From 324b9171aed00757e5391adf84de3864b0e45074 Mon Sep 17 00:00:00 2001
From: GitHub Action <action@github.com>
Date: Tue, 13 Feb 2024 08:21:38 +0000
Subject: [PATCH] refreshing index

---
 index.json | 1215 ++++++++++++++++++++++++++--------------------------
 1 file changed, 608 insertions(+), 607 deletions(-)

diff --git a/index.json b/index.json
index 0e20d84..e73bdc2 100644
--- a/index.json
+++ b/index.json
@@ -1,268 +1,273 @@
 {
     "blocks": [
         {
-            "path": "Connectors/BigQuery/Google BigQuery Import Table",
-            "displayName": "Google BigQuery Import Table",
+            "path": "Preparation/JSON/Normalise",
+            "displayName": "JSON Normalise",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "google",
-                "big",
-                "import",
+                "JSON",
+                "normalise",
                 "table",
-                "query"
+                "semi",
+                "structured"
             ],
-            "category": "Connectors",
-            "description": "Allows to import a table from Google BigQuery.",
-            "subcategory": null,
-            "icon": "data:image/png;base64,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"
+            "category": "Preparation",
+            "description": "Normalise semi-structured JSON strings into a flat table, appending data record by record.",
+            "subcategory": "JSON",
+            "icon": "data:image/png;base64,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"
         },
         {
-            "path": "Connectors/BigQuery/Google BigQuery Custom SQL",
-            "displayName": "Google BigQuery Custom SQL",
+            "path": "Preparation/Add row ID field",
+            "displayName": "Add row ID field",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "google",
-                "big",
-                "query",
-                "sql",
-                "custom"
+                "row",
+                "ID",
+                "sequence"
             ],
-            "category": "Connectors",
-            "description": "Executes a SQL query on Google BigQuery and imports the query results",
+            "category": "Preparation",
+            "description": "Adds a Row ID field with a sequential number.",
             "subcategory": null,
-            "icon": 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"
+            "icon": null
         },
         {
-            "path": "Connectors/Slack API WebClient",
-            "displayName": "Slack API WebClient",
-            "language": "PYTHON",
+            "path": "Preparation/Field Renamer",
+            "displayName": "Field Renamer",
+            "language": "R",
             "optionsVersion": 1,
-            "tags": [],
-            "category": "Connectors",
-            "description": "Allows you to call public Slack endpoints.",
-            "subcategory": "Slack",
-            "icon": 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"
+            "tags": [
+                "field",
+                "organiser",
+                "rename",
+                "name"
+            ],
+            "category": "Preparation",
+            "description": "Renames the fields of a data set given a list of current names and new names.",
+            "subcategory": null,
+            "icon": 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"
         },
         {
-            "path": "Connectors/Etherscan",
-            "displayName": "Etherscan",
+            "path": "Preparation/Pivot/Melt De-pivot",
+            "displayName": "Melt De-pivot",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "blockchain",
-                "ethereum",
-                "etherscan",
-                "crypto"
+                "pivot",
+                "depivot",
+                "melt",
+                "variable",
+                "fixed"
             ],
-            "category": "Connectors",
-            "description": "The Ethereum Blockchain Explorer.",
-            "subcategory": null,
-            "icon": 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YxLmTRqpJOORGNizZoN/65kZb+slWus7R5U5VIOLR1zK+NzSqbHnnwGR7mLLTsv8pZUBTv0hnw+/0uO48I3A6xg4ajoNoS2tm5ETYEAM6SoVJiTEjAMApGuPMSsh/e5lIAQDIKCEBqRoAHSjPb2ZoQiUWitKte6BOlUcQhLUSzkkJwa/t1NG9at3bJp0C2VMhQIMoaHi3MeOyeZgsE4bTOG6O677tAf/MD7EA2bX7Btizwr98rqbbFgBpFEQzQC2BqmpEqZQlQeFZenBM/zAEkQVZ4rxxAINogVCECsoakqlS5ljPxjiYTK5tLNp06OfPqpZ5/BieHjpPMxRBrUnCc/ZzX342f2ketaXCjDOHDosCuE/FeTE6NvIyKNFSv3JUEphba2ZjQ3xuAUS5AhABxCRdtgkK91nEeo+Ao4g0CsIVCGBkAw0NKSwNnTYm7lfYFgZgghpGVZmJwc/dc3v+FNf0mMfalMWr52tqjLzAjNusg5kqmzox3xiEE7tm12t23ZFMikpj9lWxaoIv+u9nhc8xAkAA0ISSBWYM0VCjEqStEFHsSVJR0BYK5MSYK8geTKlLkU//caZVxlc2njyKuv/te73z6Aj//qu3iwqxnj49lzrlInmfbu3Yt8ZpI1hLH/wCGXSNxrWdZmAhQAuSStfJ1DM8MRgAgZqB34hXYtU2WSYxUBKw0ZBLSuGELr7eqLh++7cxwH+UL+7r/93ms3mwaenp5OSafonqPy1Emmj3zkIxRtSNANO7e7O7dvDRTy2f/gOjZICLGidC8dmKrlcyurNMAvc3JRD8LMco8gvOLRSw+fKFJKlc9lMT169t898Ndfwd6HvsNta9pw8nS67sJ1kunIkSNslUrG4cOvuELIj9pWeTMDmgCxIpUuMxb9X73cf/KKdLJtG6nk5D13fejXdxPwYmpiUvasbtG1v6yS6YUXDhJzAVu3bnYdVwf3vvDcv7dtG0KIFUfulcBCupcXe+DiG0ZEKpvJyPGhMx/LpE7+GwKB+U2wckzBhkpN/iqZPvjB9+GjH/0l8dDDP1bTU9N33XnnbVuDwYBf1fYK9egKliP8lZ1t28hl0x+8cc/tvw/g1NR0WuSKukptAwD+5ze+iTfvuRk//enDMM0A3vLmW+6xrCK8AujXvbV7RfKeHzN2J1K5XCY6Pjbx0XK5/AVmFm0draq6hQgA/NovfRhf+cof0QsvHFbf+tZ3Ntl2+Re8E7wuogJqdzA4H2q/90NG5vrueoUQQti2jWQy9YHevp5A39pe18opvHImSYAnmb774BP0lje/RWpmvXv3nrscxzFN01DMfN2SqVYaua5bqeYujQuSQmv2xD5BEFWOFeKSQkCuBXj3RlprTqamt06MZd6htbrfsi3Z1rpaAYDxzf/7fWpJBLF+w22OMEPmxMmXf8W2LFzPyQHVUFWtIQShtbUVHR0dMAwT5JFkPtiOCylNCNLIF4rI5PMoZNIoFovXdbBgbUSBZZVlOp98d2bsxP2sFa+KJVBihvGRX3gnHnjwcSoUT3KxbG1sCGKDz8LrsWNqY3ai0Rg2bBhEa2uzR6y5JUx9HJLXsQy0gWERIZvN4/jB/cjlC9c1obx7F67rIp1KvucNb77jtwAkT5waFqI1AgGAQ6aQAgoNYfMurbVJgLpe6wP4pAiHw1jTvxHt7a1eMFplSzF/Zyg/fqj2va8nVX6nwKxRKlkIRqLYtOsGhMMRvA5KBhEzs+vYLaeG03edHErC0VIYAMR37nuAskVynt93JBwwxS97fzpxHfIIgCdlAPStW4+WjmY4rgIzUC6Xa+Kwz334x1qW5XUpwXUVJDSCkhAIR9C/aStM07iuJRNQmepKpRKsUvqOg3ufw8svPs82A8aBlw4KIUjlcoWb87meTaZpALM297uewMwwAiZiLW3+FkoQQiAWiwGoi4euO8b/PBTyyh8TEAwEYDBDVeIe0d7WhPF4AlOTk5DGdV1JSAAajYnoz777ve+IAijYABnvete7BBFUsVjcWirloPX1HUmptUY8Hkc4ZEJrG0T1g14rheb6fOYD8gJBXEhU4pAAwDCDdWlI1yeItNZg0BoAbwDwODSE2LVri7tz5xacPPnau73Q7utPHJ3TFaJCBFJzfEfzPup/CBBVzlEJULMRABCJRiqJAdehVJ/pIwYzlPdHvM3rDmEcPnyEk8lkU29vz55isQjhRQhcr53hmwQsp4yICZAvRLyQjgsdW4EvvaqeKwjvM631df9vZAakNCgcjQDALQBwgOAaf/1//h75QunGd7z91oZwOITrvRKub3CMiABMrUF+CMhCgoAqwUSVeCId9CJJeCaU5DpFTSo5WlrbKBgOQDPvFuCW3SSmjQ9+8BeQTCa3sraue31pBgQBUStaFskBP8yWr2sSVe/WW4QIKdHVu9YXOY0gsQPAY2Lnjk04eeLYbfJ1llO5NEoy4/LHEy0X+NO4QseqNYg1JABm5VV2uwEAxKlJhNb2rx10XAf+RoEruFjM1Au4nuHXJlXKRSQaQ1dvH0zBYOUtYIi2AID45Q/cNmCVC71eavFK7NIK6lBrY4tEYujp24REQwgGMQxRjSDexqwN0bG6d73juKGrRaKllIQrMnVpMUMkAKyxbmAQXV0tMIjB2gGUK7xo9PUANRmf/MSv95ZLOSilNBEt2fYXC2ms/1opteBzVNKpPVsQVgLdlgpVIgHQ2kVv3wDa2tsg4EK5TsWhjWqOXgSMdcaRVw7s6e9fB631JTdgUY31JJNhGGhuabl4DcQ7NpfNwnVdKKUgpLzkWkUrqJdIzAo9vQPo619X8VNCwc98s4olBMJREEmTgXXG8PDIuvXrB/xyzFdkEGYTqb29HR2rVqO9vW2maMP52uFPjUQolgoYGZlAJpVEKjVdyXRdIdQlYWa20Fi9ugv9/b2QpCCIQGSCjADA7BFJaFRS5nqNnTfczK7jgOjKZTPNsJ6xZs0aDAwMVKYrAOT7vC4UQouKoTAUNLG2vxfF4hqcOHYckxOnZ8rXrBBqwaiN9+rs7MS69etgyEpqu5Sm9yuqFhjDjKraLaKRwDrNDLqQL2HJG+2is6sXAxs2QQiCZVkVnakm3f58Dx+O68J2HEQiBgYGN2D16u6q5Foh0sJQIZKA67qIxWLoWbce4XAYWjMCgQAcx5krDp68z1aLxnik7YrrS1ojFIqhdVUXhMCcjtQLOVyr1lhBMKSE7WoIg9DcvgpCius5OO2yYCaU2UVDQwO27tqFUDhQiXH3+twwjLpxopo9TYioTaTS6WoluCsFzRqRaBThaAyisp8BgsEgDC8G6EISZaZMH8GQJoQgmIYACUY00YTGxiboRawMX6/wJZLWCqYZRM+6zYhFwiAUYRgChpdo4a+ca3VefxWeyWRajNovrti0wIAGIxiacWsx6hXz87XF18+rejgYTBpggmFQRTJd7RG6BjBTZZfgug5isQb0rt+K1pYEmG2IaiEEeJXrzo3xYmbKZDIYGRlpMK6WoioNs0oiT05WlbaLaUs9oRiABpGsVBlx9UpphAugdsy11mhoaMDmnbsQj0a8epqAKcPVxZI/RrXBg0IIWJaFbDYLKWVLJUb3Cvc8M8OQAq5SkACElPOUl50f1TogJABWILBX/IogxUopqfNhds5ga2srNmzdimAoBMUuBABTmoAGWPjDMneSxPT0NJRSCIfDoSseqOxLwmAgiKiUizSWzsg0zZUCWpIZgIABgnIvvXLa9YpayzZrjc7OTqxd249oMATlKhhGJQKJQGDBdccAqFb0JSIkk0l4AZXQWl+4qPzlwsyUtlgXSIVQXPXce+YAxopUmgd1U5vroruvHwPr1sE0CLZtVQghQ+ekas1WhYQQyOVymJ6eri6aAC89fDkY9xZe7WyFMQtBbRYza421AxvQsaYXQgBKaQQCgXnNKbULIyklXNfF9PQ0pJR134vZro0VXH+oJVIwEMDagUGsXdcPg1SVILXT12wHPM1SvEdHR+sK2PvPV201t4LLj5oNdiqBbZEIOnoG0NuzCo5rI2AIGF6K1ny2Rl+nVUrBMAyMj4/DsmY2YQIq02Aul8uKeDx+xSMGVnBlUFWaXQfRaAxbbtiNnt5V1VWvgKgmRpCgcySQvxOCbdswTRNTU1PI5/Nz1lOQUuYNQ74O8gdeb/AUUGYGWKGztx+re3oQiQahHBsggilMaIchTQITV/ewmz11OY6DUCiEVCqFZDIJ0zRnE4mZmUzTzBqpbCEjhEhc7ftfwcWjdrEyl6YrBHnxXQLdPRvR3d8HQYB2HRiSYAoJZfM5C57ZurM/Y1mWhampKZimiflgGMa0AYghAAksZkG1gqsELwDH2+xQGBLaBQxP7bFtG42NCQys34TGlkbP/wmQNKpWFMOscdjOZP0BqJBIKYVAIADLsjA2NnbOyq16LBEzM0kpJw1Wrltt4ooifk2A/MLxRBCCoRRgmIBtMxgaLS1tWN8/iKamBlTcTDUeAZ4lgWp8nLW+OvKq4k1OTlZrV83FD65koYCIRkUyOfmyaZrQWvMKka4heA6zyrZh7NU+0Ojt6ceuHTcgEolUfJSiEqftF7Cv+G5nJqHaOlw+gfys7uHhYbiuez4iVU0OuVxuVOzcufO0/93V7p8VXBx8qcTM0AyQILgOEAqF0bumD0IKBIIChlEzHbG3asPMDge1jl4/uhLeuYeGhuA4zsVEcUgAcBznhAEzdsIj+ZUNalrBkmBmMc5INDYAXoxR/djPLVH8qa7WNMTMGB4ehuM4FyypWGMVdwYHB4+L+/7526O2bYG8nQhWsPwxs3nPjH2oEsLjTXfCe1+dvlBTFqE+7Na27bqgxJGRkepnF4wrm5nmiqlU6rh4z7ve+XQi0Zi83qufXF+oasxg1mBoVMIN/Qh5DZD/+YzmPZtIQoiqr81xHJw+fboqkS6GD0SkPcl0rKurKyUGNwzkSpZzyp8+V6TTNQSvlA9B1zzramwXUS15MHtJX/GneRJoZGTkvMr2PGAAME3z4KOPPuqKr3/tq24kZL7oycGVFd21BhKVbVarzzWv4e/yXn+I1rqaKGDbNoaGhrxUposvO10rdIrF4ss9PT0w3njLrZBCvFIoFKqe4BUsd9Cs5wuhXuH2pY/jOBgdHYXruguuX+6dT3rFZfcxM4xIJIZgKPxIKjmllFIrjrprChfa66XyXLu896cy13UxMjKyYIk0c+5KyRzbttOO47xk2zaEA4myouMMGq/53dXupesSM67Rc15cwhnnftTqSHUxR4aBYrGIs2fPnhOTtKArV9woCIVCLz733HPTH/vYx0gMv/yUPLX/8SKAh70T65Wp7vKhspRmOLYLf9CXGrXk8W1IjuMAAKampjA6OjpnINxCzg/vn2BZ1lM7duzA17/+dcPo6N8uGFBSypemJkZnqoGt4CJwcUyoC+JnBSkBDQPKLUNIE8Ivr3kZ/sO+VVsIgWQyiXRN0u1ifbG+vkRECAaDPwoGg2BmbUQicRWJREFCPpRNTdsltxhYib68WMwkMsyH+kB8QiTaCIhKGIhjKZBYwmJnNZGP/mvTNOE4DtLpNLLZ7CUTyTuWve3CziqlnveiMLXQhsFrulrQvbrxsBkK7/PtTStEuljQeRdV5JmftVaQ0kD76lVwHUArBcMwlzw1f3ZMkuu6GBsbQyqVWhIiedfQzAzTNB8uFAqFYrEoW1tbYbTGE3jyyWdNZnYCgcB9Qsg3rkQQXAi+150BcgES8MpbnTNIWisorRGLxrGufxDNDTEvohEAjJq6CZfQmllJlX7sUT6fx/j4OIioLkLyUsfW34tQSvmDlpYWAKBgMAjR3hpjIxBWDY0tCIQT3xdCOsws/Uau4Hxgb4uDGZ3IciplgZRSgNaIRhqwacNW7N55IzpXtVZ/S1Vf2cUTaa6tXGe/94mSTCYxPj5+TrGJS75jr/QuM48ZhvGoR0514sQJiJFUkU6ePMs7t20Qu7avPxgKRR7wWKxWpNOFQJXdCgDASwetFC0jtLa3Y932Xdi18w3o61sDIUzYtvKIw94qbmFSolaq1G2kiBlfm1IKp06dQjKZvCzVbYhIA4CU8oHp6elUOp2WgUAAW7ZsYaOzKcIvvnSUfvDgQ5K11kLInwjCvxBiRQmfF3UG6IpO5Ec/NjQ0YOvuPYi3toI1Q7iAY2sEggTLcqG1V6WFaUES6ZydODGzDwyAqiFyaGgIWutzwmyXahz9VZyU8h/9bdXC4UqBCwMADu17EU8+/ZgKh8M4OzT84Mf/7cdsAIEVIl0MZv79BKCzox1EgAsGScCUM2G24YgJX9eiRRakryWW60Vc+8v+bDZbfb/UJPKuzd52uwfS6fRj3o6iOplMAvDSw9fuuJHffMdbsfdHP5TGzTcdbmpu/XYqOfURb6pbcbGcA1/h8SUSV2smSO+r2jwOrnvmanmaejLNPX3Nfu0TBahUcrNtGxMTE8jn89XKbpdrRvFWcZKIvhUKhRxmNuLxuIpEIgx4ZLr1hgEMbNiIf77vASQSjRgbG/uKaQY+aFlluaxtTvOsDy7UUn9br0u9I67dX4zZ24etopBX5VVtESqe7faoOd5/NUecde3nfnC/Ugr5fB5TU1MAUJeGtNRj5Vu8iUgycyYQCPydp9jrYDCIeDwOoEZG3/fA95C0bL2mswV7btiyF2bgWa9Ryzfd14su9CeOyh3PzbG6fDDWXmDZIjt3pgE1n1BV8tQGrTFXAtcqyzau0bPOJdJcewLPpXD7TtqJiYm6Hc8vXzfP8MAwjG9blnXWdV0phOBisVj9XbUeypYN6/jZvQfopf37pZRCtSYavjhhlb7ruu6y20+F/PU0MyRXhlGD4ArAYsCdZUesHRQiIBZJwPC2CPO1l4UkDdI577hm80Pph4bNOoDmPdPs+kf+ch6YCfb3Z4jJyUlkMhlIKavhtldo5pBEpJubm/+Xr6tZloXOzs7qjdbVZ3rjjdv5wIEDeuu2bQDwYDabPZzP5zdjRve63A2+SMwosf6OQ375xRkXZM2vawYmEomhs7MLggClGMKfxhcx6dHsV76ZYF7yzH10LWqJBMws+X13iGVZ51iyL+e4eNdQAKRS6p8OHDjwomEYoqenR9u2XffbOjIdzWSA06fx05deMgSR3RCPf6lULv+F6zhaCCGXm+7E8CQUsTfdkR9bCN+Go9mv/sWQgtDTuw4NDWFYRRtCMISWILF4QtVJNC9nv47Mc56yniy1z/6UVZs5Mj4+jlwuB8Mw6pb8l71/Z1aOkogQCAS+1N3dDQAUiUS4VioBs9KbNiQSGEunORIKq507dtBdd277y+ampsc9Ii0zfx3NkgwMAa7UyCT2QlMdEDQAwurObmzcuhurOztgWTZkQEIYcjGr81mtmPWOZj3mjDeaGazqkXPEHvkhtYVCAaZp1pVLvtwSyW+Tb7wWQny7XC4/XSwWRTgc1kNDQ+dc/5wyhFJKmp6e4pePHDWkEK4Q8puZTOZ227aWdu9QQVAMiBo316JOX1tdmDWEBkwhEI5E0NjUgVhDMxriETQ3R6H8fPyAWbVEL4Vv7FLgGx79VRoAlEolpFIplMtlf0yuGIFq4V1TKqWccDj8RcMwEIvF6NSpU3zTTTedIxrPsbffcsstnGiMU0AI9fu//wWxYf3a/20YxkNas0RlO58Fi9haUV7ZGQiQgiBqisMvNuqQeGZKI2hIaGwY2Igbd9+ELRs3oqe7Da2NUZALBLy6DcKbGn23xuUen9pV2mx/mmVZ1TpIlmVhcnISw8PDKBaL5/z2SsLXlbw2/M9CobDPsiyZTCb1qlWr5iT1nN24d+9xvPepfvz9TS/KYDCklFZ73FLuqWKhYNIiwxhqi0cpV2H7DTegrbUF0JUtKxZ0vrr6Q/ASDhW0tmHbLoJmDGBAOfCSEuFdQ4MkgQR7S3YAJBexnlv4wFQ7fFaIiB8NmUqlkM1mqwr3MgB7+6KMGIbxRtd1zxKRiMVibJome9ECdZiz1TfeuA4Pvv01Orb/KeWWC4aVz73Q3Lb6C9I0wayV3ymzO2quzqteSAgopZFOp9DS2opIohla1xSGXwgxzzUcw/fGm4YBrVwoW0MwQQr29CaFWi9//bGXp9BCrWSZvdT3kc1mcebMGWQymWo/zdeHVwI1bWbPZfOH2Wz2bLlcNtavX6/z+TzmItKs7jwXP3ryWYq19WG3uB//au+H5G/uHH1ianToTWWrrKU0xPySxCurXDUHVQY6n88hnc7gzne/F83NCbC2EKYABC0o8W/mxmviimhGSIE1Vxyp/jxa0yx/Rq0l5OWQTLUkqtWJiAiO4yCXyyGXy1Xz15bT4sZbbAml1BMDAwNvBUAvv/wyYrEY+vr65mX5ee/g+JlxAsCNLXEZiYQUM9+UnEz/5Oypk2Yxn/LKr9Sfm6hS4kUIghAzxTdDoRAaW9rR1tmFSDQECAeuthAXsUWTqXLnM3cyk9rjd8rs95e2ddj5pqv53ruuW/WZ2baNTCaDXC53jnFyuZhdalKiHK31WwA8ZxiGXLNmjZqcnKQ1a9YsjkwA8P0fPk7vePttGE5lRFtTQrHmzyvGf8ln8mpqYlzmc2mYxkzlVa0FTMNEY1MCsVgELjPCkRg0SQQCEpVdgBUADUkCJuSMs/SS7Dz1qdAztzfL+UFzHbs47z1QT6K5SmD7ux5ls1mk0+lq1dq5BvBqoqbdCoAUQnxea/17juPI/v5+fejQIezevfu8c+8F7+BsykKDWSKtbTQ1tOHwT58y1mze/kQgEH6jAamkJqldhtYEaaCq8DJVyKHB0MRQrMGsUFl5oVJO2HURlIuf5q5kR89l46l9X9epNd9NTk4il8tVP1+u9+jdpyIiqbV+lplvHRgYcM+ePYvJyUmsXr2aOzs7z3v8Rd1ZsRLgzEIXJGtXgcQ2Q4gnBEQjcZDBNCMc6vxSDC2synsvTpogPcWm4hiVWNqw0svUydU2WpaFQCBQ9dz701Wtw7VUKiGZTMJxnGoG7XKbzmbfH7zRU0qlAoHAra7rHjIMQ5qmqYiIurq6LrgiuKg1aISIrUKZQnseUJJCRkgGDkqYnyVIEGkNoUBCgaT3LDQgGCQYggwIBCBgQJIBAQHBorL7EomqRrzcOtjv5NnujlAoVFWq/UB9v5JINpvF8PAwRkZGUCqVqhGPc0VJLjcQkSYiGIbxWcMwDgkhjHvvvVdls9mLIhKwAGUhZ6URME0K6AhcYUuDAi4YX2Pwx5m4sjd5dRSqDaxs4OwbFv0rVkLSa+9kWdT6na3vzKUP+QXWfSmklEKhUEA6nYZt21Vb2uzzLlcS+cZJrbWUUn5teHj4E4lEwujr61OpVAqhUIg7Ojou6lwLukPFZRIIsmYIoaFBHHWJHmXgJvib3c86udRcT5RzbERUXa5fbTLN7uTzwS9prJSa09WxnAlU2z5fT2Lm54jojv7+/sLIyIjo7OzUx44do4GBgYs2eC34blkrAoOZSDKRIvA6AI8A6EMlgErUppWh5uW8BvhlIJF8yeOTw9dzapVt3z6Uz+fhuu45JFruBJrjvjURCSI6FQ6H71BKnRBCSCmlEkJQW1vbgiyni7pzrdk3/RlEcAHsQYVQcdTx5sp37OxMjrqbnePz+dwbPplc10W5XEYmk4FlWXX2oWuJOLP7CJUwXGLmbDAYvNMwjBeKxaIxMTHhRqNR2r59+4JN8IvqDZcZwoveIcAgwGXg/QC+jVnxqHShJl2G8Zit58ynAM9HOK01SqUSisVi1V/m1W6c9zrXCrz+YO+ZAoHABwzD+AcARjQaddPpNLW0tHA0Gl3wuRfdC8x1HjIJwAXwCQBfAbwgoissmuYyGM5Vx3G+z7XWSKfTyGQyddVD5iLgtUSgWf3BXtisME3zk+Vy+atSSqOnp0dNTEzAsiz09/cvyjG46G1VvUb56pDyzvVVAGEAf4SZRITL3uvzkcO3NtcmKvr6kL8/SD6fh1IKpVIJjuNUp7G5pNC1PrURVb3cwjCMT6dSqa8qpYytW7cqwzDYtm1aLJGAJRjoGkJ50fRwAfwnAH+IJZRQc0UlztFZ0FrDcZw60gSDwTpfWTabrZLHV6R98lyrZLlQ3/kSCRUi/Wel1H+3bdvo6upS0WiUU6kUtba2XlKowpL03DyE+i3MSKiLItT5luPzKdWzj/dL69XGUtu2jVwuB8dxqgSqXaldr6iR2FUiEdGni8XiHxuGYbS1tSnTNNmyLGpvb7/kmJcl68l5COXrUFXxutgOmY9Is5Vsv+BnNptFoVCAbdtQStWlDM2V6Hi9kspb/vt/5k9OT09/1TRNo7OzUwFgy7Kop6dnSYKnlrQH5yHUPQD+ChWzwTmGTe+4OjL4n/motTZLKetcFFprlMtllMtlCCGQzWar5AEwL3n8765XePfoGySzwWDwV6ampv7Rtm1j27ZtSgjBU1NT1N3dvWRReEvem/MQ6kYA/wxgDTxCzWfvsW27TgGuDdnwDYrlchn5fB5EhEKhUCVVrSvj9SR9Zt+nd/8KlcTJs6Zp/nwoFNo7PT1tDA4OKgA8NTW1YKPkhXBZenceQvUB+DYz7/EC1YV//bkCy3yPvJQShUIBjuNAaw2/+H2tbjQfSV4vBKq9V2/Zr7XWEsALra2t75dSns5ms0ZXV5cCwOl0mpqampY8Lviy9XSNHaoSaVKRSBEAfwLg33o3zcwsfKnCzFVLs+91NwyjbkO9+cjzeiLOXPD+jBoAefjz0dHR/zg6OlrcvXu37OjoUEIIGh0dxdq1ay9LgPll7f1ZhBJKKZJSKgC/lslk/qhQKMQBqFKpJKSU5C/dZ/vE5iMPcH3rPReDGvuRBiBd181KKX9r//79f9Hc3CzXrVvHjuPoYDBIjuMs2iB5MbgiBkXvOqy1FlNTU2J4eNi1LGtTS0vLXxPRHiGEv5uUWJmyLg41/VHbdy8YhvEvm5ubX0kmk4Zt29qyLK21pr6+PvZL31wuXLHRqdWjHMeRpmm6AAInT578bcdx/pNhGAGuJLPRMqqQsSxRa83mSuVbW0r5h8Vi8Q+klDYRGf39/UoIwdPT00uuaM+HKzpo5XKZvvCFLyAQCPA999wjg8Eg2tra1NjY2G4p5VeI6E2oEM6vULYikTzUmk/8BQwRkWEYTxPRb5RKpRcdx5GhUAiGYajW1lYaHR3FQuKRLhVXfJRGRkZgWRYFAgEGIDKZDL366qtqenpa3H333R8vFAr/BUCbp5xrz07yuibUrEA24Un5SSL6r/39/V8HoA8ePChbW1tZKaXz+Ty1t7fzfMmSlwtXbYROnTpFvb29DIAOHz4spJRoaWlRtm2vLhaLn2XmX5VSBvylLnu1yV8vpJr1B6pKIqWUpbX+i1gs9gda67F8Pi9DoRDWrl2rAXAqlaLm5uarkg58VUcml8vh8OHDFA6HuaGhgUzTFKgEbelCobDVNM3fVUr9PCpuGJ9Uwgvquq6J5fnStHfvBEAZhvEd0zQ/39nZeSibzYqhoSHq7u7Wpmny2NgYNTU1cVNT01Vr87IYjTNnzlBjYyOy2SxrrUU6naZUKqUbGxs5EonsAfAZIcR7mNnwxT2qBU2WxS0sCXxbkadc+5LYZeb7lVJfnJ6efqFcLlNHR4eIRCKczWZ1KBSieDyOVatWXfXtJJbVSJw+fZo6Ojo4GAzSsWPHhOde0Uopbmtr25HP5z8B4P1SyoR3SPXf69u0riVy1Vj9mSqV/wUA8qIdMlLKf2hvb/9qNBp9CQAdPXpUBINB9PX16XvvvZc/97nP0YYNG646iXwsu5739kSjtWvXslKKjh8/LlzX5c2bNzMAfu2117oCgcBHHMe5F8DmmcKn5K8CxXI2Lcx2e6CGQB4OA/hrIvpWZ2fncCgUogMHDlAwGKTBwUENgI8cOUIbN25cdpslLa/W1CCdTuPkyZO0a9cufvXVVykcDot0Oo3HH39ceznvZltb223BYPAXtdZ3a607gDo/n/J0q6rd6mroWbPI49uGJNX7I8eFEN9zHOfviehH4XDYuf/+++ltb3ubaGpqwtDQkN61axfv27ePdu3atexI5GN5tqoGzIyjR4/Sl7/8Zf7whz9MnZ2dVCwWxd/93d+pT33qUwgGg1woFJomJiZ+JhqN/pzjOHcKIbprY71ryAVUpEBV11oqt8wcSQwafvHfGvL4EaBKqSEhxCNKqQei0ehjXV1dKcuy6LnnnkNHR4cMBoO6XC7zE088wXfccQetW7du2ZLIx/Ju3Syk02lKJBIMgE6cOAGllMxkMnzjjTf6CjmfOXMmmkgk3pBOp29j5je7rnuDYRiNWuv5Kpf4g06YmW5mCujOkeHigb3v/QB9wAutmf1bIQRs204Hg8F9juM8aZrmj7TWz/f39xcAkNaaDx48KCORCBmGoSKRCDo6OnhkZOSiU7OXA64pMvlgZpw9e5aYmRsaGqhUKlEulxNDQ0N8xx13+MQCAB4aGmppb2/fOT4+fgMzb1VKbXFdd71pmhEApu9YrnbIPHFQcyVc1n7vB+NJKR2tdVEIcSwQCLxcKpUOJRKJfYVCYX9fX980ANq7dy/a29s5lUrJUChEkUhEAcDJkye5u7ub2tvbuaGh4Wp384JxTZKpFsyMsbExmpqa4nXr1tHExAQaGhrE9PS0OHbsmGsYBofDYWppacEjjzzC27Ztk319fc2rVq1a57ruuqNHj/Y2NTWtkVKuUkq127bdrLWOM3MsGAzGZ0umfD6flVLmTdPMGoaRNAxjAsB4Lpcb1lqfGBgYOJ5Op48//PDDybNnz6rbb7+dJiYmwMxcKBRo+/btRiKR0B5w33338S233EI7d+7kcDh8tbvzkvD/AXgGwBi4MoL+AAAAJXRFWHRkYXRlOmNyZWF0ZQAyMDIyLTA2LTIyVDE0OjI3OjM4KzAwOjAwtupxGQAAACV0RVh0ZGF0ZTptb2RpZnkAMjAyMi0wNi0yMlQxNDoyNzozOCswMDowMMe3yaUAAAAASUVORK5CYII="
+            "category": "Preparation",
+            "description": "Keep all selected fixed fields in the output, de-pivot all other fields",
+            "subcategory": "Pivot",
+            "icon": "data:image/png;base64,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"
         },
         {
-            "path": "Connectors/XPT Reader",
-            "displayName": "XPT Reader",
-            "language": "PYTHON",
+            "path": "Preparation/Join/Fuzzy Join",
+            "displayName": "Fuzzy Terms Join",
+            "language": "R",
             "optionsVersion": 1,
             "tags": [
-                "xpt",
-                "xport",
-                "sas"
+                "fuzzy",
+                "join"
             ],
-            "category": "Connectors",
-            "description": "Reads a SAS Transport *xpt* file, extracting a dataset.",
-            "subcategory": null,
-            "icon": "data:image/png;base64,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"
+            "category": "Preparation",
+            "description": "Performs a join between the first (left) and second (right) input. The field on which the join is performed must be text containing multiple terms. The result will contain joined records based on how many terms they share, weighted by inverse document frequency.",
+            "subcategory": "Join",
+            "icon": "data:image/png;base64,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"
         },
         {
-            "path": "Connectors/HubSpot",
-            "displayName": "HubSpot",
+            "path": "Preparation/Join/Interval Join",
+            "displayName": "Interval Join",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "hubspot",
-                "crm"
+                "inequality",
+                "interval",
+                "join"
             ],
-            "category": "Connectors",
-            "description": "Retrieves contacts, companies, deals and lists",
-            "subcategory": null,
-            "icon": "data:image/png;base64,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"
+            "category": "Preparation",
+            "description": "Performs a join between values in the first input and intervals in the second input. Rows are joined if the value is contained in an interval.",
+            "subcategory": "Join",
+            "icon": "data:image/png;base64,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"
         },
         {
-            "path": "Connectors/Weather/OpenWeatherMap",
-            "displayName": "OpenWeatherMap",
+            "path": "Preparation/Join/Inequality Join",
+            "displayName": "Inequality Join",
             "language": "R",
             "optionsVersion": 1,
             "tags": [
-                "weather",
-                "map",
-                "longitude",
-                "latitude",
-                "forecast"
+                "inequality",
+                "join"
             ],
-            "category": "Connectors",
-            "description": "Retrieves current weather and forecasts from OpenWeatherMap",
-            "subcategory": "Weather",
-            "icon": "data:image/png;base64,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"
+            "category": "Preparation",
+            "description": "Performs a join between the first (left) and second (right) input. The join can be performed using equality/inequality comparators ==, <=, >=, <, > , which means the result will be a constraint cartesian join including all records that match the inequalities.",
+            "subcategory": "Join",
+            "icon": "data:image/png;base64,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"
         },
         {
-            "path": "Connectors/Trello",
-            "displayName": "Trello",
+            "path": "Preparation/URL Encode",
+            "displayName": "URL Encode",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "trello",
-                "board",
-                "card"
+                "URL",
+                "quote",
+                "encode"
             ],
-            "category": "Connectors",
-            "description": "Retrieves boards, lists and cards, and allows you to search in Trello.",
+            "category": "Preparation",
+            "description": "URL encode strings in a field using the UTF-8 encoding scheme",
             "subcategory": null,
-            "icon": "data:image/png;base64,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"
+            "icon": null
         },
         {
-            "path": "Connectors/Jira",
-            "displayName": "Jira",
+            "path": "Preparation/ForEach/ForEach",
+            "displayName": "For Each (Separate Workflows)",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "jira",
-                "issue",
-                "bug"
+                "for",
+                "foreach",
+                "paramater",
+                "iteration"
             ],
-            "category": "Connectors",
-            "description": "Retrieves projects and issues from Jira",
-            "subcategory": null,
-            "icon": "data:image/png;base64,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"
+            "category": "Preparation",
+            "description": "Executes another Omniscope project multiple times, each time with a different set of parameter values.",
+            "subcategory": "Workflow",
+            "icon": "data:image/png;base64,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"
         },
         {
-            "path": "Connectors/Overpass/Street Coordinates",
-            "displayName": "Overpass Street Coordinates",
+            "path": "Preparation/ForEach/ProjectParameters",
+            "displayName": "Project Parameters Batch Setting",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "overpass",
-                "street",
-                "coordinates",
-                "map"
+                "for",
+                "foreach",
+                "parameter",
+                "batch",
+                "iteration"
             ],
-            "category": "Connectors",
-            "description": "Finds all matching streets given a street name and requests multiple coordinates along the street using data from Overpass API. It will create a row for each point found that is part of a street that matches the given street name. The resulting rows will include the street name, the street Id and the coordinates of the point. The script needs an input with a field with the street name.",
-            "subcategory": "Overpass",
-            "icon": 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"
+            "category": "Preparation",
+            "description": "",
+            "subcategory": "ForEach",
+            "icon": "data:image/png;base64,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"
         },
         {
-            "path": "Connectors/YahooFinance",
-            "displayName": "Yahoo Finance",
+            "path": "Preparation/ForEach/ForEachMultiStage",
+            "displayName": "ForEach multi stage",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "stock",
-                "finance",
-                "ticker",
-                "price",
-                "yahoo"
+                "for",
+                "foreach",
+                "iteration",
+                "paramater",
+                "stage",
+                "orchestration"
             ],
-            "category": "Connectors",
-            "description": "Fetches price data for tickers from Yahoo Finance",
-            "subcategory": null,
-            "icon": "data:image/png;base64,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"
+            "category": "Preparation",
+            "description": "The ForEach multi stage block allows to orchestrate the execution of another Omniscope project and running the workflow multiple times, each time with a different set of parameter values. Unlike the ForEach block allows multiple stages of execution, executing/refreshing from source a different set of blocks in each stage.",
+            "subcategory": "ForEach",
+            "icon": "data:image/png;base64,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"
         },
         {
-            "path": "Connectors/Flightstats/Airports",
-            "displayName": "Flightstats Airports",
-            "language": "R",
+            "path": "Preparation/Unescape HTML",
+            "displayName": "Unescape HTML",
+            "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "flightstats",
-                "airports"
+                "HTML",
+                "escape",
+                "unescape",
+                "encode",
+                "decode"
             ],
-            "category": "Connectors",
-            "description": "Downloads a list of airports as provided by flightstats (https://www.flightstats.com). The script needs your flightstats app id and key which needs to be obtained either through buying their service or signing up for a test account.",
-            "subcategory": "Flightstats",
-            "icon": "data:image/png;base64,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"
+            "category": "Preparation",
+            "description": "Convert all named and numeric character references to the corresponding Unicode characters",
+            "subcategory": null,
+            "icon": null
         },
         {
-            "path": "Connectors/Flightstats/Airlines",
-            "displayName": "Flightstats Airlines",
-            "language": "R",
+            "path": "Preparation/Geo/Gridsquare",
+            "displayName": "Gridsquare",
+            "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "flightstats",
-                "airlines"
+                "gridsquare",
+                "maidenhead",
+                "coordinates",
+                "locator",
+                "latitude",
+                "longitude"
             ],
-            "category": "Connectors",
-            "description": "Downloads a list of airlines as provided by flightstats (https://www.flightstats.com). The script needs your flightstats app id and key which needs to be obtained either through buying their service or signing up for a test account.",
-            "subcategory": "Flightstats",
-            "icon": 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"
-        },
-        {
-            "path": "Connectors/Flightstats/Flights",
-            "displayName": "Flightstats Flights",
-            "language": "R",
+            "category": "Preparation",
+            "description": "Converts gridsquare / Maidenhead ",
+            "subcategory": "Geo",
+            "icon": 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"
+        },
+        {
+            "path": "Preparation/Geo/Shapefile",
+            "displayName": "Shapefile",
+            "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "flightstats",
-                "flights"
+                "shapefile",
+                "geo",
+                "geojson",
+                "shp"
             ],
-            "category": "Connectors",
-            "description": "Requests information about flights specified in the input data from flightstats (https://www.flightstats.com). If the flight exists the result will contain live information, otherwise it will not be part of it. The script needs your flightstats app id and key which needs to be obtained either through buying their service or signing up for a test account.",
-            "subcategory": "Flightstats",
-            "icon": 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"
+            "category": "Preparation",
+            "description": "Match regions in shapefile with geographical points having latitude and longitude",
+            "subcategory": "Geo",
+            "icon": 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nPKtCmfixa/92BXQ4zHAf/iZsoEMK0iaXIBhR08ARh2ZJrZ2ZRfh0ZnBx0tpd4vk2q4b0uCy94cSRBVtDNvjHO9OJKQPHfWx7scCMy7q04UQQ3XZ8IExpV3WSX9kf2pKYxg9ra6mEXdnd1ODBuGc0ZQcYW5CLup1J9XxGuUQsUDPBf5NdwwcVCZ2frTdx4jDyQERMGkwxeIxCrun7JDh5Y8uTKE4WwfPVsSXILFQk2q5K2QhJZY5IyE/QXlr0ypSziXSQg9O78NtD0wq37E4wcMMljWdwXoWU43R+Kc5Sp0pZ+V1vx3AbfmL0O1vamE819Houv+zq/kxjFsbGq7uruW5ITJTug5HqOLCWp5e8VBpW9xZVwakeIouTaKl8kpLVl1cO4Lc6ctmJYTJtUaxJ46gJl/nTzHa94iUBTTeM3YgY0aqRVKqC2ghd6Ctoxo0CF9CLq5nvNKAaf+Ggw+L4PB52XoumxU2giDxtfGnIF2PKQjLLd0bYSUd/KYeruc3FWvTXF+3jE3ovx/9OlTVxnIkcSDuDneJ/XiSOy1QMy3GTf9if2Dxw+fLUCr/61r4CP5O7fUP1HGa8zDzklU1i2ojZmVFVR0jSXxdFEImqF+lozyEVfo73j38X3v49s+wdzT4xXhzodYlQ/+91zyC3/2zjQ6JChBB66MOw6e2eZSnusFJBZG2hfQhEyhRt+KkQRNU9+hh9f8V/WWprdJ5QAClOHH1j7r5Nz75BBT0akt1RHheiBSApoQKE2aHjDykC+nfMtpORGkwtQ00w1kf26NvnhZqHdeZKXFBBdjJgvPJFJdpxbxAx4T8P2SSHKmskqX9qFSLmadLqjjRmexDYtxrmusAWKvhAbBhUNk0jMTm3/oBSS+HWBXwfBS5qt3b//48EXnOROL4+Rf6JrASPzul+kZdCKluY40hIIvD3BXwb9B4UGJq5JQpEyuRadmBklNtqUSfQZkeUc1JJ+XkWo6Ez9paQeSYluD5WRkMoEQWMfWGlniP688uvEqVYVIfYXZfjxqCTLO/ulB1qErxN+J98zECtQjO0x60RwLkLByePaT5d1kElHYkQm4c2iOpKyaxPIor+yIUrO9qJuKcISwAncRzB4RQcinNHUXO+xuByNH4lZ6WKYeFAApMC6Avba5RBauDiiPO9MAf6dY2pnEbFIzkJSYNYLq6HVZIcWD0hSZivCmaAea6pNpIlQNkgZZhGuBQ/h1+zfUQpmp+2oQXJlSy1uRzjGPEoNfx9vu6EQ/vt+nWCX56zBI05c+WjmSmBT/+CZXsX+h1tS0+nZkrnKcQ74L6uNPf4mK3GyBm3uvKo+kSElMDJ6NQqEQ2mgNCqI0QNtCHSDSfz0x17gF9bzkaPa83AxZi0mCC2oEkgAxuYq3ODOk5JK5JmNMu/GVLOcn3cBOxlXc1raqCYV9ck3Uxsyg0iPU/sIPr5216PtTmN8kNdcyvB7AHM26k4oaM/r+upiioInJEDIVvaqXieEZM2vdWmESyx8XhzgYG9DdNNPtb+UnIBs/HeF2kMJLf/Iyqn0rVoF43ObLo/zEgaXY2QS/jXU+k4qDzt7XFKGRqIMWrPWSxp04Z+rxq7e3+VhYRMGGqgoidt5w+9MptcKGg/O8favjz2FZw0fDJ5NxGfBxU30b1bia8TOInbU0QEnvfSBPunQe50LsrXSApoWQ1PGnt+T6mB2ZgUVp5p5zvIzj+gQ4tA940zWwU60cSVh0e0g8GDB1ELfQ769lmxpNonc0cIyDig8ICTbEVkRqavA0TERDQ4bZpGT7+XXtnZfeXpVx3fufr4a8MvN3Tw9F1phN+a8yU1FlXh+OGk83R5JZwVvo7i8uthMuP/KLsUAYWx9BEBp0DdBp/0IxyYQXcb0yyqlZN4TmrRYoe20qqxJPaEzSYuxciPqba4OnFQTX3hxuAlG97pFwZaempkLtfR+vZP2KEl7DiKrpzP2SoEl4Mrd1AwQ0FSD0FtnwIkjqVtFR7jnULN4JNZHruzqfzHn65PHt8rjTZrfHoa78de/Pn3Jy91d3TB1OmtpHv+TOXZkZUjbAmdZUXKjeTkJ1xJb80IrDA5QFenamG0hKQwTbb6+LmQH99vXsw+TF1mUsXnYy5fOLRAgWk3eA617dhC0IaiYtj7YxY05GxGfAVURvIMngC0u+XnQwuJRqcGVyOupq0CdzwaSzKV9qvD4MqkvZUYYy3xxQjzgnW0S2ls7eQ1oSEF7PuoiB/64bSFxgIC7KPy68vCs3rHy/nu2bpZh7KjKuVQhUb6NDrVqK4h8WxV491L2j1evjdQir8KBc39dqEhTvmmUiao36Fy50bIj94mlqKrQmGKyJGmoM2q8fuNd0KvaorKDd67Cnp7HlnQmysMDcyIp6S0xMvY/gQ6v/2Nej/NnBiSGTcRWPhxTTKSASz1YpH0TNy/Ar+5o0udCyEAgXg6mTnJ57b222WaVV6hy5MtYEAFw6sPukSITZ5+qr6RkMJMWqPBVaz7H6TdyhD0cGp9ziGd+okzXEIYY409/4KHbyv1YUYB5R/Fr0r9nBR/+XePWghj0j+sHyMbu4I5a2iEisifxbbkj5XzDLBui/YsCb5dAKAQDA709EP9+rpLPT1hXmjQQXqvfJynh1tmJ7y3XMQQdPSa9pXOoBlahlEivDl+eEVXwKLe2wMJwwc3kTE3M3gFlZYS5m+HdLXDnUx57SyGNn+pfJDQ2drZEj4SrtCGqb8PDWozczgor/NBoAJMaw5sACz6ZdX17SJbGSrzd1G+eL2YN9IT2bPW0velzs2d4YPS19/dybCCIiX0egZX1yLffPWUFl/28k5h5Z7DunPOb0t9psizSNaRCQxHaUUodOSWdLbw01EVbawj2/mzYTyQs/ts9SE16T9uqipp0tl3UBSQTkHYqz+NVRWyUhZbnqY3/z6tZn/vlKrlHZXw2lh+CDcP/bt9q7q+LOngo+vPrN2i1n9pJbrB6NQZkNA1N1LqgCKmBxbRCgDJKufNFZ6bywgFtTrUdbFimyNn2hx6ntlz/TCSQczRChziwyWAM7eU2ROHs/M/f6xqrAeUVh8lZD1HVYBPx2l3YE5lckD3spSndxsJmfzi76zMCxmL04O8drujzpwk9DTpJyxcCMlLmyrm21HOcMTq3FnVwNJcjukzEL319XcnnYZ+T249Hu3cq+lTZ0Rnv6+qKKRCnqkY2axwjJ0MkZJLWqKwC4st7Xm6Kfe4dZ3G5Mv5asG69g2b/0IbOLNNDSQ8OD7GiM+X33rz2uWaPA9UwXsTYVeTkVfXvhsYj6qq+pEkVsF9RyN2VxykY0Ha+uean/nlgdsTU3tCJf1DTw+jdUOxZNoWDohKjl12V6hp9Maa7UGfQd15O2BTXbiT2qnxjT50jWNeS+DMeJbK2LmJHDLh8xBbi+xAqp9XSw63i5k0xXr2m70ZE3MvzQ6viKLafz9e1zJMvBMKINguZb+r7wkNePFHn0qscdjzmHw6vfGskJaOrbP9vrD438C+/AzAHQWkHr+ChmnNElbP/i6Do5LMvJ90qoT7iAATgSl/zh0NMva2nkieSLx/QNIAG3VoRfFbVXElr2xnA6tESdDTms6QtuKjCTTJ1hpinno1/aK1PB+OfAB1s/aoNb8nzfF6YcYB65YYkxG9sHPEp87gZXt7i1MNxb+2PaEdbM6jrYUX9Ghh1axamK/6jeUHGcqqeEVxApKu1Olke/nhVY8i9jiWyJ+phgt+nCIKUEVCVCc011l4NRDSQne9niev6nn+sUcKiUVbNoUP0kwiI07lbwZuxnlpg1B4gpQObKWK7QXld0Dp308yPG5O6urt5x48YBdz/H7wWIBDtaKEliVJ4RQXk+S2riT4/oQ7GmyYcfXulfueVcIyp1s5VxW4fvIKpm4ri1KpyXE3KsxBQEN3UbrPSF0Yodl0thu8k1vPDMoJJBntT86qi9ji52B/avKLgJYKYFmLAGASrL+vjyiCV5EeUjBuaGQlZ8eWzTIGlyUAAkQylH4U4oNEQL5yB8IMYiLI/Gb0dDTFR6QIk0pT4qLj2w9LAeY6TjgfABwq/hjxcHWSYJWMxJrykl6y7cIc4vKEFGHlmbUh57KjNYstC9Ounzr9QGT8OemoYTjI/ysAwFBqimS6rm/mM0xNzRAyC9i8gAl5GhaFdlG0W67ieUbfzALOls/Q4u3wjoADja09qedXC6PL3VG0uqgr2A44kxqcH20BY324cfO71Imzrxhpwt79T7cXXA2Yg7tfoqaMTDeHI1GpsZLC3Qm0JWUlBQh+6XBB19bcAz1sbSgKVHouSfmGoOsceCQo9sqBv4ckIxGsbNK/GQYwBRACaNanRMphPU9FKuBhwUZjS93+3+bMvEJzee7JIES1NNNa/R1k5cTfAMBA8UCKNEdW+r5QVncEqqjBloYNZiVh+j44euWa434IsK5DbYnkYrb3VDKLxjDGDi9X8IELW9gCTVRc0BMIgMQxkiCS6ziuuGMTSHdVXvj9A3QihSJtagL2UHSw0OvRKU7zOzLuHs4HoG2JzoPRG8Teq9MulY1JwXXulre3DXgbGfmX9f77bGUMH+rVUL0TcUMJOPxjZlsnOXrgI0+2u07h7mr71d4+mI8kZv79Mv/rKd9wsLEcG3TCxVA+Q8mMcD4yjqp8dTjL50JThx15nYF99fVfCDvvXGcjkVkJTzhsxCKgI0ptqZpY0QmGJ3qK3QhBQkjKrurri2IN1v9D18m3CqBjU10GaHIogQreAChJKIyaoACsykfRDnEUvCoO4NL0zaFNVGzUvnlLYa3sLYrKGXWx02dQsn7RqK3KQPysmYGQfXjJwPymhaErpdBkbTaPFjABpeoSgAlnawGW2kIMbjn7HsResDsj+kWu/W8hBOTkRV7WglsAXHRbM+ILFASACkwrumCPShRWtipTEnSY1NgMRrD6A/LnCyn7awLOa4RbIbWBCwQV0F5nn6HN/afNb6gKRMhaoQYOcsnqGkAYdAn1XXd81O61QqmxgAMK0imqVxCwGhwcwP1gmk6roElEoAWKlMOhMwYZIWwuQfusgjP3u3zEhbb7hywziiWrZ2eMHS5ZWbL34KbxTWCSRBrwF6W1VSUKpxFkfiGVKb9Cmpcnzti83T9i08bPLIj5aFr783VSwFQBMK8ffIkRqIWfvVoqmyFuOroWgTd87/+cKVjXepihjr5khNs6VwKQyF7fCknXbnkT2mLnSc4tjz/MTO59P8qm5qqur7/pJQV5eJSGnMqUqzLjIjGg8t8ZpSHXPhjnpS7rEHJHXbVdPvwslDjdTjrquu9Ru/IK3p/MTLEk4kf5r/e3ms/3sBBceNoLNZqnKyvbfVJX2cGVK8+s81G8/8fUCQe3znGRtAUnXFBCkRoExp2LSir7N7Es2W1mM7TnnlPVZpvyG2EiCBuZ6Lpkx1+SmfLW8XVKJuWaHSFrMgYWSju+RspzS2nLCoUy1PtUVq/UCqnYWQ65i+ANx96rounV2MGShTv+TaqL+Ot7Et3R9w5NsBf6/nrswMtEw8HUNx5crZk/994+4DDwCAmN/cQ2R/p4Jp/UBCquBKAgBUJh7Q+ziphpssCSrL1EW05PqoGBoCOjICRz7tkraxRlf5vzjXqetWS7sLzfm+sxLmlFbXfVs/kBqcWXfU88IOsktHrcCia3ERv+86HfNGmm+JXqFhxgSQWA7noDa62GP0mjrqC56mcoIaNFkcLMvaegz9J92GBlwmP7P/7145d0RKEU2Exz6wXiDxa4YIiJAWWQvijjrYCEF+hzEEs6a66knTrA9IqjZHwaS3XZuDwBin2+qjfTMCi0dNcGBzLop1Gcs2nUz5tJiaDMa6gKQYHVPtYvl1vBgxu6TInMQbTW17pc12urDr24fURwPrAXKgXRGmqUmu48VmskvGnNGxrkXDlbmPk3FbYWQs8j5pHUDirx3wVQMqxl9woU+3Bq8qXYAM53d2zsqU+sSPstRdKUY3kGrGYDvlgbYwsOFYUHwbCmJgzlLWBCfHKwNyJDlCAAADHUlEQVTSY1AaGZ1ADlR806hxgHY2Rvse8DNOqPE76JFAp9s4AXpvtw0duWL/jO03vz7qmVudcJ5MKWEooc1bHqFh2hyKmQu5qeJ/H11AUv05VJEpYcA/bAJvXuQ7PbjzICWLXfZ3Y4kGRXdtbYTn+6LHtiiGneo+Mn9NwNH4UyZTvEcUsbYt/JYpaXOtoU+9FdyrLTna6AISC9YA85L1W6nvati04v31hSYNhs9KZdnYT7Kn3x13t+9l55f7YHxUePZCcGHsurDSNTMrok5iutjA9CUr5NsvkYlcQo+sCPz+9r/PLJ3l7Z4TVjEoVzO3xm+G3QPavfaH7Qse9fVii9CewQDjHRyeKic9xdrs+LFzxgcpXw6yyV2X5RHMsKHdBaDj286ZT54qWN+rknzr4XoxuoDEY+RM2njP5qur7X8Uc0p05hlZl+m5Rdnd+82EZ8YvqtzyUbo+3DooAzllJyDS3seUskOLouoqVfFumI6P7z1chtjbvTptxrPFElbNzwe+2Tlu56wDA7Kpc/KXB09ycm4+sqFhyGCMxBjZYo/5XT09Ts4uTjdl3NODXOLhw3GLG4oQoUiHAnRUAUnkshLURqVkcUqHBCVQ4ulzXNCsMfp/YN5il/r4S3c1gcrO906RJ5zPGpQ3hOIZRtzPdn8WNvHmNzaPSELiDZKxb0SA5vf8Im+G/fjb8i3n+gMSk7lSBo6AMEOB3mZYXIN+OyNA9Vobju/MqAKSCHAQVx6y6PCGKiy5Gh7n+zGVJJQzjnQcgql2YRmqIVZwwerfVcd+SHqJsfNW7Jk7b5rYqVHWOUSYF9jOwJyOODDO9/9Nk/CbB708EEIIlu4XBpUnPi1BnYikiFi+Sjw1lLGJQUcVkPj8MUIy81+ym79qnjJttryTX8fbI2aXYDFh/cVL13fddmxycbtLk6GtGs0d4Ypvc/Wgw3DSqHSa/S+/uG6xsen9pG7LJVWchKHOHPVHak2uEgO3YtogDjKh55k+RwUsM7qAHKiCw8wbIZe6u7vTibQNMJ3CAEdY9Zlq2CKJIup2LvoGfteXmCNZ7v8Aoyh3BhJ+26EAAAAASUVORK5CYII="
         },
         {
-            "path": "Connectors/Flipside",
-            "displayName": "Flipside",
+            "path": "Preparation/Split Address",
+            "displayName": "Split Address",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "flipside",
-                "crypto",
-                "analytics",
-                "blockchain"
+                "split",
+                "address",
+                "street",
+                "zip",
+                "country"
             ],
-            "category": "Connectors",
-            "description": "Executes a SQL query on Flipside and retrieves the blockchain data",
+            "category": "Preparation",
+            "description": "Splits an address field into streetname, streetnumber, and suffix.",
             "subcategory": null,
-            "icon": "data:image/png;base64,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"
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"
         },
         {
-            "path": "Connectors/Dune",
-            "displayName": "Dune",
-            "language": "PYTHON",
+            "path": "Preparation/Standardisation/Standardise",
+            "displayName": "Standardise",
+            "language": "R",
             "optionsVersion": 1,
             "tags": [
-                "dune",
-                "crypto",
-                "analytics",
-                "blockchain"
+                "standardise",
+                "normalise",
+                "range",
+                "interval"
             ],
-            "category": "Connectors",
-            "description": "Execute queries and retrieve blockchain data from any public query on dune.com, as well as any personal private queries your Dune account has access to",
-            "subcategory": null,
-            "icon": "https://dune.com/docs/images/dune-icon-only.png"
+            "category": "Preparation",
+            "description": "Standardises the values in the selected fields so that they are in the range between 0 and 1. I.e. The new value of the highest value in each field is going to be 1, and the lowest value 0. All other values are scaled proportionally.",
+            "subcategory": "Standardisation",
+            "icon": "data:image/png;base64,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"
         },
         {
-            "path": "Connectors/Azure Data Lake Blob",
-            "displayName": "Azure Data Lake Storage Gen2 Blob",
+            "path": "Preparation/Unstack rows",
+            "displayName": "Unstack Records",
             "language": "PYTHON",
             "optionsVersion": 1,
-            "tags": [
-                "azure",
-                "storage",
-                "lake",
-                "datalake",
-                "blob",
-                "gen2",
-                "parquet",
-                "csv"
-            ],
-            "category": "Connectors",
-            "description": "Storage Gen2 Blob connector to load a CSV or Parquet blob/file in Omniscope.",
-            "subcategory": "Azure",
-            "icon": 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"
+            "tags": [],
+            "category": "Preparation",
+            "description": "Unstack all records by splitting on text fields with stacked values, filling records with empty strings where needed.",
+            "subcategory": null,
+            "icon": "data:image/png;base64,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"
         },
         {
             "path": "Preparation/Interfaces/Kedro",
@@ -280,288 +285,184 @@
             "icon": 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RYjsizPTKVSb0QJWmTA2VSbw6lU6jqvsIzrFUX5CkJob9SgRQocaeVFGVrkwNn0eVtpmr7erfIURfkqQuiVWqXpwbB577bbe7fqutANwN04blZtiqLoCp6iKF9DCL0UZWiRVJwB1mKoMIwQsuzzOgVapMHZpM2baJpeX69cRVFOQwjt0TStDx88UD3eMVLpMRIzJ25Spk3BciNCaPzsaEVRTkUIvRj19NhR4KzgybJ8daVSuSCRSFyJEHq5k6BFPlXWfgPr+jz9VSc1/SF+RFSOenrsOMVZFCz4XQkYnn7uZidB6yjFWcDT/xyl8Znbvj2S4zi3znXydTG4iNL9P0EznFNmWi5QAAAAAElFTkSuQmCC"
         },
         {
-            "path": "Preparation/Standardisation/Standardise",
-            "displayName": "Standardise",
-            "language": "R",
+            "path": "Preparation/Partition",
+            "displayName": "Partition",
+            "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "standardise",
-                "normalise",
-                "range",
-                "interval"
+                "partition",
+                "division",
+                "grouping"
             ],
             "category": "Preparation",
-            "description": "Standardises the values in the selected fields so that they are in the range between 0 and 1. I.e. The new value of the highest value in each field is going to be 1, and the lowest value 0. All other values are scaled proportionally.",
-            "subcategory": "Standardisation",
-            "icon": "data:image/png;base64,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"
+            "description": "Partitions the data into chunks of the desired size. There will be a new field called \"Partition\" which contains a number unique to each partition.",
+            "subcategory": "Partition",
+            "icon": 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"
         },
         {
-            "path": "Preparation/Join/Interval Join",
-            "displayName": "Interval Join",
+            "path": "Analytics/Data Profiler",
+            "displayName": "Data Profiler",
             "language": "PYTHON",
             "optionsVersion": 1,
-            "tags": [
-                "inequality",
-                "interval",
-                "join"
-            ],
-            "category": "Preparation",
-            "description": "Performs a join between values in the first input and intervals in the second input. Rows are joined if the value is contained in an interval.",
-            "subcategory": "Join",
-            "icon": "data:image/png;base64,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"
+            "tags": [],
+            "category": "Analytics",
+            "description": "Provides detailed statistics about a dataset",
+            "subcategory": null,
+            "icon": "data:image/png;base64,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"
         },
         {
-            "path": "Preparation/Join/Inequality Join",
-            "displayName": "Inequality Join",
+            "path": "Analytics/Network Analysis/Attribute Analysis",
+            "displayName": "Attribute Analysis",
             "language": "R",
             "optionsVersion": 1,
             "tags": [
-                "inequality",
-                "join"
+                "attribute",
+                "analysis",
+                "network",
+                "graph"
             ],
-            "category": "Preparation",
-            "description": "Performs a join between the first (left) and second (right) input. The join can be performed using equality/inequality comparators ==, <=, >=, <, > , which means the result will be a constraint cartesian join including all records that match the inequalities.",
-            "subcategory": "Join",
-            "icon": "data:image/png;base64,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"
+            "category": "Analytics",
+            "description": "Given a dataset in which each record represents an edge between two nodes of a network, and each node has an associated categorical attribute, the block analyses connections between attributes, based on connections between associated nodes. The result of the analysis is a list of records in which each record specifies a connection from one attribute to another. The connection contains a probability field, which gives an answer to the question that if a node has the specified categorical attribute, how probable it is that it has a connection to another node with the linked categorical attribute.",
+            "subcategory": "Network Analysis",
+            "icon": 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"
         },
         {
-            "path": "Preparation/Join/Fuzzy Join",
-            "displayName": "Fuzzy Terms Join",
+            "path": "Analytics/Network Analysis/TSNE",
+            "displayName": "TSNE",
             "language": "R",
             "optionsVersion": 1,
             "tags": [
-                "fuzzy",
-                "join"
+                "relationship",
+                "analysis",
+                "network",
+                "graph"
             ],
-            "category": "Preparation",
-            "description": "Performs a join between the first (left) and second (right) input. The field on which the join is performed must be text containing multiple terms. The result will contain joined records based on how many terms they share, weighted by inverse document frequency.",
-            "subcategory": "Join",
-            "icon": "data:image/png;base64,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"
+            "category": "Analytics",
+            "description": "Given a dataset in which each record represents an edge between two nodes of a network, the block will project all the nodes onto a (e.g. 2)- dimensional plane in such a way that nodes which share many connections are close together, and nodes that do not share many connections are far apart.",
+            "subcategory": "Network Analysis",
+            "icon": "data:image/png;base64,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"
         },
         {
-            "path": "Preparation/Geo/Shapefile",
-            "displayName": "Shapefile",
-            "language": "PYTHON",
+            "path": "Analytics/Clustering/GMM",
+            "displayName": "Gaussian Mixture Model",
+            "language": "R",
             "optionsVersion": 1,
             "tags": [
-                "shapefile",
-                "geo",
-                "geojson",
-                "shp"
+                "gmm",
+                "gaussian",
+                "mixture",
+                "model"
             ],
-            "category": "Preparation",
-            "description": "Match regions in shapefile with geographical points having latitude and longitude",
-            "subcategory": "Geo",
-            "icon": 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nPKtCmfixa/92BXQ4zHAf/iZsoEMK0iaXIBhR08ARh2ZJrZ2ZRfh0ZnBx0tpd4vk2q4b0uCy94cSRBVtDNvjHO9OJKQPHfWx7scCMy7q04UQQ3XZ8IExpV3WSX9kf2pKYxg9ra6mEXdnd1ODBuGc0ZQcYW5CLup1J9XxGuUQsUDPBf5NdwwcVCZ2frTdx4jDyQERMGkwxeIxCrun7JDh5Y8uTKE4WwfPVsSXILFQk2q5K2QhJZY5IyE/QXlr0ypSziXSQg9O78NtD0wq37E4wcMMljWdwXoWU43R+Kc5Sp0pZ+V1vx3AbfmL0O1vamE819Houv+zq/kxjFsbGq7uruW5ITJTug5HqOLCWp5e8VBpW9xZVwakeIouTaKl8kpLVl1cO4Lc6ctmJYTJtUaxJ46gJl/nTzHa94iUBTTeM3YgY0aqRVKqC2ghd6Ctoxo0CF9CLq5nvNKAaf+Ggw+L4PB52XoumxU2giDxtfGnIF2PKQjLLd0bYSUd/KYeruc3FWvTXF+3jE3ovx/9OlTVxnIkcSDuDneJ/XiSOy1QMy3GTf9if2Dxw+fLUCr/61r4CP5O7fUP1HGa8zDzklU1i2ojZmVFVR0jSXxdFEImqF+lozyEVfo73j38X3v49s+wdzT4xXhzodYlQ/+91zyC3/2zjQ6JChBB66MOw6e2eZSnusFJBZG2hfQhEyhRt+KkQRNU9+hh9f8V/WWprdJ5QAClOHH1j7r5Nz75BBT0akt1RHheiBSApoQKE2aHjDykC+nfMtpORGkwtQ00w1kf26NvnhZqHdeZKXFBBdjJgvPJFJdpxbxAx4T8P2SSHKmskqX9qFSLmadLqjjRmexDYtxrmusAWKvhAbBhUNk0jMTm3/oBSS+HWBXwfBS5qt3b//48EXnOROL4+Rf6JrASPzul+kZdCKluY40hIIvD3BXwb9B4UGJq5JQpEyuRadmBklNtqUSfQZkeUc1JJ+XkWo6Ez9paQeSYluD5WRkMoEQWMfWGlniP688uvEqVYVIfYXZfjxqCTLO/ulB1qErxN+J98zECtQjO0x60RwLkLByePaT5d1kElHYkQm4c2iOpKyaxPIor+yIUrO9qJuKcISwAncRzB4RQcinNHUXO+xuByNH4lZ6WKYeFAApMC6Avba5RBauDiiPO9MAf6dY2pnEbFIzkJSYNYLq6HVZIcWD0hSZivCmaAea6pNpIlQNkgZZhGuBQ/h1+zfUQpmp+2oQXJlSy1uRzjGPEoNfx9vu6EQ/vt+nWCX56zBI05c+WjmSmBT/+CZXsX+h1tS0+nZkrnKcQ74L6uNPf4mK3GyBm3uvKo+kSElMDJ6NQqEQ2mgNCqI0QNtCHSDSfz0x17gF9bzkaPa83AxZi0mCC2oEkgAxuYq3ODOk5JK5JmNMu/GVLOcn3cBOxlXc1raqCYV9ck3Uxsyg0iPU/sIPr5216PtTmN8kNdcyvB7AHM26k4oaM/r+upiioInJEDIVvaqXieEZM2vdWmESyx8XhzgYG9DdNNPtb+UnIBs/HeF2kMJLf/Iyqn0rVoF43ObLo/zEgaXY2QS/jXU+k4qDzt7XFKGRqIMWrPWSxp04Z+rxq7e3+VhYRMGGqgoidt5w+9MptcKGg/O8favjz2FZw0fDJ5NxGfBxU30b1bia8TOInbU0QEnvfSBPunQe50LsrXSApoWQ1PGnt+T6mB2ZgUVp5p5zvIzj+gQ4tA940zWwU60cSVh0e0g8GDB1ELfQ769lmxpNonc0cIyDig8ICTbEVkRqavA0TERDQ4bZpGT7+XXtnZfeXpVx3fufr4a8MvN3Tw9F1phN+a8yU1FlXh+OGk83R5JZwVvo7i8uthMuP/KLsUAYWx9BEBp0DdBp/0IxyYQXcb0yyqlZN4TmrRYoe20qqxJPaEzSYuxciPqba4OnFQTX3hxuAlG97pFwZaempkLtfR+vZP2KEl7DiKrpzP2SoEl4Mrd1AwQ0FSD0FtnwIkjqVtFR7jnULN4JNZHruzqfzHn65PHt8rjTZrfHoa78de/Pn3Jy91d3TB1OmtpHv+TOXZkZUjbAmdZUXKjeTkJ1xJb80IrDA5QFenamG0hKQwTbb6+LmQH99vXsw+TF1mUsXnYy5fOLRAgWk3eA617dhC0IaiYtj7YxY05GxGfAVURvIMngC0u+XnQwuJRqcGVyOupq0CdzwaSzKV9qvD4MqkvZUYYy3xxQjzgnW0S2ls7eQ1oSEF7PuoiB/64bSFxgIC7KPy68vCs3rHy/nu2bpZh7KjKuVQhUb6NDrVqK4h8WxV491L2j1evjdQir8KBc39dqEhTvmmUiao36Fy50bIj94mlqKrQmGKyJGmoM2q8fuNd0KvaorKDd67Cnp7HlnQmysMDcyIp6S0xMvY/gQ6v/2Nej/NnBiSGTcRWPhxTTKSASz1YpH0TNy/Ar+5o0udCyEAgXg6mTnJ57b222WaVV6hy5MtYEAFw6sPukSITZ5+qr6RkMJMWqPBVaz7H6TdyhD0cGp9ziGd+okzXEIYY409/4KHbyv1YUYB5R/Fr0r9nBR/+XePWghj0j+sHyMbu4I5a2iEisifxbbkj5XzDLBui/YsCb5dAKAQDA709EP9+rpLPT1hXmjQQXqvfJynh1tmJ7y3XMQQdPSa9pXOoBlahlEivDl+eEVXwKLe2wMJwwc3kTE3M3gFlZYS5m+HdLXDnUx57SyGNn+pfJDQ2drZEj4SrtCGqb8PDWozczgor/NBoAJMaw5sACz6ZdX17SJbGSrzd1G+eL2YN9IT2bPW0velzs2d4YPS19/dybCCIiX0egZX1yLffPWUFl/28k5h5Z7DunPOb0t9psizSNaRCQxHaUUodOSWdLbw01EVbawj2/mzYTyQs/ts9SE16T9uqipp0tl3UBSQTkHYqz+NVRWyUhZbnqY3/z6tZn/vlKrlHZXw2lh+CDcP/bt9q7q+LOngo+vPrN2i1n9pJbrB6NQZkNA1N1LqgCKmBxbRCgDJKufNFZ6bywgFtTrUdbFimyNn2hx6ntlz/TCSQczRChziwyWAM7eU2ROHs/M/f6xqrAeUVh8lZD1HVYBPx2l3YE5lckD3spSndxsJmfzi76zMCxmL04O8drujzpwk9DTpJyxcCMlLmyrm21HOcMTq3FnVwNJcjukzEL319XcnnYZ+T249Hu3cq+lTZ0Rnv6+qKKRCnqkY2axwjJ0MkZJLWqKwC4st7Xm6Kfe4dZ3G5Mv5asG69g2b/0IbOLNNDSQ8OD7GiM+X33rz2uWaPA9UwXsTYVeTkVfXvhsYj6qq+pEkVsF9RyN2VxykY0Ha+uean/nlgdsTU3tCJf1DTw+jdUOxZNoWDohKjl12V6hp9Maa7UGfQd15O2BTXbiT2qnxjT50jWNeS+DMeJbK2LmJHDLh8xBbi+xAqp9XSw63i5k0xXr2m70ZE3MvzQ6viKLafz9e1zJMvBMKINguZb+r7wkNePFHn0qscdjzmHw6vfGskJaOrbP9vrD438C+/AzAHQWkHr+ChmnNElbP/i6Do5LMvJ90qoT7iAATgSl/zh0NMva2nkieSLx/QNIAG3VoRfFbVXElr2xnA6tESdDTms6QtuKjCTTJ1hpinno1/aK1PB+OfAB1s/aoNb8nzfF6YcYB65YYkxG9sHPEp87gZXt7i1MNxb+2PaEdbM6jrYUX9Ghh1axamK/6jeUHGcqqeEVxApKu1Olke/nhVY8i9jiWyJ+phgt+nCIKUEVCVCc011l4NRDSQne9niev6nn+sUcKiUVbNoUP0kwiI07lbwZuxnlpg1B4gpQObKWK7QXld0Dp308yPG5O6urt5x48YBdz/H7wWIBDtaKEliVJ4RQXk+S2riT4/oQ7GmyYcfXulfueVcIyp1s5VxW4fvIKpm4ri1KpyXE3KsxBQEN3UbrPSF0Yodl0thu8k1vPDMoJJBntT86qi9ji52B/avKLgJYKYFmLAGASrL+vjyiCV5EeUjBuaGQlZ8eWzTIGlyUAAkQylH4U4oNEQL5yB8IMYiLI/Gb0dDTFR6QIk0pT4qLj2w9LAeY6TjgfABwq/hjxcHWSYJWMxJrykl6y7cIc4vKEFGHlmbUh57KjNYstC9Ounzr9QGT8OemoYTjI/ysAwFBqimS6rm/mM0xNzRAyC9i8gAl5GhaFdlG0W67ieUbfzALOls/Q4u3wjoADja09qedXC6PL3VG0uqgr2A44kxqcH20BY324cfO71Imzrxhpwt79T7cXXA2Yg7tfoqaMTDeHI1GpsZLC3Qm0JWUlBQh+6XBB19bcAz1sbSgKVHouSfmGoOsceCQo9sqBv4ckIxGsbNK/GQYwBRACaNanRMphPU9FKuBhwUZjS93+3+bMvEJzee7JIES1NNNa/R1k5cTfAMBA8UCKNEdW+r5QVncEqqjBloYNZiVh+j44euWa434IsK5DbYnkYrb3VDKLxjDGDi9X8IELW9gCTVRc0BMIgMQxkiCS6ziuuGMTSHdVXvj9A3QihSJtagL2UHSw0OvRKU7zOzLuHs4HoG2JzoPRG8Teq9MulY1JwXXulre3DXgbGfmX9f77bGUMH+rVUL0TcUMJOPxjZlsnOXrgI0+2u07h7mr71d4+mI8kZv79Mv/rKd9wsLEcG3TCxVA+Q8mMcD4yjqp8dTjL50JThx15nYF99fVfCDvvXGcjkVkJTzhsxCKgI0ptqZpY0QmGJ3qK3QhBQkjKrurri2IN1v9D18m3CqBjU10GaHIogQreAChJKIyaoACsykfRDnEUvCoO4NL0zaFNVGzUvnlLYa3sLYrKGXWx02dQsn7RqK3KQPysmYGQfXjJwPymhaErpdBkbTaPFjABpeoSgAlnawGW2kIMbjn7HsResDsj+kWu/W8hBOTkRV7WglsAXHRbM+ILFASACkwrumCPShRWtipTEnSY1NgMRrD6A/LnCyn7awLOa4RbIbWBCwQV0F5nn6HN/afNb6gKRMhaoQYOcsnqGkAYdAn1XXd81O61QqmxgAMK0imqVxCwGhwcwP1gmk6roElEoAWKlMOhMwYZIWwuQfusgjP3u3zEhbb7hywziiWrZ2eMHS5ZWbL34KbxTWCSRBrwF6W1VSUKpxFkfiGVKb9Cmpcnzti83T9i08bPLIj5aFr783VSwFQBMK8ffIkRqIWfvVoqmyFuOroWgTd87/+cKVjXepihjr5khNs6VwKQyF7fCknXbnkT2mLnSc4tjz/MTO59P8qm5qqur7/pJQV5eJSGnMqUqzLjIjGg8t8ZpSHXPhjnpS7rEHJHXbVdPvwslDjdTjrquu9Ru/IK3p/MTLEk4kf5r/e3ms/3sBBceNoLNZqnKyvbfVJX2cGVK8+s81G8/8fUCQe3znGRtAUnXFBCkRoExp2LSir7N7Es2W1mM7TnnlPVZpvyG2EiCBuZ6Lpkx1+SmfLW8XVKJuWaHSFrMgYWSju+RspzS2nLCoUy1PtUVq/UCqnYWQ65i+ANx96rounV2MGShTv+TaqL+Ot7Et3R9w5NsBf6/nrswMtEw8HUNx5crZk/994+4DDwCAmN/cQ2R/p4Jp/UBCquBKAgBUJh7Q+ziphpssCSrL1EW05PqoGBoCOjICRz7tkraxRlf5vzjXqetWS7sLzfm+sxLmlFbXfVs/kBqcWXfU88IOsktHrcCia3ERv+86HfNGmm+JXqFhxgSQWA7noDa62GP0mjrqC56mcoIaNFkcLMvaegz9J92GBlwmP7P/7145d0RKEU2Exz6wXiDxa4YIiJAWWQvijjrYCEF+hzEEs6a66knTrA9IqjZHwaS3XZuDwBin2+qjfTMCi0dNcGBzLop1Gcs2nUz5tJiaDMa6gKQYHVPtYvl1vBgxu6TInMQbTW17pc12urDr24fURwPrAXKgXRGmqUmu48VmskvGnNGxrkXDlbmPk3FbYWQs8j5pHUDirx3wVQMqxl9woU+3Bq8qXYAM53d2zsqU+sSPstRdKUY3kGrGYDvlgbYwsOFYUHwbCmJgzlLWBCfHKwNyJDlCAAADHUlEQVTSY1AaGZ1ADlR806hxgHY2Rvse8DNOqPE76JFAp9s4AXpvtw0duWL/jO03vz7qmVudcJ5MKWEooc1bHqFh2hyKmQu5qeJ/H11AUv05VJEpYcA/bAJvXuQ7PbjzICWLXfZ3Y4kGRXdtbYTn+6LHtiiGneo+Mn9NwNH4UyZTvEcUsbYt/JYpaXOtoU+9FdyrLTna6AISC9YA85L1W6nvati04v31hSYNhs9KZdnYT7Kn3x13t+9l55f7YHxUePZCcGHsurDSNTMrok5iutjA9CUr5NsvkYlcQo+sCPz+9r/PLJ3l7Z4TVjEoVzO3xm+G3QPavfaH7Qse9fVii9CewQDjHRyeKic9xdrs+LFzxgcpXw6yyV2X5RHMsKHdBaDj286ZT54qWN+rknzr4XoxuoDEY+RM2njP5qur7X8Uc0p05hlZl+m5Rdnd+82EZ8YvqtzyUbo+3DooAzllJyDS3seUskOLouoqVfFumI6P7z1chtjbvTptxrPFElbNzwe+2Tlu56wDA7Kpc/KXB09ycm4+sqFhyGCMxBjZYo/5XT09Ts4uTjdl3NODXOLhw3GLG4oQoUiHAnRUAUnkshLURqVkcUqHBCVQ4ulzXNCsMfp/YN5il/r4S3c1gcrO906RJ5zPGpQ3hOIZRtzPdn8WNvHmNzaPSELiDZKxb0SA5vf8Im+G/fjb8i3n+gMSk7lSBo6AMEOB3mZYXIN+OyNA9Vobju/MqAKSCHAQVx6y6PCGKiy5Gh7n+zGVJJQzjnQcgql2YRmqIVZwwerfVcd+SHqJsfNW7Jk7b5rYqVHWOUSYF9jOwJyOODDO9/9Nk/CbB708EEIIlu4XBpUnPi1BnYikiFi+Sjw1lLGJQUcVkPj8MUIy81+ym79qnjJttryTX8fbI2aXYDFh/cVL13fddmxycbtLk6GtGs0d4Ypvc/Wgw3DSqHSa/S+/uG6xsen9pG7LJVWchKHOHPVHak2uEgO3YtogDjKh55k+RwUsM7qAHKiCw8wbIZe6u7vTibQNMJ3CAEdY9Zlq2CKJIup2LvoGfteXmCNZ7v8Aoyh3BhJ+26EAAAAASUVORK5CYII="
+            "category": "Analytics",
+            "description": "Performs GMM clustering on the first input data provided. The output consists of the original input with a Cluster field appended. If a second input is available, it will be used as output instead.",
+            "subcategory": "Clustering",
+            "icon": 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"
         },
         {
-            "path": "Preparation/Geo/Gridsquare",
-            "displayName": "Gridsquare",
+            "path": "Analytics/Clustering/DBScan",
+            "displayName": "DBScan",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "gridsquare",
-                "maidenhead",
-                "coordinates",
-                "locator",
-                "latitude",
-                "longitude"
+                "dbscan",
+                "clustering"
             ],
-            "category": "Preparation",
-            "description": "Converts gridsquare / Maidenhead ",
-            "subcategory": "Geo",
-            "icon": 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"
+            "category": "Analytics",
+            "description": "Performs DBScan clustering on the first input data provided. The output consists of the original input with a Cluster field appended. If a second input is available, it will be used as output instead.",
+            "subcategory": "Clustering",
+            "icon": 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"
         },
         {
-            "path": "Preparation/JSON/Normalise",
-            "displayName": "JSON Normalise",
-            "language": "PYTHON",
+            "path": "Analytics/Clustering/KMeans",
+            "displayName": "KMeans",
+            "language": "R",
             "optionsVersion": 1,
             "tags": [
-                "JSON",
-                "normalise",
-                "table",
-                "semi",
-                "structured"
+                "kmeans"
             ],
-            "category": "Preparation",
-            "description": "Normalise semi-structured JSON strings into a flat table, appending data record by record.",
-            "subcategory": "JSON",
-            "icon": "data:image/png;base64,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"
+            "category": "Analytics",
+            "description": "Performs KMeans clustering on the first input data provided. The output consists of the original input with a Cluster field appended. If a second input is available, it will be used as output instead.",
+            "subcategory": "Clustering",
+            "icon": 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"
         },
         {
-            "path": "Preparation/Pivot/Melt De-pivot",
-            "displayName": "Melt De-pivot",
+            "path": "Analytics/Validation/Model Validation",
+            "displayName": "Model Validation",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "pivot",
-                "depivot",
-                "melt",
-                "variable",
-                "fixed"
+                "model",
+                "validation",
+                "confusion",
+                "matrix",
+                "precision",
+                "accuracy",
+                "fscore"
             ],
-            "category": "Preparation",
-            "description": "Keep all selected fixed fields in the output, de-pivot all other fields",
-            "subcategory": "Pivot",
-            "icon": "data:image/png;base64,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"
+            "category": "Analytics",
+            "description": "Computes a confusion matrix as well as model validation statistics",
+            "subcategory": "Validation",
+            "icon": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAFQAAABUCAYAAAAcaxDBAAAABGdBTUEAALGPC/xhBQAAACBjSFJNAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAABmJLR0QA/wD/AP+gvaeTAAAAB3RJTUUH5gYWDC8g1pNOHQAAFC5JREFUeNrtnXmUXFWdxz/3vqrurk5CJySQhYRNTCIEQlhkyIkRNI4LLgiIKMhiBnA5ggyKjjoLozOjZnRURkVkGAEJwZFdwAgoYUkIIYQkhOxbp7On0+nudG1v+c4f772q150K3Uk61c05/s55p6tfvXfv737vb7nL7/4KekD5vJCEJLOsScbz5UhKgcyjj4qrrlaN7+m8oqtPtLTqmoWL9ZVdzXpJkoJAvvoJxbzsbNbcBYv1lZZWXV1w9XHP03svvkQ1s2aJseNkJKX8QI4kWvbISDJ/eFk9gQrT3QNzX5dZuQENzGA++l4sIqirA0BzX+OsyWfwmV0tHHtkAxdbi5HAdFtq/6CY1yBAzS08dNRQNjw3l1nnTeZVwLguBML+8HaCrTvQxR/GTJti3hLZ/TZ9T0401GEAeQGOBWMtAnw/4GZrmF4oMqiultESGPDVuVxjTP8COOZFQlC6wkaCYwzk8myuraENwy+t4TbACYS1Bh8I4sfNfhpV8e6WDhmvFsnHpNowo4YBEHge73UcngQyEVgCPMASMSQRxIweavsBawxWUWmJ8uOGHSqZRPk+4GNII4wxsGINs4YN4wdbtvP6ySeRzhXwBtUjwLy2Gp05dl8W9rmzdK1M0UMIc/LxmLpaAmB0Ns8/1qT5VMphSAI0G/YWSNj+IokHSpHkBhGwhqjDjMF6PruKRR6oz/ADYFPRxalJ4wPm1SVw9sTOJqATBEs2ilOPjTRAWAzBlt1MGDmE/zOG8RI+kSRGjHjGkJJAwrWW9PLVzBS8cMxI2lIpsiZkjqgDTKleE/1VSZrj741jUN4l25HlklFHc13UgRiDXb+Je4Ycwb2ZDPV+WLIsKIjejco2VBAWAwQBxvOo37qDAcYwZdyJXBEEFI2hJhIMLwLTibUhauOinXu4IBBbHYtz1OAQ1P1JKgCFgoznid1tSq3ZLJp26gzX0/bIQ+aDoJO39Iqu3GxOb0r6yxurdYYrDVKHbDwiOBhp+d1TMlJYxrpNuiaqzwsCeZK0dLluiMq3/3P/wdUhyUhCRdm8NHDFBp0u6S/5vJa6nopxG4NAiq68JHm+1jbt1AmN20Vzm5ztu8X23WFZ+62k6Cq1a49oadNo19PqqHA3+hvEFUnS7j36BqgexBe+GTGpEiDOwVzRkCwtiaZt+lJXQFet0y1RPSnXO7g6osvGbf7Kt2UiH1W3q1k37mzWa4n2BkkMXE8r3lijYwMv5KGr8BiABUtkAA0fih0ziiCbZXAmwxzgtEjkbVL8PZ+Zvs+M2hqW7dmLd8QAUuub8N8xBj3zPGxthkmnglLgKNR5CawJHUvyCoLwu/j7lhaYPCnUzqZtXD96BLdHpgZjcFau45ZxJzIDsLNfQGOOgboA8jbUfceAteXyYzsSe3jPA78Ary+H0UfB1MmwcQvm2BE41uIbg045g7qlr3KXH3CZY0OnG2NgDNYPWLljD+cfdSRb12/ENjaFNnfaFCPufTC8gqCkqoODQEsiUS9JpqSgI6t239ftuXCg77TsUfpHd8qCmDBJZuOeg1PBJD0zt6zym7bq+q4SumKtvh6r/COzD72+5maZaZ8JJfQXv5FpbVf63ReE///mAX2kI6um5KQgCORG2LwhaYgUYpfLiVxOIImf/UYmVrNsTjNim5ksKJtT430PakrUGMfzlGprk3FdkcsdesNKgM5LALpN170VoA/3AqAxvbywpPZm3mslk8WTz2pioaA1XUDNS1JHVj+IeEl//usymzbJcPu9MvHL2ZwmeZ5cSUHUC0H04u7HZmvyn+eJXS2qlURLWzglW7y6N4acZXr2pQOQ0D/2HqAA67ar7LAk89ICpV9eKF5ZpNM6stqWsKuSFLie8h1ZTWltF/mCrOfJpM6eCMbgS7B9JzccP4ZUZCtKzGbq+N1HP8DcjZtIP/okxfj+9MtN76J5gNS7cMKJww2AHnpCZsMm9OZKvIsuIOU4LHlsNtdc8lGeSKVCXCRMyqHWqeMJYIQx5ABjn5sHDzwGP76DU8eM4nIIx3sSgYTxPGYZwxeWvolZsxGvuRVl6jGHC0zZxD+VAVOFT71KF11gNO6dGN+gugz+6SdjLruQp6zlt5EDDRJT2CMkrg0CCAJs6ppPk7IWNwj4D2NIA0E065EEjsNtAKeMx57mEGxownz2k4dPMm0CRFMBMlOeCGAPo35c8H6jh56Qmb8QGgZhfno3qq/jpusvZ/ywIzk7MdkwEp+2lp8BNrV6A27K4dQJ4zjL2lCUjcF3PZz2dr5VdJlbV0c6n8UbMQKu/ezhVXPTeYVl37pM+V7QoxIPni66wCgaYwbjT6LG9Wh2LPf4Pmc7DpKwAPkCY196lXM9j3n23RNh0gQuSacYLuESNsIpFlk29Eh+MnI4zPgl/rY96PUVh0vJyhQkalA3gPbK8kg3tGoret93YcbPcRsGYQcO4L89n0WAQzhIcuszDJs2hY98+HxIAbS1c0LDoJJ0IkGmjl1+QA6RLhRwAVNff/idUHcVJFXeHG4RBcaNMjyzOJz4BAEpYwhq0rQmlgItwPZdTBh5NDXW95l6xEA+R7h8lSI0Tf6SldzcnoMde/BvvKk6YAJhv8dk3xrf3vby+6NpE41efQHTnsfLu7BuM18UFIiWLQH/6KFc6Lr8jfVhcCyV8SqSMbinjmd5Qz2MOBLd/L3qMN4jkBKjADlUjS7/BDRkIJOGE0ey0caL0xF21oK1pGz7XoaWGIzkQQFe4CPPC+fa44+q3njT6bLs319ozBijBUvAWDAOufVNzIwwKxmetr2Msm1tDE/w7wOs28j30ilyqRT2mReqy3i3Xl6Vn60GrW0s20xgToIjA7BsBaNs0xbqur7YMJhNMc+1g6rLdLcqnwTUry5vZ00KqwUYcTRtia8MwInHcak98zSui26WrFNtTXl6ecSA6jJ9QGpeZZvgJuy3sRSij4qn6SOHc6atzzASuuxMJgfPtvuKqkx9Z1qTw7Sy6puS7xGBjeajdFrFD8pMW6/P2O+Wqr0p6CRHFckpcnl0ZG0srvtjzvYnV9vHpB44wYoKbRL4VxvPPl0P7IZ6ohEVAU2aCq/KNrRvV1gPnSrClRy6+FUW0WpNJw8XdSt/1W5f0Pnz2w7e/QFatqHVltA+BKM3qFsJ7Yln61U6gA6sNmsH7eU7ta/KEmo6D57f+tkqI3rQXr4TVZvpzoDu04RO849qm6PekNBqe13TeZOuUu195qiCzp1dEd5UxTcTj+7ZCY8/IyNT3mWUDZ+xivaAnNA72+he3Akm+uxHyNgg7EE/ijsiCKXMRJtHxkAhMdUNugHUMfD4sypt2xBvS9ho0Te6fEIe42ecJI+E9wMb/mP9KOzRhvyKiEegqRHeNfqtO7YioLYsueZ952BsFQf3nhezUIpU7kSJTTzzwfOhJl093hIBuVhbGdCKUOWLZOMy7vsDMsYE1boWvRkKBUDaYR+4alKle5ozr7q83TGzHJefz5WWODtHMCsRLRqH661t5LdFl5dqa6hXEDYuWpYOt/8sQiXNV7zcZyqYXJVrNCiMMlSpbkylMw0CpVKkjxvFrcZQ3yXGPrdpC98puHiOCd/vxJ8JXVl0kEIm5tGWrJ4qSVEQR1UHIY+BwZgg5DXmMQz6xxSL5AfUc/aYUVwTY1YR0L/SoVMnG5rYa+6tkxyHSk5X8U2c2OhrKp0gSfLY/9bj3+b0V5XvZeqq8oEx2HWN3FdwebE2TQaFI5WkYS4ZexOeUYqCkHraMaZUUOWZkBEoZak5bjS3GkOmq1Nq3Mw/uh6uTTil/VGS39I9i2LPKAMmKDu27g7+GYMtuGQH1nP2mJF8vqtTosvhUk+StjfrY3Ho9x0PVneu9PKicgTzlh36ciKC2ZekNev1rTiC+ennqsvb5f9UjvZu3av3JU+HxFRxYF9XS33cIdMvxFx7kGeODoaSA3vfw018pej7+J6Zei4HfR7qYCg5sM/Ukon5SD5TEdCg7EP13DyUyyemiNFfSzg9K0376BwftT+G4uMuKAxU6DpNddLwoanRDMlUUOfyHc2eg4yNeKpYYYLfhIrLKQ+GZUI+jMI2xgPX0rGc8siHugxMOzfsWF9QaZJWeS6f4K5hCLz/lOotlD3zckLiTAW7nOAtEFw4rXq8rd7SSRt6PvWku7cOIyXDvG1lR1cOwuiHi9/9bhyaHMR1h1fV8ewM6MFJaLWpu1XxZEReFcNDe0zdLzBXmaEDmWZUewukC28Va+/ehvbDfZtSi96WWyBVNgo9kYKY+jhM6AAkNHFYQFVcEYc+2LY+EN568Ey/c0oHtC9fdQPf/SO2uxerHXbd7bGaPpTgTh24n13PygG3fdiAA3E01ZbQHql8xYBb9V186IEcq6l6VEtPVD6bYxvsM0MpmYKqBzp0d6ymD6k+udVRXmdQpxj7hUu4K/qitAdeLFITP9zRXl2m+3N8aD5h3z1RG300nWLsR48o7cFDpGVtbRxTKiTbXTW9S/0YT15/vfx5VzOlA0fxabqmrdxpGwazJb5PpOrvOI5/cD0ynk8wbUpfN6P/0NgxQKTJFqZGt+MNHRCzU0cMYkf8QkJ0Uw441gkfXblFZtyot3v0+6FRMh4EqB89kiuj/22cs+WYkTRbA23JVenwZdJLV3Hy7jbYshNz6juqyXj3jxzAs71Gd87CrN0Eu1pgzgIGBV3GodEKf2Adywvte7mbcEjtEWYPc04dzw8HD4IRw3DaWlA+Xx130YM1x6qHqz/1ZzF6OGpvw+7YCQNruVRBaU9JgBMEZAtF9liAQYNojHpbsbTm8gxzLAMcB/f7v8Cu3IAWrzj8ItHtbKTCCbbDSeslPnQ+5sHZmNNPgZPHwsST+ZzjlJyRB+D6/LS2hsWp+YvBcfj9aeO4vibN0dHMya+p4ZTmFm7MF/j3TB1OGuQ3lHqkOoD24rMHS8dHTufn/0pNNkfRWr6YTnM2UUhsbD9rUuQksONOID32eJZkc7wcBBClF3LSKfwhDfzbyKOZPKQBNzMQ2wD8+r7+M1I83BL64nzx9IuwsQk74xcUZz3KIN/nUseWcjYFQFrwmjF81xisnfkY3p/nwT0P850oK46NHJQB8Hy+CrB8JcHTz8Ou3Wjmw4cP1P6SJXf+GzJTzoG6gTBsCHz7RrjmMn5Sn+E8Cd8YHKKTyK7HA0UPCi7GnjsJLvwA5oarWLp5G3cDRC9YwjjNT0n86uR3obEnkho+FJPPobtmHh5Quxwz7xN4n18gzpkQ+pMhGeybawjmvMK5Crgq8uZO5GsM0J5OcVtNCmrTBPaRh4FoQD/8KG6L0uc68SqUAXJ5Lnniac5dvxn34x+m5vezMB/52zBqY2kvJxPkAFbse9uENjeHSQTfc1aooavWkaqrwR86mLHnnsED1oa4RGCqWCTY3cpFzc3ksh3YbAHYJpnzp8t07FVq107x2hu6KpeTpFLK3DDdZVaNMx/S5ES6S2fv3t5Pd/ns/HJs0+Ydmt41O+PKtbo5jm169Kneq/eFV8uZGdc2ysbZa19ZqpNcTyu6pLssSNKu3fpZnG13+k0yi9bKMH+RmL9IDBhTDoTKF/Ri9GKcjziQFGSzavN93dGRLSdk/a9fhwlZJ03s/YSsTdv0d28FaG+ku1wvmf/9eZg79Pb7ZXJ5pe/5vQyjxNPPa3KhqHWVErL6vla15TSqLStyBdnmdtHcLlLnTDIsWCbz3B/RlmaCkUMhH/DJdMAca3lXHK6n8BcWMsZwbU2ahnyB7w9u4I0rL0U3TCe9vgn/2Ab0zByZzTvh9IlhnFJNCugAbHkImYxxEpA35fik1pZyYyuFLCXDF+sHworGENQ49XAce1kX+eAgE+V3ilMGu0ARFi2Dk8bA8cD4qbBgMc5Zp8GaVbhXXoIJAu6VuMIY3CQGxpAqFtmwupGPjRrBlhWrsOs2hCmDr7g4MT2fcbvMf/5KrN4gx5XYs1cjPV/LYkmNE7R2Smrdom/GSa2/dEvvJLX2E0mtN2/fNyHrqrX6Rimptds7Sa2//f1Sdlunfa+ua+/Q8xWSWnuSlMtrw9Mv6LSiG2pp16TW5Z6XaC/IdBRFR17OrlbR0q4xfqCVCVEPEuK/T9r1ojRQ2UNMu/5kWeXXV0i7vmS5boxV/q5Zh5Z2PdcqR9KAP83XmLkL9fe+rzc9T7kkgMm064WimpYs14RFy8S6RqVWrhfL18qs276f/IarNop3Rj8M0J7FGVSPDxxVKPKI73NmfYaaaLoVL7VW+mGA+4E5o0axpyZFhzEomRSmC4Xq2uWmtdhcnmI2x8XHDGd6px8GaOTewQ3cm8kwKFDiRwc6p2lVKdDMdo6TlsBzqd/ZTL0xTD1hDJ8PAgqBqE2F08nkDwOU2hgELFu9iQsztazZsROneXd4sMMYzAff+xYrcUs2hrmVJbFkhZxQ0mRfXawrJbXGyfKjUYAfSWvJDLwdKfmDBwmz5ktSoaCWllb9WtIxkmhtk3PeZTJXf030OKn22i3lIUTOlfF8pdduEK1tmtDYpDsjLxcoTH5diD1fgimvFy434Vm7NtrtpTqS5XuRr4jNWhBIHbmCpkYmxlm6omzOZs85QHMjiRm3y1x1k5j+NZn5i+TcdluoSTN+qQnNLXrcdUs/ayFJntSJ2SDZ+/2BEp0SJDs+4luS1NauHZ6vla5XGp45nqdUwS07oNVbD2FK8crroQf84BUyrivH9comYd5CnS5pRnOL7o8ktl8B2FOAfV9q2qLHJf34oSdKEmna2mTa2uX8y49kvvrP4v5Hemki8dSzYQWnnBGCOetR2e07Q/u6Yb24+26lgkBTFOj89r369MKl+vKu3f33J9S27dArcxfoluYWXe0Her/r6j1Tpqj+1lsFhBMc15XzwJ/KwtOa75lU/j9sITVon0pQcQAAACV0RVh0ZGF0ZTpjcmVhdGUAMjAyMi0wNi0yMlQxMjo0NzoxOSswMDowMBroaE4AAAAldEVYdGRhdGU6bW9kaWZ5ADIwMjItMDYtMjJUMTI6NDc6MTkrMDA6MDBrtdDyAAAAAElFTkSuQmCC"
         },
         {
-            "path": "Preparation/Unstack rows",
-            "displayName": "Unstack Records",
-            "language": "PYTHON",
-            "optionsVersion": 1,
-            "tags": [],
-            "category": "Preparation",
-            "description": "Unstack all records by splitting on text fields with stacked values, filling records with empty strings where needed.",
-            "subcategory": null,
-            "icon": "data:image/png;base64,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"
-        },
-        {
-            "path": "Preparation/Field Renamer",
-            "displayName": "Field Renamer",
-            "language": "R",
-            "optionsVersion": 1,
-            "tags": [
-                "field",
-                "organiser",
-                "rename",
-                "name"
-            ],
-            "category": "Preparation",
-            "description": "Renames the fields of a data set given a list of current names and new names.",
-            "subcategory": null,
-            "icon": 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0b9LMFY0vL6lcSzzVeYbs4cRSmY2WYGIDwQjjU3pWI5PVdKD6KG5fRcPDq3wcwte7lAbUtDD0ND/crpQVpSb1C1OUabvTdoGl0A7MfS3aYApF4WxpE3c8e5T9tAf71V9usWVp4+qgsrpz5JwGn8TzA1WwnSoK0r05H6GUe6yy1lvnZx9SK9qXmjsrh6v/L6XbqiTnn9nfozjRt4ef02UReDeMCCS+v/BTt4gqWj5wELTulNGuQjNp5p/oiNNiEZRUK3SdqruOpxOlLima6AECvWXkm3C0WjYFn0cZ3efrM+jUPabOSLGmGde9xG/Su3k3hEPl07hwsJBsM1/DnGP2qtSJ2tPWI1YlTnUVuStFkXCKEPar1G76O2BvSwtUm6afAPW2sTbLLH7R3LDZxKzHZ6eZTl7KrxuL0FzOZwIp7hi80ft9cuAIz1By6O+Udu5iBo8UNXD9JBOkgHaUzT/wNgbxsL2iK+ggAAACV0RVh0ZGF0ZTpjcmVhdGUAMjAyMi0wNi0xNFQxMzo0NzowNSswMDowMMx/6UEAAAAldEVYdGRhdGU6bW9kaWZ5ADIwMjItMDYtMTRUMTM6NDc6MDUrMDA6MDC9IlH9AAAAAElFTkSuQmCC"
-        },
-        {
-            "path": "Preparation/Partition",
-            "displayName": "Partition",
-            "language": "PYTHON",
-            "optionsVersion": 1,
-            "tags": [
-                "partition",
-                "division",
-                "grouping"
-            ],
-            "category": "Preparation",
-            "description": "Partitions the data into chunks of the desired size. There will be a new field called \"Partition\" which contains a number unique to each partition.",
-            "subcategory": "Partition",
-            "icon": 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qFQEK7rqpMnT1bS6bQOglAuEf0NM/8ygNsB5ACUtdafB4BUKqUdx1lY4s9KvhBExhgLgAvgzwD8Cfx9Vq2iI0+HhZ7APyaivygWi/bLL79cXU5nMLOZn5+HZVmio6PD2rBhg0kmk54xhuF7BD9NRL8WmAULSS0rrF84kO4noluz2ax19OhRFwCklEIIobq6unR3d7cWQvQy8xB8s9QEAm2amb9QLBa/6jhOVvjHtnHkyBEul8t1OX9QKpXgOI6IxWLWpk2bTCKR0MYPrLwJwK9IKR81xtD+/ftZKXXWSz9qPIFi3YrAWtUdjUaF4ziqUCi4hULBBUBzc3PWoUOHDmit3wPgagD/j1+f/LnszCAAc57nfTGcRa7rLpBvjPF6enoo6KffC8jXwaDTzNwJ4DOxWOwFy7L2CCFUuVzWtm2rgYEBhp98sSqxGI1GhWVZqlKpuNls1gXAuVzOOnDgwOhNN930aLlcpvHx8XOSjwtFBJ5pNZicnMTQ0BDGxsaot7dXWJYliejZPXv2vNMYcyOA52rMJo0zm021gRMw81csy3qFmdWBAwdMW1vbQsh0cHAQwSz+JQC/E/yvxOuTSPoBfA3APsuy3p9Op1kp5e7atUtu3rzZqs0PWE0fzMzMYHh4mEZGRqinpwePPfaYnJiY4JmZmWWRf8F4AnEGsykk5vDhw97Jkyf13NycuuOOO2wp5fcBvBXA+5j5F8HMptOYTbX+fglgAsBfsp9jaIwxCx00Ojpqgg4DgNsAJE5jihKdmkSyUwjx98lk8qexWOyGcrmsx8bGvM2bN5vBwUFKJOLnRT6WiEVm9iYmJvTQ0JDOZrPnddfTuheBZ5sJbW1tmJiYECdOnKBga5Baa8rlcv87l8tdCeC3iWgEp5pNtStd+Lt/SUQniUgNDQ0h7Mh8Pm9uuOEGSUSe1vpKIro53DLO4uiq9dy9BcD3bNv+0c6dO6+JxWIeALNxY4+VSqXsarXqVavVNUnGvSA8gSsxm4rFohkZGdHHjx/nXC7X5vmfBwBcxsx/BGAapyZ/moCso0T0FWMMRkdHudYP8cMf/hDhpU1CiNsAWFhetO4Uzx0RvZ2InpJSftcYsyuVSrlbtmypXHHFFeKqq64S5XK56eSve0/gSrFIJIJMJiNKpZKanJysdHZ2aimlAjBPRF8AsBPAXQDyNaYjiOgLzJwrlUpWJpORoddPa42PfOQjqqOjw2XmtwH49aCfVuJzqPXcGWZ+DxH9nJm/CWAbAG96epoGBgac7u5u3dfXZzKZTEPJx4UsApeLKaXM008/zfv27TMvvfSSlc/nLSLKGMN3aK1/mZn/Cn64+gCAB++8804aGxuj0FyrVCpIpVJibm4uDGN/IiDfrKS9NZgMBn/oav5NAC8Q0Zfi8XhvX1/f/MaNG3nDhg2yr69vYX9vFPkXtAhcLhaLxYRt26pcLoemI3K5rHXo0KEJZv4o/P35QwCqH/jAB+xqtbpgq0ciERGLxZytW7eW4Wce3xgoyNWukjLYHjT7R8I+6jjOCwA+xcztruuGR7ialnVde8Km2SKw3KxGTk5OYmpqCgCot7cXUko5PT291/M8MTc3ZxcKBdd1XRONRkHBlW1EVNVaQ0p5By8esqhXGLrWdEwB+FMAvyOlvHfz5s33SyldpRQFrvCWCFwtFrpy4UfcdCaT0cLP1bOnp6e9EydOmGg0CiyePOK+vj4tpfxPzPyrwe+IBvRLaDp6ADYS0cccx3GEEBgfH2+KCFRhxQLbt+EikIgaIgKXi6XTaYyMjAghhDDGVJPJpEkkEgt/F4/HPSGEISKL/fN7zZgUYQ7CZ5g5UywWrenpaWq0CCQipYrFIgXRwDCY4jWgkR580eUG282akH86rNYGZ2Zv69atgpkN+9nHV2DR39+oVZLha4NDAB4gIrz22muC/QTahonAsL0qn8+rWCxWralUmNTZiFHvLDcc3GyMg5NHgZMpIYS4PagzLbNt54uFh08+C6A0OjraVqlUKqsNHi0HIyKj2traqsGs/DEz38PMpSUmIQVLt14F5hJRHMAPiPwTqul02u3v72cANDY2JkqlkopGo15fX19dsGKxqKrVqmvbtqlUKmcNxHBwP5AxJjx59EEA/yKw32UDyQ9Xl+eKxeJDr7zyih2LxSr5fN7E46t3Fy9HBNKLL75IWmtkMhm4rsuRSAT1Pho2NDRE8Je6RftTCLNjxw7q6elBX18fRkdHIYTwsxuNof7+/vPGBgYGMDQ0hPHxcUxPT7PW+qydkc/nzdve9jYCwNVqtduyrH1EtMnnqq4ndpZi4amg9xLRP+RyOWt0dNR1Xbfh5IfhYLVz504A4Gw22x6PxyNSSs3M0FpTpVJRjuNoKaUBsFpM5vP5+fb29ix8e7rd8zxx7NgxFYlEPKUUA4DneeH/nhdWLpdVJBLRSikNIHv06FGRTCbPGlMIrmyzkslk1bKsDwPYhMafPArJ/7EQ4ruvvfaa7O3t9XK5HMJIZDNEIOXzeTsej1eZ+RNEdBf85AZV54Z7ADbAj6Z9nog6mPlZAD0AqnXeZ5mILADTnudd+Y53vCP7yCOP0NTUVJhff0pnlEolbN++XXZ3d2tjTL8QYj+AzsDx08jDJ8zMZIy5Xkr5OAC5f/9+vZqcgZVizOypQqGg4vF4lYicoFbtqL8I9AIswsww/vWo7QBi5B8aracTioPf8yqVivXUU08ZABQ4gl7XGYlEQlSrVRvAvBDidwPyz3XZ1Goxw8yCmf9JSvl4oJOMEKKpz/fQkpzAWrOEg47kYCbUAwOCDJ9yuRyGTFHnMkIMANhxHM8s3P14+s6Ix+PhlW2/Av/wiUFjZ34YFGMiuiv0wtbr5NGKyF/iCaTFehIFsyh0UtQDW4gG2rbt1WD1LINq2xFqg7N51WomQJSZx7B4Zv9MSSSrJT/cih4G8FMikgcPHqzryaPlkL+m4eCQmGa4n4UQKBQKZ+yMEydOeOVymTzPe3pubu4yAB8hojGcOYlktfWTAMpE9FkAmJmZIWNM0/0fax4Obgb5nucREVE8Hj9jZ7z66qvYu3cvjYyMOMxc1Vp/Gf6x7U8CyODUJJLV1i88zPIgM7+Qz+etwOvXVPLXPBxsjKn1fzdsQFQqFcXM3Nvbe8bO2LJli0gmk2p2dtbt6OjQwj+WViKiuwHsAPA5ACU+9fLp862fJKIsllzZ1gyv35mwNckJLJfLCvVdVk+LLUcEhljtse0jR46ESSQzxpjbjTFvZuavwzdZz+fYNmoGzpcBvBpe2bZWdzTSWoaDAxHYUPKBRa2x3NBqeGx7fn6+JokkZw0PD5/wPG8PgEEA/6tmL1/u+YPwyrZJY8x90WiUpJTVQqFghBCnrUujyW+JwGWcPxgeHsaJEyeov7+fbNuWRPQSgP+stb4GwD/T8o5tgxbf8P2SEGJiYmLCmpmZ0bFYrOnkoyUCzyuJxJuZmdGzs7PyxIkTkdnZ2WcB3ADgOmZ+GovJnwaLIeawLgx/tThWrVb/BgCSyaSbyWTOGqBqicA6YMsRgefCmNm0t7djbGxMjI2NicnJyWp3d7cwxsihoaEfPPPMM78K4D1ENAx/NQiTPxecX8Hsv9dxnFljjNq3b98p9/U1k3y0RODqzh8AMC+99JI+fPgwd3R0tO3YsUOUy/OPAngLM9/MjFexaDq6zCyZcVBr83XP0xBCnHJl21qR3xKB59mRlmVhfn5eaK3V9PR0JZ1Oa8eJKOOPtL8Tgi4D8DEAE8xs+XXD3UrJeaWkarbLF+cQgWuSE7hGIrDuKVZCCPPMM8/Atm0TjUZVX18fJxKJitb6Pmb+hpTyTiK6nIi+XSqVMD4+btaafNSIQCJqicDVYm1tbcKyLFWtVr25uTkXABcKBfvQoUPFxx9//DZmfjcAfeTIEVEqldYF+bzWV8QYYxYCRM0QgQDMyZMnG965s7OzyGQyBABdXV24/PLLZUA+UYNP+5wPRkRrczBkfn5etbW1NU0ENtPZEi7x09PTZnR0NHzcal2S3xKBDcSMMbBtW8zPz6+LZR9nEIEtT+BFirU8geuAhLXCWp7AdUDCesGanRPINZ7AdZETeLFioQhUZ8kJrOesDH9PEhEcx9G0eN6uUasBKaXYGBOKQLneSFhL8sMXQ2o9gdVAEM7Bv9SxXuQDftbLBmauEBGklBrAHPuXJFRrxkg9yGcAFvmZNyyEQD6fb4gn8ELGFg6GvPTSS2JsbExcc801Edu2YwCM1lpUKhXpOI4npfST2VaHUbVatZVSc0KIgpRSaK07q9Wqbdu2W6cyajEAMJOTk5nR0VGxZcsW7uzsJAC85Fk7AwCrwcIn8RzHqUopvYcffhhXX331uiH6bBg3+9k4ZibP8xi+wyTcu2lwcJBX8/u1n+HhYQp/F/5KppvRNrOKJ/HWCqPTPBt3I/vPxoWJDKH9Xs9n436cyWRS7e3tHyKiGBbj5astoxYTzFz2PO9LlmUViOhtzPxu9l8ta1Tb/i+AJ44dOxbJ5XJVXsNEz+WSL07zbNwfUuOfjftxMpl0yH8yLR1IdqpDGSEW/t6cbdtfY+YCM78djX8SzxDRE11dXWZmZsYopdYF0ViBCKxtUL3tco/99+rdQAR68F/IAvxzg40QgV74nTFGN6FthogQi8VC83bNicYyRKAqFosiiAaGJCw881anDqqdMVwTeArLUHU2O0Ofg9Jak5SydvZa3KAj32Gkk4houTd2rxXGNVfEvOE9geHACzCudxlLsWKxSOuZ/KXYGz4nsJmxByL/DsT1FvrFWUTgGzocHPoImtm2ZDK5qvcAmkX+RRMODtoWbnP1dnG/rm0reRd5LbGLIhystQ51hmli2+hcDzatNXZRhYMDrCUCWyKwJQLREoEtEdgSgQ1uW0sEtkRgSwSeC2uJwPWDtURgndvWEoEtEdgSgS0RuD6IRksEtkQgWiKwJQJbIrCBZSxtW0sEtkRgSwS2ROD6IBrLEIFr9mxcgAGAV2diwhdDvJqcwHDFcRugA17XtgspJ3BNno0z/oshkQBTqK/+CFcXValUVPAcrNWEtklmRrFYFEQk1gPR58JoybNxP2LmGDMXw20haGS9no2LAXiCiJDNZiupVOpuIkrAX3FEHcrQiwsJK2YuK6VyNW37PPuHXhrxJN5C26ampgQzV9cT0TiLCFyzZ+OoCce1iKjhbVtyFM1ab0TjLCJQSinUsWPHOBaL0ebNm52uri4Ti8VMuJTV8xBlsVgUU1NT4ujRo5XrrrsOAFT4d4VCoW5lLMH0E088gW3btjnd3d0Nb9vIyEjlxRdf5KuvvnpdEI1ziEAiUjQ0NLSuBUsLaxzGzN7SJXnNK9XCmuwJNMase8dFC2ucCFRSSg4dQWtdqRbWdBFIylxglxq0sPqKQLGeKtXCmoOdEg5uicCLG2uJwIsUa4nAixhricAW1hKBFzPWEoEtrCUCL3asJQIvYqwlAltYSwRezFhLBLawU0Wg1vq0+0SQ/9bC3qCYEIKElJKVUqeIwPAfWtgbFwP8s5NKa83MTFJKf08QwnieByJqYW9QTGsNAEJKqf4/sFK1NPACliMAAAAldEVYdGRhdGU6Y3JlYXRlADIwMjItMDYtMTRUMTM6NTY6NDErMDA6MDCSNndWAAAAJXRFWHRkYXRlOm1vZGlmeQAyMDIyLTA2LTE0VDEzOjU2OjQxKzAwOjAw42vP6gAAAABJRU5ErkJggg=="
-        },
-        {
-            "path": "Preparation/ForEach/ProjectParameters",
-            "displayName": "Project Parameters Batch Setting",
-            "language": "PYTHON",
-            "optionsVersion": 1,
-            "tags": [
-                "for",
-                "foreach",
-                "parameter",
-                "batch",
-                "iteration"
-            ],
-            "category": "Preparation",
-            "description": "",
-            "subcategory": "ForEach",
-            "icon": "data:image/png;base64,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"
-        },
-        {
-            "path": "Preparation/ForEach/ForEachMultiStage",
-            "displayName": "ForEach multi stage",
-            "language": "PYTHON",
-            "optionsVersion": 1,
-            "tags": [
-                "for",
-                "foreach",
-                "iteration",
-                "paramater",
-                "stage",
-                "orchestration"
-            ],
-            "category": "Preparation",
-            "description": "The ForEach multi stage block allows to orchestrate the execution of another Omniscope project and running the workflow multiple times, each time with a different set of parameter values. Unlike the ForEach block allows multiple stages of execution, executing/refreshing from source a different set of blocks in each stage.",
-            "subcategory": "ForEach",
-            "icon": "data:image/png;base64,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"
-        },
-        {
-            "path": "Preparation/ForEach/ForEach",
-            "displayName": "For Each (Separate Workflows)",
-            "language": "PYTHON",
-            "optionsVersion": 1,
-            "tags": [
-                "for",
-                "foreach",
-                "paramater",
-                "iteration"
-            ],
-            "category": "Preparation",
-            "description": "Executes another Omniscope project multiple times, each time with a different set of parameter values.",
-            "subcategory": "Workflow",
-            "icon": "data:image/png;base64,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"
-        },
-        {
-            "path": "Preparation/Unescape HTML",
-            "displayName": "Unescape HTML",
-            "language": "PYTHON",
+            "path": "Analytics/Prediction/KNN",
+            "displayName": "K-Nearest-Neighbours",
+            "language": "R",
             "optionsVersion": 1,
             "tags": [
-                "HTML",
-                "escape",
-                "unescape",
-                "encode",
-                "decode"
+                "knn",
+                "nearest",
+                "neighbours",
+                "prediction"
             ],
-            "category": "Preparation",
-            "description": "Convert all named and numeric character references to the corresponding Unicode characters",
-            "subcategory": null,
-            "icon": null
+            "category": "Analytics",
+            "description": "Performs k-nearest-neighbour prediction on the data. The prediction for a new point depends on the k-nearest-neighbours around the point. The majority class is used as the prediction.",
+            "subcategory": "Prediction",
+            "icon": 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"
         },
         {
-            "path": "Preparation/Add row ID field",
-            "displayName": "Add row ID field",
+            "path": "Analytics/Prediction/SVM",
+            "displayName": "Support Vector Machine",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "row",
-                "ID",
-                "sequence"
+                "svm",
+                "support",
+                "vector",
+                "machine",
+                "prediction"
             ],
-            "category": "Preparation",
-            "description": "Adds a Row ID field with a sequential number.",
-            "subcategory": null,
-            "icon": null
+            "category": "Analytics",
+            "description": "Predicts classes of new data from old data by drawing a boundary between two classes whereas the margin around the bondary is made as large as possible to avoid touching the points.",
+            "subcategory": "Prediction",
+            "icon": 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NjYvmXLlgEIPznXrHE7CDHXk4WxCyVxnIROhJHn82MZ+T4dviDeyVmtCLPyD+VmSKlBhELoyMjIyL/927/9v6WCagaev/u77XsJ2b0+Go3aHI6FQcZK4MNbisezUjKZTIASvBwQ3RUAwgdWDxY2NDQ0wi+pDwKHOzc3Nwtxry1btvS2trY2YgzI6XTiWiEtLyKFKbUXGrDCMrFY44svvjjz+uuv/2pyctK/bdu2fXv27Hm4s7Ozt7GxsYkXBDtjxFfKBktdpEJhk8jEkJEQQFMKiN1uz8qynAVJn+O4AguNFTRZbIaend/vT1y6dOnSq6+++mqpoFrttnbvXtcZi6FYwBmryWS1/HJoE1mt1sTw8PDkmTNnTtZjie4aAOGCqEebXS6XoL5I4XA45Ha7hW3btt2PRmlPTw/4PUa73Y5KMqyPNutSu64cLsz169ev/+53v/vdwYMH30Kc8eijjz67b9++J7Zu3bqnbfv2DjshTo4QEwURbyScJFcdTEucJBFcRZ7PpSORNOd06qyEcA6HAyDKCYIgI/PiFR6JyhLRAB8ZGVouANHU1FR0eHh4+Mc//vGP1UG1uqf4xBPdWyIRAfNoBUBWC3o1kGAJ7fZY7OTJG6OVKMGlbpq7CkB4g2pxBczJ43sQoVyzZo3vsccee+LBBx/cs2vXrrZIJOJwOp1GBTxgFKrpGYibJRDGkLJbrdbM6Oho4MMPP/zo7bfffltdtPvmN7/5ykMPPURFEVgwTYuCFEQGwkm6iiCSciBE50naGUJSDkKiY2NjcUEQrI2NjTaHw2Gw2WyoT+lLxWnpdDrOVO8YiCYmJkLIzP7xH//xRwA7zuWRRx55HAIO/f39A16vt9HrtTvq7Seq62mhkJiYn5+fx/TrkSNHPihFCS7nFu86AKnfqLok//jjjz/+wgsvvLBp06beBgyue71wcZT4pWYVKtXmtMFgwMVCECvPz88nL1++fPWzzz47MTw8PBQMBgPz8/N+xpsGiJ566qkXup96qt8TDnvNLhcNVgr0C54v2VRlMY8S7JBsPJ5JJpPxWZ9v5tLvf39Wp9PFH3vssd3gYcfjcRPqQXgocVbBYqqCaqpapgTVuZGRET8yM0ySMOvz4IMPPrJ+/frOtWuhemYy11rFV58vGx0iRMpMT8eijNEJV1ZtZfyuBhD7sJgcffnll1/es2cPtT6hUMgmCIIJWRRmidUFR9Q6OI5DvceQTCYpRzkSiURHR0fhwUZxgRGLQ6Thxo0bV65cuULHhEuACBjKWyL1A6U5OrGjevD8IvC88847b+K1/+Ef/uF/P/fcc891dXV54/E4TeWRupdos1B2gCYzk86dO3fz8OHDR/BP3YxuFgSXuMwRapUVoozOQCDgZ4MJ77zzDnXzlQLyux5AsEJq6+N2u90+n4/OkkmSpA1Kk0pnnXbYEcBGo9FMXkk3GkX6r4g0RCORyDyC8itXrlw4fPgwPSw1iNYT4kaGx9ntgIxuEZBwsgoRLBPNpB0Oh3hRr5+G5QF4PvroI1pVBvhhQcCObGtrcyklB0YxKaLVatN7hYabOXv27OTly5dHotFoQtEfWuP1rigdhhCSTF67Nue/cePGSC0iDnc9gJj1wQjy/v371wWDQQfK1uik0wEuVa9LIW9R8IB7A/eGWAidcJQCcEEBoHxPNZ2CPvPU1NT4xYsXvzp48CAyswKI2tvbu5ybNrnXEGKNEIKBeUO+Us3EMXk5jZI4IZkkIYm5s2epOKYaPOzuxRj1D37wgx9s3bp1q8/nsyrAKMnNVqX39Od5nPLZ0dHR0MzMTBCyN729vmZCPFYR72cFaMH5UkNYvH59QQUEcsPVuLG7GkDM+rAxZEEQhLa2NmcymWTqLwV2YSKRSGcyGT3cFvjLbBQnpQPDB5xp+qCqqEQUcymel1KyLMZ1uuj0iRMTp06d+lgNou3bt+9bv359tyAIHrQ64CrRP2OgQFoOFMbj8WgoFMJI0ZXDhw+/zSyP1vRjnPpv//Zv/6azs3NDY2MjhhqR3jMQaTnaKVEUM/h5NBpFOUCHvl0ikUDGlmlt5a2iSMlGYBtUDPKXckMqN5a6fn0qwGRk/lsACNbn2Wef/Ys9e/bs7ujo6IDWss/ns4AEptYPwhQE4iGQtdS01CWFLZWmWiocTsVcruj4iRMjahAhxd+6devOjo6OigLhY2NjV4eGhj4vBx52AeHKvvOd7/yPgYGBdZhFU74PThLSey3dlvXvwKzUgwYSiURkSZKy9mxWJssYymTvZwE8sEDz/71cGNPtef7555+HCALS1paWFiGXy5nUIzkADAAETg/+qWmjhTsPdhbc5myqZACciUZTEYcjpAURMp+uri66osBut0O7yG6xWGwsEI/FYnRFwcjISNUrCjBe/c1vfvMv+/r61iKoZul9mRpRQsUh4vR6PZuklXm+ej2BchaomBqTiGOq5WsVRKvTdG3Ej5+99NJLL8IK9fT09Pp8vsa2tjbEDwAQNfkMPHBbbAiQpckIcJeipEqcLBFxYWQZHOVSIGIkLcTuHo+nIHvHSgH1LElBr0vJzHwsqMb7VhiURem9wiECVQVMS9obpCog+Kogo143VgCQLOf80WhkZmZmmlWkq4l/8OfvSAykZc6p2XJ4UwCT2n21tra2CoLgbmtrs0UiEShnwP9zsDjg8yDmUU+D4mAr8Znxd7R1nFQ4nAi6XIErhw4Nnzhx4hOWneG5pWii1aS5pe5+vNY//dM/vapkZgLiIZ7nEc/AReFrvmCkTMoi1kK1Aq6MfT8vJZMfAqonkFZbHyZQcfTotSu1inneVgAx4Kg317ADQUaEdgUENJUDIUwAqqmpqamnpwdjpXwikeAhg4cMC3dsMpmk7kv9oOJgC9LLS8WQhYukpORZs2yODA8PT5w7d+7MF198cezkyZOrImqJG+SnP/3pT+GeW1paHAxEmODQTs0iM0un01lYGnWMl/fHxrpouAUAEYxk5sUp2FBmLQpotw1ArBSPrjsAgc01JpOpaE4vHA7T2gxUSSG+4PF47N3d3d14Pg4ZN6LL5bIYjUab4r4ogNQSLDlYJhiXGoR5JENWorNaPE+oFQoGZ8fGxkaGhoZOfvTRR6smrYv0/kc/+tGPent74aJZel+y3aGUHnI6nQ6iWxT4bGRaxExiDe5MfWCZDGpY8/NHjlw5X0v9h/292wIgNhf2wAMP7Fi7dm0n0nFG1wBrQzHVWdRqIpFIBIU/jMJA7ReVW0jQtbe3e0Aeg6QcIcQKq4O6D9yXGkBpsAIlqebPtdBJJ5nQ9HQIleqhoaETx44dO8Kq1RVNWR1PYOl9V1dXFxgISruD9vDUVXbljER87sUgAoukWIKmVFyk7cbjvG22aJQ1Ug8ePHigVh3qmg+61jNi4IEuEHSUe3udLkJazYTMc4RA1MusTybThOflXDCYJ/4gf8WH0+l0AEgSjUYACLI5ABEhBKk8THcpAHH6GtxXIdZY6KZnY4EA+kI3V9uNsb+N9P673/3ud5GZJRIJI8hnSqWdhTdqbjed7gBldZHrriQwqbp4ecsTjy8HPHi5VQUQA88zzzzz/MMPt6+bm3M6GxqMJjnBccSymDmnBIVZiyWTlWXIfWUzfr8k6nS6rNfrpfRVgAgAKmOBuBzkXOuxQFxW4okZmU0uFghEoEcNpY9PP/300GrFQex64pxUPbMmpc6lw5CA0WhEUK1VESkDonxEXQ5HON9kMpdLpzNplyseY6pu9VieVXdhavDs37+hOxKxO5xOE+T/K4KWaRkkk3LWYoFby2XAq8H0hMPh4I1Go7VMDAQAZeHGagmiqXvgiEREifJ5bjeA8Pc1PTMPxrIBnnQ6nTOZTEU9P8Wd0cA6m81SQh27oHkVERRUESPlvyvLcs5qlaRQKJdzuVKpGzcS8enp6Snwf5YDnlWzQFrwxGJOl3ZKgNnmggvRiBEyEClTxhgYzbpcLrQp6MiOIlOnzcKoqVfS+Jq8LaJunvD43dvuwtgbZT0zJTMTlDgI9R+ACL0/rSWCC4fXh5gs1EUKn5lV4QEeZHEgSIHCGgqF0FPD6oQLywXPqgBIHfNghDkc9iDbWhATrKL5V1whXTDJVqtVhioqBBTUdSBNy4ITCZG5Gt2YpJMkmhXzvBSanp6/XUG0FuUsM9u4ceNGVN4VQhwq0AiqYWmK5PhwVgAJ0n9lOrbIwieTyVw2m6W62liZMD4+PlZPtlXubqzoTmq5jbWsObp4RDV5WkvFVF2nyDNF8U7ykvU4SDBYmRuLJ+JSLlu4+2p2Y4WCIs8TCPSExsfnRkdHL692Gl/ubNUgamxsFJAwgF+dTqepyoeyRqRI7QqWCNI2mL2H64PsMIw6JkKQjIBUh+z2ypUrlw8fPvzBv/7rv1ac/Kjm2q8ogIoJ3+0dhCDjWlCfrwVAip+nBQ/eTIiYyk84gOMDWTlUZvEzFBPRC4PvVz2wlxRWaEmN50Xuk+flRDAYVUz8bQmgS10kdiN+//vf/xtIAnq9Xjs+H6rVEGZgLk0rSYzXQktGsUZw9XBrGLYEeT6r0+mSV69eDQ4NDQ1+8MEHh5Y7Tl3RhS3Vq9J+8JLWZ5lrjxiIiuOhfBiA1B71EHUzVaVLCDeGdkbFm6jQEwOfWdNUXc0iYrk3hpsQfbd169Z1DwwMILYeaG5uboJsjSKzB4437crnGSa6AiNTfUPIsgweVE6WZUyk4EbKRSKRjCAImbGxscTly5cvv/nmmweWC6KyFoh1w0E/npubC/j9fv+tW7emyvV/1BMDVAdxPZ00rVsHWmsdVC6sIG4JfR+lpYEZp6JrUk0/TF2BlgnJxlX1n6W4PRVRWecTQCEBcLB7DKNNZrPZgjk5zMCBDeByubAyHFkVpjcQD1Kzi9ZHKWukhAGIf2iVEfyiSCSSdTqdiJeSR48evfLrX//6N8sBUUkAMfDgk7S3t7fjj4fD4cjo6OjoxYsXL2iBxHQQ2dY+bL5pb3R5kpD4qiJoXuq8F4jf+WBaGdSjE59wY7grEUSjqMj6RGpdw1ItDW0TlWpKhULxWbfbf+4///NMKVZhnZio+tdAp928efPWDRs2bAZosPyFVesBJDAQkH3yPG+E9UXgjGFF6CCBCqLT6fTKYGVRwsKsODYyIIYC5xrPx8PpdKaOHj16cTkgWgQgLXjQh8IURCKRwGwf3Rr45ZdfDqmBxNzXrl279oGzC9B5PG6B53V0+06tsY/61NUZGQOQsgeFujEElMyNZTKZHCgPSGer2ZKMyByWJxUKJe80eDBahDFrMCABGGNTk9EQDnM6l0tnEkVdhucxE4QYCBkoxoToxAlKWD6fj86cKdaICacXBdnKOaLKT2fQEDuiiOR0OqOHDx8+X687KwJQOfBgGA6qD3iDiOSDweD8jRs3rjMgwQqgw347AIT3ADYHLBACagAI30MgHQ6HMc6jC4VCOgPPU9Wn8mR4Imei0XTK4RDndbq5O2l5AJ6+v/zLB9yy3GCKRMwmJ+Yb6XR0gcxPxa/onYV0ncaBOoNOJ9tNJgqipqYmAIhaFxoY5WPEomFLxRqhip3FxC7cGbjhXq838vbbbw//4Q9/+EOt7qwAIAaed999t+df/uVf1v793/99MyEEBCq8W/vly5etPT093MjICJ3rRkFqampqGkC6cePGOPg48N1YZdCIofN2lyeZXB0XhpsQVgb1RAYg3GHBYDChjM3IoXQaa5h0eiLqlJPPf1Y+T4Z3EpKZrECGr9r/1PlENtQI8DTOz/vATtBbrVzZXgT7O8VZBXQas263GwEzLArAB4vECPkAorZ2BGXPjNK4FScmJpK4nidPnjyJwctadIrooTLwYAXS2rVr1zK3xSyPzWYDeBDPmP1+v9Hn8+Hmzo6MjEAoMoDYCAByOBwNbre7sampqbm1FWuvVy6IVs4OhwKRAjp1gTRVDSB08m02Wxbu6+bNmwgyaS0km10oEtVKhq8TGxV/TQseumQ4r5KW/104ItPCeLXEGSQipwhZUCLOF8fyfh1kD4pZAAAYqUlEQVTteLgmubW1FTERVWuDFYKrwg2HmXyN4BabQ5OmpqbElpaWyNGjR6c/+uijj9977713q9Up0mndFizP4cOHsYnYun79+nWY8Jyfn09lMhkEbyafzwcxbvyTBUGQJiYmxHg8HovFYgm4MpvNBu6wg+d5m9NkMi+HdllUkRYJScoyOvSoRNNuNVQ5WIsEzwWAPB4P7q70+fPn0StirQ5M89CMRa3UVS0ZviIaanyCepy6sbGx2eb1OnT5sSHoR1fUJ6JgQmEsb1GJnExKWYtF0sXjaavVmuvo6KBWB+NNsMhMZhgipBqd7FQsFksKgpAaGRnBYCE1BqgR/elPf6qKSKdD1ZNlW7Ac3/rWt7xHjx4179y5cwvP8x52NlBXwfmDOQckKxOU1CIo2oCJkZERNGXwJs2CYDCLotG8XNqlsvaIJHI5CWoSsHwul0sPyVyLxVLEHwaKBUEAZiMnT56MQ30sEAiEZ2dn5/A5lkOGrxEjZZ++SNAhFnNydnte0AGyUKS6MR119ZzeGJEIuEKJXC6XhGKJ1+u1ejweE66XYqlhjYqk/3DTxWIxzPDj5ooMDg6Gsdz3q6+++qpaK0QBxDYWo+bz5JNPWgYHB239/f3rjEajW3sSYMYBMAqxCRcRTwGIKJCQCc3OziJZQM3CpHbXsMroCFZzMdStjDglPkHWLpeGBfJ4PMi0QK4vrC1QDqMAIJjjmZmZ+StXrmCkeRyzW/i7yyHDV/O+l3oOamVlJWXqELZSg0giJJ2Q5Xh0dDQoy3KiubnZ6fP5XNAQgBuDS0OLA8OIODeVYYgHg8G4z+dLDQ4OwgjNXrhw4cKRI0eODA0NVdwOrWObizdv3ozxlfYnn3wSihjW0dFRY3t7u5vjuCKZFdUfRpkXUXyBCscqpIp0LpfLJbCNUFdkhfSEgINYbp+omjWHTxmj8iPptN8vpXK5HMTBsyDXS5JkUUu6lAIQHXz/6qsLx44dO4bpCfbe6yXDLwdACBW6u7vvh5zMiopaMRYDT3KJYCKmUILHZFlOPvDAA73Nzc2NmORVlgjTyQ48VBO9kNuDd0mcO3cOK7NuAUDIsKtxYzSIVq2/3tnZ2bkeExAInBHz4Ca3Wq0oTi1KCfG7ygWHVG6hGYXoHqKXaZoJJej6blQv1Iv/sFwSJgvOOpXNj4yqHwBdND9zHidkJHrpUgYfku7B6OjoaERMph4uVMdAMMewQADQp59+eqyag1gOOKr5XQBo9+7dj2Pita+v74G8rJ7LRcTl7ZgvxEPgcxOSDI+PB9EInpmZGYeGwO7duweQFAmCgGtIa0OKogmTP86gLOPz+aTh4eF5rMyCG6v23AppPAPRP//zP+8cHBzsGRgYaIAlCoVCdP4ctQVsJla80CI3pACJ1iSo7nJ+rzrAbkTKnVekwI9R2tDCRQMeICCdTjEdv88+i85ls1kRd1NbW9sakO1BpEKAXyKIRk8jfrcCSKVFtEYR9tTVKy2s8QaoTqexNwEguHbt2nAmk4lt27Zt87Zt2/ohSgFjwAyWWlMAKbzX68W54cabq+XGKyokqizRjm9961ubJiYm6DAfEkoAh/WbjEYjwhlkQCXjGQVMVNYNIghsJ8YCTJBBMGH3hRE5tllJp7OkZXkuNTIyH5yZmZmMxWLzbW1tzXCxmM7AnYTMQiuuoGRhIu4k7J6o5SCqsSLLeQ6zQLRo2Nc3gOxLEARbvu3CEU6iqxLqetDzzscJtD4HLtPNmzdHUXDt6upas2nTJnCLkBBhzz39G6sCII07oxOhAwMDNCZSzB+lRyiLamlghveCjIgJYrITYGqkMER405isLH06i3i8lFifSCTiGPOZn5+fUdRY1zDxTdAaUCyjm2wX1DkyHMdF4BHPnTsXhC+vxRTXdeVq+KXbCSC/3z8dCoVmm5qaXN3d3Wvcbje0JGEEmNsqAhCsFlwYBuQPHz5cnwtTn4XaEgFEDQ0Nno6ODuZDC2V2RPcARjabhSuh4zmIz9hWY6WARV8aMiulNiCzv5uftOQh/5/N5XLI5sREIhHGayjmFwOFWJCCwTtkE1oLVAgGh4eHIdsyWUswWAMW6nqqGkCKnN6CC1uGNrUS01CzIvF8Oj4zExNFMarX6yVkYm632+bxeIwKKY2OSCMcUd14KY7jYrAL9ZxbWTqHNjuDuIFSkUbebvL7/WYE14hFMHKLzAvVYSauzUhfqIYyIClbkFHPKSyzZVcDz0FvCzwXs9mMehIKg+AC0zI8ioboJuN1VSPABV1E+Pv5+fmEyWRKQQ+x1nS0LlTU8EurF0QXhgGQiEiZSARlljSUX6HihmkWRckNNzmsNkIPtZ4kBiljdafxS50BQOTz+Xys0Lhu3br1Ho/H7fV6m8xmc4PT6eQVrR66MgkPNqPOQIMLjuANQEJVFM9jAGKiT8pdhC9oBMqoLYGKCbK4EiwV3iYOAUE6Imi1H1eKiFB8x4h0zQWxGrBQ11NXLY3ndBIRM6ytQZMYWHKPx0OpL6jXIdSAp4cV0mauyz23ipRWxkpsbW1t2bRp0+Y9e/bs27x5826w5kwmkx3WENlQqYW0OGmlY06752x/FkWKaoJAAR41w/nFfnTFdsn3hh+qTbACPhFj4xhInJycrKskXxcqavylJQuJOtTHqiuysj9bVI0WRWLgeWI1GuHikf7KyuYiWRWzFrEXMcOJc0OLv+5WRi1ngAN4+eWXv799+/aHGhoafGZBQDBipJYF24uXeDHFElEqBqzRUhwh7Qiu9mU1TUGqV2e1WtEUjLe0tETraQrWcg7Lee6SrQwjIZxcHYgoeGSZ8Kr7DGeMawFRdXCgMcmB94qRIC1jURn1waIZ7C2r+9wqWiB2WLBEL7300v/ctWvXI+v37Om2RaMOo8NopmyPEqOQ+CBaI4L/j+9D809ZB0kxVy/hDEADKcpsNsN/wvcnz507F6iHllALKGrhipd63SWbqajU65cGEcBDD06zihxniyUuFouFWvNSwFEsNpWKxGK+YDCY8ng8yFzrOreqAKQGT/uePV3OaFQwOhxo8y6cD6UfaJZiw6wqC0fYE+GLlRhHMhqNlPyEVZAs1qkWTCrw0DPBEpXlEKMqAYiBprGxsYWJTan7avj9WlokFekc9CgXVk9Ri0O/QxuOi3bY45zhunieh8A6ZS2WEViAwZZAiYFIOwYOPR5PaNmEsnIHWBo8RjpETh9lOsjsA9Pn5FN0RY5ER5evcRwnuVwurCig22rYQw0mrXVi1W4lzS8sUcHtJAgCWHVf1sOqqwQe9aQElEVAcGe/A5k7FO+gOa0WL6/0mvh5rYQy7c2ovinhtsAyLCe8CYvDugRWqxVFYVmhtIZXjNKq/dAAz5NPPvkiFpKoVdwLlqeCjBw1Dcqdg99BV5VtRHYYjdgemMEaAnO+x4H6EmiW1G+jB6KsygYxjL01SgbH/xEEQR8MBlEzgvBCYrXAo52UcDqdixgKas1pAKmS2Kb6nNUgopRWQsym/FksorSWAqV2V732OcrAISwN7SMpS/ng9pGMiJ9//vmVt95663evvfZaXYOGS471LG7+NbnoB6tjMa02Y1A4RIhbxI6ODlgidPXpyDJzZ4FAYGHYG/xar5ceQiwWy0ajUbmlpSV7+fLl2MWLFy+thuVhAS+blHA4HE5UDxRiO71WuItRjoDCGoSprl27hu7/kXpAVESqh4KExwOpO70mMChgRIkn6V5WLXBgrXFzYf05Y9GgDqfU6mD10Tebw7rNd9999w/nz5+vWihU/bfKAghm+9FHH/2LrVu3QmK3y+Px+MwulyVPoaxzLSS0WSg5XCQy4TPpcBgNPLAdI3q9Hq0IxEdYOEe7xh0dHfjCkjtKGwFpDV8xIZJOpzOXL1++tFJTluqDAXiefPLJ57u7u/t9Ph/6cPYQ4ltCDEaS1OvoGkBZzhAL1aGRCJES4+OQigMLYLjWuTLtWA9qbiaTyaa0behbQxICAOCcUBZBfQ2qJezBmBH4CjYE2k/IzGC0UZ/LD3RQkU70KLE+a+769esjy9lZX3YujHFX6CqktrZ2+H5GB6g20F10VzAqpjIFCvGocDg8l0qlQtFodNrhcGD+CapkZqORMmiLRlMAGLwmJkNQ82GtilonCSrFJww8276zbU9DttfHR6MWk9OJ95KnnaoeqmmJbDqSzogOR3Lu0iWMPp2sFUTMXf71X//1/2ppaWlDpV+hXjBrR4EDXQB8VTJZRqmBtaE3lyzLtM6qETwpjDopMaQEkfTl7lEtCyC1+2ptbW23e72Aup5IYtW0y8UAym/0Y2uRIpOTVBPxxo0bl+Lx+IwoihGXy+W02+12tE6sVmvR0jm8Hu4arLEGeNBtr2WCoBJw8PMF8HxnT1Mg0AKye1WTEvhlHqyuBDKc5EW9/taX//EfJ2oF0RtvvHFgz549DzU1NXmxMIb1FdHGQVoO8OB7qv2rcFWw4qj/YH8I+l0GWKB8GWUB8NAMosI41AyRLPQD8jtoF/aoYl3DwYMH3zx06FBVG6SXBJCWu5LfFVHbIrZSvlm5Syh3BULdY2NjV86cOXMsHA4HYJ5BrUUDF0vnACj2GpiOhZIrgINR62onB6oBTinwQFi8aFICKg9EVyQhnE8SxIXrlKdVIJ2O1woiiJBDP/q+++7bkEgkKHMwnU5T5oEykUpjHRXTgY4tAykg8THw5FtAmNZVJshKl+oKx4LcJhLBHlX8RnDurbeGz1QLoooAAnelYWODz0M84K6gsVWVBnO5i1YQsxTFDKwJAHT+/PnBCxcuDEFKTv17aJ+gF6edz8dzaqm5VAMgZnkYW9DR1ORETY9ehSqKe/gb6go63ETMYolOK5LBlSwRdBKhyr9///4tkUjEiPkuNrKM5icsjzrWgdUBeJSVn3I0GkXdBwkGOhq6GuQS6csC94lEjsZGHo/fXy2IKgJITT1YLQsEAJVTQ11u1bca8LCEAVX2RYXSGtoLRSCqQe0DSq1Ypvf8889vxy40m81mQpEVWj8YTtC0bugiFmW23QDGAmgucFmxWEyv8M8XdZUqkP8KIIK1CwYT8WpBVHsMlBYrltrLWh9lqzHgzoScJicnx7Dk5E5IqeB9luDpYIe8g94sdbZZVAxBNDKX1BvCVAwsD1TrVZsYsWAFAXNJ8GC2HV12WCAsuMYmHwTMdnuOJBIcMq8CgKpNeIpV4TA+FYsNDk6MV9otX0sWhgIa88F10S8lLicRMUftpRgKgb8D2uk57VqBaqzGSj1HXa7o7OzsEdrbUczjWbBfi2B5wcUYsKgs7xcwrxUKhUoqnrF1DlhnpeyBpVuoS+1VxQpzAIapa+BvoR3h9/uhugGJOxRkLR6PlS7jq0fUQg2iSASc9FDo6NEbI0utP6iqDkQPVhAaTE6ncrD114HYSoEY4WO3zp4dhxYz4oPVFPMuBzY1R6dQrmgW3ASJZp3WRx2nKP9d4CmrhcvxM/UmRugJsPEbrSoraBeKIALddKgEzfKtW7ewLDc6NzcXQdbqdDoF1zLXQDE5nWRSRrExhq0BkDv+8MMPD5VagVCxEs0yMZDA7V4vMiI6v12taVx0oJq78uOPP/7gyy+/PFarQvpKWCAtS7C1tXWtUq4wIMmtllpR6r2oAmqohmATzqRauBwJgmoPbAfG+hE4K1Rd7R7YFLIsUIiV9VBIIrAHdvzq1aujUP/PS+p4lu8llOIRYqn5+SREF4JsCd3777+/aO3Dkt34IvP+6KO9QjjsUe+3qBZEUhY19IU6CbKT8TNnRtkdWUvZfyWAw15DG/8UbhKabUIirzpuTkkA6SSJN+Rlg8MzM2EACDUWCJdjv9h99923GYFzf39/n8vlwgQpBheQyRVpQsfj8YQyZwdeOWUvYNJgeHj4wvHjx09iVTkUUdra2tb6fA4nIXkB92qvTTnw54dXI6mbN8Pzk5OTk2ynvHYNVEU6B0D03HPPvUxN/PbtHXY6y82ZCvWpJepCi5TAWH3k4sXJeiq1KwmeUgE0AxBPiF5cAaK70raRwjPFFigWiwWefvrpJ1RbqO2CIID4DmpLQYoFVgx0CxQIERcBSFNTU5GrV69effXVV18VBMGLPfIruYi3uByRyQSD0RCmW+DGTp06dUzrxioCCC9YGkSKIAC7qgBsfm4wv4VIvftDqdCKopiY8Xqn6qnQrjR41ABi06J5F2Z30GnR5VqghT5CUQx0+fLlIZ/P53n88ccf3rRpUy+i5paWFlsikQBvHIZDTXjHpElh3ys4T+Pj4zdfe+21186ePXsBol4AUH9//4DX6/WtxCZnBiClHADNpfllA6gUiKyRiN3khICUStNGe5Xz70LGYo+k3Z6YMxj8dwt4GIAW9fyam91EFJcVRDPLCwsU0ehOT09Pj+7cuXOrsoW6ze/3O3w+H2bXAaCi2Eeh6tI5POyUn52d9b///vvvHzhw4AB0Kh955JHHiy2QzUaIRY8RqeW6sPylXLBAdbswNSbUlsi3Y0dLuyzbIpS/Qg9cRwgG/mjvBTJscjoSkSSns7AWm2VcdyrmKWXFFqXxRdlmfROjhQBaozs9PDx8GmzMvXv3sqFNVyqVcqZSKYxIwXUVmBuYi0MxERsMY7EYdHxiX3zxxRnQVjDrj88CAEFWEDFQa2vrmuZmwQUS6HIyyOJ6UJrGQNevXx8tl8pX5cJKgQgcGQRwbCW2dheGsmWv6rXYq+GiqnlNbSC9adOmFpGQfOO4jjtZK7kSJmSeLfOF9enp6em8//7770fWhMJhR0eHNRKJmNjULXMhYFlCSAsrPe12e/LUqVOjJ06cOAHrgx6gWhn3vvvuG1i/fn1nY6PLAyb0SgEoFBITmK+7ePHiuePHj3/yzjvvvKVtIdUMIObOwBXSrsRGAJg3fBmog0GMCrPJc3dKCawaAKk/D7hPnSzbZDWvGupBateFTjdTvUfCgFJFPB4PYI0npHRQt1EELCBVA+UTrPSkD2gwgRyPNRgcx6VHRkYCw8PD55j1YRcR1nPv3r37HnzwwYc3btyIrYdNHo/VXu8eVXX8Q5K5HLHEoupqdKlFvHUBiB16qZXY+NndoARWLXjY8ypnm0sXTyUuK1EBVTzY6kxlge+hQ4f+gEYxCofbtm3bCgBhkXBnZ6crkUhYBEEAiY4u2lXIYNT6IBXx+/2JiYmJiaNHj37CrI/6sz311FPPMn3u3bt3dxASdRFipPLKtVoitfsKh1MiGIuoAR05cuSDzz77rGStrm4A4c3BjKqnFNQf7E4qgdUKnqpBRMGBI4OXoCwLECrVlBs0QeRUOJVk258Bnt///ve/xVkBQA899NA+uC8AqK+vD4oZEAK1MwuEi8hxHNNaksbGxoJYS/DBBx98cPDgwUUuBG/p29/+9isIpl98sfe+QKClxeu1O9R72aoJqNXgiceLV2F+8sknR8rxg5YFIHbw6o65+uKtNOWiXmDU8ntaS5TPNqmyxYKCaqkX5HkCmbl0MCiGPZ4QVocz8LCbjQEIo+LYA9vX12ebmJgwNjc3Y4csdWFwX0yCF9ZH2aY8XEn4EiB65plnXnjuuc39oVCDVxB4q5bSUWbMp7BQBBF8UpYxVxYfHh6eZDvklyKXrQiAarlAX4fnls02kd5DindhQyBMEBYDQu0+IwpCcl6vnxt///1ravCUAhAI+tu2bTONjIyYOzo6mlUuDGuZUoIg5EZGRmgVuFqdIy2IeB4rEixQMql47Ki45C0Plo5M3XrjjTMn3nvvvbcrMRPvAajM0TIQlcg2TXqvXk+IB4qdLGZJKXpGIQg7oV0Bt6V+abULQwANC7R//370FsFd7lBZoBjHcRg2iJ06dcrPxNyr1SxUg4iQLrdOFzXLMo2JSl5rRW5TYSQmEoTM+qsFDz7fPQAtcW+yGlGpbBNli/zwQyYDcjpmw27evHkdzEoteNQWqLOzs3PNmjWtjY2Nvm984xseRRF3vdFopGKmkiQFxsbGwo2NjcEPP/zwFtLoycnJW6dPn/68GtVUFhPBnWHXWH+/yx0IWK1eL0BkUvarUrK0TEhG5vlcNhDIZLzeRGJwMOCvxP/RHtc9AFUw7gBRuWwTv8oyTsjKLTVUCAvEFE4YiL73ve81YUYyFothndNGcH6i0eik2+2eJ4RMvf76636Ah21JqoUDDkuEImNnZ2e32+32YNcJhMbVZDNlbgzlluj8/DzEOTGkUNFtqY/sHoAqRgdLZ5v4dZZxVqKklAIRLJHb7Ra8Xi+q0tDzEXft2jWzHPCwj4QUf2BgYOeGDRs2ut3uBrvd7kK7hP1cXau7du3a1cHBwc8rxTz3LFAVgCn3lHLZJp5fbcapBtFvf/vbrtdff70Flujo0aOY+iX79+8PATzf+973pl555ZURrNWqxfJo3zssKJqu7e3tnQjcMTIF0jVGo9AewUStMthQ12Tq/wfuguHjdx7GrwAAAABJRU5ErkJggg=="
         },
         {
-            "path": "Preparation/URL Encode",
-            "displayName": "URL Encode",
-            "language": "PYTHON",
+            "path": "Analytics/Survival",
+            "displayName": "Survival Analysis",
+            "language": "R",
             "optionsVersion": 1,
-            "tags": [
-                "URL",
-                "quote",
-                "encode"
-            ],
-            "category": "Preparation",
-            "description": "URL encode strings in a field using the UTF-8 encoding scheme",
+            "tags": [],
+            "category": "Analytics",
+            "description": "Computes an estimate of a survival curve for truncated and/or censored data using the Kaplan-Meier or Fleming-Harrington method",
             "subcategory": null,
-            "icon": null
+            "icon": 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"
         },
         {
-            "path": "Preparation/Split Address",
-            "displayName": "Split Address",
+            "path": "Analytics/Websites/Website Analysis",
+            "displayName": "Website Analysis",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "split",
-                "address",
-                "street",
-                "zip",
-                "country"
+                "http",
+                "website",
+                "scraping",
+                "analysis"
             ],
-            "category": "Preparation",
-            "description": "Splits an address field into streetname, streetnumber, and suffix.",
-            "subcategory": null,
-            "icon": 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xatt2PHgu0mU7RJqXBxnzKEI/8tQO0CK3QUFhYW0DpsVPdG1dXH0DB+JAAioRg406ZN03+RYEDF1WjcuHGmlJwxEY5vCsjoVnsNaxipx1BjsZMdg022rLP383ydOw/273FvcPkGAZVrlM9XvPjp8GJjbQvhmCwk4mEbWAfpI/QJmNgKAMlnvAdQ3sMYrALgu317ir2x78DCtywd0EDaVlj824BadIvoed1DBi6VL0aMmFe5RFC9Z2qUOfAKVIv7AigksJnV1BFRV0QA4ZdfftE3T3gQcO019KSjJRjoZVJtZHVImhtdK3FbytWqVbiR773+7SsFV2xco25QIf+CAX82SR2LmmACUAjurAEokxMAxSYQOwEpBDTuC+OOzzmexv80fnsl7Wz/gAJlv4iOjj5nb3ufmwFQl/p10pQVjerUzvcdoELF1BNRgcBKM6gRIACUIATxXXSmu+FDWwDI1RJThsKZPPjBSBT6GGOJF8B5EqI0XkOWgvAXCRTwAJT3AAq9IqGiVmAiVAtS/euuX67cUyXwkxoBdV+NiIo8b0lsWfXpzQSoU/1au7bK1/fFvT2rldo3nRgtg8wqNNJl1A0BLDOYm6d2iAF3FmWyBZIBZkChO6gdaxfrmknkSvKciqXNl0gdkxEppa+iL/nLi8+5LsByjwIy7hiu29btW1VB//zmkDpNeq9aFf+ZUhSO/7Ne52YE1Aisfi/V+bz/8MMPzUgLM5obJubLavCSJUtqXQOVffTRR7p2F4rGqHAUmUICkAbAZ8Bk+aEnAHl6LPRNvwGOe2ASCpj85Tt0P0DKd/SLSYbK+GnHrrS0q3l9alStsfC339b1NLhB6ZtJeNqhG3B8RifcbDZpB7xYmEk1V+ah72yanXpkUU8MGG6UanpAQX8CJlL5zjvvaCmAnt1NoN+A+7JegkQCgAIY9wG9AiZ6k/cAyneAirTyPyyBpU6GiXtNvdZi/cpV5R8KDByebNSlNwOg1j6448sNe2PtPJ+k+B6MDukywoEUiTGDcXOQVHvbtt5IwJxdC71J5AvpBEyjzhRQoX0A51gYBmqWnWX4X2LbZcqcKjxs2JxzUhv9b0joP+Bx9fQHCVhDWnaiKKa2PXsW6xPctmCbckE1E+sWj0EquSl0pjd1mz0QuBZGEhSPQcIgsykHSQB39pKwPSfnAywkVPQl54T6kUjxQZFMvuczsXL5DGsaK10CENeubQ8YPz72rEWP6jzt9ZRQe+DJNfWTIGxv+P6uXQuNbB3m36hcpRoFWtaK4wbx5bJqsWZHKnv27KkzN1AehgyDSDt5Uqlp00a5DPbbu7ZlzyQNKPeGioBmARVa5TOsaqRWpJVJwHsBlCgWjf4cPbqp+LRp8SeVDnikj6e3AM2k9yw3pJ1fB/HLvLNfGu3fqOUzgffkKVnzSsjVTwkWeEKXZFhwV8iDutMYrGXLlukBxL3hxSDju7755psZTkHIkEA9xhKMAKWjuwj0U7Pk6aYfnFxAE8sVnYgRhN7kvgEUsMXahpaZUKJjJV8LMwH0zp0rSs6ate54dgF1Bh7SpIMCNs3vqxdeDagd2KRk5dr17k9rWeRTV7uNuQIIKxZwiL+yGpxdUmS/oj179mjjiOX6UJTsdEb2hfpc0mToJz7HLUFKCBEao0gsdCLHSpACNwg/0NNqf7kHaoSZeKyJ5XysvUH6mcCEGykQJzqFbgVgGEFiuQAHsICKgcc5mFiw1tat8WVnzIg+bAFUp9fckVCHes8BeL4Deg4o0un+hoU6hHSoY25YZOH1oExWa3/99dd6IRIrzQjUk1FBChgoBoIAATpQGtULgIO0ATQGFK4A+pEBZOBlpRoAxyzdrvo+28ZhuNDVpON7olpU/MsYkHdF8tlSQIrc6Ds0C6BCtwAoOpS/vOgvfjf3x3HrtsVWnPPBogRjJYU9QCXYK0vYnek9n1YVWxV7vGVosU4tQu/3aVdyHpLhTj2QIyPEVnJxP4zZFMoyIyMjdfoMymLDKYqoAYoFvkRyoEkGB+oiQE9GhgagDC5AE5xAPxNlImjPRGCdKcsRaRrQmO3qiScaZakEVO4PK5yaYtQCdU2UmjLxAJqtenCxoFLAxWCCdsU4YpIxMSVcyHE8GgWGQa+u/mZ10JyIORSEWx+Nkok+DTVBUh/0z9jXVvnervt2QHjLLtX82xZfjW7xdCW2o1mNkjcuMYRSWQLITGRW45LQGAykDhBYkMTNf//999ryJLZLXJcJxeaNgEpQXpYSUnQNoDVq1NCVDUweJgWpNSxZKh6IDTOIejlizI+qe1jDLFcLQul169ZVDRo00GUxNCkuR0V88MEHWi0AFBIJoESsuD7/Q7U0dD4vxppVdLgtfBcXF1dz5syZO92VUOpZCw8v36pCsRK16iW28IuUQXWHatw9htwmuo2dTHr16mX9GQVeGB/QI1UHrGHBYKAOl0EBBDbOoHEcVfVIAVJG41zQMnRM5Ig2c+ZMvR8Du4vxHQ1HHan5+eeftX4FUIDg/DExW1WvXq21ZGe10ae5c79W8fGRugoCqUMQYAjqmGAX1ssKo0iqTEKCgMv9Aig6lJpjDDM+i1m1rsHMKZN+dAaoPH+F3F96qD+bDaMF4wXehyZt64Ko2tu2bZvCTTCu3yT7wQAjtdT3ID2LFi3SxyDNrGOBrmg9evTQuhRgAIyGxBLEx9iA3mj8ZWUaC4fRv+g1gESKkVTSckwYBhHKXbp8uwptW0OzAoYWkuSqNsl2uAhHcm8wAFKJ/4oq4PowDBJKDRMVjBKsN4YCsXLFb2VyoxJQGRrQuEOh777VbVVUlNk3PDy9vNRIuVIobTYvOdhedSkb6w6Wxvgn7+kgF2amQykkoyU7gaUIwFJDJNLBYJK2YkClsdCIindmM+E9JBBJZHLQABRrEYsQKcTQAGCpv4VauTbUKwXWUO6oUaN07BfKpdFH9CxuAwVi4rgz8Cx7hN4Y1ISEq6ply6q6/MVRk5om2+/RjzAGUkYlBflbitgokYF9+B/QmKj85XpIsgQYkFr5DgkVi3hx/Mmu77weutQVoOwMkqm8AeMEMx4Fz0wFBFmvKX4gBgq67a233tL7G5D5hyaQMPQg1qTcEDeNLhQpRP+xc4lspsF7KBUDBzOdgQdQBocbZoCQRmY2N0k5J6DLulOWHgIUk4F+0aB3KJmJRd/4DRMBPUwNklAzExNdtTzud1W6RLLuA8ew0s3RGhmkC8lyFDvGgm7WrLsaObKv9nmZGEguAoCOZTyZnLgtgMe50Km8AJTvuHfKTFlSSVAiKuZw9zGjQqMcAmopFcxtNptTjLOMtSNQGBeDrtggEaWNc41koZto6AQoDKrDGsWvA3RolRvgplmwKxLKADOIKHuolXUpkhnBiGDlNd8xmMxKDB4GG0OJSQR9sgyid+/eWgdRuSf7HyHtTAj0JYByfiiPcyKNDJiEDrGMjddm8NChy1ZsVy2aVdZGFX1GkrJqR9BvhIHJiX3Ai3tgzLBc//rrL21pC5BIJ2MsWRf6BM1ia2BPcL8LvzzSc8yo1vOjzGbfcEtFvy3lolcKmM3mdPPK0pitXIibghKQDk6M7mGrGYqacdBR+gAOkDzGgwEGMEe7hkE5VKyj5AEJ44OojUgTq60BlBtlQCAOKJiJwW+RWKScPRToHzQqFQliAFF9gFRh+SK19Ie+InUASo4RHTxhwgTr/XI9rNxly35SvXq1yZZRBHUjUdgAsALnps8kFFAVTEqiUvjDjIFYulKSIrVF6HskFH1OIIKo19I1if1eeq7BTOM6VmOmQ6/bCApqXnT37vUn5e6QRjZ5ojqgYsWKmoroJB1htmOoIGkMMkYPHeeiDDjGDpJKGSUzzDasBz3KOhYohGuwvTiNczP4AIGE0jCsaAwK1I4RhDEEe3AMk0M27RAJZTIJlaOvkHyCB0we9DzGCZEm40ImK6ArflK9emYPUCYokxCJ5/6QSK6L+mLScB+y95JkXiSxLdUMfE5j8sFG+NUI1rJVpwc+P6DeDKeAvvBIj1LvfzXviAAKWFiHbCVD0ACpY+kB9ANY6ANAhDIwZKBbjA5mPbMIxx1JkPCbnBdq5oagbeiQWcuEECsSaxadiM/I7GXCQI0ci4+K/oQloFOq/pBg9JSE50SHohLQ3zT6yv2gs+gX6gBgmahMDNHfXAujCwl9+ul2eleVrDZCfFyHiQsQYskTLCAQQtaIvkOhTCSRUAkuSJ0RoDLGWMboewQgJu74oKH96023B6h1KeC43iMqjJg9br8RUOiLAcBwMTboFsUOzdIhvgcEGtIHlTJg9nYNY5DRTcxQGjSNrhVXhFnMek4MKqxNVpMxAbgxwmawBpOGfgEoUsCkE0CJwgAeE0bWhWKAEGXCdcLKhIoZbCSf88h2AgDKtZYv36Z6926rV4F7u4ktIAACkFT9YQ2LhctnGEn0CdeJSQxLIsWLYxMHD+5b4yOngE4bPLba4Gmv/CE3wMAzOCzVs+eDQb8MNo2ZAxVjsbFuE7phMhjDeUK9UCq0OH36dG39YsBwLnQZ+gKHe/z4RSoi4hW9tAGjiuIvQIMJeM/xSBZ94xroUFmyj35FXaBvJOiOXmeCASjswnm5JxLIWNFMKJoE69ev26MGPtc5gzvlLWABVMJ8Uk6D0SNA8h1g0zc+Y9wAHeZAF+sI2ldpLw8eVGqCU0DnDZl6X4+pQ36RjqOI4X9okxmOhWVsDDxRD1wBpIvBgiIZHPSXuALyG2Ya58JflCV/6FqkFV0GyBhfSM64cQvUihUfa30D7SGpgIKuZNsaggCcA+OMm0bKZL9epBcKBnTZykZAxtLGH8YPRhKZgJxDnsdG+A3mWbl6l+rXp601bOctMOU8YtUKoEgmEmlMdEvkSPKnAIrFz/+LFl95/fmB5d5xCuiy12aHdHq79/fGzjO46E4iFEiHUeKQAvxO0kIiCey4yUAhHUbrkXNCH7ygZDqLjmNAiaRAl+hg/jL46G4onAEmSICUcl5it/zP+bEWAUOcdslTYjlSDY/fhtFEw4ii/xwj/iSTggll9DG5HhEZJhL+NIOI1GKI8MJG4LPsNqQOAAVQrisBeiPlSn4UtYZwYR2jk7+Iufrm8wMDR9sL/ZnMUWYfU7gpdf2E5c2bv9hxvW1nmfE85IaBNq61xJhhQBkA9B6NvCQDz4tojnFGYlhh1NAxAgSAi8GEHuOFvmPAWKCLO4Rk4qzjg2LUABRlIOg1LGKMK/QwtA3IuE7QFHoGIwLfUx6cx8REMpl8/KUBNjoZQ49IEf3j99wPRguRIgaZQYUmjx3zVb17t/GKXpWSFHsSKhauuC0cwz0ybrAORlX0EjVuyICSr9oH1FKx/sv0TR3vG9Ak0yaMUBuDzOyWQAIDQpAbQMUv5DOiL5jX0C40Tccl+oFfiVONcseYQs9CsdAlUgYAWINYc7ACA08OkEgVtM7sZKBxlwAGwwxAMRjEyMLyxWIGHEASV4YJgdTjF4qhhH2AXsWo4nq4NAAI/TOAwkbodVlExYA62mLOU6mFdgFLyjqllkgiRFJ+IjVIXBsdij5dubrgxAH9Al5yCuiuzzaHVe3ZMMpex/AvEXsGRQYJcHDsCeGJxUrhM4ARkkMvQS1IIUqf3CY6DaNIgtV8jzTDAOhhpANAAQYplc0eOQ5jC3oGUNlVTAL8ktEHCFiCG5fAvEgjQEKv4ndCwYCPTsIWwAaAXrkfBhX6ZxLiXhAlQmLJjnirPFQqEmAFpA5weY/Rg3TSL/5nQnNtGJKJKkVrTZs2zfC0CwksyGLW1ENLdj9VpkuQ3QeeAwISA7VJiI0BQTfhX0lMF92D0SOlIRgfSDc+JdYpK8CwSAFLNidGwgGQv/iYUDysAABIIjfOjUE53DR9gbppSK4E8pEoqJxIEKAbH8mFhBIqhKqZcDQGER3KYHm6bbqn0ujoeAnvCahSCYj0IgTizmAzkF+VMtCVa4t88PLwMsMtD+vLUIJiBfT0yn19AtpVTA/O2jQMCcx7Ih6yiSLmP7MZR15A4TWpAAAIdklEQVSWsTNoSBhxWdwegIKWMUAAFIMIygBUqUYgioNFLHQGtTLQbFODhELZ3BxWNpYsOhIXBqmCVrkm0oh0Eb/FHfHGA/O8BZqj88hElaoEMYo4HinFI4DuYQjUkxSW8f3SuPz93ni1MqG/TBULVkAvL0sY5NepvM4IY3Xh4xE8ZkYjmdAlzrkAikGB7oIqBVA6B3jQBZKGngM8pAq9iZFC1Afpgz6gYyxkJBOTnInD7wl1GaWM/uB3cg4sXmiViYEO9GT3susNkrPz47YxOWERSWpJjZAEG+RzqUEWcCVqJJI7bVpsiVWrpp801GxZ86FWQPfu3WvG4qNBaegzfDJoFf5mIJEaCYIjFYCJe4JUSUNCGHwMCToie/VhrRIqRGeK34duxHdF9yLVUDXuATdKbtVepIlz49JgxHj6PG93AaVfSAmDT2OgGWT+hyYlZcbnTEwMK/pMeFJKUsmu2MawjTlke30xVikYg/NcR9bjxG3dW/3D0UN26WL19CVoGda22AUUicDfQh9hBOGoQ2MS3uMsUDB0ScTH+FxtAIKGKVOEItFxnAPKQFcAnKOtatwZcAYbXeJqB21n52KApJYHgOgXE5AmoPAesJhUjIMndcO21zammWWiCKVKlQLHMFEI7WErbDlUbJI5+fAR5VfwVH5lPprqk3Ygasn7B7ctX37J3p4LmYyiM2fOmI376eF24FJgbGDlQZuS4qLDRG0I/eGA27P80Am2EmZvhkrAwZOt5NwB3vYYWS+ClCFF2a0Ptj2/SLVINFLLtbguVLn3aJFBly5eOHfx0umkpEuJF3xT1XFfv2uJh/bsu5aQ8EPqhg1/mS/u3J2WoBKgBdJMdkuBHG3SbARUP3EoISHBbDvrCXLTGXSpu/vhGm/UNtPiLhD8TmK/oif4DB0N8EgORhABBPQ19TveiODY6x/3L0v8AQjp+fVK0eGpJ8+eyJWcfNYnlzqb7648iWmXL186fHjXpX1rd6R8Hvn5tR1qB8AASpZqtAAu2rLCIUxoNX23NmMGK9OCX2u25eDBg2bbrIq7ALhzHIAwILKoFemVGQwoUB5SA1i29AY9Ulkg+8ejszGMiBrBDqSqMNI4J5/DJI4kUPZBQkphB1lBfSC5Qh9TmkpMSVbnlFmd8z214+ieJR9dWBG57epytU3Asbc6wOntW57oZCwocLozmIt9ixz+NhPlHj9+3OzONuPugoeuYPCgHqSM9/bAcud8SCMGFUlvzit1vPzPRCFChUrgGkSTMO4IFPDdgV3JHa9dSTr712+/nzh75Nil/Yf2Xz157GTqX/s2Xl6+bRsZZLclyLLsMcPKOXv9N+ntpWwxtLtYwa1t39wZo0yUe3TajvYlB9dKr6q6iRp0h69J8J73gMhfpFhKWAja48IwcfgOCj6SdvmZsAdbuSystZGgf3bxsGzJlm5IWlc+GkYmE0BeAycrw28F1MDJvlfj/6ifK7TaZj7DmYcSJdaIdFFJl50mQemjKQUeyZ96LalA3lyXChRQSTxz7vzv5xMTDm25cubIkdTkn6PMHabFyxPuU0d9/PtHKQc+G0glBFSJBIrzDbC4MrhPuDpYj1iKZ5OuDnwwJJhACQ+xy7Q1amYpynRn/ypAno6zo6UQGc4zJ/rnVlVKJ+uCVMJsJJHt6aajqerRgr7qQqpS564qdb6IUudOnTqVVHTxYqq6eHKRx7pHdyTd1zJ/8803vLQRRAgQy5psC9EqpJR8KRkffGTir2RlTp65OrxNy/of2GxefEuB5AmohpVlZnZVt+51a7uyOmry5Lxhw4bJowd57ELKoXnzrn0/fHhKuLtAmfVukxlYwcFmwv9YbfoRc/p35s2bN6cR34VKCdJjdaM3Kb4WQEkQEMViwlGzu//wmRdbNK4zyeaxHZ6M0S11rKPlhPYk1+HW3PJMTgd37sqas/2Z7fGWvftNrLFMI7uD2wLVQv+yuxgUTBSLqBEBC7a8IRZ8KS3fK+1b1Bt/pwMqg2xdWmjRsU7NbjvrQL1BbboP/fqp3EOG/JpCkAODB8sVqxdqJbyGziReTNSKqAt90esqr/qN6tI6ZEwOoPbFzZ6Zd70pyQpo//7bUsiuEKbjRTqOv0gqoJLVkdVj6HqMotPnrr7Zqll9yjR8b/b94r0xkO6s4PbGdbJzDg1oRITKXb36xhSS4GRvSLOR1yS+TLE0AJKHJStE7RPRLgymA0cvjnm4Y9NROYBmBwLv/lYDOnu2uqtSpW+SyciQrae2iFIVKBiQCYZg5VIURmoOKsZgOnj80phHOzXPAdS7mGTrbBrQ8ePr+tetOz6RzSfIAmEUYQhJ3JnwHRJJbpYwHnQMwCmmYmMe7RiSA2i2IPDij8V/jIjoV7R8+UdO4ltSU4RbgiFEiQl/iQMT7iNJjgRL7pLwYMOGDYlV5+hQL+KS5VMJoLGxC8r7+ZVIIAAvew8QKcLwwU3BsmW9J24L/ieRI2K6Zy/6vf1op8YjcwDNMgTe/aEAumX7lirnTpzbQ/E0kSKKyQhBktoj+0I4EcmkzhYDCf8UHZqY4jeue6fm1K7mSKh3ocnS2axpvVXrF9fMowr/ig5FEilpIfdJ/BajCH3Ksg3KZGQfQBYqo2sbN26cQ7lZGn7v/8j6hMdpC98rtf/HB/6r1LKhlIrge0r9zvcHgtfnSjEnXUk6cfKtl2vqpwTglyLJly8X6tinT8fYW+ExV94YvlvBD7Vs7qHjzDwjm3gy+UtCkfKX7/j/iiUGLblNYyBEol7eGLeb9hw3PaDy1APbpx84G1HZdzf9+XPsHZnhCVQ3LRje6NhND6jxUZaWG7btszvxYqePl/LGQN4s57gVALUdq38jnnyz4OWyH7cioC5v6k4+IAfQ2wz9HEBvM0D/D3v85/Tm2LyAAAAAAElFTkSuQmCC"
+            "category": "Analytics",
+            "description": "Extracts the structure and content of a website and its pages.",
+            "subcategory": "Website",
+            "icon": "data:image/png;base64,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"
         },
         {
             "path": "Inputs/Rds Batch Append",
@@ -578,6 +479,21 @@
             "subcategory": "R",
             "icon": "data:image/png;base64,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"
         },
+        {
+            "path": "Inputs/Rdata",
+            "displayName": "R Data Reader",
+            "language": "R",
+            "optionsVersion": 1,
+            "tags": [
+                "rdata",
+                "rda",
+                "rds"
+            ],
+            "category": "Inputs",
+            "description": "Joins regions defined in a shapefile with points defined as latitudes and longitudes, and gives meta information about the content of the shapefile",
+            "subcategory": "R",
+            "icon": 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"
+        },
         {
             "path": "Inputs/Databases/MongoDB",
             "displayName": "MongoDB",
@@ -621,21 +537,6 @@
             "subcategory": null,
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         },
-        {
-            "path": "Inputs/Rdata",
-            "displayName": "R Data Reader",
-            "language": "R",
-            "optionsVersion": 1,
-            "tags": [
-                "rdata",
-                "rda",
-                "rds"
-            ],
-            "category": "Inputs",
-            "description": "Joins regions defined in a shapefile with points defined as latitudes and longitudes, and gives meta information about the content of the shapefile",
-            "subcategory": "R",
-            "icon": 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"
-        },
         {
             "path": "Inputs/Sharepoint Online",
             "displayName": "Sharepoint Online Downloader",
@@ -653,322 +554,422 @@
             "icon": 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"
         },
         {
-            "path": "Custom scripts/ExecuteCommand",
-            "displayName": "Execute Command",
+            "path": "Outputs/Slack Bot",
+            "displayName": "Slack Bot",
+            "language": "PYTHON",
+            "optionsVersion": 1,
+            "tags": [
+                "slack",
+                "bot"
+            ],
+            "category": "Outputs",
+            "description": "Posts messages on a channel.",
+            "subcategory": "Slack",
+            "icon": 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"
+        },
+        {
+            "path": "Outputs/Report tab to PDF",
+            "displayName": "Report tab to PDF",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "command",
-                "execute",
-                "command line",
-                "script",
                 "batch",
-                "terminal"
+                "output",
+                "pdf",
+                "report",
+                "print",
+                "tab",
+                "convert"
             ],
-            "category": "Custom scripts",
-            "description": "Execute a system command.",
-            "subcategory": null,
-            "icon": 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"
+            "category": "Outputs",
+            "description": "Prints Report tabs to PDF files for each record of the input data.",
+            "subcategory": "PDF",
+            "icon": "data:image/png;base64,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"
         },
         {
-            "path": "Analytics/Websites/Website Analysis",
-            "displayName": "Website Analysis",
+            "path": "Outputs/Report to PowerPoint",
+            "displayName": "Report to PowerPoint",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "http",
-                "website",
-                "scraping",
-                "analysis"
+                "screenshot",
+                "image",
+                "pptx",
+                "output",
+                "print",
+                "powerpoint",
+                "export",
+                "slide"
             ],
-            "category": "Analytics",
-            "description": "Extracts the structure and content of a website and its pages.",
-            "subcategory": "Website",
-            "icon": "data:image/png;base64,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"
+            "category": "Outputs",
+            "description": "Export a Report to a PowerPoint pptx file",
+            "subcategory": "PowerPoint",
+            "icon": 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"
         },
         {
-            "path": "Analytics/Network Analysis/Attribute Analysis",
-            "displayName": "Attribute Analysis",
-            "language": "R",
+            "path": "Outputs/GitHub",
+            "displayName": "GitHub",
+            "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "attribute",
-                "analysis",
-                "network",
-                "graph"
+                "git",
+                "repository",
+                "read",
+                "write"
             ],
-            "category": "Analytics",
-            "description": "Given a dataset in which each record represents an edge between two nodes of a network, and each node has an associated categorical attribute, the block analyses connections between attributes, based on connections between associated nodes. The result of the analysis is a list of records in which each record specifies a connection from one attribute to another. The connection contains a probability field, which gives an answer to the question that if a node has the specified categorical attribute, how probable it is that it has a connection to another node with the linked categorical attribute.",
-            "subcategory": "Network Analysis",
-            "icon": 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"
+            "category": "Outputs",
+            "description": "Reads from and writes data to GitHub",
+            "subcategory": "Github",
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"
         },
         {
-            "path": "Analytics/Network Analysis/TSNE",
-            "displayName": "TSNE",
-            "language": "R",
+            "path": "Outputs/Web Image-PDF output",
+            "displayName": "Web Image-PDF output",
+            "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "relationship",
-                "analysis",
-                "network",
-                "graph"
+                "screenshot",
+                "image",
+                "pdf",
+                "output",
+                "print"
             ],
-            "category": "Analytics",
-            "description": "Given a dataset in which each record represents an edge between two nodes of a network, the block will project all the nodes onto a (e.g. 2)- dimensional plane in such a way that nodes which share many connections are close together, and nodes that do not share many connections are far apart.",
-            "subcategory": "Network Analysis",
-            "icon": "data:image/png;base64,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"
+            "category": "Outputs",
+            "description": "Grabs screenshots of webpages, optionally producing a PDF document.",
+            "subcategory": "PDF",
+            "icon": "data:image/png;base64,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"
         },
         {
-            "path": "Analytics/Clustering/GMM",
-            "displayName": "Gaussian Mixture Model",
-            "language": "R",
+            "path": "Outputs/Google BigQuery Writer",
+            "displayName": "Google BigQuery Export",
+            "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "gmm",
-                "gaussian",
-                "mixture",
-                "model"
+                "google",
+                "big",
+                "query",
+                "export"
             ],
-            "category": "Analytics",
-            "description": "Performs GMM clustering on the first input data provided. The output consists of the original input with a Cluster field appended. If a second input is available, it will be used as output instead.",
-            "subcategory": "Clustering",
-            "icon": 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"
+            "category": "Outputs",
+            "description": "Allows to write data to a Google BigQuery table. The table can be created/replaced, or records can be appended to an existing table",
+            "subcategory": "BigQuery",
+            "icon": 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"
         },
         {
-            "path": "Analytics/Clustering/DBScan",
-            "displayName": "DBScan",
+            "path": "Outputs/Append PDF files",
+            "displayName": "Append PDF files",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "dbscan",
-                "clustering"
+                "pdf",
+                "append",
+                "combine"
             ],
-            "category": "Analytics",
-            "description": "Performs DBScan clustering on the first input data provided. The output consists of the original input with a Cluster field appended. If a second input is available, it will be used as output instead.",
-            "subcategory": "Clustering",
-            "icon": 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"
+            "category": "Outputs",
+            "description": "Append multiple PDF files combining them into one PDF file.",
+            "subcategory": "PDF",
+            "icon": "data:image/png;base64,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"
         },
         {
-            "path": "Analytics/Clustering/KMeans",
-            "displayName": "KMeans",
-            "language": "R",
+            "path": "Outputs/Report to PDF batch output",
+            "displayName": "Multi-tenant Report to PDF",
+            "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "kmeans"
+                "batch",
+                "output",
+                "pdf",
+                "report",
+                "print",
+                "tab",
+                "convert"
             ],
-            "category": "Analytics",
-            "description": "Performs KMeans clustering on the first input data provided. The output consists of the original input with a Cluster field appended. If a second input is available, it will be used as output instead.",
-            "subcategory": "Clustering",
-            "icon": 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"
+            "category": "Outputs",
+            "description": "Prints Report tabs to PDF files for each record of the input data.",
+            "subcategory": "PDF",
+            "icon": "data:image/png;base64,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"
         },
         {
-            "path": "Analytics/Prediction/KNN",
-            "displayName": "K-Nearest-Neighbours",
+            "path": "Connectors/Overpass/Street Coordinates",
+            "displayName": "Overpass Street Coordinates",
+            "language": "PYTHON",
+            "optionsVersion": 1,
+            "tags": [
+                "overpass",
+                "street",
+                "coordinates",
+                "map"
+            ],
+            "category": "Connectors",
+            "description": "Finds all matching streets given a street name and requests multiple coordinates along the street using data from Overpass API. It will create a row for each point found that is part of a street that matches the given street name. The resulting rows will include the street name, the street Id and the coordinates of the point. The script needs an input with a field with the street name.",
+            "subcategory": "Overpass",
+            "icon": 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"
+        },
+        {
+            "path": "Connectors/Jira",
+            "displayName": "Jira",
+            "language": "PYTHON",
+            "optionsVersion": 1,
+            "tags": [
+                "jira",
+                "issue",
+                "bug"
+            ],
+            "category": "Connectors",
+            "description": "Retrieves projects and issues from Jira",
+            "subcategory": null,
+            "icon": "data:image/png;base64,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"
+        },
+        {
+            "path": "Connectors/Trello",
+            "displayName": "Trello",
+            "language": "PYTHON",
+            "optionsVersion": 1,
+            "tags": [
+                "trello",
+                "board",
+                "card"
+            ],
+            "category": "Connectors",
+            "description": "Retrieves boards, lists and cards, and allows you to search in Trello.",
+            "subcategory": null,
+            "icon": "data:image/png;base64,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"
+        },
+        {
+            "path": "Connectors/Azure Data Lake Blob",
+            "displayName": "Azure Data Lake Storage Gen2 Blob",
+            "language": "PYTHON",
+            "optionsVersion": 1,
+            "tags": [
+                "azure",
+                "storage",
+                "lake",
+                "datalake",
+                "blob",
+                "gen2",
+                "parquet",
+                "csv"
+            ],
+            "category": "Connectors",
+            "description": "Storage Gen2 Blob connector to load a CSV or Parquet blob/file in Omniscope.",
+            "subcategory": "Azure",
+            "icon": 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"
+        },
+        {
+            "path": "Connectors/XPT Reader",
+            "displayName": "XPT Reader",
+            "language": "PYTHON",
+            "optionsVersion": 1,
+            "tags": [
+                "xpt",
+                "xport",
+                "sas"
+            ],
+            "category": "Connectors",
+            "description": "Reads a SAS Transport *xpt* file, extracting a dataset.",
+            "subcategory": null,
+            "icon": "data:image/png;base64,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"
+        },
+        {
+            "path": "Connectors/Weather/OpenWeatherMap",
+            "displayName": "OpenWeatherMap",
             "language": "R",
             "optionsVersion": 1,
             "tags": [
-                "knn",
-                "nearest",
-                "neighbours",
-                "prediction"
+                "weather",
+                "map",
+                "longitude",
+                "latitude",
+                "forecast"
             ],
-            "category": "Analytics",
-            "description": "Performs k-nearest-neighbour prediction on the data. The prediction for a new point depends on the k-nearest-neighbours around the point. The majority class is used as the prediction.",
-            "subcategory": "Prediction",
-            "icon": 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"
+            "category": "Connectors",
+            "description": "Retrieves current weather and forecasts from OpenWeatherMap",
+            "subcategory": "Weather",
+            "icon": 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To0ODoxMiswMDowMGXfpDMAAAAASUVORK5CYII="
         },
         {
-            "path": "Analytics/Prediction/SVM",
-            "displayName": "Support Vector Machine",
-            "language": "PYTHON",
+            "path": "Connectors/Flightstats/Airlines",
+            "displayName": "Flightstats Airlines",
+            "language": "R",
             "optionsVersion": 1,
             "tags": [
-                "svm",
-                "support",
-                "vector",
-                "machine",
-                "prediction"
+                "flightstats",
+                "airlines"
             ],
-            "category": "Analytics",
-            "description": "Predicts classes of new data from old data by drawing a boundary between two classes whereas the margin around the bondary is made as large as possible to avoid touching the points.",
-            "subcategory": "Prediction",
-            "icon": 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"
+            "category": "Connectors",
+            "description": "Downloads a list of airlines as provided by flightstats (https://www.flightstats.com). The script needs your flightstats app id and key which needs to be obtained either through buying their service or signing up for a test account.",
+            "subcategory": "Flightstats",
+            "icon": 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"
         },
         {
-            "path": "Analytics/Survival",
-            "displayName": "Survival Analysis",
+            "path": "Connectors/Flightstats/Flights",
+            "displayName": "Flightstats Flights",
             "language": "R",
             "optionsVersion": 1,
-            "tags": [],
-            "category": "Analytics",
-            "description": "Computes an estimate of a survival curve for truncated and/or censored data using the Kaplan-Meier or Fleming-Harrington method",
-            "subcategory": null,
-            "icon": 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"
+            "tags": [
+                "flightstats",
+                "flights"
+            ],
+            "category": "Connectors",
+            "description": "Requests information about flights specified in the input data from flightstats (https://www.flightstats.com). If the flight exists the result will contain live information, otherwise it will not be part of it. The script needs your flightstats app id and key which needs to be obtained either through buying their service or signing up for a test account.",
+            "subcategory": "Flightstats",
+            "icon": 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dHRUZFCiDgpZWJZRUWH0tLSVCG0JF3XEomojX0Pe+Atq5HW/8IaTM021Oq8ybG3mUWuBpAAMZRSkFJ6A4HAZ2WVlWsLCwo+mzBhQmVpaSkdOXLEFhWqtgJsDJgBO5Z4XDD9n67U175f1W/CbbSd4tOv/dkIE6oXHJL/fFZWJ2HMDNM0CQDdddc9SEqKeT42NuY3zJBEqC6xBQuu9P+eQ1mjpezcCjULve1rHRvOsU0OISjvyeY3SJCSUpoHDMPYWlFRsamg4OSXEyeOOHXmzBnqkJCA0tJSW1BIO1Ww2tXsQpDfsEl7Xb956juD33hh/pNap/SZHGYhYo1ZoJqievxeQ6Up7tcvDZGREUmWtRiwZLgde/jcHWWcBMEiwiaIah2wp15YcQhKMaSUp6WUBYZh7gwEqr4qLi7e9vzz/1Pw2mtzjEAgQJ06dWIppWbdQ1o1RWqqRdU9eDVW5jdAmoAp15VpOP7okqmLExb/9ek5f3Z3HDKJ2w/vHi7CFLTD9t/1la4aIoz27t0Pw5AnPR4AgAuOnKhexW09nKpVIoL1SL5cKS5h8E+maR4xDXnANAP7y8rKvt+7d2/hxIkTvbaqS0pKYmamlSvv1QFASqlQa2uacBFVV/Or9z8kSM6EyHrMq2NE5kO/nTz2P/543/jH46P9qa4OQ8c1ZxMwACiv4EJNB/BF/Q856xxxZwzr2rWL+vrrHYNiYiIe1XU9FjVqkZk5AIABYbk3JZWCn5nPEnEpM8oBLgsEAmeqqoyTmgZvZWWl7/jx497Dhw/7pk+fXimlJNuFOVRpbfepHG37xSevOPdAJAJzJgSug8AYyF69buGCgg9o0aLyR383+tno+BiO0whxuoZogGOZKEojuDh+aKI6z95T5PumYNTkxb3tqdvhVInNLl82kC44N1O1z7+oZhc5LI6ZQciCQCIIY6BIQHEdeVSvXr3QsWNHZGRkZDrfX7IEeOyx4C6kQI0bDmcepkK8nmr97czX7NdaZaGLi5hQ4CAvKJ2WgnAVCIkgHADZDy2rt8+beh4iskDi9qA2CEul4zy1wBA7VS06LjfQOY+Y6xs9Ds26WtGKVrSiFa1oRSta0YpWtKIVvzz+D6POI+QlWQsDAAAAJXRFWHRkYXRlOmNyZWF0ZQAyMDIyLTA2LTIwVDA5OjMxOjA2KzAwOjAw+UkGSwAAACV0RVh0ZGF0ZTptb2RpZnkAMjAyMi0wNi0yMFQwOTozMTowNiswMDowMIgUvvcAAAAASUVORK5CYII="
         },
         {
-            "path": "Analytics/Validation/Model Validation",
-            "displayName": "Model Validation",
-            "language": "PYTHON",
+            "path": "Connectors/Flightstats/Airports",
+            "displayName": "Flightstats Airports",
+            "language": "R",
             "optionsVersion": 1,
             "tags": [
-                "model",
-                "validation",
-                "confusion",
-                "matrix",
-                "precision",
-                "accuracy",
-                "fscore"
+                "flightstats",
+                "airports"
             ],
-            "category": "Analytics",
-            "description": "Computes a confusion matrix as well as model validation statistics",
-            "subcategory": "Validation",
-            "icon": 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"
+            "category": "Connectors",
+            "description": "Downloads a list of airports as provided by flightstats (https://www.flightstats.com). The script needs your flightstats app id and key which needs to be obtained either through buying their service or signing up for a test account.",
+            "subcategory": "Flightstats",
+            "icon": 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"
         },
         {
-            "path": "Analytics/Data Profiler",
-            "displayName": "Data Profiler",
+            "path": "Connectors/Slack API WebClient",
+            "displayName": "Slack API WebClient",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [],
-            "category": "Analytics",
-            "description": "Provides detailed statistics about a dataset",
-            "subcategory": null,
-            "icon": "data:image/png;base64,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"
+            "category": "Connectors",
+            "description": "Allows you to call public Slack endpoints.",
+            "subcategory": "Slack",
+            "icon": 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"
         },
         {
-            "path": "Outputs/Slack Bot",
-            "displayName": "Slack Bot",
+            "path": "Connectors/Dune",
+            "displayName": "Dune",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "slack",
-                "bot"
+                "dune",
+                "crypto",
+                "analytics",
+                "blockchain"
             ],
-            "category": "Outputs",
-            "description": "Posts messages on a channel.",
-            "subcategory": "Slack",
-            "icon": 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"
+            "category": "Connectors",
+            "description": "Execute queries and retrieve blockchain data from any public query on dune.com, as well as any personal private queries your Dune account has access to",
+            "subcategory": null,
+            "icon": "https://dune.com/docs/images/dune-icon-only.png"
         },
         {
-            "path": "Outputs/Report to PDF batch output",
-            "displayName": "Multi-tenant Report to PDF",
+            "path": "Connectors/HubSpot",
+            "displayName": "HubSpot",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "batch",
-                "output",
-                "pdf",
-                "report",
-                "print",
-                "tab",
-                "convert"
+                "hubspot",
+                "crm"
             ],
-            "category": "Outputs",
-            "description": "Prints Report tabs to PDF files for each record of the input data.",
-            "subcategory": "PDF",
-            "icon": "data:image/png;base64,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"
+            "category": "Connectors",
+            "description": "Retrieves contacts, companies, deals and lists",
+            "subcategory": null,
+            "icon": "data:image/png;base64,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"
         },
         {
-            "path": "Outputs/Report tab to PDF",
-            "displayName": "Report tab to PDF",
+            "path": "Connectors/Etherscan",
+            "displayName": "Etherscan",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "batch",
-                "output",
-                "pdf",
-                "report",
-                "print",
-                "tab",
-                "convert"
+                "blockchain",
+                "ethereum",
+                "etherscan",
+                "crypto"
             ],
-            "category": "Outputs",
-            "description": "Prints Report tabs to PDF files for each record of the input data.",
-            "subcategory": "PDF",
-            "icon": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGIAAAB4CAYAAAAJ6CfzAAAAAXNSR0IArs4c6QAADNlJREFUeF7tnV1wG9UVx/9rWba+bElxEkkQnGGCYepS0vIQhoeUB15C4a3lyRlmIMURX4lI7CQkaaw4QBxbBJEJwUnTmZbGfWGm0wdoMv2GdMpUmYFSiqfgpBl7AtJiO/pcrWXJUufKkrJaryzJWlm6yupRe/fee87vnnP23r33LAOJX3d395OdnZ0Pd3R03PvDNabuuZbWdfPNai2n1aqkyjfaf3qeT7Qk4nOa+djMRzcDX8zOzn41NTX18fj4+HvVkpURV7xt27aTGzdufOQek+k+TqvVV6thmurV83z0aiDw38nJyQ8vXbq0pxp9zwPR09Pz/hbb+u/5DcbOajRGe53mcMjr8bGfjI2NPSG3LDkQaQhWy/f9be13yt1II9VnDof8Hh/7D7lhpEEQd/TY/d/5cSFL0KeSaNZooTEYAJUKYJZ4tMbQdSoJJBKY46JIzPHgmCZJuUzhUPCKj/27nDAYEpi3bt16wGazPShuVb+wAIPNBvtzzzWGosuUYnR0FBGfD5zEwDOHQyGPj70sFwyGWMOjmzf3igOziUlh9xFnmV1vzOLuE0MIzsWWCGcOh8IeH/uRHDAYEhu6uroeF7ZCLKFvcLAxtbpCqdzDwwjyfNVgMO69e9hAW/t6YQsWiwV2u32FXW7c29wjwwhGJWFwHh/7t0osgxnZv5/ntFpNVn0kMPc5jzauNiuUzD0ygmA0KmUZFcFgnE5nSlirsbUVjgMHKuxuY9/udrkQ5LilMEKhqIdl/7oSy1gCwtLRAfuLLza2JmWQTm4YS0GsX3/bPq6Wy8f9hgvBiIRlhEO8x8f+pRzLUECUq31ReffJkwiGw1IxI+bxsX8qFYYCokIQ5HY5YCggZACRhvHmSQRDkpYx7/Gxf/z0009/tdwyugJCJhCLMN5EMBRaUiN5v3E1EPj3csvoCggZQSwHg1wz+/3XPTMz41JxQwEhM4g0DLcbwWBQsuZCMBQQVQBRFEbUf93zdb5lKCCqBKIoDM5//eLnX/4u++pVAVFFEMVg2LzeT966fHmIPE0pIKoMYjkYep6P/+vGjT+Q4E0dCPfQEIKxxZc0tvkYkhYr7Lt2rYI6K2uiUAA3R0IzF/8z/muqQLhdIwhyi0vQ+uQCuKbFbVZGrRaOffsq09Qq3F0IxsTExAdUgTh6dPE9idFsBlIpID6fA0PLyyz3yTcQDEfysJsjoWnqQJjUzdh98FBaEOEI06tU6Dt8eBXGdeVNuJwDeTtE9DwfowbEhXffxbXr1yEc+WSXBcuyOc0MDAxUrqVVqEEY57LNUQdCqGwxCFrc0+ipU2D9/jzkCohVsABxE6OnT4OdnaUTBOk1CdabOjux/emn00JQaxGNAMKi08Lev/ioqoCogTlnLcKWSqI3s91HOK8g16mJEbRbxLnDB+FVtyIbsF1OZ96+VFqemqiPEaMuF1iOS4988hM+uupTKfQ56dirSz2I7FzCaDSmQQhfvhh1WjgysaNGnrPkZqkHkY0TJoYBr1YjNj+fE56W+JB+yKA9RhAhzg0cgbdJBXUqhbjg3IIComSDlKdgoRVMWgJ1w1hE1j0JserUavQfPCgP6VWopSFcE9HT605nnlsiwdvhcKyCCuVpomFAZN9LZNVCU3xoGNcktZtOsQh5LLWsWlxHjoAjR4wFP51Oh/7+/rLqqWVh6l2TeJFPqEya3BP1IEaOH0dUMIlTNzVBrVYjGovBYmyH3fFyLQd6yW1TD0IcpEls0Gg0aPrmBrwqNSxka419Z8kKqVVBqkFITeSyk7jRs2fB+nxpvdLgoqgG4RoYANd0KzeGrrUV/YLTr6PvnAH77TTuUTHoOXykVoO9pHapBSEVpKVG/phrBFe5KDbZLNjeW78H9qkFMfL6a4jGE7nRpmdS6CuQJ+Q3Rw5hQtUCSx2fjqUShJQ1GI0mOBy7Jd0ADfGCShAjQ0Ppx1Phr9hKq/vUKQT9fthiPKLWO+DYtQsXzv8ciPJgYnNYE58HE+WAKId/GjtyVdviMaC5BXqDHmGNtmoH/6kDIWUN4iAtNgvyFs/79Q0w8Ti4vOQW+SUf4iOATgeAwRS5lFiAV9W8xMqMWg0c+/aXFIRLLUQdiJHhYURFqXnE60oXzp8HNzOdHuFkLiH8mRcS8Kua0zvHE1odNDpd2jqW+42eO4fYzAwC8XiumEmlwm4Z99VSB0I8gVMzDNZYrUAkjKZQCF5RqjcrwyCl18G+ty+tRBIvwHFgM5kBjHo9NG1tsO8sPulzHTsGLpnMwdh0113Y/swzpQ765WHT9KpUagInfj1qY1JIthlhf3n5pY3Rt98GOzOTVk6ph1tGXC5EBRlohBvbKqVBlUWI9yxlhbc2q5AymsoOpMTlMKwPvuStwEFSImnWrIG9t3eJbl2vvgpuYSH3v7W1FTtlSp9EDQjXoBNcKj+Tpk6rRb8Mp4Iu/PKXuHn9f/BnThsRTZtTSbQYDDB0rEWE5xELBPJiBCmzacOd2L7jp5Uaw6LLrHfXdHbQCZ8IAOk48e2OvkW/L9ePLImkg7LAQgrVbWxtgePAK3I1Xb8gzjqd8GW2xqhSKSwItsmQJ56+o9VN9EisBMEAGC4ChiyzJ5MIN7cgpdXkNjzLRqEeLeLsscGcz9apVIg3NSEueGwkwltI3lkJHy6nYla7rrpxTaMnhsBm8qhaNK2AeQ3CwSCioqSFRvLcT9Er0FKB1hwEcQHXJifT/bWqm7EzcyiRzKDDN2fzF/YaOPdsTUH8/hfnceXG1+iKhpH87gPY/tRTuQEktTOjEV1SVuCagbjoGoaH47HF1I7HdudPvqQmbsa2Njj2VOUzDaV6j6qWqwmIC243rgWDucMlQgkll7hb1HC8Qs/2yZUQqwmI9AFEsxnbJRbbxLPn2yUx/KqDuHDyDVwLR2CxWpcstL01OIgASeOQ+ZkXFrDrNkkMv+ogxl47hquJZH62gHPnEPnmm7yzb+bUAnY5qztpW4kLqdY9qw6CCEJcE1m5nGtWg+H5JWs4cq/1V0t5ctZbExBnXz0G38Ktdf2sQOmvtaxbB/tLL8kpIxV11QREVjNjI8Pp7/eEm5thsFjz5hFUaE/GTtYUhIxyUF+VAqJOECogFBB1ooE66YZiEQqIOtFAnXRDsQgFRJ1ooE66oViEAqJONFAn3VAsQgGRr4HTp08v2bFBjukaDAY8++yzBdV14sQJMKJPGre1teG5Ej/zfPz48aIoyJGwZ2TabFyosbqxCPEub6kOa7Va7BNtsSzlvkKHWA4dOoSWlpaiIEiBYgdhSqpkmUJUgSByEAvZu3dvTqRSQBRSpAJCYmQIFSoefS6XC5xgO7zw+nL3iSGJ6xWCsNls6K3h7sG6tAgpN1BI4cuBILyF19etW4fnn38+NwyEINauXYsXXnihUg+z4vupBCF0NcVAiGEIISsgynRNYmUqIFZseMVvLDayhdfvvvtuPJXZnlnsvlItQuy2ivdY3hJUuKYzZ85geno6J3mpwTp7gxCW1WrFzszBRfFTk3jneSqVwvDwsLwaL1BbXYIoJnklIMhkb09mD20pj6/Vnj9kZaUOhFgx5bqmDRs2YMeOHWn5FRBFgrXwMnEPnZ2dBZcYygVR6KlJ6LKKWWQ1rtelRZTjDuQCocwjMsOrFIVKjcRS7itURplHrGAeUcgdFAMhvC5eNFRArBKIctaaFNcko2uan58vuKwtFXcUi6iSRRRyX6W8j1AsQkaLEIIgs2Ty9m65H5k185ncT8Jlk2o8nhars24eX4t1tNGvKyDqhLACQgFRJxqok24oFkEViLVrYa/h+9w60VVVu1HS96xJnjuHTLnrqioNxZW/dfx1BOZvpTPV83ycGdm/f47TaluzculTSfRlvo5Lsax13XXx53nM4VCIce/dwwba2tcLe07DNxjqWtPLdE4qJZJtdvoq09PT835XV9fjwntXI48erYqspN8k5Snr9S6pYmJi4gNm27ZtJx/dvLmX02r1whKmJga7f1bfH8SoRCm1uFcql62e58N//uyz80x3d/eTW7duPWCz2R4Ud45YhsF2B+z2+v0oRi0UWm6bo++8g4jXu+SzbaQer9f7yeXLl4fSWW6JVfzo/u6f3DS03yXVCPlod7NOl86rDfINOEF6n3I7dVuUJ0cHEgnMRSJIRKN5mXiE8pvDocmLX4z/9tKlS3ty6YZJrNhitfzA39Z+x22hrBoLaQ4Hb3h83342Njb2BOlKXt5nAuMhm+WBQpZR4743TPPmUGjKw7KfZyEsAZF1Uxs3bnzkHpPpPnEAbxhN1EgQPc9zVwOBLycnJz8k7kjYjfxM6JkrJIB3dnY+3NHRce8Wm2XLvErdLpz01UgOKpvV83xMnYgHr/jYK7Ozs19NTU19PD4+/p5YmP8DucKIwMUmGH0AAAAASUVORK5CYII="
+            "category": "Connectors",
+            "description": "The Ethereum Blockchain Explorer.",
+            "subcategory": null,
+            "icon": 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"
         },
         {
-            "path": "Outputs/Report to PowerPoint",
-            "displayName": "Report to PowerPoint",
+            "path": "Connectors/YahooFinance",
+            "displayName": "Yahoo Finance",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "powerpoint",
-                "pptx",
-                "export",
-                "print",
-                "screenshot",
-                "png",
-                "slide"
+                "stock",
+                "finance",
+                "ticker",
+                "price",
+                "yahoo"
             ],
-            "category": "Outputs",
-            "description": "Export a Report to a PowerPoint pptx file",
-            "subcategory": "PowerPoint",
-            "icon": 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B02Vixel+4R+5aZABv94PZADtR7w6+QpxgFyNcysG/sCTT5pntfMjtGTFj0ZuPIG++ew35fxvw82Mx32/XzRtc19vORdB+5CvpB4YK3pBQSEQQoPFM25BLymQXI2wnv+JOrEh4X+vDRBRP7l40+9qvGzR9UsarYT+t0sgD2++FyubrPYbQUnV5OHgaOwhqoH/3rRPesf0joxRBNEKgXbohSD6rcMkolgRI50BHklghuIeCiwOvkJQq8ZTzflKFz0pm3nDjvsQWWFSATsov5R8vPnPRt28Hvs28BwZ9EgTnwRIBEBEVjVLgk9HEJlMkSSmQRUABJwCMIbknAJQhy8HhB+dcT6B05Du3XPeQ6dcwwpUcVYPE9t5Hk9561Z/WqdXGrhGcc/O7xKYIIggCZCG5BcEkCJYLgkQQ8QqBEJnhEoBvYLQnIRJCIIAlA5FtXsKZh3Bufk922yqismPeb7XVrVh0jZBdlUwsCwZwAEQcADfbxuwTgERLcguCRKEj9IqAcwS1M/3mkAKSqOPyz2xZMu+TyW3JKAQBg8c9n7mur/WIQKPMvX6OIJIeIIShgyZIguIggC8BFAm5ZwEOIAl8WAnKM/8+XtLCj5vSVk+964mfJtVUWZMlF09m7vy5bBBB4RwEQVgBBgCxCLBBQBrcQYauXRWAkkJSHI4G8o8a3THp0eWXyxpJhWfvSMpJKSif/sOTRzWprU3ZigOCPQBAYAFWiEMgBFogEXxBFWT/yRAH8g0ag5unXKZW2yoq8teD35B4yoqblT3/4BIova0wggmBKEeCG4oHQ2L/Q/m7fnx/0r1ZWY8KS9yhVY8mavHnzleSqrBynfLxxG/zezMcB4WAw4AJEEHyJQn8HI/7gZxGhMLlu/WpFFSYsW09paadsyrpbriSpd9VYbF3/JRR/VtJBCv4OpXVSOD7oVoT8AZ+h9qrChOUbKG2G0hOy/vXXRkvLHqylDChBVC0/SAMiIjgUEYohIoLFEPg5S/3MUI4YiuMXraZ0t1OPyPt/fmV42aqF/6Su9gynhN0sEPLsguL35TT4YPiGHYWJC1dRJtqnR2XLVWez1JipknE8sLHAh1xFLgd93uHHbJm0YOVJ6TynyJWHm7zkHfJOnRkO0NKxhXJ+WSAY+XdvAdqnQEcPRcQJuYi8pqH5zMv+nG7wc1LZN7y68vyqlfNfz+StxdYiczrSL63AvntfHvOvY4bWZtpF5oz87bknysu3vtvmOrQ/82liroqmob3mNJx0z8KM3mpOt8PGFc/v7vuXp0aCCnrYZjz27hIcvuCGO6fPvvzhTF8r51v2w9/d1EduOXS4dNc2/enlBSTk93LLKbNqB8/9/dgjszRtIW9M65ObZg+W2lv2yQ37ACosRSBV4bbxp5B/8Mih066/dV9Wr51PDfUSszz8mUcuLv943Uq5qSHvGYFUP3eMmUj+waOm/uTmezf3yD3kY8MtZnbV/HrWOIC2euq+YpbdefUcwtfFrSecTSxJU6bcteCjHlXCfKfPTX+4fRr5fe9UfLa+BJqWs6xAqgK1vBJtNadv9Q04ct5pl1/zTk7cV6H40ff/+pcxfd5eOlrpN2R12Y5NIGawkHoe9JIydIybBupqv+CkB597I+cUsxCj6XXvvjei31uLJvv7D3vZs/tzeH7YC5ZdGVcIUhWANXQN/TH8w45qgrdzzpT7nlqR03FIMeTV769aeX351vcEEZ1Hiu8cV0M95OYGkOIDSAQUI1QPNhPWQMyApgGayuwpJX/fQfBXD4HU1fa0t2rglx2Tz6k765xz38qbQBRFLB+88uKdru++RkndTrgOfy8Jv88Fw/ybyV81oFMtKSffkFHwDT/affrFl90PRxxxxBFHHHHEEUccccQRRxzJH/l/yV2EmzNRGrwAAAAASUVORK5CYII="
+            "category": "Connectors",
+            "description": "Fetches price data for tickers from Yahoo Finance",
+            "subcategory": null,
+            "icon": 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"
         },
         {
-            "path": "Outputs/Google BigQuery Writer",
-            "displayName": "Google BigQuery Export",
+            "path": "Connectors/BigQuery/Google BigQuery Import Table",
+            "displayName": "Google BigQuery Import Table",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
                 "google",
                 "big",
-                "query",
-                "export"
+                "import",
+                "table",
+                "query"
             ],
-            "category": "Outputs",
-            "description": "Allows to write data to a Google BigQuery table. The table can be created/replaced, or records can be appended to an existing table",
-            "subcategory": "BigQuery",
+            "category": "Connectors",
+            "description": "Allows to import a table from Google BigQuery.",
+            "subcategory": null,
             "icon": 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"
         },
         {
-            "path": "Outputs/Append PDF files",
-            "displayName": "Append PDF files",
+            "path": "Connectors/BigQuery/Google BigQuery Custom SQL",
+            "displayName": "Google BigQuery Custom SQL",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "pdf",
-                "append",
-                "combine"
+                "google",
+                "big",
+                "query",
+                "sql",
+                "custom"
             ],
-            "category": "Outputs",
-            "description": "Append multiple PDF files combining them into one PDF file.",
-            "subcategory": "PDF",
-            "icon": "data:image/png;base64,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"
+            "category": "Connectors",
+            "description": "Executes a SQL query on Google BigQuery and imports the query results",
+            "subcategory": null,
+            "icon": 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"
         },
         {
-            "path": "Outputs/GitHub",
-            "displayName": "GitHub",
+            "path": "Connectors/Flipside",
+            "displayName": "Flipside",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "git",
-                "repository",
-                "read",
-                "write"
+                "flipside",
+                "crypto",
+                "analytics",
+                "blockchain"
             ],
-            "category": "Outputs",
-            "description": "Reads from and writes data to GitHub",
-            "subcategory": "Github",
-            "icon": 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"
+            "category": "Connectors",
+            "description": "Executes a SQL query on Flipside and retrieves the blockchain data",
+            "subcategory": null,
+            "icon": "data:image/png;base64,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"
         },
         {
-            "path": "Outputs/Web Image-PDF output",
-            "displayName": "Web Image-PDF output",
+            "path": "Custom scripts/ExecuteCommand",
+            "displayName": "Execute Command",
             "language": "PYTHON",
             "optionsVersion": 1,
             "tags": [
-                "screenshot",
-                "image",
-                "pdf",
-                "output",
-                "print"
+                "command",
+                "execute",
+                "command line",
+                "script",
+                "batch",
+                "terminal"
             ],
-            "category": "Outputs",
-            "description": "Grabs screenshots of webpages, optionally producing a PDF document.",
-            "subcategory": "PDF",
-            "icon": "data:image/png;base64,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"
+            "category": "Custom scripts",
+            "description": "Execute a system command.",
+            "subcategory": null,
+            "icon": 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ZNpl9CMKKEHHdZoioMOSW8EZcMrmR6jIx8AqIXOVtFbQrWH7s82GN4IoE7IPgxlQWfWpbKxohUptYRex92CpWbq4zNfAcmdIz+JvhtHvoKR9pXoezDkV4x2EZr5BjS+ZsoPzGt+sA/xmSE/7cS1Yg70+Gvn70DPfbdAHcXIgGoeVFbUfXwfIBIHoo/lCmUbhtsDeEABial5ReBqqmZfJMZGVkWkjKVlTINLFEvEAsWPtO+Y34o1rhpLwVEtxI9GJsP9Ia9KxlCEiKZyU4xu6WY99YV3YABrOGaGx9LiVh2jd+GXjvpsGkum1pDA8DHFCIcTzPgMKSg1hYqiD78g3lqHtUF7HMbIEwFXa/Y4H+gzmahSFakHYuM3UoNnSFmIlKIGaBChuHz4WWmPRDzVjEF0rJdVk6dKpLiY9Y7fjd7PvSqfKTmvVnIFvVuFAlE3pkTmJgkRfEGiuhb7rJbLqrok22WbrHvXjgvWkjE/2DAASdEEuBmsbl4KyVwhMWYHL0PhHU3gmlyyjw42nuRUv526fz+6LPTslUPXt48vqfXH12+NT2ZVyls0/CGkoTMA+gsaakUtWLS/PkyQFwzMoo2lD1HobqHgUDJSJaOe2m4wQUO0E2mDLI/I4QZdynAUs95XLdSuRbFLYzl+ttVhfi8btNzfitp8V9U/LWrzVNVjLPKcQiTygEnUPpNaY+qPvJ3hbE1rdq9WqTPCT0lFlsD7ZOoMQW806+OHUW8rwCEqJBbdU32sn94AzqutIk3vFJ6boHrjoUDRy82c1FUNc00kLdRm7Q0PJbuxRHpHM9zukqlDKmc0We+v4ckSdaK7fIvK2fpEl0Mftd4tsJC2GzV6tZWgO0UilCSsnYgoQogYDQoEYT0SdYxli+QLtaW3YfXBC7Xr1FxW16596GMaiJjAJeflp/dFiVQFMs2r8FYVfhKz76YxJm65GR2AVms+sQi756m3myBEJbV/NX3f0vy+6fuW5vdN37c0v2/6vqX5fdP3Lc1/1hQ4bAoNMaByQDZOasiL4IcJfq9N/kAys1Kx8Ik+xFNDbTkNMM/FAGqo0YdjBhzTK5ftuPNoyT3NJlmSzdgqD4s+JhH0DnybKD/pnsQHyygqwNChP/BIA+lRa7w2P7ROENa6OeJ1IobawjCJlu0DvSWQkFFtnqiqKD0oGCFgG4K34+4b47TqJJeK4bLAZBPLJZr4PTlGrQ/cnkVXOYi56ril17fqbiQOjFFJ0Rx23+DR9n3sjHYFvKjdQk0BCn5QnvAJjnZY+KDplr7eelQBo6CbhMUcmkc5gZa6Yy3jeEhfULEBRayPQS3dN/QB+ViqX9hI0ade9CbPoIO1u7dAbMfyGuS7Cr/e9CGGhUiMOt6mQ8Lz9p5YTGDRe3WDereX0SPMRR+roKpRa1rTjamtMS8vDdBp4rAgpI1L+bo73PXusGZcUKo/TL90t00fDHpuaX7f9H1L8/um71ua3zd939L8vun7luYvC/zc0pymOYpFdKLgC4GPVG1rNoV1s/kqsFAjWImG+x0gfuoL4VjwaRN5RVEHo4VHReE1qkxMydJNSkQjlEJ+N/XnnOl6D9MCDQg2lCOv7fGj/jyPgwraRTdZUGJoxogu8uc2zg6WS6pZEH1yBf5YtVW9GVatPrsSxsa/4jydT7p1g4nLun0YisP0ybfbGPqwDlILBKnYzuWCjBDgTGw0YgNg41K2DN32dO2qA0bKrCIOPRQWIQ78qAcUO3pId3SC+KKWcge3QlH9A681FI560pJn16cZsffYTNSrf3uakYtS2P7j44z6MKOKmvM4o7k/z/ijxxm7XCN+8YSd+fbxvH80fm5r/tL4ua35S+PntuYvjZ/bmr80/mKx//0449ldImX0cca2XfRZdMsZN6VP3ThvShF0+JTifcdUqVfo5H06DwWiiMkkNQ51kX5ldVQllkxEKUrtoZ9BIAs/jQfWW1CTPppuKGWKyuFYNBUd2rWCAR5YeO9rPx/Ndyf89PifdcTy4q6s+X8+fh9CeHl7TgAAAYVpQ0NQSUNDIHByb2ZpbGUAAHicfZE9SMNAHMVfU7Uq9QPsIOKQoTpZEBVx1CoUoUKoFVp1MLn0C5o0JCkujoJrwcGPxaqDi7OuDq6CIPgB4ubmpOgiJf4vKbSI9eC4H+/uPe7eAUK1yDSrbRzQdNtMxKJiKr0qBl4RRD860IsumVnGnCTF0XJ83cPH17sIz2p97s/Ro2YsBvhE4llmmDbxBvH0pm1w3icOsbysEp8Tj5l0QeJHrisev3HOuSzwzJCZTMwTh4jFXBMrTczypkY8RRxWNZ3yhZTHKuctzlqxzOr35C8MZvSVZa7THEYMi1iCBBEKyiigCBsRWnVSLCRoP9rCP+T6JXIp5CqAkWMBJWiQXT/4H/zu1spOTnhJwSjQ/uI4HyNAYBeoVRzn+9hxaieA/xm40hv+UhWY+SS90tDCR0DfNnBx3dCUPeByBxh8MmRTdiU/TSGbBd7P6JvSwMAt0L3m9Vbfx+kDkKSu4jfAwSEwmqPs9Rbv7mzu7d8z9f5+APtgcnf++xzBAAAPoGlUWHRYTUw6Y29tLmFkb2JlLnhtcAAAAAAAPD94cGFja2V0IGJlZ2luPSLvu78iIGlkPSJXNU0wTXBDZWhpSHpyZVN6TlRjemtjOWQiPz4KPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyIgeDp4bXB0az0iWE1QIENvcmUgNC40LjAtRXhpdjIiPgogPHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj4KICA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIgogICAgeG1sbnM6aXB0Y0V4dD0iaHR0cDovL2lwdGMub3JnL3N0ZC9JcHRjNHhtcEV4dC8yMDA4LTAyLTI5LyIKICAgIHhtbG5zOnhtcE1NPSJodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvbW0vIgogICAgeG1sbnM6c3RFdnQ9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC9zVHlwZS9SZXNvdXJjZUV2ZW50IyIKICAgIHhtbG5zOnBsdXM9Imh0dHA6Ly9ucy51c2VwbHVzLm9yZy9sZGYveG1wLzEuMC8iCiAgICB4bWxuczpHSU1QPSJodHRwOi8vd3d3LmdpbXAub3JnL3htcC8iCiAgICB4bWxuczpkYz0iaHR0cDovL3B1cmwub3JnL2RjL2VsZW1lbnRzLzEuMS8iCiAgICB4bWxuczp0aWZmPSJodHRwOi8vbnMuYWRvYmUuY29tL3RpZmYvMS4wLyIKICAgIHhtbG5zOnhtcD0iaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wLyIKICAgeG1wTU06RG9jdW1lbnRJRD0iZ2ltcDpkb2NpZDpnaW1wOmY4YTNmNWVhLWY3ZDAtNGY0Ni1hNzQ5LTYwNjk2ODliNWMxMiIKICAgeG1wTU06SW5zdGFuY2VJRD0ieG1wLmlpZDpmNjQwMDRjNS1iOGQ5LTQzN2ItOGJjMC0xZDgzMzZmZWQ4M2MiCiAgIHhtcE1NOk9yaWdpbmFsRG9jdW1lbnRJRD0ieG1wLmRpZDoyYzg4NmMxYS0wZmZmLTRhMjctYjM0OC0zZTkxYTYzZGUyNzAiCiAgIEdJTVA6QVBJPSIyLjAiCiAgIEdJTVA6UGxhdGZvcm09Ik1hYyBPUyIKICAgR0lNUDpUaW1lU3RhbXA9IjE2Mzk2MzUyNjc1Njc1OTEiCiAgIEdJTVA6VmVyc2lvbj0iMi4xMC4yMiIKICAgZGM6Rm9ybWF0PSJpbWFnZS9wbmciCiAgIHRpZmY6T3JpZW50YXRpb249IjEiCiAgIHhtcDpDcmVhdG9yVG9vbD0iR0lNUCAyLjEwIj4KICAgPGlwdGNFeHQ6TG9jYXRpb25DcmVhdGVkPgogICAgPHJkZjpCYWcvPgogICA8L2lwdGNFeHQ6TG9jYXRpb25DcmVhdGVkPgogICA8aXB0Y0V4dDpMb2NhdGlvblNob3duPgogICAgPHJkZjpCYWcvPgogICA8L2lwdGNFeHQ6TG9jYXRpb25TaG93bj4KICAgPGlwdGNFeHQ6QXJ0d29ya09yT2JqZWN0PgogICAgPHJkZjpCYWcvPgogICA8L2lwdGNFeHQ6QXJ0d29ya09yT2JqZWN0PgogICA8aXB0Y0V4dDpSZWdpc3RyeUlkPgogICAgPHJkZjpCYWcvPgogICA8L2lwdGNFeHQ6UmVnaXN0cnlJZD4KICAgPHhtcE1NOkhpc3Rvcnk+CiAgICA8cmRmOlNlcT4KICAgICA8cmRmOmxpCiAgICAgIHN0RXZ0OmFjdGlvbj0ic2F2ZWQiCiAgICAgIHN0RXZ0OmNoYW5nZWQ9Ii8iCiAgICAgIHN0RXZ0Omluc3RhbmNlSUQ9InhtcC5paWQ6YTg0NDYyZjctYTZhNC00MDM3LWJjM2MtOWUyMWQyMTFmM2VmIgogICAgICBzdEV2dDpzb2Z0d2FyZUFnZW50PSJHaW1wIDIuMTAgKE1hYyBPUykiCiAgICAgIHN0RXZ0OndoZW49IjIwMjEtMTItMTZUMDY6MTQ6MjcrMDA6MDAiLz4KICAgIDwvcmRmOlNlcT4KICAgPC94bXBNTTpIaXN0b3J5PgogICA8cGx1czpJbWFnZVN1cHBsaWVyPgogICAgPHJkZjpTZXEvPgogICA8L3BsdXM6SW1hZ2VTdXBwbGllcj4KICAgPHBsdXM6SW1hZ2VDcmVhdG9yPgogICAgPHJkZjpTZXEvPgogICA8L3BsdXM6SW1hZ2VDcmVhdG9yPgogICA8cGx1czpDb3B5cmlnaHRPd25lcj4KICAgIDxyZGY6U2VxLz4KICAgPC9wbHVzOkNvcHlya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         }
     ]
 }
\ No newline at end of file