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src/assets/synthetic-data.tsx

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@@ -128,7 +128,7 @@ def run():
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setResult(json.dumps(
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{'type': 'heading', 'data': sdgMethod}
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))
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if isDemo:
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if isDemo:
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setResult(json.dumps({
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'type': 'heading',
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'headingKey': 'syntheticData.demo.heading'
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))
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setResult(json.dumps(
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{'type': 'heading', 'data': 'Output file:'}
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{'type': 'heading', 'data': 'Output data'}
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))
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setResult(json.dumps({'type': 'table', 'data': synthetic_data.head().to_json(orient="records")}))
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src/locales/en.json

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"shareButton": "Share",
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"exportButton": "Export to .json",
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"getStarted": "Let's get started! Fill out the form.",
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"fileUploadError": "Please upload a valid CSV file.",
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"fileUploadError": "Please upload a valid csv file.",
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"removeButton": "Remove",
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"dropzoneLabel": "Drag and drop your CSV file here, or click to select a file",
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"dropzoneLabel": "Drag and drop your csv file here, or click to select a file",
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"datasetPreview": "Dataset preview showing the first 5 rows.",
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"error": "Sorry, something went wrong.",
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"loadingMessage": "Setting up environment...",
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"mostBiasedCluster": "Most biased\n cluster",
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"cluster": "Cluster {{value}}",
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"exportToPDF": "Export to PDF",
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"exportToJSON": "Export synthetic data to JSON",
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"exportToPDF": "Export to pdf",
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"exportToJSON": "Export synthetic data to json",
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"downloadButton": "Download",
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"biasSettings": {
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"exportToPDF": "Download bias detection report as PDF",
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"exportToJSON": "Export clusters as JSON",
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"exportToPDF": "Download bias detection report as pdf",
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"exportToJSON": "Export clusters as json",
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"form": {
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"fieldsets": {
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"data": {
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"title": "Data",
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"dataSet": "Data set",
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"dataSet": "Dataset",
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"performanceMetric": "Performance metric column"
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},
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"parameters": {
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}
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},
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"errors": {
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"csvRequired": "Please upload a CSV file.",
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"csvRequired": "Please upload a csv file.",
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"targetColumnRequired": "Please select a target column.",
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"dataTypeRequired": "Please select a data type.",
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"noNumericColumns": "No numeric columns found. Please upload a valid data set.",
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"noNumericColumns": "No numeric columns found. Please upload a valid dataset.",
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"analysisError": "Error while analysing",
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"noData": "No data loaded"
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},
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},
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"demoCard": {
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"title": "Try it out!",
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"description": "Do you not have a dataset at hand? No worries use our demo data set."
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"description": "Do you not have a dataset at hand? No worries use our demo dataset."
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}
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},
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"syntheticData": {
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"demo": {
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"heading": "Information about demo dataset",
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"description": "**UI text** A subset of the [Law School Admission Bar*](https://www.kaggle.com/datasets/danofer/law-school-admissions-bar-passage) dataset is used as a demo. Synthetic data will be generated for the following columns:\n \n  \n- sex: student gender, i.e. 1 (male), 2 (female)\n- race1: race, i.e. asian, black, hispanic, white, other\n- ugpa: The student's undergraduate GPA, continous variable;\n- bar: Ground truth label indicating whether or not the student passed the bar, i.e. passed 1st time, passed 2nd time, failed, non-graduated\n \n  \n\nThe CART method will be used evaluate the distribution and correlation differences between the real and synthetic data.\n \n  \n\n*The original paper can be found [here](https://files.eric.ed.gov/fulltext/ED469370.pdf)."
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"description": "A subset of the [Law School Admission Bar](https://www.kaggle.com/datasets/danofer/law-school-admissions-bar-passage)* dataset is used as a demo. Synthetic data will be generated for the following columns:\n \n  \n- sex: student gender, i.e. 1 (male), 2 (female);\n- race1: race of student, i.e., asian, black, hispanic, white, other;\n- ugpa: undergraduate GPA of student (average course grades), continous variable;\n- bar: Ground truth label indicating whether or not the student passed the bar, i.e., passed 1st time, passed 2nd time, failed, non-graduated.\n \n  \n\nThe CART method will be used evaluate the distribution and correlation differences between the real and synthetic data.\n \n  \n\n*The original paper can be found [here](https://files.eric.ed.gov/fulltext/ED469370.pdf)\n \n  \n 0. Preview of the dataset"
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},
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"exportToPDF": "Download evaluation report as PDF",
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"exportToJSON": "Download synthetic data as JSON",
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"exportToCSV": "Download synthetic data as CSV",
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"exportToPDF": "Download evaluation report as pdf",
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"exportToJSON": "Download synthetic data as json",
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"exportToCSV": "Download synthetic data as csv",
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"form": {
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"errors": {
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"csvRequired": "Please upload a CSV file.",
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"csvRequired": "Please upload a csv file.",
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"analysisError": "Error while analysing"
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},
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"fieldset": {
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"sourceDataset": "Source Dataset",
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"sourceDataset": "Source dataset",
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"sdgMethod": {
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"title": "Method",
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"cart": "cart",
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"cart": "CART",
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"gc": "Gaussian Copula"
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},
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"samples": "Number of samples"
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},
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"actions": {
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"tryItOut": "Try it out",
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"runGeneration": "Run Synthetic Data Generation",
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"runGeneration": "Run synthetic data generation",
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"analyzing": "Analyzing...",
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"initializing": "Initialising..."
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}
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},
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"demoCard": {
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"title": "Try it out!",
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"description": "Do you not have a dataset at hand? No worries use our demo data set."
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"description": "Do you not have a dataset at hand? No worries use our demo dataset."
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},
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"columnsInDataset": "Detected column data-types in dataset",
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"columnsInDatasetInfo": "If detected data types are incorrect, please change this locally in the dataset before uploading it again.",
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"univariateCharts": "Univariate distributions of the uploaded dataset",
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"synthVsReal": "Synthetic vs Real data",
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"heatmapCorrelation": "Heatmap correlation"
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"columnsInDataset": "1. Data types detection",
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"columnsInDatasetInfo": "If detected data types are incorrect, please change this locally in the dataset before attaching it again.",
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"univariateCharts": "Univariate distributions of the attached dataset",
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"synthVsReal": "Univariate distributions of synthetic vs real data",
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"heatmapCorrelation": "Correlation matrix"
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},
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"biasAnalysis": {
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"demo": {
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"heading": "Information about demo dataset",
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"description": "A demo data set is loaded below. Based on features of tweets, from the dataset [Twitter15], a BERT-based classifier has predicted whether a tweet is fake news or not. False positive classifications are marker in the FP column and will be used as a performance metric is this use case."
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"description": "A demo dataset is loaded below. Based on features of tweets, from the dataset [Twitter15], a BERT-based classifier has predicted whether a tweet is fake news or not. False positive classifications are marker in the FP column and will be used as a performance metric is this use case."
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},
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"parameters": {
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"heading": "Parameters selected",

src/locales/nl.json

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@@ -3,9 +3,9 @@
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"shareButton": "Delen",
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"exportButton": "Exporteren naar .json",
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"getStarted": "Laten we beginnen! Vul het formulier in.",
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"fileUploadError": "Upload een geldig CSV-bestand.",
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"fileUploadError": "Upload een geldig csv-bestand.",
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"removeButton": "Verwijderen",
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"dropzoneLabel": "Sleep je CSV-bestand hierheen of klik om een bestand te selecteren",
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"dropzoneLabel": "Sleep je csv-bestand hierheen of klik om een bestand te selecteren",
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"datasetPreview": "Voorbeeld van dataset met de eerste 5 rijen.",
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"error": "Sorry, er is iets misgegaan.",
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"loadingMessage": "Omgeving instellen...",
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"downloadButton": "Download",
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"biasSettings": {
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"exportToPDF": "Download bias detection rapport als PDF",
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"exportToJSON": "Export clusters als JSON",
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"exportToPDF": "Download bias detection rapport als pdf",
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"exportToJSON": "Export clusters als json",
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"form": {
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"fieldsets": {
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"syntheticData": {
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"demo": {
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"heading": "Informatie over demodataset",
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"description": "**UI-tekst** Een subset van de [Law School Admission Bar*](https://www.kaggle.com/datasets/danofer/law-school-admissions-bar-passage) dataset wordt gebruikt als demo. Er zullen synthetische gegevens worden gegenereerd voor de volgende kolommen:\n \n  \n\n- sex: geslacht van de student, d.w.z. 1 (man), 2 (vrouw)\n- race1: etniciteit, d.w.z. aziatisch, zwart, hispanic, wit, anders\n- ugpa: Het GPA van de student tijdens de undergraduate-opleiding, continue variabele;\n- bar: Waarheidslabel dat aangeeft of de student is geslaagd voor de bar, d.w.z. geslaagd 1e keer, geslaagd 2e keer, gezakt, niet-afgestudeerd\n \n  \n\nDe CART-methode zal worden gebruikt om de verschillen in distributie en correlatie tussen de echte en synthetische gegevens te evalueren.\n \n  \n\n*Het oorspronkelijke artikel is [hier](https://files.eric.ed.gov/fulltext/ED469370.pdf) te vinden."
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"description": "Een subset van de [Law School Admission Bar](https://www.kaggle.com/datasets/danofer/law-school-admissions-bar-passage)* dataset wordt gebruikt als demo. Synthetische data worden gegenereerd voor de volgende kolommen:\n \n  \n\n- sex: geslacht van de student, zijnde 1 (man), 2 (vrouw);\n- race1: etniciteit van de student, d.w.z. aziatisch, zwart, hispanic, wit, anders\n- ugpa: Het GPA van de student tijdens de undergraduate-opleiding, continue variabele;\n- bar: Waarheidslabel dat aangeeft of de student is geslaagd voor de bar, d.w.z. geslaagd 1e keer, geslaagd 2e keer, gezakt, niet-afgestudeerd\n \n  \n\nDe CART-methode zal worden gebruikt om de verschillen in distributie en correlatie tussen de echte en synthetische gegevens te evalueren.\n \n  \n\n*Het oorspronkelijke artikel is [hier](https://files.eric.ed.gov/fulltext/ED469370.pdf) te vinden."
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},
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"exportToPDF": "Download evulatie rapport als PDF",
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"exportToJSON": "Download synthetische data als JSON",
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"exportToCSV": "Download synthetische data als CSV",
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"exportToPDF": "Download evulatie rapport als pdf",
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"exportToJSON": "Download synthetische data als json",
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"exportToCSV": "Download synthetische data als csv",
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"form": {
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"errors": {
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"csvRequired": "Upload een CSV-bestand.",
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"csvRequired": "Upload een csv-bestand.",
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"analysisError": "Fout tijdens analyse"
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},
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"fieldset": {
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"sourceDataset": "Brondataset",
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"sdgMethod": {
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"title": "Methode",
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"cart": "cart",
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"cart": "CART",
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"gc": "Gaussian Copula"
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},
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"samples": "Aantal samples"
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"title": "Probeer het uit!",
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"description": "Heeft u geen dataset bij de hand? Geen zorgen, gebruik onze demodataset."
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},
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"columnsInDataset": "Gedetecteerde kolom data-types in de dataset",
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"columnsInDatasetInfo": "Als de gedetecteerd data types niet correct zijn, pas dit dan lokaal aan in de dataset voordat u deze opnieuw uploadt.",
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"univariateCharts": "Univariate distributies van de geuploade dataset",
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"synthVsReal": "Synthetic vs Real data",
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"heatmapCorrelation": "Heatmap correlatie"
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"columnsInDataset": "1. Detectie van datatypes",
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"columnsInDatasetInfo": "Als de gedetecteerd data types niet correct zijn, pas dit dan lokaal aan in de dataset voordat u deze opnieuw aan de app koppelt.",
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"univariateCharts": "Univariate distributies van de gekoppelde dataset",
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"synthVsReal": "Univariate distributies in synthetische vs originele data",
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"heatmapCorrelation": "Correlatiematrix"
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},
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"biasAnalysis": {
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"demo": {

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