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At the domains of agriculture and livestock farming a large amount of data are produced through numerous heterogeneous sources including sensor data, weather/climate data, statistical and government data, drone/satellite imagery, video, and maps. This plethora of data can be used at precision agriculture and precision livestock farming in order to provide predictive insights in farming operations, drive real-time operational decisions, redesign business processes and support policy-making. The predictive power of the data can be further boosted if data from diverse sources are integrated and processed together, thus providing more unexplored insights. However, the exploitation and integration of data used in precision agriculture is not straightforward since they: i) cannot be easily discovered across the numerous heterogeneous sources and ii) use different structural and naming conventions hindering their interoperability. The aim of this paper is to: i) study the characteristics of data used in precision agriculture & livestock farming and ii) study the user requirements related to data modeling and processing from nine real cases at the agriculture, livestock farming and aquaculture domains and iii) propose a semantic meta-model that is based on W3C standards (DCAT, PROV-O and QB vocabulary) in order to enable the definition of metadata that facilitate the discovery, exploration, integration and accessing of data in the domain.

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

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

This paper investigates the factors influencing user acceptance of eParticipation systems drawing experience not only from previous eParticipation research but also from the Information Systems domain. Subsequently, a model for eParticipation acceptance is constructed by integrating the concepts from TAM literature and eParticipation evaluation literature. The model is validated through statistical analysis and hypothesis testing based on the findings of a multi-national user survey (gathering 299 reliable responses through a structured online questionnaire) and findings are discussed. Finally, a revised model is proposed called ePAM, including TAM's main concepts of Perceived Usefulness, Perceived Ease of Use, and Behavioural Intention to Use, and the four additional concepts of Technological Confidence, Integration to Governmental Processes, Perceived Facilitating Conditions and Social Influences.

Problem based learning (PBL) supports the development of transversal skills and could underpin the training of a workforce competent to withstand the constant generation of new information. However, the application of PBL is still facing challenges, as educators are usually unsure how to structure student-centred courses, how to monitor students’ progress and when to provide guidance. Recently, the analysis of educational data, namely learning analytics (LA), has brought forth new perspectives towards informative course monitoring and design. However, existing research shows that limited studies have combined PBL with LA to explore their potential in offering data-driven, student-centred courses. This paper presents a framework, termed PBL_LA, that aims to address this gap by combining PBL with LA. The framework is populated from the literature and discussions with PBL and LA experts. The paper also presents results from redesigning, delivering and assessing ten courses in different disciplines and countries using the proposed framework. Results showed positive feedback on all different testing settings, exhibiting reliability of the framework and potential across countries, disciplines and sectors.

With Open Data becoming more popular and more public bodies publishing their datasets, the need for educating prospective graduates on how they can use them has become prominent. This study examines the use of the Problem Based Learning (PBL) method and educational technologies to support the development of Open Data skills in university students. The study follows a Design Based Research approach and consists of three phases: a) examination of stakeholders' needs, b) design of an Open Data module, and c) re-design of the module based on the outcomes of its first run. The data collected throughout the three phases come from various sources, namely interviews with practitioners, focus groups with students, and tutors' reflection. The findings suggest that while the PBL method is suitable for Open Data education, special care should be taken to ensure that the potential of educational technologies is fully realised. The study concludes with design principles that aim to guide instructors on how they can incorporate the PBL method and digital tools into Open Data education effectively.The online version contains supplementary material available at 10.1007/s10639-022-10995-9.

The provision of integrated public services (IPS) results in increased citizens’ satisfaction and is a prerequisite for achieving important eGovernment goals, like one-stop government. However, currently relevant policies, research and practice is fragmented. As a result, current research practices (such as on IPS co-creation) and technologies (e.g. mobile) have not yet fed relevant policy, such as the European Interoperability Framework. In this poster, inGov project is outlined, which is a research and innovation project funded by the EU under Horizon2020 to overcome this problem. The vision of inGov project is to provide innovative ICT-supported governance models as well as mobile apps including chatbots, which will enable stakeholders’ collaboration in co-producing inclusive and accessible IPS. The project results will be piloted in Malta, Austria, Greece and Croatia. We aim to feed the results back to EU policies hence achieving alignment between policies, research and practice.

Nowadays, education is called upon to fulfill its mission in complex and fast-changing environments. According to the OECD, globalization, democracy issues, security risks, ageing societies and modern cultures are the global mega-trends affecting the way education deploys its functions. Enterprise Architecture (EA), as a key enabler of strategy formulation and business-IT alignment, could play a central role in helping ministries and educational organizations develop their full IT strategy and gain a competitive advantage. In this perspective, a systematic EA mapping study was conducted on major research databases, academic journals and conference proceedings, in order to identify the major approaches and challenges of EA in the education domain. A total amount of 60 articles from the past 10 years were found and analyzed (e.g. the EA lifecycle phase of each initiative). Our analysis reveals that most research focuses on the development of EA for Higher Education Institutions, while the primary, secondary education and lifelong learning are almost ignored. A notable research gap is also uncovered, namely, the small number of articles focusing on EA implementation and assessment even in the educational systems of countries considered pioneers in the field.

Part 3: Open Data: Social and Technical Aspects; International audience; An important part of Open Data is of statistical nature and describes economic and social indicators monitoring population size, inflation, trade, and employment. Combining and analysing Open Data from multiple datasets and sources enable the performance of advanced data analytics scenarios that could result in valuable services and data products. However, it is still difficult to discover and combine open statistical data that reside in different data portals. Although Linked Open Statistical Data (LOSD) provide standards and approaches to facilitate combining statistics on the Web, various interoperability challenges still exist. In this paper, we define interoperability conflicts that hamper combining and analysing LOSD from different portals. Towards this end, we start from a thorough literature review on databases and data warehouses interoperability conflicts. Based on this review, we define interoperability conflicts that may appear in LOSD. We defined two types of schema-level conflicts namely, naming conflicts and structural conflicts. Naming conflicts include homonyms and synonyms and result from the different URIs used in the data cubes. Structural conflicts result from different practices of modelling the structure of data cubes.

An important part of Open Data is of a statistical nature and describes economic and social indicators monitoring population size, inflation, trade, and employment. Combining and analyzing Open Data from multiple datasets and sources enable the performance of advanced data analytics scenarios that could result in valuable services and data products. However, it is still difficult to discover and combine Open Statistical Data that reside in different data portals. Although Linked Open Statistical Data (LOSD) provide standards and approaches to facilitate combining statistics on the Web, various interoperability challenges still exist. In this paper, we propose an Interoperability Framework for LOSD, comprising definitions of LOSD interoperability conflicts as well as modelling practices currently used by six official open government data portals. Towards this end, we combine a top-down approach that studies interoperability conflicts in the literature with a bottom-up approach that studies the modelling practices of data portals. We define two types of LOSD schema-level conflicts, namely naming conflicts and structural conflicts. Naming conflicts result from using different URIs. Structural conflicts result from different practices of modelling the structure of data cubes. Only two out of the 19 conflicts are currently resolved and 11 can be resolved according to literature.

Producing new generation of digital public services from open data is of major interest to policymakers, practitioners and academia in the digital government community. Recent efforts in the area of Linked Statistical Data suggest that the associated multidimensional data cubes are excellent resources that could underpin data-driven digital public services. We describe in this paper a set of tools and approach to exploiting linked statistical data produced from the integration of streams of open marine datasets for developing digital services to support Marine Rescue Operations. We also highlight the opportunities enabled through co-creation activities as well as the benefit and challenges for scaling and sustaining the initiative.

Linked Open Cube Analytics (LOCA) systems enable the performance of analytics on top of multiple open statistical data (OSD) that reside in disparate portals. We present OSD's potential and highlight the problems hampering its integration and reuse. To overcome these problems, we introduce an approach for OSD integration. The proposed approach capitalizes on the data cube model and linked data technologies to enable unified access to multiple OSD published in disparate portals. Finally, we present an online analytical processing (OLAP) browser for linked data cubes as a proof of concept of LOCA systems. Throughout this article, we also outline the challenges that need to be addressed for the wide adoption of LOCA systems.

Electronic Government (eGovernment) is currently facing significant challenges particularly due to the economic crisis which suggests governments should provide more with fewer resources. During the last few years, cloud computing emerged as a new computing paradigm with significant potential. The use of cloud computing in eGovernment has inevitably attracted significant attention from the research community. However, views of researchers and practitioners about the ways and the extent to which cloud computing can be adopted at the national level in order to facilitate eGovernment vary notably. The aim of this paper is to map cloud computing adoption by governments. For this purpose, a stage model was developed by employing the thematic analysis method based on both academic and grey literature. The proposed model is applicable at the national level and consists of four stages: ad-hoc eGov cloud solutions, cloud-based public services, eGov cloud(s) and diffusion of cloud computing in eGov. It should be noted that at this phase the authors' research is of an exploratory nature thus the model has not been validated with empirical data. Its purpose is to facilitate further research on the topic and help governments determine their progress in adopting cloud computing.

Open Data initiatives worldwide are boosting with an aim to increase transparency and contribute to economic growth. With a global annual economic potential value estimated to $3 trillion, this boost seems justified. Current progress however is not satisfactory. We believe a main reason is the lack of relevant skills and competencies. Current education and training activities are scarce and do not exploit practice-oriented learning methods such as Problem Based Learning (PBL) or novel assessment opportunities such as Learning Analytics (LA). As a result, stakeholders are missing skills related to publishing and reusing Open Data. The aim of this tutorial is to introduce to the audience Open Data education and training based on the PBL strategy and by exploiting LA for performance assessment. We will present a curriculum structure and learning content on Open Data for academia, business and the public sector that can be reused by all interested stakeholders. We will also demonstrate a university and a VET course model that have been designed in order to facilitate innovative and data-driven Open Data education and training. Finally, we will share lessons learnt from the practical application of the above in universities and businesses across multiple European countries.

Learning Analytics (LA) aims to improve the learning process by analysing learning data, and communicating the results of this analysis to both educators and learners. LA has been employed in a few cases for improving Problem Based Learning (PBL) courses but the literature has yet to discuss how PBL project work could benefit by LA. This paper presents a novel approach for enhancing PBL with LA in order to produce a new educational paradigm (PBL_LA). This paper presents a trial that took place in an engineering study in order to draw evidence-based conclusions on the PBL_LA approach. The trial run during one semester and aimed at introducing LA in PBL semester projects. For this trial, we adapted a set of software tools for supporting PBL_LA to Moodle, which is a Learning Management System (LMS), and its analytics tools. In this paper, we present this adaptation and some preliminary results of the trial. Finally, we discuss the potential of the PBL_LA approach for improving learning and teaching in this kind of engineering PBL projects.

A large number of public authorities worldwide publish descriptions of their administrative procedures (public services) in public service catalogues. These public services descriptions are often based on a national or an ad hoc description template (also referred to as model). Moreover, usually they are not provided in a machine-readable format. At the end of 2016, the European Commission released CPSV-AP 2.0 (Core Public Sector Vocabulary-Application Profile) which is a Core Vocabulary for public service modeling and publishing, incorporating Linked Data technologies. In this paper, we present an approach for using CPSV-AP 2.0 to model and publish, as Linked Data, public services descriptions of a regional public service catalogue. Concequently, we discuss the challenges that we encountered during the modeling and publishing procedure.

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.

Over the past years electronic participation (eParticipation) became a political priority worldwide. Consequently, research on the field has dramatically grown. However, eParticipation is still an unconsolidated research area that lacks generally agreed upon definitions, research disciplines, methods and boundaries. The aim of this paper is to contribute to the establishment of the eParticipation identity by investigating the scientific collaborations in the domain. The study of the nature of academic collaboration reveals the structure and the intellectual roots of the research community and the most influential authors. The approach followed in this paper includes the construction of the co-authorship network and the calculation of the social network analysis (SNA) metrics that describe the nature of the collaboration. The results revealed that eParticipation is a rather active academic field in the last decade including a high degree of collaboration and a core network of very influential researchers.

Part 5: Big and Open Linked Data; International audience; Governments are creating and maintaining increasing amounts of data, and, recently, releasing data as open government data. As the amount of data available increases, so too should the exploitation of this data. However, this potential currently seems to be unexploited. Since exploiting open government data has the potential to create new public value, the absence of this exploitation is something that should be explored. It is therefore timely to investigate how the potential of existing datasets could be unleashed to provide services that create public value. For this purpose, we conducted a literature study and an empirical survey of the relevant drivers, barriers and gaps. Based on the results, we propose a framework that addresses some of the key challenges and puts forward an agile co-production process to support effective data-driven service creation. The proposed framework incorporates elements from agile development, lean startups, co-creation, and open government data literature and aims to increase our understanding on how open government data may be able to drive public service co-creation.

Open data have tremendous potential which however remains unexploited. A large part of open data is numerical and highly structured. We concentrate on those data and capitalize on linked open data (LOD) as the underlying technology. In this paper, we present a number of tools to facilitate publishing and reusing of linked open statistical data. We propose an architecture and implementation that allows developing custom visualization and analysis tools without knowledge of LOD technologies. We further present work towards deploying relevant tools in six different countries to improve decision-making and transparency and thus support public administration.

An increasing number of governments, organisations and enterprises publish huge amounts of data as open data. The major part of this data concern statistics, e.g. data in the Open Data Portal of the European Commission (open-data.europa.eu). This data has the potential for innovative uses including performing advanced data analytics and visualizations on top of combined data that were previously isolated. From a technological point of view, statistical data can be combined by employing linked open data technologies and, specifically, the W3C RDF Data Cube vocabulary [1]. This has the potential to realise the vision of performing data analytics on top of integrated but previously isolated statistical data across the Web [2] [3]. Although several practical solutions have been developed during the last years for creating and exploiting Linked Open Statistical Data (e.g. [4] [5]), these solutions are mainly technology-driven and are not able to address the complexity and dynamics of public sector organisations and public-private collaboration with regards to (a) opening up statistical data and (b) co-producing data-driven public service. The aim of this paper is to present the Linked Open Statistical Data (LOSD) Innovation Ecosystem that enables exploiting the full potential of LOSD. This ecosystem comprises of a framework, which includes processes, roadmaps, best practices etc., and a number of open specifications and software tools for publishing and reusing LOSD. REFERENCES [1] R. Cyganiak and D. Reynolds, The RDF data cube vocabulary: W3C recommendation, W3C, Tech. Rep., January 2014. [2] A. Abello, J. Darmont, L. Etcheverry, J.-N. Mazon,F. Naumann, T. Pedersen, S. B. Rizzi, J. Trujillo, P. Vassiliadis, and G. Vossen, Fusion cubes: Towards self- service business intelligence, 9, 2, (2013), 66–88. [3] J. Perez, R. Berlanga, M. Aramburu and T. Pedersen, Integrating data warehouses with web data: A survey, IEEE Transactions on Knowledge and Data Engineering, 20, 7, (2008) 940–955. [4] E. Kalampokis, E. Tambouris and K. Tarabanis, Linked Open Cube Analytics Systems: Potential and Challenges, IEEE Intelligent Systems, 31, 5, (2016) 89-92. [5] P. E. R. Salas, F. M. Da Mota, K. K. Breitman, M. A. Casanova, M. Martin and S. Auer, Publishing statistical data on the web, International Journal of Semantic Computing, 6, 4, (2012), 373–388.

In many democratic countries, including the European Union Member States, it is increasingly important that policy makers are aware of young people’s opinion. In this respect, the European Union prioritizes the discovery of new forms of participation in democratic processes and access to political decision-making through online and offline tools. The aim of this paper is to demonstrate and evaluate the idea of promoting young people’s participation in political decision-making by brokering discussions on Twitter. Towards this end, we present a case study that utilizes and makes a preliminary evaluation of the #ask Dashboard, a software solution for brokering and analyzing discussions on Twitter between policy makers and young people. In terms of the case study, we discovered Twitter handles of 74 policy makers and 229 young people interested in ten topics. We used the #ask Dashboard to broker more than 275 discussions about these topics. So far, we have achieved 367 replies, 15 retweets and 105 likes mostly from young people. Students who participated in brokered discussions for a month performed the evaluation of the case study. Although students believe that the case study gave them the opportunity to be active on political issues, they were sceptical on whether Twitter discussion brokering successfully reaches young people and policy makers.

Electronic participation (eParticipation) refers to the use of Information and Communication Technologies to enhance political participation and citizen engagement. During the last few years, eParticipation researchers have focused on analysing the field, investigating the main challenges, studying different initiatives, and proposing relevant evaluation criteria. However, current literature lacks comprehensive studies for understanding the factors influencing success when designing eParticipation initiatives. The main aim of this paper is to determine a concrete set of success factors to be considered when designing an eParticipation initiative. For this purpose, we review the literature on eGovernment success and on eParticipation success and evaluation, and we conduct a European-wide survey and desktop research amongst eParticipation practitioners. By comparing findings from the literature and the practitioners' research, we thereafter construct a proposed model of eParticipation success factors. The model includes 23 success factors and specific activities associated with each factor. We anticipate that our model will be useful to both eParticipation researchers and practitioners interested in the design of eParticipation initiatives.

The integration of medical data coming from multiple sources is important in clinical research. Amongst others, it enables the discovery of appropriate subjects in patient-oriented research and the identification of innovative results in epidemiological studies. At the same time, the integration of medical data faces significant ethical and legal challenges that impose access constraints. Some of these issues can be addressed by making available aggregated instead of raw record-level data. In many cases however, there is still a need for controlling access even to the resulting aggregated data, e.g., due to data provider’s policies. In this paper we present the Linked Medical Data Access Control (LiMDAC) framework that capitalizes on Linked Data technologies to enable controlling access to medical data across distributed sources with diverse access constraints. The LiMDAC framework consists of three Linked Data models, namely the LiMDAC metadata model, the LiMDAC user profile model, and the LiMDAC access policy model. It also includes an architecture that exploits these models. Based on the framework, a proof-of-concept platform is developed and its performance and functionality are evaluated by employing two usage scenarios.

Journal article This paper proposes a collaborative methodology for developing semantic data models. The proposed methodology for the semantic model development follows a meet-in-the-middle approach. On the one hand, the concepts emerged in a bottom-up fashion from analyzing the domain and interviewing the domain experts regarding their data needs. On the other hand, it followed a top-down approach whereby existing ontologies, vocabularies and data models were analyzed and integrated with the model. The identified elements were then fed to a multiphase abstraction exercise in order to get the concepts of the model. The derived model is also evaluated and validated by domain experts. The methodology is applied on the creation of the Cancer Chemoprevention semantic model that formally defines the fundamental entities used for annotating and describing inter-connected cancer chemoprevention related data and knowledge resources on the Web. This model is meant to offer a single point of reference for biomedical researchers to search, retrieve and annotate linked cancer chemoprevention related data and web resources. The model covers four areas related to Cancer Chemoprevention: i) concepts from the literature that refer to cancer chemoprevention, ii) facts and resources relevant for cancer prevention, iii) collections of experimental data, procedures and protocols and iv) concepts to facilitate the representation of results related to virtual screening of chemopreventive agents. GRANATUM FP7 ICT Project - grant 270139. peer-reviewed

Part 2: Open and Smart Government; International audience; A significant part of open data provided by governments and international organizations concerns statistics such as demographics and economic indicators. The real value, however, of open statistical data will unveil from performing analytics on top of combined datasets from disparate sources. Linked data provide the most promising technological paradigm to enable such analytics across the Web. Currently, however, relevant processes and tools do not fully exploit the distinctive characteristics of statistical data. The aim of this paper is to present a process that enables publishing statistical raw data as linked data, combining statistics from multiple sources, and exploiting them in data analytics and visualizations. Moreover, the capability of existing software tools to support the vision of linked statistical data analytics is evaluated. We anticipate that the proposed process will contribute to the development of a roadmap for future research and development in the area.

– The purpose of this paper is to consolidate existing knowledge and provide a deeper understanding of the use of social media (SM) data for predictions in various areas, such as disease outbreaks, product sales, stock market volatility and elections outcome predictions. , – The scientific literature was systematically reviewed to identify relevant empirical studies. These studies were analysed and synthesized in the form of a proposed conceptual framework, which was thereafter applied to further analyse this literature, hence gaining new insights into the field. , – The proposed framework reveals that all relevant studies can be decomposed into a small number of steps, and different approaches can be followed in each step. The application of the framework resulted in interesting findings. For example, most studies support SM predictive power, however, more than one-third of these studies infer predictive power without employing predictive analytics. In addition, analysis suggests that there is a clear need for more advanced sentiment analysis methods as well as methods for identifying search terms for collection and filtering of raw SM data. , – The proposed framework enables researchers to classify and evaluate existing studies, to design scientifically rigorous new studies and to identify the field's weaknesses, hence proposing future research directions.

PurposeThe purpose of this paper is to develop a domain model for online community building and collaboration in e‐government and policy modelling.Design/methodology/approachThe authors followed a structured approach including five distinct steps: define the domain to be investigated; collect domain knowledge from both existing online community building and collaboration platforms and domain experts; analyse the gathered knowledge; develop and evaluate the domain model.FindingsA domain model was developed for community building and collaboration in eGovernment and policy modelling, including the domain definition, the domain lexicon and the conceptual models modelling its basic entities and functions of the domain. In particular, the UML class diagram was used for modelling the domain entities and the UML use cases diagram for modelling the domain functions.Originality/valueA literature search revealed a lack of domain models for online community building and collaboration, not only in e‐government and policy modelling but also in general. The proposed model provides a better understanding of the domain. It can also be used in the development of relevant platforms, leading to the reduction of software development costs and delivery time, as well as the improvement of software quality and reliability, by minimising domain analysis errors.

Part 1: E-Government Foundations; International audience; A large part of open data concerns statistics, such as demographic, economic and social data (henceforth referred to as Open Statistical Data, OSD). In this paper we start by introducing open data fragmentation as a major obstacle for OSD reuse. We proceed by outlining data cube as a logical model for structuring OSD. We then introduce Open Statistics as a new area aiming to systematically study OSD. Open Statistics reuse and extends methods from diverse fields like Open Data, Statistics, Data Warehouses and the Semantic Web. In this paper, we focus on benefits and challenges of Open Statistics. The results suggest that Open Statistics provide benefits not present in any of these fields alone. We conclude that in certain cases OSD can realise the potential of open data.