Geavanceerde computerondersteuning van kwantitatief onderzoek

Kwantitatief onderzoek vindt in belangrijke mate op de computer plaats. Toch is de computerondersteuning van kwantitatief onderzoek niet ideaal. Er zijn meerdere, ongekoppelde systemen nodig en veel informatie blijft impliciet en dus niet toegankelijk voor automatische verwerking. De oplossing schuilt in semantische ontsluiting van de kwantitatieve informatie. Daartoe werd een kwantitatief vocabulaire ontwikkeld, die daarna werd toegepast in ontwikkelde tools, waarna het gebruik van deze tools met kwantitatieve onderzoekers werd geëvalueer

Semantic Support for Quantitative Research

Top, J.L. [Promotor

Towards Customizable Chart Visualizations of Tabular Data Using Knowledge Graphs

Scientific articles are typically published as PDF documents, thus rendering the extraction and analysis of results a cumbersome, error-prone, and often manual effort. New initiatives, such as ORKG, focus on transforming the content and results of scientific articles into structured, machine-readable representations using Semantic Web technologies. In this article, we focus on tabular data of scientific articles, which provide an organized and compressed representation of information. However, chart visualizations can additionally facilitate their comprehension. We present an approach that employs a human-in-the-loop paradigm during the data acquisition phase to define additional semantics for tabular data. The additional semantics guide the creation of chart visualizations for meaningful representations of tabular data. Our approach organizes tabular data into different information groups which are analyzed for the selection of suitable visualizations. The set of suitable visualizations serves as a user-driven selection of visual representations. Additionally, customization for visual representations provides the means for facilitating the understanding and sense-making of information

Future Food Basket : methodology for the forecasting of the future food demand

Research by Wageningen University & Research (WUR) on global food and nutrition security focuses on the question how to achieve transitions to a food system that will be adequately equipped to nourish the growing world population. One of the challenges of this transition is to evolve to a food system that will be sustainable (resource-efficient and with minimal impact on climate change and global warming), yielding affordable, trustworthy (safe), high-quality food products. This particular report is part of a study on the redesign of food value chains from linear value chains into circular adaptive value chain networks for nutrition and food security (Redesign or Adaptive Value Chain Networks for food and nutrition security (AdVaNs)). In view of the global trends of world population growth, urbanization, the efficient use of natural resources, mitigation of the impact of food production on climate change and global warming, this research addresses global food and nutrition security by developing a forecast model for the content and composition of local food baskets. Enablers of changes in these future food baskets are the growing economic welfare, advancing information technologies and sustainability issues that affect regional and global value chains. Knowledge about these trends in this future demand on food is searched for by policy makers and governments that are in need of accurate and reliable quantitative information for strategic decision-making. By developing forecasting models that are dedicated to human nutritional needs and consumption patterns, historic quantitative data can be transferred into future trends and predictions regarding food demand in specific regions. A methodology, using autonomous time based linear regression, was developed by the authors to predict a future food basket in terms of energy, composition and products for the near future in 2030 based on available historical data. The methodology was used for 4 regions in Mexico (Mexico City, North-, South- and Central Mexico). Also the amount of micro-nutrients, including vitamins and minerals, in the food was estimated. The forecasted results were also categorised by two demographic characteristics: income class (low income vs. high income) and the residential environment (urban vs. rural environment). The forecasting is based on FAO data in combination with national data for the prediction of the specific regional food baskets in Mexico. The results show that the urban region obtains more energy and vegetables, fruit and meat, having also the more wealthy class of the population. Also in Mexico most proteins and carbohydrates are consumed as part of staple foods. In this research validation of the methodology was carried out by using data from the past to predict the situation in 2011 of the composition of the food basket. This comparison of the present data with the forecasted data shows that this linear regression method can be used to forecast the food basket in 2030 for a majority of product groups, but to a smaller extent for milk and pulses in particular

Exploring forest structural complexity by multi-scale segmentation of VHR imagery

Forests are complex ecological systems, characterised by multiple-scale structural and dynamical patterns which are not inferable from a system description that spans only a narrow window of resolution; this makes their investigation a difficult task using standard field sampling protocols. We segment a QuickBird image covering a beech forest in an initial stage of old-growthness – showing, accordingly, a good degree of structural complexity – into three segmentation levels. We apply field-based diversity indices of tree size, spacing, species assemblage to quantify structural heterogeneity amongst forest regions delineated by segmentation. The aim of the study is to evaluate, on a statistical basis, the relationships between spectrally delineated image segments and observed spatial heterogeneity in forest structure, including gaps in the outer canopy. Results show that: some 45% of the segments generated at the coarser segmentation scale (level 1) are surrounded by structurally different neighbours; level 2 segments distinguish spatial heterogeneity in forest structure in about 63% of level 1 segments; level 3 image segments detect better canopy gaps, rather than differences in the spatial pattern of the investigated structural indices. Results support also the idea of a mixture of macro and micro structural heterogeneity within the beech forest: large size populations of trees homogeneous for the examined structural indices at the coarser segmentation level, when analysed at a finer scale, are internally heterogeneous; and vice versa. Findings from this study demonstrate that multiresolution segmentation is able to delineate scale-dependent patterns of forest structural heterogeneity, even in an initial stage of old-growth structural differentiation. This tool has therefore a potential to improve the sampling design of field surveys aimed at characterizing forest structural complexity across multiple spatio-temporal scales.L'articolo è disponibile sul sito dell'editore www.sciencedirect.co

Relating some stuff to other stuff

Traceability in food and medicine supply chains has to handle stuffs—entities such as milk and starch indicated with mass nouns—and their portions and parts that get separated and put together to make the final product. Implementations have underspecified ‘links’, if at all, and theoretical accounts from philosophy and in domain ontologies are incomplete as regards the relations involved. To solve this issue, we define seven relations for portions and stuff-parts, which are temporal where needed. The resulting theory distinguishes between the extensional and intensional level, and between amount of stuff and quantity. With application trade-offs, this has been implemented as an extension to the Stuff Ontology core ontology that now also imports a special purpose module of the Ontology of units of Measure for quantities. Although atemporal, some automated reasoning for traceability is still possible thanks to using property chains to approximate the relevant temporal aspects