scholarly journals Agricultural Data Science as a Potential Field and Promoting Agricultural Activities & Sustainable Agriculture

2020 ◽  
Vol 7 (2) ◽  
Author(s):  
P.K. Paul
Keyword(s):  
Agricultural Production ◽  
Data Analytics ◽  
Data Science ◽  
Complex Data ◽  
Agricultural Systems ◽  
Computing Technology ◽  
Daily Lives ◽  
The Core ◽  
Leading Role ◽  
Systems Practice

Agriculture is important for everyone due to its importance in our daily lives. Cultivation is valuable for all of us and required in the building of healthy agricultural systems development and in this context different technologies and also emerging Agricultural Informatics plays a leading role. In respect of developing modern agricultural systems,various methods are useful and enhancing. Apart from the core technologies various supporting technologies, also beneficial in modernizing agricultural production and systems practice. Among the technologies few important are genetic engineering, computing technology, nanoscience, Management Science, and so on. Information Technology and Agricultural Sciences result in the development of Agricultural Informatics with its various components. Recently various other components have developed viz. Data Analytics, AI & Robotics, Cloud Computing & Virtualization, Internet of Things, etc. And within these technologies, Big Data and Analytics is emerging and enhancing more on Agricultural Informatics since it holds the solution for managing data effectively with a large amount and also the complex data.

scholarly journals Big Data and Data Analytics in Agricultural Space: Towards Sustainable and Intelligent Agro Sector Development

2020 ◽  
pp. 95-120
Author(s):  
P. K. Paul ◽  
◽  
Anil Bhuimali ◽  
R.R. Sinha ◽  
K.S. Tiwary ◽  
...  
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Complex Data ◽  
Agricultural Systems ◽  
Daily Lives ◽  
Leading Role ◽  
Rapid Changes ◽  
It Applications ◽  
Nano Science ◽  
The Impact

Agriculture has become important for each and everyone for its importance in the daily lives. Cultivation and farming is most important and valuable in our life as it is needed for all of us. Furthermore it is essential to have better healthy agricultural systems and in this context Agricultural Informatics play a leading role. Here proper mechanism is very important in healthy and modern agricultural systems and development and for this various initiatives and methods are useful and enhancing. There are rapid changes and growth in respect of the support of various technologies which help in modernizing agricultural production and systems like genetic engineering and technologies, computing and information technology, nano-science and technology, Management Science etc. The combination of Information Technology and Agricultural Sciences has led to the developed the Agricultural Informatics. Agricultural Informatics is simply IT applications in Agriculture and allied areas with its various components. Though in recent past more emerging technologies of IT are enhancing the traditional growth of the Agricultural Informatics and among the technologies important are Big Data and Analytics, AI & Robotics, Cloud Computing & Virtualization, Internet of Things etc. And among these, Big Data and Analytics is emerging and changing the entire arena of the Agricultural Informatics with its periphery and functioning. As the data is changing and rapidly growing therefore, Big Data and Analytics is the solution for managing data effectively with large amount and also the complex data. This paper is theoretical and various aspects of Agricultural Informatics are mentioned such as features, applications and specially the impact of Big Data and Analytics. The Paper is also focused on possibilities of Big Data and Analytics in Agricultural Informatics with challenges, issues etc.

Orchestrating Data, Design, and Narrative

2012 ◽  
Vol 2 (2) ◽  
pp. 1-15
Author(s):  
Charles Miller ◽  
Lucas Lecheler ◽  
Bradford Hosack ◽  
Aaron Doering ◽  
Simon Hooper
Keyword(s):  
Online Learning ◽  
Information Visualization ◽  
Essential Element ◽  
Complex Data ◽  
Daily Lives ◽  
Future Design ◽  
Science And Politics ◽  
Interactive Visualizations ◽  
Online Educators ◽  
Data Design

Information visualization involves the visual, and sometimes interactive, presentation and organization of complex data in a clear, compelling representation. Information visualization is an essential element in peoples’ daily lives, especially those in data-driven professions, namely online educators. Although information visualization research and methods are prevalent in the diverse fields of healthcare, statistics, economics, information technology, computer science, and politics, few examples of successful information visualization design or integration exist in online learning. The authors provide a background of information visualization in education, explore a set of potential roles for information visualization in the future design and integration of online learning environments, provide examples of contemporary interactive visualizations in education, and discuss opportunities to move forward with design and research in this emerging area.

scholarly journals New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
César de Oliveira Ferreira Silva ◽  
Mariana Matulovic ◽  
Rodrigo Lilla Manzione
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Data Science ◽  
Final Decision ◽  
Aquifer Recharge ◽  
Groundwater Governance ◽  
Ethical Attitude ◽  
New Information ◽  
Modeling Experiment ◽  
Using Data

Abstract Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract

scholarly journals Mathematical Issues in Data Science and Applications for Health care

2019 ◽  
Author(s):  
Sitti Zuhaerah Thalhah ◽  
Mohammad Tohir ◽  
Phong Thanh Nguyen ◽  
K. Shankar ◽  
Robbi Rahim
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Decision Models ◽  
Computational Techniques ◽  
Healthcare Applications ◽  
Use Of Data ◽  
Source Data ◽  
Military Applications

For development in military applications, industrial and government the predictive analytics and decision models have long been cornerstones. In modern healthcare system technologies and big data analytics and modeling of multi-source data system play an increasingly important role. Into mathematical models in these domains various problems arising that can be formulated, by using computational techniques, sophisticated optimization and decision analysis it can be analyzed. This paper studies the use of data science in healthcare applications and the mathematical issues in data science.

scholarly journals Data for Students: The Potential of Data and Analytics for Student Success

2020 ◽  
Author(s):  
Katherine Leu
Keyword(s):  
Student Success ◽  
Financial Aid ◽  
Postsecondary Education ◽  
Data Analytics ◽  
Data Science ◽  
Postsecondary Institutions ◽  
College Completion ◽  
Ethical Challenges ◽  
Education Data ◽  
Data Linkages

Postsecondary education is awash in data. Postsecondary institutions track data on students’ demographics, academic performance, course-taking, and financial aid, and have put these data to use, applying data analytics and data science to issues in college completion. Meanwhile, an extensive amount of higher education data are being collected outside of institutions, opening possibilities for data linkages. Newer sources of postsecondary education data could provide an even richer view of student success and improve equity. To explore this potential, this brief describes existing applications of analytics to student success, presents a framework to structure understanding of postsecondary data topics, suggests potential extensions of these data to student success, and describes practical and ethical challenges.

scholarly journals Data Wrangling using Python

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3491-3495
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Research Work ◽  
Research Area ◽  
Mapping Data ◽  
Data Format ◽  
Data Analyst ◽  
Data Engineering ◽  
Data Scientist ◽  
Term Data

The term Data Engineering did not get much popularity as the terminologies like Data Science or Data Analytics, mainly because the importance of this technique or concept is normally observed or experienced only during working with data or handling data or playing with data as a Data Scientist or Data Analyst. Though neither of these two, but as an academician and the urge to learn, while working with Python, this topic ‘Data engineering’ and one of its major sub topic or concept ‘Data Wrangling’ has drawn attention and this paper is a small step to explain the experience of handling data which uses Wrangling concept, using Python. So Data Wrangling, earlier referred to as Data Munging (when done by hand or manually), is the method of transforming and mapping data from one available data format into another format with the idea of making it more appropriate and important for a variety of relatedm purposes such as analytics. Data wrangling is the modern name used for data pre-processing rather Munging. The Python Library used for the research work shown here is called Pandas. Though the major Research Area is ‘Application of Data Analytics on Academic Data using Python’, this paper focuses on a small preliminary topic of the mentioned research work named Data wrangling using Python (Pandas Library).

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