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3Het idee om overheidsdata vrij beschikbaar te stellen aan de samenleving is relatief nieuw en heeft een groot innovatief potentieel. Rotterdam speelt in op internationale bewegingen om informatie uit de publieke sector vrij te geven als open data. Om dit proces in gang te zetten en te bespoedigen heeft de Hogeschool Rotterdam samen met een aantal gemeentelijke clusters en afdelingen het onderzoeksproject ‘Professionals Supported – Rotterdam Open Data’ (PSROD) opgezet. Dit project verkent de drempels en kansen van data ontsluiting binnen de gemeente Rotterdam. Het is een praktijkgericht onderzoeksproject, dat niet alleen streeft naar het vergaren en ontwikkelen van kennis, maar ook naar het daadwerkelijk ontsluiten van data. De Hogeschool, gemeente en bedrijven hebben open data in Rotterdam de laatste jaren gezamenlijk op de rails gezet, met projecten als PS-ROD en ROD(S) 2.0. Dit boek toont de resultaten van PS-ROD, aangevuld net interviews met en tekstuele bijdragen van betrokkenen.
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Some organizations have faced serious obstacles for disseminating their data according to the Open Data requirements and characteristics, e.g., for everybody and any use. Often this is the case when the data is of low quality, has (potentially) sensitive information, or has non-interoperable data format and semantics. Not being able to (completely) satisfy the Open Data requirements may have made such organizations to appear incompliant with the ideals and objectives of Open Data, despite their full commitments and efforts for data opening. In this contribution we propose a new paradigm -- called Semi-Open Data paradigm -- in order to frame, acknowledge, and encourage such initiatives and efforts that strive along the Open Data objectives but do not comply with Open Data requirements completely due to some practical constraints. For the proposed Semi-Open Data paradigm we further present an assessment method to measure and categorize Semi-Open Data initiatives objectively. This method offers a better way to assess and reward the extent of organizations' efforts to meet the Open Data characteristics than the current method that checks whether all Open Data requirements are met or not (i.e., by making a binary decision).
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De druk op een beter presterende en informatiegedreven overheid is bijzonder hoog. Gebruik van open data heeft een directe relatie met de rol, positie en dienstverlening van overheden. Er ontstaan nieuwe mogelijkheden om taken en verantwoordelijkheden in de maatschappij terug te leggen. De grote waarde zit daarbij in ketens. Overheidsinstanties zullen dus moeten samenwerken met private partijen.
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Judiciary systems comprise various partner organizations (e.g., police, public prosecutor, courts, and rehabilitation centres) that collaboratively resolve criminal cases. These partner organizations have their own data administration and management systems, which are setup/operated separately and integrated barely. This chapter explains the approach of the authors' organization for integrating the data sets of the Dutch judiciary systems, and for opening the data integration outcomes to the public and/or to specific groups. These outcomes (e.g., data sets and reports) are meant to provide useful insights into (the performances of) the partner organizations individually and collectively. Such data opening efforts do not comply with all Open Data requirements, mainly due to the quality, (privacy) sensitivity and interoperability issues of the raw data. Nevertheless, since these initiatives aim at delivering some benefits of Open Data, the chapter introduces the new paradigm of Semi-Open Data for acknowledging such data opening initiatives.
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For a successful public value strategy, the elements “public values/strategic goals”, authorizing environment” and “operational capability” should be coherently aligned. In this paper, we discuss how we have aligned these elements in the context of Open Data. We focus on the relationships between Open Data and public values, in particular, trust, transparency, privacy and security. Several contradictions exist between these values. To succeed, Open Data policy has to reconcile these values. For reconciliation purposes, we introduce the notion of precommitment, which is a restriction of one’s choices. Precommitment is conceptualized as a policy instrument whereby an organization imposes some restraint on its policy in order to restrict the extent to which values may conflict and stakeholders have to worry about the trustworthiness of that policy. We demonstrate how precommitment implemented as a data request procedure combined with a proper data infrastructure for Open Data may reconcile potentially conflicting values.
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Due to expected benefits such as citizen participation and innovation, the release of Public Sector Information as open data is getting increased attention on various levels of government. However, currently data release by governments is still novel and there is little experience and knowledge thus far about its benefits, costs and barriers. This is compounded by a lack of understanding about how internal processes influence data release. Our aim in this paper is to get a better understanding of these processes and how they influence data release, i.e., to find determinants for the release of public sector information. For this purpose, we conducted workshops, interviews, questionnaires, desk research and practice based cases in the education program of our university, involving six local public sector organizations. We find that the way data is stored, the way data is obtained and the way data is used by a department are crucial indicators for open data release. We conclude with the lessons learned based on our research findings. These findings are: we should take a nuanced approach towards data release, avoid releasing data for its own sake, and take small incremental steps to explore data release.
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Reconciling Contradictions of Open Data Regarding Transparency, Privacy, Security and Trust
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During the 2024 Open Science Retreat, the Measuring Open Science team collected, reviewed, and analyzed existing research into open science practices. As a team, we developed an interactive overview of open science surveys, which may be used e.g. to reuse questionnaire items on different open science practices.
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Some public organisations, despite being committed to attain the ideals of Open Data, struggle to meet the Open Data requirements fully. Often this is the case when their data are of low quality, have (potentially) sensitive information, or have non-interoperable format and semantics. These restrictions, for example, apply quite often to the datasets of justice domain. In practice, nevertheless, many of such public organisations do share their data in a way that partially satisfies the Open Data Requirements in order to be, e.g., transparent. To acknowledge such data opening initiatives, we advocate and describe a method to assess the degree of data openness, as a first step for recognizing such so-called Semi-Open Data initiatives. The proposed method relies on a multi-dimensional method to quantify these initiatives in terms of their adherence, i.e., distance, to the Open Data requirements. We carry out eight case studies and present the results in a way that it shows how to construct and fine-tune the parameters of the proposed method incrementally in a sense making way (i.e., based on consensus among stakeholders involved in a given domain). We report on the feasibility and applicability of the proposed model in practice and the encountered challenges.
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