updated

.github/scripts/update_stats.py

@@ -5,6 +5,7 @@ import matplotlib.pyplot as plt
 from datetime import datetime
 from pathlib import Path
 import os
+import re
 
 # Get the repository root (works both for local and GitHub Actions)
 if 'GITHUB_WORKSPACE' in os.environ:
@@ -39,10 +40,23 @@ def update_csv():
     except FileNotFoundError:
         rows = [["Date", "Total Downloads"]]
     
+    # Check if today's date is already in the CSV
+    today_exists = False
+    for i, row in enumerate(rows):
+        if len(row) > 0 and row[0] == today:
+            rows[i] = [today, downloads]
+            today_exists = True
+            break
+    
+    # If today's date is not in the CSV, add it
+    if not today_exists:
+        rows.append([today, downloads])
+    
     with open(CSV_PATH, mode='w', newline='') as file:
         writer = csv.writer(file)
         writer.writerows(rows)
-        writer.writerow([today, downloads])
+    
+    print(f"Updated CSV with {downloads} downloads for {today}")
 
 def generate_image():
     data = pd.read_csv(CSV_PATH)
@@ -60,27 +74,32 @@ def generate_image():
     plt.tight_layout()
     plt.savefig(IMAGE_PATH)
     plt.close()
+    
+    print(f"Generated download statistics image at {IMAGE_PATH}")
 
 def update_readme():
+    # The badge in the README is already dynamic and will update automatically
+    # This function ensures the download_statistics.png image is properly referenced
+    
     with open(README_PATH, 'r') as file:
         content = file.read()
     
-    start_marker = "## Download Statistics"
-    image_text = f"\n\n![Download Statistics](download_statistics.png)\n"
-    
-    if start_marker not in content:
-        content += f"\n\n{start_marker}{image_text}"
-    else:
-        parts = content.split(start_marker)
-        next_section = parts[1].find("##")
-        if next_section == -1:
-            parts[1] = image_text
+    # Check if we need to add a download statistics section with the image
+    if "## Download Statistics" not in content and "![Download Statistics]" not in content:
+        # Find a good place to insert the download statistics section
+        maintenance_section = "## 🔧 Development and Maintenance"
+        if maintenance_section in content:
+            # Insert before the maintenance section
+            parts = content.split(maintenance_section)
+            content = parts[0] + "\n## Download Statistics\n\n![Download Statistics](download_statistics.png)\n\n" + maintenance_section + parts[1]
         else:
-            parts[1] = image_text + parts[1][next_section:]
-        content = start_marker.join(parts)
+            # Insert at the end of the file
+            content += "\n\n## Download Statistics\n\n![Download Statistics](download_statistics.png)\n"
     
     with open(README_PATH, 'w') as file:
         file.write(content)
+    
+    print(f"Updated README with download statistics section")
 
 if __name__ == "__main__":
     update_csv()

.github/workflows/update_downloads.yml

@@ -12,13 +12,13 @@ jobs:
       - uses: actions/setup-python@v3
         with:
           python-version: '3.x'
-      - run: pip install requests
+      - run: pip install -r requirements.txt
       - name: Update stats
         run: python .github/scripts/update_stats.py
       - name: Commit changes
         run: |
           git config --local user.email "[email protected]"
           git config --local user.name "GitHub Action"
-          git add daily_downloads.csv
+          git add daily_downloads.csv download_statistics.png
           git commit -m "Update download stats" || exit 0
           git push

README.md

@@ -3,7 +3,7 @@ language:
 - en
 pretty_name: IFVI Value Factors - Derivative Dataset For Analysis
 ---
-![alt text](resources/images/graphics/3.png)
+![IFVI Value Factors](images/graphics/3.png)
 
 [![GitHub Repository](https://img.shields.io/badge/GitHub-Repository-blue?logo=github)](https://github.com/danielrosehill/Global-Value-Factors-Explorer-Dataset)  
 [![Hugging Face Dataset](https://img.shields.io/badge/Hugging%20Face-Dataset-orange?logo=huggingface)](https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv)  
@@ -55,8 +55,11 @@ ifvi_valuefactors_deriv/
 │
 ├── examples/              # Example applications using the dataset
 │
-└── resources/             # Additional resources
-    └── images/            # Images used in documentation
+├── images/                # Images used in documentation
+│   ├── cards/             # Card images for visual representation
+│   └── graphics/          # Graphics and visual elements
+│
+└── resources/             # Additional resources and reference data
 ```
 
 For more details on the repository structure, see [REPOSITORY_STRUCTURE.md](REPOSITORY_STRUCTURE.md).
@@ -69,10 +72,6 @@ This repository reflects GVFD Version 1 (October 15th, 2024).  It is not guarant
 
 This derivative dataset is subject to the same terms of use as the original database, available in `license.md` at the repository root. These licensing conditions are stipulated by the International Foundation for Valuing Impacts. At the time of writing, the licensing terms provide for wide use of the data on a complimentary basis (including by account preparers) with limited exclusions to that position for those looking to integrate the data into commercial data products for which licensing charges apply. Questions regarding licensing of the database and requests for clarification regarding allowable uses and any other queries regarding compliance with the terms of their license should be referred to the IFVI.
 
-## 📅 Versioning
-
-This repository reflects GVFD Version 1 (October 15th, 2024).  It is not guaranteed to be the most recent version.  Consult the IFVI website for the latest data and updates.  While this repository aims to mirror the original GVFD, using this data for official purposes requires referencing the complete IFVI documentation, which is not included here.
-
 <a id="data-formatting"></a>
 ## 🗂️ Data Formatting
 
@@ -97,6 +96,22 @@ No material data changes were made.  Modifications are limited to formatting and
 * Removal of US dollar signs for easier database integration.
 * Standardization of 12 country names to more common versions (e.g., "Bahamas, The" to "Bahamas") and mapping all territories to their ISO-3166 Alpha-2 codes for clarity.
 
+## 🔧 Development and Maintenance
+
+This repository includes scripts for maintaining download statistics and other metadata. To contribute or run these scripts locally:
+
+1. Install the required dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+2. Update download statistics manually:
+   ```bash
+   python3 .github/scripts/update_stats.py
+   ```
+
+The repository is configured with GitHub Actions to automatically update download statistics daily.
+
 <a id="release-notes-for-v2"></a>
 
 ---

REPOSITORY_STRUCTURE.md

@@ -32,8 +32,11 @@ ifvi_valuefactors_deriv/
 │   ├── economic-impact/   # Examples for economic impact assessment
 │   └── regional-comparison/ # Examples for regional comparisons
 │
-├── resources/             # Additional resources
-│   └── images/            # Images used in documentation
+├── images/                # Images used in documentation
+│   ├── cards/             # Card images for visual representation
+│   └── graphics/          # Graphics and visual elements
+│
+├── resources/             # Additional resources and reference data
 │
 └── internal/              # Internal repository management (not for public use)
     ├── archive/           # Archived files

archive/old-readme.md

@@ -1,36 +0,0 @@
----
-language:
-- en
-pretty_name: IFVI Value Factors - Derivative Dataset For Analysis
-size_categories:
-- 1K<n<10K
----
-
-## Dataset Description
-This dataset contains value factors for quantifying environmental impacts in monetary terms. It includes JSON files for structured data, PDFs for detailed reports, and CSVs for tabular data.
-
- This repository contains a derivative data set of the International Foundation for Valuing Impacts Global Value Factor Database, which was released during UN Climate Week in 2024. 
- 
- This derivative data set was originally compiled by me, Daniel Rosehill, released onto Github as a repository and is now copied and made available also on Hugging Face. 
-
- The derivative data set is merely a reformatting of the original data, which was released in XSLM format. 
- 
- The reformatted and reshaped data set provides the same data, but in JSON and CSV format to support data analysis and visualization workloads. 
- 
- Some other minor formatting changes were made, such as removing the US dollar symbol from numeric fields where they appeared (instead the currency is noted in metadata files), but no substantive changes were made to either the data or its constitution.
- 
- The dataset consists of a range of value factors which are almost all segmented on a geographical basis. 
- 
- The purpose of these value factors is to enable companies to convert their environmental impacts into monetary terms in support of a practice known as impact accounting.
- 
-In the envisioned use, these value factors would be paired with companies' data about their greenhouse gas emissions (GHG), land use, wastewater consumption and pollution and other impacts. 
-
-Those would be integrated into the preparation of financial accounts. This data set is shared to this platform with the kind permission of the IFVI whose only stipulation is that users should adhere to the license which is provided at the root of this repository. Although this summary is not a substitute for reading the license, the licensing generally permits non commercial use of the data. Usage requests can additionally be made directly to IFVI.
-
-Beyond the use case for account preparers there are other potentially interesting use cases in these value factors, which have been formulated on the basis of the best scientific opinion. 
-
-They could be used in modeling government policy decisions to incentivise or disincentivise environmental activities by non profit seeking to. understand or estimate the extent of companies' non financial impacts among other uses. 
-
-This dataset may or may not be the latest version of the GVFD and the original database should in all cases be considered the authoritative source that can be downloaded via the IFVI's website at ifvi.org. 
-
-Thank you for your interest and if you have made any interesting use of this data, please do feel free to reach out. 

daily_downloads.csv

@@ -1,3 +1,4 @@
 Date,Total Downloads
 2025-01-05,275
 2025-01-07,288
+2025-03-18,847

requirements.txt

@@ -0,0 +1,4 @@
+requests>=2.25.0
+pandas>=1.2.0
+matplotlib>=3.3.0
+numpy>=1.19.0