Anne Fleur Van Veenstra
email: annefleur.vanveenstra@tno.nl

Tijs Van Den Broek
email: tijs.vandenbroek@tno.nl

Opening Moves -Drivers, Enablers and Barriers of Open Data in a Semi-public Organization

Keywords: Open Data, Open Government, Drivers, Enablers, Barriers

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Since President Obama of the United States announced his strategy for 'open government' on his first day in office [START_REF] Mcdermott | Building open government[END_REF], government organizations around the world began opening up their data. The main goals of open data for these organizations are to become more transparent and accountable to citizens [START_REF] Jaeger | Transparency and technological change: Ensuring equal and sustained public access to government information[END_REF] and to realize economic activity by stimulating re-use [START_REF] Harrison | Creating Open Government Ecosystems: A Research and Development Agenda[END_REF]. By now, also semi-public organizations, such as cultural heritage foundations, public transport organizations and research institutes, have jumped on the bandwagon. Although these organizations also see the benefits of transparency and re-use, their purpose of opening up data extends to enhance the value of their data for their own (commercial) purposes. They, for example, aim to commercially exploit their data or forge strategic partnerships with app developers.

While strong drivers are present spurring open data developments, many public and semi-public organizations find the process of opening up data cumbersome [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF]. Many challenges remain as changes need to be made to different organizational aspects [START_REF] Janssen | Open data and transformational government[END_REF]. This paper investigates the process of opening up data to find out which drivers, enablers and barriers exist in organizations that open up their data to the public. By investigating a case study of a research and technology organization (RTO) in the Netherlands, this paper looks at which factors influence the process of opening up data. This paper uses a two-pronged approach. First, based on literature, we develop a framework for identification of enablers and challenges to open data. Then, using longitudinal case study research, we identify the main drivers, enablers and barriers within the RTO. When organizations have better insight in this process, they are more likely to successfully open up their data.

The next section identifies drivers, enablers and barriers of open government and open data from literature and subsequently presents an overview that can be used for the identification of factors influencing the process of opening up data. In the third section, we present the case study methodology, followed by the case study of the RTO in the fourth section. The fifth section presents and discusses the main findings from the case study. Finally, the sixth section formulates conclusions and recommendations for further research.

Drivers, enablers and barriers of open data

Information technologies, such as the semantic web and social media, have increased the ability to collaboratively produce, share, distribute and innovate [START_REF] Harrison | Creating Open Government Ecosystems: A Research and Development Agenda[END_REF] Currently, an increasing number of government agencies around the world is publishing public sector information (PSI) such as weather forecasts, legal documents, crime statistics, geospatial data, traffic data and educational data [START_REF] Huijboom | Open data: an international comparison of strategies[END_REF][START_REF] Alani | Unlocking the potential of public sector information with Semantic Web technology[END_REF][START_REF] Bizer | The emerging Web of linked data[END_REF]. While PSI is traditionally published in a humanreadable or proprietary format on the internet (e.g. PDF or HTML) [START_REF] Alani | Unlocking the potential of public sector information with Semantic Web technology[END_REF][START_REF] Bizer | The emerging Web of linked data[END_REF], open data requires a machine-readable format (e.g. CSV, XML or RDF) and a minimum of juridical, economical and technical barriers to re-use the data for social or commercial purposes [START_REF] Dawes | Stewardship and usefulness: Policy principles for information-based transparency[END_REF][START_REF] Kalampokis | A classification scheme for open government data: towards linking decentralised data[END_REF]. After open data strategies were put in place at the national level, the revision of the European PSI directive is currently pushing the open data movement [START_REF]European Commission: Open data, An engine for innovation, growth and transparent governance[END_REF]. An extension of open data is linked data: the semantics of the data are modelled and the data can be linked to and from external data sets [START_REF] Bizer | Linked data-the story so far[END_REF][START_REF] Hausenblas | Exploiting linked data to build web applications[END_REF].

Government agencies generally have three driving forces to open up their data: transparency, innovation and efficiency [START_REF] Huijboom | Open data: an international comparison of strategies[END_REF][START_REF] Hyland | Preparing for a linked data enterprise[END_REF][START_REF] Janssen | The influence of the PSI directive on open government data: An overview of recent developments[END_REF]. Firstly, open data is seen as an instrument to increase transparency and accountability [START_REF] Jaeger | Transparency and technological change: Ensuring equal and sustained public access to government information[END_REF][START_REF] Hyland | Preparing for a linked data enterprise[END_REF], e.g. by facilitating the Freedom of Information act [START_REF] Janssen | The influence of the PSI directive on open government data: An overview of recent developments[END_REF][START_REF] Horsley | Introduction on open government information implementation[END_REF]. Predominantly in the US and the UK, citizens and NGOs push for open data to increase their ability to evaluate the process and performance of government agencies [START_REF] Harrison | Creating Open Government Ecosystems: A Research and Development Agenda[END_REF]. Secondly, open data is seen as an instrument to foster innovation [START_REF] Harrison | Creating Open Government Ecosystems: A Research and Development Agenda[END_REF]. The European Commission claims that a European open data strategy can lead to a yearly economic value of 70-140 billion euros in the European Union alone. The promise of open data is that re-use of PSI by private and public parties will grow, resulting in new commercial and public services [START_REF]European Commission: Open data, An engine for innovation, growth and transparent governance[END_REF][START_REF] Robinson | Government data and the invisible hand[END_REF]. Thirdly, open data is seen as a way to make information exchange within and outside the government agency more efficient [START_REF] Hyland | Preparing for a linked data enterprise[END_REF].

The process of opening up data, however, is perceived as cumbersome and many challenges remain [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF]. One reason is that opening up data is often presented too simplistically [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF], while, in reality, developing an open data strategy requires organizational transformation [START_REF] Janssen | Open data and transformational government[END_REF], with changes taking place on multiple levels. Research on public sector change and the implementation of information systems in (government) organizations often looks at drivers and enablers to identify those aspects that need to be in place to realize this change [START_REF] Gil-Garcia | Collaboratieve e-government: impediments and benefits of information-sharing in the public sector[END_REF][START_REF] Ebrahim | E-government adoption: architecture and barriers[END_REF]. Furthermore, also the identification of barriers is found important to deal with them in undertaking transformational efforts [START_REF] Veenstra | Barriers and impediments to transformational government: insights from literature and practice[END_REF]. Drivers, enablers and barriers are -while not the samerelated to each other and, therefore, we categorize these factors into four groups used in research or organizational change: information technology, organizational and managerial, legal and regulatory, and institutional and environmental [START_REF] Gil-Garcia | E-government success factors: Mapping practical tools to theoretical foundations[END_REF]. For every category we reviewed literature to identify the factors that influence the process of opening up data. An overview of these factors is shown in table 1. Linked data [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Bizer | Linked data-the story so far[END_REF][START_REF] Hausenblas | Exploiting linked data to build web applications[END_REF] Usefulness of the databases [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Kalampokis | A classification scheme for open government data: towards linking decentralised data[END_REF] Poor data structures [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Alani | Unlocking the potential of public sector information with Semantic Web technology[END_REF]; Legacy systems [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Alani | Unlocking the potential of public sector information with Semantic Web technology[END_REF]; Fragmented databases [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Alani | Unlocking the potential of public sector information with Semantic Web technology[END_REF]; Limited data quality [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Huijboom | Open data: an international comparison of strategies[END_REF]; Lack of standardization [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Huijboom | Open data: an international comparison of strategies[END_REF]

Organizational and managerial

Efficiency and budget cuts [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Huijboom | Open data: an international comparison of strategies[END_REF][START_REF] Hyland | Preparing for a linked data enterprise[END_REF] Data stewardship [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Kalampokis | A classification scheme for open government data: towards linking decentralised data[END_REF] Complexity of the changes to be made [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Janssen | Open data and transformational government[END_REF] Legal and regulatory PSI directive [START_REF] Dawes | Stewardship and usefulness: Policy principles for information-based transparency[END_REF]; Law enforcement [START_REF] Huijboom | Open data: an international comparison of strategies[END_REF] Privacy and data protection [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Huijboom | Open data: an international comparison of strategies[END_REF]; classified information [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Huijboom | Open data: an international comparison of strategies[END_REF]

Institutional and environmental

Transparency and accountability [START_REF] Jaeger | Transparency and technological change: Ensuring equal and sustained public access to government information[END_REF][START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Hyland | Preparing for a linked data enterprise[END_REF]; Enabling reuse [START_REF] Harrison | Creating Open Government Ecosystems: A Research and Development Agenda[END_REF][START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF]European Commission: Open data, An engine for innovation, growth and transparent governance[END_REF][START_REF] Robinson | Government data and the invisible hand[END_REF] Political leadership [START_REF] Huijboom | Open data: an international comparison of strategies[END_REF]; Value for users [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF]European Commission: Open data, An engine for innovation, growth and transparent governance[END_REF] Closed culture of government [START_REF] Huijboom | Open data: an international comparison of strategies[END_REF]; Lack of support of user feedback [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF] The drivers of the process of opening up data are linking data on the information technology layer, efficiency on the organizational layer, compliance on the legal and regulatory layer, and transparency, accountability, and enabling re-use and innovation on the institutional and environmental layer. The enablers found in literature are the usefulness of databases, including easier access to data on the information technology layer, data stewardship and management on the organizational and managerial layer, and political leadership and value for users (including economic as well as societal value) on the institutional and environmental layer. The barriers on the information technology layer are poor data structures, legacy systems, fragmented databases, limited data quality, and lack of standardization. On the organizational and managerial layer the barrier found is the complexity of the changes to be made. On the legal and regulatory layer we identified privacy, data protection, and classified information as barriers, and on the institutional and environmental layer the closed culture of government and the lack of support of user feedback were found. We will use the case study to validate and elaborate these drivers, enablers and barriers.

Case study methodology

In the previous section, we used literature to identify drivers, enablers and barriers to opening data in an organization. The second step of this research is to identify the factors influencing the process of opening up data in practice to validate, refine and/or elaborate the findings from literature. For investigating the process of opening data we use an interpretivist methodology for in-depth research, which fits the complexity of the matter [START_REF] Klein | A Set of Principles for Conducting and Evaluating Interpretive Field Studies in Information Systems[END_REF]. Using a longitudinal case study approach we aim to identify the drivers, enablers and barriers to opening data. The case selected is TNO, an RTO based in the Netherlands. As this semi-public organization is in the middle of opening up its data to the public, we were able to collect data throughout the process.

For the data collection we used a triangulation of methods [START_REF] Mingers | Combining IS research methods: Towards a pluralist methodology[END_REF]: action research, surveys and interviews with relevant stakeholders. Firstly, we were involved in the process of opening up data, supporting the relevant stakeholders during the process. Secondly, a survey was sent out twice to different stakeholders of open data within the organization (such as data owners, senior management, information officers, strategists and lawyers). These surveys aimed to capture the attitude of stakeholders with regard to the importance of open data for the RTO to identify the drivers, enablers and barriers within the organization. These surveys were sent out at different moments in time: the first survey was sent before the process of opening data commenced in September 2012 and the second survey was sent out in November 2012 after the first data sets had been opened up.

Three main questions were asked in the survey. The first question concerned the reasons (drivers) for opening up data and for implementing an open data strategy. The second question concerned the enablers of opening up the data. And the third question concerned the barriers and impediments to opening data. Options for answering the questions were provided and the respondents were asked to indicate the importance of the different options on a five-point scale ranging from very important to very unimportant. The second survey was an evolution of the first. While the main questions remained the same, some answers were added based on new insights. The response rate for both surveys approximated 50% (14 and 15 responses out of approximately 30 invitations, respectively). The second survey showed that most respondents considered themselves data owners, collectors, analysts or having a commercial role. Technology developers, legal and communication professionals were relatively underrepresented.

To validate the survey findings, we conducted nine semi-structured interviews with different types of stakeholders. These interviews were undertaken in November 2012 and in January 2013 and lasted around 45 minutes. These interviews were held with a five data owners, a director or research, a strategist and an information manager to reflect on the process of opening up their data. Central questions concerned the strategic choices for opening up data of the RTO, and their experiences with opening data, such as the main driving, enabling and impeding factors.

Case study: RTO

The RTO has a history in opening up data. The organization has long opened some of its research data to the public; for some time, the organization even was the largest contributor of datasets to the national open data portal data.overheid.nl. However, opening up data never took place in a structured manner; occasionally, datasets were opened. Therefore, during the fall of 2012 and beginning of 2013 the RTO undertook a pilot project to investigate the process of opening up data with the purpose of learning from this process. In this pilot project three datasets from different domains (transportation, working conditions, and geology) were opened up. The datasets took part in a hackathon, a workshop in which programmers can re-use the data to develop their own services. The first survey was sent to the relevant stakeholders during the preparation of the databases for the hackathon, and the second survey was sent during the publication phase. Afterwards, we evaluated the process during the interviews.

Open data strategies

The RTO is a semi-public organization and, as such, the organization is accountable to the Freedom of Information act. Furthermore, the central government has demanded the RTO to open up all research results and data that are not harmful to the privacy of individuals nor to the security of society. In addition, the RTO aims to adopt a strategy of enabling others to use data that are gathered using public funds. At the same time, as the organization competes on the European as well on the national market for research projects on a daily basis, the RTO aims to develop a business model using its data to attract new research projects. It aims to do so either by creating new business models for existing datasets, or by helping other organizations to develop an open data strategy. The organization is thus in need of an open data strategy that supports these different objectives.

In the surveys and the interviews, we asked questions about the relevance for the RTO of having an open data strategy in place. In the surveys, the respondents were asked what they consider to be the importance of a number of drivers for opening up data, based on a five-point scale ranging from very important to very unimportant. Based on the answers in the first survey, the second survey was somewhat altered. The main difference between the two surveys is that we added drivers that are related to the commercial proposition of open data. The drivers that were included in the surveys and in the interviews, as well as their results, can be found in table 2.

Enablers of open data

In both surveys and in the interviews, we asked questions on the enablers the RTO envisaged for opening up the data. Hence, we asked the respondents and interviewees to indicate which enablers would be most important in each phase. Using a five-point scale (ranging from very important to very unimportant) we asked the respondents to indicate how they value the enablers. Based on the insights gained from the first survey, the second survey was changed somewhat. The main differences between the surveys are the addition of business-related indicators, and the introduction of risks such as privacy violation and reputation damage. The results from the surveys and the interviews can be found in table 3.

Table 3: Enablers of open data in the RTO

Open data enablers

Preparation phase survey

Publication phase survey

Evaluation phase interviews

Not included in survey

The pilot that was undertaken was considered very useful to gain understanding in how to open up data

Barriers to opening data

Questions on the barriers to opening data were asked in both surveys and during the interviews. Hence, we asked the respondents and interviewees to indicate which barriers are most important in each phase. Using a five-point scale (ranging from very important to very unimportant) we asked the respondents to indicate how they value the barriers. We changed the second survey based on the findings from the first survey. The main changes were the exclusion of security risks and the inclusion of business-related barriers. The results can be found in table 4.

Findings and discussion

In all three investigations, being a semi-public organization was seen as the main driver for having an open data strategy in place. In other words: open data was considered mostly from the viewpoint of transparency and accountability in the RTO. The second most important driving force for opening up data was the business value it could generate for the organization, either by developing business models for open data, or by using open data as a sales instrument. However, as the director of research stated, there may be a tension between the transparency and the commercial objective of the organization: "Our role in the world of open data is quite interesting. We want transparency, we want to stimulate re-use of our data and at the same time, we have to make money. In a way, this makes open data even more challenging for us than for public organisations". The third driver that was found important was enabling re-use by third parties. The strategist: "The public deserves an optimal return on investment in data we collect with public funding. We collected the data with a certain objective, but it can be useful for many other objectives we cannot even think of". This shows, firstly, that semi-public organizations often need to balance more strategies regarding open data than government organizations. Further research on the drivers of open data may focus on open data business models and how these models balance commercial and social goals [START_REF] Vafopoulos | A Framework for Linked Data Business Models[END_REF][START_REF] Ferro | Business Models for PSI Re-Use: A Multidimensional Framework[END_REF][START_REF]Deloitte: Pricing Of Public Sector Information Study (POPSIS)[END_REF].

The first survey shows the importance for organizational enablers, such as undertaking pilot projects, developing a strategy for open data, management commitment, and clearly dividing roles and responsibilities in order to control the access to the data as well as the data quality. These were not mentioned in the literature and are thus added to the refined overview of drivers, enablers and barriers in table 5. In the second survey, factors related to the re-use of data were found especially important, such as privacy, standardization and publishing metadata, as well as connecting with re-users of data through setting up a data portal and partnerships with third parties. Especially factors related to building partnerships with end were not found in literature and therefore added to table 5. It thus appears that throughout the process of opening data, attention shifted from organizational issues to issues related to re-use. The interviewees corroborate this shift in their discussion on how to ensure the re-use of data. They found that randomly uploading data does not automatically lead to re-use. Instead, they consider the development of open data communities useful for stimulating re-use of data. The second finding is thus that throughout the process of open data, focus shifts from the internal organization to external users of data.

Also the importance attached to the barriers shifted between the first and the second survey. In the preparation phase barriers that were found most important were low data quality and the security risks of opening up data, for example the risk of data leaks, while the lack of interest from third parties, such as open data re-users was considered the least important challenge. After publication, security risks were replaced by privacy risks (and related reputation damage) as an important barrier to opening data. The runner-up barrier was the uncertainty of how open data would generate future revenue (added to table 5, as it was not found in literature). The interviewees still had doubts about how to balance privacy and security risks with the requirements of opening up data by the central government. A data owner: "It was difficult to decide which data to open, and on what level, because we also have to comply with the data protection act". The third finding is thus that throughout the process the more technically oriented barriers became less important as they were addressed, while factors regarding the impact of open data during re-use became more important. Still, the interviewees also maintained that a major challenge was to mobilize organizational support for an organization-wide open data strategy, and embed the open data strategy in the current data management processes (added to table 5). The director of research: "We need top-down as well as bottom-up support for open data". Linked data [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Bizer | Linked data-the story so far[END_REF][START_REF] Hausenblas | Exploiting linked data to build web applications[END_REF] Usefulness of the databases [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Kalampokis | A classification scheme for open government data: towards linking decentralised data[END_REF];

Findability of the data (technically as well as through advertisements)

Poor data structures [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Alani | Unlocking the potential of public sector information with Semantic Web technology[END_REF]; Legacy systems [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Alani | Unlocking the potential of public sector information with Semantic Web technology[END_REF]; Fragmented databases [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Alani | Unlocking the potential of public sector information with Semantic Web technology[END_REF]; Limited data quality [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Huijboom | Open data: an international comparison of strategies[END_REF]; Lack of standardization [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Huijboom | Open data: an international comparison of strategies[END_REF] Organizational and managerial Efficiency and budget cuts [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Huijboom | Open data: an international comparison of strategies[END_REF][START_REF] Hyland | Preparing for a linked data enterprise[END_REF] Data stewardship [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Kalampokis | A classification scheme for open government data: towards linking decentralised data[END_REF]; Clear implementation strategy Complexity of the changes to be made [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Janssen | Open data and transformational government[END_REF]

; Lack of business case for generating revenue from re-use; embedding open data in the strategy and work processes

Legal and regulatory PSI directive [START_REF] Dawes | Stewardship and usefulness: Policy principles for information-based transparency[END_REF];

Law enforcement [START_REF] Huijboom | Open data: an international comparison of strategies[END_REF] Privacy and data protection [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Huijboom | Open data: an international comparison of strategies[END_REF]; national security [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Huijboom | Open data: an international comparison of strategies[END_REF]

Institutional and environmental

Transparency and accountability [START_REF] Jaeger | Transparency and technological change: Ensuring equal and sustained public access to government information[END_REF][START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF] Hyland | Preparing for a linked data enterprise[END_REF]; Enabling re-use [START_REF] Harrison | Creating Open Government Ecosystems: A Research and Development Agenda[END_REF][START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF]European Commission: Open data, An engine for innovation, growth and transparent governance[END_REF][START_REF] Robinson | Government data and the invisible hand[END_REF] Political leadership [START_REF] Huijboom | Open data: an international comparison of strategies[END_REF]; Value for users [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF][START_REF]European Commission: Open data, An engine for innovation, growth and transparent governance[END_REF] Closed culture of government [START_REF] Huijboom | Open data: an international comparison of strategies[END_REF]; Lack of support of user feedback [START_REF] Janssen | Adoption Barriers and Myths of Open Data and Open Government[END_REF] By looking at the process of opening up data in a semi-public organization, we found that during the process attention shifted from organizational concerns such as ensuring publication to concerns regarding re-use of the data and forging partnerships to with end users. While many government organizations regard opening up their data mainly as a technical process, this study finds that organizational aspects, both within the organization and with third parties re-using the data are found more important for realizing open data. The case study demonstrates that open data is an opening move for a more fundamental strategic process, in which multiple barriers (such as ensuring security, privacy and re-use) need to be dealt with. Based on this case study of the RTO we thus find that open data needs to be properly embedded both in the business proposition of organizations, as well as in the technology. This study thus corroborates findings that open data requires transformational changes to the organization of (semi-)public agencies [START_REF] Janssen | Open data and transformational government[END_REF].

The survey results also reflect the tension that is felt between the different drivers of open data: between those that reflect the public function of the organization and the commercial function of the organization. At different times, different enablers and barriers become more or less important. This may be an indication that this paradox is false: openness as a strategy can shift the commercial value of these data to intelligent services based on the data, but perhaps not simultaneously. Semi-public organizations require open, networked forms of innovation that, for example, change the way they interact within a networks of stakeholders [START_REF] Janssen | Open data and transformational government[END_REF]. Further research may look into how these different objectives can be achieved. One way of investigating this matter is by applying recent insights on institutional complexity and how organizations deal with competing logics triggered by information technologies [START_REF] Greenwood | Institutional complexity and organizational responses[END_REF][START_REF] Robey | Accounting for the contradictory organizational consequences of information technology: Theoretical directions and methodological implications[END_REF], to better understand the organizational difficulties of opening up data.

Conclusion

This paper investigated the process of opening up data in a semi-public organization. Semi-public organizations differ in their objectives for opening up their data from public organizations. While government organizations mainly pursue open data because they aim to be transparent and accountable as well as enabling re-use of their data for economic purposes, semi-public organizations also aim to use open data to enhance their own strategic position and become more efficient and generate new income. Based on the case study of an RTO in the Netherlands, we found that while the drivers for open data remain the same throughout the process, the enablers and barriers shift. While internally focused, organizational factors were the main focus at the beginning of the process, attention shifted gradually to externally oriented factors, such as stimulating re-use and forging strategic partnerships with end users. Furthermore, we found that especially semi-public organizations have to deal with a variety of drivers for open data, based on their public and commercial goals, that they need to balance. Further research should thus focus on how to develop well-balanced open data business models, foster re-use and build strategic partnerships with end users around datasets as well. Furthermore, scholars can focus on how organizations can deal with these competing logics and with how organizations could align open data with their general strategy as well as their information technology.

  . These technologies gave rise to openness as an organizational strategy: open innovation, open source, open standards, open web platforms and, predominantly in the public sector, open government and open data.

Table 1 :

 1 Overview of organizational drivers, enablers and barriers to open data.

	Drivers	Enablers	Barriers
	Information
	technology

Table 2 :

 2 Drivers of open data in the RTO

	Open data drivers	Preparation	Publication phase	Evaluation phase
		phase survey	survey	interviews
	Open data should be	11 out of 14	10 out of 15	All interviewees
	part of the mission	respondents	respondents consider	consider this the most
	of the RTO	consider this	this important	important reason for
		important		opening data

Table 4 :

 4 Enablers of open data in the RTO

	Barriers to	Preparation phase	Publication phase	Evaluation phase
	opening data	survey	survey	interviews
	Security risks	9 out of 14	Not included in	Due to a lack of
		respondents	survey	experience with open
		consider this		data, the interviewees
		important		are uncertain about the
	Privacy risks	6 out of 14	11 out 15	optimal degree of
		respondents	respondents consider	openness, taking into
		consider this	this important	account privacy and
		important		security
	Lack of interest	3 out of 14	7 out 15 respondents	Respondents at the RTO
	by third parties	respondents	consider this	stated that problems in
		consider this	important	the communication
		important		between the data-owner
	Lack of a	Not included in	9 out 15 respondents	and potential re-users
	business case for	survey	consider this	was an important
	open data		important	challenge
	Reputation	10 out of 14	9 out 15 respondents
	damage to the	respondents	consider this
	organization as a	consider this	important
	result of low data	important
	quality
	Reputation	8 out of 14	11 out 15
	damage to the	respondents	respondents consider
	organization as a	consider this	this important
	result of re-use of	important
	data
	Embedding open	5 out of 14	6 out 15 respondents	Respondents indicated
	data in the	respondents	consider this	that translating open
	organizational	consider this	important	data into the existing
	strategies	important		organizational
				strategies, e.g. on
				innovation, and
				processes is an
				important barrier too

Table 5 :

 5 Refined overview of organizational drivers, enablers and barriers to open data.

	Drivers	Enablers	Barriers
	Information
	technology


Pedro Rui

Lourenço

Rui Pedro Lourenço

Open Government Portals Assessment: A Transparency for Accountability Perspective

Keywords: Dataset portals, transparency, accountability, assessment

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The idea of open government is not new: Linders and Wilson [START_REF] Linders | What is Open Government? One Year after the Directive[END_REF] trace the origins of open government back to the Swedish Freedom of the Press Act in 1766. But perhaps the most notorious and visible recent effort to promote the principles of open government can be attributed to the Open Government Directive (OGD) [START_REF] Orszag | Open government directive. Memorandum for the Heads of Executive Departments and Agencies[END_REF], issued by the Obama Administration in 2009. Open government strategies such as the OGD include as one objective the promotion of governmental transparency which involves the "publication of government data intended to support accountability and reuse for social or economic value" [START_REF] Linders | What is Open Government? One Year after the Directive[END_REF]. In this work we are particularly interested in transparency for public accountability, that is, the disclosure of data which provides citizens and other public stakeholders "with the information needed for judging the propriety and effectiveness of the conduct of the government" [START_REF] Bovens | Analysing and Assessing Accountability: A Conceptual Framework[END_REF]. Dataset portals (described as "services supporting the location of public sector information (PSI)" [START_REF] Shadbolt | Linked Open Government Data: Lessons from Data.gov.uk[END_REF]) such as Data.gov and Data.gov.uk quickly became flagship initiatives of open government, but they are not isolated initiatives. Data.gov, for instance, provides an extensive list of other open data sites, as part of open government initiatives, including 39 concerning US states, 39 related to US cities and counties, 40 maintained by other countries and 140 from international regions 1 . Huijboom and Broek [START_REF] Huijboom | Open data: an international comparison of strategies[END_REF] have also identified and analyzed open data strategies from five different countries (Australia, Denmark, Spain, the United Kingdom and the United States) which include the financing of open data portals.

Despite this widespread movement towards the promotion of governmental open data (dataset) portals, two questions arise: Are these portals effectively making data available for accountability (transparency) purposes? How can that be assessed? The aim of this paper is to identify and propose a set of requirements as part of an assessment framework that may be used to answer these questions.

The lack of evaluation guidelines regarding the implementation of open government principles was emphasized by Darbishire [START_REF] Darbishire | Proactive Transparency: The future of the right to information?[END_REF] who recognized that "limited examples of monitoring of proactive disclosure by official or oversight bodies" were found and recommended that monitoring of proactive disclosure initiatives should be put in place [START_REF] Darbishire | Proactive Transparency: The future of the right to information?[END_REF]. Even the OGD did not provide an assessment framework on how to evaluate US agencies plans and, more importantly, on how to evaluate their implementation and results [START_REF] Bertot | Measurement of Open Government: Metrics and Process[END_REF]. More recently, Huijboom and Broek [START_REF] Huijboom | Open data: an international comparison of strategies[END_REF] noted that, from the five countries analyzed in their work, only the UK and the US have evaluated their open data policies but none of them have assessed their economic and social impacts. Harrison et al. [START_REF] Harrison | Open government and e-government: Democratic challenges from a public value perspective[END_REF] also acknowledge the lack of frameworks and procedures to assess open government.

Regarding the actual data being disclosed, McDermott [START_REF] Mcdermott | Building open government[END_REF] recognized that, in what concerns Data.gov, "no one has done an overall assessment of the data sets". According to Harrison et al. [START_REF] Harrison | Open government and e-government: Democratic challenges from a public value perspective[END_REF], even if the data disclosed is "both usable and of high quality", simply making data available does not necessarily mean that government is being more transparent and accountability of public agents is facilitated. As a consequence, these authors developed and proposed a "public value assessment tool" intended to guide assessment exercises and record the analysis product. In a similar effort, Reggi and Ricci [START_REF] Reggi | Information Strategies for Open Government in Europe: EU Regions Opening Up the Data on Structural Funds[END_REF] analyzed 434 EU Cohesion policy and Structural Funds related datasets, and classified them into three clusters according to their characteristics concerning the policy principles of "stewardship" and "usefulness" [START_REF] Dawes | Stewardship and usefulness: Policy principles for information-based transparency[END_REF].

Another strand of assessment research dedicated to technical aspects of data disclosure, that is, the way data is being disclosed within dataset portals. Such research is usually focused on the respect for principles of Open Government Data [START_REF]Government Working Group: 8 Principles of Open Government Data[END_REF] and Linked Open Government Data [START_REF] Shadbolt | Linked Open Government Data: Lessons from Data.gov.uk[END_REF]. In this sense, Kalampokis et al. [START_REF] Kalampokis | A classification scheme for open government data: towards linking decentralized data[END_REF] recognize two broad technological approaches to classify Open Government Data and distinguish between "downloadable files" and "linked data". Despite all research efforts described, no dataset portals assessment framework were found, specifically in what concerns the ability of those portals to promote transparency and allow for accountability of public agents. The goal of this paper is therefore to provide such a framework in the form of requirements derived from the analysis of transparency research literature. Even before the emergence of open gov-ernment related dataset portals, such literature used assessment models and procedures that relied on individual web site analysis to evaluate the transparency degree of public entities. The assessment models used, and their underlying informational and procedural requirements, should apply to the new context of 'dataset portals' by defining a minimum set of requirements dataset portals must fulfill to respond to the needs of citizens and other stakeholders when conducting accountability processes. Therefore, these requirements form the basis of our assessment framework proposal.

The remainder of this paper is structured in the following way. Section 2 briefly describes the transparency assessment literature considered. Section 3 analyses those papers according to 4 relevant dimensions which will be considered in section 4 to propose a set of requirements that compose the assessment framework. Some final remarks are presented in section 5 (conclusions).

Transparency assessment literature

The proposed framework assumes that dataset portals, in order to promote government transparency and accountability, should (at least) fulfill the requirements identified in the transparency assessment literature. Table 1 presents the list of research papers considered (the more recent works are presented in the end), the majority of which address internet-based transparency, with one exception [START_REF] Coy | The public accountability index: crafting a parametric disclosure index for annual reports[END_REF] (which considers paper-based disclosure). The list does not result from a formal literature review process, but rather from an ongoing research effort.

Analysis

To analyze these papers two important dimensions were considered: the type of public entities studied and the information types sought-after. The sequence by which these studies define those two dimensions characterize different information seeking scenarios. Once these two dimensions are established, studies also consider in their assessment technical and qualitative aspects of the data being disclosed. Only the latter will be considered in our analysis.

Entities covered

Several types of entities were addressed by these studies, including different types of local, regional and central authorities, and universities. Clearly, local governments are the most chosen research target. Some studies address a single type of entities while others consider several (although similar) entity types. In some cases the authors perform international comparisons, although most research concentrates on entities from a single country. The number of entities considered in each study varies. Some studies consider all entities of a particular type (for entity type characterization purposes), while others select a sample of entities of a particular type based on some characteristic criteria (e.g. [START_REF] Cucciniello | Assessing Transparency in Government: Rhetoric, Reality and Desire[END_REF]) or using a random selection process (e.g. [START_REF] Stewart | Governance Disclosure on the Internet: The Case of Australian State Government Departments[END_REF]).

Information types covered

Noticeably, the most sought-after data is related to financial, budgetary and management information (including performance indicators). More detailed data concerning public spending (still with a financial impact), such as government contracts [START_REF] Armstrong | Providing a clearer view: An examination of transparency on local government websites[END_REF] and public funds management programs [START_REF] Reggi | Information Strategies for Open Government in Europe: EU Regions Opening Up the Data on Structural Funds[END_REF], is also considered. Cucciniello et al. [START_REF] Cucciniello | Assessing Transparency in Government: Rhetoric, Reality and Desire[END_REF] add to this financial dimension three other types of information associated with a political, service delivery and institutional dimension. The latter is based on the required items suggested in the Web Site Attribute Evaluation System (WAES). This was developed by the Cyberspace Policy Research Group ( [START_REF] Cyprg | Web attribute evaluation system (WAES)[END_REF] apud [START_REF] Pina | Is e-Government leading to more accountable and transparent local governments? An overall view[END_REF]) and was originally used for evaluating US federal websites [START_REF] Pina | Is e-Government leading to more accountable and transparent local governments? An overall view[END_REF]. This institutional dimension is adapted and used on several works ( [START_REF] Cucciniello | Assessing Transparency in Government: Rhetoric, Reality and Desire[END_REF][START_REF] Pina | Are ICTs Improving Transparency and Accountability in the EU Regional and Local Governments? An Empirical Study[END_REF][START_REF] Pina | Is e-Government leading to more accountable and transparent local governments? An overall view[END_REF][START_REF] Pina | Is E-Government Promoting Convergence Towards More Accountable Local Governments[END_REF]) and it refers to items such as ownership (of web site), contacts, or activities (static information [START_REF] Cucciniello | Assessing Transparency in Government: Rhetoric, Reality and Desire[END_REF]). Governance information (such as strategy, planning and capability building) is also considered in the studies [START_REF] Stewart | Governance Disclosure on the Internet: The Case of Australian State Government Departments[END_REF]. Finally, Grimmelikhuijsen and Welch [START_REF] Grimmelikhuijsen | Developing and Testing a Theoretical Framework for Computer-Mediated Transparency of Local Governments[END_REF] used the process and event transparency concepts proposed by Heald [START_REF] Heald | Varieties of Transparency[END_REF] and search for data concerning decision-making processes, policy content, and policy outcomes.

From the different main types of information identified in our survey it is possible to conclude that the nature and type of the information sought-after in transparency studies is not always 'compatible' with the dataset format: sometimes researchers look for mainly textual reports with little supporting (numerical) data. Also, some data is clearly produced in continuum (such as contract related data) while other is produced at a single point in time (such as budgets).

3.3

Information seeking strategies

In general, research papers address a particular type (universe) of entities and aim at assessing the degree of web-based transparency of all or part of entities belonging to that universe by seeking on their individual web sites for relevant information items (and the way they are disclosed). The work by Reggi and Ricci [START_REF] Huijboom | Open data: an international comparison of strategies[END_REF] is somehow distinctive in the sense that it begins by identifying the type of information sought after (European Regional Development Fund and the European Social Fund), and then identifies the entities that have the responsibility, at national level, to disclose it. Although the common goal of these studies is to assess transparency, some aim to perform a descriptive analysis of the information disclosed (which entities do not report the expected items, etc) [START_REF] Reggi | Information Strategies for Open Government in Europe: EU Regions Opening Up the Data on Structural Funds[END_REF][START_REF] Stewart | Governance Disclosure on the Internet: The Case of Australian State Government Departments[END_REF], others aim to quantify the level of transparency and build a disclosure index (e.g. [START_REF] Bolívar | E-Government and Public Financial Reporting: The Case of Spanish Regional Governments[END_REF]), while still others identify factors (disclosure incentives) that account for the levels of measured transparency (e.g. [START_REF] Cárcaba García | Determinants of Online Reporting of Accounting Information by Spanish Local Government Authorities[END_REF]). Despite some differences, the authors usually adopt the following general procedure:

1. Select and define the type of entities to be assessed; 2. Identify a subset or the entire universe of entities of that particular type. In the former case, selection may be based on a specific characteristic (such the number of inhabitants of a municipality [START_REF] Gandia | Determinants of web site information by Spanish city councils[END_REF]) or entities may be randomly selected (e.g. [START_REF] Stewart | Governance Disclosure on the Internet: The Case of Australian State Government Departments[END_REF]);

3. Determine and characterize the information item(s) expected to be disclosed by each of the entities considered; 4. Define other analysis dimensions of data availability, including desired qualitative characteristics, and technical characteristics of data disclosure and/or web site; 5. Define the global assessment model which considers all analysis dimensions, including determining weights for the different criteria and developing a scoring methodology (if applicable); 6. Visit each entity web site, and collect the data required by the assessment model to characterize or evaluate the entity. 7. Describe the results or compute the disclosure indexes according to that model.

As mentioned before, a slightly different information seeking scenario would start by identifying the required information types (step 3) and only then determine which types of entities should be assessed (steps 1 and 2): typically those responsible for the information considered. Dataset portals assessment frameworks must take into consideration these information seeking scenarios and other possible variations.

3.4

Qualitative requirements of data

The assessment exercises described in the literature usually consider in their models qualitative characteristics of the data available and technical characteristics of the web sites. The required qualitative characteristics of data are commonly selected based on international best practices (e.g. Caba Pérez et al. [START_REF] Caba Pérez | Citizens' access to on-line governmental financial information: Practices in the European Union countries[END_REF]) and most commonly include the requirements for completeness, timeliness, understandability or clarity, comparability, relevance and reliability [START_REF] Caba Pérez | Citizens' access to on-line governmental financial information: Practices in the European Union countries[END_REF][START_REF] Bolívar | Cultural contexts and governmental digital reporting[END_REF]. Some of these qualitative requirements are also considered as part of the eight principles of Open Government Data [START_REF]Government Working Group: 8 Principles of Open Government Data[END_REF].

Assessment framework requirements

Based on the analysis presented in previous section, it is now possible to propose a set of requirements that form an open government dataset portals assessment framework. These requirements will be associated with each of the three main topics addressed in the previous section (entities covered, information types, and information seeking strategies). Whenever relevant, references will be made to desired qualitative characteristics of data although strictly technical aspects of data provision, such as data formats or linked data principles will not be considered.

Entities covered

According to the literature analyzed, dataset portals, in order to fulfill transparency informational needs, should address the whole range of entity types possible in each country institutional arrangement. Portals curated by international organizations, might even be expected to cover entities from different countries. In this respect, users should be able to assess the completeness of the data provided. R1: Portals should present a master list of all entity types covered (regardless of the entities that actually have provided datasets).

This means that dataset portals should cover the entire set of entities of a particular type. Again, in this context, users should be able to assess the completeness of the data provided. Some portals do indeed present a list on entities which have actually disclose datasets, but that information is not sufficient to identify which entities should provide the same information but are not doing so. R2: Portals should present a master list of entities belonging to each one of the types covered (regardless of the entities that actually have provided datasets).

Search mechanisms alone do not meet the requirements identified in the transparency literature. A browsing mechanism, based on the master list of entities, offers a more reliable way to determine which entities, from each type, do not provide data. R3: Portals should present a list of disclosed datasets associated with each entity.

Information types covered

To fulfill transparency informational needs recognized in the literature, portals should provide all datasets related to, at least, the whole range of information types identified in the previous section. These datasets should be clearly identified and associated with a specific type of transparency item. It should also be possible to easily determine which information items are not being covered by the datasets or are not being provided by a certain entity (for instance).

R4: Portals should provide a master list of accountability-oriented information items, and related datasets, which should be available (regardless of the actual provided datasets).

Some portals allow users to characterize their datasets using tags such as "transparency" or "accountability" (e.g., Data.gov) which are not consistently used and do not provide a complete view of all transparency/accountability related information provided. This master list should clearly distinguish the different types of transparency datasets (accountability or re-use).

R5: Portals should associate each dataset disclosed with each information type defined.

The literature also showed the importance to access institutional (entity characterization) information needed to allow for characteristic-based selection of entities. R6: Portals should provide, for each entity, a minimum of institutional (characterization) information regardless of the other datasets provided by the entity.

Transparency literature also showed the importance of 'non-dataset compatible' information (such as textual reports) to accountability. In the context of a broader open government policy, portals should be adapted or complemented to cope with such type of information, despite the limitations of their current specific dataset nature. R7: Portals should be able to accommodate 'non-dataset compatible' transparency-related information items (such as reports and other documents).

The literature also revealed the importance of clearly knowing the date and time associated with each dataset, and to follow the evolution of indicators (for instance). Sometimes, entities provide several datasets concerning the same information item but related with different time periods (years, for instance).

R8: Portals should explicitly indicate, for each information type, which update periodicity is expected for individual datasets.

Also, without and explicit dataset time organization structure, it becomes very difficult to identify missing datasets from a particular entity at a given time, and to perform longitudinal analysis concerning a particular entity or information item. R9: Portals should explicitly associate each dataset with a specific time/period tag.

Whenever possible and relevant, entities should provide the data as it is created and not only on a periodic (yearly) base, thus allowing for real-time transparency [START_REF] Heald | Varieties of Transparency[END_REF]. The timeliness of the data is one important qualitative characteristic valued in the literature. R10: Portals should be prepared to allow datasets to be updated in real-time, thus reflecting the circumstances at each moment.

The assessment exercises reported in the literature rely on the comparability of data provided by different entities. This implies that for each information type, a common set of characterizing elements must exist to allow for such comparability. R11: Portals should define the minimum set elements that must be disclosed by each entity type, for each information type.

This set of characterizing elements should also take into consideration the principles of completeness and relevance, two qualitative characteristics of data identified in the literature.

4.3

Information seeking strategies Different possible information seeking strategies were identified from the literature. Portals, as key components of open government initiatives, should support citizens to pursue accountability regardless of the way information is sought-after.

R12: Portals should adopt and make visible an overall organization structure, according to the requirements previously presented, and provide related browsing and selection tools.

This organization structure and corresponding tools should facilitate the identification of non-reporting entities, missing datasets by some entities and longitudinal studies (for instance).

Conclusions

There is a significant body of scientific literature dedicated to the assessment of webbased public entities transparency. Most of this literature considers the data provided in each entity individual web site as a proxy for its transparency. But the emergence of dataset portals, in the context of open government programmes, has an impact on the way transparency is 'traditionally' assessed: from now on it is necessary to consider that at least part of the data expected to be disclosed in an entity web site might be available through 'collective' dataset portals. This means that the procedures adopted by this assessment literature might no longer be totally adequate.

Also, dataset portals were created to meet the objectives of open government strategies, including promoting transparency and therefore facilitating the accountability of public agents. However, no evidence was found in the open government literature that the way data is provided by these portals, and the data itself, might in fact contribute to meet such objective. Research has been mainly focusing on open government strategies formulation, implementation and impact assessment, and technical aspects of data availability. One matter seems clear: disclosing huge amounts of datasets does not necessarily equate to more transparency and does not necessarily facilitate accountability.

Despite the shortcomings of the assessment methodologies identified in the literature to deal with the new portal reality, they may still be considered as proxies for the way information is sought-after in the context of accountability processes. Therefore, this work analyzed the 'traditional' transparency assessment literature and identified a set of requirements that dataset portals need to fulfill in order to contribute to the transparency of public entities and allow for the accountability of public officials. Such requirements concern the type of entities covered by dataset portals, the type of information types provided, the information seeking strategies supported and some qualitative aspects regarding the data provided.

Further research is needed to complement the proposed set of requirements by considering, for instance, qualitative aspects of data delivery and the informational needs of 'real' citizens in what concerns public accountability. The next step is to fully develop an assessment model and procedure based on the requirements identified which could be used to evaluate dataset portals and other elements of open government strategies. The ultimate goal is to give governments the information they need to improve their open government strategies and therefore contribute to increase transparency and allow for better accountability.

Table 1 .

 1 Transparency assessment literature

	adfa, p. 4, 2011.
	© Springer-Verlag Berlin Heidelberg 2011

Spanish regional governments Economic and financial information; Budgetary information; Performance indicators Pina et al. [26] 319 EU regional and local governments (76 cities and 242 regions or counties of the first 15 EU countries) Transparency (Ownership, Contacts, Organizational or operational information, Issues, Citizen consequences, Security and Privacy) adfa, p. 6, 2011. © Springer-Verlag Berlin Heidelberg 2011

http://www.data.gov/opendatasites (last visited on 3/3/2013).

Acknowledgements. This work was partially supported by Fundação para a Ciência e a Tecnologia (FCT) under project grant PEst-C/EEI/UI0308/2011.


Evangelos Kalampokis

Efthimios Tambouris
email: tambouris@uom.gr

Konstantinos Tarabanis

Linked Open Government Data Analytics

Keywords: Open government data, linked data, statistics, data analytics

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Open data refers to data that "can be freely used, reused and redistributed by anyone" [START_REF]Open Knowledge Foundation: The Open Data Handbook[END_REF]. During the last years, an increasing number of governments and public authorities have started to open up their data. The so-called Open Government Data (OGD) movement has resulted in the launch of numerous OGD portals that aim at providing a single point of access for governmental data [START_REF] Kalampokis | A Classification Scheme for Open Government Data: Towards Linking Decentralized Data[END_REF].

OGD have promised to provide a variety of benefits to society such as to increase government transparency and accountability, stimulate innovation, contribute to economic growth and improve administrative processes [START_REF] Janssen | Benefits, Adoption Barriers and Myths of Open Data and Open Government[END_REF]- [START_REF] Tammisto | Definition of Open Data Services in Software Business[END_REF]. However, a recent empirical study concluded that a simplistic view is often adopted with regards to open data, which automatically correlates the publicizing of data with use and benefits [START_REF] Janssen | Benefits, Adoption Barriers and Myths of Open Data and Open Government[END_REF]. In practice however, gaining access to raw data, placing it into a meaningful context, and extracting valuable information is extremely difficult. As a result, during the last couple of years different solutions have been developed to support the whole lifecycle of OGD reuse i.e. data discovery, cleaning, integration, browsing and visualization (e.g. [START_REF] Böhm | Linking Open Government Data: What Journalists Wish They Had Known[END_REF]- [START_REF] Shadbolt | Linked Open Government Data: Lessons from Data[END_REF]). However, despite the huge efforts that have been put into supporting both OGD publishing and consuming, the OGD movement has not yet realized its expected potential.

The difficulty in exploiting open data seems surprising if we consider the huge importance data have in modern societies. Indeed, during the last years, businesses, academia and government employ various data analytics methods on their own data with great success. For example, business intelligence methods are employed by enterprises to help them survive in the global economy [START_REF] Jourdan | Business Intelligence: An Analysis of the Literature[END_REF]. In addition, evidence based policy-making relies on data analytics to assist policy makers in producing better policies [START_REF]What works? Evidencebased Policy and Practice in Public Services[END_REF]. Finally, academia employ data analytics to test hypotheses, understand patterns, predict future points, estimate hidden parameters etc. in various domains and problem areas [START_REF]Computational Models of Political Economy[END_REF].

We claim that the real value of OGD will unveil from performing data analytics on top of combined statistical datasets that were previously closed in disparate sources and can now be linked in order to provide unexpected and unexplored insights into different domains and problem areas.

For this purpose, we deem that the linked data paradigm must be first adopted for constructing the technical infrastructure that is essential for employing data analytics in a decentralized manner on the Web. The term linked data refers to "data published on the Web in such a way that it is machine-readable, its meaning is explicitly defined, it is linked to other external datasets, and can in turn be linked to from external datasets" [START_REF] Bizer | Linked data -the story so far[END_REF]. Currently the most promising implementation of linked data is based on Semantic Web philosophy and technologies but in contrast to the fullfledged Semantic Web vision, it is mainly about publishing structured data in RDF using URIs rather than focusing on the ontological level or inferencing [START_REF] Hausenblas | Exploiting linked data to build web applications[END_REF].

The objectives of this paper is to (a) introduce the concept of data analytics on top of distributed statistical linked OGD (b) describe the technical prerequisites that will enable the effective exploit of statistical OGD in data analytics and (c) demonstrate the end-user value of the linked OGD analytics approach based on a case study that is related to the general elections of the United Kingdom (UK) using data from data.gov.uk, the official UK's OGD portal.

The remaining of the paper is organized as follows. In section 2 we describe the concept of data analytics based on open and linked government data. In section 3 we describe the technical prerequisites for realizing this vision. Section 4 presents the results of an analysis use case related to the UK general elections. Finally, in section 5 the related work is presented while in section 6 conclusions are drawn along with future work.

The concept of Linked Open Government Data Analytics

A big portion of Open Government Data (OGD) concerns statistics such as population figures, economic and social indicators. Major providers of statistics on the international level include Eurostat1 , World Bank2 , OECD 3Factbook 4The availability of accurate and reliable statistical OGD in formats that enable easy reuse and combination can provide new potentials to businesses, academia and governments. The combination of statistical OGD that refer to different domains and is published by different public authorities with other data (e.g. enterprise's own data) could enable creating and evaluating models that were previously hard or even impossible to develop.

. Moreover, public agencies at all administrative levels collect, produce and disseminate statistical data through their OGD portals. Accurate and reliable statistics provide the solid ground for developing models that could support academia to better understand the world and businesses to make better decisions. These models enable the identification of patterns, prediction of future points and estimation of hidden parameters.

The potential of performing analytics on top of combined OGD and third party data could be summarized in the following user stories: "As a business manager I want to be able to combine enterprise's data with accurate and timeliness demographics, economic and social indicators in order to make better decision regarding business operations and strategies". For instance, the correlation of product sales with economic and social indicators in various locations can reveal valuable information regarding consumer behaviour, hence supporting marketing or logistic departments.

"As a researcher I want to be able to combine statistical data from disparate sources and domains in order to empirically identify novel hypotheses or test existing ones with more data as well as to understand patterns, predict values and estimate hidden parameters". For instance, developing models that integrate biodiversity information from a variety of datasets to assess biodiversity change, including remote sensing and in situ observations. "As a policy maker I want to be able to combine statistical data regarding economic and social indicators in order to identify evidences regarding policy interventions and hence evaluate policies." For instance, the correlation of data about education, unemployment and criminality in different geographical or administrative units and different time intervals could support or challenge existing policies.

However, putting together statistics in a meaningful manner so that to enable the creation of added value is usually a labour intensive task that introduces significant burdens to data users. It requires the manual discovery, collection, cleaning, transformation, integration, visualization and statistical analysis of data. The vision that we present in this paper suggests shifting this effort from the end-users to the data providers enabling this way the easier and wider reuse of statistical OGD in various problem areas. As a result, statistical OGD will be openly available for reuse in a way that will facilitate the performance of data analytics on top of combined open data and thus will enable the creation of useful information in an easy and cost effective manner.

For this purpose, the data should be provided in such a way that facilitates the whole lifecycle of statistical data reuse:

Data discovery: Metadata that describes statistical data should facilitate the effective and easy identification of datasets that could be combined for statistical analysis. This includes the identification of datasets that share common joint points (i.e. parliament constituencies, local authorities, schools etc.) and thus allow for further analysis. For instance, it is not feasible to correlate schools' expenditures with hospitals' inpatients because there are no joint points between them. In addition, it includes the identification of datasets that describe variables measured using similar categories of units e.g. continuous or discrete. Finally, the metadata should enable the identification of variables of a specific category or class.

Data cleaning: The statistical data should be of high quality i.e. timely, accurate and relevant.

Data linking: The data should be linked in order to enable analysis in different levels of granularity e.g. unemployment that refer to parliamentary constituencies' level with criminality that refer to local authorities' level. Data linking should also facilitate the disambiguation of entities, concepts, units, codes etc. that are described in the datasets.

Data visualization and statistical analysis:

The data should enable easy visualization and statistical analysis. Towards this end, the provided data should facilitate the automatic identification and matching of the unit of measurement of the described variables. This will allow the automatic visualization and selection of the method to be used for the statistical analysis. For example, in the case of continuous units data analytics could be performed through linear regression analysis while in the case of discrete unit (i.e. categorical measures) through a classification analysis method such as logistic regression.

Technical Prerequisites

In order to realize the vision of linked open government data analytics as it was described in the previous section a number of technical prerequisite should be met.

The main assumption is that the statistical data is published as linked data in order to enable the combination of datasets that are published in disparate sources on the Web. This includes publishing the data following the linked data principles as well as establishing typed links at the instance and schema level. However, both the publishing and linking of the statistical data should be made in a statistically rigorous manner. Statistical data should be modelled as data cubes that describe data in a multi-dimensional fashion. Towards this end, the RDF data cube vocabulary can be employed [START_REF] W3c | The RDF Data Cube Vocabulary[END_REF]. This vocabulary is currently a W3C candidate recommendation for modelling multi-dimensional data, such as statistics, as RDF and thus adhering to the linked data principles. Although more vocabularies have been proposed in the area (e.g. the Open Cube vocabulary [START_REF] Etcheverry | Enhancing OLAP Analysis with Web Cubes[END_REF]), RDF data cube is the most popular and stable one.

Data cubes are characterised by their dimensions, their measures and possibly by additional attributes. The dimensions in the cube define what each observation is about. For instance, a cube related to unemployment might have the following dimensions: location, time period, gender and age group. An individual observation refers to a unique value along each of these dimensions. In the unemployment example, a single observation would define the unemployment rate of women (gender) between 22 and 30 years old (age group) that live in Kensington (location) for the first six months of 2009 (time period). The possible values for each dimension are taken from a code list. A code list is a controlled vocabulary such as a list of constituencies in the UK or possible age groups. The measure defines what kind of quantity is being measured e.g. the unemployment rate. An observation could also have attached attributes, which facilitate the interpretation of the observation value e.g. the unemployment rate is measured as percentage and is an estimation of the Ministry of Labour.

Moreover, the data that has been modelled based on data cubes should be also connected through typed links (e.g. owl:sameAs property) at the instance level in the following joint points:

• Dimensions definitions: This will enable the identification of datasets that share at least two common dimensions and thus can be considered for comparative statistical analysis and visualization. Only datasets that share at least two common dimensions can be considered for analysis. • The values of dimensions: This will enable the matching of observations that refer to the same values of the dimensions. For instance, the identification of the unemployment rate and the number of crimes that refer to the same parliament constituency e.g. Kensington.

• The categories of measures: This will enable the identification of measures of the same category and the analysis of variables that are linked to a number of different measures. Relevant measures should be categorized under a predefined set of categories enabling users to identify measures relevant to the research or business question. In addition, well known statistical concept schemes, code lists and classifications should be employed in order to enable maximum reusability.

The representation of data as RDF cubes will enable the linking of data published in disparate sources and thus facilitating statistical data reuse and combination for performing data analytics.

However, we should also note the current version of the RDF data cube vocabulary does not enable the aggregation of data from different granularity levels along a dimension hierarchy and thus doesn't facilitate the comparative analysis of this type of data.

In summary, the proposed open government data (OGD) analytics framework capitalizes on linked data principles and thus assumes data is published as linked data and meets a set of technical prerequisites. Clearly, this assumption is not yet true for all OGD initiatives. However, it is true that from all different technologies approaches for OGD, linked data seems the most promising and fast adopting [START_REF] Kalampokis | A Classification Scheme for Open Government Data: Towards Linking Decentralized Data[END_REF]. Actually, in UK data.gov.uk was one of the first OGD portals providing data following the linked data principles [START_REF] Sheridan | Linking UK government data[END_REF]. In fact, we believe that our framework might act as a catalyst for further speeding the adoption of linked data paradigm.

In this section we employ the use case of general elections in the United Kingdom (UK) in order to demonstrate the end-user value of the proposed linked open government data analytics approach. In particular, our aim is to demonstrate how one can gain insights about UK elections from available Open Government Data (OGD) that is published on data.gov.uk. Towards this end, we use as a starting point open data regarding the outcome of two UK general elections from 2005 and 2010. In particular we employ datasets that are published on Guardian's web site under an open license regarding the elections' results. In particular, these datasets contain the final results of all participating political parties in the country but also results of the main parties per parliamentary constituency along with the winning party per constituency. These datasets from Guardian's web sites were published as spreadsheets.

As it was described in the previous sections, in order to be able to perform data analytics on top of combined datasets we need to identify datasets that share the same dimensions. In the elections case, we need to identify datasets with statistics on data.gov.uk that describe data in the parliament constituency level. Towards this end, we searched and collected datasets regarding two categories, namely unemployment and poverty. For the former we have identified two datasets describing the unemployment and employment rate while for the latter two datasets describing the proportion of children in poverty and the families receiving child benefits. We identified datasets in different time periods from 2005 until 2010. These datasets was published on data.gov.uk as spreadsheets.

Before we proceed with the actual analysis, the data goes through a data conditioning phase in which all the datasets from both data.gov.uk and Guardian are being published following the linked data principles and based on the technical requirements that we have specified before.

We have created an RDF data cube for each measure. For instance, one cube has unemployment rate as measure and year and parliament constituencies as dimensions while another one has the percentage of children in poverty as measure and the same dimensions. We have also published the elections datasets as linked data and according to the same requirements. In this case we have as a measure the number of votes and as dimensions the years, parliament constituencies and political parties. We have also created a cube that describes the winning party as measure and parliament constituencies and year as dimensions. In order to disambiguate the dimensions we have created typed links between our datasets and DBpedia, the linked data version of Wikipedia. We have used owl:sameAs links in order to denote that an entity described in our dataset is the same as the corresponding entity in DBpedia. In this way, it becomes possible to disambiguate entity instances.

Two types of links are being established. The first one refers to the dimensions level while the second one to the observation level. Figure 1 presents the structure of a small part of the RDF data representing the data cubes along with the links among them. In particular, it describes the data structure definition of the unemployment rate and the election results cubes along with one observation per cube. It also presents the links that have been established between the two cubes. At the dimension level the graph denotes that one of the dimensions in both cubes is same as the dbpedia:United_Kingdom_constituencies entity while at the observation level that one observation in both cubes refers to an entity that is same as the dbpedia:Kensington_(UK_Parliament_constituency) entity.

The final task is to annotate the described measure with a set of categories. In our case the elections datasets could be categorized under elections while the data.gov.uk datasets are being published based a predefined set of categories from the Office of National Statistics. The data conditioning process gets the data into a state where data analytics can be performed in a transparent and easy manner. Initially, data that is related to elections can be discovered based on the category annotation. In our case scenario, two datasets that describe the number of votes and the winning party are discovered. Thereafter, datasets that share at least one dimension with the elections dataset are identified through the typed links that have been established to other datasets. In our case these datasets describe unemployment and poverty.

In addition, the datasets describe that the measures has been computed in either continuous (i.e. number of votes) or discrete (i.e. winning party) units. So, the way of visualizing the data or even the statistical analysis method to be followed can be emerged from the unit that characterizes the measure. For example, in the case of continuous units data analytics could be performed through linear regression analysis while in the case of discrete unit (i.e. categorical measures) through a classification analysis method such as logistic regression. Logistic regression measures the relationship between a categorical dependent variable and one or more continuous independent variables, by converting the dependent variable to probability scores through the logistic function:

P(A) = 1 1+ e -y
For example, in the elections case the logistic function could correlate the unemployment rate of a parliament constituency to the probability P(A) a particular political party to win the elections in the same constituency. On the other hand, the linear regression would correlate the unemployment rate of a parliament constituency to the number of votes that a specific political party received in the same constituency.

In our case we assume that the elections related dataset that includes a continuous measure is analyzed based on a linear regression while the one that includes a discrete measure based on logistic regression. The analysis can be also combined with a comparative visualization depending on the selected analysis method. The comparative visualization enables the easy understanding of the data analytics results and facilitates their interpretation.

In Figures 2 and3 the visualizations of linked open government data analytics are depicted. These figures visualize logistic regression analyses for the winners per constituency datasets in relation to the unemployment rate per constituency datasets for two consecutive UK elections in 2005 and 2010. In particular, the visualizations depict the percent of constituencies of a particular unemployment rate in which a party has won the elections. For example, in Figure 2 in the diagram that refers to the 2005 elections we see that the conservatives did not win in any parliament constituency with ten percent (10%) unemployment rate.

In addition, as the same figure indicates for parliament constituencies with more than five percent (5%) unemployment rate the Conservative Party has very small probability to win. However, in the same Figure and in the diagram that refers to the 2010 elections the unemployment rate above of which the Conservative party has very small probability to win goes up to thirteen percent (13%).

These visualizations enable users to evaluate the performed analyses and thus to understand the correlation between measures described in different datasets. As Figures 2 and3 suggest there is a significant relationship between the probability one of the two main UK parties (i.e. Labour party and Conservatives) to win in a parliament constituency and the unemployment rate in the same parliament constituency. It is notable that the same patterns holds for the two consecutive elections. The Conservative Party seems to win in areas that are characterized by small unemployment rate while the Labour Party in areas with high unemployment rate. Here we should note that the average unemployment rate in 2005 was 3.35 percent while in 2010 7.5 percent. This difference in the average unemployment rate between 2005 and 2010 could explain the moving of the data points and the data pattern to the right in the case of the unemployment measure. Finally, we should note that other connections that could have been produced by visualizing or correlating other measures (e.g. poverty) are not presented in this paper for shortness.

The identified patterns as presented through visualization could be valuable for supporting decision-making. For example, political parties and candidates could intensify their campaigns in areas that the analytics predict negative results. The presented approach also enables the creation of a statistical model out of the identified datasets. This can be used as a predictive model for estimating a future value of the dependent variable. For example, in our case one could create a model in order to estimate who will be the winner of a particular constituency in the future based on the unemployment rate at that time. Towards this end, we used R statistical package and we computed the coefficients of a logistic function. In particular, the model that correlates the probability P(A) the Labour party to win in a specific parliament constituency as a linear function of the unemployment rate of 2010 in the same parliament constituency using the logistic function is as follows:

P(A) = 1 1+ e -y
where y = -3.823 + 0.437 × x and x is the unemployment rate of the constituency. So for example, in constituencies with 12 percent unemployment rate the y variable is y=1.421 and the probability P(A)=0.8.

Therefore, based on this naïve prediction model and using a dataset that includes an estimation of the unemployment for 2015 we could also produce a prediction about the winning party in a particular constituency.

The UK elections case could also demonstrate the applicability of the linked open government analytics approach into a business setting where enterprise's own data could be combined with OGD. In this case the election results could have been replaced by product sales related data enabling this way the better understanding of an enterprise's sales.

Related Work

In the linked data community a few recent research endeavours worked on demonstrating the potential of linked statistical data. Towards this end, they showed how using linked data principles in statistical data for decision support services bears advantages such as easier integration and enrichment with other data sources [START_REF] Cyganiak | Official Statistics and the Practice of Data Fidelity[END_REF]- [START_REF] Kampgen | DC Proposal: Online Analytical Processing of Statistical Linked Data[END_REF]. They suggested that linking statistical data to the Linked Data Web could enable the enrichment of a particular dataset and thus the extraction of interesting and previously hidden insights related to particular events.

In particular, van Hage et al. [START_REF] Van Huge | Linked Open Piracy: A Story about e-Science, Linked Data and Statistics[END_REF] created linked data from piracy reports and employed statistical analysis to classify the events based on the weapons used. They also performed statistics on top of linked data in order to answer questions such as "what are the most active areas", "what are the differences in ships attacked in different regions" etc.

In addition, Paulheim [START_REF] Paulheim | Generating Possible Interpretations for Statistics from Linked Open Data[END_REF] employed datasets that are published on the Linked Data Web in order to enrich statistical data with attributes e.g. from DBpedia. He showed through a prototype that these attributes could generate new hypotheses. This approach was implemented through a prototype and analyzed the perceived quality of these hypotheses in a case study.

However, the proposed in this paper linked open government data analytics approach describes the value and the prerequisites of combining disparate statistical datasets that could enable the identification of unexpected and unexplored insights into different domains and problems areas.

Similarly to OGD, open data is also created and disseminated through Social Media such as Twitter, Facebook, blogs and forums. This data differs from OGD because it mainly expresses subjective opinions and thoughts. Social media data has been used as a primary source for the creation of predictive analytics models regarding various phenomena such as elections, stock market and product sales. An analysis of 52 relevant empirical studies concluded that social media data should go through a data conditioning process, which is similar to the one that we have presented in the proposed linked open government data analytics approach [START_REF] Kalampokis | Understanding the Predictive Power of Social Media[END_REF]. The same study revealed that this kind of open data could be successfully used for the development of predictive models in various problem areas.

Conclusions and Future Work

During the last years the Open Government Data (OGD) movement has been introduced evangelizing the need to open up governments and public authorities data for everyone to reuse. The OGD movement promised that open data would increase government transparency and accountability, stimulate innovation, contribute to economic growth and improve administrative processes. However, recent studies have shown that the full potential of OGD has not yet realized, despite the significant efforts put into developing tools and application facilitating open data publishing and consumption.

Today, data that are closed into isolated systems are fed in data analytics methods and tools to provide valuable information to businesses, governments and academia. In this paper we claimed that the real value of OGD will emerge from performing data analytics on top of combined statistical datasets that were previously closed in disparate sources and can now be linked to provide unexpected and unexplored insights into different domains and problem areas. To support this claim, in this paper we describe the linked open government data analytics vision along with its technical requirements as well as we demonstrate its applicability and value using a use case related to UK general elections. The case revealed that there is a strong correlation between the unemployment rate in a constituency and the possibility one of the two main political parties (i.e. Conservatives and Labour party) to win the elections in the constituency.

Future work includes the development of a platform that will enable the semiautomatic identification of important relations between variables that are described in datasets published as open data by different sources or even with variables that resides in enterprise's own data and thus also demonstrate the value of the proposed framework in business settings.

Fig. 1 .

 1 Fig. 1. A part of the RDF graph that presents two data structure definitions of two cubes along with two observations and the respective links between them.

Fig. 2 .

 2 Fig. 2. The correlation of Conservatives' wins and Unemployment using the unemployment datasets for the general elections of 2005 and 2010.

Fig. 3 .

 3 Fig. 3. The correlation of Labours' wins and Unemployment using the unemployemnt datasets for the general elections of 2005 and 2010

http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/themes and CIA's World

http://data.worldbank.org

http://www.oecd.org/statistics/

https://www.cia.gov/library/publications/the-world-factbook/index.html