├── .gitignore ├── CNAME ├── README.md ├── css ├── footer.css ├── loader.css └── main.css ├── data └── acclab_data_innovation.csv ├── favicon.ico ├── imgs ├── branding │ ├── UNDP_Acc_Labs_All_Partners_horiz_white.png │ ├── UNDP_accelerator_labs_logo_vertical_color_RGB.png │ └── UNDP_accelerator_labs_logo_vertical_white.png ├── map │ ├── ai_intervention_areas.ai │ └── ai_intervention_areas.svg └── stats │ ├── area.png │ ├── data_and_applications.png │ ├── data_and_applications.svg │ ├── data_and_applications_colors.png │ ├── data_and_applications_colors.svg │ ├── data_and_applications_colors_dark.png │ ├── data_and_applications_colors_dark.svg │ ├── data_and_applications_colors_light.png │ ├── data_and_applications_colors_light.svg │ ├── datasources.png │ ├── sankey_20240402.png │ ├── sankey_20240402.svg │ └── technique.png ├── index.html ├── js ├── Array.prototype.extensions.js ├── d3.prototype.extensions.js └── sankey.js ├── notebooks ├── .ipynb_checkpoints │ └── descriptive_analysis-checkpoint.ipynb └── descriptive_analysis.ipynb ├── requirements.txt └── signals.html /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | env/ 3 | imgs/stats/sankey_20240402_* 4 | *.ai 5 | imgs/solutions_mapped/ -------------------------------------------------------------------------------- /CNAME: -------------------------------------------------------------------------------- 1 | data-innovation.sdg-innovation-commons.org -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Mainstreaming data innovation through the UNDP Accelerator Labs Network 2 | 3 | - [Download the data](https://github.com/UNDP-Accelerator-Labs/AI-map/blob/main/data/acclab_data_innovation.csv) 4 | 5 | ![Areas x datasources x labs](/imgs/stats/sankey_20240402.png) 6 | *Main data sources and application areas per lab* 7 | 8 | Over the past four years, the [UNDP Accelerator Labs Network](https://www.undp.org/acceleratorlabs) has been pushing for the mainstreaming of data innovation in sustainable development practice. Here we take stock of these efforts and highlight a certain convergence between computational techniques, datasources, and application areas. Note the work of the labs is action oriented. Other application areas and convergences undoubtably exist, typically in academic work. The purpose of this compilation is to highlight the application readiness of these techniques x datasources for specific development application areas around the world. 9 | 10 | The focus is intentionally reductive. It is on data innovation "projects". It should be noted however, that these are all part of broader portfolios of interventions. Overall, the UNDP Accelerator Labs Network sees data innovation a means to an end, not as an objective in itself. 11 | 12 | The next frontier for many of these projects is to establish data collaboratives to ensure their sustainability. More to come on this soon… 13 | 14 | ## Descriptive analysis 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 |
Computational techniqueMain datasourceAreaApplicationLab
Feature detectionDrone imageryAgriculture and pastoralismCrop disease detectionCameroon
Cabo Verde
Satellite imageryPollution and waste managementDump site detectionGuatemala
Serbia
Vietnam
Plastic waste detection in river systemsThe Philippines
Brick kiln detectionIndia
Environmental monitoringMine detectionBolivia
Land cover and use classificationSatellite imageryAgriculture and pastoralismDetection of sustained grazing areas over timeSomalia
Detection of sustained dry croplands over timeNiger
Detection of forest boundaries over timeEcuador
Multilayer map generationIndia
Disaster risk managementFlood detectionMauritania
Landslide detectionKyrgyzstan
Topic analysis and classificationOnline reviewsTourismTopic analysis of toursit reviewsJordan
Sentiment analysis of tourist reviewsMalawi
Tanzania
Social media postsGenderGender-related toxic speech detectionKyrgyzstan
Uruguay
MisinformationJordan
Generative AIPre-trained text modelOperations supportR script generation for simple, baseline data analyses and visualizationsGuatemala
EducationPersonalized ducational material generationCameroon
Pre-trained text model + political speechesDemocratic governanceUnified political speech generationArgentina
Pre-trained image modelGenderAnalysis of gender stereotypes in STEM fieldsSerbia
Pre-trained image model + urban and vegetation photosUrban developmentGreen urbanism image generation during public consultationsNorth Macedonia
Causal analysisLinkedIn profilesGenderGender inequality in the workplaceSerbia
157 | 158 | *Note we do not cover initiatives like crowdmapping here, as these are covered by the collective intelligence work of the labs.* 159 | 160 | Remote sensing data, whether low quality satellite imagery of high resolution drone imagery, is the most commonly used data source in the Accelerator Labs Network. This is likely due in part to the relative simplicity of accessing imagery and the maturity of the computational techniques. 161 | 162 | We also see a trend in applicaion areas for this type of data. Agriculture and pastoralism, and pollution and waste management are most common across labs, often trying to detect features in the imagery, like the aggregation of solid waste or crop diseases, or for deriving labd cover and land use maps, and vegetation indices. 163 | 164 | We also see a trend across several labs on the use of online posts, typically to travel sites, to understand sub-national toursim dynamics. 165 | 166 | -------------------------------------------------------------------------------- /css/footer.css: -------------------------------------------------------------------------------- 1 | footer { 2 | position: relative; 3 | background-color: #0468B1; 4 | background: linear-gradient(180deg, #005687 0%, #0468B1 30%, #32BEE1 100%); 5 | color: #fff; 6 | display: flex; 7 | flex-wrap: wrap; 8 | justify-content: space-between; 9 | font-family: Arial, sans-serif; 10 | font-size: 0.8rem; 11 | z-index: 10000; 12 | } 13 | 14 | .footer-row { 15 | display: flex; 16 | flex-wrap: 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24px; 397 | } 398 | .node rect.bg { 399 | fill: #FFF; 400 | pointer-events: none; 401 | } 402 | .leaf { 403 | font-weight: bold; 404 | font-size: 18px; 405 | } 406 | .node text, 407 | .leaf text { 408 | opacity: 0; 409 | } 410 | g.title text { 411 | font-weight: bold; 412 | font-size: 24px; 413 | opacity: 0; 414 | } 415 | g.title text .emph { 416 | font-size: 36px; 417 | } 418 | path.connection.dim { 419 | /* stroke: #e5e5e5 !important;*/ 420 | stroke: #646464 !important; 421 | stroke-opacity: .25 !important; 422 | } 423 | g.base.dim text { 424 | /* fill: #e5e5e5 !important;*/ 425 | fill: #646464 !important; 426 | fill-opacity: .25 !important; 427 | } 428 | -------------------------------------------------------------------------------- /data/acclab_data_innovation.csv: -------------------------------------------------------------------------------- 1 | Technique,Aggregate datasource,Datasource,Area,Application,Lab,Link,UNDP region,Note 2 | Object detection,Earth observation,Drone imagery,Precision agriculture and pastoralism,Crop disease detection,Cameroon,https://www.undp.org/cameroon/blog/will-drone-and-artificial-intelligence-help-increase-income-cameroonian-cocoa-planters,RBA, 3 | Object detection,Image data,Photo/ video,Precision agriculture and pastoralism,Crop disease detection,Cabo verde,,RBA, 4 | Object detection,Image data,Photo/ video,Precision agriculture and pastoralism,Pest control,Zimbabwe,https://www.undp.org/acceleratorlabs/publications/labs-20-evolving-global-research-and-development-capability-sdgs,RBA, 5 | Object detection,Earth observation,Satellite imagery,Pollution,Dump site detection,Guatemala,,RBLAC, 6 | Object detection,Earth observation,Satellite imagery,Pollution,Dump site detection,Serbia,https://www.undp.org/serbia/blog/can-artificial-intelligence-and-satellite-images-help-make-our-rivers-free-waste,RBEC,Low accuracy because of lack of training data 7 | Object detection,Earth observation,Satellite imagery,Pollution,Dump site detection,Vietnam,https://www.undp.org/vietnam/blog/remote-sensing-smart-city-solution-municipal-waste-management,RBAP, 8 | Object detection,Earth observation,Satellite imagery,Pollution,Plastic waste detection in river systems,The Philippines,https://www.undp.org/philippines/blog/experiment-satellite-remote-sensing-plastic-waste-pasig-river,RBAP, 9 | Object detection,Earth observation,Satellite imagery,Pollution,Brick kiln detection,India,https://www.undp.org/india/blog/how-geoai-platform-helping-target-brick-kiln-hotspots-air-pollution,RBAP, 10 | Object detection,Earth observation,Satellite imagery,Environmental monitorig,Mine detection,Bolivia,https://learningplans.sdg-innovation-commons.org/en/edit/pad?id=2060&source=1895&display=adjacent-source,RBLAC, 11 | "Land use/ land cover classification, and vegetation indices",Earth observation,Satellite imagery,Precision agriculture and pastoralism,Detection of sustained grazing areas over time ,Somalia,,RBAS, 12 | "Land use/ land cover classification, and vegetation indices",Earth observation,Satellite imagery,Precision agriculture and pastoralism,Detection of sustained dry croplands over time,Niger,,RBA, 13 | "Land use/ land cover classification, and vegetation indices",Earth observation,Satellite imagery,Precision agriculture and pastoralism,Detection of forest boundaries over time,Ecuador,,RBLAC, 14 | "Land use/ land cover classification, and vegetation indices",Earth observation,Satellite imagery,Precision agriculture and pastoralism,Multilayer map generation for agriculture,India,https://dicra.undp.org.in/,RBAP, 15 | "Land use/ land cover classification, and vegetation indices",Earth observation,Satellite imagery,Disaster risk management,Flood detection,Mauritania,,RBA, 16 | "Land use/ land cover classification, and vegetation indices",Earth observation,Satellite imagery,Disaster risk management,Landslide detection,Kyrgyzstan,,RBEC,Lack of training data 17 | Text classification,Online posts,Online reviews,Tourism,Topic analysis of tourist reviews,Jordan,https://public.tableau.com/app/profile/mohtaz.younes/viz/SentimentAnalysisofTourisminJordanonTripAdvisor/Main?publish=yes,RBAS, 18 | Text classification,Online posts,Online reviews,Tourism,Sentiment analysis of toursit reviews,Malawi,,RBA, 19 | Text classification,Online posts,Online reviews,Tourism,Sentiment analysis of toursit reviews,Tanzania,https://www.undp.org/tanzania/blog/re-thinking-tourism-zanzibar-how-artificial-intelligence-creating-value-industry,RBA, 20 | Text classification,Online posts,Social media posts,Misinformation,Misinformation on Covid19,Tanzania,https://www.undp.org/tanzania/blog/using-artificial-intelligence-tackle-infodemic-tanzania,RBA, 21 | Text classification,Online posts,Social media posts,Gender,Gender-related toxic speech detection,Kyrgyzstan,,RBEC, 22 | Text classification,Online posts,Social media posts,Gender,Gender-related toxic speech detection,Uruguay,,RBLAC, 23 | Text classification,Online posts,Social media posts,Misinformation,???,Jordan,https://learningplans.sdg-innovation-commons.org/en/edit/pad?id=2039&source=1917&display=adjacent-source,RBAS, 24 | Generative AI,Synthetic data,Pre-trained text model,Operations support,"R script generation for simple, baseline data analyses and visualizations",Guatemala,https://chat.openai.com/share/7c1972d8-a36d-4b79-8730-4a361d13d2da,RBLAC, 25 | Generative AI,Synthetic data,Pre-trained text model,Education,Personalized ducational material generation,Cameroon,https://learningplans.sdg-innovation-commons.org/en/edit/pad?id=1996&source=1928&display=adjacent-source,RBA, 26 | Generative AI,Synthetic data,Pre-trained text model + political speeches,Democratic governance,Unified political speech generation,Argentina,https://www.undp.org/es/argentina/ai-argentine-intelligence,RBLAC, 27 | Generative AI,Synthetic data,Pre-trained image model,Gender,Analysis of gender stereotypes in STEM fields,Serbia,https://www.undp.org/serbia/blog/reproducing-inequality-how-ai-image-generators-show-biases-against-women-stem,RBEC, 28 | Generative AI,Synthetic data,Pre-trained image model + urban and vegetation photos,Urbanism,Green urbanism image generation during public consultations,North Macedonia,,RBEC, 29 | Predictive modelling,Network data,Linked profiles,Gender,Gender inequality in the workplace,Serbia,,RBEC, 30 | Generative AI,Synthetic data,Pre-trained text model + political speeches,Urbanism,Intergenerational urban planning,Panama,https://www.linkedin.com/feed/update/urn:li:activity:7208501007826178050?updateEntityUrn=urn%3Ali%3Afs_feedUpdate%3A%28V2%2Curn%3Ali%3Aactivity%3A7208501007826178050%29,RBLAC, -------------------------------------------------------------------------------- /favicon.ico: -------------------------------------------------------------------------------- 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42 | production 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | POLICY 51 | 52 | 53 | 54 | 55 | 56 | 57 | enabling 58 | 59 | 60 | 61 | 62 | 63 | 64 | constraining 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | APPLICATION 75 | 76 | 77 | 78 | 79 | 80 | critical 81 | 82 | 83 | 84 | 85 | 86 | analytic 87 | 88 | 89 | 90 | 91 | 92 | creative 93 | 94 | 95 | 96 | 97 | 98 | ? 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | Infrastructure 108 | 109 | 110 | 111 | 112 | 113 | Business 114 | 115 | 116 | 117 | AIpossible areas of intervention 118 | 119 | 120 | 121 | 122 | 123 | 124 | -------------------------------------------------------------------------------- /imgs/stats/area.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/UNDP-Accelerator-Labs/AI-map/50dccccfa57a512626ef4198b9f47cad87ef19c9/imgs/stats/area.png -------------------------------------------------------------------------------- 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| Education 27 | Environmental 28 | monitorig 29 | Gender 30 | Misinformation 31 | Operations support 32 | Pollution and waste 33 | management 34 | Tourism 35 | Urban 36 | development 37 | Network data 38 | Online posts 39 | Pre-trained AI models 40 | Remote sensing 41 | Argentina 42 | Bolivia 43 | Cabo verde 44 | Cameroon 45 | Ecuador 46 | Guatemala 47 | India 48 | Jordan 49 | Kyrgyzstan 50 | Malawi 51 | Mauritania 52 | Niger 53 | North 54 | Macedonia 55 | Serbia 56 | Somalia 57 | Tanzania 58 | The 59 | Philipines 60 | Uruguay 61 | Vietnam 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | -------------------------------------------------------------------------------- /imgs/stats/data_and_applications_colors_dark.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | 2 | 3 | 5 | 25 | Agriculture 26 | and pastoralism 27 | Democratic governance 28 | Disaster 29 | r 30 | isk management 31 | Education 32 | Environmental 33 | monitorig 34 | Gender 35 | Misinformation 36 | Operations support 37 | Pollution and waste 38 | management 39 | Tourism 40 | Urban 41 | development 42 | Network data 43 | Online posts 44 | Pre-trained AI models 45 | Remote sensing 46 | Argentina 47 | Bolivia 48 | Cabo verde 49 | Cameroon 50 | Ecuador 51 | Guatemala 52 | India 53 | Jordan 54 | Kyrgyzstan 55 | Malawi 56 | Mauritania 57 | Niger 58 | North 59 | Macedonia 60 | Serbia 61 | Somalia 62 | Tanzania 63 | The 64 | Philipines 65 | Uruguay 66 | Vietnam 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 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30 | 35 |

36 | UNDP Accelerator Labs
37 | Towards
38 | mainstreaming
39 | data innovation
40 | for sustainable
41 | development 42 |

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50 | 51 | Over the past four years, the UNDP Accelerator Labs Network has been pushing the mainstreaming of data innovation* inside and outside UNDP. Here, we take stock of these efforts, focusing specifically on work that leverages existing, third-party data, and includes some form of advanced computational technique—as opposed to work that focuses on more manual means of inputting and analyzing "new" data, like Collective Intelligence. The work is action oriented, rather than (solely) research oriented, meaning it is not focused on pushing the limits of data innovation, but rather on its integration into practice. 52 | 53 |

54 | The interactive graphic below highlights exemplar applications from around the world. It is a living document, meaning we plan to update it regularly. 55 |

56 |

57 | *We refer to data innovation as the use of unusual data sources and advanced computational techniques for sustainable development practice. We prefer the terms "unusual data sources" and "advanced computational techniques" over "big (or new) data" and "Artificial Intelligence (AI)", as we believe that the prior more adequately reflect the situated notions of resource and tool, while the latter are, in our opinion, generally confounded by the current hype around Generative AI. We see data innovation as a means to an end, not an end in itself. 58 |

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DATA SOURCE

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COMPUTATIONAL
TECHNIQUE

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AREA

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LAB/COUNTRY

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Hover over the diagram to highlight connections

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← Click on the Lab/Country to read more

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87 | The Accelerator Labs “work out loud”, meaning they continuously publish updates on their work, whether through blogs or action learning plans and reflections. Here, we sample 29 projects from 21 different Labs using these sources of information, looking for terms like “data innovation”, “unusual data”, “big data”, “data science”, “machine learning”, “artificial intelligence”, and “AI”. 88 |

89 | We code each project as a geographically situated triptych of data source—computational technique—application area. Our intention is to uncover convergences that might suggest maturity of given data innovation techniques. Note that we use relatively high-level descriptive classes for our coding to facilitate aggregation. For example, we characterize computational techniques as “object detection” or “text classification”, rather than by specific types of classification or of machine learning models. 90 |

91 | Read more about this work in our blog post: Dismantling the AI Monolith for Sustainable Development – Part 1: Observations on Our Use of Data and Computing 92 |

93 | Find all source materials on Github. 94 |

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97 | 98 | 181 | 182 | 183 | 184 | -------------------------------------------------------------------------------- /js/Array.prototype.extensions.js: -------------------------------------------------------------------------------- 1 | Array.prototype.filterInPlace = function (condition, thisArg) { 2 | let jx = 0; 3 | this.forEach((el, ix) => { 4 | if (condition.call(thisArg, el, ix, this)) { 5 | if (ix !== jx) { 6 | this[jx] = el; 7 | } 8 | jx += 1; 9 | } 10 | }); 11 | this.length = jx; 12 | return this; 13 | }; 14 | Array.prototype.flat = function () { 15 | return [].concat.apply([], this); 16 | }; 17 | Array.prototype.flatObj = function () { 18 | // FLATTEN OBJECT: https://stackoverflow.com/questions/31136422/flatten-array-with-objects-into-1-object 19 | return Object.assign.apply(Object, this); 20 | }; 21 | Array.prototype.fillNaN = function (key, value) { 22 | if (!value) value = 0; 23 | if (!key) { 24 | return this.map((v) => (isNaN(v) || v == null ? value : v)); 25 | } else { 26 | return this.map((v) => { 27 | if (isNaN(v[key]) || v[key] == null) v[key] = value; 28 | return v; 29 | }); 30 | } 31 | }; 32 | Array.prototype.sum = function (key) { 33 | if (this.length === 0) return 0; 34 | if (!key) { 35 | return this.reduce((accumulator, value) => accumulator + value); 36 | } else { 37 | return this.reduce((accumulator, value) => { 38 | const obj = {}; 39 | obj[key] = accumulator[key] + value[key]; 40 | return obj; 41 | })[key]; 42 | } 43 | }; 44 | Array.prototype.max = function (key, onkey) { 45 | const max = this.sort((a, b) => { 46 | if (key) return b[key] - a[key]; 47 | else return b - a; 48 | })[0]; 49 | if (onkey) return max[key]; 50 | else return max; 51 | }; 52 | Array.prototype.mean = function (key) { 53 | return this.sum(key) / this.length; 54 | }; 55 | Array.prototype.variance = function (key) { 56 | if (this.length === 0) throw new Error('no values'); 57 | const mean = this.mean(key); 58 | if (!key) return this.map((d) => Math.pow(d - mean, 2)).sum() / this.length; 59 | else return this.map((d) => Math.pow(d[key] - mean, 2)).sum() / this.length; 60 | }; 61 | Array.prototype.sd = function (key) { 62 | if (this.length === 0) throw new Error('no values'); 63 | return Math.sqrt(this.variance(key)); 64 | }; 65 | Array.prototype.nest = function (key, keep) { 66 | // THIS IS NOT QUITE THE SAME FUNCTION AS IN distances.js, THIS MORE CLOSELY RESEMBLES d3.nest 67 | const arr = []; 68 | this.forEach((d) => { 69 | const groupby = typeof key === 'function' ? key(d) : d[key]; 70 | if (!arr.find((c) => c.key === groupby)) { 71 | if (keep) { 72 | const obj = {}; 73 | obj.key = groupby; 74 | obj.values = [d]; 75 | obj.count = 1; 76 | if (Array.isArray(keep)) keep.forEach((k) => (obj[k] = d[k])); 77 | else obj[keep] = d[keep]; 78 | arr.push(obj); 79 | } else arr.push({ key: groupby, values: [d], count: 1 }); 80 | } else { 81 | arr.find((c) => c.key === groupby).values.push(d); 82 | arr.find((c) => c.key === groupby).count++; 83 | } 84 | }); 85 | return arr; 86 | }; 87 | Array.prototype.dotProduct = function (V2) { 88 | // USED IN distances.cosine 89 | return this.map((v1, i) => { 90 | const v2 = V2[i]; 91 | return v1 * v2; 92 | }).sum(); 93 | }; 94 | Array.prototype.matrixMultiplication = function (V) { 95 | return this.map((row) => { 96 | return [ 97 | row 98 | .map((c, j) => { 99 | return c * V[j]; 100 | }) 101 | .sum(), 102 | ]; 103 | }); 104 | }; 105 | Array.prototype.intersection = function (V2) { 106 | const intersection = []; 107 | this.sort(); 108 | V2.sort(); 109 | for (let i = 0; i < this.length; i += 1) { 110 | if (V2.indexOf(this[i]) !== -1) { 111 | intersection.push(this[i]); 112 | } 113 | } 114 | return intersection; 115 | }; 116 | Array.prototype.diff = function (V2) { 117 | const diff = []; 118 | this.forEach((d) => { 119 | if (!V2.includes(d)) diff.push(d); 120 | }); 121 | V2.forEach((d) => { 122 | if (!this.includes(d)) diff.push(d); 123 | }); 124 | return diff; 125 | }; 126 | Array.prototype.union = function (V) { 127 | V.forEach((d) => { 128 | if (this.indexOf(d) === -1) this.push(d); 129 | }); 130 | return this; 131 | }; 132 | Array.prototype.unique = function (key, onkey) { 133 | const arr = []; 134 | this.forEach((d) => { 135 | if (!key) { 136 | if (arr.indexOf(d) === -1) arr.push(d); 137 | } else { 138 | if (onkey) { 139 | if (arr.map((c) => c).indexOf(d[key]) === -1) arr.push(d[key]); 140 | } else { 141 | if (typeof key === 'function') { 142 | if (arr.map((c) => key(c)).indexOf(key(d)) === -1) arr.push(d); 143 | } else { 144 | if (arr.map((c) => c[key]).indexOf(d[key]) === -1) arr.push(d); 145 | } 146 | } 147 | } 148 | }); 149 | return arr; 150 | }; 151 | Array.prototype.group = function (key, keep) { 152 | const arr = []; 153 | this.forEach((d) => { 154 | if (!key) { 155 | if (arr.map((c) => c.key).indexOf(d) === -1) 156 | arr.push({ key: d, count: 1 }); 157 | else arr.find((c) => c.key === d).count += 1; 158 | } else { 159 | if (arr.map((c) => c.key).indexOf(d[key]) === -1) { 160 | const obj = {}; 161 | obj.key = d[key]; 162 | obj.count = 1; 163 | if (keep) obj[keep] = [d[keep]]; 164 | arr.push(obj); 165 | } else { 166 | const obj = arr.find((c) => c.key === d[key]); 167 | obj.count += 1; 168 | if (keep) obj[keep].push(d[keep]); 169 | } 170 | } 171 | }); 172 | arr.forEach((d) => (d.p = d.count / this.length)); 173 | return arr; 174 | }; 175 | Array.prototype.zip = function (V2) { 176 | return this.map((d, i) => [d, V2[i]]); 177 | }; 178 | Array.prototype.multizip = function (arr) { 179 | return this.map((d, i) => { 180 | const zip = [d]; 181 | arr.forEach((c) => zip.push(c[i])); 182 | return zip; 183 | }); 184 | }; 185 | Array.prototype.last = function () { 186 | return this[this.length - 1]; 187 | }; 188 | Array.prototype.chunk = function (size) { 189 | const groups = []; 190 | for (let i = 0; i < this.length; i += size) { 191 | groups.push(this.slice(i, i + size)); 192 | } 193 | return groups; 194 | }; 195 | Array.prototype.median = function () { 196 | this.sort((a, b) => a - b); 197 | const half = Math.floor(this.length / 2); 198 | return this[half]; 199 | }; 200 | Array.prototype.shuffle = function () { 201 | let currentIndex = this.length; 202 | let temporaryValue; 203 | let randomIndex; 204 | 205 | while (0 !== currentIndex) { 206 | randomIndex = Math.floor(Math.random() * currentIndex); 207 | currentIndex -= 1; 208 | temporaryValue = this[currentIndex]; 209 | this[currentIndex] = this[randomIndex]; 210 | this[randomIndex] = temporaryValue; 211 | } 212 | return this; 213 | }; 214 | Array.prototype.duplicates = function (key, onkey) { 215 | const arr = []; 216 | this.forEach((d, i) => { 217 | if (!key) { 218 | if (i === 0) arr.push(d); 219 | else if (arr.indexOf(d) !== -1) arr.push(d); 220 | } else { 221 | if (onkey) { 222 | if (i === 0) arr.push(d[key]); 223 | else if (arr.map((c) => c).indexOf(d[key]) !== -1) arr.push(d[key]); 224 | } else { 225 | if (i === 0) arr.push(d); 226 | if (arr.map((c) => c[key]).indexOf(d[key]) !== -1) arr.push(d); 227 | } 228 | } 229 | }); 230 | return arr; 231 | }; 232 | -------------------------------------------------------------------------------- /js/d3.prototype.extensions.js: -------------------------------------------------------------------------------- 1 | const staticElement = function (_sel, _method, _element, _class) { 2 | return _sel[typeof _method === 'object' ? _method[0] : _method](_element, typeof _method === 'object' ? _method[1] : null) 3 | .attr('class', _class) 4 | } 5 | const dynamicElement = function (_sel, _method, _element, _class, _data, _key) { // THIS REPLACES DOMnode 6 | const node = _sel.selectAll(_class ? `${_element.replace(/xhtml:/g, '')}.${_class.replace(/\s/g, '.')}` : `${_element.replace(/xhtml:/g, '')}`) 7 | .data(_data ? (typeof _data === 'function' ? d => _data(d) : _data) : d => [d], 8 | (d, i) => _key ? (typeof _key === 'function' ? _key(d) : d[_key]) : i) 9 | node.exit().remove() 10 | return node.enter() 11 | [typeof _method === 'object' ? _method[0] : _method](_element, typeof _method === 'object' ? _method[1] : null) 12 | .attr('class', _class ? _class : null) 13 | .merge(node) 14 | } 15 | 16 | d3.selection.prototype.insertElem = function (_before, _element, _class) { 17 | return new staticElement(this, ['insert', _before], _element, _class ? _class : null) 18 | } 19 | d3.selection.prototype.insertElems = function (_before, _element, _class, _data, _key) { 20 | return new dynamicElement(this, ['insert', _before], _element, _class ? _class : null, _data, _key) 21 | } 22 | d3.selection.prototype.addElem = function (_element, _class) { 23 | return new staticElement(this, 'append', _element, _class ? _class : null) 24 | } 25 | d3.selection.prototype.addElems = function (_element, _class, _data, _key) { 26 | return new dynamicElement(this, 'append', _element.trim(), _class ? _class.trim() : null, _data, _key) 27 | } 28 | d3.selection.prototype.findAncestor = function (_target) { 29 | if (!this.node().classList || this.node().nodeName === 'BODY') return null 30 | if (this.classed(_target) || this.node().nodeName === _target?.toUpperCase()) return this 31 | return d3.select(this.node().parentNode)?.findAncestor(_target); 32 | } 33 | d3.selection.prototype.hasAncestor = function (_target) { 34 | if (this.node().nodeName === 'BODY') return false 35 | if (typeof _target === 'string') { 36 | if (this.classed(_target) || this.node().nodeName === _target?.toUpperCase()) return true 37 | } else { 38 | if (this.node() === _target) return true 39 | } 40 | return d3.select(this.node().parentNode)?.hasAncestor(_target); 41 | } 42 | d3.selection.prototype.moveToFront = function() { 43 | return this.each(function(){ 44 | this.parentNode.appendChild(this) 45 | }) 46 | } 47 | d3.selection.prototype.moveToBack = function() { 48 | // CREDIT: https://stackoverflow.com/questions/14167863/how-can-i-bring-a-circle-to-the-front-with-d3 49 | return this.each(function() { 50 | const firstChild = this.parentNode.firstChild 51 | if (firstChild) { 52 | this.parentNode.insertBefore(this, firstChild) 53 | } 54 | }) 55 | } 56 | d3.selection.prototype.toggleClass = function (_class) { 57 | return this.classed(_class, !this.classed(_class)) 58 | } 59 | // https://stackoverflow.com/questions/25405359/how-can-i-select-last-child-in-d3-js 60 | d3.selection.prototype.last = function() { 61 | return d3.select(this.nodes()[this.size() - 1]) 62 | } -------------------------------------------------------------------------------- /js/sankey.js: -------------------------------------------------------------------------------- 1 | const basecolors = { 2 | 'dark-blue': '#005687', 3 | 'mid-blue': '#0468b1', 4 | 'light-blue': '#32bee1', 5 | 'dark-red': '#a51e41', 6 | 'mid-red': '#fa1c26', 7 | 'light-red': '#f03c8c', 8 | 'dark-green': '#418246', 9 | 'mid-green': '#61b233', 10 | 'light-green': '#b4dc28', 11 | 'dark-yellow': '#fa7814', 12 | 'mid-yellow': '#ffc10e', 13 | 'light-yellow': '#fff32a', 14 | 'dark-grey': '#000000', 15 | 'mid-grey': '#646464', 16 | 'light-grey': '#969696', 17 | 'light-2': '#e5e5e5', 18 | } 19 | const colors = [ 20 | basecolors['mid-blue'], 21 | basecolors['mid-yellow'], 22 | basecolors['light-red'], 23 | basecolors['light-green'], 24 | basecolors['light-yellow'] 25 | ] 26 | 27 | const offsetFirstRow = false 28 | 29 | function setupSVG (kwargs) { 30 | const { width, height } = kwargs 31 | const svg = d3.select('svg') 32 | .attrs({ 33 | 'x': 0, 34 | 'y':0, 35 | 'viewBox': `0 0 ${width} ${height}`, 36 | 'preserveAspectRatio': 'xMidYMid meet' 37 | }) 38 | return svg 39 | } 40 | 41 | function findBestTextDisplay (svg, text, maxwidth) { 42 | const terms = text.split(' '); 43 | const lines = []; 44 | let start = 0; 45 | let end = 1; 46 | 47 | function getWidth (terms, start, end, maxwidth) { 48 | const txt = svg.addElems('text', 'dummy hide') 49 | .text(terms.slice(start, end)); 50 | const { width } = txt.node().getBBox(); 51 | txt.remove() 52 | if (width < maxwidth && end < terms.length) { 53 | return getWidth(terms, start, end + 1, maxwidth); 54 | } else { 55 | return [start, end] 56 | } 57 | } 58 | 59 | if (end === terms.length) return terms 60 | else { 61 | while (end <= terms.length) { 62 | const bounds = getWidth(terms, start, end, maxwidth); 63 | start = bounds[0]; 64 | end = bounds[1]; 65 | const line = terms.slice(start, end); 66 | lines.push(line.join(' ')) 67 | 68 | start = end; 69 | end = end + 1; 70 | } 71 | } 72 | 73 | return lines; 74 | } 75 | 76 | function drawSankey () { 77 | const { clientWidth: cw, clientHeight: ch, offsetWidth: ow, offsetHeight: oh } = d3.select('body').node() 78 | const width = Math.min(cw ?? ow, 1080) //Math.round(Math.min(cw ?? ow, ch ?? oh)) 79 | const height = ch ?? oh //width 80 | const padding = 60 81 | const edit_layout = true 82 | 83 | const svg = setupSVG({ width, height }) 84 | 85 | d3.csv('data/acclab_data_innovation.csv') 86 | .then(data => { 87 | const levels = ['Aggregate datasource', 'Technique', 'Area', 'Lab']//.reverse() 88 | 89 | const lists = levels.map(d => { 90 | return { 91 | key: d, 92 | values: data.unique(d, true).sort((a, b) => a?.localeCompare(b)) 93 | } 94 | }) 95 | 96 | const x = levels.map((d, i) => { 97 | return d3.scaleLinear() 98 | .range([padding, width - padding]) 99 | .domain([0, lists.find(c => c.key === d).values.length + 2]) 100 | }) 101 | 102 | const y = d3.scaleLinear() 103 | .range([padding * 1.5, height - padding * 2]) 104 | .domain([0, levels.length - 1]) 105 | 106 | 107 | let hierarchy = [] 108 | levels.forEach((d, i) => { 109 | if (i > 0) { 110 | const prev = levels[i - 1] 111 | const nest = data.nest(c => `${c[prev]}---${c[d]}`) 112 | nest.forEach(c => { 113 | const sources = lists.find(c => c.key === prev).values 114 | const targets = lists.find(c => c.key === d).values 115 | 116 | c.source = c.key.split('---')[0] 117 | c.target = c.key.split('---')[1] 118 | 119 | let x1 = x[levels.indexOf(prev)](sources.indexOf(c.source) + 1) 120 | let x2 = x[levels.indexOf(d)](targets.indexOf(c.target) + 1) 121 | let y1 = levels.indexOf(prev) % 3 === 0 ? y(levels.indexOf(prev)) : y(levels.indexOf(prev)) + 10 122 | let y2 = levels.indexOf(d) % 3 === 0 ? y(levels.indexOf(d)) : y(levels.indexOf(d)) - 15 123 | 124 | if (offsetFirstRow) { 125 | if (i - 1 === 0) { 126 | if (sources.indexOf(c.source) % 2 !== 0) y1 -= 35 127 | } else if (i % 3 === 0) { 128 | if (targets.indexOf(c.target) % 2 !== 0) y2 += 45 129 | } 130 | } else { 131 | if (i % 3 === 0) { 132 | if (targets.indexOf(c.target) % 2 !== 0) y2 += 45 133 | } 134 | } 135 | 136 | c.from = [x1, y1] 137 | c.q1 = [x1, y1 + (y2 - y1) / 2] 138 | c.q2 = [x2, y1 + (y2 - y1) / 2] 139 | c.to = [x2, y2] 140 | c.level = d 141 | c.level_id = i 142 | }) 143 | hierarchy.push(nest) 144 | } 145 | }) 146 | hierarchy = hierarchy.flat() 147 | 148 | const w = d3.scaleLinear() 149 | .domain([1, d3.max(hierarchy, d => d.count)]) 150 | .range([1, 10]) 151 | 152 | svg.addElems('path', 'connection', hierarchy) 153 | .attr('d', d => `M ${d.from.join(' ')} C ${d.q1.join(' ')}, ${d.q2.join(' ')}, ${d.to.join(' ')}`) 154 | .styles({ 155 | 'stroke': d => { 156 | const { values } = lists.find(c => c.key === 'Aggregate datasource') 157 | if (values.includes(d.values[0]['Aggregate datasource'])) return colors[values.indexOf(d.values[0]['Aggregate datasource'])] 158 | else if (values.includes(d.target)) return colors[values.indexOf(d.target)] 159 | 160 | },'fill': 'none', 161 | 'stroke-width': d => w(d.count), 162 | 'stroke-linecap': 'round', 163 | 'stroke-opacity': .5 164 | }) 165 | /*.on('mouseover', function () { 166 | const sel = d3.select(this); 167 | highlight(sel); 168 | }).on('mouseout', _ => { 169 | resetHighlight(); 170 | })*/ 171 | 172 | const rows = svg.addElems('g', 'level', levels) 173 | .attr('transform', (d, i) => `translate(${[0, y(i)]})`) 174 | const base = rows.addElems('g', 'base', d => { 175 | const entries = lists.find(c => c.key === d).values 176 | return entries.map(c => { 177 | const obj = {} 178 | obj.level = levels.indexOf(d) 179 | obj.value = c 180 | return obj 181 | }) 182 | }).attr('transform', (d, i) => { 183 | let y = 0 184 | 185 | if (offsetFirstRow) { 186 | if (d.level === 0) { 187 | if (i % 2 !== 0) y = -45 188 | else y = -15 189 | } else if (d.level % 3 === 0) { 190 | if (i % 2 !== 0) y = 60 191 | else y = 15 192 | } 193 | } else { 194 | if (d.level === 0) { 195 | y = -15 196 | } else if (d.level % 3 === 0) { 197 | if (i % 2 !== 0) y = 60 198 | else y = 15 199 | } 200 | } 201 | 202 | return `translate(${[x[d.level](i + 1), y]})` 203 | }) 204 | 205 | const multiline = true 206 | if (multiline) { 207 | base.addElems('text') 208 | .styles({ 209 | 'text-anchor': 'middle', 210 | 'font-face': 'Noto Sans', 211 | 'font-size': '12px', 212 | 'fill': basecolors['light-2'], 213 | 'cursor': d => d.level === levels.length - 1 ? 'pointer' : null, 214 | }).addElems('tspan', 'line', d => { 215 | const maxwidth = (width - padding * 2) / (Math.max(...x[d.level].domain()) + 2); 216 | const lines = findBestTextDisplay(svg, d.value, maxwidth); 217 | return lines 218 | }).attrs({ 219 | 'x': 0, 220 | 'dy': (d, i) => i === 0 ? 0 : 12 * 1.3, 221 | }).text(d => d) 222 | } else { 223 | base.addElems('text') 224 | .styles({ 225 | 'text-anchor': 'middle', 226 | 'font-face': 'Noto Sans', 227 | 'font-size': '12px', 228 | 'fill': basecolors['light-2'], 229 | 'cursor': d => d.level === levels.length - 1 ? 'pointer' : null, 230 | }).text(d => d.value) 231 | .on('mouseover', function (d) { 232 | const connection = d3.selectAll('path.connection').filter(c => c.target === d.value); 233 | highlight(connection); 234 | }).on('mouseout', _ => { 235 | resetHighlight(); 236 | }) 237 | } 238 | 239 | base.each(function () { 240 | const sel = d3.select(this) 241 | const text = sel.select('text') 242 | const { width: txt_width, height: txt_height } = text.node().getBBox(); 243 | 244 | sel.addElems('rect', 'interaction-padding') 245 | .attrs({ 246 | 'width': txt_width + 50, 247 | 'height': txt_height + 30, 248 | 'x': -(txt_width + 50) / 2, 249 | 'y': -(txt_height + 30) / 2, 250 | }).styles({ 251 | 'fill': 'transparent', 252 | // 'stroke': 'orange', 253 | 'cursor': 'pointer', 254 | }) 255 | }).on('mouseover', function (d) { 256 | const connection = d3.selectAll('path.connection').filter(c => c.source === d.value || c.target === d.value); 257 | highlight(connection); 258 | }).on('mouseout', function () { 259 | const { toElement } = d3.event; 260 | if (!d3.select(toElement)?.hasAncestor(this)) resetHighlight(); 261 | }).on('click', function (d) { 262 | if (d.level !== levels.length - 1) return false 263 | 264 | const sel = d3.select(this).moveToFront(); 265 | const text = sel.select('text').moveToFront(); 266 | const parentLevel = d3.select(this).findAncestor('level').moveToFront(); 267 | const { width: txt_width, height: txt_height } = text.node().getBBox(); 268 | const { left } = text.node().getBoundingClientRect(); 269 | const { value, level } = d 270 | const paths = data.filter(c => { 271 | return c[levels[level]] === value 272 | }).map((c, j) => { 273 | return { label: levels.reduce((acc, val, idx) => { 274 | if (idx < levels.length - 1) { 275 | if (c[val].length > 15) return acc += `${c[val].slice(0, 15)}…—`; 276 | else return acc += `${c[val]}—`; 277 | } else { 278 | if (c[val].length > 15) return acc += `${c[val].slice(0, 15)}…`; 279 | else return acc += `${c[val]}`; 280 | } 281 | }, '🔗 '), datasource: c['Aggregate datasource'], link: c['Link'], line: j } 282 | }).filter(c => c.link?.length) 283 | 284 | const bubble = sel.addElems('g', 'bubble', paths.length ? [paths] : []) 285 | const labels = bubble.addElems('text') 286 | .styles({ 287 | 'text-anchor': 'start', 288 | 'font-face': 'Noto Sans', 289 | 'font-size': '12px', 290 | // 'stroke': d => { 291 | // const { values } = lists.find(c => c.key === 'Aggregate datasource') 292 | // if (values.includes(d.values[0]['Aggregate datasource'])) return colors[values.indexOf(d.values[0]['Aggregate datasource'])] 293 | // else if (values.includes(d.target)) return colors[values.indexOf(d.target)] 294 | 295 | // }, 296 | }) 297 | labels.addElems('a', 'link', c => c) 298 | .attrs({ 299 | 'xlink:href': c => c.link, 300 | 'target': '_blank', 301 | }) 302 | .addElems('tspan', 'link') 303 | .attrs({ 304 | 'x': 0, 305 | 'dy': c => c.line === 0 ? 0 : 12 * 1.7, 306 | }).styles({ 307 | 'fill': c => { 308 | const { values } = lists.find(c => c.key === 'Aggregate datasource'); 309 | return colors[values.indexOf(c.datasource)]; 310 | } 311 | }).html(c => c.label) 312 | // .on('mouseover', c => { 313 | // console.log(c) 314 | // }); 315 | 316 | if (labels.size()) { 317 | const { width: bubble_w, height: bubble_h } = labels.node().getBBox(); 318 | 319 | bubble.insertElems('text', 'rect', 'interaction-padding') 320 | .attrs({ 321 | 'width': bubble_w + 50, 322 | 'height': bubble_h + 50, 323 | 'x': -25, 324 | 'y': (-bubble_h / paths.length) - 25, 325 | }).styles({ 326 | 'fill': 'transparent', 327 | // 'stroke': 'orange', 328 | }); 329 | bubble.insertElems('text', 'rect', 'label-bg') 330 | .attrs({ 331 | 'width': bubble_w + 27, 332 | 'height': bubble_h + 27, 333 | 'x': -10, 334 | 'y': (-bubble_h / paths.length) - 7, 335 | }).styles({ 336 | 'fill': '#FFF' 337 | }); 338 | bubble.attr('transform', _ => { 339 | let x = (bubble_w + txt_width / 2 + 30) * -1 340 | let y = 0 341 | if (left <= width / 2) x = txt_width / 2 + 30 342 | if (paths.length > 1) y = -bubble_h * .25 343 | return `translate(${[x, y]})` 344 | }); 345 | } 346 | }) 347 | 348 | function highlight (sel) { 349 | const d = sel.data(); 350 | let level_id = d.unique('level_id', true)[0]; 351 | let source = d.unique('source', true); 352 | let target = d.unique('target', true); 353 | 354 | const highlights = d.map(c => c.values) 355 | .flat() 356 | .map(c => { 357 | const connections = [] 358 | const arr = levels.map((b, k) => { return { level: k, value: c[b] } }) 359 | arr.forEach(b => { 360 | if (b.level > 0) { 361 | arr.filter(a => a.level === b.level - 1) 362 | .forEach(a => { 363 | connections.push(`${a.value}---${b.value}`) 364 | }) 365 | } 366 | }) 367 | return { nodes: arr.map(a => a.value), connections } 368 | }) 369 | const nodesToHighlight = highlights.map(c => c.nodes).flat().unique() 370 | const connectionsToHighlight = highlights.map(c => c.connections).flat().unique() 371 | 372 | // sel.classed('highlight', true) 373 | // .classed('dim', false) 374 | // .moveToFront(); 375 | 376 | d3.selectAll('path.connection') 377 | .classed('highlight', c => connectionsToHighlight.includes(c.key)) 378 | .classed('dim', c => !connectionsToHighlight.includes(c.key)); 379 | 380 | d3.selectAll('g.base') 381 | .classed('highlight', c => nodesToHighlight.includes(c.value)) 382 | .classed('dim', c => !nodesToHighlight.includes(c.value)); 383 | } 384 | function resetHighlight () { 385 | d3.selectAll('path.connection, g.base') 386 | .classed('highlight dim', false); 387 | d3.selectAll('g.bubble').remove(); 388 | } 389 | 390 | }).catch(err => console.log(err)) 391 | } 392 | 393 | 394 | document.addEventListener('DOMContentLoaded', drawSankey) -------------------------------------------------------------------------------- /notebooks/.ipynb_checkpoints/descriptive_analysis-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 5 6 | } 7 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | anyio==4.2.0 2 | appnope==0.1.3 3 | argon2-cffi==23.1.0 4 | argon2-cffi-bindings==21.2.0 5 | arrow==1.3.0 6 | asttokens==2.4.1 7 | async-lru==2.0.4 8 | attrs==23.2.0 9 | Babel==2.14.0 10 | beautifulsoup4==4.12.3 11 | bleach==6.1.0 12 | certifi==2023.11.17 13 | cffi==1.16.0 14 | charset-normalizer==3.3.2 15 | comm==0.2.1 16 | contourpy==1.2.0 17 | cycler==0.12.1 18 | debugpy==1.8.0 19 | decorator==5.1.1 20 | defusedxml==0.7.1 21 | exceptiongroup==1.2.0 22 | executing==2.0.1 23 | fastjsonschema==2.19.1 24 | fonttools==4.47.2 25 | fqdn==1.5.1 26 | idna==3.6 27 | ipykernel==6.29.0 28 | ipython==8.20.0 29 | ipywidgets==8.1.1 30 | isoduration==20.11.0 31 | jedi==0.19.1 32 | Jinja2==3.1.3 33 | json5==0.9.14 34 | jsonpointer==2.4 35 | jsonschema==4.21.1 36 | jsonschema-specifications==2023.12.1 37 | jupyter==1.0.0 38 | jupyter-console==6.6.3 39 | jupyter-events==0.9.0 40 | jupyter-lsp==2.2.2 41 | jupyter_client==8.6.0 42 | jupyter_core==5.7.1 43 | jupyter_server==2.12.5 44 | jupyter_server_terminals==0.5.2 45 | jupyterlab==4.0.11 46 | jupyterlab-widgets==3.0.9 47 | jupyterlab_pygments==0.3.0 48 | jupyterlab_server==2.25.2 49 | kiwisolver==1.4.5 50 | MarkupSafe==2.1.4 51 | matplotlib==3.8.2 52 | matplotlib-inline==0.1.6 53 | mistune==3.0.2 54 | nbclient==0.9.0 55 | nbconvert==7.14.2 56 | nbformat==5.9.2 57 | nest-asyncio==1.6.0 58 | notebook==7.0.7 59 | notebook_shim==0.2.3 60 | numpy==1.26.3 61 | overrides==7.6.0 62 | packaging==23.2 63 | pandas==2.2.0 64 | pandocfilters==1.5.1 65 | parso==0.8.3 66 | pexpect==4.9.0 67 | pillow==10.2.0 68 | platformdirs==4.1.0 69 | prometheus-client==0.19.0 70 | prompt-toolkit==3.0.43 71 | psutil==5.9.8 72 | ptyprocess==0.7.0 73 | pure-eval==0.2.2 74 | pycparser==2.21 75 | Pygments==2.17.2 76 | pyparsing==3.1.1 77 | python-dateutil==2.8.2 78 | python-json-logger==2.0.7 79 | pytz==2023.3.post1 80 | PyYAML==6.0.1 81 | pyzmq==25.1.2 82 | qtconsole==5.5.1 83 | QtPy==2.4.1 84 | referencing==0.32.1 85 | requests==2.31.0 86 | rfc3339-validator==0.1.4 87 | rfc3986-validator==0.1.1 88 | rpds-py==0.17.1 89 | Send2Trash==1.8.2 90 | six==1.16.0 91 | sniffio==1.3.0 92 | soupsieve==2.5 93 | stack-data==0.6.3 94 | terminado==0.18.0 95 | tinycss2==1.2.1 96 | tomli==2.0.1 97 | tornado==6.4 98 | traitlets==5.14.1 99 | types-python-dateutil==2.8.19.20240106 100 | typing_extensions==4.9.0 101 | tzdata==2023.4 102 | uri-template==1.3.0 103 | urllib3==2.1.0 104 | wcwidth==0.2.13 105 | webcolors==1.13 106 | webencodings==0.5.1 107 | websocket-client==1.7.0 108 | widgetsnbextension==4.0.9 109 | -------------------------------------------------------------------------------- /signals.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 24 | 25 | 26 | 27 | 28 |
29 | Contribute 30 | 31 |
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40 | 42 | 43 | 44 | 45 | 46 | 47 | AI 48 | (possible) areas 49 | of intervention 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 |
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