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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
name: string
title: string
description: string
licenses: list<item: struct<name: string, path: string, title: string>>
child 0, item: struct<name: string, path: string, title: string>
child 0, name: string
child 1, path: string
child 2, title: string
homepage: string
version: timestamp[s]
keywords: list<item: string>
child 0, item: string
sources: list<item: struct<title: string, path: string>>
child 0, item: struct<title: string, path: string>
child 0, title: string
child 1, path: string
resources: list<item: struct<name: string, path: string, format: string, mediatype: string, description: string (... 2 chars omitted)
child 0, item: struct<name: string, path: string, format: string, mediatype: string, description: string>
child 0, name: string
child 1, path: string
child 2, format: string
child 3, mediatype: string
child 4, description: string
eligibility: struct<worldwide: int64, europe: int64>
child 0, worldwide: int64
child 1, europe: int64
medianPayUsd: struct<value: int64, sample: int64, of: int64, method: string>
child 0, value: int64
child 1, sample: int64
child 2, of: int64
child 3, method: string
bands: list<item: struct<category: string, sample: int64, total: int64, min: int64, median: int64, max: int (... 4 chars omitted)
child 0, item: struct<category: string, sample: int64, total: int64, min: int64, median: int64, max: int64>
child 0, category: string
child 1, sample:
...
child 3, total: int64
child 4, overlapEurope: int64
child 5, overlapAmericas: int64
child 6, medianPayUsd: int64
child 7, medianPaySample: int64
child 8, wageMultiple: double
child 9, wage: struct<perMonthUsd: double, asOf: string, source: string>
child 0, perMonthUsd: double
child 1, asOf: string
child 2, source: string
child 10, europe: bool
live: int64
license: string
generatedAt: string
countriesTotal: int64
attribution: string
method: struct<pay: string, wageMultiple: string, sample: string>
child 0, pay: string
child 1, wageMultiple: string
child 2, sample: string
history: list<item: struct<day: timestamp[s], at: int64, live: int64, worldwide: int64, europe: int64, withPa (... 31 chars omitted)
child 0, item: struct<day: timestamp[s], at: int64, live: int64, worldwide: int64, europe: int64, withPay: int64, r (... 19 chars omitted)
child 0, day: timestamp[s]
child 1, at: int64
child 2, live: int64
child 3, worldwide: int64
child 4, europe: int64
child 5, withPay: int64
child 6, reconstructed: bool
at: int64
categories: list<item: struct<slug: string, count: int64>>
child 0, item: struct<slug: string, count: int64>
child 0, slug: string
child 1, count: int64
skills: list<item: struct<slug: string, count: int64>>
child 0, item: struct<slug: string, count: int64>
child 0, slug: string
child 1, count: int64
source: string
to
{'source': Value('string'), 'license': Value('string'), 'attribution': Value('string'), 'generatedAt': Value('string'), 'method': {'pay': Value('string'), 'wageMultiple': Value('string'), 'sample': Value('string')}, 'at': Value('int64'), 'live': Value('int64'), 'eligibility': {'worldwide': Value('int64'), 'europe': Value('int64')}, 'medianPayUsd': {'value': Value('int64'), 'sample': Value('int64'), 'of': Value('int64'), 'method': Value('string')}, 'countries': List({'cc': Value('string'), 'slug': Value('string'), 'eligible': Value('int64'), 'total': Value('int64'), 'overlapEurope': Value('int64'), 'overlapAmericas': Value('int64'), 'medianPayUsd': Value('int64'), 'medianPaySample': Value('int64'), 'wageMultiple': Value('float64'), 'wage': {'perMonthUsd': Value('float64'), 'asOf': Value('string'), 'source': Value('string')}, 'europe': Value('bool')}), 'countriesTotal': Value('int64'), 'skills': List({'slug': Value('string'), 'count': Value('int64')}), 'categories': List({'slug': Value('string'), 'count': Value('int64')}), 'sources': List({'slug': Value('string'), 'count': Value('int64')}), 'bands': List({'category': Value('string'), 'sample': Value('int64'), 'total': Value('int64'), 'min': Value('int64'), 'median': Value('int64'), 'max': Value('int64')}), 'history': List({'day': Value('timestamp[s]'), 'at': Value('int64'), 'live': Value('int64'), 'worldwide': Value('int64'), 'europe': Value('int64'), 'withPay': Value('int64'), 'reconstructed': Value('bool')})}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
name: string
title: string
description: string
licenses: list<item: struct<name: string, path: string, title: string>>
child 0, item: struct<name: string, path: string, title: string>
child 0, name: string
child 1, path: string
child 2, title: string
homepage: string
version: timestamp[s]
keywords: list<item: string>
child 0, item: string
sources: list<item: struct<title: string, path: string>>
child 0, item: struct<title: string, path: string>
child 0, title: string
child 1, path: string
resources: list<item: struct<name: string, path: string, format: string, mediatype: string, description: string (... 2 chars omitted)
child 0, item: struct<name: string, path: string, format: string, mediatype: string, description: string>
child 0, name: string
child 1, path: string
child 2, format: string
child 3, mediatype: string
child 4, description: string
eligibility: struct<worldwide: int64, europe: int64>
child 0, worldwide: int64
child 1, europe: int64
medianPayUsd: struct<value: int64, sample: int64, of: int64, method: string>
child 0, value: int64
child 1, sample: int64
child 2, of: int64
child 3, method: string
bands: list<item: struct<category: string, sample: int64, total: int64, min: int64, median: int64, max: int (... 4 chars omitted)
child 0, item: struct<category: string, sample: int64, total: int64, min: int64, median: int64, max: int64>
child 0, category: string
child 1, sample:
...
child 3, total: int64
child 4, overlapEurope: int64
child 5, overlapAmericas: int64
child 6, medianPayUsd: int64
child 7, medianPaySample: int64
child 8, wageMultiple: double
child 9, wage: struct<perMonthUsd: double, asOf: string, source: string>
child 0, perMonthUsd: double
child 1, asOf: string
child 2, source: string
child 10, europe: bool
live: int64
license: string
generatedAt: string
countriesTotal: int64
attribution: string
method: struct<pay: string, wageMultiple: string, sample: string>
child 0, pay: string
child 1, wageMultiple: string
child 2, sample: string
history: list<item: struct<day: timestamp[s], at: int64, live: int64, worldwide: int64, europe: int64, withPa (... 31 chars omitted)
child 0, item: struct<day: timestamp[s], at: int64, live: int64, worldwide: int64, europe: int64, withPay: int64, r (... 19 chars omitted)
child 0, day: timestamp[s]
child 1, at: int64
child 2, live: int64
child 3, worldwide: int64
child 4, europe: int64
child 5, withPay: int64
child 6, reconstructed: bool
at: int64
categories: list<item: struct<slug: string, count: int64>>
child 0, item: struct<slug: string, count: int64>
child 0, slug: string
child 1, count: int64
skills: list<item: struct<slug: string, count: int64>>
child 0, item: struct<slug: string, count: int64>
child 0, slug: string
child 1, count: int64
source: string
to
{'source': Value('string'), 'license': Value('string'), 'attribution': Value('string'), 'generatedAt': Value('string'), 'method': {'pay': Value('string'), 'wageMultiple': Value('string'), 'sample': Value('string')}, 'at': Value('int64'), 'live': Value('int64'), 'eligibility': {'worldwide': Value('int64'), 'europe': Value('int64')}, 'medianPayUsd': {'value': Value('int64'), 'sample': Value('int64'), 'of': Value('int64'), 'method': Value('string')}, 'countries': List({'cc': Value('string'), 'slug': Value('string'), 'eligible': Value('int64'), 'total': Value('int64'), 'overlapEurope': Value('int64'), 'overlapAmericas': Value('int64'), 'medianPayUsd': Value('int64'), 'medianPaySample': Value('int64'), 'wageMultiple': Value('float64'), 'wage': {'perMonthUsd': Value('float64'), 'asOf': Value('string'), 'source': Value('string')}, 'europe': Value('bool')}), 'countriesTotal': Value('int64'), 'skills': List({'slug': Value('string'), 'count': Value('int64')}), 'categories': List({'slug': Value('string'), 'count': Value('int64')}), 'sources': List({'slug': Value('string'), 'count': Value('int64')}), 'bands': List({'category': Value('string'), 'sample': Value('int64'), 'total': Value('int64'), 'min': Value('int64'), 'median': Value('int64'), 'max': Value('int64')}), 'history': List({'day': Value('timestamp[s]'), 'at': Value('int64'), 'live': Value('int64'), 'worldwide': Value('int64'), 'europe': Value('int64'), 'withPay': Value('int64'), 'reconstructed': Value('bool')})}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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Remote vacancies open to Georgia
Aggregate statistics on remote job adverts that accept applicants from Georgia, rebuilt daily from live listings.
Updated 2026-09-02. Licence CC-BY-4.0. Source: https://donator.ge/jobs/dataset
What is in it
| File | Contents |
|---|---|
data.json |
Eligibility split, published pay by category, skill demand, daily board size, country table. Every figure carries the number of adverts it was computed over. |
countries.csv |
One row per country: vacancies open to it, median published monthly pay in USD, and that pay as a multiple of the country's average monthly wage. |
What is not in it
The vacancies themselves. Those are listings from other boards under their own terms; only aggregates are published here.
Method
Pay figures are the median published monthly rate in USD. Hourly and daily rates are excluded, as is any advert whose pay period we inferred rather than read. The wage multiple uses ILO average monthly earnings in USD, with the observation year on each row.
Citation
Remote vacancies open to Georgia. Donator, 2026-09-02. https://donator.ge/jobs/dataset. Licensed CC BY 4.0.
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