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Academics have a duty towards peers and scholars alike to engage in research work and to publish their findings. This also assists in establishing personal academic success as well as the attainment of research grants. In the past, authors used to publish their research articles for free but access to these articles was restricted to subscription users only. Recently, open access publishing has gained momentum, whereby such articles are made freely accessible online. However open access publishing comes with a price tag for the author through article processing charges. Open access may also question a journal's credibility within the academic world if improperly implemented. This is particularly so following the unsolicited bombardment of researchers' email accounts with invitations for submissions to predatory open access journals. For these reasons, authors needs to rigorously weigh the pros and cons of whether to choose a subscription based or an open access journal for publication.

The paper presents and discusses the method adopted by Compagnia di San Paolo, one of the largest European philanthropic institutions, to monitor the advancement, despite the COVID-19 situation, in providing specific input to the decision-making process for dedicated projects. An innovative approach based on the use of daily open data was adopted to monitor the metropolitan area with a multidimensional perspective. Several open data indicators related to the economy, society, culture, environment, and climate were identified and incorporated into the decision support system dashboard. Indicators are presented and discussed to highlight how open data could be integrated into the foundation's strategic approach and potentially replicated on a large scale by local institutions. Moreover, starting from the lessons learned from this experience, the paper analyzes the opportunities and critical issues surrounding the use of open data, not only to improve the quality of life during the COVID-19 epidemic but also for the effective regulation of society, the participation of citizens, and their well-being.

The economic impact of open data in Europe has an estimated value of € 140 billions a year between direct and indirect effects. The social impact is also known to be high, as the use of more transparent open data have been enhancing public services and creating new opportunities for citizens and organizations. We are assisting at a staggering growth in the production and consumption of Linked Data (LD). Exploring, visualizing and analyzing LD is a core task for a variety of users in numerous scenarios. This paper deeply analyzes the state of the art of tools for LD visualization. Linked Data visualization aims to provide graphical representations of datasets or of some information of interest selected by a user, with the aim to facilitate their analysis. A complete list of 77 LD visualization tools has been created starting from tools listed in previous surveys or research papers and integrating newer tools recently published online. The visualization tools have been described and compared based on their usability, and their features. A set of goals that LD tools should implement in order to provide clear and convincing visualizations has been defined and 14 tools have been tested on a big LD dataset. The results of this comparison and test led us to define some suggestions for LD consumers in order for them to be able to select the most appropriate tools based on the type of analysis they wish to perform.

Worldwide, small-scale fisheries (SSFs) contribute over half of global fish and invertebrate catch and generate employment for 90% of those working in the fishing capture industry, the majority of whom live in developing countries. Despite their importance, most of the world's estimated 10,000 SSFs are data deficient. Community data is critical to understanding fish stocks, and evaluating fisheries management policies, particularly in remote areas. This pilot study explores the potential for smartphones and the Open Data Kit software to assist in the collection of shark landings data in southwest Madagascar, where sustainable fisheries management is critical to economic and food security. The pilot builds on a previous study of participatory data collection using paper notebooks (2003–2016), which continued in eight villages throughout the smartphone trial (2013–2016), allowing comparisons in speed, accuracy and user experience to be drawn. Initial challenges, which included limited electricity supplies to charge the smartphones; typing errors caused by wet hands; and interpretation difficulties, were overcome during the trial with additional training and data accuracy improved as a result, with only 5% fewer records recorded on phones vs. paper notebooks by 2015. One major challenge - limited mobile network coverage – often prevented data from being uploaded from phones to an online database, meaning manual data extraction was required, with associated travel costs. With appropriate training, smartphones show promise as a useful and accurate tool for participatory fisheries data collection. However, this method may be better suited to regions with stronger mobile coverage.

This paper presents the features and capabilities of a vertical axis wind turbine (VAWT) designed and built by the authors, which is meant to be the reference machine for open data code validation purposes under the project VODCA, VAWT Open Data for Code Assessment. The machine, whose design details are provided openly and summarized in the present document, has Individual Pitch Control (IPC) capability and it will be used for wind tunnel tests. Related results will be shared with participants who want to validate their own code, in terms of prediction of the aerodynamics or assessment of IPC strategies. The article reports a comprehensive overview of the state of the art of laboratory tests for VAWTs as well as the motivation of VODCA project, further to the need of code validation against experimental tests in controlled environment. The various phases of the project are also presented. Furthermore, the design of the carbon fiber blades, the structural components, as well as the mechatronics of the machine are summarized in this paper. Moreover, the experimental characterization of the machine's effective capabilities and properties, carried out after the completion of the building process, is reported and then the testing possibilities are defined and discussed.

This paper explores the characteristics of DataCite to determine its possibilities and potential as a new bibliometric data source to analyze the scholarly production of open data. Open science and the increasing data sharing requirements from governments, funding bodies, institutions and scientific journals has led to a pressing demand for the development of data metrics. As a very first step towards reliable data metrics, we need to better comprehend the limitations and caveats of the information provided by sources of open data. In this paper, we critically examine records downloaded from the DataCite's OAI API and elaborate a series of recommendations regarding the use of this source for bibliometric analyses of open data. We highlight issues related to metadata incompleteness, lack of standardization, and ambiguous definitions of several fields. Despite these limitations, we emphasize DataCite's value and potential to become one of the main sources for data metrics development., This study has been partially supported by the European Commission project RTD-B6-00964-2013 Monitoring the evolution and benefits of Responsible Research and Innovation (MoRRI). Nicolas Robinson-Garcia is currently supported by a Juan de la Cierva-Formación grant from the Spanish Ministry of Economy and Competitiveness.

Recent emphasis and requirements for open data publication have led to significant increases in data availability in the Earth sciences, which is critical to long-tail data integration. Currently, data are often published in a repository with an identifier and citation, similar to those for papers. Subsequent publications that use the data are expected to provide a citation in the reference section of the paper. However, the format of the data citation is still evolving, particularly with regards to citing dynamic data, subsets, and collections of data. Considering the motivations of both data producers and consumers, the most pressing need is to create user-friendly solutions that provide credit for data producers and enable accurate citation of data, particularly integrated data. Providing easy-to-use data citations is a critical foundation that is required to address the socio-technical challenges around data integration. Studies that integrate data from dozens or hundreds of datasets must often include data citations in supplementary material due to page limits. However, citations in the supplementary material are not indexed, making it difficult to track citations and thus giving credit to the data producer. In this paper, we discuss our experiences and the challenges we have encountered with current citation guidance. We also review the relative merits of the currently available mechanisms designed to enable compact citation of collections of data, such as data collections, data papers, and dynamic data citations. We consider these options for three data producer scenarios: a domain-specific data collection, a data repository, and a large-scale, multidisciplinary project. We posit that a new mechanism is also needed to enable citation of multiple datasets and credit to data producers.

This paper reports on a project currently underway to investigate how an open exchange standard for modelling information at the scale of an urban precinct can be used to support integrated solutions to achieve low carbon targets in the built environment. The project is part of a major research initiative to deliver on low carbon targets in Australia. The project builds on the concept of BIM to develop an object-oriented approach to modelling the built environment at a broader urban scale, focusing in the first instance on a precinct, being any region within an urban context that can be regarded as an integrated whole for the purposes of planning, design or management. This approach is referred to as precinct information modelling (PIM) and provides a key mechanism to bridge the information modelling gap between building scale (BIM) and the spatial scale. The paper argues the case for such an approach, proposing that the current IFC data model, and recent work that is investigating how that data model can be extended to handle transport infrastructure elements such as roads and bridges, can be adapted with modest extensions to serve this purpose. The paper describes this approach, proposing an initial data model and addressing several key strategies and principles that influence the work (e.g. commonality of concepts to maintain semantic integrity and the use of data dictionaries to define concept hierarchies). The paper offers a review of current approaches, reflects on a couple of trial implementations and provides a discussion of how this work can be carried forward.

The paper provides an overview of recent research and publications on the integration of research data in Current Research Information Systems (CRIS) and addresses three related issues, i.e. the object of evaluation, identifier schemes and conservation. Our focus is on social sciences and humanities. As research data gradually become a crucial topic of scientific communication and evaluation, current research information systems must be able to consider and manage the great variety and granularity levels of data as sources and results of scientific research. More empirical and moreover conceptual work is needed to increase our understanding of the reality of research data and the way they can and should be used for the needs and objectives of research evaluation. The paper contributes to the debate on the evaluation of research data, especially in the environment of open science and open data, and will be helpful in implementing CRIS and research data policies.

This paper describes a case study to explore how we continue to develop our CRIS [at the University of St Andrews] and support the University’s research needs and how it has become an embedded tool for researchers to manage their research outputs and to enable Open Access and Open Data. The paper will show how we used researchers’ feedback and comments to develop a simple and easy to remember workflow mapped against existing and familiar research lifecycles. We examine some of the technical, practical and cultural issues we have encountered in implementing these workflows, and show how the CRIS as a single portal has streamlined tasks and reduced duplication of effort. Postprint Other

Transit-Oriented Development (TOD) degree, the correlation between transit-based accessibility and urban development, reflects to what extent TOD shapes urban form. This paper empirically examines the shifting patterns of TOD degree and its correlates by using publicly available big and/or open data (BOD) such as point of interest (POI) and OpenStreetMap data for Shenzhen, China, a city experiencing rapid growth/changes in recent decades. The BOD used contain up-to-date information of human activities in finer spatiotemporal resolutions than traditional data. The paper illustrates how BOD can be utilized to quantify TOD degree and its correlates in 2014 and 2017. The main findings are twofold. First, bus stops, a proxy for bus-based accessibility, were found to markedly influence urban development (measued by the number of POIs with social media check-ins) around a (planned) metro station in 2014, when the local metro system was still in devleopment. Second, the impacts of the metro-based accessibility (measured by the average time to the two central business districts) on urban development became larger than those of the accessibility to bus stops as the local metro system matured. It is recommended that decision-makers should systematically consider and harness those effects over time to better promote TOD degree.

As the world's population is becoming progressively urban-dwelling, sustainable development challenges are increasingly concentrated in cities, placing tremendous pressure on society to build more sustainable, innovative, and equitable urban environments. Consequently, today's cities require integrated policies and new innovative ways to manage and improve the complexity of urban living conditions. The growing volume and variety of data produced in the urban ecosystem are crucial for obtaining the city's insights and building knowledge-based solutions for a smarter and more sustainable urban development. In this paper, we look at the open data impacts on these complex ecosystems and its crucial enabler role for the generation and analysis of contextual and actionable data aimed at understanding, managing, and planning the city. Despite the importance of open data, the literature is scarce in systematic and structured research that evaluates its impacts on the smart city context. This paper explores this gap by proposing a theoretical framework, composed of a model and an experiment grounded on the use of Randomized Controlled Trials (RCT), designed to give a more detailed view concerning the context and characteristics of the impacts of open data initiatives on smart cities' sustainable development. This work will contribute to open data management and smart city development, providing boundaries and theoretical insights for further research and experimentation on how open data can be leveraged to develop better smart cities.

This paper introduces the EGI Open Data Platform and the EGI DataHub, outlines their functionality and explains how this meets the requirements of EGI end users. The paper also explains how these new services can support the European Open Science Cloud and will fit into the future European Strategy Report on Research Infrastructures (ESFRI).

From the Heads of State of the G8 nations, the European Union, international organisations, through to national laws and policies, there is a strong momentum for full, free and open access to public sector digital data so that the benefits of public investment in such data can be maximised with as few barriers to use as possible. While open data for society at large is a relatively new phenomenon, the space sector has been accustomed to open data for all of its existence, especially in astronomy and navigation, but also in Earth observation. In the context of the open data debate, the purpose of this paper is to examine 21 policy and legal instruments concerning open data made by a range of organisations that in one way or another relate to Earth observation data and therefore contribute to the critical examination of the effectiveness of the open data movement. Based on these documents, the paper discusses common issues such as changing perspectives on the desirability of open data, commercial use, and licences and exceptions, and concludes that open access to Earth observation data requires more specification in order to clarify the conditions of access and so to realise fully the potential benefits.

The recent buzz around open data highlighted the crucial problem of anonymization in the context of data publishing. Many research efforts were devoted to the definition of techniques performing such an anonymization. However the selection of the most relevant technique and the adequate algorithm is complex. Successful decision depends firstly on the ability of data publishers to understand the anonymization techniques and their associated algorithms. In this paper, we focus on the choice of an algorithm among the different ones implementing one of the anonymization techniques, namely generalization. Through an abstraction process presented in this paper, we provide data publishers with simplified descriptions for the generalization technique and its algorithms. These descriptions facilitate the understanding of the algorithms by data publishers having low programing skills. We present also some other use cases of these abstractions as well as an experimentation conducted to validate them.

This paper presents a model of semantic ontologies integration into workflow co mposition design process via Virtual Simu lation Objects (VSO) concept and technology. Doma in knowledge distributed over open linked data sources may be usefully applied for new VSO-images design and used for organization co mputational-intensive simulation e xpe riments. In this paper we describe the VSO- architecture e xtended with novel functionality regarding integration with lin ked open data sources. We also provide a computational-scientific e xa mp le of do ma in-specific use-case offering a solution for some public-transportation domain problem.

Analysing large amounts of data goes to the heart of the challenges confronting intelligence and law enforcement professionals today. Increasingly, this involves Internet data that are ‘open source’ or ‘publicly available’. Projects such as the European FP7 VIRTUOSO are developing platforms for open-source intelligence by law enforcement and public security, which open up opportunities for large-scale, automated data gathering and analysis. However, the mere fact that data are publicly available does not imply an absence of restrictions to researching them. This paper investigates one area of legal constraints, namely criminal-procedure law in relation to open-source data gathering by the police. What is the legal basis for this activity? And under what conditions can domestic and foreign open sources be investigated? These questions are addressed from the perspectives of European and Dutch law. First, the international legal context for gathering data from openly accessible and semi-open sources is analysed, including the issue of cross-border gathering of data. In particular, article 32 of the Cybercrime Convention and some national implementations are discussed, as well as data protection requirements from European Union law. Next, the paper zooms in on the Dutch legal context for open-source investigations, to illustrate how the issues of a legal basis and other legal requirements are addressed in a specific legal framework. The paper draws the conclusion that technology-facilitated investigations of open sources by the police often constitute an interference with the right to privacy; hence, they require a legal, statutory basis that is sufficiently clear for citizens to understand what the police are doing. Moreover, open-source investigation tools and practices used must meet general data-protection requirements and forensic reliability standards. The discussion also shows that interpreting existing legal provisions to accommodate open-source investigation tools can lead to convoluted interpretations, suggesting that legal frameworks of investigation powers with a focus on physical-space investigations may need to be revised to accommodate the particularities of open-source Internet investigations.

This paper presents a comparative analysis of rail transit and taxi by travel cost and time based on the large-scale taxi trip data and public transit schedule information in two major U.S. cities. To quantify the relative advantage of one mode over the other, we introduce the notion of travel gradient, which is travel cost difference divided by travel time difference. Based on the signs of travel cost and time differences, we classify all trips into four quadrants. Quadrant II trips are selected for further analysis because rail transit is identified to be competitive with taxi for such trips. We also explore the relation between various trip characteristics and travel gradient with and without considering the spatial variation of such a relation. Main research findings include: (1) around 70% of the taxi trips in the considered datasets can be substituted with rail transit trips if the maximum walking distance is 0.5 miles at each trip end; (2) for around 10% of taxi trips with both modes being viable, rail transit dominates taxi in both travel cost and time; for the rest, rail transit is competitive with taxi; (3) the marginal travel cost saving due to mode switching from taxi to rail transit is about $70; and (4) there exist clearly spatial variations of the relation between trip characteristics and travel gradient. The main policy recommendation from this study is that rail transit can be better marketed by highlighting its relative advantage over taxi in travel time and cost, especially for travels in certain directions and time periods.

International audience; The availability of transportation scenarios from open data introduces new research motivations, such as route pricing and pollution. Unfortunately, these data are often anonymized to protect the privacy of the survey participants. In this paper, we present a generation of a mobility simulation from anonymized data based on a modal choice model. We show how to reconstruct missing information such as the municipalities of residence and activities and the coordinates of the origins/destinations (O/D) of trips necessary for the estimation of the model. This model integrates an inter-modal alternative mixing car and public transport (pt) for commutes through park and ride (PR) facilities. A first application of the simulation model was made on the perimeter of the European Metropolis of Lille (MEL) where the activities zones were blurred and the O/D coordinates of trips were withdrawn from the household trip survey. The preliminary results obtained show that the simulation is capable of reproducing the behaviors of trips performed by combining car and pt. Therefore, this work can serve as a basis for testing inter-modality incentive policies (designated by “car + pt”) such as intra-urban tools or the creation of PR facilities.

Biomedical text mining is becoming increasingly important as the number of biomedical documents grow rapidly. Deep learning has boosted the development of biomedical text mining models. However, as deep learning models require a large amount of training data, a hierarchical attention based transfer learning model is proposed in this paper for the question answering task in biomedical field which lacks of sufficient training data. We adopt BERT (Bidirectional Encoder Representation Transformers), which has the ability to learn from large-scale unsupervised data, to enrich the semantic representation in our model. Especially, the scaled dot-product attention mechanism captures the question interaction clues for passage encoding. The domain adaptation technique of fine-tuning is used to reinforce the performance, which penalizes the deviations from the source model’s parameters and remembers the knowledge of source domain. We evaluate the system performance on the open data set of BioASQ-Task B. The results show that our system achieves the state-of-the-art performance without any handcrafted features and outperforms the best solution for factoid questions in 2016 and 2017 BioASQ-Task B.

Nowadays, the development of data sharing technologies allows to involve more people to collaboratively contribute knowledge on the Web. The shared knowledge is usually represented as Collaborative Open Data (COD), for example, Wikipedia is one of the well-known sources for COD. The Wikipedia articles can be written in different languages, updated in real time, and originated from a vast variety of editors. However, COD also bring different data quality problems such as data inconsistency and low data objectiveness due to the crowd-based and dynamic nature. These data quality problems such as biased information may lead to sentimental changes or social impacts. This paper therefore proposes a new measurement model to assess the quality of COD. In order to evaluate the proposed model, A preliminary experiment is conducted with a large scale of Wikipedia articles to validate the applicability and efficiency of this proposed quality model in the real-world scenario.

Virtual Research Environments (VREs), also known as science gateways or virtual laboratories, assist researchers in data science by integrating tools for data discovery, data retrieval, workflow management and researcher collaboration, often coupled with a specific computing infrastructure. Recently, the push for better open data science has led to the creation of a variety of dedicated research infrastructures (RIs) that gather data and provide services to different research communities, all of which can be used independently of any specific VRE. There is therefore a need for generic VREs that can be coupled with the resources of many different RIs simultaneously, easily customised to the needs of specific communities. The resource metadata produced by these RIs rarely all adhere to any one standard or vocabulary however, making it difficult to search and discover resources independently of their providers without some translation into a common framework. Cross-RI search can be expedited by using mapping services that harvest RI-published metadata to build unified resource catalogues, but the development and operation of such services pose a number of challenges. In this paper, we discuss some of these challenges and look specifically at the VRE4EIC Metadata Portal, which uses X3ML mappings to build a single catalogue for describing data products and other resources provided by multiple RIs. The Metadata Portal was built in accordance to the e-VRE Reference Architecture, a microservice-based architecture for generic modular VREs, and uses the CERIF standard to structure its catalogued metadata. We consider the extent to which it addresses the challenges of cross-RI search, particularly in the environmental and earth science domain, and how it can be further augmented, for example to take advantage of linked vocabularies to provide more intelligent semantic search across multiple domains of discourse.

Reliable detection of clouds is a critical pre-processing step in optical satellite based remote sensing. Currently, most methods are based on classifying invidual pixels from their spectral signatures, therefore they do not incorporate the spatial patterns. This often leads to misclassifications of highly reflective surfaces, such as human made structures or snow/ice. Multi-temporal methods can be used to alleviate this problem, but these methods introduce new problems, such as the need of a cloud-free image of the scene. In this paper, we introduce the Remote Sensing Network (RS-Net), a deep learning model for detection of clouds in optical satellite imagery, based on the U-net architecture. The model is trained and evaluated using the Landsat 8 Biome and SPARCS datasets, and it shows state-of-the-art performance, especially over biomes with hardly distinguishable scenery, such as clouds over snowy and icy regions. In particular, the performance of the model that uses only the RGB bands is significantly improved, showing promising results for cloud detection with smaller satellites with limited multi-spectral capabilities. Furthermore, we show how training the RS-Net models on data from an existing cloud masking method, which are treated as noisy data, leads to increased performance compared to the original method. This is validated by using the Fmask algorithm to annotate the Landsat 8 datasets, and then use these annotations as training data for regularized RS-Net models, which then show improved performance compared to the Fmask algorithm. Finally, the classification time of a full Landsat 8 product is 18.0 ± 2.4 s for the largest RS-Net model, thereby making it suitable for production environments.

The open data initiative allows the organizations from different countries to share and integrate their data to produce value added information products and services. However, the trust relationship between any two organizations can hardly be established due to the data sensitivity and their specific responsibilities. In this paper, we propose a secure string pattern query processing framework for multiple data owners. With this framework, we design an efficient and secure indexing structure called S ecure S tring PA ttern S earch tree (S2PAStree). S2PAStree allows the execution of the secure string pattern query with logarithmic time complexity. We further propose a set of secure index construction protocols under the scenario that multiple data owners share and integrate their sensitive data. Finally, we conduct comprehensive experiments on three public datasets to evaluate the efficiency and the scalability of our solutions.

The rising number of data portals has been increasing demand for new techniques to assess data openness in an automated manner. Some methods have emerged that presuppose well-organized data catalogs, the availability of API interfaces and natively exposed metadata. However, many data portals, particularly those of local governments, appear to be misimplemented and developed with the classic website model in mind, which provides access to data only through user interaction with web forms. Data in such portals resides in the hidden part of the web, as it is dynamically produced only in response to direct requests. This paper proposes an automated method for assessing government-related data in the deep web on the basis of compliance with open data principles and requirements. To validate our method, we apply it in an experiment using the government websites of the 27 Brazilian capitals. The method is fully carried out for 22 of the capitals' websites, resulting in the analysis of 5.6 million government web pages. The results indicate that the keyword search approach utilized in the method, along with the checking of web pages for multifield web forms, is effective for identifying deep web data sources, as 1.5% of web pages with potential government data that are analyzed are found to contain data stored in the deep web. This work contributes to the development of a novel method that allows for the continuous checking and identification of government data from surface web data portals. In addition, this method can be scaled and repeated to assure the widest possible content coverage.

International audience; This paper presents a new experimental test bench, called Friction-Induced Vibration and noisE atÉcole Centrale de Lyon (FIVE@ECL). This experimental bench aims at discussing some of the open issues in understanding squealing disc brakes. Measurements for friction-induced vibration and squeal noise are performed to investigate the dynamic behavior of the system under study and its squeal characterization through experiments. One of the main original contributions is to share the data sets to give the opportunity to researchers for conducting new analysis and testing numerical models of brake system with the proposed data of squeal noise. The data provided include all the measurements on the two pads, the caliper and the disc, as well as the measurement of sounds in near-field and in far-field.

This paper presents a conceptual model to analyze motivation factors and perceived risk factors of open data measurement in Indonesia local government. The conceptual model is developed from a theoretical background and literature reviews of related research, which describes open data government, indicators of open data measurement, motivation factors and perceived risks factor in open data. In result, there are eight factors that construct in the model. There are four motivation indicators and four perceived risk indicator that influence open data measurement. For each indicator are determined from the literature review and previous research. In the future, the conceptual model is expected to be able to provide insight to Indonesian local governments on open government data through an analysis of the relationship of identified motivational factors and perceived risks factors.

With the growing availability of public open data, the protection of citizens’ privacy has become a vital issue for governmental data publishing. However, there are a large number of operational risks in the current government cloud platforms. When the cloud platform is attacked, most existing privacy protection models for data publishing cannot resist the attacks if the attacker has prior background knowledge. Potential attackers may gain access to the published statistical data, and identify specific individual’s background information, which may cause the disclosure of citizens’ private information. To address this problem, we propose a fog-computing-based differential privacy approach for privacy-preserving data publishing in this paper. We discuss the risk of citizens’ privacy disclosure related to governmental data publishing, and present a differential privacy framework for publishing governmental statistical data based on fog computing. Based on the framework, a data publishing algorithm using a MaxDiff histogram is developed, which can be used to realize the function of preserving user privacy based on fog computing. Applying the differential method, Laplace noises are added to the original data set, which prevents citizens’ privacy from disclosure even if attackers get strong background knowledge. According to the maximum frequency difference, the adjacent data bins are grouped, then the differential privacy histogram with minimum average error can be constructed. We evaluate the proposed approach by computational experiments based on the real data set of Philippine families’ income and expenditures provided by Kaggle. It shows that the proposed data publishing approach can not only effectively protect citizens’ privacy, but also reduce the query sensitivity and improve the utility of the data published.

With more diverse transport policies being proposed and the advent of novel transport services and technologies, the transport system is becoming more individualized in many aspects. Transport models, the most important tool to assess policies and schemes, need to be sufficiently expressive to address these developments. Agent-based transport models, where travelers with individual properties and the ability to act and decide autonomously are resolved individually, allow to appropriately model and analyze such policies. This paper describes the MATSim Open Berlin Scenario, a transport simulation scenario for the Berlin metropolitan area implemented in the agent-based transport simulation framework MATSim. The scenario is solely based on open data and the demand for transport is created based on a fully synthetic procedure. Contrary to most transport simulation scenarios, no information from a travel diary survey is required as input. As such, the scenario generation procedure described in this study is spatially transferable and facilitates the creation of agent-based transport simulation scenarios for arbitrary regions.

This paper aims to contribute to a better understanding of the literature on open data in three ways. The first is to develop a descriptive analysis of journals and authors to identify the knowledge areas in which open data are applied. The second is to analyse the conceptual structure of the field using a bibliometric technique. The co-word analysis enabled us to create a map of the main themes that have been studied, identifying their importance and relevance. These themes were analysed and grouped. The third is to propose future research trends. According to our results, the main knowledge areas are Engineering, Health, Public Administration, Management and Education. The main themes are big data, open-linked data and data reuse. Finally, several research questions are proposed according to knowledge area and theme.

Practitioners bemoan lack of data as one of the biggest obstacles to progress towards global sustainable development goals. This paper explores a scaled-up participatory method developed by YouthMappers, for creating missing geospatial data derived from remotely sensed imagery in order to contribute to persistent data needs in the context of the United Nations Sustainable Development Goals (SDGs). We explore the application of this method to a case related to SDG 3 on Health. We document how our approach centered on creating a global academic network designed to engage and empower university students and their faculty mentors to participate in broader efforts to create open, free spatial data on open platforms to inform humanitarian and development objectives outlined by the funding agency, the United States Agency for International Development (USAID). This approach expressly links supply and demand for geospatial knowledge by connecting specific needs for geographic information to specific development objectives in targeted places where USAID works to end extreme poverty. We discuss the rationale and context for the methodology as it draws from and builds upon prominent literature of participatory GIS (PGIS) and volunteered geographic information (VGI). We demonstrate how the mapping of building and road infrastructure in Mozambique and Kenya was carried out in order to provide information for an insecticide spray campaign to prevent malaria and protect public health. Throughout these efforts, steps are taken to ensure spatial data quality and to offer opportunities for youth volunteer embeddedness in mapping tasks and themes in places where students otherwise would not engage with real world data or connect with peers from different countries. We reflect on the opportunities and challenges for how this scaled-up “remote participatory sensing” approach to spatial data creation can inform development projects in the context of the SDGs.

Initiatives of open data promote the online publication and sharing of large amounts of geologic data. How to retrieve information and discover knowledge from the big data is an ongoing challenge. In this paper, we developed an ontology-driven data integration and visualization pilot system for exploring information of regional geologic time, paleontology, and fundamental geology. The pilot system ( http://www2.cs.uidaho.edu/∼max/gts/ ) implemented the following functions: modeling and visualization of a geologic time scale ontology of North America, interactive retrieval and display of fossil information, geologic map information query and comparison with fossil information. A few case studies were carried out in the pilot system for querying fossil occurrence records from Plaeobiology Database and comparing them with information from the USGS geologic map services. The results show that, to improve the compatibility between local and global geologic standards, bridge gaps between different data sources, and create smart geoscience data services, it is necessary to further extend and improve the existing geoscience ontologies and use them to support functions to explore the open data.

Open science practices including open access (OA) publication, open methods, study preregistration, and open data are gaining acceptance across diverse fields of research. These practices are promoted as strategies to improve the reproducibility of research findings and the replicability of studies to accumulate knowledge and advance science. However, these arguments may raise concerns for qualitative researchers, and open science practices pose several challenges for qualitative researchers. The purpose of this paper is: (1) to review the state of open science practices within sport and exercise psychology, and (2) to discuss the implications of open science for qualitative inquiry. We examined open science practices across quantitative and qualitative articles in 11 sport and exercise psychology journals. While OA publication is a relatively recent phenomenon, OA articles were cited slightly more often than non-OA articles, although this difference was not significant. Some researchers provided supplementary materials alongside published articles, but researchers do not appear to be openly sharing the methods and data from their studies. No articles were published as preregistered studies at the time of our review. Some benefits of open science practices for qualitative inquiry include transparent documentation of the research process, opportunities for collaborative and pluralistic analyses, access to data across multiple research sites and from difficult-to-access settings and participants, and opportunities for teaching qualitative inquiry. We conclude by addressing several key questions including participant consent, confidentiality and anonymity, analyzing de-contextualized qualitative data, storing and accessing data, study preregistration, and the principle of emergent design within qualitative inquiry.

In the last decade, governments around the world have created open government data (OGD) repositories to make government data more accessible and usable by the public, mostly motivated by values such as improved government transparency, citizen collaboration and participation, and spurring innovation. The basic assumption is that once data are more discoverable, accessible, available in alternative formats, and with licensing schemes that allow free re-use, diverse stakeholders will develop innovative data applications. Despite OGD's potential transformative value, there is limited evidence for such transformation, particularly due to scarce data use, which is partly attributable to the lack of technical skills and user training. To advance the dialogue around methods to increase awareness of OGD, improve users' skills to work with OGD, and encourage data use, the paper compares and contrasts how three training interventions in Spain, Italy, and the United States have sought to increase awareness of OGD, improve users' skills and potentially engage them in their use of OGD. We report three main findings. First, introduction and analysis skills are taught in combination to encourage use of open data. Being aware of OGD and its benefits is insufficient to promote use. Second, OGD training seems to be more effective when complemented with knowledge about context and interactions with government. Finally, embedding the training interventions in the specific contexts and considering the unique characteristics, interests, and expectations of different types of users is critical to success.

This editorial opens by introducing Internet Plus Government, a new government initiative emerging after the US presidential election in 2008. Comparing to the more descriptive definitions of e-government, supporters of ‘Internet Plus Government’ emphasize the transformative and normative aspect of the newest generation of Information and Communication Technology (ICTs). They argue that the new initiative designates how government should operate and in turn implies how state-citizen relationships are transformed. To understand the core of this initiative and whether it offers new opportunities to solve public problems, we conducted analyses of research articles published in the e-governance 1 area between 2008 and 2017. Our analysis suggests that the Internet Plus Government initiative has enriched the government information infrastructure. That is, it has enabled the accumulation and use of huge volumes of data for better decision making. The advancement of open data, the wide use of social media, and the potential of data analytics have also generated pressure to address challenging questions and issues in e-democracy. 2 However, the analysis leads us to deliberate on whether Internet Plus Government initiatives worldwide have actually achieved their goal. After introducing papers included in this special issue, we present challenges to be addressed before Internet Plus Government initiatives realize their potential towards better public governance.

With the rising adoption of BIM in the AECO (Architecture, Engineering, Construction and Owner-operator) industry, BIM content is increasingly more complete and valuable. Unfortunately, the data is largely locked inside the respective BIM authoring tool with limited facility for users to perform queries beyond simple queries on objects and their properties. Even with the use of open data exchange format such as IFC (Industry Foundation Classes), getting the data out of BIM is still not an easy task. This research focuses on defining a schema and transformation rules to bring BIM data into a form that is easily queried. The schema is defined using popular relational database structure following an established star schema model that is well-defined within the data warehouse domain. The primary aim of the concept is to allow flexible and efficient queries into the BIM data using standard SQL that removes restrictions on predefined queries, effectively transforming BIM data into an open and queryable database. Another major contribution of this research is the significant potential for supporting spatial queries. This is a critical feature often neglected in the current approach to BIM-based databases. This paper presents the integration of spatial operations into standardized SQL queries making the BIM data accessible for wide ranges of query capabilities. Such feature will allow much better visibility into BIM data for better decision-making processes.

With plenty valuable information, open data are often deemed as great assets to academia or industry. In spite of some de-identification processing that most of data owners will perform before releasing the data, the more datasets are opened to public, the more likely personal privacy will be exposed. According to previous real case studies, even though the personally identifiable information has been de-identified, sensitive personal information could still be uncovered by heterogeneous or cross-domain data joining operations. The involved privacy re-identification processes are usually too complicated or obscure to be realized by data owners, not to mention that this problem will be more severe as the scale of data will get larger and larger. For preventing the leakage of sensitive information, this paper shows how to use a novel visualization analysis tool for open data de-identification (ODD Visualizer) to verify whether there exists sensitive information leakage problem in the target datasets. The high effectiveness that the ODD Visualizer can provide mainly comes from implementing a scalable computing platform as well as developing an efficient data visualization technique. Our demonstrations show that the ODD Visualizer can indeed uncover real vulnerability of record linkage attacks among open datasets available on the Internet.

Open government IS are theorized using Complex Adaptive Systems (CAS).Insights are drawn from a case study of an open government IS in Australia.Tension between transparency and datification effects is non-linear and unavoidable.We theorize seven datification patterns and discuss their societal effects.We discuss important strategic implications of open government IS. Government agencies worldwide continue their commitment to providing open data in order to increase transparency of education, healthcare and other public services. Focusing on open government information systems (IS) that provide performance-related data, this paper explores the ongoing tension between governments goal of transparency and the resulting largely opaque datification effects. Our research insights are derived from an empirical longitudinal study of a controversial open government IS called My School, currently providing performance data on almost 10,000 schools in Australia. We investigate the tension between transparency intended with schools open performance data and datification effects they create within the education system and a broader society, through the theoretical lens of Complex Adaptive Systems (CAS). Our study reveals how the tension emerges due to unpredictable use, propagation and reinterpretation of open data by more and more users. Consequently, the original meaning of data gets distorted, as these users continue to reconstruct and reinterpret data in their own contexts and adapt their behavior in pursuit of their strategic goals. We also identify and theorize seven datification patterns underlying the tension and the ways they produce various social consequences. Based on these research contributions we discuss important strategic implications for government decision makers and identify new opportunities for future research on open government IS.

As of 2015, there are over 60 million smart meters installed in the United States; these meters are at the forefront of big data analytics in the building industry. However, only a few public data sources of hourly non-residential meter data exist for the purpose of testing algorithms. This paper describes the collection, cleaning, and compilation of several such data sets found publicly on-line, in addition to several collected by the authors. There are 507 whole building electrical meters in this collection, and a majority are from buildings on university campuses. This group serves as a primary repository of open, non-residential data sources that can be built upon by other researchers. An overview of the data sources, subset selection criteria, and details of access to the repository are included. Future uses include the application of new, proposed prediction and classification models to compare performance to previously generated techniques.

In recent years there have been significant advances in online planning and decision support systems that encompass spatial and statistical analysis and visualisation services. In this paper we provide an analysis and evaluation of one particular online spatial-statistical platform, known as the Australian Urban Research Infrastructure Network (AURIN) portal. By adopting the Statistical Data and Metadata Exchange (SMDX) format, more than 500 socio-economic data products can be dynamically searched, browsed, accessed and analysed via the AURIN portal. The significance of this approach is that urban geographers can access both the datasets and statistical analysis tools via the same online Portal and thus in theory, conduct their research more efficiently. In conducting our research we specifically focus on three practical use cases which utilise machine-to-machine access from the AURIN portal to the national census data maintained by the Australian Bureau of Statistics (ABS). The first use case involves location quotient analysis in New South Wales to analyse the financial sector. The second use case focuses on understanding economic growth across industry sectors across Queensland using shift-share analysis. The third use case aims to identify spatial autocorrelation between low income and other spatial variables in South Australia. Through this in-depth case study approach we have identified there are some strengths and weaknesses with the AURIN portal. We have demonstrated that the portal can successfully search, interrogate and visualise spatial-statistical data from across Australia. We have also demonstrated that the AURIN portal can successfully conduct simple spatial-statistical analysis all via a single online platform. However, we have also found there remains significant challenges in manipulating and visualising complex multi-dimensional datasets through the portal. Yet these challenges are not considered insurmountable and further research should endeavour to address them. In conclusion, it is important to highlight that online platforms such as the AURIN portal hold significant promise as effective planning and decision support systems which can be used to better shape our cities.

This paper presents the efforts at PSFC to comply with a new directive from the White House Office of Science and Technology Policy, which requires that all publications supported by federal funding agencies (e.g. Department of Energy Office of Science, National Science Foundation) include machine-readable datasets for figures and tables (“open data requirement”). The memorandum [ 1 ] includes a specific language indicating that the data must be “stored and publicly accessible to search, retrieve and analysis”, which discourages the use of proprietary formats such as an IDL save sets and promotes the use of more openly available data formats. The manual effort required for producing a well documented, and thereby potentially useful dataset is not trivial. At PSFC, we defined a standard schema for data and metadata to comply with the new open data requirement. We chose HDF5 for our dataset file format and developed dataset file generation tools in several commonly used scientific languages (IDL, MATLAB, Python), and πScope [2,3], a python data analysis environment. A new web-based document submission and distribution system was also developed for the PSFC library to manage and distribute the collection of files associated with each manuscript.

International audience; Synthetic populations of travelers and their detailed mobility behavior are an important basis for agent-based transport simulations, which are increasingly used in transport planning and research today. To date, research based on such simulations is rarely replicable as it is based on proprietary data and tools. To foster the discussion and steer research towards reproducible transport simulations, this paper introduces a process for generating a synthetic travel demand with individual households, persons, and their daily activity chains for Paris and its surrounding region Île-de-France — entirely based on open data and open software and replicable by any researcher. The resulting travel demand is published for others to use as a comprehensive data basis for agent-based transport simulations and as a test bed for population and demand synthesis algorithms. Furthermore, it is discussed how implicit correlation structures impact the potential use cases of the synthetic travel demand for simulation and analysis purposes and how the common practice of using population samples for downstream simulations affects the results.

Traffic congestion and air pollution continue to be serious concerns, especially in large cities, and going forward, this is not sustainable. Urban transport around the world is facing challenges, such as air pollution and inefficient use of resources, that often inhibit economic development. Simply building more roads cannot address such challenges. There is a need to integrate the urban infrastructure through smart connectivity. Smart mobility, as a vital cornerstone of a smart city, will potentially reduce traffic jams, commuting times, and road crashes and create an opportunity for passengers to customize their journeys. In fact, planning smart mobility solutions is among the top challenges for large cities around the world. It involves a set of deliberate actions backed by sophisticated technologies. The different elements and dimensions that characterize smart mobility are investigated to depict the overall picture surrounding the smart mobility domain. Additionally, the trends, opportunities, and threats inherent to smart mobility are addressed. There are four segments of smart mobility that are highlighted in this paper: intelligent transport systems, open data, big data analytics, and citizen engagement. These segments are all inter-related and play a crucial role in the successful implementation of smart mobility.

This might be the most opportune time for Human Resource Management (HRM) to benefit from psychometric meta-analysis. Explosion of empirical research, often with conflicting results, hide important takeaways that can guide evidence-based applications of HRM. The science of HRM can turn to meta-analyses and meta-analytic thinking as the antidote to the so-called replication crisis afflicting social sciences in general. In this paper, we focus on issues and potential problems that may threaten the veracity and usefulness of contemporary meta-analyses in HRM. We contend that these problems must be correctly tackled for meta-analyses to realize their full potential in advancing HRM science and practice. We address the problems of identification and inclusion of all relevant effect sizes, as well as appropriate corrections for unreliability and range restriction. We offer concrete proposals to enable inclusion of unpublished, practitioner research and data in HRM meta-analyses.

Cities are restless systems. Increasing urbanisation and the cumulative growth of urban management issues are pressing local governments to provide valuable services. Information and Communication Technologies (ICTs) are established as drivers of this new wave of change in the redefinition of the relationship between the city and its citizens. Smart Cities rely strongly on strategies and solutions enabled by ICT5 involving directly local governments, citizens and communities. Open data alone, though a relevant component of these strategies, cannot provide enough incentives for the engagement of citizens necessary to establish a collaborative and open governance system; data need to be appropriately elaborated, communicated and used. The main objective of this paper is to investigate how open data together with simple and standardised elaborations and innovative visualisation techniques may be used to provide new and updated services to citizens and communities: free and readily available services based on the wealth of information 'owned' by local governments. Adopting a design science research methodology we develop and test a collaborative ICT-based tool called UrbanSense. We use it to highlight how systemic connections between citizens and city-government may be devised. On this point, the focus is on the feedforwarding of open data integrated with basic elaborations and visualisations as a means for the local government to create new and open services for citizens and communities. The use of the services prompts citizens to feedback new information in real-time to the city government. These interactions may be used to foster an open innovation ecosystem. (C) 2016 Elsevier Inc. All rights reserved

In this paper, we present the results of research on features and content of open data portals in American cities. Five scales are developed to categorize and describe these portals: the Open Data Portal Index (ODPI), Data Content Index (DCI), a compilation of the two (Overall Index), the Number of Datasets and Number of Datasets per 100,000. Regression models explaining variation between cities on these scales indicate city population as an important influence, along with participation in a regional consortium. More variation could be explained in the number of datasets model (79.8%) than in any other model. Overall, results indicate portals are in a very early stage of development and need a great deal of work to improve user help and analysis features as well as inclusion of features to help citizens understand the data, such as more charting and analysis.

Modern Chinese cities are defined from the administrative view and classified into several administrative categories, which makes it inconsistent between Chinese cities and their counterparts in western countries. Without easy access to fine-scale data, researchers have to rely heavily on statistical and aggregated indicators available in officially released yearbooks, to understand Chinese city system. Not to mention the data quality of yearbooks, it is problematic that a large number of towns or downtown areas of counties are not addressed in yearbooks. To address this issue, as a following study of Long et al. (2016), we have redefined the Chinese city system, using percolation theory in the light of newly emerging big/open data. In this paper, we propose our alternative definition of a city with road/street junctions, and present the methodology for extracting city system for the whole country with national wide road junctions. A city is defined as “a spatial cluster with a minimum of 100 road/street junctions within a 300 m distance threshold”. Totally we identify 4629 redefined cities with a total urban area of 64,144 km2 for the whole China. We observe total city number increases from 2273 in 2009 to 4629 in 2014. We find that expanded urban area during 2009 and 2014, comparing with urban areas in 2009 are associated with 73.3% road junction density, 25.3% POI density and 5.5% online comment density. In addition, we benchmark our results with the conventional Chinese city system by using yearbooks.

Smart disclosure constitutes a form of open data policy that has the objective of promoting more sustainable economies and innovation by providing consumers with information to help them make better purchasing decisions. Trust in the information regarding product and certification is crucial for the adoption and usage of smart disclosure tools that make use of such information. In this paper, we investigate the determinants of trust in sustainable product information through a survey administered in Mexico and the United States. Our results suggest that information indicating brands and certificates reputation are important factors that encourage the development of trust. Our results also suggest that additional information to verify labels does not emerge as significant predictor to induce trust. We argue that to be useful, such information should be aggregated and presented to consumers in a simple way right at their fingertips. Finally, we found that information indicating support from government agencies and endorsement from non-for-profit organizations significantly influence consumer's trusting beliefs on sustainable practices information.

Recent advances in public sector open data and online mapping software are opening up new possibilities for interactive mapping in research applications. Increasingly there are opportunities to develop advanced interactive platforms with exploratory and analytical functionality. This paper reviews tools and workflows for the production of online research mapping platforms, alongside a classification of the interactive functionality that can be achieved. A series of mapping case studies from government, academia and research institutes are reviewed. The conclusions are that online cartography's technical hurdles are falling due to open data releases, open source software and cloud services innovations. The data exploration functionality of these new tools is powerful and complements the emerging fields of big data and open GIS. International data perspectives are also increasingly feasible. Analytical functionality for web mapping is currently less developed, but promising examples can be seen in areas such as urban analytics. For more presentational research communication applications, there has been progress in story-driven mapping drawing on data journalism approaches that are capable of connecting with very large audiences.

Energy transitions are reshaping global and national energy systems and appropriate decision-making strategies are needed to drive an effective change in response to pressing global issues. Governmental institutions, industry, academia, and civil society are all participating to this global change, playing different roles. Open energy models and associated data are essential to promote open science practices, and create an effective science-policy interaction. For example, they can foster multi-disciplinary research addressing the co-evolution of energy technologies and human behaviour more transparently and, more in general, they can improve the interaction of multiple linked models and data, by improving them with respect to the current state of the art. In this paper, we present an analysis of features of open energy models and data, highlighting essential information that can be shared among communities of researchers in the energy field to foster multidisciplinary research. This information inherently embodies different key concepts and perspectives in modelling that affect both simulation and optimization processes employed for energy systems planning, design and operation. Indeed, this shared knowledge is crucial to overcome critical technical issues (e.g. end-use energy efficiency improvements, energy conversion processes, energy infrastructures operation, etc.) that may inhibit successful energy transitions. Finally, ecosystems of interacting open data and models are key assets for the development of next generation energy services and technologies, based on innovative business models in which the problem of monitoring, verifying and tracking performance transparently (at multiple levels) will be fundamental.