Datasets:
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Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 6 new columns ({'Explore link', 'Started', 'Ended', 'Trend breakdown', 'Search volume', 'Trends'}) and 2 missing columns ({'tag', 'location'}).
This happened while the csv dataset builder was generating data using
zip://500/trending_500_1d_20241129-0251.csv::/tmp/hf-datasets-cache/medium/datasets/59346467273913-config-parquet-and-info-aurman-GoogleTrendArchive-d23446ee/hub/datasets--aurman--GoogleTrendArchive/snapshots/0cf1acde79341c0337002587bc3e600ea20ee29f/daily_compressed.zip
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
Trends: string
Search volume: string
Started: string
Ended: string
Trend breakdown: string
Explore link: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 976
to
{'tag': Value('string'), 'location': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1339, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 972, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1833, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 6 new columns ({'Explore link', 'Started', 'Ended', 'Trend breakdown', 'Search volume', 'Trends'}) and 2 missing columns ({'tag', 'location'}).
This happened while the csv dataset builder was generating data using
zip://500/trending_500_1d_20241129-0251.csv::/tmp/hf-datasets-cache/medium/datasets/59346467273913-config-parquet-and-info-aurman-GoogleTrendArchive-d23446ee/hub/datasets--aurman--GoogleTrendArchive/snapshots/0cf1acde79341c0337002587bc3e600ea20ee29f/daily_compressed.zip
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
tag
string | location
string |
|---|---|
AL
|
Albania
|
DZ
|
Algeria
|
AO
|
Angola
|
AR
|
Argentina
|
AR-C
|
Autonomous City of Buenos Aires
|
AR-B
|
Buenos Aires Province
|
AR-K
|
Catamarca Province
|
AR-H
|
Chaco Province
|
AR-U
|
Chubut Province
|
AR-X
|
Cordoba
|
AR-W
|
Corrientes Province
|
AR-E
|
Entre Rios
|
AR-P
|
Formosa Province
|
AR-Y
|
Jujuy
|
AR-L
|
La Pampa Province
|
AR-F
|
La Rioja Province
|
AR-M
|
Mendoza Province
|
AR-N
|
Misiones Province
|
AR-Q
|
Neuquen
|
AR-R
|
Río Negro
|
AR-A
|
Salta Province
|
AR-J
|
San Juan Province
|
AR-D
|
San Luis Province
|
AR-Z
|
Santa Cruz Province
|
AR-S
|
Santa Fe Province
|
AR-G
|
Santiago del Estero Province
|
AR-V
|
Tierra del Fuego Province
|
AR-T
|
Tucumán
|
AM
|
Armenia
|
AU
|
Australia
|
AU-ACT
|
Australian Capital Territory
|
AU-NSW
|
New South Wales
|
AU-NT
|
Northern Territory
|
AU-QLD
|
Queensland
|
AU-SA
|
South Australia
|
AU-TAS
|
Tasmania
|
AU-VIC
|
Victoria
|
AU-WA
|
Western Australia
|
AT
|
Austria
|
AT-1
|
Burgenland
|
AT-2
|
Carinthia
|
AT-3
|
Lower Austria
|
AT-5
|
Salzburg
|
AT-6
|
Styria
|
AT-7
|
Tyrol
|
AT-4
|
Upper Austria
|
AT-9
|
Vienna
|
AT-8
|
Vorarlberg
|
AZ
|
Azerbaijan
|
BH
|
Bahrain
|
BD
|
Bangladesh
|
BY
|
Belarus
|
BE
|
Belgium
|
BE-BRU
|
Brussels
|
BE-VLG
|
Flanders
|
BE-WAL
|
Walloon Region
|
BJ
|
Benin
|
BO
|
Bolivia
|
BA
|
Bosnia & Herzegovina
|
BR
|
Brazil
|
BR-DF
|
Federal District
|
BR-AC
|
State of Acre
|
BR-AL
|
State of Alagoas
|
BR-AP
|
State of Amapá
|
BR-AM
|
State of Amazonas
|
BR-BA
|
State of Bahia
|
BR-CE
|
State of Ceará
|
BR-ES
|
State of Espírito Santo
|
BR-GO
|
State of Goiás
|
BR-MA
|
State of Maranhão
|
BR-MT
|
State of Mato Grosso
|
BR-MS
|
State of Mato Grosso do Sul
|
BR-MG
|
State of Minas Gerais
|
BR-PA
|
State of Pará
|
BR-PB
|
State of Paraíba
|
BR-PR
|
State of Paraná
|
BR-PE
|
State of Pernambuco
|
BR-PI
|
State of Piauí
|
BR-RJ
|
State of Rio de Janeiro
|
BR-RN
|
State of Rio Grande do Norte
|
BR-RS
|
State of Rio Grande do Sul
|
BR-RO
|
State of Rondônia
|
BR-RR
|
State of Roraima
|
BR-SC
|
State of Santa Catarina
|
BR-SP
|
State of São Paulo
|
BR-SE
|
State of Sergipe
|
BR-TO
|
State of Tocantins
|
BG
|
Bulgaria
|
BF
|
Burkina Faso
|
KH
|
Cambodia
|
CM
|
Cameroon
|
CA
|
Canada
|
CA-AB
|
Alberta
|
CA-BC
|
British Columbia
|
CA-MB
|
Manitoba
|
CA-NB
|
New Brunswick
|
CA-NL
|
Newfoundland and Labrador
|
CA-NT
|
Northwest Territories
|
CA-NS
|
Nova Scotia
|
CA-NU
|
Nunavut
|
Google Trend Archive: Global Real-Time Search Trends (2024-2026)
Dataset Details
Dataset Description
This dataset contains over 7.6 million trending search instances from Google's Trending Now feature, collected continuously from November 28, 2024 to January 3, 2026 across all available geographic locations (200+ countries/regions). Unlike aggregated retrospective tools like Google Trends, Trending Now captures search queries experiencing real-time surges, offering unprecedented temporal granularity for studying collective attention dynamics.
Each instance represents a moment when specific search terms or query clusters became "trending" according to Google's algorithm, complete with search volume buckets, precise timestamps, trend durations, geographic locations, and related query variations.
- Curated by: Aleksandra Urman, Anikó Hannák, Joachim Baumann (Social Computing Group, University of Zurich & Stanford Artificial Intelligence Laboratory)
- Language(s): Multilingual
- License: CC-BY-4.0
- DOI: https://doi.org/10.57967/hf/7531
Dataset Sources
- Repository: https://huggingface.co/datasets/aurman/GoogleTrendArchive
- Contact: [email protected]
Uses
Direct Use
This dataset enables research across multiple domains:
- Information Diffusion Modeling: Track how topics cascade across geographic regions and analyze diffusion pathways
- Event Detection: Identify breaking news, crises, and significant events from search surge patterns
- Comparative Cultural Studies: Analyze differences in collective attention across countries and cultures
- Crisis Communication: Understand information needs during emergencies
- Temporal Pattern Analysis: Study daily, weekly, and seasonal rhythms in collective attention
- Predictive Modeling: Develop models to forecast trend emergence, duration, and spread
- Media Ecosystem Analysis: Compare search trends with social media and news coverage
- Political Communication: Examine attention to political topics across different political systems
Out-of-Scope Use
The dataset should not be used for:
- Surveillance or monitoring of specific populations
- Marketing or commercial targeting (intended for academic research only)
- Drawing deterministic conclusions about population beliefs (search shows information-seeking, not settled beliefs)
- Identifying or tracking individuals (data is aggregated, but de-anonymization attempts would violate ethical norms)
- Training models for harmful applications (discrimination, manipulation, surveillance)
Dataset Structure
Each instance contains six fields:
- Trend Identifier: The search query or representative term for a cluster of related queries (e.g., "man united vs bodø/glimt")
- Search Volume: Bucketed traffic ranges (e.g., "100+", "50K+", "2M+") indicating approximate search volume
- Start Timestamp: When Google's system first flagged the trend as emerging (ISO format with timezone)
- End Timestamp: When the trend ended and returned to baseline (ISO format with timezone)
- Trend Breakdown: Comma-separated list of related query variations that Google clustered together
- Explore Link: URL to the corresponding Google Trends page for further investigation
Total Instances: 7,600,000+ trending searches
Temporal Coverage: November 28, 2024 - January 3, 2026 (with ~14 days of gaps due to technical issues)
Geographic Coverage: 1358 countries and regions
Format: CSV files with UTF-8 encoding
Dataset Creation
Curation Rationale
This dataset is a comprehensive archive of Google Trending Now data spanning over one year (from November 28, 2024 to January 3, 2026) across all available geographic locations. Unlike aggregated retrospective tools like Google Trends, Trending Now captures search queries experiencing real-time surges, offering temporal granularity for studying collective attention dynamics --- however, this data is not available retrospectively through Google directly. Our dataset addresses this gap by presenting an archive of Trending Now data.
Source Data
Data Collection and Processing
Data was collected using automated software that continuously monitored Google Trends Trending Now pages (e.g., https://trends.google.com/trending?geo=US) for all available geographic locations from November 28, 2024 onward.
Validation procedures included:
- Automated checks for data completeness across time periods and locations
- Verification of timestamp consistency
- Deduplication to handle multiply-collected trends
- Comparison against manually spot-checked trends
- Logging of collection failures (~14 days of gaps)
Preprocessing steps:
- Timestamp standardization to ISO 8601 format with timezone information
- Duration calculation from start/end timestamps
- Geographic code standardization
- Format consolidation from daily files into unified dataset
- Data validation and quality checks
All preprocessing code and raw data files are available in the repository for transparency and replication.
Who are the source data producers?
Google's Trending Now system, which aggregates and anonymizes search activity from Google users worldwide.
Personal and Sensitive Information
No personally identifiable information is included.
Bias, Risks, and Limitations
Technical Limitations:
- Search volumes provided in buckets rather than exact counts
- Google's trend identification algorithm is proprietary (clustering and thresholding decisions not transparent)
- ~14 days of missing data due to technical collection issues
- Trends represent relative surges, not absolute search volumes
- Some trends may have estimated rather than actual end dates
Representativeness Issues:
- Search behavior does not uniformly represent entire populations
- Digital divides and varying internet penetration mean certain demographics are over/underrepresented
- Search engine market share varies by country (Google dominance differs across regions)
- Geographic coverage limited to locations where Google provides Trending Now data
Algorithmic Mediation:
- Google determines what qualifies as "trending" using proprietary algorithms
- Content filtering based on Google's policies may exclude certain trends
- Reflects Google's selection of notable search spikes, not all search activity
Interpretation Risks:
- Search trends show what people search for, not why they search or what they conclude
- Risk of drawing inappropriate generalizations or stereotypes about populations
- Patterns in sensitive topics (health, politics) could stigmatize communities
- Missing social context can lead to misinterpretation
Recommendations
Users should:
- Acknowledge representativeness limitations in cross-country comparisons
- Account for structural inequalities in internet access when interpreting patterns
- Avoid causal claims without additional evidence
- Exercise caution when analyzing/presenting sensitive topics
- Consider social context when interpreting collective attention patterns
- Present findings with appropriate epistemic humility
- Recognize this represents one platform's perspective on collective attention
Citation
BibTeX:
@dataset{urman2026googletrendarchive,
title={Google Trend Archive: Global Real-Time Search Trends},
author={Urman, Aleksandra and Hann{\'a}k, Anik{\'o} and Baumann, Joachim},
year={2026},
publisher={Hugging Face},
doi={10.57967/hf/7531},
url={https://huggingface.co/datasets/aurman/GoogleTrendArchive}
}
APA:
Urman, A., Hannák, A., & Baumann, J. (2026). Google Trend Archive: Global Real-Time Search Trends [Dataset]. Hugging Face. https://doi.org/10.57967/hf/7531
More Information
Funding
Research activities during dataset construction received support from:
- Swiss National Science Foundation – PostDoc Mobility fellowship P500-2 235328 (JB)
- SNSF Project Grant 215354 (AU and AH)
Maintenance
The dataset is hosted on Hugging Face and maintained by the authors. The current release represents a snapshot from November 28, 2024 to January 3, 2026. Updates are planned every 2-3 months as data collection is ongoing, communicated through changelogs in the repository.
All versions will be permanently archived and accessible through Hugging Face's versioning system to ensure reproducibility.
Contributions
We welcome community contributions:
- Derived datasets: Researchers adding annotations or features are encouraged to share as separate datasets with attribution
- Corrections: Report errors to [email protected] for review and incorporation into updated versions
- Extensions: Researchers collecting additional data using our methodology may collaborate on future releases
Dataset Card Authors
Aleksandra Urman, Anikó Hannák, Joachim Baumann
Dataset Card Contact
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