Digital Curation Centre

The evolution of research data management (RDM) practices, driven by policy changes, technological advances, and the growing emphasis on FAIR (Findable, Accessible, Interoperable, Reusable) data principles, has created significant skills gaps within the community of RDM professionals. The FAIR's FAIR Impact Award Scheme, coordinated by the Digital Curation Centre at the University of Edinburgh as part of the international FAIR Impact consortium project, represents an innovative response to these challenges, offering a structured mentorship programme that pairs experienced practitioners with emerging professionals to foster knowledge transfer, skill development, and community building within the RDM ecosystem.

FAIR-IMPACT aims to realise a FAIR EOSC using proven solutions, tools and methods developed during the FAIRsFAIR and other initiatives. One of the goals of the project is to enable the ‘FAIRification’ of different research objects such as datasets, software and semantic artefacts originating from a large range of scientific disciplines. This includes the provision and extension of FAIR assessment metrics and associated tools and their adoption to the needs and requirements of a variety of research communities. In particular FAIRsFAIR data object assessment metrics as well as the F-UJI tool are intended to become more disciplinary-context aware and to include more discipline-specific tests in cooperation with FAIR-IMPACT use case partners, domain data repositories, research infrastructures and e-infrastructures. This deliverable provides the first set of discipline specific tests and metrics developed in cooperation with FAIR-IMPACT Social Sciences and Humanities (SSH) use case partners. We present an analysis of SSH community FAIR-aligned habits and practices carried out using available literature and whitepapers, data collected using standard interfaces provided by the community, as well as FAIR Implementation Profiles (FIPs) from a number of SSH data repositories. Based on this analysis we identified an appropriate SSH sub-community, the social sciences, for which we defined a set of discipline specific metrics and tests derived from the FAIRsFAIR data assessment metrics which are also presented in this deliverable.

<p>The NFDI4Ing Conference 2022 took place online on 26 and 27 October 2022.</p>\n\n<p>The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) - project number 442146713.<br>\n </p>\n\n<p>If not stated otherwise within the respective document, the files go by CC-BY 4.0 license.<br>\nPlease refer to the license comments in the resprective slide decks.</p>

The NFDI4Ing Conference 2022 took place online on 26 and 27 October 2022. The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) - project number 442146713.<br> If not stated otherwise within the respective document, the files go by CC-BY 4.0 license.<br> Please refer to the license comments in the resprective slide decks.

Automotive radar systems are improving road-user safety by their integration into collision avoidance systems. Often these radar systems need a high probability of detection using superior range and angular resolution. In this paper, the concept of highly separated radar antennas is introduced in the context of automotive radar systems. This novel technique can improve detection accuracy due to the processing gain improvement of the system, reduced antenna inter-element coupling and increased diversity gain by the combination of multiple-input multiple-output (MIMO) antennas at highly separated distances. The diverse spatial scattering reflected from the targets improves the overall response and in this paper results are compared for $\mathrm{a}0.5 \lambda$ and 45 cm separation for an frequency modulated continuous wave (FMCW) multiple input multiple output (MIMO) antenna system using substrate-integrated waveguide (SIW) receiver antennas.

Data interoperability is key to the FAIR principles, yet can be challenging to put into practice. This document provides guidance on practices involved in achieving data interoperability, more specifically, practices around data citation, persistent identifiers (PIDs), semantic resources, and metadata. All of these create a data interoperability framework and are important building blocks of a FAIR ecosystem. The purpose of such a framework is to set some specific requirements for the digital objects that it will be applied to. Generally these include that the digital object/data need to be accompanied by standardised metadata for it to be cited and be unambiguously identified, using a persistent identifier. The metadata should also describe the object according to a community-endorsed vocabulary, richly enough for it to be understandable and reusable by anyone in that community. In addition, the data files that comprise the object need to be represented in common and open formats. ACME-FAIR is a 7-part guide developed in the FAIRsFAIR project, whose main purpose is to help managers of Research Data Management and related professional services to self-assess how they are enabling researchers, and the professional staff who support them, to put the FAIR data principles into practice (for short we refer to this as ‘FAIR-enabling practice’). This part addresses the key issue of <strong>Defining Data Interoperability Frameworks. </strong>

To put the FAIR principles into practice research projects need to make choices, assisted by the professional support staff in their organisations. Research performing organisations (RPOs) therefore need to offer a supportive environment for selecting data, services, and repositories. Consider these choices from the investigators’ point of view. As a researcher, how should my project decide which data, of all that it produces, it should focus effort to make FAIR? Of all the services available, how do we identify some that will help manage data in a FAIR way? And which of the multitude of repositories we could potentially use to safeguard the data at the end of the project should we choose to do that? This guide aims to help organisations assess the support they offer researchers, data stewards and others whose role involves making such choices.

This seminar is part of a series to provide societies and their journals with information and resources to help their communities be more knowledgeable and prepared to share data (and software) in a way that is relevant and meaningful for each discipline. This is a 12-month series. <strong>The Data You Document are the Data We Love</strong> 5 November 2021, 10am ET (1400 UTC) <strong>Speakers:</strong> <strong>Joy Davidson</strong>, Coordinator, Digital Curation Centre (DCC) <strong>Eva Méndez</strong>, Associate Professor, Universidad Carlos III de Madrid <strong>Moderator:</strong> <strong>Zsolt Silberer</strong>, Director of Publishing, Ecological Society of America <strong>Description:</strong> Our research data are critical to our work. We don’t always document them as well as we should. If we restructure our thinking that our research data are just as important a contribution to the scientific record as our peer-reviewed papers, then what would “well-documented” data look like? Our speakers Joy Davidson and Eva Méndez will share current work on helping researchers improve their data documentation (i.e., metadata) and give us reasonable steps as societies to help encourage our researchers with their own documentation practices. Seminar Recording: https://youtu.be/SoFxBN-Jnbg

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.

&amp;lt;p&amp;gt;This report is the seventh&amp;nbsp;in a series of SPARC Europe and DCC analyses of national Open Science policies in Europe and covers the period between September 2020 and April&amp;nbsp;2021. This issue provides an update on activity across European Member States and&amp;nbsp;relevant countries from the European Research Area. This issue includes a new section on the Open Data Directive and Horizon Europe as well as&amp;nbsp;updates on&amp;nbsp;policy-related activities among the&amp;nbsp;European Commission supported INFRAEOSC 5b projects.&amp;nbsp;&amp;lt;/p&amp;gt;\n\n&amp;lt;p&amp;gt;To access previous versions of the analysis of Open Data and Open Science policies in Europe and&amp;nbsp;other SPARC Europe reports related to Open Data please see&amp;nbsp;&amp;lt;a href="https://sparceurope.org/what-we-do/open-data/sparc-europe-open-data-resources/"&amp;gt;https://sparceurope.org/what-we-do/open-data/sparc-europe-open-data-resources/&amp;lt;/a&amp;gt;.&amp;lt;/p&amp;gt;