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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
ai_precheck_flags: list<item: null>
child 0, item: null
aliases: list<item: string>
child 0, item: string
canonical_name: string
entity_type: string
flags: struct<flag_scope: string, human_resolution_status: string, quarantined_evidence_in_related_record: (... 282 chars omitted)
child 0, flag_scope: string
child 1, human_resolution_status: string
child 2, quarantined_evidence_in_related_record: list<item: struct<quarantined_mention_ids: list<item: string>, risk_text_id: string>>
child 0, item: struct<quarantined_mention_ids: list<item: string>, risk_text_id: string>
child 0, quarantined_mention_ids: list<item: string>
child 0, item: string
child 1, risk_text_id: string
child 3, semantic_record_issues: list<item: struct<issues: list<item: string>, risk_text_id: string>>
child 0, item: struct<issues: list<item: string>, risk_text_id: string>
child 0, issues: list<item: string>
child 0, item: string
child 1, risk_text_id: string
child 4, unresolved_alias_candidate_ids: list<item: string>
child 0, item: string
child 5, unresolved_causal_conflict_ids: list<item: null>
child 0, item: null
identity_key: list<item: string>
child 0, item: string
node_id: string
research_status: string
review_status: string
risk_text_ids: list<item: string>
child 0, item: string
source_locators: list<item: string>
child 0, item: string
source_resolutions: list<item: string>
child 0, item: string
source_sha256s: list<item: string>
child 0, item: string
source_types: list<item: string>
child 0, item: string
sources: list<item: string>
child 0, item: string
definition: string
english_name: string
evidence_origin: string
model_extraction: bool
native_id: string
preferred_chinese_name: string
to
{'ai_precheck_flags': List(Value('null')), 'aliases': List(Value('string')), 'canonical_name': Value('string'), 'entity_type': Value('string'), 'flags': {'flag_scope': Value('string'), 'human_resolution_status': Value('string'), 'quarantined_evidence_in_related_record': List({'quarantined_mention_ids': List(Value('string')), 'risk_text_id': Value('string')}), 'semantic_record_issues': List({'issues': List(Value('string')), 'risk_text_id': Value('string')}), 'unresolved_alias_candidate_ids': List(Value('string')), 'unresolved_causal_conflict_ids': List(Value('null'))}, 'identity_key': List(Value('string')), 'node_id': Value('string'), 'research_status': Value('string'), 'review_status': Value('string'), 'risk_text_ids': List(Value('string')), 'source_locators': List(Value('string')), 'source_resolutions': List(Value('string')), 'source_sha256s': List(Value('string')), 'source_types': List(Value('string')), 'sources': List(Value('string'))}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
ai_precheck_flags: list<item: null>
child 0, item: null
aliases: list<item: string>
child 0, item: string
canonical_name: string
entity_type: string
flags: struct<flag_scope: string, human_resolution_status: string, quarantined_evidence_in_related_record: (... 282 chars omitted)
child 0, flag_scope: string
child 1, human_resolution_status: string
child 2, quarantined_evidence_in_related_record: list<item: struct<quarantined_mention_ids: list<item: string>, risk_text_id: string>>
child 0, item: struct<quarantined_mention_ids: list<item: string>, risk_text_id: string>
child 0, quarantined_mention_ids: list<item: string>
child 0, item: string
child 1, risk_text_id: string
child 3, semantic_record_issues: list<item: struct<issues: list<item: string>, risk_text_id: string>>
child 0, item: struct<issues: list<item: string>, risk_text_id: string>
child 0, issues: list<item: string>
child 0, item: string
child 1, risk_text_id: string
child 4, unresolved_alias_candidate_ids: list<item: string>
child 0, item: string
child 5, unresolved_causal_conflict_ids: list<item: null>
child 0, item: null
identity_key: list<item: string>
child 0, item: string
node_id: string
research_status: string
review_status: string
risk_text_ids: list<item: string>
child 0, item: string
source_locators: list<item: string>
child 0, item: string
source_resolutions: list<item: string>
child 0, item: string
source_sha256s: list<item: string>
child 0, item: string
source_types: list<item: string>
child 0, item: string
sources: list<item: string>
child 0, item: string
definition: string
english_name: string
evidence_origin: string
model_extraction: bool
native_id: string
preferred_chinese_name: string
to
{'ai_precheck_flags': List(Value('null')), 'aliases': List(Value('string')), 'canonical_name': Value('string'), 'entity_type': Value('string'), 'flags': {'flag_scope': Value('string'), 'human_resolution_status': Value('string'), 'quarantined_evidence_in_related_record': List({'quarantined_mention_ids': List(Value('string')), 'risk_text_id': Value('string')}), 'semantic_record_issues': List({'issues': List(Value('string')), 'risk_text_id': Value('string')}), 'unresolved_alias_candidate_ids': List(Value('string')), 'unresolved_causal_conflict_ids': List(Value('null'))}, 'identity_key': List(Value('string')), 'node_id': Value('string'), 'research_status': Value('string'), 'review_status': Value('string'), 'risk_text_ids': List(Value('string')), 'source_locators': List(Value('string')), 'source_resolutions': List(Value('string')), 'source_sha256s': List(Value('string')), 'source_types': List(Value('string')), 'sources': List(Value('string'))}
because column names don't match
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/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 1694, 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 1879, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
ai_precheck_flags list | aliases list | canonical_name string | entity_type string | flags dict | identity_key list | node_id string | research_status string | review_status string | risk_text_ids list | source_locators list | source_resolutions list | source_sha256s list | source_types list | sources list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[] | [
"RTT001082"
] | RTT001082 | RiskText | {
"flag_scope": "Associated original evidence records; does not assert every aggregate edge is erroneous",
"human_resolution_status": "NOT_PERFORMED",
"quarantined_evidence_in_related_record": [],
"semantic_record_issues": [],
"unresolved_alias_candidate_ids": [],
"unresolved_causal_conflict_ids": []
} | [
"RiskText",
"RTT001082"
] | IV4E_00030e214b1747391cf7f3d7 | CANDIDATE_PENDING_HUMAN_REVIEW | first_pass | [
"RTT001082"
] | [
"EventText"
] | [
"{\"current_path\":\"MAUDE_1154_original_bytes.csv\",\"historical_original_path\":\"https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfMAUDE/Detail.CFM?MDRFOI__ID=10338142&pc=HAW\",\"historical_source_sha256\":\"\",\"note\":\"Historical source_sha256 is empty. New container hash is separate; no web page hash is f... | [] | [
"MAUDE_EVENT"
] | [
"3009185973-2020-00183"
] |
[] | [
"Use errors: tasks performed incorrectly or not performed at all"
] | Use errors: tasks performed incorrectly or not performed at all | Cause | {
"flag_scope": "Associated original evidence records; does not assert every aggregate edge is erroneous",
"human_resolution_status": "NOT_PERFORMED",
"quarantined_evidence_in_related_record": [],
"semantic_record_issues": [],
"unresolved_alias_candidate_ids": [],
"unresolved_causal_conflict_ids": []
} | [
"Cause",
"Use errors: tasks performed incorrectly or not performed at all",
"General",
"",
"POSSIBLE"
] | IV4E_000612f4f3197f967b3a5ab7 | CANDIDATE_PENDING_HUMAN_REVIEW | first_pass | [
"FT4_b198decada31ae89c21c"
] | [
"pdf_page=16;block=5;lines=14-18"
] | [
"{\"actual_source_sha256\":\"9b49b108f88e04eaa63143f9ace07b0fef878e40353b198581dfb603c24e1b20\",\"current_path\":\"FDA_2026_Applying_Human_Factors_and_Usability_Engineering.pdf\",\"expected_source_sha256\":\"9b49b108f88e04eaa63143f9ace07b0fef878e40353b198581dfb603c24e1b20\",\"historical_original_path\":\"FDA_2026_A... | [
"9b49b108f88e04eaa63143f9ace07b0fef878e40353b198581dfb603c24e1b20"
] | [
"FDA_GUIDANCE"
] | [
"FDA_GUIDE_HF_USABILITY_2026"
] |
[] | [
"3009185973-2019-00425"
] | 3009185973-2019-00425 | SourceDocumentOrEvent | {
"flag_scope": "Associated original evidence records; does not assert every aggregate edge is erroneous",
"human_resolution_status": "NOT_PERFORMED",
"quarantined_evidence_in_related_record": [],
"semantic_record_issues": [],
"unresolved_alias_candidate_ids": [],
"unresolved_causal_conflict_ids": []
} | [
"SourceDocumentOrEvent",
"3009185973-2019-00425"
] | IV4E_00154ded1f9717a954b3f1b0 | CANDIDATE_PENDING_HUMAN_REVIEW | first_pass | [
"RTT000904"
] | [
"EventText"
] | [
"{\"current_path\":\"MAUDE_1154_original_bytes.csv\",\"historical_original_path\":\"https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfMAUDE/Detail.CFM?MDRFOI__ID=9393731&pc=HAW\",\"historical_source_sha256\":\"\",\"note\":\"Historical source_sha256 is empty. New container hash is separate; no web page hash is fa... | [] | [
"MAUDE_EVENT"
] | [
"3009185973-2019-00425"
] |
[] | [
"FDA_GUIDE_DEVICE_SOFTWARE_2023"
] | FDA_GUIDE_DEVICE_SOFTWARE_2023 | SourceDocumentOrEvent | {
"flag_scope": "Associated original evidence records; does not assert every aggregate edge is erroneous",
"human_resolution_status": "NOT_PERFORMED",
"quarantined_evidence_in_related_record": [],
"semantic_record_issues": [
{
"issues": [
"PRIMARY_RT_RE_INCONSISTENT"
],
"risk_text_... | [
"SourceDocumentOrEvent",
"FDA_GUIDE_DEVICE_SOFTWARE_2023"
] | IV4E_001790bcf11271943f191c68 | CANDIDATE_PENDING_HUMAN_REVIEW | first_pass | [
"FT4_007be0ced0ca94c037f9",
"FT4_00cf7a5e8d8a12acbd2e",
"FT4_02a87939d8a21c8cfb94",
"FT4_02c2fdb1d0f1ef108b75",
"FT4_0332594f40d917c05854",
"FT4_062b07571ae0df3a0616",
"FT4_06a5a39490bf0083f9fa",
"FT4_06a81a14a4b6d0cbaea7",
"FT4_073f24cd36d48d266723",
"FT4_07d97e6b16ef47e2b70f",
"FT4_07f25a5b4e2... | [
"pdf_page=10;block=2;lines=2-4",
"pdf_page=10;block=3;lines=5-6",
"pdf_page=10;block=4;lines=7-15",
"pdf_page=10;block=8;lines=26-28",
"pdf_page=10;block=9;lines=29-37",
"pdf_page=11;block=2;lines=2-4",
"pdf_page=11;block=3;lines=5-5",
"pdf_page=11;block=4;lines=6-15",
"pdf_page=11;block=5;lines=16-... | [
"{\"actual_source_sha256\":\"dee0c15e11499682ca281120a9828180ec16710eea3d9e272cee5807050a704a\",\"current_path\":\"FDA_2023_Device_Software_Functions.pdf\",\"expected_source_sha256\":\"dee0c15e11499682ca281120a9828180ec16710eea3d9e272cee5807050a704a\",\"historical_original_path\":\"FDA_2023_Device_Software_Function... | [
"dee0c15e11499682ca281120a9828180ec16710eea3d9e272cee5807050a704a"
] | [
"FDA_GUIDANCE"
] | [
"FDA_GUIDE_DEVICE_SOFTWARE_2023"
] |
[] | [
"FDARECALL_156928"
] | FDARECALL_156928 | SourceDocumentOrEvent | {
"flag_scope": "Associated original evidence records; does not assert every aggregate edge is erroneous",
"human_resolution_status": "NOT_PERFORMED",
"quarantined_evidence_in_related_record": [],
"semantic_record_issues": [],
"unresolved_alias_candidate_ids": [],
"unresolved_causal_conflict_ids": []
} | [
"SourceDocumentOrEvent",
"FDARECALL_156928"
] | IV4E_0017a533b31d9e92cccadf5d | CANDIDATE_PENDING_HUMAN_REVIEW | first_pass | [
"FT4_9d0512423e1ea5861270",
"FT4_ca6a8082fa377f9a3116",
"FT4_dea19e249880bf4ee685",
"FT4_ef96c5dc889a8a00ea95"
] | [
"html:nth-of-type(1) > body:nth-of-type(1) > div:nth-of-type(3) > span:nth-of-type(2) > br:nth-of-type(1) > table:nth-of-type(1) > tr:nth-of-type(1) > td:nth-of-type(1) > table:nth-of-type(1) > tr:nth-of-type(2) > td:nth-of-type(1) > table:nth-of-type(1) > tr:nth-of-type(1) > td:nth-of-type(1) > span:nth-of-type(1)... | [
"{\"actual_source_sha256\":\"b73e318c272017cbf7baa8bd127eb7c69ef5f45d1e96d86d577c2e846925772c\",\"current_path\":\"FDA_recall_156928.html\",\"expected_source_sha256\":\"b73e318c272017cbf7baa8bd127eb7c69ef5f45d1e96d86d577c2e846925772c\",\"historical_original_path\":\"FDA_recall_156928.html\",\"inventory_id_hash_matc... | [
"b73e318c272017cbf7baa8bd127eb7c69ef5f45d1e96d86d577c2e846925772c"
] | [
"FDA_RECALL_DETAIL"
] | [
"FDARECALL_156928"
] |
[] | [
"Fast follow upgrade to Windows 10"
] | Fast follow upgrade to Windows 10 | Cause | {
"flag_scope": "Associated original evidence records; does not assert every aggregate edge is erroneous",
"human_resolution_status": "NOT_PERFORMED",
"quarantined_evidence_in_related_record": [],
"semantic_record_issues": [],
"unresolved_alias_candidate_ids": [],
"unresolved_causal_conflict_ids": []
} | [
"Cause",
"Fast follow upgrade to Windows 10",
"ROSA family",
"",
"POSSIBLE"
] | IV4E_00203c6aac0cf7f101beec01 | CANDIDATE_PENDING_HUMAN_REVIEW | first_pass | [
"RTT001103"
] | [
"EventText"
] | [
"{\"current_path\":\"MAUDE_1154_original_bytes.csv\",\"historical_original_path\":\"https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfMAUDE/Detail.CFM?MDRFOI__ID=10533808&pc=HAW\",\"historical_source_sha256\":\"\",\"note\":\"Historical source_sha256 is empty. New container hash is separate; no web page hash is f... | [] | [
"MAUDE_EVENT"
] | [
"3009185973-2020-00214"
] |
[] | [
"FT4_3c0ee8d2f3340e615019"
] | FT4_3c0ee8d2f3340e615019 | RiskText | {
"flag_scope": "Associated original evidence records; does not assert every aggregate edge is erroneous",
"human_resolution_status": "NOT_PERFORMED",
"quarantined_evidence_in_related_record": [],
"semantic_record_issues": [],
"unresolved_alias_candidate_ids": [],
"unresolved_causal_conflict_ids": []
} | [
"RiskText",
"FT4_3c0ee8d2f3340e615019"
] | IV4E_00238690cdaa7719eaf3a5a2 | CANDIDATE_PENDING_HUMAN_REVIEW | first_pass | [
"FT4_3c0ee8d2f3340e615019"
] | [
"pdf_page=26;block=28;lines=51-53"
] | [
"{\"actual_source_sha256\":\"3e59a32a27c41956b63de9ba2fc53c8099155a7c903460b4ebd7c82b616f958d\",\"current_path\":\"DICOM_PS3.10_Media_Storage_and_File_Format.pdf\",\"expected_source_sha256\":\"3e59a32a27c41956b63de9ba2fc53c8099155a7c903460b4ebd7c82b616f958d\",\"historical_original_path\":\"DICOM_PS3.10_Media_Storag... | [
"3e59a32a27c41956b63de9ba2fc53c8099155a7c903460b4ebd7c82b616f958d"
] | [
"DICOM_STANDARD"
] | [
"DICOM_PS3_10"
] |
[] | [
"FT4_37b262892d6e84053682"
] | FT4_37b262892d6e84053682 | RiskText | {
"flag_scope": "Associated original evidence records; does not assert every aggregate edge is erroneous",
"human_resolution_status": "NOT_PERFORMED",
"quarantined_evidence_in_related_record": [],
"semantic_record_issues": [],
"unresolved_alias_candidate_ids": [],
"unresolved_causal_conflict_ids": []
} | [
"RiskText",
"FT4_37b262892d6e84053682"
] | IV4E_00250ac37b165dfcb72248b2 | CANDIDATE_PENDING_HUMAN_REVIEW | first_pass | [
"FT4_37b262892d6e84053682"
] | [
"pdf_page=306;table=1;row=4"
] | [
"{\"actual_source_sha256\":\"b88e59c32ddb4b74416cd0aff7f579269695087a74806ef462311ef58091c01a\",\"current_path\":\"DICOM_PS3.2_Conformance.pdf\",\"expected_source_sha256\":\"b88e59c32ddb4b74416cd0aff7f579269695087a74806ef462311ef58091c01a\",\"historical_original_path\":\"DICOM_PS3.2_Conformance.pdf\",\"inventory_id... | [
"b88e59c32ddb4b74416cd0aff7f579269695087a74806ef462311ef58091c01a"
] | [
"DICOM_STANDARD"
] | [
"DICOM_PS3_2"
] |
[] | [
"FT4_e67b591f8a1ad8e4dd5e"
] | FT4_e67b591f8a1ad8e4dd5e | RiskText | {
"flag_scope": "Associated original evidence records; does not assert every aggregate edge is erroneous",
"human_resolution_status": "NOT_PERFORMED",
"quarantined_evidence_in_related_record": [],
"semantic_record_issues": [],
"unresolved_alias_candidate_ids": [],
"unresolved_causal_conflict_ids": []
} | [
"RiskText",
"FT4_e67b591f8a1ad8e4dd5e"
] | IV4E_00267c711078bc25cfad0dc2 | CANDIDATE_PENDING_HUMAN_REVIEW | first_pass | [
"FT4_e67b591f8a1ad8e4dd5e"
] | [
"pdf_page=19;block=6;lines=19-19"
] | [
"{\"actual_source_sha256\":\"f85b80efb4d81292aa63fecca81058e991ed2abe454facef05d3948d4fe9bc1b\",\"current_path\":\"FDA_2026_Content_of_Human_Factors_in_Marketing_Submissions.pdf\",\"expected_source_sha256\":\"f85b80efb4d81292aa63fecca81058e991ed2abe454facef05d3948d4fe9bc1b\",\"historical_original_path\":\"FDA_2026_... | [
"f85b80efb4d81292aa63fecca81058e991ed2abe454facef05d3948d4fe9bc1b"
] | [
"FDA_GUIDANCE"
] | [
"FDA_GUIDE_HF_SUBMISSION_2026"
] |
[] | [
"RE23"
] | RE23 | RiskElement | {"flag_scope":"Associated original evidence records; does not assert every aggregate edge is erroneo(...TRUNCATED) | [
"RiskElement",
"RE23"
] | IV4E_0026ea034fa06458664e9a17 | CANDIDATE_PENDING_HUMAN_REVIEW | first_pass | ["FT4_0007a0bfa17107363488","FT4_0009bf5c7c389ef37dcc","FT4_0012a1be024d09786367","FT4_00157d2e98c49(...TRUNCATED) | ["EventText","pdf_page=100;block=0;lines=3-3","pdf_page=100;block=27;lines=69-70","pdf_page=100;bloc(...TRUNCATED) | ["{\"actual_source_sha256\":\"160332664060f09ace80bed765e4c78fa641988e89a1b2956c4ebb58a8d7fa58\",\"c(...TRUNCATED) | ["160332664060f09ace80bed765e4c78fa641988e89a1b2956c4ebb58a8d7fa58","3596818deb0c9e56ad18127d7e8db30(...TRUNCATED) | [
"DEVICE_USER_MANUAL",
"DICOM_STANDARD",
"FDA_510K_SUMMARY",
"FDA_GUIDANCE",
"MAUDE_EVENT"
] | ["3009185973-2017-00826","3009185973-2018-00163","3009185973-2019-00005","3009185973-2019-00041","30(...TRUNCATED) |
End of preview.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
# NeuroNav Knowledge Graph
Public research dataset published and maintained by KlisSu for neuronavigation robot failure-risk research and the NeuroNav Agent application. This package contains the complete current logical knowledge graph: 10,984 nodes, 19,980 aggregated relations, and 20,198 relation-evidence records.
Files
| File | Content |
|---|---|
candidate_entities.jsonl |
All graph nodes, original canonical names, aliases, types, and research fields. |
candidate_relations.jsonl |
All aggregated edges, endpoint IDs/types, causal predicates, assertions, and research fields. |
relation_evidence.jsonl |
All linked evidence, original source text/context, page/section locators, and provenance references. |
ontology/risk_types.csv |
Frozen v1.2 risk-type configuration, copied without byte changes. |
ontology/risk_elements.csv |
Frozen v1.2 risk-element configuration, copied without byte changes. |
examples.json |
Three prepared English interface examples. |
manifest.json |
Counts, file hashes, source hashes, exact export transformations, and verification results. |
The JSONL records can be read directly by an application or imported into a graph database. This is a complete logical graph export, not a Neo4j binary database backup. Node names, aliases, relations, assertion/review status, evidence text, and original source body/context are retained. Existing source text is preserved as recorded, including any source encoding artifacts.
To limit unrelated disclosure, duplicated model-call audit data within provenance is excluded: raw request/response content, duplicated parsed annotations, token usage, response IDs, run timestamps, and related transport/run fields. Local absolute paths in metadata are replaced with their file basenames; public source URLs and source locations are preserved. The manifest lists each changed or excluded field path and its occurrence count. This export is not a byte-for-byte copy of the original JSONL files.
The dataset does not include separate model-response archives, API credentials, experiment outputs, gold-standard annotations, expert score sheets, historical working folders, or source PDF files. The dataset is publicly available at https://huggingface.co/datasets/KlisSu/neuro-nav-kg. Anyone can browse and download its files without a Hugging Face account or access token. Related research source code is publicly available at https://gitlab.com/shanxuex/neuronav-agent.
The graph is research data pending human review (first_pass); it does not provide verified clinical guidance. Suspected and confirmed causal predicates remain distinct. MAUDE report counts describe the composition of reports and must not be interpreted as failure rates or comparisons of device safety.
DICOM excerpts are from DICOM PS3.2 and PS3.10, © NEMA; the applicable part and source coordinates are identified in the evidence records. DICOM is continuously maintained; consult the current standard at https://www.dicomstandard.org/current. Excerpt conditions are described in NEMA Procedures section 8.8: https://dicom.nema.org/Dicom/Geninfo/Procedures.pdf. DICOM material is provided "AS IS", without warranty; NEMA disclaims liability for damages arising from its use. Source excerpts remain in their original wording. This package does not assign an open-source license to third-party source text.
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