Government Data Openness and Coverage. How do They Affect Trust in European Countries?

Purpose: This paper aims to assess if the extent of openness and the coverage of data sets released by European governments have a significant impact on citizen trust in public institutions.Design/methodology/approach: Data for openness and coverage have been collected from the Open Data Inventory 2018(ODIN), by Open Data Watch;institutional trust is built up as a formative construct based on the European Social Survey(ESS), Round 9. The relations between the open government data features and trust have been tested on the basis of structural equation modelling(SEM).Findings: The paper reveals that as European governments improve data openness, disaggregation, and time coverage, people tend to trust them more. However, the size of the effect is still small and, comparatively, data coverage effect on citizens' confidence is more than twice than the impact of openness.Research limitations: This paper analyzes the causal effect of Open Government Data(OGD) features captured in a certain moment of time. In upcoming years, as OGD is implemented and a more consistent effect on people is expected, time series analysis will provide with a deeper insight.Practical implications: Public officers should continue working in the development of a technological framework that contributes to make OGD truly open. They should improve the added value of the increasing amount of open data currently available in order to boost internal and external innovations valuable both for public agencies and citizens.Originality/value: In a field of knowledge with little quantitative empirical evidence, this paper provides updated support for the positive effect of OGD strategies and it also points out areas of improvement in terms of the value that citizens can get from OGD coverage and openness.

LSTM-based lane change prediction using Waymo open motion dataset: The role of vehicle operating space

Lane change prediction is critical for crash avoidance but challenging as it requires the understanding of the instantaneous driving environment.With cutting-edge artificial intelligence and sensing technologies,autonomous vehicles(AVs)are expected to have exceptional perception systems to capture instantaneously their driving environments for predicting lane changes.By exploring the Waymo open motion dataset,this study proposes a framework to explore autonomous driving data and investigate lane change behaviors.In the framework,this study develops a Long Short-Term Memory(LSTM)model to predict lane changing behaviors.The concept of Vehicle Operating Space(VOS)is introduced to quantify a vehicle's instantaneous driving environment as an important indicator used to predict vehicle lane changes.To examine the robustness of the model,a series of sensitivity analysis are conducted by varying the feature selection,prediction horizon,and training data balancing ratios.The test results show that including VOS into modeling can speed up the loss decay in the training process and lead to higher accuracy and recall for predicting lane-change behaviors.This study offers an example along with a methodological framework for transportation researchers to use emerging autonomous driving data to investigate driving behaviors and traffic environments.

Bolzano Traffic: An Example of Open ITS Deployment for Advanced Traveller Information Services

This paper aims to present the experience gathered in the Italian alpine city of Bolzano within the project“Bolzano Traffic”whose goal is the introduction of an experimental open ITS platform for local service providers,fostering the diffusion of advanced traveller information services and the future deployment of cooperative mobility systems in the region.Several end-users applications targeted to the needs of different user groups have been developed in collaboration with local companies and research centers;a partnership with the EU Co-Cities project has been activated as well.The implemented services rely on real-time travel and traffic information collected by urban traffic monitoring systems or published by local stakeholders(e.g.public transportation operators).An active involvement of end-users,who have recently started testing these demo applications for free,is actually on-going.

FAIR + FIT: Guiding Principles and Functional Metrics for Linked Open Data(LOD) KOS Products

Purpose:To develop a set of metrics and identify criteria for assessing the functionality of LOD KOS products while providing common guiding principles that can be used by LOD KOS producers and users to maximize the functions and usages of LOD KOS products.Design/methodology/approach:Data collection and analysis were conducted at three time periods in 2015–16,2017 and 2019.The sample data used in the comprehensive data analysis comprises all datasets tagged as types of KOS in the Datahub and extracted through their respective SPARQL endpoints.A comparative study of the LOD KOS collected from terminology services Linked Open Vocabularies(LOV)and BioPortal was also performed.Findings:The study proposes a set of Functional,Impactful and Transformable(FIT)metrics for LOD KOS as value vocabularies.The FAIR principles,with additional recommendations,are presented for LOD KOS as open data.Research limitations:The metrics need to be further tested and aligned with the best practices and international standards of both open data and various types of KOS.Practical implications:Assessment performed with FAIR and FIT metrics support the creation and delivery of user-friendly,discoverable and interoperable LOD KOS datasets which can be used for innovative applications,act as a knowledge base,become a foundation of semantic analysis and entity extractions and enhance research in science and the humanities.Originality/value:Our research provides best practice guidelines for LOD KOS as value vocabularies.

A Novel Method for Resolving and Completing Authors' Country Affiliation Data in Bibliographic Records

Purpose: Our work seeks to overcome data quality issues related to incomplete author affiliation data in bibliographic records in order to support accurate and reliable measurement of international research collaboration(IRC).Design/methodology/approch: We propose, implement, and evaluate a method that leverages the Web-based knowledge graph Wikidata to resolve publication affiliation data to particular countries. The method is tested with general and domain-specific data sets.Findings: Our evaluation covers the magnitude of improvement, accuracy, and consistency. Results suggest the method is beneficial, reliable, and consistent, and thus a viable and improved approach to measuring IRC.Research limitations: Though our evaluation suggests the method works with both general and domain-specific bibliographic data sets, it may perform differently with data sets not tested here. Further limitations stem from the use of the R programming language and R libraries for country identification as well as imbalanced data coverage and quality in Wikidata that may also change over time.Practical implications: The new method helps to increase the accuracy in IRC studies and provides a basis for further development into a general tool that enriches bibliographic data using the Wikidata knowledge graph.Originality: This is the first attempt to enrich bibliographic data using a peer-produced, Webbased knowledge graph like Wikidata.

Why Open Government Data? The Case of a Swedish Municipality

Purpose: The purpose of this exploratory study is to provide modern local governments with potential use cases for their open data, in order to help inform related future policies and decision-making. The concrete context was that of the Vaxjo municipality located in southeastern Sweden.Design/methodology/approach: The methodology was two-fold: 1) a survey of potential end users(n=151) from a local university;and, 2) analysis of survey results using a theoretical model regarding local strategies for implementing open government data.Findings: Most datasets predicted to be useful were on: sustainability and environment;preschool and school;municipality and politics. The use context given is primarily research and development, informing policies and decision making;but also education, informing personal choices, informing citizens and creating services based on open data. Not the least, the need for educating target user groups on data literacy emerged. A tentative pattern comprising a technical perspective on open data and a social perspective on open government was identified. Research limitations: In line with available funding, the nature of the study was exploratory and implemented as an anonymous web-based survey of employees and students at the local university. Further research involving(qualitative) surveys with all stakeholders would allow for creating a more complete picture of the matter. Practical implications: The study determines potential use cases and use contexts for open government data, in order to help inform related future policies and decision-making.Originality/value: Modern local governments, and especially in Sweden, are faced with a challenge of how to make their data open, how to learn about which types of data will be most relevant for their end users and what will be different societal purposes. The paper contributes to knowledge that modern local governments can resort to when it comes to attitudes of local citizens to open government data in the context of an open government data perspective.

Research on the Opening of Adverse Drug Reaction Data in the EU

Systematically analyze the composition of post-marketing adverse drug reaction data and the open mode in the EU, and summarize its characteristics. EU post-marketing ADR data is open to six categories of stakeholders: EMA, EC, medicines regulatory authorities in EEA member states, healthcare professionals and the public, Marketing Authorization Holders, academia, WHO and medicines regulatory authorities in third countries. The EU has implemented hierarchical opening for ADRs, with different levels containing different data and facing different stakeholders. Openness is divided into active and passive openness. In opening up data, the EU complies with relevant personal data protection laws to protect the privacy of individuals. The EU’s post-marketing adverse drug reaction data openness is characterized by a combination of data openness and privacy protection, active and passive openness, and a hierarchy of data openness. It is hoped that this can provide a reference for the opening up of post-marketing adverse drug reaction data in China.

New application of open source data and Rock Engineering System for debris flow susceptibility analysis

This research describes a quantitative,rapid,and low-cost methodology for debris flow susceptibility evaluation at the basin scale using open-access data and geodatabases.The proposed approach can aid decision makers in land management and territorial planning,by first screening for areas with a higher debris flow susceptibility.Five environmental predisposing factors,namely,bedrock lithology,fracture network,quaternary deposits,slope inclination,and hydrographic network,were selected as independent parameters and their mutual interactions were described and quantified using the Rock Engineering System(RES)methodology.For each parameter,specific indexes were proposed,aiming to provide a final synthetic and representative index of debris flow susceptibility at the basin scale.The methodology was tested in four basins located in the Upper Susa Valley(NW Italian Alps)where debris flow events are the predominant natural hazard.The proposed matrix can represent a useful standardized tool,universally applicable,since it is independent of type and characteristic of the basin.

Specialized deep neural networks for battery health prognostics:Opportunities and challenges

Lithium-ion batteries are key drivers of the renewable energy revolution,bolstered by progress in battery design,modelling,and management.Yet,achieving high-performance battery health prognostics is a significant challenge.With the availability of open data and software,coupled with automated simulations,deep learning has become an integral component of battery health prognostics.We offer a comprehensive overview of potential deep learning techniques specifically designed for modeling and forecasting the dynamics of multiphysics and multiscale battery systems.Following this,we provide a concise summary of publicly available lithium-ion battery test and cycle datasets.By providing illustrative examples,we emphasize the efficacy of five techniques capable of enhancing deep learning for accurate battery state prediction and health-focused management.Each of these techniques offers unique benefits.(1)Transformer models address challenges using self-attention mechanisms and positional encoding methods.(2) Transfer learning improves learning tasks within a target domain by leveraging knowledge from a source domain.(3) Physics-informed learning uses prior knowledge to enhance learning algorithms.(4)Generative adversarial networks(GANs) earn praise for their ability to generate diverse and high-quality outputs,exhibiting outstanding performance with complex datasets.(5) Deep reinforcement learning enables an agent to make optimal decisions through continuous interactions with its environment,thus maximizing cumulative rewards.In this Review,we highlight examples that employ these techniques for battery health prognostics,summarizing both their challenges and opportunities.These methodologies offer promising prospects for researchers and industry professionals,enabling the creation of specialized network architectures that autonomously extract features,especially for long-range spatial-temporal connections across extended timescales.The outcomes could include improved accuracy,faster training,and enhanced generalization.

面向城镇住宅建筑光伏一体化的GIS开放数据协同仿真平台

The rising awareness of environmental issues and the increase of renewable energy sources(RESs)has led to a shift in energy production toward RES,such as photovoltaic(PV)systems,and toward a distributed generation(DG)model of energy production that requires systems in which energy is generated,stored,and consumed locally.In this work,we present a methodology that integrates geographic information system(GIS)-based PV potential assessment procedures with models for the estimation of both energy generation and consumption profiles.In particular,we have created an innovative infrastructure that co-simulates PV integration on building rooftops together with an analysis of households’electricity demand.Our model relies on high spatiotemporal resolution and considers both shadowing effects and real-sky conditions for solar radiation estimation.It integrates methodologies to estimate energy demand with a high temporal resolution,accounting for realistic populations with realistic consumption profiles.Such a solution enables concrete recommendations to be drawn in order to promote an understanding of urban energy systems and the integration of RES in the context of future smart cities.The proposed methodology is tested and validated within the municipality of Turin,Italy.For the whole municipality,we estimate both the electricity absorbed from the residential sector(simulating a realistic population)and the electrical energy that could be produced by installing PV systems on buildings’rooftops(considering two different scenarios,with the former using only the rooftops of residential buildings and the latter using all available rooftops).The capabilities of the platform are explored through an in-depth analysis of the obtained results.Generated power and energy profiles are presented,emphasizing the flexibility of the resolution of the spatial and temporal results.Additional energy indicators are presented for the self-consumption of produced energy and the avoidance of CO_(2) emissions.

Search for production of dark fermion candidates in association with heavy neutral gauge boson decaying to dimuon in proton-proton collisions at√s=8 TeV using CMS open data

In this study,we conducted a search for dark matter using a part of the data recorded by the CMS experiment during run-I of the LHC in 2012 with a center of mass energy of 8 TeV and an integrated luminosity of 11.6 fb−1.These data were gathered from the CMS open data.Dark matter,in the framework of the simplified model(mono-Z′),can be produced from proton-proton collisions in association with a new hypothetical gauge boson,Z′.Thus,the search was conducted in the dimuon plus large missing transverse momentum channel.One benchmark scenario of mono-Z′,which is known as light vector,was used for interpreting the CMS open data.No evidence of dark matter was observed,and exclusion limits were set on the masses of dark matter and Z′at 95%confidence level.

World-Wide Semantic Web of Agriculture Knowledge

The rapid increase in the publication of knowledge bases as linked open data （LOD） warrants serious consideration from all concerned, as this phenomenon will potentially scale exponentially. This paper will briefly describe the evolution of the LOD, the emerging world-wide semantic web （WWSW）, and explore the scalability and performance features Of the service oriented architecture that forms the foundation of the semantic technology platform developed at MIMOS Bhd., for addressing the challenges posed by the intelligent future internet. This paper＂ concludes with a review of the current status of the agriculture linked open data.

Dictionary,Thesaurus or Ontology? Disentangling Our Choices in the Semantic Web Jungle

The Semantic Web seems finally close to maintaining its promise about a real world-wide graph of interconnected resources. The SPARQL query language and protocols and the Linked Open Data initiative have laid the way for endless data endpoints sparse around the globe. However, for the Semantic Web to really happen, it does not suffice to get billions of triples out there： these must be shareable, interlinked and conform to widely accepted vocabularies. While more and more data are converted from already available large knowledge repositories of companies and organizations, the question whether these should be carefully converted to semantically consistent ontology vocabularies or find other shallow representations for their content naturally arises. The danger is to come up with massive amounts of useless data, a boomerang which could result to be contradictory for the success of the web of data. In this paper, I provide some insights on common problems which may arise when porting huge amount of existing data or conceptual schemes （very common in the agriculture domain） to resource description framwork （RDF）, and will address different modeling choices, by discussing in particular the relationship between the two main modeling vocabularies offered by W3C： OWL and SKOS.

Barcelona Smart City: The Heaven on Earth(Internet of Things: Technological God)

Cities are the most preferable dwelling places, having with better employment opportunities, educational hubs, medical services, recreational facilities, theme parks, and shopping malls etc. Cities are the driving forces for any national economy too. Unfortunately now a days, these cities are producing circa 70% of pollutants, even though they only oeeupy 2% of surface of the Earth. Pub- lic utility services cannot meet the demands of unexpected growth. The filthiness in cities causing decreasing of Quality of Life. In this light our research paper is giving more concentration on necessity of ＂ Smart Cities＂, which are the basis for civic centric services. This article is throwing light on Smart Cities and its important roles. The beauty of this manuscript is scribbling ＂Smart Cities＂ concepts in pictorially. Moreover this explains on ＂Barcelona Smart City＂ using lnternet of Things Technologies. It is a good example in urban paradigm shift. Braeelona is like the heaven on the earth with by providing Quality of Life to all urban citizens. The GOD is Interenet of Things.

Multi-Hazard Evaluation Using Cluster Analysis—For Designated Evacuation Centers of Yokohama

Hazard maps are usually prepared for each disaster, including seismic hazard maps, flood hazard maps, and landslide hazard maps. However, when the general public attempts to check their own disaster risk, most are likely not aware of the specific types of disaster. So, first of all, we need to know what kinds of hazards are important. However, the information that integrates multiple hazards is not well maintained, and there are few such studies. On the other hand, in Japan, a lot of hazard information is being released on the Internet. So, we summarized and assessed hazard data that can be accessed online regarding shelters (where evacuees live during disasters) and their catchments (areas assigned to each shelter) in Yokohama City, Kanagawa Prefecture. Based on the results, we investigated whether a grouping by cluster analysis would allow for multi-hazard assessment. We used four natural disasters (seismic, flood, tsunami, sediment disaster) and six parameters of other population and senior population. However, since the characteristics of the population and the senior population were almost the same, only population data was used in the final examination. From the cluster analysis, it was found that it is appropriate to group the designated evacuation centers in Yokohama City into six groups. In addition, each of the six groups was found to have explainable characteristics, confirming the effectiveness of multi-hazard creation using cluster analysis. For example, we divided, all hazards are low, both flood and Seismic hazards are high, sediment hazards are high, etc. In many Japanese cities, disaster prevention measures have been constructed in consideration of ground hazards, mainly for earthquake disasters. In this paper, we confirmed the consistency between the evaluation results of the multi-hazard evaluated here and the existing ground hazard map and examined the usefulness of the designated evacuation center. Finally, the validity was confirmed by comparing this result with the ground hazard based on the actual measurement by the past research. In places where the seismic hazard is large, the two are consistent with the fact that the easiness of shaking by actual measurement is also large.

Think global,cube local:an Earth Observation Data Cube’s contribution to the Digital Earth vision

The technological landscape for managing big Earth observation(EO)data ranges from global solutions on large cloud infrastructures with web-based access to self-hosted implementations.EO data cubes are a leading technology for facilitating big EO data analysis and can be deployed on different spatial scales:local,national,regional,or global.Several EO data cubes with a geographic focus(“local EO data cubes”)have been implemented.However,their alignment with the Digital Earth(DE)vision and the benefits and trade-offs in creating and maintaining them ought to be further examined.We investigate local EO data cubes from five perspectives(science,business and industry,government and policy,education,communities and citizens)and illustrate four examples covering three continents at different geographic scales(Swiss Data Cube,semantic EO data cube for Austria,DE Africa,Virginia Data Cube).A local EO data cube can benefit many stakeholders and players but requires several technical developments.These developments include enabling local EO data cubes based on public,global,and cloud-native EO data streaming and interoperability between local EO data cubes.We argue that blurring the dichotomy between global and local aligns with the DE vision to access the world’s knowledge and explore information about the planet.

An open compute and data federation as an alternative to monolithic infrastructures for big Earth data analytics

An adequate compute and storage infrastructure supporting the full exploitation of Copernicus and Earth Observation datasets is currently not available in Europe.This paper presents the cross-disciplinary open-source technologies being leveraged in the C-SCALE project to develop an open federation of compute and data providers as an alternative to monolithic infrastructures for processing and analysing Copernicus and Earth Observation data.Three critical aspects of the federation and the chosen technologies are elaborated upon:(1)federated data discovery,(2)federated access and(3)software distribution.With these technologies the open federation aims to provide homogenous access to resources,thereby enabling its users to generate meaningful results quickly and easily.This will be achieved by abstracting the complexity of infrastructure resource access provisioning and orchestration,including discovery of data across distributed archives,away from the end-users.Which is needed because end-users wish to focus on analysing ready-to-use data products and models rather than spending their time on the setup and maintenance of complex and heterogeneous IT infrastructures.The open federation will support processing and analysing the vast amounts of Copernicus and Earth Observation data that are critical for the implementation of the Destination Earth resp.Digital Twins vision for a high precision digital model of the Earth to model,monitor and simulate natural phenomena and related human activities.

How to assess the success of the open data ecosystem?

Open data are currently a hot topic and are associated with realising ambitions such as a more transparent and efficient government,solving societal problems,and increasing economic value.To describe and monitor the state of open data in countries and organisations,several open data assessment frameworks were developed.Despite high scores in these assessment frameworks,the actual(re)use of open government data(OGD)fails to live up to its expectations.Our review of existing open data assessment frameworks reveals that these only cover parts of the open data ecosystem.We have developed a framework,which assesses open data supply,open data governance,and open data user characteristics holistically.This holistic open data framework assesses the maturity of the open data ecosystem and proves to be a useful tool to indicate which aspects of the open data ecosystem are successful and which aspects require attention.Our initial assessment in the Netherlands indicates that the traditional geographical data perform significantly better than non-geographical data,such as healthcare data.Therefore,open geographical data policies in the Netherlands may provide useful cues for other OGD strategies.

Healthcare data analytics:using a metadata annotation approach for integrating electronic hospital records

The data in electronic medical records(EMR)are complex in structure.They are independent,yet related to each other.In order to improve information access through the use of EMR,annotating work on these data is necessary.The annotation on metadata,the resource data which contain a meta-model of the database,is the basis of the annotating work if a semi-automated or an automated annotating approach which aims at making the database more accessible is expected.In this study,a method has been proposed to transform the terms which cannot be matched directly by changing them literally but maintaining their semantics,and then annotating them indirectly.After the transforming work,a refinement method which is reducible to phrase sense disambiguation(PSD)is employed to ensure accuracy.A pilot study on a hospital database has been conducted to test the accuracy and effectiveness of the proposed method.

Gap analysis on open data interconnectivity for disaster risk research

Open data strategies are being adopted in disaster-related data particularly because of the need to provide information on global targets and indicators for implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030.In all phases of disaster risk management including forecasting,emergency response and post-disaster reconstruction,the need for interconnected multidisciplinary open data for collaborative reporting as well as study and analysis are apparent,in order to determine disaster impact data in timely and reportable manner.The extraordinary progress in computing and information technology in the past decade,such as broad local and wide-area network connectivity(e.g.Internet),highperformance computing,service and cloud computing,big data methods and mobile devices,provides the technical foundation for connecting open data to support disaster risk research.A new generation of disaster data infrastructure based on interconnected open data is evolving rapidly.There are two levels in the conceptual model of Linked Open Data for Global Disaster Risk Research(LODGD)Working Group of the Committee on Data for Science and Technology(CODATA),which is the Committee on Data of the International Council for Science(ICSU):data characterization and data connection.In data characterization,the knowledge about disaster taxonomy and data dependency on disaster events requires specific scientific study as it aims to understand and present the correlation between specific disaster events and scientific data through the integration of literature analysis and semantic knowledge discovery.Data connection concepts deal with technical methods to connect distributed data resources identified by data characterization of disaster type.In the science community,interconnected open data for disaster risk impact assessment are beginning to influence how disaster data are shared,and this will need to extend data coverage and provide better ways of utilizing data across domains where innovation and integration are now necessarily needed.