2022 Interoperability for Cross-Domain Research: Machine-Actionability & Scalability

Owned by Joachim Wackerow
Last updated: Sept 28, 2022
6 min read

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Introduction

This workshop builds on the outcomes of three previous Dagstuhl Workshops in 2018, 2019, and 2021 on the alignment of standards and technologies for cross-domain data combination. The first three workshops in this series have produced draft guidelines and use case documentation to provide insight into the cross-domain challenges which form the focus of the ISC CODATA Decadal Programme on ‘Making Data Work for Cross-Domain Grand Challenges’.

Scope and Background

A significant challenge facing the wide-scale implementation of the FAIR principles for data stewardship is the ready availability of metadata in a processable format, with sufficient context to support accurate and informed reuse and harmonisation of that data. This is an ambitious undertaking, and some parts of this challenge are more easily met than others.

Topics and Activities

This workshop will use a set of use cases to inform how a framework of domain-agnostic standards and models can be usefully employed to permit the communication of the needed metadata, and how such metadata can be collected. Integral to this approach
is the idea that both for collection and use, full advantage must be taken of the existing and emerging capabilities of artificial intelligence and machine-actionable metadata harvesting.

The focus of this workshop is three-fold:

  1. Identifying the set of data models and standards which can be shared across domains for purposes of supporting machine actionable use and AI methods.

  2. Exploring strategies for the practical harvesting and dissemination of the metadata, based on the identified standards and existing technology approaches.

  3. Defining the contextual "package" of information which is needed to accurately share, reuse, and harmonise data.

These areas are interconnected, reflecting exploratory work from earlier Dagstuhl workshops over the past several years, and discussions in other fora. The scope spans a variety of concerns in the data sharing space in order to explore a range of current and emerging topics.

The standards and models to be considered will include, but will not be limited to, many which were considered in the 2021 Dagstuhl session Interoperability for Cross-Domain Research: Use Cases for Metadata Standards, which covered the FDOF, DDI-CDI, Schema.org, DCAT, DataCube, SDMX, PROV-O, ODRL, and DPV. More recent work around observable properties, based on the I-ADOPT work in RDA will also be addressed. Relevant cross-domain areas of practice such as DRUM (Digital Representation of Units of Measurement) will also be considered. The relationship of these standards to use-case-specific domain standards and vocabularies will be addressed, in order to explore how the interchangeable expression of metadata can be practically realised at scale.

Cross-domain interoperability framework

The Cross-domain interoperability framework (CDIF) is a set of recommended best practices for using a coordinated set of domain-agnostic standards – most often as specific subsets or profiles of those standards – to support a core set of functions for cross-domain FAIR reuse. The goal of CDIF is – to the greatest extent possible – to build on standards and models which are already in existence, and which have been widely adopted, or are likely to be widely adopted. CDIF does not represent a new standard itself, but is intended to be a set of guidelines for using existing standards and models in a coordinated way, to ensure a degree of FAIR exchange in as automated a fashion as possible.

Use Cases

Real-world use cases will be used to test the ideas put forward in the workshop, and demonstrate their practicability. The final selection of use cases has not yet been finalized, and will depend to some extent on who is available to attend. Currently, the case studies being considered include:

  1. Primary and Reference Data - Integration of reference data and primary data (for example, the European Social Survey case with environmental data; Smart Energy Research Laboratory; projects using geolocated social data, government statistics being integrated across ministries for use as an integrated resource, etc.). The temporal and geographical matches between data streams can be very important here, along with practical approaches to making data useful from the perspective of research questions and potential policy uses.

  2. Sensitive Data/Micro- and Macro-Data - Reuse of microdata across institutional boundaries often conflicts with the need to ensure data confidentiality. Data is often held in disparate systems, complicating access. The linking of aggregate data with the supporting microdata most useful for scientific research is also inhibited by the same barriers. Public health data - especially as regards the recent COVID epidemic - is an example where the microdata themselves require integration across institutions, and feed upward into highly visible and high-demand data such as the SDG indicators. Navigating the links between data at different levels while protecting confidentiality is a difficult challenge which will benefit from agreed approaches and standards.

  3. Oceans and Disasters/Geography and Phenomena Terminology - There has been a lot of work done in the UN agencies (for example in relation to the Ocean Data Information System) and in other domains around harmonizing semantics, and we have also seen some relevant work coming out of the fields of disaster risk reduction, geophysics and environmental monitoring data. Investigation of the effects of climate change is a key element in avoiding disasters and mitigating their impact. This area remains challenging, but it is very important to have a more general approach to combining population data with hard science data in the context of climate change. The idea is to integrate the approaches from the Oceans project and elsewhere into the broader guidance for interoperability.

  4. Describing Physical Samples (not in 2022 workshop) - For many sciences (including biodiversity, crystallography, nanomaterials and geochemistry) the description and characterization of physical samples and specimens is central to the integration and reuse of data. Some progress has been made toward sufficient digital description of samples within specific domains. These approaches can potentially be used as the basis for a more generalized way of describing physical samples to support data sharing across domains. Connecting descriptions of samples, their environment, and their connection to other quantitative data, are all topics of interest.

Deliverables

The workshop will produce recommendations based on the proposed standards and models and on their application to the use cases considered. Proposed next steps for the use cases will be documented, to illustrate the basis on which the recommendations are formed. Any extensions or changes to the standards and models considered will be documented for communication to the relevant groups which maintain them. The intent of the workshop is to provide concrete input into the formulation of a core interoperability framework for FAIR data-sharing taking into account requirements emerging from user-driven communities, large scale infrastructures and significant domain organisations. Although space and logistics at Dagstuhl mean that not all the WorldFAIR project partners and case studies can be included, this event will certainly feed into the work of that project.

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Date and Location

The workshop takes place at Schloss Dagstuhl – Leibniz Center for Informatics on August 28 to September 2, 2022. It has the Dagstuhl event number 22353 and a related web page.

See the separate pages with practical information and information about COVID-19.

Workshop Schedule

Framework

Use Cases

Related Material

DDI-CDI Webinars (including slides and recordings)

Organizers and Participants

Organizers

Participants

  • Ari Asmi, RDA Europe - Finland

  • Darren Bell, UKDA - UK

  • Libby Bishop, GESIS - Germany

  • Adrian Burton, ARDC - Australia

  • Pier-Luigi Buttigieg, AWI - Germany

  • Pierre-Antoine Champin, W3C - France

  • Franck Cotton, INSEE - France

  • Bapon Fakruhddin, Tonkin & Taylor - New Zealand

  • Lara Ferrighi, Met. No. - Norway

  • Tom Gilders, UKDA - UK

  • Luis Gonzalez Morales, UN - USA

  • Lauren Maxwell, Uni. Heidelberg - Germany

  • Laura Molloy, CODATA - France

  • Milan Ojsteršek, Uni. Maribor - Slovenia

  • Steve Richard, Earthcube - USA

  • Flavio Rizzolo, StatCan - Canada

  • Keith Tomlin, LSHTM - UK