Is a More Data-driven Approach the Future of Tuberculosis Transmission Modeling?

La orientación al dato en el contexto de la transformación digital lleva aparejada la aparición de nuevas regulaciones, dinámicas de gobernanza y roles, y servicios, junto con las correspondientes prácticas, instrumentos y estándares. A la vez se suscitan retos en relación con la ciberseguridad y la preservación de los datos. En este artículo se exponen la transformación digital y la orientación al dato, la proyección de lo anterior en la administración digital, el contexto de la Unión Europea, trayectoria y su orientación, aspectos de la interoperabilidad, ciberseguridad y preservación de los datos, cuestiones de gobernanza y roles en la orientación al dato y, finalmente, unas conclusiones. The data-driven approach in the context of digital transformation entails the appearance of new regulations, governance dynamics and roles, and services, together with the corresponding practices, instruments and standards. At the same time new challenges appear in relation to cybersecurity and data preservation. This article presents the digital transformation and data-driven approach, the impact in digital administration, the context of the European Union, trajectory and orientation towards the future, along with aspects of interoperability, cybersecurity and data preservation, as well as issues of governance and roles in data orientation and finally some conclusions.

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Towards a resilient perspective for the future of offshore platforms. Insights from a data driven approach

Transforming Government People Process and Policy ◽

10.1108/tg-04-2021-0067 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Francesca Loia ◽

Nunzia Capobianco ◽

Roberto Vona

Keyword(s):

Local Governments ◽

Alternative Energy ◽

Data Driven ◽

Environmental Research ◽

Offshore Platforms ◽

Territorial Development ◽

Content Type ◽

Circular Model ◽

The Future ◽

Data Driven Approach

Purpose This study aims to investigate the collective perception regarding the future of offshore platforms and frame the main categories of meanings associated by the community with the investigated phenomenon. Design/methodology/approach A data driven approach has been conducted. The collection of the peoples’ opinions has been realized on two specific social network communities as follows: Twitter and Instagram. The text mining processes carried out a sentiment and a cluster analysis. Findings The sentiment analysis of the most frequent words has been shown. The following four main homogeneous categories of words are emerged in relation to the decommissioning of offshore platforms: technological areas, green governance (GG), circular economy and socio-economic sphere. Research limitations/implications The alternative use of the offshore platforms, including tourism initiatives, aquaculture, alternative energy generation, hydrogen storage and environmental research, could improve the resilience of communities by offering the development of new jobs and the growth of local and innovative green businesses. Practical implications The adoption of a circular model and GG initiatives aims to limit the input of resources and energy, minimize waste and losses, adopt a sustainable approach and realize new social and territorial value. Originality/value The analysis underlines the importance to adopt a systems perspective, which takes into account the social, economic and environmental system as a whole, the different phenomena that occur and the variety of categories of stakeholders, from users to local governments that participate in the territorial development.

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Are machines stealing our jobs?

Cambridge Journal of Regions Economy and Society ◽

10.1093/cjres/rsz025 ◽

2020 ◽

Vol 13 (1) ◽

pp. 153-173 ◽

Cited By ~ 1

Author(s):

Andrea Gentili ◽

Fabiano Compagnucci ◽

Mauro Gallegati ◽

Enzo Valentini

Keyword(s):

Cluster Analysis ◽

International Federation ◽

Data Driven ◽

The Political ◽

Human Labour ◽

The Future ◽

Data Driven Approach ◽

Technological Transition ◽

Cross Country ◽

Academic Debate

Abstract This study aims to contribute empirical evidence to the debate about the future of work in an increasingly robotised world. We implement a data-driven approach to study the technological transition in six leading Organisation for Economic Co-operation and Development (OECD) countries. First, we perform a cross-country and cross-sector cluster analysis based on the OECD-STAN database. Second, using the International Federation of Robotics database, we bridge these results with those regarding the sectoral density of robots. We show that the process of robotisation is industry- and country-sensitive. In the future, participants in the political and academic debate may be split into optimists and pessimists regarding the future of human labour; however, the two stances may not be contradictory.

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A View to the Future â A Data-Driven Approach to Mergers and Acquisitions

Understanding Mergers and Acquisitions in the 21st Century ◽

10.1057/9781137278074.0018 ◽

2013 ◽

Author(s):

Auke-Dirk Pietersma ◽

Killian J. McCarthy

Keyword(s):

Mergers And Acquisitions ◽

Data Driven ◽

The Future ◽

Data Driven Approach

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The Future of Transatlanticism: Effects of a Raise of US Import Tariffs on Exports in the German Automotive Sector. A Quantitative, Data Driven Approach

Progress in International Business Research - International Business in a VUCA World: The Changing Role of States and Firms ◽

10.1108/s1745-886220190000014008 ◽

2019 ◽

pp. 103-130

Author(s):

Moritz Kath ◽

Natalia Ribberink

Keyword(s):

Quantitative Data ◽

Data Driven ◽

Automotive Sector ◽

The Future ◽

Data Driven Approach

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Partially versus Purely Data-Driven Approaches in SARS-CoV-2 Prediction

Applied Sciences ◽

10.3390/app10165696 ◽

2020 ◽

Vol 10 (16) ◽

pp. 5696 ◽

Cited By ~ 1

Author(s):

Samar A. Shilbayeh ◽

Abdullah Abonamah ◽

Ahmad A. Masri

Keyword(s):

Prediction Models ◽

Machine Learning Algorithms ◽

Data Driven ◽

Sir Epidemic ◽

Past Behavior ◽

The Us ◽

The Future ◽

Data Driven Approach ◽

Expert Input ◽

Death Cases

Prediction models of coronavirus disease utilizing machine learning algorithms range from forecasting future suspect cases, predicting mortality rates, to building a pattern for country-specific pandemic end date. To predict the future suspect infection and death cases, we categorized the approaches found in the literature into: first, a purely data-driven approach, whose goal is to build a mathematical model that relates the data variables including outputs with inputs to detect general patterns. The discovered patterns can then be used to predict the future infected cases without any expert input. The second approach is partially data-driven; it uses historical data, but allows expert input such as the SIR epidemic algorithm. This approach assumes that the epidemic will end according to medical reasoning. In this paper, we compare the purely data-driven and partially-data driven approaches by applying them to data from three countries having different past pattern behavior. The countries are the US, Jordan, and Italy. It is found that those two prediction approaches yield significantly different results. Purely data-driven approach depends totally on the past behavior and does not show any decline in the number of the infected cases if the country did not experience any decline in the number of cases. On the other hand, a partially data-driven approach guarantees a timely decline of the infected curve to reach zero. Using the two approaches highlights the importance of human intervention in pandemic prediction to guide the learning process as opposed to the purely data-driven approach that predicts future cases based on the pattern detected in the data.

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Investing in the future: a call for strategies to empower and expand representation of women in neurosurgery worldwide

Neurosurgical FOCUS ◽

10.3171/2020.12.focus20963 ◽

2021 ◽

Vol 50 (3) ◽

pp. E8

Author(s):

Samantha J. Sadler ◽

Ho Kei Yuki Ip ◽

Eliana Kim ◽

Claire Karekezi ◽

Faith C. Robertson

Keyword(s):

Positive Change ◽

Data Driven ◽

Representation Of Women ◽

The Future ◽

Data Driven Approach ◽

Improve Representation

As progress is gradually being made toward increased representation and retention of women in neurosurgery, the neurosurgical community should elevate effective efforts that may be driving positive change. Here, the authors describe explicit efforts by the neurosurgery community to empower and expand representation of women in neurosurgery, among which they identified four themes: 1) formal mentorship channels; 2) scholarships and awards; 3) training and exposure opportunities; and 4) infrastructural approaches. Ultimately, a data-driven approach is needed to improve representation and empowerment of women in neurosurgery and to best direct the neurosurgical community’s efforts across the globe.

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