scholarly journals AN OVERVIEW OF BIG DATA IN COVID-19 AS A CONTRIBUTION TO THE MANAGEMENT OF SCIENTIFIC AND TECHNOLOGICAL KNOWLEDGE

2021 ◽  
Vol 16 (2) ◽  
pp. 102-111
Author(s):  
Jorge Magalhães ◽  
Henrique Koch Chaves ◽  
Viviane Theodora Muniz
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
State Of The Art ◽  
The United States ◽  
Technological Knowledge ◽  
Research Data ◽  
Processing Data ◽  
Pubmed Database ◽  
Patent Applications ◽  
New Perspective

In times of pandemic, rapid sharing of research data is urgently needed, as is the intensification of networking. The COVID-19 pandemic brought a new perspective in relation to knowledge management in various organizational means, whether through the search for innovation or the improvement of its processes. Thus, to calculate the state of the art and track scientific and technological knowledge in the COVID-19 spectrum, the keyword “Coronavir*” was used in the PubMed and Espacenet databases. Data were processed by Carrot Search Lingo4G® and PatentInspiration®. In the Pubmed database, 1,000 documents were retrieved, which were organized into 81 groups of sub-themes, with emphasis on the sub-theme “treatment during coronavirus disease”, with 188 articles (18.8% of the total). Regarding technological innovation, China and the United States were the countries that filed the most patent applications, especially in 2020 and 2021, corresponding to 68.5% of the total. The first 4 (four) applicants with the highest number of patents were Pfizer, Gilead Sciences Inc., Center Nat Rech, Crucell Holland. The results obtained over a period of time demonstrate a partnership between universities and companies towards the fight against the pandemic. The tools for identifying, extracting and processing data (or free), are needed efficiently in the management of scientific and technological knowledge in COVID -19, thus being able to contribute to more assertive decision-making at various organizational levels. Keywords: Big Data, COVID-19, Knowledge Management, coronavirus patents

scholarly journals The Application of Big Data Technology in the Practical Research Work of Ideological and Political Courses

2021 ◽  
Vol 290 ◽  
pp. 02026
Author(s):  
Yuhong Bai
Keyword(s):  
Big Data ◽  
Research Work ◽  
Research Data ◽  
Data Management System ◽  
Survey Method ◽  
Processing Data ◽  
Practical Research ◽  
Survey Results ◽  
Curriculum Construction ◽  
Big Data Technology

This article analyzes the application advantages, application core and technical nature of big data technology. The author studies the specific application of big data technology in the allocation of topics, pre-class research, data collection and processing, data statistical analysis, and data rational application. This article studies how to do well in survey method training, how to improve the data management system, how to strengthen the curriculum construction of colleges and universities, and how to actively learn from successful cases. The author’s purpose is to enhance the application value of big data technology and improve the reliability of the practical survey results of ideological and political courses.

More than a number: The telephone and the history of digital identification

2021 ◽  
pp. 136754942199457
Author(s):  
Jennifer Holt ◽  
Michael Palm
Keyword(s):  
United States ◽  
Big Data ◽  
Digital Age ◽  
The United States ◽  
Cultural Identities ◽  
Consumer Identity ◽  
Digital Identification ◽  
History Of ◽  
Digital Transactions ◽  
New Perspective

This article examines the telephone’s entangled history within contemporary infrastructural systems of ‘big data’, identity and, ultimately, surveillance. It explores the use of telephone numbers, keypads and wires to offer new perspective on the imbrication of telephonic information, interface and infrastructure within contemporary surveillance regimes. The article explores telephone exchanges as arbiters of cultural identities, keypads as the foundation of digital transactions and wireline networks as enacting the transformation of citizens and consumers into digital subjects ripe for commodification and surveillance. Ultimately, this article argues that telephone history – specifically the histories of telephone numbers and keypads as well as infrastructure and policy in the United States – continues to inform contemporary practices of social and economic exchange as they relate to consumer identity, as well as to current discourses about surveillance and privacy in a digital age.

scholarly journals Double Screen Innovation: Building Sustainable Core Competence through Knowledge Management

2019 ◽  
Vol 11 (16) ◽  
pp. 4266 ◽  
Author(s):  
Ximing Yin ◽  
Jin Chen ◽  
Chuang Zhao
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Core Competence ◽  
Management Practice ◽  
Core Competencies ◽  
Knowledge Management Practice ◽  
Learning Mechanisms ◽  
Business Sustainability ◽  
Knowledge Based ◽  
New Perspective

How to exploit the precipitated internal and external knowledge to build dynamic capability in the era of big data remains a big challenge for innovation and business sustainability. This paper documents a novel perspective to address this challenge by exploring the double screen innovation knowledge management practice in Commercial Aircraft Corporation of China Ltd. (COMAC). Drawing from the literature on knowledge management and knowledge-based view, this paper elaborates how the new type of knowledge management practice represented by the case of Double Screen Innovation (DSI) in COMAC could help enterprise build sustainable core competence, which provides new perspective for multi-level knowledge management towards business sustainability. DSI, as a novel way of knowledge management, optimizes the micro-level knowledge co-creation and sharing and macro-level organizational learning mechanisms to accelerate the knowledge accumulation and dissemination within the organization. The process of knowledge creation, transformation, and application helps to integrate and transform big data into useful business information, thus provides an endless driving force conducive to the establishment and promotion of the core competencies of enterprises.

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.

Big data investments in knowledge and non-knowledge intensive firms: what the market tells us

2017 ◽  
Vol 21 (3) ◽  
pp. 623-639 ◽  
Author(s):  
Tingting Zhang ◽  
William Yu Chung Wang ◽  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Stock Market ◽  
Value Chain ◽  
Stock Prices ◽  
Economic Value ◽  
Information Value ◽  
Publicly Traded ◽  
Content Type ◽  
Knowledge Intensive

Purpose This paper aims to investigate the value of big data investments by examining the market reaction to company announcements of big data investments and tests the effect for firms that are either knowledge intensive or not. Design/methodology/approach This study is based on an event study using data from two stock markets in China. Findings The stock market sees an overall index increase in stock prices when announcements of big data investments are revealed by grouping all the listed firms included in the sample. Increased stock prices are also the case for non-knowledge intensive firms. However, the stock market does not seem to react to big data investment announcements by testing the knowledge intensive firms along. Research limitations/implications This study contributes to the literature on assessing the economic value of big data investments from the perspective of big data information value chain by taking an unexpected change in stock price as the measure of the financial performance of the investment and by comparing market reactions between knowledge intensive firms and non-knowledge intensive firms. Findings of this study can be used to refine practitioners’ understanding of the economic value of big data investments to different firms and provide guidance to their future investments in knowledge management to maximize the benefits along the big data information value chain. However, findings of study should be interpreted carefully when applying them to companies that are not publicly traded on the stock market or listed on other financial markets. Originality/value Based on the concept of big data information value chain, this study advances research on the economic value of big data investments. Taking the perspective of stock market investors, this study investigates how the stock market reacts to big data investments by comparing the reactions to knowledge-intensive firms and non-knowledge-intensive firms. The results may be particularly interesting to those publicly traded companies that have not previously invested in knowledge management systems. The findings imply that stock investors tend to believe that big data investment could possibly increase the future returns for non-knowledge-intensive firms.

scholarly journals The WxChallenge: Forecasting Competition, Educational Tool, and Agent of Cultural Change

2013 ◽  
Vol 94 (10) ◽  
pp. 1501-1506 ◽  
Author(s):  
Bradley G. Illston ◽  
Jeffrey B. Basara ◽  
Christopher Weiss ◽  
Mike Voss
Keyword(s):  
Cultural Change ◽  
State Of The Art ◽  
Learning Experience ◽  
The United States ◽  
Higher Learning ◽  
Educational Tool ◽  
Web Interfaces ◽  
Wind Speeds ◽  
University Of Oklahoma ◽  
The University

The WxChallenge, a project developed at the University of Oklahoma, brings a state-of-the-art, fun, and exciting forecast contest to participants at colleges and universities across North America. The challenge is to forecast the maximum and minimum temperatures, precipitation, and maximum wind speeds for select locations across the United States over a 24-h prediction period. The WxChallenge is open to all undergraduate and graduate students, as well as higher-education faculty, staff, and alumni. Through the use of World Wide Web interfaces accessible by personal computers, tablet computer, and smartphones, the WxChallenge provides a state-of-the-art portal to aid participants in submitting forecasts and alleviate many of the administrative issues (e.g., tracking and scoring) faced by local managers and professors. Since its inception in 2006, 110 universities have participated in the contest and it has been utilized as part of the curricula for 140 classroom courses at various institutions. The inherently challenging nature of the WxChallenge has encouraged its adoption as an educational tool. As its popularity has grown, professors have seen the utility of the Wx-Challenge as a teaching aid and it has become an instructional resource of many meteorological classes at institutions for higher learning. In addition to evidence of educational impacts, the competition has already begun to leave a cultural and social mark on the meteorological learning experience.

scholarly journals Accelerating organic solar cell material's discovery: high-throughput screening and big data

2021 ◽  
Author(s):  
Xabier Rodríguez-Martínez ◽  
Enrique Pascual-San-José ◽  
Mariano Campoy-Quiles
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
High Throughput ◽  
Organic Solar Cells ◽  
High Throughput Screening ◽  
Organic Solar Cell ◽  
State Of The Art ◽  
Review Article ◽  
Machine Learning Algorithms ◽  
Device Optimization

This review article presents the state-of-the-art in high-throughput computational and experimental screening routines with application in organic solar cells, including materials discovery, device optimization and machine-learning algorithms.

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