scholarly journals The Brazilian Science Data Center (BSDC)

2017 ◽  
Vol 45 ◽  
pp. 1760075 ◽  
Author(s):  
Ulisses Barres de Almeida ◽  
Benno Bodmann ◽  
Paolo Giommi ◽  
Carlos H. Brandt
Keyword(s):  
Data Center ◽  
Large Data ◽  
Scientific Work ◽  
Scientific Data ◽  
Science Data ◽  
Data Accessibility ◽  
Related Data ◽  
Space Agency ◽  
Data Archives ◽  
Access To Data

Astrophysics and Space Science are becoming increasingly characterised by what is now known as “big data”, the bottlenecks for progress partly shifting from data acquisition to “data mining”. Truth is that the amount and rate of data accumulation in many fields already surpasses the local capabilities for its processing and exploitation, and the efficient conversion of scientific data into knowledge is everywhere a challenge. The result is that, to a large extent, isolated data archives risk being progressively likened to “data graveyards”, where the information stored is not reused for scientific work. Responsible and efficient use of these large data-sets means democratising access and extracting the most science possible from it, which in turn signifies improving data accessibility and integration. Improving data processing capabilities is another important issue specific to researchers and computer scientists of each field. The project presented here wishes to exploit the enormous potential opened up by information technology at our age to advance a model for a science data center in astronomy which aims to expand data accessibility and integration to the largest possible extent and with the greatest efficiency for scientific and educational use. Greater access to data means more people producing and benefiting from information, whereas larger integration of related data from different origins means a greater research potential and increased scientific impact. The project of the BSDC is preoccupied, primarily, with providing tools and solutions for the Brazilian astronomical community. It nevertheless capitalizes on extensive international experience, and is developed in full cooperation with the ASI Science Data Center (ASDC), from the Italian Space Agency, granting it an essential ingredient of internationalisation. The BSDC is Virtual Observatory-complient and part of the “Open Universe”, a global initiative built under the auspices of the United Nations.

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 Virtual European Solar & Planetary Access (VESPA): Progress and prospects

2020 ◽  
Author(s):  
Stéphane Erard ◽  
Baptiste Cecconi ◽  
Pierre Le Sidaner ◽  
Angelo Pio Rossi ◽  
Carlos Brandt ◽  
...  
Keyword(s):  
Distribution System ◽  
Data Science ◽  
Large Data ◽  
Open Science ◽  
Planetary Science ◽  
Science Data ◽  
Horizon 2020 ◽  
Data Services ◽  
Related Data ◽  
Research And Innovation

<p>The H2020 Europlanet-2020 programme, which ended on Aug 31<sup>st</sup>, 2019, included an activity called VESPA (Virtual European Solar and Planetary Access), which focused on adapting Virtual Observatory (VO) techniques to handle Planetary Science data [1] [2]. The outcome of this activity is a contributive data distribution system where data services are located and maintained in research institutes, declared in a registry, and accessed by several clients based on a specific access protocol. During Europlanet-2020, 52 data services were installed, including the complete ESA Planetary Science Archive, and the outcome of several EU funded projects. Data are described using the EPN-TAP protocol, which parameters describe acquisition and observing conditions as well as data characteristics (physical quantity, data type, etc). A main search portal has been developed to optimize the user experience, which queries all services together. Compliance with VO standards ensures that existing tools can be used as well, either to access or visualize the data. In addition, a bridge linking the VO and Geographic Information Systems (GIS) has been installed to address formats and tools used to study planetary surfaces; several large data infrastructures were also installed or upgraded (SSHADE for lab spectroscopy, PVOL for amateurs images, AMDA for plasma-related data).</p><p>In the framework of the starting Europlanet-2024 programme, the VESPA activity will complete this system even further: 30-50 new data services will be installed, focusing on derived data, and experimental data produced in other Work Packages of Europlanet-2024; connections between PDS4 and EPN-TAP dictionaries will make PDS metadata searchable from the VESPA portal and vice versa; Solar System data present in astronomical VO catalogues will be made accessible, e.g. from the VizieR database. The search system will be connected with more powerful display and analysing tools: a run-on-demand platform will be installed, as well as Machine Learning capacities to process the available content. Finally, long-term sustainability will be improved by setting VESPA hubs to assist data providers in maintaining their services, and by using the new EU-funded European Open Science Cloud (EOSC). In addition to favoring data exploitation, VESPA will provide a handy and economical solution to Open Science challenges in the field.</p><p>The Europlanet 2020 & 2024 Research Infrastructure project have received funding from the European Union's Horizon 2020 research and innovation programme under grant agreements No 654208 & 871149.</p><p>[1] Erard et al 2018, Planet. Space Sci. <strong>150</strong>, 65-85. 10.1016/j.pss.2017.05.013. ArXiv 1705.09727  </p><p>[2] Erard et al. 2020, Data Science Journal <strong>19</strong>, 22. doi: 10.5334/dsj-2020-022.</p>

scholarly journals Data Cleaners for Pristine Datasets: Visibility and Invisibility of Data Processors in Social Science

2018 ◽  
Vol 44 (1) ◽  
pp. 52-73 ◽  
Author(s):  
Jean-Christophe Plantin
Keyword(s):  
Social Science ◽  
Data Processing ◽  
Data Sharing ◽  
Social Study ◽  
Scientific Data ◽  
Data Sets ◽  
Processing Unit ◽  
Science Data ◽  
Data Set ◽  
Data Archives

This article investigates the work of processors who curate and “clean” the data sets that researchers submit to data archives for archiving and further dissemination. Based on ethnographic fieldwork conducted at the data processing unit of a major US social science data archive, I investigate how these data processors work, under which status, and how they contribute to data sharing. This article presents two main results. First, it contributes to the study of invisible technicians in science by showing that the same procedures can make technical work invisible outside and visible inside the archive, to allow peer review and quality control. Second, this article contributes to the social study of scientific data sharing, by showing that the organization of data processing directly stems from the conception that the archive promotes of a valid data set—that is, a data set that must look “pristine” at the end of its processing. After critically interrogating this notion of pristineness, I show how it perpetuates a misleading conception of data as “raw” instead of acknowledging the important contribution of data processors to data sharing and social science.

scholarly journals IMPLEMENTING THE GROUP ON EARTH OBSERVATIONS DATA MANAGEMENT PRINCIPLES: OBSERVATIONS OF A SCIENTIFIC DATA CENTER

2017 ◽  
Vol XLII-3/W2 ◽  
pp. 51-55
Author(s):  
R. R. Downs
Keyword(s):  
Data Management ◽  
Data Center ◽  
Management Practices ◽  
Earth Science ◽  
Scientific Data ◽  
Geospatial Data ◽  
Data Repository ◽  
Science Data ◽  
Earth Observations ◽  
Earth Science Data

The Group on Earth Observations (GEO) Data Management Principles (DMP) provide direction for managing geospatial data and related information products and services. Offering opportunities for enabling discovery, accessibility, usability, preservation, and curation, the GEO DMP challenge repositories, such as scientific archives and data centers, to improve practices that foster the use of Earth science data today and in the future. In addition, the Data Management Principles Implementation Guidelines (IG) offer many practical suggestions for implementing the DMP with examples that can inform the consideration of options for improving geospatial data management practices. Implementing such improvements offers value to the users of geospatial data by enabling data providers to support the use of the data products and services that they disseminate. Adopting these improvements also can assist repositories in their efforts to meet the requirements for attaining data repository certification, which offers value for repositories and their stakeholders. This article shows how repositories can improve data management practices for geospatial data by adopting the GEO DMP, with examples drawn from a scientific data center. Current and future opportunities for improving data management practices to attain data repository certification also are described along with practical approaches that repositories can adopt in the short term.

An empirical study on the performance evaluation of scientific data sharing platforms in China

2015 ◽  
Vol 33 (2) ◽  
pp. 211-229 ◽  
Author(s):  
Li Si ◽  
Yueting Li ◽  
Xiaozhe Zhuang ◽  
Wenming Xing ◽  
Xiaoqin Hua ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Data Sharing ◽  
Data Center ◽  
Evaluation System ◽  
Meteorological Data ◽  
Indicator System ◽  
Scientific Data ◽  
Data Availability ◽  
Science Data ◽  
Content Type

Purpose – The purpose of this paper is to conduct performance evaluation of eight main scientific data sharing platforms in China and find existing problems, thus providing reference for maximizing the value of scientific data and enhancing scientific research efficiency. Design/methodology/approach – First, the authors built an evaluation indicator system for the performance of scientific data sharing platforms. Next, the analytic hierarchy process was employed to set indicator weights. Then, the authors use experts grading method to give scored for each indicator and calculated the scoring results of the scientific data sharing platform performance evaluation. Finally, an analysis of the results was conducted. Findings – The performance evaluation of eight platforms is arranged by descending order by the value of F: the Data Sharing Infrastructure of Earth System Science (76.962), the Basic Science Data Sharing Center (76.595), the National Scientific Data Sharing Platform for Population and Health (71.577), the China Earthquake Data Center (66.296), the China Meteorological Data Sharing Service System (65.159), the National Agricultural Scientific Data Sharing Center (55.068), the Chinese Forestry Science Data Center (56.894) and the National Scientific Data Sharing & Service Network on Material Environmental Corrosion (Aging) (52.528). And some existing shortcomings such as the relevant policies and regulation, standards of data description and organization, data availability and the services should be improved. Originality/value – This paper is mainly discussing about the performance evaluation system covering operation management, data resource, platform function, service efficiency and influence of eight scientific data sharing centers and made comparative analysis. It reflected the reality development of scientific data sharing in China.

Science Data Center Extends Interdisciplinary Role

1983 ◽  
Vol 61 (13) ◽  
pp. 25-27
Author(s):  
DERMOT A. O'SULLIVAN
Keyword(s):  
Data Center ◽  
Science Data

Comprehensive Analysis of Automatic Identification System (AIS) Data in Regard to Vessel Movement Prediction

2014 ◽  
Vol 67 (5) ◽  
pp. 791-809 ◽  
Author(s):  
Philipp Last ◽  
Christian Bahlke ◽  
Martin Hering-Bertram ◽  
Lars Linsen
Keyword(s):  
Large Data ◽  
Automatic Identification ◽  
Identification System ◽  
Very High Frequency ◽  
Database Queries ◽  
Related Data ◽  
Movement Prediction ◽  
Time Period ◽  
Current Usage ◽  
Exchange Vessel

AIS was primarily developed to exchange vessel-related data among vessels or AIS stations by using very-high frequency (VHF) technology to increase safety at sea. This study evaluates the formal integrity, availability, and the reporting intervals of AIS data with a focus on vessel movement prediction. In contrast to former studies, this study is based on a large data collection of over 85 million AIS messages, which were continuously received within a time period of two months. Thus, the evaluated data represent a comprehensive and up-to-date view of the current usage of AIS systems installed on vessels. Results of previous studies concerning the availability of AIS data are confirmed and extended. New aspects such as reporting intervals are additionally evaluated. Received messages are stored in a database, which allows for performing database queries to evaluate the obtained data in an automatic way. This study shows that almost ten years after becoming mandatory for professional operating vessels, AIS still lacks availability for both static and dynamic data and that the reporting intervals are not as reliable as specified within the technical AIS standard.

Multimodal Systems

2014 ◽  
Author(s):  
Elisabeth André ◽  
Jean-Claude Martin
Keyword(s):  
Natural Language ◽  
Rapid Growth ◽  
Large Data ◽  
Access To Information ◽  
Language Technology ◽  
Data Archives ◽  
Multimodal Systems ◽  
Integral Role ◽  
Effective Output

Recent years have witnessed a rapid growth in the development of multimodal systems. Improving technology and tools enable the development of more intuitive styles of interaction and convenient ways of accessing large data archives. Starting from the observation that natural language plays an integral role in many multimodal systems, this chapter focuses on the use of natural language in combination with other modalities, such as body gestures or gaze. It addresses the following three issues: (1) how to integrate multimodal input including spoken or typed language in a synergistic manner; (2) how to combine natural language with other modalities in order to generate more effective output; and (3) how to make use of natural language technology in combination with other modalities in order to enable better access to information.

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