scholarly journals Compartilhamento de dados e e-Science: explorando um novo conceito para a comunicação científica │ Data sharing and e-Science: exploring a new concept for scientific communication

2012 ◽  
Vol 8 (2) ◽  
Author(s):  
Jackson Da Silva Medeiros ◽  
Sônia Elisa Caregnato
Keyword(s):  
Data Sharing ◽  
Scientific Collaboration ◽  
Data Centers ◽  
Research Work ◽  
Large Data ◽  
Scientific Communication ◽  
Scientific Data ◽  
Data Exploration ◽  
New Methods ◽  
De Se

Resumo O trabalho objetiva apresentar um novo conceito que surge visando o compartilhamento de dados científicos primários: a e-Science. Este conceito nasce a partir dos novos métodos de se fazer pesquisa, onde grandes quantidades de dados científicos primários são gerados por pesquisas em diversos ramos das ciências e processados e armazenados em grandes centros de dados e repositórios responsáveis pelo gerenciamento que permita a cientistas distribuídos pelo mundo acessar e analisar esses dados a fim de reutilizá-los em suas pesquisas. Finaliza observando que a perspectiva de compartilhar dados entre cientistas pode fazer com que as possibilidades de colaboração se tornem cada vez mais reais, criando ambientes onde as viabilidades de compartilhamento de resultados de pesquisas promovam o desenvolvimento da ciência e tecnologia e permitindo que recursos sejam utilizados de forma a avançar a ciência.Palavras-chave e-science; compartilhamento de dados científicos; exploração de dados; colaboração científicaAbstract The study aims to present a new concept of scientific data sharing. This concept originates from the new methods of doing research, in which large amounts of primary scientific data are generated by scientific research in various branches of science, processed and stored in large data centers and repositories allowing scientists world-wide to access and analyze these data in order to reuse them in their research work. It concludes noting that the prospect of sharing data among scientists can make collaboration become ever more real, creating environments where feasibility of sharing research results promotes the development of science and technology and allows resources to be used in order to advance science. Keywords e-science; scientific data sharing; data exploration; scientific collaboration

National Tibetan Plateau Data Center

2020 ◽  
Author(s):  
Xin Li ◽  
Xiaoduo Pan ◽  
Xuejun Guo ◽  
Xiaolei Niu ◽  
Xiaojuan Yang ◽  
...  
Keyword(s):  
Big Data ◽  
Tibetan Plateau ◽  
Data Sharing ◽  
Data Center ◽  
Environmental Changes ◽  
Large Data ◽  
Scientific Data ◽  
Data Repository ◽  
Observation System ◽  
The Tibetan Plateau

<p>National Tibetan Plateau Data Center (TPDC) is one of the first 20 national data centers authorized by the Ministry of Science and Technology of China in 2019 . It is the only data center in China with the most complete scientific data for the Tibetan Plateau and surrounding regions. There are more than 1700 datasets covering many disciplines such as geography, atmospheric science, cryospheric science, hydrology, ecology, geology, geophysics, natural resource science, social economy, and other fields. All data are sorted and integrated in a strict way accordance with the data standards specified by TPDC and the relevant data acquisition specifications. The mission of the data center is to establish a big data center for Third-Pole Earth System Sciences to integrate ThirdPole data resources, particularly those obtained through the implementation of the Third-Pole "Super Monitoring" plan; to develop cutting edge observation technology for extreme environments; and to build a comprehensive and intelligent Internet of Things (IoT) observation system for the Pan-Third Pole region. These developments will facilitate the modeling of environmental changes in the Pan-Third Pole with improved accuracy and performance, as well as support decision-making for sustainable development of the Pan-Third Pole region.</p><p>TPDC complies with the “findable, accessible, interoperable and reusable (FAIR)” data sharing principles, in which, the scientific data and metadata can be 'findable' by anyone for exploring and using, can be 'accessible' for being examined, can be 'interoperable' for being analyzed and integrated with comparable data through the use of common vocabulary and formats, can be 'reusable' for public as a result of robust metadata, provenance information and clear usage license. Under the guidance of FAIR data sharing principle, Pan-Third big data system provides online sharing manner for data users, supplemented by offline sharing manner, with bilingual data sharing in Chinese and English.</p><p>TPDC has joined WMO (World Meteorological Organization) to promote the project of Integrated Global Cryosphere Information System (IGCryoIS), aiming to collect and share multi-source data in global regions where data is difficult to obtain. Recently TPDC and NSIDC (National Snow and Ice Data Center) officially signed a memorandum of collaboration on data sharing and research to start comprehensive cooperation. TPDC is strengthening cooperation with the international data organizations (e.g. CODATA, WDS) and providing data support for the international science programs of the Tibetan Plateau (e.g. TPE, ANSO). TPDC is applying to become a recommended data repository for the international mainstream journals so as to encourage data authors to share their well-documented, useful and preserved data by giving them credit and recognition.</p><p> In a word, TPDC stores, integrates, analyses, excavates and publishes scientific data such as resources, environment, ecology and atmosphere in Pan-third polar region, gathers Pan-third polar core data resources, forms Pan-third polar key scientific data products, and gradually develops online large data analysis, model application and other functions. Furthermore, a cloud service platform will be built for the extensive integration of data, methods, models and services in Pan-Third Pole Science and to promote the application of large data methods in Pan-Third Pole Science Research.</p>

scholarly journals Open Data in Scientific Communication

2018 ◽  
Vol 60 (3) ◽  
pp. 192-198
Author(s):  
Dorota Grygoruk
Keyword(s):  
Information Technology ◽  
Open Access ◽  
Data Management ◽  
Data Sharing ◽  
Open Data ◽  
Scientific Communication ◽  
Scientific Data ◽  
Scientific Publications ◽  
Management Policies ◽  
Access Model

Abstract The development of information technology makes it possible to collect and analyse more and more data resources. The results of research, regardless of the discipline, constitute one of main sources of data. Currently, the research results are increasingly being published in the Open Access model. The Open Access concept has been accepted and recommended worldwide by many institutions financing and implementing research. Initially, the idea of openness concerned only the results of research and scientific publications; at present, more attention is paid to the problem of sharing scientific data, including raw data. Proceedings towards open data are intricate, as data specificity requires the development of an appropriate legal, technical and organizational model, followed by the implementation of data management policies at both the institutional and national levels. The aim of this publication was to present the development of the open data concept in the context of open access idea and problems related to defining data in the process of data sharing and data management.

scholarly journals A Hybrid-parallel Architecture for Applications in Bioinformatics

2017 ◽  
Author(s):  
Jan Christian Kässens
Keyword(s):  
Graphics Processing Units ◽  
High Performance ◽  
Data Centers ◽  
Association Studies ◽  
Parallel Architecture ◽  
Large Data ◽  
Scientific Data ◽  
Genome Wide Association Studies ◽  
Application Development ◽  
Concurrent Software

Since the advent of Next Generation Sequencing (NGS) technology, the amount of data from whole genome sequencing has been rising fast. In turn, the availability of these resources led to the tapping of whole new research fields in molecular and cellular biology, producing even more data. On the other hand, the available computational power is only increasing linearly. In recent years though, special-purpose high-performance devices started to become prevalent in today’s scientific data centers, namely graphics processing units (GPUs) and, to a lesser extent, field-programmable gate arrays (FPGAs). Driven by the need for performance, developers started porting regular applications to GPU frameworks and FPGA configurations to exploit the special operations only these devices may perform in a timely manner. However, applications using both accelerator technologies are still rare. Major challenges in joint GPU/FPGA application development include the required deep knowledge of associated programming paradigms and the efficient communication both types of devices. In this work, two algorithms from bioinformatics are implemented on a custom hybrid-parallel hardware architecture and a highly concurrent software platform. It is shown that such a solution is not only possible to develop but also its ability to outperform implementations on similar- sized GPU or FPGA clusters in terms of both performance and energy consumption. Both algorithms analyze case/control data from genome- wide association studies to find interactions between two or three genes with different methods. Especially in the latter case, the newly available calculation power and method enables analyses of large data sets for the first time without occupying whole data centers for weeks. The success of the hybrid-parallel architecture proposal led to the development of a high- end array of FPGA/GPU accelerator pairs to provide even better runtimes and more possibilities.

scholarly journals A Hybrid-parallel Architecture for Applications in Bioinformatics

2017 ◽  
Author(s):  
Jan Christian Kässens
Keyword(s):  
Graphics Processing Units ◽  
High Performance ◽  
Data Centers ◽  
Association Studies ◽  
Parallel Architecture ◽  
Large Data ◽  
Scientific Data ◽  
Genome Wide Association Studies ◽  
Application Development ◽  
Concurrent Software

Since the advent of Next Generation Sequencing (NGS) technology, the amount of data from whole genome sequencing has been rising fast. In turn, the availability of these resources led to the tapping of whole new research fields in molecular and cellular biology, producing even more data. On the other hand, the available computational power is only increasing linearly. In recent years though, special-purpose high-performance devices started to become prevalent in today’s scientific data centers, namely graphics processing units (GPUs) and, to a lesser extent, field-programmable gate arrays (FPGAs). Driven by the need for performance, developers started porting regular applications to GPU frameworks and FPGA configurations to exploit the special operations only these devices may perform in a timely manner. However, applications using both accelerator technologies are still rare. Major challenges in joint GPU/FPGA application development include the required deep knowledge of associated programming paradigms and the efficient communication both types of devices. In this work, two algorithms from bioinformatics are implemented on a custom hybrid-parallel hardware architecture and a highly concurrent software platform. It is shown that such a solution is not only possible to develop but also its ability to outperform implementations on similar- sized GPU or FPGA clusters in terms of both performance and energy consumption. Both algorithms analyze case/control data from genome- wide association studies to find interactions between two or three genes with different methods. Especially in the latter case, the newly available calculation power and method enables analyses of large data sets for the first time without occupying whole data centers for weeks. The success of the hybrid-parallel architecture proposal led to the development of a high- end array of FPGA/GPU accelerator pairs to provide even better runtimes and more possibilities.

Earth System Scientific Data Sharing Research and Practice

2010 ◽  
Vol 12 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Yunqiang ZHU ◽  
Jiulin SUN ◽  
Shunbao LIAO ◽  
Yapeng YANG ◽  
Huazhong ZHU ◽  
...  
Keyword(s):  
Data Sharing ◽  
Scientific Data ◽  
Earth System ◽  
Research And Practice

Research on Earth System Scientific Data Sharing Platform Based on SOA

2010 ◽  
Vol 11 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Yunqiang ZHU ◽  
Min FENG ◽  
Jia SONG ◽  
Runda LIU
Keyword(s):  
Data Sharing ◽  
Scientific Data ◽  
Earth System

Why they shared: recovering early arguments for sharing social scientific data

2020 ◽  
Vol 33 (2) ◽  
pp. 101-119
Author(s):  
Emily Hauptmann
Keyword(s):  
Social Science ◽  
Data Sharing ◽  
Financial Stability ◽  
Social Research ◽  
Scientific Data ◽  
Social Scientists ◽  
Ethical Imperative ◽  
Social Scientific ◽  
The University ◽  
University Consortium

ArgumentMost social scientists today think of data sharing as an ethical imperative essential to making social science more transparent, verifiable, and replicable. But what moved the architects of some of the U.S.’s first university-based social scientific research institutions, the University of Michigan’s Institute for Social Research (ISR), and its spin-off, the Inter-university Consortium for Political and Social Research (ICPSR), to share their data? Relying primarily on archived records, unpublished personal papers, and oral histories, I show that Angus Campbell, Warren Miller, Philip Converse, and others understood sharing data not as an ethical imperative intrinsic to social science but as a useful means to the diverse ends of financial stability, scholarly and institutional autonomy, and epistemological reproduction. I conclude that data sharing must be evaluated not only on the basis of the scientific ideals its supporters affirm, but also on the professional objectives it serves.

Research and Application on Manufacturing Scientific Data Sharing Service Customization

2012 ◽  
Vol 522 ◽  
pp. 770-775
Author(s):  
Yu Zheng ◽  
Yan Rong Ni ◽  
Deng Zhe Ma
Keyword(s):  
Data Sharing ◽  
Scientific Data ◽  
Prototype System ◽  
Data Service ◽  
Service Model ◽  
Resource Model ◽  
Data Resource ◽  
Key Technologies ◽  
Service Customization ◽  
Registration Service

In order to satisfy the needs of fast and convenient customization of manufacturing scientific data sharing service, the data service customization process and its key technologies were studied. First the data resource model and the customization oriented professional data service model were studied. Then the processes of service customization, from resource registration, service definition, service parsing, to service generating, were analyzed. The parsing engine based on service parsing technology and incubator based on service generating technology was emphasized. Finally the prototype system was developed and validated by an example.

scholarly journals On Students Training in a New Masters Program “Molecular Biology and Plant Agrobiotechnology” at SaintPetersburg State University (SPbSU)

2020 ◽  
Vol 3 (1) ◽  
pp. 7-12
Author(s):  
I. A. Tikhonovich ◽  
L. A. Lutova ◽  
T. V. Matveeva
Keyword(s):  
Molecular Biology ◽  
Agricultural Research ◽  
Research Work ◽  
Scientific Data ◽  
Teaching Staff ◽  
Industrial Complex ◽  
State University ◽  
High Productivity ◽  
Research Institutes ◽  
Active Implementation

The development of an agro-industrial complex under present-day conditions is impossible to imagine without the development of agro-biotechnology, which in turn requires specialists with profound knowledge of biology, chemistry and related sciences. In this regard, training of personnel is needed to ensure active implementation of modern technologies in agricultural sciences. Until recently, such specialists have not been trained at classical universities, to which St. Petersburg State University belongs. To deal with this challenge, a Masters Program «Molecular Biology and Agrobiotechnology of Plants» has been developed and is being implemented in SPbSU. Teaching staff from eight departments of the Biological Faculty of SPbSU is involved in the creation and implementation of the Program. The Program in question is focused on familiarizing students with the modern problems, achievements, methodology of agro-biotechnology of plants, as well as on practical application of the obtained knowledge. Special attention is paid to the formation of trainees’ perceptions of the possibility and necessity of bringing plant breeding to the level of requirements and possibilities of the «post-genome era» to achieve high productivity and sustainability of agricultural production with minimal environmental risks. The Program seamlessly integrates practical exercises and students’ research work in the SPbSU facilities, as well as that performed at St. Petersburg research institutes. Much attention is paid to the development of students’ skills in conducting scientific discussions and in presenting their scientific data in different formats, for instance in English, which is very important for monitoring current scientific trends and integrating own research into world science. The Program is popular with students and many of its graduates have been employed by the leading biological and agricultural research institutes.

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