Water Education: STEM, Youth Empowerment and Workforce Development

[00:00:12] Max Herzog: Thanks so much, attendees for your patience as we get things kicked off here. But really, really excited to be here today for this month's edition of the Water Data Forum. Water Data Forum is a collaborative webinar series put on by the Cleveland Water Alliance, the Water Environment Federation, and the Midwest Big Data Hub.

[00:00:35] For the purpose of sharing and discussing topics in the field of water data, demystifying those topics for a broad audience. I'm really excited about this month's discussion on water and education. I'll be focusing on STEM, issues of youth empowerment, and how that ties into workforce development.

[00:01:01] And it's my great pleasure, as Max Herzog, a representative of Cleveland Water Alliance, to introduce today's facilitator for the panel, Julianne Jones. Julianne is with, WEF, the Water Environment Federation, and specifically works on their Water Leadership Institute program. So it's really well positioned to facilitate today's session.

[00:01:24] So with that, I'll hand it over to Julianne. Oh sorry, one quick point, please, throughout the conversation, feel free to insert your questions in the Q& A tab. It's a little confusing. There's two different tabs, one for chat and one for Q& A. Feel free to drop any technical issues you have.

[00:01:47]  Like if you can't hear or having trouble, you know, someone's having trouble connecting, something like that in the chats, and feel free to direct those towards me. But any questions for the panelists, please share in the Q& A.  If you do write your question in the chat, we may get to it.

[00:02:00]  But you know, there tend to be a lot of questions in these. So if you want to make sure yours gets answered, please send in the shared in the Q& A. So, with that aside, it's my great pleasure to turn things over to Julianne to kick off today's panel. Julianne.

[00:02:17] Julianne Jones: Alright, so, welcome everyone. As mentioned, Julianne Jones from the Water Environment Federation.

[00:02:20] So I do run our Water Leadership Institute and we work on getting people. In the water industry is learning more about leadership and some of those soft skills and things like that. But I don't want to talk too long as we've got a lot of panelists here. So I thought maybe we would just go straight to the questions.

[00:02:39] That sounds okay to you, and I'll be monitoring the Q& A as well to help, make sure those get answered as well. So, the first question we have here is, what does, like, water mean to you?

[00:02:52] Laureline Josset: Water is something that we're all very familiar with, so we take it a bit as granted.  Especially in the US, but we don't realize how much insecurities that can be around water, whether it is total access, whether it is too much, whether it is the quality and because we have this notion that we know about it.

[00:03:11] That is, we experienced it from a very young age. We do not necessarily understand it. And we do not necessarily quantify it, measure it. So we do not understand how to data document it and process that information. So for me in water education, the way I experienced it, whether it is at the high school level or university level, is that there's a lot of bias that we need to fight again and really get into the critical thinking around water notions.

[00:03:39] Nicholas Passerelli: Good afternoon. I'm adding my two cents to your question.  kind of very basic, I guess, from here at the DC border. We've, I think, what it means in regards to education and educating not only your customer base, but your future customer base, reaching out to the community, and especially schools, be they middle schools, high schools.

[00:04:10] What not teaching them what you do right? What we do is so in the background. No one really thinks about it or understands and turns on the tap or they flush the toilet. It just does stuff. They don't know where it goes. They don't know what happens. We try and be very proactive with our community and bringing them, you know, engaging in programs about the services we perform.

[00:04:38] Bringing them on tours here to the Blue Plains Wastewater Plant, right, things like that, where it really, I think it changes the perception that they have, and we're also always discussing in these things, in these programs, the opportunities that lay for them in the future, be it, engineers, obviously water, wastewater engineers, those type of engineers in STEM. But also trades, mechanics, electricians, operators, if these are all important jobs and always emphasizing the opportunities and the feeling that you get are returning to your things to your community.

[00:05:25]  So I think that's important. Always keeping a very positive attitude. Big picture and not just focusing on say the engineers, right? That's the easy one.

[00:05:40] Mandy Hering: You know, I'll add on to that. I'll give it a little context about who I am and my perspective,  as it will be different from Nick's who's at DC water,  thinking more about, you know, Interfacing with the public. I'm a professor of statistical science at Baylor University and have been working with collaborators in water and wastewater treatment in both natural and engineered environments for the last 10 years.

[00:06:12] You know, what I've seen is that really, even from a more, maybe say technical perspective, water data is special, it's different from what you may typically see in a data science course, right? So there are data science courses that are proliferated across the country. online programs.  You can get a data science certificate in six weeks in some places, but a lot of the kind of data that you see in the water sector is very different from what you would encounter in one of these sort of blanket data science programs.

[00:06:49]  You know, they don't take into account differences like You're getting measurements over time,  you know, the order in which measurements are taken from upstream to downstream, thinking about batch processes, hydraulic retention time, you know, what kinds of information do your sensors give you versus not give you, how do sensor faults play into the data that you get.

[00:07:14]  And so there's a lot of things that with respect to water data that I think are not typically addressed in a, you know, standard statistical data science machine learning computer science academic program. And as Nick was saying, there's just tons of opportunity. You know, there's tons of water data out there and opportunities for students to really encounter a real problem in data science through the use of working with water data.

[00:07:48] And so that's yeah. One of the things that in my group, we try to facilitate is, hey, let's get students early on in their academic journey, interested in a data science career by encountering some of these really,  kind of complex questions with respect to water data, but the water data is, you know,  Easy to get right.

[00:08:16] There's lots of it out there. And so there's lots of opportunity for even students early on in their academic career to look at it, to analyze it and to offer some insight into that data,  back to utilities or consulting firms. So that's a large part of what, of what we do. 

[00:08:38] Julianne Jones: I'm sure. Did you have anything you wanted to add?

[00:08:40] Drew Ferguson: I'll step in here. Sorry, I'm late. I'm Drew Ferguson. I'm the CEO of Argonaut and we partner with the Cleveland Metropolitan School District to operate the Davis Aerospace and Maritime High School. So, quite simply, water to us is adventure, it's exploration, and it's opportunity, as you said, and the opportunities that we present are in two frontiers that our students don't readily have access to.

[00:09:07] And that's, You know, the waters in the heavens,  we put students on the water first as a step in accomplishing something or reaching out to something that most of them haven't done.  Cleveland sits on the south shore of Lake Erie, connected to the largest source of freshwater on the planet and the Great Lakes and 80 percent of them at age 15 have never touched it.

[00:09:35]  So quite simply,  at the most rudimentary,approach, it's purely about exposure,  you know, changing expectations, getting over fears. I believe Nicholas mentioned that is, you know, you've got to get past fears and you've got to gain an understanding, and it, and it appears, you know, it not only gets us out on the water to participate in opportunities, such as, as water research, recreational activities, so, water is a basic element.

[00:10:13] It's one of the basic elements of our high school education that we present here in Cleveland.

[00:10:18] Julianne Jones: Wonderful. Great things to note. I will say, at our stormwater conference this year, we had some students that are affiliated with the Poseidon Education in California come out and speak. And they are implementing stormwater things into middle and high schools, where they'll go out and actually check the parking lots and clean storm drains and trying to get that education.

[00:10:39] So that people actually know this industry exists from a young age and like. Feed them in so agreed people need to know there's options out there.  Before I move on to the next question, I realized I didn't allow you all day, like, fully introduce yourself. So I want to give you a second to make sure I know a couple of you did that, but make sure everybody knows who you are. So, Nicholas, do you want to. Do a quick intro?

[00:11:04] Nicholas Passerelli: Sure. I'm Nick passerelli. I am director of process and operations engineering here at the, uh. D. C. waters, blue planes, wastewater treatment plan in Washington, D. C. that's me.

[00:11:21] Julianne Jones: Great. And, Lauren.

[00:11:24] Laureline Josset: Yeah. Hi, everyone. My name is Lauren. I'm an associate research scientist at the Columbia water center.

[00:11:30] And I work on the nsf grants where we're trying to use data platforms inside high schools to convince the underrepresented population to switch to stem because there's. Tremendous bias in who works in that department, and we really need to make sure that it translates all the way through the data that is collected. Right? So, as a researcher, I get biased data from the lack of her presentation.

[00:11:58] Julianne Jones: Excellent. I know Mandy drew you gave little interest, but I'll still throw it to you to, do that as well. So, Mandy.

[00:12:04] Mandy Hering: Yeah, I'm a professor in the statistics department at Baylor University.  I'm also the PI of an NSF award.

[00:12:17] That we've titled Modernizing Water and Wastewater Treatment Through Data Science Education and Research. And that's been the thrust of a lot of our education activities over the last three or four years.

[00:12:29] Drew Ferguson: I'm Drew Ferguson, the founder, CEO of a 12 year old non-profit organization, non profit in Cleveland called Argonaut.

[00:12:36] We created and opened an urban high school in the city of Cleveland. It is a public high school and we, you know, assure its success. It's grades 9 through 12. It's a unique public private partnership that is built around a stem school. It is very much a vocational school. It's very much a college prep school, but we differentiate ourselves from those other programs just by saying we are a theme school.

[00:13:03] So, our students engage in activities on the water and in the air from flying airplanes to drones to submersibles driving boats and collecting water samples so we pretty much just. Establish that it's anything water, anything air and the students find their own way and we build programs around what's working, what's popular and what's needed.

[00:13:26] Julianne Jones: Excellent, thank you for that and sorry for skipping that up front, but I want to make sure we get back to, so. The next question I wanted to ask is, how does your work leverage water data for impact?

[00:13:39] Nicholas Passerelli: We have a lot of small projects here at DC Water with regards to data and analytics, i.e. So we have large control systems, databases, and historians.

[00:13:56] Full of data on the operation to say this plant the planes we get like 60, 000 hardwired IO points that come in. So we get data from these every second and they get put into this database. So we have tons and tons of data. We have a number of people within our groups that look for situations. i. eWe've started operating a tunnel system for our long term control plan, actually controlling wet weather flows and avoiding overflows to the river.

[00:14:35] So we've started, we've teamed with Virginia Tech, and we're doing a program where we are analyzing that data, looking for correlations to rainfall, rainfall intensity. Plant flows, other, other, all sorts of the data that we have to try and predict how best to operate the plant and the pumping systems such that we can get to the minimum amount of overflows and the maximum capture we can.

[00:15:06] Other things are a little smaller, like we were having an issue where we were breaking some of our final effluent filters. So we just threw, basically taking all the data, throwing it into a blender, getting a data analyst to kind of massage it and see if we can come up with correlations as to predicting which filters might be at risk of being broken in the near future, and we can mitigate that.

[00:15:34] So we're doing a lot of small projects around our utility to try and understand, let's just say, predict the future so that we can better react. In a nutshell.

[00:15:50] Mandy Hering: And I'll hop in and say, I think that. Any work that I do is incredibly interdisciplinary.  So, the water data that I get is from my partner collaborators.

[00:16:08] It may come from industry consulting companies and, you know, part of the problem is in the process of trying to understand the process that the data was generated from. And, you know, regardless of what type of system it is, the kinds of questions that we're trying to answer are generally how can we improve the operation of the system, right?

[00:16:34] Is that either by, as Nick mentioned, anticipating changes in the system that require a change in set points so you can, you know, control blowers better control pumps better, you know, reduce maintenance, improve water quality or quantity. And so that's one thing is sort of, how can you make forecasts in order to improve the control of the system.

[00:17:03] And so there are various ways to do that. And so we work on developing those methods. We also look at how we can identify faults in the system that could be faults in could be a faulty sensor, or a faulty pump. And then, you know, the data begins to, you know, show some signs that there's some fault or some break in the system.

[00:17:25] It could be a change in the influence. We look for methods where we can monitor all of the data for changes in possibly just the average levels or if there's a change in the variability in the system. And it's not enough to just say yes, we've identified a problem. We also need to give operators information about which particular sensor measurements were the ones that were primarily associated with that fault in order to diagnose the problem, right?

[00:17:58] Because it's not enough to know there's a problem. You need to know,  where do I need to go or to look to begin to fix it? So we think a lot about the methods that we might design in terms of controlling, models are controlling systems better or identifying faults in the system or changes and influence, in such a way that, you know, these are things that operators might.

[00:18:24] Reasonably used and reasonably trustworthy. And so we try to build methods that are intuitive from an operator standpoint, that are accessible. And so they don't, you know, just say, okay, I'm just ignoring this fancy method over here that no one understands. It's too opaque.  so we really. That's part of that interdisciplinary aspect of thinking about building monitoring systems or control systems that are accessible to those who will actually be using them.

[00:18:56] So I think there can be lots of impacts, you know, we're looking for improvement and impacts in terms of the environment, the system itself to reduce maintenance, make it safer, last longer, reduce energy, you know. Operate at lower cost and, and also just to be able to provide the water that is needed, the quantity that's needed at the time that it's needed in the quality that it's needed at.

[00:19:27] Julianne Jones: Drew, you got it? Perfect.

[00:19:28] Drew Ferguson: Yeah, go ahead, jump in. So the water data for a Davis Aerospace and Maritime High School student is hands-on experiential learning.  While studying marine and environmental sciences, they will utilize real world, real time, relevant data. Further, they're involved in the actual collection of the data or supporting other partner organizations as we launch buoys. 

[00:19:56]  Go out and assist.ater collections, operating vessels, in order to get those,  researchers and equipment out on the water, to fixing the vessels that are going out and doing all that work. So, overall, they are not only recipients of the data and connect to it, but they are able to contribute to that data collection. And this offers our kids endless opportunities to get involved and make those meaningful connections to their individual lives and bring about change through the community through them.

[00:20:29] Laureline Josset: In our case, we work directly with the instructors because the goal is to.

[00:20:35] Enhance the experience of the students by using federal or state data platforms that are up to date because they're managed by the government and that give data the best data that the state of the art data,  that you can have access to make sense of the news. Of what's happening currently, when we look at drought, when we look at water quality.

[00:21:00] This is because we rely on these data platforms because it makes it 1st, we don't necessarily have access to someone like Drew to help all of the teachers, but also it makes it quite interesting to switch scales,  to augment lab experiences. Showing what's happening at the state level. What's happening at the city level or the national level where it gets really tricky is that oftentimes the narrative is quite depressing.

[00:21:26]  It's very hard to be confronted with the lack of information or the information can be extremely scary itself.  The news for water in the US are very difficult when we talk about infrastructure, whether it is up to date, whether it is monitored, whether the quality of the water is sufficient or not poisoning.

[00:21:44]  It gets very hard to have this conversation. This is why I'm not stepping into the classroom, but the instructors are really working towards the social emotional learning that goes around the question of water and trying to find solutions and be hopeful for a better future. But the diagnosis of what's going on right now, and the gaps in information is not a happy story.

[00:22:09] So we're hoping that we can convince them to step up and hopefully ourselves. We can also improve the current situation.

[00:22:16] Mandy Hering:.May I add on to that? Laura. That was great. I mean, I love hearing about things that are more macro scale water data. I feel like I agree that there's a lack of these larger scale data sets available certainly available for students to work with.

[00:22:39]  And even the types of data that I work with might be more plant or facility level. Oftentimes, people will tell us we have so much data we have so much data. And then when we really drill down and begin to look at it. They have a lot of measurements, but they might not all be measured at the same point in time, so they might not all be concurrent and so.

[00:23:03] And there's often, you know, just lots of missing observations here.  Well, this sensor went down. We forgot that sensor went down for a period of time. And so when you really begin to look at it all together, well, you have a lot of data, but it's simply not sufficient to answer the question that you want.

[00:23:19]  And so sometimes it's, you know, we wind up giving recommendations for here's what we would measure if we could, you know, in order to answer your question. And I think a lot of those. You know, water is the problem, it's everywhere, right? And there's so many different scales of it, that it is a very challenging problem to think about being able to measure it,  and monitor it, in the environment and in facilities.

[00:23:52] Julianne Jones: Right. Those are all really good points. Thank you.

[00:23:54] Julianne Jones: So we have another question here that I want to ask you all, but I also want to mention to those. Participating today, if you have any questions, throw them in the Q&A section and we'll get to those here shortly. But as we're talking about our education, what do you think the future looks like from your perspective, thinking about maybe exciting trends, or there are sections with other areas for transformation.

[00:24:18]  Are there any, like, particular interesting goals, strategies, or long term initiatives that you all are thinking about?

[00:24:27] Laureline Josset: Maybe I can start because I think I was quite depressing in my previous answer.  So what was the idea behind our proposal for that grant? We just started basically. And was that when I used a data platform, the formidable Utah water rights, a platform that exists to assess whether your well is going to have an impact on creating depletion for surrounding farmers.

[00:24:53]  I use that at the master level class where I was giving lectures that might have been a bit boring at first, but when we used the platform, I realized how much knowledge was not going across. So, it was a great way to verify that knowledge was working by using data platforms, these official data platforms.

[00:25:13] It was also very useful to see students making, sorting out between what they care about and what they don't care about. And also enlightening was, Amanda was, sorry, Mandy was mentioning before, is that the transdisciplinary dimension. If you look at Wells data around Pennsylvania and New York State, what you see is where fracking is allowed and where it isn't.

[00:25:36] So you see political decisions having a very physical impact in terms of where you have data points or not. And so this really like water and data platforms and data in general,  just to reinforce the point that Mandy was getting across before, really helps getting at the transdisciplinary notions, disciplines, knowledge, skills and attitudes that we need to address these questions and find solutions.

[00:26:03] And it cannot be limited to 1 individual. So it really helps building a community of actors that are needed to make sure that every voice is heard, that we can take on those problems in a more holistic way. And there's no easy answer to that.

[00:26:20] Mandy Hering: I also didn't want to be negative that there's no water data out there.

[00:26:24] That's not what I'm saying. But it's often a, at least in our experience up to date, has been, you know, personal network connections, trying to get data from a facility. But I think that some of that is beginning to change. So water dams are a data repository where water data sets can be put. That's part of a Department of Energy project.

[00:26:49] We are beginning to archive some of our data that we've received throughout the program publicly through the Harvard data repository. So I think that more and more of these data repositories, I think, will provide some of these data sets that people would like to use for educational purposes.

[00:27:09]  And so it's not so bleak at all that I think that that might be one of the trends in the next 10 years that really changes.  And I think that, you know, more people need to take courses, take some coursework, some fundamental coursework and some data driven disciplines and more and more departments I'll say this from an academic perspective more and more departments are offering courses with the understanding that.

[00:27:39] People are going to go and apply these methods to real life problems. And so they try to make the course accessible for people with those goals. Now, I think that, providing the tools that people in water and wastewater treatment industries actually need to apply these, there's still some gap there.

[00:28:00] And that's something that we, in our group, are working on to provide some training. So, for example, next summer. We're going to be hosting a week long workshop for students in upper level and graduate students in water and wastewater treatment, environmental engineers, who want to learn how to apply data science and want to learn how to do some programming, say, in R and RStudio, who want to apply data science to their particular problems in water and wastewater treatment.

[00:28:31] So,  we've been hosting undergraduate summer research programs, and we've also in the middle right now doing an industry short course for professionals and water and wastewater treatment and now we're kind of transitioning a lot of these efforts to the upper level undergraduate and graduate level, because these are the students who are going to be entering the water and wastewater treatment field in the next few years and they're the ones that are really going to be transforming the way that data is thought about, you know, when they're working for utilities consulting firms.

[00:29:13] Working in education, etc. So we're trying to give them the skills that they need to go out and, and work with the data that they'll encounter every day.

[00:29:32] Julianne Jones:  Great. Nicholas or George, do you have anything you wanted to add?

[00:29:35] Nicholas Passerelli: Sure.  So I think from, at least from my point of view, from the utility,  what I see as important in the education field is to keep in mind two things that are very important to us. One thing that I found here And I've heard a number of our panelists talk about is the need to build a very varied team with different technical backgrounds.

[00:30:01] So, we've worked hard in our process group to basically bring in people of different, very divergent talents that may or may not necessarily be directly tied to traditional water backgrounds, i. e. computer scientists that, you know, could work for any computer company, not necessarily a water agency. But, chemists, biologists, and typical environmental engineers.

[00:30:32] You bring all these groups together to work on these projects. And in essence, the sum is so much greater, the, what you get out of it is so much greater than the sum of their individual talents. They really are able to build off each other, but I also want to make sure that we stress that. The nature of the industry is changing, be it water treatment or wastewater treatment or any resources, is it's become much more advanced and technological, the processes are becoming more complex, the equipment is more complex, certainly the instrumentation and the controls, all these things combined. Needing a greater skill set in all your workforce from the operators mechanics all along.

[00:31:28] So,  one of the things we did is we've been creating and rolling out an apprenticeship program, working with local high schools and trying to bring in promising young students and, and start to train them so that we can, we can get ourselves good, strongly based operation staff or maintenance staff that can help build into this team that we are trying to create, that helps us act independently and do these projects and Work efficiently for our ratepayers,

[00:32:13] Julianne Jones: Great. Those are really good points.  Drew, did you have anything you wanted to add?

[00:32:18] Drew Ferguson: Yeah, I'd say, you know, and in our environment again, we're looking at, you know, the big words on big push moving forward with most organizations is diversity, equity and inclusion as it applies here, moving into the future.

[00:32:33]  We should be engaging all of our various communities and individuals, making sure they have a voice and making sure that they're involved in setting the direction, setting priorities.  Each community has its concerns. I think that our challenges is bringing concerns about the environment to the forefront and a higher priority, which is very challenging because, you know, the issues we're facing in our urban communities have a much more immediate impact and challenge.

[00:33:12] To the types of kids and young people that we're trying to get into the environmental and maritime world. So the challenge moving forward is we have to address the immediate concerns and issues that are going on in our, in our urban communities and our super rural communities. So that we can elevate the environmental concerns.

[00:33:35] I think I've been spending a lot of time this morning watching a funeral, which isn't really me, but the environment just keeps coming up. I mean, we have to start tracking data. We have to start understanding data and under and really educating our next generation to appreciate that this is an immediate and, and while, you know, the immediate threats to them are often violence and food shortages.

[00:34:06]  We have to figure out a way to address those while also bringing the issues that we have in the environment to the forefront. And then elevating it so that we can tackle this as an entire community.

[00:34:24] Julianne Jones: Yeah, I think those are all really good points. I did want to mention, like, at WEF, we have started an inflow program. So you're talking about diversity, equity, inclusion. And so, the inflow program stands for introducing future leaders to opportunities and water. And so what we've been doing is we've had 2 different paths.

[00:34:46] There's a stem path where we're partnering with undergraduate and graduate degree programs. Looking for students that are historically like underrepresented in the water industry, racially, ethnically gender as well. So just trying to give people the opportunity to get involved.

[00:35:07]  And they get to learn about the water industry. They get to come to our annual conference, left tech and meet professionals and often find jobs in the water industry because they're meeting so many different folks. And then we also have a career check. Portion, which is focusing more on the operator side of things.

[00:35:25]  So there we are working with community based organizations and exposing scholars in the job readiness programs to a variety of rewarding career opportunities. So they also get to go to web tech. We also work with people that are in that area, so that they have time to actually join us and be there.

[00:35:44] So we're working on trying to get more people in the industry. I know some different states have also tried to start programs like this as well. I know PNC, WA. I believe Illinois as well has started that. But thinking about things like that, do you have I know you've mentioned. We need to get more people and we need to talk to scholars.

[00:36:06] Do you have ideas about how to make this happen or not knowing that you know you're not going to solve all the world's problems, but just thought it might be a good discussion for that.

[00:36:17] Drew Ferguson: I'll jump in again. Now, obviously. You know, what we're doing is just starting at the most fundamental level, getting the kids on the water.

[00:36:28] We started with learning to swim. You can't get a kid near the water to collect a sample if they're definitely afraid. And oftentimes that's generational. So when we have a freshman come in, ninth graders are walking into their first week of school.  We start with the pool. We start with a five ft deep pool that gradually gets deeper and we overcome those fears.

[00:36:53]  We teach them how to rescue themselves, how to rescue their friends and we have fun. It's no longer a challenge. It's something that's fun, that's something that's an accessible part of their lives. And then we move into Lake Erie and, you know, you move from a pool where you can see your feet to where you can't see your feet anymore when you're in 3 feet of water.

[00:37:14]  And not only being excited and realizing that they can go into a different type of water source and, you know, it goes from ankle deep to 50, 60, 70 feet. And you know, if they have the skills, they're okay. But then really discussing why the water looks the way it does. And so we do it very organically.

[00:37:34] And I think that, you know, you got to meet kids where they're at. You're trying to get more women involved. You're trying to get more under representative individuals involved. The first thing you do is look around and see, okay, why is this difficult for them? You know, if you walk onto a yacht club, or you meet up at your 1st session at a yacht club, and then the kids can see it clearly.

[00:37:55] They're not there, or you go out on a research vessel, or in our case, an airplane, and it's. All white men. So, trying to break that down, trying to get individuals who want to get involved with the kids and might look like them.  Not that those are the most qualified, but you have to put an extra effort to try to find folks that can help, to make the transition easier.

[00:38:18] So it's again, we take baby steps, we meet the kids where they're at, and then we, we open it up to opportunity. We do have a good relationship with the Northeast regional sewer district, working now with limb note tech case Western Reserve University, Cleveland State. So starting to really reach out and show the kids that just the variety of pathways, you can have a job next summer, you can have a job on graduation, you can continue on to college and get a job in this field.

[00:38:47]  You know, you can go up to 10, 15, 20 years of schooling, to achieve what goals you want, but just making it accessible and showing that it's fun. And it's exciting.

[00:39:00] Mandy Hering: I feel like I want to come experience Drew's program now and get out and do some sampling because I feel like I'm behind a computer all the time.

[00:39:08]  On my end of the spectrum, we're not out collecting water data, but we are on the receiving end of water data. Yeah. And I think some of the things that we try to do to get students excited is to give them real data, you know, so that's one of the challenges I would say in the data science community is the kinds of problems and the kinds of data sets that students are given to work on.

[00:39:33] Are already so easy in a sense, and they're not real.  And so, we bring students in and their freshman and sophomore years, and we throw them in the deep end and start teaching them, not just how to program because that's kind of dry and boring. We want to teach them how to think about data, how to ask questions of the data, and then how to get those answers out of the data.

[00:40:01] And to get the answers out of the data, they have to learn how to program. So in the course of trying to answer questions with data, they learn how to program in R. So it's a means to an end, right? It's a tool to use. And They recognize it early on in their career.  Well, this is what real data analysis looks like.

[00:40:18] This is what a career in data science might look like. It's not just that I have, you know, 30 observations and I can do a T test. So most people in statistics think about it. And so when they see that early on in their career they get really inspired to continue on. And it's working in groups. So even in the classroom, we have students working in groups and in teams in our summer program.

[00:40:43] We have them working in groups with a graduate student who mentors them. Sometimes both a statistics graduate student and an environmental engineering student. They meet with the stakeholders that provided the data. And that makes it really real that there, somebody spent the money to collect this data, the time, the effort, they understand it.

[00:41:06] They know what all the variables mean. And they're just sitting there, you know, wishing the best for you to find something from their data. That's interesting. And they're motivated to do something cool with it. And I think making it a more of a community aspect and not just a, and I say community I use that word loosely because what I mean is, it's not just students sort of working individually on their own by themselves.

[00:41:35] I mean, I think typically the statistics computer science fields have been somewhat solitary, you know, you went into those fields because you don't have to work with people, but we're getting a lot of students who, you know, they want to talk about things they don't want to be making decisions by themselves and maybe go down a wrong path you can, you know, brainstorm ideas about how to approach the data with they're with their peers, and that makes it more exciting for them because they're doing it together. And it's not an individual pursuit.

[00:42:08] Laureline Josset: Another thing maybe to mention is the current events that are happening, I think, in terms of motivations and showing the need to have a capable workforce is very clear it has been part of the NSF big ideas for over 10 years now, and the news.

[00:42:24] Only has reinforced this over the summer. There's no doubt in terms of Lower East Side, just in New York City, what's happening in Jackson, Mississippi, these events, make it very clear that there's a need for competent individuals working in these disciplines where we need to make sure is that at the university level, if we look at the statistics of who's coming into the programs and who's dropping out, I would say, like, lectures at the university level, often the source of the issue.

[00:42:54] And The Center for Teaching and Learning has been working against that for years, and the documents are immense in proof and strategies that we can adopt. So that's our practice of educating at a higher level. I'm not pushing anyone to the site. And I feel like if we were all to adopt the strategies that Mandy has been describing, we would be in a much better place.

[00:43:16] So make sure to have this constant attention.

[00:43:23] Julianne Jones: Did she freeze for everyone else? Well, hopefully we get her back in just a second. But I do, there was a follow up question, Mandy, to what you were saying, so. In their check that real fast. But do you believe that current students have the appropriate level of data literacy?

[00:43:43] Mandy Hering:  They could always have more and it's certainly something that,  I think students, regardless of what their area of expertise ultimately will be in, need more of.

[00:43:57] So I know a lot of students, when they finish our intro to data science class, they say, everyone needs to know this. I'm like, yes, everyone needs to know this. We're hoping that, you know, everyone will know it, whether it's applied to water or not. But you know, I'm seeing just with my own kids and their curriculum at school, I'm not, and I'm not a K through 12 expert,  in terms of, you know what students are being exposed to, but they're already learning about median mode, variability, they're beginning to think about those concepts, I think, earlier and earlier and K through 12.

[00:44:35] But I'll say that oftentimes, even when they get to college. There's some variability in terms of who's been exposed to those ideas and who hasn't. So we always try to do what we can to shore up those deficiencies and, but yes, it would be, I think that Basically, any major on campus,  can benefit from some data literacy and studies have shown that if you have a good background in data analysis, regardless of your field, that you can really excel in your field.

[00:45:15] Julianne Jones: Nick, did you have anything you wanted to add to this?

[00:45:20] Nicholas Passerelli: Sure. Quickly, because I think what I, my answer is very similar to what Drew was talking about where you're engaging early with. Students say in high school or what not, where you're trying to excite them about this whole water field and the whole breadth of the different jobs that might be available to them or career paths that might be available to them throughout rom college to trades to whatnot.

[00:45:54]  I mean, there's a whole plethora of careers that they don't, they may not know about. And, and really it's incumbent upon a lot of, especially utilities, I think, to get out into their community and explain what they do and excite the students about the field, because I think it is, can be very rewarding.

[00:46:17] Because not only do you get yourself a good career path, but you're doing something good for your community and how often do you get to say that? In your job, right? Not every job can do that. But I think that's an important thing. And I think I applaud that for any efforts in that area.

[00:46:42] Drew Ferguson: I want to jump in and follow up as well. Mandy said our program sounds so fun. I said, well, I didn't, I didn't really speak to the other staff because I'm clearly more of an operational guy. I found that our most attended and sought after programming is based around coding.  Our students have grown up in a digital world and they like adventure and problem solving.

[00:47:10] And so how that is translated into getting more involved in this and finding data and analyzing it and even developing programming to understand and make it relevant.  We're still developing that.  I know that. I looked at some of the Great Lakes data resources. There's a lot of information out there and the kids go out and grab what they can find easily, but identify 2 or 3 sources that you can stick with that way.

[00:47:41] The kids can compare results versus kids using all these different data sets. Our kids are very, you know, the basics of reading, writing and arithmetic, I think, have fallen back a little bit, but the ability to utilize computers and technologies to understand things is ingrained in our kids, regardless of where they come from.

[00:48:05] So, I talk about swimming and going on the water flying airplanes, but our most popular programs are the fabrication lab, coding and system designed. So I think that with this generation that's grown up with iPads and iPhones and everything at the palm of their hand, I think there's definitely a more natural hunger for information and converting it to understandable information.

[00:48:41] Laureline Josset: If I may add something on that part. I hope, where I see a lack of data literacy is often also at the highest level of researchers. There have been many,  documentation and probably not at the system level that much because we're talking about real data the way, all of you work actually with data is probably at the I don't know what's happening, but global models or national models or state models, there's a lot of mispractice.

[00:49:07] And I would say that there's 1 example. That is very typical that you had this group of researchers that developed a global model and showed that the groundwater was going up in southern India when, in fact, the levels kept going down and was purely from, an issue with that literacy, which is just questioning where you wells were being located. And it turns out that all of the wells that were actually used were dropped out of their data sets because they screened against that wells that actually had measurements and the wells that had measurements where in area, we had still water when it dries out, you no longer have water levels.

[00:49:47] And so they removed these points and they can. I liberated their models against these data sets. So this is just 1 example, but there's actually many of them when we talk about drinking water quality, making any kind of model beyond linear regression. And even the linear regression would be pushing it a bit.

[00:50:03]  Sometimes we have terrible statistical scores on them. So what I would often see is a lot of will and desire to use fancy, neural techniques, neural networks techniques. When we don't have the data set for that, they tend to not work quite well with time series, which is what we're interested in to deal with seasonality in water sciences.

[00:50:25] So there's a lot of gaps in the community that are currently teaching in terms of data literacy. So this is where I see us collectively, we could make some improvement in terms of what it means and defining the notion of water data literacy. I would be excited to see that written out and have a manifesto about it.

[00:50:49] Julianne Jones: Right. Thank you all so much. So I think you're just hitting the top of the hour so we want to be respectful of time but I do want to thank everyone for joining us today and all the panelists for the great discussion.  Max, I don't know if you have anything you wanted to add to wrap us up.

[00:51:07] Max Herzog: Just thanks again everyone for joining and for sticking with us through a couple of technical difficulties, we will be reconvening the water data forum next in November, to have a discussion about water equity and justice and the relationship between those issues and water data, but really want to thank our panelists here today for having such a robust discussion about the education side of things and how STEM and water and data can really be empowering the next generation to take on these issues. And thanks so much, Julianne, for facilitating a really great discussion.

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