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liangyou03
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SimplexTasks-12-data

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conformal-prediction
uncertainty-estimation
simplex
benchmark
task-collection
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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'affectivetext_emotions' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
                  df = pandas_read_json(f)
                File "/usr/local/lib/python3.14/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/python3.14/site-packages/pandas/io/json/_json.py", line 791, in read_json
                  json_reader = JsonReader(
                      path_or_buf,
                  ...<16 lines>...
                      engine=engine,
                  )
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 905, in __init__
                  self.data = self._preprocess_data(data)
                              ~~~~~~~~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 917, in _preprocess_data
                  data = data.read()
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 839, in read_with_retries
                  out = read(*args, **kwargs)
                File "<frozen codecs>", line 325, in decode
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 7: invalid start byte
              
              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 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4523, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2768, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2972, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2483, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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 327, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                  return check_status(status)
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0

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SimplexTasks-12-data

Dataset-style artifact for the SimplexUQ benchmark.

Please cite: If you use SimplexUQ or SimplexTasks-12 in your research, please cite our paper: Liang You, Hengyu Shi, and Dongwen Ou. SimplexUQ: An Evaluation Framework and Benchmark for Conformal Uncertainty on Simplex-Valued Predictions, 2026.

Official repository

Authors: Liang You, Hengyu Shi, and Dongwen Ou.

This personal-account release is synchronized from the anonymous review artifact at revision a3c1f1f3975cecaae991e8dd256c2e66cd42adf6. The frozen data arrays are unchanged; this is a repository and metadata update, not a new experiment run. The source repository is anonymous0523ly/SimplexTasks-12-data. Archived result summaries retain their original experiment protocols; they are not a claim that every historical summary matches the final manuscript.

The repository retains the source artifact's license metadata. This does not override task-specific source terms or grant new rights to third-party assets; consult LICENSE, ASSET_PROVENANCE_AND_TERMS.md, and the task cards.

This bundle is artifact-first. Its primary contents are:

  • task_cards/ with one card per synthetic or real task
  • configs/ with method specs, benchmark task specs, and split policies
  • data/synthetic/ with frozen synthetic benchmark arrays
  • data/derived/ with frozen real-task derived arrays
  • data/summaries/ with metric summaries and figure/table inputs
  • data/viewer/tasks.jsonl as the flat table used by the Hugging Face Dataset Viewer
  • docs/ with provenance, restricted-asset notes, and reviewer quickstart

The benchmark is designed to run on frozen derived arrays and fixed predictor outputs. Restricted raw assets are intentionally excluded.

Top-Level Files

  • BENCHMARK_PROTOCOL.md
  • EVALUATION_PROTOCOL.md
  • ASSET_PROVENANCE_AND_TERMS.md
  • COMPUTE_ENV.md
  • REPRODUCIBILITY.md
  • MANIFEST.json
  • VALIDATION_REPORT.json

Real-Task Derived Data

  • data/derived/cifar10_softmax/
  • data/derived/topics_20newsgroups/
  • data/derived/samson_unmixing/
  • data/derived/pbmc_pseudobulk/
  • data/derived/utkface_ldl/
  • data/derived/affectivetext/

Synthetic Data

  • data/synthetic/D1/
  • data/synthetic/D2/
  • data/synthetic/D3/
  • data/synthetic/D4/
  • data/synthetic/D5/
  • data/synthetic/D6/

Important Release Rule

The benchmark consumes frozen (Y, U) arrays, task-specific strata metadata, and release notes for restricted assets. It is not a raw-data mirror.

Hugging Face Dataset Viewer

The viewer-facing dataset is intentionally limited to data/viewer/tasks.jsonl. This flat JSONL table summarizes the 12 task cards with a uniform schema. The other JSON, JSONL, CSV, and NumPy files in this repository are artifact inputs for reproduction and should not be mixed into the Dataset Viewer table.

Croissant-compatible tables

The data/parquet/ directory contains one lossless table for each of the 12 tasks. sample_index is the zero-based row index in the original frozen arrays. Columns U_0, U_1, ... and Y_0, Y_1, ... preserve the original component order. Synthetic tables additionally include R and sigma_true. Floating-point values and their stored precision are unchanged. The viewer split named train is a transport convention for a frozen sample pool, not a prescribed training split. Use the benchmark split policies for conformal evaluation.

See docs/parquet_verification.json for source-array hashes, table hashes and exact round-trip verification. These are format conversions, not new experiment runs. Croissant metadata is provided under croissant/.

License scope

The repository uses the other license category to match its existing artifact license notice and CITATION.cff. The notice and task-specific source terms govern reuse; there is no blanket CC-BY-4.0 grant for this collection. Format conversion does not change those terms. See LICENSE, ASSET_PROVENANCE_AND_TERMS.md and the individual task cards.
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Paper for liangyou03/SimplexTasks-12-data