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The EOSC Metadata Cataloguing and Indexing service comprises the management of metadata in the whole life cycle from generation up to uploading and indexing metadata in a searchable catalogue.

The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the outstanding predictive power of artificial intelligence, triggered the application of deep learning to human mobility. In particular, the literature is focusing on three tasks: next-location prediction, i.e., predicting an individual's future locations; crowd flow prediction, i.e., forecasting flows on a geographic region; and trajectory generation, i.e., generating realistic individual trajectories. Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing. This survey provides: (i) basic notions on mobility and deep learning; (ii) a review of data sources and public datasets; (iii) a description of deep learning models and (iv) a discussion about relevant open challenges. Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, and trajectory generation. At the same time, it helps deep learning scientists and practitioners understand the fundamental concepts and the open challenges of the study of human mobility.

This deliverable is the second and final release of the EOSC Service Architecture. It sets the foundations characterising the EOSC System:(i) Its functionalities are provisioned as-a-Service; (ii) It is a highly distributed, evolving and heterogeneous hybrid cloud; (iii)Its operation and development is regulated by a set of Rules of Participation; (iv) It is modelled as an open and evolving System of Systems (SoS) where the component systems providing services include existing and emerging Research Infrastructures (including e-Infrastructures) and other types of Service Providers; (v) EOSC services provision is based on an open and evolving set of EOSC Nodes spread across several organisations and regions; (vi) EOSC Services should promote and support FAIRness. The deliverable identifies 47 classes of services that can be considered at this stage of development as the "Minimal Viable Product" able to match the EOSC overall goal. Such services include cross-cutting services together with services specifically envisaged to serve researchers, research administrators, third-party service providers as well as EOSC managers, service providers and service suppliers. This deliverable briefly highlights major contextual aspects already introduced in D5.1 and then describes the identified classes of services. The deliverable also discusses aspects related to "how" the system can/should be developed. The notions of "federation" and "interoperability" related to the building of this EOSC System are addressed highlighting the importance of dealing with these two concepts per-single service rather than from the perspective of EOSC as a whole.

As a TCOM area, this specification describes Security, i.e. the standards and specifications for operational security, or “cybersecurity.” Ultimately, the purpose of security, in this sense, is to ensure that the infrastructure is trustworthy, and participants are able to carry out their legitimate work and collaborations, while protecting the infrastructure and data from unauthorised parties. In order to ensure that participants in e-infrastructures, research infrastructures, and identity federations (such as those operated by NRENs) can reduce the risk of security incidents, and collaborate on investigating, managing, and resolving security incidents, it is necessary to have a shared security operations framework. Specifically, this will cover • best practices, • security contacts, • processes for assessing severity (and hence urgency), • traceability of users, • defining, updating, and tracking users’ acceptance of acceptable use policies. In addition, the standards cover how the compliance is asserted in a machine readable way. There are also constraints on human readable information but the specification on how to implement these constraints is left to the federation operator and/or participants. It should also be noted that the wider issue of establishing, maintaining, and restoring trust – between organisations, communities, and infrastructures – is not covered here.

Given the dynamic rate of change in the OA books business models landscape, the OPERAS Open Access Business Models Special Interest Group launched a survey in 2021 to 1) improve our understanding of the scholarly publishing landscape and of the challenges that publishers face in the context of publishing OA monographs; and 2) to identify main trends (including opportunities and challenges) and the knowledge of collaborative funding and infrastructure models in OA publishing in Social Science and the Humanities. This white paper updates and expands an earlier version published in 2021, which presented the preliminary analysis of the findings. Despite a small sample of presses meaning that no strong trends ought to be discussed, several insights were drawn and should be considered important directions for the future. Key findings in the report have been grouped into three main areas: collaboration, funding, and support. The report found that, although not opposed to the idea, a majority of presses do not engage in collaboration, specifically collaborative models for shared infrastructure, mainly due to the lack of knowledge and information, or perceived lack of need. This indicates that, for OA books, we are still at the early stage of the adoption curve for collaborative shared infrastructure. In terms of funding, most publishers perceive themselves to be somewhat sustainable. For institutional publishers, parent organisations are crucial as providers of financial or non-monetary support of OA. In addition, most publishers stress the need to have more resources and rely on more than one funding source, including grants and subsidies. The report found that awareness-raising and targeted support and training could be used to engage the presses but further incentivisation may be required to encourage publishers to collaborate more widely. We believe that the insights from this white paper may be interesting to a number of projects, such as DIAMAS, OPERAS-PLUS, and Palomera and have presented areas for further research and more specific actionable points for these projects.

The software quality assurance (SQA) is the process responsible for the overall supervision of the software development lifecycle ensuring that the required quality level is achieved. The SQA encompasses all software development processes starting from the definition of requirements, coding, release, testing and integration. This technical area covers ways to deliver quality software for EOSC consumption and favours the adoption of automated solutions over the traditional manual-based validation mechanisms. The automation allows not only to speed up the development tasks but as well improves the reliability of the developments “ensuring the fast execution of defined tests at each change in the codebase” and keeping them aligned with the initial user requirements and design “Fast feedback received at any development stage - the faster release of quality software”.

The EOSC Accounting service collects, stores, aggregates, and displays usage information of HTC compute, storage space, cloud VM and data set resources. This usage data is collected from the Resource Centres of the EOSC infrastructure. Accounting information is gathered from distributed sensors into a central Accounting Repository where it is processed to generate summaries that are made available through an Accounting Portal. Depending on the use case the data may go via intermediate repositories that collate accounting data for particular regions, infrastructures or communities. EOSC resource centres can either directly publish accounting information to the EOSC central Accounting Repository or via an intermediate repository that can be related to an infrastructure (European, regional or thematic etc.). It is up to the infrastructure decide to have its own accounting infrastructure connected to the EOSC one or directly leverage the EOSC accounting infrastructure.

Services of Cloud IaaS VM Management provide on-demand API-based access to computing resources as Virtual Machines that can run user-defined arbitrary software (including operating systems and applications). Services in this category also allow management of block storage that can be associated to the VMs and network management to provide connectivity between VMs and external networks.

The EOSC-hub project operates many of the core elements of the European Open Science Cloud, and acts as an important facilitator of onboarding providers, users and communities to the EOSC Portal. This document provides a snapshot about the different stakeholders that have been engaged by EOSC-hub until February 2020. The document presents our assessment of the engaged communities from different perspectives: instrument that was used for the engagement; role in the EOSC ecosystem; geographical distribution; science discipline affiliation. Based on the findings we provide recommendations that can help the project strengthen community engagement activities for its remaining lifetime.

The Horizon 2020 project EOSC-Life brings together the 13 Life Science ‘ESFRI’ research infrastructures to create an open, digital and collaborative space for biological and medical research. Sharing sensitive data is a specific challenge within EOSC-Life. For that reason, a toolbox is developed, providing information to researchers who wish to share sensitive data or to use sensitive data. The sensitivity of the data may arise from its personal nature but can also be caused by intellectual property considerations, biohazard concerns, or the Nagoya protocol. The toolbox will not create new content, instead, it will allow researchers to find existing resources that are relevant for sharing sensitive data across all participating research infrastructures (F in FAIR). The toolbox is based upon a tagging (categorisation) system, allowing consistent labelling and categorisation of resources, in terms most relevant to data sharing tasks and activities in the Life Sciences. A first version of the tagging system was provided (version 1), covering 8 dimensions (resource type, research field, research design, data type, stage in data sharing life cycle, geographical scope, specific topics or keywords and targeted group) and evaluated in a pilot study, generating major feedback for improvement of the categorisation system. Consequently, an updated version (version 2) of the categorisation system was provided with 55 tags spread over 6 dimensions, which is described in this report.