Hugging Face's logo

Datasets:
jimnoneill
/
dataset-to-field-training-10k

Tasks:
Text Classification
Sub-tasks:
topic-classification
Languages:
English
Size:
10K<n<100K
Tags:
scientific-classification
topic-classification
s-index
datacite
dataset-classification
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:      JSON parse error: Column(/topic_id) changed from string to number in row 2
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 183, in _generate_tables
                  df = pandas_read_json(f)
                       ^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                         ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1014, in read
                  obj = self._get_object_parser(self.data)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1176, in parse
                  self._parse()
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1392, in _parse
                  ujson_loads(json, precise_float=self.precise_float), dtype=None
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
              ValueError: Trailing data
              
              During handling of the above exception, another exception occurred:
              
              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 544, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 383, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 186, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 160, in _generate_tables
                  pa_table = paj.read_json(
                             ^^^^^^^^^^^^^^
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                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: JSON parse error: Column(/topic_id) changed from string to number in row 2

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.

Dataset Research Field Training Data

A dataset of 10,500 scientific records with ground truth research field classifications for training topic classifiers. The classifications are aligned with the OpenAlex topics taxonomy. Created as part of our NIH S-index Challenge Phase 2 proposal. We refer to the S-index Hub for more information about our S-index and the Challenge.

Dataset Description

This dataset contains 10,500 scientific records (titles, subjects, descriptions) with ground truth topic classifications aligned with the OpenAlex topics taxonomy hierarchy:

  • Domain (4 categories): Physical Sciences, Life Sciences, Social Sciences, Health Sciences
  • Field (~26 categories): Chemistry, Medicine, Computer Science, etc.
  • Subfield (~250 categories): More specific research areas
  • Topic (4,516 categories): Granular research topics with numeric IDs

Dataset Structure

Each record contains:

Field Type Description
record_id string DOI or unique identifier
title string Title of the scientific record
subjects list[string] Subject keywords
description string Abstract or description (truncated)
domain string Domain classification
field string Field classification
subfield string Subfield classification
topic_name string Topic name
topic_id string Topic ID (numeric)

Domain Distribution

Domain Count Percentage
Physical Sciences 5,680 54.1%
Life Sciences 2,882 27.4%
Social Sciences 1,116 10.6%
Health Sciences 822 7.8%

Topic Coverage

  • Unique Topics: 1,471 / 4,516 (32.6%)
  • Topics with 1 example: 694
  • Topics with 3+ examples: 514

Usage 

from datasets import load_dataset

dataset = load_dataset("jimnoneill/dataset-to-field-training-10k")

# Access training data
for example in dataset["train"]:
    print(f"Title: {example['title']}")
    print(f"Topic: {example['topic_name']} (ID: {example['topic_id']})")
    print(f"Domain: {example['domain']}")
    break

Training a Classifier 

from model2vec import StaticModel
from model2vec.train import StaticModelForClassification

# Load dataset
texts = [f"{r['title']}. {', '.join(r['subjects'][:10])}" for r in dataset["train"]]
labels = [r['topic_id'] for r in dataset["train"]]

# Initialize and train
classifier = StaticModelForClassification.from_pretrained(
    model_name="minishlab/potion-base-32m",
    out_dim=len(set(labels))
)
classifier.fit(texts, labels, max_epochs=30)

Source

Records sampled from DataCite with ground truth classifications derived from the OpenAlex topics taxonomy. English-language records only.

Related Resources

Citation 

@software{dataset-to-field,
  author = {O'Neill, James, Patel, Bhavesh},
  title = {Dataset Research Field Classifier},
  year = {2026},
  url = {https://github.com/data-S-index/dataset-to-field}
}

License

CC0 1.0 Universal
Downloads last month
18

Models trained or fine-tuned on jimnoneill/dataset-to-field-training-10k