Opening industry data: The private sector's role in addressing societal challenges

This commentary explores the potential of private companies to advance scientific progress and solve social challenges through opening and sharing their data. Open data can accelerate scientific discoveries, foster collaboration, and promote long-term business success. However, concerns regarding data privacy and security can hinder data sharing. Companies have options to mitigate the challenges through developing data governance mechanisms, collaborating with stakeholders, communicating the benefits, and creating incentives for data sharing, among others. Ultimately, open data has immense potential to drive positive social impact and business value, and companies can explore solutions for their specific circumstances and tailor them to their specific needs.

Application of Machine Learning Techniques to Predict Visitors to the Tourist Attractions of the Moche Route in Peru

Due to the COVID-19 pandemic, the tourism sector has been one of the most affected sectors and requires management entities to develop urgent measures to reactivate and achieve digital transformation using emerging disruptive technologies. The objective of this research is to apply machine learning techniques to predict visitors to tourist attractions on the Moche Route in northern Peru, for which a methodology based on four main stages was applied: (1) data collection, (2) model analysis, (3) model development, and (4) model evaluation. Public data from official sources and internet data (TripAdvisor and Google Trends) during the period from January 2011 to May 2022 are used. Four algorithms are evaluated: linear regression, KNN regression, decision tree, and random forest. In conclusion, for both the prediction of national and foreign tourists, the best algorithm is linear regression, and the results allow for taking the necessary actions to achieve the digital transformation to promote the Moche Route and, thus, reactivate tourism and the economy in the north of Peru.

Geographic Information Systems for Facilitating Audits of the Urban Built Environment

Data collection and large-scale urban audits are challenging and can be time consuming processes. Geographic information systems can extract and combine relevant data that can be used as input to calculation tools that provide results and quantify indicators with sufficient spatial analysis to facilitate the local decision-making process for building renovations and sustainability assessment. This work presents an open-access tool that offers an automated process that can be used to audit an urban area in order to extract relevant information about the characteristics of the built environment, analyze the building characteristics to evaluate energy performance, assess the potential for the installation of photovoltaics on available building rooftops, and quantify ground permeability. A case study is also presented to demonstrate data collection and processing for an urban city block, and the relevant results are elaborated upon. The method is easily replicable and is based on open data and non-commercial tools.

The Impact of COVID-19 on Authoring Open Data Workshop Settings in High School

According to the Open Knowledge Foundation, Open Data are data that can be freely used, created and shared by anyone. Initiatives to let K-12 learners exploit Open Data are rare in literature, and the situation is even worse if we look for opportunities to move them in the position of Open Data publishers. To advance the dialogue around methods to increase awareness of Open Data, improve users' skills to author and use Open Data, HETOR regularly organises workshops with secondary school learners to let them create, publish, and exploit Open Data by SPOD since 2016. While workshops were organised as physical meetings, during the COVID-19 pandemic, HETOR required to revise the performed protocol. This article reports changes applied to the workshops proposed by HETOR and the observed results in terms of quantity and quality of produced open datasets, and quality of presenting and disseminating the authored Open Data by comparing workshops' results before and after the COVID-19. According to the discussion, the quantity and quality of the workshops outcome increased during the workshops that took place after the COVID-19 pandemic demonstrating that Open Data based initiatives can successfully survive in remote settings. On the opposite, the quality of the presentations authored by scholars is more heterogeneous during after-COVID workshops demonstrating that remote settings make educational inequalities worse. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Thinking Beyond COVID-19: How Has the Pandemic Impacted Future Time Horizons?

Older age is reportedly protective against the detrimental psychological impacts of the COVID-19 pandemic, consistent with the theory that reduced future time extension (FTE) leads to prioritization of socioemotional well-being. We investigated whether depression severity and pandemic-related factors (regional severity, threat, social isolation) reduce FTE beyond chronological age and whether these relationships differ between younger and older adults. In May 2020, we recruited 248 adults (younger: 18-43 years, older: 55-80 years) from 13 industrialized nations. Multigroup path analysis found that depression severity was a better predictor of FTE than the reverse association in both age groups, suggesting an affective foreshortening of future time. In both age groups, older age was protective against depression severity, and younger age was associated with heightened vulnerability to the negative impacts of pandemic-related factors. Future research should consider the complex interrelationships between FTE, age, and depression severity and the potential impacts of the broader psychosocial milieu.

Integrating Telehealth for Strengthening Health Systems in the Context of the COVID-19 Pandemic: A Perspective from Peru.

The COVID-19 pandemic forced the government to rapidly modify its legal framework to adopt telemedicine and promote the implementation of telehealth services to meet the healthcare needs of patients in Peru. In this paper, we aim to review the main changes to the regulatory framework and describe selected initiatives to promote the telehealth framework that emerged in Peru during the COVID-19 pandemic. In addition, we discuss the challenges to integrate telehealth services for strengthening health systems in Peru. The Peruvian telehealth regulatory framework began in 2005, and in subsequent years, laws and regulations were established that sought to progressively implement a national telehealth network. However, mainly local initiatives were deployed. In this sense, significant challenges remain to be addressed, such as infrastructure in healthcare centers, including high-speed Internet connectivity; infostructure of health-information systems, including interoperability with electronic medical records; monitoring and evaluation of the national agenda for the health sector in 2020-2025; expanding the healthcare workforce in terms of digital health; and developing the capacities of healthcare users on health literacy, including digital aspects. In addition, there is enormous potential for telemedicine as a key strategy to deal with the COVID-19 pandemic and to improve access to rural and hard-to-reach areas and populations. There is thus an urgent need to effectively implement an integrated national telehealth system to address sociocultural issues and strengthen the competencies of human resources in telehealth and digital health in Peru.

Dados governamentais abertos sobre a COVID-19 no Brasil: avalia??o do potencial de re?so

In the COVID-19 pandemic, access to data on the disease has become strategic for controlling public health measures. Faced with the health emergency, a large volume of data needed to be minimally organized and made available in a quick and automated way, composing the open government data. After two years of a pandemic and in order to present an overview of the publication of open data by the federal government of Brazil, on COVID-19, this study sought to evaluate the open government data made available through the Application Programming Interface (API). The methodology involved the identification of datasets on COVID-19 in Brazil, in Application Programming Interface, until April 2022, the analysis of the documentation and the evaluation using the DGABr metric. The evaluation considered the five perspectives of the metric that measures fundamental elements about the open government data, essential for interoperability and consequently reuse of the data and was based on the published documentation. As results, the open government data on COVID-19, made available in Application Programming Interface, presented a good score in the metric, reaching level 4. This result indicates that the use of APIs was an important and agile technological resource for the organization and availability of open government data, promoting its reuse. However, it is important to highlight that this availability to society was late, it needs constant improvements, mainly in technical issues such as the connection of data with other sources, and that the effective reuse actions were limited to data visualization panels on COVID-19.

Are African economies open for entrepreneurship: How do we know?

Inspired by the concept of entrepreneurship as a fundamental human right, this chapter interrogates the readiness of African states for entrepreneurial activity open to all. Given the scale of economic, demographic, political and environmental challenges facing African countries, exacerbated by the Coronavirus pandemic, it asks if the scale of ambition needs to be raised to support universal access to enterprise and innovation across Africa. From a shortlist of eight internationally recognised indices, five were selected to develop a framework for assessing the openness of African states for entrepreneurship. The most recent datasets from these five indices were standardised into a set of 54 African states. The countries were ranked by mean scores to enable pan-African comparisons. The chapter contributes to existing knowledge of African entrepreneurship and development through the development of a composite pan-African framework which maps national levels of economic openness and related factors critically affecting entrepreneurial development. This is more useful than global indices which typically conflate similarities and differences between African states, whilst masking historic causes, such as colonial legacies and instances of poor governance, conflict or recovery from natural disasters. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022. All rights reseverd.

Open Science services and tools to get information from outreach articles

The pandemic caused by the SARS-CoV-2 virus has caused in the scientific community the need to collaborate as well as promoting Open Science practices, including the open data paradigm to stimulate resing. In this article a set of outreach articles published in The Conversation are analyzed together with some Open Science services and tools to carry out an analysis of their content and context, including information about their authors, institutions and disciplines. This information can be analyzed to better understand the entire research life cycle while facilitating the discovery of relationships at the article, topics or experts.

Analysis of Public Transportation in Montevideo, Uruguay During the COVID-19 Pandemic

This article presents the analysis of the demand and the characterization of mobility using public transportation in Montevideo, Uruguay, during the COVID-19 pandemic. A urban data-analysis approach is applied to extract useful insights from open data from different sources, including mobility of citizens, the public transportation system, and COVID cases. The proposed approach allowed computing significant results to determine the reduction of trips caused by each wave of the pandemic, the correlation between the number of trips and COVID cases, and the recovery of the use of the public transportation system. Overall, results provide useful insights to quantify and understand the behavior of citizens in Montevideo, regarding public transportation during the COVID-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Model of Visualization and Analytics for Open Data (Case: Election Voters & Kids Disability Category)

Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 percent.In this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities. © 2023 IEEE.

Uncoupling Inequality: Reflections on the Ethics of Benchmarks for Digital Media

Our collaboration seeks to demonstrate shared interrogation by exploring the ethics of machine learning benchmarks from a socio-technical management perspective with insight from public health and ethnic studies. Benchmarks, such as ImageNet, are annotated open data sets for training algorithms. The COVID-19 pandemic reinforced the practical need for ethical information infrastructures to analyze digital and social media, especially related to medicine and race. Social media analysis that obscures Black teen mental health and ignores anti-Asian hate fails as information infrastructure. Despite inadequately handling non-dominant voices, machine learning benchmarks are the basis for analysis in operational systems. Turning to the management literature, we interrogate cross-cutting problems of benchmarks through the lens of coupling, or mutual interdependence between people, technologies, and environments. Uncoupling inequality from machine learning benchmarks may require conceptualizing the social dependencies that build structural barriers to inclusion. © 2022 IEEE Computer Society. All rights reserved.

The Empirical Study of Pandemic and War Influence on the University Students' Education

The pandemic's impact on junior students' educational process was analyzed based on a survey conducted by the authors. The study is supplemented by an analysis of the effects of the war on learning based on open data. It is concluded that the conditions of natural disasters, which create both a pandemic and war, have caused and continue to cause significant damage to both the educational process and the psychological state of students. The ongoing online educational process is essential for continuing education and psychological support for young people throughout the educational process. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Open government data for crisis management: Rapid information sharing and co-creation of services

During the COVID-19 pandemic, open government data (OGD) was often used as a valuable crisis management resource. Unfortunately, there is limited research that explores how OGD can be used during times of crisis as a crisis management tool. To ensure that OGD can be used effectively in future crises, there is a need to understand how it may be used and what benefits its usage may bring. This paper brings new insight into this topic by conducting a comparative exploratory case study of three Central and Eastern European (CEE) countries – Czech Republic, Estonia and Latvia, where OGD was used at different levels to help manage different aspects of the COVID-19 pandemic. As a result of this research, three contributions are made: (1) it integrates OGD into previous crisis management literature, offering new and initial conceptual propositions;(2) it demonstrates how OGD enables the co-creation of new services that create public value during times of crisis;and (3) it provides empirical examples of OGD-driven co-created services. [ABSTRACT FROM AUTHOR] Copyright of Information Polity: The International Journal of Government & Democracy in the Information Age is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Elementary statistics projects using covid data

This article describes five elementary statistics projects involving the Covid-19 data made available to the public in csv files by the Centers for Disease Control and Prevention. The first project examined data available at the beginning of the covid surge in New York City in spring, 2020, and used the correlation coefficient to estimate the total number of deaths that could be expected as the spike ran its course. The second project is an easy one on the concept of excess deaths and on the mechanics of extracting parts of a data file that answer relevant questions. The data is from a spike in deaths in the particularly bad flu surge in the winter of 2017-2018. The third and fourth projects ask the student to fit a logistic growth curve to observed cumulative numbers of deaths in a spike, like the Covid spikes in New York City and Wisconsin and the nationwide 2017-2018 flu spike. The method is a simple linear regression with transformed variables. The fifth project involves hypothesis testing and judging when a Poisson model might be useful. The paper also documents difficulties and adaptations of the sort familiar to all teachers who have taught during the Covid-19 pandemic. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Active Keyword Selection to Track Evolving Topics on Twitter

How can we study social interactions on evolving topics at a mass scale? Over the past decade, researchers from diverse fields such as economics, political science, and public health have often done this by querying Twitter's public API endpoints with hand-picked topical keywords to search or stream discussions. However, despite the API's accessibility, it remains difficult to select and update keywords to collect high-quality data relevant to topics of interest. In this paper, we propose an active learning method for rapidly refining query keywords to increase both the yielded topic relevance and dataset size. We leverage a large open-source COVID-19 Twitter dataset to illustrate the applicability of our method in tracking Tweets around the key sub-topics of Vaccine, Mask, and Lockdown. Our experiments show that our method achieves an average topic-related keyword recall 2x higher than baselines. We open-source our code along with a web interface for keyword selection to make data collection from Twitter more systematic for researchers. © 2022 IEEE.

USED CAR CLUSTERING in BANDUNG CITY USING K-PROTOTYPES ALGORITHMS

Since the case of the 2019 Coronavirus Disease pandemic or commonly referred to as Covid-19, the use of public transportation has slowly begun to become an option as transportation to reduce the spread of the corona virus cluster, therefore some people prefer to buy private vehicles. However, due to the increasing price of cars, some people prefer to buy used cars. On the used car buying and selling platform, OLX Autos Indonesia, the demand for used cars increased by 15% to 20%. Therefore, this study was conducted to determine the characteristics of the cluster formed from the used car sales dataset taken from AtapData (atapdata.ai). AtapData is an open data site in Indonesia that can be used for research related to Data Science. This cluster model was created using the K-Prototypes algorithm, Silhouette Score and Davies Bouldin Index to evaluate the resulting cluster results. This clustering model will produce three clusters. The results of the three clusters will have one thing in common, namely brands that dominate sales, including Toyota, Honda, Daihatsu, Nissan, and Mitsubishi. Clustering evaluation using the Silhouette Score method produces a value of 0.7744140503593034. And for the evaluation of the Davies-Bouldin Index it produces a value of 0.4999221950856398. © 2022 IEEE.

Store Congestion Forecast under the Pandemic using Point of Sales Statistics

Under the COVID-19 pandemic, it is necessary to balance social distancing and continuous economic activities. In this study, we report on our developed service that forecasts the congestion level of regional commercial facilities using point-of-sales (POS) statistics. POS statistics data were collected for over a year from 150 commercial facilities in Tokyo. Through the analysis of a total of over 100 million customers, we clarified the factors that affect congestion levels of commercial facilities in each ward of Tokyo. Based on this analysis, we developed a congestion forecast model that predicts future congestion levels from several factors such as a big event, business restrictions, and weather. We implemented a web service incorporating this model and published estimated congestion levels both on our website and a television program. The experimental results show that the model has a high prediction accuracy with a coefficient of determination greater than 0.95 on average, which implies that big data from POS has great potential for value creation under the pandemic. © 2022 IEEE.

Open science progress: A literature assessment of open access articles

The open science movement has been widely adopted in multiple scientific fields across nations. Its benefit has been proven in many cases, most notably when the practice accelerated the search for solutions to the Covid-19 pandemic both in medical and socio-economic contexts. Still, the movement has faced multiple challenges, including an imbalance in the adoption of its numerous aspects. For example, the open access aspect which indicates the starting point of the movement has been widely practiced. Unfortunately, while open access is essential, an open access practice alone is not enough to pursue open science. In this work, we would like to assess the imbalance of the adoption, especially to measure how open access practice contributes to other practices, namely open data and open source as a sub-aspect of the open reproducibility research. Our assessment is based on descriptive statistic analysis of 300 open access articles from three domains, that is engineering, social and life science. Our findings indicated that the free and open source computer codes were dominantly adopted by the three scientific fields. However, social science has the lowest involvement in public data. © 2022 ACM.

Open-Access Data and Toolbox for Tracking COVID-19 Impact on Power Systems

Intervention policies against COVID-19 have caused large-scale disruptions globally, and led to a series of pattern changes in the power system operation. Analyzing these pandemic-induced patterns is imperative to identify the potential risks and impacts of this extreme event. For this purpose, we developed an open-access data hub (COVID-EMDA+), an open-source toolbox (CoVEMDA), and a few evaluation methods to explore what the U.S. power systems are experiencing during COVID-19. These resources could be broadly used for research, public policy, and educational purposes. Technically, our data hub harmonizes a variety of raw data such as generation mix, demand profiles, electricity price, weather observations, mobility, confirmed cases and deaths. Typical methods are reformulated and standardized in our toolbox, including baseline estimation, regression analysis, and scientific visualization. Here the fluctuation index and probabilistic baseline are proposed for the first time to consider data fluctuation and estimation uncertainty. Furthermore, we conduct three empirical studies on the U.S. power systems, and share new solutions and findings to address several issues of public concerns. This conveys a more complete picture of the COVID-19 impact and also opens up several attractive topics for future work. Python, Matlab source codes, and user manuals are all publicly shared on a Github repository.