scholarly journals Adoption-Driven Data Science for Transportation Planning: Methodology, Case Study, and Lessons Learned

2020 ◽  
Vol 12 (15) ◽  
pp. 6001 ◽  
Author(s):  
Eduardo Graells-Garrido ◽  
Vanessa Peña-Araya ◽  
Loreto Bravo
Keyword(s):  
Transportation Planning ◽  
Data Science ◽  
Cost Effective ◽  
Lessons Learned ◽  
Data Driven ◽  
Mobile Phone Data ◽  
Data Set ◽  
Policy Makers ◽  
Massive Data Set ◽  
Planning Methodology

The rising availability of digital traces provides a fertile ground for data-driven solutions to problems in cities. However, even though a massive data set analyzed with data science methods may provide a powerful and cost-effective solution to a problem, its adoption by relevant stakeholders is not guaranteed due to adoption barriers such as lack of interpretability and interoperability. In this context, this paper proposes a methodology toward bridging two disciplines, data science and transportation, to identify, understand, and solve transportation planning problems with data-driven solutions that are suitable for adoption by urban planners and policy makers. The methodology is defined by four steps where people from both disciplines go from algorithm and model definition to the development of a potentially adoptable solution with evaluated outputs. We describe how this methodology was applied to define a model to infer commuting trips with mode of transportation from mobile phone data, and we report the lessons learned during the process.

PROMOTING STATISTICS IN THE ERA OF DATA SCIENCE AND DATA-DRIVEN INNOVATIONS

2020 ◽  
Vol 19 (1) ◽  
pp. 226-237
Author(s):  
IBRAHIM SIDI ZAKARI
Keyword(s):  
Communication Skills ◽  
Data Science ◽  
Statistics Education ◽  
Effective Interaction ◽  
Oral Communication ◽  
Public Institutions ◽  
Future Generations ◽  
Data Driven ◽  
Policy Makers ◽  
Research Journal

This paper aims at highlighting initiatives in developing future statisticians directed at high-school and university levels in Niger. More specifically, it focuses on collaborations, partnerships, outreach initiatives and supporting mechanisms, which may contribute to increase engagement and interest in and attraction to the field of statistics in the era of data science and data-driven innovations. Providing sufficient exposure to modern statistical analysis, computational and graphical tools, written and oral communication skills, and the ever-growing interdisciplinary use of statistics are key activities for building future generations of statisticians. Furthermore, current curricula as well as pedagogical approaches, teaching materials, and assessment methods need to be re-thought in order tomeet the requirements of the skills needed in the 21st century ensuring effective interaction with scientists, public institutions, industry, civil society, and policy makers. First published February 2020 at Statistics Education Research Journal Archives

scholarly journals Data-Driven Construction Safety Information Sharing System Based on Linked Data, Ontologies, and Knowledge Graph Technologies

2022 ◽  
Vol 19 (2) ◽  
pp. 794
Author(s):  
Akeem Pedro ◽  
Anh-Tuan Pham-Hang ◽  
Phong Thanh Nguyen ◽  
Hai Chien Pham
Keyword(s):  
Information Sharing ◽  
Industry 4.0 ◽  
Linked Data ◽  
Data Science ◽  
Safety Information ◽  
Construction Safety ◽  
Lessons Learned ◽  
Data Driven ◽  
Knowledge Graph ◽  
Ontological Approach

Accident, injury, and fatality rates remain disproportionately high in the construction industry. Information from past mishaps provides an opportunity to acquire insights, gather lessons learned, and systematically improve safety outcomes. Advances in data science and industry 4.0 present new unprecedented opportunities for the industry to leverage, share, and reuse safety information more efficiently. However, potential benefits of information sharing are missed due to accident data being inconsistently formatted, non-machine-readable, and inaccessible. Hence, learning opportunities and insights cannot be captured and disseminated to proactively prevent accidents. To address these issues, a novel information sharing system is proposed utilizing linked data, ontologies, and knowledge graph technologies. An ontological approach is developed to semantically model safety information and formalize knowledge pertaining to accident cases. A multi-algorithmic approach is developed for automatically processing and converting accident case data to a resource description framework (RDF), and the SPARQL protocol is deployed to enable query functionalities. Trials and test scenarios utilizing a dataset of 200 real accident cases confirm the effectiveness and efficiency of the system in improving information access, retrieval, and reusability. The proposed development facilitates a new “open” information sharing paradigm with major implications for industry 4.0 and data-driven applications in construction safety management.

scholarly journals Where Is My Crop? Data-Driven Initiatives to Support Integrated Multi-Stakeholder Agricultural Decisions

2021 ◽  
Vol 5 ◽  
Author(s):  
Robert Andrade ◽  
Sergio Urioste ◽  
Tatiana Rivera ◽  
Benjamin Schiek ◽  
Fridah Nyakundi ◽  
...  
Keyword(s):  
Decision Making ◽  
Open Data ◽  
Lessons Learned ◽  
Data Driven ◽  
Economic Systems ◽  
Full Potential ◽  
Data Generation ◽  
Policy Makers ◽  
Advance Research ◽  
Multi Stakeholder

Globally, there has been an explosion of data generation in agriculture. With such a deluge of data available, it has become essential to create solutions that organize, analyze, and visualize it to gain actionable insights, which can guide farmers, scientists, or policy makers to take better decisions that lead to transformative actions for agriculture. There is a plethora of digital innovations in agriculture that implement big data techniques to harness solutions from large amounts of data, however, there is also a significant gap in access to these innovations among stakeholders of the value chains, with smallholder's farmers facing higher risks. Open data platforms have emerged as an important source of information for this group of producers but are still far from reaching their full potential. While the growing number of such initiatives has improved the availability and reach of data, it has also made the collection and processing of this information more difficult, widening the gap between those who can process and interpret this information and those who cannot. The Crop Observatories are presented in this article as an initiative that aims to harmonize large amounts of crop-specific data from various open access sources to build relevant indicators for decision making. Observatories are being developed for rice, cassava, beans, plantain and banana, and tropical forages, containing information on production, prices, policies, breeding, agronomy, and socioeconomic variables of interest. The Observatories are expected to become a lighthouse that attracts multi-stakeholders to avoid “not see the forest for the trees” and to advance research and strengthen crop economic systems. The process of developing the Observatories, as well as the methods for data collection, analysis, and display, is described. The main results obtained by the recently launched Rice Observatory (www.riceobservatory.org), and the about to be launched Cassava Observatory are presented, contextualizing their potential use and importance for multi-stakeholders of both crops. The article concludes with a list of lessons learned and next steps for the Observatories, which are also expected to guide the development of similar initiatives. Observatories, beyond presenting themselves as an alternative for improving data-driven decision making, can become platforms for collaboration on data issues and digital innovations within each sector.

scholarly journals DASHBOARDS FOR INPUT-EVALUATION OF POLICY PROGRAMS: LESSONS LEARNED FROM AN ANTWERP DASHBOARD FOR GARDEN STREETS

2020 ◽  
Vol VI-4/W2-2020 ◽  
pp. 173-179
Author(s):  
J. Vannieuwenhuyze
Keyword(s):  
Performance Indicators ◽  
Development Process ◽  
Subjective Evaluation ◽  
Evaluation Research ◽  
Lessons Learned ◽  
Data Driven ◽  
Policy Makers ◽  
Different Types ◽  
Development Processes ◽  
The City

Abstract. There is an ever-growing trend to pursue policies based on evidence-based and data-driven program evaluation research. In order to facilitate such evaluation research, electronic dashboards are increasingly used for translating sources of big and unstructured data into low-level summary visualizations understandable by layman policy-makers. In this paper, we report on the dashboard development process for an input-evaluation of new garden streets in the city of Antwerp. During this process, different lessons were learned. First, developers should start from a clearly defined policy question and analysis units in order to optimize the development process. Second, different types of key performance indicators exist, which should also be well-defined in advance so that appropriate data can be collected. Third, a dashboard should not be restricted to purely objective data-analyses but may also include features that facilitate subjective evaluation guided by assumptions and believes of the dashboard-user. These lessons helped us to make the dashboard requirements of Antwerp more concrete. Likewise, they may help other policy supporting dashboard developers to optimize their development processes.

Retraction data can bring more insights and implications for not just authors and their institutions, but funders, policy-makers, and editors

2019 ◽  
Author(s):  
Quan-Hoang Vuong
Keyword(s):  
Research Field ◽  
Academic Community ◽  
Lessons Learned ◽  
Sql Server ◽  
Scientific Enterprise ◽  
Positive Role ◽  
Data Set ◽  
Policy Makers ◽  
Social Responsibilities ◽  
Open Review

Although retractions are commonly considered as negative outcomes, the fact remains that they play a positive role in the academic community, for instance, help scientific enterprise perform its self-correcting function; become lessons learned for future researchers; represent social responsibilities or let open review communities offer better "monitoring services" in keeping problematic studies in check. This study provides retraction data, which is believed to give useful insights into retraction and its powerful function. By using RetractionWatch data, a database built based on SQL Server 2016, and some home-made AI, a data set of 18,603 retractions from 1753 to 2019 February covering 127 research field was contributed. The results show a long way of retraction with the popularity of retraction practices since 1999, and the burden in 2010; IEEE, Elsevier, and Springer account for nearly 60% of all retracted papers globally with the record belongs to IEEE even though it is not the organization that publishes most journals; a paper could be retracted for diverse reasons but "fake peer review" becomes a major one.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

2020 ◽  
Vol 13 ◽  
Author(s):  
Ritu Khandelwal ◽  
Hemlata Goyal ◽  
Rajveer Singh Shekhawat
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Data Science ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Future Trends ◽  
Data Set ◽  
Learning Stage ◽  
Learning Techniques ◽  
Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

scholarly journals Food Security, Environmental Health, and the Economy in Mexico: Lessons Learned with the COVID-19

2021 ◽  
Vol 13 (13) ◽  
pp. 7470
Author(s):  
Rebeca Monroy-Torres ◽  
Ángela Castillo-Chávez ◽  
Erika Carcaño-Valencia ◽  
Marco Hernández-Luna ◽  
Alex Caldera-Ortega ◽  
...  
Keyword(s):  
Food Security ◽  
Food Insecurity ◽  
Environmental Health ◽  
Public Policies ◽  
Cost Effective ◽  
Lessons Learned ◽  
Civil Society Organizations ◽  
Human Right ◽  
Healthy Environment ◽  
Mesh Terms

The COVID-19 pandemic showed an impact mainly on the health of people and the economy of households. The levels of food security in the world’s households, especially in Mexico, have decreased. When people do not have food security, their health is compromised and they have financial problems; on the other hand, environmental deterioration has a link with food security. The purpose of this review is to analysis of the current situation in Mexico of food security, environmental health and economy, the main lessons learned in these areas and their proposals integrating public policies. A review was carried out in the main databases (MEDLINE, Embase, CINAHL Plus, Web of Science, CAB Abstracts y PAIS Index) with the following keywords and according to the MeSH terms: Food security, food insecurity, environmental health, public policies, environmental, production, integrating the word COVID-19 in English and Spanish. Only 44.5% of Mexican households presented food security. For food insecurity, 22.6% had moderate and severe food insecurity, while 32.9% had mild insecurity. Food insecurity and the health impacts of environmental origin (waste management during the coronavirus pandemic, water contaminated by bacteria, viruses, and toxins; air pollution) generates impacts on economic activity by not offering food that meets health regulations. Without the application of cost-effective measures and interventions for the prevention and control of patients with obesity, the direct costs for 2023 will amount to 9 million dollars, which worsens the household economy. Despite having laws and policies on the right to food, a healthy environment (water), and opportunities for economic growth, these human rights are not fulfilled. The conclusion is that it is necessary to use a health and agroecological model to promote public policies (health, environment, and economy) that aims to prevent the discussed issues, with multidisciplinary and intersectoral interventions (government, academia, researchers, civil society organizations, industry, and population). This upholds the human right that all people should enjoy an adequate, healthy environment and have access to high-quality food.

Lessons learned from a search for common ground

2021 ◽  
Vol 102 (5) ◽  
pp. 29-32
Author(s):  
Rick Hess ◽  
Pedro Noguera
Keyword(s):  
School Choice ◽  
Common Ground ◽  
Social And Emotional Learning ◽  
Lessons Learned ◽  
Policy Makers ◽  
Teachers College ◽  
Social And Emotional ◽  
Education Leaders ◽  
K 12 ◽  
Common Understanding

In 2020, Rick Hess and Pedro Noguera engaged in a long-running correspondence that tackled many of the biggest questions in education — including topics like school choice, equity and diversity, testing, privatization, the achievement gap, social and emotional learning, and civics. They sought to unpack their disagreements, better understand one another’s perspectives, and seek places of agreement or points of common understanding. Their correspondence appears in their book, A Search for Common Ground: Conversations About the Toughest Questions in K-12 Education (Teachers College Press, 2021). In this article, they reflect on the exercise, what they learned from it, and what lessons it might offer to educators, education leaders, researchers, and policy makers.

scholarly journals Despite vaccination, China needs non-pharmaceutical interventions to prevent widespread outbreaks of COVID-19 in 2021

2021 ◽  
Author(s):  
Juan Yang ◽  
Valentina Marziano ◽  
Xiaowei Deng ◽  
Giorgio Guzzetta ◽  
Juanjuan Zhang ◽  
...  
Keyword(s):  
Reproduction Number ◽  
Synergetic Effect ◽  
Data Driven ◽  
Natural Immunity ◽  
Epidemic Threshold ◽  
Policy Makers ◽  
Large Populations ◽  
One Year ◽  
Pharmaceutical Interventions

AbstractCOVID-19 vaccination is being conducted in over 200 countries and regions to control SARS-CoV-2 transmission and return to a pre-pandemic lifestyle. However, understanding when non-pharmaceutical interventions (NPIs) can be lifted as immunity builds up remains a key question for policy makers. To address this, we built a data-driven model of SARS-CoV-2 transmission for China. We estimated that, to prevent the escalation of local outbreaks to widespread epidemics, stringent NPIs need to remain in place at least one year after the start of vaccination. Should NPIs alone be capable of keeping the reproduction number (Rt) around 1.3, the synergetic effect of NPIs and vaccination could reduce the COVID-19 burden by up to 99% and bring Rt below the epidemic threshold in about 9 months. Maintaining strict NPIs throughout 2021 is of paramount importance to reduce COVID-19 burden while vaccines are distributed to the population, especially in large populations with little natural immunity.

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