Datasets:

IntelliProcure
/

sustainability_criteria

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

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

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

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

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

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

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

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

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

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:

@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