House Directions and Institutions Committee and the House Judiciary Committee to receive the report of the Council of State Governments Justice Center on Justice Reinvestment II. And we're pleased to have representatives from the Justice Center here this morning and I'm gonna turn it over to Madeline, I think. Thank you, Senator. Madeline Dardo, my senior policy analyst at the Council State Governments Justice Center and I'm actually gonna turn it over to my colleague, Sarah Bostomsky, to get us started this morning. Hey. Great, thanks, Madeline. Thank you, Senator Sears. It's great to be here this morning. My name is Sarah Bostomsky. I am a research manager with the CSG Justice Center. So we're really excited to be able to share some of our work from our racial equity analysis that was connected as part of the Justice Reinvestment II work. So I'm gonna go ahead and share my screen. Just a quick note, once I have this slideshow shared, it is a little difficult for me to see everyone. So if you need to get my attention, please just speak up. That would be very helpful. Okay, and is everyone able to see that title slide? I'm gonna look for some nods. Okay, great, we are going to go. So as I mentioned, we're very glad to be here this morning. The work that we'll share today, I believe most of you are already familiar with, we did present the results of our racial equity analysis to the Justice Reinvestment Working Group in November and we were asked to present to you all as well today. So we're really looking forward to discussion around the results and the recommendation. So before we get started, I believe you are already familiar with CSG Justice Center since we have partnered with all of you for some time. But just a quick refresher, we are a National Nonpartisan and Nonprofit Membership Association. We work with all three branches of government to improve criminal justice systems. The work that we're sharing today is part of JRI, which is a data-driven approach to improve public safety and it's funded by the Bureau of Justice Assistance and the Pew Charitable Trust. Just a quick note as well, so our deliverables for the work that we did, this racial equity analysis included this presentation. We also have an accompanying report that we will be releasing to the state in early February, so you can keep an eye out for that as well. It'll just have more details around the analysis. So to give you kind of a roadmap for where we're headed today, the first three sections of this presentation really focus on a quantitative analysis. So our goal here is to ensure that you understand the results but also the approach that we took. The methods that we use are more rigorous than some of the work we've been able to do in the past. So we'll provide a quick background refresher on some of the findings in racial equity. In Vermont, the CST has presented in the past through JR. I'll also present an overview of what we know about racial equity and criminal justice across the United States as part of the introduction. Then we'll move to the analytic approach. Again, just the framework and methodology that we use to obtain our results. And then I will go over the results of the analysis. At that point, after that third section, we will pause and open up for questions and answers. We'll have a chance for some discussion. And then I'll pass over to Madeline who will share the policy recommendations. And again, after we go through that, we'll have a second chance for Q&A and discussion. So just to give everyone kind of a headset for how this is all organized. So this slide of the next few review key findings from the National Research Literature on Racial Equity and Criminal Justice. So this background is important for situating the project. Just so we have that kind of broader context in mind when we're thinking about the findings in Vermont. So when we look at the national research, we see that black, white racial disparities in incarceration have declined during the last two decades but nonetheless remain a very persistent feature of the criminal justice system in the US. So nationally, we know that black people are five times more likely to be incarcerated in a state prison relative to white people. Those disparities are more pronounced in the federal system.  When we look at research on drug offenses in particular, it's an area where incarceration disparities are particularly pronounced nationally. So for example, research has shown that uses drugs, drugs and sales of drugs are very similarly, there are very similar rates, I should say, across both black and white populations. But despite this kind of similarity in behavior, we know that black people are arrested three to four times more often than white folks for drug offenses. And then at the same time, there is a more pronounced disparity in incarceration. So black people are up to sign times more likely to be incarcerated in a state prison for a drug offense relative to white folks. National research really points to sentencing outcomes as a particularly important decision-making point when we're trying to understand potential drivers for those downstream incarceration disparities. So for example, the research shows that black defendants on average are more likely to receive harsher sentences relative to comparable white defendants. The magnitude of that difference or disparity in sentencing outcomes does vary depending on the type of crime, being investigated in the jurisdiction, but it's more often than not statistically significant and therefore meaningful from a policy intervention perspective. In terms of what we know, before we kind of embarked on this project, in terms of what we knew about racial disparities in Vermont, CSG had done sort of initial analysis of corrections data. It has found evidence of disparities across all corrections populations. So that is the results that are shown in this particular figure. So for some context here, we know that black people account for about 1.4% of Vermont's general population, but CSG's analysis, again, shown in this figure, found that black people are overrepresented across all of these corrections populations. That overrepresentation is especially pronounced when we look at the sentenced incarcerated population. When we examine this disparity using a different kind of framework or way of kind of crunching the numbers, we see that black people are over six times more likely to be incarcerated in Vermont relative to white people. So you think back to those national statistics that I presented, this is actually a little bit higher than the national average. So just to give that context. In CSG's previous analysis, again, before we launched this particular project, we also saw results that show that black Vermonters, as well as black people from out of state, are more likely to be incarcerated for drug offenses in particular relative to the total incarcerated population. And so those results are depicted in these pie charts. We can see the total sentenced population on the left-hand side, about 5% of people there are incarcerated for a drug offense. But then when we look at the black population, we see that that percent of the pie is really much larger. So these earlier findings are troubling, but they don't give us a lot of information about the factors that could contribute to these observed differences. So that was really some of the motivation for the project that we'll share today. Another component of the motivation for the project or the need for this more in-depth analysis was based off of stakeholder engagement throughout justice between investments. So one of the things that CSG staff had kind of heard consistently from some stakeholders in Vermont was the perception that any racial disparities, such as the ones that we just saw, in Vermont's criminal justice system, are due to crimes that are committed by out of state people. And so the implication of that perception was that any identified racial disparities are not really due to inequitable treatment of remonters by the state's criminal justice system. So we wanted to dig more into that and really look at the in-state risks out of state status of folks. And so with all of that context in mind, our current project aimed to provide a much more rigorous and in-depth analysis of sentencing to provide insight into whether race plays a role once we account for other factors. And if so, kind of under what circumstances. So this next section is the analytic approach. Again, kind of the framework for the project, the different methods that we use to arrive at our results. So our project focus is informed by that broader research literature that I spoke to earlier. And that literature shows that racial and ethnic disparities in criminal justice are a complex problem. So there are numerous factors and decision-making points that can play a role in any particular observed disparity as affected in the graphic here. And so with that framework in mind, knowing that this is a complex problem, our analysis really focused in on court processes in particular in order to provide more in-depth and rigorous analysis. And this is strategic because we knew that any findings here would be something that justice reinvestment working groups folks could address within their power to address things through policy and practice changes within the criminal justice system. In terms of the framework for the project, again, we're strategically focusing in on sentencing and we examined three major decision-making points within the Vermont court system. So at the front end, we looked at the inflow of misdemeanor and felony cases, whether there are disproportionalities there to get a sense of the lay of the land. So what is it that the courts are facing in terms of the cases that are coming in? The second decision-making point was the likelihood of conviction in the case. And the third one that we looked at were some outcomes around incarceration. The key point that I wanna underscore here is that this project was designed to provide a really rigorous assessment of racial disparity using research best practices, while also kind of accounting for key questions that we know Vermonters are interested in, especially around whether racial disparity has anything to do with in-risk status of the defendant. There are two primary data sources that we used. First, judiciary data. We looked at cases between 2014 and 2019, both misdemeanor and felony cases. We were also able to obtain in-state criminal history records for people who we examined in the court data, and that data came from the Department of Public Safety. So just to make one note as well in the framing, our analysis did focus in on black weight disparities in particular, and the reason for that is that first, the methods that we're using do require a minimum sample size, and just due to the demographics of Vermont's population and the criminal justice population, there aren't enough folks in other groups to run the analysis that we needed to do. The second reason for this kind of focus or limitation in what we did is that the judiciary's race data is actually drawn from law enforcement reports, and the judiciary doesn't receive data on Hispanic ethnicity. Yeah, I'm there. So we're unable to look at that as well. So those are important limitations. It is possible there are other disparities that we... Well, if there was an email, the only one I had was for... Okay, someone just went on to be, okay. Somebody is not muted. I guess they're muted now, thank you. Yeah, I think they muted pretty quickly, so that worked out. So just two more slides on the methodological approach, and we'll dive into the results. So as with any research project, we weren't able to use every observation available to us in the raw data. We had to exclude a certain number of cases due to missing or incomplete data. And again, we also intentionally restricted to black and white defendants. So after we processed the data, this diagram just kind of shows what the funnel looks like. Ultimately, we had 79,570 cases across that six-year period. And then the table on the right-hand side of this chart just gives you some basics or scripted statistics for the group that we analyzed. So it's predominantly white, about 94% white, 6% black, predominantly male. And then in terms of in-state versus out-of-state status, the courts have information on a defendant's place of residence at the time a case is filed. So the vast majority, 91% of defendants have a Vermont residence, 9% have an out-of-state one. We also have average age at case filing. Sarah, may I ask a question? The 79,000 is individual cases, but is it 79,000 individuals or individual cases? I mean, are there 79,000 different people in this or do some of them have five cases? That's a great question. So it is 79,000 and a little more cases, criminal cases. There are some folks who show us more than one, but that doesn't kind of drive the results of the analysis. Thank you. Yeah, great question. And please folks, feel free to jump in at any time if you have a clarification question or that sort of thing. In terms of those statistical methods that we used or quantitative methods, there are two major ones that we employed. So first we use relative rate indices which are commonly used in analysis of the criminal justice system. The goal of that type of calculation is to examine whether there are group differences in a particular outcome. We also relied on regression analysis, which is really kind of the centerpiece of this work. So this is a statistical method for examining relationships between one variable and an outcome. It's a way to assess group differences such as racial differences, while accounting for key factors that could otherwise explain a difference in outcomes. And I'm happy to talk about that more if folks would like. So now we'll move into the results. So the first set of results are looking at case info, as I mentioned earlier. So we examined whether there are disproportionalities in the volume of cases coming into the system. So like I mentioned, we used a calculation called a relative rate index. This is a standard approach for examining disproportionality or disparity. And it gives us very general information on any differences across groups. So this diagram just has an example of how we would calculate a relative rate index or RRI. It's just a series of division. So it's fairly straightforward. And for example, this uses misdemeanor data from 2019. You can see in the orange boxes at the top for black folks, we had 550 misdemeanor cases that were disposed that year. We divide that number by the number of black residents in Vermont in 2019 using census data and we arrive at a proportion. We do the same set of calculations for white folks as shown in the blue boxes. And we then get an RRI of 3.5. And what that tells us is that black people are 3.5 times more likely than white folks to be defendants in a misdemeanor case. So this next graphic is a series of RRIs for misdemeanor cases disposed across these different time periods.  We did the same series of calculations for felony cases as well. And we see a similar pattern anytime that number is above one, it indicates that black people are overrepresented relative to white folks. And consistently throughout this time period, we're seeing that black people are more likely to be a defendant in a felony case. We wanted to dig in a little bit more into the felony cases. So we divided the data up into different types of crime. Excuse me, I'm just gonna grab some water. So we look here, we can see again, the same type of pattern across 2014 to 2019, black people are more likely than white folks to be defendants in felony cases. But there are some differences, right? So there are some types of crimes for which that disparity is a little bit more pronounced. Excuse me, I'm getting some throat irritations. May just grab water again. What's important to note here is that the disparity is the most pronounced for felony drug cases. So that middle bar shows that black people are over 14 times more likely than white people to be defendants in a felony drug case. So we can just kind of despair that in mind. This analysis doesn't tell us a lot about the drivers of these differences. The additional analysis we did does get more in depth into some of those drivers. But this gives us a sense of the disproportionalities in the cases coming before the court. Can I ask a quick question about the previous slide? Yeah, absolutely, please. Just what was the crime that was represented with the bar on the furthest left? It says felony person and I'm not quite tracking what that means. Absolutely. So felony person cases could be a number of different crimes that fall into that category. But an example would be an aggravated assault. So it's that sort of crime. The categories that we use here come from a matrix that CRG, the Crime Research Group provided, that categorizes all of the different types of offenses into these larger buckets. And it's really consistent with how the National Center for State Courts will categorize these types of things. Thank you, that's very helpful. I just got a quick question. Yeah. About the felony drug, did you have information about the number of out-of-staters versus in-staters on that as well? That's a great question. So we didn't write this down, this particular analysis down in that way. But when we start to look at conviction and incarceration, we do account for that very precisely. So I'm happy to talk about that more when we look at those regression results. Thanks. I have a quick, I have a question also. I'm trying to, to a previous question, you said we were talking about cases, not individual people. And you said it didn't impact the results. And I'm trying to figure out if somebody has, is a repeat offender, are they more likely to get a higher sentence? Or if they're a repeat offender, can there, is there more likely that their crime will be up to a felony as opposed to a misdemeanor? That's a great question. So in terms of criminal history, information, the analysis, but those three sides we just looked at, the relative rate indices are a simpler type of analysis. So we're not accounting for that background information yet. When we move to the conviction analysis and the incarceration analysis, that analysis is more rigorous and we are able to account for criminal history for in-state folks using that Department of Public Safety data. So what that means is with the results that we'll see in just a moment, they account for criminal history, meaning any differences that we show are really isolated to racial differences. It's after accounting for the extent of criminal history that people have. Does that answer your question in terms of how we accounted for that type of information? I believe so. So when that analysis is done, you're in a way of seeing the more as individuals, individual people rather than individual cases. To some extent, yeah, yes. And I think it may be helpful. We should also probably discuss this a little more in just a moment because I think it's gonna, your question is extremely relevant for some of the additional results. The goal of the relative rate indices, so just to go back, this first slide on misdemeanor cases, felony cases, types of felony cases. The goal of these was to get a sense of the lay of the land. So what is it that the courts are seeing, right? What types of disproportionalities are coming to the court before court actors even have a chance to make decisions? It's not the more, we do have more rigorous analyses that dig a little further into some of these things which I can present in just a moment. So let's definitely return to the criminal history question when we look at these more rigorous results as well. Is there anything that should help kind of answer some of those broader concerns? Okay, thank you. Okay, great. So then first set of regression results. Focus is on conviction as an outcome. The second set is incarceration. So we'll go through each of those in turn to take a step back, the analysis that we did, regression analysis. The purpose of using that approach is that it's possible to compare outcomes, in this case criminal justice outcomes, between two groups, black and white folks in this case, after we account for group differences. So we can account for observable differences such as the type of offense that someone was charged with from on residential status, criminal history, and we can make an apples to apples comparison. So the next set of results really are accounting for those sort of group differences or individual differences that folks are having questions about. The results that we'll share will be expressed as percentage point differences. So just keep that in mind when you see that. It's a signal that we use regression analysis. In terms of the specific outcomes that we looked at, there are three particular outcomes. The first is the likelihood of conviction. The alternatives are a case dismissal or an acquittal. The second outcome is the likelihood of a straight incarceration sentence among people who have been convicted. Alternatives here would be things like a sentence deprivation, pre-approved for a low, a split sentence, or a suspended or deferred sentence. Finally, among people who were sentenced to incarceration, we also looked at differences in sentence length. Again, thinking about how is it that we design this work to make those apples to apples comparisons, it was important that we controlled for case and defendant characteristics. And so this is the list of factors that are accounted for in our analysis. So we have case characteristics, defense type and severity for the case, the total number of charges filed in the case, because of course that can impact some things. We also looked at disposition year and then the county in which this case was heard. For defendants, we were able to look at race, of course, demographic information like sex and age, Vermont residential status at case filing, and then for people who were residents of Vermont at the time the case was filed, again, that's where we were able to look at lifetime criminal history records as well. We did run two sets of analyses to produce the most rigorous results that we could. So primary results, look at both Vermont and out-of-state residents. They control for all of that information that I just showed, except for criminal history. Go ahead, yeah. I'm curious, go back to the previous slide, if you would, and I have a question about that. Why couldn't you account for the criminal history of out-of-state residents? Is that because you didn't have access to it or? But so the, does the courts not have access to that information as well? That's a great question. Yeah, that's a great question. So court, of course, court actors would have access to some of that information when they're making determinations in a case, of course, but what matters to us is what is recorded in the court's data. The court data system does not track, along with the case information, does not also track criminal history. So in order to get anything from criminal history, we had to submit, we had to go through a whole other four or five month process with the Department of Public Safety to get permission to match people from the court record to that in-state criminal history, lifetime criminal history information. It's a really cumbersome process in pretty much every state. It's virtually impossible in a project that has less than a few years to get out-of-state criminal history records as well, right? Because that requires going to Massachusetts, New York, all of those states might have a six month to year long process. So, yeah, so we focused in on Vermont. Yeah, you're welcome. Thanks. First, so that primary set of results is everyone, Vermont and out-of-staters. The secondary set of results focused in on Vermont residents and accounts for all of those control variables that I mentioned and in-state criminal history. So when we talk about results today from this direct-based set of regression analyses, we always compared the primary and secondary results before arriving at a conclusion. So the results are very consistent that we're sharing. We examine differences across 10 offense categories. We are only presenting results when there was a statistically significant black-white difference in one of these categories. So we have our misdemeanor categories on the left and then the felony categories on the right. The reason that we're only presenting a statistically significant result is that that threshold means we have sufficient evidence to rule out the possibility that a difference we're observing is due to random chance and is less representative of the whole population. So our first set of results around conviction are shown in this graphic and our results indicated that this particular decision-making point doesn't appear to be a major driver of black-white disparities. So what you see in this graphic, these were the offense categories where we found a statistically significant difference. You can see from misdemeanor property offenses. Once we account for all of those case and individual characteristics that I mentioned earlier, we see that black people are actually experiencing lower conviction rates for misdemeanor property cases than white folks. That's what that first set of bars tells us. The second one for misdemeanor driver offenses tells us that black people shown in that orange bar are actually more likely to see a conviction. So overall, the results do not show consistent evidence that the disparity is sort of the big takeaway here. And other studies that have been conducted in the Northeast have actually found very similar results. So specifically that black folks are more likely to have a case of misdemeanor and less likely to see a conviction, but it's somewhat consistent with some of the other work that has been done. The next set of results focus on incarceration. The first particular outcome is the incarceration in-out decision. So was there a sentence to straight incarceration or not? So the results here show that black people are more likely to in-face incarceration for certain offenses, especially felony offenses. So the bars that are shown in this chart indicate that we found a statistically significant and therefore meaningful difference. So what you can see with the bars here, the blue bars are lower for white folks, so orange bars are higher for black folks. And that indicates that there's a higher likelihood that black people with the same type of case characteristics, the same type of individual characteristics are more likely to be sentenced to incarceration. The results are certainly sobering and they're the most pronounced when we look at felony property and felony drug cases. So here, black people are 18 percentage points more likely to be incarcerated for those types of offenses. And again, like I mentioned earlier, when we restrict this analysis to only a Vermont resident, they do not, the results do not substantively change. Sarah, this is right side of it. Sarah, this is right side of the Vamons. On this bar chart where it says misdemeanor person, should that be felony person? That's a great question. I think our title in that slide, in this last slide is a little bit confusing because we said for certain felony offenses, but no, it is misdemeanor person. So we have the three felony crimes and then one misdemeanor crime for which that disparity exists. But thanks for the clarification on that. So the folks are not confused. Having seen these results, we wanted to dig in a little further to make sense of the types of specific offenses that are showing up in these cases. So we dug into the drug felony cases a little bit more and among cases in which a black defendant is sentenced to incarceration, the most common top-filed charge is a cocaine possession or sales charge. And so we have these tabulations shown on this slide. The key takeaway here is that the most common types of offenses that are leading to black people being incarcerated typically are having to do with a cocaine possession or sales charge. It's different for white folks in Vermont. So for white people who are convicted of a felony drug offense and sentenced to prison or to incarceration, the most common types of drugs that are involved in their cases is more often heroin. So the reason that we did this additional analysis was to inform some of the policy recommendations. So Madeline will speak more to kind of how we are leveraging that type of results in our policy recommendations. The final set of results is around sentenced lengths. When we examined the 10 offense categories that I mentioned earlier, we didn't see any consistent statistically significant black-white differences. So that was the key kind of finding there. So the results around incarceration are actually very consistent with that broad national research literature that shows that black folks are more likely to be sentenced partially relative to comparable white folks. So to sum up, the next couple of slides are a high-level summary of these results. We did find that there are observable and substantively important black-white disparities in both the contact with criminal courts as well as sentencing outcomes in Vermont, right? So the first set of results, the relative rate indices, are indicative of overrepresentation in criminal cases. And for example, in 2019, black people were 3.5 times more likely to be a defendant in the misdemeanor case, 5.9 times more likely to be defendants in a felony case. And the key takeaway there again is that's a result that is a difference in terms of the cases that are coming to the courts. So it's before court actors are able to make decisions, but it gives us just a general sense of what those disparities look like before court actors are making decisions. When we looked at the decisions that folks in the courts are making, we looked at conviction first. We found that there was not consistent evidence of a racial disparity there. And so we concluded that it's not a major driver of black-white incarceration disparities that I mentioned earlier. When we looked at the incarceration decisions, the sort of centerpiece finding I think of this work is that there are very kind of robust consistent disparities around the decision to sentence someone to straight incarceration. Those disparities are especially pronounced when we're looking at felony drug and property cases. And so we'll talk more about the recommendations that we have and how Vermont can kind of address some of those disparities. Finally, for sentence length, this was another area where there was no evidence of a disparity. So just to sum up kind of the high-level takeaways across the work that CSD did in both the 2019 analysis and the more recent analysis. First, we know that black people are about six times more likely to be incarcerated, relative to white folks in Vermont. We know that there are disparities present in the cases that are coming to the courts, meaning that there's something in the community that may have to do with policing or prosecutorial decision-making. It's not kind of in the scope of our work, but clearly important. So there are disparities in those cases coming in and they're the most pronounced for felony drug cases. Then finally, once before the court, black people are not more likely to be convicted, for most offenses, they're not more likely to be sentenced to a longer incarceration term, but there are statistically significant racial disparities in who receives an incarceration sentence, especially for felony property and drug offenses. That last set of results doesn't change when we examine Vermont residents exclusively and account for their in-state lifetime criminal history. So the takeaway there is that the racial disparities exist independent of those sorts of factors. So I'll pause now. We finished up some reables. Quantitative analysis. Madeleine has a lot of content around the policy recommendations to share, but I wanna pause while those results are kind of fresh and remind us to see if there are questions or discussion points that folks have. Why don't I jump right in with a question? Sure. A lot of people didn't like hearing this information and there was concern about where the information came from on addresses, whether or not, you know, there were very temporary addresses and people really were from another state. And I realized what has happened here is we're one of the first state type of work in and I think to be, well, I mean, obviously it was information that we had a lot of anecdotal thoughts about and we've debunked a lot of theories and so naturally there's pushback. How comfortable are you with these numbers? How comfortable am I? The results that we have use really the best, most comprehensive data available in Vermont and use research best practices. So the methods we use mimic the types of methods that folks use in peer reviewed journal articles that are being published. So I'm very comfortable with them. It's a rigorous analysis. The results around incarceration disparities are very consistent no matter how we run the numbers. And then one final thing that I look at when I do this type of work, I look at other research that has been published to try to make sense of kind of where these findings line up with other studies that have been conducted and the results are very consistent with that large body of work that has been published in the United States. Thank you. And this is just from, there's a little background. I think other states will be looking at what the justice center has developed here from not be requested. Similar in us, a lot of us follow things that happen in New Jersey, for example, in Delaware or whatever and say that's a good idea here. So I suspect that others will be looking at this. We are the first in the nation where the justice center has done this type of analysis. Absolutely, no, I mean, Vermont is a front runner in tackling this type of question with this level of data. Absolutely. Yes, and to underscore what you said, Senator, that Vermont is the first state to use a justice reinvestment approach to really dive into resequity in this way. And so not only is it kind of a first for the justice center to be able to have the opportunity to do this level of analysis, but you all are the front runners in justice reinvestment for really taking a hard look at race equity and a rigorous look at race equity in this way. I think sometimes we're here in this little state and don't realize that others are watching. It's really all. Any other questions or comments? Representative Grad or Representative Copy, I saw I had her hand, I think. No? Oh, that's my hand. Representative Emmons or Representative Grad, thank you, Val. And Sarah, thank you so much. Very helpful. I'm happy to go into the policy recommendations now, Sarah. Yep, I will get the slide back up. Okay, thank you. So before we go into the recommendations themselves, we do just want to bring you all back briefly to a framework that Sarah introduced previously because we think it's really important to just emphasize that racial disparities have the opportunity to compound at each of these key decision points as someone moves through the justice system and that the impact of those compounding disparities are what's ultimately reflected in the racial disparities and incarceration that you see in Vermont and nationally. However, the factors that can perpetuate disparity in the system, they're listed here in the green box, those are also the same factors that can be used as mechanisms for improving racial disparity outcomes. So this includes changes to statute and policy and practice, as well as really understanding and engaging with implicit and explicit bias and how it impacts the use of discretion throughout the system. So recognizing this complexity, the recommendations that we'll share with you all today are really strategically focused on how these factors can be harnessed to address the compounding impact of racial disparities and sentencing that were identified in this analysis. And so our first recommendation is actually focused on the classification of drug offenses. As Sarah emphasized, and I'm sure you noticed a key through line in our analysis results so that racial disparities are particularly pronounced for felony drug offenses. And nationally, it's really important to note that these inequities are the result of targeted disproportionate enforcement of drug laws in black communities following desegregation and this includes the war on drugs. So with this context, we also understand that Vermont has been discussing offense classification and in particular reclassification within drug offenses. And so we really believe that these analysis results are an opportunity for you all to bring a race equity lens to your current conversations, particularly related to felony drug possession. We've proposed one way to do this here, which is to defelonize drug possession to the extent possible given Vermont statutory context. It's important to note that when thinking about of the defelonization of drug possession, unlike other states where intent to distribute is an enhancement that's provable by a number of factors, Vermont relies on weight is really that de facto intent to distribute mechanism. So it does make it a little bit more complicated to think about fully defelonizing possession regardless of weight as other states have done, because the next question that comes up is, well, what if it is at amount is well above quote unquote personal use, is that still possession? So given that particular context, we propose one path forward here, which is to reduce lower to mid-level felony drug possession offenses to misdemeanors. And then to look at that, read about that highest level of possession to reflect significant amounts intended for distribution. During our conversations throughout this whole process, we've heard from you all that there are other approaches that are discussed around decriminalization or potentially introducing intent to distribute. We also heard there have been conversations about defelonizing all levels of drug possession or regardless of weight, and then using trafficking to charge large amounts of drugs. We really think all of these options point to a similar approach, which is moving away from the filimization of drug possession. And the results of our analysis show that this is also a way where you can really address an area where we've identified the most pronounced racial disparities and defendant representation and sentencing in Vermont. And before we move on, we also understand that it's part of this classification process that the sentencing commission was actually specifically directed to look at penalty reductions related to opioid use. And as Sarah pointed out, kind of based on the results of this analysis, just focusing on opioid related possession offenses could potentially increase racial disparities in Vermont. Our findings indicate that while heroin possession is the most common felony drug offense for which white people are sentenced to incarceration, cocaine possession is the most common for black people. So really this is another example of a way to bring a racial equity lens, the classification process, and avoid perpetuating this historical system inequities that we talked about earlier related to drug policy. It's really important to look at penalty reductions across substances and not really just focus in on opioids. So on the next slide, you'll see a couple of examples of states that have reclassified drug possession from a felony to a misdemeanor, five having done so up to the third conviction regardless of amount. And two of these states have seen significant reductions in racial disparity. So in California, there were substantial reductions in racial disparities in arrest, jail bookings and incarceration. And then in Oregon found a 61% decrease in racial and ethnic disparities in felony convictions. But this is something that other states have done and seen to be able to reduce racial disparities around drug offenses. So our next recommendation relies on a mechanism that Vermont actually currently has on the books, which is non-binding sentencing guidance. So Vermont has non-binding sentencing guidance for determining misdemeanor and felony probation term links. And so as you see here, this guidance seems to have ensured that there are actually no differences in probation term length by race. Not shown here, but our team also found there were no geographic disparities as well. So this is in line actually with national research, which indicates that sentencing guidelines or guidance are successful at reducing racial disparities by providing a structure that doesn't remove but actually informs judicial discretion. And so for our next recommendation, on the next slide, thank you, Zira. For, we suggest actually using this mechanism that's been proven to be effective in Vermont or another mechanism like presumptive probation to provide some structure around that in out incarceration decision for drug and property offenses where incarceration disparities were found to be most pronounced. So as the analysis results showed, black defendants are more likely to be incarcerated for certain offenses compared to white defendants. And sentencing guidance or presumptive probation can really help spread structure around this in out decision to support the use of discretion and encourage consistency. And it can also focus really on where those own offenses where racial disparities were found to be most pronounced, as well as where there's an opportunity to support the use of probation rather than incarceration. And so our final two recommendations, our final two policy recommendations will also have some data recommendations for you as well. Our next recommendation focuses on identifying racial disparities and diversion and pretrial services. So national research has shown that BIPOC individuals are less likely to receive diversion than white people. And the Vermont Attorney General's pretrial services and court diversion report to you all does not currently include a breakdown by race and ethnicity. So it's not really possible to understand if these same disparities are actually present in Vermont. So there is an opportunity here to add to that report, not only who is accessing services, but also who is eligible in declining services by race and ethnicity to really understand if there are any disparities in the use of diversion or pretrial services. I will share that when we started having conversations about this recommendation with the Attorney General's office, we found that they've actually started collecting this information. Some of this information are very much headed in the direction of this recommendation. So that's really exciting progress to share with you all today. And then our last policy recommendation is actually a newer one for us at the Justice Center, but something that we're really excited about has seen to be successful in other jurisdictions for reducing racial disparities for specific offenses. So as some of you may remember, analysis for justice for the investment too found that 99% of cases in Vermont are resolved through the plea bargaining process. And this is not uncommon across the country. And so in recognizing the importance of charging and plea bargaining as part of that court process, our recommendation is for state's attorney's offices to explore the use of internal guidance to support the use of discretion and charging and plea bargaining decisions. So this guidance again, can be limited to specific offenses where disparities are most pronounced with the goal of really helping to increase consistency in decision-making. And that guidance can address when and when not to charge, can provide structure on what to charge in specific circumstances and factors, prioritize diversion and non-custodial responses, then also really just provide a framework for guiding discretion during the plea bargaining process. And so probably unsurprising to this group, this type of guidance has been found to be most effective when paired with the regular collection and monitoring of relevant data to understand implementation. Some jurisdictions have also used an internal charge review process as an additional reinforcement. And we do have a couple of examples for you today of jurisdictions that have used guidance to reduce racial disparities. The first is the Milwaukee district attorney's office. They were able to actually eliminate disparities and drug peer-familiar charges when they established guidance that prioritize diversion or dismissal. They also coupled this guidance with a limited internal charge review process and then regular data collection in order to benchmark progress. And a second example is the Philadelphia district attorney's office. They implemented sentencing policies governing charging and plea bargaining decisions, which resulted in decreased disparities in community supervision sentencing. And so our final recommendations are focused around data. Before we go into them, I just want to recognize we know that Vermont is in the process of doing a really deep dive into how to increase the collection analysis of race and ethnicity data through the work that our DAP has done developed the Office of Racial Justice Statistics, which they submitted to a report to you all on in November. So I just want to make sure that underscore that several of our recommendations here also reinforce some of the key focuses of that work and the need for intentional investment and thinking about the expansion of race and ethnicity data collection analysis in Vermont. So looking at the first recommendation as Sarah mentioned previously, Hispanic ethnicity data separate from race is not currently available in Vermont DOC court data, or excuse me, Vermont court data. DOC actually has begun this best practice in kind of race and ethnicity data collection and so we recommend kind of exploring ways to actually have this information available at the law enforcement and the court level. It's really interesting to note that the initial data that we have received from DOC under their new data collection method for Hispanic ethnicity is showing a much larger Hispanic population than was previously reflected in their prior data or the court data that we received for this analysis. So it is like the possible that you will have the volume of the ability to do a similar analysis on Hispanic disparities as we did here focused on black and white disparities. And then you'll see our remaining recommendations include investing in staffing and system improvements to really increase data collection analysis capacity, collecting and analyzing data statewide as well as by judicial district, looking for opportunities to make racial disparity data public and then engaging impacted communities around that data collection and particularly thinking about potential ways to gain qualitative information as well as quantitative information. And then continuing that engagement as you're thinking through policy development and implementation. We know that this is a key kind of cornerstone of some of the work that our DAP has done in the Office of Racial Justice Statistics. And then our last recommendation is that, once you have this information, you can use this to also, in addition to ongoing policy development, you can look at ongoing judicial training to support consistency and decision-making. And so just to close out, what we've shared with you all today are really strategic data-driven ways to address disparities at sentencing. But we do wanna make sure we acknowledge that we examine really only one driver of those incarceration disparities identified early on in the Justice Reinvestment II process. You all do though have two additional efforts in the state that can help you identify other drivers of disparity outside sentencing. The first is the work that I mentioned previously by our DAP around the Office of Racial Justice Statistics. If sufficiently resourced, this entity would really provide you all with a more complete understanding of how those disparities compound as a person moves through the system, as well as give you the information to develop additional targeted data-driven policy reforms. And then the second is actually, we're being done by some of our colleagues here at the Justice Center with the Vermont Network Against Domestic and Sexual Violence and the Department of Public Safety related to domestic violence, which also includes an equity focus. So as you all are thinking about moving your criminal justice focus, racial equity work forward this session, you are a really, really well positioned to both make concrete policy changes now to improve the racial disparities identified in this analysis, as well as continue to improve your data analysis capacity to identify other areas of focus. And again, just wanting to really recognize and congratulate you all on the Justice Reinvestment II working group for taking this on and really leading the way and using a justice reinvestment approach to look at this really, really critical area of criminal justice system reform. And with that, we're happy to take any additional questions about the policy recommendations or the analysis itself. Representative Grad, do you wanna, Representative Colburn has a question, but I didn't mean to as representative Nolan. So why don't we start with Representative Colburn? Thank you. Thank you, Senator Sears. I have two questions, which I hope is okay. And the first relates to your first recommendation. To really defalinize, which I was excited to see. But your recommendation as it stands really looks at making possession misdemeanor crimes. And I know increasingly we're starting to see more and more examples of places that have made these civil penalties rather than criminal penalties. So I'm wondering if you have started to look, I know some of the data is quite new coming out of those places, but Oregon, for example, you use the data from their shift to misdemeanor, the misdemeanor approach, they've estimated since kind of moving to the civil penalty decriminalization approach that they've reduced thousands of charges just based on prior arrest data. So I'm wondering how much you looked at that model and also just what data points you think might be important to track moving forward as we see more and more examples of that approach moving forward. Laura, thanks for that question. I think we did have in conversations with folks as we were talking about potential policy recommendations with stakeholders and obviously decriminalization came up. And I think it's something that we're interested in pursuing further are the data that we had available about what other states have done and what has worked directly to reduce racial disparities, pointed more toward the decriminalization because that's really just what we have, we have the most access to you at this point, as you mentioned, it's relatively new. But again, I think it all kind of points to the same thing, right? It's changing that approach to what is currently felony drug possession and really being able to use the information that we have in this analysis to inform that whatever steps that you all decide to take, whether it's going down a path toward decriminalization or decriminalization, either one of those are really, want to point to the same thing, which is changing that approach and hopefully bringing in a race equity lens to be able to reduce those racial disparities for felony drug possession. And as far as thinking about data points moving forward, you know, I think one of the good things about this particular analysis is I can pry you all a little bit of a benchmark. And so as you think about moving forward to be able to do something similar and be able to develop out other ways to be able to bring in additional data points, particularly if you move forward with the Office of Racial Justice Statistics to write you an even more complete picture of what's going on. We were very kind of focused at court processes. There's a lot of different decision points around that that you could also bring in as well. I think one point that Sarah has spoken to before was just thinking about accessing prosecutorial data. So to be able to understand a little bit more and then obviously going back a little bit further, you're looking at law enforcement data. So there's a lot of different opportunities to be able to use this analysis to build on it moving forward as you're thinking about benchmarking progress. Great, thank you so much. I had one other question about your recommendations if I may, which is just that 14% number for the detained of black people represented in the detained population really stood out to me. And I believe there's research showing that pretrial detention increases the likelihood of more severe sentencing and even incarceration. So I'm thinking about your well taken point throughout your presentation about the compounding effects. I wondered if you also thought about bail reformer, if you think there's some room to think about bail reform as a secondary policy approach to sort of start to nip some of those downstream effects in the, sort of thing. Sure, and that stood out to us as well. And while the focus of our analysis was a little bit more downstream and Sarah can talk a little bit about that as well. That did stand out to us as well. And while we, again, there's several places in the analysis where we think they're kind of flags that come up that say, hey, this is something that you could be dived into a little bit more. And I think that that's one of those certainly. And I don't know, Sarah, if you wanna add, I know we talked about pretrial early on in this analysis. Sure, I can add a few thoughts. And I think just for the group's reference, I think representative Colerain, you're referring to one of the additional signs. So we looked at some relative rate indices across the different corrections populations. If you look at side 52, one of the findings there was that there's really a disproportionality in the detained incarcerated population, so pretrial. We weren't, like Madeline said, able to look at that in depth with the data available to us. But I guess I'll just sort of reemphasize what representative Colerain said, which is that there is analysis from the national literature that shows that pretrial detention can correlate with likelihood of being sentenced to more harshly. And so it's certainly something that's important to look at. And I would hope as the Office of Racial Justice Statistics is growing and being built out that those could be areas where more data collection can be built. So hopefully that's helpful in response to your question. Yes, thank you both very much. Representative Dolan. Thank you. And thanks for everybody that was involved in this work and creating this report. It is extremely helpful to kind of get that confirmation that these disparities do exist and to help point us in the direction of where we can work for policy recommendations. I have a question, I think it's maybe the policy recommendation too that had to do with pretrial services and diversion. I'm curious, it looks like, or it seems like Community Justice Center work was left out of that recommendation. And I'm curious the rationale for that with my understanding of the work with Community Justice Center, it's not associated with the court at all. So it seems like that would be even more beneficial because pretrial services and diversion are connected to court. So wouldn't it be great to also look at racial disparities before even entering the court system? So if you can speak to that and if that is something that should be looked into that would be helpful. Sure. And so because of the nexus that we had in this particular project to court processes, that's why you see that focus on pretrial and diversion. I mean, that being said, there are obviously a lot of other areas and particularly moving downstream that would be beneficial to have this similar type of information to be able to do this similar type of analysis. So definitely wanna not and imply that, just because it's not kind of included within the scope of our work, that it's not important because it definitely is. I think for us what we saw was what we were looking for data around, which is just a lack of data are basically to be around diversion and pretrial services and kind of honed in on that. But again, that doesn't mean that there's not other places where there's also that same lack of data. Representative Taylor. Yes, I can see with most of your policy recommendations I can see how they would modify behavior. Except the first one, I'm not clear on how reclassification can modify behavior. I can see how it would modify the statistics, but how does that work where it would make a person or we would reduce racial inequities by changing the reclassification, not just the numbers, but the actual behavior of racial inequity. Thank you, that's a really great question. So I think you're absolutely right. And then that is one that will, it's a little bit different than the others, but when you think about the, well, I'll go back to what Sarah mentioned around the use, the similarities in drug use and drug sales for black and white people and then the disparities that we see in arrest and incarceration. And so recognizing those disparities and understanding that we have a law that's being applied inequitably and we have a potential public safety, smart public safety approach to look at drug possession from a public health perspective from a different way in order to be able, instead of kind of moving to a penalized approach, but be able to think about how you're thinking about possession as a way from a public health perspective as a way to meet people where they are and help improve those outcomes for their lives, that you have an opportunity to be able to both make a difference when you're talking about addressing that disproportionate enforcement, but you can also take a smart public safety approach to what is also a public health problem. So we see those two things kind of moving together as one and as an opportunity for you all, an opportunity that also several other states have seen and taken advantage of when you think about both defilemization and decriminalization to move away from penalizing possession and moving toward a more public health approach for that particular offense. And in this way, you're also able to then think about reducing those racial disparities that are the result of some historic inequities in the enforcement of drug policies. Okay, very good. That makes sense. Thank you. Actually, I have a question, Madeline, and you said something about most states don't use weights of drugs to determine and how do they do that? And should we be looking at a different, I mean, is our whole idea of weights. I'm thinking about fentanyl. You know, how a fairly dangerous drug at any rate, any weight, and it doesn't really fit in well with whatever we do, whatever we've done on the criminal justice side. I'm curious about other ways to go about dealing with the problem. Sure, and we had a lot of conversations throughout this process with stakeholders and Justice Reinvestment II members about intent to distribute and how we see other states handling intent to distribute as an enhancement that can be provable by a number of factors. And one of those could be weight, right? But it could also be communications, it could be other circumstances. And so, and then what we see in Vermont is that it's really specifically tied to the weight of the amount of the substance. And so what we heard was really kind of mixed opinions about some of the challenges that could happen of introducing an intent to distribute mechanisms similar to what other states may have into Vermont. We heard concerns around kind of, it would increase searches, there would be, they could actually end up with increasing the level of the charge. And to be frank with you, we really didn't have what we found to be kind of solid understanding of what the unintended consequences would be of introducing that intent to distribute mechanism. So it's one reason why you see in our presentation that we really talk about working within kind of the statutory scheme that you all already have, but recognizing that there are places that do that differently, but we also know that potentially introducing that intent to distribute could have some other consequences that we heard from stakeholders that there were significant concerns about in the state. Thank you. Other questions from Madeline or Sarah? We're gonna meet our 12 noon adjournment time. I thank the Justice Center for everything that you've done for us throughout this process, perhaps from Representative Grad and Representative Emmond's section. I'll defer to the Dean Vaz. No, no. I just echo Senator Sears gratitude and appreciation. Thank you for the past two years, two and a half years of the work that you've done. It's really helped us refine our discussions within our respective committees. And you've really given us a path forward over the past few years with your recommendations. And I really appreciate the work that you've done. And this report in particular is very helpful for us to really refine our thinking and really process how we move forward. So thank you. And I'll just, yeah, I'll just also echo the remarks of Senator Sears and Representative Emmond's really does give us a path forward that we are starting with this afternoon hearing from our DAP and so really, really thank you. Your work really brings a lot of frankly, credibility to our work, puts the politics aside. It's data driven and it's so, so important. So can't thank you enough and hope we get an opportunity to work with you again. So, and I also wanna thank Senator Sears and Peggy for putting this together and for Representative Emmond's for your committee joining us. I think it really is helpful for all of us to hear this information together as we work together. So, thank you. Thank you all. Thank you all so much. We appreciate the opportunity not just to speak with you today, but to work with you. I think it has been two and a half years Representative Emmond, it's been a long time. You all have stuck through some really hard work during some really just unprecedented times. And we appreciate that and are excited to be able to share with other states the good stuff that you have done. We'll make sure as Sarah mentioned that we have a report that's coming out probably in early February and we'll make sure that you will have access to that and look forward to any questions you might have. Before we adjourn, I just wanna express my gratitude also to both the administration as well as the Chief Justice and the Judiciary. They were involved every step of the way in this process. It was taking all three branches of government as well as many advocacy groups from the ACLU and the Victims' Rights Community. So I think that that process helped us to achieve some consensus on many of these issues. Your process, thank you all very much.


I think we have to start better. That's the only thing I think this is. Pert was the game that we didn't start very well as a team, and that we have to be watching against Newcastle Jets, that we start from minute one, that we are fully focused and we keep the concentration, that we don't have to chase the game again, but we show good character in that game to come back twice, so we have to be ready from the start. I was thinking, no, I think it's only happened in England, I play in Holland and Germany, so most of the time you just have the winter break. So it's going to be the first time playing a game and a new experience as well, so see how it goes. I mean, it's amazing, you know. If I had a possibility with my dad, he was not a soccer player, but I was always with him when I was younger, and that's where I get it from, and it's a good time to spend with your son. He lost football, so it's good to have him around and have a dad and son thing. Yeah, look, that depends on other things, but we've got to control what we can and we've got to play well once more in front of our home fans, and if we can do that, then we have a great opportunity to take another three and enjoy Christmas, as you say. Good, he's trained now, he's done three sessions, which is probably the most he's done in the last three weeks, leading into a game, so he's strong and fit and he's available for tomorrow, that's for sure. Dimas is good, he trained yesterday, so I expect him to be ready. Look, they've shown that if they do sit back, they can be very dangerous on the break. They had some opportunities last week against Adelaide and they always looked dangerous, so we know what to expect. It will be a tough game, but we need to worry about what we do and what we can do with the ball and obviously be aware of their threats without it. And if we do that, then we've got to be confident to take three points.


Good morning and welcome to this tutorial video from ST Microelectronics. I am Andrei Vitale, sensor ecosystem manager in the Internet of Things Excalement Center in Santa Clara. Today I am going to present the sensor tile development kit. We will see what are the components of the sensor tile and how the sensor tile can be used standalone or with host motherboards. We will see what is included in the sensor tile kit and how the parts in the kit can be used or assembled. We will see how the sensor tile can be used out of the box with its companion app ST BlueMS running on your smartphone. For advanced and technical uses, we will present the block diagram and the pin out of the sensor tile. Let me introduce the sensor tile. This sensor tile is a reference design, an evaluation tool and a development platform. This sensor tile is a tiny square shaped module. It is only 13.5 by 13.5 mm. This module packs a powerful microcontroller, STM32L4, a Bluetooth low energy radio and network processor, Blue Energy MS with its balloon filter and antenna, motion sensors, 3-axis accelerometer and 3-axis gyroscope, environmental sensors, 3-axis magnetometer and the barometer, and a digital microphone. This sensor tile can work standalone, just connect the power supply and ground pin to a power source, such as a coin cell battery. This sensor tile can also work with a host board, for this purpose it comes in two flavors, one with a connector on the back and the other without the connector. Not having a connector makes it easier to solder the module on a host board because the back of the board is flat. This sensor tile is part of the sensor tile kit. The sensor tile kit includes two different motherboards that can extend its capabilities. The sensor tile cradle board and the sensor tile cradle expansion board. The first motherboard, the sensor tile cradle, is compact and includes additional sensors, in this case the ambient temperature and relative humidity sensor. It also includes the battery management circuits, a micro USB connector together with other connectors and an SD card slot. The SD card enables the local storage of all the data, removing the need to stream them out immediately. You need to solder the sensor tile on this motherboard, connect the battery, close the protective plastic enclosure and then you can power up the device by pushing the slider towards the USB connector. The second motherboard, the sensor tile cradle expansion, is slightly larger and includes an audio digital to analog converter, an audio jack connector and Arduino connectors, together with other components such as the USB micro connector. The Arduino connectors enable the expansion of the system. As an example, one can add a Wi-Fi or NFC expansion, a stepper motor control expansion or any other expansion board with Arduino connectors. You need to plug the sensor tile on this motherboard and then you can power up the device by connecting the USB connector to a USB port or power source. Once you have assembled your sensor tile, you can connect it to your smartphone. You just need to download and install the ST BlueMS app from the iTunes or the Android Store. If you want to learn more about the sensor tile, you can find other tutorial videos from ST Micro Electronics. As an example, search for the unboxing the sensor tile video on YouTube. Advanced and technical users can keep watching this video to learn more. This is the sensor tile block diagram. The core is the microcontroller STM32L4, a Cortex M4 with a floating point unit. The microcontroller reads the data from the sensors through a 3-wire SPI. The digital microphone needs a dedicated 2-wire bus to transfer the high data rate bitstream to the microcontroller. The bitstream is known as pulse density modulation or PDM. The PDM is a stream of bits. The typical speed is 1 to 2 Mbps. The microcontroller has enough computational power to process all the data coming from the sensors and the audio coming from the microphone. Once processing is completed, the microcontroller transfers the data to the network processor, which includes the Bluetooth low-energy radio and the Cortex M0, which is needed to run the radio protocol stack. The sensor tile can be connected to a host system. For this purpose, several pins are available. Digital buses such as I2C, SPI, UART or USB are available. They use different pins. Therefore, they can work simultaneously. An additional digital microphone can be connected to the system to enable beamforming and sensors localization. There is also the option to use the analog-to-digital converter embedded in the microcontroller. There is a voltage regulator on board. It regulates the primary input voltage to provide the 1.8 volts for the entire system. The regulated voltage is also available on a dedicated pin so that it can be used to power the host system. The secondary input voltage powers the input-output pins so that there is no need for leaven translator when the host system works at a higher voltage. By default, the secondary voltage is the same as the primary voltage. However, one may need to set the secondary voltage to at least 3 volts in order to enable the USB on-deco functionality. Now, let's have a look at the pin-out of the sensor tile module. The first group of pins are the power supply and ground pins. The sensor tile can work standalone. Just connect the primary input voltage pin and the ground pin to a power source. By default, the secondary input voltage is internally shorted with the primary voltage. However, one may want to use a different, higher secondary voltage to match the host system voltage or to use the USB on-deco functionality. The second group of pins are dedicated to programming and the parking. The third group of pins is dedicated to the UART or USB interfaces. Note that a low-power version of the UART is also available to minimize the power consumption while data transfer is active. The fourth group of pins is dedicated to the PDM, PULS Density Modulation Bitstream, used by digital microphones. On the same pins, a 12-bits analog to digital converter is available. The fifth group of pins is dedicated to the I2C and SPI interfaces. As you can see, all interfaces are available on different pins, so that they can work simultaneously. This concludes the sensor tile tutorial. For more information, please visit the dedicated web page on the ST website, www.st.com. On the web page, you will find the schematic and Gerber files, so you can use the sensor tile as a reference design. You will also find the reference software, pre-compiled binaries and the corresponding source code, and the documentation. A good starting point is the quick guide. During this tutorial, we have seen what are the components of the sensor tile and what is included in the sensor tile kit. For advanced and technical uses, we have also presented the block diagram and the pinout of the sensor tile. I hope you have seen how small yet complete the sensor tile is. It is a wonderful reference design to jumpstart your projects. It is a very convenient development platform. It is also an evaluation tool that can be used in the lab or in the field to immediately start your data collection campaign. Thank you for watching. Bye.