Open Government Data (OGD) has become a topic of prominence during the last decade. However, most governments have not realized the desired outcomes from OGD, which implies that the envisaged value streams have not been realized. This study defines three objectives that will help address this shortcoming. First, it seeks to identify the causal mechanisms that lead to effective institutionalization and sustainability of OGD initiatives in a developing country context. Second, it seeks to identify the social, economic, cultural, political structures and components that describe the OGD context. Third, it seeks to identify the underlying contextmechanism- outcome (CMO) configurations in the Kenya Open Data Initiative (KODI). The guiding philosophy for this qualitative study is critical realism, which is implemented using Pawson & Tilley's realist evaluation model. Data is obtained through observation of open data events, semi-structured interviews and documentary materials from websites and policy documents. Fereday & Muir-Cochrane's five-stage thematic analysis model is applied in conducting data analysis. Three main contributions arise from this study. The first contribution is the open data institutionalization analysis guide. This study collates several institutionalization concepts from literature with the aim of developing a lens for analyzing OGD initiatives. The second contribution is the identification of supporting mechanisms, including a description of the current CMO configurations. The resulting case study provides an in-depth account of KODI between 2011 and 2016. This will assist policy makers in understanding the current setup, identifying gaps, and establishing or supporting existing support structures and mechanisms. The third contribution is related to scarcity of empirical work based on critical realism in the field of information systems. This research will act as a reference point for future IS research, in determining how critical realism can be applied to conduct similar studies.

Die MOSAiC-Expedition war die größte Polarexpedition, die je durchgeführt wurde. Mehr als ein Jahr driftete das Forschungsschiff Polarstern durch den Arktischen Ozean und erhob dabei unzählige Forschungsdaten. Die Umsetzung stellte große logistische und technische Herausforderungen. Gleichzeitig setzte das Projekt Meilensteine in der Digitalisierung der MOSAiC-Daten. Das vorhandene Datenrepositorim PANGEA wurde als Datenbasis für die Abspeicherung der erhobenen und gewonnenen Daten genutzt. Das Datenmanagement hatte ein frühestmögliches Teilen der Daten zum Ziel. Außerdem stand von Anfang an das Datenmanagement als ein Teil von open science und einer frühen Datenzitierbarkeit. Ab 2023 sollen alle MOSAiC-Daten im Repositorium frei verfügbar sein. MOSAiC ist der bisher größte Anwendungsfall für das Projekt Nationale Forschungsdateninfrastruktur (NFDI). / The MOSAiC expedition was the largest polar expedition ever conducted. For more than a year, the research vessel Polarstern drifted through the Arctic Ocean collecting countless research data. The implementation posed major logistical and technical challenges. At the same time, the project set milestones in the digitization of MOSAiC data. The existing data repositoryim PANGEA was used as a database for storing the collected and acquired data. Data management aimed at sharing the data as early as possible. In addition, from the beginning, data management stood as a part of open science and early data citability. Starting in 2023, all MOSAiC data should be freely available in the repository. MOSAiC is the largest use case to date for the National Research Data Infrastructure (NFDI) project.

Data governance reaches across the field of information technology and is increasingly important for big data efforts, regulatory compliance, and ensuring data integrity. The purpose of this qualitative case study was to explore strategies for implementing data governance practices. This study was guided by institutional theory as the conceptual framework. The study's population consisted of informatics specialists from a small hospital, which is also a research institution in the Washington, DC, metropolitan area. This study's data collection included semi structured, in-depth individual interviews (n = 10), focus groups (n = 3), and the analysis of organizational documents (n = 19). By using methodological triangulation and by member checking with interviewees and focus group members, efforts were taken to increase the validity of this study's findings. Through thematic analysis, 5 major themes emerged from the study: structured oversight with committees and boards, effective and strategic communications, compliance with regulations, obtaining stakeholder buy-in, and benchmarking and standardization. The results of this study may benefit informatics specialists to better strategize future implementations of data governance and information management practices. By implementing effective data governance practices, organizations will be able to successfully manage and govern their data. These findings may contribute to social change by ensuring better protection of protected health information and personally identifiable information.

This work proposes the system for dynamic enforcement of access rights on Android. Each suspicious application can be repackaged by this system, so that the access to selected private data is restricted for the outer world. The system intercepts the system calls using Aurasium framework and adds an innovative approach of tracking the information flows from the privacy-sensitive sources using tainting mechanism without need of administrator rights. There has been designed file-level and data-level taint propagation and policy enforcement based on Android binder.

Les robots opèrent dans le monde réel, dans lequel essayer quelque chose prend beaucoup de temps. Pourtant, les methodes d’apprentissage par renforcement actuels (par exemple, deep reinforcement learning) nécessitent de longues périodes d’interaction pour trouver des politiques efficaces. Dans cette thèse, nous avons exploré des algorithmes qui abordent le défi de l’apprentissage par essai-erreur en quelques minutes sur des robots physiques. Nous appelons ce défi “Apprentissage par renforcement micro-data”. Dans la première contribution, nous avons proposé un nouvel algorithme d’apprentissage appelé “Reset-free Trial-and-Error” qui permet aux robots complexes de s’adapter rapidement dans des circonstances inconnues (par exemple, des dommages) tout en accomplissant leurs tâches; en particulier, un robot hexapode endommagé a retrouvé la plupart de ses capacités de marche dans un environnement avec des obstacles, et sans aucune intervention humaine. Dans la deuxième contribution, nous avons proposé un nouvel algorithme de recherche de politique “basé modèle”, appelé Black-DROPS, qui: (1) n’impose aucune contrainte à la fonction de récompense ou à la politique, (2) est aussi efficace que les algorithmes de l’état de l’art, et (3) est aussi rapide que les approches analytiques lorsque plusieurs processeurs sont disponibles. Nous avons aussi proposé Multi-DEX, une extension qui s’inspire de l’algorithme “Novelty Search” et permet de résoudre plusieurs scénarios où les récompenses sont rares. Dans la troisième contribution, nous avons introduit une nouvelle procédure d’apprentissage du modèle dans Black-DROPS qui exploite un simulateur paramétré pour permettre d’apprendre des politiques sur des systèmes avec des espaces d’état de grande taille; par exemple, cette extension a trouvé des politiques performantes pour un robot hexapode (espace d’état 48D et d’action 18D) en moins d’une minute d’interaction. Enfin, nous avons exploré comment intégrer les contraintes de sécurité, améliorer la robustesse et tirer parti des multiple a priori en optimisation bayésienne. L'objectif de la thèse était de concevoir des méthodes qui fonctionnent sur des robots physiques (pas seulement en simulation). Par conséquent, tous nos approches ont été évaluées sur au moins un robot physique. Dans l’ensemble, nous proposons des méthodes qui permettre aux robots d’être plus autonomes et de pouvoir apprendre en poignée d’essais / Robots have to face the real world, in which trying something might take seconds, hours, or even days. Unfortunately, the current state-of-the-art reinforcement learning algorithms (e.g., deep reinforcement learning) require big interaction times to find effective policies. In this thesis, we explored approaches that tackle the challenge of learning by trial-and-error in a few minutes on physical robots. We call this challenge “micro-data reinforcement learning”. In our first contribution, we introduced a novel learning algorithm called “Reset-free Trial-and-Error” that allows complex robots to quickly recover from unknown circumstances (e.g., damages or different terrain) while completing their tasks and taking the environment into account; in particular, a physical damaged hexapod robot recovered most of its locomotion abilities in an environment with obstacles, and without any human intervention. In our second contribution, we introduced a novel model-based reinforcement learning algorithm, called Black-DROPS that: (1) does not impose any constraint on the reward function or the policy (they are treated as black-boxes), (2) is as data-efficient as the state-of-the-art algorithm for data-efficient RL in robotics, and (3) is as fast (or faster) than analytical approaches when several cores are available. We additionally proposed Multi-DEX, a model-based policy search approach, that takes inspiration from novelty-based ideas and effectively solved several sparse reward scenarios. In our third contribution, we introduced a new model learning procedure in Black-DROPS (we call it GP-MI) that leverages parameterized black-box priors to scale up to high-dimensional systems; for instance, it found high-performing walking policies for a physical damaged hexapod robot (48D state and 18D action space) in less than 1 minute of interaction time. Finally, in the last part of the thesis, we explored a few ideas on how to incorporate safety constraints, robustness and leverage multiple priors in Bayesian optimization in order to tackle the micro-data reinforcement learning challenge. Throughout this thesis, our goal was to design algorithms that work on physical robots, and not only in simulation. Consequently, all the proposed approaches have been evaluated on at least one physical robot. Overall, this thesis aimed at providing methods and algorithms that will allow physical robots to be more autonomous and be able to learn in a handful of trials

<p>  </p> <p>The management of water resources in East Africa is inherently challenged by rainfall variability and the uneven spatial distribution of freshwater resources. In addition to these issues, meteorological and water data collection has been inconsistent over the past decades, and unclearly defined purposes or end goals for collected data have left many datasets ineffectively curated. In light of the data intensiveness of current modelling and planning methods, data scarcity and inaccessibility have become substantial impediments to informed decision-making. Among the outputs of this research are 1) a revised technique for evaluating bias correction performance on reanalysis data for use in regions where precipitation data is temporally discontinuous which can potentially be applied to other types of climate data as well, 2) a new methodology for quantifying qualitative information contained in legislation and official documents and websites for the assessment of relationships between documented meteorological and water data policies and resulting outcomes in terms of data availability and accessibility, and 3) a fresh look at data needs and the value data holds with respect to water resources decision-making and management in the region.</p>