Dear DDI Alliance community,
We are ready to vote on Version 1.0 of SDTL (Structured Data Transformation Language) for publication. Next week, Designated Member Representatives will receive a link to cast their votes. It is important to vote; a two-thirds majority is required for approval.
SDTL is a new product for DDI and we are asking you to approve its inclusion in the product line of the DDI Alliance. Over the past year this product has provided the Technical Committee with:
- Statement of content and functionality
- Information on the business case for the product
- Objectives for the product
- Position within the suite of products supported by the DDI Alliance
- A maintenance plan for the product
The public review was completed between July 1 through August 31, 2020. All issues have been addressed and the product prepared for the final vote on publication.
To assist you in evaluating this product for inclusion in the suite of DDI products a webinar will be presented on
Monday, November 2, 2020 at 10:00 Eastern Time (16:00 European Time)
To join the Webinar:
Meeting ID: 820 4137 8650
Find your local number: https://us02web.zoom.us/u/
The following content should also be informative:
- SDTL User Guide: http://c2metadata.gitlab.io/
- Introduction to SDTL: https://deepblue.lib.umich.
- Overview of the C2Metadata Project: https://deepblue.lib.umich.
- SDTL Working Group: https://ddi-alliance.
atlassian.net/wiki/spaces/ DDI4/pages/899547182/SDTL+-+ Structured+Data+ Transformation+Language
SDTL is an independent language for representing data transformation commands in statistical analysis packages, such as SPSS, Stata, SAS, R, and Python. Commands like RECODE, MERGE FILES, and VARIABLE LABELS are rendered in a structured format (JSON, XML, RDF) that is easy for machines to read and process. Command scripts translated into SDTL produce variable-level data transformation histories, which can be translated into natural language. SDTL can be added to Data Documentation Initiative (DDI) and other metadata standards for use in data catalogs, codebooks, and other documentation.
We wish to thank George Alter and the SDTL Working Group, as well as the Technical Committee, for the preparation of the SDTL product for publication.