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ted_2025_07_sample

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csv
Languages:
English
Size:
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Tags:
dataset
procurement
europe
tenders
machine-learning
nlp
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pandas
License:
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1
Dataset Viewer
The dataset viewer is not available for this dataset.
The JWT signature verification failed. Check the signing key and the algorithm.
Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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EU Public Procurement — July 2025 (Enriched CSV)

This dataset contains all public procurement notices from July 2025,
parsed and enriched from the European Union's TED (Tenders Electronic Daily).

The full dataset (200,000+ rows) is available for purchase here: Full Dataset on Gumroad

Free Sample vs Full Dataset

Feature Free Sample (this repo) Full Dataset (Gumroad)
Rows 100 260,000+
File size ~50 KB ~150 MB+
Format CSV (UTF-8) CSV (UTF-8)
Columns 14 (see below) 14 (same schema)

Contents

  • Procurement notices parsed from official TED XML
  • Normalized, analysis-ready columns:
    • notice_id — unique identifier of the notice
    • publication_date — publication date (ISO 8601, may include timezone offset)
    • buyer_id — anonymized buyer/organization ID
    • cpv_code — Common Procurement Vocabulary (CPV 2008, 8-digit)
    • lot_id — identifier for the procurement lot
    • lot_name — contract title (local language)
    • lot_description — contract description (local language)
    • award_value — contract award value (when available, numeric)
    • final_value — overall contract value at notice/result level (if present)
    • best_value — most reliable value selected among award, final, and estimated
    • best_value_source — which field the best_value came from
    • currency — contract currency
    • source_file — original TED XML file
    • cpv_label — CPV 2008 English description

Added Value

While the raw TED data is free and open, this dataset provides:

  • Parsed and normalized structure (from thousands of XML files → single CSV)
  • Automatic CPV 2008 code enrichment with human-readable labels
  • Multiple contract value fields (award, final, best) for flexible analysis
  • Ready for machine learning pipelines and analytics without preprocessing

Source & License

  • Contains information from the EU’s TED portal.
    © European Union. Reuse governed by Commission Decision 2011/833/EU.
    No endorsement by the European Union is implied.
  • Enrichment (parsing, CPV mapping, packaging) © 2025 OpenML Datasets,
    released under Creative Commons Zero v1.0 Universal (CC0).

Suggested Uses

  • Machine learning: NER, classification, contract value forecasting
  • Market intelligence: sector and supplier mapping
  • Procurement research: transparency, competition, and cross-country studies
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