George Alter , Darrell Donakowski1, Jack Gager , Pascal Heus2, Carson Hunter2, Sanda Ionescu1, Jeremy Iverson , Hosagrahar V Jagadish1, Carl Lagoze1, Jared Lyle1, Alexander Mueller1, Sigbjorn Revheim , Matthew A. Richardson1, Ornulf Risnes4, Karunakara Seelam1, Dan Smith3, Tom Smith , Jie Song1, Yashas Jaydeep Vaidya1, Ole Voldsater4
The Continuous Capture of Metadata for Statistical Data Project is funded by National Science Foundation grant ACI-1640575.
Structured Data Transformation Language (SDTL) provides a structured format for describing data processing steps in scripts used with statistical analysis software. SDTL creates a machine-actionable representation of the script showing which variables were affected by each command. SDTL can be embedded in DDI metadata files to provide variable- and file-level derivation histories (provenance) for use in data catalogs, codebooks, and other documentation. SDTL operates at a more granular level than most previous data provenance tools, which treat each program as a black box that reads and writes files. SDTL has been defined in JSON schemas for common operations, such as RECODE, MERGE FILES, and VARIABLE LABELS. The C2Metadata Project (http://c2metadata.org/) has developed software that automates the creation of SDTL from program scripts in five leading statistical analysis packages: SPSS, SAS, Stata, R, and Python. SDTL can also be translated into natural language, such as “Set the value of varX to the natural logarithm of varY.”
Benefits of SDTL:
- SDTL fills a gap in DDI standards (Codebook, Lifecycle, Cross Domain Integration). DDI standards include places to insert provenance information, but there has been no standard way to describe how variables are computed, recoded, aggregated, etc.
- SDTL can be translated into natural language, so that data users do not need to understand the programming language that transformed the data.
- SDTL is machine actionable. Application developers can easily add SDTL to existing applications, like data catalogs and codebooks. Future applications can use SDTL to visualize, analyze, and validate the working of data transformation programs. For example, if there is a question about the quality of a question in a survey, SDTL will reveal all of the variables derived from that question.
- The C2Metadata Project has produced software that automates the creation of SDTL from program scripts in five leading statistical analysis packages: SPSS, SAS, Stata, R, and Python.
- SDTL-based tools can be used by both large data collection and distribution projects and by individual researchers. The latter can quickly create annotated codebooks to satisfy data and code documentation requirements of journals.
|||University of Michigan|
|||Metadata Technologies North America|
|||Norwegian Centre for Research Data|