Okay, welcome everybody to our very first keynote of CSVConf 2021. I'm really, really excited to have the incredible team behind the COVID tracking project. For those of you that are not familiar with the effort, it is a volunteer effort that came together in the early days of the pandemic. When data was really, really hard to come by. And there was a lot of missing information everywhere. And so a group of technologists and volunteers came together to fill in the gaps, collect and publish data to help states and other agencies understand what was happening with the outbreak. And the project has done an incredible amount of effort over the past year and they are just, they have just about wound down at this point. So this is a really great time to hear from them as the project wraps up and to learn all the challenges that they've overcome in the past year. I'll do a quick round of introductions. We have Michele Mark, who is one of the data quality leads for the COVID tracking project. We have Kara Scheckman, who is another data quality lead as well. And she will be starting a master's of Stanford very soon. We've got Julia Kodish, who is a data infrastructure lead for the project. And last but not least, Kevin Miller, who is the website lead for the project. So thank you all for speaking here today and I will let you take over. Thank you for the introduction and hello all. Nice to meet you. So my name is Kara. And I along with all Kevin and Julia will be taking you along our journey of building the COVID tracking project, all the way up from a single spreadsheet to the critical data infrastructure it became. So just a quick note before we get started that we're going to be touching on a lot of different things during this presentation. We'll be moving relatively quickly through them just to touch on as many different corners of the project as we can. And so if you'd like to learn more about anything that we talked about today, we've compiled this resource sheet that you can access at this URL. It just has like links to articles that we've written about some of the things that we're going to be talking about documentation around our website. So you can check that out if you're interested in learning anything more about something that we talked about today. So just to provide a little bit of general background on what the COVID tracking project is. So we're a volunteer organization that collected COVID-19 data from the websites of all US states territories in the District of Columbia. We started doing that in March 7th of 2020 because it was really the only way to get the data back then. The federal government first put out a COVID data tracker in May of 2020. And when that data come out, we did an analysis comparing it to our own data and found that there were some discrepancies. So we continue to track data coming from directly from state and territorial sources. We recently wrapped up data collection in March 7th of 2021. So exactly a year after we started, because we felt the federal data had improved enough over the course of the year and that this work is properly the role of the federal government. And we're currently in sort of an archiving phase. We're doing that until the end of the month, just cleaning all the data, making sure everything's tidy and doing some wrap up analysis work. So the COVID tracking project publishes three data sets. The first is our testing outcomes data set which tracks test cases, hospitalizations and deaths across all 56 jurisdictions in the United States. You may be familiar with this from the four of you that we produced each day and posted on Twitter test cases, hospitalizations and deaths. We also have the COVID racial data tracker, which tracks cases test hospitalizations and deaths broken down by race and ethnicity to understand racial disparities in the pandemic. And our long term care facilities data set, which tracks cases and deaths in nursing homes, assisted listening facilities and other long term care facilities, which were disproportionately impacted by COVID. And we're mostly going to be talking today about the testing and outcomes data set just because it's what the four of us here have worked with the most. But whenever we say, you know, talk about a difficult dashboard or a difficult, you know, data set to work with just multiply by 20. And you'll have a sense of the raw data that CRDT and LTC managed to make into really amazing data sets. So the tracking project really started because two staff writers to the Atlantic and Alexis Madrigal and Robinson Meyer wanted to know basic information early on in the pandemic like how many tests were states running. And there really wasn't any data set at the federal level, or any clean aggregates data set that they could find so they started just thinking well this is simple we'll call every state and ask them. Pretty quickly they found that a friend of Alexis's Jeff Hammerbocker had been doing a similar thing. So they joined forces, along with another co founder Aaron Cassane to start a little project and maybe in a thinking maybe in a few weeks. This was early March 2020 they were thinking maybe in a few weeks. The federal government would be doing this and they wouldn't have to do this anymore. In the beginning our website was basically a big button that took you to a Google sheet, but quickly they found that the sheet was crashing from just the sheer number of people who were trying to access and use it. Over time, we became one of the most critical pieces of information about the pandemic for the media so our charts were oftentimes just copied and pasted into graphics on television and print media. So we were within a very short period of time went from a Google sheet managed by a handful of people to a large piece of data infrastructure about COVID in the US. Just a few examples of how we were used we were used by two presidential administrations the Biden administration used our data during their transition planning for COVID. We were cited in dozens of congressional reports we've we were been at least cited for 140,000 times in news articles. We have a lot of mentions and academic citations, and Elizabeth Warren once said that she must be 10% of all of our website traffic. Hi, I'm Michael. So we wanted to describe to you what it was like to build this emergency response organization and to encapsulate what it felt like we often say that we felt like we were building the plane as we were flying it. So here is that dinner that we took that we took. We'll talk about the data entry process which was manual and why we did it manually. We'll talk about how we store the data or database and the choices about what data points we stored in it. We'll talk about where the data comes from because we're just an aggregator and how we created an archive of our data sources. We're going to explain how we presented the data and made it clear and accessible to all. And finally, we will talk about the community that came together to do all these things. So how did we get the data I talked to a non CTP friend a few weeks ago and he asked me. So what did you actually do at the COVID tracking project where you like calling hospitals and asking them how many patients they had. So no, what we did do is look at state dashboards. We would meet every day on the slack channel like five or 10 of us and we would each go to state dashboards and get the numbers and enter them into one gigantic spreadsheet and every data point that was entered. The slide control is a little one piece. Every data point was then double checked by another human and interesting questions were always popping up and we would hash them out on the dedicated per state thread until we had the daily data set that was ready to be published. So huge disclaimer here there's going to be some frustration with the state reporting. We all think that the state and local health departments are the real data heroes of the pandemic. They had to stand up systems to track and coordinate the response, as well as report them out. And overall they did an incredible job. But as the consumers of the data they published, we do reserve the right to vent a little bit. So this data was not easy to get. And the slides are also not easy to get early on there were much less dashboards and we relied a lot on press conferences. We had an amazing team of reporters and outreach people that was led by Kara Oler and artist Chris and they would watch the press conferences and summarize the relevant numbers for our data and treating. And this was also an amazing two way system where we could submit questions to the states. If anything needed further clarification. Another example of how difficult it was to get the data is the frequent use of hovers. And the image that you're about to see shows how hard it was to like find a very narrow strip and hover over it, in order to get the daily number. And this caused us to react by creating an emoji to express our feelings about hovering. There were multiple other issues like grounding and percentages that really made it very challenging to get this data. And all these challenges prompted us to become very efficient in our data entry process. We came up with a great system of source notes. It was created by JD Moresco and Elliott Klug.  Later on this was iterated on and brilliantly maintained by Hannah Hoffman. And this system consisted of a list of links and associated steps on where to get each data point. When you hovered over a data point, and yes, sometimes hovers can be good. You would see very detailed instructions on where to get the data and you could easily click through and follow them. So this system gave us the ability to sail or glide much more smoothly through a data entry shift. Hi, I'm Julia. I'll start here by trying to answer what is probably kind of an obvious question at this juncture. We've talked a lot about all of our manual processes for collecting this data. So why didn't we just scrape it off? It seems like this project is something that should lend itself to that. But unfortunately, the data landscape that CTP operated in was overall a pretty large lack of consistency or standards in COVID data reporting, especially early on, and everything changed really often. So there were changing dashboards, which to begin with many of these dashboards seem like they're actually designed to obfuscate or at least be hard to scrape the data from. And then generally there were kind of lots of changes to the data formats over time, changes to the metrics themselves that are being reported. And then what's possibly the hardest thing to catch automatically, there could be a change to the meaning of a reported metric part of the way through. So even if we'd set up a perfectly automated system on day one, it's wrong by day five in ways that are hard to impossible to notice with automated checks, especially early on. So you still come back to human scrutiny being a core part of our process and ultimately we needed our eyes on the data kind of context and annotations, at least somewhere in the data pipeline. So this is just one example. It's actually one of the better ones. This is Rhode Island's COVID data from July and September of last year. Over the space of two months, they changed the dashboard layout, like you can see here. So disclaimer, Rhode Island is actually great. They provide a great sheet with all the data available, but in many other cases that look like this, this kind of formatting change might also come with the change in the data export also if a state website even supports the data export. And in cases where they didn't, we did a lot of begging of state health departments for CSV files, which sometimes worked. And kind of overall, as the world of COVID data reporting started reaching a little bit more of a steady state, we were eventually able to take more advantage of automation, but finding that sweet spot was something that can only really be done with the experience of starting out with a pretty manual process at first. And even then, once we did set up some data automation, it still needs a lot of manual care. So no matter the bots, you still need the people. Okay, so now we have all this data that we collect very carefully with our eyes and our hands over time. Where does it go? So this part of the talk is going to involve a lot of our feelings about Google Sheets. Just a quick recap of our data entry process. So during data entry shifts, we have a lot of volunteers that would scour state dashboards, and then everyone writes their findings into a giant Google Sheet, which we call Worksheet 2. No one talks about Worksheet 1. There's also lots and lots of metadata attached to every state, and everything is ultimately stored in interconnected sheets. So Google Sheets is wonderful in many, many ways. It's a fantastic tool for collaborative data entry, especially for data structure at the table. It's generic and flexible enough to accommodate lots of folks with different backgrounds and respond to different circumstances like different types of data changes, which came up a lot. Lastly, we probably don't need to convince anyone listening here, but the CSV format is very, very important to general data users, and so it's baked into our data entry processes and pipelines pretty much every step of the way. So all of that meant that for a long time Google Sheets was our world for anything we needed. We had really awesome tooling within Sheets to make all these things possible. It was a mix of Google Apps Script magic and lots of Excel type power usage, and all this powered very kind of domain specific data collection to publish processes. So using Sheets for data entry was awesome. The problem was in the beginning we were also using it as a data store for our time series data, which was less awesome because we started running into a bunch of problems. For starters, our data was too big. We were adding 56 rows and an increasing number of columns at least once a day, and since the project ran much longer than the few weeks, we originally thought that we would need to be doing all this for. This added up pretty quickly and so Sheets performance started to degrade with this much data and people editing all at once during a shift. We ran to a bunch of other issues like accidentally losing edit history if Excel got deleted and then rolling back mistakes was also pretty hard because all of our tooling was dependencies coded within the space of Sheets. So it was amazing, but somewhat brittle machine and problems like that were also harder to prevent within Sheets because as our data got more complex, so did validating it. So along with Zach Lipton, co-leading the data infrastructure team, we added a database layer that took some of the data store pressure off of Google Sheets. The main challenge here was adding a critical piece of infrastructure in the middle of a production data pipeline, essentially amounted to building a second plane while flying it and connecting it to the first plane mid-flight very carefully with no interruptions. Those kind of changes are never easy, but it was worth it for the separation of concerns between data entry and data storage. Just a brief overview of our data stack. So we set up a postgres database on Amazon RDS and then the data in that database became a published source of truth. Then we also had an internal API layer, which was the only programmatic route to the database. This was a flask app that's set between that and our spreadsheets. And it took care of transforming and validating incoming data and would also transform the data to feed it back out to our website and public API. This is getting a little bit in the weeds, but just a few words about the way that we represented our data because we think it's pretty cool. So in order to preserve history, we set it up so that data was never modified. It was only added. And the way the data gets added is through batches, which we can sort of interpret chronologically and play forward as needed. This bar is a bit from the idea of database transaction logging, except applied on the data modeling level. So for example, if we had to go back and revise our data in response to a state data revision, we would know that the newer batch is the data that we should treat as sort of currently accurate. But since we started all of it, we were able to trace these kinds of changes over time. And then we left most of the kind of semantics heavy work to the internal API layer around the database, which meant that we could keep our internal data model pretty simple. And in general, keeping the data store simple as possible was our friend in this work. And having a sort of working model that was a combination of a simple data representation and a layer that would transform it as necessary for all the use cases that we had worked pretty well. And as kind of a more, more general point, all of our tech ended up being on one hand generic solutions. On the other hand, pretty custom and scrappy. On the other hand, we still had things like unit tests and code reviews. So from the engineering standpoint, it was a, it was a cool balance to strike. And then I'll leave off here on the right is very important. This became an emblem of our database with spreadsheets and data and data in the style of other code tracking project logos. This was a brilliant work of emoji by Nikki Campbell, which as a theme will come back later in this talk. So we talked about the systems and the techniques that we use to enter the data. What data to capture and report. So the first data CSV in our GitHub repository was for March 4. And on that day we captured up to four metrics in 14 jurisdictions, and it was a total of 45 data points. On the last day that we captured data, we covered 56 jurisdictions, we captured up to 33 metrics and total of 784 data points. So you can see that we definitely expanded the scope of what we captured and reported. And finally on the reporting landscape was extremely varied and there were many nuanced wrinkles in the data that seemed like they might be meaningful to capture and report. So, for example, we considered capturing the number of quarantine people, coding cruise ships as their own locations, tracking what type of exposure people had and the type of labs that conducted testing. Those were some of the things that were very relevant to understanding the landscape at that point. None of these things ended up being things that we captured and reported. Instead, we found that what was relevant was to provide a national summary on a daily basis of some key metrics. How many tests results were reported, how many cases were found, how many people were currently hospitalized and how many have died. And all these numbers come with huge caveats that we talked about often and we'll do some more later in this presentation. And they were reported very differently by different jurisdictions. So we did our best to cobble together a cohesive picture. And oftentimes this meant that we had to take a metric that we reported early on and expand it into multiple metrics. So one such example is our positive field, which represents unique people with a case of COVID-19. At first glance, positive seems like a simple enough concept and really a binary thing. And in this dictionary definition, it's defined as a medical test that shows a person has the disease or condition for which they are being tested. However, on dashboards, it wasn't always clear what kind of a positive test was reported. And it turns out that different tests could result in a different kind of positive and a different kind of case. So the COVID-19 case definition that was adopted by the CDC is quite long. It's currently scrolling in this animated gif on the left and it defines a confirmed case, a probable case and a suspect case. And those are based on the types of tests, symptoms and exposure that a person had. And when this recommendation was released and then it was updated, it impacted the reporting on state dashboards of positive cases. And as these reporting recommendations rippled through the dashboards, we adjusted our capture to match. So we ended up with three different fields to reflect cases. We added two columns to provide a more nuanced view for the jurisdictions that separated their reporting. And our original field became the catch all for either some of probable and confirmed cases or just whatever that jurisdiction reported in case it wasn't clear. And unfortunately, in a few cases, it still isn't clear what kind of cases a jurisdiction is reporting. And so when we were figuring out the process of or figuring out what kinds of cases that a state reported, that involved a lot of research, because oftentimes when a state puts up a case number on their dashboard, it'll just say something like cases and you have to go deep into the footnotes deep into the weeds to kind of understand what they're actually reporting. And so we spent a lot of time at the COVID tracking project really staring at dashboard footnotes in size eight font hidden at the very bottom of state dashboard trying to understand what was going on, what these states were reporting, because every state was kind of doing things differently. And what we started to learn down in these footnotes was not just the information about what states were reporting, but also the way that it was produced. And for the most part, people in the COVID tracking project really aren't health informatics people, we weren't super familiar with the systems that sort of produce public health surveillance data. And so we kind of realized from these footnotes that we just had a lot to learn about the way this data was produced. And it was going to be a really uphill battle. We soon figured out, as soon as we started to understand sort of like the sort of like the axiom, if you will, of public health reporting in the United States, which is that it's really a state by state thing. It's a very, very Patrick landscape with really different state data pipelines, all at different levels of health. So you have to build up that individual state understanding. And some of them are really falling out of date. They have been underfunded. And so in one state, every test might be automatically transmitted to the health department via the most up to date electronic protocols. And in another state, you may have as many as 40% of case reports coming into the state health department via facts. And you know, people just have to read through these paper reports in order to produce the data that we get on the dashboards. And what we started to realize when we began researching these questions of where the data comes from was that it really would shape what we saw on the other end on a dashboard. And we needed to sort of pay attention to those limits of the data to understand how to use it responsibly because of the way it shaped states overall reporting. So here's an example of why that's the case from Georgia, which is a really common difference in sourcing for state numbers. And this is a difference between the number of total tests that a state conducts, as opposed to confirmed cases in the states. And this one is especially important because these two numbers coming together form a metric that many people use called test positivity to understand viral prevalence to divide the number of cases by the number of tests. So that way, if the state is not doing very much testing, you're not sort of getting an artificially low case number you're sort of waiting it by the total number of testing. So you'd think that total tests and confirmed cases would have the same sources because total test counts the number of PCR tests and confirmed cases of people with a PCR test. But it turns out when states are positive, it turns out when tests are hard to track an estate like lots or sent the effects these numbers actually tend to diverge in their sourcing. And that's because of what's urgent for the state health department. So cases are really urgent for health department to know about because when you have a case, you need to understand or you need to sort of do a lot as a health department. You need to get in touch with people who may have been close contacts, you need to follow up and see if like how that person is diseases progressing if they're getting better. And so states are very thorough. When they count the number of cases, they go through the facts stacks or the stacks of faxes and count every single case and make sure it ends up into their systems. But when it comes to negative tests, there's not really anything the health department needs to do with that data.  The health department doesn't need to follow up. And so most states, many states will either sort of like leave those faxes in the corner and just like go through them all in one day. Which results in a big data dump, or they'll restrict themselves to only counting the tests that are easy to count for negative tests. And that's electronic reporting as opposed to fax reporting. So you'll see here in the total test number for Georgia, if you look at the footnotes, the only source for that number is electronic laboratory reporting. They're leaving out the faxes. And in fact, when you compare the number of positive tests, which you'd expect to be higher than confirmed cases, because it counts repeat tests, it's actually lower. Because it's excluding all those fax reports. And so what you end up with is if you divide the number of cases by the number of tests in Georgia, because positivity, that's actually not about a calculation, because it's top heavy. It's including testing numerator that aren't in denominator. You need to divide the number of positive tests number, which only includes the ELR, the electronic laboratory reporting by the number of total tests to have a responsible calculation. And we tried to sort of provide that context to our data users and make sure that people understood that the way that this data is shaped, the way that it's produced affects how you can actually use it. So one thing that helped us a bit over time with understanding the evolution of this kind of data context, and in general, the evolution of the data over time was having a store of state data screenshots. So I'll just say a few words about our screenshot system, which we built for a bunch of reasons. Most importantly was data provenance. It was really important to us to be able to show our work and where the data came from. This is very closely connected to data quality. We needed the ability to go back and check our numbers against what had been published on specific dates by the states themselves. So we can answer questions like what were the testing counts that New Hampshire reported on March 7. And how do they show that data, which gets us into data annotations. So states would make changes to data definitions pretty often. And this kind of context had a good chance of being reflected in more on the visual version of a website as opposed to like a data CSV. Screenshots also helped us maintain history. So it was particularly useful for situations where we needed to update our data in response to state data revisions. So the database was one side of that. But having older screenshots helped us keep track of what the states themselves put out. And then lastly we did this for archival purposes. So eventually we want to be able to present a record of what was reported and when and have that record be as complete as possible. So we built a system to essentially screen grab state websites. In a nutshell, it was a Python script that used a cloud service called phantom JS cloud to render websites and capture screenshots and also run various kind of custom JavaScript to navigate around as needed. And that behavior was encoded in a YAML config for every state. All of this lives on GitHub. We'll link you folks later. So this is an example of a config. It's structured to our use cases. So there's a state URL. There's some web browser settings like page size. And then we have a message that we used to remind ourselves roughly what this config is doing. The interesting part here is this bit in the middle called the overseer script. This example is far from the most complicated thing we had to do. But really often we would need to do things like click on the fifth tab of this dashboard, which of course doesn't have a reliable selector name so instead it's like click on the fifth HTML element of a certain type. Wave 49.3 seconds for the page to load, click our heels, spin around three times, hope for the best. A lot of the time this worked. Sometimes it didn't. And in cases where it didn't, one of our amazing volunteers would take a screenshot manually and put it into our screenshot story. So along the way we ended up building an accidental brain trust of automatically navigating around ArcGIS and Power BI dashboards. Jonathan Gilmore, Hannah Hoffman, so many folks on the project put a ton of work into maintaining these screenshot configs over time. We actually reached 100% coverage on our main data points toward the end of the project, which was a big kind of accountability milestone for us. So as I mentioned before, we started as a series of spreadsheets but found that we needed to provide more context and visualization to this data, especially as news organizations, media organizations and government institutions were starting to present this data and visualize it, sometimes in ways that would be misleading. So we really wanted to set an example of how to put that data into context. We had a number of principles, first and foremost, everyone should have access to the story of the visualization regardless of their ability or the device that they're using. Over 50% of our web traffic was on mobile devices, so we really wanted to ensure that regardless of what community you were a member of, in terms of access to device, you could get the information you needed. We also wanted to provide consistent and direct charts that people could use as something to look at when comparing one day or trend to the next. We also created an entire visualization guide that talked about how to avoid misleading maps and fancy visualizations that might obscure what's really going on in the ground. And in all of our visualizations, we wanted to ensure that we called out anomalies. Kara mentioned things like data dumps from faxes could really throw off a chart and make it look like maybe there's a huge problem in the state when in fact that's a big chunk of data from another period of time. So as part of making all of our content and our website accessible to people regardless of ability, every chart we had had keyboard navigation. So you could, if you were a person who was had site but was not able to use a mouse because of, for example, motor impairment, you could toggle around all of our charts and maps using just a keyboard. And we also made automated alt text for all of our automatically generated charts, as well as provided every all the raw data for every chart. Every day we produced this series of charts with now you've seen several times we replicated that chart in terms of design and color on our website. And people really told us in our feedback forms that they held on to this chart this chart was the one sort of constant in their lives and I found that feedback really interesting that people were like, there's so much information and everything's very confusing, especially in the early days of the pandemic and just having something that was consistent and clear was really helpful. You'll see in the lower right hand side of this slide, there's two examples of our annotations. So in new deaths here we have these big spikes. So we would always flag those and provide more information when you clicked on them about what why that spike might be not necessarily accurate. We found early on this is a screenshot of some of our state interfaces from May that the presentation of table data in the table layout sometimes created cognitive frameworks that would infer correlation between columns and rows that wasn't necessarily accurate. So for example, in these three states. This column is actually different units. So what we ended up doing is in one of our redesign many redesigns of our website. We instead put data into cards that we could add more context to so every element had a definition so if you clicked on and this is on our website today as well. If you click on a definition will pop up a very specific definition of each field and we also included per data set per state flags if there was something that you should really be aware of. And you'll also see in here examples that says total test specimens and some other states might be different units. As a result, our data page is as bad as long as the CVS receipt, but a lot of people gave us that feedback that having just one big page where you can look at every state was really helpful for them. That's all great but I really want to talk about the great state of Delaware. So Delaware was people for thousands of years the native Lenine Lenape people lived in Delaware and down the eastern seaboard. But then the Dutch came and set up a colony who I won't attempt to pronounce in 1624. The Swedes, which I didn't know lived there had a little colony in 1654, and then they were kicked out by the English and they were like we're going to give it to Maryland. But then Pennsylvania said well we want Delaware and they were it had a lawsuit for over 100 years until these two guys named Charles Mason and Jeremiah Dixon came to settle the issue. And they mapped out the boundaries of Delaware and this is where Mason Dixon line gets its name. The reason I bring that up is Mason Dixon were maybe great cartographers, but they were not very good user interface designers. So as a result, this is a classic example of the kind of interface you would see on newspaper websites or even maybe federal agencies, which was a map of the US that showed COVID data. But the problem was these maps were sometimes the only way to navigate to data it was not only a map that is also a means of navigation. So little Delaware was really hard to get information for if you had motor skill issues and couldn't use a mouse to the fidelity needed to get down to little Delaware. And or if you're on a mobile device where your thumb this is an actual size of the average American thumb covers the entire eastern seaboard. So as a result, when we did want to do state level maps, we show spent a lot of time on developing hex maps that provision for presented every state as a consistent sized target. That also allowed us to make it more of a grid layout so it meant if you're navigating with the keyboard you could use the arrows to move up and down really easily. Another part of being present not only presenting but delivering our data to as many people as possible was we had an API from day one and both CSV and JSON format. Our API is traffic was orders of magnitude larger than our website traffic we were delivering upwards and nine terabytes of API data a month. And that was great because it meant that small newspapers could present to their users a daily automatically daily updated chart of cova data for their state. And a lot of that was powered by the internal API and database that Julia mentioned, and we would then just host it as a static website, which helps our volunteers not have to focus on things like operations. So to conclude, just wanted to share a little bit about the community that made all of this, making the sheets, collecting the data, producing the charts, building the website that made all of that possible. It's a pretty awesome community. So producing this data was a very large group of dedicated, wonderful humans who contrary to this photo were not all named Bob. And it was really when I say producing this data, I do really mean down in this data. This was a manual data set like we've covered every data point had two eyes on it.  And so what this amounted to is there was a big community of people around the United States. And in fact, the entire globe was that was recording this experience as we also live through it. And that could be very intense, you know, for many people, I think the project was a form of collective morning, a way to remember each person affected by the pandemic by sort of making sure they were counted in the numbers. But despite the nature of the task and the nature of the data that we were dealing with, we didn't want CTP to be a grim place for people to log on to each day. And in the end, I don't think it was. Rather, we wanted people to find meaning and connections through this work to support each other through the pandemic and also the task of conflict. And so we took very seriously creating the virtual place, the slack where we gathered. And this is a piece of art that our data science lead Dave law made. It's called emoji trope it emoji trope lists. And I think it speaks pretty well to that. So, even though it's not quite obvious, it's actually a graph, it's a graph of custom emoji that were uploaded into our slack or virtual home, and it's bucketed by week. So the emoji that were uploaded each week to create something that looks like a city skyline. And I just wanted to tell the brief history of how we came to have so many emoji in our slack. And it's a little bit of a silly yarn, but I think it's a portal to understand our community and also the work that came out of that community. So just for context, this is our logo. It's the circle with the little art around it, and two really fundamental and consequential questions were first posited about this logo in May of 2020 that ushered in the emoji ages we know now. So the first was posed by our community lead, Amanda French, who had the good sense to wonder, why is there not yet a logo emoji. It's been two months with this project. And that was quickly remedied. And then the lead of the COVID racial data tracker Alice Goldfarve asked the second pivotal question, which is what if it's fun. And what this kind of did was it spawned a realization that our logo is infinitely memeable. And the first grip I was sort of able to track down that slack was a few days after the CTPs consequential CTP spin logo was uploaded. Someone wrote to our email to ask whether he might have permission to use our logo as his car companies insignia. And we said no, but we did try out the concept for ourselves just to see what it looked like. And this kind of opened the floodgates. Before you knew it, you blink, and then you'd open your eyes and there would be 20 new CTP emoji in the slack. And then you blink again. And there would be 1000 CTP emoji in the slack. And then, you know, you go and grab a cup of water and maybe take a walk around the block. And then you come back and the official CTP emoji team led by Nikki Hamburg was organizing March Madness emoji bracket to choose the favorite CTP emoji, the winner of which, by the way, was CTP. This is fine, which I did draw enough to brag. And, you know, this says a lot about CTP, that it's a community of extremely creative people just full of energy who really enjoy building things together, whether that's a creative data infrastructure or a critical data infrastructure or an emoji infrastructure. And what you see with emoji really is kind of the way things felt on this project in general.  But I have one favorite emoji, or really 10 favorite emoji, because it's the emoji with most variation in our slack other than below itself, but I think really cuts to the heart of CTP and organization. And that's the thank you emoji. There are 10 ways to say thank you on the CTP slack, including a few that are specifically devoted to Kevin, which I think tells you about many miracles that Kevin has worked on our project. But CTP has a real culture of gratitude. There's even a channel of gratitude that's just dedicated to thanking people. It's a place, I think, where people really wanted to show that they cared about each other, that they cared that people were putting in the work to make this sort of to fill this fundamental hole in the US pandemic infrastructure. And that care was at the heart of our work, I think, the care for each other was care for the data as well. And we wanted to do everything that we could to get to do the right. So we had over 800 volunteers in data entry, data quality, outreach, reporting, infrastructure, communications, visualization and the website team. Just in data entry, we calculated over 20,000 hours of time were dedicated to the COVID tracking project. This were people were who many people volunteered full time, many people volunteered more than full time. They were all doing volunteer work that was really filling gaps that other institutions should have been doing. And I think I just want to commend them for that. This has been a very hard, difficult, tragic year. And the people that you see here, along with hundreds of others who are not pictured in the slide have both sustained me and each other. I'd like to quote from Aaron Cassane who's one of our co-founders.  But it also came at a high cost, especially when we found ourselves working with numbers that include digits representing our own friends and family members. For many of us doing the work together in community with each other made all the difference. I'm going to hit the next slide for it. So here's the resources again, we have a lot of links to blog posts and especially lately on our website. You can see a lot of analysis and updates that we've done in a retrospective format that I think are very powerful and interesting that I would encourage you to check out. And here is all of our contact information. And I think we'll now take it to questions. Thank you so much. Mihao, Kara, Julia and Kevin for such a great talk and thank you for being part of such an incredible effort. We're so glad we got a chance to hear about it. I do have a few questions from from the audience and I'll start with a question from Paula, which is, did you encounter issues with data licenses? Once that we're not open, for example. I wanted to say something to this. Not so much licenses per se, but we had a lot of instances where we would write to states and ask them for specific data or time series data to smooth out data dumps. And a lot of times what would happen was that they would send it to us in a CSV or Excel. And we didn't want to use data that like on the testing and outcomes side of the project that wasn't publicly available. So we did a lot of back and forth of just asking them to publish the data themselves on their website so that it will be available to the public. And sometimes it worked and sometimes it didn't work. Sometimes we even have to go back and scrape their power BI for the same data just so we know that we are using publicly available data. I also know that Puerto Rico I think ended up being the only state that had an API. And we did want to use that API and there was a data issue with that we were chatting with them and they tried to work on it. And as far as I know it never got resolved. We have another question which is a little bit depressing which is that sadly this isn't going to be our last pandemic. We're likely to run into something like this in the future. Does the COVID tracking project team have any plans to write some sort of playbook for future volunteers that might take on a similar effort or for organizations like the ones that you've been dealing with to be able to better provide more usable data. Because something that I've seen often is people want to do the right thing but they have no way what the best practices are. So do you have any plans to do something along these lines. I would say we've done some of that work already in terms of the posts that we've made to our website that have been much more in descriptive in terms of how we work rather than just what kind of analysis we're doing. We are working on, as we mentioned before, and like an official archive to ensure that not only our data but our organizational structure methods are preserved. I'm not sure if anyone else wants to speak to that. I think just a quick follow up in general sort of at this part of the project, even though our public data collection stopped on March 7, we're now kind of in the phase where we are actually trying to think about that question and answer it as best we can. And we're very much still putting out blog posts on co-attracting.com, a lot of which try to get at the kind of different parts of the system. And there are components to pretty much all of those posts that are a little bit of like a how to or takeaways or things that would hopefully, not hopefully but hopefully come in useful if something like this ever needs to be done again. Great. I have a time for one more question. I'm not sure if this is already covered, but how often did you run into proprietary data formats that were not as simple as CSVs that you could just easily process? If no one else wants to talk about this, I can. I actually have a recent blog post that talks about all our automated data scraping, and there were PDFs, HTML, CSV, Excel, and also just the APIs that are powering different dashboards and also hacking dashboards that all participate things. It's all there and the link is in our resources. How often I would say all the time and sometimes there just was no data available at all. Yeah. Well, thank you very much again for this great talk.


Girl soups brainered at home hosting sock rapids rice early in the first half Reagan Mcelfish to Kara Helgaston down low then back out to Mcelfish who knocks down the three they're about five two then later this time trailing Lexi Roby though finds Allison Breski in the corner she nails the triple from downtown that one ties the game and Ashley Huber's turning she'll take it herself all the way to the bucket get out of her way this one also goes to overtime and the Warriors grind out the win at 59 to 54 If you enjoyed this segment of Lakeland news please consider making a tax-deductible contribution to Lakeland PBS


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Let's be honest, studying the Bible sometimes can be a little bit difficult, can be a little bit daunting, especially when you've got people over here saying one thing, people over there saying another thing, and they're saying it about the same scripture, about the same passage. People can look at the same passage and walk away with different views, different understanding. And so if you're trying to be genuine and honest about studying the scriptures, how do you do it? How do you make sure that you get the right understanding? How do you make sure that you receive what the writer is trying to convey? What I want to do is give you some things that I have learned in well over 30 years of doing this that have kind of helped me, some things that I have learned the hard way. And I want to point out to you something that I've noticed to be almost foolproof. And sometimes that's kind of how we need to be because we can be foolish in our thinking sometimes. So I want to share with you some things. Now I want to caution you in as you read the Bible to understand that one, and it makes sense, obviously, the Bible was not written in English. And so there are going to be times where you're going to need to actually look up the words, not the English word, but the word in either the Hebrew or the Greek. And so that necessarily means that you're going to need a certain type of a Bible, meaning you're going to need a word for word Bible. You've got different types of Bible that aren't written for the same purpose. Why is that? Well, first of all, you have a word for word, which tries to convey what each word in Hebrew or each word in Greek is trying to convey. You try to convey that in English. You also have what's called a thought for thought Bible, such as NIV, where it's trying to convey the thought of the writer, of the person speaking. And then you also have what would you call these paraphrase Bible, we won't get into those. But Bibles like the NIV, they're very helpful. They're very good. But if you're going to do a Bible study, you don't want to lean on those as much. You do want to kind of have those in mind, but it's going to be hard to do a word study using a thought for thought Bible. So because it wasn't written in English, the translators are trying to give you the entire meaning. If you listen to me, you can hear not only my words, but you can also understand the thought behind it, because you can hear my, my tone, my inflection and so forth. Well, you can't get that out of reading words in black and white. It's difficult to do so. And so that's the reason for both of them. And so you're going to see that certain translations might render a word or a phrase or a verse a little differently. There is no way to 100% of the time give a word for word translation, which means you're going to have to know and look up the word. Sometimes you're going to have to look at different aspects of the word. If it's a noun, what type of a noun is it? If it's a verb, what's the tense? What's the mood? What's the voice? So these are things that you're going to have to deal with as you go deeper. But what if I'm just a beginner? I just want to know how to read the Bible and get a good understanding. Well, I would say this. One, you want to read the Bible the way that you read everything else. I'll tell you more about this as we go further, but God expects you to read this Bible really the way that you read everything else. He doesn't expect you to change how you do things naturally in order to get his word. God is speaking to you on your own level so that the person who is the scholar or the person who's the beginner, the person who is in their later years in life or the younger person can still get the same understanding. As you read the Bible, you want to avoid bringing in any preconceived notions, meaning whatever you were taught in the past, whatever grandma may have spoken to you, whatever your past, your former pastor, your favorite preacher or theologian, whatever they have spoken, you might want to do your very best to leave those aside as best you can. I know it's difficult. I know it's hard not to, as you read the scriptures, not to think about what someone else said about that particular topic somewhere else, some of that resonated with you. It's going to be hard not to read that in the scriptures, but the problem is you end up reading someone else's opinion into the scriptures and that might not be the case. So how you can avoid bringing in preconceived notion is to try to place yourself as best you can in the place of the audience, the person that is reading it, or the people that's being spoken to directly. Try to imagine yourself being there, thinking about one what you have heard thus far, what you know about the situation, and also you can take into account what is happening there on the ground versus reading it from the standpoint of someone who was in 2023 in the 21st century America or some western civilization. Now as you read, another thing that you want to keep in mind is look for opportunities to be wrong. Look for areas where you are wrong. As a matter of fact, make that intentional. Where am I wrong at here? The reason why is it's going to sound strange, but if you find opportunities or if you find areas where you are wrong, when it does take a level of humility to admit that you are wrong, especially if you do so publicly, but in doing so a couple of things happen. One, it's not a bad thing that you found out that you were wrong about something. Look at the converse of it. You have now discovered something new and how joyful is that to learn something new in the Bible? The other thing is this, you also release yourself from the burden of being perfect, meaning you don't have to know everything. As a matter of fact, news flash, you don't know everything. You will not know everything. There are going to be some things that are just going to be so difficult. And so I would caution you as a matter of fact, I would encourage you to develop this one phrase that is, I don't know. It's good to be able to come across a passage and when someone asks you or you trying to figure out to be able to send yourself, I don't know. I'm not sure what that means and there are passages in the Bible. I don't care who the scholar is, who's been studying for the longest. There are passages in the Bible where we just don't fully know what's being spoken of.  So how then can I tell you heavenly things? There are some things that he might allude to that are really what we would call heavenly things that we just don't get. The Bible doesn't speak a whole lot about certain topics and so because it might broach those and we don't get enough information, avoid trying to be perfect and trying to force in an answer that you just don't know because then you end up creating a problem one at that moment but also down the road. Now with that mindset in place, this is kind of how you ought to approach the scriptures but the first thing that you do every time you approach the scriptures is to pray. I know that sounds a little bit obvious but the truth is you need to pray. Lord, I'm reading your word. Give me clarity. Give me insight. Help me to understand your word. Help me to be humble in looking at things and also be open to seeing new things to see your truth because as we say guys and you hear me say this often again and you'll hear me say this often it's not who's right but what's right. The truth is we all want to be right. No one wants to be wrong so when someone says you just want to be right well sure who wants to be wrong but if you know that you are wrong and this is the truth then certainly you want to preach that teach that believe that and live that and that's the goal. We want to love it. We want to learn it and then live it. Amen. Something that people tend to ask a lot of especially in the beginning is where do I start? If I want to start my studies all over, if I want to focus become a good Bible scholar, a good theologian, which by the way fancy yourself as being a scholar. Fancy yourself as being a theologian. You are trying to study God's word. You are trying to do it in a scholarly thoughtful fashion and so where do you begin? Well the same way you would if you're going to watch a movie or read any other book you start in the beginning. Kind of makes sense right? Because how many of you have watched a movie or in the middle of watching a movie and someone comes in and asks questions about the movie that they could have understood if they had simply watched the beginning and now you're trying to explain something to catch them up and they still don't get it. Same thing with the Bible. There's a reason why we have Genesis, Exodus, Leviticus numbers. There's a reason why our Bible does not start off in Matthew, Mark, Luke and John. Now if you start off in Matthew, Mark, Luke and John that's fine, but understand that this is building off of something. This is a culmination of what has happened in the past, what God has said previously. What prophecies are now being fulfilled, what God is doing. It's not something new. It's something that he said he is going to do and then you'll also be able to see if you go to the beginning you'll see why. You will have developed a foundation. It's just like when we have in math one of these real long tedious problems where it might be one plus something minus this divided by this plus this times that. The problems in and of themselves aren't difficult because it's so long if we make a mistake in anywhere in the process then further down the rest of the problem will be incorrect because we made a mistake somewhere else and so we want to get a good foundation on what's happening, what God aims to do, what is his purpose, what is his purpose in the entirety of the book, what is his purpose in this particular letter, what is his purpose, what is he trying to do here with these people, why. The best way to get that is if we understand from the beginning. Now this I want to caution you because I think that this is probably the thing that causes the most amount of problems in the church. There are people who have good intentions and good motives on both sides and you'll have people that believe this and people who believe that about the same topic and there's those who are in between. How do we get to that point? Well one of the ways is because of how we read the Bible. I mentioned this earlier so I want to talk about it now. How you read is important. I would recommend that you have what's called a literal grammatical historical hermeneutic. A hermeneutic is a method that you use when reading. Believe it or not we all have one. The problem is oftentimes it's inconsistent. When we come to the Bible sometimes we think that the best way to do it is to look for this deep spiritual hidden meaning. But can I bust our bubbles a little bit? Can I bring a little bit of a news flash? God does not think that highly of our intellect. He doesn't think that we are so smart, so clever, so intelligent, so wise, and so spiritual that he can put a hidden meaning in front of us and then we can get past all of our preconceived notions, all of things that are going on in life and then see the deep hidden meaning. No, that's not what he's trying to do. He's trying to communicate to us. The same way when you communicate to someone you communicate to them because you want them to understand and so you do so in a way they can understand. If I speak two languages and one of Spanish doesn't speak English and I know you only speak English well then I'm not going to speak to you in Spanish and then hope that you catch on. That's not how I do so. Now in doing so I want to be as consistent as possible. I don't want to allot arise or spiritualize the text meaning if I go looking for a deep meaning well when the next person does the exact same thing look for a spiritual meaning and his understanding is different or her understanding is different well then the next question is going to be who's right? We all can have an opinion and then what we're going to say we're going to say that I just use the Holy Spirit to guide me but the person who disagrees will say the same thing well God is after us using just these words. Words have meaning when we speak languages have rules and the rules are meant to be used to understand and so for that reason I'll have a literal I'll read the Bible as literally as I possibly can knowing that there are times where they're going to be similes there's going to be idioms there's going to be metaphors there's going to be different figures of speech there's going to be different types of writing such as poetry and so forth we understand that but similarly the way we do in English if I'm speaking to you I speak to you straightforward because I want you to understand and I might throw in metaphors or things like that if I say it's raining cats and dogs I don't mean that felines and catans are coming out of the sky I mean it's coming down pretty heavy and just like if I say I think that's really cool I'm not saying what I think the temperature of something is I'm saying that it's a figure speech today to say I think that's really nice that's all right that's nice now words do have different meanings such as the word fine or the word bank the word bank means a place where you put something in your money the edge of a river or something like that if I hit something off of something I've banked it off of the wall that kind of thing and so we understand that with the words that we use and the same thing has to be has to hold true in reading the Bible and so sometimes the words that we might use in English might be a different a certain word in Greek or Hebrew and so we look up that word see what it means that's just showing that I'm looking at the word what it means how it's used the tense the mood the voice we understand past present and future but then sometimes there's an aspect that's that's added to it for example if I say I watched versus I was watching in the Bible we're here where someone believes or believed but what if we say instead of believe what if we say they are believing and so now we get an even better picture because one may convey an end whereas the other one may convey a continuation and so those things are vitally important as we read the Bible that part that portion is very important a literal grammatical hermeneutic take it as literally as a possibly can until the literal doesn't make any sense and then the grammatical portion which is what we just described using the actual words but then the historical portion of this the literal grammatical historical hermeneutic meaning I'm also going to keep in mind the history of it these things that Jesus is speaking of that we see the apostles acting out and so forth the foundation of the church the coming of the Holy Spirit his death prayer and resurrection on the cross what he does with the church what he does with Israel these are things that we can see God speaking of that are going to happen we see what has happened in the past and we are told what's going to happen in the future and so there's a historical aspect to what God is doing again Jesus doesn't just show up on the scene without any sort of forewarning we have been told about him and we're told about what God is going to do we see what he tells what he tells Adam in the garden Genesis 3 we see what he tells Abram or Abraham in Genesis 12 and so these things are spoken of prior and so we can see where God is going as a matter of fact in doing so we also see the nature in character of God maintaining a consistency helps us to see the trueness of the word again God does not think that highly of us if anyone does think that highly of himself he needs to take heed lest he falls you are going to fall if you think that you are standing on solid ground and you cannot be shaken there's a reason why God calls us sheep because sheep just are not that intellectually superior to any other animal they just are not might shake us internally to realize that God didn't think that much of us but it's a good thing though that he desired to come and show his love for us in order to save us as you read the scripture remember there is a primary application and a secondary application meaning this there are some passages that just were not meant for you to have a primary application but you could glean something from it for example in second chronicles the bible says my people who are called by my name if they will humble themselves and seek my face and then turn from their wicked ways then i will hear from heaven and heal their land now sometimes we use that passage to apply to ourselves maybe to our country we see that oftentimes when it comes to elections but if we keep reading we'll find out that this is not about us or shouldn't be applied to us this is about israel because he says that if they do the opposite if they don't do right well then what will God do he says that he will take them out of the land well that clearly doesn't refer to us that's referring to them now are there some things that we can glean from that is a secondary application sure but to make it as though everything the bible is saying about one person should be applied to us we set ourselves up for a fall when God talks about doing certain things through the apostles and then we want in turn to take those and apply them to us we'll find out really quickly or at least others will if if we're humble enough we will that those powers and those things that were given to the apostles were never meant for us and so they're in lives an option to see a primary application or a secondary application trying to apply something in a primary way when it should be applied in a secondary application meaning that we should we can glean from the power of God working in man and really to see what God is trying to do not who he's trying to do it through as always make sure that when we read we read things in context there's a greater context in the entirety of the bible what God is trying to convey i would be careful about separating the old testament with the new testament i wouldn't do so because God did not just stop in midthought and decided to change courses no he did not everything is really one big continual moving up the lower from genesis to revelation now how he moves with different people in what we call these different dispensations that is there how he operates how he deals with mankind in the garden how he deals with mankind after the fall prior to the flood how he deals with mankind after the flood prior to the law how he does with mankind during the law and then after after the cross we see this happening but these aren't these aren't or shouldn't be surprised because we see God speaking of those things and so if we keep the full context of what God is trying to accomplish as we read the scriptures that would be helpful but even still the writers themselves have a context a meaningful purpose of why they are writing first Corinthians is written to speak about unity in the bible talks about the the body at large but then also dealing with marriages and so forth and family and it also talks about unity and divisions as it relates to spiritual gifts and so let's keep those in mind and every now and then you'll see that there are also some more immediate context of a letter inside different chapters and verses and remember the chapters and verses were added not when they wrote it but later on for our benefit to keep an idea of where we are in our reading now one of you five things to remember to kind of help keep all of this in your mind as you're reading remember the five w's the who what when where and why you can apply this to the scriptures who who is writing who is the writer writing to that helps that makes sense as a matter of fact that kind of keeps things keeps your preconceived notions out and you're focusing on what's happening what what is the point of the writer what is he writing about what's happening on the ground and what is he trying to get across when when is this taking place now i want to stop and show you something because i think this is very important as we read the bible i think that these little this little graph i'm going to show you is i think very helpful in your study to know when certain things are happening if we look on the screen at the at the bottom right hand side you'll see where it talks about the prophetic books and we see that there's a northern southern exile and restoration these are prophets who are prophesying during these times either to the northern kingdom the southern kingdom the exile or the restoration of the nation and then we have below that we have these prophets who are prophesying to the nation and so it is helpful to know when these things are happening and so when someone is prophesying for example Jeremiah if he's prophesying to the southern kingdom and when it's just before the exile we know there's going to be an exile and so when he makes a statement we know what's getting ready to happen so the win is very important similarly to this old testament timeline there is a new testament timeline that i think you all have to be aware of and so in that we can see the approximate years of when certain books were written what was happening at that time we can look at when the paul and the pistols were written which is important the reason why that's important is because somebody may come back and say for example paul that paul evolved he in this book that is in our bible written previously where he says that there is no jew nor greek nor male or female that book is in our bible after he has made the statement regarding women in their role then someone who doesn't know when these books were written might come back and say and they have may come and say that paul may have evolved in his understanding but that's not the case as we look at this again we see that uh he wrote timothy at the latter end of his ministries and so he did not evolve he was true to what he said so be careful to know when certain things were written and when they were taking place where something is taking place or even sometimes even where the writer is for example when paul is writing and giving comfort and we know that he's where he is in prison that kind of adds a lot more to when paul is talking about us having joy and comforting us knowing where he is as a matter of fact even knowing what he's about to face it also helps to know that where the people are at this very moment when these things are taking place very helpful in our understanding of the overall picture and then why the last thing is why why is this written why what is the overall purpose why should we be hearing this so if you use those things it's just like how we read them read a book or watch a movie we actually unbeknownst to ourselves employ all of these these different things we know what's happening we know who's doing what we know where it is why where when all of those things the same thing holds true in the bible and so if you want to take your bible study to a different level apply these things because again if you go back and watch and pay attention to everything else that you do everything else that you read you read everything else the same way you read the newspaper this way you read a menu this way you read directions this way if you were making a cake and i'm not very good at making a cake but if they say that i need an ounce of i don't know sugar again i'm not a cook but if i take that and don't give an ounce of sugar and i think well they're about sort of an ounce is okay and i give an ounce and a half or if i don't if i think that he really didn't mean sugar anything sweet maybe sweet nerd will will suffice then i may mess up let's say if i'm baking a cake this is why i don't bake this is why i'm not a good cook because i would again allegorize what the plain reading of the menu is we use this way of reading everything even if we're reading i don't care if it's a story i don't care if it's an if it's some sort of story in a newspaper or magazine this is how we read everything this is how we does even how we watch tv we understand what's happening and so the bible is laid out the exact same way however we sometimes read things out of context we'll read the passage here today and then we'll go over there and then we'll go over there and read this passage what's in our mind what we read yesterday so do we take what we read yesterday and superimpose that in the text sometimes that can be a problem and so guys i hope this helps it's helped me this is honestly this is what a lot of scholars use i think this keeps us consistent because if you're not consistent in your reading and then you change the way you read something just arbitrarily then what you're going to end up having is more instead of the facts you're going to state more of an opinion this is how you feel but god gave us his word for a reason not just to enjoy it but to learn it and if you can't learn it it's going to be hard to really enjoy it because you're going to be confused so guys i hope this helped prayerfully it will bless your reading and guess what i know it will


Hi, welcome to nursing school explained. Today's topic is hydrocephalus. So let's take a look here. So hydrocephalus is an imbalanced production and absorption of cerebral spinal fluid. And remember that cerebral spinal fluid is in the brain and also flows all the way down into the spinal cord. And it's usually contained within the ventricles of the brain, but when there's too much cerebral spinal fluid, the ventricles inside the brain will dilate. And when that happens, the brain gets compressed. And I wrote down here these three terms. So basically in our cranial cavity, we have three components. We have the brain, we have blood inside the blood vessels, and we have cerebral spinal fluid. And usually that's all contained in our skull. So this is a nice protective mechanism to protect the brain blood blood and cerebral spinal fluid. But when there's now increased production of cerebral spinal fluid, there's an imbalance between these three structures or compartments within the brain. And so then something has to give.  Now hydrocephalus is usually something that occurs in infants or children. And the good thing about infants is that their skull is not completely fused yet. So they have the anterior and posterior fontanelles open still. And so when the cerebral spinal fluid, there's an increased production, then it had the skull, there's basically room for that to expand. And so then you will see fontanelles that are bulging because now the cerebral spinal fluid is causing increased intracranial pressure. And these fontanelles will kind of allow the expansion of the cerebral spinal fluid. So when there's open fontanelles, then the skull will basically enlarge. You will see the bulging fontanelles but then over time because the skull, the bones of the skull are still kind of formable, pliable. It will result in hydrocephalus.  Now that's the advantage of having open fontanelles, but if the fontanelles are closed, there's no way for the skull to expand. Everything is already set. And then the patient will exhibit signs and symptoms of increased intracranial pressure, which will be all kinds of neurological signs. So what causes hydrocephalus? It's either congenital. So that can be either by tumors that are causing increased cerebral spinal fluid production or chiarimal formation. And chiarimal formation is a complication of spinal bifidocystica or myeloma ninjoseal, which is a normal tube defect. And keep in mind that in embryonic development, the embryo is basically just a tube that eventually starts to develop and elongate and develop into the different parts. And this neural tube can have a defect called spinal bifidoma or myeloma ninjoseal, where there is then at the level of L5S1, kind of like a sac-like protrusion. And if this protrusion happens, the brain kind of gets sucked down into the spinal cord, and this is called a chiarimal formation. And in turn, cerebral spinal fluid will increase causing the increased pressure in the cranial cavity and then the enlargement of the skull with all those complications. Now, another cause is a quiet hydrocephalus. And this might be due to neonatal meningitis, as well as subarachnoid hemorrhage. So bleeding in that intracranial cavity. So in this case, the cause would be the actual blood taking up more space here rather than the cerebral spinal fluid. But nonetheless, the cerebral cavity will expand. Now, how do we diagnose this? That's why we measure head-to-conference in infants at every visit. And sometimes it's pretty obvious already if the patient has spinal bifida that is detected during the prenatal checkups or some other abnormalities that occur, but head-to-conference is number one. And then certainly a CAT scan or MRI to help get some pictures of the exact structures of the brain to see what might be causing it. Or an ultrasound can also be helpful in transcranial ultrasound, as well as the lumbar puncture, which will have to detect the subarachnoid hemorrhage. Now, the treatment goal for hydrocephalus is to decrease that CSF accumulation so that the skull cavity doesn't get enlarged, which helps to decrease disability and death. And the way this is accomplished is by a ventricular peritoneal shunt, or also called a VP shunt. And bear with me here, I'm not the greatest drawer there is, but I drew out our child here. And the VP shunt basically means there's a tube placed inside the ventricle of the brain that's then threaded down through one of the blood vessels of the neck, all the way down into the abdomen. And basically the contents are just emptied, indeed, into the abdominal cavity, within the body just reabsorbs that CNF, allowing CSF allowing it to drain and not accumulate in the brain. And that's usually done in smaller children. If children need a shunt for hydrocephalus and they're a little bit older, this might be a ventricular atrial shunt. So basically again, stemming from the ventricles of the brain, and that shunt now ends up inside the right atrium, when a CSF just enters the brain, sorry, the bloodstream, and then is circulated throughout the blood and then reabsorbed by the body. Now, as you can imagine, any time you have a surgical procedure that involves placing a shunt inside the brain, there's a high risk for infection. So complications of any, either one of these shunts are infection or blockage. And keep in mind, these shunts are usually there for a lifetime. So you might even see adults that have an EP shunt or VA shunt implanted, and now they're maybe brought to the hospital for altered level of consciousness. And then your thoughts should be maybe there's a blockage or an infection of that shunt. And if the shunt is now blocked, that CSF again is accumulating, causing the brain to be compressed, causing altered level of consciousness and neurological deficits. And the shunt also might need to be changed, which means that there's a surgical procedure involved. And again, whenever we have a surgical procedure involved in the central nervous system, we always have to be concerned about infection. So thank you for watching this video on hydrocephalus. If you would like more information on spinal bifida, there's a separate video about that. Thanks for watching Nursing School Explained.


Hello everybody. Welcome back to another Adobe After Effects 2022 tutorial. This one, I'm going to show you how to face track and change faces into caricatures into funny faces. For example, here I've got one for you. I've got Johnny Depp and Amber Heard both talking on the stand and then all of a sudden Johnny becomes a bit clownish. I got a unique little effect going on with his face and then Amber Heard while her forehead turns into a five head. How do we do that and how do we make these caricature changes? If I hit spacebar you'll see that the face is tracked so it's not like in Photoshop where it's a still shot. This moves with her face like these things move with them as you can see here. So how the heck did we do that? Well let's start from scratch and I will show you step by step. Let's delete everything. Why not? Let's start right from scratch so we're going to go new project might as well. Don't save new project whatever. Okay grab your footage. So where do I have my footage? I have it in frequently used. Here it is. Here is the Depp Heard footage. I'm going to drag and drop that into my project and then I'm just going to drag and drop it here and now presto it's on my timeline. The next step is you got to determine which one of these characters do you want to make the funny face on. So let's go with Amber Heard. Let's work on her first. So I'll show you how to do both of them in both of them but we'll work on her face. So the next step is making sure you're selected on the layer here. Go up to the top and select your ellipse tool. Draw an ellipse around her face. This is face tracking and this is basically going to create a mask. You'll see it's created a mask because it says mask one. So there you go. So there you go. We've made it a mask and now you want to go from add switch that to none and the reason why we do that is because then we can see the rest of the video instead of just that face bobbing around in discombobulated space. So there we go. We've got a mask on her face. All right. Next step here is making sure that you're selected on the mask. So the mask is selected. Go over to your tracker. If you don't see that go to window. Make sure the check mark beside tracker wherever your tracker is. The next step here is they hit basically the play button. We're just going to track selected masks forward. So we're going to track it. So there we go. We're tracking her face and this is a short clip so she'll only take a few seconds but presto that is it. We've got a mask and we've got a tracked mask and you can see the tracking points on her face. So we know it's a good mask. So we're on our way. We're doing good here. Okay. The next step is you want to basically get a rest pose for her. So if there's a shot where one of your character or your person is looking forward towards at the camera that's generally the best one to use. So I'm just going to left click set rest pose. Beauty. Now we want to extract and copy face measurements. Very important. So left click on that now and boom. You'll see under the effect controls panel that we've got facial measurements set up. So we have now tracked the mask and we've got the measurements and we're now ready to use the plugin. Now this is a plugin by the motion bro and it is a well there's a paid plugin and there's a free plugin. You can check here. I'm going to click on this little folder icon and when I do that you'll see here a couple of things.  The starter pack is currently free although that could change at any time but let's click on that and get to work. Okay good stuff. Now when I do that you'll see here. Well let's just go ahead and click on it. There we go. You'll see here a whole bunch of different options. We'll see here we've got distortion. I don't really like this being here but what are you going to do? I'm going to make it a bit bigger here. Hang on. There we go. Okay distortion. So if you'd like you can just hover over them. So if you're thinking about what kind of a caricature or what type of special effect you want to apply to her face. Go ahead like this is a puffer fish style. If you want to change your forehead why not let's change your forehead in this one. Okay we're going to go from a forehead to a five head. Which one do I like? Do I like this one or this one? Oh let's go with Shanaido Connor. Okay we're going to do this one. The next step actually is just to go ahead and left click on apply. Now I'm going to minimize this for a second and boom. Here's the original click. Now here's the caricature. She's got a five head people. How great is that? Now one more thing I'll show you before I click off here is if you want to show this as a gradual transition you can go ahead and just move this to like let's say 10 frames. And what I would do is there's no forehead, forehead, forehead, forehead, five head. Okay go to the first frame. Click here. Make sure you're selected and not hit the T button on your keyboard. T stands for opacity. Left click on the stop wash and now drop that down to zero percent. And then let's go forward to say the 20th frame and then go up to 100 percent. And what this does is an opacity transition. So basically it goes oh let's just hit spacebar. You'll see. Yep she's got a five head. That's how you do it guys. That's how you do face tracking caricatures and special effects for faces inside Adobe After Effects. Thanks for watching. Be back soon.