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Environmental conservation has ascended to a prominent position on the global agenda, and China, recognizing the urgent need for environmental protection, has implemented nationwide measures. However, varying levels of environmental attentiveness among local governments have resulted in uneven implementation of these national directives across regions. Therefore, it is crucial to investigate the factors that drive local governments' environmental attention. Our study explores the impact of open government data (OGD) on local governments' environmental attention. Utilizing city-level data from 2010 to 2020, we employ a staggered difference-in-differences (DID) model for empirical analysis. The results reveal that OGD significantly and positively influences local governments' environmental attention. This influence is partly attributed to OGD's role in promoting government digitization, mitigating fiscal pressures, and increasing energy demand. Further analysis, including heterogeneity assessments, demonstrates that OGD has a more pronounced positive effect on environmental attention in cities with higher online political participation activity and a larger internet user base. Such empirical insights underscore the imperative for an integrative policy framework that accentuates the refinement of OGD platform in tandem with strategic enhancements in political participatory mechanisms and digital infrastructure investments, thereby fostering robust local environmental stewardship paradigms.

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[Purpose/Significance]Government open data has been in China for 10 years, however, the actual use of it has been criticised. This study analyses citizens' willingness to use government open data from their perspectives and constructs a model, aiming to promote in-depth research on citizens' use of government open data. [Methodology/Process] Meta-ethnographic qualitative research method was adopted to synthesise and integrate the 25 original studies. [Results/Conclusions] The study finally constructed a model of factors influencing citizens' willingness to use government open data, and identified four dimensions: "citizen", "platform", "government" and "environment". The four dimensions of "citizen", "platform", "government" and "environment" were identified, of which "citizen" involves "intrinsic motivation", "behavioural attitude", "information attitude" and "information security". The four dimensions of "Citizen" include "Intrinsic Motivation", "Behavioural Attitude", "Information Literacy", and "Demographic Characteristics", while the four dimensions of "Platform" include "Data Quality", "Information Literacy", and "Demographic Characteristics". platform" involves the sub-dimensions of "data quality" and "platform quality", "government" involves the sub-dimensions of "organisational conditions" and "policies and regulations", and "government" involves the sub-dimensions of "organisational conditions" and "policies and regulations". government" involves two sub-dimensions, "organisational conditions" and "policies and regulations", and "environment" involves two sub-dimensions, "social environment" and "type of context", with each sub-dimension having a different impact on the use of open government data by citizens. The different factors under each sub-dimension have different degrees of influence on citizens' use of open government data. Finally, suggestions are made to enhance citizens' willingness to use open government data.

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Since 2019, the Korean government's investments in making data more accessible to the public have grown by 337%. However, open government data, which should be accessible to everyone, are not entirely accessible to people with low vision, who represent an information-vulnerable class. Emergencies, such as the COVID-19 pandemic, decrease face-to-face encounters and inevitably increase untact encounters. Thus, the information gap experienced by low-vision people, who are underprivileged in terms of information, will be further widened, and they may consequently face various disadvantages. This study proposed visual communication design accessibility (VCDA) guidelines for people with low vision. Introduced screens enhanced by accessibility guidelines were presented to 16 people with low vision and 16 people with normal vision and the speed of visual information recognition was analyzed. No statistically significant difference (p > 0.05) was found due to the small sample size; however, this study's results approached significance with improved visual recognition speed for people with low vision after adopting VCDA. As a result of the intervention, the visual information recognition speed of both normal and low-vision people improved. Thus, our results can help improve information recognition speed among people with normal and low vision.

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This study identifies differences in the content of open public data managed by the central government, local governments, public institutions, and the office of education in Korea through keyword network analysis. Pathfinder network analysis was performed by extracting keywords assigned to 1,200 data cases, open to the Korean Public Data Portals. Subject clusters were derived for each type of government and their utility was compared using download statistics. Eleven clusters were formed for public institutions with specialized information on national issues such as Health care and Real estate, while 15 clusters were formed for the central government with national administrative information, including Crime and Safety policing. Local governments and offices of education were assigned 16 and 11 topic clusters respectively, with data focusing on regional life such as Local factories and manufacturing, Resident registration, and Lifelong education. Usability was higher in public and central governments that deal with national-level specialized information than for regional-level information. It was also confirmed that subject clusters such as Health care, Real estate, and Crime showed high usability. Furthermore, there was a large gap in data utilization because of the existence of popular data that showed extremely high usage. Supplementary Information: The online version contains supplementary material available at 10.1007/s11135-023-01630-x.

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Open Government Data (OGD), including statistical data, such as economic, environmental and social indicators, are data published by the public sector for free reuse. These data have a huge potential when exploited using Machine Learning methods. Linked Data technologies facilitate retrieving integrated statistical indicators by defining and executing SPARQL queries. However, statistical indicators are available in different temporal and spatial granularity levels as well using different units of measurement. This data article describes the integrated statistical indicators that were retrieved from the official Scottish data portal in order to facilitate the exploitation of Machine Learning methods in OGD. Multiple SPARQL queries as well as manual search in the data portal were employed towards this end. The resulted dataset comprises the maximum number of compatible datasets, i.e., datasets with matching temporal and spatial characteristics. In particular, the data include 60 statistical indicators from seven categories such as health and social care, housing, and crime and justice. The indicators refer to the 6,976 "2011 data zones" of Scotland, while the year of reference is 2015. Data are ready to be used by the research community, students, policy makers, and journalists and give rise to plenty of social, business, and research scenarios that can be solved using Machine Learning technologies and methods.

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Dynamic data (including environmental, traffic, and sensor data) were recently recognized as an important part of Open Government Data (OGD). Although these data are of vital importance in the development of data intelligence applications, such as business applications that exploit traffic data to predict traffic demand, they are prone to data quality errors produced by, e.g., failures of sensors and network faults. This paper explores the quality of Dynamic Open Government Data. To that end, a single case is studied using traffic data from the official Greek OGD portal. The portal uses an Application Programming Interface (API), which is essential for effective dynamic data dissemination. Our research approach includes assessing data quality using statistical and machine learning methods to detect missing values and anomalies. Traffic flow-speed correlation analysis, seasonal-trend decomposition, and unsupervised isolation Forest (iForest) are used to detect anomalies. iForest anomalies are classified as sensor faults and unusual traffic conditions. The iForest algorithm is also trained on additional features, and the model is explained using explainable artificial intelligence. There are 20.16% missing traffic observations, and 50% of the sensors have 15.5% to 33.43% missing values. The average percent of anomalies per sensor is 71.1%, with only a few sensors having less than 10% anomalies. Seasonal-trend decomposition detected 12.6% anomalies in the data of these sensors, and iForest 11.6%, with very few overlaps. To the authors' knowledge, this is the first time a study has explored the quality of dynamic OGD.

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Open government data (OGD) holds great potential for firms and the digital economy as a whole and has attracted increasing interest in research and practice in recent years. Governments and organizations worldwide are struggling in exploiting the full potential of OGD and require a comprehensive understanding of this phenomenon. Although scientific debates in OGD research are intense and heterogeneous, the field lacks theoretical integration of OGD topics and their systematic consideration in the context of the digital economy. In addition, OGD has been widely neglected by information systems (IS) research, which promises great potential for advancing our knowledge of the OGD concept and its role in the digital economy. To fill in this gap, this study conducts a systematic literature review of 169 empirical OGD studies. In doing so, we develop a theoretical review framework of Antecedents, Decisions, Outcomes (ADO) to unify and grasp the accumulating isolated evidence on OGD in context of the digital economy and provide a theory-informed research agenda to tap the potential of IS research for OGD. Our findings reveal six related key topic clusters of OGD research and substantial gaps, opening up prospective research avenues and particularly outlining how IS research can inform and advance OGD research. Supplementary information: The online version contains supplementary material available at 10.1007/s12525-022-00582-8.

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Extracting knowledge from open data of traffic accidents has been attracting increasing attention to policymakers responsible for road safety. This article presents a knowledge elicitation approach to exploring the determinants of traffic accidents from open government data of an urban area in Taiwan. The collected open dataset contains 34 decisional attributes and one predictive attribute (i.e., type of injury, including head, breast, leg), and 47,974 cases. Prediction models using a classification-oriented mechanism and generated rules that considered datasets from before (B-dataset; 30,116 cases) and after (A-dataset; 17,868 cases) beginning to combat the Covid-19 pandemic in an urban area of Taiwan were compared. The findings showed that prediction accuracy was acceptable but not high, at 70.73% for B-dataset and 74.77% for A-dataset. Determinants in the human and vehicle categories revealed higher classification ranks than those in the temporal and environment categories. Traffic accidents involving motorcycles were 5.13% higher in A-dataset, whereas those involving cars were 4.11% lower. Injury on leg or foot was 3.46% higher in A-dataset, whereas other types of injury were up to 1.00% lower. The average support for rules in the A-dataset rule base and the simplicity of the A-dataset decision tree were higher than those of B-dataset. The research demonstrates the value of open government data in prediction model development and knowledge elicitation to support policymaking in the traffic safety domain.

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A study on the feasibility of a national open data policy in Zimbabwe was done to document open government data globally and in Zimbabwe. The study showcases the benefits of open government data and the opportunities and challenges toward the development of a national open data policy. Web content analysis and document analysis were used to collect data concerning the readiness of the country in implementing open data activities. The open data barometer was used to gather qualitative data which is essential in assessing the preparedness of the country in opening up government and research data. Content analysis was used to analyse the data which was presented thematically based on the objectives of the study. The findings indicated that the Government of Zimbabwe has endorsed a couple of open data frameworks though some projects are done by non-governmental organizations. The major challenge is implementation of these conventions and commitment to make the data accessible. The results indicated that open data must be made available and accessible within Zimbabwe as a matter of national policy. The author recommends the need for advocacy and continuous awareness creation among the stakeholders so that a national open data policy can be crafted and enacted. The enactment of a national open data policy would guide the use of and access to government data and research data which is valuable in research.

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Environmental monitoring of rivers is a cornerstone of the European Union's Water Framework Directive. It requires the estimation and reporting of environmental flows in rivers whose characteristics vary widely across the EU member states. This variability has resulted in a fragmentation of estimation and reporting methods for environmental flows and is exhibited by the myriad of regulatory guidelines and estimation procedures. To standardise and systematically evaluate environmental flows at the pan-European scale, we propose to formalise the estimation procedures through automation by reusing existing river monitoring resources. In this work, we explore how sensor-generated hydrological open government data can be repurposed to automate the estimation and monitoring of river environmental flows. In contrast to existing environmental flows estimation methods, we propose a scalable IoT-based architecture and implement its cloud-layer web service. The major contribution of this work is the demonstration of an automated environmental flows system based on open river monitoring data routinely collected by national authorities. Moreover, the proposed system adds value to existing environmental monitoring data, reduces development and operational costs, facilitates streamlining of environmental compliance and allows for any authority with similar data to reuse or scale it with new data and methods. We critically discuss the opportunities and challenges associated with open government data, including its quality. Finally, we demonstrate the proposed system using the Estonian national river monitoring network and define further research directions.

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Nowadays, governments launch open government data (OGD) portals that provide data that can be accessed and used by everyone for their own needs. Although the potential economic value of open (government) data is assessed in millions and billions, not all open data are reused. Moreover, the open (government) data initiative as well as users' intent for open (government) data are changing continuously and today, in line with IoT and smart city trends, real-time data and sensor-generated data have higher interest for users. These "smarter" open (government) data are also considered to be one of the crucial drivers for the sustainable economy, and might have an impact on information and communication technology (ICT) innovation and become a creativity bridge in developing a new ecosystem in Industry 4.0 and Society 5.0. The paper inspects OGD portals of 60 countries in order to understand the correspondence of their content to the Society 5.0 expectations. The paper provides a report on how much countries provide these data, focusing on some open (government) data success facilitating factors for both the portal in general and data sets of interest in particular. The presence of "smarter" data, their level of accessibility, availability, currency and timeliness, as well as support for users, are analyzed. The list of most competitive countries by data category are provided. This makes it possible to understand which OGD portals react to users' needs, Industry 4.0 and Society 5.0 request the opening and updating of data for their further potential reuse, which is essential in the digital data-driven world.

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Governments around the world have made data on COVID-19 testing, case numbers, hospitalizations and deaths openly available, and a breadth of researchers, media sources and data scientists have curated and used these data to inform the public about the state of the coronavirus pandemic. However, it is unclear if all data being released convey anything useful beyond the reputational benefits of governments wishing to appear open and transparent. In this analysis we use Ontario, Canada as a case study to assess the value of publicly available SARS-CoV-2 positive case numbers. Using a combination of real data and simulations, we find that daily publicly available test results probably contain considerable error about individual risk (measured as proportion of tests that are positive, population based incidence and prevalence of active cases) and that short term variations are very unlikely to provide useful information for any plausible decision making on the part of individual citizens. Open government data can increase the transparency and accountability of government, however it is essential that all publication, use and re-use of these data highlight their weaknesses to ensure that the public is properly informed about the uncertainty associated with SARS-CoV-2 information.

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Open government data (OGD) portals are official websites where governments can publish OGD in a controlled way. OGD portals foster discoverability, accountability, and reusability for stakeholders. This data article presents the data collected while monitoring the OGD portals in the 28 countries of the European Union. Several parameters and indicators observed over a period of 3 years in the official national open data portals were located and recorded to create this dataset. Data were manually obtained from existing public data sources and official OGD portals freely available on the Web. Clustering techniques using Density-based spatial clustering of applications with noise (DBSCAN) were applied to elaborate a dataset showcasing similar countries with respect to different parameters and indicators. Cluster data validation was carried out using the Davies-Bouldin index. The data presented in this article are related to the research article entitled "Open government data portals in the European Union: Considerations, development and expectations" [1].