The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 91, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 193, in _generate_tables
examples = [ujson_loads(line) for line in batch.splitlines()]
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.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.
Synthetic FHIR Interoperability Dataset — 20,000 Patients This dataset contains 20,000 fully synthetic patient records generated with Synthea™, an open-source patient simulator developed by MITRE Corporation. All records are 100% fictional — no real patient data, no HIPAA restrictions, free to use for research, education, and development. Why this dataset exists Real healthcare data is locked behind privacy laws, institutional agreements, and de-identification requirements. This dataset was purpose-built to fill that gap — providing a realistic, multi-format, interoperability-ready practice environment that mirrors the complexity of actual health system data without any of the legal friction. What makes it different The generation was deliberately structured across four separate batches to simulate real-world interoperability challenges:
Runs A & B generate the same patients in two different FHIR versions — R4 (current standard) and DSTU2 (legacy, still in production at many hospitals) — making this dataset ideal for practicing format migration and version translation. Runs C & D generate two independent patient populations, with 500 patients intentionally appearing in both — but with realistic data entry variations: name nicknames, birth date offsets of 1–2 days, address abbreviation differences, and phone number transpositions. A ground truth file is included, enabling objective precision/recall scoring of Master Patient Index (MPI) and record linkage algorithms.
Formats included FormatDescriptionFHIR R4 (JSON + NDJSON bulk)Current US national standard, US Core 6.1 compliantFHIR DSTU2 (JSON)Legacy version, widely deployed in older EHR systemsC-CDA (XML)Clinical document standard used in transitions of careCSVFlat-file export simulating legacy EHR database extractsPlain TextHuman-readable clinical summaries Patient population Patients are aged 45–90, living at time of generation, simulated in Texas. This age range targets chronic disease populations — the most clinically complex and interoperability-relevant cohort. All genders and a realistic demographic mix are included. HL7 v2 Synthea does not natively generate HL7 v2 messages. This is a known gap in the tooling. HL7 v2 can be reconstructed from the FHIR bundles or CSV exports included in this dataset — a reconstruction exercise that is itself a practical interoperability skill. Intended use cases
FHIR API development and testing C-CDA parsing and transformation FHIR version migration (DSTU2 → R4) Master Patient Index and record linkage algorithm development and benchmarking Clinical analytics and population health queries Terminology validation and SNOMED/LOINC/RxNorm code testing Clinical decision support and CQL rule execution HL7 v2 message reconstruction from structured data
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