[ES] El Gobierno Abierto es un modo de política pública que se basa en los pilares de colaboración y participación ciudadana, transparencia y rendición de cuentas y derecho de acceso a la información pública. De la mano de las tecnologías de la información y las comunicaciones, gobiernos y administraciones llevan a cabo iniciativas de apertura de datos, movimiento conocido como Open Data (Datos Abiertos). Las plataformas digitales donde estas entidades ponen a disposición de la sociedad civil los datos son conocidas como portales de datos abiertos. Se trata de fuentes de información donde los conjuntos de datos son potencialmente reutilizables, con cualquier fin y sin ningún tipo de restricción, únicamente de referencia de autoría de los datos. La comunidad científica, personal altamente cualificado dentro de la sociedad, pueden llegar a ser reutilizadores potenciales de estas fuentes de información. El producto derivado se traduce en producción científica: artículos, usos de datos abiertos en proyectos de investigación, comunicaciones y docencia. Este estudio aborda, por una parte, el conocimiento que tienen los investigadores e investigadoras acerca de los datos abiertos. Por otra, el uso y la reutilización de los datos abiertos para generar conocimiento científico. Para llevar a cabo el estudio se ha desarrollado una metodología cuantitativa. Se ha elaborado una encuesta, distribuida en un bloque inicial de contexto con 6 preguntas y 6 bloques de carácter técnico con 24 preguntas, es decir, un cuestionario con 30 preguntas. Se obtienen un total de 783 respuestas, procedentes de 34 provincias españolas. Los investigadores e investigadoras proceden de 47 universidades españolas y 21 centros de investigación, y existe representación 19 áreas de investigación de la Agencia Estatal de Investigación. Con los datos obtenidos a través de esta metodología cuantitativa, se procesan, se normalizan y se lleva a cabo un análisis. Además, con los datos se desarrolla una plataforma para visualizar los resultados de la encuesta. / [CA] El Govern Obert és una mena de política basada en els pilars de col·laboració i participació ciutadana, transparència i rendició de comptes i dret d'accés a la informació pública. De la mà de les tecnologies de la informació i de la comunicació, els governs i les administracions duen a terme iniciatives d'apertura de dades, moviment conegut com Open Data (Dades Obertes). Les plataformes digitals, on aquestes entitats posen a disposició de la societat civil les dades, són conegudes com portals de dades obertes. Es tracta de fonts d'informació on els conjunts de dades són potencialment reutilitzables, amb qualsevol fi i sense cap mena de restricció, únicament de referència d'autoria de les dades. La comunitat científica, personal altament qualificat dins de la societat, poden arribar a ser reutilizadors potencials d'aquestes fonts d'informació. El producte derivat es tradueix en producció científica: articles, usos de dades obertes en projectes d'investigació, comunicacions i docència. Aquest estudi aborda, per una banda, el coneixement que tenen els investigadors i investigadores sobre les dades obertes; per una altra, l'ús i la reutilització de les dades obertes per a generar coneixement científic. Per a dur a terme l'estudi s'ha desenvolupat una metodologia quantitativa. S'ha elaborat una enquesta, distribuïda en un bloc inicial de context, amb 6 preguntes i 6 blocs de caràcter tècnic amb 24 preguntes, és a dir, un qüestionari amb 30 preguntes. S'obtenen un total de 783 respostes, procedents de 34 províncies espanyoles. Els investigadors i investigadores procedeixen de 47 universitats espanyoles i 21 centres de recerca, i existeix representació de 19 àrees de recerca de l'Agència Estatal de Recerca. Amb les dades obtingudes a través d'aquesta metodologia quantitativa es processen, es normalitzen i es duu a terme una anàlisi. A més, amb les dades, es desenvolupa una plataforma per a visualitzar els resultats de l'enquesta. / [EN] Open Government is a mode of public policy that is based on the pillars of collaboration and citizen participation, transparency and accountability, and right of access to public information. Hand in hand with information and communication technologies, governments and administrations carry out initiatives to open data, a movement known as Open Data. The digital platforms, where these entities make the data available to civil society, are known as Open data portals. These are sources of information where the data sets are potentially reusable, for any purpose and without any type of restriction, only for reference of authorship of the data. The scientific community, highly qualified personnel within society, can become potential re-users of these information sources. The by-product translates into scientific production: articles, uses of open data in research projects, communications and teaching. This study addresses, on the one hand, the knowledge that researchers have about open data; on the other, the use and reuse of open data to generate scientific knowledge. In order to carry out the study, a quantitative methodology has been developed. A survey has been prepared, distributed in an initial block of context with 6 questions and 6 technical blocks with 24 questions, that is, a questionnaire with 30 questions. A total of 783 responses were obtained, from 34 Spanish provinces. The researchers come from 47 Spanish universities and 21 research centers, and 19 research areas of the State Research Agency are represented. The data obtained through this quantitative methodology are processed, normalized and analyzed. In addition, a platform is developed with the data, in order to visualize the results of the survey. / Vidal Cabo, C. (2022). Conocimiento, uso y reutilización de los datos abiertos en la ciencia española [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/184664 / TESIS

Sammanfattning  Syftet med den här uppsatsen är att öka kunskapen om hur myndigheter arbetar med öppna data, samt hur man förhåller sig till demokratiska principer kopplat till öppna data. Jag har applicerat en kvalitativ metodologi med fallstudie som metod. Jag har utfört intervjuer med ett antal informanter som är verksamma inom området öppna data på sina respektive institutioner. Intervjuerna har genomförts med en informant från varje institution och för detta ändamål valdes tre statliga myndigheter, och tre kommuner ut för närmare efterforskningar. Utöver det har två remissrundor kopplade till ett nytt förslag på en öppna data-lag i Sverige, konsulterats för ytterligare information om de valda institutionernas förhållningssätt till öppna data. Institutionernas hemsidor har också studerats, samt olika data-set på sidan kallad ”Sveriges Dataportal” (dataportal.se). En litteraturgenomgång har genomförts, samt en översikt över andra relevanta rapporter och dokument.  Det teoretiska ramverket består av dels innovationsteori (Greenacre et al., 2012), som belyser öppna datas effekter som innovationsskapare, samt ”Records Continuum-modellen” (RCM) (Upward, 2000), som är en arkivteoretisk modell som kan vara till hjälp vid hantering av data. Flera myndigheter har idag helt automatiserade publiceringsprocesser där man externt delar API:er som ändå finns och används internt av organisationen. Hos kommunerna varierar hanteringen dock något mer i förfining och kvalitet. Kunskapen om vad öppna data är, varierar även den mellan de olika institutionerna. Detta gör att enstaka tjänstemän ibland får agera som ensamt ansvariga, samtidigt som mängden öppna data, och engagemanget från resten av organisationen är ganska litet beroende på institution. I undersökningen belyses också flera andra aspekter av hantering och publicering av öppna data, såsom exempelvis tids- och resursbrist och en brist på användare. Man hanterar, särskilt i kommunerna, öppna data till synes så gott man kan, men oftast ganska osystematiskt. Inom myndigheterna är arbetet mer rutinmässigt utfört, men även där uttrycks bristen på drivkraft i hela processen. I undersökningen uppmärksammas de två parallella arkivkontexter som finns, där skapandet och initiala hanteringen av data som arkivhandlingar, är fysiskt och organisatoriskt separerat från publiceringen av öppna data. De som är med i skapandet av data är sällan aktiva inom hantering och publicering av öppna data, trots att det rör sig om till synes samma datamängder. Detta gör dels att en process som skulle kunna ha varit integrerad, i dagsläget är funktionsuppdelad. Effekten blir att kunskapen om datamängderna finns i en del av organisationen och vidare hantering och publicering som öppna data sköts av dem som kanske inte har samma kännedom om datamängderna som sådana. Man planerar inte heller vid skapandet av data för dess publicering. Enligt RCM så är det helt ok att dela upp processer enligt olika syften, men i en kontext om öppna data så skulle arbetet antagligen kunna fortskrida bättre om hela organisationen var delaktig och hade kännedom om öppna data. Ansvarsskyldighet och transparens är något som informanterna tycker är viktigt, men det hanteras inte som en grundläggande förutsättning för öppna data inom någon institution. / Abstract  The aim of this study is to increase knowledge of the management and publication processes of open data in Swedish institutions. The aim is also to increase the understanding of how public authorities regard the democratic principles connected with open data. The study looked at three governmental authorities and three municipal authorities that were the objects of the investigation. It applied a qualitative research methodology with case studies as the method. Interviews were used as the main data collection technique along with document studies and a literature review of previous research on the topic was also conducted. Two theoretical frameworks were used to analyse the collected data. Innovation theory was used to create a general understanding of how innovation can happen in the framework of open data. The Records Continuum-theory was applied to understand the management and publication of open data. A few conclusions could be made, regarding the actual management process as well as publishing, but also about the state of open data in public institutions in Sweden today. A discrepancy between the official policy, the prospect of a new open data law, and the actual work being done by public servants has been acknowledged. Also, the user base is said to be rather weak. The situation is analysed through a lens of the officially stated prospect of billions of kroner in gains for society, where the outcome is still to be realised. The conclusion is that a lot has to be done to get enough momentum to start a process of innovation when it comes to open data. Regarding the actual management and publishing of open data, different public institutions have varying ways to solve problems. The available resources seem to be a defining factor when it comes to how far public institutions have developed in publishing open data. Resources are not the only factor though, as a lack of knowledge regarding the principles of open data also plays a part, as well as motivation and lack of support from further up in the hierarchy. With regards to the question of accountability and transparency, it seems to be an understated benefit, that isn’t a priority for the policymaking, though several informants expressed concern for the democratic value of open data.

Digital transformation is highlighted as a way of solving many of the problems and challenges that the public sector faces in terms of cost developments and increased demands for better public services. Open data is a strategic resource that is necessary for this development to takeplace and the municipal sector collects information that could be published to create value inmany stages. Previous research believes that economic value is generated through new innovative services and productivity gains, but also social values such as increased civic participation and transparency. But despite previous attempts to stimulate open data, Sweden is far behind comparable countries and there is a lack of research that looks at exactly how these economic values should be captured. To investigate why this is the case and what role open datahas in value creation in the municipal sector, this study has identified several themes through qualitative interviews with an inductive approach. The study resulted in a deeper theoretical analysis of open data and its properties. By considering it as a public good, it is possible to use several explanatory models to explain its slow spread and but also understand the difficult conditions for value capture which results in incentive problems. In addition, there are structural problems linked to legislation and infrastructure that hamper the dissemination of open data and its value-creating role in the municipal sector. / Digital transformationen lyfts som ett sätt att lösa många av de problem och utmaningar som den offentliga sektorn står inför gällande kostnadsutveckling och ökade krav på bättre samhällsservice. Öppna data är en sådan strategisk resurs som är nödvändig för att dennautveckling ska ske och kommunsektorn samlar på sig information som skulle kunna publiceras för att skapa värden i många led. Dels menar tidigare forskning att ekonomiska värden kan genereras genom nya innovativa tjänster och produktivitetsökningar, men även sociala värden som ökad medborgardelaktighet och transparens. Men trots tidigare försök att stimulera öppna data, ligger Sverige långt efter jämförbara länder och det saknas forskning som tittar på exakt hur dessa ekonomiska värden ska fångas. För att undersöka varför så är fallet och vilken roll öppna data har på värdeskapande i kommunsektorn har denna studie genom kvalitativa intervjuer med en induktiv ansats identifierat flertalet teman. Studien resulterade i en djupare teoretisk analys av öppna data och dess egenskaper. Genom att betrakta det som en kollektiv vara går det att använda flera förklaringsmodeller för att förklara dess långsamma spridning och förstå de svåra förutsättningarna för värdefångst vilket resulterar i incitamentsproblem. Till det finns det strukturella problem kopplat till lagstiftning och infrastruktur som hämmarspridningen av öppna data och dess värdeskapande roll i kommunsektorn.

Digital twins (DTs) are digital copies of a physical system that incorporates the system environment, interactions, etc. to mirror the system accurately in real time. As effective decision support systems (DSS) in complex multivariate situations, DTs could be the next step in the digitalization of water management. This study is done in cooperation with the Open Waters project group at the Swedish environmental research institute (IVL). The aim of the project group is to investigate the possibility to realize DTs with the use of open data (OD), and shared design (SD), in Swedish water management while also promoting ecosystems for innovation in virtual environments. This study will aid the project group by bridging the gap between project stakeholders and water managers. A DSS developed by IVL for automatic dosage of coagulants in water treatment which is based on the same industry 4.0 technology as DTs will be evaluated as a possible starting point for DTs, OD, and SD. In depth interviews were held with representatives from water management, and experts in DTs, OD, and SD. This was to identify key opportunities and threats, and to understand water managers perception and opinion of the project. This is complimented by a brief review of Swedish water management, and the international state of DTs. There were 4 main opportunities and threats. Challenges and goals are very similar between different WTPs    Water managers are already collaborating to reach common goals    WTPs are unique in terms of treatment steps and composition/properties of raw water WTPs are objects of national security which raises questions regarding safety when digitalization is discussed. / Digitala tvillingar (DT) är digitala kopior av fysiska system som inkluderar systemets miljö, interaktioner, etc. för att noggrant spegla systemet i realtid. Som effektiva beslutsunderlag i komplexa, multivariabla situationer har DT fått uppmärksamhet inom vattensektorn och kan vara nästa steg i industrins digitalisering. Denna studie utförs i samarbete med svenska miljöinstitutets (IVLs) projektgrupp Open Waters. Syftet är att utforska möjligheten att förverkliga DT med hjälp av öppna data (OD) och delad design (SD) i den svenska vattensektorn, samt att främja innovationsekosystem i virtuella miljöer. Målet med denna studie är att överbygga klyftan mellan projektgruppen och dess målgrupp. Till hjälp kommer den IVL utvecklade DOS-modellen för automatisk dosering av fällningskemikalier för vattenrening. Denna är baserad på samma industri 4.0 teknologi som DT och ses som en startpunkt för DT, OD, och SD. Djupintervjuer hölls med representanter inom vattensektorn, såväl som experter inom DT, OD, och SD. Målet med detta var att identifiera centrala möjligheter och hot för projektet, samt för att förstå vattensektorns bild och åsikt av DT. Detta kompletteras med en övergripande genomgång av den svenska vattensektorn, och DT. 4 huvudsakliga möjligheter och hot identifierades.    Utmaningar och mål är väldigt lika mellan olika vattenverk    Det sker redan samarbeten i vattensektorn när gemensamma mål identifieras    Vattenverk är unika i förhållande till reningssteg och råvatten Vattenverk är skyddsobjekt vilket höjer frågor gällande informationssäkerhet när digitalisering diskuteras.

Ambient air pollution is among the top 10 health risk factors in the US. With increasing concerns about adverse health effects of ambient air pollution among stakeholders including environmental scientists, health professionals, urban planners and community residents, improving air quality is a crucial goal for developing healthy communities. The US Environmental Protection Agency (EPA) aims to reduce air pollution by regulating emissions and continuously monitoring air pollution levels. Local communities also benefit from crowd-sourced monitoring to measure air pollution, particularly with the help of rapidly developed low-cost sampling technologies. The shift from relying only on government-based regulatory monitoring to crowd-sourced effort has provided new opportunities for air quality data. In addition, the fast-growing data sciences (e.g., data mining) allow for leveraging open data from different sources to improve air pollution exposure assessment. My dissertation investigates how new data sources of air quality (e.g., community-based monitoring, low-cost sensor platform) and model predictor variables (e.g., non-government open data) based on emerging modeling approaches (e.g., machine learning [ML]) could be used to improve air quality models (i.e., land use regression [LUR]) at local, regional, and national levels for refined exposure assessment. LUR models are commonly used for predicting air pollution concentrations at locations without monitoring data based on neighboring land use and geographic variables. I explore the use of crowd-sourced low-cost monitoring data, new/open dataset from government and non-government sponsored platforms, and emerging modeling techniques to develop LUR models in the US. I focus on testing whether: (1) air quality data from community-based monitoring is feasible for developing LUR models, (2) air quality data from non-government crowd-sourced low-cost sensor platforms could supplement regulatory monitors for LUR development, and (3) new/open data extracted from non-government sponsored platforms could serve as alternative datasets to traditional predictor variable sources (e.g., land use and geographic features) in LUR models. In Chapter 3, I developed LUR models using community-based sampling (n = 50) for 60 volatile organic compounds (VOC) in the city of Minneapolis, US. I assessed whether adding area source-related features improves LUR model performance and compared model performance using variables featuring area sources from government vs. non-government sponsored platforms. I developed three sets of models: (1) base-case models with land use and transportation variables, (2) base-case models adding area source variables from local business permit data (government sponsored platform), and (3) base-case models adding Google point of interest (POI) data for area sources. Models with Google POI data performed the best; for example, the total VOC (TVOC) model had better goodness-of-fit (adj-R2: 0.56; Root Mean Square Error [RMSE]: 0.32 µg/m3) as compared to the permit data model (0.42; 0.37) and the base-case model (0.26; 0.41). This work suggests that VOC LUR models can be developed using community-based samples and adding Google POI could improve model performance as compared to using local business permit data. In Chapter 4, I evaluated a national LUR model using annual average PM2.5 concentrations from low-cost sensors (i.e., PurpleAir platform) in 6 US urban areas (n = 149) and tested the feasibility of using low-cost sensor data for developing LUR models. I compared LUR models using only the PurpleAir sensors vs. hybrid LUR models (combining both the EPA regulatory monitors and the PurpleAir sensors). I found that the low-cost sensor network could serve as a promising alternative to fill the gaps of existing regulatory networks. For example, the national regulatory monitor-based LUR (i.e., CACES LUR developed as part of the Center for Air, Climate, and Energy Solutions) may fail to capture locations with high PM2.5 concentrations and the within-city spatial variability. Developing LUR models using the PurpleAir sensors was reasonable (PurpleAir sensors only: 10-fold CV R2 = 0.66, MAE = 2.01 µg/m3; PurpleAir and regulatory monitors: R2 = 0.85, MAE = 1.02 µg/m3). I also observed that incorporating PurpleAir sensor data into LUR models could help capture within-city variability and merit further investigation on areas of disagreement with the regulatory monitors. This work suggests that the use of crowd-sourced low-cost sensor networks for LUR models could potentially help exposure assessment and inform environmental and health policies, particularly for places (e.g., developing countries) where regulatory monitoring network is limited. In Chapter 5, I developed national LUR models to predict annual average concentrations of 6 criteria pollutants (NO2, PM2.5, O3, CO, SO2 and PM10) in the US to compare models using new data (Google POI, Google Street View [GSV] and Local Climate Zone [LCZ]) vs. traditional geographic variables (e.g., road lengths, area of built land) based on different modeling approaches (partial least square [PLS], stepwise regression and machine learning [ML] with and without Kriging effect). Model performance was similar for both variable scenarios (e.g., random 10-fold CV R2 of ML-kriging models for NO2, new vs. traditional: 0.89 vs. 0.91); whereas adding the new variables to the traditional LUR models didn't necessarily improve model performance. Models with kriging effect outperformed those without (e.g., CV R2 for PM2.5 using the new variables, ML-kriging vs. ML: 0.83 vs. 0.67). The importance of the new variables to LUR models highlights the potential of substituting traditional variables, thus enabling LUR models for areas with limited or no data (e.g., developing countries) and across cities. The dissertation presents the integration of new/open data from non-government sponsored platform and crowd-sourced low-cost sensor networks in LUR models based on different modeling approaches for predicting ambient air pollution. The analyses provide evidence that using new data sources of both air quality and predictor variables could serve as promising strategies to improve LUR models for tracking exposures more accurately. The results could inform environment scientists, health policy makers, as well as urban planners interested in promoting healthy communities. / Doctor of Philosophy / According to the US Centers for Disease Control and Prevention (CDC), a healthy community aims at preventing disease, reducing health gaps, and creating more accessible options for a wider population. Outdoor air pollution has been evidenced to cause a wide range of diseases (e.g., cardiovascular diseases, respiratory diseases, diabetes and adverse birth outcome), ranking as the top 10 health risks in the US. Thus, improving understanding of ambient air quality is one of the common goals among environmental scientists, urban planners, health professionals, and local residents to achieving healthy communities. To understand air pollution exposures in different areas, US Environmental Protection Agency (EPA) has regulatory monitors for outdoor air pollution measurements across the country. For locations without these regulatory monitors, land use regression (LUR) models (one type of air quality models) are commonly employed to make a prediction. Usually, information including number of people, location of bus stops, and type of roads are shared online from government websites. These datasets are often used as significant predictor variables for developing LUR models. Questions remain on whether new air quality data and alternative land use data from non-government sources could improve air quality modeling. In recent years, local communities have been actively involving in air pollution monitoring using rapidly developed low-cost sensors and sampling campaigns with the help of local residents. In the meantime, advances in data sciences make open data much easier to acquire and use, particularly from non-government sponsored platforms. My dissertation aims to explore the use of new data sources including community-based low-cost monitoring data and open dataset from non-government websites in LUR modes based on emerging modeling techniques (e.g. machine learning) to predict air pollution levels in the US. I first built LUR models for volatile organic compounds (VOC: organic chemicals with a high vapor pressure at room temperature [e.g., Benzene]) based on community-based sampling data in the City of Minneapolis, US. I added information on number of neighboring gas stations, dry cleaners, paint booths, and auto shops from both the local government and Google website into the model and compared the model performance for both data sources (Chapter 3). Then, I used PM2.5 data from a non-government website (PurpleAir low-cost sensors) for 6 US cities evaluating an existing air quality model that used air quality data from government websites. I further developed LUR models using the PurpleAir PM2.5 data to see whether this non-government source of low-cost sensor data could be as reasonable as the government data for LUR model development. I finally extracted new/open data from non-government sponsored platforms (e.g., Google products and local climate zone [LCZ: a map that describes the development patterns of land, such as high-rise vs. low-rise or trees vs. sands]) in the US to investigate if these data sources can be used to alternate the land use and geographic data often used in national LUR model development. I found that: (1) adding information (e.g., number of neighboring gas stations) from non-government sponsored sources (e.g., Google) could improve the air quality model performance for VOCs, (2) integrating non-government low-cost PM2.5 sensor data into government regulatory monitoring data to develop LUR models could improve model performance and offer more insights on the air pollution exposure, (3) new/open data from non-government sponsored platforms could be used to replace the land use and geographic data previous obtained from government websites for air quality models. These findings mean that air quality data and street-level land use characteristics could serve as alternative data sources and are capable of developing better air quality models for promoting healthy communities.

During the early stages of any epidemic, simple interventions such as quarantine and isolation may be sufficient to halt the spread of a novel pathogen. However, should this opportunity be missed, substantially more resource-intensive, complex, and societally intrusive interventions may be required to achieve an acceptable outcome. These disparities place a differential on the value of a given unit of knowledge across the time-domains of an epidemic. Within this dissertation we explore these value-differentials via extension of the business concept of the time-value of knowledge and propose the C4 Response Model for organizing the research response to novel pathogenic outbreaks. First, we define the C4 Response Model as a progression from an initial data-hungry collect stage, iteration between open-science-centric connect stages and machine-learning centric calibrate stages, and a final visualization-centric convey stage. Secondly we analyze the trends in knowledge-building across the stages of epidemics with regard to open and closed access article publication, referencing, and citation. Thirdly, we demonstrate a Twitter message mapping application to assess the virality of tweets as a function of their source-profile category, message category, timing, urban context, tone, and use of bots. Finally, we apply an agent-based model of influenza transmission to explore the efficacy of combined antiviral, sequestration, and vaccination interventions in mitigating an outbreak of an influenza-like-illness (ILI) within a simulated military base population. We find that while closed access outbreak response articles use more recent citations and see higher mean citation counts, open access articles are published and referenced in significantly greater numbers and are growing in proportion. We observe that tweet viralities showed distinct heterogeneities across message and profile type pairing, that tweets dissipated rapidly across time and space, and that tweets published before high-tweet-volume time periods showed higher virality. Finally, we saw that while timely responses and strong pharmaceutical interventions showed the greatest impact in mitigating ILI transmission within a military base, even optimistic scenarios failed to prevent the majority of new cases. This body of work offers significant methodological contributions for the practice of computational epidemiology as well as a theoretical grounding for the further use of the C4 Response Model. / Doctor of Philosophy / During the early stages of an outbreak of disease, simple interventions such as isolating those infected may be sufficient to prevent further cases. However, should this opportunity be missed, substantially more complex interventions such as the development of novel pharmaceuticals may be required. This results in a differential value for specific knowledge across the early, middle, and late stages of epidemic. Within this dissertation we explore these differentials via extension of the business concept of the time-value of knowledge, whereby key findings may yield greater benefits during early epidemics. We propose the C4 Response Model for organizing research regarding this time-value. First, we define the C4 Response Model as a progression from an initial knowledge collection stage, iteration between knowledge connection stages and machine learning-centric calibration stages, and a final conveyance stage. Secondly we analyze the trends in knowledge-building across the stages of epidemics with regard to open and closed access scientific article publication, referencing, and citation. Thirdly, we demonstrate a Twitter application for improving public health messaging campaigns by identifying optimal combinations of source-profile categories, message categories, timing, urban origination, tone, and use of bots. Finally, we apply an agent-based model of influenza transmission to explore the efficacy of combined antiviral, isolation, and vaccination interventions in mitigating an outbreak of an influenza-like-illness (ILI) within a simulated military base population. We find that while closed access outbreak response articles use more recent citations and see higher mean citation counts, open access articles are growing in use and are published and referenced in significantly greater numbers. We observe that tweet viralities showed distinct benefits to certain message and profile type pairings, that tweets faded rapidly across time and space, and that tweets published before high-tweet-volume time periods are retweeted more. Finally, we saw that while early responses and strong pharmaceuticals showed the greatest impact in preventing influenza transmission within military base populations, even optimistic scenarios failed to prevent the majority to new cases. This body of work offers significant methodological contributions for the practice of computational epidemiology as well as a theoretical grounding for the C4 Response Model.

Bei der Bewältigung der Folgen des demographischen Wandels stehen viele Länder vor der Herausforderung, staatliche Dienstleistungen weiterhin flächendeckend anzubieten. Hierbei geht es darum, Dienstleistungen des öffentlichen Sektors wie Schulen, Krankenhäuser, Feuerwachen, etc. möglichst gleichwertig und kosteneffizient bereitzustellen, wenn die Standorte optimal gewählt sind. Bei dieser Optimierung sind Bevölkerungsdaten eine wichtige Eingangsgröße. Das Ziel dieser Arbeit bestand darin, die sehr spezifische und aufgrund ihrer multifaktoriellen Problemstellung hochkomplexe Thematik der Standortanalyse und -planung zu generalisieren und eine benutzerfreundliche Desktop-Applikation auf Basis der von Esri Inc. bereitgestellten ArcGIS-Technologie (Location-Allocation-Analyse, Einzugsgebiet-Analyse etc.) zu entwickeln. Dabei wurde sich auf die Location-Allocation-Funktionalität fokussiert. Die beiden Hauptkomponenten dieser sind Einrichtungen und Bedarfsstellen bzw. -punkte. Einrichtungen können sowohl bestehende als auch potenzielle Standorte darstellen. Bedarfsstellen repräsentieren die Anzahl der Bürger oder Verbraucher in einem bestimmten Gebiet. Um dieses Ziel zu erreichen, wurde ein generisches Modell für Standortanalysen und -planungen entwickelt und bereitgestellt. Dies geschah nach einer Exploration von Ausgangsdatenquellen zu Einrichtungen öffentlicher Dienstleistungen und Bedarfsstellen zur Bevölkerungsverteilung in Deutschland. Die benutzerfreundliche Applikation wurde gemäß der Methodik des Software Engineerings entwickelt. Hierbei wurden eine Anforderungsanalyse und Entscheidungen des Entwurfsprozesses, konkret, berücksichtigt. Die Implementierung wurde als Add-in namens „LA-Application“ in ArcGIS Pro integriert. Ein Add-in ist eine Erweiterung des Desktop-GIS auf Basis des ArcGIS Pro SDK for .NET. Abschließend wurde ein Testdatenbestand für die Komponenten „Einrichtungen“ und „Bedarfsstellen“ erstellt, um Lösungen für die Analyse und Planung von Standorten aufzuzeigen.:Inhaltsverzeichnis Abkürzungsverzeichnis Vorwort 1. Einleitung 2. Theorie und verwandte Arbeiten 3. Exploration von Ausgangsdatenquellen 4. Bereitstellung eines generischen Modells für Standortanalysen und -planungen 5. Implementierung einer benutzerfreundlichen Applikation 6. Anwendung der Applikation und Auswertungen 7. Zusammenfassung und Ausblick Glossar Literaturverzeichnis Abbildungsverzeichnis Tabellenverzeichnis A. Geschäftsverteilungsplan der Landeshauptstadt Dresden B. Anforderungsspezifikation C. Erklärungen zur Testdatenbestandskomponente 'Network Dataset' D. Digitale Anlagen Erklärung über die eigenständige Erstellung der Arbeit

Purpose - The objective of this study is to explore how Data-Intelligence Service-Providers (DISP) can adapt existing Business Model (BM) dimensions to leverage the potential value and mitigate the emerging challenges Open Data (OD) introduces. Method – By developing a multiple case study, we intend to qualitatively explore what BM practices DISPs employ when incorporating OD. Interviews are conducted in multiple phases with a total of 25 interviews and results generated using a thematic analysis. Findings – Through empirical investigation and analysis of DISPs actions and strategies, the study uncovers how these firms navigate challenges and opportunities presented by OD. By portraying the strategies across three BM dimensions—value creation, delivery, and capture—this study identifies six key practices that help DISPs competitively differentiate themselves in the OD environment. The identified practices include Use-case understanding and Data-driven Service Innovation for value creation, Enhanced Data Delivery and Collaborative Data Optimization for value delivery, and AdjustedRevenue Model and Market Expansion for value capture. Implications – In our contribution to existing literature, we present empirical evidence spanning across all dimensions of the BM, shedding light on the competitive advantages facilitated by OD. Additionally, through identifying key practices, this thesis uncovers several areas where there is a lack of understanding on ODs impact in a commercial context. Specifically, by solely focusing on the perspective of DISPs, we offer detailed insight into how these practices are practically unfolding. Furthermore, the thesis presents a framework categorizing practices based on priority and ecosystem dependency. This framework delineates certain practices that are considered fundamental when incorporating OD while also recognizing their intricate requirement of involving external parties, offering managers a visual overview of how to systematically adapt their BMs to incorporate OD into their services. In addition, we manage to address the common distortions about OD by offering a thorough theoretical foundation and defining it clearly within a commercial context, making this complex topic more accessible and better understood. Limitations and future research – As this study is limited to data-providers and DISPs, this thesis advocates for exploring end-user perspectives in future research deemed crucial for gathering a comprehensive understanding of their needs and interactions with OD solutions to solidify findings in this study. Additionally, it is encouraged that future research should investigate misalignments between data-providers and DISPs (e.g. regulatory and technical matters) which currently, are leading to massive inefficiencies in data supply chains. Understanding these issues and implementing strategies to address them can optimize OD resource utilization, thereby facilitating greater innovative potential for service-providers leveraging it.

Over the last years, the Linked Open Data (LOD) has evolved from a mere 12 to more than 10,000 knowledge bases. These knowledge bases come from diverse domains including (but not limited to) publications, life sciences, social networking, government, media, linguistics. Moreover, the LOD cloud also contains a large number of crossdomain knowledge bases such as DBpedia and Yago2. These knowledge bases are commonly managed in a decentralized fashion and contain partly verlapping information. This architectural choice has led to knowledge pertaining to the same domain being published by independent entities in the LOD cloud. For example, information on drugs can be found in Diseasome as well as DBpedia and Drugbank. Furthermore, certain knowledge bases such as DBLP have been published by several bodies, which in turn has lead to duplicated content in the LOD . In addition, large amounts of geo-spatial information have been made available with the growth of heterogeneous Web of Data. The concurrent publication of knowledge bases containing related information promises to become a phenomenon of increasing importance with the growth of the number of independent data providers. Enabling the joint use of the knowledge bases published by these providers for tasks such as federated queries, cross-ontology question answering and data integration is most commonly tackled by creating links between the resources described within these knowledge bases. Within this thesis, we spur the transition from isolated knowledge bases to enriched Linked Data sets where information can be easily integrated and processed. To achieve this goal, we provide concepts, approaches and use cases that facilitate the integration and enrichment of information with other data types that are already present on the Linked Data Web with a focus on geo-spatial data. The first challenge that motivates our work is the lack of measures that use the geographic data for linking geo-spatial knowledge bases. This is partly due to the geo-spatial resources being described by the means of vector geometry. In particular, discrepancies in granularity and error measurements across knowledge bases render the selection of appropriate distance measures for geo-spatial resources difficult. We address this challenge by evaluating existing literature for point set measures that can be used to measure the similarity of vector geometries. Then, we present and evaluate the ten measures that we derived from the literature on samples of three real knowledge bases. The second challenge we address in this thesis is the lack of automatic Link Discovery (LD) approaches capable of dealing with geospatial knowledge bases with missing and erroneous data. To this end, we present Colibri, an unsupervised approach that allows discovering links between knowledge bases while improving the quality of the instance data in these knowledge bases. A Colibri iteration begins by generating links between knowledge bases. Then, the approach makes use of these links to detect resources with probably erroneous or missing information. This erroneous or missing information detected by the approach is finally corrected or added. The third challenge we address is the lack of scalable LD approaches for tackling big geo-spatial knowledge bases. Thus, we present Deterministic Particle-Swarm Optimization (DPSO), a novel load balancing technique for LD on parallel hardware based on particle-swarm optimization. We combine this approach with the Orchid algorithm for geo-spatial linking and evaluate it on real and artificial data sets. The lack of approaches for automatic updating of links of an evolving knowledge base is our fourth challenge. This challenge is addressed in this thesis by the Wombat algorithm. Wombat is a novel approach for the discovery of links between knowledge bases that relies exclusively on positive examples. Wombat is based on generalisation via an upward refinement operator to traverse the space of Link Specifications (LS). We study the theoretical characteristics of Wombat and evaluate it on different benchmark data sets. The last challenge addressed herein is the lack of automatic approaches for geo-spatial knowledge base enrichment. Thus, we propose Deer, a supervised learning approach based on a refinement operator for enriching Resource Description Framework (RDF) data sets. We show how we can use exemplary descriptions of enriched resources to generate accurate enrichment pipelines. We evaluate our approach against manually defined enrichment pipelines and show that our approach can learn accurate pipelines even when provided with a small number of training examples. Each of the proposed approaches is implemented and evaluated against state-of-the-art approaches on real and/or artificial data sets. Moreover, all approaches are peer-reviewed and published in a conference or a journal paper. Throughout this thesis, we detail the ideas, implementation and the evaluation of each of the approaches. Moreover, we discuss each approach and present lessons learned. Finally, we conclude this thesis by presenting a set of possible future extensions and use cases for each of the proposed approaches.

Angetrieben durch die Akkretion von Materie in ein super massives Schwarzes Loch in ihrem Zentrum, stellen aktive Galaxien die stärksten und beständigsten Strahlungsquellen im Universum dar. Ihre elektromagnetische Emission kann sich bis in den Gammastrahlenbereich ausbreiten. Das Ziel dieser Arbeit ist, diese Mechanismen und die Orte jenseits der hoch energetischen Emission zu charakterisieren. Dafür werden die Observationen von zwei Aktiven Galaxien im Bereich von hunderten von GeV verwendet, welche mit den Cherenkov Teleskopen MAGIC aufgenommen wurden. Die physikalische Interpretation wird durch Beobachtungen mit dem Fermi Gamma-ray Space Teleskop und durch Multiwellenlängendaten unterstützt. Es werden zwei Aktive Galaxien mit Jet untersucht: PKS 1510-089 und NGC 1275. Die MAGIC Teleskope, welche PKS 1510-089 seit 2012 immer wieder beobachten, detektieren eine signifikante Emission über dutzende von Observationsstunden, was auf schwache aber kontinuierliche Gammastrahlung aus dieser Quelle hinweist. NGC 1275 zeigte in der Periode von September 2016 bis Februar 2017 einen großen Ausbruch im Gammerstrahlenbereich: MAGIC zeichnete eine Variabilität in der Größenordnung von wenigen Stunden und die erstmalige Emission von TeV Photonen. Aus beiden untersuchten Quellen ist ersichtlich, dass die Kombination von Daten aus verschiedenen Instrumenten die physische Diskussion entscheidend beeinflusst. Der Übergang zu zugänglichen und interoperablen Daten wird zu einem zwingenden Thema für Gammastrahlenastronomen, und diese Arbeit stellt das technische Bestreben dar, standardisierte hochrangige Daten für Gammastrahleninstrumente zu erzeugen. Ein Beispiel für eine zukünftige Analyse, die einheitliche High-Level-Daten von einem Gammastrahlensatelliten und vier Cherenkov-Teleskopen kombiniert, wird vorgestellt. Der neue Ansatz, der vorgeschlagen wird, führt die Datenanalyse durch und verbreitet die Ergebnisse, wobei nur Open-Source-Ressourcen verwendet werden. / Powered by the accretion of matter to a supermassive black hole, active galactic nuclei constitute the most powerful and persistent sources of radiation in the universe, with emission extending in the gamma-ray domain. The aim of this work is to characterise the mechanisms and sites beyond this highly-energetic radiation employing observations of two galaxies at hundreds of GeV, conducted with the MAGIC imaging Cherenkov telescopes. The physical interpretation is supported with observations by the Fermi Gamma-ray Space Telescope and with multi-wavelength data. Two peculiar jetted galaxies are studied: PKS 1510-089 and NGC 1275. The first source, monitored by MAGIC since 2012, presents a significant emission over tens of observation hours, in what appears to be a low but persistent gamma-ray state. The second source has instead shown, in the period between September 2016 and February 2017, a major outburst in its gamma-ray activity with variability of the order of few hours and emission of TeV photons. The broad band emission of jetted galaxies is commonly modelled with the radiative processes of a population of electrons accelerated in the jet. While PKS 1510-089 conforms to this scenario, modelling the gamma-ray outburst of NGC 1275 requires placing the acceleration and radiation of electrons close to the event horizon of the black hole. From both the sources studied it is evident that the combination of data from different instruments critically drives the physical discussuion. Moving towards accessible and interoperable data becomes a compelling issue for gamma-ray astronomers and this thesis presents the technical endeavour to produce standardised high-level data for gamma-ray instruments. An example of a future analysis combining uniformed high-level data from a gamma-ray satellite and four Cherenkov telescopes is presented. The novel approach proposed performs the data analysis and disseminates the results making use only of open-source assets.