45:["$","title",null,{"children":"2024 NCAA Football Transfers | Eli Standard | Observable"}] 4d:T1fb1,chart = { const {nodes, links} = sankey({ nodes: nodesArray.map(d => Object.assign({}, d)), links: linksArray.map(d => Object.assign({}, d)) }); const margin = { top: 90, right: 20, bottom: 20, left: 20 }; const svgWidth = width + margin.left + margin.right; const svgHeight = height + margin.top + margin.bottom + 200; const svg = d3.select(DOM.svg(width, height + 100)) .style("background", "#ffffff") .style("width", "100%") .style("height", "auto"); // Add a Set to track selected paths let selectedPaths = new Set(); // Title svg.append("text") .attr("x", 10) .attr("y", margin.top - 50) .attr("text-anchor", "left") .style("font-size", "27px") .style("font-family", "Test Tiempos Headline, Times New Roman, sans-serif") .style("font-weight", "bold") .style("fill", "black") .text("NCAA Football Transfers") //.text("Transferred to SEC") .append("tspan") .attr("x", 10) .attr("dy", "1.6em") .style("font-size", "18px") .style("font-family", "Lato, sans-serif") .style("font-weight", "normal") .style("fill", "#696969") .text("2024"); // Grey rectangles for nodes svg.append("g") .selectAll("rect") .data(nodes) .join("rect") .attr("x", d => d.x0 + 1) .attr("y", d => d.y0 + margin.top) .attr("width", d => d.x1 - d.x0 - 2) .attr("height", d => d.y1 - d.y0) .attr("fill", d => { let c; for (const link of d.targetLinks) { if (c === undefined) c = link.color; else if (c !== link.color) c = null; } //return (d3.color(c) || d3.color(color)).darker(0); return c || "#cccccc"; // Default to light gray if no color is found }) .attr("fill-opacity", 0.6) // Reduce opacity to make it lighter .append("title") .text(d => `${d.name}\n${d.value}`); const link = svg.append("g") .attr("fill", "none") .selectAll("g") .data(links.sort((a, b) => b.width - a.width)) .join("g") //.attr("stroke", d => d3.color(d.color) || color) .attr("stroke", "#cccccc") // Set default color to grey .style("mix-blend-mode", "multiply"); // Tooltip for hover effect const tooltip = d3.select("body").append("div") .style("position", "absolute") .style("visibility", "hidden") .style("background", "white") // White background .style("color", "black") // Black text .style("padding", "8px 12px") // Padding around the number .style("border-radius", "6px") // Rounded corners .style("font-family", "Lato, sans-serif") .style("font-weight", "bold") .style("font-size", "14px") .style("text-align", "center") // Center the text .style("box-shadow", "0 1px 3px rgba(0,0,0,0.2)") // Optional: subtle shadow .style("border", "1px solid rgba(0,0,0,0.1)"); // Optional: subtle border // Modify the path section link.append("path") .attr("d", d3.sankeyLinkHorizontal()) .attr("stroke-width", d => Math.max(1, d.width)) .attr("stroke-opacity", 0.2) .attr("transform", `translate(0,${margin.top})`) .on("mouseover", (event, d) => { // Show hover effects and tooltip for unselected paths if (!selectedPaths.has(event.currentTarget)) { tooltip.style("visibility", "visible") .html(`${d.value}`) .style("left", (event.pageX + 10) + "px") .style("top", (event.pageY - 10) + "px"); highlightPath(event.currentTarget, d); } }) .on("mousemove", (event, d) => { if (!selectedPaths.has(event.currentTarget)) { tooltip.style("left", (event.pageX + 10) + "px") .style("top", (event.pageY - 10) + "px"); } }) .on("mouseout", (event, d) => { // Hide tooltip and unhighlight if not selected if (!selectedPaths.has(event.currentTarget)) { tooltip.style("visibility", "hidden"); unhighlightPath(event.currentTarget); } }) .on("click", (event, d) => { if (selectedPaths.has(event.currentTarget)) { // If clicking a selected path, deselect it selectedPaths.delete(event.currentTarget); unhighlightPath(event.currentTarget); if (event.currentTarget.persistentTooltip) { event.currentTarget.persistentTooltip.remove(); event.currentTarget.persistentTooltip = null; } } else { // Add new selection selectedPaths.add(event.currentTarget); highlightPath(event.currentTarget, d); // Create persistent tooltip const persistentTooltip = d3.select("body").append("div") .attr("class", "persistent-tooltip") .style("position", "absolute") .style("background", "white") .style("color", "black") .style("padding", "8px 12px") .style("border-radius", "6px") .style("font-family", "Lato, sans-serif") .style("font-weight", "bold") .style("font-size", "14px") .style("text-align", "center") .style("box-shadow", "0 1px 3px rgba(0,0,0,0.2)") .style("border", "1px solid rgba(0,0,0,0.1)") .style("left", (event.pageX + 10) + "px") .style("top", (event.pageY - 10) + "px") .html(`${d.value}`); event.currentTarget.persistentTooltip = persistentTooltip; } }); function highlightPath(path, d) { d3.select(path) .attr("stroke-opacity", 0.8) .attr("stroke", d => d3.color(d.color) || color); } function unhighlightPath(path) { d3.select(path) .attr("stroke-opacity", 0.2) .attr("stroke", "#cccccc"); } // Helper functions function showTooltip(event, d) { tooltip.style("visibility", "visible") .html(`${d.value}`) .style("left", (event.pageX + 10) + "px") .style("top", (event.pageY - 10) + "px"); } function createPersistentTooltip(event, d) { // Create a new tooltip div for this selection const persistentTooltip = d3.select("body").append("div") .attr("class", "persistent-tooltip") .style("position", "absolute") .style("background", "white") .style("color", "black") .style("padding", "8px 12px") .style("border-radius", "6px") .style("font-family", "Lato, sans-serif") .style("font-weight", "bold") .style("font-size", "14px") .style("text-align", "center") .style("box-shadow", "0 1px 3px rgba(0,0,0,0.2)") .style("border", "1px solid rgba(0,0,0,0.1)") .style("left", (event.pageX + 10) + "px") .style("top", (event.pageY - 10) + "px") .html(`${d.value}`); // Store the tooltip with the path event.currentTarget.tooltip = persistentTooltip; } function hideTooltip(path) { d3.select(path) .attr("stroke-opacity", 0.2) .attr("stroke", "#cccccc"); // Remove persistent tooltip if it exists if (path.tooltip) { path.tooltip.remove(); path.tooltip = null; } } //link.append("title") //.text(d => `${d.source.name} → ${d.target.name}\n${d.value}`); // Text labels svg.append("g") .style("font", "bold 10px sans-serif") .style("font-family", "Lato, sans-serif") .style("stroke", "white") .style("stroke-width", ".1px") .attr("pointer-events", "none") .selectAll("text") .data(nodes) .join("text") .attr("x", d => d.x0 < width / 2 ? d.x1 + 6 : d.x0 - 6) .attr("y", d => (d.y1 + d.y0) / 2 + margin.top) .attr("dy", "0.35em") .attr("text-anchor", d => d.x0 < width / 2 ? "start" : "end") .text(d => d.name) .append("tspan") .attr("fill-opacity", 0.7) .text(d => ` ${d.value}`); return svg.node(); } 4e:T4fa,## Improvements To improve this vizualization further, I'd introduce a way to filter the dataset within the graphic, like selecting certain conferences at a time. I'd also make a way to drill down into the data further, like clicking on a starting conference and viewing all of the individual players' names and schools from which they transferred and where they transferred to.

I'd also like to look at transfer data from multiple years, not just 2024. Are the transfer patterns the same amongst the SEC and Big 10 every year? What about transfer patterns before 2021, when NIL spending was approved?

Additionally, what's the average age of transfer students? How many transferred out after their junior year?

I'd also make improvements to the readability of the diagram, like making the tooltip's background color a gradient from the starting conference's color to the ending conference's color, so it's easier to see which number applies to which conference transition in the static graphics.

The structure of the plot could be even further customized to include logos for the conferences instead of just the names. Drawing arrows of varying sizes might be more unique and engaging than just a generic Sankey 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49:{"id":"925e87a7aa127f44","slug":"2024-ncaa-football-transfers","title":"2024 NCAA Football Transfers","thumbnail":"65b107560b08d0006dea91dead894689558e2285a48860b7966956bf1855ceb3","access_level":"public","create_time":"$D2025-01-30T02:49:48.297Z","update_time":"$D2025-01-31T16:12:04.926Z","view_time":null,"first_public_time":"$D2025-01-30T02:49:48.297Z","has_unpublished_edits":null,"fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"1fc4efa9ca05c06e","login":"elistandard","name":"Eli Standard","avatar_url":"https://avatars-next.observableusercontent.com/4c1b8090cff6c12c6bf97e8cdb4931f3e0e6321dbef5c66c031ce98e49bf9c75","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":1236,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"b21f049d995eb1d5","login":"elistandard","name":"Eli Standard","avatar_url":"https://avatars-next.observableusercontent.com/4c1b8090cff6c12c6bf97e8cdb4931f3e0e6321dbef5c66c031ce98e49bf9c75","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"team","id":"1fc4efa9ca05c06e","login":"elistandard","name":"Eli Standard","avatar_url":"https://avatars-next.observableusercontent.com/4c1b8090cff6c12c6bf97e8cdb4931f3e0e6321dbef5c66c031ce98e49bf9c75","tier":"starter_2024","role":"editor"},{"type":"individual","id":"b21f049d995eb1d5","login":"elistandard","name":"Eli Standard","avatar_url":"https://avatars-next.observableusercontent.com/4c1b8090cff6c12c6bf97e8cdb4931f3e0e6321dbef5c66c031ce98e49bf9c75","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"titling":"auto","title":"2024 NCAA Football Transfers","cells":[{"id":0,"value":"md`# 2024 NCAA Football Transfers\n\nBased on work by [chartr](https://twitter.com/chartrdaily/status/1101764681599877126); built with [d3-sankey](/@d3/sankey-diagram). Data: [247 Sports](https://247sports.com/season/2024-football/transferportaltop/)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1061,"value":"I just finished reading The Athletic article [Alabama and Georgia lost the upper hand in college football last season. Is NIL to blame?](https://www.nytimes.com/athletic/6091910/2025/01/28/alabama-georgia-nil-college-football/) and I wanted to visualize some data to support the story. \n

\nI really wanted to look at NIL spending to find out which teams spent the most on players last year, but as the article mentions, \"the spending by collectives and the outside endorsement deals for players are not public.\" So without reliable NIL spending data, what could I visualize?\n

\nI read some comments at the bottom of the article and stumbled upon this take from Ryan K.:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1090,"value":"highlighted = FileAttachment(\"highlighted.jpg\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1083,"value":"Is this true? Do higher paid and bigger name athletes transfer less frequently? It's impossible to know the salaries of these athletes, so we won't be able to find out which team's money is drawing in specific players, but we can find out which conferences these players are tranferring to and from.\n

\nSo let's find out, do players from SEC schools primarily transfer to other SEC schools? Do Big 10 athletes stay in the Big 10?\n

\nTo find out, we need 2024 NCAA football transfer data, a way to map those schools to their respective conferences, and then a way to visualize it.\n

\nFor data, I Googled college football transfer data looking for a site that could be easily scraped.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1101,"value":"googleTransferData = FileAttachment(\"google-transfer-data.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1103,"value":"[247 Sports](https://247sports.com/season/2024-football/transferportaltop/) gave me the most accessible data. I could sort players by rating, so I knew I was getting the top athletes, and the html looked well organized.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1106,"value":"sports = FileAttachment(\"247-sports.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1105,"value":"sportsInspected = FileAttachment(\"247-sports-inspected.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1109,"value":"I scraped the data for the top 1000 athletes using Python's BeautifulSoup package:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1111,"value":"python1 = FileAttachment(\"python-1.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1113,"value":"Resulting in this dataset:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1115,"value":"python5 = FileAttachment(\"python-5.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1117,"value":"Then I used a dictionary to map the schools to the divisions they belong in:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1119,"value":"python2 = FileAttachment(\"python-2.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1121,"value":"This gave me a more complete dataset with school and conference names:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1123,"value":"python6 = FileAttachment(\"python-6.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1129,"value":"1000 Athletes meant a lot of schools. Since we're only focusing on Division 1-A conferences for this graphic, let's filter out all of the D2 and Division 1-AA schools.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1131,"value":"python3 = FileAttachment(\"python-3.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1127,"value":"This leaves us with 859 transfers. From here, I turned this data into something that was usable for a Sankey diagram. I looped through the final dataset and tallied up the total number of transfers going to and from each school pair:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1126,"value":"python4 = FileAttachment(\"python-4.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1137,"value":"Finally we can make the Sankey diagram. I used [d3-sankey](/@d3/sankey-diagram) as a starting point. Below is the finished chart. Hover over a path to see the number of transfers from one division to the next. Click on a path to freeze the tooltip so it can be compared with others. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3,"value":"$4d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1140,"value":"## Analysis\nSo lets answer the question using some static graphics. Do SEC athletes primarily transfer to other SEC schools? What about Big 10 athletes?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1159,"value":"sankeyFromSec1 = FileAttachment(\"sankey-from-sec@1.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1171,"value":"It looks like there is some truth to Ryan K.'s take. Of the 217 players that transferred from an SEC team, 56 of them went to another SEC team. This is higher than the number of SEC transfers to any other conference, including the Big 10, which only got 39. That being said, 47 players went from the SEC to the ACC, which is close to the same amount that stayed in the SEC.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1161,"value":"sankeyFromBig10 = FileAttachment(\"sankey-from-big10.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1175,"value":"Now what about transfers from the Big 10? Wow, it looks like the SEC got more Big 10 players than the Big 10 itself (38 to the SEC vs. 33 staying in the Big 10). It looks like lots of players want to go to the SEC. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1168,"value":"sankeyToSec = FileAttachment(\"sankey-to-sec.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1177,"value":"But how many players want to go to the SEC? Last year 204 did. The SEC saw both the most departures (217) and the most transfers (204) of any conference. This is notable because the SEC only has 16 schools while the Big 10 has 18, so this trend can't be attributed to sheer size alone.\n

\nSchools per conference:
\nSEC: 16 schools
\nBig Ten: 18 schools
\nACC: 17 schools
\nBig 12: 16 schools
\nMountain West: 12 schools
\nAAC (American): 14 schools
\nSun Belt: 14 schools
\nConference USA: 9 schools
\nMAC (Mid-American): 12 schools
\nIndependent: 3 schools (Notre Dame, UConn, UMass)

\n\nThe AAC and the Big 12 were the only conferences with net gains, where more players transferred in than out. The SEC saw a net loss of 13 players and the Big 10 saw a net loss of 7 players.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1165,"value":"sankeyToBig101 = FileAttachment(\"sankey-to-big10@1.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1190,"value":"Interestingly, the Big 10 also got most of its transfers from the SEC, followed by the Big Ten and then the ACC","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1074,"value":"$4e","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1055,"value":"### Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":470,"value":"nodesArray = FileAttachment(\"nodesArray@8.csv\").csv({typed: true});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":477,"value":"linksArray = FileAttachment(\"linksArray@13.csv\").csv({typed: true});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":23,"value":"sankey = d3.sankey()\n .nodeId(d => d.name)\n .nodeAlign(d3.sankeyCenter)\n .nodeSort((a, b) => b.value - a.value) // .nodeSort(null)\n .nodeWidth(15)\n .nodePadding(padding)\n .extent([[0, 5], [width, height - 5]])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"width = 1075","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":28,"value":"height = 600","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1045,"value":"color = \"#dddddd\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":333,"value":"padding = 22","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":21,"value":"d3 = require(\"d3@6\", \"d3-sankey@0.12\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4f","stdlib":"1"}} 44:null