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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.

AGINFRA+ aspires to provide a sustainable channel addressing adjacent but not fully connected user communities around Food & Agriculture, by exploiting core European e-infrastructures such as EGI.eu, OpenAIRE, EUDAT and D4Science. To this end, the project develops, extends and provides the necessary specifications and components for allowing the rapid and intuitive development of variegating data analysis workflows, where the functionalities for data storage and indexing, algorithm execution, results visualization and deployment are provided by specialized services utilizing European large-scale, cloud-based infrastructural assets. Furthermore, AGINFRA+ aspires to establish a framework facilitating the transparent documentation, exploitation and publication of research assets (datasets, mathematical models, software components and publications), to enable their reuse and repurposing from the wider research community. Thus, the vision of AGINFRA+ project is to develop a common technical infrastructure that could initially serve three user communities (namely, Agro-climatic and Economic Modelling, Food Safety & Risk Assessment and Food Security) and it could be evolved to an AGINFRA food cloud demonstrator that will be positioned as the European Open Science Cloud (EOSC)1 agri-food thematic cloud. WP8 concentrates on the dissemination of the project and its results among the identified target groups by using online and offline dissemination channels and activities. More specifically, WP8 focuses on creating awareness and engaging further the scientific communities that are related to each one of the three user communities of the project (namely, Agro-climatic and Economic Modelling, Food Safety & Risk Assessment and Food Security) and (b) creating general awareness about AGINFRA+ and the types of innovative services that scientists may use, in other scientific communities and networks (Task 8.2). Moreover, linking AGINFRA+ with international initiatives and networks that are working on open, big and interoperable data for agriculture and nutrition towards contributing to the corresponding standardisation work falls within the scope of WP8 under Task 8.3. Additionally, alignment of the work held in AGINFRA+ with the conception, development and deployment of the European Open Science Cloud (EOSC) and its integration with the existing core e-infrastructures is part of Task 8.4. Finally, WP8 aims at establishing a sustainable legal entity form (through a not-for-profit association) for the further operation and evolution of AGINFRA+ as a joint venture of involved stakeholders of innovative services that scientists may use, in other scientific communities and network (Task 8.5). WP8 has created until M18 an online infrastructure (including project website and social media channels) and a set of print dissemination materials to promote the project (Annex D). By the end of M18, the project website has been visited by 523 people (unique visitors). The social media channels count 251 community members (Facebook) and 849 followers (Twitter). These numbers are expected to be further increased as soon as additional tangible results are available via the deployed Virtual Research Environments (VREs) for the targeted project communities. Finally, the project has organized 13 major events with selected group of researchers and practitioners (see Section 3.1 and Annex A and Annex B). Moreover, the project has conducted 29 AGINFRA+ presentations and panel participations at scientific conferences, workshops and other events and was represented at 4 conferences and fairs with a booth or distributing leaflets (see Section 3.2 and Annex B). In total, more than 4615 stakeholders have been reached and 7 scientific publications (3 journal papers, 2 conference papers and 1 book chapter) were published or have submitted for publication (See Section 3.3 and Annex C). This deliverable describes online and offline dissemination channels, as well as activities, which were conducted until M18 of the project. Moreover, it provides an outlook of the dissemination activities that are planned for the next 18 months of the project, namely M19-M36. Furthermore, Key Performance Indicators are described and applied to measure the effectiveness of dissemination until M18 and allow measuring the progress in the next 18 months of the project.

We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, CRC and Compute Canada, Canada; COST, ERC, ERDF, Horizon 2020, and Marie Sklodowska-Curie Actions, European Union; Investissements d' Avenir Labex and Idex, ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; CERCA Programme Generalitat de Catalunya, Spain; The Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF(Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of comp Measurements of the azimuthal anisotropy in lead–lead collisions at sNN−−−√ = 5.02 TeV are presented using a data sample corresponding to 0.49 nb−1 integrated luminosity collected by the ATLAS experiment at the LHC in 2015. The recorded minimum-bias sample is enhanced by triggers for “ultra-central” collisions, providing an opportunity to perform detailed study of flow harmonics in the regime where the initial state is dominated by fluctuations. The anisotropy of the charged-particle azimuthal angle distributions is characterized by the Fourier coefficients, v2–v7, which are measured using the two-particle correlation, scalar-product and event-plane methods. The goal of the paper is to provide measurements of the differential as well as integrated flow harmonics vn over wide ranges of the transverse momentum, 0.5

Comprehensive report of the second year of the StratusLab project.

The observation of Higgs boson production in association with a top quark pair ($t\bar{t}H$), based on the analysis of proton–proton collision data at a centre-of-mass energy of 13 TeV recorded with the ATLAS detector at the Large Hadron Collider, is presented. Using data corresponding to integrated luminosities of up to 79.8 f$^{−1}$ , and considering Higgs boson decays into $b\bar{b}, WW^⁎ , τ^+ τ^− , γγ$ , and $ZZ^⁎$ , the observed significance is 5.8 standard deviations, compared to an expectation of 4.9 standard deviations. Combined with the $t\bar{t}H$ searches using a dataset corresponding to integrated luminosities of 4.5 fb$^{−1}$ at 7 TeV and 20.3 fb$^{−1}$ at 8 TeV, the observed (expected) significance is 6.3 (5.1) standard deviations. Assuming Standard Model branching fractions, the total $t\bar{t}H$ production cross section at 13 TeV is measured to be 670 ± 90 (stat.)$_{−100}^{+110}$ (syst.) fb, in agreement with the Standard Model prediction. Physics letters / B 784, 173 - 191 (2018). doi:10.1016/j.physletb.2018.07.035 Published by North-Holland Publ., Amsterdam

A search in an all-jet final state for new massive resonances decaying to $\text{ W }{}{}\text{ W }{}{}$, $\text{ W }{}{}\text{ Z }{}{}$, or $\text{ Z }{}{}\text{ Z }{}{}$ boson pairs using a novel analysis method is presented. The analysis is performed on data corresponding to an integrated luminosity of 77.3 $\,\text {fb}^{-1}$ recorded with the CMS experiment at the LHC at a centre-of-mass energy of 13 $\text {Te}\text {V}$. The search is focussed on potential narrow-width resonances with masses above 1.2 $\text {Te}\text {V}$, where the decay products of each $\text{ W }{}{}$ or $\text{ Z }{}{}$ boson are expected to be collimated into a single, large-radius jet. The signal is extracted using a three-dimensional maximum likelihood fit of the two jet masses and the dijet invariant mass, yielding an improvement in sensitivity of up to 30% relative to previous search methods. No excess is observed above the estimated standard model background. In a heavy vector triplet model, spin-1 ${\text {Z}}^{\prime }$ and ${\text {W}}^{\prime }$ resonances with masses below 3.5 and 3.8 $\text {Te}\text {V}$, respectively, are excluded at 95% confidence level. In a bulk graviton model, upper limits on cross sections are set between 27 and 0.2 $\,\text {fb}$ for resonance masses between 1.2 and 5.2 $\text {Te}\text {V}$, respectively. The limits presented in this paper are the best to date in the dijet final state. The European physical journal / C Particles and fields C 80(3), 237 (2020). doi:10.1140/epjc/s10052-020-7773-5 Published by Springer, Heidelberg

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.