Transcript#
This transcript was generated automatically and may contain errors.
Hey there, welcome to the Paws at Data Science Hangout. I'm Libby Herron, and this is a recording of our weekly community call that happens every Thursday at 12 p.m. U.S. Eastern Time. If you are not joining us live, you miss out on the amazing chat that's going on. So find the link in the description where you can add our call to your calendar and come hang out with the most supportive, friendly, and funny data community you'll ever experience.
Can't wait to see you there. I'm super excited to introduce our featured leader today. He has tons of fun. We're going to have a great conversation. I'd like to introduce Florent Bled, biostatistician at the Florida Fish and Wildlife Conservation Commission, which is a very long phrase, but you can just say FWC. Florent, it's so nice to have you here. Could you introduce yourself, tell us a little bit about what you do and stuff you like to do for fun?
Hey, yes. Well, thanks for having me. As you mentioned, I'm a biostatistician here at FWC. So we do a lot of things here. Our job is to basically protect resources, like for our resources, so for us to be able to use an Android right now and for generations to come. So that covers a lot, a lot of things. We handle both on the water and regular wildlife. So that's a lot of work. More specifically, what I do is basically provide support to other scientists and help them with sampling design and data analysis and getting you some graphs, data visualization, all of these kinds of things. What I like to do for fun, I am a huge scuba diver. We like scuba diving. That's going to be the big one. Also like art, woodworking. I like drawing, painting, origami. And because it's the season, like pumpkin carving right now. This is what we're doing right now.
Pumpkin carving. If anybody has carved their pumpkin already, we have not. We usually do it the day of or like the morning of Halloween. Please put your pumpkins in the chat and share them in the Discord too, because I really want to see them. I have so much love for pumpkin carving and I'm not going to get to do a full pumpkin this year. So share with me. And also I know that we have other divers in the chat here. I see Arsenis saying, yes, diving rules. We know Arsenis is a diver. James, are you a diver as well? I know that there are lots of others. I wanted you to tell just a little bit about where you learned to scuba dive, because not only is it a cool story, you were in South Africa, but you were also doing two postdocs at the same time.
Yeah, that was a lot of work and not a lot of sleep. That was back before family life, I want to say, before I had kids. I was at that time working with USGS here in the US, and somebody contacted my advisor at the time and said, hey, we need somebody to help personalize some bird data. Do you have anybody that would be interested? And my advisor came to me and asked me, hey, do you want to go there to work on that project while still working on what you're doing right now? So, for a little while, I went to South Africa twice to work on the South African bird data project. It's a citizen science project. It's fantastic. Lots of work. It's great and really interesting for its own merit. But when I was over there, well, I basically was walking, sleeping really little, walking as much as I could during the first four days of the week and just cramming as much. And then I would just spend the rest of the week pretty much in the water, getting my dive master. So, it's a beautiful place over there. Great dives. We have the chance to just dive with sharks on a weekly basis, seals, dolphins, all the cool stuff.
Background and career path
Amazing. And that speaks a little bit to your background because you mentioned you were working with birds. And I know that you sort of started in the ornithology space, right? Could you talk just a little bit about your education and your interest in that? Yes. So, it's one of those things where as a kid, you're asked, what job do you want to do when you grow up? And the first one I can remember was ornithologist. I don't even know I knew that word back at that time, but that's what I wanted to do. And over the years, you know, goals change. But I'm always interested into something wildlife-related. Throughout my life experience, I had the chance to meet great teachers that introduced me to a lot of other things. I've always loved math too. So, kind of trying to find this point of contact between those two worlds. And I had the chance to have a great teacher back in France that introduced me to all at the same time as I was going through all of this. So, for me, that was kind of like the perfect combination of everything I like to do. I like to tinker. I like wildlife. I like being in nature. I like science, asking questions. And just using math to explore all this, we're trying to understand how things work at a fundamental level.
So, I had the chance to do a Ph.D. that was focusing on what we call phyto occupancies, trying to figure out where things are and how you can account for whether you see them or not when they're there. And I was working, like, one of my projects was here in the U.S. with the invasive species, the Eurasian coral dove. I was also working on black-legged kittiwakes back in France. So, a lot of fun. And after that, I came in the U.S. to keep working on, like, more specifically on the breeding bird survey with CSGS. And after that, I worked with, I had the chance to work with, like, looking for bigger species. I worked on carnivore ecology. So, I worked with, you know, kajak bears, lions, lynx, bobcats, all of those fun species. And then, like, you know, one thing led to another. I met people here at FWC, and now I'm working on pretty much every single thing that has scales, feathers, hair, all of that jazz.
No longer just kittiwakes. No longer just birds. I put a couple in the chat so people could see. I think I put a red-legged kittiwake baby and then a black-legged kittiwake. I don't know as much about kittiwakes as Florent does. Amazing. Thank you so much for the background. I absolutely could not be happier talking to so many people who are doing lots of animal tracking lately. Like, we had Dwight Berry on who was, he started in ecology and was doing bear tracking. This has been so much fun to talk about. It gives me an excuse to, like, Google animals during the Hangout, which is amazing.
The chlorophyll background image
So, I wanted to also dive in with Christy's question. She hasn't asked her, so I'm going to ask it. Because it's the same thing that I asked you the first time I met you. What is the image behind you that's so beautiful? This one? Yep. That's a mistake. That's basically just that. One day, I was exploring some data. More specifically, that's the chlorophyll level here in Tampa Bay. And I was just trying to do a map, you know, just X, Y, or, like, let launch and have kind of my sun level on the Z axis. And I was coding really fast and I ended up putting my Z against my X axis. And so, basically, what you see is, like, the chlorophyll level up and down and just, like, looking at it from the side. Only in base R because I like to do a lot of, like, you know, exploration. Really just simply, like, in base R before diving into, like, cooler plots with CG plot. And yeah, that just came out and I just, man, I kind of really, really like it. And I just try to polish it a little bit so we have something really, really clean. And, yeah, just I think that makes a nice background. Like, if you want to see, like, over here in front of me, what I can see is my kids' picture. Drawings for my kids. It's a lot more beautiful than what I can do. But for the background, this is pretty nice. It's gorgeous. We all love it. I think we'd all like a print.
That's a mistake. That's basically just that. One day, I was exploring some data. More specifically, that's the chlorophyll level here in Tampa Bay. And I was just trying to do a map, you know, just X, Y, or, like, let launch and have kind of my sun level on the Z axis. And I was coding really fast and I ended up putting my Z against my X axis.
A typical week and the role of internal support
Okay. Well, I have another question for you from Natasha who has an asterisk in her question. So, I will ask it. And it's going to help us understand a little bit more about what data work looks like for you. She asks, what does a typical day or week look like for you? What areas do you split your time between, like, programming or meetings, maybe planning, maybe collaborating? So, there's no, like, typical week. It's not really, like, oh, on Monday I'm going to work on this. On Tuesday I'm going to work on that. It's a lot of answering help. So, we need to have scientists that are going to come to us and say, I have this data set. I'm trying to figure out what I can tell about my data in there. And so, we need to start with meetings, just figure out what are the needs, what are the questions. Whenever possible, we try to get involved in every project from the start. Because if we can provide help from the moment of, like, data collection, we can usually be a lot more successful than, you know, coming at the end and say, hey, I have this data. What can I do with it?
So, depending on the needs of the people, we need to just meet and establish, okay, what are your questions about the data that you have? What can we work? Like, what can we do with that? So, that can be, like, part of the job. Or the part is just developing our own Shiny apps or Quarto so we can help people. So, a lot of programming there. So, a lot of fun, basically, creating, like, creating toys that, you know, I like having, hey, I have a Shiny app. I did that. So, some programming to do. So, programming stats, help, meetings here and there, depending on the needs. But it always changes. We do have, like, during the week, during the year where people come back from the field, usually at this time of the year, and that's when they have their data. So, we usually have a huge workload coming in at this time of the year. And by the time we get back to managing to take care of everything, people are back in the field and collecting more data.
Enabling subject matter experts with Shiny apps
Nice. So, you are a sort of a consultant internally, maybe a little bit, where you're helping lots of different people accomplish different things. I like this. I would love to be a problem solver and just be, like, let's go fix everything for everybody else. It sounds like you're also enabling the subject matter experts and the scientists with the technology, like Shiny apps. Are most of your Shiny apps internal? Or do you have any of the dashboards that are public for people? So, yeah. That's one of the things I love about my job. I have the chance to work on a bunch of different projects with people that are really smart and experts in their field. So, that's giving me a chance to interact with people that are passionate about what they're doing and, like, know so, so, so much. And I get to learn from everyone. So, that's great. And that's something I want to say that is unique in our team. It's, like, really, like, having this team dedicated just to help people with just data needs in this specific context. Rather than letting people figure out what they're supposed to do.
But for, like, what we keep in mind is, like, the person that are important is, like, the subject matter experts. So, trying to just figure out what our thing is. And so, we can just, like, work with that and match that. That means one of the tools that we offer to people, like, things to connect is making them easy. Making it easy for them to share their knowledge with management, with the public at large, with stakeholders. We have some people that are good coders. We have people that don't know R at all. In that case, that's where, like, you know, some of us are going to come in and develop the apps for them. A lot of what we do is going to be internally, at least at this point.
We have a couple of apps that are going to be either, like, in the works to be available publicly or, like, have been published or in the process of being published publicly. That's going to be things to, like, figuring out, like, what's happening on our lakes in terms of management instruments. What kind of fish species we have in there. That's going to be information about imperiled wading bird colonies. So, people can gather that information and figure out, hey, like, what kind of wildlife do I have, like, around me? What's the status of those populations and what can I do to help with some of that?
Amazing. I put in the chat, I'm wondering if anybody else has a job that's kind of like this. Problem solving, internal consulting, enablement, stuff like that. Because I know that we've had a couple people on recently who had a little bit of this in their role. And we have someone coming up at GenMab. We're going to be joined by Sud, who is even more on the enablement side and is in, sort of, like, an admin type of role. And I'd like to talk with more people like that.
Chime in and let me know. Jonathan in the chat is talking about the rough world series. Y'all, I'm out of the sports game. What's going on with the world series?
I heard something about Milwaukee.
So, that was actually happening right here. Just like a few hundred yards away from where I am right now. We just lost. You just lost. The Brewers and the Rays, right? Am I wrong?
Yes. Somebody let me know. Okay.
That's correct. And it was a hard talk with my kid this morning saying, I'm really, really sorry to tell you that we have lost the world series.
Yeah. Kristen says, reframe. The Brewers just won for the first time in 40 plus years. That's the story that I was looking at because we have friends in Milwaukee who are just so over the moon excited. Carl Grealis says, brew crew. Okay. We'll get back to data science, but if anybody wants to talk about data for the world series, let me know in the Discord so we can all talk about it.
All right. I have a question from Rachel. Rachel, would you like to ask your question live?
Sure. So, Florent, I remember when we were talking, maybe this was a few months ago, but you were telling me this fun example with data from gators, I believe, or is it crocodiles or gators? American crocodile.
Okay. I got it wrong too. I thought that would be a really fun one to share with everybody.
Relocating problem animals: the crocodile study
Yeah. So, we have a place in the US where you can find both American crocodiles and alligators. One of the important jobs that our agency has is to handle human-wildlife conflicts.
We are all animal lovers here. We all care deeply about our animal resources, and also, we deeply care about people because that's why we do what we do.
That's why we do what we do. So, on occasion, we can have conflicts between gators or crocodiles that are going to become used to human population and are going to become problem animals. One of our preferred solutions, whenever possible, is to basically take those animals and relocate them within a more natural setting so they don't bother human populations anymore. So, that specific study was kind of like a test study to figure out, hey, is that actually a good solution for us to do that? And we are trying to figure out, depending on how far away we relocate them, how effective are those techniques?
So, it turns out that those animals are incredibly good at finding their way back, a lot better than I am. We would move them by, you know, a couple dozen miles, and they would make it back to like 14-ish miles, and they would make it back after just a little bit of time trying to figure out where they are, turning around, and they would just find the location they were in and zoom right back in where they were from. And that was up to almost 100 miles, where it would take them a little bit longer, but we did have some individuals making it back after a couple of months from more than 100 miles away. So, it is a valuable tool, because if you pass a certain distance, it can become difficult for the animals to get back, and they just have to settle where they are. But it was also important for us to figure out, you know, like, is it a stressful situation for the animals? Because we want to avoid stress as much as possible. But, yeah, it was pretty quick, you think, to see, like, we could use GPS data to kind of track movements, so it was just like a movement analysis, and figure out, you could see, like, literally see the animals going, I don't know what I'm doing, I don't, oh, where am I?
Oh, I do know. Bring back on. And, yeah, that was a really interesting project.
That's amazing. I'm glad that you were worried about the stress level, because I was worried about the stress level the whole time you were talking. I was like, oh, poor crocodiles, they're going to be stressed out. Imagine just, like, getting too used to humans, and then getting a little too friendly with them, which happens with bears, too, all the time, right, up in the woods. And, you know, and then somebody just taking you and relocating you 15 miles away.
Okay, well, I'm glad that someone's looking out for the crocodiles. Oh, we have so many questions inside it. Let's get to another one. Noor, I saw you had a great question, and it has risen to the top here. Would you like to ask that live? I'll put it in the chat as well.
Working in the public sector
Bonjour. Bonjour. That's the extent, because I forgot the rest of my French. Anyway, you're in the public sector, particularly in the field of, sort of, like, ecology, wildlife, and so forth. What are challenges that are unique to working as a data scientist in the public sector versus other domains? What are the pros, cons? And if you have experience working in other domains, what makes you stick in the public sector? Oh, good question. Thank you, Noor.
Well, that's really important to me, because I like helping people. That's one of my favorite things to do. And before that, I was in the academic field.
Now, we mentioned doing a couple of postdocs. I wanted to be able to make a difference. I wanted to be able to help people. I wanted to just have things. I love, I still love, you know, just pure research and figuring out, like, understanding, just improve understanding. But also, we enjoy being able to take that understanding and do something with it.
I, my job here is to kind of, like, help with this connection between ecologists and management and stats. I'm not as good in ecology as a pure ecologist, and I'm not as good as, in stats, as a pure statistician. But I'm a lot better in stats as an ecologist and a lot better in ecology as a pure statistician. So, I'm able to make this bridge. And coming here as a public sector also helped me make that connection with management and hopefully, like, one level removed with the general public.
But I'm a lot better in stats as an ecologist and a lot better in ecology as a pure statistician. So, I'm able to make this bridge.
It does come with some difficulties in the sense that, yeah, we deal with potentially sensitive topics, and what we do has consequences. And that's really important. But in my position, what we try to do at our agency is to provide the right information in the right format at the right time. So, basically, use the most novel, most strongly, like, the strongest possible science so we can better inform our decisions.
And then somebody's going to, somebody that has a bigger picture of, like, of the situation than I do can come and just, okay, look at this is what my stakeholders and the public want. This is the current status of appropriation. So, it can be, like, fisheries when we have to figure out, like, can we fish this species at this time of year? How much can we get? That can be related to just general population management, species, dealing with deer, like, white-tailed deer, or turkey, these kind of things.
So, based on my experience, I think that it can be more challenging to get, to collect data, because there's so much of it. We can't, you know, we're trying to just deal with the whole ecosystem in Florida, and we're not, you know, disconnected from the rest of the U.S. So, it's kind of, like, interagency discussions that are happening on a daily basis, trying to find, like, stakeholders that are willing to talk with us. Sometimes when I go out with, you know, my work outfit, people are going to see the badge and be like, oh, I have my fishing license with me. That's great for you. That's not what I do, but we appreciate the support.
So, we do have some great interaction with the public most of the time, and it's always kind of a great chance for us to be able to share what we do with everyone. Especially, like, for us here at, you know, Research Institute, where we can bring people in, we have these great events. If you are, like, in the area, every year we have this thing called Marine Quest, which is we open our doors for the public to come. We have two days where, like, students can come from schools, and one day we're, like, we open our doors to everyone, and our scientists get to share our work, and basically say, hey, you live in Florida, or you don't have to live in Florida to come and visit, but you live in Florida, this is what we do for you.
So, what are the challenges of working in the public sector? I think one important thing is, like, what are the great aspects of it? And one of it is you get to work with people and make a difference for others at a very large scale. I know, like, you know, data science community, and especially here, like, with Vanguard, everybody's interested in that. Like, all of us are just interested in what can we do with data, with our data, to make a difference.
And we have great people that have been, like, it was a couple of weeks ago, we have people that work in hospital settings. That's a great way of making a difference, you know, just using your knowledge to be able to help people. And this, working with the public sector, is just another approach to it. I put the link to Marine Quest in the chat, so everybody can look at it. I think it's a very cool thing to do. I don't know if anybody else does anything like this, but there is a school days portion where school age kids can come in, and then there's a Saturday where it's open to everyone. I know that some of our folks here are local to Florida. So, if you've never done Marine Quest, maybe go visit Florent and see what's going on. Open invitation to the next time that happens.
Types of data and workflows
Okay, we have so many questions in Slido. We better get to some questions. We're about the halfway mark, and I saw somebody asked about what types of data you use, and I think that that would be a fantastic context building question. Let me go find that in the Slido. It was right here. Oh, here it is. It was an anonymous question, and it says, what kinds of data do you use in your work, like remote sensing data, satellite imagery, input from biologists in the field, citizen scientists, collected information, et cetera, and how do you manage all of that? I'm curious about sources and workflows. What a great question, anonymous asker.
So, I love that question because that's really at the core of what we do here in our group. So, the group I work with is called the Center for Biostatistics and Modeling. In our group, we have two teams. We travel to biostatisticians where we take care of the analysis, and we can travel ecological data specialists that are going to be the first step that people should go to to handle their data. As you mentioned in your question, we do have a really wide array of different types of data. We're going to have remote sensing, satellites. We're going to have a lot of GPS data. We're going to have mortality, survival, records. We're going to have hunting data. We're going to have fisheries data, both independent and dependent, where we're going to go out there and count the number of fish that we can detect, or talk with fisheries folks and figure out the size of the catch that they're going to get.
We're going to have DNA data. We're going to have simple chemical data. We're going to have measurements of profile levels. That's one of the things that is super interesting with the job here. Every project that we work is different. I'm in no way a specialist on all of those, but we are always passionate about learning. Usually, what happens is we just try to figure out what kind of data are needed to answer a specific question. It's always important. I cannot advocate enough to just start with your question. What are you trying to figure out? What are your objectives? How can you test for that to answer those questions?
I cannot advocate enough to just start with your question. What are you trying to figure out? What are your objectives? How can you test for that to answer those questions?
Then, the data that you need, if you want to use those specific tests. If you want to have those data, how are you going to just collect them? We try to just match the needs for every project.
I don't have an easy answer of, hey, this is exactly the kind of data that we use. It is really product-specific. It's a wide area of data. We have a lot of special GIS data, as well as everything I mentioned earlier. In terms of workflow, once we have this pipeline set up of, that's your question, that's the kind of data that you need to collect, we then need to kind of format that and store that in a safe and secure way that we can then access easily and take care of. It is a large teamwork, lots of teamwork there.
The hard question to answer when it sounds like, yeah, every single project, you're going to have a different type of data. Kristen Bot in the chat said, the juggle is real about this sort of problem with data sources, which makes me laugh so much.
She says, it can be a challenge to vet the quality, incorporate all of the data, manage all the sources. It sounds like a lot of people have a similar experience with their sort of project-specific work. Thank you, Anonymous Asker, for that fantastic question.
You sent us in a great direction. We have an AI question. Rizwan, you asked an AI question. Would you like to ask that live? Let's see if we can get Rizwan. There he is. Hey.
Yeah, hi, Florenda. Hi, Florenda. Just a generic doubt. Do you guys use large language models to aggregate your research flow? I'm just asking, not just simple cloud UI or any other UI. Do you guys actually use that in research purposes? So, it's something we are currently looking at.
and that is a lot of telltale s and there are a lot of lemmas that are passed through there. In that context, we do have specialized elements that are going to be just for analysing those specific data. So it is kind of like really project-specific, and we are in the process of trying to figure out ways of expanding that as needed, but it is really, at this time, project- specific.
Thank you. We have another question that has arisen, which is a quick one. Favorite and least favorite animal. I don't know if least favorite is fair.
I have a hard time picking favorites in general. I like, okay, so there are going to be my favorite one is probably the platypus. It has nothing to do with Florida, but it is kind of my top three. That being said, more specifically, when I work, I love bears. I had the chance to work with scojag bears. They are the largest bears that we have in the world with polar bears. That has been such a humbling experience. They are just fantastic.
I also love looking at things like ants, because they are just so beautiful. They are very tactile, and their behavior can emerge from a very simple mechanism between the individual levels. So, I, and black-legged kittywags always enjoy a special place in my heart, seeing them against a blue sky when you have these beautiful black W pattern on their wings soaring through the sky.
It just grounds you the moment the world is a beautiful place. There is magic out there. So, those will be my favorites, probably animals. Least favorites, I'm sorry, I think you have to say mosquitoes and ticks.
You're not alone. I was just going to say Michael Morales in the chat says ticks are the least like for me, and I think I agree. Yeah, I mean, there is an argument for the important eco speaking, but in Florida, it can be challenging during the summer months to come out and say, oh, is that fog out there? No, it's mosquitoes.
Yeah, mosquitoes are pretty bad. I feel like anyone in Minnesota would also agree. Mosquitoes are pretty bad.
Okay, I asked in the chat if anybody could guess my favorite animal. Nobody did. Excellent. I love spiders. I'm obsessed with spiders. I've been obsessed with spiders for a very long time. My family calls me the spider lady. I love them. I know that that's not a normal question or answer, but spiders are absolutely fascinating, and there's so few of them that can actually hurt you.
Especially if you know the ones in your state that can hurt you, and even the ones that can probably can't hurt you that bad. You shouldn't be that afraid of a black widow. Don't smush them. Don't get in bed with them or anything. I feel that for spiders, that's kind of a scale. Tiny spiders are cute and adorable. Big spiders are fluffy, and they look like little plushies. They're awesome. It's like the in-between where people are scared. I love them all. The worst thing you can do around me is kill a spider. I will lose it.
Okay, thank you so much for your question and least favorite and favorite animal question. I don't know who that was. It was anonymous, but it was adorable. Thank you.
Shiny apps and tools at the agency
Y'all, I am not going to ask this Florida man story question. It's in the chat. You are killing me. I would love to ask a Shiny-R question, though, because we had anonymous questions. I think we had a couple people asking how you happen to do Shiny-R apps internally. Do you containerize? Do you use Docker? Stuff like that.
I figured it's a good time to ask a tool question as well. Are you mainly working in R doing biostats? Do you have people working in Python? Do you work in Python as well? Yeah, R, Python, Shiny-R. A little bit of a glimpse into how you use your tools and how you deploy.
For Shiny-R, one thing that I find is I'm going to take a really quick tangent. Not that I do that on a constant basis. I feel like we always see those really cool Shiny-R apps. Really big, really complex. Those are awesome tool caps. But I also like the smaller ones that have just, hey, I want to be able to share my data, the really simple one. I want to be able to show to somebody the path that my bears are taking or that my cows are taking. It can be just as simple as a simple territory visualization app.
I like this kind of range, this gradient of you can go from really simple apps to the really big ones and all of those are important because all of them have a purpose. For us, right now, we had to develop. We recently brought, now that is a couple of years, brought Connect to the agency. People were really enthusiastic for that, and we get requests for apps on a really regular basis. We had to develop a whole governance process with that. I took care of that with brilliant, brilliant researcher too, and that was kind of our job to put all of that together.
I like this kind of range, this gradient of you can go from really simple apps to the really big ones and all of those are important because all of them have a purpose.
Some of those apps are really simple. It's one of those things where through my career, my job description changes little by little where I came from to now also helping with all this app development and other things, and one of the things that I do now is trying to get better, helping people bring their apps in production. We take that into consideration, and each app is going to have different level of scrutiny that it has to go through.
That's one of the things where we try to make it possible for the subject matter expert to share their passion with everyone and their data and their conclusions depending on the scale they need to do that at.
In terms of like what we use mostly in our team, we're like most of them are more familiar with R. We do have some people that work with SAS, and we're trying to bring to us, to the lights with R, and we do have people that work with Python. That's one of the skills where I'm personally trying to work on it. I'm still trying to get better at developing it. I've heard once that Python is not the best language for any project, but it's the second best language for all of them. Just trying to get better at it. But we do have people that work with Python.
Amazing. I didn't even mention SAS in my list of options and flavors, but, of course, SAS is there. Very valid option. Does anybody use SPSS at your office? Hmm. I still shamelessly kind of like SPSS sometimes. We don't have to go there. I've seen some SPSS code a couple of years ago, but it's not that I can think of right now. It's tough. You have to have a license for it, just like SAS, but it's cheaper. It's a cheaper license from IBM.
Most rewarding project
Okay. Let's hop back into the Slido here. I had a question that we already asked some of the challenges questions. There it is. I can highlight it here. It was anonymous. It says, how are ongoing challenges handled with invasive species and habitats? But I really liked the follow-up question they included, which is, which project has been most rewarding for you?
That's a great question, and there's no easy answer to it. I am not a specialist when it comes to taking care of it. I have a couple of projects right now where we are helping some of our IPM invasive species, even its plant management group, to kind of develop and bring their apps to life.
What I try to do in my job is basically provide the best information possible for other people to be able to make those decisions. I don't have, personally, I could direct contact with the policy element of our agency, the law enforcement. This is kind of removed from what I can do. I'm not the best person to answer that question. I just know that we do a lot of work, and I know the people that work on it, and that we are always passionate to try to make a better place for the long-term for everyone.
When it comes to specific policies, I'm not the best person to answer that. I'm going to take the coward way out on this one and say I would recommend contacting the people that are better suited to give a full and knowledgeable answer to that. There was a second part to that question. The best part. Which project has been most rewarding for you?
There are so many of them. That's a good problem to have. Lots to pick from. Oh, my God. I'm going to get some of my coworkers angry if I don't pick their project.
One that I really like is a white tailed deer project. Because it was such a complex project, trying to figure out ways of taking really advanced stats aspects, like techniques, to help us optimise the data that we collect so we can get better information about the white-tailed deer population in the context of Florida panther recovery and protection.
It was a really interesting project because it is really complex in the sense that we are going to be dealing with multiple trophic levels that are also going to be impacted by management decisions that are not just everywhere, but we have a restoration project that is happening and that is going to change the whole hydrographic landscape that we are going to have in this area. What does it mean for a white-tailed deer? How is that going to impact the Florida panther?
Our population was down to a few individuals back in the 90s, and thanks to the work of amazing people here at WWT and others outside of the agency, we have NPS, we have so many people and I'm really sorry for leaving people, but it was such a huge, huge work inter-agency and with people that are passionate, and in general with the public, because trying to educate the public and make sure that they really understand why we're doing that and why it's important.
In that regard, the project was really interesting because it just made me think about all those different elements and how they connect. Then, we were able to use that to take into action some of those information that we learned. Something as simple as, this is how many cameras we need to put out there for as long. We don't need to do it as much there because we have some information that we're getting from this other option that we can use to supplement that using Bayesian statistics. People, if you can,
use informative priors. That's a useful tool to have. We have that, use it.
That was, in that regard, really rewarding because I had the chance to work with so many different people and this project that you're interested in has implications in my day-to-day work. That was one of the big ones. I love that part of the rewarding part was getting to work with so many good people. It's not always about the impact that your project is making. It's also about the impact that all of these human beings are making on each other.
It's not always about the impact that your project is making. It's also about the impact that all of these human beings are making on each other.
I love it. Thank you so much for that great question, Anonymous Asker.
Career advice
What are we going to do with our last eight minutes? I would love to ask Florent a question we always ask, which is, do you have a piece of career advice for people? Something you wish you knew when you were getting started or something that might help somebody else entering maybe the Biostats space?
It's a loaded question because it's one of those things where I'm happy where I'm at and I can tell you about the things that have happened to me and the chances that it took and that brought me there, but I can't tell you the path that I missed and I'm not aware of. That's why I mean by it's a loaded question. Oftentimes if you want to get to a place, it's more informative to look at the path that didn't take you there or didn't take people there.
With that being said, I was given an advice a long time ago where I was told don't say that you want to work with dolphins. Don't say that you want to work with bears. Think about the actual scientific question that you want to ask. From there, if it so happens that it's on dolphins, that's what you need to be working with. That was really helpful for me to figure out, yeah, what I'm trying to figure out is understanding the scientific aspect of it.
To take a step back, there are two elements that I think are really important. There is this passion that we all have, all of us. I know that in the data science Hangout community that we have here that you guys are fostering, we are all really passionate and we all care. Take that passion with you.
There are times that are going to be hard. There are times when you want to give up. Just remember why you're doing all of that. Just keep that drive. If you have a goal, figure out how to get there. Just look at the people that are doing that. Contact them. We all love talking about what we're doing and sharing. If you have some people that are doing what you want to do, shoot them an email. I'm sure they'll be more than happy to answer you and say, hey, this is what I've done. Maybe it doesn't work for you, maybe not, but at least you have this information. That's the first thing. Stay focused.
Second piece of advice, though, is don't miss opportunities. Be open to also other ideas. I wanted to be an autologist when I grew up. Then I switched to a vet. That didn't work for me in terms of that just didn't happen. What can I do then? I still love animals. I want to be a bivouac ecologist. Okay, perfect. Now I can do that. I also love math. I have those people that want to talk to me about stats. How can I integrate that?
If you have something you really want to do, try and go for it. If that doesn't pan out the way you want to, there are other ways to get there, where you can be happy with what you're doing. I know that for me, going through the academic setting, becoming a professor was always, oh, that's what I have to do, because that's what everybody else that I'm talking with is doing. At some point, I realized, well, actually, that's not exactly what I want to do. Then you open the door, and oh, now I'm here working in the public sector, and I'm so happy doing that.
It is important to just keep the energy. Use your passion to stay focused, and have the energy to pursue your passions.
Keep in mind that we are a large community working on so many different topics, and we are all here to help each other.
If you try to look at the people in the chat right now, we are all coming from different backgrounds and work on different things, and we all took a different path to get there. Sometimes, it's a straightforward path, and people say, I want to do this, I want to do that. Other times, it's bumpier. I think that's important to just not close the door in that regard. Sometimes, you can go through a bumpy path, but I think with the passion that we all have, we can get through it.
That's wonderful. Thank you so much for sharing. I think that I am looking at the faces of my grid, and I know some of these bumpy paths, and the bumps are the best part. When I talk to them and hear their stories, the parts that seem squiggly are where they gained a lot of their skills and passion and picked up interesting stuff along the way.
Well, I have a question. Just one in that realm. A quote that I keep with me all the time is a ship is safe in a harbor, but that's not what ships are made for. That's true, yeah. That has really helped me. Go out there, explore, because that's what we're made for.
a ship is safe in a harbor, but that's not what ships are made for.
What's that saying? Smooth seas never meet a skilled sailor? Somebody in the chat can help me with that one.
I saw a question from Gregory, and I like it so much, I want to just pop it in here right at the end, when we only have two minutes left. Gregory asks, if you could wave a magic wand and solve a day-to-day problem, what problem would you solve?
Dealing with dates. Oh, my gosh. Good choice. I mean, we do have this magic wand with dates. It's an amazing package. I love it. That has helped me a lot. This is one of the most frustrating things in my day-to-day basis, dealing with dates. Going from that, and do I have data in Excel? You open Excel and just mess with everything, then you bring it back in R, and then you have to deal. Yes, that's a big one. If you could just make dealing with dates even easier, that would be that one.
I've never seen the chat explode so thoroughly and quickly as everybody agreeing that dates and times are the absolute worst problem in data. Yes, we love LuberDate. We love all of the packages, including wonderful libraries in Python to deal with dates and times too, but oh, my gosh. I will say date and time problems will keep me from enjoying a project, for sure. Something that I would otherwise enjoy will make me miserable.
I see some other public sector people in the chat as well, and lots of people who came from academia to public sector. I see a question from Paul that I wish we had gotten to today. Paul, I bet this is Paul Lloyd, who said he can't do video right now. We will have to get some public sector people together and have a public sector day. I think that would be so much fun.
Thank you, Paul. This has been so much fun. Thank you for hanging out with us. Florent, I hope you had a wonderful time. Thank you for sharing all of your wisdom with us. Well, thank you for having me. It was great to have the chance to just share with all of you, like seeing you every week. That was nice to be part of this.
If anybody in the chat has any questions, feel free to just shoot me an email or send me a message on Discord. I'm not super active on Discord, but I will probably get your message. Thank you to the team for having me.
Wonderful. Well, Florent, I'm putting your contact information in the chat for everybody that you gave to us. There's his LinkedIn, his GitHub, his website, which is PreludeInR. If anyone has seen PreludeInR, that is Florent's work. Thank you so much, everybody. This was so much fun. I hope that you will come join us on Tuesday at the Data Science Lab, where I think we're going to be joined by Frank Hull, who has been on the Data Science Hangout before. He's going to be talking about well, he's going to be walking through building a modeling assistant in Elmer. He's also going to be talking a little bit about TabPFN, so some tabular model stuff. Very, very exciting, very fun. Please come join us and hang out.
All right, everybody. Oh, if you want to join the lab, Rachel put it in the chat, pos.it.dslab. It's Tuesdays at this same time. It's the Tuesday version, where we share screens and do technical stuff. All right, everybody. If you want to grab the chat, you can click the top three dots or top right corner three dots, unless Zoom has changed for you or you're in the browser, and click Save Chat. Then we will see you in the Discord server, where we also attempt to put the chat if we can.
Oh, yes, and the time is changing next week. We just talked about how much we hate dates and times, but for people who... It will be 12 to 1 Eastern time, so just a heads up for people who are observing daylight savings time. Okay, yes. In the U.S., on Sunday, November 1st, we are going to fall back an hour from 2 a.m. to 1 a.m.
If you have any questions, I'm going to go on to the Discord. I'm going to try very hard to go on to the Discord and change the little event time in Discord so that it will match, because it never does. It doesn't update for me, unfortunately. I'm so sorry. But, yes. Reach out anywhere in the world But, yes. Reach out anywhere on LinkedIn if you're worried about your timing stuff. We will try to hook you up with a good time, okay? All right, everybody. Super fitting with the conversation in the chat. Thank you so much, Florin. It was so nice to see you again, and great to get to meet you at Conf, too. Yes, you're welcome. Thank you so much. Bye. Happy Halloween, everybody.
