34:["$","title",null,{"children":"Visualizing Results: 17 Incidents with Zero-Shot, Few-shot CoT and ToT Prompting | Irresponsible AI Atlas | Observable"}] 3c:T40b,# Visualizing Results: 17 Incidents with Zero-Shot, Few-shot CoT and ToT Prompting From our initial attempt to run 15 first incidents, we noticed that we did not have any examples of some of the taxonomy classes such as: mental health, pseudo-science, copyright, and environmental impacts. We wanted to include these examples in our manually classified results and run these same incidents through the LLM to compare the overall results. We also included multiple classes of biases for one incident, in contrast to strictly assigning one class to one incident in the previous manually classifications. 17 manually classified incidents can be found [here](https://docs.google.com/spreadsheets/d/1ryL6P6Djd7yL0GsdewFhRtUzNyai7V8G-vhCpMIszlY/edit#gid=331525361) In this notebook, we also included additional prompting techniques: Few-shot Chain-of-Thoughts and Tree-of-Thoughts prompting. Our goal is to explore different types of prompting techniques and see how they compare with each other and to our manually classified results. 3d:Ta8a,function hierarchy(items, type) { const hierarchy = new Map(); items.forEach(item => { // Ensure item['Classes of irresponsible AI use'] is treated as an array const classes = item ? Array.isArray(item['Classes of irresponsible AI use']) ? item['Classes of irresponsible AI use'] : [item['Classes of irresponsible AI use']] : []; classes.forEach(classKey => { // Initialize a Map for this classKey if it doesn't exist if (!hierarchy.has(classKey)) { hierarchy.set(classKey, new Map()); } const classMap = hierarchy.get(classKey); // Process subclasses if this classKey is a key in the subclass object if (item['Subclasses'] && item['Subclasses'][classKey]) { const subclassArray = item['Subclasses'][classKey]; subclassArray.forEach(subclassItem => { // Initialize a Map for this subclassItem under classKey if (!classMap.has(subclassItem)) { classMap.set(subclassItem, new Map()); } const subclassMap = classMap.get(subclassItem); // Process sub-subclasses based on the type if (type === 'zeroShot' && item['Sub-subclasses'] && item['Sub-subclasses'][subclassItem]) { const subsubclassArray = item['Sub-subclasses'][subclassItem]; subsubclassArray.forEach(subsubclassItem => { // Initialize an array for this subsubclassItem under subclassItem if it doesn't exist if (!subclassMap.has(subsubclassItem)) { subclassMap.set(subsubclassItem, []); } }); } else if (type === 'zeroShotRules' && item['Sub-subclass'] && item['Sub-subclass'][classKey] && item['Sub-subclass'][classKey][subclassItem]) { const subsubclassArray = item['Sub-subclass'][classKey][subclassItem]; subsubclassArray.forEach(subsubclassItem => { // Initialize an array for this subsubclassItem under subclassItem if it doesn't exist if (!subclassMap.has(subsubclassItem)) { subclassMap.set(subsubclassItem, []); } }); } else if (type === 'latest_prompt' && item['Sub-subclass'] && item['Sub-subclass'][subclassItem]) { const subsubclassArray = item['Sub-subclass'][subclassItem]; subsubclassArray.forEach(subsubclassItem => { // Initialize an array for this subsubclassItem under subclassItem if it doesn't exist if (!subclassMap.has(subsubclassItem)) { subclassMap.set(subsubclassItem, []); } }); } }); } }); }); return hierarchy; }3e:T502,zeroShotArray = { //zeroShotFile consists of 16 incidents, including the awful AI incident const zeroShotFile = await FileAttachment("zero_shot_1_processed_results_2024_03_26_17_08.json").json(); //zeroShotFile2 consists of incident 39 const zeroShotFile2 = await FileAttachment("zero_shot_39_processed_results_2024_03_26_17_16.json").json(); const zeroShotArray = Object.entries(zeroShotFile).map(([key,value]) => { return {id : key, ...value}; }) const obj_39 = Object.entries(zeroShotFile2).map(([key,value]) => { return {id: key, ...value} }) //merging the incidents together to one in zeroShotArray zeroShotArray.push(obj_39[0]) // post-processing here to merge the rest of the fields from incident AI database, such as date, title, description, to the zeroShotArray results let zeroShotMergedArray = zeroShotArray.map((item,index)=>{ const incident = selectedIncidents.find(i=> +i["incident id"] === +item.id) if(incident){ return { ...item, title: incident.title, description: incident.description, "date occurrence": incident["date occurrence"], "date publicly known": incident["date publicly known"] } } } ) return zeroShotMergedArray }3f:T60a,fewShotCOTArray = { //fewShotFile consists of 15 incidents const fewShotFile = await FileAttachment("cot_1_processed_results_2024_03_26_16_21.json").json(); //fewShotFile2 consists of incident 0 (from awful ai database) const fewShotFile2 = await FileAttachment("cot_0_processed_results_2024_03_26_16_40.json").json(); //fewShotFile3 consists of incident 39 const fewShotFile3 = await FileAttachment("cot_39_processed_results_2024_03_26_17_19.json").json(); const fewShotArray = Object.entries(fewShotFile).map(([key,value]) => { return {id : key, ...value}; }) const obj_39 = Object.entries(fewShotFile3).map(([key,value]) => { return {id: key, ...value} }) const obj_0 = Object.entries(fewShotFile2).map(([key,value]) => { return {id: key, ...value} }) //merging the incidents together to one in fewShotArray fewShotArray.push(obj_39[0]) fewShotArray.push(obj_0[0]) // post-processing here to merge the rest of the fields from incident AI database, such as date, title, description, to the fewShotArray results let fewShotMergedArray = fewShotArray.map((item,index)=>{ const incident = selectedIncidents.find(i=> +i["incident id"] === +item.id) if(incident){ return { ...item, title: incident.title, description: incident.description, "date occurrence": incident["date occurrence"], "date publicly known": incident["date publicly known"] } } } ) return fewShotMergedArray }40:T4db,ToTFewShotsArray = { //ToTFile consists of 16 incidents including one awful AI data (incident id 0) const ToTFile = await FileAttachment("tot_1_processed_results_2024_03_26_16_58.json").json(); //ToTFile2 consists of incident 39 (from awful ai database) const ToTFile2 = await FileAttachment("tot_39_processed_results_2024_03_26_17_26.json").json(); const ToTArray = Object.entries(ToTFile).map(([key,value]) => { return {id : key, ...value}; }) const obj_39 = Object.entries(ToTFile2).map(([key,value]) => { return {id: key, ...value} }) //merging the incidents together to one in ToTArray ToTArray.push(obj_39[0]) // post-processing here to merge the rest of the fields from incident AI database, such as date, title, description, to the ToTArray results let ToTMergedArray = ToTArray.map((item,index)=>{ const incident = selectedIncidents.find(i=> +i["incident id"] === +item.id) if(incident){ return { ...item, title: incident.title, description: incident.description, "date occurrence": incident["date occurrence"], "date publicly known": incident["date publicly known"] } } } ) return ToTMergedArray }41:T639,mergedIncidentsArr = { const obj_0 = {"title":"AI's Environmental Footprint","incident id":0,"url":"https://earth.org/the-green-dilemma-can-ai-fulfil-its-potential-without-harming-the-environment/","news":null,"description":"The environmental footprint of AI, particularly in training large models, is significant. According to a study by researchers at the University of Massachusetts, the energy used in training certain popular large AI models can produce about 626,000 pounds of carbon dioxide. This amount is equivalent to roughly 300 round-trip flights between New York and San Francisco, highlighting the substantial carbon footprint associated with advanced AI technologies. This data underscores the need for more sustainable practices in the field of AI to mitigate its impact on climate change.","awful-ai md":null,"date":"2023-07-18","date published":"2023-07-18","Affected Population":"General Human","Actual Impact":"unknown","Potential Impact":"unknown","Application":"AI training","Area":"tech","class":"environmental impact","subclass":null,"subsubclass":null,"city":null,"state":null,"country":"United States of America","continent":"North America","company ":null,"company city":null,"company state":null,"company country":null,"Online":"Not online","source":"awful-ai","thumb":null,"tags":null,"curated":null,"Researcher":null,"Comments":null,"News Outlet":null} // mergedIncidentsArray.push(obj_0) let mergedIncidentsArr = [] mergedIncidentsArr = [...mergedIncidentsArray,obj_0] const i = mergedIncidentsArray.find(i=> +i["incident id"]===0) return mergedIncidentsArr 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38:{"id":"c644b6c2996288e7","slug":"visualizing-results-17-incidents-three-promting-techniques","title":"Visualizing Results: 17 Incidents with Zero-Shot, Few-shot CoT and ToT Prompting","thumbnail":"31f0e8d02559f221f501ba89e319897103857b4eb4f275ad6b985d773fa52746","access_level":"public","create_time":"$D2024-03-27T00:26:34.891Z","update_time":"$D2024-05-06T18:01:29.925Z","view_time":null,"first_public_time":"$D2024-05-24T17:20:10.619Z","has_unpublished_edits":null,"fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"bf0181f806479314","login":"irresponsible-ai","name":"Irresponsible AI Atlas","avatar_url":"https://avatars-next.observableusercontent.com/f31fcdf005aa4e1c8c0875116f37029d1167f9946e745d02fa78604064472c7e","tier":"starter_2024","bio":"","following":false},"version":1012,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"02864ecdf909a1c2","login":"yianchen261","name":"Yian Chen","avatar_url":"https://avatars-next.observableusercontent.com/f5c211532591ea6d22a37f1c0b8087283b5465d037993473711dbb7f73b0dc13","tier":null,"bio":"","following":false}],"members":[{"type":"team","id":"bf0181f806479314","login":"irresponsible-ai","name":"Irresponsible AI Atlas","avatar_url":"https://avatars-next.observableusercontent.com/f31fcdf005aa4e1c8c0875116f37029d1167f9946e745d02fa78604064472c7e","tier":"starter_2024","role":"editor"},{"type":"individual","id":"02864ecdf909a1c2","login":"yianchen261","name":"Yian Chen","avatar_url":"https://avatars-next.observableusercontent.com/f5c211532591ea6d22a37f1c0b8087283b5465d037993473711dbb7f73b0dc13","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":2,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Visualizing Results: 17 Incidents with Zero-Shot, Few-shot CoT and ToT Prompting","cells":[{"id":0,"value":"$3c","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":359,"value":"Results from zero-shot prompting for 17 incidents","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":772,"value":"navio(zeroShotArray, {height: 500, attribWidth: 16})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":355,"value":"// viewof zeroShot = navio(zeroShotArray, {height: 500, attribWidth: 16})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":922,"value":"// navio(ToTFewShotsArray, {height: 500, attribWidth: 16})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":666,"value":"// viewof zeroShotWithRules = navio(zeroShotWithRulesArray,{height: 500, attribWidth: 16})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":728,"value":"Results of few-shot learning combined with Chain-of-Thoughts prompting technique","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":783,"value":"Results of Tree-of-Thoughts technique combined with Chain-of-Thought few-shot learning","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":747,"value":"// {\n// return html`\n//

\n//

${viewof zeroShot}

\n//

${viewof CoTFewShots}

\n//

${viewof zeroShotWithRules}

\n//

`}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":712,"value":"{\n const treeSvg = Tree(hierarchyZeroShot, {\n label: (d) => {\n return d[0];\n },\n width: 1100,\n fontSize: 14\n });\n \n return htl.html`

Class/Subclass/Subsubclass Taxonomy Zero Shot

${treeSvg}

Class/Subclass/Subsubclass Taxonomy Few-Shot Chain-of-Thought Prompting

${treeSvg}

Class/Subclass/Subsubclass Taxonomy Tree-of-Thoughts Few-Shot Prompting

${treeSvg}

`;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":584,"value":"## Notes:\n1. Classification done by LLM is different from our manually classified data. \n - Example 1: Human Incompetence sub classes are administrative and technical, not Algorithmic/Data\n - Example 2: Algorithm bias subclass should have sub-subclass interaction, feedback loop, optimization function, other\n\n2. Each incident has MULTIPLE classification instead of just 1 case. \n ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":553,"value":"myPanel = panel","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":551,"value":"import { panel } with {\n zeroShotArray as data,\n defaultGroupBy,\n defaultColorBy,\n defaultLayout\n} from \"a578473b724795d5\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":565,"value":"defaultLayout = \"pack\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":560,"value":"defaultColorBy = \"Country\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":556,"value":"defaultGroupBy = \"Country\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":541,"value":"import {Tree} from \"@john-guerra/tidy-tree-component\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":618,"value":"// hireachyFewShot = hierarchy(fewShotsProcessed)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":604,"value":"hierarchyZeroShot = hierarchy(zeroShotArray,\"latest_prompt\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":888,"value":"hierarchyfewShotCOT = hierarchy(fewShotCOTArray,\"latest_prompt\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":905,"value":"hierarcyToT = hierarchy(ToTFewShotsArray,\"latest_prompt\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":737,"value":"$3d","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":367,"value":"viewof zeroShotUnrolled = {\n // Generates an object where each groupBy attribute is a field and the value is a function spliting by that attribute\n const objForDerive = Object.fromEntries(\n groupByZeroShot.map((a) => [a, aq.escape((d) => op.split(d[a], \",\"))])\n );\n\n let df = aq\n .from(zeroShotArray)\n .derive(objForDerive);\n\n for (let a of groupByZeroShot) {\n df = df.unroll(a);\n }\n\n return df\n .view();\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":340,"value":"viewof zeroShotResultsTable = {\nlet df = aq.from(zeroShotArray);\n\nreturn df.view();\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":38,"value":"attribs_zeroShot = Object.keys(zeroShotArray[0])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":437,"value":"## Selecting 17 incidents we handpicked from mergedIncidents to compare with classifications from zero-shot and few-shots prompting","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":259,"value":"selectedIncidents = {\n const ids = [0,1,2,3,4,5,6,9,10,11,13,14,451,382,505,167,39]\n const id_set = new Set(ids)\n const selectedFieldsArray = mergedIncidentsArr.map(item => {\n return {\n title: item.title,\n 'incident id': item['incident id'],\n description: item.description,\n 'date occurrence': item.date,\n 'date publicly known': item['date published'],\n };\n});\n \n const selectedIncidentsArr = selectedFieldsArray.filter(item => id_set.has(+item['incident id']))\n\n return selectedIncidentsArr\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":941,"value":"zeroShotArray","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":830,"value":"# THIS IS THE ZERO SHOT ARRAY/DATA FOR THE POSTER","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":832,"value":"This zero-shot array is merged with fields that AIID already classified (such as title, description, dates). The rest of the fields are from the LLM","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":979,"value":"zeroShotArray","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":265,"value":"$3e","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":883,"value":"# Few-Shot Chain-of-Thought Prompting Results Array","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":885,"value":"This fewShotsCot array is merged with fields that AIID already classified (such as title, description, dates). The rest of the fields are from the LLM","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":881,"value":"$3f","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":907,"value":"# Tree-of-Thoughts Few-Shots Prompting Results","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":909,"value":"This ToTMergedArray is merged with fields that AIID already classified (such as title, description, dates). The rest of the fields are from the LLM","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":901,"value":"$40","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":826,"value":"selectedIncidents","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":16,"value":"// mergedIncidentsArray = [...mergedIncidents.values()]\nmergedIncidentsArray = incidents\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":810,"value":"$41","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3,"value":"import {mergedIncidents,incidents} from \"126c228131c034d7\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"import {aq, op} from \"@uwdata/arquero\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":57,"value":"// import { BubbleChart } from \"@d3/bubble-chart-component\"\nimport {Pack } from \"@d3/pack-component\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":104,"value":"import {Icicle} from \"@d3/icicle-component\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":129,"value":"import {multiAutoSelect} from \"@john-guerra/multi-auto-select\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":240,"value":"import {SpaceTree} from \"@john-guerra/spacetree\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":299,"value":"d3 = require(\"d3@7\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":353,"value":"import {navio} from \"@john-guerra/navio\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W42","stdlib":"1"}} 33:null