed, 555 00:25:09.300 --> 00:25:11.120 it goes out and has a huge home range. 556 00:25:11.600 --> 00:25:13.180 So I thought, well, this is really interesting. 557 00:25:13.280 --> 00:25:16.780 I looked back through all of the data for people who said their cats 558 00:25:17.560 --> 00:25:20.840 were inside at night and looked at their home ranges, 559 00:25:20.900 --> 00:25:24.520 and I found that 39% of them had home ranges over a hectare at night. 560 00:25:24.800 --> 00:25:26.880 So there's a lot of clandestine cats out there 561 00:25:26.880 --> 00:25:28.020 sneaking out through the cat flap. 562 00:25:28.280 --> 00:25:30.060 So that's projects I've run. 563 00:25:30.120 --> 00:25:31.900 I'm going to quickly run you through some projects. 564 00:25:32.000 --> 00:25:36.480 I've got maybe 10 minutes, maybe five, to run you through some projects. 565 00:25:36.560 --> 00:25:38.180 There's some local projects that are great. 566 00:25:38.180 --> 00:25:40.480 We've just finished collecting data on... 567 00:25:40.480 --> 00:25:42.560 So you can't participate in this one still now, 568 00:25:42.800 --> 00:25:47.360 but we've just finished collecting data on people's produce from their gardens. 569 00:25:47.460 --> 00:25:50.000 And this one was cool because people were uploading information 570 00:25:50.000 --> 00:25:52.120 about how much water they were putting on their gardens 571 00:25:52.120 --> 00:25:53.580 and how much produce they were collecting, 572 00:25:53.760 --> 00:25:56.180 and then it automatically graphed online for them 573 00:25:56.180 --> 00:25:59.040 their per square metre productivity and their per square metre water use. 574 00:25:59.220 --> 00:26:03.120 And then they were using that information to modify their gardening habits 575 00:26:03.120 --> 00:26:05.840 to make sure they were being as efficient as they could be. 576 00:26:06.100 --> 00:26:07.460 I now have a PhD student, 577 00:26:07.460 --> 00:26:08.420 Georgia Pollard, 578 00:26:08.560 --> 00:26:11.440 who's analysing the data from all of the gardens that we studied, 579 00:26:11.660 --> 00:26:14.360 looking at what are the most effective ways to grow foods, 580 00:26:14.420 --> 00:26:15.500 particularly in Adelaide, 581 00:26:15.580 --> 00:26:17.540 what are the most effective crops to grow 582 00:26:17.540 --> 00:26:18.840 and how should we be growing them? 583 00:26:18.900 --> 00:26:21.380 Should we be looking at wicking beds or other things 584 00:26:21.380 --> 00:26:23.220 to make these as effective as possible? 585 00:26:23.420 --> 00:26:26.180 So that's another example of a little bit different citizen science project. 586 00:26:27.420 --> 00:26:30.380 Echidna CSI is one run out of Adelaide Uni. 587 00:26:31.480 --> 00:26:33.820 It's one of a group of citizen science projects 588 00:26:33.820 --> 00:26:36.460 that I affectionately call the Poo Projects, 589 00:26:36.460 --> 00:26:37.780 and they're very popular. 590 00:26:38.120 --> 00:26:41.780 So with Echidna CSI there's a smartphone app 591 00:26:41.780 --> 00:26:44.920 and you can record if you find an echidna, 592 00:26:45.000 --> 00:26:46.740 you can report an echidna sighting, 593 00:26:47.200 --> 00:26:50.300 but you can also collect echidna scats, 594 00:26:50.420 --> 00:26:50.940 echidna poo, 595 00:26:51.120 --> 00:26:52.660 and send that into the team. 596 00:26:52.760 --> 00:26:56.780 They're doing some great research on the DNA that's found within those scats. 597 00:26:56.900 --> 00:26:58.200 They're looking at the actual echidnas, 598 00:26:58.400 --> 00:26:59.700 they're looking at the plants they're eating, 599 00:26:59.760 --> 00:27:00.520 the food they're eating, 600 00:27:01.480 --> 00:27:05.060 and they're also looking at the hormones and the stress and so on 601 00:27:05.060 --> 00:27:06.200 and if the echidnas are pregnant. 602 00:27:06.280 --> 00:27:06.440 So they're looking at the hormones and the stress and so on, 603 00:27:06.460 --> 00:27:07.460 they're doing a whole bunch of research 604 00:27:07.460 --> 00:27:09.720 based on what they can find in the echidna scats. 605 00:27:10.060 --> 00:27:12.500 And they're getting echidna scats posted to them 606 00:27:12.500 --> 00:27:14.480 from around Australia, which is great. 607 00:27:14.740 --> 00:27:18.940 I hear that one person recently sent them virtually a full esky full of scats. 608 00:27:19.020 --> 00:27:21.220 So they're a particularly enthusiastic participant, 609 00:27:21.260 --> 00:27:22.360 which is wonderful to see. 610 00:27:23.020 --> 00:27:24.260 It's an interesting enthusiasm. 611 00:27:26.000 --> 00:27:28.900 Frogwatch SA is a great citizen science project here in South Australia. 612 00:27:29.100 --> 00:27:30.980 I love it because you can do two things. 613 00:27:31.380 --> 00:27:34.460 One, you can use an app to go down and record frog calls. 614 00:27:34.580 --> 00:27:35.620 So if you hear frog calls 615 00:27:35.620 --> 00:27:37.920 and you've got the FrogSpotter app, 616 00:27:38.060 --> 00:27:40.240 you can just quickly turn that on for three minutes 617 00:27:40.240 --> 00:27:41.640 and record the calls. 618 00:27:41.880 --> 00:27:46.580 And then they go in and Steve Walker at the NRM board identifies those frogs. 619 00:27:47.160 --> 00:27:48.440 The other thing you can do, 620 00:27:48.500 --> 00:27:52.840 if you see in the top right-hand corner of the website screenshot there, 621 00:27:53.340 --> 00:27:55.020 is you can become a frog identifier. 622 00:27:55.420 --> 00:27:57.780 So you can help Steve do the analysis. 623 00:27:58.200 --> 00:28:01.080 And there's training there to learn how to identify frog calls. 624 00:28:01.280 --> 00:28:02.740 And then a bit like Galaxy Zoo, 625 00:28:02.840 --> 00:28:04.980 you can go online and start to identify, 626 00:28:04.980 --> 00:28:07.100 listen to the frog calls that other people are submitting 627 00:28:07.100 --> 00:28:12.080 and analyse them and identify the frogs that are calling. 628 00:28:12.380 --> 00:28:16.340 So you can either record data or analyse data for this project. 629 00:28:16.940 --> 00:28:18.800 There's great national projects too. 630 00:28:18.900 --> 00:28:24.040 Bowerbird is a project that is for recording biodiversity generally. 631 00:28:24.620 --> 00:28:28.420 What I think is great about Bowerbird is that one of the big issues 632 00:28:28.420 --> 00:28:32.260 with becoming more knowledgeable about nature 633 00:28:32.260 --> 00:28:33.780 or getting engaged with nature, 634 00:28:33.780 --> 00:28:37.340 is that if you find something and you don't know what it is, 635 00:28:37.480 --> 00:28:39.820 it's really hard to find out more about it. 636 00:28:40.280 --> 00:28:42.300 If you know the name of it, you can Google it 637 00:28:42.300 --> 00:28:44.240 or you can look it up in a library 638 00:28:44.240 --> 00:28:47.600 or you can do all sorts of things to find out more information about it 639 00:28:47.600 --> 00:28:49.140 and learn about it. 640 00:28:49.320 --> 00:28:50.920 But if you don't know what something's called, 641 00:28:51.020 --> 00:28:51.800 it can be quite tricky. 642 00:28:52.680 --> 00:28:57.080 Bowerbird is a website where you can take a photograph of something 643 00:28:57.480 --> 00:28:58.840 and load it up onto the website 644 00:28:58.840 --> 00:29:01.520 and Ken Walker and his team at the Museum of Victoria 645 00:29:01.520 --> 00:29:03.400 will identify it for you. 646 00:29:03.840 --> 00:29:05.820 So that's the first cool thing about Bowerbird. 647 00:29:06.080 --> 00:29:08.020 The second cool thing about Bowerbird is that 648 00:29:08.660 --> 00:29:12.240 you can store all of your observations together 649 00:29:12.240 --> 00:29:14.220 and you can put them together in projects. 650 00:29:14.420 --> 00:29:16.180 So you can start a project for your back garden. 651 00:29:16.360 --> 00:29:18.780 You can call it Bill's Backyard Biodiversity 652 00:29:19.420 --> 00:29:20.460 or whatever you like. 653 00:29:20.600 --> 00:29:24.180 You can take photographs of all the biodiversity 654 00:29:24.180 --> 00:29:27.240 you find in your backyard or at a local park or in a schoolyard 655 00:29:27.900 --> 00:29:30.960 and keep them all in one spot and have them identified. 656 00:29:31.280 --> 00:29:32.280 It works really neatly. 657 00:29:32.320 --> 00:29:33.760 I call it Facebook for Photos. 658 00:29:33.760 --> 00:29:34.560 It's a great way to get information about flora and fauna. 659 00:29:34.840 --> 00:29:37.200 It has a bit of a feed like Facebook does 660 00:29:37.200 --> 00:29:38.560 where you see photographs come by. 661 00:29:38.880 --> 00:29:40.860 And you'll often see the same thing go by 662 00:29:41.260 --> 00:29:43.280 because one person will upload the photograph 663 00:29:43.780 --> 00:29:45.740 and say, you know, what's this? 664 00:29:45.960 --> 00:29:48.040 The next person will come in and identify it 665 00:29:48.040 --> 00:29:49.500 so you see it go through the feed again 666 00:29:49.500 --> 00:29:51.040 and then someone else might comment on it. 667 00:29:51.420 --> 00:29:55.780 And they have had some really interesting findings through Bowerbird. 668 00:29:56.000 --> 00:29:58.240 They've had new pest species identified in areas 669 00:29:58.240 --> 00:29:59.160 where they weren't known to be 670 00:29:59.160 --> 00:30:00.980 and so there's then had to be some management action 671 00:30:00.980 --> 00:30:01.880 to eradicate them. 672 00:30:01.880 --> 00:30:05.140 They've found new species outside of their known ranges and so on. 673 00:30:05.220 --> 00:30:08.280 So all of the information, once it's identified from Bowerbird, 674 00:30:08.360 --> 00:30:10.840 goes further into the Atlas of Living Australia 675 00:30:10.840 --> 00:30:14.420 which is the place where scientists store biodiversity data. 676 00:30:14.940 --> 00:30:18.040 So there's a number of reasons why Bowerbird is cool. 677 00:30:18.240 --> 00:30:19.740 You can get things identified. 678 00:30:19.820 --> 00:30:21.600 You can start your own projects 679 00:30:21.600 --> 00:30:23.680 or you can contribute to other people's projects. 680 00:30:24.000 --> 00:30:25.320 It's really easy to use 681 00:30:25.320 --> 00:30:28.060 and the data goes into the Atlas of Living Australia. 682 00:30:28.420 --> 00:30:29.760 So great project, Bowerbird. 683 00:30:30.640 --> 00:30:31.120 QuestaGame. 684 00:30:31.120 --> 00:30:33.920 QuestaGame was set up by a couple in Canberra 685 00:30:33.920 --> 00:30:37.000 who realised that some of the kids they knew 686 00:30:37.540 --> 00:30:41.280 knew how to identify more Pokemon than they did bird species. 687 00:30:42.120 --> 00:30:44.120 And that was a bit sad to them. 688 00:30:44.160 --> 00:30:46.920 So they thought, well, we need to engage kids how they get engaged. 689 00:30:47.180 --> 00:30:48.420 So they set up QuestaGame. 690 00:30:48.540 --> 00:30:51.080 And QuestaGame is another wildlife type of project 691 00:30:51.080 --> 00:30:53.400 where you can go out and take photographs of wildlife with the app 692 00:30:53.400 --> 00:30:55.540 and they also identify things for you. 693 00:30:55.720 --> 00:30:57.760 It's app-based, so it's mobile phone-based. 694 00:30:57.800 --> 00:31:00.860 It doesn't have a website where you can upload data, 695 00:31:01.120 --> 00:31:01.780 but it's just an app. 696 00:31:02.800 --> 00:31:03.880 Well, just an app. 697 00:31:03.940 --> 00:31:04.980 An app's a good thing. 698 00:31:05.060 --> 00:31:07.120 It does the location data, does all those sorts of things. 699 00:31:08.020 --> 00:31:09.900 Now, it was originally set up for kids 700 00:31:09.900 --> 00:31:13.480 and it's a game so that you go online, 701 00:31:13.720 --> 00:31:17.140 you take photographs of plants and animals that you see 702 00:31:17.140 --> 00:31:18.260 and you get points. 703 00:31:18.840 --> 00:31:21.360 And if it's rare, you get more points. 704 00:31:21.680 --> 00:31:23.960 And if it's a quest, so they actually set quests, 705 00:31:24.220 --> 00:31:25.640 can you find X? 706 00:31:25.960 --> 00:31:28.140 If you find X, you get more points. 707 00:31:28.320 --> 00:31:29.600 So it's a game where you get points. 708 00:31:29.660 --> 00:31:30.620 And you can compete against me. 709 00:31:30.620 --> 00:31:32.040 You can find me on there if you want. 710 00:31:32.500 --> 00:31:35.300 I'm about ranked number 235 or something. 711 00:31:35.760 --> 00:31:37.060 But if you look through the leaderboard, 712 00:31:37.180 --> 00:31:40.300 what's really interesting is that while it was set up for kids, 713 00:31:40.840 --> 00:31:43.900 it's clearly been really engaging for all ages 714 00:31:43.900 --> 00:31:45.220 because you can look at the leaderboard 715 00:31:45.220 --> 00:31:50.140 and see there are a lot of adults in the top 100 as well 716 00:31:50.140 --> 00:31:51.000 as a lot of kids. 717 00:31:51.760 --> 00:31:54.260 So QuestaGame's a great game to get people engaged. 718 00:31:54.520 --> 00:31:56.380 My 10-year-old daughter loves QuestaGame. 719 00:31:56.540 --> 00:31:57.060 Oh, there you go. 720 00:31:57.100 --> 00:31:59.540 You can see on the screen there I'm ranked 203 721 00:31:59.540 --> 00:32:00.700 when that screenshot was taken. 722 00:32:01.500 --> 00:32:02.600 RedMap. Anyone into fishing? 723 00:32:03.200 --> 00:32:03.640 Yep. 724 00:32:04.040 --> 00:32:07.880 RedMap is a project set up for people who go fishing 725 00:32:07.880 --> 00:32:09.020 or walking on the beach. 726 00:32:09.060 --> 00:32:12.220 If you see anything unusual, you can use their app to record it. 727 00:32:12.340 --> 00:32:17.880 And they're looking at how species ranges are changing over time. 728 00:32:18.260 --> 00:32:19.700 So RedMap's if you're into fishing. 729 00:32:20.160 --> 00:32:22.260 There's some great international projects as well. 730 00:32:22.280 --> 00:32:23.660 I've already talked about Galaxy Zoo, 731 00:32:24.160 --> 00:32:25.720 but that's been so popular. 732 00:32:25.780 --> 00:32:27.360 And there's a screenshot of what it looks like 733 00:32:27.360 --> 00:32:29.460 with the different galaxies. 734 00:32:29.540 --> 00:32:30.720 There's a lot of galaxies down in the bottom right-hand corner 735 00:32:30.720 --> 00:32:31.360 for you to click on. 736 00:32:31.700 --> 00:32:34.940 But it's been so popular that they've expanded Galaxy Zoo 737 00:32:35.540 --> 00:32:38.080 into a bigger project called Zooniverse. 738 00:32:38.640 --> 00:32:42.320 And on Zooniverse, there are a whole lot of citizen science projects. 739 00:32:42.880 --> 00:32:44.980 There are some of them that are like Galaxy Zoo 740 00:32:44.980 --> 00:32:47.060 where they ask you to classify or identify things. 741 00:32:47.680 --> 00:32:49.260 Down the bottom right-hand corner there, 742 00:32:49.320 --> 00:32:52.500 there's a project called Go Wild in the Serengeti 743 00:32:52.500 --> 00:32:54.420 where they're studying animal migration 744 00:32:54.420 --> 00:32:56.140 across the Serengeti Plains in Africa. 745 00:32:56.400 --> 00:32:59.520 They have automatic cameras set up that are motion-activated. 746 00:32:59.540 --> 00:33:01.480 And they ask people to go online 747 00:33:01.480 --> 00:33:04.660 and identify the animals that walk past these cameras. 748 00:33:04.900 --> 00:33:06.640 So that's a project. 749 00:33:06.760 --> 00:33:08.540 In the middle there, the humanities projects, 750 00:33:09.320 --> 00:33:10.800 we have a war diary project. 751 00:33:11.160 --> 00:33:13.360 There are a whole range of citizen science projects 752 00:33:13.360 --> 00:33:14.980 that are about transcribing things. 753 00:33:15.200 --> 00:33:19.020 So historians and social scientists want to know more 754 00:33:19.020 --> 00:33:23.080 about the experience of people in the trenches in the world wars. 755 00:33:23.540 --> 00:33:25.200 And they have their diaries. 756 00:33:25.380 --> 00:33:26.940 But they don't have time to read them all. 757 00:33:27.060 --> 00:33:28.120 So they put them online. 758 00:33:28.120 --> 00:33:31.260 And people can read them and transcribe them 759 00:33:31.260 --> 00:33:33.260 for the historians and so on. 760 00:33:33.500 --> 00:33:36.380 And then the historians can just do automatic searches 761 00:33:36.380 --> 00:33:38.140 through the digital information 762 00:33:38.140 --> 00:33:41.500 rather than having to read all of the handwritten information. 763 00:33:42.260 --> 00:33:46.260 If you're good at reading cursive writing, 764 00:33:46.500 --> 00:33:49.600 older style writing, this is a great project for you. 765 00:33:49.700 --> 00:33:53.340 If you're just learning linked script at primary school, 766 00:33:53.380 --> 00:33:58.000 it might be really tricky for you to read the writing in these war diaries. 767 00:33:58.000 --> 00:34:00.880 There's all sorts of projects like that as well. 768 00:34:01.280 --> 00:34:04.480 Digivol in Australia runs all sorts of projects as well. 769 00:34:04.660 --> 00:34:09.280 If you're into the records of the city of Parramatta 770 00:34:09.280 --> 00:34:11.420 from the 1897 council meetings, 771 00:34:11.680 --> 00:34:13.440 then you can go online and read those. 772 00:34:13.840 --> 00:34:16.100 If you're interested in the different gemstones 773 00:34:16.100 --> 00:34:19.000 in the library of the Australian Museum, 774 00:34:19.840 --> 00:34:21.860 then you can go on and help classify those. 775 00:34:21.980 --> 00:34:23.660 There's a whole range of projects. 776 00:34:24.040 --> 00:34:26.740 The last one I'll talk about in any great detail is Foldit. 777 00:34:26.740 --> 00:34:29.480 This is quite different because it's a health-focused project. 778 00:34:30.080 --> 00:34:31.700 It's about proteins. 779 00:34:32.380 --> 00:34:34.700 Proteins are made up of strings of amino acids. 780 00:34:35.060 --> 00:34:37.820 They can be anywhere from less than 100 amino acids 781 00:34:37.820 --> 00:34:38.840 to more than 1,000. 782 00:34:39.120 --> 00:34:41.560 They're in particular shapes, if you like, 783 00:34:41.700 --> 00:34:43.660 and those shapes determine their functions. 784 00:34:44.180 --> 00:34:47.220 It's really complex with the variable number of amino acids 785 00:34:47.220 --> 00:34:52.120 and the different number of ways they can be folded 786 00:34:52.120 --> 00:34:54.160 to create different shapes. It's very complex. 787 00:34:54.300 --> 00:34:56.000 Scientists have tried using computers 788 00:34:56.000 --> 00:35:00.700 to manipulate proteins in order to create new functions 789 00:35:00.700 --> 00:35:02.820 for all sorts of things like fighting disease 790 00:35:02.820 --> 00:35:04.560 or creating new biofuels. 791 00:35:04.720 --> 00:35:08.480 There's any number of reasons that these proteins can be used in research. 792 00:35:08.920 --> 00:35:10.220 But they realised that computers, 793 00:35:10.380 --> 00:35:12.500 while they're really good at iteratively working through things, 794 00:35:12.760 --> 00:35:14.540 they're not great at thinking sideways, 795 00:35:14.660 --> 00:35:15.780 at looking at something and thinking, 796 00:35:15.940 --> 00:35:19.340 oh, the problem is this, we should try something completely different, 797 00:35:19.460 --> 00:35:23.000 or we should manipulate this, we should start on a different track 798 00:35:23.000 --> 00:35:25.080 because I think there's something else going on here. 799 00:35:25.620 --> 00:35:27.440 That's what human minds are really good at. 800 00:35:27.660 --> 00:35:30.240 And Foldit has been set up so that human minds 801 00:35:30.240 --> 00:35:31.660 can work on these protein issues. 802 00:35:31.840 --> 00:35:35.800 You can see a rather complex protein being manipulated on the screen there. 803 00:35:36.140 --> 00:35:39.800 When you start playing Foldit, it starts with much simpler forms 804 00:35:39.800 --> 00:35:41.180 and gives you all sorts of training. 805 00:35:41.400 --> 00:35:43.340 And when you get things right, it gives you fireworks 806 00:35:43.340 --> 00:35:44.640 and makes it very exciting. 807 00:35:45.440 --> 00:35:49.960 But as you work up through Foldit, you get to more complex things 808 00:35:49.960 --> 00:35:53.060 and you start to help the scientists do their research. 809 00:35:53.340 --> 00:35:54.980 And there's a great example of a... 810 00:35:55.080 --> 00:35:58.180 a scientific paper published in a nature journal on the screen there, 811 00:35:58.220 --> 00:36:00.500 which is the sort of top tier of scientific journals. 812 00:36:00.740 --> 00:36:02.920 And the names, the authors on that paper, 813 00:36:03.020 --> 00:36:05.720 the first couple are scientists, Firas and Frank, 814 00:36:05.920 --> 00:36:08.860 and then there's the Foldit Contenders Group 815 00:36:08.860 --> 00:36:11.240 and the Foldit Void Crushers Group. 816 00:36:11.780 --> 00:36:14.000 These are groups of online game players 817 00:36:14.000 --> 00:36:16.540 who've been manipulating proteins to help the scientists, 818 00:36:16.620 --> 00:36:19.240 and they're being acknowledged in these scientific papers, 819 00:36:19.300 --> 00:36:19.940 which is fantastic. 820 00:36:20.820 --> 00:36:22.440 So that's a whole range of projects. 821 00:36:22.560 --> 00:36:24.400 Old Weather is another transcription project, 822 00:36:25.080 --> 00:36:25.180 and it's a great way to go online 823 00:36:25.180 --> 00:36:29.420 and help people transcribe records from ships 824 00:36:29.420 --> 00:36:33.040 to help climate scientists understand how the weather has been changing 825 00:36:33.040 --> 00:36:34.660 over the last... or the climate's been changing 826 00:36:34.660 --> 00:36:36.560 over the last few hundred years. 827 00:36:36.820 --> 00:36:38.280 So if you want to find projects, 828 00:36:38.500 --> 00:36:41.260 there is an Australian citizen science project finder 829 00:36:41.260 --> 00:36:44.420 and there is an international project finder called SciStarter. 830 00:36:44.680 --> 00:36:48.200 You can find out more information about all of those projects 831 00:36:48.200 --> 00:36:50.920 that I've mentioned today and the project finders 832 00:36:50.920 --> 00:36:53.280 on my website, on the Discovery Circle website, 833 00:36:54.340 --> 00:36:55.060 discoverycircle.com, 834 00:36:55.080 --> 00:36:55.760 and discoverycircle.org.au. 835 00:36:56.140 --> 00:36:57.540 I've got some cards in the bag, 836 00:36:57.600 --> 00:36:59.940 so if you do want to get that written on a card, 837 00:36:59.980 --> 00:37:00.900 you can come and grab one afterwards. 838 00:37:01.540 --> 00:37:03.320 But I've been given the two-minute call, 839 00:37:03.440 --> 00:37:04.920 so enough from me. 840 00:37:04.980 --> 00:37:06.000 I think we're going to do some questions.