In this thesis I undertake a study of Canadian municipal open data initiatives in order to assess the current state of the programs and to gauge the role(s) that these initiatives may play in regards to improving public engagement in local government issues. After an initial literature review, I adopt two separate approaches. The first approach involves the creation of an inventory and evaluation of the contents of all twenty three (23) Canadian municipal open data catalogues in existence during the summer of 2012. The second approach involves asking questions of key informants in the field through the execution of nineteen (19) semi-structured interviews with open data experts from both government and civic realms in ten (10) case study cities across the country. The results of the research illustrate the major differences and similarities between the structure, output, and roles of open data initiatives in various Canadian cities. The data provided by these programs mostly consists of politically neutral geographic data, though there are a few exceptions. I find two major program structures in Canadian cities: (1) The first type of open data program is created and operates within a specific municipal department and the (2) second type of program operates across a number of departments. Each approach has its own benefits and challenges. The open data initiatives across Canadian cities also appear to have different approaches to public engagement. Several cities have developed strong collaborative relationships with local open data advocates which are explored in some detail. Larger themes about the current state of open data, its current and future role, and the challenges faced by operators and users, are also described in this thesis. I conclude with some recommendations for improving municipal open data initiatives in the future. / Thesis (Master, Geography) -- Queen's University, 2013-08-12 11:27:50.037

There is a growing body of literature recognizing the benefits of Open Data. However, many potential data providers are unwilling to publish their data and at the same time, data users are often faced with difficulties when attempting to use Open Data in practice. Despite various barriers in using and publishing Open Data still being present, studies which systematically collect and assess these barriers are rare. Based on this observation we present a review on prior literature on barriers and the results of an empirical study aimed at assessing both the users' and publishers' views on obstacles regarding Open Data adoption. We collected data with an online survey in Austria and internationally. Using a sample of 183 participants, we draw conclusions about the relative importance of the barriers reported in the literature. In comparison to a previous conference paper presented at the conference for E-Democracy and Open Government, this article includes new additional data from participants outside Austria, reports new analyses, and substantially extends the discussion of results and of possible strategies for the mitigation of Open Data barriers.

The open data concept is based on the EU directive from 2003 with the goal to highlight the need for member states to increase and digitalize their public data and thus achieving transparency and stimulating economic growth based on new innovations. In Sweden, the development has fallen behind expectations and are currently the worst performing nordic country in open data development. In relation, research on open data is limited, especially when it come to barriers and challenges for adopting strategies to handle open data for local public organizations. This research is therefore aimed towards understanding and identifying barriers for adopting open data in local public organizations in Sweden. To answer this, we have conducted a case study based on the municipality of Örnsköldsviks. Our findings suggest that existing research on open data is somewhat lacking in the areas of ethical issues,  the private sector and the challenge of  moving towards openness; these are important aspects to consider when it comes to open data development. Furthermore, our results indicate that collaboration on both national and regional levels is advantageous and combined with PSI Directive improvements, is something that can bridge the gap between open data barriers and ambitions.

With the adoption of European Commission directives in 2003 related to open data,member States of EU were encouraged to provide citizens access to previously inaccessiblepublic sector data. This published public data could be used, reused and distributed free ofcharge. Following these directives, many municipalities within Sweden and Europe ingeneral created open data portals for publishing public sector data. With such datapublished, expectations of third party innovations were highly envisaged. This thesis adoptsa qualitative research approach to investigate the challenges and proposed solution ofusing open data for third party innovation. The thesis identifies various aspects ofgovernance, architecture and business model that public organizations should take intoconsideration in order to attract third party innovations on open data. Specifically, theresults of this thesis suggest that in order for open data to act as a platform for innovation,there is need for integration of open data policies. This involves developing commonstandards relating to governance, data format, and architecture. Harmonizing thesestandards across municipalities within Sweden and Europe, would provide the muchneededuser based which is necessary to enhance the two-sided nature of innovations onopen data platforms.

In the past years Open Data has become a trend among governments to increase transparency and public engagement by opening up national, regional, and local datasets. However, while many of these datasets come in semi-structured file formats, they use di ff erent schemata and lack geo-references or semantically meaningful links and descriptions of the corresponding geo-entities. We aim to address this by detecting and establishing links to geo-entities in the datasets found in Open Data catalogs and their respective metadata descriptions and link them to a knowledge graph of geo-entities. This knowledge graph does not yet readily exist, though, or at least, not a single one: so, we integrate and interlink several datasets to construct our (extensible) base geo-entities knowledge graph: (i) the openly available geospatial data repository GeoNames, (ii) the map service OpenStreetMap, (iii) country-specific sets of postal codes, and (iv) the European Union's classification system NUTS. As a second step, this base knowledge graph is used to add semantic labels to the open datasets, i.e., we heuristically disambiguate the geo-entities in CSV columns using the context of the labels and the hierarchical graph structure of our base knowledge graph. Finally, in order to interact with and retrieve the content, we index the datasets and provide a demo user interface. Currently we indexed resources from four Open Data portals, and allow search queries for geo-entities as well as full-text matches at http://data.wu.ac.at/odgraph/ .

Intuitively, most datasets found in Open Data are organised by spatio-temporal scope, that is, single datasets provide data for a certain region, valid for a certain time period. For many use cases (such as for instance data journalism and fact checking) a pre-dominant need is to scope down the relevant datasets to a particular period or region. Therefore, we argue that spatio-temporal search is a crucial need for Open Data portals and across Open Data portals, yet - to the best of our knowledge - no working solution exists. We argue that - just like for for regular Web search - knowledge graphs can be helpful to significantly improve search: in fact, the ingredients for a public knowledge graph of geographic entities as well as time periods and events exist already on the Web of Data, although they have not yet been integrated and applied - in a principled manner - to the use case of Open Data search. In the present paper we aim at doing just that: we (i) present a scalable approach to construct a spatio-temporal knowledge graph that hierarchically structures geographical, as well as temporal entities, (ii) annotate a large corpus of tabular datasets from open data portals, (iii) enable structured, spatio-temporal search over Open Data catalogs through our spatio-temporal knowledge graph, both via a search interface as well as via a SPARQL endpoint, available at data.wu.ac.at/odgraphsearch/ / Series: Working Papers on Information Systems, Information Business and Operations

Main goal of this diploma thesis is characterization of open data, standards and analyzation of adoption and utilization of open principles in the public sector of the Czech Republic. And comparison with European Union and chosen countries. Identifies technology and tools for linked data, used for deployment of highest rating of data openness. Defines geographical data, its standards and INSPIRE directive for spatial information in Europe. The goal of practical part of thesis is to analyze adoption of open principles for geographical data between Czech institutions. Focusing on what data are available, if open principles are applied and on what circumstances are data available. Foreign countries are also covered for the comparison.

In the past years Open Data has become a trend among governments to increase transparency and public engagement by opening up national, regional, and local datasets. However, while many of these datasets come in semi-structured file formats, they use different schemata and lack geo-references or semantically meaningful links and descriptions of the corresponding geo-entities. We aim to address this by detecting and establishing links to geo-entities in the datasets found in Open Data catalogs and their respective metadata descriptions and link them to a knowledge graph of geo-entities. This knowledge graph does not yet readily exist, though, or at least, not a single one: so, we integrate and interlink several datasets to construct our (extensible) base geo-entities knowledge graph: (i) the openly available geospatial data repository GeoNames, (ii) the map service OpenStreetMap, (iii) country-specific sets of postal codes, and (iv) the European Union¿s classification system NUTS. As a second step, this base knowledge graph is used to add semantic labels to the open datasets, i.e., we heuristically disambiguate the geo-entities in CSV columns using the context of the labels and the hierarchical graph structure of our base knowledge graph. Finally, in order to interact with and retrieve the content, we index the datasets and provide a demo user interface. Currently we indexed resources from four Open Data portals, and allow search queries for geo-entities as well as full-text matches at http://data.wu.ac.at/odgraph/.

Intuitively, most datasets found on governmental Open Data portals are organized by spatio-temporal criteria, that is, single datasets provide data for a certain region, valid for a certain time period. Likewise, for many use cases (such as, for instance, data journalism and fact checking) a pre-dominant need is to scope down the relevant datasets to a particular period or region. Rich spatio-temporal annotations are therefore a crucial need to enable semantic search for (and across) Open Data portals along those dimensions, yet -- to the best of our knowledge -- no working solution exists. To this end, in the present paper we (i) present a scalable approach to construct a spatio-temporal knowledge graph that hierarchically structures geographical as well as temporal entities, (ii) annotate a large corpus of tabular datasets from open data portals with entities from this knowledge graph, and (iii) enable structured, spatio-temporal search and querying over Open Data catalogs, both via a search interface as well as via a SPARQL endpoint, available at http://data.wu.ac.at/odgraphsearch/ / Series: Working Papers on Information Systems, Information Business and Operations

Intuitively, most datasets found on governmental Open Data portals are organized by spatio-temporal criteria, that is, single datasets provide data for a certain region, valid for a certain time period. Likewise, for many use cases (such as, for instance, data journalism and fact checking) a pre-dominant need is to scope down the relevant datasets to a particular period or region. Rich spatio-temporal annotations are therefore a crucial need to enable semantic search for (and across) Open Data portals along those dimensions, yet -- to the best of our knowledge -- no working solution exists. To this end, in the present paper we (i) present a scalable approach to construct a spatio-temporal knowledge graph that hierarchically structures geographical as well as temporal entities, (ii) annotate a large corpus of tabular datasets from open data portals with entities from this knowledge graph, and (iii) enable structured, spatio-temporal search and querying over Open Data catalogs, both via a search interface as well as via a SPARQL endpoint, available at http://data.wu.ac.at/odgraphsearch/ / Series: Working Papers on Information Systems, Information Business and Operations