an independent intermediate language for representing data transformation commands for data stored in statistical analysis packages.
as a machine actionable description of variable level data transformation
a representation of the SDTL model in different formats
a crosswalk between a standard SDTL representation of each function and the implementations of that function in various statistical languages (the Function Library)
a human readable translation of SDTL commands (the Pseudo-Code Library)
Comments are requested on the content and scope of SDTL
SDTL Command Language
Function Library
Pseudo-Code Library
Documentation
Starting point of review is through the SDTL User Guide
Structured Data Transformation Language (SDTL) is an independent intermediate language for representing data transformation commands. Statistical analysis packages (e.g., SPSS, Stata, SAS, and R) provide similar functionality, but each one has its own proprietary language. SDTL consists of JSON schemas for common operations, such as RECODE, MERGE FILES, and VARIABLE LABELS. SDTL provides machine-actionable descriptions of variable-level data transformation histories derived from any data transformation language. Provenance metadata represented in SDTL can be added to documentation in DDI and other metadata standards.
SDTL greatly enhances the value of DDI, because it is a key component of an automated metadata production process. Currently, DDI metadata is almost always created by data repositories not by data producers. Even when data are born digital, data producers discard provenance information that could be transported into DDI, because they do data management and variable transformations in statistical packages with minimal metadata capabilities. SDTL and the tools created by the C2Metadata Project are designed to create a metadata life cycle that parallels the data life cycle. The same scripts that are used to transform and manage data files can be used to update metadata files. As a result, data producers can create more accurate and complete DDI metadata with less time and effort for them and for data repositories.
Areas of interest for review:
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July 1 - August 31, 2020
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