Suchergebnisse

Data Development for International Research (DDIR) DDIR II: Event Data Research

Research data management (RDM) is a major priority for many institutions as they struggle to cope with the plethora of pronouncements including funder policies, a G8 statement, REF2020 consultations, all stressing the importance of open data in driving everything from global innovation through to more accountable governance; not to mention the more direct possibility that non-compliance could result in grant income drying up. So, at the coalface, how do we become part of this global movement? In this article the author explains the approach being taken at the University of St Andrews, building on the research information management infrastructure (data, systems and people) that has evolved since 2006. Continuing to navigate through the rapidly evolving research policy and cultural landscape, they aim to establish services to support their research community as it moves to this 'open by default' requirement of funders and governments.

: The increasing employment of artificial intelligence and machine learning in the biomedical sector as well as the growing number of partnerships aimed at pooling together different types of digital health data, stress the importance of an effective regulation and governance of data sharing in the health and life sciences. This paper explores the emerging economic reality of health data pools from the perspective of European Union policy and law.

While disaggregation has gained prominence in policy research, it is sometimes flawed by invalid & unreliable policy measures, & by improper inferences derived from policy data. Examples of research of several types are compared, including studies which disaggregate within a level of analysis, between levels of analysis, or within a level of analysis with interlevel inferences. Reviewed are both the usefulness of disaggregation & the errors which it may generate. 1 Table. Modified HA.

This article explores the implicit philosophical framework that underpins, and provides the moral and political justification for, the move towards treating data as a so-called common resource. It begins by tracing the emergence of the idea of viewing data as an open access common resource. It then outlines the regulatory, policy and legislative mechanisms that have been instituted to encourage and ensure that researchers comply with data sharing requirements, and that are institutionalising new ownership regimes away from research data being treated de facto as private property towards it becoming public property. It also spells out the case being made for treating data as a public good, including scientific, moral, economic and political arguments. The article then moves on to suggest that positioning data as a common resource is dependent on a Cartesian and representational understanding of data, their production, and their use in the making of knowledge, drawing in particular on the work of Karen Barad. Barad's critique of classical Cartesian and Newtonian metaphysical assumptions helps to reveal the positionality of the assumed universalism of treating research data as a given and a priori common resource. The final section of the article considers what treating data as a common resource and public good, and the exclusion of the labour and relations of data producers that it depends on, does ontologically, epistemologically, morally and politically. In particular, it suggests that emerging regulatory, policy, legislative and discursive practices reinforce, institutionalise and legitimise power differentials and inequalities precisely along the lines that feminist scholars have been contesting for over four decades.

While references to the complexity of the subject matter permeate both policy rhetoric & policy research, remarkably little effort is generally expended on the search for nonlinear & nonadditive relationships among variables. The importance of incorporating such a search into exploratory data analysis is emphasized, & the theoretical & methodological problems that may arise are considered. A number of strategies for coping with excessive interaction are presented; seven prescriptions are offered. 24 References. HA.

The article explores the intersections between the different perspectives of institutional & policy research & discusses the characteristic purposes & conditions of theory-oriented policy research, where the usefulness of statistical analyses is generally constrained by the complexity & contingency of causal influences. Although comparative case studies are better able to deal with these conditions, their capacity to empirically identify the causal effect of differing institutional conditions on policy outcomes depends on a restrictive case selection that would need to hold constant the influence of two other sets of contingent factors-the policy challenges actually faced & the preferences & perceptions of the actors involved. When this is not possible, empirical policy research may usefully resort to a set of institutionalist working hypotheses that are derived from the narrowly specified theoretical assumptions of rational-choice institutionalism. Although these hypotheses will often be wrong, they are useful in guiding the empirical search for factors that are able to explain policy outcomes that deviate from predictions of the rationalist model. 90 References. Adapted from the source document.

This paper urges organizational researchers to collect data from subjects in the form of pictures, diagrams, computer graphics, and other visual representations. Drawing on theoretical and empirical work in cognitive psychology, neurophysiology, linguistics, and artificial intelligence, it presents a rationale for collecting visual data, provides examples, and suggests research questions and settings where visual data may be preferable to verbal data.

As part of a response to the challenges that confront Canadian policy research, a joint task force assembled by the Social Sciences and Humanities Research Council (SSHRC) and Statistics Canada proposed the creation of a series of Research Data Centres (RDCs). The network of RDCs was formally launched in December 2000 with the opening of the centre at McMaster University in Hamilton, Ontario. The RDCs are located throughout the country, so researchers are not obliged to travel to Ottawa to access Statistics Canada data. At the same time, the centres are administered in accordance with all the confidentiality rules required under the Statistics Act. The Research Data Centres meet, in a single location, both the need to facilitate access to detailed micro-data for crucial social research and the need to protect the confidentiality and security of Canadians' information. (Original abstract)