2b:null 2c:["$","title",null,{"children":"Quantifying \"Smart Scatter\" TV Advertising, Part 2: Data-Driven Linear TV and AVOD Reach and Ad Impressions | Simulmedia | Observable"}] 31:T84f,md`# Quantifying "Smart Scatter" TV Advertising, Part 2: Data-Driven Linear TV and AVOD Reach and Ad Impressions Maximizing unique audience reach is a time-honored advertising strategy that is proven to power both performance in the near term and growth over the long term. With all of the changes in TV viewing habits, including cord shaving, cord cutting, and the emergence of more over-the-air networks, this article reveals just how much reach brands and agencies should expect from two approaches to what we call "smart scatter" TV advertising: data-driven linear TV and ad-supported video on demand, or AVOD, otherwise known as over-the-top (OTT). For this update, we also look at the available ad impressions in both formats. That's because reach is just one variable to consider; advertisers also need access to these audiences in the form of the number and length of ad breaks. **TL:DR** - Simulmedia's data-driven linear TV platform can reach about 112 million households, approximately 57M of which are not reachable via AVOD. - We estimate that AVOD can reach approximately 58 million households today, 55M of which are also reachable via Simulmedia. - This leaves just 3M that are AVOD-only. - Linear TV also offers nearly 14x as many ad impressions as AVOD. - That's why Simulmedia recommends data-driven linear as the foundation of any video advertising strategy. What is "smart scatter?" The "smart" part refers to relying on a data layer to guide audience targeting, automation to drive execution, and rapid reporting that clearly shows the link between ad exposure and business outcomes. The "scatter" part indicates inventory that's planned and bought only when it's needed, not Upfront (i.e. months in advance). Using smart scatter is going to be essential this year because of the diminished size of the 2020 Upfronts. First, here's the theoretical maximum of all linear TV households, as reported by Nielsen, for the following 10 audiences: Adults 18-34, Adults 18-49, Adults 25-54, Adults 25-54 Black, Adults 25-54 Hispanic, Men 18-34, Persons 2+, Kids 2-11, HH with Kids 2-11, and total Households.`32:T687,chart = { const svg = d3.create("svg").attr("viewBox", [0, 0, width, height]); svg .append("g") .selectAll("rect") .data(data) .join("rect") .attr("fill", "#ddd") .attr("x", x(0)) .attr("y", (d, i) => y(d.name)) .attr("width", x(1) - x(0)) .attr("height", y.bandwidth()); svg .append("g") .selectAll("rect") .data(data) .join("rect") .attr("fill", d => color(d.period)) .attr("x", x(0)) .attr("y", (d, i) => y(d.name)) .attr("width", d => x(d.value) - x(0)) .attr("height", y.bandwidth()); svg .append("g") .attr("fill", "white") .attr("text-anchor", "end") .attr("font-family", "sans-serif") .attr("font-weight", "bold") .attr("font-size", 12) .selectAll("text") .data(data) .join("text") .attr("x", d => x(d.value)) .attr("y", d => y(d.name) + y.bandwidth() / 3) .attr("dy", "0.35em") .attr("dx", -4) .text(d => d3.format(".3s")(d.absValue)) .call(text => text .filter(d => x(d.value) - x(0) < 20) // short bars .attr("dx", +4) .attr("fill", "black") .attr("text-anchor", "start") ); svg .append("g") .attr("fill", "#555") .attr("text-anchor", "start") .attr("font-family", "sans-serif") .attr("font-weight", "bold") .attr("font-size", 12) .selectAll("text") .data(audienceData) .join("text") .attr("x", d => x(1)) .attr("y", d => y(d.name) + y.bandwidth() / 3) .attr("dy", "0.35em") .attr("dx", 4) .text(d => d3.format(".3s")(d.value)); svg.append("g").call(xAxis); svg.append("g").call(yAxis); return svg.node(); }33:Teb2,md` As you can see, Simulmedia's direct relationships with 120 national networks has made it possible for our advertisers to reach **112 million HHs, which is nearly 93% of the theoretical maximum number of households, and at least more than 75% of every other audience we show here.** The other approach to “smart scatter,” AVOD, also known as OTT, has a HH reach that is growing. Based on recent data from JP Morgan Chase and other sources, we estimate AVOD can reach about 58 million HHs today. (Some expect it to surpass 80 million by the end of the year.) Most of this audience is reachable via data-driven linear. Here's why: - There are approximately 115M households (HHs) that receive the internet, which is required to watch AVOD.* - Based on data from JP Morgan Chase and other sources, we estimate that there are 58M AVOD HHs. - Most AVOD HHs are reachable via data-driven linear because AVOD is typically watched via a connected TV, or CTV. - CTVs also receive linear broadcast TV signals. - Simulmedia can reach 112M HHs. - We therefore estimate that the maximum number of AVOD-only HHs is 3M. - The overlap of HHs that receive both AVOD and linear is therefore approximately 55M. - This means there are approximately 57M HHs that are only reachable via linear TV. *Asterisk indicates data from Statista through 2018, which we've increased to estimate 2020: https://www.statista.com/statistics/183614/us-households-with-broadband-internet-access-since-2009/*

${vennDiagram}

${barChartVenn}

While both approaches to smart scatter belong in today’s video advertising mix, as this Venn diagram shows, nearly all AVOD viewers are reachable via Simulmedia, while Simulmedia also enables a reach of 57M HHs that are not reachable via AVOD. **This is why data-driven linear TV belongs at the foundation of any video advertising strategy.** The difference becomes even more significant when we look at the number of HH impressions available in data-driven linear and AVOD. For linear TV, the impressions delivered on Nielsen-rated networks over the past few years have been ${d3.format( ".3s" )(yoyImps[4].impressions)} (${yoyImps[4].year}), ${d3.format(".3s")( yoyImps[3].impressions )} (${yoyImps[3].year}), ${d3.format(".3s")(yoyImps[2].impressions)} (${ yoyImps[2].year }) and ${d3.format(".3s")(yoyImps[1].impressions)} (${ yoyImps[1].year }). Even though [TV viewing has increased significantly post-COVID](https://observablehq.com/@simulmedia/simulmedia-tv-marketplace-update-for-may-15-2020), we assume that the number of impressions for 2020 will be the same as 2019: ${d3.format( ".3s" )(yoyImps[1].impressions)}. To estimate AVOD impressions, we make some assumptions and let you play around with them. [eMarketer estimates total CTV ad spend to be $${d3 .format(".3s")(avod2019rev) .replace( "G", "B" )} in 2019 and $10.81B in 2021](https://www.emarketer.com/content/us-connected-tv-advertising-2019) (25% CAGR). To take into account the impact of COVID on CTV viewing, one can assume the growth rate increases to ${selCagr}%. Also assuming a CPM of $${selCpm}, we can approximate the total number of impressions on AVOD in 2020 to be

${d3
  .format(".3s")(impsBarChartData[1].value)
  .replace(
    "G",
    "B"
  )}

. You may think that the AVOD growth rate is higher than this model or that CPMs are lower. Below, you can change these input values for compound annual growth rate, or CAGR, and CPM and see the effects propagate in the comparison chart below (as well as the numbers above). `34:T6bd,md`In addition to making linear TV data-driven, Simulmedia is also buttressing direct relationships with national networks with workflow automation between the brand, Simulmedia, and the TV networks. Along with our data-driven approach, automation of the media supply-chain enables us to provide our clients the ability to drive performance and reach with precision, speed, and scale on linear TV. Direct relationships with national networks is just one element that benefits Simulmedia advertisers. Now more than ever, a data-driven approach to all premium video advertising is necessary to maximize performance, precision, speed and efficiency. This includes AVOD and data-driven linear. Here's why. The typical American cable subscriber gets 200 networks - up from under 30 in 1990. This means viewers are much harder to reach than ever before. Evidence of our data-driven approach and reliance on software can be seen in the average Simulmedia media plan: over the past two years, it has included **${d3.format( ",.1f" )(avgNetworks)} networks** and **${d3.format(",.0f")( avgSpots )} ads**. Plans need this sort of breadth in order to maximize the reach of *any* audience, not just the ones we've listed. The chart below visualizes this breadth. It shows the number of networks and spots for every Simulmedia campaign over the past two years. The x-axis is on log scale to account for the very high number of ads for some campaigns, while the size of the dots indicates the length of the campaign: from 1 day to 2 months. Hover over a dot to get more details. `35:[{"id":"ce1e3cbe5855dd4b7780abc293c4c31a36adfe295ef10ab5fba8891ed3803fd443e6b87ff64a3283173caa50cf7139c883a9264b8f386d965ea03bd3a2e29bc3","href":"https://static.observableusercontent.com/files/ce1e3cbe5855dd4b7780abc293c4c31a36adfe295ef10ab5fba8891ed3803fd443e6b87ff64a3283173caa50cf7139c883a9264b8f386d965ea03bd3a2e29bc3","name":"yoy_imps.csv","type":"text/csv","lastModified":1590019402918.913,"size":119}] 2f:{"id":"43e3768f45bf69dd","slug":"quantifying-the-reach-of-smart-scatter-tv-advertising-data","title":"Quantifying \"Smart Scatter\" TV Advertising, Part 2: Data-Driven Linear TV and AVOD Reach and Ad Impressions","thumbnail":"2a4b621aaaf328d0cdccc75a27baae202bc275f6dccda4789575268448934081","access_level":"public","create_time":"$D2020-05-14T21:04:37.802Z","update_time":"$D2023-01-20T13:56:43.164Z","view_time":null,"first_public_time":"$D2020-05-15T21:00:25.398Z","has_unpublished_edits":null,"fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"65b839140443db96","login":"simulmedia","name":"Simulmedia","avatar_url":"https://avatars-next.observableusercontent.com/9ba73130625325fdd2559f4ecf06018cb93f0fae71f9314f6595f9259c95f168","tier":"pro"},"version":737,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"5f9d94d9fec7c169","login":"n-ricci","name":"Nico Ricci","avatar_url":"https://avatars-next.observableusercontent.com/30c466d5e63760c99357eb7c57959ad1c88f69ec044f2e34582e67fe89f23dc9","tier":null}],"members":[{"type":"team","id":"65b839140443db96","login":"simulmedia","name":"Simulmedia","avatar_url":"https://avatars-next.observableusercontent.com/9ba73130625325fdd2559f4ecf06018cb93f0fae71f9314f6595f9259c95f168","tier":"pro","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Quantifying \"Smart Scatter\" TV Advertising, Part 2: Data-Driven Linear TV and AVOD Reach and Ad Impressions","cells":[{"id":1,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":735,"value":"Plot.plot({\n marginLeft: 120,\n marks: [\n Plot.barX(audienceData, {\n x: \"value\",\n y: \"name\",\n sort: { y: \"x\", reverse: true }\n }),\n Plot.ruleX([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":355,"value":"md`\nLinear TV reaches 121 million HHs in spite of the gradual decline in households that pay for a cable bundle because of the number of households that access over-the-air networks, or OTAs, for free. Most of these OTAs reach more than 50 million households. \n\nNext, let's show Simulmedia's ability to reach these audiences via its data-driven platform, which includes targeting, planning, execution/activation, measurement and optimization.\n\nBelow, we show the actual size of each audience that Simulmedia has been able to reach since January 27, 2020, over a one day, one week, one month and three month basis. \n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":297,"value":"key = legend({ title: \"Period\", color, tickSize: 0 })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":19,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":371,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":637,"value":"viewof selCagr = slider({\n min: 25,\n max: 50,\n step: 5,\n value: 35,\n title: \"CAGR (%)\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":651,"value":"viewof selCpm = slider({\n min: 10,\n max: 30,\n step: 5,\n value: 15,\n title: \"CPM ($)\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":693,"value":"html`
`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":686,"value":"md`###### Estimated Impressions Delivered in 2020`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":680,"value":"barChartImps","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":696,"value":"md`**It is striking to see that, even in the rosiest of scenarios (50% CAGR and $10 average CPM), the amount of impressions delivered on AVOD would still be a fraction of those available on linear TV.** `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":643,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":424,"value":"scatterPlot","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":523,"value":"md` If you want to reach more customers using data-driven linear, visit [build.simulmedia.com](https://build.simulmedia.com/brand-insights). It's a free, interactive tool to help you get started. You can select a brand, target audience, a competitor, and a time frame to see how many you’ve already reached - and how to reach even more. And, as usual, if you have questions, just contact Matt Collins, Simulmedia SVP of Marketing, at mcollins@simulmedia.com.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":441,"value":"html`

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":361,"value":"html`

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":358,"value":"md`# Appendix`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":341,"value":"md`### Charts`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":347,"value":"import { audienceChart, audienceData } from '5d62e6bc349bb35d'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":560,"value":"import { chart as vennDiagram } from '869ba57119a2d3cf'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":579,"value":"import { chart as barChartVenn } from '@simulmedia/horizontal-bar-chart'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":673,"value":"import { chart as barChartImps } with {\n impsBarChartData as data\n} from '@simulmedia/horizontal-bar-chart/2'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":448,"value":"audienceData","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":421,"value":"import { chart as scatterPlot, avgSpots, avgNetworks } from \"ffcd74c893bb81e5\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":180,"value":"format = x.tickFormat(20, data.format)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":78,"value":"x = d3\n .scaleLinear()\n .domain([0, 1]) //d3.max(data, d => d.value)])\n .range([margin.left, width - margin.right])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"y = d3\n .scaleBand()\n .domain(audiences)\n .rangeRound([margin.top, height - margin.bottom])\n .padding(0.2)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":105,"value":"xAxis = g => g\n .attr(\"transform\", `translate(0,${margin.top})`)\n .call(d3.axisTop(x).ticks(width / 80, data.format))\n .call(g => g.select(\".domain\").remove())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":110,"value":"yAxis = g =>\n g\n .attr(\"transform\", `translate(${margin.left},0)`)\n .call(d3.axisLeft(y).tickSizeOuter(0))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":274,"value":"color = d3\n .scaleOrdinal()\n .domain(periods)\n // .range(d3.schemeSpectral[periods.length])\n .range([\"#98abc5\", \"#8a89a6\", \"#7b6888\", \"#6b486b\"].reverse()) //[\"#9ecae1\", \"#6baed6\", \"#3182bd\", \"#08519c\"])\n .unknown(\"#ccc\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":215,"value":"barHeight = 32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":69,"value":"height = Math.ceil((audiences.length + 0.1) * barHeight) +\n margin.top +\n margin.bottom","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":74,"value":"margin = ({ top: 30, right: 80, bottom: 10, left: 90 })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6,"value":"d3 = require(\"d3@5\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":243,"value":"md`### Data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":246,"value":"import { smData as data } from \"5d62e6bc349bb35d\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":268,"value":"data.reverse()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":285,"value":"(data.format = \"%\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":618,"value":"yoyImps = d3.csvParse(await FileAttachment(\"yoy_imps.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":659,"value":"avod2019rev = 23000000000 //6940000000","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":665,"value":"avodImps = (1000 * avod2019rev * (1 + selCagr / 100)) / selCpm","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":658,"value":"impsBarChartData = [\n {\n name: \"Linear TV\",\n value: yoyImps[1].impressions\n },\n {\n name: \"AVOD\",\n value: avodImps\n }\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":251,"value":"// audiences = [...new Set(data.map(obj => obj.name))].sort()\naudiences = [\n \"Households\",\n \"A25-54 Black\",\n \"P2+\",\n \"A25-54\",\n \"A18-49\",\n \"HH with Kids 2-11\",\n \"A25-54 Hispanic\",\n \"A18-34\",\n \"Kids 2-11\",\n \"M18-34\"\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":260,"value":"periods = [...new Set(data.map(obj => obj.period))]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":299,"value":"import { legend } from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":585,"value":"jQuery3 = require(\"https://code.jquery.com/jquery-3.3.1.slim.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":588,"value":"BootStrap4 = require(\"https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/js/bootstrap.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":591,"value":"BootstrapCss = importcss(\n \"https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css\"\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":593,"value":"import { importjs, importcss } from \"@chomtana/import-external-library\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":628,"value":"import { slider } from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W35","stdlib":"1"}} 30:"$2f:body"