Metadata

Metadata, or data about data, play a crucial rule in social sciences to ensure that high quality documentation and community knowledge are properly captured and surround the data across its entire life cycle, from the early stages of production to secondary analysis by researchers or use by policy makers and other key stakeholders. The paper provides an overview of the social sciences metadata landscape, best practices and related information technologies. It particularly focuses on two specifications - the Data Documentation Initiative (DDI) and the Statistical Data and Metadata Exchange Standard (SDMX) - seen as central to a global metadata management framework for social data and official statistics. It also highlights current directions, outlines typical integration challenges, and provides a set of high level recommendations for producers, archives, researchers and sponsors in order to foster the adoption of metadata standards and best practices in the years to come.social sciences, metadata, data, statistics, documentation, data quality, XML, DDI, SDMX, archive, preservation, production, access, dissemination, analysis

Generating evidence of aid effectiveness in global health: the case of the Global Fund

Global health has been identified as a ‘tracer-sector’ for advancement in regards to aid-effectiveness. This paper interrogates how evidence of aid effectiveness has been generated within one of the central, most resource-rich global health actors: The Global Fund to fight Tuberculosis, Aids and Malaria. Key terms are defined, processes for generating evidence of aid-effectiveness within both the public and global health arenas examined, and conclusions around the predominance of vertical interventions in the global health arena proposed. Ultimately, it is argued that the need for strategic and financial legitimacy has driven the Global Fund to generate very specific kinds of evidence of AE and that the Global Fund only generates the kind of evidence it can take

Miniaturized bioactivity screening of complex samples

Irth, H. [Promotor]Niessen, W.M.A. [Promotor]Kool, J. [Copromotor

Turbulent dispersion in cloud-topped boundary layers

Compared to dry boundary layers, dispersion in cloud-topped boundary layers has received less attention. In this LES based numerical study we investigate the dispersion of a passive tracer in the form of Lagrangian particles for four kinds of atmospheric boundary layers: 1) a dry convective boundary layer (for reference), 2) a "smoke" cloud boundary layer in which the turbulence is driven by radiative cooling, 3) a stratocumulus topped boundary layer and 4) a shallow cumulus topped boundary layer. We show that the dispersion characteristics of the smoke cloud boundary layer as well as the stratocumulus situation can be well understood by borrowing concepts from previous studies of dispersion in the dry convective boundary layer. A general result is that the presence of clouds enhances mixing and dispersion ¿ a notion that is not always reflected well in traditional parameterization models, in which clouds usually suppress dispersion by diminishing solar irradiance. The dispersion characteristics of a cumulus cloud layer turn out to be markedly different from the other three cases and the results can not be explained by only considering the well-known top-hat velocity distribution. To understand the surprising characteristics in the shallow cumulus layer, this case has been examined in more detail by 1) determining the velocity distribution conditioned on the distance to the nearest cloud and 2) accounting for the wavelike behaviour associated with the stratified dry environmen

Automated Tracking of Shallow Cumulus Clouds in Large Domain, Long Duration Large Eddy Simulations

This paper presents a method for feature tracking of fields of shallow cumulus convection in large eddy simulations (LES) by connecting the projected cloud cover in space and time, and by accounting for splitting and merging of cloud objects. Existing methods tend to be either imprecise or, when using the full three-dimensional (3-D) spatial field, prohibitively expensive for large data sets. Compared to those 3-D methods, the current method reduces the memory footprint by up to a factor 100, while retaining most of the precision by correcting for splitting and merging events between different clouds. The precision of the algorithm is further enhanced by taking the vertical extent of the cloud into account. Furthermore, rain and subcloud thermals are also tracked, and links between clouds, their rain, and their subcloud thermals are made. The method compares well with results from the literature. Resolution and domain dependencies are also discussed. For the current simulations, the cloud size distribution converges for clouds larger than an effective resolution of 6 times the horizontal grid spacing, and smaller than about 20% of the horizontal domain size

Large-Eddy Simulation of Organized Precipitating Trade Wind Cumulus Clouds

Trade wind cumulus clouds often organize in along-wind cloud streets and across-wind mesoscale arcs. We present a benchmark large-eddy simulation which resolves the individual clouds as well as the mesoscale organization on scales of O(10 km). Different methods to quantify organization of cloud fields are applied and discussed. Using perturbed physics large-eddy simulation experiments, the processes leading to the formation of cloud clusters and the mesoscale arcs are revealed. We find that both cold pools as well as the sub-cloud layer moisture field are crucial to understand the organization of precipitating shallow convection. Further sensitivity studies show that microphysical assumptions can have a pronounced impact on the onset of cloud organization