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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 10 new columns ({'engine', 'temporal_alignment', 'direction_agreement', 'cfc_005_count', 'pass_rate', 'cfc_007_count', 'description', 'pass_count', 'type', 'n_runs'}) and 20 missing columns ({'r3_pass', 'r2_accel_g', 'r3_direction', 'r1_accel_g', 'cfc_007', 'r2_direction', 'r3_gyro_deg_s', 'verdict', 'r1_gyro_deg_s', 'r1_direction', 'r1_jitter_ms', 'r3_jitter_ms', 'direction_match_rate', 'r2_jitter_ms', 'r2_pass', 'cfc_008', 'r2_gyro_deg_s', 'r3_accel_g', 'r1_pass', 'run_id'}).
This happened while the csv dataset builder was generating data using
hf://datasets/ContinuityLab-Org/myshape-576/engine_summary.csv (at revision bff632c8af0d9dddf4f92c76d5dff616343e056c), ['hf://datasets/ContinuityLab-Org/myshape-576@bff632c8af0d9dddf4f92c76d5dff616343e056c/ee003_runs.csv', 'hf://datasets/ContinuityLab-Org/myshape-576@bff632c8af0d9dddf4f92c76d5dff616343e056c/engine_summary.csv', 'hf://datasets/ContinuityLab-Org/myshape-576@bff632c8af0d9dddf4f92c76d5dff616343e056c/tracker_comparison.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
engine: string
description: string
type: string
n_runs: int64
pass_count: int64
pass_rate: double
temporal_alignment: double
direction_agreement: double
cfc_005_count: double
cfc_007_count: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1494
to
{'run_id': Value('string'), 'verdict': Value('string'), 'direction_match_rate': Value('float64'), 'r1_direction': Value('string'), 'r1_gyro_deg_s': Value('int64'), 'r1_accel_g': Value('float64'), 'r1_jitter_ms': Value('int64'), 'r1_pass': Value('string'), 'r2_direction': Value('string'), 'r2_gyro_deg_s': Value('int64'), 'r2_accel_g': Value('float64'), 'r2_jitter_ms': Value('int64'), 'r2_pass': Value('string'), 'r3_direction': Value('string'), 'r3_gyro_deg_s': Value('int64'), 'r3_accel_g': Value('float64'), 'r3_jitter_ms': Value('int64'), 'r3_pass': Value('string'), 'cfc_007': Value('bool'), 'cfc_008': Value('bool')}
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 10 new columns ({'engine', 'temporal_alignment', 'direction_agreement', 'cfc_005_count', 'pass_rate', 'cfc_007_count', 'description', 'pass_count', 'type', 'n_runs'}) and 20 missing columns ({'r3_pass', 'r2_accel_g', 'r3_direction', 'r1_accel_g', 'cfc_007', 'r2_direction', 'r3_gyro_deg_s', 'verdict', 'r1_gyro_deg_s', 'r1_direction', 'r1_jitter_ms', 'r3_jitter_ms', 'direction_match_rate', 'r2_jitter_ms', 'r2_pass', 'cfc_008', 'r2_gyro_deg_s', 'r3_accel_g', 'r1_pass', 'run_id'}).
This happened while the csv dataset builder was generating data using
hf://datasets/ContinuityLab-Org/myshape-576/engine_summary.csv (at revision bff632c8af0d9dddf4f92c76d5dff616343e056c), ['hf://datasets/ContinuityLab-Org/myshape-576@bff632c8af0d9dddf4f92c76d5dff616343e056c/ee003_runs.csv', 'hf://datasets/ContinuityLab-Org/myshape-576@bff632c8af0d9dddf4f92c76d5dff616343e056c/engine_summary.csv', 'hf://datasets/ContinuityLab-Org/myshape-576@bff632c8af0d9dddf4f92c76d5dff616343e056c/tracker_comparison.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.
run_id string | verdict string | direction_match_rate float64 | r1_direction string | r1_gyro_deg_s int64 | r1_accel_g float64 | r1_jitter_ms int64 | r1_pass string | r2_direction string | r2_gyro_deg_s int64 | r2_accel_g float64 | r2_jitter_ms int64 | r2_pass string | r3_direction string | r3_gyro_deg_s int64 | r3_accel_g float64 | r3_jitter_ms int64 | r3_pass string | cfc_007 bool | cfc_008 bool |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
EE003-001 | PASS | 0.67 | → | 253 | 1.36 | 317 | FAIL | → | 289 | 1.55 | 140 | PASS | ← | 194 | 1.85 | 999 | PASS | false | false |
EE003-002 | PASS | 1 | ↓ | 389 | 2.41 | 80 | PASS | ↑ | 152 | 1.87 | 447 | PASS | ↑ | 126 | 1.57 | 341 | PASS | false | false |
EE003-003 | PASS | 0.67 | ← | 357 | 0.93 | 592 | PASS | → | 268 | 2.03 | 631 | PASS | ↑ | 161 | 1.76 | 706 | PASS | false | false |
EE003-004 | PASS | 0.67 | ← | 409 | 1.11 | 369 | PASS | → | 328 | 2.36 | 397 | FAIL | ↑ | 168 | 2.19 | 386 | PASS | false | false |
EE003-005 | PASS | 0.67 | ↑ | 164 | 2.78 | 529 | PASS | → | 427 | 3.88 | 967 | FAIL | ↓ | 347 | 2.42 | 272 | PASS | false | false |
EE003-006 | PASS | 0.67 | ↓ | 387 | 2.69 | 902 | PASS | → | 371 | 2.49 | 575 | FAIL | ← | 263 | 1.43 | 455 | PASS | false | false |
EE003-007 | PASS | 0.67 | ↓ | 328 | 2.36 | 579 | PASS | ↓ | 346 | 2.81 | 384 | PASS | ↑ | 109 | 2.21 | 420 | PASS | false | false |
EE003-008 | PASS | 0.67 | ← | 379 | 1.78 | 896 | PASS | ↓ | 318 | 3.71 | 486 | PASS | ↓ | 331 | 3.21 | 772 | PASS | false | false |
EE003-009 | INSUFFICIENT | 0.33 | → | 452 | 3.51 | 12 | FAIL | ← | 241 | 1.54 | 364 | FAIL | → | 176 | 1.68 | 840 | PASS | false | false |
EE003-010 | INSUFFICIENT | 0.33 | ↓ | 418 | 2.82 | 450 | PASS | → | 417 | 3.51 | 285 | FAIL | → | 340 | 3.51 | 237 | FAIL | false | false |
EE003-011 | INSUFFICIENT | 0.33 | → | 428 | 2.49 | 49 | FAIL | → | 266 | 2.86 | 536 | PASS | ↑ | 275 | 3.47 | 152 | FAIL | false | false |
EE003-012 | PASS | 0.67 | ↑ | 203 | 2.07 | 370 | PASS | ← | 281 | 1.13 | 980 | PASS | → | 350 | 2.54 | 626 | FAIL | false | false |
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YAML Metadata Warning:The task_categories "signal-processing" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
YAML Metadata Warning:The task_categories "verification" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
YAML Metadata Warning:The task_categories "time-series" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
MyShape Continuity Protocol — Experimental Dataset
576 structured experimental runs across 4 evidence engines, validating cross-modal continuity verification on consumer hardware.
Dataset Summary
This dataset contains the structured output of 576 verification sessions conducted on an iPhone with desktop camera (where applicable) between 2026-07-15 and 2026-07-19. Each run includes per-engine verdicts, component-level evidence scores, CFC (Continuity Failure Condition) check results, and round-level gyroscope telemetry.
Engines
| Engine | N | Pass Rate | Description |
|---|---|---|---|
| EE-003 | 200 | 59% | Gyroscope challenge-response (3 randomized rounds) |
| PE-001 single-device | 50 | 93% | Cross-modal coupling, camera + IMU on same phone |
| PE-001 independent | 266 | 58% | Cross-modal coupling, desktop camera + phone IMU |
| VS-001 | 60 | 93% | Dual-engine pipeline (EE-001 + EE-003) |
Files
| File | Content |
|---|---|
engine_summary.csv |
Per-engine aggregate statistics |
ee003_runs.csv |
Per-run EE-003 diagnostics with round-level telemetry |
pe001_runs.csv |
Per-run PE-001 diagnostics with tracker metadata |
vs001_runs.csv |
Per-run VS-001 diagnostics with passive scores |
tracker_comparison.csv |
Three-tracker comparison (pixel diff, color-blob, moving-blob) |
Data Collection
All experiments were conducted on a single iPhone model with the following calibrated parameters (v0.3):
- JERK_MIN_THRESHOLD: 0.15 m/s³
- MAD multiplier: 2×
- MATCH_WINDOW_MS: ±500ms
- Refractory period: 150ms
- CAMERA_PIPELINE_LATENCY_MS: 80ms
- CFC-005 INVERSION_THRESHOLD_MS: 250ms
Limitations
- Single-operator dataset (one entity, one device model)
- Camera motion tracking uses pixel-frame differencing or color-blob tracking, not MediaPipe (loading issues on test hardware)
- Lighting conditions varied (daylight vs. artificial light) and are not recorded per-run
- Raw sensor data (IMU samples, camera frames) was not persisted — only structured evidence output was retained
Citation
@misc{continuity-lab-2026,
author = {{The Continuity Lab}},
title = {MyShape Continuity Protocol — Experimental Dataset},
year = {2026},
url = {https://huggingface.co/datasets/TheContinuityLab/myshape-576},
}
License
MIT
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