Open data platforms and their usability: Proposing a framework for evaluating citizen intentions

Governments across the world are releasing public data in an effort to increase transparency of how public services are managed whilst also enticing citizens to participate in the policy decision-making processes. The channel for making open data available to citizens in the UK is the data.gov.uk platform, which brings together data relating to various public services in one searchable website. The data.gov.uk platform currently offers access to 25,500 datasets that are organized across key public service themes including health, transport, education, environment, and public spending in towns and cities. While the website reports 5,438,159 site visits as of June 2015, the average time spent on the site has been recorded at just 02:12 min per visitor. This raises questions regarding the actual use and usability of open data platforms and the extent to which they fulfill the stated outcomes of open data. In this paper, the authors examine usability issues surrounding open data platforms and propose a framework that can be used to evaluate their usability

Impact of Urban Conditions of Firm Performance of Migrant Entrepreneurs: A Comparative Dutch - US Study

Recent studies on ethnic entrepreneurship have pointed at an increasing share of migrants in urban small- and medium-sized entrepreneurial businesses. These migrant activities are crucial to the urban economy in many countries, as they employ a significant part of the workforce. The main objective of our study is to identify success conditions of ethnic entrepreneurship by using concepts from social capital and human capital from the literature on empirical factors that are responsible for successful ethnic entrepreneurship. The empirical part of the paper is based on a survey questionnaire among migrant entrepreneurs in the city of Amsterdam in the Netherlands and in Fairfax, County in the state of Virginia in the US. We present an overview of cultural, ethno-psychological and motivational aspects that contribute to the understanding of similarities and differences between ethnic entrepreneurs in both locations. The analysis is structured around several dimensions of social and human capital including personal and business characteristics, and network participation for improving business performance. The findings of the two studies are compared to explore a possible correspondence in business performance patterns. The research tool used to assess performance is Data Envelopment Analysis (DEA), a technique for comparative efficiency analysis in various types of corporate organizations. Finally, concluding remarks are presented and possible extensions of the analysis are suggested. © Springer-Verlag 2009

Examining the role of genetic risk and longitudinal transmission processes underlying maternal parenting and psychopathology and children’s ADHD symptoms and aggression: utilizing the advantages of a prospective adoption design

Although genetic factors may contribute to initial liability for ADHD onset, there is growing evidence of the potential importance of the rearing environment on the developmental course of ADHD symptomatology. However, associations between family-level variables (maternal hostility, maternal depressive symptoms) and child behaviors (developmental course of ADHD and aggression) may be explained by genes that are shared by biologically related parents and children. Furthermore, ADHD symptoms and aggression commonly co-occur: it is important to consider both simultaneously to have a better understanding of processes underlying the developmental course of ADHD and aggression. To addresses these issues, we employed a longitudinal genetically sensitive parent–offspring adoption design. Analyses were conducted using Cohort I (n = 340) of the Early Growth and Development Study with cross-validation analyses conducted with Cohort II (n = 178). Adoptive mother hostility, but not depression, was associated with later child ADHD symptoms and aggression. Mothers and their adopted children were genetically unrelated, removing passive rGE as a possible explanation. Early child impulsivity/activation was associated with later ADHD symptoms and aggression. Child impulsivity/activation was also associated with maternal hostility, with some evidence for evocative gene-environment correlation processes on adoptive mother depressive symptoms. This study provides novel insights into family-based environmental influences on child ADHD and aggression symptoms, independent of shared parental genetic factors, implications of which are further explicated in the discussion

Exploiting Data for Supporting Developing Countries

The development in the field of ICT has opened doors for huge data collections. In order to take advantage of the huge data collections, research and development evolves in different directions, which are not necessarily divergent. We have set ourselves the question how developing and developed countries may share best practices to benefit from the explosive growth of data due to ICT- developments. We argue that existing apps can be tailored and fed by data to solve problems, such as safety issues, in specific countries. Furthermore, we stress the importance of sharing datasets amongst different countries. Data sharing contributes to a better understanding of emerging phenomena on our planet

Blackbox Meets Blackbox: Representational Similarity & Stability Analysis of Neural Language Models and Brains

In this paper, we define and apply representational stability analysis (ReStA), an intuitive way of analyzing neural language models. ReStA is a variant of the popular representational similarity analysis (RSA) in cognitive neuroscience. While RSA can be used to compare representations in models, model components, and human brains, ReStA compares instances of the same model, while systematically varying single model parameter. Using ReStA, we study four recent and successful neural language models, and evaluate how sensitive their internal representations are to the amount of prior context. Using RSA, we perform a systematic study of how similar the representational spaces in the first and second (or higher) layers of these models are to each other and to patterns of activation in the human brain. Our results reveal surprisingly strong differences between language models, and give insights into where the deep linguistic processing, that integrates information over multiple sentences, is happening in these models. The combination of ReStA and RSA on models and brains allows us to start addressing the important question of what kind of linguistic processes we can hope to observe in fMRI brain imaging data. In particular, our results suggest that the data on story reading from Wehbe et al./ (2014) contains a signal of shallow linguistic processing, but show no evidence on the more interesting deep linguistic processing.<br/