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Datasets:
flamiinngo
/
applied-science-qa

Tasks:
Question Answering
Text Generation
Languages:
English
Size:
1K<n<10K
Tags:
science
chemistry
biology
physics
earth-science
applied-science
License:
Dataset card Data Studio Files
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The dataset generation failed because of a cast error
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 2 new columns ({'question', 'answer'}) and 6 missing columns ({'question_url', 'answer_score', 'license', 'site', 'question_score', 'answer_id'}).

This happened while the csv dataset builder was generating data using

hf://datasets/flamiinngo/applied-science-qa/science_v2_dataset.csv (at revision b8683b2afee455c662c8ecd8a9cd04ab26988f4d), ['hf://datasets/flamiinngo/applied-science-qa@b8683b2afee455c662c8ecd8a9cd04ab26988f4d/science_v2_attribution.csv', 'hf://datasets/flamiinngo/applied-science-qa@b8683b2afee455c662c8ecd8a9cd04ab26988f4d/science_v2_dataset.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
              question: string
              answer: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 508
              to
              {'question_url': Value('string'), 'answer_id': Value('int64'), 'question_score': Value('int64'), 'answer_score': Value('int64'), 'site': Value('string'), 'license': 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 2 new columns ({'question', 'answer'}) and 6 missing columns ({'question_url', 'answer_score', 'license', 'site', 'question_score', 'answer_id'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/flamiinngo/applied-science-qa/science_v2_dataset.csv (at revision b8683b2afee455c662c8ecd8a9cd04ab26988f4d), ['hf://datasets/flamiinngo/applied-science-qa@b8683b2afee455c662c8ecd8a9cd04ab26988f4d/science_v2_attribution.csv', 'hf://datasets/flamiinngo/applied-science-qa@b8683b2afee455c662c8ecd8a9cd04ab26988f4d/science_v2_dataset.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.

question_url
string
answer_id
int64
question_score
int64
answer_score
int64
site
string
license
string
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End of preview.

Applied Science QA

2,472 question and answer pairs covering seven scientific disciplines, written by practitioners and scored by their peers on the Stack Exchange network.

The set is built for applied science. The questions have a real-world referent behind them, and the answers explain a mechanism rather than assert a fact: why a metallic smell is not actually the metal, whether stored tap water goes bad and what governs it, how flint ends up banded through chalk cliffs.

What is in it

Column Description
question The question title plus the opening of the asker's own description
answer The highest-scoring answer, trimmed to its substantive core
Statistic Value
Rows 2,472
Distinct answers 2,472 (100%)
Answer length median 113 words
Community score on the answer median +14
Disciplines 7
Discipline Rows Share
Chemistry 692 28.0%
Biology 562 22.7%
Physics 494 20.0%
Earth Science 305 12.3%
Engineering 211 8.5%
Astronomy 108 4.4%
Sustainability 100 4.0%

No single discipline dominates, so the set reads as science broadly rather than as one field.

How it was built

Sourced through the official Stack Exchange API. Every candidate had to clear the following before it was kept:

  • The answer scored at least +3 from the community, and the question at least +1
  • Code blocks removed, then the answer trimmed to whole sentences, 45 to 125 words
  • Empty openers dropped, so answers begin with content rather than "Great question"
  • Rejected if the answer points at an image, table or link the reader cannot see
  • Rejected if it hedges rather than answers
  • Rejected if the question reads as homework rather than a real situation
  • Required to contain causal or quantitative language, so it explains rather than asserts
  • Deduplicated on the question

Licence and attribution

Stack Exchange content is CC BY-SA, so this dataset is released under CC BY-SA 4.0 as the share-alike term requires.

science_v2_attribution.csv carries one row per training row, in the same order, recording the source URL, the answer id, the question and answer scores, the site, and the exact licence version of that individual post. Posts written under earlier versions of the licence keep the version that was in force when they were written, which is why three appear.

Every row is traceable to a live public post. The quality figures above can be checked against the source rather than taken on trust.

Intended use

Adapting a base model toward explaining applied science to a non-specialist: grounded in mechanism, at the length of a real answer rather than a summary.

Built for the Adaption Labs AutoScientist Challenge, Part 2, Science category.
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