The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type list<item: struct<timestamp: string, metric: string, value: int64, unit: string, anomaly_flag: bool>> to string
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2303, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2143, in cast_array_to_feature
return array_cast(
array,
...<2 lines>...
allow_decimal_to_str=allow_decimal_to_str,
)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2007, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
TypeError: Couldn't cast array of type list<item: struct<timestamp: string, metric: string, value: int64, unit: string, anomaly_flag: bool>> to stringNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:The task_ids "synthetic-data" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation
Dataset Card for SynData medical research Synthetic Dataset
Dataset Summary
This dataset is a fully synthetic, high-fidelity benchmark generated by SynData Studio. It maintains realistic mathematical covariance, non-linear feature correlations, and multi-modal time-series telemetry while guaranteeing zero exposure of real-world Personally Identifiable Information (PII) or Protected Health Information (PHI).
Designed for zero-shot ML training, synthetic pre-training, algorithm benchmarking, and cross-border privacy-preserving analytics.
Dataset Structure
Data Fields
patient_guid(uuid): Patient GUID - De-identified HIPAA patient identifierage_years(number): Age (Years) - Participant age (18-88)gender_cat(enum): Gender Category - Biological sex assigned at birthbmi_index(number): BMI Index - Body Mass Indexsystolic_bp_mmHg(number): Systolic BP (mmHg) - Blood pressure readingcrp_biomarker_mg_L(number): C-Reactive Protein (mg/L) - Inflammation markerhba1c_pct(number): HbA1c (%) - Glycated hemoglobintreatment_arm(enum): Treatment Arm - Trial cohort assignmentsurvival_months(number): Follow-up Survival (Mo) - Observed trial periodoutcome_flag(boolean): Primary Endpoint Met - Boolean treatment response indicatortime_series_vitals(timestamp_series): Vitals Telemetry Series - Multi-modal time-stamped ECG & SpO2 readings
Sample Python Code to Load
from datasets import load_dataset
import pandas as pd
# Load synthetic dataset directly from Hugging Face Hub
ds = load_dataset("my-org-or-user/syndata-medical-research", split="train")
# Convert to Pandas DataFrame
df = ds.to_pandas()
print(df.head())
Direct Python Upload Script
# Install required libraries
# pip install huggingface_hub datasets pandas
import pandas as pd
from datasets import Dataset
# Load local synthetic dataset
df = pd.read_json("syndata_medical_research_500000_records.json")
# Convert to Hugging Face Dataset object
hf_dataset = Dataset.from_pandas(df)
# Push to Hugging Face Hub
hf_dataset.push_to_hub("my-org-or-user/syndata-medical-research", private=False)
Licensing Information
Dedicated to the public domain under CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. Free for commercial and non-commercial model training without restriction.
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