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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.

Comprehensive report of the second year of the StratusLab project.

Cross-section measurements of top-quark pair production where the hadronically decaying top quark has transverse momentum greater than $355$ GeV are presented using 139 fb$^{-1}$ of data collected by the ATLAS experiment during proton-proton collisions at the LHC. The fiducial cross-section at $\sqrt{s}=13$ TeV is measured to be $\sigma = 1.267 \pm 0.005 \pm 0.053$ pb, where the uncertainties reflect the limited number of data events and the systematic uncertainties, giving a total uncertainty of $4.2\%$. The cross-section is measured differentially as a function of kinematic variables characterising the $t\bar{t}$ system and also as a function of variables that characterise the additional radiation in the events. The results are compared with various Monte Carlo generators, including comparisons where the generators are reweighted to match a parton-level calculation at next-to-next-to-leading order. The reweighting improves the agreement between data and theory. The measured distribution of the top-quark transverse momentum is used to set limits on the Wilson coefficients of the dimension-six operators $O_{tG}$ and $O_{tq}^{8}$ in the effective field theory framework. The obtained $95\%$ credibility intervals are $C_{tG} \in [-0.68, 0.21]$ and $C_{tq}^{8} \in [-0.30, 0.36]$. 1-39 (2021).

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.

The LHCb upgrade will take place in the second long shutdown of the LHC, currently scheduled to begin in 2018. The upgrade will enable the experiment to run at luminosities of $2 \times 10^{33}cm^{-2}s^{-1}$ and will read out data at a rate of 40MHz into a exible software-based trigger. All sub-detectors of LHCb will be re-designed to comply with these new operating conditions. This Technical Design Report presents the upgrade plans of the Ring Imaging Cherenkov (RICH) system, the calorimeter system and the muon system, which together provide the particle identication capabilities of the experiment.

This document presents the features implemented for the automatic deployment and dynamic provision of grid services, and for the scalable cloud-like management of grid site resources in the second year of the project. These features, developed largely in Work Package 6 (WP6), are integrated into the StratusLab Toolkit by Work Package 4 (WP4).

The aim of this deliverable is to present a process specification for secure sharing of and access to personalized medicine (PM) data. The intention is that a producer of data can share and the user of the data can gain access to personalized medicine (PM) data in a secure and legal, yet easiest possible manner. For the specification described in this deliverable, close cooperation with the Secure Access Work Package (WP) 5 has been of high relevance. Previous work in WP5 started with the specification of a usage scenario for PM, and the identification of regulations, privacy and security requirements, which were presented by deliverable D5.1 [1]. Deliverable D5.2 further elaborated the work of D5.1 and published templates of relevant forms under http://www.biomedbridges.eu/deliverables/52-0. Next, a security architecture and framework has been developed in WP5 and described in deliverable D5.3. Secure access to and sharing of PM data is one of the most relevant use cases for this architecture. Deliverable D8.1 on its part will massively build upon D5.3. As a follow-up, a proof of concept is planned, which will be covered by a forthcoming deliverable, D8.3. Cooperation with the Technical Integration Work Package 4 will be sought for this step. Deliverable D8.1 relies on the security and privacy architecture which has been developed and put forward in deliverable D5.3 of the Secure Access Work Package 5. This architecture has been developed to support the security and privacy requirements of all the Use Case (UC) WPs, i.e., WP6-10, including WP8 the use case of personalized medicine. Deliverable D8.1 revisits the generic security and privacy architecture presented in D5.3 to address the data management challenges of the BioMedBridges (BMB) project as a whole. It builds upon Usage Scenarios described in D5.1 and on the Data Flow Diagrams (DFDs) described in D5.3. Alltogether, D8.1 can be perceived as a particular “instantiation” of the general security architecture of BMB, with a specific focus on PM. Deliverable D8.1 is structured as follows: Section 3 provides an overview of the background of personalized medicine. Section 4 describes the methodology applied, which essentially follows the approach described in D5.3. Section 5 elaborates on the process specification conducted as a basis of a threat and risk analysis that is described in Section 6. Section 7 then explains the design of the security framework derived from the threat and risk analysis results. Section 8 puts forward processes for secure sharing of and access to personalized medicine data based on work carried out in WP5.