README.md

---
license: cc-by-4.0
configs:
- config_name: default
  data_files: sustainability_criteria.jsonl
task_categories:
  - text-classification
  - text-retrieval
language:
  - en
  - de
tags:
  - sustainability
  - procurement
  - criteria
  - german
pretty_name: Sustainability Procurement Criteria
---

# Sustainability Procurement Criteria

This dataset contains sustainability procurement criteria organized by groups of goods and services (GGS) (German: Waren- und Dienstleistungsgruppen; WDG).
It originates from validated Excel files and has been converted to JSONL format for easy consumption.

## Dataset Structure

### Format
- **Format**: JSONL (JSON Lines) - one JSON object per line
- **Encoding**: UTF-8
- **Compression**: None

### Files
The dataset is provided as a single merged JSONL file (`sustainability_criteria.jsonl`) containing criteria from all groups of goods and services (WDG; Waren- und Dienstgruppen). Each record includes WDG identifiers (`WDG_ID`, `wdg_name_en`, `wdg_name_de`) to distinguish criteria by category.

### Fields

Each JSONL line contains the following fields:

| Field | Type | Description |
|-------|------|-------------|
| `WDG_ID` | string | Waren- und Dienstleistungsgruppe identifier |
| `wdg_name_en` | string | Waren- und Dienstleistungsgruppe (Goods and Services Group) - English name |
| `wdg_name_de` | string | Waren- und Dienstleistungsgruppe (Goods and Services Group) - German name |
| `source_file` | string | Original Excel filename (e.g., Food_V1.0.xlsx) |
| `Handlungsfeld-ID` | string | Action field identifier (WDG-specific) |
| `Handlungsfeld` | string | Action field name (German) (WDG-specific) |
| `Kriterium-ID` | string | Criterion identifier (WDG-specific) |
| `Kategorie Kriterium` | string | Criterion category (EK, TS, ZK, TB) |
| `Ausschreibungskriterium` | string | Procurement criterion description |
| `Ambitionsniveau: Basis` | string | Basic ambition level |
| `Ambitionsniveau: Gute Praxis` | string | Good practice ambition level |
| `Ambitionsniveau: Vorbild` | string | Best practice ambition level |
| `Nachweise` | string | Evidence/documentation requirements |
| `Nachhaltigkeitsdimensionen` | string | Sustainability dimensions |
| `Quelle` | string | Source reference identifiers |
| `Kommentar` | list | Additional comments and notes |

### Notes

- **Empty fields**: Fields with no value are represented as empty strings
- **Forward-filled hierarchical data**: `Handlungsfeld-ID` and `Handlungsfeld` values are propagated downward within each file
- **Source references**: Use the `Quelle` field to look up source details in the corresponding `*_metadata.json` file

### WDG-Specific Fields

Fields marked as **(WDG-specific)** are unique to each Waren- und Dienstleistungsgruppe (goods and services group). These fields may vary across different WDGs. All other fields are standardized across all WDGs.

## Data Description

### Criterion Categories
- **EK** (Eignungskriterium): Selection criterion
- **TS** (Technische Spezifikation): Technical specification
- **ZK** (Zuschlagskriterium): Award criterion
- **TB** (Zwingende Teilnahmebedingung): Mandatory participation condition

### Sustainability Dimensions
- **ökologisch**: Environmental/Ecological
- **sozial**: Social
- **ökonomisch**: Economic

### Ambition Levels
- **Basis**: Basic level
- **Gute Praxis**: Good practice
- **Vorbild**: Best practice / exemplary

## Usage Examples

### Load with Hugging Face Datasets Library

#### Basic Loading
```python
from datasets import load_dataset

# Load the entire dataset
catalog = load_dataset("IntelliProcure/sustainability_criteria")

# Access the train split
criteria = catalog['train']
print(f"Total criteria: {len(criteria)}")
print(f"Columns: {criteria.column_names}")
```

#### Working with the Dataset
```python
from datasets import load_dataset

catalog = load_dataset("IntelliProcure/sustainability_criteria")
criteria = catalog['train']

# Convert to pandas DataFrame
df = criteria.to_pandas()

# Access specific records
first_record = criteria[0]
print(first_record['Ausschreibungskriterium'])

# Get multiple records
first_ten = criteria[:10]

# Filter criteria by category
selection_criteria = criteria.filter(lambda x: x['Kategorie Kriterium'] == 'EK')

# Filter by sustainability dimension
ecological = criteria.filter(
    lambda x: 'ökologisch' in x['Nachhaltigkeitsdimensionen']
)

# Get records from specific action field
food_criteria = criteria.filter(lambda x: x['Handlungsfeld'] == 'Food')
```

#### Advanced Filtering and Analysis
```python
from datasets import load_dataset
import pandas as pd

catalog = load_dataset("IntelliProcure/sustainability_criteria")
df = catalog['train'].to_pandas()

# Find all criteria by multiple dimensions
multi_dim = df[
    df['Nachhaltigkeitsdimensionen'].str.contains('ökologisch|sozial', na=False)
]

# Group by action field
by_field = df.groupby('Handlungsfeld').size()
print(by_field)

# Get statistics on ambition levels
print(df[['Ambitionsniveau: Basis', 'Ambitionsniveau: Gute Praxis']].notna().sum())

# Find criteria with all three ambition levels
complete = df[
    (df['Ambitionsniveau: Basis'] != '') &
    (df['Ambitionsniveau: Gute Praxis'] != '') &
    (df['Ambitionsniveau: Vorbild'] != '')
]
```

#### Stream Large Datasets
```python
from datasets import load_dataset

# Stream data without downloading entirely (useful for large datasets)
catalog = load_dataset("IntelliProcure/sustainability_criteria", streaming=True)
criteria_stream = catalog['train']

# Iterate through records
for i, record in enumerate(criteria_stream):
    if i >= 100:  # Process first 100
        break
    print(record['Kriterium-ID'], record['Ausschreibungskriterium'])
```

#### Access Dataset Information
```python
from datasets import load_dataset

catalog = load_dataset("IntelliProcure/sustainability_criteria")
criteria = catalog['train']

# Dataset info
print(criteria.info)
print(criteria.features)
print(f"Number of records: {len(criteria)}")

# Column names and types
print(criteria.column_names)
for feature_name, feature_type in criteria.features.items():
    print(f"  {feature_name}: {feature_type}")

# Get unique values
print(f"Unique action fields: {criteria.unique('Handlungsfeld')}")
print(f"Unique categories: {criteria.unique('Kategorie Kriterium')}")
```

### Load with Pandas
```python
import pandas as pd

# Load the merged JSONL file
df = pd.read_json('sustainability_criteria.jsonl', lines=True)

# Filter by WDG
food_criteria = df[df['wdg_name_en'] == 'Food']

# Filter by category
procurement_criteria = df[df['Kategorie Kriterium'] == 'EK']

# Filter by sustainability dimension
ecological = df[df['Nachhaltigkeitsdimensionen'].str.contains('ökologisch', na=False)]
```

### Load with Json Module
```python
import json

with open('sustainability_criteria.jsonl', 'r', encoding='utf-8') as f:
    for line in f:
        record = json.loads(line)
        print(record['Ausschreibungskriterium'])
```

### Resolve Source References
```python
import json

# Load metadata to resolve source references
with open('sustainability_criteria_metadata.json', 'r', encoding='utf-8') as f:
    metadata = json.load(f)

sources = metadata['sources']

# Example: resolve a source reference
quelle_value = "Q-1, Q-2"
source_ids = [s.strip() for s in quelle_value.split(',')]
for sid in source_ids:
    if sid in sources:
        print(f"{sid}: {sources[sid]}")
```

#### Filter by WDG
```python
from datasets import load_dataset

catalog = load_dataset("IntelliProcure/sustainability_criteria")
criteria = catalog['train']

# Filter by English WDG name
food_criteria = criteria.filter(lambda x: x['wdg_name_en'] == 'Food')

# Filter by German WDG name
food_criteria_de = criteria.filter(lambda x: x['wdg_name_de'] == 'Lebensmittel')

# Filter by source file
food_v1 = criteria.filter(lambda x: x['source_file'] == 'Food_V1.0')

# Get unique WDGs
df = criteria.to_pandas()
print(df['wdg_name_en'].unique())
print(df['wdg_name_de'].unique())
```

## Sources

Each criterion includes references to source documents. Source details are provided in the metadata files.

Common sources include:
- EU GPP (Green Public Procurement) criteria
- German environmental labels and standards
- Industry-specific guidelines
- Sustainability certifications

## License

CC-BY-4.0

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{sustainability_criteria,
title = {Sustainability Procurement Criteria},
year = {2025},
url = {https://huggingface.co/datasets/IntelliProcure/sustainability_criteria}
}
```

---

**Last Updated**: 2026-02-18
**Dataset Version**: 1.0
**Format Version**: 1.0