scholarly journals Defining Data Science by a Data-Driven Quantification of the Community

2018 ◽  
Vol 1 (1) ◽  
pp. 235-251 ◽  
Author(s):  
Frank Emmert-Streib ◽  
Matthias Dehmer
Keyword(s):  
Quantitative Analysis ◽  
Data Science ◽  
Data Driven ◽  
Statistical Regression ◽  
Important Research ◽  
Academic Field ◽  
Research Fields ◽  
Science Community ◽  
Fully Automatic ◽  
Using Data

Data science is a new academic field that has received much attention in recent years. One reason for this is that our increasingly digitalized society generates more and more data in all areas of our lives and science and we are desperately seeking for solutions to deal with this problem. In this paper, we investigate the academic roots of data science. We are using data of scientists and their citations from Google Scholar, who have an interest in data science, to perform a quantitative analysis of the data science community. Furthermore, for decomposing the data science community into its major defining factors corresponding to the most important research fields, we introduce a statistical regression model that is fully automatic and robust with respect to a subsampling of the data. This statistical model allows us to define the ‘importance’ of a field as its predictive abilities. Overall, our method provides an objective answer to the question ‘What is data science?’.

scholarly journals Data-Driven Management and Interoperable Metrics for Special Collections and Archives User Services

2012 ◽  
Vol 13 (2) ◽  
pp. 129-151 ◽  
Author(s):  
Joyce Chapman ◽  
Elizabeth Yakel
Keyword(s):  
Decision Making ◽  
Quantitative Analysis ◽  
Data Driven ◽  
Significant Challenge ◽  
Special Collections ◽  
Quantitative Metrics ◽  
Day To Day Operations ◽  
Using Data ◽  
Institutional Boundaries ◽  
Operational Data

While special collections and archives managers have at times recognized the importance of using data to drive decision making, translating this objective into reality and integrating data analysis into day-to-day operations has proven to be a significant challenge. There have also been obstacles to formulating quantitative metrics for special collections and archives and rendering them interoperable across institutional boundaries. This article attempts to focus a conversation around two issues: 1) the importance of quantitative analysis of operational data for improving research services in special collections and archives; and 2) the need for the profession to achieve consensus on definitions for . . .

scholarly journals Four Generations in Data Engineering for Data Science

2021 ◽  
Author(s):  
Meike Klettke ◽  
Uta Störl
Keyword(s):  
Data Science ◽  
Data Curation ◽  
Data Driven ◽  
Environmental Sciences ◽  
Scientific Methods ◽  
Domain Experts ◽  
The Past ◽  
Research Fields ◽  
Data Engineering ◽  
The Moment

AbstractData-driven methods and data science are important scientific methods in many research fields. All data science approaches require professional data engineering components. At the moment, computer science experts are needed for solving these data engineering tasks. Simultaneously, scientists from many fields (like natural sciences, medicine, environmental sciences, and engineering) want to analyse their data autonomously. The arising task for data engineering is the development of tools that can support an automated data curation and are utilisable for domain experts. In this article, we will introduce four generations of data engineering approaches classifying the data engineering technologies of the past and presence. We will show which data engineering tools are needed for the scientific landscape of the next decade.

scholarly journals Using Data Expedition as a Formative Assessment Tool in Data Science Education: Reasoning, Justification, and Evaluation

2019 ◽  
Vol 14 (11) ◽  
pp. 107
Author(s):  
Olga Maksimenkova ◽  
Alexey Neznanov ◽  
Irina Radchenko
Keyword(s):  
Science Education ◽  
Digital Media ◽  
Data Science ◽  
Evaluation Method ◽  
Assessment Tool ◽  
Structural Features ◽  
Data Driven ◽  
Distinctive Features ◽  
Accurate Evaluation ◽  
Using Data

The paper addresses the questions of data science education of current im-portance. It aims to introduce and justify the framework that allows flexibly evaluate the processes of a data expedition and a digital media created during it. For these purposes, the authors explore features of digital media artefacts which are specific to data expeditions and are essential to accurate evaluation. The ru-brics as a power but hardly formalizable evaluation method in application to digi-tal media artefacts are also discussed. Moreover, the paper documents the experi-ence of rubrics creation according to the suggested framework. The rubrics were successfully adopted to two data-driven journalism courses. The authors also formulate recommendations on data expedition evaluation which should take into consideration structural features of a data expedition, distinctive features of digital media, etc.

Development of a Pediatric Early Warning System Using Data-Driven Vital Signs

PEDIATRICS ◽  
2016 ◽  
Vol 137 (Supplement 3) ◽  
pp. 256A-256A
Author(s):  
Catherine Ross ◽  
Iliana Harrysson ◽  
Lynda Knight ◽  
Veena Goel ◽  
Sarah Poole ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Vital Signs ◽  
Warning System ◽  
Data Driven ◽  
Using Data

Enhanced Resilient State Estimation Using Data-Driven Auxiliary Models

2020 ◽  
Vol 16 (1) ◽  
pp. 639-647 ◽  
Author(s):  
Olugbenga Moses Anubi ◽  
Charalambos Konstantinou
Keyword(s):  
State Estimation ◽  
Data Driven ◽  
Using Data

scholarly journals Lifestyle carbon footprints and changes in lifestyles to limit global warming to 1.5 °C, and ways forward for related research

2021 ◽  
Author(s):  
Ryu Koide ◽  
Michael Lettenmeier ◽  
Lewis Akenji ◽  
Viivi Toivio ◽  
Aryanie Amellina ◽  
...  
Keyword(s):  
Global Warming ◽  
Quantitative Analysis ◽  
Sustainability Science ◽  
Scenario Development ◽  
Carbon Footprints ◽  
Related Research ◽  
Living Lab ◽  
Per Capita ◽  
Using Data ◽  
Subnational Analysis

AbstractThis paper presents an approach for assessing lifestyle carbon footprints and lifestyle change options aimed at achieving the 1.5 °C climate goal and facilitating the transition to decarbonized lifestyles through stakeholder participatory research. Using data on Finland and Japan it shows potential impacts of reducing carbon footprints through changes in lifestyles for around 30 options covering food, housing, and mobility domains, in comparison with the 2030 and 2050 per-capita targets (2.5–3.2 tCO2e by 2030; 0.7–1.4 tCO2e by 2050). It discusses research opportunities for expanding the footprint-based quantitative analysis to incorporate subnational analysis, living lab, and scenario development aiming at advancing sustainability science on the transition to decarbonized lifestyles.

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