Read a Dataset-JSON File

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Summary

EditUnder reviewWhat this benchmark means

README.md

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

Goal: carry the actual treatment (TRT01A), age (AGE), baseline height (HEIGHTBL) and the date and time of first exposure (TRTSDT, TRTSDTM) into ADSL from a Dataset-JSON file, each value as the stored data means it.

Input: one spec.yaml reading input/adsl_raw.json, a Clinical Data Interchange Standards Consortium (CDISC) Dataset-JSON 1.1 file in its single-object form, with one subject-level record per subject. Each column of the file states its own data type.

Variables:

TRT01A is the actual treatment; missing when the subject was never treated, which the file stores as null.
AGE is the age in whole years.
HEIGHTBL is the baseline height in centimetres, which the file stores as decimal text; missing when not measured.
TRTSDT is the date of first exposure; missing when the subject was never treated.
TRTSDTM is the date and time of first exposure; missing when the subject was never treated.

Note: each value takes the type its column states: whole numbers stay whole, a decimal written as text is read as a number, and dates and times are read from their ISO 8601 text, a time to the minute gaining zero seconds. A null is missing. The file's labels, lengths and display formats, and its request that a receiving system convert dates to SAS numbers, decide nothing.

YAML specification

1 spec file

spec.yaml

Edit52 lines

1yamaa: "1.0"2type: spec3model: ADaM4domain: ADSL5keys: [STUDYID, USUBJID]6input:7  # A Dataset-JSON 1.1 file in its single-object form. Each column's dataType8  # types its field, so no types entry is written: AGE is an integer, HEIGHTBL9  # a decimal read as a float, and TRTSDT and TRTSDTM a date and a datetime,10  # whatever targetDataType asks a receiving system to convert them to.11  ADSL_RAW: input/adsl_raw.json1213output:14  path: adsl.csv15  columns: [STUDYID, USUBJID, TRT01A, AGE, HEIGHTBL, TRTSDT, TRTSDTM]1617columns:18  - name: STUDYID19    type: str20    label: Study Identifier21    derivation: ADSL_RAW.STUDYID2223  - name: USUBJID24    type: str25    label: Unique Subject Identifier26    derivation: ADSL_RAW.USUBJID2728  # A null value is missing.29  - name: TRT01A30    type: str31    label: Actual Treatment for Period 0132    derivation: ADSL_RAW.TRT01A3334  - name: AGE35    type: int36    label: Age37    derivation: ADSL_RAW.AGE3839  - name: HEIGHTBL40    type: float41    label: Baseline Height (cm)42    derivation: ADSL_RAW.HEIGHTBL4344  - name: TRTSDT45    type: date46    label: Date of First Exposure to Treatment47    derivation: ADSL_RAW.TRTSDT4849  - name: TRTSDTM50    type: datetime51    label: Datetime of First Exposure to Treatment52    derivation: ADSL_RAW.TRTSDTM

Inputs

Under review1 source file

input/adsl_raw.json

Edit

{
  "datasetJSONCreationDateTime": "2026-09-30T10:15:00",
  "datasetJSONVersion": "1.1.0",
  "fileOID": "www.example.org/PILOT7/ADSL_RAW",
  "originator": "Example CRO",
  "sourceSystem": {"name": "Example EDC", "version": "5.1"},
  "studyOID": "PILOT7",
  "metaDataVersionOID": "MDV.1",
  "itemGroupOID": "IG.ADSL_RAW",
  "records": 3,
  "name": "ADSL_RAW",
  "label": "Subject-Level Source",
  "columns": [
    {"itemOID": "IT.ADSL_RAW.STUDYID", "name": "STUDYID", "label": "Study Identifier", "dataType": "string", "length": 6, "keySequence": 1},
    {"itemOID": "IT.ADSL_RAW.USUBJID", "name": "USUBJID", "label": "Unique Subject Identifier", "dataType": "string", "length": 6, "keySequence": 2},
    {"itemOID": "IT.ADSL_RAW.TRT01A", "name": "TRT01A", "label": "Actual Treatment for Period 01", "dataType": "string", "length": 7},
    {"itemOID": "IT.ADSL_RAW.AGE", "name": "AGE", "label": "Age", "dataType": "integer", "length": 3},
    {"itemOID": "IT.ADSL_RAW.HEIGHTBL", "name": "HEIGHTBL", "label": "Baseline Height (cm)", "dataType": "decimal", "displayFormat": "6.2"},
    {"itemOID": "IT.ADSL_RAW.TRTSDT", "name": "TRTSDT", "label": "Date of First Exposure to Treatment", "dataType": "date", "targetDataType": "integer", "displayFormat": "DATE9."},
    {"itemOID": "IT.ADSL_RAW.TRTSDTM", "name": "TRTSDTM", "label": "Datetime of First Exposure to Treatment", "dataType": "datetime", "targetDataType": "integer", "displayFormat": "DATETIME20."}
  ],
  "rows": [
    ["PILOT7", "P7-741", "Drug A", 24, "172.50", "2025-03-04", "2025-03-04T09:15"],
    ["PILOT7", "P7-742", "Placebo", 67, null, "2025-03-05", "2025-03-05T14:02:37"],
    ["PILOT7", "P7-743", null, 52, "165.20", null, null]
  ]
}

Expected output

Under review3 expected rows

expected/adsl.csv

Edit3 rows

expected/adsl.csv
STUDYID	TRT01A	AGE	HEIGHTBL	TRTSDT	TRTSDTM
PILOT7	Drug A	24	172.5	2025-03-04	2025-03-04T09:15:00
PILOT7	Placebo	67	—	2025-03-05	2025-03-05T14:02:37
PILOT7	—	52	165.2	—	—

Shaded columns are derived— = missing value

Human review and assessment

Dataset Name	Variable Name	Variable Label	Type	Codelist/Controlled Terms	Computational Method	Origin
ADSL	STUDYID	Study Identifier	character		Copy value from ADSL_RAW.STUDYID.	Predecessor
ADSL	USUBJID	Unique Subject Identifier	character		Copy value from ADSL_RAW.USUBJID.	Predecessor
ADSL	TRT01A	Actual Treatment for Period 01	character		Copy value from ADSL_RAW.TRT01A.	Predecessor
ADSL	AGE	Age	numeric		Copy value from ADSL_RAW.AGE.	Predecessor
ADSL	HEIGHTBL	Baseline Height (cm)	numeric		Copy value from ADSL_RAW.HEIGHTBL.	Predecessor
ADSL	TRTSDT	Date of First Exposure to Treatment	numeric	ISO 8601	Copy value from ADSL_RAW.TRTSDT.	Predecessor
ADSL	TRTSDTM	Datetime of First Exposure to Treatment	numeric	ISO 8601	Copy value from ADSL_RAW.TRTSDTM.	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

R

Edit3 lines

1library(yamaa)23yamaa_spec("spec.yaml")$run()$output

Python

Edit3 lines

1import yamaa23yamaa.spec("spec.yaml").run().output

input/adsl_raw.json

expected/adsl.csv

Your review

Mapping spec

yamaa code

R

Python