The thesis deals with the presence of the technology team in various types of newsrooms of investigative journalism. The aim of the work is to find out the degree of representation of the technological component in the Slovak and Czech newsrooms. In case of their absence to find out the work process of journalists in more technologically demanding tasks. The theoretical part introduces the basic terminology of this field and this part is based on the data from quantitative study from 2019 conducted under the auspices of the ICFJ. In the practical part, qualitative research will be carried out in the form of semi-structured interviews with representatives of selected newsrooms.

Historic open data can be an asset to community gardens in land use disputes, the preservation and sharing of cultural traditions, and adaptation to climate change. Yet scholarship has not yet provided an accessible guide to the many issues of labor and technology involved in producing open data. This thesis addresses this gap by offering a guide to producing, preserving, and interpreting open data oriented toward community gardens from a public history perspective. This thesis examines the history of community gardens and related community data stretching to the Progressive Era, drawing comparisons to to that of historic open data in the gallery, library, archives, and museum (GLAM) world. The thesis also considers the worth of crowdsourcing and other volunteer labor models in data production, offers basic considerations for structuring and maintaining historic open datasets, and reviews the role of data visualization as a means of data communication and interpretation. Ultimately, I contend that open data is doable in public history and urgently worthy of consideration for gardens. / History

Integrating up-to-date information into databases from different heterogeneous data sources is still a time-consuming and mostly manual job that can only be accomplished by skilled experts. For this reason, enterprises often lack information regarding the current market situation, preventing a holistic view that is needed to conduct sound data analysis and market predictions. Ironically, the Web consists of a huge and growing number of valuable information from diverse organizations and data providers, such as the Linked Open Data cloud, common knowledge sources like Freebase, and social networks. One desirable usage scenario for this kind of data is its integration into a single database in order to apply data analytics. However, in today's business intelligence tools there is an evident lack of support for so-called situational or ad-hoc data integration. What we need is a system which 1) provides a flexible storage of heterogeneous information of different degrees of structure in an ad-hoc manner, and 2) supports mass data operations suited for data analytics. In this paper, we will provide our vision of such a system and describe an extension of the well-studied property graph model that allows to 'integrate and analyze as you go' external data exposed in the RDF format in a seamless manner. The proposed integration approach extends the internal graph model with external data from the Linked Open Data cloud, which stores over 31 billion RDF triples (September 2011) from a variety of domains.

No description available.

Transport models are key in predicting travel behaviour and planning transport systems. Transport models can be either aggregated or disaggregated. Disaggregation means that travel behaviour is represented on an individual level, which can be beneficial because it offers a higher detail level and reduces aggregation bias. Input data for transport models can be both expensive and inaccessible, especially comprehensive data. Thus, it is advantageous to explore the utilisation of open data, which is free and accessible. The objective of the thesis was to evaluate how OpenStreetMap and other Open Data can be utilised in disaggregated transport modelling. The scope of the study was Uppsala, Sweden. In the thesis, a disaggregate transport model was designed, which only considered commuting trips made by public transport. Destinations and a synthetic population were estimated based on OpenStreetMap map features, SCB census data, and LuTRANS land use data. A travel survey was utilised in model calibration, and UL boarding data was used for model validation. The results showed that OpenStreetMap provided sufficient data for estimating a synthetic population and destinations for a disaggregate transport model when combined with other open data sources. Population and land usecensus data were essential for calibrating the model. However, the model came with limitations caused by assumptions, generalisation, technical constraints, and the partial incompleteness of open data. The thesis concludes that Open Data, such as OpenStreetMap, can be utilised sufficiently for transport modelling, with proper assumptions and processing. The openness of the data also increases the replicability of such a model. / Transportmodeller är viktiga i att förutspå resvanemönster och för att kunna planera transportsystemet. Transportmodeller kan vara antingen aggregeradeeller disaggregerade. Disaggregering betyder att resvanor är representerade påindividuell nivå, vilket kan vara fördelaktigt då det innebär en högre detalj nivå och mindre partiskhet orsakad av aggregering (aggregation bias). Indata förtransportmodeller kan vara både dyrt och svåråtkomligt, speciellt för mer omfattande data. Därav kan det vara till stor nytta att utforska möjligheten att använda öppnadata (Open Data), som är gratis och lättåtkomligt. Syftet med examensarbetetvar att utvärdera hur OpenStreetMap och annan Open Data kan användas idisaggregerad transportmodellering. Den geografiska omfattningen av studien är Uppsala tätort. En disaggregerad transportmodell togs fram i examensarbetet, sombara tog hänsyn till jobbresor med kollektivtrafik. Destinationer och en syntetiskbefolkning uppskattades utifrån OpenStreetMap objekt, befolkningsdata från SCB, samt markanvändningsdata från LuTRANS. En resvaneundersökning utnyttjadesför modellkalibrering och påstigningsdata från UL användes för modellvalidering.Resultaten visade att OpenStreetMap erbjöd tillräckligt med data för att ta framoch uppskatta en syntetisk befolkning och destinationer för en disaggregeradtransportmodell, om den kombineras med andra öppna datakällor. Befolkning- ochmarkanvändningsdata var avgörande i att kalibrera modellen. Dock så innefattar modellen vissa begränsningar som är orsakada av antaganden, generalisering, tekniskabegränsningar, samt ofullständigheten av Open Data. Slutsatsen är att Open Data, så som OpenStreetMap, kan utnyttjas för transportmodellering, om det kombineras med välformulerade antaganden och processering av datan. Datans öppenheten medför även en ökad replikerbarhet för en sådan modell.

No description available.

This study explores Open Data actors in Kenya, focusing on the issue of transparency and accountability. Drawing on an exploratory quantitative analysis of existing statistical material of usage of the Kenya Open Data Initiative website and 15 qualitative interviews conducted primarily in Nairobi, the study analyses key factors – both enabling and disabling – that shape transparency initiatives connected to Open Data in Kenya. The material is analysed from three perspectives: a) a review based on existing research around impact and effectiveness of transparency and accountability initiatives; b) based on theories on human behaviour in connection to transparency and accountability; and c) introducing a critical perspective on power relations based on Michel Foucault’s concept of ‘governmentality’. The study shows that the Kenya Open Data Initiative has potential to become an effective transparency and accountability initiative in Kenya, but that its future is heavily dependent on current trends within the political context and fluctuations in power relations. Applying a stronger user-perspective and participatory approach is critical.Open Data is a relatively new area within the governance and development field, and academia can play an important role in enhancing methodology and impact assessments to create more effective and sustainable initiatives and ensure that future Open Data initiatives can be both accessible and constitute a base for accountability.

Yes / In recent years, there has been a focus in social psychology on efforts to improve the robustness, rigour, transparency and openness of psychological research. This has led to a plethora of new tools, practices and initiatives that each aim to combat questionable research practices and improve the credibility of social psychological scholarship. However, the majority of these efforts derive from quantitative, deductive, hypothesis-testing methodologies, and there has been a notable lack of in-depth exploration about what the tools, practices and values may mean for research that uses qualitative methodologies. Here, we introduce a Special Section of BJSP: Open Science, Qualitative Methods and Social Psychology: Possibilities and Tensions. The authors critically discuss a range of issues, including authorship, data sharing and broader research practices. Taken together, these papers urge the discipline to carefully consider the ontological, epistemological and methodological underpinnings of efforts to improve psychological science, and advocate for a critical appreciation of how mainstream open science discourse may (or may not) be compatible with the goals of qualitative research.

Year after year, technologies are evolving in an incredible rapid pace, becoming faster, more complex, more accurate and more immersive. Looking back just a decade, especially interaction technologies have made a major leap. Just two years ago in 2013, after being researched for quite some time, the hype around virtual reality (VR) arouse renewed enthusiasm, finally reaching mainstream attention as the so called head-mounted displays (HMD), devices worn on the head  to grant a visual peek into the virtual world, gain more and more acceptance with the end-user. Currently, humans interact with computers in a very counter-intuitive two dimensional way. The ability to experience digital content in the humans most natural manner, by simply looking around and perceiving information from their surroundings, has the potential to be a major game changer in how we perceive and eventually interact with digital information. However, this confronts designers and developers with new challenges of how to apply these exciting technologies, supporting interaction mechanisms to naturally explore digital information in the virtual world, ultimately overcoming real world boundaries. Within the virtual world, the only limit is our imagination. This thesis investigates an approach of how to naturally interact and explore information based on open data within an immersive virtual reality environment using a head-mounted display and vision-based motion controls. For this purpose, an immersive VR application visualizing information as a network of European capital cities has been implemented, offering interaction through gesture input. The application lays a major focus on the exploration of the generated network and the consumption of the displayed information. While the conducted user interaction study with eleven participants investigated their acceptance of the developed prototype, estimating their workload and examining their explorative behaviour, the additional dialog with five experts in the form of explorative discussions provided further feedback towards the prototype’s design and concept. The results indicate the participants’ enthusiasm and excitement towards the novelty and intuitiveness of exploring information in a less traditional way than before, while challenging them with the applied interface and interaction design in a positive manner. The design and concept were also accepted through the experts, valuing the idea and implementation. They provided constructive feedback towards the visualization of the information as well as emphasising and encouraging to be even bolder, making more usage of the available 3D environment. Finally, the thesis discusses these findings and proposes recommendations for future work.

Com o propósito de auxiliar os usuários no processo de tomada de decisão, diversos tipos de sistemas Web passaram a incorporar sistemas de recomendação. As abordagens mais utilizadas são a filtragem baseada em conteúdo, que recomenda itens com base nos seus atributos, a filtragem colaborativa, que recomenda itens de acordo com o comportamento de usuários similares, e os sistemas híbridos, que combinam duas ou mais técnicas. A abordagem baseada em conteúdo apresenta o problema de análise limitada de conteúdo, o qual pode ser reduzido com a utilização de informações semânticas. A filtragem colaborativa, por sua vez, apresenta o problema da partida fria, esparsidade e alta dimensionalidade dos dados. Dentre as técnicas de filtragem colaborativa, as baseadas em fatoração de matrizes são geralmente mais eficazes porque permitem descobrir as características subjacentes às interações entre usuários e itens. Embora sistemas de recomendação usufruam de diversas técnicas de recomendação, a maioria das técnicas apresenta falta de informações semânticas para representarem os itens do acervo. Estudos na área de sistemas de recomendação têm analisado a utilização de dados abertos conectados provenientes da Web dos Dados como fonte de informações semânticas. Dessa maneira, este trabalho tem como objetivo investigar como relações semânticas computadas a partir das bases de conhecimentos disponíveis na Web dos Dados podem beneficiar sistemas de recomendação. Este trabalho explora duas questões neste contexto: como a similaridade de itens pode ser calculada com base em informações semânticas e; como semelhanças entre os itens podem ser combinadas em uma técnica de fatoração de matrizes, de modo que o problema da partida fria de itens possa ser efetivamente amenizado. Como resultado, originou-se uma métrica de similaridade semântica que aproveita a hierarquia das bases de conhecimento e obteve um desempenho superior às outras métricas na maioria das bases de dados. E também o algoritmo Item-MSMF que utiliza informações semânticas para amenizar o problema de partida fria e obteve desempenho superior em todas as bases de dados avaliadas no cenário de partida fria. / In order to assist users in the decision-making process, several types of web systems started to incorporate recommender systems. The most commonly used approaches are content-based filtering, which recommends items based on their attributes; collaborative filtering, which recommends items according to the behavior of similar users; and hybrid systems that combine both techniques. The content-based approach presents the problem of limited content analysis, which can be reduced by using semantic information. The collaborative filtering, presents the problem of cold start, sparsity and high dimensionality of the data. Among the techniques of collaborative filtering, those based on matrix factorization are generally more effective because they allow us to discover the underlying characteristics of interactions between users and items. Although recommender systems have several techniques, most of them lack semantic information to represent the items in the collection. Studies in this area have analyzed linked open data from the Web of data as source of semantic information. In this way, this work aims to investigate how semantic relationships computed from the knowledge bases available in the Data Web can benefit recommendation systems. This work explores two questions in this context: how the similarity of items can be calculated based on semantic information and; as similarities between items can be combined in a matrix factorization technique, so that the cold start problem of items can be effectively softened. As a result, a semantic similarity metric was developed that leverages the knowledge base hierarchy and outperformed other metrics in most databases. Also the Item-MSMF algorithm that uses semantic information to soften the cold start problem and obtained superior performance in all databases evaluated in the cold start scenario.