Open Data Quality Research Articles

Historical Performance Gap, Readiness of Government, and Open Data Quality: Transnational Empirical Analysis Based on Performance Feedback Theory

Open government data has become an important movement for government administrations in numerous countries. Previous studies have paid less attention to the complex relationship among the historical performance gap, the readiness of government (including policy and action dimensions), and open data quality. Therefore, on the basis of performance feedback theory, a theoretical model has been developed and tested to analyze the impact of the historical performance gap on the readiness of government and open data quality. The model is empirically tested by the partial least squares method using sample data from 115 countries. The results show that the historical performance gap could indirectly contribute to a country's open data quality by promoting the readiness of government. In addition, the impact of this gap on the readiness of government and open data quality shows a U-shaped curve. In other words, a greater change in the effect of open data utilization in a country in the previous year entails a more significant promotion of readiness of government and open data quality in the next year. These findings drive governments to pay more attention to performance evaluation in open data and improve the readiness of government to improve their respective country's open data quality.

(m)App my data! Developing a Map-ability Rating and App to Rapidly Communicate Data Quality and Interoperability Potential of Open Data

(m)App my data! Developing a Map-ability Rating and App to Rapidly Communicate Data Quality and Interoperability Potential of Open Data

Cybersecurity and Efficacity of Open Data Platforms

Cybersecurity is critical for protecting open data. Transparency and innovation are facilitated by open data platforms; however, concerns about cybersecurity and privacy persist. This study examines the role of cybersecurity in public institutions in the Republic of Kosovo to determine methods of safeguarding data integrity. The main aim of this study was to examine the role of cybersecurity in securing open data in public organizations in the Republic of Kosovo. The study aimed to identify optimal cybersecurity practices in the context of open data and provide a comprehensive overview of the implementation of cybersecurity measures. This study employed a structured and methodical approach to assess cybersecurity and the effectiveness of open data platforms in public organizations in the Republic of Kosovo. Results: The study provides an overview of the status of open data platforms in the Republic of Kosovo and highlights the importance of cybersecurity, data privacy, and data integrity. Despite the stated concerns, such as enhancing security measures and increasing user knowledge, it is evident that public institutions have made significant progress in securing and enhancing their open data platforms. It is suggested that institutions in the Republic of Kosovo continue to invest in cybersecurity, promote privacy protection measures, and focus on enhancing the quality of open data to develop in this sector. Furthermore, collaboration and coordination across institutions and government agencies are required to enhance the efficiency and effectiveness of these platforms.

Do geographic location and historical conditions affect the quality and availability of open cadastral data? From early cadastral maps till now

Abstract Since the early days, cadastral data represent the geographic extent of the past, current, and future rights and interests in real property. Thus, these data are extremely important for the proper development of statehood and society. The article analyses the process of formation of the real estate cadastre in two countries – Poland and Ukraine – from the time of its establishment to the present. Both countries were in the past (and Ukraine still is) victims of various historical events that deprived them of statehood, stability and opportunities for development; therefore, it affected the development of the cadastre and its accessibility to all stakeholders. The authors attempt to answer the question of if and how geographic location and historical conditions can affect the quality and availability of open cadastral data (OCD). After all, our perception of reality is a direct result of processes originating in history. This paper contains comparisons between a post-Soviet (Ukraine) and a post-communist country (Poland) and their path from early cadastral maps to OCD.

Data-driven scientific research based on public statistics: a bibliometric perspective

Official statistics provide information on different areas of citizens’ lives and are widely used in scientific research as a source of data due to their open data nature and quality assurance. In this context, a bibliometric analysis is carried out using all Scopus publications from 1960 to 2020 that use official statistics as data sources. Thus, 10,777 publications are analyzed using the SciMAT bibliometric analysis software, providing a complete conceptual analysis of the main research topics in the literature through the quantification of the main bibliometric performance indicators, identifying the most important authors, organizations, countries, sources, and intellectual structures corresponding to the main fields of research and bringing classification by subject area as an innovation to the methodology.

Energy signature-based clustering using open data for urban building energy analysis toward carbon neutrality: A case study on electricity change under COVID-19

Under the necessity for urban energy savings and the importance of energy demand changes, this paper proposes energy analytics of urban buildings using a novel clustering method with open data. In the proposed method, the concept of energy signatures is introduced, and the signatures are defined as the representative symbols in the symbolic hierarchical clustering. This method can advance the existing clustering method or symbolic aggregate approximation (SAX) with limited energy usage patterns by introducing energy signatures with various pieces of energy information into the symbolic transformation. The proposed method can scientifically support tracking building energy usage and patterns, evaluating the existing concepts (such as green retrofitting and zero energy buildings) and advanced technologies, and the decision-making process for new policies under the global carbon neutrality scenarios. In a case study applied to a city using open energy data (in South Korea), the proposed method determined five representative clusters/areas, revealing the open data quality problems (anomaly and missing data), energy usage changes (e.g., energy usage polarization) caused by COVID-19, and the necessity of classifying building types in terms of energy usage patterns. Specifically, the cluster for decreasing energy patterns accounted for approximately 37%, and the increasing patterns accounted for 25%. Educational buildings accounted for 70% of the decreasing patterns, and technical and medical research facilities accounted for 76% of the increasing patterns under the COVID-19. Approximately 93% of missing data was found in the residential buildings. Anomaly data accounted for 10.9% in the total data.

Assesment of Rospatent open data quality within the context of integration with national current research information systems

Current Research Information Systems (CRIS) are to aggregate data on organizational research projects and their funding, on employers’ publications and intellectual property subject matters. The scientometric analysis is based on CRIS data to assess research output and innovative potential of organizations, and to make management solutions. The early loading of quality and reliable information is the most important task for CRIS. The Rospatent open data that allow for automated processing based on free (free of charge) licensing makes the potential source for data on intellectual property subject matters (patent and government registration certificates) for national CRIS. The studies evidence that despite publishing OD in machine-readable formats, their practical application is impeded by incorrect, incomplete and uncoordinated entries. Therefore, before loading. Rospatent OD have to be assessed for quality and to be improved, if possible. As for today, Rospatent OD quality is assessed by several criteria: accessibility, metadata completeness, feedback. However, at the content level, the open data have not been assessed. The purpose of the article is to evaluate the internal quality of Rospatent OD sets including information on inventions, utility models, industrial designs, computer programs, databases, circuit layouts, within the context of OD integration in CRIS systems. The quality is assessed in several characteristics: completeness, accuracy, consistency, expedience, and relevancy. The study has revealed incomplete, inaccurate and uncoordinated entries.

OPEN SCIENTIFIC DATA: ANALYTICS OF EUROPEAN AND NATIONAL STANDARDS FOR QUALITY, INTEROPERABILITY AND SAFETY

The article analyzes European and national standards regarding the quality, interoperability and security of data in the context of open scientific data. Today, in the European Union, there are progressive developments at the legislative level regarding the quality, interoperability and security of open data, in particular scientific data — these are ISO Standards, Directives, Recommendations, Guidelines, Regulations, etc. However, national legislative norms mostly concern the field of interoperability and security of open data. Regarding data interoperability, the Law of Ukraine “On Access to Public Information” provides for public information to be provided in the form of open data by default, while research data (open scientific data) does not belong to its scope. To ensure management, information security and information protection, the government plans to implement ISO 20000, ISO 27001 standards in the field of research infrastructure management and information protection in the field of research. It should be noted that in the decree of the Cabinet of Ministers of Ukraine No. 892‑r “On the approval of the national plan for open science”, as in other resolutions, the main emphasis is placed on the interoperability and security of open scientific data. On the basis of a comparison of the European and national regulatory framework, we proposed to make changes to: Laws of Ukraine “On access to public information”, in particular, on access to research data, “On information”, “On scientific and technical information”, “On scientific and scientific — technical activity”, in particular, that all scientific publications obtained as a result of state-funded research should be publicly available. In addition, we emphasize the implementation of the ISO/IEC 25012:2008 “Data Quality Model” standard and a practical guide to obtaining quality data.

Flood risk assessment data access and equity in Metro Vancouver

Flood risk is increasing in many urban regions in Canada. Flood risk is the product of interaction between a flood hazard, the exposure of built assets, and the vulnerability of people to flood impacts. Flood risk assessment seeks to quantify each of these factors for a geographic space to identify areas with greater risk, which can inform public- and private-sector decision making. This study conducts a flood risk assessment of Metro Vancouver in British Columbia using only government-provided open data. It finds that flood hazards and social vulnerability are uneven across the study area, and it reveals inequity in open data access and quality between municipalities. It concludes that more standardized or centralized provision of open data could better support flood risk analysis in Metro Vancouver, which could inform flood management and help to reduce local risk.

Validation of an Empirical Subwaveform Retracking Strategy for SAR Altimetry

The sea level retrievals from the latest generation of radar altimeters (the SAR altimeters) are still challenging in the coastal zone and areas covered by sea ice and require a dedicated fitting (retracking) strategy for the waveforms. In the framework of the European Space Agency’s Baltic + Sea Level (ESA Baltic SEAL) project, an empirical retracking strategy (ALES + SAR), including a dedicated sea state bias correction, has been designed to improve the sea level observations in the Baltic Sea, characterised by a jagged coastline and seasonal sea ice coverage, without compromising the quality of open ocean data. In this work, the performances of ALES + SAR are validated against in-situ data in the Baltic Sea. Moreover, variance, crossover differences and power spectral density of the open ocean data are evaluated on a global scale. The results show that ALES + SAR performances are of comparable quality to the ones obtained using physical-based retrackers, with relevant advantages in coastal and sea ice areas in terms of quality and quantity of the sea level data.

Metrics proposal to measure the quality of governmental datasets

The government challenge is to provide citizens with information efficiently and transparently. In this context, the new challenges that arise can be considered as an opportunity to rethink the methodologies of designing and implementing public policies and promoting a State with collaborating public officials. All this leads to a new paradigm in the treatment of open and public information. Currently, data is an indispensable resource for any public management activity, so it is necessary to keep it updated and complete. Although it can be determined that more and more governments are embarking on this new concept of open data, there is still a long way to go. Several related works on this subject are increasingly focused on the quality of open data from the portals of government entities, but it is still necessary to reinforce and understand the importance of the data, that is, if a data has quality, it can be better used, manipulated and distributed to citizens for various purposes. This work focuses on the proposal of quality metrics to analyze the contents of published datasets and thus obtain an analysis to improve their dissemination and interoperability between databases and software.

A Comprehensive Method for Improving the Quality of Open Government Data and Increasing Citizens’ Willingness to Use Data by Analyzing the Complex System of Citizens and Organizations

In recent years, the amount of data in the world is growing rapidly. Data growth also occurs in the government sector. All ministries and institutions at every level are data producers. These government‐owned data have a high potential if they can be used properly. Open government data can stimulate innovation and economic growth and enhance business models. In order to increase the willingness of citizens to use open government data and enjoy the benefits mentioned, the quality of open government data needs to be improved. The quality of open government data encompasses a variety of dimensions and criteria. Also, the importance of each dimension and criterion in increasing the quality of open government data is different. Therefore, we are faced with a complex system that requires proper decision‐making and management. In fact, we are dealing with decision‐making in the complex management system. Given the importance of this issue, the purpose of this study is to provide a new and comprehensive method to improve the quality of open government data and increase the willingness of citizens to use the data by considering the complex network of citizens and organizations. For this purpose, library studies have been used to extract comprehensive and effective dimensions and criteria. The statistical population includes all articles related to the criteria of improving the quality of open government data and increasing the willingness of citizens to use the data. The probabilistic sampling method of simple random samples has been used, and 10 articles in this field have been reviewed. After extracting the criteria as well as the data of 112 governmental organizations and institutions related to each criterion from the open data portal, the complex network of citizens and governmental organizations and institutions has been analyzed in order to identify high‐degree centrality organizations. Then, the data characteristics of the organizations that were most desired by the citizens were extracted using data mining techniques including the regression model. Also, field method and multicriteria decision‐making technique including the DEMATEL technique have been used to express the solutions and identify the cause‐and‐effect relationships between the solutions. The criteria extracted in improving the quality of open government data and increasing the willingness of citizens to use the data are included: “data originality,” “license openness,” “up‐to‐datedness,” “data access,” “metadata completeness,” “number of data sets,” “format openness,” “nondiscrimination,” “understandable,” “number of categories of data sets,” “free,” “lack of missing data,” “data request ability,” “visualization,” “feedback,” and “data subject matter.” Based on the results obtained from the analysis of the complex network and the regression model, the criterion of “society subject” with a coefficient of 72.564 and a positive sign has the greatest impact on increasing the number of citizens' visits to open government data. After that, the criterion of “format openness” with a coefficient of 52.682 and a positive sign has the second rank in increasing the number of visits. Extracting comprehensive and effective criteria in improving the quality of open government data and increasing citizens' willingness to use data, calculating the weight and importance of each criterion by analyzing the complex network of citizens and organizations, as well as providing solutions, can help managers in decision‐making and proper management in the complex system of citizens and government organizations.

GRQA: Global River Water Quality Archive

Abstract. Large-scale hydrological studies are often limited by the lack of available observation data with a good spatiotemporal coverage. This has affected the reproducibility of previous studies and the potential improvement of existing hydrological models. In addition to the observation data themselves, insufficient or poor-quality metadata have also discouraged researchers from integrating the already-available datasets. Therefore, improving both the availability and quality of open water quality data would increase the potential to implement predictive modeling on a global scale. The Global River Water Quality Archive (GRQA) aims to contribute to improving water quality data coverage by aggregating and harmonizing five national, continental and global datasets: CESI (Canadian Environmental Sustainability Indicators program), GEMStat (Global Freshwater Quality Database), GLORICH (GLObal RIver CHemistry), Waterbase and WQP (Water Quality Portal). The GRQA compilation involved converting observation data from the five sources into a common format and harmonizing the corresponding metadata, flagging outliers, calculating time series characteristics and detecting duplicate observations from sources with a spatial overlap. The final dataset extends the spatial and temporal coverage of previously available water quality data and contains 42 parameters and over 17 million measurements around the globe covering the 1898–2020 time period. Metadata in the form of statistical tables, maps and figures are provided along with observation time series. The GRQA dataset, supplementary metadata and figures are available for download on the DataCite- and OpenAIRE-enabled Zenodo repository at https://doi.org/10.5281/zenodo.5097436 (Virro et al., 2021).

Quality, Reuse and Governance of Open Data

AbstractIn recent years, the amount of open data from governments and academic communities have increased rapidly. Open data are expected to promote the transparency and accountability of governments and academic communities, enable public participation, and facilitate digital innovation. However, open data are still facing problems such as unsatisfactory quality, insufficient data governance, increasing preservation cost and ineffective data reuse at present. This panel will invite experts in related fields to discuss the quality, reuse, and governance of open data, and propose feasible solutions from an international perspective. We will verify and promote the action plan in practice, and have more academic discussion with ASIS&T and relevant academic communities.

Determinant Factors for Adoption of Government as a Platform in South Korea: Mediating Effects on the Perception of Intelligent Information Technology

This study aims to shed light on determinant factors for the introduction of Government as a Platform (GaaP) by public officers who can be platform providers or main stakeholders in GaaP. It focuses on public officers in 261 Korean public agencies. In order to verify the research model’s hypotheses, we take advantage of Structural Equation Modeling and the Technology Acceptance Model. As a first result, concerning the direct effect on GaaP, the quality of open data, the scope of the stakeholders, and attitudes to civic engagement have a positive relationship on the perceived usefulness of Intelligent Information Technology (IIT). Second, in terms of the effect of IIT on GaaP, the perceived usefulness of IIT mediates positively on the intention to adopt GaaP based on the quality of open data, the scope of the stakeholders, and attitudes towards civic engagement. Based on these results, policy implications can be described as follows. Besides managing internal open data, governments should actively mine new open data that can create added value for innovation. They need to create an easily cooperative environment with other stakeholders, especially non-public participants, and governments should encourage public officers to more actively accept and utilize IIT in their jobs.

Quality of government health data in COVID-19: definition and testing of an open government health data quality evaluation framework

PurposeCurrently, countries worldwide are struggling with the virus COVID-19 and the severe outbreak it brings. To better benefit from open government health data in the fight against this pandemic, this study developed a framework for assessing open government health data at the dataset level, providing a tool to evaluate current open government health data's quality and usability COVID-19.Design/methodology/approachBased on the review of the existing quality evaluation methods of open government data, the evaluation metrics and their weights were determined by 15 experts in health through the Delphi method and analytic hierarchy process. The authors tested the framework's applicability using open government health data related to COVID-19 in the US, EU and China.FindingsThe results of the test capture the quality difference of the current open government health data. At present, the open government health data in the US, EU and China lacks the necessary metadata. Besides, the number, richness of content and timeliness of open datasets need to be improved.Originality/valueUnlike the existing open government data quality measurement, this study proposes a more targeted open government data quality evaluation framework that measures open government health data quality on a range of data quality dimensions with a fine-grained measurement approach. This provides a tool for accurate assessment of public health data for correct decision-making and assessment during a pandemic.

LODQuMa: A Free-ontology process for Linked (Open) Data quality management

For many years, data quality is among the most commonly discussed issue in Linked Open Data (LOD) due to the huge volume of integrated datasets that are usually heterogeneous. Several ontology-based approaches dealing with quality problems have been proposed. However, when datasets lack a well-defined schema, these approaches become ineffective because of the lack of metadata. Moreover, the detection of quality problems based on an analysis between RDF (Resource Description Framework) triples without requiring ontology statistical and semantical information is not addressed. Keeping in mind that ontologies are not always available and they may be incomplete or misused. In this paper, a novel free-ontology process called LODQuMa is proposed to assess and improve the quality of LOD. It is mainly based on profiling statistics, synonym relationships between predicates, QVCs (Quality Verification Cases), and SPARQL (SPARQL Protocol and RDF Query Language) query templates. Experiments on the DBpedia dataset demonstrate that the proposed process is effective for increasing the intrinsic quality dimensions, resulting in correct and compact datasets.

Open Collaboration, Data Quality, and COVID-19

The flavor of this "Impact" department is somewhat different. In a pandemic, everybody has to come together. In April 2020, a call went out in the United Kingdom for groups to informally form and collaborate to study this brutal pathogen in whatever way they could. The five authors of this article, old friends from the geophysical industry with decades of experience in numerical modeling and big data, formed such a group.

GRQA: Global River Water Quality Archive

Abstract. Large-scale hydrological studies are often limited by the lack of available observation data with a good spatiotemporal coverage. This has affected the reproducibility of previous studies and the potential improvement of existing hydrological models. In addition to the observation data themselves, insufficient or poor-quality metadata have also discouraged researchers from integrating the already-available datasets. Therefore, improving both the availability and quality of open water quality data would increase the potential to implement predictive modeling on a global scale. The Global River Water Quality Archive (GRQA) aims to contribute to improving water quality data coverage by aggregating and harmonizing five national, continental and global datasets: CESI (Canadian Environmental Sustainability Indicators program), GEMStat (Global Freshwater Quality Database), GLORICH (GLObal RIver CHemistry), Waterbase and WQP (Water Quality Portal). The GRQA compilation involved converting observation data from the five sources into a common format and harmonizing the corresponding metadata, flagging outliers, calculating time series characteristics and detecting duplicate observations from sources with a spatial overlap. The final dataset extends the spatial and temporal coverage of previously available water quality data and contains 42 parameters and over 17 million measurements around the globe covering the 1898–2020 time period. Metadata in the form of statistical tables, maps and figures are provided along with observation time series. The GRQA dataset, supplementary metadata and figures are available for download on the DataCite- and OpenAIRE-enabled Zenodo repository at https://doi.org/10.5281/zenodo.5097436 ( Virro et al. , 2021 ) .

Completeness of open access FluNet influenza surveillance data for Pan-America in 2005\u20132019

For several decades, the World Health Organization has collected, maintained, and distributed invaluable country-specific disease surveillance data that allow experts to develop new analytical tools for disease tracking and forecasting. To capture the extent of available data within these sources, we proposed a completeness metric based on the effective time series length. Using FluNet records for 29 Pan-American countries from 2005 to 2019, we explored whether completeness was associated with health expenditure indicators adjusting for surveillance system heterogeneity. We observed steady improvements in completeness by 4.2–6.3% annually, especially after the A(H1N1)-2009 pandemic, when 24 countries reached > 95% completeness. Doubling in decadal health expenditure per capita was associated with ~ 7% increase in overall completeness. The proposed metric could navigate experts in assessing open access data quality and quantity for conducting credible statistical analyses, estimating disease trends, and developing outbreak forecasting systems.