diff --git a/.gitignore b/.gitignore
index 5aa39e8..828fb59 100644
--- a/.gitignore
+++ b/.gitignore
@@ -144,6 +144,7 @@ data.zip
 data/*
 data1
 data
+data_raw/
 
 data_mid/
 data_out/
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 5b08b0e..1652a8b 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,4 +1,13 @@
 fail_fast: true
+
+exclude: |
+  (?x)^(
+      notebooks/process_sandbox\.ipynb|
+      src/cerf/data_acquisition_scrape\.py|
+      src/disaster_charter/data_acquisition_scrape\.py|
+      src/glide/data_acquisition_scrape\.py
+  )$
+
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
     rev: v0.6.8
diff --git a/Makefile b/Makefile
index 05cdf32..90a0c28 100644
--- a/Makefile
+++ b/Makefile
@@ -24,6 +24,30 @@ clean:
 	@rm -rf .venv
 	@poetry env remove --all
 
+run_gdacs_download:
+	@echo "Running GDACS download"
+	@poetry run python -m src.gdacs.data_acquisition_api
+
+run_glide_download:
+	@echo "Running Glide download"
+	@poetry run python -m src.glide.data_acquisition_scrape
+
+run_cerf_download:
+	@echo "Running CERF download"
+	@poetry run python -m src.cerf.data_acquisition_scrape
+
+run_disaster_charter_download:
+	@echo "Running Disaster-Charter download"
+	@poetry run python -m src.disaster_charter.data_acquisition_scrape
+
+run_idus_download:
+	@echo "Downloading IDUS dump → data_raw/idmc_idu/idus_all.json"
+	@mkdir -p data_raw/idmc_idu
+	@curl -L --compressed \
+		-o data_raw/idmc_idu/idus_all.json \
+		"https://helix-copilot-prod-helix-media-external.s3.amazonaws.com/external-media/api-dump/idus-all/2025-06-04-10-00-32/5mndO/idus_all.json"
+	@echo "✅  Saved (decompressed): data_raw/idmc_idu/idus_all.json"
+
 run_glide_normal:
 	@echo "Running Glide normalisation"
 	@poetry run python -m src.glide.data_normalisation_glide
diff --git a/docs/DATASETS.md b/docs/DATASETS.md
index 258424b..969cf4a 100644
--- a/docs/DATASETS.md
+++ b/docs/DATASETS.md
@@ -21,6 +21,21 @@ update any of the datasets:
 **Important**: Always preserve the folder structure to avoid breaking downstream
 processes.
 
+## API-Based Dataset: GDACS
+
+The GDACS dataset is unique in that it provides an official API, made available directly by the GDACS team.
+
+To update this dataset:
+
+1. Modify the date range in the `src/gdacs/data_acquisition_api.py` script and the `main` method.
+2. Run the following command:
+
+    ```sh
+    make run_gdacs_download
+    ```
+
+This process will automatically download and save the updated records to the appropriate location.
+
 ## Web-Scraped Legacy Datasets (Now Blocked or Fragile)
 
 Some datasets were initially extracted using automated **web scraping scripts**.
@@ -31,7 +46,6 @@ category:
 - CERF Activations
 - Disaster Charter Activations
 - GLIDE Events
-- GDACS Events
 - WFP ADAM
 
 ### How to Update These
diff --git a/notebooks/process_sandbox.ipynb b/notebooks/process_sandbox.ipynb
new file mode 100644
index 0000000..a995f31
--- /dev/null
+++ b/notebooks/process_sandbox.ipynb
@@ -0,0 +1,999 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "7fd4519c-da46-4871-a4fc-7a3901a72ada",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from datetime import datetime\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "## pycirclize package is only necessary for the last plot. Installed it with pip instead of conda on my machine\n",
+    "from pycirclize import Circos\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78e5a63e-5ce3-412a-a527-c0afac949f4d",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "## Pre-process sources"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "838deefe-151a-4450-8136-f638f6e59c54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def convert_date(x,date_format):\n",
+    "    \"\"\"Different sources have different date formats stored as strings (DD/MM/YYYY, MM-DD-YYYY, etc...).\n",
+    "    This simple function converts them to a datetime single format.\n",
+    "    \"\"\"\n",
+    "    try:\n",
+    "        return(datetime.strptime(x,date_format))\n",
+    "    except:\n",
+    "        return(np.nan)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "2879a7f7-2a67-4cb2-b210-2f567c79fa40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def filter_rename_columns(df,col_list,new_col_list):\n",
+    "    \"\"\"Simple function used to rename a dataframe columns based on a\n",
+    "    renaming list. Returns the same dataframe with only\n",
+    "    selected columns and rename them\n",
+    "    \"\"\"\n",
+    "    rename_dict = dict()\n",
+    "    for i in range(len(col_list)):\n",
+    "        rename_dict[col_list[i]] =  new_col_list[i]\n",
+    "\n",
+    "    df = df[col_list]\n",
+    "    df.rename(columns = rename_dict, inplace = True)\n",
+    "\n",
+    "    return(df)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "13c3271a-9d45-4d6a-832f-55e0453254f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# This dictionary is just a placeholder for a preliminary event taxonomy. Has to be replaced\n",
+    "# by a proper function with less hard-coding. Event type who are not on this dictionary\n",
+    "# are not converted on the current version.\n",
+    "\n",
+    "# The following events are covered as output:\n",
+    "# Drought (DR) / Floods (FL) / Epidemics (EP) / Earthquake (EQ)\n",
+    "# Volcano (VO) / Landslide (LS) / Storm + Tropical Cyclone (TC/ST)\n",
+    "# Fires (WF) / Heat + Cold Waves (HW / CW) / Displacement (-)\n",
+    "\n",
+    "event_dict = {\n",
+    "    \"Drought\":\"DR\",\n",
+    "    \"Flood\":\"FL\",\n",
+    "    \"Floods\":\"FL\",\n",
+    "    \"floods\":\"FL\",\n",
+    "    \"Flooding\":\"FL\",\n",
+    "    \"FF\":\"FL\",\n",
+    "    \"Pluvial/Flash Flood\":\"FL\",\n",
+    "    \"Epidemic\":\"EP\",\n",
+    "    \"Cholera\":\"EP\",\n",
+    "    \"Ebola\":\"EP\",\n",
+    "    \"Measles\":\"EP\",\n",
+    "    \"Earthquake\":\"EQ\",\n",
+    "    \"Earthquakes\":\"EQ\",\n",
+    "    \"Volcano\":\"VO\",\n",
+    "    \"Volcanic activity\":\"VO\",\n",
+    "    \"Volcanic Eruption\":\"VO\",\n",
+    "    \"Landslide\":\"LS\",\n",
+    "    \"landslide\":\"LS\",\n",
+    "    \"Mass Movement\":\"LS\",\n",
+    "    \"Mass movement (wet)\":\"LS\",\n",
+    "    \"Mass movement (dry)\":\"LS\",\n",
+    "    \"SL\":\"LS\",\n",
+    "    \"MS\":\"LS\",\n",
+    "    \"TC\":\"TC/ST\",\n",
+    "    \"ST\":\"TC/ST\",\n",
+    "    \"Cyclone\":\"TC/ST\",\n",
+    "    \"Cyclones\":\"TC/ST\",\n",
+    "    \"Typhoon\":\"TC/ST\",\n",
+    "    \"cyclone\":\"TC/ST\",\n",
+    "    \"Storm\":\"TC/ST\",\n",
+    "    \"Storm Surge\":\"TC/ST\",\n",
+    "    \"Fire\":\"WF\",\n",
+    "    \"Fires\":\"WF\",\n",
+    "    \"FR\":\"WF\",\n",
+    "    \"Wildfires\":\"WF\",\n",
+    "    \"Wildfire\":\"WF\",\n",
+    "    \"Forest fire\":\"WF\",\n",
+    "    \"HT\":\"HW/CW\",\n",
+    "    \"HW\":\"HW/CW\",\n",
+    "    \"CW\":\"HW/CW\",\n",
+    "    \"Heat Wave\":\"HW/CW\",\n",
+    "    \"Cold Wave\":\"HW/CW\",\n",
+    "    \"Extreme temperature\":\"HW/CW\",\n",
+    "    \"Extreme Temperature\":\"HW/CW\",\n",
+    "    \"Heat/Cold Wave\":\"HW/CW\",\n",
+    "    \"Population Movement\":\"Displacement\",\n",
+    "    \"Displacement/Migration\":\"Displacement\",\n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6961aea6-fe84-4b31-b098-77765a7f8869",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "### Source-specific loading and pre-processing\n",
+    "\n",
+    "This should be improved / replaced by the work Evangelos has doing. Lots of hard-coding for now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "1f56bb68-7e47-46bd-a892-e2db7f20e668",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "############## GDACS ##############\n",
+    "\n",
+    "\n",
+    "#Load all unique files (per year) into one single dataframe\n",
+    "input_gdacs_df = pd.DataFrame()\n",
+    "for file_year in range(2000,2025):\n",
+    "    filename = \"../data/gdacs/gdacs_events_\" + str(file_year) +\".csv\"\n",
+    "    #filename = '../data_raw/gdacs/gdacs_events_' + str(file_year) +'.csv'\n",
+    "    new_df = pd.read_csv(filename)\n",
+    "\n",
+    "    input_gdacs_df = pd.concat([input_gdacs_df, new_df])\n",
+    "\n",
+    "#input column names\n",
+    "col_list = [\"event_id\",\"event_type\",\"country_iso3\",\"country_name\",\"from_date\",\"to_date\"]\n",
+    "df = input_gdacs_df.copy()\n",
+    "\n",
+    "#retrieve first country and first iso-code per event (should be improved in the future to account for all countries)\n",
+    "df[\"countries\"] = df[\"countries\"].fillna(\"\")\n",
+    "df[\"country_name\"] = df[\"countries\"].apply(lambda x : x.split(\",\")[0].split(\"(\")[0][:-1])\n",
+    "df[\"country_iso3\"] = df[\"countries\"].apply(lambda x : x.split(\",\")[0].split(\"(\")[-1][:-1])\n",
+    "df = df[col_list]\n",
+    "\n",
+    "#Format both initial and end date\n",
+    "df.rename(columns = {\"event_id\" : \"gdacs_id\"}, inplace = True)\n",
+    "df[\"from_date\"] = df[\"from_date\"].apply(lambda x : convert_date(x[:10],\"%Y-%m-%d\"))\n",
+    "df[\"to_date\"] = df[\"to_date\"].apply(lambda x : convert_date(x[:10],\"%Y-%m-%d\"))\n",
+    "\n",
+    "#Rename event types following our custom taxonomy\n",
+    "for event in event_dict:\n",
+    "    df.loc[df[\"event_type\"] == event, \"event_type\"] = event_dict[event]\n",
+    "\n",
+    "df.drop_duplicates(inplace = True)\n",
+    "df.reset_index(drop = True, inplace = True)\n",
+    "\n",
+    "gdacs_df = df.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "fe157c75-b24e-4c5c-ae2a-cf7638162c1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_785094/621467075.py:13: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df.rename(columns = rename_dict, inplace = True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "############## GLIDE ##############\n",
+    "\n",
+    "#Load data\n",
+    "date_col = \"event_date\"\n",
+    "filename = \"../data/glide/glide_events.csv\"\n",
+    "#filename = '../data_raw/glide/glide_events.csv'\n",
+    "input_glide_df = pd.read_csv(filename)\n",
+    "\n",
+    "#Rename key columns\n",
+    "col_list = [\"GLIDE_number\",\"Event_Code\",\"Country_Code\",\"Country\",\"Date_\"]\n",
+    "new_col_list = [\"glide_id\",\"event_type\",\"country_iso3\",\"country_name\",date_col]\n",
+    "df = filter_rename_columns(input_glide_df.copy(),col_list,new_col_list)\n",
+    "\n",
+    "#Format event date\n",
+    "df[date_col] = df[date_col].apply(lambda x : convert_date(x,\"%Y/%m/%d\"))\n",
+    "df = df[~df[date_col].isna()].reset_index(drop = True)\n",
+    "\n",
+    "#Rename event types following our custom taxonomy\n",
+    "for event in event_dict:\n",
+    "    df.loc[df[\"event_type\"] == event, \"event_type\"] = event_dict[event]\n",
+    "\n",
+    "glide_df = df.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "1495b634-8a88-454d-8b30-2efbad9da4ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_785094/621467075.py:13: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df.rename(columns = rename_dict, inplace = True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "############## CERF ##############\n",
+    "\n",
+    "#Load data\n",
+    "filename = \"../data/cerf/cerf_emergency_data_dynamic_web_scrape.csv\"\n",
+    "#filename = '../data_raw/cerf/cerf_emergency_data_dynamic_web_scrape.csv'\n",
+    "input_cerf_df = pd.read_csv(filename)\n",
+    "date_col = \"approval_date\"\n",
+    "\n",
+    "#Rename key columns\n",
+    "col_list = [\"project_code\",\"emergency_type\",\"country\",\"approval_date\"]\n",
+    "new_col_list = [\"cerf_id\",\"event_type\",\"country_name\",date_col]\n",
+    "df = filter_rename_columns(input_cerf_df.copy(),col_list,new_col_list)\n",
+    "\n",
+    "#Format event date\n",
+    "df[date_col] = df[date_col].apply(lambda x : convert_date(x,\"%d/%m/%Y\"))\n",
+    "df = df[~df[date_col].isna()].reset_index(drop = True)\n",
+    "\n",
+    "#Rename event types following our custom taxonomy\n",
+    "for event in event_dict:\n",
+    "    df.loc[df[\"event_type\"] == event, \"event_type\"] = event_dict[event]\n",
+    "\n",
+    "cerf_df = df.copy()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "a9bf0407-3d1c-4c3e-bf04-8f9b0069d99b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_785094/621467075.py:13: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df.rename(columns = rename_dict, inplace = True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "############## DISASTER CHART ##############\n",
+    "\n",
+    "#Load data\n",
+    "filename = \"../data/disaster-charter/charter_activations_web_scrape_2000_2025.csv\"\n",
+    "#filename = '../data_raw/disaster-charter/charter_activations_web_scrape_2000_2025.csv'\n",
+    "input_charter_df = pd.read_csv(filename)\n",
+    "date_col = \"date\"\n",
+    "\n",
+    "#Rename key columns\n",
+    "col_list = [\"Activation ID\",\"Type of Event\",\"Location of Event\",\"Date\"]\n",
+    "new_col_list = [\"charter_id\",\"event_type\",\"country_name\",date_col]\n",
+    "df = filter_rename_columns(input_charter_df.copy(),col_list,new_col_list)\n",
+    "\n",
+    "#Format event date\n",
+    "df[date_col] = df[date_col].apply(lambda x : convert_date(x,\"%Y-%m-%d\"))\n",
+    "df = df[~df[date_col].isna()].reset_index(drop = True)\n",
+    "\n",
+    "#Rename event types following our custom taxonomy\n",
+    "for event in event_dict:\n",
+    "    df.loc[df[\"event_type\"] == event, \"event_type\"] = event_dict[event]\n",
+    "\n",
+    "# Not all events have an id. For now, we use the DataFrame id (column index)\n",
+    "# combined with the original id (column charter_id)\n",
+    "df.reset_index(inplace = True)\n",
+    "df[\"index\"] = df[\"index\"].apply(lambda x : \"custom_\" + str(x))\n",
+    "df[\"charter_id\"] = df[\"charter_id\"].astype(\"str\")\n",
+    "df.loc[df[\"charter_id\"] == \"nan\", \"charter_id\"] = df.loc[df[\"charter_id\"] == \"nan\", \"index\"]\n",
+    "df.drop(columns = \"index\", inplace = True)\n",
+    "\n",
+    "charter_df = df.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "2355c4c3-4bce-49ef-a7b6-84ec89f0cc81",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_785094/621467075.py:13: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df.rename(columns = rename_dict, inplace = True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "############## EM-DAT ##############\n",
+    "\n",
+    "#Load data\n",
+    "filename = \"../data/emdat/public_emdat_custom_request_2025-06-04_c1e3334f-e027-4f8a-92d5-7ce401c7654c.xlsx\"\n",
+    "#filename = '../data_raw/emdat/public_emdat_custom_request_2025-06-04_c1e3334f-e027-4f8a-92d5-7ce401c7654c.xlsx'\n",
+    "input_emdat_df = pd.read_excel(filename)\n",
+    "date_col = \"start_date\"\n",
+    "\n",
+    "#Filter events without a start day and create date column from year, month and day\n",
+    "df = input_emdat_df.copy()\n",
+    "df = df[~df[\"Start Day\"].isna()]\n",
+    "df[date_col] = df.apply(lambda row : str(int(row[\"Start Year\"])) + \"/\" + str(int(row[\"Start Month\"])) + \"/\" + str(int(row[\"Start Day\"])), axis = 1)\n",
+    "\n",
+    "#Rename key columns\n",
+    "col_list = [\"DisNo.\",\"Disaster Type\",\"ISO\", \"Country\",\"start_date\"]\n",
+    "new_col_list = [\"emdat_id\",\"event_type\",\"country_iso3\",\"country_name\",date_col]\n",
+    "df = filter_rename_columns(df.copy(),col_list,new_col_list)\n",
+    "\n",
+    "#Format event date\n",
+    "df[date_col] = df[date_col].apply(lambda x : convert_date(x,\"%Y/%m/%d\"))\n",
+    "df = df[~df[date_col].isna()].reset_index(drop = True)\n",
+    "\n",
+    "#Rename event types following our custom taxonomy\n",
+    "for event in event_dict:\n",
+    "    df.loc[df[\"event_type\"] == event, \"event_type\"] = event_dict[event]\n",
+    "\n",
+    "\n",
+    "emdat_df = df.copy()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "88cadbb1-1716-408a-86c0-1546f8d8cd22",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_785094/621467075.py:13: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df.rename(columns = rename_dict, inplace = True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "############## IDMC ##############\n",
+    "\n",
+    "#Load data\n",
+    "filename = \"../data/idmc_idu/idus_all.json\"\n",
+    "#filename = '../data_raw/idmc_idu/idus_all.json'\n",
+    "input_idmc_df = pd.read_json(filename)\n",
+    "date_col = \"event_start_date\"\n",
+    "\n",
+    "df = input_idmc_df.copy()\n",
+    "#df = df[df['displacement_type'] == 'Disaster'] # we may want to filter out events who are not disaster\n",
+    "\n",
+    "#Rename key columns\n",
+    "col_list =  [\"id\",\"type\",\"iso3\",\"country\",\"event_start_date\"]\n",
+    "new_col_list = [\"idmc_id\",\"event_type\",\"country_iso3\",\"country_name\",date_col]\n",
+    "df = filter_rename_columns(df.copy(),col_list,new_col_list)\n",
+    "\n",
+    "#Format event date\n",
+    "df[date_col] = df[date_col].apply(lambda x : convert_date(x,\"%Y-%m-%d\"))\n",
+    "df = df[~df[date_col].isna()].reset_index(drop = True)\n",
+    "\n",
+    "#Rename event types following our custom taxonomy\n",
+    "for event in event_dict:\n",
+    "    df.loc[df[\"event_type\"] == event, \"event_type\"] = event_dict[event]\n",
+    "\n",
+    "idmc_df = df.copy()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "89887462-b053-4769-84e0-9e7f680bf19f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_785094/621467075.py:13: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df.rename(columns = rename_dict, inplace = True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "############## IFRC DREF ##############\n",
+    "\n",
+    "#Load data\n",
+    "filename = \"../data/ifrc_dref/IFRC_emergencies.csv\"\n",
+    "#filename = '../data_raw/ifrc_dref/IFRC_emergencies.csv'\n",
+    "input_dref_df = pd.read_csv(filename, on_bad_lines=\"skip\")\n",
+    "date_col = \"disaster_start_date\"\n",
+    "df = input_dref_df.copy()\n",
+    "\n",
+    "#Rename key columns\n",
+    "col_list =  [\"id\",\"dtype.name\",\"countries.iso3\",\"countries.name\",\"disaster_start_date\"]\n",
+    "new_col_list = [\"dref_id\",\"event_type\",\"country_iso3\",\"country_name\",date_col]\n",
+    "df = filter_rename_columns(df.copy(),col_list,new_col_list)\n",
+    "\n",
+    "#Format event date\n",
+    "df[date_col] = df[date_col].apply(lambda x : convert_date(x[:10],\"%Y-%m-%d\"))\n",
+    "df = df[~df[date_col].isna()].reset_index(drop = True)\n",
+    "df = df[df[\"disaster_start_date\"].apply(lambda x : x.year > 1900)].reset_index(drop = True)\n",
+    "df[\"disaster_start_date\"] = df[\"disaster_start_date\"].astype(\"datetime64[ns]\")\n",
+    "\n",
+    "#Rename event types following our custom taxonomy\n",
+    "for event in event_dict:\n",
+    "    df.loc[df[\"event_type\"] == event, \"event_type\"] = event_dict[event]\n",
+    "\n",
+    "dref_df = df.copy()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "513f9f5c-0e7b-434c-8d00-ef2462ca0272",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "## Match sources"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "757c5a8d-f9b0-4045-820b-58a0ff533e35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def initialize_disaster_df(ref_df):\n",
+    "    \"\"\"Create the reference dataframe based on the first source. GDACS is used as the first one\n",
+    "    but anyother could fill this role.\n",
+    "    \"\"\"\n",
+    "    df = pd.DataFrame()\n",
+    "\n",
+    "    disaster_col_list = [\"event_type\",\"country_iso3\",\"country_name\",\"from_date\",\"to_date\",\"gdacs_id\"]\n",
+    "    df = ref_df[disaster_col_list].copy()\n",
+    "\n",
+    "    df.rename(columns = {\"from_date\":\"initial_date\", \"to_date\":\"end_date\"}, inplace = True)\n",
+    "    df.reset_index(inplace = True)\n",
+    "    df.rename(columns = {\"index\" : \"unique_id\"}, inplace = True)\n",
+    "\n",
+    "    return(df)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "63f0d274-f799-499c-a0a9-668e055d9f9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def initialize_new_source(source_name):\n",
+    "    \"\"\"Define some key parameters depending on the source being added.\n",
+    "    These parameters are: \n",
+    "    new_id => source unique id column name \n",
+    "    match_col => columns used for the matching (besides event date)\n",
+    "    date_col => event date colum name\n",
+    "    delta_day_threshold => threshold for matching dates \n",
+    "    \"\"\"\n",
+    "    if source_name == \"glide\":\n",
+    "        new_id = \"glide_id\"\n",
+    "        match_col = [\"event_type\",\"country_iso3\"]\n",
+    "        date_col = \"event_date\"\n",
+    "        delta_day_threshold = 7\n",
+    "\n",
+    "    elif source_name == \"cerf\":\n",
+    "        new_id = \"cerf_id\"\n",
+    "        match_col = [\"event_type\",\"country_name\"]\n",
+    "        date_col = \"approval_date\"\n",
+    "        delta_day_threshold = 14\n",
+    "\n",
+    "    elif source_name == \"charter\":\n",
+    "        new_id = \"charter_id\"\n",
+    "        match_col = [\"event_type\",\"country_name\"]\n",
+    "        date_col = \"date\"\n",
+    "        delta_day_threshold = 7\n",
+    "\n",
+    "    elif source_name == \"emdat\":\n",
+    "        new_id = \"emdat_id\"\n",
+    "        match_col = [\"event_type\",\"country_iso3\"]\n",
+    "        date_col = \"start_date\"\n",
+    "        delta_day_threshold = 7\n",
+    "\n",
+    "    elif source_name == \"idmc\":\n",
+    "        new_id = \"idmc_id\"\n",
+    "        match_col = [\"event_type\",\"country_iso3\"]\n",
+    "        date_col = \"event_start_date\"\n",
+    "        delta_day_threshold = 7\n",
+    "\n",
+    "    elif source_name == \"dref\":\n",
+    "        new_id = \"dref_id\"\n",
+    "        match_col = [\"event_type\",\"country_iso3\"]\n",
+    "        date_col = \"disaster_start_date\"\n",
+    "        delta_day_threshold = 7\n",
+    "\n",
+    "\n",
+    "    return new_id, match_col, date_col, delta_day_threshold\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "497d8a4a-a991-4498-8ffb-ac87ae3ef5f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_new_source(disaster_df, new_source_df, source_name):\n",
+    "    \"\"\"Match a new source to all the previous one added to the combined DataFrame.\n",
+    "    For now the matching is based on event date, type and country.\n",
+    "    \"\"\"\n",
+    "    #Retrieve source parameters\n",
+    "    new_id, match_col, date_col, delta_day_threshold = initialize_new_source(source_name)\n",
+    "\n",
+    "    #First match based on matching columns (event type and country by default)\n",
+    "    merge_df = disaster_df.merge(new_source_df, on = match_col, how = \"inner\", suffixes = (\"\",\"_new\"))\n",
+    "    #Check if new event date is within the defined threshold in days to check if the dates match\n",
+    "    merge_df[\"date_match\"] = ((merge_df[date_col] - merge_df[\"initial_date\"]).apply(lambda x : x.days) >= - delta_day_threshold) & ((merge_df[date_col] - merge_df[\"end_date\"]).apply(lambda x : x.days) <= delta_day_threshold)\n",
+    "    merge_df = merge_df[merge_df[\"date_match\"]].reset_index(drop = True)\n",
+    "    #Update initial / end dates based on the new added event\n",
+    "    merge_df[\"initial_date\"]  = merge_df[[\"initial_date\",date_col]].min(axis=1)\n",
+    "    merge_df[\"end_date\"]  = merge_df[[\"end_date\",date_col]].max(axis=1)\n",
+    "    merge_df.drop(columns = [\"country_name_new\",\"country_iso3_new\", date_col,\"date_match\"], inplace = True, errors=\"ignore\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "    #Left outer dataframe contain events within the reference dataframe that are not present on the new source\n",
+    "    left_outer_df = disaster_df[~disaster_df[\"unique_id\"].isin(list(merge_df[\"unique_id\"].unique()))]\n",
+    "    #Right outer dataframe contain events within the new source that are not present on the reference dataframe\n",
+    "    new_source_df[\"initial_date\"] = new_source_df[date_col]\n",
+    "    new_source_df[\"end_date\"]  = new_source_df[date_col]\n",
+    "    new_source_df.drop(columns = [date_col], inplace = True)\n",
+    "    right_outer_df = new_source_df[~new_source_df[new_id].isin(list(merge_df[new_id].unique()))]\n",
+    "\n",
+    "    #Combine events that are present both in the reference and new source (merge_df),\n",
+    "    #those who are only present on the reference (left_outer_df) and those only\n",
+    "    #present on the new source (right_outer_df).\n",
+    "    df = pd.concat([merge_df,left_outer_df,right_outer_df])\n",
+    "    df.drop(columns = \"unique_id\", inplace = True)\n",
+    "    df.reset_index(inplace = True)\n",
+    "    df.rename(columns = {\"index\" : \"unique_id\"}, inplace = True)\n",
+    "\n",
+    "    return(df)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "cb4a66c2-3f1c-47dc-9c17-5d2d9150fb09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "disaster_df = initialize_disaster_df(gdacs_df)\n",
+    "disaster_df = add_new_source(disaster_df, glide_df, \"glide\")\n",
+    "disaster_df = add_new_source(disaster_df, cerf_df, \"cerf\")\n",
+    "disaster_df = add_new_source(disaster_df, charter_df, \"charter\")\n",
+    "disaster_df = add_new_source(disaster_df, emdat_df, \"emdat\")\n",
+    "disaster_df = add_new_source(disaster_df, idmc_df, \"idmc\")\n",
+    "disaster_df = add_new_source(disaster_df, dref_df, \"dref\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93e177c7-14de-4a60-a355-d2dea4df8c3e",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d698df3-2e11-4c41-abc0-01638a6ea280",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62d10bcf-3a0b-492a-a3da-b8fd2e2195b4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23d981b1-7e84-4383-ba63-5a9e60948860",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "## Analysis\n",
+    "\n",
+    "Analysis code is not well structured / commented. For now it was just a quick analysis but we could format it in the future\n",
+    "to make repetitive analysis easier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "902a85d2-4d59-4a65-9289-51117cc0dd15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "analysis_df = disaster_df.copy()\n",
+    "\n",
+    "analysis_df[\"gdacs\"] = analysis_df[\"gdacs_id\"].notna()\n",
+    "analysis_df[\"glide\"] = analysis_df[\"glide_id\"].notna()\n",
+    "analysis_df[\"cerf\"] = analysis_df[\"cerf_id\"].notna()\n",
+    "analysis_df[\"charter\"] = analysis_df[\"charter_id\"].notna()\n",
+    "analysis_df[\"emdat\"] = analysis_df[\"emdat_id\"].notna()\n",
+    "analysis_df[\"idmc\"] = analysis_df[\"idmc_id\"].notna()\n",
+    "analysis_df[\"dref\"] = analysis_df[\"dref_id\"].notna()\n",
+    "\n",
+    "analysis_df[\"nb_sources\"] = analysis_df[[\"gdacs\",\"glide\",\"cerf\",\"charter\",\"emdat\",\"idmc\", \"dref\"]].sum(axis = 1)\n",
+    "\n",
+    "#analysis_df.loc[analysis_df['nb_sources'] > 1, ['nb_sources','gdacs','glide','cerf','charter','emdat','idmc', 'dref']].value_counts().reset_index().head(10)\n",
+    "#analysis_df['nb_sources'].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "5c94a4c3-ca89-4e1b-9376-681b64a55d24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Number of events per source')"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nb_events = []\n",
+    "valid_events = []\n",
+    "\n",
+    "source_list = [\"gdacs\",\"idmc\",\"cerf\",\"emdat\",\"glide\", \"dref\",\"charter\"]\n",
+    "source_df_dict = {\n",
+    "    \"gdacs\" : input_gdacs_df,\n",
+    "    \"idmc\" : input_idmc_df,\n",
+    "    \"cerf\" : input_cerf_df,\n",
+    "    \"emdat\" : input_emdat_df,\n",
+    "    \"glide\" : input_glide_df,\n",
+    "    \"dref\" : input_dref_df,\n",
+    "    \"charter\" : input_charter_df,\n",
+    "}\n",
+    "\n",
+    "for source in source_list:\n",
+    "    col_id = source + \"_id\"\n",
+    "    nb_events.append(source_df_dict[source].shape[0])\n",
+    "    valid_events.append( len(analysis_df.loc[(analysis_df[col_id].notna()), col_id].unique()) )\n",
+    "\n",
+    "\n",
+    "\n",
+    "count_df = pd.DataFrame()\n",
+    "count_df[\"source\"] = source_list\n",
+    "count_df[\"Database size\"] = nb_events\n",
+    "count_df[\"Unique valid events\"] = valid_events\n",
+    "\n",
+    "ax = count_df.plot.bar(x = \"source\", rot = 45)\n",
+    "ax.set_title(\"Number of events per source\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "38b0d03c-894c-46f9-83fe-0ec10de76b10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#df = input_gdacs_df['event_type'].copy()\n",
+    "#df = input_idmc_df['type'].copy()\n",
+    "#df = input_cerf_df['emergency_type'].copy()\n",
+    "#df = input_emdat_df['Disaster Type'].copy()\n",
+    "#df = input_glide_df['Event_Code'].copy()\n",
+    "#df = input_dref_df['dtype.name'].copy()\n",
+    "#df = input_charter_df['Type of Event'].copy()\n",
+    "\n",
+    "#df.fillna('', inplace = True)\n",
+    "#print(df.value_counts().shape[0])\n",
+    "#df.value_counts(True).head(20)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "1e27b2fc-3563-4eb4-b17e-94c2bbfdfb12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "no_match_list = []\n",
+    "match_list = []\n",
+    "\n",
+    "source_list = [\"gdacs\",\"idmc\",\"cerf\",\"emdat\",\"glide\", \"dref\",\"charter\"]\n",
+    "\n",
+    "for source in source_list:\n",
+    "    col_id = source + \"_id\"\n",
+    "\n",
+    "    x = len(analysis_df.loc[(analysis_df[col_id].notna()) & (analysis_df[\"nb_sources\"] == 1), col_id].unique())\n",
+    "    y = len(analysis_df.loc[(analysis_df[col_id].notna()) & (analysis_df[\"nb_sources\"] > 1), col_id].unique())\n",
+    "    no_match_list.append(x)\n",
+    "    match_list.append(y)\n",
+    "\n",
+    "plot_df = pd.DataFrame()\n",
+    "plot_df[\"source\"] = source_list\n",
+    "plot_df[\"no_match\"] = no_match_list\n",
+    "plot_df[\"match\"] = match_list\n",
+    "plot_df[\"total\"] = plot_df[\"no_match\"] + plot_df[\"match\"]\n",
+    "plot_df[\"score\"] = plot_df[\"match\"]/plot_df[\"total\"]\n",
+    "plot_df[\"legend\"] = plot_df.apply(lambda row : row[\"source\"] + \" - \" + str(int(row[\"score\"]*100)) + \"%\", axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "2fcb7241-8b08-495b-81e4-35bf06685cf3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Matching events')"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "ax = plot_df[[\"legend\",\"match\",\"no_match\"]].plot.bar(x = \"legend\", stacked=True, rot = 45)\n",
+    "ax.set_title(\"Matching events\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "0b38c9b8-7320-459c-bded-eaafd679e1fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = analysis_df.copy()\n",
+    "N = len(diag_source_list)\n",
+    "\n",
+    "value_matrix = list()\n",
+    "\n",
+    "for i in range(N):\n",
+    "\n",
+    "\n",
+    "    new_matrix = list()\n",
+    "    for j in range(N):\n",
+    "\n",
+    "        col_i = diag_source_list[i].lower()\n",
+    "        col_j = diag_source_list[j].lower()\n",
+    "        if i == j or i > j:\n",
+    "            z = 0\n",
+    "        else:\n",
+    "            z = df[(df[col_i]) & (df[col_j])].shape[0]\n",
+    "\n",
+    "        new_matrix.append(z)\n",
+    "    value_matrix.append(new_matrix)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "28dbe9cc-4162-4970-933b-ced26899c489",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         GDACS   IDMC  CERF  EMDAT  GLIDE  DREF  CHARTER\n",
+      "GDACS        0  22274  2862  17312  15842  9205     1622\n",
+      "IDMC         0      0   812  15900  13150  8179     1655\n",
+      "CERF         0      0     0   1749   2397  1432      136\n",
+      "EMDAT        0      0     0      0  15367  8558     1730\n",
+      "GLIDE        0      0     0      0      0  8072     1427\n",
+      "DREF         0      0     0      0      0     0     1025\n",
+      "CHARTER      0      0     0      0      0     0        0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create matrix dataframe (3 x 6)\n",
+    "\n",
+    "diag_source_list = [\"GDACS\", \"IDMC\", \"CERF\", \"EMDAT\", \"GLIDE\", \"DREF\", \"CHARTER\"]\n",
+    "\n",
+    "row_names = diag_source_list\n",
+    "col_names = diag_source_list\n",
+    "matrix_data = value_matrix\n",
+    "matrix_df = pd.DataFrame(matrix_data, index=row_names, columns=col_names)\n",
+    "\n",
+    "# Initialize from matrix (Can also directly load tsv matrix file)\n",
+    "circos = Circos.initialize_from_matrix(\n",
+    "    matrix_df,\n",
+    "    start=-265,\n",
+    "    end=95,\n",
+    "    space=5,\n",
+    "    r_lim=(93, 100),\n",
+    "    cmap=\"tab10\",\n",
+    "    label_kws=dict(r=94, size=12, color=\"white\"),\n",
+    "    link_kws=dict(ec=\"black\", lw=0.5),\n",
+    ")\n",
+    "\n",
+    "print(matrix_df)\n",
+    "fig = circos.plotfig()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47802143-877e-47fd-a7bc-933817b98bfc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c677540c-0bf5-48c8-9bd1-e85f542b5a89",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "## Notes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f30423f-3d06-47f3-a29e-69f3c5f3f58f",
+   "metadata": {},
+   "source": [
+    "Notes\n",
+    "\n",
+    "\n",
+    "- Miscellaneous:\n",
+    "\n",
+    "  - No unique ID for Charter source as activation id is sometimes NULL.\n",
+    "  - GDACS seems to have lots of duplicates. Some cleanup is needed\n",
+    "  - Should we drop conflict events? What about epidemics? What about displacement without a identified cause?\n",
+    "  - Some sources have a glide_number id (but very often empty). Add this to the matching\n",
+    "  - GDACS matching with other sources is highly dependent on the alert level (72% for red, 61% for orange, 19% for green)\n",
+    "\n",
+    "\n",
+    "####################################\n",
+    "\n",
+    "To do:\n",
+    "\n",
+    "- Create a mapping for GLIDE event type categories\n",
+    "\n",
+    "- Evaluate CERF event categories, specially if we can have more info on displacement activations\n",
+    "\n",
+    "- Approval date (CERF) may be a few days later than the event date. For now I've used 14 days as a threshold but more investigation is needed\n",
+    " (maybe depending on event type)\n",
+    "\n",
+    "- Map CERF and Charter country names to iso_code\n",
+    "\n",
+    "\n",
+    "- Some sources have multiple event types => think about how to add multi-event index\n",
+    "\n",
+    "- Think about how to handle event with only month and year data (no day)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f585e090",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "348f4e39",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/cerf/data_acquisition_scrape.py b/src/cerf/data_acquisition_scrape.py
new file mode 100644
index 0000000..8d0c71c
--- /dev/null
+++ b/src/cerf/data_acquisition_scrape.py
@@ -0,0 +1,164 @@
+from datetime import datetime
+
+import pandas as pd
+import requests
+from bs4 import BeautifulSoup
+
+BASE_URL = "https://cerf.un.org/what-we-do/allocation/all/emergency/"
+DETAILS_BASE_URL = "https://cerf.un.org/what-we-do/allocation/"
+
+def fetch_emergency_list():
+    cerf_url = "https://cerf.un.org/fundingByEmergency/all"
+    response = requests.get(cerf_url)
+    if response.status_code == 200:
+        return response.json()
+    print(f"Error fetching CERF emergency list: {response.status_code}")
+    return []
+
+def clean_text(cell):
+    return cell.get_text(separator=" ").strip()
+
+def clean_approved_amount(amount):
+    """Remove 'US$' and commas from approved_amount field and return as a number string"""
+    return amount.replace("US$", "").replace("US $", "").replace(",", "").strip()
+
+def clean_date_format(date_text):
+    """Reformat date from 'DD MMM YYYY' to 'DD/MM/YYYY'"""
+    try:
+        return datetime.strptime(date_text, "%d %b %Y").strftime("%d/%m/%Y")
+    except ValueError:
+        return date_text  # Keep as-is if it doesn't match expected format
+
+def fetch_project_details(allocation_code, project_code, year, emergency_id):
+    """Dynamically build the link for each project and scrape additional details.
+    """
+    details_url = f"{DETAILS_BASE_URL}{year}/emergency/{emergency_id}/{allocation_code}/{project_code}"
+    response = requests.get(details_url)
+
+    if response.status_code == 200:
+        soup = BeautifulSoup(response.content, "html.parser")
+
+        # Scrape the 'Group(s) of people targeted' if available
+        groups_targeted_element = soup.find("div", string="Group(s) of people targeted")
+        groups_targeted = groups_targeted_element.find_next("div").text.strip() if groups_targeted_element else "N/A"
+
+        # Scrape the 'Number of people targeted' if available
+        number_of_people_element = soup.find("div", string="Number of people targeted")
+        number_of_people = number_of_people_element.find_next("div").text.strip() if number_of_people_element else "N/A"
+
+        # Scrape the 'Implementation dates' if available
+        implementation_dates_element = soup.find("div", string="Implementation dates")
+        if implementation_dates_element:
+            implementation_dates = implementation_dates_element.find_next("div").text.strip()
+            implementation_dates = implementation_dates.replace("\n", " | ").replace("Project start:", "Start:").replace("Project end:", "End:")
+        else:
+            implementation_dates = "N/A"
+
+        return {
+            "groups_targeted": groups_targeted,
+            "number_of_people_targeted": number_of_people,
+            "implementation_dates": implementation_dates,
+        }
+
+    print(f"Error fetching project details from {details_url}: {response.status_code}")
+    return {
+        "groups_targeted": "N/A",
+        "number_of_people_targeted": "N/A",
+        "implementation_dates": "N/A",
+    }
+
+def fetch_emergency_details(link_id):
+    url = f"{BASE_URL}{link_id}"
+    response = requests.get(url)
+
+    if response.status_code == 200:
+        soup = BeautifulSoup(response.content, "html.parser")
+
+        year_range_element = soup.find("div", {"class": "field--name-field-year"})
+        year_range = year_range_element.text.strip() if year_range_element else "Unknown"
+
+        total_amount_element = soup.find("div", {"class": "field--name-field-total-amount"})
+        total_amount = total_amount_element.text.strip() if total_amount_element else "Unknown"
+
+        table = soup.find("table")
+
+        table_data = []
+
+        if table:
+            rows = table.find_all("tr")
+
+            for row in rows[1:]:
+                cells = row.find_all("td")
+
+                # Skip summary or total rows
+                if any(cell.has_attr("colspan") for cell in cells):
+                    continue
+
+                if len(cells) >= 8:
+                    try:
+                        allocation_code = clean_text(cells[0])
+                        allocation = clean_text(cells[1])
+                        emergency_type = clean_text(cells[2])
+                        agency = clean_text(cells[3])
+                        country = clean_text(cells[4])
+                        project_code = clean_text(cells[5])
+                        project_description = clean_text(cells[6])
+                        window = clean_text(cells[7]).replace("Window:", "").strip()
+                        sector = clean_text(cells[8]).replace("Sector:", "").strip()
+                        approved_amount = clean_approved_amount(clean_text(cells[9]))
+                        approval_date = clean_date_format(clean_text(cells[10]).replace("Approval date:", "").strip())
+                        disbursement_date = clean_date_format(clean_text(cells[11]).replace("Disbursement date:", "").strip())
+
+                        # Fetch additional project details dynamically
+                        project_details = fetch_project_details(allocation_code, project_code, year_range.split("-")[0], link_id)
+
+                        table_data.append({
+                            "link_id": link_id,
+                            "allocation_code": allocation_code,
+                            "allocation": allocation,
+                            "emergency_type": emergency_type,
+                            "agency": agency,
+                            "country": country,
+                            "project_code": project_code,
+                            "project_description": project_description,
+                            "window": window,
+                            "sector": sector,
+                            "approved_amount": approved_amount,
+                            "approval_date": approval_date,
+                            "disbursement_date": disbursement_date,
+                            "groups_targeted": project_details["groups_targeted"],
+                            "number_of_people_targeted": project_details["number_of_people_targeted"],
+                            "implementation_dates": project_details["implementation_dates"],
+                        })
+                    except IndexError as e:
+                        print(f"Error processing row: {cells} - {e}")
+                        continue
+
+        return table_data
+    print(f"Error fetching data for link ID {link_id}: {response.status_code}")
+    return []
+
+def main():
+    emergency_list = fetch_emergency_list()
+
+    if not emergency_list:
+        print("No emergency data found.")
+        return
+
+    all_table_data = []
+
+    for emergency in emergency_list:
+        link_id = emergency["link_id"]
+        name = emergency["name"]
+        print(f"Fetching data for link ID {link_id} ({name})...")
+        table_data = fetch_emergency_details(link_id)
+        all_table_data.extend(table_data)
+
+    df = pd.DataFrame(all_table_data)
+    output_csv = "./data_raw/cerf/cerf_emergency_data.csv"
+    df.to_csv(output_csv, index=False)
+
+    print(f"Data saved to {output_csv}")
+
+if __name__ == "__main__":
+    main()
diff --git a/src/disaster_charter/data_acquisition_scrape.py b/src/disaster_charter/data_acquisition_scrape.py
new file mode 100644
index 0000000..40c26ff
--- /dev/null
+++ b/src/disaster_charter/data_acquisition_scrape.py
@@ -0,0 +1,147 @@
+"""Disaster Charter activations scraper
+• Loops 2025→2000 through the JSON filter API
+• Visits each legacy detail page (/web/guest/activations/-/article/<slug>)
+• Extracts full table (type, location, timezone …)
+• Writes CSV identical to the October-2024 format
+"""
+
+import csv
+import os
+import re
+import time
+from datetime import datetime
+
+import requests
+from bs4 import BeautifulSoup
+
+# ----------------------------------------------------------------------
+# CONFIG
+# ----------------------------------------------------------------------
+
+BASE_API  = "https://disasterscharter.org/api-proxy/cos-api/api/public/library/activations"
+BASE_SITE = "https://disasterscharter.org"
+YEAR_FROM = 2000
+YEAR_TO   = 2025          # inclusive
+HEADERS   = {"User-Agent": "Mozilla/5.0"}
+SLEEP_SEC = 0.35          # polite delay between page hits
+
+OUT_DIR   = "./data_raw/disaster_charter/"
+CSV_PATH  = os.path.join(OUT_DIR, "disaster_activations_web_scrape_2000_2025.csv")
+os.makedirs(OUT_DIR, exist_ok=True)
+
+# ----------------------------------------------------------------------
+# LABEL MAPPING & DETAIL SCRAPER
+# ----------------------------------------------------------------------
+
+LABEL_PATTERNS = {
+    "type_of_event"      : r"type of event",
+    "location_of_event"  : r"location of event",
+    "date_of_activation" : r"date of charter activation",
+    "time_of_activation" : r"time of charter activation",
+    "timezone"           : r"time zone of charter activation",
+    "charter_requestor"  : r"charter requestor",
+    "activation_id"      : r"activation id",
+    "project_management" : r"project management",
+    "value_adding"       : r"value adding",
+}
+
+def scrape_detail_page(slug: str) -> dict:
+    """Slug e.g. 'flood-in-nigeria-activation-963-'
+    Fetches /web/guest/activations/-/article/<slug>
+    """
+    url = f"{BASE_SITE}/web/guest/activations/-/article/{slug}"
+    resp = requests.get(url, headers=HEADERS, timeout=25)
+    if resp.status_code != 200:
+        print("  ⚠️  detail page 404:", slug)
+        return {k: "N/A" for k in LABEL_PATTERNS}
+
+    soup = BeautifulSoup(resp.content, "html.parser")
+
+    def pull(regex: str) -> str:
+        pat = re.compile(regex + r":?$", re.IGNORECASE)
+        lbl = soup.find(lambda tag: tag.string and pat.search(tag.string.strip()))
+        if not lbl:
+            return "N/A"
+        val_cell = lbl.find_next("td") if lbl.name == "th" else lbl.find_next()
+        return val_cell.get_text(strip=True) if val_cell else "N/A"
+
+    return {k: pull(rgx) for k, rgx in LABEL_PATTERNS.items()}
+
+# ----------------------------------------------------------------------
+# MAIN COLLECTION LOOP
+# ----------------------------------------------------------------------
+
+def collect_rows():
+    rows = []
+
+    # ▶︎ reverse order: newest year first
+    for yr in range(YEAR_TO, YEAR_FROM - 1, -1):
+        print(f"• Year {yr}")
+        params = {
+            "from": f"{yr}-01-01 00:00:00",
+            "to"  : f"{yr}-12-31 23:59:59",
+        }
+        try:
+            data = requests.get(BASE_API, headers=HEADERS, params=params, timeout=25).json()
+            acts = data.get("activations", [])
+        except Exception as e:
+            print("  ↳ API error:", e)
+            continue
+
+        for act in acts:
+            slug = act.get("slug", "")
+            if not slug:
+                continue
+
+            ts = act.get("dateAsTimestamp")
+            if ts:
+                dt  = datetime.utcfromtimestamp(ts / 1000)
+                year, month = dt.year, dt.strftime("%B")
+                date_str    = dt.strftime("%Y-%m-%d")
+                pretty_date = dt.strftime("%d %A")          # ▶︎ 16 Wednesday
+            else:
+                year = month = date_str = pretty_date = "N/A"
+
+            title     = act.get("title", "N/A")
+            page_link = f"{BASE_SITE}/web/guest/activations/-/article/{slug}"
+
+            details = scrape_detail_page(slug)
+            time.sleep(SLEEP_SEC)
+
+            rows.append([
+                year, month, date_str, title, pretty_date, page_link,
+                details["type_of_event"],
+                details["location_of_event"],
+                details["date_of_activation"],
+                details["time_of_activation"],
+                details["timezone"],
+                details["charter_requestor"],
+                details["activation_id"],
+                details["project_management"],
+                details["value_adding"],
+            ])
+
+    return rows
+
+# ----------------------------------------------------------------------
+# CSV WRITER
+# ----------------------------------------------------------------------
+
+def save_csv(rows):
+    header = [
+        "Year","Month","Date","Disaster","Formatted Date","Details Link",
+        "Type of Event","Location of Event","Date of Activation","Time of Activation",
+        "Timezone","Charter Requestor","Activation ID","Project Management","Value Adding",
+    ]
+    with open(CSV_PATH, "w", newline="", encoding="utf-8") as fh:
+        csv.writer(fh).writerows([header] + rows)
+    print(f"✅  Saved {len(rows)} rows → {CSV_PATH}")
+
+# ----------------------------------------------------------------------
+# RUN
+# ----------------------------------------------------------------------
+
+if __name__ == "__main__":
+    rows = collect_rows()
+    if rows:
+        save_csv(rows)
diff --git a/src/gdacs/data_acquisition_api.py b/src/gdacs/data_acquisition_api.py
new file mode 100644
index 0000000..6fbe0e6
--- /dev/null
+++ b/src/gdacs/data_acquisition_api.py
@@ -0,0 +1,141 @@
+"""GDACS Data Acquisition Script."""
+
+import logging
+import pathlib
+from datetime import datetime, timedelta, timezone
+
+import pandas as pd
+import requests
+
+SEARCH_URL = "https://www.gdacs.org/gdacsapi/api/events/geteventlist/SEARCH"
+OUTPUT_DIR = "./data_raw/gdacs/"
+pathlib.Path(OUTPUT_DIR).mkdir(parents=True, exist_ok=True)
+
+
+def fetch_events(
+    start_date: datetime,
+    end_date: datetime,
+    event_type: str,
+) -> list[dict]:
+    """Fetch GDACS events of a specific type within a date range."""
+    params = {
+        "fromDate": start_date.strftime("%Y-%m-%d"),
+        "toDate": end_date.strftime("%Y-%m-%d"),
+        "alertlevel": "Green;Orange;Red",
+        "eventlist": event_type,
+        "country": "",
+    }
+
+    logging.info(
+        "Fetching %s events from %s to %s...",
+        event_type,
+        params["fromDate"],
+        params["toDate"],
+    )
+    response = None
+    try:
+        response = requests.get(SEARCH_URL, params=params, timeout=10)
+        response.raise_for_status()
+
+        if not response.text.strip():
+            logging.warning(
+                "Empty response body for %s events %s to %s",
+                event_type,
+                params["fromDate"],
+                params["toDate"],
+            )
+            return []
+
+        data = response.json()
+
+        return [
+            {
+                "event_id": feature["properties"].get("eventid", "N/A"),
+                "event_type": feature["properties"].get("eventtype", "N/A"),
+                "event_name": feature["properties"].get("name", "N/A"),
+                "from_date": feature["properties"].get("fromdate", "N/A"),
+                "to_date": feature["properties"].get("todate", "N/A"),
+                "alert_level": feature["properties"].get("alertlevel", "N/A"),
+                "countries": ", ".join(
+                    f"{c['countryname']} ({c.get('iso3','')})"
+                    for c in feature["properties"].get("affectedcountries", [])
+                ),
+                "population": feature["properties"].get("population", "N/A"),
+                "severity": feature["properties"]
+                .get("severitydata", {})
+                .get("severity", "N/A"),
+                "alert_score": feature["properties"].get("alertscore", "N/A"),
+                "bbox": feature.get("bbox", []),
+                "coordinates": feature.get("geometry", {}).get("coordinates", []),
+            }
+            for feature in data.get("features", [])
+        ]
+
+    except requests.exceptions.JSONDecodeError:
+        logging.exception(
+            "JSON decode error for %s events %s to %s first 200 chars: %r",
+            event_type,
+            params["fromDate"],
+            params["toDate"],
+            response.text[:200] if response is not None else "No response",
+        )
+        return []
+
+    except requests.RequestException:
+        logging.exception(
+            "Request failed for %s events %s to %s",
+            event_type,
+            params["fromDate"],
+            params["toDate"],
+        )
+        return []
+
+
+def main() -> None:
+    """Main function to fetch GDACS events and save them to CSV files."""
+    start_date = datetime(2000, 1, 1, tzinfo=timezone.utc)
+    end_date = datetime(2025, 5, 28, tzinfo=timezone.utc)
+    interval = timedelta(days=30)
+    event_types = ["EQ", "TS", "TC", "FL", "VO", "DR", "WF"]
+
+    all_data = pd.DataFrame()
+    current_date = start_date
+
+    while current_date < end_date:
+        next_date = min(current_date + interval, end_date)
+        try:
+            for event_type in event_types:
+                events = fetch_events(current_date, next_date, event_type)
+                if events:
+                    events_df = pd.DataFrame(events)
+                    all_data = pd.concat([all_data, events_df], ignore_index=True)
+                else:
+                    logging.info(
+                        "No events found for %s from %s to %s",
+                        event_type,
+                        current_date.date(),
+                        next_date.date(),
+                    )
+        except Exception:
+            logging.exception(
+                "Unexpected error occurred while processing events from %s to %s",
+                current_date.date(),
+                next_date.date(),
+            )
+        current_date = next_date
+
+    if not all_data.empty:
+        all_data["year"] = pd.to_datetime(
+            all_data["from_date"],
+            errors="coerce",
+        ).dt.year
+        for year, group in all_data.groupby("year"):
+            output_file = pathlib.Path(OUTPUT_DIR) / f"gdacs_events_{year}.csv"
+            group.to_csv(output_file, index=False)
+            logging.info("Saved %d events to %s", len(group), output_file)
+    else:
+        logging.info("No data found.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/glide/data_acquisition_scrape.py b/src/glide/data_acquisition_scrape.py
new file mode 100644
index 0000000..cbb052f
--- /dev/null
+++ b/src/glide/data_acquisition_scrape.py
@@ -0,0 +1,104 @@
+"""Data acquisition script for Glide Number using Selenium and BeautifulSoup."""
+
+from pathlib import Path
+
+import pandas as pd
+from bs4 import BeautifulSoup
+from selenium import webdriver
+from selenium.webdriver.common.by import By
+from selenium.webdriver.firefox.options import Options as FirefoxOptions
+from selenium.webdriver.firefox.service import Service as FirefoxService
+from selenium.webdriver.support import expected_conditions as ec
+from selenium.webdriver.support.ui import WebDriverWait
+
+URL = "https://glidenumber.net/glide/public/result/report.jsp"
+GECKODRIVER_PATH = "/usr/local/bin/geckodriver"
+PROFILE_PATH = (
+    "/home/evangelos/snap/firefox/common/.mozilla/firefox/"
+    "cf7shfvv.selenium_profile"
+)
+
+
+Path("./data_raw/glide_v2/").mkdir(parents=True, exist_ok=True)
+CSV_OUTPUT = "./data_raw/glide/glide_data_combined_all.csv"
+
+def scrape_with_selenium() -> str:
+    """Use Selenium to interact with the Glide Number website and return the rendered.
+
+    HTML as a string.
+    """
+    options = FirefoxOptions()
+    options.headless = False # type: ignore[attr-defined]
+    options.add_argument("-profile")
+    options.add_argument(PROFILE_PATH)
+
+    service = FirefoxService(GECKODRIVER_PATH)
+    driver = webdriver.Firefox(service=service, options=options)
+
+    try:
+        driver.get(URL)
+
+        WebDriverWait(driver, 20).until(
+            ec.presence_of_element_located((By.NAME, "variables")),
+        )
+
+        variables_field = driver.find_element(By.NAME, "variables")
+        for option in variables_field.find_elements(By.TAG_NAME, "option"):
+            driver.execute_script("arguments[0].selected = true;", option)
+
+        unlimited_checkbox = driver.find_element(By.NAME, "unlimited")
+        if not unlimited_checkbox.is_selected():
+            unlimited_checkbox.click()
+
+        continue_button = driver.find_element(By.NAME, "continueReport")
+        continue_button.click()
+
+        WebDriverWait(driver, 60).until(
+            ec.presence_of_element_located(
+                (
+                    By.XPATH,
+                    "//table[@border='1' and @width='100%']//tr",
+                ),
+            ),
+        )
+
+        return driver.page_source
+
+    finally:
+        driver.quit()
+
+def parse_html_to_dataframe(html: str) -> pd.DataFrame:
+    """Parse the rendered HTML using BS and return the data as a DataFrame."""
+    soup = BeautifulSoup(html, "html.parser")
+
+    data_table = soup.find(
+        "table",
+        {
+            "cellspacing": "1",
+            "cellpadding": "1",
+            "border": "1",
+            "width": "100%",
+        },
+    )
+    if not data_table:
+        return pd.DataFrame()
+
+    headers = [th.text.strip() for th in data_table.find_all("th")] # type: ignore[attr-defined]
+
+    rows = []
+    for tr in data_table.find_all("tr")[1:]: # type: ignore[union-attr]
+        cells = [td.text.strip() for td in tr.find_all("td")] # type: ignore[attr-defined]
+        # Filter out rows like "Hits:0"
+        if len(cells) == len(headers) and not any("Hits:" in cell for cell in cells):
+            rows.append(cells)
+
+    return pd.DataFrame(rows, columns=headers)
+
+if __name__ == "__main__":
+    rendered_html = scrape_with_selenium()
+    data_df = parse_html_to_dataframe(rendered_html)
+
+    if not data_df.empty:
+        data_df.to_csv(CSV_OUTPUT, index=False)
+    else:
+        pass