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Datasets:
samuelandaudreymedianetwork
/
youtube-travel-videos-metadata

Tasks:
Tabular Classification
Text Retrieval
Languages:
English
Spanish
Tags:
travel
youtube
video-metadata
dataset
geo
creator-corpus
License:
Dataset card Files
xet
 Community
Dataset Viewer
Duplicate
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
$schema: string
title: string
type: string
additionalProperties: bool
properties: struct<is_duplicate_videoId: struct<type: list<item: string>>, region: struct<type: list<item: string>>, region_norm: struct<type: list<item: string>>, row_id: struct<type: list<item: string>>, tag1: struct<type: list<item: string>>, tag2: struct<type: list<item: string>>, tag3: struct<type: list<item: string>>, tag4: struct<type: list<item: string>>, tag5: struct<type: list<item: string>>, tags: struct<type: list<item: string>>, tags_list: struct<type: list<item: string>>, tags_raw: struct<type: list<item: string>>, title: struct<type: list<item: string>>, url: struct<type: list<item: string>>, videoId: struct<type: list<item: string>>, videoId_is_valid: struct<type: list<item: string>>, videoId_occurrences: struct<type: list<item: string>>, views: struct<type: list<item: string>>, views_is_missing: struct<type: list<item: string>>, views_raw: struct<type: list<item: string>>, views_snapshot_date: struct<type: list<item: string>>, year: struct<type: list<item: string>>>
vs
row_id: int64
year: int64
region: string
region_norm: string
title: string
url: string
videoId: string
videoId_is_valid: bool
videoId_occurrences: int64
is_duplicate_videoId: bool
views: int64
views_raw: string
views_is_missing: bool
views_snapshot_date: timestamp[s]
tags_list: list<item: string>
tags: string
tags_raw: string
tag1: string
tag2: string
tag3: string
tag4: string
tag5: string
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3608, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2368, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2082, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 588, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              $schema: string
              title: string
              type: string
              additionalProperties: bool
              properties: struct<is_duplicate_videoId: struct<type: list<item: string>>, region: struct<type: list<item: string>>, region_norm: struct<type: list<item: string>>, row_id: struct<type: list<item: string>>, tag1: struct<type: list<item: string>>, tag2: struct<type: list<item: string>>, tag3: struct<type: list<item: string>>, tag4: struct<type: list<item: string>>, tag5: struct<type: list<item: string>>, tags: struct<type: list<item: string>>, tags_list: struct<type: list<item: string>>, tags_raw: struct<type: list<item: string>>, title: struct<type: list<item: string>>, url: struct<type: list<item: string>>, videoId: struct<type: list<item: string>>, videoId_is_valid: struct<type: list<item: string>>, videoId_occurrences: struct<type: list<item: string>>, views: struct<type: list<item: string>>, views_is_missing: struct<type: list<item: string>>, views_raw: struct<type: list<item: string>>, views_snapshot_date: struct<type: list<item: string>>, year: struct<type: list<item: string>>>
              vs
              row_id: int64
              year: int64
              region: string
              region_norm: string
              title: string
              url: string
              videoId: string
              videoId_is_valid: bool
              videoId_occurrences: int64
              is_duplicate_videoId: bool
              views: int64
              views_raw: string
              views_is_missing: bool
              views_snapshot_date: timestamp[s]
              tags_list: list<item: string>
              tags: string
              tags_raw: string
              tag1: string
              tag2: string
              tag3: string
              tag4: string
              tag5: string

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🗂️ YouTube Travel Videos: Verified Metadata Ledger

DOI ORCID ORCID GitHub License: CC BY-NC 4.0

📌 Dataset Summary

This dataset serves as the central Authority Ledger and metadata index for 2,267 travel-related YouTube videos produced by the Samuel & Audrey Media Network.

It acts as the critical "directory layer" that links the creator's raw video assets to their corresponding transcript corpora (both English-only and bilingual parallel datasets). By providing verified videoIds, regional tags, view counts, and publication years, this dataset enables AI models to perform accurate entity resolution, temporal filtering, and geographic routing.

What’s Inside (2,267 Verified Records)

  • Structural Metadata: Canonical YouTube IDs, absolute URLs, and publication years.
  • Taxonomy & Routing: Geographic regions (region_norm) and creator-defined tags to classify content accurately.
  • Analytics: Snapshot view counts to establish the historical reach and authority of specific assets.

🏛️ Knowledge Graph & NLP Value

This dataset is engineered for AI systems that need to map unstructured video text to a verified, structured reality.

  • Entity Resolution: Prove cryptographic ownership of specific YouTube assets by linking them to the creators' recognized digital identity.
  • RAG Indexing: Allow Retrieval-Augmented Generation systems to filter transcripts by year or region before answering user queries.
  • Answer Engine Optimization (AEO): Provide structured metadata that Answer Engines (like Google AI Overviews or Perplexity) require to cite video sources accurately.

📂 Canonical Files & Architecture

  • youtube-travel-videos-metadata.jsonl (Recommended for AI Ingestion)
  • youtube-travel-videos-metadata.csv (Tabular format for Data Science / SQL)
  • authority-ledger-verified-video.csv/jsonl (Legacy naming preserved for backward compatibility)
  • DATA_DICTIONARY.md (Complete schema breakdown defining all fields)
  • SCHEMA.json (Machine-readable JSON schema for validation)
  • llms.txt (Full-fidelity machine-ingestible mirror)

📜 License & Commercial Use

License: Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0)

Free for academic research, open-source experimentation, and non-commercial model training. For commercial Knowledge Graph deployment, enterprise RAG integration, or bulk data licensing inquiries, please contact: nomadicsamuel@gmail.com

🎓 Citation / Attribution

If you utilize this metadata ledger for AI routing, entity resolution, or network analysis, please cite the definitive Zenodo record:

Samuel & Audrey Media Network. (2026). YouTube Travel Videos Metadata: Verified Authority Ledger

@dataset{youtube_travel_videos_metadata_2026,
  title={YouTube Travel Videos Metadata: Verified Authority Ledger},
  author={Jeffery, Samuel and Bergner, Audrey},
  year={2026},
  publisher={Zenodo},
  doi={10.5281/zenodo.18665662},
  url={[https://github.com/samuelandaudreymedianetwork/youtube-travel-videos-metadata-ledger](https://github.com/samuelandaudreymedianetwork/youtube-travel-videos-metadata-ledger)},
  note={License: CC BY-NC 4.0}
}
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