Datasets:

brightkey
/

intl-schools-2026

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 16 new columns ({'fees_range', 'inspection_rating', 'boarding', 'inspection_year', 'eal_support', 'a_level_results', 'city', 'website', 'languages_of_instruction', 'school_type', 'founded', 'curricula', 'ib_average', 'age_range', 'inspection_body', 'accreditations'})

This happened while the csv dataset builder was generating data using

hf://datasets/brightkey/intl-schools-2026/schools.csv (at revision 7fece05a1bb537a9267692db18b054fd5fc39729), ['hf://datasets/brightkey/intl-schools-2026@7fece05a1bb537a9267692db18b054fd5fc39729/school-cities.csv', 'hf://datasets/brightkey/intl-schools-2026@7fece05a1bb537a9267692db18b054fd5fc39729/schools.csv']

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/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              slug: string
              name: string
              city: string
              country: string
              school_type: string
              founded: double
              website: string
              curricula: string
              age_range: string
              languages_of_instruction: string
              eal_support: bool
              fees_range: string
              boarding: bool
              accreditations: string
              inspection_body: string
              inspection_rating: string
              inspection_year: double
              ib_average: double
              a_level_results: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2578
              to
              {'slug': Value('string'), 'name': Value('string'), 'country': 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 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              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 16 new columns ({'fees_range', 'inspection_rating', 'boarding', 'inspection_year', 'eal_support', 'a_level_results', 'city', 'website', 'languages_of_instruction', 'school_type', 'founded', 'curricula', 'ib_average', 'age_range', 'inspection_body', 'accreditations'})
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/brightkey/intl-schools-2026/schools.csv (at revision 7fece05a1bb537a9267692db18b054fd5fc39729), ['hf://datasets/brightkey/intl-schools-2026@7fece05a1bb537a9267692db18b054fd5fc39729/school-cities.csv', 'hf://datasets/brightkey/intl-schools-2026@7fece05a1bb537a9267692db18b054fd5fc39729/schools.csv']
              
              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.

slug string	name string	country string
singapore	Singapore	singapore
tokyo	Tokyo	japan
hong-kong	Hong Kong	hong-kong
dubai	Dubai	uae
bangkok	Bangkok	thailand
kuala-lumpur	Kuala Lumpur	malaysia
seoul	Seoul	south-korea
jeju	Jeju	south-korea
taipei	Taipei	taiwan
sydney	Sydney	australia
melbourne	Melbourne	australia
auckland	Auckland	new-zealand
shanghai	Shanghai	china
beijing	Beijing	china
shenzhen	Shenzhen	china
guangzhou	Guangzhou	china
abu-dhabi	Abu Dhabi	uae
doha	Doha	qatar
switzerland	Switzerland (boarding)	switzerland
london	London	uk
paris	Paris	france
mumbai	Mumbai	india
delhi	Delhi NCR	india
jakarta	Jakarta	indonesia
manila	Manila	philippines
amsterdam	Amsterdam	netherlands
germany	Germany (international)	germany
madrid	Madrid	spain
barcelona	Barcelona	spain
ho-chi-minh-city	Ho Chi Minh City	vietnam
saudi-arabia	Saudi Arabia	saudi-arabia
tanglin-trust	Tanglin Trust School	singapore
dulwich-college-singapore	Dulwich College (Singapore)	singapore
uwcsea	UWC South East Asia	singapore
singapore-american-school	Singapore American School	singapore
stamford-american	Stamford American International School	singapore
canadian-international-school	Canadian International School	singapore
australian-international-school	Australian International School	singapore
iss-international-school	ISS International School	singapore
nexus-international-school	Nexus International School	singapore
overseas-family-school	Overseas Family School	singapore
etonhouse	EtonHouse International Pre-School	singapore
chiltern-house	Chiltern House (now Julia Gabriel Preschool)	singapore
blue-house	Blue House Nursery & International Preschool	singapore
the-schoolhouse-by-busy-bees	The Schoolhouse by Busy Bees	singapore
asij	The American School in Japan	japan
nishimachi	Nishimachi International School	japan
british-school-tokyo	The British School in Tokyo	japan
tokyo-international-school	Tokyo International School	japan
seisen-international	Seisen International School	japan
st-marys-international	St. Mary's International School	japan
aoba-japan-international	Aoba-Japan International School	japan
k-international-tokyo	K. International School Tokyo	japan
esf-hong-kong	English Schools Foundation (ESF)	hong-kong
cdnis-hong-kong	Canadian International School of Hong Kong	hong-kong
harrow-hong-kong	Harrow International School Hong Kong	hong-kong
hkis-hong-kong	Hong Kong International School	hong-kong
cis-hong-kong	Chinese International School	hong-kong
gsis-hong-kong	German Swiss International School	hong-kong
kellett-hong-kong	Kellett School	hong-kong
french-international-hong-kong	French International School of Hong Kong	hong-kong
gems-wellington-dubai	GEMS Wellington International School	uae
dubai-college	Dubai College	uae
gems-jumeirah-college	GEMS Jumeirah College	uae
jess-dubai	Jumeirah English Speaking School	uae
desc-dubai	Dubai English Speaking College	uae
dubai-american-academy	Dubai American Academy	uae
kings-school-al-barsha	Kings' School Al Barsha	uae
dubai-international-academy	Dubai International Academy Emirates Hills	uae
gems-modern-academy-dubai	GEMS Modern Academy	uae
nist-international	NIST International School	thailand
international-school-bangkok	International School Bangkok	thailand
bangkok-patana	Bangkok Patana School	thailand
shrewsbury-bangkok	Shrewsbury International School Bangkok	thailand
bangkok-prep	Bangkok Prep	thailand
harrow-bangkok	Harrow International School Bangkok	thailand
iskl	The International School of Kuala Lumpur	malaysia
garden-international-kl	Garden International School	malaysia
alice-smith-kl	The Alice Smith School	malaysia
mont-kiara-international	Mont'Kiara International School	malaysia
nexus-malaysia	Nexus International School Malaysia	malaysia
igb-international	IGB International School	malaysia
seoul-foreign-school	Seoul Foreign School	south-korea
seoul-international-school	Seoul International School	south-korea
dwight-school-seoul	Dwight School Seoul	south-korea
chadwick-international	Chadwick International	south-korea
yongsan-international-seoul	Yongsan International School of Seoul	south-korea
nlcs-jeju	North London Collegiate School Jeju	south-korea
branksome-hall-asia	Branksome Hall Asia	south-korea
kis-jeju	Korea International School Jeju	south-korea
st-johnsbury-jeju	St. Johnsbury Academy Jeju	south-korea
taipei-american-school	Taipei American School	taiwan
taipei-european-school	Taipei European School	taiwan
morrison-academy-taipei	Morrison Academy Taipei	taiwan
dominican-international-taipei	Dominican International School	taiwan
sydney-grammar-school	Sydney Grammar School	australia
international-grammar-sydney	International Grammar School	australia
knox-grammar-sydney	Knox Grammar School	australia
abbotsleigh-sydney	Abbotsleigh	australia
reddam-house-sydney	Reddam House	australia

End of preview.

BrightKey International Schools Dataset 2026

An independent, verifiable-public-data profile of 184 international schools across 31 cities / 23 countries (Asia-Pacific, the Gulf, China, Europe, India and Southeast Asia), published by BrightKey — an independent bilingual (English + Simplified Chinese) education-advisory service.

What this is

The verifiable spine of each school: curricula, age range, languages of instruction, EAL support, fee range, boarding, accreditations, and — where a public inspection regime exists (e.g. Singapore's BSO, Dubai's KHDA) — the official inspection body, rating, and year, plus IB average and A-Level results where published.

What it is NOT: this dataset contains only public, verifiable facts. It deliberately excludes BrightKey's editorial tier ratings and rich-profile prose (those are copyrighted editorial work, not released here). Facts such as fees and accreditations are not copyrightable (cf. Feist v. Rural Telephone Service); this release covers only that factual spine.

What changed in v2

Expanded from 114 schools / 18 cities / 12 countries to 184 schools across 31 cities / 23 countries, broadening beyond Asia-Pacific + the Gulf + China to add Europe (London, Paris, Madrid, Barcelona, Amsterdam, Germany, Switzerland), India (Delhi, Mumbai) and more of Southeast Asia (Jakarta, Manila, Ho Chi Minh City), plus Saudi Arabia. Quality grades are still never imputed — only the verifiable factual spine is released.

Method (how BrightKey compiles it)

No payments from schools — placement and inclusion are never for sale.
Public, verifiable data only. Missing facts are left blank, never imputed.
University placement is excluded — school-reported placement lists are marketing, not verifiable, and are never tiered.

Full methodology + the browsable, bilingual guide: https://brightkey.co/en/schools

Files

File	Rows	Description
`schools.csv`	184	One row per school — the verifiable spine (see columns below).
`school-cities.csv`	31	City → country lookup.

`schools.csv` columns

slug, name, city, country, school_type, founded, website, curricula, age_range, languages_of_instruction, eal_support, fees_range, boarding, accreditations, inspection_body, inspection_rating, inspection_year, ib_average, a_level_results

Related dataset

BrightKey also publishes an open university rankings dataset (299 universities across 55 countries, 6 dimensions): on Hugging Face, Kaggle, and Zenodo (DOI 10.5281/zenodo.20603842). Both datasets share the Zenodo concept DOI 10.5281/zenodo.20603841.

Citation

BrightKey (2026). BrightKey International Schools Dataset 2026 (v2). Editorial reviewer: Priscilla Han. CC-BY-4.0. https://brightkey.co/en/schools — Zenodo DOI: 10.5281/zenodo.20603842; Wikidata: Q140002954.

License

CC-BY-4.0 — free to use, including commercially, with attribution to BrightKey (https://brightkey.co).

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