News

Over the past few years, the DDI Alliance has developed and adopted a broad product line, including Codebook, Lifecycle, Controlled Vocabularies, XKOS, and most recently the Structured Data Transformation Language. The term “DDI” no longer refers to a single metadata standard or schema, but has evolved to refer to a suite of different data management tools. This evolution is being reflected in the ongoing reorganization of the DDI Alliance website (https://ddialliance.org/products) to highlight and describe the different products offered by the DDI Alliance.

The DDI Marketing and Partnerships Group, in collaboration with the DDI Training Group, has developed a series of new product logos to differentiate the components of the DDI product suite (https://ddialliance.org/membership/promoting-ddi/logos). We hope the community will embrace the new logos and use the information on this page to promote the DDI product suite.

Members of the DDI Alliance will be participating in the "Standards in Action: Emergence of Data Standards to Drive Business Outcomes" session of the Government of Canada Data Conference 2021 (19 Feb at 14:00 EST).  Cory Chobanik, from Statistics Canada, is organizing and moderating the session. Dan Gillman, from the U.S. Bureau of Labor Statistics, is a panelist. The panel will explore the value and how to boost the adoption of common data standards across government.  

For program and registration details, visit the conference web page.

As we enter 2021, we want to acknowledge the many DDI accomplishments in 2020 made possible by our passionate community volunteers and our 40+ member organizations.  Achievements include:

DDI Lifecycle 3.3 public release. The result of six years of work addressing new requirements filed by the DDI community, DDI Lifecycle 3.3 offers better coverage for research and data management including non-survey data collection, sampling, and classification management. 

New DDI product: SDTL. The Alliance membership approved an addition to the DDI product suite called Structured Data Transformation Language (SDTL). Designed as another tool to facilitate a DDI-based workflow through the research data lifecycle, SDTL provides machine-actionable descriptions of variable-level data transformation histories derived from any data transformation language (SPSS, SAS, Stata, R, etc.).  The same scripts that are used to transform and manage variables and data files can be used to update metadata files, thereby increasing efficiency in the research process and reducing information loss.

DDI-CDI public review. The Alliance announced the public review of a new specification called DDI – Cross Domain Integration (DDI-CDI). DDI-CDI is a model-driven specification that is designed to provide support for integrating data across domain and disciplinary boundaries, describing disparate data sources, and documenting their provenance. DDI-CDI is technology-agnostic and adaptable to any platform or representation, designed to meet emerging needs for the integration of old and new forms of research data. The development of DDI-CDI also spurred new partnerships with open science organizations like CODATA.

International standards. The DDI Alliance is now a category A liaison to ISO/TC46/SC 4, This moves DDI-Codebook and DDI-Lifecycle closer to being recognized as official International Standards Organizations (ISO) standards. DDI work products are now also catalogued in FAIRsharing, a curated resource on data and metadata standards dedicated to making research data Findable, Accessible, Interoperable and Reproducible. 

Organizational updates. The Alliance Bylaws were updated to reorganize the Scientific Board and a new Scientific Board was elected.  These changes will improve the management and direction of the Alliance’s scientific and technical work activities.   The Training Group was refreshed with new co-chairs and many new members.  Finally, the Alliance website was reorganized to improve navigation and increase its usefulness for novices and experts alike, assigning responsibility for content to specific working groups. 

As we start a new year, we look forward to building on these accomplishments and connecting with many of you on Alliance activities and at meetings in the coming year.

CODATA, the Committee on Data of the International Science Council, and the Data Documentation Initiative (DDI) Alliance recently signed a letter of collaboration building on the longstanding and ongoing collaboration between the two organisations. The letter details activities where the two organisations intend to cooperate and support each other in the achievement of their common objectives. Each of the organisations has become a dues-paying member of the other organisation and intends to work as an active member of that organisation's activities.

Furthermore, to strengthen cooperation, Steve McEachern, Chair of the Executive Board, will be the DDI Alliance delegate to CODATA, while Simon Hodson, Executive Director of CODATA, is a candidate for the Scientific Board of the DDI Alliance.

Two developments make this collaboration opportune and appropriate.  On the one hand, CODATA has been mandated by the International Science Council, as part of the latter’s Action Plan 2019-2021, to develop and implement a global, decadal programme entitled ‘Making Data Work for Cross-Domain Grand Challenges’. The objectives of the programme are multiple, but first among these is to assist cross-domain research and policy monitoring by facilitating the interoperability, combination and integration of data from many different sources. The mechanisms being explored to assist this are also multiple and include: developing conceptual and semantic alignment between key metadata specifications, developing semantic tools to assist interoperability, developing methodologies and processes to assist harmonisation and data ‘cleaning’, and exploring the contribution that machine learning can make to some of these processes.

On the other hand, DDI - Cross Domain Integration (DDI-CDI) is a potentially significant development which includes definitions for core concepts, applicable across a range of research areas, and which will therefore greatly increase interoperability between data sets.  Taken alongside developments such as DCAT, Schema.org, initiatives to standardise representation of experiments, observations and measurements, and the improved management of ontologies, DDI-CDI can make a positive contribution across domains and the collaboration between the DDI Alliance and CODATA is very timely.

In the recent past, the DDI Alliance and CODATA have engaged in a number of activities together:

• Jointly organized two "cross-domain" workshops on metadata standards, hosted at Schloss Dagstuhl in Wadern, Germany (2018, 2019).
• CODATA has supported the DDI-CDI public review, helping to promote and conduct a series of webinars since May 2020 with an attendance of over 400 people across a wide range of domains. (Given the need for input to the specification from other domains, CODATA's connections were invaluable in identifying the right group of participants.)
• CODATA is leading an EOSC project to produce a report on the application of DDI-CDI within the EOSC environment, working with members of the DDI Modelling, testing and Representation (MRT) working group.
• Engaged in supporting projects which are implementing DDI, as part of the Decadal Programme ("Making Data Work for Cross-Domain Grand Challenges"). Notably, the INSPIRE Network development in east Africa is serving both as a test case for using DDI Codebook and DDI-CDI as part of their implementation, and acting as one of the initial use cases for the Decadal Programme on the subject of infectious disease. 
• Discussed collaboration between the DDI Training Committee and various training programs on the topic of research data management which CODATA participates in, including the RDA-CODATA School of Research Data Science and work with FAIRsFAIR and GO FAIR around their competence centers in this area. A DDI introductory training workshop (51 participants) was conducted as part of the International FAIR Convergence Symposium in December 2020 as a first step in this collaboration.
• DDI in general and DDI-CDI in particular featured in a number of other sessions at the FAIR Convergence Symposium including a high level panel setting the key technical themes for the week (over 200 participants), a session on combining infectious disease data, including the INSPIRE Network above (over 30 participants) and a session on FAIR data provenance (over 60 participants).

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ABOUT THE DDI ALLIANCE

The DDI Alliance shares a commitment to meet worldwide demand for publicly available standards and semantic products that support the documentation and integration of social science data and other data necessary for understanding the human condition. The DDI Alliance is an international collaboration dedicated to establishing metadata standards and semantic products for describing social science data, data measuring human activity, and other data derived from observational methods. The DDI Alliance is composed of its Member and Associate Member Organizations and is governed by an Executive Board, a Scientific Board, and a Technical Committee. In addition, there are working groups that focus on developing specific areas of DDI.

ABOUT CODATA

CODATA’s mission is to connect data and people to advance science and improve our world. CODATA is an international non-profit organization mobilizing a global expert community. As the Committee on Data of the International Science Council (ISC), CODATA supports the ISC mission of advancing science as a global public good by promoting the availability and usability of data for all fields of research. CODATA works to advance the interoperability and the usability of such data: research data should be FAIR (Findable, Accessible, Interoperable and Reusable). CODATA promotes the principle that data produced by research and of potential value for research should be as open as possible and only as closed as necessary. CODATA pursues its mission through strategic activities to build consensus and implement good practice, and through advancing data policies, data science and data education.