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Various reasons explain why Europe lags behind other jurisdictions in Empirical Legal Studies, especially regarding judicial decision-making. One of them is a scarcity of field data, even on the highest tiers of adjudication. Civil law jurisdictions differ markedly from their common law counterparts in the emphasis they place on individual judges. Hence very little empirical data is available on how courts are composed and how that composition changes over time. The present project attempts to change that by easing access to such data and thus lowering the threshold for empirical studies on the composition of the judiciary. It creates a resource for Empirical Legal Scholarship in order to start closing a research gap and to inspire more European scholars to engage with the empirical aspects of civil law adjudication. Several thousand pages of German court documentation were digitized, transcribed into machine-readable tables (ready to be imported into statistics software) and published online. To explore new avenues for sharing public domain data, they were also modeled as linked open data and imported into a prominent data repository.

The Programmable City is a European Research Council (ERC) and Science Foundation Ireland (SFI) funded social science research project under the directorship of the primary investigator (PI) Professor Rob Kitchin. The project is based at the National Institute for Regional and Spatial Analysis (NIRSA), National University of Ireland Maynooth. Dr Tracey P. Lauriault, is a post doctoral researcher and a member of the open data PBWG.Dr Lauriault solicited input from data experts at the AIRO, IQDA, Dublin Dashboard, NCG and the Programmable City to inform this response. These organizations rely on public data to inform research and public policy, they add value to these data by creating analytical products such as maps, charts, reports, papers and software. In addition, they collaborate with public bodies to disseminate their data, to transform data for re-use, and to make them tangible and accessible to the public. The NCG collect and manage their own data in addition to doing primary research with government data and developing open source software. The IQDA neither creates nor uses government data, but is a repository of primary date collected by social science researchers.The following briefly discusses how each of these 4 institutions and the Dublin Dashboard work with data and how intellectual property issues are addressed. This will be followed by responses to the Issues for Consideration in Section 4 of this submission.

My area of research on the Programmable City Project examines how digital data materially and discursively support and process cities and their citizens. This work falls within an emerging field called critical data studies. A recent working paper entitled Small Data, Data Infrastructures and Big Data, is an example of critically thinking about data, as is the forthcoming book The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences and my earlier work on data preservation, access, and data infrastructures. The new Big Data and Society journal also provides some theoretical orientation. Dublin and Boston, are the Programmable City’s formal study sites and I will add a little bit of Canada in that mix from time to time.The first part of my research consists of constructing ‘data landscapes’ for Dublin and Boston, by which I mean mapping out the different organisations involved in producing, analyzing, and selling data and analytic services, of which big data are an important component. A key element is reading a number of reports, papers, and consultations on the topic, as well as conducting interviews and attending industry and academic events, I will share questions and observations here in blog posts. At first, these will mainly be descriptive and comparative. However, this reading combined with empirical case studies will lead to a more critical examination of the data assemblages of big data, open data, indicators, maps and data infrastructures. I will be guided by two theoretical frameworks, Rob Kitchin’s ‘Data Assemblages’ and a modified Ian Hacking Making Up People and Spaces framework.

Background This study examined regional inequalities in dental care utilization in Japan and the association of dental care utilization with socioeconomic factors. Methods Using the Fourth National Database of Health Insurance Claims and Specific Health Checkups of Japan Open data, this ecological study analyzed 216 million pieces of aggregated data from April 2017 to March 2018. Nine indicators of dental care utilization were used: outpatient visits, outreach services, cavity fillings, pulpectomies, dental calculus removals, periodontal surgeries, tooth extractions, dental bridges, and dentures. Standardized claim ratios (SCRs) for these indicators were calculated for Japan's 47 prefectures, which were divided into three groups based on the number of dental clinics per population, average income per capita, and the proportion of university enrollments. Associations of the dental care utilization with dental supply and regional socioeconomic factors were examined. Findings The ratios of maximum to minimum of SCRs were 1·4 for outpatient visits, 19·3 for outreach services, and 17·6 for periodontal surgeries. Dental supply was positively associated with outpatient visits, outreach services, dental calculus removal, and periodontal surgeries. Regional average income and educational level were positively associated with dental calculus removals, and negatively associated with pulpectomies, tooth extractions, dental bridges, and dentures. Interpretation In Japan, regional inequalities in dental care utilization exist for periodontal care and outreach services but are smaller for urgent and substantial dental care. Regional income and educational levels appear to have influence on dental care utilization. Funding Ministry of Health, Labour and Welfare of Japan (H31–19FA1001).

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.

This paper describes MapX, a spatial data infrastructure that aims to support the sustainable use of natural resources and the environment by increasing access to the best available geospatial information and related monitoring technologies. MapX is supported by an online platform that provides authoritative spatial data at local, national and global scales, an authentication data integrity framework using a scorecard and a set of on-line tools to visualize, analyze and access geospatial data. Originally created for stakeholders involved in the extractives sector, MapX has recently expanded to other fields where spatial data can help inform stakeholder dialog, prioritization of investments and impact monitoring such as disaster risk reduction, chemicals management, biodiversity planning, renewable energy and environmental security. This paper describes the open source software stack of the platform, its key functionalities as well as its planned features and concludes by capturing some of the key lessons learned. Keywords: Spatial data infrastructure, Open data, Data integrity, Natural resources

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.

Author(s): Boeing, Geoff | Abstract: This paper was presented as the 8th annual Transactions in GIS plenary address at the American Association of Geographers annual meeting in Washington, DC. The spatial sciences have recently seen growing calls for more accessible software and tools that better embody geographic science and theory. Urban spatial network science offers one clear opportunity: from multiple perspectives, tools to model and analyze nonplanar urban spatial networks have traditionally been inaccessible, atheoretical, or otherwise limiting. This paper reflects on this state of the field. Then it discusses the motivation, experience, and outcomes of developing OSMnx, a tool intended to help address this, then reviews the literature of multidisciplinary empirical spatial network science recently conducted using it to highlight upstream and downstream benefits of open-source software development. Tool-building is an essential but poorly incentivized component of academic geography and social science more broadly. To conduct better science, we need to build better tools. The paper concludes with paths forward, emphasizing open-source software and reusable computational data science beyond mere reproducibility and replicability.

Input subsidies are a popular redistributive policy measure in many developing countries to support climate change adaptation through yield stabilization and food security in a small-farm context. Nevertheless, the evidence of the effectiveness of the programs is mixed. One main point of critique is that these programs are vulnerable to political interference leading to misuse. In this paper we assess if targeted investments in public data and information infrastructure can reduce entry points for political interference. We present a case study on Guatemala in which fertilizer distribution was accompanied by an effort of open data provision and transparency under the Zero Hunger Pact. For this case study, we used a mixed-method approach. We show that political interference was a significant determinant of fertilizer distribution in the 2012–2015 election period and to analyze the role that information and data played with this regard. The paper closes by proposing four action points that could help to harvest the potential of information, data, and digital tools to reduce political interference into public redistributive decision-making.

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

With the development of biodiversity informatics, more and more data repositories have emerged, providing massive stores of open scientific data. The use of open data is increasingly valuable for scientific research and related decision making but little is known about which topics are addressed, especially in non-English publications. Further, how this data should be cited has been called into question. The Global Biodiversity Information Facility (GBIF) is the world’s largest and most widely-used open data access platform on the occurrence of species in nature. Here, we analyze the use of GBIF-mediated data in Chinese language publications. From 623 publications containing the word “GBIF”, we obtained 324 peer-reviewed publications. Among these publications, there were 237 journal papers, 17 doctoral theses, 66 master theses, and 4 conference papers. The publications came from 114 journals and 49 research institutes and universities. We found that the most common topics addressed by these publications were species distribution, climate change, biological invasion and medicinal plants. For data citation, multiple forms coexist. 295 publications (90% of publications) only cited the name of “GBIF” in the main text, without a Digital Object Identifier (DOI). Among the 29 publications that cited GBIF in the references, 17 cited the GBIF website, 8 publications listed the URL of a species page in GBIF, and only 4 publications cited a DOI, which is what GBIF recommends. Based on a discussion on the use of GBIF-mediated data and the resulting data citation issues, this study makes three suggestions to improve data citation practice, which are applicable to data citation practice in other non-English publications.

Energy system models can contribute to the transition to low-carbon energy systems by helping devise pathways and calculate costs of energy policies and targets. As such, they are intended to provide support for energy planning, especially at the national level. Since energy system models are often developed outside urban contexts, their municipal policy relevance cannot be taken for granted. It is still unclear to what extent and how energy system models are applied to create municipal energy strategies and who uses them. This exploratory study aims to shed light on these aspects by examining the use of modelling tools and their outputs by municipal planners. We conducted interviews with practitioners from Danish municipalities and evaluated them using qualitative content analysis. This paper finds that the interviewees' use of software tools depends on how they perceive the functionality and complicatedness of the tools. The planners we interviewed prefer spreadsheet-based CO2 calculation or evaluation tools to energy system models, which in turn are used by heat supply companies, consultancies or universities. The practitioners we interviewed collaborate with model developers and users to further utilize model outputs. The incorporation of results from models or spreadsheet tools takes place mostly in the beginning of energy planning projects. This study suggests that models and the modelling practice can be improved with: open data, assumptions and models, collaboration across planning levels and improving links between technical modelling and practical implementation. Energy system models can contribute to the transition to low-carbon energy systems by helping devise pathways and calculate costs of energy policies and targets. As such, they are intended to provide support for energy planning, especially at the national level. Since energy system models are often developed outside urban contexts, their municipal policy relevance cannot be taken for granted. It is still unclear to what extent and how energy system models are applied to create municipal energy strategies and who uses them. This exploratory study aims to shed light on these aspects by examining the use of modelling tools and their outputs by municipal planners. We conducted interviews with practitioners from Danish municipalities and evaluated them using qualitative content analysis. This paper finds that the interviewees' use of software tools depends on how they perceive the functionality and complicatedness of the tools. The planners we interviewed prefer spreadsheet-based CO2 calculation or evaluation tools to energy system models, which in turn are used by heat supply companies, consultancies or universities. The practitioners we interviewed collaborate with model developers and users to further utilize model outputs. The incorporation of results from models or spreadsheet tools takes place mostly in the beginning of energy planning projects. This study suggests that models and the modelling practice can be improved with: open data, assumptions and models, collaboration across planning levels and improving links between technical modelling and practical implementation.

In this paper we propose an innovative architecture, called Mo.Re.Farming, for handling agricultural data in an integrated fashion and supporting decision making in the precision agriculture domain. This architecture is oriented to data analysis and is inspired by Business Intelligence 2.0 approaches. It is hybrid in that it couples traditional and big data technologies to integrate heterogeneous data, at different levels of detail, from several owned and open data sources; its goal is to demonstrate that such integration is feasible and beneficial in supporting situ-specific and large-scale analyses. The proposed architecture has been developed in the context of the Mo.Re.Farming project, aimed at providing a Decision Support System for agricultural technicians in the Emilia-Romagna region and to enable analyses related to the use of water and chemical resources. The architecture is fully deployed and serves as a hub for agricultural data in Emilia-Romagna; the integrated data are made available in open access mode and can be accessed through web interfaces and through a set of web services. The paper describes the architecture from the technological and functional points of view and discusses the Mo.Re.Farming project outcomes and lessons learnt.

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).

This paper introduces the EUREGIO database: the first time-series (annual, 2000-2010) of global IO tables with regional detail for the entire large trading bloc of the European Union. The construction of this database, which allows for regional analysis at the level of so-called NUTS2 regions, is presented in detail for its methodology and applications. The tables merge data from WIOD (the 2013 release) with, regional economic accounts, and interregional trade estimates developed by PBL Netherlands Environmental Assessment Agency, complemented with survey-based regional input-output data for a limited number of countries. All used data are survey data and only non-behavioral assumptions have been made to estimate the EUREGIO dataset. These two general rules of data construction allow empirical analyses focused on impacts of changes in behavior (of economies, firms, policies) without endogenously having this behavior embedded already by construction. The tables are publicly available to the research community, from the Dutch government open data website. In this project, both a regional trade database, and production and consumption data of different actors in different regions, were used. The focus is thus intentionally on the regionalization of both trade and the regional use and supply of products by different economic actors. The paper is organized around the successive steps of the data construction: (1) adjustment of WIOD, (2) regional information, (3) Construction of tables, (4) conclusions on the usefulness of this type of regional IO tables with an overview of current applications of the EUREGIO database.

A major part ofOpen Dataconcerns statistics such aseconomicandsocial indicators.Statistical dataare structured in a multidimensional manner creatingdata cubes. Recently, National Statistical Institutes andpublic authoritiesadopted the Linked Data paradigm to publish their statistical data on the Web. Many vocabularies have been created toenablemodeling data cubes as RDF graphs, and thus creating Linked Open Statistical Data (LOSD). However, the creation of LOSD remains a demanding task mainly because of modelingchallenges relatedeither to the conceptual definition of the cube, or to the way of modeling cubes as linked data. The aim of this paper is to identify and clarify (a) modeling challenges related to the creation of LOSD and (b) approaches to address them. Towards this end, nine LOSD experts were involved in an interactive feedback collection andconsensus-buildingprocess that was based on Delphi method. We anticipate that the results of this paper will contribute towards the formulation of best practices for creating LOSD, and thus facilitate combining and analyzing statistical data from diverse sources on the Web.

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 examines some of the challenges presented by the transition from ‘closed’ to open data within the municipal context, using municipal transit data as a case study. The particular lens through which this paper examines these challenges is intellectual property law. In a ‘closed data’ system, intellectual property law is an important means by which legal control over data is asserted by governments and their agencies. In an ‘open data’ context, the freedom to use and distribute content is a freedom from IP constraints. The evolution of approaches to open municipal transit data offers some interesting examples of the role played by intellectual property at every stage in the evolution of open municipal transit data, and it highlights not just the relationship between municipalities and their residents, but also the complex relationships between municipalities, residents, and private sector service providers.

Economists are increasingly using publicly shared data and code to check each other’s work, an exercise often called ‘replication’ testing. But this much-needed trend has not been accompanied by a consensus about what ‘replication’ means. If a follow-up study does not ‘replicate’ an original result, according to current usage of the term, this can mean anything from an unremarkable disagreement over methods to scientific incompetence or misconduct.This paper proposes an unambiguous definition of replication. Many social scientists already use the term in the way suggested here, but many more do not. The paper contrasts this definition with decades of unsuccessful attempts to standardize terminology, and argues that many prominent results described as replication tests should not be described as such. It argues that professional associations should formally adopt this definition, thereby improving incentives for researchers to conduct more and better replication tests.

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.

Formal identification is a prerequisite for development in the modern world. The inability to authenticate oneself when interacting with the state — or with private entities such as banks inhibits access to basic rights and services, including education, formal employment, financial services, voting, social transfers, and more. Unfortunately, underdocumentation is pervasive in the developing world. Civil registration systems are often absent or cover only a fraction of the population. In contrast, people in rich countries are almost all well identified from birth. This “identity gap” is increasingly recognized as not only a symptom of underdevelopment but as a factor that makes development more difficult and less inclusive.Many programs now aim to provide individuals in poor countries with more robust official identity, often in the context of the delivery of particular services. Many of these programs use digital biometric identification technology that distinguish physical or behavioral features, such as fingerprints or iris scans, to help “leapfrog” traditional paper-based identity systems. The technology cannot do everything, but recent advances enable it to be used far more accurately than previously, to provide identification (who are you?) and authentication (are you who you claim to be?). Technology costs are falling rapidly, and it is now possible to ensure unique identity in populations of at least several hundred million with little error.This paper surveys 160 cases where biometric identification has been used for economic, political, and social purposes in developing countries. About half of these cases have been supported by donors. Recognizing the need for more rigorous assessments and more open data on performance, the paper draws some conclusions about identification and development and the use of biometric technology. Some cases suggest large returns to its use, with potential gains in inclusion, efficiency, and governance. In others, costly technology has been ineffective or, combined with the formalization of identity, has increased the risk of exclusion.One primary conclusion is that identification should be considered as a component of development policy, rather than being seen as just a cost on a program-by-program basis. Within such a strategic framework, countries and donors can work to close the identification gap, and in the process improve both inclusion and the efficiency of many programs.

Resumen: Los datos abiertos (open data) son datos que pueden ser utilizados, reutilizados y redistribuidos libremente por cualquier persona, y que se encuentran sujetos, cuando más, al requerimiento de atribución y de compartirse de la misma manera en que aparecen. En este trabajo se describe el concepto de datos abiertos y de datos de investigación y por qué es necesario compartirlos. Se exponen las ventajas e inconvenientes de compartir datos y el procedimiento para llevar a cabo esta práctica. Se especifican los elementos que deben constituir el plan de gestión de los datos o documento que describe el tratamiento que van a recibir los datos de investigación recopilados o generados en el curso de un estudio o proyecto. Por último, se exponen las vías verde y dorada para la publicación de los datos y la necesidad de poner al servicio de los investigadores el asesoramiento técnico y las infraestructuras necesarias para que puedan llevar a cabo estas prácticas. Abstract: Open data are data that can be freely used, reused and redistributed by any person, and that are, at most, subject to the attribution requirement and shared in the same way in which they appear. This paper describes the concept of open data and research data and why it is important to share them. The advantages and disadvantages of data sharing and the procedure for carrying out this practice are discussed. We specifies the elements that should constitute the data management plan or document that describes the treatment that will receive the research data collected or generated in the course of a study or project. Finally, the green and gold routes for the publication of the data and the need to put at the service of the researchers the technical advice and the infrastructures necessary to carry out these practices are presented.

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.

To support power grid operators to detect and evaluate potential power grid congestions due to the electrification of urban private cars, accurate models are needed to determine the charging energy and power demand of battery electric vehicles (BEVs) with high spatial and temporal resolution. Typically, e-mobility traffic simulations are used for this purpose. In particular, activity-based mobility models are used because they individually model the activity and travel patterns of each person in the considered geographical area. In addition to inaccuracies in determining the spatial distribution of BEV charging demand, one main limitation of the activity-based models proposed in the literature is that they rely on data describing traffic flow in the considered area. However, these data are not available for most places in the world. Therefore, this paper proposes a novel approach to develop an activity-based model that overcomes the spatial limitations and does not require traffic flow data as an input parameter. Instead, a route assignment procedure assigns a destination to each BEV trip based on the evaluation of all possible destinations. The basis of this evaluation is the travel distance and speed between the origin of the trip and the destination, as well as the car-access attractiveness and the availability of parking spots at the destinations. The applicability of this model is demonstrated for the urban area of Berlin, Germany, and its 448 sub-districts. For each district in Berlin, both the required daily BEV charging energy demand and the power demand are determined. In addition, the load shifting potential is investigated for an exemplary district. The results show that peak power demand can be reduced by up to 31.7% in comparison to uncontrolled charging.

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.

The current paper aims to discuss the viability of digital fabrication and its optimization requirements in architectural design processes, through the analysis of an emergency sheltering solution scenario. The emergency study case is ideal to explain the necessity for optimal cost and time production, while keeping up with structural and aesthetic requirements in a semi-automated design and fabrication process. Therefore, the design of a shelter is used as a paradigm, in order to explore, implement and evaluate technologically advanced tools and techniques, such as generative design, topological optimization and additive manufacturing in small building scale. The aim of the project is not to provide a standardized design outcome, but to create a methodology which later in time may set up a dynamic solution data-base for shelter designs. The main objective relies on how optimization tools assist in producing mass customized sheltering, capable of performing efficiently in various environments, given the fact that open data and open technologies are spread across the world.

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 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.

The proliferation of disconnected information systems accumulating increasing volumes of heterogeneous data is a formidable challenge in the healthcare domain. It is necessary to address this challenge as the entire domain gradually transitions into e-healthcare and patient-centered healthcare delivery models. The lack of data integration at systems level prohibits clinicians from building a comprehensive understanding of the patient condition and further deprives patients of developing a holistic awareness of their medical conditions. In this paper, we propose an open data integration platform for patient, clinical, medical and historical data siloed across multiple health information systems. As an open platform, it can accommodate and integrate further heterogeneous data sources such as data streams generated by wearable IoT devices. As an integration platform, it facilitates the centralization of data assets. This centralization empowers every stakeholder in a patient-centered care setting to actively participate in decision-making. A range of analytics and reporting solutions, such as data warehouse, interactive dashboards, and predictive analytics tools, can be deployed upon this open data integration platform. The proposed platform is currently being adapted and implemented to address patient-centered healthcare and clinical decision support requirements in a sports injury clinic at a not-for-profit private hospital in Melbourne, Australia. Use Case based demonstration of the platform’s suitability for holistic information management, decision support, and predictive analytics justify its role in the advancement of e-healthcare.

Changes in climate conditions are expected to pose significant challenges to the food industry, as it is very likely that they will affect the production of various crops. As a consequence, decisions associated with the sourcing of food items will need to be reconsidered in the years to come. In this paper, we investigate how environmental changes are likely to affect the suitability and risk of different regions—in terms of growing certain food items—and whether companies should adapt their sourcing decisions due to these changes. In particular, we propose a three-stage approach that guides food sourcing decisions by incorporating climate change data. The methodology utilises environmental data from several publicly available databases and models weather uncertainties to calculate the suitability and risk indices associated with growing a crop in a particular geographical area. The estimated suitability and risk parameters are used in a mean-variance analysis to calculate the optimal sourcing decision. Results from a case example indicate that sourcing decisions of popular food items are likely to require significant adaptations due to changes to the suitability of certain regions.

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 presents an exhaustive and unified repository of judgments documents, called ECHR-OD, based on the European Court of Human Rights. The need of such a repository is explained through the prism of the researcher, the data scientist, the citizen, and the legal practitioner. Contrarily to many open data repositories, the full creation process of ECHR-OD, from the collection of raw data to the feature transformation, is provided by means of a collection of fully automated and open-source scripts. It ensures reproducibility and a high level of confidence in the processed data, which is one of the most important issues in data governance nowadays. The experimental evaluation was performed to study the problem of predicting the outcome of a case, and to establish baseline results of popular machine learning algorithms. The obtained results are consistently good across the binary datasets with an accuracy comprised between 75.86% and 98.32%, having the average accuracy equals to 96.45%, which is 14pp higher than the best known result with similar methods. We achieved a F1-Score of 82% which is aligned with the recent result using BERT. We show that in a multilabel setting, the features available prior to a judgment are good predictors of the outcome, opening the road to practical applications.

Objectives: Adverse drug events (ADEs) are among the top causes of hospitalization and death. Social media is a promising open data source for the timely detection of potential ADEs. In this paper, we study the problem of detecting signals of ADEs from social media. Methods: Detecting ADEs whose drug and AE may be reported in different posts of a user leads to major concerns regarding the content authenticity and user credibility, which have not been addressed in previous studies. Content authenticity concerns whether a post mentions drugs or adverse events that are actually consumed or experienced by the writer. User credibility indicates the degree to which chronological evidence from a user's sequence of posts should be trusted in the ADE detection. We propose AC-SPASM, a Bayesian model for the authenticity and credibility aware detection of ADEs from social media. The model exploits the interaction between content authenticity, user credibility and ADE signal quality. In particular, we argue that the credibility of a user correlates with the user's consistency in reporting authentic content. Results: We conduct experiments on a real-world Twitter dataset containing 1.2 million posts from 13,178 users. Our benchmark set contains 22 drugs and 8089 AEs. AC-SPASM recognizes authentic posts with F1 - the harmonic mean of precision and recall of 80%, and estimates user credibility with precision@10 = 90% and NDCG@10 - a measure for top-10 ranking quality of 96%. Upon validation against known ADEs, AC-SPASM achieves F1 = 91%, outperforming state-of-the-art baseline models by 32% (p

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.

Open government data has led to public policy innovation in pursuit of various expected benefits. One of the intended goals of open data innovation to improve transparency and accountability. However, our current understanding of open data innovation and its ability to generate transparency and accountability is limited, particularly with regard to empirical evidence. In this paper, we describe how a state agency redesigned its organizational processes around visualization tools and how such efforts helped bridge the transparency-accountability gap by enhancing the understandability and usability of open government data. We conclude that open data innovation does not stop with the adoption of an open data policy, but rather involves an ongoing cycle of improvements through which the organization responds to its various stakeholders' use of open data, thereby increasing usefulness of those datasets and, subsequently, improving overall accountability.

The advancement of natural language processing and text mining techniques facilitate automatic non-trivial pattern extraction and knowledge discovery from text data. However, text-based research has received less attention compared to image- and sensor-based research in the construction industry. Hence, this paper performs a comprehensive review to understand the current state and future insights of text analytics focusing on the data source and analysis method. This study identifies various kinds of text data sources from project documents as well as open data in the websites. In addition, the review finds that the ontology- and rule-based approach has been dominant, at the same time, recent research has attempted to apply the state-of-the-art machine learning methods. It is envisioned that there are potential advancements in construction engineering and management based on the latest text analysis methods along with the enriched data by the digital transformation.

Researcher bias occurs when researchers influence the results of an empirical study based on their expectations, either consciously or unconsciously. Researcher bias might be due to the use of Questionable Research Practices (QRPs). In research fields like medicine, blinding techniques have been applied to counteract researcher bias. In this paper, we present two studies to increase our body of knowledge on researcher bias in Software Engineering (SE) experiments, including: (i) QRPs potentially leading to researcher bias; (ii) causes behind researcher bias; and (iii) possible actions to counteract researcher bias with a focus on, but not limited to, blinding techniques. The former is an interview study, intended as an exploratory study, with nine experts of the empirical SE community. The latter is a quantitative survey with 51 respondents, who were experts of the above-mentioned community. The findings from the exploratory study represented the starting point to design the survey. In particular, we defined the questionnaire of this survey to support the findings from the exploratory study. From the interview study, it emerged that some QRPs (e.g., post-hoc outlier criteria) are acceptable in certain cases. Also, it appears that researcher bias is perceived in SE and, to counteract researcher bias, a number of solutions have been highlighted. For example, duplicating the data analysis in SE experiments or fostering open data policies in SE conferences/journals. The findings from the interview study are mostly confirmed by those from the survey, and allowed us to delineate recommendations to counteract researcher bias in SE experiments. Some recommendations are intended for SE researchers, while others are purposeful for the boards of SE research venues.

This paper describes the process of creating uncertainty-infused synthetic profiles of building performance. The synthetic profiles are utilized as a resource for evaluating the response of trained machine learning models to unseen events. Applications of the introduced method can benefit researchers and practitioners who train data-driven building models on limited historical data and is particularly useful when a physics-based model of the building is unavailable. As an original contribution, we propose a conditional deep convolutional Generative Adversarial Network (GAN) for projecting multi-dimensional time-series profiles of building performance. The proposed GAN reflects climate and operation variations into the synthetic building performance profiles, while preserving the internal consistency within the generated data. To ensure high quality synthetic profiles, this study validates the plausibility of generated data through qualitative (visualization) and quantitative (Pearson correlation, Wasserstein distance) assessments. Synthetic profiles are fed to a trained reinforcement learning model and a rule-based controller to compare their performances in the presence of uncertainty. Results show that with limited training data, a reinforcement learning model's response can be fairly sensitive to uncertainties and disturbances, insofar, some advantages over rule-based controllers may be overestimated. To ensure the reproducibility of the presented results, this study is conducted on open data and models are shared as open source.