scholarly journals CASEarth Poles: Big Data for the Three Poles

2020 ◽  
Vol 101 (9) ◽  
pp. E1475-E1491 ◽  
Author(s):  
Xin Li ◽  
Tao Che ◽  
Xinwu Li ◽  
Lei Wang ◽  
Anmin Duan ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Big Data ◽  
Data Acquisition ◽  
Data Science ◽  
Environmental Changes ◽  
Water Cycle ◽  
Ecosystem Dynamics ◽  
Polar Regions ◽  
The North ◽  
Integration Service

Abstract Unprecedented changes in the climate and environment have been observed in the three poles, including the North Pole, the South Pole, and the Third Pole–Tibetan Plateau. Although considerable data have been collected and several observation networks have been built in these polar regions, the three poles are relatively data-scarce regions due to inaccessible data acquisition, high-cost labor, and difficult living environments. To address the obstacles to better understanding the unprecedented changes in the three poles and their effects on the global environment and humans, there is a pressing need for better data acquisition, curation, integration, service, and application to support fundamental scientific research and sustainable development for the three poles. CASEarth Poles, a project within the framework of the “CAS Big Earth Data Science Engineering” program of the Chinese Academy of Sciences, aims to construct a big data platform for the three poles. CASEarth Poles will be devoted to 1) breaking the bottleneck of polar data curation, integration, and sharing; 2) developing high-resolution remote sensing products over the three poles; 3) generating atmospheric reanalysis datasets for the polar regions; 4) exploring the synchronization, asynchronization, and teleconnection of the environmental changes in the three poles; 5) investigating the climate, water cycle, and ecosystem dynamics and the interactions among the multispheres in the polar regions and their global effects; and 6) supporting decision-making with regard to sea ice forecasting, infrastructure, and sustainable development in polar regions. CASEarth Poles will collaborate with international efforts to enable better data and information services for the three poles in the big data era.

scholarly journals Research on River Governance Index System in the Big Data Era

2021 ◽  
Vol 275 ◽  
pp. 03058
Author(s):  
Chengzhi Niu ◽  
Yougan Zhu
Keyword(s):  
Sustainable Development ◽  
Big Data ◽  
Index System ◽  
Data Science ◽  
Science Methods ◽  
Ecological Health ◽  
Analytic Hierarchy ◽  
Governance Index ◽  
Multi Level ◽  
Hierarchy Process

Realizing the ecological health and sustainable development of rivers has become a worldwide research hotspot. Sustainable development is a large system with multi-system, multi-level and multi-index, In the era of big data, data science methods have made great changes in water resources governance methods. We can obtain more detailed data through big data methods to comprehensively and scientifically measure river governance. Based on the analytic hierarchy process, this paper invites experts in related fields to construct and score the river governance index system, and establish a river governance index system in the era of big data. We hope that the river governance index system will achieve advanced river governance in the era of big data.

scholarly journals Data-Driven Methodology for Sustainable Urban Mobility Assessment and Improvement

2021 ◽  
Vol 13 (13) ◽  
pp. 7162
Author(s):  
Marko Šoštarić ◽  
Krešimir Vidović ◽  
Marijan Jakovljević ◽  
Orsat Lale
Keyword(s):  
Big Data ◽  
Transport System ◽  
Urban Areas ◽  
Data Science ◽  
System Analysis ◽  
Environmental Changes ◽  
Telecommunication Network ◽  
Urban Mobility ◽  
Mobility Data ◽  
Sustainable Urban Mobility

The transport system is sensitive to external influences generated by various economic, social and environmental changes. The society and the environment are changing extremely fast, resulting in the need for rapid adjustment of the transport system. Traffic system management, especially in urban areas, is a dynamic process, which is why transport planners are in need of a proven and validated methodology for fast and efficient transport data collection, fusion and analytics that will be used in sustainable urban mobility policy creation. The paper presents a development of a methodology in data rich reality that combines traditional and novel data science approach for transport system analysis and planning. The result is overall process consisting of 150 steps from first desktop research to final solution development. It enables urban mobility stakeholders to identify transport problems, analyze the urban mobility situation and to propose dedicated measures for sustainable urban mobility strengthening. The methodology is based on a big data research and analysis on anonymized big data sets originating from mobile telecommunication network, where the extraction of mobility data from the big dataset is the most innovative part of the proposed process. The extracted mobility data were validated through a “conventional” field research. The methodology was, for additional testing, applied in a pilot study, performed in the City of Rijeka in Croatia. It resulted in a set of alternative measures for modal shift from passenger cars to sustainable mobility modes, that were validated by the local public and urban mobility stakeholders.

Issues in security and privacy of big data

2018 ◽  
Vol 7 (12) ◽  
pp. 1
Author(s):  
Shaveta Bhatia
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Approximate Method ◽  
Biomedical Research ◽  
Cyber Security ◽  
Data Science ◽  
Third Party ◽  
Security And Privacy ◽  
Security Threats ◽  
The Third

 The epoch of the big data presents many opportunities for the development in the range of data science, biomedical research cyber security, and cloud computing. Nowadays the big data gained popularity.  It also invites many provocations and upshot in the security and privacy of the big data. There are various type of threats, attacks such as leakage of data, the third party tries to access, viruses and vulnerability that stand against the security of the big data. This paper will discuss about the security threats and their approximate method in the field of biomedical research, cyber security and cloud computing.

INTERCONNECTION OF SUSTAINABLE DEVELOPMENT COMPONENTS FOR THE INDIGENOUS PEOPLES OF THE NORTH

2020 ◽  
Vol 2 (8) ◽  
pp. 101-110
Author(s):  
N. N. ILYSHEVA ◽  
◽  
E. V. KARANINA ◽  
G. P. LEDKOV ◽  
E. V. BALDESKU ◽  
...  
Keyword(s):  
Climate Change ◽  
Sustainable Development ◽  
Economic Development ◽  
Indigenous Peoples ◽  
Ecological Footprint ◽  
Positive Relationship ◽  
The North ◽  
Strong Positive Relationship ◽  
The Face ◽  
The Relationship

The article deals with the problem of achieving sustainable development. The purpose of this study is to reveal the relationship between the components of sustainable development, taking into account the involvement of indigenous peoples in nature conservation. Climate change makes achieving sustainable development more difficult. Indigenous peoples are the first to feel the effects of climate change and play an important role in the environmental monitoring of their places of residence. The natural environment is the basis of life for indigenous peoples, and biological resources are the main source of food security. In the future, the importance of bioresources will increase, which is why economic development cannot be considered independently. It is assumed that the components of resilience are interrelated and influence each other. To identify this relationship, a model for the correlation of sustainable development components was developed. The model is based on the methods of correlation analysis and allows to determine the tightness of the relationship between economic development and its ecological footprint in the face of climate change. The correlation model was tested on the statistical materials of state reports on the environmental situation in the Khanty-Mansiysk Autonomous Okrug – Yugra. The approbation revealed a strong positive relationship between two components of sustainable development of the region: economy and ecology.

Big Data Driven Clinical Informatics & Surveillance (BDD_CIS) – A Multimodal Database Focused Clinical, Community, and Multi-Omics Surveillance Plan for COVID-19: A study Protocol (Preprint)

2020 ◽  
Author(s):  
Bankole Olatosi ◽  
Jiajia Zhang ◽  
Sharon Weissman ◽  
Zhenlong Li ◽  
Jianjun Hu ◽  
...  
Keyword(s):  
Big Data ◽  
South Carolina ◽  
Data Science ◽  
Age Groups ◽  
The Elderly ◽  
The United States ◽  
Data Sources ◽  
Patient Registries ◽  
Multiple Partner ◽  
Multimodal Data

BACKGROUND The Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) remains a serious global pandemic. Currently, all age groups are at risk for infection but the elderly and persons with underlying health conditions are at higher risk of severe complications. In the United States (US), the pandemic curve is rapidly changing with over 6,786,352 cases and 199,024 deaths reported. South Carolina (SC) as of 9/21/2020 reported 138,624 cases and 3,212 deaths across the state. OBJECTIVE The growing availability of COVID-19 data provides a basis for deploying Big Data science to leverage multitudinal and multimodal data sources for incremental learning. Doing this requires the acquisition and collation of multiple data sources at the individual and county level. METHODS The population for the comprehensive database comes from statewide COVID-19 testing surveillance data (March 2020- till present) for all SC COVID-19 patients (N≈140,000). This project will 1) connect multiple partner data sources for prediction and intelligence gathering, 2) build a REDCap database that links de-identified multitudinal and multimodal data sources useful for machine learning and deep learning algorithms to enable further studies. Additional data will include hospital based COVID-19 patient registries, Health Sciences South Carolina (HSSC) data, data from the office of Revenue and Fiscal Affairs (RFA), and Area Health Resource Files (AHRF). RESULTS The project was funded as of June 2020 by the National Institutes for Health. CONCLUSIONS The development of such a linked and integrated database will allow for the identification of important predictors of short- and long-term clinical outcomes for SC COVID-19 patients using data science.

Produção internacional sobre ciência orientada a dados: análise dos termos data science e e-science na scopus e na web of science

2017 ◽  
Vol 12 (1) ◽  
Author(s):  
Leilah Santiago Bufrem ◽  
Fábio Mascarenhas Silva ◽  
Natanael Vitor Sobral ◽  
Anna Elizabeth Galvão Coutinho Correia
Keyword(s):  
Big Data ◽  
Open Access ◽  
Grid Computing ◽  
Digital Library ◽  
Data Science ◽  
Web Of Science ◽  
Computer Systems ◽  
Distributed Computer Systems

Introdução: A atual configuração da dinâmica relativa à produção e àcomunicação científicas revela o protagonismo da Ciência Orientada a Dados,em concepção abrangente, representada principalmente por termos como “e-Science” e “Data Science”. Objetivos: Apresentar a produção científica mundial relativa à Ciência Orientada a Dados a partir dos termos “e-Science” e “Data Science” na Scopus e na Web of Science, entre 2006 e 2016. Metodologia: A pesquisa está estruturada em cinco etapas: a) busca de informações nas bases Scopus e Web of Science; b) obtenção dos registros; bibliométricos; c) complementação das palavras-chave; d) correção e cruzamento dos dados; e) representação analítica dos dados. Resultados: Os termos de maior destaque na produção científica analisada foram Distributed computer systems (2006), Grid computing (2007 a 2013) e Big data (2014 a 2016). Na área de Biblioteconomia e Ciência de Informação, a ênfase é dada aos temas: Digital library e Open access, evidenciando a centralidade do campo nas discussões sobre dispositivos para dar acesso à informação científica em meio digital. Conclusões: Sob um olhar diacrônico, constata-se uma visível mudança de foco das temáticas voltadas às operações de compartilhamento de dados para a perspectiva analítica de busca de padrões em grandes volumes de dados.Palavras-chave: Data Science. E-Science. Ciência orientada a dados. Produção científica.Link:http://www.uel.br/revistas/uel/index.php/informacao/article/view/26543/20114

Impact of Big Data over Telecom Industry

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Muhammad Waqar Khan ◽  
Muhammad Asghar Khan ◽  
Muhammad Alam ◽  
Wajahat Ali
Keyword(s):  
Big Data ◽  
Data Science ◽  
Cell Phones ◽  
Smart Phones ◽  
World Population ◽  
Huge Amount ◽  
Scale Down ◽  
Telecom Industry ◽  
Telecom Sector ◽  
Theoretical Computing

<p>During past few years, data is growing exponentially attracting researchers to work a popular term, the Big Data. Big Data is observed in various fields, such as information technology, telecommunication, theoretical computing, mathematics, data mining and data warehousing. Data science is frequently referred with Big Data as it uses methods to scale down the Big Data. Currently<br />more than 3.2 billion of the world population is connected to internet out of which 46% are connected via smart phones. Over 5.5 billion people are using cell phones. As technology is rapidly shifting from ordinary cell phones towards smart phones, therefore proportion of using internet is also growing. There<br />is a forecast that by 2020 around 7 billion people at the globe will be using internet out of which 52% will be using their smart phones to connect. In year 2050 that figure will be touching 95% of world population. Every device connect to internet generates data. As majority of the devices are using smart phones to<br />generate this data by using applications such as Instagram, WhatsApp, Apple, Google, Google+, Twitter, Flickr etc., therefore this huge amount of data is becoming a big threat for telecom sector. This paper is giving a comparison of amount of Big Data generated by telecom industry. Based on the collected data<br />we use forecasting tools to predict the amount of Big Data will be generated in future and also identify threats that telecom industry will be facing from that huge amount of Big Data.</p>

scholarly journals Record linkage of routine data with cohorts’ data of infants under European and Portuguese law

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Doetsch ◽  
I Lopes ◽  
R Redinha ◽  
H Barros
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Protection ◽  
Record Linkage ◽  
Data Science ◽  
Personal Data ◽  
Routine Data ◽  
Cohort Data ◽  
Education Data ◽  
Explicit Consent

Abstract The usage and exchange of “big data” is at the forefront of the data science agenda where Record Linkage plays a prominent role in biomedical research. In an era of ubiquitous data exchange and big data, Record Linkage is almost inevitable, but raises ethical and legal problems, namely personal data and privacy protection. Record Linkage refers to the general merging of data information to consolidate facts about an individual or an event that are not available in a separate record. This article provides an overview of ethical challenges and research opportunities in linking routine data on health and education with cohort data from very preterm (VPT) infants in Portugal. Portuguese, European and International law has been reviewed on data processing, protection and privacy. A three-stage analysis was carried out: i) interplay of threefold law-levelling for Record Linkage at different levels; ii) impact of data protection and privacy rights for data processing, iii) data linkage process' challenges and opportunities for research. A framework to discuss the process and its implications for data protection and privacy was created. The GDPR functions as utmost substantial legal basis for the protection of personal data in Record Linkage, and explicit written consent is considered the appropriate basis for the processing sensitive data. In Portugal, retrospective access to routine data is permitted if anonymised; for health data if it meets data processing requirements declared with an explicit consent; for education data if the data processing rules are complied. Routine health and education data can be linked to cohort data if rights of the data subject and requirements and duties of processors and controllers are respected. A strong ethical context through the application of the GDPR in all phases of research need to be established to achieve Record Linkage between cohort and routine collected records for health and education data of VPT infants in Portugal. Key messages GDPR is the most important legal framework for the protection of personal data, however, its uniform approach granting freedom to its Member states hampers Record Linkage processes among EU countries. The question remains whether the gap between data protection and privacy is adequately balanced at three legal levels to guarantee freedom for research and the improvement of health of data subjects.

scholarly journals Research Gap Analysis of Remote Sensing Application in Fisheries: Prospects for Achieving the Sustainable Development Goals

2021 ◽  
Vol 13 (5) ◽  
pp. 1013
Author(s):  
Kuo-Wei Yen ◽  
Chia-Hsiang Chen
Keyword(s):  
Sustainable Development ◽  
Indian Ocean ◽  
Atlantic Ocean ◽  
The Sustainable Development ◽  
The North ◽  
The Pacific ◽  
Development Goals ◽  
The Indian Ocean ◽  
The Pacific Ocean ◽  
Research Hotspots

Remote sensing (RS) technology, which can facilitate the sustainable management and development of fisheries, is easily accessible and exhibits high performance. It only requires the collection of sufficient information, establishment of databases and input of human and capital resources for analysis. However, many countries are unable to effectively ensure the sustainable development of marine fisheries due to technological limitations. The main challenge is the gap in the conditions for sustainable development between developed and developing countries. Therefore, this study applied the Web of Science database and geographic information systems to analyze the gaps in fisheries science in various countries over the past 10 years. Most studies have been conducted in the offshore marine areas of the northeastern United States of America. In addition, all research hotspots were located in the Northern Hemisphere, indicating a lack of relevant studies from the Southern Hemisphere. This study also found that research hotspots of satellite RS applications in fisheries were mainly conducted in (1) the northeastern sea area in the United States, (2) the high seas area of the North Atlantic Ocean, (3) the surrounding sea areas of France, Spain and Portugal, (4) the surrounding areas of the Indian Ocean and (5) the East China Sea, Yellow Sea and Bohai Bay sea areas to the north of Taiwan. A comparison of publications examining the three major oceans indicated that the Atlantic Ocean was the most extensively studied in terms of RS applications in fisheries, followed by the Indian Ocean, while the Pacific Ocean was less studied than the aforementioned two regions. In addition, all research hotspots were located in the Northern Hemisphere, indicating a lack of relevant studies from the Southern Hemisphere. The Atlantic Ocean and the Indian Ocean have been the subjects of many local in-depth studies; in the Pacific Ocean, the coastal areas have been abundantly investigated, while offshore local areas have only been sporadically addressed. Collaboration and partnership constitute an efficient approach for transferring skills and technology across countries. For the achievement of the sustainable development goals (SDGs) by 2030, research networks can be expanded to mitigate the research gaps and improve the sustainability of marine fisheries resources.

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