Even though considerable progress with making research data and also research data processing services more FAIR, there is still lacking sufficient detail in the description of workflows, data and services for their immediate application and they are mostly not machine-actionable. CLARIN has developed the Language Resource Switchboard helping users find and invoke specific services for specific types of data. We consider and explore mapping this approach onto an FDO architecture and see what additional gains can be made.

CLARIN stands for “Common Language Resources and Technology Infrastructure”. In 2012 CLARIN ERIC was established as a legal entity with the mission to create and maintain a digital infrastructure to support the sharing, use, and sustainability of language data (in written, spoken, or multimodal form) available through repositories from all over Europe, in support of research in the humanities and social sciences and beyond. Since 2016 CLARIN has had the status of Landmark research infrastructure and currently it provides easy and sustainable access to digital language data and also offers advanced tools to discover, explore, exploit, annotate, analyse, or combine such datasets, wherever they are located. This is enabled through a networked federation of centres: language data repositories, service centres, and knowledge centres with single sign-on access for all members of the academic community in all participating countries. In addition, CLARIN offers open access facilities for other interested communities of use, both inside and outside of academia. Tools and data from different centres are interoperable, so that data collections can be combined and tools from different sources can be chained to perform operations at different levels of complexity. The strategic agenda adopted by CLARIN and the activities undertaken are rooted in a strong commitment to the Open Science paradigm and the FAIR data principles. This also enables CLARIN to express its added value for the European Research Area and to act as a key driver of innovation and contributor to the increasing number of industry programmes running on data-driven processes and the digitalization of society at large.

This paper gives a summary of implementation activities in the realm of FAIR Digital Objects (FDO). It gives an idea which software components are robust and used for many years, which components

Background In the past decade many different national, EU and global projects have been successful in raising awareness about Open Science and the importance of making data findable and accessible such as stated in the FAIR principles (Wilkinson et al. 2016).In this respect, there have been many advances with respect to options for discovering data. A multitude of either thematic or general catalogues are providing faceted browsing interfaces for humans and Application Programming Interfaces (APIs) for use by machines and similarly, data-citations in publications offer references to resources hosted by repositories. However, using such catalogues and data-citations, researchers are not guaranteed to obtain access to the data itself. Mostly the resource link in the catalogue (and also in the metadata) or citation is a “landing-page”, a description of the resource meant for human consumption. The landing-page may contain instructions how to access or download the resource itself but usually it is difficult to parse by machines.FAIR data accessThus the approach sketched above does not meet the requirements in scenarios where applications need assured and quick access to data. Also the FAIR principles interpretation from GO FAIR states*1 that these “emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.” The requirement for providing a Persistent Identifier (PID) for a resource*2, is mostly interpreted as meaning a PID for the resource’s metadata or landing-page only. Note that we ignore the need for user authentication and authorization prior to accessing data, here we will only consider data that is ‘freely’ accessible.To improve the situation with respect to machine data accessibility a number of technologies and approaches that have been discussed in the CLARIN and Social Sciences and Humanities (SSH) infrastructure domain can be useful. We present some and comment on their suitability.SignpostingSignposting*3 is a technology proposed by van de Sompel (Sompel and Nelson 2015) to release relevant technical and bibliographical attributes from a resource URI. It's well described, and uses the HTTP protocol to provide additional information via HTTP Link Headers*4. Alternatively, for HTML type resources, the information may also be provided in HTML Link elements. In the CLARIN community the signposting concept was accepted, but its proposed implementation deviated from van de Sompel and made it less dependent on the HTTP protocol (Arnold et al. 2021). However on the downside, the signposting information is embedded in the CLARIN specific Component Metadata (CMDI) (Broeder et al. 2012), and so makes it CLARIN specific, or at least requires clients to have specific knowledge about CMDI.CLARIN Digital Object Gateway (DOG)One approach that is currently worked on for the CLARIN research infrastructure is the creation of a DOG library*5 and (later) a service that provides a proxy gateway from the resource PID to the actual data. DOG uses implicit knowledge about the different repository solutions that are used by the CLARIN B-type centres*6 and some repositories outside the CLARIN infrastructure. DOG works in two steps: first obtaining metadata from the resource PID and secondly extracting resource links from the metadata. Each of the repositories registered within DOG has a minimal configuration specifying how to parse fields of interest from the resource's metadata. For B-type CLARIN centres DOG uses content negotiation as the primary way of obtaining the metadata in CMDI format. For repositories outside the CLARIN infrastructure, DOG primarily relies on the API provided by the repository in order to access metadata and data resources. The DOG solution does have scalability problems, but within the limited domain of CLARIN centres, it can offer a solution until a better one becomes available.Limited PID kernel informationThe (limited) PID kernel information approach assumes that for every Digital Object (DO) (Berg-Cross et al. 2015) and its metadata a Handle type PID (CNRI 2020) is issued and that the Handle information record can be used to store and associate additional important information with the (meta) data PID using handle value types such as for example a checksum and references to the data or metadata. This is a simplification of the architecture proposed in the work done in RDA context: PID Kernel Info recommendations (Weigel et al. 2018). Consistent use of Handle information records could solve the data access problem, but just as for the signposting strategy, it requires strong discipline to maintain the additional information source. Examples from smaller projects and repositories exist that do manage this information in the Handle record eg. the DARIAH-DE repository*7. FAIR Digital Objects (FDO)FDOs*8 attempt to overcome the data management challenges posed by the heterogeneity and complexity of data using a combination of abstraction, virtualization and encapsulation (Schwardmann 2020). In practice, in the context of our access to data problem, the FDO solution can be seen as both a generalization and upgrade of the PID kernel information approach. The key characteristics here are the (conceptual) encapsulation of data objects with data structure and services that allow aware applications to recognize the data objects metadata and bitstream format, and process as intended by the programmer. Eligible data processing services, either general ones from communities, can be found through the FDO typing mechanism, or can be directly linked from the FDO.A rich set of FDO attributes permit signaling machines processing FDOs where and how to access bitstream data including for instance additional information about supported protocols and APIs.What to do?For our community and in our collaboration with others, we need solutions now but would prefer not to invest and get closed in unscalable technologies.We would propose to combine the DOG approach with signposting. First testing URIs (obtained by resolving the Handle PID) for the presence of HTTP Link Headers. If these are missing, (extended) DOG could use its idiosyncratic workflow. Long term we see advantages of the general, scalable and protocol independent approach that FDOs offer. Hybrid solutions are conceivable where FDO proxies can sit between the FDO machinery and data hosted by signposting compliant repositories.

This report documents the programme and the outcomes of Dagstuhl Perspectives Workshop 18472 "Implementing FAIR Data Infrastructures". The workshop aimed at bringing together computer scientists with digital infrastructure experts from different domains to discuss open issues implementing and adopting the FAIR principles in research data infrastructures and to shape the role that the field of computer science has to play.

This report is the first deliverable of Task 8.2 “Trust & Quality Assurance” within WP8 of the SSHOC project. The distributed character of data infrastructures within the SSHOC communities requires developing an agreed approach to assessing the trustworthiness and quality of data repositories. This deliverable provides an overview of Trusted Digital Repository (TDR) standards offering a certification framework for communities represented in the SSHOC project (CESSDA, CLARIN, DARIAH, E-RIHS). Moreover, the deliverable lays the ground for the SSHOC trust work that is needed in order to facilitate the adoption of TDR standards and the FAIR principles in SSH data repositories across the board. In this report, ‘trust’ refers to the landscape of issues, standards and processes related to trustworthy digital repositories. Trust between all parties in the quality of data and services is critical for research infrastructure in terms of people, processes and technologies. The level of trustworthiness can be assessed through evaluation against agreed requirements.

Since 2009 initiatives that were selected for the roadmap of the European Strategy Forum on Research Infrastructures started working to build research infrastructures for a wide range of research disciplines. An important result of the strategic discussions was that distributed infrastructure scenarios were now seen as “complex research facilities” in addition to, for example traditional centralised infrastructures such as CERN. In this paper we look at five typical examples of such distributed infrastructures where many researchers working in different centres are contributing data, tools/services and knowledge and where the major task of the research infrastructure initiative is to create a virtually integrated suite of resources allowing researchers to carry out state-of-the-art research. Careful analysis shows that most of these research infrastructures worked on the Findability, Accessibility, Interoperability and Reusability dimensions before the term “FAIR” was actually coined. The definition of the FAIR principles and their wide acceptance can be seen as a confirmation of what these initiatives were doing and it gives new impulse to close still existing gaps. These initiatives also seem to be ready to take up the next steps which will emerge from the definition of FAIR maturity indicators. Experts from these infrastructures should bring in their 10-years’ experience in this definition process.

CLARIN is a European Research Infrastructure providing access to language resources and technologies for researchers in the humanities and social sciences. It supports the use and study of language data in general and aims to increase the potential for comparative research of cultural and societal phenomena across the boundaries of languages and disciplines, all in line with the European agenda for Open Science. Data infrastructures such as CLARIN have recently embarked on the emerging frameworks for the federation of infrastructural services, such as the European Open Science Cloud and the integration of services resulting from multidisciplinary collaboration in federated services for the wider domain of the social sciences and humanities (SSH). In this paper we describe the interoperability requirements that arise through the existing ambitions and the emerging frameworks. The interoperability theme will be addressed at several levels, including organisation and ecosystem, design of workflow services, data curation, performance measurement and collaboration. For each level, some concrete outcomes are described.

In this paper, we introduce a new type of shared task - which is collaborative rather than competitive - designed to support and foster the reproduction of research results. We also describe the first event running such a novel challenge, present the results obtained, discuss the lessons learned and ponder on future undertakings.