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1. INFN and the evolution of distributed scientific computing in Italy

Author

Salomoni Davide, Alkhansa Ahmad, Antonacci Marica, Belluomo Patrizia, Biasotto Massimo, Carbone Luca Giovanni, Cesini Daniele, Ciangottini Diego, Ciaschini Vincenzo, Costantini Alessandro, Doria Alessandra, Donvito Giacinto, Duma Doina Cristina, Fanzago Federica, Foggetti Nadina, Fornari Federico, Giorgio Emidio Maria, Italiano Alessandro, Malatesta Giada, Martelli Barbara, Michelotto Diego, Morganti Lucia, Gasparetto Jacopo, Peco Gianluca, Pellegrino Carmelo, Rendina Andrea, Sgaravatto Massimo, Sinisi Francesco, Spiga Daniele, Spinoso Vincenzo, Spisso Bernardino, Stalio Stefano, Strizzolo Lucio, Traldi Sergio, Verlato Marco, and Vianello Enrico

Subjects
Physics, QC1-999
Abstract

INFN has been running a distributed infrastructure (the Tier-1 at Bologna-CNAF and 9 Tier-2 centres) for more than 20 years which currently offers about 150000 CPU cores and 120 PB of space both in tape and disk storage, serving more than 40 international scientific collaborations. This Grid-based infrastructure was augmented in 2019 with the INFN Cloud: a production quality multi-site federated Cloud infrastructure, composed by a core backbone, and which is able to integrate other INFN sites and public or private Clouds as well. The INFN Cloud provides a customizable and extensible portfolio offering computing and storage services spanning the IaaS, PaaS and SaaS layers, with dedicated solutions to serve special purposes, such as ISO-certified regions for the handling of sensitive data. INFN is now revising and expanding its infrastructure to tackle the challenges expected in the next 10 years of scientific computing adopting a “cloud-first” approach, through which all the INFN data centres will be federated via the INFN Cloud middleware and integrated with key HPC centres, such as the pre-exascale Leonardo machine at CINECA. In such a process, which involves both the infrastructures and the higher level services, initiatives and projects such as the "Italian National Centre on HPC, Big Data and Quantum Computing" (funded in the context of the Italian "National Recovery and Resilience Plan") and the Bologna Technopole are precious opportunities that will be exploited to offer advanced resources and services to universities, research institutions and industry. In this paper we describe how INFN is evolving its computing infrastructure, with the ambition to create and operate a national vendorneutral, open, scalable, and flexible "datalake" able to serve much more than just INFN users and experiments.

Published
2024
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2. ICSC: The Italian National Research Centre on HPC, Big Data and Quantum computing

Author

Grandi Claudio, Bettoni Diego, Boccali Tommaso, Carlino Gianpaolo, Cesini Daniele, dell’Agnello Luca, Donvito Giacinto, Salomoni Davide, and Zoccoli Antonio

Subjects
Physics, QC1-999
Abstract

ICSC (“Italian Center for SuperComputing”) is one of the five Italian National Centres created within the framework of the NextGenerationEU funding by the European Commission. The aim of ICSC, designed and approved through 2022 and eventually started in September 2022, is to create the national digital infrastructure for research and innovation, leveraging existing HPC, HTC and Big Data infrastructures and evolving towards a cloud data-lake model. It will be available to the scientific and industrial communities through flexible and uniform cloud web interfaces and will be relying on a high-level support team; as such, it will form a globally attractive ecosystem based on strategic public-private partnerships to fully exploit top level digital infrastructure for scientific and technical computing and promote the development of new computing technologies. The ICSC IT infrastructure is built upon existing scientific digital infrastructures as provided by the major national players: GARR, the Italian NREN, provides the network infrastructure, whose capacity will be upgraded to multiples of Tbps; CINECA hosts Leonardo, one of the world largest HPC systems, with a power of over 250 Pflops, to be further increased and complemented with a quantum computer; INFN contributes with its distributed Big Data cloud infrastructure, built in the last decades to respond to the needs of the HEP community. On top of the IT infrastructure, several thematic activities will be funded and will focus on the development of tools and applications in several research domains. Of particular relevance to this audience are the activities on "Fundamental Research and Space Economy" and "Astrophysics and Cosmos Observations", strictly aligned with the INFN and HEP core activities. Finally, two technological research activities will foster research on "Future HPC and Big Data" and "Quantum Computing".

Published
2024
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3. The DODAS Experience on the EGI Federated Cloud

Author

Spiga Daniele, Fernandez Enol, Spinoso Vincenzo, Ciangottini Diego, Tracolli Mirco, Donvito Giacinto, Antonacci Marica, Salomoni Davide, Ceccanti Andrea, Cristina Duma Doina, and Gaido Luciano

Subjects
Physics, QC1-999
Abstract

The EGI Cloud Compute service offers a multi-cloud IaaS federation that brings together research clouds as a scalable computing platform for research accessible with OpenID Connect Federated Identity. The federation is not limited to single sign-on, it also introduces features to facilitate the portability of applications across providers: i) a common VM image catalogue VM image replication to ensure these images will be available at providers whenever needed; ii) a GraphQL information discovery API to understand the capacities and capabilities available at each provider; and iii) integration with orchestration tools (such as Infrastructure Manager) to abstract the federation and facilitate using heterogeneous providers. EGI also monitors the correct function of every provider and collects usage information across all the infrastructure. DODAS (Dynamic On Demand Analysis Service) is an open-source Platform-as-a-Service tool, which allows to deploy software applications over heterogeneous and hybrid clouds. DODAS is one of the so-called Thematic Services of the EOSC-hub project and it instantiates on-demand container-based clusters offering a high level of abstraction to users, allowing to exploit distributed cloud infrastructures with a very limited knowledge of the underlying technologies.This work presents a comprehensive overview of DODAS integration with EGI Cloud Federation, reporting the experience of the integration with CMS Experiment submission infrastructure system.

Published
2020
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4. The eXtreme-DataCloud project solutions for data management services in distributed e-infrastructures

Author

Cesini Daniele, Donvito Giacinto, Costantini Alessandro, Aguilar Gomez Fernando, Duma Doina Cristina, Fuhrmann Patrick, Dutka Lukasz, Viljolen Matthew, Battaglia Serena, Poireau Vincent, Dell’Agnello Luca, Keeble Oliver, Lemrani Rachid, Ohmann Christian, and Marco de Lucas Jesus

Subjects
Physics, QC1-999
Abstract

The eXtreme DataCloud (XDC) project is aimed at developing data management services capable to cope with very large data resources allowing the future e-infrastructures to address the needs of the next generation extreme scale scientific experiments. Started in November 2017, XDC is combining the expertise of 8 large European research organisations. The project aims at developing scalable technologies for federating storage resources and managing data in highly distributed computing environments. The project is use case driven with a multidisciplinary approach, addressing requirements from research communities belonging to a wide range of scientific domains: Life Science, Biodiversity, Clinical Research, Astrophysics, High Energy Physics and Photon Science, that represent an indicator in terms of data management needs in Europe and worldwide. The use cases proposed by the different user communities are addressed integrating different data management services ready to manage an increasing volume of data. Different scalability and performance tests have been defined to show that the XDC services can be harmonized in different contexts and complex frameworks like the European Open Science Cloud. The use cases have been used to measure the success of the project and to prove that the developments fulfil the defined needs and satisfy the final users. The present contribution describes the results carried out from the adoption of the XDC solutions and provides a complete overview of the project achievements.

Published
2020
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5. Advancements in data management services for distributed e-infrastructures: the eXtreme-DataCloud project

Author

Cesini Daniele, Donvito Giacinto, Costantini Alessandro, Aguilar Gomez Fernando, Duma Doina Cristina, Fuhrmann Patrick, Dutka Lukasz, Viljolen Matthew, Battaglia Serena, Poireau Vincent, Dell’Agnello Luca, Keeble Oliver, Lemrani Rachid, Ohmann Christian, and Marco de Lucas Jesus

Subjects
Physics, QC1-999
Abstract

The development of data management services capable to cope with very large data resources is a key challenge to allow the future einfrastructures to address the needs of the next generation extreme scale scientific experiments. To face this challenge, in November 2017 the H2020 eXtreme DataCloud - XDC project has been launched. Lasting for 27 months and combining the expertise of eight large European research organisations, the project aims at developing scalable technologies for federating storage resources and managing data in highly distributed computing environments. The targeted platforms are the current and next generation e-Infrastructures deployed in Europe, such as the European Open Science Cloud (EOSC), the European Grid Infrastructure (EGI), and the Worldwide LHC Computing Grid (WLCG). The project is use-case driven with a multidisciplinary approach, addressing requirements from research communities belonging to a wide range of scientific domains: High Energy Physics, Astronomy, Photon and Life Science, Medical research. XDC is aimed at implementing data management scalable services, combining already established data management and orchestration tools, to address the following high level topics: policy driven data management based on Quality-of-Service, Data Life-cycle management, smart placement of data with caching mechanisms to reduce access latency, meta-data with no predefined schema handling, execution of pre-processing applications during ingestion, data management and protection of sensitive data in distributed e-infrastructures, intelligent data placement based on access patterns. This contribution introduces the project, presents the foreseen overall architecture and the developments that are being carried on to implement the requested functionalities.

Published
2019
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6. Exploiting private and commercial clouds to generate on-demand CMS computing facilities with DODAS

Author

Spiga Daniele, Antonacci Marica, Boccali Tommaso, Ceccanti Andrea, Ciangottini Diego, Di Maria Riccardo, Donvito Giacinto, Duma Cristina, Gaido Luciano, López García Álvaro, Palacio Hoz Aida, Salomoni Davide, and Tracolli Mirco

Subjects
Physics, QC1-999
Abstract

Minimising time and cost is key to exploit private or commercial clouds. This can be achieved by increasing setup and operational efficiencies. The success and sustainability are thus obtained reducing the learning curve, as well as the operational cost of managing community-specific services running on distributed environments. The greater beneficiaries of this approach are communities willing to exploit opportunistic cloud resources. DODAS builds on several EOSC-hub services developed by the INDIGO-DataCloud project and allows to instantiate on-demand container-based clusters. These execute software applications to benefit of potentially “any cloud provider”, generating sites on demand with almost zero effort. DODAS provides ready-to-use solutions to implement a “Batch System as a Service” as well as a BigData platform for a “Machine Learning as a Service”, offering a high level of customization to integrate specific scenarios. A description of the DODAS architecture will be given, including the CMS integration strategy adopted to connect it with the experiment’s HTCondor Global Pool. Performance and scalability results of DODAS-generated tiers processing real CMS analysis jobs will be presented. The Instituto de Física de Cantabria and Imperial College London use cases will be sketched. Finally a high level strategy overview for optimizing data ingestion in DODAS will be described.

Published
2019
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7. Developing a monitoring system for Cloud-based distributed data-centers

Author

Elia Domenico, Vino Gioacchino, Donvito Giacinto, and Antonacci Marica

Subjects
Physics, QC1-999
Abstract

Nowadays more and more datacenters cooperate each others to achieve a common and more complex goal. New advanced functionalities are required to support experts during recovery and managing activities, like anomaly detection and fault pattern recognition. The proposed solution provides an active support to problem solving for datacenter management teams by providing automatically the root-cause of detected anomalies. The project has been developed in Bari using the datacenter ReCaS as testbed. Big Data solutions have been selected to properly handle the complexity and size of the data. Features like open source, big community, horizontal scalability and high availability have been considered and tools belonging to the Hadoop ecosystem have been selected. The collected information is sent to a combination of Apache Flume and Apache Kafka, used as transport layer, in turn delivering data to databases and processing components. Apache Spark has been selected as analysis component. Different kind of databases have been considered in order to satisfy multiple requirements: Hadoop Distributed File System, Neo4j, InfluxDB and Elasticsearch. Grafana and Kibana are used to show data in a dedicated dashboards. The Root-cause analysis engine has been implemented using custom machine learning algorithms. Finally, results are forwarded to experts by email or Slack, using Riemann.

Published
2019
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8. Digital repository as a service: automatic deployment of an Invenio-based repository using TOSCA orchestration and Apache Mesos

Author

Antonacci Marica, Brigandì Alberto, Caballer Miguel, Cetinić Eva, Davidovic Davor, Donvito Giacinto, Moltó Germán, and Salomoni Davide

Subjects
Physics, QC1-999
Abstract

In the framework of the H2020 INDIGO-DataCloud project, we have implemented an advanced solution for the automatic deployment of digital data repositories based on Invenio, the digital library framework developed by CERN. Exploiting cutting-edge technologies, such as Docker and Apache Mesos, and standard specifications to describe application architectures such as TOSCA, we are able to provide a service that simplifies the process of creating and managing repositories of various digital assets using cloud resources. An Invenio-based repository consists of a set of services (e.g. database, message queue, cache, workers and frontend) that need to be properly installed, configured and linked together. These operations, along with the provisioning of the resources and their monitoring and maintenance, can be challenging for individual researchers or small-to-moderate-sized research groups. To this purpose, the INDIGO-DataCloud platform provides advanced features for orchestrating the deployment of complex virtual infrastructures on distributed cloud environments: it is able to provision the required resources automatically over heterogeneous and/or hybrid cloud infrastructures and to configure them automatically ensuring dynamic elasticity and resilience. This approach has been successfully adapted to support the needs of the researchers and scholars in the domain of the Digital Arts and Humanities.

Published
2019
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9. Search for gamma-ray spectral lines with the DArk Matter Particle Explorer

Author

Alemanno, Francesca, An, Qi, Azzarello, Philipp, Barbato, Felicia Carla Tiziana, Bernardini, Paolo, Bi, Xiao-Jun, Cai, Ming-Sheng, Casilli, Elisabetta, Catanzani, Enrico, Chang, Jin, Chen, Deng-Yi, Chen, Jun-Ling, Chen, Zhan-Fang, Cui, Ming-Yang, Cui, Tian-Shu, Cui, Yu-Xing, Dai, Hao-Ting, De Benedittis, Antonio, De Mitri, Ivan, de Palma, Francesco, Deliyergiyev, Maksym, Di Santo, Margherita, Ding, Qi, Dong, Tie-Kuang, Dong, Zhen-Xing, Donvito, Giacinto, Droz, David, Duan, Jing-Lai, Duan, Kai-Kai, D'Urso, Domenico, Fan, Rui-Rui, Fan, Yi-Zhong, Fang, Fang, Fang, Kun, Feng, Chang-Qing, Feng, Lei, Fusco, Piergiorgio, Gao, Min, Gargano, Fabio, Gong, Ke, Gong, Yi-Zhong, Guo, Dong-Ya, Guo, Jian-Hua, Han, Shuang-Xue, Hu, Yi-Ming, Huang, Guang-Shun, Huang, Xiao-Yuan, Huang, Yong-Yi, Ionica, Maria, Jiang, Wei, Kong, Jie, Kotenko, Andrii, Kyratzis, Dimitrios, Lei, Shi-Jun, Li, Shang, Li, Wen-Hao, Li, Wei-Liang, Li, Xiang, Li, Xian-Qiang, Liang, Yao-Ming, Liu, Cheng-Ming, Liu, Hao, Liu, Jie, Liu, Shu-Bin, Liu, Yang, Loparco, Francesco, Luo, Chuan-Ning, Ma, Miao, Ma, Peng-Xiong, Ma, Tao, Ma, Xiao-Yong, Marsella, Giovanni, Mazziotta, Mario Nicola, Mo, Dan, Mu, Maria, Niu, Xiao-Yang, Pan, Xu, Parenti, Andrea, Peng, Wen-Xi, Peng, Xiao-Yan, Perrina, Chiara, Qiao, Rui, Rao, Jia-Ning, Ruina, Arshia, Shangguan, Zhi, Shen, Wei-Hua, Shen, Zhao-Qiang, Shen, Zhong-Tao, Silveri, Leandro, Song, Jing-Xing, Stolpovskiy, Mikhail, Su, Hong, Su, Meng, Sun, Hao-Ran, Sun, Zhi-Yu, Surdo, Antonio, Teng, Xue-Jian, Tykhonov, Andrii, Wang, Jin-Zhou, Wang, Lian-Guo, Wang, Shen, Wang, Shu-Xin, Wang, Xiao-Lian, Wang, Ying, Wang, Yan-Fang, Wang, Yuan-Zhu, Wei, Da-Ming, Wei, Jia-Ju, Wei, Yi-Feng, Wu, Di, Wu, Jian, Wu, Li-Bo, Wu, Sha-Sha, Wu, Xin, Xia, Zi-Qing, Xu, En-Heng, Xu, Hai-Tao, Xu, Zhi-Hui, Xu, Zun-Lei, Xu, Zi-Zong, Xue, Guo-Feng, Yang, Hai-Bo, Yang, Peng, Yang, Ya-Qing, Yao, Hui-Jun, Yu, Yu-Hong, Yuan, Guan-Wen, Yuan, Qiang, Yue, Chuan, Zang, Jing-Jing, Zhang, Sheng-Xia, Zhang, Wen-Zhang, Zhang, Yan, Zhang, Yi, Zhang, Yong-Jie, Zhang, Yun-Long, Zhang, Ya-Peng, Zhang, Yong-Qiang, Zhang, Zhe, Zhang, Zhi-Yong, Zhao, Cong, Zhao, Hong-Yun, Zhao, Xun-Feng, Zhou, Chang-Yi, Zhu, Yan, and Liang, Yun-Feng

Subjects
Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Experiment
Abstract

The DArk Matter Particle Explorer (DAMPE) is well suitable for searching for monochromatic and sharp $\gamma$-ray structures in the GeV$-$TeV range thanks to its unprecedented high energy resolution. In this work, we search for $\gamma$-ray line structures using five years of DAMPE data. To improve the sensitivity, we develop two types of dedicated data sets (including the BgoOnly data which is the first time to be used in the data analysis for the calorimeter-based gamma-ray observatories) and adopt the signal-to-noise ratio optimized regions of interest (ROIs) for different DM density profiles. No line signals or candidates are found between 10 and 300 GeV in the Galaxy. The constraints on the velocity-averaged cross section for $\chi\chi \to \gamma\gamma$ and the decay lifetime for $\chi \to \gamma\nu$, both at 95% confidence level, have been calculated and the systematic uncertainties have been taken into account. Comparing to the previous Fermi-LAT results, though DAMPE has an acceptance smaller by a factor of $\sim 10$, similar constraints on the DM parameters are achieved and below 100 GeV the lower limits on the decay lifetime are even stronger by a factor of a few. Our results demonstrate the potential of high-energy-resolution observations on dark matter detection., Comment: 14 pages, 8 figures. Update the content to keep up with the published version

Published
2021
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11. The GENIUS Grid Portal and robot certificates: a new tool for e-Science

Author

La Rocca Giuseppe, Falzone Alberto, Donvito Giacinto, Barbera Roberto, Milanesi Luciano, Maggi Giorgio, and Vicario Saverio

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Grid technology is the computing model which allows users to share a wide pletora of distributed computational resources regardless of their geographical location. Up to now, the high security policy requested in order to access distributed computing resources has been a rather big limiting factor when trying to broaden the usage of Grids into a wide community of users. Grid security is indeed based on the Public Key Infrastructure (PKI) of X.509 certificates and the procedure to get and manage those certificates is unfortunately not straightforward. A first step to make Grids more appealing for new users has recently been achieved with the adoption of robot certificates. Methods Robot certificates have recently been introduced to perform automated tasks on Grids on behalf of users. They are extremely useful for instance to automate grid service monitoring, data processing production, distributed data collection systems. Basically these certificates can be used to identify a person responsible for an unattended service or process acting as client and/or server. Robot certificates can be installed on a smart card and used behind a portal by everyone interested in running the related applications in a Grid environment using a user-friendly graphic interface. In this work, the GENIUS Grid Portal, powered by EnginFrame, has been extended in order to support the new authentication based on the adoption of these robot certificates. Results The work carried out and reported in this manuscript is particularly relevant for all users who are not familiar with personal digital certificates and the technical aspects of the Grid Security Infrastructure (GSI). The valuable benefits introduced by robot certificates in e-Science can so be extended to users belonging to several scientific domains, providing an asset in raising Grid awareness to a wide number of potential users. Conclusion The adoption of Grid portals extended with robot certificates, can really contribute to creating transparent access to computational resources of Grid Infrastructures, enhancing the spread of this new paradigm in researchers' working life to address new global scientific challenges. The evaluated solution can of course be extended to other portals, applications and scientific communities.

Published
2009
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12. Observations of Forbush Decreases of cosmic ray electrons and positrons with the Dark Matter Particle Explorer

Author

Alemanno, Francesca, An, Qi, Azzarello, Philipp, Barbato, Felicia Carla Tiziana, Bernardini, Paolo, Bi, XiaoJun, Cai, MingSheng, Casilli, Elisabetta, Catanzani, Enrico, Chang, Jin, Chen, DengYi, Chen, JunLing, Chen, ZhanFang, Cui, MingYang, Cui, TianShu, Cui, YuXing, Dai, HaoTing, De Benedittis, Antonio, De Mitri, Ivan, de Palma, Francesco, Deliyergiyev, Maksym, Di Santo, Margherita, Ding, Qi, Dong, TieKuang, Dong, ZhenXing, Donvito, Giacinto, Droz, David, Duan, JingLai, Duan, KaiKai, D'Urso, Domenico, Fan, RuiRui, Fan, YiZhong, Fang, Fang, Fang, Kun, Feng, ChangQing, Feng, Lei, Fusco, Piergiorgio, Gao, Min, Gargano, Fabio, Gong, Ke, Gong, YiZhong, Guo, DongYa, Guo, JianHua, Han, ShuangXue, Hu, YiMing, Huang, GuangShun, Huang, XiaoYuan, Huang, YongYi, Ionica, Maria, Jiang, Wei, Kong, Jie, Kotenko, Andrii, Kyratzis, Dimitrios, Li, ., Lei, ShiJun, Li, WenHao, Li, WeiLiang, Li, Xiang, Li, XianQiang, Liang, YaoMing, Liu, ChengMing, Liu, Hao, Liu, Jie, Liu, ShuBin, Liu, Yang, Loparco, Francesco, Luo, ChuanNing, Ma, Miao, Ma, PengXiong, Ma, Tao, Ma, XiaoYong, Marsella, Giovanni, Mazziotta, Mario Nicola, Mo, Dan, Niu, XiaoYang, Pan, Xu, Parenti, Andrea, Peng, WenXi, Peng, XiaoYan, Perrina, Chiara, Qiao, Rui, Rao, JiaNing, Ruina, Arshia, Salinas, MariaMunoz, Shangguan, Zhi, Shen, WeiHua, Shen, ZhaoQiang, Shen, ZhongTao, Silveri, Leandro, Song, JingXing, Stolpovskiy, Mikhail, Su, Hong, Su, Meng, Sun, HaoRan, Sun, ZhiYu, Surdo, Antonio, Teng, XueJian, Tykhonov, Andrii, Wang, JinZhou, Wang, LianGuo, Wang, Shen, Wang, ShuXin, Wang, XiaoLian, Wang, Ying, Wang, YanFang, Wang, YuanZhu, Wei, DaMing, Wei, JiaJu, Wei, YiFeng, Wu, Di, Wu, Jian, Wu, LiBo, Wu, Sha Sha, Wu, Xin, Xia, ZiQing, Xu, EnHeng, Xu, HaiTao, Xu, ZhiHui, Xu, ZunLei, Xue, GuoFeng, Xu, ZiZong, Yang, HaiBo, Yang, Peng, Yang, YaQing, Yao, Hui Jun, Yu, YuHong, Yuan, GuanWen, Yuan, Qiang, Yue, Chuan, Zang, JingJing, Zhang, ShengXia, Zhang, WenZhang, Zhang, Yan, Zhang, Yi, Zhang, YongJie, Zhang, YunLong, Zhang, YaPeng, Zhang, YongQiang, Zhang, ZhiYong, Zhang, Zhe, Zhao, Cong, Zhao, HongYun, Zhao, XunFeng, Zhou, ChangYi, Zhu, Yan, Chen, Wei, Feng, Li, Luo, Xi, and Zhu, ChengRui

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

The Forbush Decrease (FD) represents the rapid decrease of the intensities of charged particles accompanied with the coronal mass ejections (CMEs) or high-speed streams from coronal holes. It has been mainly explored with ground-based neutron monitors network which indirectly measure the integrated intensities of all species of cosmic rays by counting secondary neutrons produced from interaction between atmosphere atoms and cosmic rays. The space-based experiments can resolve the species of particles but the energy ranges are limited by the relative small acceptances except for the most abundant particles like protons and helium. Therefore, the FD of cosmic ray electrons and positrons have just been investigated by the PAMELA experiment in the low energy range ($<5$ GeV) with limited statistics. In this paper, we study the FD event occurred in September, 2017, with the electron and positron data recorded by the Dark Matter Particle Explorer. The evolution of the FDs from 2 GeV to 20 GeV with a time resolution of 6 hours are given. We observe two solar energetic particle events in the time profile of the intensity of cosmic rays, the earlier and weak one has not been shown in the neutron monitor data. Furthermore, both the amplitude and recovery time of fluxes of electrons and positrons show clear energy-dependence, which is important in probing the disturbances of the interplanetary environment by the coronal mass ejections., Comment: This article is dedicated to the 72nd anniversary of People's Republic of China

Published
2021
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13. The Laniakea Dashboard and Storage Encryption Components: A Foundation for Developing On-Demand Cloud Services for Life Science

Author

Tangaro, Marco Antonio, Antonacci, Marica, Mandreoli, Pietro, Colombo, Daniele, Foggetti, Nadina, Donvito, Giacinto, Pesole, Graziano, Zambelli, Federico, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Agapito, Giuseppe, editor, Bernasconi, Anna, editor, Cappiello, Cinzia, editor, Khattak, Hasan Ali, editor, Ko, InYoung, editor, Loseto, Giuseppe, editor, Mrissa, Michael, editor, Nanni, Luca, editor, Pinoli, Pietro, editor, Ragone, Azzurra, editor, Ruta, Michele, editor, Scioscia, Floriano, editor, and Srivastava, Abhishek, editor

Published
2023
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14. Comparison of proton shower developments in the BGO calorimeter of the Dark Matter Particle Explorer between GEANT4 and FLUKA simulations

Author

Jiang, Wei, Yue, Chuan, Cui, Ming-Yang, Li, Xiang, Yuan, Qiang, Alemanno, Francesca, Bernardini, Paolo, Catanzani, Giovanni, Chen, Zhan-Fang, De Mitri, Ivan, Dong, Tie-Kuang, Donvito, Giacinto, Droz, David Francois, Fusco, Piergiorgio, Gargano, Fabio, Guo, Dong-Ya, Kyratzis, Dimitrios, Lei, Shi-Jun, Liu, Yang, Loparco, Francesco, Ma, Peng-Xiong, Marsella, Giovanni, Mazziotta, Mario Nicola, Pan, Xu, Peng, Wen-Xi, Surdo, Antonio, Tykhonov, Andrii, Wei, Yi-Yeng, Yu, Yu-Hong, Zang, Jing-Jing, Zhang, Ya-Peng, Zhang, Yong-Jie, and Zhang, Yun-Long

Subjects
Physics - Instrumentation and Detectors, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The DArk Matter Particle Explorer (DAMPE) is a satellite-borne detector for high-energy cosmic rays and $\gamma$-rays. To fully understand the detector performance and obtain reliable physical results, extensive simulations of the detector are necessary. The simulations are particularly important for the data analysis of cosmic ray nuclei, which relies closely on the hadronic and nuclear interactions of particles in the detector material. Widely adopted simulation softwares include the GEANT4 and FLUKA, both of which have been implemented for the DAMPE simulation tool. Here we describe the simulation tool of DAMPE and compare the results of proton shower properties in the calorimeter from the two simulation softwares. Such a comparison gives an estimate of the most significant uncertainties of our proton spectral analysis., Comment: 7 pages, 7 figures, to be published in Chinese Physics Letters

Published
2020
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15. Genome-wide identification of coding and non-coding conserved sequence tags in human and mouse genomes

Author

Maggi Giorgio P, Donvito Giacinto, Anselmo Anna, Mignone Flavio, Grillo Giorgio, and Pesole Graziano

Subjects
Biotechnology, TP248.13-248.65, Genetics, QH426-470
Abstract

Abstract Background The accurate detection of genes and the identification of functional regions is still an open issue in the annotation of genomic sequences. This problem affects new genomes but also those of very well studied organisms such as human and mouse where, despite the great efforts, the inventory of genes and regulatory regions is far from complete. Comparative genomics is an effective approach to address this problem. Unfortunately it is limited by the computational requirements needed to perform genome-wide comparisons and by the problem of discriminating between conserved coding and non-coding sequences. This discrimination is often based (thus dependent) on the availability of annotated proteins. Results In this paper we present the results of a comprehensive comparison of human and mouse genomes performed with a new high throughput grid-based system which allows the rapid detection of conserved sequences and accurate assessment of their coding potential. By detecting clusters of coding conserved sequences the system is also suitable to accurately identify potential gene loci. Following this analysis we created a collection of human-mouse conserved sequence tags and carefully compared our results to reliable annotations in order to benchmark the reliability of our classifications. Strikingly we were able to detect several potential gene loci supported by EST sequences but not corresponding to as yet annotated genes. Conclusion Here we present a new system which allows comprehensive comparison of genomes to detect conserved coding and non-coding sequences and the identification of potential gene loci. Our system does not require the availability of any annotated sequence thus is suitable for the analysis of new or poorly annotated genomes.

Published
2008
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16. Gene analogue finder: a GRID solution for finding functionally analogous gene products

Author

Licciulli Flavio, Donvito Giacinto, Tulipano Angelica, Maggi Giorgio, and Gisel Andreas

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background To date more than 2,1 million gene products from more than 100000 different species have been described specifying their function, the processes they are involved in and their cellular localization using a very well defined and structured vocabulary, the gene ontology (GO). Such vast, well defined knowledge opens the possibility of compare gene products at the level of functionality, finding gene products which have a similar function or are involved in similar biological processes without relying on the conventional sequence similarity approach. Comparisons within such a large space of knowledge are highly data and computing intensive. For this reason this project was based upon the use of the computational GRID, a technology offering large computing and storage resources. Results We have developed a tool, GENe AnaloGue FINdEr (ENGINE) that parallelizes the search process and distributes the calculation and data over the computational GRID, splitting the process into many sub-processes and joining the calculation and the data on the same machine and therefore completing the whole search in about 3 days instead of occupying one single machine for more than 5 CPU years. The results of the functional comparison contain potential functional analogues for more than 79000 gene products from the most important species. 46% of the analyzed gene products are well enough described for such an analysis to individuate functional analogues, such as well-known members of the same gene family, or gene products with similar functions which would never have been associated by standard methods. Conclusion ENGINE has produced a list of potential functionally analogous relations between gene products within and between species using, in place of the sequence, the gene description of the GO, thus demonstrating the potential of the GO. However, the current limiting factor is the quality of the associations of many gene products from non-model organisms that often have electronic associations, since experimental information is missing. With future improvements of the GO, this limit will be reduced. ENGINE will manifest its power when it is applied to the whole GODB of more than 2,1 million gene products from more than 100000 organisms. The data produced by this search is planed to be available as a supplement to the GO database as soon as we are able to provide regular updates.

Published
2007
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17. INDIGO-DataCloud:A data and computing platform to facilitate seamless access to e-infrastructures

Author

Collaboration, INDIGO-DataCloud, Salomoni, Davide, Campos, Isabel, Gaido, Luciano, de Lucas, Jesus Marco, Solagna, Peter, Gomes, Jorge, Matyska, Ludek, Fuhrman, Patrick, Hardt, Marcus, Donvito, Giacinto, Dutka, Lukasz, Plociennik, Marcin, Barbera, Roberto, Blanquer, Ignacio, Ceccanti, Andrea, David, Mario, Duma, Cristina, López-García, Alvaro, Moltó, Germán, Orviz, Pablo, Sustr, Zdenek, Viljoen, Matthew, Aguilar, Fernando, Alves, Luis, Antonacci, Marica, Antonelli, Lucio Angelo, Bagnasco, Stefano, Bonvin, Alexandre M. J. J., Bruno, Riccardo, Cetinic, Eva, Chen, Yin, Chiarello, Fabrizio, Costa, Alessandro, Pra, Stefano Dal, Davidovic, Davor, Dorigo, Alvise, Ertl, Benjamin, Fanzago, Federica, Fargetta, Marco, Fiore, Sandro, Gallozzi, Stefano, Kurkcuoglu, Zeynep, Lloret, Lara, Martins, Joao, Nuzzo, Alessandra, Nassisi, Paola, Palazzo, Cosimo, Pina, Joao, Sciacca, Eva, Segatta, Matteo, Sgaravatto, Massimo, Spiga, Daniele, Taneja, Sonia, Tangaro, Marco Antonio, Urbaniak, Michal, Vallero, Sara, Verlato, Marco, Wegh, Bas, Zaccolo, Valentina, Zambelli, Federico, Zangrando, Lisa, Zani, Stefano, and Zok, Tomasz

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to manage the demanding requirements of scientific communities, either locally or through enhanced interfaces. The middleware developed allows to federate hybrid resources, to easily write, port and run scientific applications to the cloud. In particular, we have extended existing PaaS (Platform as a Service) solutions, allowing public and private e-infrastructures, including those provided by EGI, EUDAT, and Helix Nebula, to integrate their existing services and make them available through AAI services compliant with GEANT interfederation policies, thus guaranteeing transparency and trust in the provisioning of such services. Our middleware facilitates the execution of applications using containers on Cloud and Grid based infrastructures, as well as on HPC clusters. Our developments are freely downloadable as open source components, and are already being integrated into many scientific applications., Comment: 39 pages, 15 figures.Version accepted in Journal of Grid Computing

Published
2017
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18. Improved Cloud resource allocation: how INDIGO-DataCloud is overcoming the current limitations in Cloud schedulers

Author

Garcia, Alvaro Lopez, Zangrando, Lisa, Sgaravatto, Massimo, Llorens, Vincent, Vallero, Sara, Zaccolo, Valentina, Bagnasco, Stefano, Taneja, Sonia, Pra, Stefano Dal, Salomoni, Davide, and Donvito, Giacinto

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Performing efficient resource provisioning is a fundamental aspect for any resource provider. Local Resource Management Systems (LRMS) have been used in data centers for decades in order to obtain the best usage of the resources, providing their fair usage and partitioning for the users. In contrast, current cloud schedulers are normally based on the immediate allocation of resources on a first-come, first-served basis, meaning that a request will fail if there are no resources (e.g. OpenStack) or it will be trivially queued ordered by entry time (e.g. OpenNebula). Moreover, these scheduling strategies are based on a static partitioning of the resources, meaning that existing quotas cannot be exceeded, even if there are idle resources allocated to other projects. This is a consequence of the fact that cloud instances are not associated with a maximum execution time and leads to a situation where the resources are under-utilized. These facts have been identified by the INDIGO-DataCloud project as being too simplistic for accommodating scientific workloads in an efficient way, leading to an underutilization of the resources, a non desirable situation in scientific data centers. In this work, we will present the work done in the scheduling area during the first year of the INDIGO project and the foreseen evolutions.

Published
2017
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19. Predictive medicine in a testis trio‐family through a combined multi‐omics approach

Author

Di Maggio, Federica, primary, Damaggio, Gianluca, additional, Nunziato, Marcella, additional, Buonaiuto, Silvia, additional, Crocetto, Felice, additional, Calabrese, Alessandra, additional, Aveta, Achille, additional, Vino, Gioacchino, additional, Donvito, Giacinto, additional, Pandolfo, Savio Domenico, additional, Imbimbo, Ciro, additional, Colonna, Vincenza, additional, and Salvatore, Francesco, additional

Published
2024
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21. Laniakea@ReCaS: exploring the potential of customisable Galaxy on-demand instances as a cloud-based service

Author

Tangaro, Marco Antonio, Mandreoli, Pietro, Chiara, Matteo, Donvito, Giacinto, Antonacci, Marica, Parisi, Antonio, Bianco, Angelica, Romano, Angelo, Bianchi, Daniela Manila, Cangelosi, Davide, Uva, Paolo, Molineris, Ivan, Nosi, Vladimir, Calogero, Raffaele A., Alessandri, Luca, Pedrini, Elena, Mordenti, Marina, Bonetti, Emanuele, Sangiorgi, Luca, Pesole, Graziano, and Zambelli, Federico

Published
2021
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22. TEBAKA: a technological platform for Apulian crop monitoring

Author

Cilli, Roberto, primary, Pantaleo, Ester, additional, Donvito, Giacinto, additional, Antonacci, Marica, additional, Cristella, Leonardo, additional, Vino, Gioacchino, additional, Vivaldi, Gaetano Alessandro, additional, Giannico, Vincenzo, additional, D'Avino, Vincenzo, additional, Amoruso, Vito, additional, Cagnazzo, Cosimo, additional, Sanesi, Giovanni, additional, Matarrese, Manuela, additional, Amoroso, Nicola, additional, and Bellotti, Roberto, additional

Published
2023
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23. DArk Matter Particle Explorer: 7 years in Space

Author

Tykhonov, Andrii, primary, Alemanno, Francesca, additional, Altomare, Corrado, additional, An, Qi, additional, Azzarello, Philipp, additional, Barbato, Felicia Carla Tiziana, additional, Bernardini, Paolo, additional, Bi, Xiao Jun, additional, Cagnoli, Irene, additional, Cai, Ming Sheng, additional, Casilli, Elisabetta, additional, Catanzani, Enrico, additional, Chang, Jin, additional, Chen, Deng Yi, additional, Chen, Jun Ling, additional, Chen, Zhan Fang, additional, Chen, Zi Xuan, additional, Coppin, Paul, additional, Cui, Ming Yang, additional, Cui, Tian Shu, additional, Cui, Yu Xin, additional, De Mitri, Ivan, additional, de Palma, Francesco, additional, Di Giovanni, Adriano, additional, Di Santo, Margherita, additional, Ding, Qi, additional, Dong, Tie Kuang, additional, Dong, Zhen Xing, additional, DONVITO, Giacinto, additional, Droz, David, additional, Duan, Jing Lai, additional, Duan, Kai Kai, additional, Fan, Rui Rui, additional, Fan, Yi Zhong, additional, Fang, Fang, additional, Fang, Kun, additional, Feng, Chang Qing, additional, Feng, Lei, additional, Fernandez Alonso, Mateo, additional, Frieden, Jennifer Maria, additional, Fusco, Piergiorgio, additional, Gao, Min, additional, Gargano, Fabio, additional, Ghose, Essna, additional, Gong, Ke, additional, Gong, Yi Zhong, additional, Guo, Dong Ya, additional, Guo, Jian Hua, additional, Han, Shuang Xue, additional, Hu, Yi Ming, additional, Huang, Guang Shun, additional, Huang, Xiao Yuan, additional, Huang, Yong Yi, additional, Ionica, Maria, additional, Jiang, Lu Yao, additional, Jiang, Wei, additional, Jiang, Yao Zu, additional, Kong, Jie, additional, Kotenko, Andrii, additional, Kyratzis, Dimitrios, additional, Lei, Shi Jun, additional, Li, Wei Liang, additional, Li, Wen Hao, additional, Li, Xiang, additional, Li, Xian Qiang, additional, Liang, Yao Ming, additional, Liu, Cheng Ming, additional, Liu, Hao, additional, Liu, Jie, additional, Liu, Shu Bin, additional, Liu, Yang, additional, Loparco, Francesco, additional, Luo, Chuan Ning, additional, Ma, Miao, additional, Ma, Peng Xiong, additional, Ma, Tao, additional, Ma, Xiao Yong, additional, Marsella, Giovanni, additional, Mazziotta, Mario Nicola, additional, Mo, Dan, additional, Niu, Xiao Yang, additional, Pan, Xu, additional, Parenti, Andrea, additional, Peng, Wen Xi, additional, Peng, Xiao Yan, additional, Perrina, Chiara, additional, Putti-Garcia, Enzo, additional, Qiao, Rui, additional, Rao, Jia Ning, additional, Ruina, Arshia, additional, Shangguan, Zhi, additional, Shen, Wei Hua, additional, Shen, Zhao Qiang, additional, Shen, Zhong Tao, additional, Silveri, Leandro, additional, Song, Jing Xing, additional, Stolpovskiy, Mikhail, additional, Su, Hong, additional, Su, Meng, additional, Sun, Hao Ran, additional, Sun, Zhi Yu, additional, Surdo, Antonio, additional, Teng, Xue Jian, additional, Wang, Jin Zhou, additional, Wang, Lian Guo, additional, Wang, Shen, additional, Wang, Xiao Lian, additional, Wang, Yan Fang, additional, Wang, Ying, additional, Wang, Yuan Zhu, additional, Wei, Da Ming, additional, Wei, Jia Ju, additional, Wei, Yi Feng, additional, Wu, Di, additional, Wu, Jian, additional, Wu, Li Bo, additional, Wu, Sha Sha, additional, Wu, Xin, additional, Xia, Zi Qing, additional, Xu, En Heng, additional, Xu, Hai Tao, additional, Xu, Jing, additional, Xu, Zhi Hui, additional, Xu, Zi Zong, additional, Xu, Zun Lei, additional, Xue, Guo Feng, additional, Yang, Hai Bo, additional, Yang, Peng, additional, Yang, Ya Qing, additional, Yao, Hui Jun, additional, Yu, Yu Hong, additional, Yuan, Guan Wen, additional, Yuan, Qiang, additional, Yue, Chuan, additional, Zang, Jing Jing, additional, Zhang, Sheng Xia, additional, Zhang, Wen Zhang, additional, Zhang, Yan, additional, Zhang, Ya Peng, additional, Zhang, Yi, additional, Zhang, Yong Jie, additional, Zhang, Yong Qiang, additional, Zhang, Yun Long, additional, Zhang, Zhe, additional, Zhang, Zhi Yong, additional, Zhao, Cong, additional, Zhao, Hong Yun, additional, Zhao, Xun Feng, additional, Zhou, Chang Yi, additional, and Zhu, Yan, additional

Published
2023
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24. A PCA-based Method for Electron-Proton Discrimination of the DAMPE Experiment

Author

Cui, Ming-Yang, primary, Alemanno, Francesca, additional, Altomare, Corrado, additional, An, Qi, additional, Azzarello, Philipp, additional, Barbato, Felicia Carla Tiziana, additional, Bernardini, Paolo, additional, Bi, Xiao Jun, additional, Cagnoli, Irene, additional, Cai, Ming Sheng, additional, Casilli, Elisabetta, additional, Catanzani, Enrico, additional, Chang, Jin, additional, Chen, Deng Yi, additional, Chen, Jun Ling, additional, Chen, Zhan Fang, additional, Chen, Zi Xuan, additional, Coppin, Paul, additional, Cui, Ming Yang, additional, Cui, Tian Shu, additional, Cui, Yu Xin, additional, De Mitri, Ivan, additional, de Palma, Francesco, additional, Di Giovanni, Adriano, additional, Di Santo, Margherita, additional, Ding, Qi, additional, Dong, Tie Kuang, additional, Dong, Zhen Xing, additional, DONVITO, Giacinto, additional, Droz, David, additional, Duan, Jing Lai, additional, Duan, Kai Kai, additional, Fan, Rui Rui, additional, Fan, Yi Zhong, additional, Fang, Fang, additional, Fang, Kun, additional, Feng, Chang Qing, additional, Feng, Lei, additional, Fernandez Alonso, Mateo, additional, Frieden, Jennifer Maria, additional, Fusco, Piergiorgio, additional, Gao, Min, additional, Gargano, Fabio, additional, Ghose, Essna, additional, Gong, Ke, additional, Gong, Yi Zhong, additional, Guo, Dong Ya, additional, Guo, Jian Hua, additional, Han, Shuang Xue, additional, Hu, Yi Ming, additional, Huang, Guang Shun, additional, Huang, Xiao Yuan, additional, Huang, Yong Yi, additional, Ionica, Maria, additional, Jiang, Lu Yao, additional, Jiang, Wei, additional, Jiang, Yao Zu, additional, Kong, Jie, additional, Kotenko, Andrii, additional, Kyratzis, Dimitrios, additional, Lei, Shi Jun, additional, Li, Wei Liang, additional, Li, Wen Hao, additional, Li, Xiang, additional, Li, Xian Qiang, additional, Liang, Yao Ming, additional, Liu, Cheng Ming, additional, Liu, Hao, additional, Liu, Jie, additional, Liu, Shu Bin, additional, Liu, Yang, additional, Loparco, Francesco, additional, Luo, Chuan Ning, additional, Ma, Miao, additional, Ma, Peng Xiong, additional, Ma, Tao, additional, Ma, Xiao Yong, additional, Marsella, Giovanni, additional, Mazziotta, Mario Nicola, additional, Mo, Dan, additional, Niu, Xiao Yang, additional, Pan, Xu, additional, Parenti, Andrea, additional, Peng, Wen Xi, additional, Peng, Xiao Yan, additional, Perrina, Chiara, additional, Putti-Garcia, Enzo, additional, Qiao, Rui, additional, Rao, Jia Ning, additional, Ruina, Arshia, additional, Shangguan, Zhi, additional, Shen, Wei Hua, additional, Shen, Zhao Qiang, additional, Shen, Zhong Tao, additional, Silveri, Leandro, additional, Song, Jing Xing, additional, Stolpovskiy, Mikhail, additional, Su, Hong, additional, Su, Meng, additional, Sun, Hao Ran, additional, Sun, Zhi Yu, additional, Surdo, Antonio, additional, Teng, Xue Jian, additional, Tykhonov, Andrii, additional, Wang, Jin Zhou, additional, Wang, Lian Guo, additional, Wang, Shen, additional, Wang, Xiao Lian, additional, Wang, Yan Fang, additional, Wang, Ying, additional, Wang, Yuan Zhu, additional, Wei, Da Ming, additional, Wei, Jia Ju, additional, Wei, Yi Feng, additional, Wu, Di, additional, Wu, Jian, additional, Wu, Li Bo, additional, Wu, Sha Sha, additional, Wu, Xin, additional, Xia, Zi Qing, additional, Xu, En Heng, additional, Xu, Hai Tao, additional, Xu, Jing, additional, Xu, Zhi Hui, additional, Xu, Zi Zong, additional, Xu, Zun Lei, additional, Xue, Guo Feng, additional, Yang, Hai Bo, additional, Yang, Peng, additional, Yang, Ya Qing, additional, Yao, Hui Jun, additional, Yu, Yu Hong, additional, Yuan, Guan Wen, additional, Yuan, Qiang, additional, Yue, Chuan, additional, Zang, Jing Jing, additional, Zhang, Sheng Xia, additional, Zhang, Wen Zhang, additional, Zhang, Yan, additional, Zhang, Ya Peng, additional, Zhang, Yi, additional, Zhang, Yong Jie, additional, Zhang, Yong Qiang, additional, Zhang, Yun Long, additional, Zhang, Zhe, additional, Zhang, Zhi Yong, additional, Zhao, Cong, additional, Zhao, Hong Yun, additional, Zhao, Xun Feng, additional, Zhou, Chang Yi, additional, and Zhu, Yan, additional

Published
2023
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25. Point-like Source Catalog Observed by DAMPE

Author

Duan, Kai-Kai, primary, Alemanno, Francesca, additional, Altomare, Corrado, additional, An, Qi, additional, Azzarello, Philipp, additional, Barbato, Felicia Carla Tiziana, additional, Bernardini, Paolo, additional, Bi, Xiao Jun, additional, Cagnoli, Irene, additional, Cai, Ming Sheng, additional, Casilli, Elisabetta, additional, Catanzani, Enrico, additional, Chang, Jin, additional, Chen, Deng Yi, additional, Chen, Jun Ling, additional, Chen, Zhan Fang, additional, Chen, Zi Xuan, additional, Coppin, Paul, additional, Cui, Ming Yang, additional, Cui, Tian Shu, additional, Cui, Yu Xin, additional, De Mitri, Ivan, additional, de Palma, Francesco, additional, Di Giovanni, Adriano, additional, Di Santo, Margherita, additional, Ding, Qi, additional, Dong, Tie Kuang, additional, Dong, Zhen Xing, additional, DONVITO, Giacinto, additional, Droz, David, additional, Duan, Jing Lai, additional, Duan, Kai Kai, additional, Fan, Rui Rui, additional, Fan, Yi Zhong, additional, Fang, Fang, additional, Fang, Kun, additional, Feng, Chang Qing, additional, Feng, Lei, additional, Fernandez Alonso, Mateo, additional, Frieden, Jennifer Maria, additional, Fusco, Piergiorgio, additional, Gao, Min, additional, Gargano, Fabio, additional, Ghose, Essna, additional, Gong, Ke, additional, Gong, Yi Zhong, additional, Guo, Dong Ya, additional, Guo, Jian Hua, additional, Han, Shuang Xue, additional, Hu, Yi Ming, additional, Huang, Guang Shun, additional, Huang, Xiao Yuan, additional, Huang, Yong Yi, additional, Ionica, Maria, additional, Jiang, Lu Yao, additional, Jiang, Yao Zu, additional, Kong, Jie, additional, Kotenko, Andrii, additional, Kyratzis, Dimitrios, additional, Lei, Shi Jun, additional, Li, Wei Liang, additional, Li, Wen Hao, additional, Li, Xian Qiang, additional, Liang, Yao Ming, additional, Liu, Cheng Ming, additional, Liu, Hao, additional, Liu, Jie, additional, Liu, Shu Bin, additional, Liu, Yang, additional, Loparco, Francesco, additional, Luo, Chuan Ning, additional, Ma, Miao, additional, Ma, Peng Xiong, additional, Ma, Tao, additional, Ma, Xiao Yong, additional, Marsella, Giovanni, additional, Mazziotta, Mario Nicola, additional, Mo, Dan, additional, Niu, Xiao Yang, additional, Pan, Xu, additional, Parenti, Andrea, additional, Peng, Wen Xi, additional, Peng, Xiao Yan, additional, Perrina, Chiara, additional, Putti-Garcia, Enzo, additional, Qiao, Rui, additional, Rao, Jia Ning, additional, Ruina, Arshia, additional, Shangguan, Zhi, additional, Shen, Wei Hua, additional, Shen, Zhao Qiang, additional, Shen, Zhong Tao, additional, Silveri, Leandro, additional, Song, Jing Xing, additional, Stolpovskiy, Mikhail, additional, Su, Hong, additional, Su, Meng, additional, Sun, Hao Ran, additional, Sun, Zhi Yu, additional, Surdo, Antonio, additional, Teng, Xue Jian, additional, Tykhonov, Andrii, additional, Wang, Jin Zhou, additional, Wang, Lian Guo, additional, Wang, Shen, additional, Wang, Xiao Lian, additional, Wang, Yan Fang, additional, Wang, Ying, additional, Wang, Yuan Zhu, additional, Wei, Da Ming, additional, Wei, Jia Ju, additional, Wei, Yi Feng, additional, Wu, Di, additional, Wu, Jian, additional, Wu, Li Bo, additional, Wu, Sha Sha, additional, Wu, Xin, additional, Xia, Zi Qing, additional, Xu, En Heng, additional, Xu, Hai Tao, additional, Xu, Jing, additional, Xu, Zhi Hui, additional, Xu, Zi Zong, additional, Xu, Zun Lei, additional, Xue, Guo Feng, additional, Yang, Hai Bo, additional, Yang, Peng, additional, Yang, Ya Qing, additional, Yao, Hui Jun, additional, Yu, Yu Hong, additional, Yuan, Guan Wen, additional, Yuan, Qiang, additional, Yue, Chuan, additional, Zang, Jing Jing, additional, Zhang, Sheng Xia, additional, Zhang, Wen Zhang, additional, Zhang, Yan, additional, Zhang, Ya Peng, additional, Zhang, Yi, additional, Zhang, Yong Jie, additional, Zhang, Yong Qiang, additional, Zhang, Yun Long, additional, Zhang, Zhe, additional, Zhang, Zhi Yong, additional, Zhao, Cong, additional, Zhao, Hong Yun, additional, Zhao, Xun Feng, additional, Zhou, Chang Yi, additional, and Zhu, Yan, additional

Published
2023
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28. D3.6 Periodical assessment of the services V3 (Under EC Review)

Author

Fernández, Enol, Schumacher, Benjamin, Clark, Tom, Chatzikyriakou, Charis, Šustr, Zdeněk, Donvito, Giacinto, Oonk, Raymond, Paepen, Martine, Dries, Jeroen, Triantafyllis, Nikos, Kubowicz, Łukasz, David, Mario, and Benta, Zacarias

Subjects
EOSC, Earth Observation, C-SCALE, Metrics, Virtual Access, Copernicus
Abstract

This report presents an update of the assessment of the C-SCALE services provided through the Virtual Access (VA) mechanism to the users. These include computing and storage resources. The deliverable describes the existing VA installations, their management within the project, updates the usage metrics delivered from the start of the project (January 2021) until Month 24 (December 2022) and reports on the Green Computing metrics and initiatives from the C-SCALE providers.

Published
2022
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29. EOSCPillar4EarthScience: a FAIR-enabled Virtual Data Analysis Platform for Environment and Earth System communities

Author

Vernet, Marine, Harscoat, Valérie, Queric, Antoine, Maudire, Gilbert, Antonio, Fabrizio, Kulüke, Marco, Kindermann, Stephan, Sudre, Joël, Pierkot, Christelle, Jourdain, Cédric, and Donvito, Giacinto

Subjects
EOSC, FAIR data, Earth science, Environmental science
Abstract

Challenges Studying the Earth system requires heterogeneous, voluminous data of different types, formats and sources, stored in distributed domain-dependent repositories. In this context, how to: Facilitate the discovery of datasets of interest, regardless of domains and types? Facilitate and speed up remote access to heterogeneous distributed data? Facilitate on-demand cross-domain or cross-sources analysis of Earth observation and environmental Big data? EOSC-Pillar services D4Science VRE service and associated IT resources (storage, CPU, RAM) FFDS FAIR API and multi-domain FAIR data catalogue Data services from UC’s partners: Swift, iRODS, Ophidia Learn more about the EOSC-Pillar use case 2

Published
2022
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30. Exploring reference data through existing computing services for the bioinformatics community

Author

Sanaa, Yosra, Belhaj Salem, Marwa, Mathieu, Gilles, Blanchet, Christophe, Mandreoli, Pietro, Tangaro, Marco Antonio, Donvito, Giacinto, Foggetti, Nadina, Antonacci, Marica, Burlot, Laura, Lorenzo, Jonathan, Colombo, Daniele, Zambelli, Federico, Salgado, David, Béroud, Christophe, and Savini, Gianluca

Subjects
FOS: Computer and information sciences, EOSC, Open Science, Bioinformatics
Abstract

EOSC-Pillar is a European Commission Horizon 2020 funded research and innovation project. EOSC aims to provide researchers with access to data/results that are currently fragmented. EOSC-Pillar focuses on achieving these goals in Austria, Belgium, France, Germany or Italy. EOSC-Pillar’s work package 6 focuses on scientific demonstrators that show the added value of EOSC services Learn more about Use case 6.6

Published
2022
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31. D3.5 Periodical assessment of the services V2

Author

Fernández, Enol, Ziółkowski, Marcin, Donvito, Giacinto, Erdem, Hande, Benta, Zacarias, Chatzikyriakou, Charis, David, Mario, Oonk, Raymond, Triantafyllis, Nikos, and Šustr, Zdeněk

Subjects
EOSC, Earth Observation, C-SCALE, Metrics, Virtual Access, Copernicus
Abstract

This report presents an update of the assessment of the C-SCALE services provided through the Virtual Access (VA) mechanism to the users. These include computing and storage resources. The deliverable describes the existing VA installations, their management within the project, updates the usage metrics delivered from the start of the project (January 2021) until Month 18 (June 2022) and reports on the Green Computing metrics and initiatives from the C-SCALE providers.

Published
2022
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32. A set of Common Software Quality Assurance Baseline Criteria for Research Projects

Author

Orviz, Pablo, David, Mario, Campos, Isabel, Gomes, Jorge, Moltó, Germán, Tykhonov, Vyacheslav, Duma, Doina Cristina, Lopez, Alvaro, Donvito, Giacinto, Orviz, Pablo, David, Mario, Campos, Isabel, Gomes, Jorge, Moltó, Germán, Tykhonov, Vyacheslav, Duma, Doina Cristina, Lopez, Alvaro, and Donvito, Giacinto

Abstract

The purpose of this document is to define a set of quality standards, procedures and best practices to conform a Software Quality Assurance plan to serve as a reference within the European research ecosystem related projects for the adequate development and timely delivery of software products.

Published
2022

33. A set of common software quality assurance baseline criteria for research projects

Author

European Commission, Orviz, Pablo, López García, Álvaro, Duma, Doina Cristina, Donvito, Giacinto, David, Mario, Gomes, Jorge, Campos, Isabel, Moltó, Germán, Tykhonov, Vyacheslav, European Commission, Orviz, Pablo, López García, Álvaro, Duma, Doina Cristina, Donvito, Giacinto, David, Mario, Gomes, Jorge, Campos, Isabel, Moltó, Germán, and Tykhonov, Vyacheslav

Abstract

The purpose of this document is to define a set of quality standards, procedures and best practices to conform a Software Quality Assurance plan to serve as a reference within the European research ecosystem related projects for the adequate development and timely delivery of software products.

Published
2022

34. Search for gamma-ray spectral lines with the DArk Matter Particle Explorer

Author

Liang, Yun-Feng, primary, Alemanno, Francesca, additional, An, Qi, additional, Azzarello, Philipp, additional, Tiziana Barbato, Felicia Carla, additional, Bernardini, Paolo, additional, Bi, Xiao-Jun, additional, Cai, Ming-Sheng, additional, Casilli, Elisabetta, additional, Catanzani, Enrico, additional, Chang, Jin, additional, Chen, Deng-Yi, additional, Chen, Jun-Ling, additional, Chen, Zhan-Fang, additional, Cui, Ming-Yang, additional, Cui, Tian-Shu, additional, Cui, Yu-Xing, additional, Dai, Hao-Ting, additional, Benedittis, Antonio De, additional, Mitri, Ivan De, additional, de Palma, Francesco, additional, Deliyergiyev, Maksym, additional, Santo, Margherita Di, additional, Ding, Qi, additional, Dong, Tie-Kuang, additional, Dong, Zhen-Xing, additional, Donvito, Giacinto, additional, Droz, David, additional, Duan, Jing-Lai, additional, Duan, Kai-Kai, additional, D’Urso, Domenico, additional, Fan, Rui-Rui, additional, Fan, Yi-Zhong, additional, Fang, Fang, additional, Fang, Kun, additional, Feng, Chang-Qing, additional, Feng, Lei, additional, Fusco, Piergiorgio, additional, Gao, Min, additional, Gargano, Fabio, additional, Gong, Ke, additional, Gong, Yi-Zhong, additional, Guo, Dong-Ya, additional, Guo, Jian-Hua, additional, Han, Shuang-Xue, additional, Hu, Yi-Ming, additional, Huang, Guang-Shun, additional, Huang, Xiao-Yuan, additional, Huang, Yong-Yi, additional, Ionica, Maria, additional, Jiang, Wei, additional, Kong, Jie, additional, Kotenko, Andrii, additional, Kyratzis, Dimitrios, additional, Lei, Shi-Jun, additional, Li, Shang, additional, Li, Wen-Hao, additional, Li, Wei-Liang, additional, Li, Xiang, additional, Li, Xian-Qiang, additional, Liang, Yao-Ming, additional, Liu, Cheng-Ming, additional, Liu, Hao, additional, Liu, Jie, additional, Liu, Shu-Bin, additional, Liu, Yang, additional, Loparco, Francesco, additional, Luo, Chuan-Ning, additional, Ma, Miao, additional, Ma, Peng-Xiong, additional, Ma, Tao, additional, Ma, Xiao-Yong, additional, Marsella, Giovanni, additional, Mazziotta, Mario Nicola, additional, Mo, Dan, additional, Salinas, Maria Muñoz, additional, Niu, Xiao-Yang, additional, Pan, Xu, additional, Parenti, Andrea, additional, Peng, Wen-Xi, additional, Peng, Xiao-Yan, additional, Perrina, Chiara, additional, Qiao, Rui, additional, Rao, Jia-Ning, additional, Ruina, Arshia, additional, Shangguan, Zhi, additional, Shen, Wei-Hua, additional, Shen, Zhao-Qiang, additional, Shen, Zhong-Tao, additional, Silveri, Leandro, additional, Song, Jing-Xing, additional, Stolpovskiy, Mikhail, additional, Su, Hong, additional, Su, Meng, additional, Sun, Hao-Ran, additional, Sun, Zhi-Yu, additional, Surdo, Antonio, additional, Teng, Xue-Jian, additional, Tykhonov, Andrii, additional, Wang, Jin-Zhou, additional, Wang, Lian-Guo, additional, Wang, Shen, additional, Wang, Shu-Xin, additional, Wang, Xiao-Lian, additional, Wang, Ying, additional, Wang, Yan-Fang, additional, Wang, Yuan-Zhu, additional, Wei, Da-Ming, additional, Wei, Jia-Ju, additional, Wei, Yi-Feng, additional, Wu, Di, additional, Wu, Jian, additional, Wu, Li-Bo, additional, Wu, Sha-Sha, additional, Wu, Xin, additional, Xia, Zi-Qing, additional, Xu, En-Heng, additional, Xu, Hai-Tao, additional, Xu, Zhi-Hui, additional, Xu, Zun-Lei, additional, Xu, Zi-Zong, additional, Xue, Guo-Feng, additional, Yang, Hai-Bo, additional, Yang, Peng, additional, Yang, Ya-Qing, additional, Yao, Hui-Jun, additional, Yu, Yu-Hong, additional, Yuan, Guan-Wen, additional, Yuan, Qiang, additional, Yue, Chuan, additional, Zang, Jing-Jing, additional, Zhang, Sheng-Xia, additional, Zhang, Wen-Zhang, additional, Zhang, Yan, additional, Zhang, Yi, additional, Zhang, Yong-Jie, additional, Zhang, Yun-Long, additional, Zhang, Ya-Peng, additional, Zhang, Yong-Qiang, additional, Zhang, Zhe, additional, Zhang, Zhi-Yong, additional, Zhao, Cong, additional, Zhao, Hong-Yun, additional, Zhao, Xun-Feng, additional, Zhou, Chang-Yi, additional, and Zhu, Yan, additional

Published
2022
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36. Exploring reference data through existing computing services for the bioinformatics community

Author

Sanaa, Yosra, Mathieu, Gilles, Blanchet, Christophe, Mandreoli, Pietro, Tangaro, Marco Antonio, Donvito, Giacinto, Foggetti, Nadina, Antonacci, Marica, Burlot, Laura, Lorenzo, Jonathan, Colombo, Daniele, Zambelli, Federico, Salgado, David, and Savini, Gianluca

Subjects
FOS: Computer and information sciences, EOSC, Open Science, Bioinformatics
Abstract

EOSC-Pillar is a European Commission Horizon 2020 funded research and innovation project. EOSC aims to provide researchers with access to data/results that are currently fragmented. EOSC-Pillar focuses on achieving these goals in Austria, Belgium, France, Germany or Italy. EOSC-Pillar’s work package 6 focuses on scientific demonstrators that show the added value of EOSC services Learn more about Use case 6.6:https://www.eosc-pillar.eu/use-cases/exploring-reference-data-through-existing-computing-services-bioinformatics-community

Published
2021
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37. Laniakea Shaping and Developing a Cloud-based bioinformatic platform

Author

Mandreoli, Pietro, Tangaro, Marco Antonio, Chiara, Matteo, Donvito, Giacinto, Antonacci, Marica, Pesole, Graziano, and Zambelli, Federico

Subjects
EOSC, Open Science, bioinformatics
Abstract

PhD Workshop 2021 UNIMI, 7-8 October Galaxy is currently the prevailing workflow manager for bioinformatics thanks to its many useful features and a user-friendly interface. While several Galaxy public services are available to researchers, either for general purpose or dedicated to specific research domains, there are still many scenarios where a private Galaxy instance is necessary or preferable, including heavy data analysis workloads, data privacy concerns or specific customisation needs. Cloud computing gives the possibility to the scientific community to have access to state-of-the-art computational resources without having to worry about hardware installation, configuration and maintenance. This characteristic is extremely useful in scientific fields, including life science, that deal with large amounts of data and need a lot of computational and storage resources to analyse them. Cloud computing technology is a promising solution to this problem allowing small laboratories and even large institutions to have access to adequate IT resources without the need of establishing and maintaining complex and expensive computational infrastructures. Laniakea is a software framework that facilitates the provisioning of on-demand Galaxy instances as a cloud service over e-infrastructures, by leveraging the open-source software platform developed by the INDIGO-DataCloud H2020 project, which aimed to make cloud e-infrastructures more accessible by scientific communities. End-users interact with Laniakea through a web front-end allowing a general setup of a Galaxy instance. The deployment of the virtual hardware and of the Galaxy software ecosystem is subsequently performed by the INDIGO Platform as a Service layer. At the end of the process, the user gains access to a private, production-grade,fully customisable, Galaxy virtual instance.

Published
2021
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38. D3.1 Initial Design of the Compute Federation (Under EC Review)

Author

Fernández, Enol, Oonk, Raymond, Donvito, Giacinto, and Venekamp, Gerben

Subjects
EOSC, Design, Earth Observation, C-SCALE, Federation, Compute, Copernicus
Abstract

The C-SCALE Compute Federation brings together European commercial (e.g. Copernicus DIAS) and public (from e-infrastructure) computing providers to deliver a federated infrastructure to support Copernicus and Earth Observation (EO) use cases that deal with data- and compute-intensive workloads. The federation makes computing e-Infrastructure, potentially consisting of multiple services provided collaboratively by several service providers, accessible through homogeneous interfaces using a common AAI framework. Hence, C-SCALE facilitates the deployment of research applications that make use of EO and Copernicus datasets, e.g. Sentinel-series satellite data, that are distributed across multiple providers. This document describes the overall architecture of the C-SCALE Compute Federation, that provides users with the baseline computing resources to perform Earth Observation related analytics on a distributed e-Infrastructure.

Published
2021
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39. D3.1 Initial Design of the Compute Federation

Author

Fernández, Enol, Oonk, Raymond, Donvito, Giacinto, and Venekamp, Gerben

Subjects
EOSC, Design, Earth Observation, C-SCALE, Federation, Compute, Copernicus
Abstract

The C-SCALE Compute Federation brings together European commercial (e.g. Copernicus DIAS) and public (from e-infrastructure) computing providers to deliver a federated infrastructure to support Copernicus and Earth Observation (EO) use cases that deal with data- and compute-intensive workloads. The federation makes computing e-Infrastructure, potentially consisting of multiple services provided collaboratively by several service providers, accessible through homogeneous interfaces using a common AAI framework. Hence, C-SCALE facilitates the deployment of research applications that make use of EO and Copernicus datasets, e.g. Sentinel-series satellite data, that are distributed across multiple providers. This document describes the overall architecture of the C-SCALE Compute Federation, that provides users with the baseline computing resources to perform Earth Observation related analytics on a distributed e-Infrastructure.

Published
2021
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40. Observations of Forbush Decreases of Cosmic-Ray Electrons and Positrons with the Dark Matter Particle Explorer

Author

Alemanno, Francesca, primary, An, Qi, additional, Azzarello, Philipp, additional, Carla Tiziana Barbato, Felicia, additional, Bernardini, Paolo, additional, Bi, XiaoJun, additional, Cai, MingSheng, additional, Casilli, Elisabetta, additional, Catanzani, Enrico, additional, Chang, Jin, additional, Chen, DengYi, additional, Chen, JunLing, additional, Chen, ZhanFang, additional, Cui, MingYang, additional, Cui, TianShu, additional, Cui, YuXing, additional, Dai, HaoTing, additional, De Benedittis, Antonio, additional, De Mitri, Ivan, additional, de Palma, Francesco, additional, Deliyergiyev, Maksym, additional, Di Santo, Margherita, additional, Ding, Qi, additional, Dong, TieKuang, additional, Dong, ZhenXing, additional, Donvito, Giacinto, additional, Droz, David, additional, Duan, JingLai, additional, Duan, KaiKai, additional, D’Urso, Domenico, additional, Fan, RuiRui, additional, Fan, YiZhong, additional, Fang, Fang, additional, Fang, Kun, additional, Feng, ChangQing, additional, Feng, Lei, additional, Fusco, Piergiorgio, additional, Gao, Min, additional, Gargano, Fabio, additional, Gong, Ke, additional, Gong, YiZhong, additional, Guo, DongYa, additional, Guo, JianHua, additional, Han, ShuangXue, additional, Hu, YiMing, additional, Huang, GuangShun, additional, Huang, XiaoYuan, additional, Huang, YongYi, additional, Ionica, Maria, additional, Jiang, Wei, additional, Kong, Jie, additional, Kotenko, Andrii, additional, Kyratzis, Dimitrios, additional, Li, S., additional, Lei, ShiJun, additional, Li, WenHao, additional, Li, WeiLiang, additional, Li, Xiang, additional, Li, XianQiang, additional, Liang, YaoMing, additional, Liu, ChengMing, additional, Liu, Hao, additional, Liu, Jie, additional, Liu, ShuBin, additional, Liu, Yang, additional, Loparco, Francesco, additional, Luo, ChuanNing, additional, Ma, Miao, additional, Ma, PengXiong, additional, Ma, Tao, additional, Ma, XiaoYong, additional, Marsella, Giovanni, additional, Mazziotta, Mario Nicola, additional, Mo, Dan, additional, Niu, XiaoYang, additional, Pan, Xu, additional, Parenti, Andrea, additional, Peng, WenXi, additional, Peng, XiaoYan, additional, Perrina, Chiara, additional, Qiao, Rui, additional, Rao, JiaNing, additional, Ruina, Arshia, additional, Salinas, MariaMunoz, additional, Shangguan, Zhi, additional, Shen, WeiHua, additional, Shen, ZhaoQiang, additional, Shen, ZhongTao, additional, Silveri, Leandro, additional, Song, JingXing, additional, Stolpovskiy, Mikhail, additional, Su, Hong, additional, Su, Meng, additional, Sun, HaoRan, additional, Sun, ZhiYu, additional, Surdo, Antonio, additional, Teng, XueJian, additional, Tykhonov, Andrii, additional, Wang, JinZhou, additional, Wang, LianGuo, additional, Wang, Shen, additional, Wang, ShuXin, additional, Wang, XiaoLian, additional, Wang, Ying, additional, Wang, YanFang, additional, Wang, YuanZhu, additional, Wei, DaMing, additional, Wei, JiaJu, additional, Wei, YiFeng, additional, Wu, Di, additional, Wu, Jian, additional, Wu, LiBo, additional, Wu, Sha Sha, additional, Wu, Xin, additional, Xia, ZiQing, additional, Xu, EnHeng, additional, Xu, HaiTao, additional, Xu, ZhiHui, additional, Xu, ZunLei, additional, Xue, GuoFeng, additional, Xu, ZiZong, additional, Yang, HaiBo, additional, Yang, Peng, additional, Yang, YaQing, additional, Jun Yao, Hui, additional, Yu, YuHong, additional, Yuan, GuanWen, additional, Yuan, Qiang, additional, Yue, Chuan, additional, Zang, JingJing, additional, Zhang, ShengXia, additional, Zhang, WenZhang, additional, Zhang, Yan, additional, Zhang, Yi, additional, Zhang, YongJie, additional, Zhang, YunLong, additional, Zhang, YaPeng, additional, Zhang, YongQiang, additional, Zhang, ZhiYong, additional, Zhang, Zhe, additional, Zhao, Cong, additional, Zhao, HongYun, additional, Zhao, XunFeng, additional, Zhou, ChangYi, additional, Zhu, Yan, additional, Chen, Wei, additional, Feng, Li, additional, Luo, Xi, additional, and Zhu, ChengRui, additional

Published
2021
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41. EOSC-hub D10.7 Final technical architecture update and interoperability guidelines update

Author

Costantini, Alessandro, Donvito, Giacinto, and Scardaci, Diego

Subjects
technical architecture, interoperability
Abstract

This document describes how the architecture and the interoperability guidelines developed within EOSC-hub have been updated in accordance with the evolution of the project activities and the feedback collected among various stakeholders, including user communities. It also provides information about their future evolution after the end of the project.

Published
2021
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42. EOSC-hub D3.4 Final report on dissemination and exploitation of project results

Author

Heikkurinen, Matti, Wilk, Roksana, Małgorzata Krakowian, Appleton, Owen, Weber, Pavel, Kennedy, John, Cauhé, Elisa, Robertson, Dale, Donvito, Giacinto, La Rocca, Giuseppe, Sipos, Gergely, and Testi, Debora

Subjects
dissemination, exploitation
Abstract

This document provides a final report on the key exploitable results, including aspects such as the definition, value proposition, IP management, exploitation path and dissemination activities and adoption.

Published
2021
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43. Charge Loss Correction in the Silicon-Tungsten Tracker-Converter for Proton-Helium Charge Identification in the DAMPE Detector

Author

Ruina, Arshia, primary, Stolpovskiy, M., additional, Deliyergiyev, Maksym, additional, Cui, Yu Xing, additional, Alemanno, F., additional, An, Qi, additional, Azzarello, P., additional, Barbato, F. C. T., additional, Bernardini, P., additional, Bi, Xiao Jun, additional, Cai, Ming Sheng, additional, Casilli, E., additional, Catanzani, E., additional, Chang, Jin, additional, Chen, Deng Yi, additional, Chen, Jun Ling, additional, Chen, Zhan Fang, additional, Cui, Ming Yang, additional, Cui, Tian Shu, additional, Dai, Hao Ting, additional, De Benedittis, A., additional, De Mitri, I., additional, de Palma, F., additional, Di santo, M., additional, Ding, Qi, additional, Dong, Tie Kuang, additional, Dong, Zhen Xing, additional, DONVITO, Giacinto, additional, Droz, D., additional, Duan, Jing Lai, additional, Duan, Kai Kai, additional, Durso, D., additional, Fan, Rui Rui, additional, Fan, Yi Zhong, additional, Fang, Fang, additional, Fang, Kun, additional, Feng, Chang Qing, additional, Feng, Lei, additional, Fusco, Piergiorgio, additional, Gallo, V., additional, Gao, Min, additional, Gargano, Fabio, additional, Gong, Ke, additional, Gong, Yi Zhong, additional, Guo, Dong Ya, additional, Guo, Jian Hua, additional, Han, Shuang Xue, additional, Hu, Yi Ming, additional, Huang, Guang Shun, additional, Huang, Xiao Yuan, additional, Huang, Yong Yi, additional, Ionica, M., additional, Jiang, Wei, additional, Kong, Jie, additional, Kotenko, A., additional, Kyratzis, D., additional, Lei, Shi Jun, additional, Li, Wei Liang, additional, Li, Wen Hao, additional, Li, Xiang, additional, Li, Xian Qiang, additional, Liang, Yao Ming, additional, Liu, Cheng Ming, additional, Liu, Hao, additional, Liu, Jie, additional, Liu, Shu Bin, additional, Liu, Yang, additional, Loparco, Francesco, additional, Luo, Chuan Ning, additional, Ma, Miao, additional, Ma, Peng Xiong, additional, Ma, Tao, additional, Ma, Xiao Yong, additional, Marsella, G., additional, Mazziotta, Mario Nicola, additional, Mo, Dan, additional, Niu, Xiao Yang, additional, Pan, Xu, additional, Parenti, A., additional, Peng, Wen Xi, additional, Peng, Xiao Yan, additional, Perrina, C., additional, Qiao, Rui, additional, Rao, Jia Ning, additional, Ruina, A., additional, Salinas, M. M., additional, Shang, Guan Zhi, additional, Shen, Wei Hua, additional, Shen, Zhao Qiang, additional, Shen, Zhong Tao, additional, Silveri, L., additional, Song, Jing Xing, additional, Su, Hong, additional, Su, M., additional, Sun, Hao Ran, additional, Sun, Zhi Yu, additional, Surdo, A., additional, Teng, Xue Jian, additional, Tykhonov, A., additional, Wang, H., additional, Wang, Jin Zhou, additional, Wang, Lian Guo, additional, Wang, Shen, additional, Wang, Shu Xin, additional, Wang, Xiao Lian, additional, Wang, Yan Fang, additional, Wang, Ying, additional, Wang, Yuan Zhu, additional, Wei, Da Ming, additional, Wei, Jia Ju, additional, Wei, Yi Feng, additional, Wu, Di, additional, Wu, Jian, additional, Wu, L. B., additional, Wu, Sha Sha, additional, Wu, X., additional, Xia, Zi Qing, additional, Xu, En Heng, additional, Xu, Hai Tao, additional, Xu, Zhi Hui, additional, Xu, Zi Zong, additional, Xu, Zun Lei, additional, Xue, Guo Feng, additional, Yang, Hai Bo, additional, Yang, Peng, additional, Yang, Ya Qing, additional, Yao, Hui Jun, additional, Yu, Yu Hong, additional, Yuan, Guan Wen, additional, Yuan, Qiang, additional, Yue, Chuan, additional, Zang, Jing Jing, additional, Zhang, Sheng Xia, additional, Zhang, Wen Zhang, additional, Zhang, Yan, additional, Zhang, Ya Peng, additional, Zhang, Yi, additional, Zhang, Yong Jie, additional, Zhang, Yong Qiang, additional, Zhang, Yun Long, additional, Zhang, Zhe, additional, Zhang, Zhi Yong, additional, Zhao, Cong, additional, Zhao, Hong Yun, additional, Zhao, Xun Feng, additional, and Zhou, Chang Yi, additional

Published
2021
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44. Analyzing the Fermi Bubbles with DArk Matter Particle Explorer

Author

Shen, Zhao-Qiang, primary, Duan, Kai-Kai, additional, Xu, Zun-Lei, additional, Li, Xiang, additional, Yuan, Qiang, additional, Alemanno, F., additional, An, Qi, additional, Azzarello, P., additional, Barbato, F. C. T., additional, Bernardini, P., additional, Bi, Xiao Jun, additional, Cai, Ming Sheng, additional, Casilli, E., additional, Catanzani, E., additional, Chang, Jin, additional, Chen, Deng Yi, additional, Chen, Jun Ling, additional, Chen, Zhan Fang, additional, Cui, Ming Yang, additional, Cui, Tian Shu, additional, Cui, Yu Xing, additional, Dai, Hao Ting, additional, De Benedittis, A., additional, De Mitri, I., additional, de Palma, F., additional, Ding, Qi, additional, Di Santo, I., additional, Dong, Tie Kuang, additional, Dong, Zhen Xing, additional, DONVITO, Giacinto, additional, Droz, D., additional, Duan, Jing Lai, additional, D'urso, D., additional, Fan, Rui Rui, additional, Fan, Yi Zhong, additional, Fang, Fang, additional, Fang, Kun, additional, Feng, Chang Qing, additional, Feng, Lei, additional, Fusco, Piergiorgio, additional, Gallo, V., additional, Gao, Min, additional, Gargano, Fabio, additional, Gong, Ke, additional, Gong, Yi Zhong, additional, Guo, Dong Ya, additional, Guo, Jian Hua, additional, Han, Shuang Xue, additional, Hu, Yi Ming, additional, Huang, Guang Shun, additional, Huang, Xiao Yuan, additional, Huang, Yong Yi, additional, Ionica, M., additional, Jiang, Wei, additional, Kong, Jie, additional, Kotenko, A., additional, Kyratzis, D., additional, Lei, Shi Jun, additional, Li, Wei Liang, additional, Li, Wen Hao, additional, Li, Xian Qiang, additional, Liang, Yao Ming, additional, Liu, Cheng Ming, additional, Liu, Hao, additional, Liu, Jie, additional, Liu, Shu Bin, additional, Liu, Yang, additional, Loparco, Francesco, additional, Luo, Chuan Ning, additional, Ma, Miao, additional, Ma, Peng Xiong, additional, Ma, Tao, additional, Ma, Xiao Yong, additional, Marsella, G., additional, Mazziotta, Mario Nicola, additional, Mo, Dan, additional, Niu, Xiao Yang, additional, Pan, Xu, additional, Parenti, A., additional, Peng, Wen Xi, additional, Peng, Xiao Yan, additional, Perrina, C., additional, Qiao, Rui, additional, Rao, Jia Ning, additional, Ruina, A., additional, Salinas, M. M., additional, Shang, Guan Zhi, additional, Shen, Wei Hua, additional, Shen, Zhong Tao, additional, Silveri, L., additional, Song, Jing Xing, additional, Stolpovskiy, M., additional, Su, Hong, additional, Su, Meng, additional, Sun, Hao Ran, additional, Sun, Zhi Yu, additional, Surdo, A., additional, Teng, Xue Jian, additional, Tykhonov, Andrii, additional, Wang, H., additional, Wang, Jin Zhou, additional, Wang, Lian Guo, additional, Wang, Shen, additional, Wang, Shu Xin, additional, Wang, Xiao Lian, additional, Wang, Yan Fang, additional, Wang, Ying, additional, Wang, Yuan Zhu, additional, Wei, Da Ming, additional, Wei, Jia Ju, additional, Wei, Yi Feng, additional, Wu, Di, additional, Wu, Jian, additional, Wu, Li Bo, additional, Wu, Sha Sha, additional, Wu, Xin, additional, Xia, Zi Qing, additional, Xu, En Heng, additional, Xu, Hai Tao, additional, Xu, Zhi Hui, additional, Xu, Zi Zong, additional, Xue, Guo Feng, additional, Yang, Hai Bo, additional, Yang, Peng, additional, Yang, Ya Qing, additional, Yao, Hui Jun, additional, Yu, Yu Hong, additional, Yuan, Guan Wen, additional, Yue, Chuan, additional, Zang, Jing Jing, additional, Zhang, Sheng Xia, additional, Zhang, Wen Zhang, additional, Zhang, Yan, additional, Zhang, Ya Peng, additional, Zhang, Yi, additional, Zhang, Yong Jie, additional, Zhang, Yong Qiang, additional, Zhang, Yun Long, additional, Zhang, Zhe, additional, Zhang, Zhi Yong, additional, Zhao, Cong, additional, Zhao, Hong Yun, additional, Zhao, Xun Feng, additional, and Zhou, Chang Yi, additional

Published
2021
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46. Distributed Analysis in CMS

Author

Fanfani, Alessandra, Afaq, Anzar, Sanches, Jose Afonso, Andreeva, Julia, Bagliesi, Giusepppe, Bauerdick, Lothar, Belforte, Stefano, Bittencourt Sampaio, Patricia, Bloom, Ken, Blumenfeld, Barry, Bonacorsi, Daniele, Brew, Chris, Calloni, Marco, Cesini, Daniele, Cinquilli, Mattia, Codispoti, Giuseppe, D’Hondt, Jorgen, Dong, Liang, Dongiovanni, Danilo, Donvito, Giacinto, Dykstra, David, Edelmann, Erik, Egeland, Ricky, Elmer, Peter, Eulisse, Giulio, Evans, Dave, Fanzago, Federica, Farina, Fabio, Feichtinger, Derek, Fisk, Ian, Flix, Josep, Grandi, Claudio, Guo, Yuyi, Happonen, Kalle, Hernàndez, José M., Huang, Chih-Hao, Kang, Kejing, Karavakis, Edward, Kasemann, Matthias, Kavka, Carlos, Khan, Akram, Kim, Bockjoo, Klem, Jukka, Koivumäki, Jesper, Kress, Thomas, Kreuzer, Peter, Kurca, Tibor, Kuznetsov, Valentin, Lacaprara, Stefano, Lassila-Perini, Kati, Letts, James, Lindén, Tomas, Lueking, Lee, Maes, Joris, Magini, Nicolò, Maier, Gerhild, Mcbride, Patricia, Metson, Simon, Miccio, Vincenzo, Padhi, Sanjay, Pi, Haifeng, Riahi, Hassen, Riley, Daniel, Rossman, Paul, Saiz, Pablo, Sartirana, Andrea, Sciabà, Andrea, Sekhri, Vijay, Spiga, Daniele, Tuura, Lassi, Vaandering, Eric, Vanelderen, Lukas, Van Mulders, Petra, Vedaee, Aresh, Villella, Ilaria, Wicklund, Eric, Wildish, Tony, Wissing, Christoph, and Würthwein, Frank

Published
2010
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48. Seismicity of the Gargano promontory (Southern Italy) after 7 years of local seismic network operation: Data release of waveforms from 2013 to 2018

Author

Filippucci, Marilena, primary, Miccolis, Simona, additional, Castagnozzi, Angelo, additional, Cecere, Gianpaolo, additional, de Lorenzo, Salvatore, additional, Donvito, Giacinto, additional, Falco, Luigi, additional, Michele, Maddalena, additional, Nicotri, Stefano, additional, Romeo, Annalisa, additional, Selvaggi, Giulio, additional, and Tallarico, Andrea, additional

Published
2021
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50. DEEP framework for deep learning

Author

Alic, Andy S., Antonacci, Marica, Caballer, Miguel, Campos, Isabel, Costantini, Alessandro, David, Mario, Dlugolinsky, Stefan, Donvito, Giacinto, Gomes, Jorge, Hardt, Marcus, Heredia, Ignacio, Hluchy, Ladislav, Ito, Keiichi, Kozlov, Valentin, Lloret, Lara, López García, Alvaro, Marco, Jesus, Matyska, Ludek, Moltó, Germán, Nguyen, Giang, Orviz, Pablo, Plociennik, Marcin, Šustr, Zdeněk, Tran, Viet, Wolniewicz, Pawel, and Castell, Wolfgang zu

Subjects
DATA processing & computer science, Scientific Software Development, ddc:004, AI/Machine Learning/Deep Learning, Clouds and Distributed Computing
Abstract

The DEEP-Hybrid-DataCloud project developed a distributed architecture to leverage intensive computing techniques such as needed for deep learning. The DEEP framework applies a hybrid-cloud approach and provides a set of tools and cloud services to cover the whole machine learning cycle: from models creation, training, validation and testing to model serving and sharing. These tools and services in particular include DEEP API for a web access to machine learning models, Pilot testbed with heterogeneous resources to develop and test models, DEEP Marketplace for easy sharing, DevOps approach with Data Science template and CI/CD pipeline to facilitate development, testing, and delivery of machine learning applications. The project recently published its second software release and platform, code named DEEP Rosetta, and provides distributed training facility for machine learning, artificial intelligence and deep learning via EOSC portal.

Published
2020

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