From 5c78494dee73f980146ee25108930e18f7bbfcf6 Mon Sep 17 00:00:00 2001
From: Marigold <mojmir.vinkler@gmail.com>
Date: Mon, 20 May 2024 15:45:04 +0200
Subject: [PATCH] :sparkles: chart-diff improvements

---
 .../2024-05-20/update_metadata.ipynb          | 569 ++++++++++++++++--
 .../2024-05-20/wdi.countries.json             |   1 +
 .../worldbank_wdi/2024-05-20/wdi.meta.yml     | 187 ++++--
 .../garden/worldbank_wdi/2024-05-20/wdi.py    |  28 +-
 .../worldbank_wdi/2024-05-20/wdi.sources.json | 183 +++++-
 5 files changed, 814 insertions(+), 154 deletions(-)

diff --git a/etl/steps/data/garden/worldbank_wdi/2024-05-20/update_metadata.ipynb b/etl/steps/data/garden/worldbank_wdi/2024-05-20/update_metadata.ipynb
index b025fa933501..4ec7ec8de50b 100644
--- a/etl/steps/data/garden/worldbank_wdi/2024-05-20/update_metadata.ipynb
+++ b/etl/steps/data/garden/worldbank_wdi/2024-05-20/update_metadata.ipynb
@@ -23,7 +23,325 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>topic</th>\n",
+       "      <th>indicator_name</th>\n",
+       "      <th>short_definition</th>\n",
+       "      <th>long_definition</th>\n",
+       "      <th>unit_of_measure</th>\n",
+       "      <th>periodicity</th>\n",
+       "      <th>base_period</th>\n",
+       "      <th>other_notes</th>\n",
+       "      <th>aggregation_method</th>\n",
+       "      <th>limitations_and_exceptions</th>\n",
+       "      <th>notes_from_original_source</th>\n",
+       "      <th>general_comments</th>\n",
+       "      <th>source</th>\n",
+       "      <th>statistical_concept_and_methodology</th>\n",
+       "      <th>development_relevance</th>\n",
+       "      <th>related_source_links</th>\n",
+       "      <th>other_web_links</th>\n",
+       "      <th>related_indicators</th>\n",
+       "      <th>license_type</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>indicator_code</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ag_agr_trac_no</th>\n",
+       "      <td>Environment: Agricultural production</td>\n",
+       "      <td>Agricultural machinery, tractors</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Agricultural machinery refers to the number of...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Annual</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Sum</td>\n",
+       "      <td>The data are collected by the Food and Agricul...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Food and Agriculture Organization, electronic ...</td>\n",
+       "      <td>A tractor provides the power and traction to m...</td>\n",
+       "      <td>Agricultural land covers more than one-third o...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>CC BY-4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ag_con_fert_pt_zs</th>\n",
+       "      <td>Environment: Agricultural production</td>\n",
+       "      <td>Fertilizer consumption (% of fertilizer produc...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Fertilizer consumption measures the quantity o...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Annual</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>The world and regional aggregate series do not...</td>\n",
+       "      <td>Weighted average</td>\n",
+       "      <td>The FAO has revised the time series for fertil...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Food and Agriculture Organization, electronic ...</td>\n",
+       "      <td>Fertilizer consumption measures the quantity o...</td>\n",
+       "      <td>Factors such as the green revolution, has led ...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>CC BY-4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ag_con_fert_zs</th>\n",
+       "      <td>Environment: Agricultural production</td>\n",
+       "      <td>Fertilizer consumption (kilograms per hectare ...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Fertilizer consumption measures the quantity o...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Annual</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>The world and regional aggregate series do not...</td>\n",
+       "      <td>Weighted average</td>\n",
+       "      <td>The FAO has revised the time series for fertil...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Food and Agriculture Organization, electronic ...</td>\n",
+       "      <td>Fertilizer consumption measures the quantity o...</td>\n",
+       "      <td>Factors such as the green revolution, has led ...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>CC BY-4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ag_lnd_agri_k2</th>\n",
+       "      <td>Environment: Land use</td>\n",
+       "      <td>Agricultural land (sq. km)</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Agricultural land refers to the share of land ...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Annual</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Areas of former states are included in the suc...</td>\n",
+       "      <td>Sum</td>\n",
+       "      <td>The data are collected by the Food and Agricul...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Food and Agriculture Organization, electronic ...</td>\n",
+       "      <td>Agricultural land constitutes only a part of a...</td>\n",
+       "      <td>Agricultural land covers more than one-third o...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>CC BY-4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ag_lnd_agri_zs</th>\n",
+       "      <td>Environment: Land use</td>\n",
+       "      <td>Agricultural land (% of land area)</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Agricultural land refers to the share of land ...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Annual</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Areas of former states are included in the suc...</td>\n",
+       "      <td>Weighted average</td>\n",
+       "      <td>The data are collected by the Food and Agricul...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Food and Agriculture Organization, electronic ...</td>\n",
+       "      <td>Agriculture is still a major sector in many ec...</td>\n",
+       "      <td>Agricultural land covers more than one-third o...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>CC BY-4.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "\n",
+       "                                                  topic  \\\n",
+       "indicator_code                                            \n",
+       "ag_agr_trac_no     Environment: Agricultural production   \n",
+       "ag_con_fert_pt_zs  Environment: Agricultural production   \n",
+       "ag_con_fert_zs     Environment: Agricultural production   \n",
+       "ag_lnd_agri_k2                    Environment: Land use   \n",
+       "ag_lnd_agri_zs                    Environment: Land use   \n",
+       "\n",
+       "                                                      indicator_name  \\\n",
+       "indicator_code                                                         \n",
+       "ag_agr_trac_no                      Agricultural machinery, tractors   \n",
+       "ag_con_fert_pt_zs  Fertilizer consumption \u001b[1m(\u001b[0m% of fertilizer produc\u001b[33m...\u001b[0m   \n",
+       "ag_con_fert_zs     Fertilizer consumption \u001b[1m(\u001b[0mkilograms per hectare \u001b[33m...\u001b[0m   \n",
+       "ag_lnd_agri_k2                            Agricultural land \u001b[1m(\u001b[0msq. km\u001b[1m)\u001b[0m   \n",
+       "ag_lnd_agri_zs                    Agricultural land \u001b[1m(\u001b[0m% of land area\u001b[1m)\u001b[0m   \n",
+       "\n",
+       "                  short_definition  \\\n",
+       "indicator_code                       \n",
+       "ag_agr_trac_no                 NaN   \n",
+       "ag_con_fert_pt_zs              NaN   \n",
+       "ag_con_fert_zs                 NaN   \n",
+       "ag_lnd_agri_k2                 NaN   \n",
+       "ag_lnd_agri_zs                 NaN   \n",
+       "\n",
+       "                                                     long_definition  \\\n",
+       "indicator_code                                                         \n",
+       "ag_agr_trac_no     Agricultural machinery refers to the number of\u001b[33m...\u001b[0m   \n",
+       "ag_con_fert_pt_zs  Fertilizer consumption measures the quantity o\u001b[33m...\u001b[0m   \n",
+       "ag_con_fert_zs     Fertilizer consumption measures the quantity o\u001b[33m...\u001b[0m   \n",
+       "ag_lnd_agri_k2     Agricultural land refers to the share of land \u001b[33m...\u001b[0m   \n",
+       "ag_lnd_agri_zs     Agricultural land refers to the share of land \u001b[33m...\u001b[0m   \n",
+       "\n",
+       "                  unit_of_measure periodicity base_period  \\\n",
+       "indicator_code                                              \n",
+       "ag_agr_trac_no                NaN      Annual         NaN   \n",
+       "ag_con_fert_pt_zs             NaN      Annual         NaN   \n",
+       "ag_con_fert_zs                NaN      Annual         NaN   \n",
+       "ag_lnd_agri_k2                NaN      Annual         NaN   \n",
+       "ag_lnd_agri_zs                NaN      Annual         NaN   \n",
+       "\n",
+       "                                                         other_notes  \\\n",
+       "indicator_code                                                         \n",
+       "ag_agr_trac_no                                                   NaN   \n",
+       "ag_con_fert_pt_zs  The world and regional aggregate series do not\u001b[33m...\u001b[0m   \n",
+       "ag_con_fert_zs     The world and regional aggregate series do not\u001b[33m...\u001b[0m   \n",
+       "ag_lnd_agri_k2     Areas of former states are included in the suc\u001b[33m...\u001b[0m   \n",
+       "ag_lnd_agri_zs     Areas of former states are included in the suc\u001b[33m...\u001b[0m   \n",
+       "\n",
+       "                  aggregation_method  \\\n",
+       "indicator_code                         \n",
+       "ag_agr_trac_no                   Sum   \n",
+       "ag_con_fert_pt_zs   Weighted average   \n",
+       "ag_con_fert_zs      Weighted average   \n",
+       "ag_lnd_agri_k2                   Sum   \n",
+       "ag_lnd_agri_zs      Weighted average   \n",
+       "\n",
+       "                                          limitations_and_exceptions  \\\n",
+       "indicator_code                                                         \n",
+       "ag_agr_trac_no     The data are collected by the Food and Agricul\u001b[33m...\u001b[0m   \n",
+       "ag_con_fert_pt_zs  The FAO has revised the time series for fertil\u001b[33m...\u001b[0m   \n",
+       "ag_con_fert_zs     The FAO has revised the time series for fertil\u001b[33m...\u001b[0m   \n",
+       "ag_lnd_agri_k2     The data are collected by the Food and Agricul\u001b[33m...\u001b[0m   \n",
+       "ag_lnd_agri_zs     The data are collected by the Food and Agricul\u001b[33m...\u001b[0m   \n",
+       "\n",
+       "                  notes_from_original_source general_comments  \\\n",
+       "indicator_code                                                  \n",
+       "ag_agr_trac_no                           NaN              NaN   \n",
+       "ag_con_fert_pt_zs                        NaN              NaN   \n",
+       "ag_con_fert_zs                           NaN              NaN   \n",
+       "ag_lnd_agri_k2                           NaN              NaN   \n",
+       "ag_lnd_agri_zs                           NaN              NaN   \n",
+       "\n",
+       "                                                              source  \\\n",
+       "indicator_code                                                         \n",
+       "ag_agr_trac_no     Food and Agriculture Organization, electronic \u001b[33m...\u001b[0m   \n",
+       "ag_con_fert_pt_zs  Food and Agriculture Organization, electronic \u001b[33m...\u001b[0m   \n",
+       "ag_con_fert_zs     Food and Agriculture Organization, electronic \u001b[33m...\u001b[0m   \n",
+       "ag_lnd_agri_k2     Food and Agriculture Organization, electronic \u001b[33m...\u001b[0m   \n",
+       "ag_lnd_agri_zs     Food and Agriculture Organization, electronic \u001b[33m...\u001b[0m   \n",
+       "\n",
+       "                                 statistical_concept_and_methodology  \\\n",
+       "indicator_code                                                         \n",
+       "ag_agr_trac_no     A tractor provides the power and traction to m\u001b[33m...\u001b[0m   \n",
+       "ag_con_fert_pt_zs  Fertilizer consumption measures the quantity o\u001b[33m...\u001b[0m   \n",
+       "ag_con_fert_zs     Fertilizer consumption measures the quantity o\u001b[33m...\u001b[0m   \n",
+       "ag_lnd_agri_k2     Agricultural land constitutes only a part of a\u001b[33m...\u001b[0m   \n",
+       "ag_lnd_agri_zs     Agriculture is still a major sector in many ec\u001b[33m...\u001b[0m   \n",
+       "\n",
+       "                                               development_relevance  \\\n",
+       "indicator_code                                                         \n",
+       "ag_agr_trac_no     Agricultural land covers more than one-third o\u001b[33m...\u001b[0m   \n",
+       "ag_con_fert_pt_zs  Factors such as the green revolution, has led \u001b[33m...\u001b[0m   \n",
+       "ag_con_fert_zs     Factors such as the green revolution, has led \u001b[33m...\u001b[0m   \n",
+       "ag_lnd_agri_k2     Agricultural land covers more than one-third o\u001b[33m...\u001b[0m   \n",
+       "ag_lnd_agri_zs     Agricultural land covers more than one-third o\u001b[33m...\u001b[0m   \n",
+       "\n",
+       "                  related_source_links other_web_links related_indicators  \\\n",
+       "indicator_code                                                              \n",
+       "ag_agr_trac_no                     NaN             NaN                NaN   \n",
+       "ag_con_fert_pt_zs                  NaN             NaN                NaN   \n",
+       "ag_con_fert_zs                     NaN             NaN                NaN   \n",
+       "ag_lnd_agri_k2                     NaN             NaN                NaN   \n",
+       "ag_lnd_agri_zs                     NaN             NaN                NaN   \n",
+       "\n",
+       "                  license_type  \n",
+       "indicator_code                  \n",
+       "ag_agr_trac_no       CC BY-\u001b[1;36m4.0\u001b[0m  \n",
+       "ag_con_fert_pt_zs    CC BY-\u001b[1;36m4.0\u001b[0m  \n",
+       "ag_con_fert_zs       CC BY-\u001b[1;36m4.0\u001b[0m  \n",
+       "ag_lnd_agri_k2       CC BY-\u001b[1;36m4.0\u001b[0m  \n",
+       "ag_lnd_agri_zs       CC BY-\u001b[1;36m4.0\u001b[0m  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_vars.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -96,7 +414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -114,16 +432,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
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     {
      "data": {
       "text/plain": [
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+       "\u001b[1m[\u001b[0m\u001b[1m]\u001b[0m"
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-     "execution_count": 3,
+     "execution_count": 6,
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@@ -142,67 +470,179 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Cost GPT4: $0.08\n"
+      "Cost GPT4: $0.04\n",
+      "{\n",
+      "  \"rawNames\": [\n",
+      "    {\n",
+      "      \"rawName\": \"UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed November 27, 2023. https://apiportal.uis.unesco.org/bdds.\",\n",
+      "      \"name\": \"UNESCO Institute for Statistics\",\n",
+      "      \"dataPublisherSource\": \"UIS.Stat Bulk Data Download Service - UNESCO Institute for Statistics\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"World Bank and UIS\",\n",
+      "      \"name\": \"World Bank and UNESCO Institute for Statistics\",\n",
+      "      \"dataPublisherSource\": \"World Bank and UNESCO Institute for Statistics\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.\",\n",
+      "      \"name\": \"UNICEF, WHO, World Bank\",\n",
+      "      \"dataPublisherSource\": \"Joint child Malnutrition Estimates - UNICEF, WHO, World Bank\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"International Labour Organization. “Wages and Working Time Statistics database (COND)” ILOSTAT. Accessed February 06, 2024. https://ilostat.ilo.org/data/.\",\n",
+      "      \"name\": \"International Labour Organization\",\n",
+      "      \"dataPublisherSource\": \"Wages and Working Time Statistics database (COND) - International Labour Organization\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"IEA Statistics © OECD/IEA 2014 (https://www.iea.org/data-and-statistics), subject to https://www.iea.org/terms/\",\n",
+      "      \"name\": \"International Energy Agency\",\n",
+      "      \"dataPublisherSource\": \"IEA Statistics - International Energy Agency\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).\",\n",
+      "      \"name\": \"UNICEF, WHO, World Bank\",\n",
+      "      \"dataPublisherSource\": \"Joint child Malnutrition Estimates - UNICEF, WHO, World Bank\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"(1) United Nations Population Division. World Population Prospects: 2022 Revision. (2) HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.\",\n",
+      "      \"name\": \"Data compiled from multiple sources by World Bank\",\n",
+      "      \"dataPublisherSource\": \"World Population Prospects - UN Population Division (2022), Human Mortality Database - Max Planck Institute for Demographic Research, University of California, Berkeley, French Institute for Demographic Studies\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"WHO, UNICEF, UNFPA, World Bank Group, and UNDESA/Population Division. Trends in Maternal Mortality 2000 to 2020. Geneva, World Health Organization, 2023\",\n",
+      "      \"name\": \"World Health Organization\",\n",
+      "      \"dataPublisherSource\": \"Trends in Maternal Mortality - WHO, UNICEF, UNFPA, World Bank Group, UNDESA/Population Division\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"Global Health Observatory. Geneva: World Health Organization; 2023. (https://www.who.int/data/gho/data/themes/topics/financial-protection)\",\n",
+      "      \"name\": \"World Health Organization\",\n",
+      "      \"dataPublisherSource\": \"Global Health Observatory - World Health Organization\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"International Labour Organization. “ILO modelled estimates database” ILOSTAT. Accessed February 07, 2024. https://ilostat.ilo.org/data/.\",\n",
+      "      \"name\": \"International Labour Organization\",\n",
+      "      \"dataPublisherSource\": \"ILO modelled estimates database - International Labour Organization\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"IEA Statistics © OECD/IEA 2018 (https://www.iea.org/data-and-statistics), subject to https://www.iea.org/terms/\",\n",
+      "      \"name\": \"International Energy Agency\",\n",
+      "      \"dataPublisherSource\": \"IEA Statistics - International Energy Agency\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed September 19, 2023. https://apiportal.uis.unesco.org/bdds.\",\n",
+      "      \"name\": \"UNESCO Institute for Statistics\",\n",
+      "      \"dataPublisherSource\": \"UIS.Stat Bulk Data Download Service - UNESCO Institute for Statistics\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions\",\n",
+      "      \"name\": \"World Resources Institute\",\n",
+      "      \"dataPublisherSource\": \"Climate Watch Historical GHG Emissions - World Resources Institute\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys.  Largely compiled by UNICEF.\",\n",
+      "      \"name\": \"UNICEF\",\n",
+      "      \"dataPublisherSource\": \"Demographic and Health Surveys, Multiple Indicator Cluster Surveys - UNICEF\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"Emissions data are sourced from Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions\",\n",
+      "      \"name\": \"World Resources Institute\",\n",
+      "      \"dataPublisherSource\": \"Climate Watch Historical GHG Emissions - World Resources Institute\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"Internation Union of Railways (UIC Railisa Database), OECD Statistics\",\n",
+      "      \"name\": \"International Union of Railways\",\n",
+      "      \"dataPublisherSource\": \"UIC Railisa Database - International Union of Railways, OECD Statistics\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"Global Health Observatory. Geneva: World Health Organization; 2023. (https://www.who.int/data/gho/data/themes/topics/service-coverage)\",\n",
+      "      \"name\": \"World Health Organization\",\n",
+      "      \"dataPublisherSource\": \"Global Health Observatory - World Health Organization\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"Data are available online at: https://lpi.worldbank.org/. Summary results are published in World Bank (2023): Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators.\",\n",
+      "      \"name\": \"World Bank\",\n",
+      "      \"dataPublisherSource\": \"Logistics Performance Index - World Bank\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"International Labour Organization. “Labour Force Statistics database (LFS)” ILOSTAT. Accessed February 06, 2024. https://ilostat.ilo.org/data/.\",\n",
+      "      \"name\": \"International Labour Organization\",\n",
+      "      \"dataPublisherSource\": \"Labour Force Statistics database (LFS) - International Labour Organization\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"Global Burden of Disease Collaborative Network. 2021. Global Burden of Disease Study 2019 (GBD 2019) Air Pollution Exposure Estimates 1990-2019. Seattle, United States of America: Institute for Health Metrics and Evaluation (IHME). https://doi.org/10.6069/70JS-NC54\",\n",
+      "      \"name\": \"Institute for Health Metrics and Evaluation\",\n",
+      "      \"dataPublisherSource\": \"Global Burden of Disease Study 2019 - Institute for Health Metrics and Evaluation\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"International Labour Organization. “ILO modelled estimates database” ILOSTAT. Accessed February 06, 2024. https://ilostat.ilo.org/data/.\",\n",
+      "      \"name\": \"International Labour Organization\",\n",
+      "      \"dataPublisherSource\": \"ILO modelled estimates database - International Labour Organization\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"International Labour Organization. “Labour Market-related SDG Indicators database (ILOSDG)” ILOSTAT. Accessed February 06, 2024. https://ilostat.ilo.org/data/.\",\n",
+      "      \"name\": \"International Labour Organization\",\n",
+      "      \"dataPublisherSource\": \"Labour Market-related SDG Indicators database (ILOSDG) - International Labour Organization\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"National statistical offices or national database and publications compiled by United Nations Statistics Division. The data were downloaded on February 14, 2023, from the Global SDG  API: https://unstats.un.org/sdgs/UNSDGAPIV5/swagger/index.html\",\n",
+      "      \"name\": \"United Nations Statistics Division\",\n",
+      "      \"dataPublisherSource\": \"National statistical offices, Global SDG API - United Nations Statistics Division\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database). The data was retrieved on April 7, 2023.\",\n",
+      "      \"name\": \"World Health Organization\",\n",
+      "      \"dataPublisherSource\": \"Global Health Expenditure database - World Health Organization\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"United Nations Conference on Trade and Development\",\n",
+      "      \"name\": \"United Nations Conference on Trade and Development\",\n",
+      "      \"dataPublisherSource\": \"United Nations Conference on Trade and Development\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. The WGI are produced by Daniel Kaufmann (Natural Resource Governance Institute and Brookings Institution) and Aart Kraay (World Bank Development Research Group).  Please cite Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2010).  \\\"The Worldwide Governance Indicators:  Methodology and Analytical Issues\\\".  World Bank Policy Research Working Paper No. 5430 (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1682130).  The WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent.\",\n",
+      "      \"name\": \"World Bank\",\n",
+      "      \"dataPublisherSource\": \"Worldwide Governance Indicators - World Bank\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"IEA, IRENA, UNSD, World Bank, WHO. 2023. Tracking SDG 7: The Energy Progress Report. World Bank, Washington DC. © World Bank. License: Creative Commons Attribution—NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO).\",\n",
+      "      \"name\": \"World Bank and International Energy Agency\",\n",
+      "      \"dataPublisherSource\": \"Tracking SDG 7: The Energy Progress Report - World Bank, International Energy Agency, IRENA, UNSD, WHO\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"National statistical offices or national database and publications compiled by United Nations Statistics Division.  The data were downloaded on February 14, 2023, from the Global SDG  API: https://unstats.un.org/sdgs/UNSDGAPIV5/swagger/index.html\",\n",
+      "      \"name\": \"United Nations Statistics Division\",\n",
+      "      \"dataPublisherSource\": \"National statistical offices, Global SDG API - United Nations Statistics Division\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"International Labour Organization. “ILO Modelled Estimates and Projections database (ILOEST)” ILOSTAT. Accessed February 06, 2024. https://ilostat.ilo.org/data/.\",\n",
+      "      \"name\": \"International Labour Organization\",\n",
+      "      \"dataPublisherSource\": \"ILO Modelled Estimates and Projections database (ILOEST) - International Labour Organization\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"Demographic and Health Surveys compiled by United Nations Population Fund. Retrieved on February 14, 2023, from the SDG Global database API (https://unstats.un.org/sdgs/UNSDGAPIV5/swagger/index.html).\",\n",
+      "      \"name\": \"United Nations Population Fund\",\n",
+      "      \"dataPublisherSource\": \"Demographic and Health Surveys - United Nations Population Fund\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"rawName\": \"International Labour Organization. “Education and Mismatch Indicators database (EMI)” ILOSTAT. Accessed February 06, 2024. https://ilostat.ilo.org/data/.\",\n",
+      "      \"name\": \"International Labour Organization\",\n",
+      "      \"dataPublisherSource\": \"Education and Mismatch Indicators database (EMI) - International Labour Organization\"\n",
+      "    }\n",
+      "  ]\n",
+      "}\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[{'rawName': 'International Labour Organization. “Labour Market-related SDG Indicators database (ILOSDG)” ILOSTAT. Accessed December 6, 2022. https://ilostat.ilo.org/data/.',\n",
-       "  'name': 'International Labour Organization (via World Bank)',\n",
-       "  'dataPublisherSource': 'Labour Market-related SDG Indicators Database - ILOSTAT'},\n",
-       " {'rawName': 'World Bank, World Development Indicators database. Estimates are based on employment, population, GDP, and PPP data obtained from International Labour Organization, United Nations Population Division, Eurostat, OECD, and World Bank.',\n",
-       "  'name': 'World Bank',\n",
-       "  'dataPublisherSource': 'World Development Indicators Database - World Bank'},\n",
-       " {'rawName': 'World Bank, World Development Indicators database. Estimates are based on data obtained from International Labour Organization and United Nations Population Division.',\n",
-       "  'name': 'World Bank',\n",
-       "  'dataPublisherSource': 'World Development Indicators Database - World Bank'},\n",
-       " {'rawName': 'Derived from total population. Population source: (1) United Nations Population Division. World Population Prospects: 2022 Revision, (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.',\n",
-       "  'name': 'United Nations Population Division and others (via World Bank)',\n",
-       "  'dataPublisherSource': 'World Population Prospects, Census Reports, Eurostat, United Nations Statistical Division, U.S. Census Bureau, Secretariat of the Pacific Community'},\n",
-       " {'rawName': '(1) United Nations Population Division. World Population Prospects: 2022 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.',\n",
-       "  'name': 'United Nations Population Division and others (via World Bank)',\n",
-       "  'dataPublisherSource': 'World Population Prospects, Census Reports, Eurostat, United Nations Statistical Division, U.S. Census Bureau, Secretariat of the Pacific Community'},\n",
-       " {'rawName': 'UNICEF global databases, based on administrative reports from countries (link: https://data.unicef.org/topic/nutrition/vitamin-a-deficiency/)',\n",
-       "  'name': 'UNICEF (via World Bank)',\n",
-       "  'dataPublisherSource': 'UNICEF Global Databases'},\n",
-       " {'rawName': 'International Labour Organization. “ILO Modelled Estimates and Projections database (ILOEST)” ILOSTAT. Accessed December 6, 2022. https://ilostat.ilo.org/data/.',\n",
-       "  'name': 'International Labour Organization (via World Bank)',\n",
-       "  'dataPublisherSource': 'ILO Modelled Estimates and Projections Database - ILOSTAT'},\n",
-       " {'rawName': 'Center for International Earth Science Information Network - CIESIN - Columbia University, and CUNY Institute for Demographic Research - CIDR - City University of New York. 2021. Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/d1x1-d702.',\n",
-       "  'name': 'CIESIN and CIDR (via World Bank)',\n",
-       "  'dataPublisherSource': 'Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates - CIESIN / CIDR'},\n",
-       " {'rawName': '(1) United Nations Population Division. World Population Prospects: 2022 Revision, or derived from male and female life expectancy at birth from sources such as: (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.',\n",
-       "  'name': 'United Nations Population Division and others (via World Bank)',\n",
-       "  'dataPublisherSource': 'World Population Prospects, Census Reports, Eurostat, United Nations Statistical Division, U.S. Census Bureau, Secretariat of the Pacific Community'},\n",
-       " {'rawName': 'UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed October 24, 2022. https://apiportal.uis.unesco.org/bdds.',\n",
-       "  'name': 'UNESCO (via World Bank)',\n",
-       "  'dataPublisherSource': 'UNESCO Institute for Statistics'},\n",
-       " {'rawName': 'Global Findex Database, World Bank (https://www.worldbank.org/en/publication/globalfindex).',\n",
-       "  'name': 'World Bank',\n",
-       "  'dataPublisherSource': 'Global Findex Database - World Bank'},\n",
-       " {'rawName': '(1) United Nations Population Division. World Population Prospects: 2022 Revision. (2) University of California, Berkeley, and Max Planck Institute for Demographic Research. The Human Mortality Database.',\n",
-       "  'name': 'United Nations Population Division and others (via World Bank)',\n",
-       "  'dataPublisherSource': 'World Population Prospects, Human Mortality Database - University of California, Berkeley / Max Planck Institute for Demographic Research'},\n",
-       " {'rawName': \"World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2022 Revision.\",\n",
-       "  'name': 'World Bank',\n",
-       "  'dataPublisherSource': 'World Population Prospects - United Nations Population Division'}]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "import openai\n",
+    "import os\n",
+    "from openai import OpenAI\n",
     "import random\n",
     "\n",
     "SYSTEM_PROMPT = f\"\"\"\n",
@@ -212,7 +652,7 @@
     "rawNames with name and dataPublisherSource fields filled in.\n",
     "\n",
     "Examples:\n",
-    "{json.dumps(random.sample(sources, 10))}\n",
+    "{json.dumps(random.sample(sources, 20))}\n",
     "\"\"\"\n",
     "\n",
     "all_sources = \"\\n\".join(missing_sources)\n",
@@ -228,16 +668,17 @@
     "    },\n",
     "]\n",
     "\n",
-    "# 10 missing sources / 5 examples -> 2min\n",
+    "client = OpenAI()\n",
     "\n",
-    "response = openai.ChatCompletion.create(\n",
-    "    model=\"gpt-4\",\n",
-    "    # model=\"gpt-3.5-turbo\",\n",
+    "# 10 missing sources / 5 examples -> 2min\n",
+    "response = client.chat.completions.create(\n",
+    "    model=\"gpt-4o\",\n",
     "    temperature=0,\n",
     "    messages=messages,\n",
+    "    response_format={\"type\": \"json_object\"},\n",
     ")\n",
-    "print(f\"Cost GPT4: ${response['usage']['total_tokens'] / 1000 * 0.03:.2f}\")\n",
-    "r = json.loads(response[\"choices\"][0][\"message\"][\"content\"])\n",
+    "print(f\"Cost GPT4o: ${response.usage.total_tokens / 1e6 * 7.5:.2f}\")\n",
+    "r = json.loads(response.choices[0].message.content)\n",
     "print(json.dumps(r, ensure_ascii=False, indent=2))"
    ]
   }
diff --git a/etl/steps/data/garden/worldbank_wdi/2024-05-20/wdi.countries.json b/etl/steps/data/garden/worldbank_wdi/2024-05-20/wdi.countries.json
index 1fdd0d9e2ae2..bae421a33885 100644
--- a/etl/steps/data/garden/worldbank_wdi/2024-05-20/wdi.countries.json
+++ b/etl/steps/data/garden/worldbank_wdi/2024-05-20/wdi.countries.json
@@ -201,6 +201,7 @@
   "Uzbekistan": "Uzbekistan",
   "Vanuatu": "Vanuatu",
   "Vietnam": "Vietnam",
+  "Viet Nam": "Vietnam",
   "Virgin Islands (U.S.)": "United States Virgin Islands",
   "World": "World",
   "Zambia": "Zambia",
diff --git a/etl/steps/data/garden/worldbank_wdi/2024-05-20/wdi.meta.yml b/etl/steps/data/garden/worldbank_wdi/2024-05-20/wdi.meta.yml
index f131c9cb6c46..af31fd6c6b87 100644
--- a/etl/steps/data/garden/worldbank_wdi/2024-05-20/wdi.meta.yml
+++ b/etl/steps/data/garden/worldbank_wdi/2024-05-20/wdi.meta.yml
@@ -1,7 +1,6 @@
 #
 dataset:
   update_period_days: 365
-
 tables:
   wdi:
     variables:
@@ -1469,31 +1468,31 @@ tables:
         title: Primary government expenditures as a proportion of original approved budget (%)
       hd_hci_ovrl:
         title: Human capital index (HCI) (scale 0-1)
-        unit: ""
+        unit: ''
       hd_hci_ovrl_fe:
         title: Human capital index (HCI), female (scale 0-1)
-        unit: ""
+        unit: ''
       hd_hci_ovrl_lb:
         title: Human capital index (HCI), lower bound (scale 0-1)
-        unit: ""
+        unit: ''
       hd_hci_ovrl_lb_fe:
         title: Human capital index (HCI), female, lower bound (scale 0-1)
-        unit: ""
+        unit: ''
       hd_hci_ovrl_lb_ma:
         title: Human capital index (HCI), male, lower bound (scale 0-1)
-        unit: ""
+        unit: ''
       hd_hci_ovrl_ma:
         title: Human capital index (HCI), male (scale 0-1)
-        unit: ""
+        unit: ''
       hd_hci_ovrl_ub:
         title: Human capital index (HCI), upper bound (scale 0-1)
-        unit: ""
+        unit: ''
       hd_hci_ovrl_ub_fe:
         title: Human capital index (HCI), female, upper bound (scale 0-1)
-        unit: ""
+        unit: ''
       hd_hci_ovrl_ub_ma:
         title: Human capital index (HCI), male, upper bound (scale 0-1)
-        unit: ""
+        unit: ''
       ic_bus_dfrn_xq:
         title: Ease of doing business score (0 = lowest performance to 100 = best performance)
         unit: 0=lowest performance to 100=frontier
@@ -3847,51 +3846,21 @@ tables:
       sh_tbs_incd:
         title: Incidence of tuberculosis (per 100,000 people)
         unit: per 100,000 people
-      sh_uhc_nop1_cg:
-        title: Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (USD)
-        short_unit: USD
-        unit: USD
-      sh_uhc_nop1_to:
-        title: Number of people pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure
-        short_unit: $
-        unit: $ 2011 PPP
-      sh_uhc_nop1_zg:
-        title: Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (%
-          of poverty line)
-        short_unit: '%'
-        unit: '% of poverty line'
       sh_uhc_nop1_zs:
-        title: Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure
+        title: Proportion of population pushed below the $2.15 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure
           (%)
         short_unit: '%'
         unit: '%'
-      sh_uhc_nop2_cg:
-        title: Increase in poverty gap at $3.20 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (USD)
-        short_unit: USD
-        unit: USD
-      sh_uhc_nop2_to:
-        title: Number of people pushed below the $3.20 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure
-        short_unit: $
-        unit: $ 2011 PPP
-      sh_uhc_nop2_zg:
-        title: Increase in poverty gap at $3.20 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (%
-          of poverty line)
-        short_unit: '%'
-        unit: '% of poverty line'
       sh_uhc_nop2_zs:
-        title: Proportion of population pushed below the $3.20 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure
+        title: Proportion of population pushed below the $3.65 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure
           (%)
         short_unit: '%'
         unit: '%'
-      sh_uhc_oopc_10_to:
-        title: Number of people spending more than 10% of household consumption or income on out-of-pocket health care expenditure
       sh_uhc_oopc_10_zs:
         title: Proportion of population spending more than 10% of household consumption or income on out-of-pocket health
           care expenditure (%)
         short_unit: '%'
         unit: '%'
-      sh_uhc_oopc_25_to:
-        title: Number of people spending more than 25% of household consumption or income on out-of-pocket health care expenditure
       sh_uhc_oopc_25_zs:
         title: Proportion of population spending more than 25% of household consumption or income on out-of-pocket health
           care expenditure (%)
@@ -4947,8 +4916,8 @@ tables:
         short_unit: '%'
         unit: '% of GDP'
       tm_qty_mrch_xd_wd:
-        title: Import volume index (2000 = 100)
-        unit: 2000 = 100
+        title: Import volume index (2015 = 100)
+        unit: 2015 = 100
       tm_tax_manf_bc_zs:
         title: Binding coverage, manufactured products (%)
         short_unit: '%'
@@ -5132,8 +5101,8 @@ tables:
         short_unit: '%'
         unit: '% of total merchandise imports'
       tm_val_mrch_xd_wd:
-        title: Import value index (2000 = 100)
-        unit: 2000 = 100
+        title: Import value index (2015 = 100)
+        unit: 2015 = 100
       tm_val_othr_zs_wt:
         title: Computer, communications and other services (% of commercial service imports)
         short_unit: '%'
@@ -5151,15 +5120,15 @@ tables:
         short_unit: '%'
         unit: '% of commercial service imports'
       tt_pri_mrch_xd_wd:
-        title: Net barter terms of trade index (2000 = 100)
-        unit: 2000 = 100
+        title: Net barter terms of trade index (2015 = 100)
+        unit: 2015 = 100
       tx_mnf_tech_zs_un:
         title: Medium and high-tech exports (% manufactured exports)
         short_unit: '%'
         unit: '% manufactured exports'
       tx_qty_mrch_xd_wd:
-        title: Export volume index (2000 = 100)
-        unit: 2000 = 100
+        title: Export volume index (2015 = 100)
+        unit: 2015 = 100
       tx_uvi_mrch_xd_wd:
         title: Export unit value index (2015 = 100)
         unit: 2015 = 100
@@ -5246,8 +5215,8 @@ tables:
         short_unit: '%'
         unit: '% of total merchandise exports'
       tx_val_mrch_xd_wd:
-        title: Export value index (2000 = 100)
-        unit: 2000 = 100
+        title: Export value index (2015 = 100)
+        unit: 2015 = 100
       tx_val_othr_zs_wt:
         title: Computer, communications and other services (% of commercial service exports)
         short_unit: '%'
@@ -5302,3 +5271,117 @@ tables:
       omm_goods_exp_share_gdp: {}
       omm_tax_rev_percap: {}
       omm_net_savings_percap: {}
+      cc_est:
+        title: 'Control of Corruption: Estimate'
+      cc_no_src:
+        title: 'Control of Corruption: Number of Sources'
+      cc_per_rnk:
+        title: 'Control of Corruption: Percentile Rank'
+      cc_per_rnk_lower:
+        title: 'Control of Corruption: Percentile Rank, Lower Bound of 90% Confidence Interval'
+      cc_per_rnk_upper:
+        title: 'Control of Corruption: Percentile Rank, Upper Bound of 90% Confidence Interval'
+      cc_std_err:
+        title: 'Control of Corruption: Standard Error'
+      ge_est:
+        title: 'Government Effectiveness: Estimate'
+      ge_no_src:
+        title: 'Government Effectiveness: Number of Sources'
+      ge_per_rnk:
+        title: 'Government Effectiveness: Percentile Rank'
+      ge_per_rnk_lower:
+        title: 'Government Effectiveness: Percentile Rank, Lower Bound of 90% Confidence Interval'
+      ge_per_rnk_upper:
+        title: 'Government Effectiveness: Percentile Rank, Upper Bound of 90% Confidence Interval'
+      ge_std_err:
+        title: 'Government Effectiveness: Standard Error'
+      pv_est:
+        title: 'Political Stability and Absence of Violence/Terrorism: Estimate'
+      pv_no_src:
+        title: 'Political Stability and Absence of Violence/Terrorism: Number of Sources'
+      pv_per_rnk:
+        title: 'Political Stability and Absence of Violence/Terrorism: Percentile Rank'
+      pv_per_rnk_lower:
+        title: 'Political Stability and Absence of Violence/Terrorism: Percentile Rank, Lower Bound of 90% Confidence Interval'
+      pv_per_rnk_upper:
+        title: 'Political Stability and Absence of Violence/Terrorism: Percentile Rank, Upper Bound of 90% Confidence Interval'
+      pv_std_err:
+        title: 'Political Stability and Absence of Violence/Terrorism: Standard Error'
+      rl_est:
+        title: 'Rule of Law: Estimate'
+      rl_no_src:
+        title: 'Rule of Law: Number of Sources'
+      rl_per_rnk:
+        title: 'Rule of Law: Percentile Rank'
+      rl_per_rnk_lower:
+        title: 'Rule of Law: Percentile Rank, Lower Bound of 90% Confidence Interval'
+      rl_per_rnk_upper:
+        title: 'Rule of Law: Percentile Rank, Upper Bound of 90% Confidence Interval'
+      rl_std_err:
+        title: 'Rule of Law: Standard Error'
+      rq_est:
+        title: 'Regulatory Quality: Estimate'
+      rq_no_src:
+        title: 'Regulatory Quality: Number of Sources'
+      rq_per_rnk:
+        title: 'Regulatory Quality: Percentile Rank'
+      rq_per_rnk_lower:
+        title: 'Regulatory Quality: Percentile Rank, Lower Bound of 90% Confidence Interval'
+      rq_per_rnk_upper:
+        title: 'Regulatory Quality: Percentile Rank, Upper Bound of 90% Confidence Interval'
+      rq_std_err:
+        title: 'Regulatory Quality: Standard Error'
+      se_lpv_prim:
+        title: 'Learning poverty: Share of Children at the End-of-Primary age below minimum reading proficiency adjusted by
+          Out-of-School Children (%)'
+      se_lpv_prim_fe:
+        title: 'Learning poverty: Share of Female Children at the End-of-Primary age below minimum reading proficiency adjusted
+          by Out-of-School Children (%)'
+      se_lpv_prim_ld:
+        title: Pupils below minimum reading proficiency at end of primary (%). Low GAML threshold
+      se_lpv_prim_ld_fe:
+        title: Female pupils below minimum reading proficiency at end of primary (%). Low GAML threshold
+      se_lpv_prim_ld_ma:
+        title: Male pupils below minimum reading proficiency at end of primary (%). Low GAML threshold
+      se_lpv_prim_ma:
+        title: 'Learning poverty: Share of Male Children at the End-of-Primary age below minimum reading proficiency adjusted
+          by Out-of-School Children (%)'
+      se_lpv_prim_sd:
+        title: Primary school age children out-of-school (%)
+      se_lpv_prim_sd_fe:
+        title: Female primary school age children out-of-school (%)
+      se_lpv_prim_sd_ma:
+        title: Male primary school age children out-of-school (%)
+      sh_uhc_fbp1_zs:
+        title: Proportion of population pushed further below the $2.15 ($ 2017 PPP) poverty line by out-of-pocket health care
+          expenditure (%)
+      sh_uhc_fbp2_zs:
+        title: Proportion of population pushed further below the $3.65 ($ 2017 PPP) poverty line by out-of-pocket health care
+          expenditure (%)
+      sh_uhc_fbpr_zs:
+        title: Proportion of population pushed further below the 60% median consumption poverty line by out-of-pocket health
+          care expenditure (%)
+      sh_uhc_nopr_zs:
+        title: Proportion of population pushed below the 60% median consumption poverty line by out-of-pocket health expenditure
+          (%)
+      sh_uhc_tot1_zs:
+        title: Proportion of population pushed or further pushed below the $2.15 ($ 2017 PPP) poverty line by out-of-pocket
+          health care expenditure (%)
+      sh_uhc_tot2_zs:
+        title: Proportion of population pushed or further pushed below the $3.65 ($ 2017 PPP) poverty line by out-of-pocket
+          health care expenditure (%)
+      sh_uhc_totr_zs:
+        title: Proportion of population pushed or further pushed below the 60% median consumption poverty line by out-of-pocket
+          health expenditure (%)
+      va_est:
+        title: 'Voice and Accountability: Estimate'
+      va_no_src:
+        title: 'Voice and Accountability: Number of Sources'
+      va_per_rnk:
+        title: 'Voice and Accountability: Percentile Rank'
+      va_per_rnk_lower:
+        title: 'Voice and Accountability: Percentile Rank, Lower Bound of 90% Confidence Interval'
+      va_per_rnk_upper:
+        title: 'Voice and Accountability: Percentile Rank, Upper Bound of 90% Confidence Interval'
+      va_std_err:
+        title: 'Voice and Accountability: Standard Error'
diff --git a/etl/steps/data/garden/worldbank_wdi/2024-05-20/wdi.py b/etl/steps/data/garden/worldbank_wdi/2024-05-20/wdi.py
index 9bc6be124f84..4b1e6c8013f5 100644
--- a/etl/steps/data/garden/worldbank_wdi/2024-05-20/wdi.py
+++ b/etl/steps/data/garden/worldbank_wdi/2024-05-20/wdi.py
@@ -62,29 +62,6 @@ def run(dest_dir: str) -> None:
         assert tb_garden[col].metadata.title is not None, 'Variable "{}" has no title'.format(col)
 
     ####################################################################################################################
-    # Fix issue with Papua New Guinea's electricity access.
-    # The current version of the data reports around 60% electricity access, but the newer version reports around 20%.
-    # The new version can be found here:
-    # https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS?locations=PG
-    # A similar issue happens with the electricity access as a percentage of rural or urban population.
-    # The issue is known, so, for now, and before we update the data, simply remove that data for Papua New Guinea.
-    # Therefore, rows for Papua New Guinea for the following columns will be made nan:
-    # * 'eg_elc_accs_ru_zs' - 'Access to electricity, rural (% of rural population)'.
-    # * 'eg_elc_accs_ur_zs' - 'Access to electricity, urban (% of urban population)'.
-    # * 'eg_elc_accs_zs' - 'Access to electricity (% of population)'.
-
-    # First check that the data is as expected (so that we remember to remove this code when we update the data).
-    error = (
-        "Papua New Guinea electricity access was mistakenly high and was removed from the data. But it seems that"
-        "data has changed (hopefully fixing the issue), so this piece of code can now safely be removed."
-    )
-    assert tb_garden.loc["Papua New Guinea"].loc[2020]["eg_elc_accs_ru_zs"] > 50, error
-    assert tb_garden.loc["Papua New Guinea"].loc[2020]["eg_elc_accs_zs"] > 50, error
-    assert tb_garden.loc["Papua New Guinea"].loc[2020]["eg_elc_accs_ur_zs"] > 80, error
-    # Make nan all those rows.
-    for column in ["eg_elc_accs_zs", "eg_elc_accs_ur_zs", "eg_elc_accs_ru_zs"]:
-        tb_garden.loc["Papua New Guinea", column] = None
-    ####################################################################################################################
 
     #
     # Save outputs.
@@ -373,6 +350,9 @@ def load_variable_metadata() -> pd.DataFrame:
 
     df_vars["indicator_name"] = df_vars["indicator_name"].str.replace(r"\s+", " ", regex=True)
 
+    # remove non-breaking spaces
+    df_vars.source = df_vars.source.str.replace("\xa0", " ").replace("\u00a0", " ")
+
     return df_vars.set_index("indicator_code")
 
 
@@ -392,7 +372,7 @@ def add_variable_metadata(table: Table, ds_source: Source) -> Table:
         # retrieve clean source name, then construct source.
         source_raw_name = var["source"]
         clean_source = clean_source_mapping.get(source_raw_name)
-        assert clean_source, f'`rawName` "{source_raw_name}" not found in wdi.sources.json'
+        assert clean_source, f'`rawName` "{source_raw_name}" not found in wdi.sources.json. Run update_metadata.ipynb or check non-breaking spaces.'
         source = Source(
             name=clean_source["name"],
             description=None,
diff --git a/etl/steps/data/garden/worldbank_wdi/2024-05-20/wdi.sources.json b/etl/steps/data/garden/worldbank_wdi/2024-05-20/wdi.sources.json
index 0a9fd77ca3c4..01996f12fe85 100644
--- a/etl/steps/data/garden/worldbank_wdi/2024-05-20/wdi.sources.json
+++ b/etl/steps/data/garden/worldbank_wdi/2024-05-20/wdi.sources.json
@@ -30,32 +30,32 @@
     "dataPublisherSource": "Brauer et al. (2017)"
   },
   {
-    "rawName": "CAIT data: Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at:\u00a0https://www.climatewatchdata.org/ghg-emissions.",
+    "rawName": "CAIT data: Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions.",
     "name": "World Resources Institute (via World Bank)",
     "dataPublisherSource": "Climate Watch - World Resources Institute"
   },
   {
-    "rawName": "Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at:\u00a0https://www.climatewatchdata.org/ghg-emissions.",
+    "rawName": "Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions.",
     "name": "World Resources Institute (via World Bank)",
     "dataPublisherSource": "Climate Watch - World Resources Institute"
   },
   {
-    "rawName": "Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at:\u00a0https://www.climatewatchdata.org/ghg-emissions. See SP.POP.TOTL for the denominator's source.",
+    "rawName": "Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See SP.POP.TOTL for the denominator's source.",
     "name": "Data compiled from multiple sources by World Bank",
     "dataPublisherSource": "Climate Watch - World Resources Institute, World Population Prospects - UN Population Division (2019), National statistical offices, Eurostat, Population and Vital Statistics Report - UN Statistical Division, International Database - US Census Bureau, Secretariat of the Pacific Community Statistics and Demography Programme"
   },
   {
-    "rawName": "Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at:\u00a0https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.PP.CD for the denominator's source.",
+    "rawName": "Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.PP.CD for the denominator's source.",
     "name": "Data compiled from multiple sources by World Bank",
     "dataPublisherSource": "Climate Watch - World Resources Institute, International Comparison Program - World Bank, Eurostat-OECD PPP Programme"
   },
   {
-    "rawName": "Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at:\u00a0https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.PP.KD for the denominator's source.",
+    "rawName": "Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.PP.KD for the denominator's source.",
     "name": "Data compiled from multiple sources by World Bank",
     "dataPublisherSource": "Climate Watch - World Resources Institute, International Comparison Program - World Bank, Eurostat-OECD PPP Programme"
   },
   {
-    "rawName": "Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at:\u00a0https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.KD for the denominator's source.",
+    "rawName": "Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.KD for the denominator's source.",
     "name": "Data compiled from multiple sources by World Bank",
     "dataPublisherSource": "Climate Watch - World Resources Institute, National accounts data - World Bank / OECD"
   },
@@ -436,7 +436,7 @@
     "dataPublisherSource": "Netcraft, Population estimates - World Bank"
   },
   {
-    "rawName": "Public Expenditure and Financial Accountability\u00a0(PEFA). Ministry of Finance (MoF).",
+    "rawName": "Public Expenditure and Financial Accountability (PEFA). Ministry of Finance (MoF).",
     "name": "Public Expenditure and Financial Accountability program (via World Bank)",
     "dataPublisherSource": "Public Expenditure and Financial Accountability program"
   },
@@ -951,7 +951,7 @@
     "dataPublisherSource": "Global Health Observatory Data Repository - World Health Organization"
   },
   {
-    "rawName": "WHO Global Health Observatory\u00a0 (https://www.who.int/data/gho/data/themes/air-pollution/household-air-pollution)",
+    "rawName": "WHO Global Health Observatory  (https://www.who.int/data/gho/data/themes/air-pollution/household-air-pollution)",
     "name": "World Health Organization (via World Bank)",
     "dataPublisherSource": "Global Health Observatory Data Repository - World Health Organization"
   },
@@ -1011,27 +1011,27 @@
     "dataPublisherSource": "World Trade Organization"
   },
   {
-    "rawName": "Data for up to 1990 are sourced from Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States. Data from 1990 are CAIT data: Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at:\u00a0https://www.climatewatchdata.org/ghg-emissions.",
+    "rawName": "Data for up to 1990 are sourced from Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States. Data from 1990 are CAIT data: Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions.",
     "name": "Carbon Dioxide Information Analysis Center and World Resources Institute (via World Bank)",
     "dataPublisherSource": "Carbon Dioxide Information Analysis Center, Climate Watch - World Resources Institute"
   },
   {
-    "rawName": "Data for up to 1990 are sourced from Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States. Data from 1990 are CAIT data: Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at:\u00a0https://www.climatewatchdata.org/ghg-emissions. See SP.POP.TOTL for the denominator's source.",
+    "rawName": "Data for up to 1990 are sourced from Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States. Data from 1990 are CAIT data: Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See SP.POP.TOTL for the denominator's source.",
     "name": "Data compiled from multiple sources by World Bank",
     "dataPublisherSource": "Carbon Dioxide Information Analysis Center, Climate Watch - World Resources Institute, World Population Prospects - UN Population Division (2019), National statistical offices, Eurostat, Population and Vital Statistics Report - UN Statistical Division, International Database - US Census Bureau, Secretariat of the Pacific Community Statistics and Demography Programme"
   },
   {
-    "rawName": "Data for up to 1990 are sourced from Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States. Data from 1990 are CAIT data: Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at:\u00a0https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.KD for the denominator's source.",
+    "rawName": "Data for up to 1990 are sourced from Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States. Data from 1990 are CAIT data: Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.KD for the denominator's source.",
     "name": "Data compiled from multiple sources by World Bank",
     "dataPublisherSource": "Carbon Dioxide Information Analysis Center, Climate Watch - World Resources Institute, National accounts data - World Bank / OECD"
   },
   {
-    "rawName": "Data for up to 1990 are sourced from Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States. Data from 1990 are CAIT data: Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at:\u00a0https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.PP.CD for the denominator's source.",
+    "rawName": "Data for up to 1990 are sourced from Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States. Data from 1990 are CAIT data: Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.PP.CD for the denominator's source.",
     "name": "Data compiled from multiple sources by World Bank",
     "dataPublisherSource": "Carbon Dioxide Information Analysis Center, Climate Watch - World Resources Institute, International Comparison Program - World Bank, Eurostat-OECD PPP Programme"
   },
   {
-    "rawName": "Data for up to 1990 are sourced from Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States. Data from 1990 are CAIT data: Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at:\u00a0https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.PP.KD for the denominator's source.",
+    "rawName": "Data for up to 1990 are sourced from Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States. Data from 1990 are CAIT data: Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.PP.KD for the denominator's source.",
     "name": "Data compiled from multiple sources by World Bank",
     "dataPublisherSource": "Carbon Dioxide Information Analysis Center, Climate Watch - World Resources Institute, International Comparison Program - World Bank, Eurostat-OECD PPP Programme"
   },
@@ -1211,7 +1211,7 @@
     "dataPublisherSource": "ILO Modelled Estimates and Projections Database - ILOSTAT"
   },
   {
-    "rawName": "Center for International Earth Science Information Network - CIESIN - Columbia University, and CUNY Institute for Demographic Research - CIDR - City University of New York. 2021. Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/d1x1-d702.",
+    "rawName": "Center for International Earth Science Information Network - CIESIN - Columbia University, and CUNY Institute for Demographic Research - CIDR - City University of New York. 2021. Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/d1x1-d702.",
     "name": "CIESIN and CIDR (via World Bank)",
     "dataPublisherSource": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates - CIESIN / CIDR"
   },
@@ -1239,5 +1239,160 @@
     "rawName": "World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2022 Revision.",
     "name": "World Bank",
     "dataPublisherSource": "World Population Prospects - United Nations Population Division"
+  },
+  {
+    "rawName": "UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed November 27, 2023. https://apiportal.uis.unesco.org/bdds.",
+    "name": "UNESCO Institute for Statistics",
+    "dataPublisherSource": "UIS.Stat Bulk Data Download Service - UNESCO Institute for Statistics"
+  },
+  {
+    "rawName": "World Bank and UIS",
+    "name": "World Bank and UNESCO Institute for Statistics",
+    "dataPublisherSource": "World Bank and UNESCO Institute for Statistics"
+  },
+  {
+    "rawName": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.",
+    "name": "UNICEF, WHO, World Bank",
+    "dataPublisherSource": "Joint child Malnutrition Estimates - UNICEF, WHO, World Bank"
+  },
+  {
+    "rawName": "International Labour Organization. “Wages and Working Time Statistics database (COND)” ILOSTAT. Accessed February 06, 2024. https://ilostat.ilo.org/data/.",
+    "name": "International Labour Organization",
+    "dataPublisherSource": "Wages and Working Time Statistics database (COND) - International Labour Organization"
+  },
+  {
+    "rawName": "IEA Statistics © OECD/IEA 2014 (https://www.iea.org/data-and-statistics), subject to https://www.iea.org/terms/",
+    "name": "International Energy Agency",
+    "dataPublisherSource": "IEA Statistics - International Energy Agency"
+  },
+  {
+    "rawName": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).",
+    "name": "UNICEF, WHO, World Bank",
+    "dataPublisherSource": "Joint child Malnutrition Estimates - UNICEF, WHO, World Bank"
+  },
+  {
+    "rawName": "(1) United Nations Population Division. World Population Prospects: 2022 Revision. (2) HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.",
+    "name": "Data compiled from multiple sources by World Bank",
+    "dataPublisherSource": "World Population Prospects - UN Population Division (2022), Human Mortality Database - Max Planck Institute for Demographic Research, University of California, Berkeley, French Institute for Demographic Studies"
+  },
+  {
+    "rawName": "WHO, UNICEF, UNFPA, World Bank Group, and UNDESA/Population Division. Trends in Maternal Mortality 2000 to 2020. Geneva, World Health Organization, 2023",
+    "name": "World Health Organization",
+    "dataPublisherSource": "Trends in Maternal Mortality - WHO, UNICEF, UNFPA, World Bank Group, UNDESA/Population Division"
+  },
+  {
+    "rawName": "Global Health Observatory. Geneva: World Health Organization; 2023. (https://www.who.int/data/gho/data/themes/topics/financial-protection)",
+    "name": "World Health Organization",
+    "dataPublisherSource": "Global Health Observatory - World Health Organization"
+  },
+  {
+    "rawName": "International Labour Organization. “ILO modelled estimates database” ILOSTAT. Accessed February 07, 2024. https://ilostat.ilo.org/data/.",
+    "name": "International Labour Organization",
+    "dataPublisherSource": "ILO modelled estimates database - International Labour Organization"
+  },
+  {
+    "rawName": "IEA Statistics © OECD/IEA 2018 (https://www.iea.org/data-and-statistics), subject to https://www.iea.org/terms/",
+    "name": "International Energy Agency",
+    "dataPublisherSource": "IEA Statistics - International Energy Agency"
+  },
+  {
+    "rawName": "UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed September 19, 2023. https://apiportal.uis.unesco.org/bdds.",
+    "name": "UNESCO Institute for Statistics",
+    "dataPublisherSource": "UIS.Stat Bulk Data Download Service - UNESCO Institute for Statistics"
+  },
+  {
+    "rawName": "Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions",
+    "name": "World Resources Institute",
+    "dataPublisherSource": "Climate Watch Historical GHG Emissions - World Resources Institute"
+  },
+  {
+    "rawName": "Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys.  Largely compiled by UNICEF.",
+    "name": "UNICEF",
+    "dataPublisherSource": "Demographic and Health Surveys, Multiple Indicator Cluster Surveys - UNICEF"
+  },
+  {
+    "rawName": "Emissions data are sourced from Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions",
+    "name": "World Resources Institute",
+    "dataPublisherSource": "Climate Watch Historical GHG Emissions - World Resources Institute"
+  },
+  {
+    "rawName": "Internation Union of Railways (UIC Railisa Database), OECD Statistics",
+    "name": "International Union of Railways",
+    "dataPublisherSource": "UIC Railisa Database - International Union of Railways, OECD Statistics"
+  },
+  {
+    "rawName": "Global Health Observatory. Geneva: World Health Organization; 2023. (https://www.who.int/data/gho/data/themes/topics/service-coverage)",
+    "name": "World Health Organization",
+    "dataPublisherSource": "Global Health Observatory - World Health Organization"
+  },
+  {
+    "rawName": "Data are available online at: https://lpi.worldbank.org/. Summary results are published in World Bank (2023): Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators.",
+    "name": "World Bank",
+    "dataPublisherSource": "Logistics Performance Index - World Bank"
+  },
+  {
+    "rawName": "International Labour Organization. “Labour Force Statistics database (LFS)” ILOSTAT. Accessed February 06, 2024. https://ilostat.ilo.org/data/.",
+    "name": "International Labour Organization",
+    "dataPublisherSource": "Labour Force Statistics database (LFS) - International Labour Organization"
+  },
+  {
+    "rawName": "Global Burden of Disease Collaborative Network. 2021. Global Burden of Disease Study 2019 (GBD 2019) Air Pollution Exposure Estimates 1990-2019. Seattle, United States of America: Institute for Health Metrics and Evaluation (IHME). https://doi.org/10.6069/70JS-NC54",
+    "name": "Institute for Health Metrics and Evaluation",
+    "dataPublisherSource": "Global Burden of Disease Study 2019 - Institute for Health Metrics and Evaluation"
+  },
+  {
+    "rawName": "International Labour Organization. “ILO modelled estimates database” ILOSTAT. Accessed February 06, 2024. https://ilostat.ilo.org/data/.",
+    "name": "International Labour Organization",
+    "dataPublisherSource": "ILO modelled estimates database - International Labour Organization"
+  },
+  {
+    "rawName": "International Labour Organization. “Labour Market-related SDG Indicators database (ILOSDG)” ILOSTAT. Accessed February 06, 2024. https://ilostat.ilo.org/data/.",
+    "name": "International Labour Organization",
+    "dataPublisherSource": "Labour Market-related SDG Indicators database (ILOSDG) - International Labour Organization"
+  },
+  {
+    "rawName": "National statistical offices or national database and publications compiled by United Nations Statistics Division. The data were downloaded on February 14, 2023, from the Global SDG  API: https://unstats.un.org/sdgs/UNSDGAPIV5/swagger/index.html",
+    "name": "United Nations Statistics Division",
+    "dataPublisherSource": "National statistical offices, Global SDG API - United Nations Statistics Division"
+  },
+  {
+    "rawName": "World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database). The data was retrieved on April 7, 2023.",
+    "name": "World Health Organization",
+    "dataPublisherSource": "Global Health Expenditure database - World Health Organization"
+  },
+  {
+    "rawName": "United Nations Conference on Trade and Development",
+    "name": "United Nations Conference on Trade and Development",
+    "dataPublisherSource": "United Nations Conference on Trade and Development"
+  },
+  {
+    "rawName": "Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. The WGI are produced by Daniel Kaufmann (Natural Resource Governance Institute and Brookings Institution) and Aart Kraay (World Bank Development Research Group).  Please cite Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2010).  \"The Worldwide Governance Indicators:  Methodology and Analytical Issues\".  World Bank Policy Research Working Paper No. 5430 (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1682130).  The WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent.",
+    "name": "World Bank",
+    "dataPublisherSource": "Worldwide Governance Indicators - World Bank"
+  },
+  {
+    "rawName": "IEA, IRENA, UNSD, World Bank, WHO. 2023. Tracking SDG 7: The Energy Progress Report. World Bank, Washington DC. © World Bank. License: Creative Commons Attribution—NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO).",
+    "name": "World Bank and International Energy Agency",
+    "dataPublisherSource": "Tracking SDG 7: The Energy Progress Report - World Bank, International Energy Agency, IRENA, UNSD, WHO"
+  },
+  {
+    "rawName": "National statistical offices or national database and publications compiled by United Nations Statistics Division.  The data were downloaded on February 14, 2023, from the Global SDG  API: https://unstats.un.org/sdgs/UNSDGAPIV5/swagger/index.html",
+    "name": "United Nations Statistics Division",
+    "dataPublisherSource": "National statistical offices, Global SDG API - United Nations Statistics Division"
+  },
+  {
+    "rawName": "International Labour Organization. “ILO Modelled Estimates and Projections database (ILOEST)” ILOSTAT. Accessed February 06, 2024. https://ilostat.ilo.org/data/.",
+    "name": "International Labour Organization",
+    "dataPublisherSource": "ILO Modelled Estimates and Projections database (ILOEST) - International Labour Organization"
+  },
+  {
+    "rawName": "Demographic and Health Surveys compiled by United Nations Population Fund. Retrieved on February 14, 2023, from the SDG Global database API (https://unstats.un.org/sdgs/UNSDGAPIV5/swagger/index.html).",
+    "name": "United Nations Population Fund",
+    "dataPublisherSource": "Demographic and Health Surveys - United Nations Population Fund"
+  },
+  {
+    "rawName": "International Labour Organization. “Education and Mismatch Indicators database (EMI)” ILOSTAT. Accessed February 06, 2024. https://ilostat.ilo.org/data/.",
+    "name": "International Labour Organization",
+    "dataPublisherSource": "Education and Mismatch Indicators database (EMI) - International Labour Organization"
   }
 ]