1 00:00:04,160 --> 00:00:05,880 Speaker 1: There are no girls on the Internet. As a production 2 00:00:05,920 --> 00:00:13,000 Speaker 1: of I Heart Radio and unbost Creative, I'm Bridget Todd, 3 00:00:13,200 --> 00:00:18,280 Speaker 1: and this is there are no girls on the Internet. Worldwide, 4 00:00:18,360 --> 00:00:21,200 Speaker 1: we've lost over a million people from COVID, and here 5 00:00:21,200 --> 00:00:24,200 Speaker 1: in the United States, we've lost over two hundred thousand 6 00:00:24,280 --> 00:00:27,800 Speaker 1: people to COVID according to the A p M Research Lab. 7 00:00:28,280 --> 00:00:32,000 Speaker 1: One and every one thousand and twenty Black Americans is 8 00:00:32,040 --> 00:00:37,120 Speaker 1: now dead from COVID. Let that sink in. It's an 9 00:00:37,120 --> 00:00:42,479 Speaker 1: absolutely staggering figure. Yet we've had no national, large scale 10 00:00:42,479 --> 00:00:46,200 Speaker 1: mourning of these deaths. Earlier this fall, Trump even said 11 00:00:46,240 --> 00:00:54,440 Speaker 1: that COVID impacted quote, virtually nobody. Mickey mckella, a professor 12 00:00:54,480 --> 00:00:57,000 Speaker 1: of history at the University of Connecticut and author of 13 00:00:57,000 --> 00:01:00,160 Speaker 1: the Politics of Mourning, told CNN that instead of more ing, 14 00:01:00,440 --> 00:01:03,280 Speaker 1: Americans have been fed a kind of wartime attitude about 15 00:01:03,320 --> 00:01:05,640 Speaker 1: how he must defeat the virus and must not let 16 00:01:05,680 --> 00:01:08,960 Speaker 1: the virus win, and that that response has largely been 17 00:01:09,000 --> 00:01:13,120 Speaker 1: about not marking death, not marking tragedy, and not marking 18 00:01:13,200 --> 00:01:16,400 Speaker 1: the horror of the ongoing lack of meaningful response, but 19 00:01:16,520 --> 00:01:20,240 Speaker 1: instead focusing on that this is what Americans do, but 20 00:01:20,319 --> 00:01:23,800 Speaker 1: that's now what we should do. Collective mourning is important, 21 00:01:24,319 --> 00:01:26,720 Speaker 1: and mourning is an important step of dealing with grief. 22 00:01:27,720 --> 00:01:30,600 Speaker 1: We can't just pretend these people never existed. They did, 23 00:01:31,480 --> 00:01:33,440 Speaker 1: and they're more than just data points on some chart 24 00:01:33,480 --> 00:01:36,960 Speaker 1: about COVID. There are mothers and daughters, and friends and 25 00:01:37,000 --> 00:01:40,959 Speaker 1: family and colleagues. This week, faith leaders from all over 26 00:01:41,000 --> 00:01:44,360 Speaker 1: the country held visuals in person and online to mourn 27 00:01:44,640 --> 00:01:47,480 Speaker 1: those we've lost to COVID. And I wanted to tell 28 00:01:47,520 --> 00:01:51,040 Speaker 1: you about someone we lost to. Lunika Stroser was just 29 00:01:51,080 --> 00:01:54,000 Speaker 1: thirty five, and she died from complications of COVID. She 30 00:01:54,120 --> 00:01:56,880 Speaker 1: was a gifted scientist and a researcher in the DNA 31 00:01:56,960 --> 00:01:59,520 Speaker 1: lab at the Field Museum of Natural History in Chicago, 32 00:02:00,120 --> 00:02:02,520 Speaker 1: one of the largest in the world. She didn't have 33 00:02:02,520 --> 00:02:05,960 Speaker 1: an easy life. Her mother struggled with drugs, and Lenika 34 00:02:06,040 --> 00:02:09,160 Speaker 1: lived with her grandmother. A learning disability made math and 35 00:02:09,200 --> 00:02:12,359 Speaker 1: reading a challenge, but she found creative solutions to manage 36 00:02:12,360 --> 00:02:16,200 Speaker 1: these challenges. Rather than working out complicated math equations on 37 00:02:16,200 --> 00:02:19,200 Speaker 1: a calculator, she did them on paper by hand, which 38 00:02:19,240 --> 00:02:23,160 Speaker 1: helped her visualize the numbers. She worked with a visual learner, 39 00:02:23,639 --> 00:02:27,359 Speaker 1: drawing pictures and diagrams helped her map out her lessons. 40 00:02:27,400 --> 00:02:31,040 Speaker 1: She went on to successfully earn two master's degrees. She 41 00:02:31,160 --> 00:02:34,240 Speaker 1: wasn't really sure what she wanted to study until in college, 42 00:02:34,480 --> 00:02:38,440 Speaker 1: her mentor, Yvon Harris, suggested she think about exploring the sciences. 43 00:02:39,520 --> 00:02:42,880 Speaker 1: My philosophy is that we're born scientists and mathematicians, and 44 00:02:42,919 --> 00:02:45,280 Speaker 1: we experiment and observe the world around us all the time. 45 00:02:45,800 --> 00:02:49,160 Speaker 1: Harris explained. Having the a student is nice, but we 46 00:02:49,240 --> 00:02:52,239 Speaker 1: want people who have tenacity and determination and of refusal 47 00:02:52,280 --> 00:02:56,240 Speaker 1: to fail. Harris told the Chicago Tribune in a profile 48 00:02:56,360 --> 00:02:59,679 Speaker 1: of Lenika's academic success. When Lenka got involved in the 49 00:02:59,720 --> 00:03:03,600 Speaker 1: science is it just clicked and she loved it. One 50 00:03:03,639 --> 00:03:07,600 Speaker 1: of her professors even nicknamed her Golden Hands because she 51 00:03:07,639 --> 00:03:10,600 Speaker 1: was able to get DNA from very small samples, a 52 00:03:10,639 --> 00:03:14,280 Speaker 1: difficult task. Everyone who talks about Linika was struck by 53 00:03:14,280 --> 00:03:17,720 Speaker 1: her determination. You get knocked down so many times. You 54 00:03:17,760 --> 00:03:20,520 Speaker 1: have to learn to pick yourself back up, and sometimes 55 00:03:20,560 --> 00:03:23,280 Speaker 1: it's about hard work and faith and having people who 56 00:03:23,280 --> 00:03:26,080 Speaker 1: can help you push forward. Sometimes that's all you have 57 00:03:26,160 --> 00:03:30,240 Speaker 1: to go on, she explained to the Chicago Tribune. Field 58 00:03:30,360 --> 00:03:35,440 Speaker 1: Museum president Richard Lavier calls Linika's death a devastating loss, 59 00:03:35,440 --> 00:03:38,080 Speaker 1: both to her own family and to her museum family 60 00:03:38,480 --> 00:03:41,440 Speaker 1: and all who knew Linika. Her life goal was to 61 00:03:41,440 --> 00:03:44,480 Speaker 1: be in front of a classroom teaching the sciences to others, 62 00:03:45,240 --> 00:03:48,120 Speaker 1: and right before she died, that goal had actually become 63 00:03:48,120 --> 00:03:51,360 Speaker 1: a reality. Who knows how many more lives Lenika could 64 00:03:51,360 --> 00:03:55,160 Speaker 1: have touched? Who knows how this loss will reverberate for generations. 65 00:03:55,640 --> 00:03:59,119 Speaker 1: A gifted scientist who overcame so much to accomplish so much, 66 00:03:59,680 --> 00:04:02,280 Speaker 1: a teacher, and a black woman excelling in a field 67 00:04:02,320 --> 00:04:05,480 Speaker 1: not traditionally known for its diversity. How many lives because 68 00:04:05,480 --> 00:04:07,600 Speaker 1: she have gone into shape? And how can you even 69 00:04:07,600 --> 00:04:11,440 Speaker 1: begin to measure such a loss? Like she really just 70 00:04:11,520 --> 00:04:15,920 Speaker 1: had this fire in her that she always wanted to 71 00:04:16,600 --> 00:04:20,680 Speaker 1: to succeed. Lenka's scientific research involved bugs and plants and 72 00:04:20,680 --> 00:04:24,200 Speaker 1: other kinds of organisms. It's a pretty particular subject matter 73 00:04:24,720 --> 00:04:27,279 Speaker 1: and that's something that her colleague, Corey Morous has really 74 00:04:27,279 --> 00:04:31,640 Speaker 1: brought them together. How did you get involved in being 75 00:04:31,640 --> 00:04:34,560 Speaker 1: a scientist? I wouldn't have predicted it from being a child. 76 00:04:34,960 --> 00:04:37,839 Speaker 1: I grew up in New Orleans, Louisiana, and um, neither 77 00:04:37,920 --> 00:04:40,880 Speaker 1: of my parents went to university or graduated college. So 78 00:04:41,560 --> 00:04:43,520 Speaker 1: despite the fact that I knew that I wanted to 79 00:04:43,520 --> 00:04:46,080 Speaker 1: go to college, I didn't necessarily know what I could 80 00:04:46,080 --> 00:04:48,600 Speaker 1: do with that degree when I got out. And I 81 00:04:48,640 --> 00:04:51,960 Speaker 1: always loved nature and I always loved UM science, and 82 00:04:52,000 --> 00:04:54,400 Speaker 1: so I knew that I wanted to study biology when 83 00:04:54,400 --> 00:04:57,119 Speaker 1: I went to the university, and and insects were always 84 00:04:57,120 --> 00:05:00,440 Speaker 1: my favorite. UM but I thought that, you know, the 85 00:05:00,520 --> 00:05:03,039 Speaker 1: options for me probably were limited in the sense that 86 00:05:03,120 --> 00:05:06,080 Speaker 1: the only people I knew with college degrees UM that 87 00:05:06,160 --> 00:05:08,839 Speaker 1: I interacted with personally were my high school teachers. So 88 00:05:08,880 --> 00:05:11,560 Speaker 1: I thought maybe I could teach biology, or since I 89 00:05:11,560 --> 00:05:14,719 Speaker 1: liked insects, maybe I could work for a pest extermination company, 90 00:05:14,760 --> 00:05:16,400 Speaker 1: because those were the only people I knew who had 91 00:05:16,480 --> 00:05:20,560 Speaker 1: jobs to play with bugs. UM But I loved PBS, 92 00:05:20,640 --> 00:05:22,600 Speaker 1: and I sort of always wished that I could be 93 00:05:22,640 --> 00:05:26,160 Speaker 1: one of the explorers on you know, the television shows 94 00:05:26,560 --> 00:05:29,359 Speaker 1: growing up, And essentially my dreams come true when I 95 00:05:29,360 --> 00:05:31,440 Speaker 1: got to university. The world was opened up to me 96 00:05:31,480 --> 00:05:33,360 Speaker 1: in the sense that there's so many ways you can 97 00:05:33,480 --> 00:05:37,400 Speaker 1: use a science degree, UM. And now I get to 98 00:05:37,760 --> 00:05:40,440 Speaker 1: run around jungles all over the world, collecting bugs and 99 00:05:40,440 --> 00:05:44,080 Speaker 1: it's I have the dream job. What was it about 100 00:05:44,120 --> 00:05:45,960 Speaker 1: bugs for you? Why did you like bugs so much? 101 00:05:46,720 --> 00:05:48,800 Speaker 1: I think because I grew up in a city and 102 00:05:49,160 --> 00:05:51,360 Speaker 1: I loved nature. I you know, there wasn't a lot 103 00:05:51,400 --> 00:05:54,360 Speaker 1: of it outside. I also liked that there was just 104 00:05:54,440 --> 00:05:57,039 Speaker 1: so much diversity with insects. You know, you could go 105 00:05:57,080 --> 00:06:00,680 Speaker 1: outside and catch dragonflies or beatles, or or flies, or 106 00:06:00,760 --> 00:06:03,520 Speaker 1: watched the ants on the sidewalk. And I just think 107 00:06:03,520 --> 00:06:06,520 Speaker 1: it was that there was so much wonder out there 108 00:06:06,600 --> 00:06:08,880 Speaker 1: that I could sort of take advantage of no matter 109 00:06:08,920 --> 00:06:11,600 Speaker 1: where I live, and that's true anywhere. So how did 110 00:06:11,640 --> 00:06:15,080 Speaker 1: you wind up at the Field Museum? Yeah? So I've 111 00:06:15,080 --> 00:06:18,680 Speaker 1: always been associated with natural history UM collections throughout my 112 00:06:18,800 --> 00:06:21,240 Speaker 1: entire career. So I started as an undergrad working in 113 00:06:21,279 --> 00:06:25,920 Speaker 1: the um entomological collection at San Francisco State University. I 114 00:06:25,960 --> 00:06:28,760 Speaker 1: then did my master's UM, also at San Francisco State, 115 00:06:28,760 --> 00:06:31,160 Speaker 1: but in collaboration with the California Academy and the Sciences, 116 00:06:31,279 --> 00:06:35,039 Speaker 1: again using their scientific research collections for you know, my 117 00:06:35,120 --> 00:06:38,039 Speaker 1: master's thesis. I then went away to Harvard and I 118 00:06:38,120 --> 00:06:40,920 Speaker 1: was UM using the collections at the Museum of Comparative 119 00:06:40,960 --> 00:06:44,040 Speaker 1: Zoology on a daily basis, and so I've always had 120 00:06:44,040 --> 00:06:47,960 Speaker 1: this connection with um natural history museums and the cool 121 00:06:48,000 --> 00:06:51,279 Speaker 1: science you can do by using them. So UM, when 122 00:06:51,320 --> 00:06:54,520 Speaker 1: I finally, you know, finished all my schooling and did 123 00:06:54,520 --> 00:06:57,600 Speaker 1: a post docet at Berkeley, I started a position at 124 00:06:57,600 --> 00:07:00,680 Speaker 1: the Film Museum in Chicago. And and although most people 125 00:07:00,720 --> 00:07:04,039 Speaker 1: think of natural history museums as places to sort of 126 00:07:04,400 --> 00:07:08,680 Speaker 1: go and you know, have educational and entertainment um, what 127 00:07:08,720 --> 00:07:11,040 Speaker 1: most people don't realize is that almost all natural history 128 00:07:11,120 --> 00:07:15,119 Speaker 1: collections have scientists working behind the scenes, using the vast 129 00:07:15,160 --> 00:07:19,119 Speaker 1: collections to ask scientific questions. It was during this time 130 00:07:19,920 --> 00:07:22,880 Speaker 1: playing with bugs and answering questions behind the scenes at 131 00:07:22,920 --> 00:07:26,800 Speaker 1: the Field Museum that Corey met Lnika and right away 132 00:07:26,960 --> 00:07:29,920 Speaker 1: they clicked. So is that the first time that you 133 00:07:30,160 --> 00:07:33,480 Speaker 1: met Nika? That's absolutely true. So I met Lnika in 134 00:07:33,560 --> 00:07:37,400 Speaker 1: two thousand eleven. She had done an internship with a 135 00:07:37,480 --> 00:07:41,280 Speaker 1: colleague and was looking for another internship and he knew 136 00:07:41,360 --> 00:07:44,240 Speaker 1: that I was looking to hire someone, and so um 137 00:07:44,240 --> 00:07:46,400 Speaker 1: he introduced us in Linique and I hit it off 138 00:07:46,480 --> 00:07:50,280 Speaker 1: right away. What was it about her that that made 139 00:07:50,320 --> 00:07:54,120 Speaker 1: you hit it off? I think it was her openness, 140 00:07:54,160 --> 00:07:58,000 Speaker 1: her honesty, and her tenacity. Like she really just had 141 00:07:58,080 --> 00:08:03,640 Speaker 1: this fire in her that she always wanted to to succeed. 142 00:08:03,760 --> 00:08:06,440 Speaker 1: And I don't just mean be a successful scientist, but 143 00:08:06,520 --> 00:08:08,480 Speaker 1: like even with an experiment, if she couldn't get it 144 00:08:08,520 --> 00:08:11,520 Speaker 1: to work, it would really like kind of not away 145 00:08:11,520 --> 00:08:13,360 Speaker 1: at her and she had to figure out not just 146 00:08:13,400 --> 00:08:15,520 Speaker 1: how to make it work, but why it wasn't working. 147 00:08:15,560 --> 00:08:18,200 Speaker 1: And that is something And as a scientist, you can't 148 00:08:18,240 --> 00:08:21,880 Speaker 1: teach that sort of drive or that creativity to someone. 149 00:08:22,000 --> 00:08:25,880 Speaker 1: She just already possessed it. Yeah, and reading about her life, 150 00:08:25,920 --> 00:08:28,120 Speaker 1: it seems like that kind of drive was a defined 151 00:08:28,240 --> 00:08:30,640 Speaker 1: I think that really defined her. You know, she was 152 00:08:30,680 --> 00:08:33,880 Speaker 1: someone who based a lot of limitations growing up and 153 00:08:33,920 --> 00:08:36,160 Speaker 1: still managed to get to where she was at the 154 00:08:36,240 --> 00:08:40,200 Speaker 1: end of her life. Absolutely. I mean, she was really 155 00:08:40,240 --> 00:08:43,400 Speaker 1: a very thoughtful person. She was incredibly hard working, and 156 00:08:43,440 --> 00:08:47,720 Speaker 1: she was such a loving person. She you know, anyone 157 00:08:47,800 --> 00:08:50,560 Speaker 1: she came across in her life, she really wanted to 158 00:08:50,600 --> 00:08:53,440 Speaker 1: connect with them. And I mean, I think one of 159 00:08:53,440 --> 00:08:55,760 Speaker 1: the things I always respected the most about her is 160 00:08:55,960 --> 00:09:01,040 Speaker 1: her her openness and honesty about both the things she's 161 00:09:01,120 --> 00:09:04,480 Speaker 1: experienced in the past. But you know, you know, some 162 00:09:04,520 --> 00:09:07,480 Speaker 1: people would have shame over things that they can't control. 163 00:09:08,040 --> 00:09:11,000 Speaker 1: She didn't have that at all. But the flip side 164 00:09:11,040 --> 00:09:14,240 Speaker 1: of it was she also loved to share her successes. 165 00:09:14,280 --> 00:09:16,440 Speaker 1: And so I think when you have someone who is 166 00:09:16,440 --> 00:09:19,480 Speaker 1: willing to let you see when they're down but also 167 00:09:19,600 --> 00:09:22,800 Speaker 1: let you see when they're succeeding, they're an inspiration. Can 168 00:09:22,840 --> 00:09:25,520 Speaker 1: you tell us a little bit more about her research. Yeah, 169 00:09:25,640 --> 00:09:27,640 Speaker 1: So you know, when she was at the Field Museum, 170 00:09:27,679 --> 00:09:30,720 Speaker 1: she did lots of different projects, um, because we have, 171 00:09:30,920 --> 00:09:34,160 Speaker 1: you know, dozens of scientists working on pretty much every 172 00:09:34,240 --> 00:09:36,680 Speaker 1: kind of organism you can imagine. So I know she 173 00:09:36,720 --> 00:09:38,840 Speaker 1: did a bunch of work on early land plants and 174 00:09:38,880 --> 00:09:41,480 Speaker 1: on fungi. And for me, she of course was sequencing 175 00:09:41,559 --> 00:09:44,559 Speaker 1: DNA of ants. And in that project, essentially what we 176 00:09:44,559 --> 00:09:48,840 Speaker 1: were trying to understand is the diversity, um, both genetic diversity, 177 00:09:48,880 --> 00:09:52,400 Speaker 1: but also the host associated microbiome or the microbes living 178 00:09:52,520 --> 00:09:55,480 Speaker 1: in ants from the Florida Keys. So for me, she 179 00:09:55,520 --> 00:09:59,280 Speaker 1: did a lot of sequencing of DNA of ants. But 180 00:09:59,480 --> 00:10:02,600 Speaker 1: you know, she went on to do a master's degree, um, 181 00:10:02,640 --> 00:10:05,760 Speaker 1: a research master's degree as well as an educational master's degree, 182 00:10:05,800 --> 00:10:09,080 Speaker 1: and I was on her master's committee, uh, where she 183 00:10:09,120 --> 00:10:12,080 Speaker 1: was studying the philo geography of these birds from Madagascar, 184 00:10:12,120 --> 00:10:13,880 Speaker 1: and she did a bunch of beautiful work on that 185 00:10:13,920 --> 00:10:26,800 Speaker 1: and even published that research. Let's take a quick break 186 00:10:27,840 --> 00:10:31,640 Speaker 1: and we're back. Women being in community with one another 187 00:10:31,840 --> 00:10:35,200 Speaker 1: is a powerful force. Not only did their shared interest 188 00:10:35,240 --> 00:10:38,800 Speaker 1: in science unite Corey and Linika, but it also created 189 00:10:38,800 --> 00:10:42,120 Speaker 1: the conditions to bring more underrepresented women into the field. 190 00:10:42,760 --> 00:10:45,119 Speaker 1: And the more Linika came into her own as a scientist, 191 00:10:45,679 --> 00:10:48,559 Speaker 1: the more focus she became on bringing others with her. 192 00:10:48,600 --> 00:10:51,880 Speaker 1: As a teacher and a mentor. Linika didn't have the 193 00:10:51,920 --> 00:10:56,439 Speaker 1: picture perfect a student story. Her openness around her background 194 00:10:56,520 --> 00:10:59,960 Speaker 1: and her struggles allowed others to see science as something 195 00:11:00,080 --> 00:11:03,280 Speaker 1: they could do too. I just can't get over how 196 00:11:03,320 --> 00:11:06,319 Speaker 1: interesting this body of work is. You don't even like, 197 00:11:06,480 --> 00:11:08,960 Speaker 1: I'm not a scientist, but you don't. You never think 198 00:11:08,960 --> 00:11:12,080 Speaker 1: of like someone studying birds and aunts and you know, 199 00:11:12,280 --> 00:11:15,760 Speaker 1: these very specific types of organisms. It's so interesting, how 200 00:11:16,240 --> 00:11:19,320 Speaker 1: I mean, I guess I can imagine you finding another 201 00:11:19,400 --> 00:11:22,319 Speaker 1: woman who is captivated by all of these things that 202 00:11:22,360 --> 00:11:25,920 Speaker 1: you're captivated by and really just sort of clicking absolutely. 203 00:11:26,360 --> 00:11:28,640 Speaker 1: And and that's the thing is that, I mean, what 204 00:11:28,720 --> 00:11:30,800 Speaker 1: I loved about Lenika is not just that she had 205 00:11:30,840 --> 00:11:33,240 Speaker 1: this general awe of the natural world and wanted to 206 00:11:33,320 --> 00:11:36,240 Speaker 1: learn everything she could about it. But one of her 207 00:11:36,280 --> 00:11:39,880 Speaker 1: other passions was she loved sharing it. So you know, 208 00:11:40,160 --> 00:11:42,560 Speaker 1: if I ever needed people to be trained in the lab, 209 00:11:42,679 --> 00:11:44,800 Speaker 1: she was my go to person. And not that other 210 00:11:44,800 --> 00:11:47,520 Speaker 1: people didn't have the skills, is that Lenika had joy 211 00:11:47,640 --> 00:11:51,120 Speaker 1: and showing people how to do science and helping them 212 00:11:51,120 --> 00:11:55,319 Speaker 1: succeed and overcome hurdles. And you know, she was just spectacular, 213 00:11:55,480 --> 00:11:58,360 Speaker 1: and you know, there's not a lot of people like 214 00:11:58,480 --> 00:12:00,960 Speaker 1: her in the sense that you know, she could pursue 215 00:12:01,000 --> 00:12:03,720 Speaker 1: a scientific question, but she could also talk about it 216 00:12:03,760 --> 00:12:06,880 Speaker 1: to the public and she could share her enthusiasm and 217 00:12:06,880 --> 00:12:10,000 Speaker 1: get other people to essentially want to do the same 218 00:12:10,040 --> 00:12:13,319 Speaker 1: things she's doing. Was she did she have a like 219 00:12:13,800 --> 00:12:16,840 Speaker 1: a position as a role model for other students, other 220 00:12:16,920 --> 00:12:19,920 Speaker 1: students from marginalized backgrounds. Absolutely, And that was one thing 221 00:12:20,000 --> 00:12:22,559 Speaker 1: she was very vocal about and I absolutely loved about her, 222 00:12:23,000 --> 00:12:24,800 Speaker 1: is that she wanted to make sure that we had 223 00:12:24,840 --> 00:12:30,320 Speaker 1: opportunities to engage other underrepresented, you know, students in research. 224 00:12:30,400 --> 00:12:33,120 Speaker 1: And so she was instrumental in making sure that we 225 00:12:33,200 --> 00:12:35,959 Speaker 1: always kept that as on the forefront of our minds 226 00:12:35,960 --> 00:12:38,679 Speaker 1: as we were thinking about, you know, what programming we 227 00:12:38,679 --> 00:12:41,880 Speaker 1: were creating or which positions we were hiring. UM. You know, 228 00:12:42,559 --> 00:12:45,320 Speaker 1: she was heavily involved in the field museums Women in 229 00:12:45,360 --> 00:12:48,960 Speaker 1: Science program Uh. You know, she often was the sort 230 00:12:48,960 --> 00:12:52,440 Speaker 1: of point person that was training the interns we brought 231 00:12:52,440 --> 00:12:54,760 Speaker 1: in for the summer. And you know, she was a 232 00:12:54,840 --> 00:12:58,960 Speaker 1: role model to many people across the museum. What is 233 00:12:59,000 --> 00:13:01,640 Speaker 1: your I you don't have one particular one, But if 234 00:13:01,640 --> 00:13:03,400 Speaker 1: you had to think of one of your favorite memories 235 00:13:03,440 --> 00:13:05,920 Speaker 1: of her, or the most vivid memory of her, does 236 00:13:05,960 --> 00:13:12,200 Speaker 1: anything come to mind? I think, of course I have many. Um. 237 00:13:12,240 --> 00:13:15,720 Speaker 1: I think that the thing I remember most about Lenika 238 00:13:15,920 --> 00:13:19,600 Speaker 1: is that even after she had you know, not worked 239 00:13:19,640 --> 00:13:20,920 Speaker 1: for me for a while, she had gone on and 240 00:13:20,960 --> 00:13:23,240 Speaker 1: done all these you know, amazing things, gotten these two 241 00:13:23,240 --> 00:13:27,000 Speaker 1: master's degrees, she would always pop in my office just 242 00:13:27,200 --> 00:13:29,719 Speaker 1: come by to talk to me, either to share some 243 00:13:29,800 --> 00:13:32,120 Speaker 1: success she had or if she was struggling with something, 244 00:13:32,240 --> 00:13:34,240 Speaker 1: she would often want to come and like bounce it 245 00:13:34,280 --> 00:13:36,800 Speaker 1: off of me, just to sort of, you know, have 246 00:13:36,880 --> 00:13:39,840 Speaker 1: another perspective. And most of the time she didn't need advice. 247 00:13:40,400 --> 00:13:42,280 Speaker 1: It was like she needed a sounding board. She would 248 00:13:42,320 --> 00:13:44,720 Speaker 1: say it out loud, and she would reach a conclusion 249 00:13:44,960 --> 00:13:48,200 Speaker 1: that she probably already knew herself, but she felt like 250 00:13:48,320 --> 00:13:51,240 Speaker 1: having someone else hear it, you know, gave her the 251 00:13:51,320 --> 00:13:55,040 Speaker 1: courage to come to the right decision. And I liked 252 00:13:55,120 --> 00:13:59,040 Speaker 1: watching her go through that, you know, essentially this vocal 253 00:13:59,559 --> 00:14:02,680 Speaker 1: thought experiment just right in my office. And it was 254 00:14:02,800 --> 00:14:05,760 Speaker 1: kind of you know, every time she came in, I 255 00:14:05,760 --> 00:14:08,120 Speaker 1: would kind of get a small smile because I knew 256 00:14:08,200 --> 00:14:09,920 Speaker 1: I was going to get to sort of see her, 257 00:14:10,640 --> 00:14:13,400 Speaker 1: you know, think through a problem and reach a conclusion, 258 00:14:13,480 --> 00:14:15,800 Speaker 1: and that she didn't need me. It was just she 259 00:14:15,840 --> 00:14:18,319 Speaker 1: needed a space to do it. And I just really 260 00:14:18,360 --> 00:14:21,920 Speaker 1: loved that about her. Lenika's friends and family raised almost 261 00:14:21,920 --> 00:14:25,040 Speaker 1: eighty five dollars on go fund me for funeral cost 262 00:14:25,200 --> 00:14:28,080 Speaker 1: and to establish a scholarship fund to help support young 263 00:14:28,120 --> 00:14:32,440 Speaker 1: black women with internship opportunities at science and technology institutions 264 00:14:32,480 --> 00:14:37,040 Speaker 1: in Chicago. Because Linika was so passionate about both science 265 00:14:37,080 --> 00:14:41,360 Speaker 1: but also in including marginalized communities and science, it only 266 00:14:41,400 --> 00:14:43,800 Speaker 1: seemed absolutely the right decision to do is to sort 267 00:14:43,840 --> 00:14:47,040 Speaker 1: of create a scholarship and you know, we're able to 268 00:14:47,040 --> 00:14:49,880 Speaker 1: do that because we had a very successful go fund 269 00:14:49,880 --> 00:14:53,920 Speaker 1: me on campaign um and the you know, museums and 270 00:14:53,960 --> 00:14:56,640 Speaker 1: the institutions she's been involved with are all on board. 271 00:14:56,680 --> 00:14:58,920 Speaker 1: And so we're going to make sure that the next 272 00:14:58,920 --> 00:15:02,560 Speaker 1: generation not only knows about Linika, but they actually continue 273 00:15:02,640 --> 00:15:09,480 Speaker 1: to to you know, benefit from her impact in the world. More. 274 00:15:09,480 --> 00:15:20,320 Speaker 1: After this quick break, let's get right back into it. 275 00:15:21,800 --> 00:15:24,840 Speaker 1: And this time of COVID is it's been kind of 276 00:15:24,880 --> 00:15:28,040 Speaker 1: heartbreaking to see the amount of people who have lost 277 00:15:28,080 --> 00:15:30,720 Speaker 1: their lives to COVID and yet we have not had 278 00:15:30,760 --> 00:15:35,960 Speaker 1: any kind of official, you know, large scale memorial for 279 00:15:36,040 --> 00:15:38,920 Speaker 1: these people. And sometimes it can sort of feel like 280 00:15:39,720 --> 00:15:42,800 Speaker 1: these people weren't people. They were sort of you know, 281 00:15:42,880 --> 00:15:46,200 Speaker 1: numbers or you know, data points. How can we get 282 00:15:46,200 --> 00:15:50,000 Speaker 1: to a place where we remember that these were people. 283 00:15:50,080 --> 00:15:53,920 Speaker 1: They were friends, colleagues, daughters, sisters, loved ones, and not 284 00:15:54,120 --> 00:15:58,520 Speaker 1: just you know, another number on the news. You know, 285 00:15:58,600 --> 00:16:00,920 Speaker 1: I wish I knew the ant or to that. I mean, 286 00:16:01,920 --> 00:16:06,040 Speaker 1: I remember early on in the pandemic, you know, I 287 00:16:06,080 --> 00:16:08,520 Speaker 1: didn't say it out loud to anyone, but to myself, 288 00:16:08,600 --> 00:16:11,320 Speaker 1: I had said, I really hope that I get through this, 289 00:16:12,160 --> 00:16:16,120 Speaker 1: not knowing anyone who's personally been severely affected, And that 290 00:16:16,240 --> 00:16:19,840 Speaker 1: was like this weird internal wish I had for myself, 291 00:16:20,320 --> 00:16:26,320 Speaker 1: and then when when Linika passed away, I was absolutely 292 00:16:26,360 --> 00:16:29,640 Speaker 1: devastated for days. I mean I couldn't stop crying, and 293 00:16:31,600 --> 00:16:36,360 Speaker 1: even now thinking about it, it's tremendously sad. And to 294 00:16:36,480 --> 00:16:38,960 Speaker 1: think that we have hundreds of thousands of people who 295 00:16:39,000 --> 00:16:42,000 Speaker 1: are dying and we just sort of chuck it up 296 00:16:42,000 --> 00:16:45,240 Speaker 1: to like, well, at least the infection rates low and 297 00:16:45,280 --> 00:16:48,000 Speaker 1: the death rates low. But if it's even one, it's 298 00:16:48,040 --> 00:16:51,760 Speaker 1: too many. I mean, these are people and they're they're 299 00:16:51,800 --> 00:16:54,440 Speaker 1: important than they have contributions to give to the world, 300 00:16:54,480 --> 00:16:59,320 Speaker 1: and so I just hope that we can control this 301 00:16:59,480 --> 00:17:01,880 Speaker 1: soon and we don't have to lose any more beautiful, 302 00:17:01,920 --> 00:17:06,800 Speaker 1: inspiring people like Lanika. Yeah, I mean that was one 303 00:17:06,800 --> 00:17:08,960 Speaker 1: of the reasons I was so moved by her story, 304 00:17:09,040 --> 00:17:13,359 Speaker 1: because I thought, you know, and obviously one life is 305 00:17:13,400 --> 00:17:16,159 Speaker 1: too many to lose, but when you look at people 306 00:17:16,359 --> 00:17:19,480 Speaker 1: and you think all the lives this person could have 307 00:17:19,560 --> 00:17:22,280 Speaker 1: continued to touch, all of the sort of you know, 308 00:17:23,280 --> 00:17:27,159 Speaker 1: generations of people who are missing out on knowing this person, 309 00:17:27,160 --> 00:17:30,159 Speaker 1: getting mentorship from this person, being inspired by this person, 310 00:17:30,640 --> 00:17:34,880 Speaker 1: and really taking a bird's eye look of that. At 311 00:17:34,920 --> 00:17:37,480 Speaker 1: that scale of the loss that we can't even calculate, 312 00:17:37,520 --> 00:17:39,960 Speaker 1: Like if you can't even really fathom it to say, 313 00:17:40,040 --> 00:17:42,600 Speaker 1: to say how many people are going to, you know, 314 00:17:42,720 --> 00:17:45,200 Speaker 1: could have benefited from knowing her or working with her, 315 00:17:45,480 --> 00:17:48,239 Speaker 1: learning from her, seeing her. It's it's just sort of 316 00:17:48,960 --> 00:17:52,760 Speaker 1: we'll never know that that the loss. That's absolutely true, 317 00:17:52,800 --> 00:17:55,199 Speaker 1: and I think that's why we were all so moved 318 00:17:55,800 --> 00:17:59,400 Speaker 1: to make sure that there's going to be and opportunities 319 00:17:59,480 --> 00:18:02,280 Speaker 1: for other people to still have some of those experiences 320 00:18:02,320 --> 00:18:06,399 Speaker 1: at least to have access to learning what sciences and 321 00:18:06,480 --> 00:18:10,040 Speaker 1: getting hands on experience through these internships that we're creating, 322 00:18:10,160 --> 00:18:13,800 Speaker 1: because her legacy is just so impactful, and we want 323 00:18:13,840 --> 00:18:16,040 Speaker 1: to make sure that even though she can't be there 324 00:18:16,119 --> 00:18:19,320 Speaker 1: to inspire them, they'll still know about what an amazing 325 00:18:19,400 --> 00:18:23,080 Speaker 1: woman she was. M I'm so grateful that you all 326 00:18:23,080 --> 00:18:27,119 Speaker 1: are doing this work. Lineka touched so many people's lives 327 00:18:27,160 --> 00:18:31,320 Speaker 1: that it's such a loss to have her not here anymore. 328 00:18:34,200 --> 00:18:39,200 Speaker 1: I'm so sorry. It's it's it's you know. I think 329 00:18:39,200 --> 00:18:42,359 Speaker 1: that's another thing that really moved me about her story 330 00:18:42,600 --> 00:18:45,280 Speaker 1: is I was reading an article that said that she 331 00:18:45,359 --> 00:18:47,840 Speaker 1: always wanted to have this classroom of her own and 332 00:18:47,880 --> 00:18:51,760 Speaker 1: that she was finally on track to make that goal 333 00:18:51,840 --> 00:19:01,480 Speaker 1: a reality and then this happens, and it's just so yeah, yeah, yeah. 334 00:19:01,480 --> 00:19:04,879 Speaker 1: It's somebody who had so many hurdles and none of 335 00:19:04,920 --> 00:19:10,639 Speaker 1: them stopped her, and that is remarkable and everything she 336 00:19:10,800 --> 00:19:13,360 Speaker 1: wanted to come true in her life, despite the fact 337 00:19:13,440 --> 00:19:15,439 Speaker 1: that when she first started dreaming of them that was 338 00:19:15,560 --> 00:19:20,280 Speaker 1: such a far like reach. She reached every one of them, 339 00:19:20,320 --> 00:19:22,640 Speaker 1: and that to me just shows the kind of amazing 340 00:19:22,680 --> 00:19:25,359 Speaker 1: person she was. And she got there not by like 341 00:19:25,480 --> 00:19:28,800 Speaker 1: stepping on others or you know, throwing other people under 342 00:19:28,800 --> 00:19:32,560 Speaker 1: the bus. She did it by being a loving, caring, compassionate, 343 00:19:32,880 --> 00:19:37,879 Speaker 1: dedicated person. Through this scholarship, Lineika's colleagues are using the 344 00:19:37,920 --> 00:19:40,960 Speaker 1: tragedy of her death to inspire the next generation of 345 00:19:41,000 --> 00:19:44,480 Speaker 1: girls to fall in love with science, just like Lineka did. 346 00:19:45,600 --> 00:19:49,000 Speaker 1: What do you hope the scholarship achieves? Like the like 347 00:19:49,400 --> 00:19:52,359 Speaker 1: when the scholarship is up and running, what kind of 348 00:19:52,400 --> 00:19:54,200 Speaker 1: impact do you hope that it has? In her name? 349 00:19:57,200 --> 00:20:02,639 Speaker 1: I know that the young women who will receive this 350 00:20:02,720 --> 00:20:09,480 Speaker 1: scholarship will benefit immensely, mostly because they'll have an opportunity 351 00:20:09,560 --> 00:20:13,040 Speaker 1: to continue in her footsteps. Right, they will essentially be 352 00:20:13,800 --> 00:20:17,159 Speaker 1: the first in their family to do research or to 353 00:20:17,520 --> 00:20:22,919 Speaker 1: learn how to educate and mentor others. And and the 354 00:20:22,960 --> 00:20:26,280 Speaker 1: most important thing I think for us is that we 355 00:20:26,320 --> 00:20:29,560 Speaker 1: want to make sure that the work Linika was doing 356 00:20:29,600 --> 00:20:33,960 Speaker 1: continues and it continues to impact the next generation of scientists. 357 00:20:36,240 --> 00:20:39,480 Speaker 1: I'm I have no doubt that it will already already. 358 00:20:39,520 --> 00:20:41,800 Speaker 1: I think so many people are moved by her story 359 00:20:41,840 --> 00:20:44,120 Speaker 1: and her legacy and the work that you and your 360 00:20:44,119 --> 00:20:46,399 Speaker 1: colleagues and her family are doing to keep that alive. 361 00:20:46,480 --> 00:20:49,920 Speaker 1: So I'm I'm so grateful that you all are doing 362 00:20:49,960 --> 00:21:04,760 Speaker 1: that work. Linka isn't really gone, not really. She'll live 363 00:21:04,760 --> 00:21:08,520 Speaker 1: on in classrooms wherever little black girls are getting excited 364 00:21:08,520 --> 00:21:12,240 Speaker 1: about science or bugs or any other subject that she's 365 00:21:12,280 --> 00:21:16,280 Speaker 1: realizing could be hers to master. People like Lenica mattered, 366 00:21:16,840 --> 00:21:19,359 Speaker 1: we won't forget about them or the way they shaped 367 00:21:19,359 --> 00:21:24,840 Speaker 1: our lives. Their names won't be forgotten. We won't let them. 368 00:21:24,880 --> 00:21:27,080 Speaker 1: We hope you've enjoyed listening to season one of There 369 00:21:27,080 --> 00:21:30,320 Speaker 1: Are No Girls on the Internet. We're taking a short hiatus, 370 00:21:30,440 --> 00:21:33,160 Speaker 1: but we'll be back real soon with more. In the meantime, 371 00:21:33,400 --> 00:21:36,800 Speaker 1: keep in Dutch, stay hi at Hello at tangodi dot 372 00:21:36,800 --> 00:21:40,360 Speaker 1: com and follow me Bridget at Bridget Marie in DC 373 00:21:40,520 --> 00:21:43,640 Speaker 1: on Instagram and at Bridget Marie on Twitter, and we'll 374 00:21:43,640 --> 00:21:49,040 Speaker 1: see you real soon. Got a story about an interesting 375 00:21:49,040 --> 00:21:51,080 Speaker 1: thing in tech, or just want to say hi, you 376 00:21:51,080 --> 00:21:53,399 Speaker 1: can be just at Hello at tangodi dot com. You 377 00:21:53,400 --> 00:21:56,600 Speaker 1: can also find transcripts for today's episode at tangodi dot com. 378 00:21:56,640 --> 00:21:58,280 Speaker 1: There Are No Girls on the Internet was created by 379 00:21:58,280 --> 00:22:01,679 Speaker 1: me bridgetad. It's a product iHeart Radio and Unboss creative 380 00:22:02,080 --> 00:22:05,040 Speaker 1: Jonathan Strickland as our executive producer. Terry Harrison is our 381 00:22:05,080 --> 00:22:08,600 Speaker 1: producer and sound engineer. Michael Amato is our contributing producer. 382 00:22:08,840 --> 00:22:11,520 Speaker 1: I'm your host, Bridget DoD. If you want to help 383 00:22:11,600 --> 00:22:14,560 Speaker 1: us grow, rate and review us on Apple Podcasts. For 384 00:22:14,680 --> 00:22:17,320 Speaker 1: more podcasts from iHeart Radio, check out the iHeart Radio app, 385 00:22:17,359 --> 00:22:19,400 Speaker 1: Apple podcast or wherever you get your podcasts.