scholarly journals Data Mining and Analysis of Scientific Research Data Records on COVID-19 Mortality, Immunity, and Vaccine Development - In the First Wave of the COVID-19 Pandemic

Author(s):  
Petar Radanliev ◽  
David C. De Roure ◽  
Rob Walton
2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Rui Shang ◽  
Balqees Ara ◽  
Islam Zada ◽  
Shah Nazir ◽  
Zaid Ullah ◽  
...  

Context. Educational Data Mining (EDM) is a new and emerging research area. Data mining techniques are used in the educational field in order to extract useful information on employee or student progress behaviors. Recent increase in the availability of learning data has given importance and momentum to educational data mining to better understand and optimize the learning process and the environments in which it takes place. Objective. Data are the most valuable commodity for any organization. It is very difficult to extract useful information from such a large and massive collection of data. Data mining techniques are used to forecast and evaluate academic performance of students based on their academic record and participation in the forum. Although several studies have been carried out to evaluate the academic performance of students worldwide, there is a lack of appropriate studies to assess factors that can boost the academic performance of students. Methodology. The current study sought to weigh up factors that contribute to improving student academic performance in Pakistan. In this paper, both the simple and parallel clustering techniques are implemented and analyzed to point out their best features. The Parallel K-Mean algorithms overcome the problems of simple algorithm and the outcomes of the parallel algorithms are always the same, which improves the cluster quality, number of iterations, and elapsed time. Results. Both the algorithms are tested and compared with each other for a dataset of 10,000 and 5000 integer data items. The datasets are evaluated 10 times for minimum elapse time-varying K value from 1 to 10. The proposed study is more useful for scientific research data sorting. Scientific research data statistics are more accurate.


Author(s):  
Nurul Rofiqo ◽  
Agus Perdana Windarto ◽  
Dedy Hartama

This study aims to utilize Clushtering Algorithm in grouping the number of people who have health complaints with the K-means algorithm in Indonesia. The source of this research data was collected based on the documents of the provincial population which had health complaints produced by the National Statistics Agency. The data used in this study are data from 2013-2017 consisting of 34 provinces. The method used in this research is K-means Algorithm. Data will be processed by clushtering in 3 clushter, namely clusther high health complaints, clusther moderate and low health complaints. Centroid data for high population level clusters 37.48, Centroid data for moderate population level clusters 27.08, and Centroid data for low population level clusters 14.89. So that obtained an assessment based on the population index that has health complaints with 7 provinces of high health complaints, namely Central Java, Yogyakarta, Bali, West Nusa Tenggara, East Nusa Tenggara, South Kalimantan, Gorontalo, 18 provinces of moderate health complaints, and 9 other provinces including low health complaints. This can be an input to the government to give more attention to residents in each region who have high health complaints through improving public health services so that the Indonesian population becomes healthier without health complaints.Keywords: data mining, health complaints, clustering, K-means, Indonesian residents


Somatechnics ◽  
2012 ◽  
Vol 2 (2) ◽  
pp. 284-304
Author(s):  
Patricia Adams

Contemporary scientific discoveries are rapidly modifying established concepts of embodiment and corporeality. For example, developing techniques in adult stem cell research can actively remodel the human body; whilst neuroscientists are shedding increasing light on the functioning of our brains. My research at the art/science nexus draws upon recent media theories to investigate the ways twenty-first century constructs of ‘humanness’ and the ‘self’ are affected by both historical and contemporary scientific research and developments in digital imaging technologies. In this article, examples from my artworks: “machina carnis” and “HOST” illustrate how my use of innovative digital technologies and collaborative methodologies has enabled me to immerse myself in the scientific experience at first hand. I demonstrate how my reinterpretations of what is commonly termed ‘hard’ scientific research data does not seek to emulate ‘objective’ readings of the experimental digital image data but rather recontextualises it in the context of my artworks. These artworks acknowledge the personal and visceral content in the scientific data and enable viewer/participants to reflect upon the issues raised from an emotive and individual perspective.


Author(s):  
Jadranka Stojanovski

>> See video of presentation (28 min.) The primary goal of scholarly communication is improving human knowledge and sharing is the key to achieve this goal: sharing ideas, sharing methodologies, sharing of results, sharing data, information and knowledge. Although the concept of sharing applies to all phases of scholarly communication, most often the only visible part is the final publication, with the journal article as a most common type. The traditional characteristics of the present journals allow only limited possibilities for sharing the knowledge. Basic functions, registration, dissemination, certification, and storage, are still present but they are no more effective in the network environment. Registration is too slow, there are various barriers to dissemination, certification system has many shortcomings, and used formats are not suitable for the long term preservation and storage. Although the journals today are digital and various powerful technologies are available, they are still focused on their unaltered printed versions. This presentation will discuss possible evolution of journal article to become more compliant with users' needs and to enable “the four R’s of openness” – reuse, redistribute, revise and remix (Hilton, Wiley, Stein, & Johnson, 2010).Several aspects of openness will be presented and discussed: open access, open data, open peer review, open authorship, and open formats. With digital technology which has become indispensable in the creation, collection, processing and storage of data in all scientific disciplines the way of conducting scientific research has changed and the concept of "data-driven science" has been introduced (Ware & Mabe, 2009). Sharing research data enhances the capabilities of reproducing the results, reuse maximizes the value of research, accelerating the advancement of science, ensuring transparency of scientific research, reducing the possibility of bias in the interpretation of results and increasing the credibility of published scientific knowledge. The open peer review can ensure full transparency of the entire process of assessment and help to solve many problems in the present scholarly publishing. Through the process of the open peer review each manuscript can be immediately accessible, reviewers can publicly demonstrate their expertise and could be rewarded, and readers can be encouraged to make comments and views and to become active part of the scholarly communication process. The trend to to describe the author's contribution is also present, which will certainly lead to a reduced number of “ghost”, "guest" and "honorary" authors, and will help to establish better standards for author’s identification.Various web technologies can be used also for the semantic enhancement of the article. One of the most important aspects of semantic publication is the inclusion of the research data, to make them available to the user as an active data that can be manipulated. It is possible to integrate data from external sources, or to merge the data from different resources (data fusion) (Shotton, 2012), so the reader can gain further understanding of the presented data. Additional options provide merging data from different articles, with the addition of the component of time. Other semantic enhancement can include enriched bibliography, interactive graphical presentations, hyperlinks to external resources, tagged text, etc.Instead of mostly static content, journals can offer readers dynamic content that includes multimedia, "living mathematics", “executable articles”, etc. Videos highlighting critical points in the research process, 3D representations of chemical compounds or art works, audio clips with the author's reflections and interviews, and animated simulations or models of ocean currents, tides, temperature and salinity structure, can became soon common part of every research article. The diversity of content and media, operating systems (GNU / Linux, Apple Mac OSX, Microsoft Windows), and software tools that are available to researchers, suggests the usage of the appropriate open formats. Different formats have their advantages and disadvantages and it would be necessary to make multiple formats available, some of which are suitable for "human" reading (including printing on paper), and some for machine reading that can be used by computers without human intervention. Characteristics and possibilities of several formats will be discussed, including XML as the most recommended format, which can enable granulate document structure as well as deliver semantics to the human reader or to the computer.Literature:Hilton, J. I., Wiley, D., Stein, J., & Johnson, A. (2010). The Four R’s of Openness and ALMS Analysis: Frameworks for Open Educational Resources. Open Learning: The Journal of Open, Distance and E-Learning, 25(1), 37–44. doi:10.1080/02680510903482132Shotton, D. (2012). The Five Stars of Online Journal Articles - a Framework for Article Evaluation. D-Lib Magazine, 18(1/2), 1–16. doi:10.1045/january2012-shottonWare, M., & Mabe, M. (2009). The stm report (p. 68).


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