scholarly journals A status report on a section-based stratigraphic and palaeontological database – the Geobiodiversity Database

2020 ◽  
Vol 12 (4) ◽  
pp. 3443-3452
Author(s):  
Hong-He Xu ◽  
Zhi-Bin Niu ◽  
Yan-Sen Chen
Keyword(s):  
Big Data ◽  
Quantitative Analysis ◽  
Scientific Research ◽  
Structured Data ◽  
Data Driven ◽  
Status Report ◽  
Statistical Results ◽  
Existing Data

Abstract. Big data are significant for quantitative analysis and contribute to data-driven scientific research and discoveries. Here a brief introduction is given to the Geobiodiversity Database (GBDB), a comprehensive stratigraphic and palaeontological database, and its data. The GBDB includes abundant geological records from China and has supported a series of scientific studies on the Paleozoic palaeogeography and tectonic and biodiversity evolution of China. The data that the GBDB has including those that are newly collected are described in detail; the statistical results and structure of the data are given. A comparison between the GBDB; the largest palaeobiological database, the Paleobiology Database (PBDB); and the geological rock database Macrostrat is drawn. The GBDB and other databases are complementary in palaeontological and stratigraphic research. The GBDB will continually provide users access to detailed palaeontological and stratigraphic data based on publications. Non-structured data of palaeontology and stratigraphy will also be included in the GBDB, and they will be organically correlated with the existing data of the GBDB, making the GBDB more widely used for both researchers and anyone who is interested in fossils and strata. The GBDB fossil and stratum dataset (Xu, 2020) is freely downloadable from https://doi.org/10.5281/zenodo.4245604.

scholarly journals Retrospect and prospect of a section-based stratigraphic and palaeontological database – Geobiodiversity Database

2020 ◽  
Author(s):  
Hong-He Xu ◽  
Zhi-Bin Niu ◽  
Yan-Sen Chen
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Quantitative Analysis ◽  
Quantitative Research ◽  
Scientific Research ◽  
Data Driven ◽  
Artificial Intelligence Techniques ◽  
Existing Problems ◽  
Push Forward ◽  
Early Palaeozoic

Abstract. Big data are significant to the quantitative analysis and contribute to the data-driven scientific research and discoveries. Here the thorough introduction is given on the Geobiodiversity database (GBDB), a comprehensive stratigraphic and palaeontological database. The GBDB includes abundant geological records from China and contributes a serial of scientific studies on early Palaeozoic palaeogeography, tectonic and biodiversity evolution of China. Nevertheless, the existing problems of the GBDB limited the using of its data. The turnover and improvement of the GBDB were started in 2019. Besides the data collecting, processing and visualization as the GBDB did previously, the database and the website are optimized and re-designed, the new GBDB working team pays more attention to data analyzing with the professional artificial intelligence techniques. GBDB is complementary to other related databases, and further collaborations are proposed to mutually benefit and push forward the quantitative research of palaeontology and stratigraphy in the era of big data. The datasets (Xu, 2020) are freely downloadable from http://doi.org/10.5281/zenodo.3667645.

scholarly journals Retrospect and prospect of a section-based stratigraphic and palaeontological database – Geobiodiversity Database

2020 ◽  
Author(s):  
Hong-He Xu ◽  
Zhi-Bin Niu ◽  
Yan-Sen Chen
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Quantitative Analysis ◽  
Quantitative Research ◽  
Scientific Research ◽  
Data Driven ◽  
Artificial Intelligence Techniques ◽  
Existing Problems ◽  
Push Forward ◽  
Early Palaeozoic

Abstract. Big data are significant to the quantitative analysis and contribute to the data-driven scientific research and discoveries. Here the thorough introduction is given on the Geobiodiversity database (GBDB), a comprehensive stratigraphic and palaeontological database. The GBDB includes abundant geological records from China and contributes a serial of scientific studies on early Palaeozoic palaeogeography, tectonic and biodiversity evolution of China. Nevertheless, the existing problems of the GBDB limited the using of its data. The turnover and improvement of the GBDB were started in 2019. Besides the data collecting, processing and visualization as the GBDB did previously, the database and the website are optimized and re-designed, the new GBDB working team pays more attention to data analyzing with the professional artificial intelligence techniques. GBDB is complementary to other related databases and further collaborations are proposed to mutually benefit and push forward the quantitative research of palaeontology and stratigraphy in the era of big data. The persistent snapshot of the GBDB data can be found at: http://doi.org/10.5281/zenodo.3667645 (Xu, 2020).

scholarly journals Innovation in Times of Big Data and AI: Introducing the Data-Driven Innovation (DDI) Framework

2021 ◽  
pp. 289-310
Author(s):  
Sonja Zillner
Keyword(s):  
Big Data ◽  
Frequency Analysis ◽  
Empirical Data ◽  
Research Study ◽  
Scientific Research ◽  
Data Driven ◽  
Demand And Supply ◽  
Factors Influencing ◽  
Generic Data ◽  
Art Analysis

AbstractTo support the process of identifying and scoping data-driven innovation, we are introducing the data-driven innovation (DDI) framework, which provides guidance in the continuous analysis of factors influencing the demand and supply sides of a data-driven innovation. The DDI framework describes all relevant aspects of any generic data-driven innovation and is backed by empirical data and scientific research encompassing a state-of-the-art analysis, an ontology describing the central dimensions of data-driven innovation, as well as a quantitative and representative research study covering more than 90 data-driven innovations. This chapter builds upon a short analysis of the nature of data-driven innovation and provides insights into how to best screen it. It details the four phases of the empirical DDI research study and discusses central findings related to trends, frequencies and distributions along the main dimensions of the DDI framework that could be derived by percentage-frequency analysis.

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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