scholarly journals Big Data in Biodiversity Science: A Framework for Engagement

Technologies ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 60
Author(s):  
Tendai Musvuugwa ◽  
Muxe Gladmond Dlomu ◽  
Adekunle Adebowale
Keyword(s):  
Big Data ◽  
Best Practices ◽  
Scientific Community ◽  
Data Driven ◽  
Data Generation ◽  
Global Trends ◽  
Major Threat ◽  
Biodiversity Science ◽  
Evidenced Based ◽  
Historical Developments

Despite best efforts, the loss of biodiversity has continued at a pace that constitutes a major threat to the efficient functioning of ecosystems. Curbing the loss of biodiversity and assessing its local and global trends requires a vast amount of datasets from a variety of sources. Although the means for generating, aggregating and analyzing big datasets to inform policies are now within the reach of the scientific community, the data-driven nature of a complex multidisciplinary field such as biodiversity science necessitates an overarching framework for engagement. In this review, we propose such a schematic based on the life cycle of data to interrogate the science. The framework considers data generation and collection, storage and curation, access and analysis and, finally, communication as distinct yet interdependent themes for engaging biodiversity science for the purpose of making evidenced-based decisions. We summarize historical developments in each theme, including the challenges and prospects, and offer some recommendations based on best practices.

Landscape of Big Data Research in India

2021 ◽  
pp. 178-192
Author(s):  
Ravindra Sopan Bankar ◽  
Shalini Ramdas Lihitkar
Keyword(s):  
Big Data ◽  
Scientific Community ◽  
Research Area ◽  
Data Driven ◽  
Know How ◽  
Scientometric Evaluation ◽  
Research Studies

We all know that data has become a new fuel to the fast paced technology-driven world. And the academicians and researchers are doing their best for getting better into moulding the data-driven society to keeping it updated every day. Indian academicians and researchers are also doing their best in field of big data research studies. This chapter will focus the research landscape of big data research in India. This scientometric evaluation will let us know how India is going forward in this research area with some specific statistics in scientific community.

Challenges and best practices for big data-driven healthcare innovations conducted by profit–non-profit partnerships – a quantitative prioritization

2017 ◽  
Vol 11 (3) ◽  
pp. 171-181 ◽  
Author(s):  
E. R. Witjas-Paalberends ◽  
L. P. M. van Laarhoven ◽  
L. H. M. van de Burgwal ◽  
J. Feilzer ◽  
J. de Swart ◽  
...  
Keyword(s):  
Big Data ◽  
Best Practices ◽  
Data Driven ◽  
Non Profit

Characteristics of Scientific Inquiry

2018 ◽  
Author(s):  
Peter Miksza ◽  
Kenneth Elpus
Keyword(s):  
Philosophy Of Science ◽  
Scientific Inquiry ◽  
Scientific Reasoning ◽  
Scientific Community ◽  
Scientific Progress ◽  
Statistical Tools ◽  
Basic Characteristics ◽  
Historical Developments

This chapter introduces the reader to basic characteristics of science and situates the design and analysis considerations presented throughout the book within the context of scientific inquiry. A brief description of key historical developments regarding the philosophy of science is provided. An overview of the fundamental aspects of inductive and deductive scientific reasoning and the importance of falsification to scientific progress is presented. In addition, the values of objectivity and transparency as well as the importance of scientific community are stressed. The usefulness of statistical tools for helping researchers clarify their questions, establish criteria for their judgments, and communicate evidence for their claims is also discussed.

scholarly journals Precision agriculture and geospatial techniques for sustainable disease control

2021 ◽  
Author(s):  
Daniel P. Roberts ◽  
Nicholas M. Short ◽  
James Sill ◽  
Dilip K. Lakshman ◽  
Xiaojia Hu ◽  
...  
Keyword(s):  
Big Data ◽  
Disease Control ◽  
Crop Production ◽  
Plant Disease ◽  
Production Systems ◽  
Cropping Systems ◽  
Data Driven ◽  
Agricultural Community ◽  
Plant Disease Control ◽  
Crop Germplasm

AbstractThe agricultural community is confronted with dual challenges; increasing production of nutritionally dense food and decreasing the impacts of these crop production systems on the land, water, and climate. Control of plant pathogens will figure prominently in meeting these challenges as plant diseases cause significant yield and economic losses to crops responsible for feeding a large portion of the world population. New approaches and technologies to enhance sustainability of crop production systems and, importantly, plant disease control need to be developed and adopted. By leveraging advanced geoinformatic techniques, advances in computing and sensing infrastructure (e.g., cloud-based, big data-driven applications) will aid in the monitoring and management of pesticides and biologicals, such as cover crops and beneficial microbes, to reduce the impact of plant disease control and cropping systems on the environment. This includes geospatial tools being developed to aid the farmer in managing cropping system and disease management strategies that are more sustainable but increasingly complex. Geoinformatics and cloud-based, big data-driven applications are also being enlisted to speed up crop germplasm improvement; crop germplasm that has enhanced tolerance to pathogens and abiotic stress and is in tune with different cropping systems and environmental conditions is needed. Finally, advanced geoinformatic techniques and advances in computing infrastructure allow a more collaborative framework amongst scientists, policymakers, and the agricultural community to speed the development, transfer, and adoption of these sustainable technologies.

scholarly journals DaLiF: a data lifecycle framework for data-driven governments

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Syed Iftikhar Hussain Shah ◽  
Vassilios Peristeras ◽  
Ioannis Magnisalis
Keyword(s):  
Big Data ◽  
Data Management ◽  
Good Governance ◽  
Business Community ◽  
Data Driven ◽  
Governmental Organizations ◽  
Data Lifecycle ◽  
The Government ◽  
Data Ecosystem ◽  
Governance Transparency

AbstractThe public sector, private firms, business community, and civil society are generating data that is high in volume, veracity, velocity and comes from a diversity of sources. This kind of data is known as big data. Public Administrations (PAs) pursue big data as “new oil” and implement data-centric policies to transform data into knowledge, to promote good governance, transparency, innovative digital services, and citizens’ engagement in public policy. From the above, the Government Big Data Ecosystem (GBDE) emerges. Managing big data throughout its lifecycle becomes a challenging task for governmental organizations. Despite the vast interest in this ecosystem, appropriate big data management is still a challenge. This study intends to fill the above-mentioned gap by proposing a data lifecycle framework for data-driven governments. Through a Systematic Literature Review, we identified and analysed 76 data lifecycles models to propose a data lifecycle framework for data-driven governments (DaliF). In this way, we contribute to the ongoing discussion around big data management, which attracts researchers’ and practitioners’ interest.

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