Suchergebnisse

ABSTRACT
ObjectivesDataSHIELD (www.datashield.ac.uk) was born of the requirement in the biomedical and social sciences to co-analyse individual patient data (microdata) from different sources, without disclosing identity or sensitive information. Under DataSHIELD, raw data never leaves the data provider and no microdata or disclosive information can be seen by the researcher. The analysis is taken to the data - not the data to the analysis.
Text data can be very disclosive in the biomedical domain (patient records, GP letters etc). Similar, but different, issues are present in other domains - text could be copyrighted, or have a large IP value, making sharing impractical.
ApproachBy treating text in an analogous way to individual patient data we assessed if DataSHIELD could be adapted and implemented for text analysis, and circumvent the key obstacles that currently prevent it.
ResultsUsing open digitised text data held by the British Library, a DataSHIELD proof-of-concept infrastructure and prototype DataSHIELD functions for free text analysis were developed.
ConclusionsWhilst it is possible to analyse free text within a DataSHIELD infrastructure, the challenge is creating generalised and resilient anti-disclosure methods for free text analysis. There are a range of biomedical and health sciences applications for DataSHIELD methods of privacy protected analysis of free text including analysis of electronic health records and analysis of qualitative data e.g. from social media.

<b><i>Background:</i></b> Data from individual collections, such as biobanks and cohort studies, are now being shared in order to create combined datasets which can be queried to ask complex scientific questions. But this sharing must be done with due regard for data protection principles. DataSHIELD is a new technology that queries nonaggregated, individual-level data in situ but returns query data in an anonymous format. This raises questions of the ability of DataSHIELD to adequately protect participant confidentiality. <b><i>Methods:</i></b> An ethico-legal analysis was conducted that examined each step of the DataSHIELD process from the perspective of UK case law, regulations, and guidance. <b><i>Results:</i></b> DataSHIELD reaches agreed UK standards of protection for the sharing of biomedical data. All direct processing of personal data is conducted within the protected environment of the contributing study; participating studies have scientific, ethics, and data access approvals in place prior to the analysis; studies are clear that their consents conform with this use of data, and participants are informed that anonymisation for further disclosure will take place. <b><i>Conclusion:</i></b> DataSHIELD can provide a flexible means of interrogating data while protecting the participants' confidentiality in accordance with applicable legislation and guidance.

The promise of science lies in expectations of its benefits to societies and is matched by expectations of the realisation of the significant public investment in that science. In this paper, we undertake a methodological analysis of the science of biobanking and a sociological analysis of translational research in relation to biobanking. Part of global and local endeavours to translate raw biomedical evidence into practice, biobanks aim to provide a platform for generating new scientific knowledge to inform development of new policies, systems and interventions to enhance the public's health. Effectively translating scientific knowledge into routine practice, however, involves more than good science. Although biobanks undoubtedly provide a fundamental resource for both clinical and public health practice, their potentiating ontology—that their outputs are perpetually a promise of scientiWc knowledge generation— renders translation rather less straightforward than drug discovery and treatment implementation. Biobanking science, therefore, provides a perfect counterpoint against which to test the bounds of translational research. We argue that translational research is a contextual and cumulative process: one that is necessarily dynamic and interactive and involves multiple actors. We propose a new multidimensional model of translational research which enables us to imagine a new paradigm: one that takes us from bench to bedside to backyard and beyond, that is, attentive to the social and political context of translational science, and is cognisant of all the players in that process be they researchers, health professionals, policy makers, industry representatives, members of the public or research participants, amongst others. ; Peer-reviewed ; Publisher Version ; 54597

The promise of science lies in expectations of its benefits to societies and is matched by expectations of the realisation of the significant public investment in that science. In this paper, we undertake a methodological analysis of the science of biobanking and a sociological analysis of translational research in relation to biobanking. Part of global and local endeavours to translate raw biomedical evidence into practice, biobanks aim to provide a platform for generating new scientific knowledge to inform development of new policies, systems and interventions to enhance the public's health. Effectively translating scientific knowledge into routine practice, however, involves more than good science. Although biobanks undoubtedly provide a fundamental resource for both clinical and public health practice, their potentiating ontology—that their outputs are perpetually a promise of scientific knowledge generation—renders translation rather less straightforward than drug discovery and treatment implementation. Biobanking science, therefore, provides a perfect counterpoint against which to test the bounds of translational research. We argue that translational research is a contextual and cumulative process: one that is necessarily dynamic and interactive and involves multiple actors. We propose a new multidimensional model of translational research which enables us to imagine a new paradigm: one that takes us from bench to bedside to backyard and beyond, that is, attentive to the social and political context of translational science, and is cognisant of all the players in that process be they researchers, health professionals, policy makers, industry representatives, members of the public or research participants, amongst others.

ABSTRACT
ObjectivesIn several disciplines such as in biomedicine and social sciences the analysis of individual-level data or the co-analysis of data from different studies requires the pooling and the sharing of those data. However, sharing and combining sensitive individual-level data is often prohibited by ethico-legal constraints and other barriers such as the control maintenance and the huge sample sizes. The graphical illustration of microdata is also often forbidden as can potentially be unsecured on the identification of sensitive information. For example the plot of a standard scatterplot is disclosive as can explicitly specify the exact values of two measurements for each single individual.
ApproachDataSHIELD (www.datashield.ac.uk) is a novel approach that allows the analysis of sensitive individual-level data and the co-analysis of such data from several studies simultaneously without physically pooling the data.
ResultsDataSHIELD functionality consists of several functions that provide the flexibility of performing data analysis through different statistical techniques. A part of this environment includes a number of graphical-related functions for the graphical illustration of the statistical properties and relationships between different variables. We overview the graphical functions in DataSHIELD (ds.histogram, ds.heatmapPlot, ds.contourPlot) and demonstrate a number of new functions including ds.scatterPlot and ds.boxPlot developed based on the application of different computational approaches like the k-Nearest Neighbours algorithm and ensuring privacy protected analysis.
ConclusionDataSHIELD graphical functionality has certain methodological features for the representation of the relationships between different variables preserving their statistical properties and assuring the data privacy protection. These graphical approaches can be used or enhanced for application in various areas where confidentiality and information sensitivity is considered, for example in longitudinal data and survival analysis, in epidemiological studies, in geospatial analysis and several others.

Abstract In this paper we argue that rationalist 'predict then act' approaches to disaster risk management (DRM) policy promote unrealistic public expectations of DRM provisions, the avoidance of decision making by political elites, an over-reliance on technical expertise and engineering solutions to reducing exposure to natural events, and a reactive approach to DRM overall. We propose an alternative incrementalist approach that focuses on managing uncertainties rather than reducing them and building resilience not simply through the reduction of hazard exposure, but also through the ongoing reduction of community vulnerability, the explicit consideration of normative priorities, and more effective community engagement in climate risk debates.

Emergency management and climate change adaptation will increasingly challenge all levels of government because of three main factors. First, Australia is extremely vulnerable to the impacts of climate change, particularly through the increasing frequency, duration and/or intensity of disasters such as floods and bushfires. Second, the system of government that divides powers by function and level can often act as a barrier to a well-integrated response. Third, policymaking processes struggle to cope with such complex inter-jurisdictional issues. This paper discusses these factors and explores the nature of the challenge for Australian governments. Investigations into the 2009 Victorian bushfires, the 2011 Perth Hills bushfires, and the 2011 Brisbane floods offer an indication of the challenges ahead and it is argued that there is a need to: improve community engagement and communication; refocus attention on resilience; improve interagency communication and collaboration; and, develop institutional arrangements that support continual improvement and policy learning. These findings offer an opportunity for improving responses as well as a starting point for integrating disaster risk management and climate change adaptation policies. The paper is based on the preliminary findings of an NCCARF funded research project: The Right Tool for the Job: Achieving climate change adaptation outcomes through improved disaster management policies, planning and risk management strategies involving Griffith University and RMIT. It should be noted from the outset that the purpose of this research project is not to criticise the actions of emergency service workers and volunteers who do an incredible job under extreme circumstances, often risking their own lives in the process. The aim is simply to offer emergency management agencies the opportunity to step back and rethink their overall approach to the challenge they face in the light of the impacts of climate change.