UK Data Archive

UNSTRUCTURED During public health crises, the significance of rapid data sharing cannot be overstated. In attempts to accelerate COVID-19 pandemic responses, discussions within society and scholarly research have focused on data sharing among health care providers, across government departments at different levels, and on an international scale. A lesser-addressed yet equally important approach to sharing data during the COVID-19 pandemic and other crises involves cross-sector collaboration between government entities and academic researchers. Specifically, this refers to dedicated projects in which a government entity shares public health data with an academic research team for data analysis to receive data insights to inform policy. In this viewpoint, we identify and outline documented data sharing challenges in the context of COVID-19 and other public health crises, as well as broader crisis scenarios encompassing natural disasters and humanitarian emergencies. We then argue that government-academic data collaborations have the potential to alleviate these challenges, which should place them at the forefront of future research attention. In particular, for researchers, data collaborations with government entities should be considered part of the social infrastructure that bolsters their research efforts toward public health crisis response. Looking ahead, we propose a shift from ad hoc, intermittent collaborations to cultivating robust and enduring partnerships. Thus, we need to move beyond viewing government-academic data interactions as 1-time sharing events. Additionally, given the scarcity of scholarly exploration in this domain, we advocate for further investigation into the real-world practices and experiences related to sharing data from government sources with researchers during public health crises.

This iterative working paper presents a set of high-level interactive components necessary for a functioning FAIR ecosystem. Operational repositories, and other (meta)data related services are supported by common reference information managed in registries, and by evaluation processes consisting of standards and assessment methods. <br> Findable, Accessible, Interoperable and Reusable digital objects (data and metadata) are a necessary input to, and output from, ecosystems that integrate (one provider serves many) or federate (many providers interoperate) services. <br> Users of all kinds must be empowered to find and access (meta)data that supports interoperability and reuse. All ecosystem actors depend on machine and human-mediated evaluation of digital objects’ FAIRness.

The <em>CoreTrustSeal+FAIRenabling Capability Maturity (CapMat)</em> model aligns the CoreTrustSeal Requirements with the FAIR Data Principles, allowing repositories to self-assess their practice and associated evidence with a view to their development and improvement. The FAIR Data Principles define the expectation that digital objects should be findable, accessible, interoperable and re-usable. Repositories provide the organisational context for enabling FAIR data. Trustworthy digital repositories (TDR), such as those certified to the CoreTrustSeal, offer long-term digital preservation services that can ensure digital assets remain FAIR over time. This text describes the alignment between the 15 FAIR Data Principles and the 16 CoreTrustSeal Requirements so that repositories can self assess their trustworthiness and FAIR enabling status together. The associated capability-maturity model measures the repository in terms of the policies and procedures used to deliver (meta)data services. FAIR enabling, trustworthy digital repositories are acknowledged as key nodes in the research data lifecycle and in networks of federated data infrastructures, including the European Open Science Cloud (EOSC). The community development of indicators, metrics and automated systems for assessing compliance with the FAIR Data Principles is ongoing and to date, no formal standard, process and governance structure is in place to certify FAIR objects or FAIR enabling repositories. The <em>CoreTrustSeal+FAIRenabling CapMat</em> self-assessment can be used immediately by repositories seeking to to identify current levels of capability and to plan for increased maturity. Applying this approach supports the readiness of a repository for formal CoreTrustSeal certification. Other types of data service can also use it to prepare for future assessments of FAIR enabling capability. Development of the <em>CoreTrustSeal+FAIRenabling CapMat</em> model within FAIRsFAIR has benefited from a range of internal perspectives including policy administration, data services, repository support, registry developments, and guidance and testing for object FAIRness. Iterations of the model have been improved through engaged and informed public feedback. This paper presents guidance for those applying the <em>CoreTrustSeal+FAIRenabling CapMat</em> model and provides detailed alignment of the CoreTrustSeal Requirements, FAIR Principles and Capability-Maturity levels. Concluding remarks reflect on the next steps for FAIRenabling TDRs, trustworthy data services and FAIR certification.

This draft working paper has been prepared by members of the SSHOC, EOSC Nordic and FAIRsFAIR projects working on related topics. It seeks to present some key concepts and expand on them to specify the standards and assessments required for an interoperable ecosystem of FAIR (findable, accessible, interoperable and reusable) data preserved for the long term in generalist and specialist FAIR-enabling trustworthy digital repositories (TDR) for a defined designated community of users. It seeks to provide context and define these concepts for audiences familiar with research data and technical data management systems, but with less direct experience of digital preservation and trustworthy digital repositories. This is intended to help clarify which organisations are potential candidates to receive CoreTrustSeal TDR status and to identify and support the types of organisations that may not be candidates but also play a vital role in the data ecosystem. A public comment version is available in Google Document Format.

Interview data is multimodal data: it consists of speech sound, facial expression and gestures, captured in a particular situation, and containing textual information and emotion. This workshop shows how a multidisciplinary approach may exploit the full potential of interview data. The workshop first gives a systematic overview of the research fields working with interview data. It then presents the speech technology currently available to support transcribing and annotating interview data, such as automatic speech recognition, speaker diarization, and emotion detection. Finally, scholars who work with interview data and tools may present their work and discover how to make use of existing technology.

This document represents the third alignment of CoreTrustSeal to FAIR requirements to inform repositories seeking to enable FAIR data. Further context is presented in the FAIRsFAIR project milestone: https://doi.org/10.5281/zenodo.4003598.

Aligning the CoreTrustSeal Requirements with an assessment of repositories' ability to enable FAIR data is an important part of delivering an EOSC. Trustworthy Digital Repositories (TDR) which enable FAIR data are a dependency for many components of modern, open, distributed research. This paper sets the work within the wider context of data infrastructures, describes the co-dependencies between (meta) data objects and their repository environment, and presents the developing mapping between requirements and principles. The evolving capability/maturity approach is explained and the design of a governed assessment and certification process is defined. This work will iterate alongside the wide range of ongoing data infrastructure initiatives to support a range of stakeholders on their journey towards trustworthy repository services that enable FAIR data. Extensive engagement and feedback are planned to allow us to reach this goal.

&lt;p&gt;This paper is milestone 4.1 of the FAIRsFAIR task 4.1 (Capability Maturity models towards FAIR Certification) within the FAIR Certification work package (WP4).&lt;/p&gt; &lt;p&gt;This document presents the first iterative step in aligning the characteristics of FAIR digital objects with the repositories that &lsquo;enable&rsquo; FAIRness, through the CoreTrustSeal Trustworthy Data Repository Requirements&nbsp;and the application of a capability/maturity evaluation approach.&lt;/p&gt; &lt;p&gt;&nbsp;&lt;/p&gt;

As increasingly sophisticated new technologies for working with numbers, text, sound and images come on stream, there is one type of data that begs to be explored by the wide array of available digital humanities tools, and that is <strong>interview data</strong>. (De Jong, 2014), (Corti and Fielding, 2016). A stream of concerted activities by the authors of this workshop based on their engagement with interview data from different perspectives, led to a series of 4 workshops from 2016 to 2018, held in Oxford, Utrecht, Arezzo and München, that were funded by CLARIN (Common Language Resources and Technology Infrastructure). This workshop intended to present the motivation for this DH2019-workshop by sketching the complex design and results of these series of multidisciplinary workshops. The premise was that the multimodal character (text, sound and facial expression) and multidisciplinary potential of interview data (history, oral and written language, audio-visual communication) is rarely fully exploited, as most scholars focus on the analysis of the textual representation of the interviews. This might change by getting acquainted with scholarly approaches and conventions from other disciplines.