Field-Level Documentation

DDI-CDI Specification

The DDI-Cross Domain Integration (DDI-CDI) specification provides a model for working with a wide variety of research data across many scientific and policy domains. It provides a level of detail which supports machine-actionable processing of data, both within and between systems, and is designed to be easily aligned with other standards.

It focuses on the key elements of the data management challenges facing research today: an exact understanding of data in a wide variety of formats, coming from many different sources. Two elements are critical for dealing with these challenges: a flexible means of describing data that can reveal the connections between the same data existing in different formats, and a means of describing the provenance of the data at a detailed (but comprehensible) level: the processes which produced it must be transparent.

DDI-CDI covers these areas in a fashion intended to make it optimally useful to modern systems, which often employ a variety of models, and comply with a range of related specifications for both functions related to data description and process/provenance. The model is designed to be easy to fit into such systems, by aligning with relevant external standards, and to be align-able with them into the future.

The core model field-level documentation is available in the section DDICDILibrary.

The corresponding Git repository can be found at https://github.com/ddi-cdi/ddi-cdi.

Further information on DDI-CDI is available at the related DDI Alliance web page https://ddialliance.org/Specification/DDI-CDI/.

Purpose

The DDI-CDI specification describes a model and supporting elements for implementing it in the areas of data description and process/provenance. It is not intended to supplant existing specifications for these purposes, but to fill in the information which such specifications often do not capture. For data, this is the description of a single bit of information – a datum – which can be used to play different roles in different data structures and formats. For provenance and process, this is the packaging of specific machine-level processes, which may be described in many different ways, into a structure which relates them to the business processes described at a level understandable to human users.

In order to serve this purpose, the DDI-CDI specification uses a Unified Modeling Language (UML) formalization so that it can be mapped against other models within systems more easily. Several different syntax expressions of the model are made available to support implementation.

Several important features of the specification can be highlighted, to show how it serves this purpose:

  • Domain-independence

  • Datum-Oriented Data Description

  • Provenance and Process Description

  • Foundational Metadata

  • Interoperability, Sustainability, and Alignment with Other Standards

Each of these will be addressed in more detail, and an outline of the specification documents is presented.

Key Features of the Specification

Domain Independence

DDI-CDI is designed to be used with research data from any domain. In order to do this, it is fundamentally based on the structure and other generic aspects of the things it describes. It does not attempt to be a domain model of semantics, nor a model specific to the life-cycle of a particular domain of science or research. (Historically, DDI has focused on the Social, behavioral, and Economic [SBE] sciences and some types of health research – to see how DDI-CDI relates to other DDI specifications, see below.)

DDI-CDI is intended to be complimentary to (and used in combination with) other standards and models which focus more on domain-specific aspects (such as semantics and life-cycle models). Such generic elements such as classifications and variables are given a detailed formal treatment but are agnostic as to the domain. It is left to the user to employ whatever domain semantics are demanded by the data with which they are working.

This feature of the specification makes it well-suited to combining data coming from more than one domain or system, to allow a description that supports systems which perform data integration, harmonization, and similar functions. Cross-domain data sharing is becoming increasingly common, and DDI-CDI is intended to provide support for this type of application.

Datum-Oriented Data Description

DDI-CDI embraces a form of data description which is based on its atomic components: individual datums. Any given datum can play different roles in different formatting of the same data set, depending on how it is processed and transformed. In order to retain the continuity of a given datum across different formats and throughout a series of processes, DDI-CDI allows it to be described playing different roles in different structures.

DDI-CDI provides four basic types of structural description for data sets: wide data, long data, dimensional data, and key-value data. These four types (and their sub-types) provide coverage for many common data formats today. While not comprehensive, they cover the majority of cases that the developers of this specification have seen. These include many of the newer forms of data such as streaming data, “big” data, registers, and instrument data. The underlying approach is one which could – and may be – expanded in future. By assigning appropriate roles to the variables which contain the datums across each of these different formats, however, it is possible to understand how data passes from one form to another.

Provenance and Process Description

If we are to fully understand data, we also need to know how it has been processed and transformed. Given our ability to describe how a different datum can be used in different data sets, it becomes desirable to understand also how those data sets relate to one another in terms of the processes which use them. This can be understood as an important aspect of data provenance.

There are many different ways of describing process and provenance. Popular models include the Business Process Modelling and Notation (BPMN) standard and the PROV Ontology (from W3C). There are a multitude of syntaxes for driving data transformation, cleaning, and analysis in packages such as R, SAS, Stata, MATLab, SPSS, Python, and so on. There are also some emerging standard models for specifically describing such processes (e.g., Structured Data Transformation Language [SDTL], https://ddialliance.org/products/sdtl/1.0, Validation and Transformation Language [VTL], https://sdmx.org/?page_id=5096).

DDI-CDI attempts to do something which complements the use of such models, by connecting specific processes interpretable by machines at the lowest level (described in a package-specific syntax or language) with the higher-level flows which combine these into human-readable documentation of business processes. Both traditional linear (deterministic) processing and the newer declarative (non-deterministic) processing approaches are supported.

Foundational Metadata

In order to formally describe data at a detailed level, there are many component elements which themselves must be modelled. Concepts used for statistical data but also widely applicable – including categories and variables – are a core part of this, but the range is broad. These components are included in DDI-CDI as “foundational metadata.”

Terminology for such constructs varies widely across domains. DDI-CDI has attempted to provide common terms for these components, and to adopt common models from other standards where it seemed useful.

One area which deserves particular attention is the “variable cascade” – a model for how data are described at different points in their creation, processing, and use, which is designed to optimize reuse. While many different models have a “variable” of some form, the one presented in DDI-CDI reflects the experience of working with this important construct in many of the specifications and standards which have preceded it. It is a nuanced view of how variables relate and are understood across different systems, and – although not simple – it is a powerful model which helps solve some of the commonly encountered problems in data description and management.

Interoperability, Sustainability, and Alignment with Other Standards

DDI-CDI is fundamentally a model which is intended to be implemented across a wide variety of technology platforms, and in combination with many other standards, models, and specifications. To support this use, it is formalized using a limited subset of the Unified Modelling Language (UML) class diagram part. The model is provided in the form of Canonical XMI (restricted XML Metadata Interchange) – an interchange format for UML models supporting the import into many different modelling and development tools. Further, a syntax representation is provided in XML Schema, so that direct implementation of the model is possible if needed.

The platform-independence of the model makes it more easily applicable across a broad range of applications and helps ensure that it will be sustainable even as the technology landscape evolves.

DDI-CDI builds on many other standard models and is aligned with them where appropriate. This is shown in the model itself, where formalizations from other models and specifications are refined, extended, or directly used. The specification includes a description of what these other standards and models are, and how they are used in DDI-CDI.

Credits

Members of the Cross Domain Integration (CDI) Working Group shepherded the standard into its final form and produced the final documentation. Listed in alphabetical order they are:

  • Arofan Gregory (chair)

  • Dan Gillman

  • Flavio Rizzolo

  • Hilde Orten

  • Jay Greenfield

  • Joachim Wackerow

  • Larry Hoyle

  • Oliver Hopt

  • Wendy Lee Thomas (Technical Committee contact)

Over 100 people have contributed to the development of the Data Documentation Initiative Cross Domain Integration (DDI-CDI) specification. A more complete description of their contribution to the work can be found at https://github.com/ddi-cdi/ddi-cdi/blob/main/CREDITS.md.