Add master datacard from README_Water.md

README.md

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+# Master Datacard for Water Indicators for African Countries
+
+This repository contains time-series datasets for key water-related indicators for 54 African countries. The data is sourced from The World Bank and has been cleaned, processed, and organized for analysis.
+
+Each country has its own set of files, including a main CSV dataset and a corresponding datacard in Markdown format. The data covers the period from 1960 to 2024, where available.
+
+---
+
+## Repository Structure
+
+The datasets are organized by country. Each country's folder contains:
+
+1. A CSV file with the naming convention: `{CountryName}-Water-Indicators-Dataset-{StartYear}-{EndYear}.csv`
+2. A detailed datacard named `datacard_Water.md`.
+
+---
+
+## Indicators Included
+
+This collection includes the following water indicators:
+
+- Annual freshwater withdrawals, total (% of internal resources)
+- Marine protected areas (% of territorial waters)
+- People using at least basic drinking water services (% of population)
+- People with basic handwashing facilities including soap and water (% of population)
+
+---
+
+## Countries Included
+
+This dataset covers all 54 sovereign nations of Africa as recognized by the source data:
+
+- Algeria
+- Angola
+- Benin
+- Botswana
+- Burkina Faso
+- Burundi
+- Cabo Verde
+- Cameroon
+- Central African Republic
+- Chad
+- Comoros
+- Congo, Dem. Rep.
+- Congo, Rep.
+- Cote d'Ivoire
+- Djibouti
+- Egypt, Arab Rep.
+- Equatorial Guinea
+- Eritrea
+- Eswatini
+- Ethiopia
+- Gabon
+- Gambia, The
+- Ghana
+- Guinea
+- Guinea-Bissau
+- Kenya
+- Lesotho
+- Liberia
+- Libya
+- Madagascar
+- Malawi
+- Mali
+- Mauritania
+- Mauritius
+- Morocco
+- Mozambique
+- Namibia
+- Niger
+- Nigeria
+- Rwanda
+- Sao Tome and Principe
+- Senegal
+- Seychelles
+- Sierra Leone
+- Somalia
+- South Africa
+- South Sudan
+- Sudan
+- Tanzania
+- Togo
+- Tunisia
+- Uganda
+- Zambia
+- Zimbabwe
+
+---
+
+## Data Preparation
+
+The raw data from The World Bank was processed using a Python script with the following steps:
+
+1. **Filtering**: Data was filtered for each of the 54 African countries.
+2. **Reshaping**: Wide-format data (with years as columns) was melted into a long format.
+3. **Merging**: All indicator files for a country were merged into a single time-series dataset based on the 'Year' column.
+4. **Cleaning**: The 'Year' column was converted to a standard date format (`YYYY-MM-DD`).
+5. **Missing Data Handling**: Gaps in the time-series were filled using linear interpolation followed by a back-fill to ensure data continuity.
+
+---
+
+## How to Use
+
+You can access the data directly through the Hugging Face Hub, either by downloading individual files or by using the `datasets` library to load the data.
+
+```python
+from datasets import load_dataset
+
+# Example: Load the dataset for Nigeria
+dataset = load_dataset('electricsheepafrica/Water-Indicators-For-African-Countries', data_files='Nigeria/Nigeria-Water-Indicators-Dataset-1960-2024.csv')
+
+print(dataset)
+```