Cyber Infrastructure as a Service to Empower Multidisciplinary, Data-Driven Scientific Research

Traffic data is commonly collected from widely deployed sensors in urban areas. This brings up a new research topic, data-driven intelligent transportation systems (ITSs), which means to integrate heterogeneous traffic data from different kinds of sensors and apply it for ITS applications. This research, taking into consideration the significant increase in the amount of traffic data and the complexity of data analysis, focuses mainly on the challenge of solving data-intensive and computation-intensive problems. As a solution to the problems, this paper proposes a Cyber-ITS framework to perform data analysis on Cyber Infrastructure (CI), by nature parallel-computing hardware and software systems, in the context of ITS. The techniques of the framework include data representation, domain decomposition, resource allocation, and parallel processing. All these techniques are based on data-driven and application-oriented models and are organized as a component-and-workflow-based model in order to achieve technical interoperability and data reusability. A case study of the Cyber-ITS framework is presented later based on a traffic state estimation application that uses the fusion of massive Sydney Coordinated Adaptive Traffic System (SCATS) data and GPS data. The results prove that the Cyber-ITS-based implementation can achieve a high accuracy rate of traffic state estimation and provide a significant computational speedup for the data fusion by parallel computing.

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On a cyberinfrastructure platform for multidisciplinary, data-intensive scientific research

2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC) ◽

10.1109/ccwc.2017.7868483 ◽

2017 ◽

Author(s):

Xiangrong Ma ◽

Zhao Fu ◽

Yingtao Jiang ◽

Mei Yang ◽

Haroon Stephen

Keyword(s):

Scientific Research ◽

Data Intensive ◽

Multidisciplinary Data

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

10.5194/essd-2020-164 ◽

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.

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

The Elements of Big Data Value ◽

10.1007/978-3-030-68176-0_12 ◽

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.

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Trust in the research community predicts intent to comply with COVID-19 prevention measures: An analysis of a large-scale international survey dataset

10.1101/2021.12.08.21267486 ◽

2021 ◽

Author(s):

Hyemin Han

Keyword(s):

Network Analysis ◽

Generalized Linear Model ◽

Large Scale ◽

Preventive Measures ◽

Scientific Research ◽

Research Community ◽

Data Driven ◽

Prevention Measures ◽

The Relationship ◽

Bayesian Generalized Linear Model

In the present study, I explored the relationship between people's trust in different agents related to prevention of spread of COVID-19 and their compliance with pharmaceutical and non-pharmaceutical preventive measures. The COVIDiSTRESSII Global Survey dataset, which was collected from international samples, was analysed to examine the aforementioned relationship across different countries. For data-driven exploration, network analysis and Bayesian generalized linear model (GLM) analysis were performed. The result from network analysis demonstrated that trust in the scientific research community was most central in the network of trust and compliance. In addition, the outcome from Bayesian GLM analysis indicated that the same factor, trust in the scientific research community, was most fundamental in predicting participants' intent to comply with both pharmaceutical and non-pharmaceutical preventive measures. I briefly discussed the implications of the findings, the importance of trust in the scientific research community in explaining people's compliance with measure to prevent spread of COVID-19.

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

10.5194/essd-2020-40 ◽

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).

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Using Cyber-Infrastructure for Dynamic Data Driven Laser Treatment of Cancer

Computational Science – ICCS 2007 - Lecture Notes in Computer Science ◽

10.1007/978-3-540-72584-8_128 ◽

2007 ◽

pp. 972-979 ◽

Cited By ~ 4

Author(s):

C. Bajaj ◽

J. T. Oden ◽

K. R. Diller ◽

J. C. Browne ◽

J. Hazle ◽

...

Keyword(s):

Laser Treatment ◽

Data Driven ◽

Dynamic Data ◽

Cyber Infrastructure

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A field guide to cultivating computational biology

PLoS Biology ◽

10.1371/journal.pbio.3001419 ◽

2021 ◽

Vol 19 (10) ◽

pp. e3001419

Author(s):

Gregory P. Way ◽

Casey S. Greene ◽

Piero Carninci ◽

Benilton S. Carvalho ◽

Michiel de Hoon ◽

...

Keyword(s):

Computational Biology ◽

Scientific Progress ◽

Scientific Field ◽

Data Driven ◽

Team Science ◽

Research Model ◽

The Past ◽

Multidisciplinary Data ◽

Funding Agencies ◽

Single Discipline

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

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

Earth System Science Data ◽

10.5194/essd-12-3443-2020 ◽

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.

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