IntelliProcure/sustainability_criteria

from datasets import load_dataset

criteria = load_dataset("IntelliProcure/sustainability_criteria")["train"]
ggs_summaries = load_dataset("IntelliProcure/sustainability_criteria", "ggs_summaries")["train"]
aoa_summaries = load_dataset("IntelliProcure/sustainability_criteria", "aoa_summaries")["train"]

# Combine with pandas: attach catalog- and area-of-action-level context to every criterion
df = criteria.to_pandas()
df = df.merge(ggs_summaries.to_pandas(), on="WDG_ID", how="left")
df = df.merge(aoa_summaries.to_pandas(), on=["WDG_ID", "Handlungsfeld-ID"], how="left")

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}")

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')

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'] != '')
]

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'])

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 areas of action: {criteria.unique('Handlungsfeld')}")
print(f"Unique categories: {criteria.unique('Kategorie Kriterium')}")

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)]

import json

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

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]}")

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())

@dataset{sustainability_criteria,
	author       = { Tilia Renate Ellendorff and Luca Sven Rolshoven and Veton Matoshi and Jann Jeremy Austin and Judith Binder },
	title        = {{Sustainable Procurement Criteria}},
	year         = 2026,
	url          = { https://huggingface.co/datasets/IntelliProcure/sustainability_criteria },
	doi          = { 10.57967/hf/7958 },
	publisher    = { Hugging Face }
}