1 00:00:08,790 --> 00:00:07,430 um 2 00:00:10,470 --> 00:00:08,800 some of the ways we can look at trying 3 00:00:12,549 --> 00:00:10,480 to understand who astroviolets are is 4 00:00:15,190 --> 00:00:12,559 collecting a couple points of data on 5 00:00:16,870 --> 00:00:15,200 the first is academic age academic age 6 00:00:19,029 --> 00:00:16,880 is traditionally defined as the year in 7 00:00:20,950 --> 00:00:19,039 which a person publishes their first 8 00:00:23,349 --> 00:00:20,960 paper in a peer-reviewed journal 9 00:00:24,790 --> 00:00:23,359 for my purposes i couldn't use that so i 10 00:00:26,070 --> 00:00:24,800 looked at the year in which person got 11 00:00:27,670 --> 00:00:26,080 their phd 12 00:00:28,870 --> 00:00:27,680 next is what discipline do they come 13 00:00:30,630 --> 00:00:28,880 from 14 00:00:32,229 --> 00:00:30,640 the easiest way to do that for me was to 15 00:00:34,150 --> 00:00:32,239 look at what field they say their 16 00:00:35,590 --> 00:00:34,160 dissertations in 17 00:00:37,030 --> 00:00:35,600 and the last is 18 00:00:39,350 --> 00:00:37,040 do they continue to participate in 19 00:00:41,030 --> 00:00:39,360 astrobiology meaning when someone says 20 00:00:43,270 --> 00:00:41,040 they're an astrobiologist 21 00:00:44,470 --> 00:00:43,280 do they keep on being an astrobiologist 22 00:00:46,389 --> 00:00:44,480 or do they give up and go on to a 23 00:00:49,670 --> 00:00:46,399 different discipline and are there new 24 00:00:53,110 --> 00:00:50,470 now 25 00:00:55,029 --> 00:00:53,120 um for purposes of my study 26 00:00:56,310 --> 00:00:55,039 it was difficult to find astrobiologists 27 00:00:58,470 --> 00:00:56,320 in the wild 28 00:01:00,709 --> 00:00:58,480 because they're spread across a lot of 29 00:01:02,709 --> 00:01:00,719 different diverse disciplines right 30 00:01:05,189 --> 00:01:02,719 so the easiest way to do that was to let 31 00:01:07,030 --> 00:01:05,199 them self select and look at who shows 32 00:01:09,190 --> 00:01:07,040 up to absycon 33 00:01:13,109 --> 00:01:09,200 so i randomly selected 34 00:01:15,590 --> 00:01:13,119 the presenters of apps icon 2002 to 35 00:01:17,749 --> 00:01:15,600 through 2012. 36 00:01:21,910 --> 00:01:17,759 then i figured out when they got their 37 00:01:24,230 --> 00:01:21,920 phd and what field they got it in 38 00:01:27,429 --> 00:01:24,240 this is the distribution of fields that 39 00:01:29,190 --> 00:01:27,439 people got their phds in 40 00:01:31,510 --> 00:01:29,200 now the important thing to look at here 41 00:01:32,710 --> 00:01:31,520 is you have a few core disciplines on 42 00:01:34,950 --> 00:01:32,720 the far left 43 00:01:36,710 --> 00:01:34,960 that a lot of people were in and then 44 00:01:39,590 --> 00:01:36,720 you have a whole bunch where there was 45 00:01:42,469 --> 00:01:39,600 only one person in all of those 46 00:01:44,789 --> 00:01:42,479 this is a measure of multidisciplinarity 47 00:01:46,870 --> 00:01:44,799 and the question is how much is good for 48 00:01:48,469 --> 00:01:46,880 astrobiology to continue growing and 49 00:01:51,350 --> 00:01:48,479 then is there a point at which you get 50 00:01:53,270 --> 00:01:51,360 too much in the field fractures 51 00:01:54,469 --> 00:01:53,280 like do some of these people team up 52 00:01:56,789 --> 00:01:54,479 with some of the other ones and then you 53 00:01:58,389 --> 00:01:56,799 end up with two different things though 54 00:02:03,109 --> 00:01:58,399 astrobiology right 55 00:02:06,830 --> 00:02:04,550 you have a couple weird ones up there 56 00:02:08,550 --> 00:02:06,840 that i don't fully understand like 57 00:02:09,589 --> 00:02:08,560 neuroscience 58 00:02:10,869 --> 00:02:09,599 um 59 00:02:12,630 --> 00:02:10,879 i get 60 00:02:14,790 --> 00:02:12,640 paleobiology 61 00:02:16,710 --> 00:02:14,800 and you know geology and chemistry those 62 00:02:17,910 --> 00:02:16,720 were the two biggest 63 00:02:19,750 --> 00:02:17,920 um 64 00:02:21,270 --> 00:02:19,760 genetics yeah but 65 00:02:23,270 --> 00:02:21,280 it was also difficult because it's hard 66 00:02:24,869 --> 00:02:23,280 to come up with an ontology or taxonomy 67 00:02:27,110 --> 00:02:24,879 for science because some of these are 68 00:02:28,390 --> 00:02:27,120 clearly related but it's hard to like 69 00:02:32,710 --> 00:02:28,400 mush them together into larger 70 00:02:36,390 --> 00:02:33,830 so now we're going to talk about 71 00:02:38,390 --> 00:02:36,400 academic age so what year did the people 72 00:02:39,670 --> 00:02:38,400 get their phd 73 00:02:42,309 --> 00:02:39,680 you have 74 00:02:44,070 --> 00:02:42,319 one at 1950 which would be your oldest 75 00:02:46,550 --> 00:02:44,080 and then you have one at two in the two 76 00:02:50,390 --> 00:02:46,560 decades of the 2010s which makes sense 77 00:02:51,750 --> 00:02:50,400 because my data stopped in 2012. 78 00:02:53,430 --> 00:02:51,760 so what does a healthy feel like 79 00:02:55,270 --> 00:02:53,440 astrobiology look like 80 00:02:57,830 --> 00:02:55,280 you'd expect to find people that are 81 00:03:00,949 --> 00:02:57,840 older and people that are younger which 82 00:03:04,229 --> 00:03:00,959 is what the data shows 83 00:03:06,470 --> 00:03:04,239 this is the academic age calculated at 84 00:03:07,990 --> 00:03:06,480 the time of the conference 85 00:03:10,070 --> 00:03:08,000 and what's interesting at this is that 86 00:03:12,790 --> 00:03:10,080 you have a wide distribution 87 00:03:14,309 --> 00:03:12,800 right if astrobiology wasn't 88 00:03:15,750 --> 00:03:14,319 progressing very healthily you would see 89 00:03:18,149 --> 00:03:15,760 like a whole bunch of people 90 00:03:19,990 --> 00:03:18,159 at the bottom some people at the top but 91 00:03:21,670 --> 00:03:20,000 nobody in the middle 92 00:03:23,430 --> 00:03:21,680 meaning that 93 00:03:25,190 --> 00:03:23,440 as people get trained as astrobiologists 94 00:03:26,790 --> 00:03:25,200 they're going up the ranks 95 00:03:28,869 --> 00:03:26,800 they're getting a postdoc and going on 96 00:03:30,149 --> 00:03:28,879 to fact so on and so forth 97 00:03:32,070 --> 00:03:30,159 and then the people with negative 98 00:03:34,470 --> 00:03:32,080 numbers for academic age which is the 99 00:03:36,789 --> 00:03:34,480 sorry academic age is the blue bars 100 00:03:38,229 --> 00:03:36,799 and the orange bars of participation 101 00:03:39,589 --> 00:03:38,239 people the negative numbers means they 102 00:03:42,149 --> 00:03:39,599 got their 103 00:03:43,430 --> 00:03:42,159 phd after the conference which is also 104 00:03:46,470 --> 00:03:43,440 good because that means that the grad 105 00:03:49,830 --> 00:03:46,480 students that showed up progressed on 106 00:03:54,149 --> 00:03:49,840 and you can see the pattern holds 107 00:03:58,149 --> 00:03:56,149 now we'll look at participation 108 00:03:59,830 --> 00:03:58,159 this is the part where we talk about 109 00:04:01,270 --> 00:03:59,840 if the field is healthy people show up 110 00:04:04,470 --> 00:04:01,280 at the conference and then keep showing 111 00:04:09,990 --> 00:04:07,750 and then obviously uh 2000 and each year 112 00:04:12,149 --> 00:04:10,000 that i pick is going to be uh 20 you 113 00:04:15,270 --> 00:04:12,159 know the full 20 114 00:04:18,150 --> 00:04:15,280 um and then it's the following years 115 00:04:20,069 --> 00:04:18,160 so you see this pattern develop here 116 00:04:21,189 --> 00:04:20,079 where you know it's pretty strong people 117 00:04:23,110 --> 00:04:21,199 are showing up 118 00:04:25,030 --> 00:04:23,120 at pretty good rates 119 00:04:26,390 --> 00:04:25,040 then you get here we get more on the 120 00:04:28,070 --> 00:04:26,400 front end 121 00:04:29,510 --> 00:04:28,080 meaning that they showed up in 2008 when 122 00:04:31,189 --> 00:04:29,520 i collected their name 123 00:04:33,030 --> 00:04:31,199 and then they the participation rate was 124 00:04:34,390 --> 00:04:33,040 pretty high going on 125 00:04:36,310 --> 00:04:34,400 and you get this one 126 00:04:38,230 --> 00:04:36,320 same pattern 127 00:04:40,150 --> 00:04:38,240 this one because it's 2012 you can't 128 00:04:41,510 --> 00:04:40,160 really look at it but what that means is 129 00:04:43,670 --> 00:04:41,520 that you have new people coming into the 130 00:04:45,189 --> 00:04:43,680 field and they continue to stay at least 131 00:04:48,310 --> 00:04:45,199 for a few years 132 00:04:48,320 --> 00:04:51,189 this is you guys 133 00:04:54,550 --> 00:04:51,909 now 134 00:04:57,110 --> 00:04:54,560 what's the big problem with my study 135 00:04:58,390 --> 00:04:57,120 how do you find an astrobiologist 136 00:04:59,590 --> 00:04:58,400 obviously there's a bunch of you here 137 00:05:00,710 --> 00:04:59,600 now 138 00:05:02,070 --> 00:05:00,720 but 139 00:05:04,230 --> 00:05:02,080 how do you identify them because they're 140 00:05:05,590 --> 00:05:04,240 in all sorts of different disciplines 141 00:05:07,110 --> 00:05:05,600 i can tell you for sure that there's one 142 00:05:08,790 --> 00:05:07,120 person up there that's not an 143 00:05:10,390 --> 00:05:08,800 astrobiologist and they're not in the 144 00:05:11,590 --> 00:05:10,400 right category 145 00:05:13,350 --> 00:05:11,600 that would be me 146 00:05:14,950 --> 00:05:13,360 education 147 00:05:16,150 --> 00:05:14,960 i'm actually an information scientist 148 00:05:18,310 --> 00:05:16,160 like i said 149 00:05:19,670 --> 00:05:18,320 but how many of you think that 150 00:05:21,830 --> 00:05:19,680 you would like me to collect out on you 151 00:05:25,270 --> 00:05:21,840 and call you a planetary scientist 152 00:05:27,029 --> 00:05:25,280 or do you think it's something else 153 00:05:29,590 --> 00:05:27,039 you have something 154 00:05:33,189 --> 00:05:29,600 oh you are a planetary scientist so we 155 00:05:35,189 --> 00:05:33,199 have one one planetary scientist 156 00:05:36,550 --> 00:05:35,199 now a few okay 157 00:05:38,390 --> 00:05:36,560 but that's the problem with these sorts 158 00:05:40,070 --> 00:05:38,400 of studies is getting good data on 159 00:05:42,310 --> 00:05:40,080 academics because they bounce around and 160 00:05:44,150 --> 00:05:42,320 so forth 161 00:05:45,909 --> 00:05:44,160 and also i guess the question is how 162 00:05:47,909 --> 00:05:45,919 much multidisciplinarity do you guys 163 00:05:49,430 --> 00:05:47,919 want in the field how much do you think 164 00:05:51,270 --> 00:05:49,440 is healthy 165 00:05:52,950 --> 00:05:51,280 is it good that we have this many 166 00:05:54,950 --> 00:05:52,960 biologists showing up 167 00:05:57,510 --> 00:05:54,960 do we need more astronomy people i mean 168 00:05:58,950 --> 00:05:57,520 what's what's the mix you're after 169 00:06:00,710 --> 00:05:58,960 now obviously that's not a question that 170 00:06:02,629 --> 00:06:00,720 any individual can answer but when you 171 00:06:05,510 --> 00:06:02,639 talk about studying emerging fields 172 00:06:07,029 --> 00:06:05,520 these questions become relevant 173 00:06:10,230 --> 00:06:07,039 i think i got through that quick enough 174 00:06:10,240 --> 00:06:26,309 questions questions 175 00:06:29,830 --> 00:06:28,070 so you were studying absycon which 176 00:06:31,189 --> 00:06:29,840 obviously is an american-based 177 00:06:33,110 --> 00:06:31,199 conference how much do you think your 178 00:06:35,350 --> 00:06:33,120 data would have been affected by people 179 00:06:36,870 --> 00:06:35,360 who may have attended for a couple of 180 00:06:39,189 --> 00:06:36,880 times because they were studying in the 181 00:06:40,710 --> 00:06:39,199 us and then stopped attending because 182 00:06:42,629 --> 00:06:40,720 they had to come back to the us to 183 00:06:45,270 --> 00:06:42,639 attend like by the the presence of an 184 00:06:47,749 --> 00:06:45,280 international community a lot but 185 00:06:49,510 --> 00:06:47,759 because there was no good methodology 186 00:06:51,189 --> 00:06:49,520 that i could come up with 187 00:06:52,790 --> 00:06:51,199 that was the frankly the best i could do 188 00:06:54,710 --> 00:06:52,800 and the data is not perfect it's far 189 00:06:57,830 --> 00:06:54,720 from but that was as close as i could 190 00:07:01,110 --> 00:06:59,909 uh so i noticed that there 191 00:07:02,629 --> 00:07:01,120 you pointed out that there are a couple 192 00:07:04,710 --> 00:07:02,639 of spikes and things like that in your 193 00:07:06,950 --> 00:07:04,720 in your graphs um but how much of it do 194 00:07:08,950 --> 00:07:06,960 you think is related to how much funding 195 00:07:10,309 --> 00:07:08,960 we're getting for doing astrobiology 196 00:07:12,070 --> 00:07:10,319 research or have you not looked into 197 00:07:14,070 --> 00:07:12,080 that yeah 198 00:07:16,070 --> 00:07:14,080 well i think a lot of these spikes like 199 00:07:18,629 --> 00:07:16,080 these ones are probably related to the 200 00:07:20,469 --> 00:07:18,639 size of the conference but and it's 201 00:07:21,990 --> 00:07:20,479 probably related to how much funding 202 00:07:23,670 --> 00:07:22,000 people are getting 203 00:07:25,670 --> 00:07:23,680 but that would be an interesting data 204 00:07:30,870 --> 00:07:25,680 point i could try to pull out 205 00:07:35,830 --> 00:07:32,550 the title of your talk was health and 206 00:07:38,790 --> 00:07:35,840 robustness of our field um maybe i 207 00:07:39,749 --> 00:07:38,800 missed it did you give us an answer 208 00:07:41,029 --> 00:07:39,759 well 209 00:07:42,870 --> 00:07:41,039 just let me follow up on that i was 210 00:07:45,029 --> 00:07:42,880 wondering if you'd done similar 211 00:07:47,029 --> 00:07:45,039 evaluations of other emerging fields 212 00:07:49,990 --> 00:07:47,039 that would help us get an idea of you 213 00:07:51,430 --> 00:07:50,000 know are we robust and healthy or 214 00:07:53,110 --> 00:07:51,440 decrepit 215 00:07:55,670 --> 00:07:53,120 well 216 00:07:59,110 --> 00:07:55,680 okay so the data so far many of the 217 00:08:01,909 --> 00:07:59,120 years for academic age and participation 218 00:08:04,150 --> 00:08:01,919 show that there are people at all three 219 00:08:06,629 --> 00:08:04,160 sort of levels right so you have grad 220 00:08:08,390 --> 00:08:06,639 students uh people sort of mid-career 221 00:08:10,390 --> 00:08:08,400 and people later in the career 222 00:08:12,070 --> 00:08:10,400 that's good because it indicates the 223 00:08:14,230 --> 00:08:12,080 likelihood that people that are grad 224 00:08:16,230 --> 00:08:14,240 students have the opportunity to pursue 225 00:08:17,990 --> 00:08:16,240 this way it also means that people that 226 00:08:20,070 --> 00:08:18,000 it were around before there was much of 227 00:08:21,990 --> 00:08:20,080 a notion of astrobiology are coming into 228 00:08:23,749 --> 00:08:22,000 the field so you're getting a good mix 229 00:08:26,230 --> 00:08:23,759 of ages which is good for a healthy 230 00:08:27,670 --> 00:08:26,240 field an unhealthy field would would be 231 00:08:29,909 --> 00:08:27,680 where you have a lot of people at this 232 00:08:31,589 --> 00:08:29,919 end and a lot of people at this end 233 00:08:34,790 --> 00:08:31,599 indicating that the people at the the 234 00:08:36,070 --> 00:08:34,800 younger people are unable to get jobs 235 00:08:38,149 --> 00:08:36,080 in the field 236 00:08:39,750 --> 00:08:38,159 and there's a bunch of basically there's 237 00:08:41,509 --> 00:08:39,760 a bunch of grad students working for a 238 00:08:43,829 --> 00:08:41,519 bunch of old farts 239 00:08:45,910 --> 00:08:43,839 that would be unhealthy 240 00:08:48,310 --> 00:08:45,920 and the fact that they keep showing up 241 00:08:49,990 --> 00:08:48,320 to the conferences also indicates a 242 00:08:57,829 --> 00:08:50,000 level of interest and participation in 243 00:09:02,790 --> 00:09:00,150 are you going to compare that with the 244 00:09:05,829 --> 00:09:02,800 astrobiology conference in europe 245 00:09:07,350 --> 00:09:05,839 um well probably not because the next 246 00:09:09,030 --> 00:09:07,360 phase of this research is to pick 247 00:09:11,269 --> 00:09:09,040 another discipline 248 00:09:12,150 --> 00:09:11,279 probably one that's like how do you say 249 00:09:14,070 --> 00:09:12,160 uh 250 00:09:15,670 --> 00:09:14,080 more well developed along the lines like 251 00:09:17,030 --> 00:09:15,680 it's been around for a lot longer and 252 00:09:18,949 --> 00:09:17,040 then to see if they have a similar 253 00:09:21,590 --> 00:09:18,959 pattern or a different pattern because i 254 00:09:23,910 --> 00:09:21,600 can't really talk about uh you know do a 255 00:09:28,949 --> 00:09:23,920 lot of strong conclusions until i get 256 00:09:39,670 --> 00:09:30,630 any other questions 257 00:09:43,990 --> 00:09:41,590 yeah i'm not sure if your um your data 258 00:09:46,470 --> 00:09:44,000 can can give an answer to that but did 259 00:09:48,310 --> 00:09:46,480 you get an idea how multidisciplinary 260 00:09:50,630 --> 00:09:48,320 the field really is i mean are people 261 00:09:52,790 --> 00:09:50,640 like really just presenting 262 00:09:55,030 --> 00:09:52,800 projects from their particular field or 263 00:09:57,190 --> 00:09:55,040 how many of the projects are actually 264 00:09:58,949 --> 00:09:57,200 combination of different fields did you 265 00:10:00,310 --> 00:09:58,959 get an idea about that well that's 266 00:10:01,990 --> 00:10:00,320 actually an incredibly difficult 267 00:10:03,990 --> 00:10:02,000 question to answer because just reading 268 00:10:04,870 --> 00:10:04,000 the titles and abstracts it's hard to 269 00:10:06,070 --> 00:10:04,880 place 270 00:10:07,910 --> 00:10:06,080 what uh 271 00:10:09,670 --> 00:10:07,920 field the person's presenting it the 272 00:10:10,710 --> 00:10:09,680 only thing i could really do is let them 273 00:10:12,550 --> 00:10:10,720 tell me 274 00:10:15,190 --> 00:10:12,560 what their field was 275 00:10:16,710 --> 00:10:15,200 and i did that by looking at their cvs 276 00:10:19,430 --> 00:10:16,720 and also well digging through a lot of 277 00:10:21,670 --> 00:10:19,440 different databases but primarily cvs so 278 00:10:24,389 --> 00:10:21,680 if somebody said i graduated with a phd 279 00:10:25,829 --> 00:10:24,399 in chemistry i said okay put you on 280 00:10:27,509 --> 00:10:25,839 there for chemistry 281 00:10:29,269 --> 00:10:27,519 but it was almost impossible to do it 282 00:10:32,550 --> 00:10:29,279 from abstracts