Reject a Dataset-JSON Value Against Its Column's Type

Review ask

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Summary

EditUnder reviewWhat this benchmark means

README.md

Public entries: Python and R. Passing coverage requires an installed inventory.

Goal: build a subject listing carrying SITEID and AGE from demographics received as a Dataset-JSON file.

Input: the received demographics input/dm.json, a Clinical Data Interchange Standards Consortium (CDISC) Dataset-JSON 1.1 file whose AGE column states that it holds integers, while the second subject's age is written as 67.5.

Variables:

SITEID would identify the site where the subject enrolled. The rejected file produces no output value.
AGE would give the subject's age in whole years. The rejected file produces no output value.

The column's type is part of the dataset: a value that is not a whole number in a column of integers is a defect in the data, not a reason to round it or to read the whole column as decimals. The file is rejected while it is read, naming the column, the record and the value, and no artifact is accepted.

How to fix

Confirm the subject's age with the sender and have the file corrected at its source. If the study really collects a fractional age, the sender states the column as a floating-point or decimal column, and the study reads it as one:

{"itemOID": "IT.DM.AGE", "name": "AGE", "label": "Age", "dataType": "float"}

Expected failure

Editthe run is rejected; no artifact is accepted

expected/error.yaml

phase: ingest
condition: field_parse_failed
rule: DSJSON-3

expected/error.yaml

phase: ingest
condition: field_parse_failed
spec_paths:
  - input.DM.path
rule: DSJSON-3
context:
  dataset: DM
  field: AGE
  type: int
  value: '67.5'
  record: 2

YAML specification

1 spec file

spec.yaml

Edit33 lines

1yamaa: "1.0"2type: spec3model: ADaM4domain: ADSL5keys: [STUDYID, USUBJID]6input:7  # Demographics received as a Dataset-JSON file. AGE is an integer column.8  DM: input/dm.json910output:11  path: adsl.csv12  columns: [STUDYID, USUBJID, SITEID, AGE]1314columns:15  - name: STUDYID16    type: str17    label: Study Identifier18    derivation: DM.STUDYID1920  - name: USUBJID21    type: str22    label: Unique Subject Identifier23    derivation: DM.USUBJID2425  - name: SITEID26    type: str27    label: Study Site Identifier28    derivation: DM.SITEID2930  - name: AGE31    type: int32    label: Age33    derivation: DM.AGE

Inputs

Under review1 source file

input/dm.json

Edit

{
  "datasetJSONCreationDateTime": "2026-09-30T10:15:00",
  "datasetJSONVersion": "1.1.0",
  "fileOID": "www.example.org/PILOT7/DM",
  "sourceSystem": {"name": "Example EDC", "version": "5.1"},
  "studyOID": "PILOT7",
  "metaDataVersionOID": "MDV.1",
  "itemGroupOID": "IG.DM",
  "records": 2,
  "name": "DM",
  "label": "Demographics",
  "columns": [
    {"itemOID": "IT.DM.STUDYID", "name": "STUDYID", "label": "Study Identifier", "dataType": "string", "length": 6, "keySequence": 1},
    {"itemOID": "IT.DM.USUBJID", "name": "USUBJID", "label": "Unique Subject Identifier", "dataType": "string", "length": 6, "keySequence": 2},
    {"itemOID": "IT.DM.SITEID", "name": "SITEID", "label": "Study Site Identifier", "dataType": "string", "length": 3},
    {"itemOID": "IT.DM.AGE", "name": "AGE", "label": "Age", "dataType": "integer", "length": 3}
  ],
  "rows": [
    ["PILOT7", "P7-741", "101", 24],
    ["PILOT7", "P7-742", "102", 67.5]
  ]
}

Unexpected Output

Under reviewNo artifact is produced

No fixture files in this directory.

Shaded columns are derived— = missing value

Human review and assessment

Dataset Name	Variable Name	Variable Label	Type	Computational Method	Origin
ADSL	STUDYID	Study Identifier	character	Copy value from DM.STUDYID.	Predecessor
ADSL	USUBJID	Unique Subject Identifier	character	Copy value from DM.USUBJID.	Predecessor
ADSL	SITEID	Study Site Identifier	character	Copy value from DM.SITEID.	Predecessor
ADSL	AGE	Age	numeric	Copy value from DM.AGE.	Predecessor

Version	Date	Author	Description
1.7	2026-10-20	yamaa docs build	Read a specification in the 1.0 surface as its 0.5 counterpart; state that missing values stay missing where a mapping declares no missing handler, and leave out a declared missing answer that restates a handler's default
1.6	2026-10-17	yamaa docs build	Describe collected date parts written as ISO 8601 date text and times of day counted in seconds since midnight
1.5	2026-10-17	yamaa docs build	Name collected precision in words instead of letter codes; state imputation fallbacks, minimum source precision, bounds ignored outside the collected period, and invalid text with no handler as an error; describe date and datetime shifts, imputation flag codes, and partial text giving no date part
1.4	2026-10-15	yamaa docs build	State datetime precision codes through the hour and minute, and complete only the time parts a collected datetime lacks
1.3	2026-09-29	yamaa docs build	Show study codelist names, external dictionaries, and ISO 8601 formats in variable rows; list the document's codelist values and metadata
1.2	2026-09-29	yamaa docs build	Classify ADaM copies from a declared input as Predecessor and recodes as Derived, and SDTM recodes of locally derived columns as Derived
1.1	2026-09-29	yamaa docs build	Resolve lookups into the donor record they select, and give every derivation shape a plain-language rule instead of its raw YAML
1.0		yamaa docs build	Initial generation from spec.yaml

How to fix

input/dm.json

Your review

Mapping spec