The Global Protection of Personal Health Data

The workshop is an activity of the IMIA Working Group ‘Security in Health Information Systems’ (SiHIS). It is focused to the growing global problem: how to protect personal health data in today’s global eHealth and digital health environment. It will review available trust building mechanisms, security measures and privacy policies. Technology alone does not solve this complex problem and current protection policies and legislation are considered woefully inadequate. Among other trust building tools, certification and accreditation mechanisms are dis-cussed in detail and the workshop will determine their acceptance and quality. The need for further research and international collective action are discussed. This workshop provides an opportunity to address a critical growing problem and make pragmatic proposals for sustainable and effective solutions for global eHealth and digital health.

Increasing research data management capability within your university

A Powerpoint presentation on increasing research data management capability within your university, presented from the university library perspective, and focusing on collaborations with university partners to develop and implement university wide data management services and infrastructure.

Straightforward data management at QUT

QUT Library Research Support has simplified and streamlined the process of research data management planning, storage, discovery and reuse through collaboration and the use of integrated and tailored online tools, and a simplification of the metadata schema. This poster presents the integrated data management services a QUT, including QUT’s Data Management Planning Tool, Research Data Finder, Spatial Data Finder and Software Finder, and information on the simplified Registry Interchange Format – Collections and Services (RIF-CS) Schema. The QUT Data Management Planning (DMP) Tool was built using the Digital Curation Centre’s DMP Online Tool and modified to QUT’s needs and policies. The tool allows researchers and Higher Degree Research students to plan how to handle research data throughout the active phase of their research. The plan is promoted as a ‘live’ document’ and researchers are encouraged to update it as required. The information entered into the plan can be made private or shared with supervisors, project members and external examiners. A plan is mandatory when requesting storage space on the QUT Research Data Storage Service. QUT’s Research Data Finder is integrated with QUT’s Academic Profiles and the Data Management Planning Tool to create a seamless data management process. This process aims to encourage the creation of high quality rich records which facilitate discovery and reuse of quality data. The Registry Interchange Format – Collections and Services (RIF-CS) Schema that is used in the QUT Research Data Finder was simplified to “RIF-CS lite” to reflect mandatory and optional metadata requirements. RIF-CS lite removed schema fields that were underused or extra to the needs of the users and system. This has reduced the amount of metadata fields required from users and made integration of systems a far more simple process where field content is easily shared across services making the process of collecting metadata as transparent as possible.

Clinical data warehousing

Clinical Data Warehousing: A Business Analytic approach for managing health data

Design of a tangible data visualization (extended)

In this paper we describe the design of DNA Jewelry, which is a wearable tangible data representation of personal DNA profile data. An iterative design process was followed to develop a 3D form-language that could be mapped to standard DNA profile data, with the aim of retaining readability of data while also producing an aesthetically pleasing and unique result in the area of personalised design. The work explores design issues with the production of data tangibles, contributes to a growing body of research exploring tangible representations of data and highlights the importance of approaches that move between technology, art and design.

Leveraging the repository infrastructure to curate institutional heritage materials, learning objects and data assets

Developing and maintaining a successful institutional repository for research publications requires a considerable investment by the institution. Most of the money is spent on developing the skill-sets of existing staff or hiring new staff with the necessary skills. The return on this investment can be magnified by using this valuable infrastructure to curate collections of other materials such as learning objects, student work, conference proceedings and institutional or local community heritage materials. When Queensland University of Technology (QUT) implemented its repository for research publications (QUT ePrints) over 11 years ago, it was one of the first institutional repositories to be established in Australia. Currently, the repository holds over 29,000 open access research publications and the cumulative total number of full-text downloads for these document now exceeds 16 million. The full-text deposit rate for recently-published peer reviewed papers (currently over 74%) shows how well the repository has been embraced by QUT researchers. The success of QUT ePrints has resulted in requests to accommodate a plethora of materials which are ‘out of scope’ for this repository. QUT Library saw this as an opportunity to use its repository infrastructure (software, technical know-how and policies) to develop and implement a metadata repository for its research datasets (QUT Research Data Finder), a repository for research-related software (QUT Software Finder) and to curate a number of digital collections of institutional and local community heritage materials (QUT Digital Collections). This poster describes the repositories and digital collections curated by QUT Library and outlines the value delivered to the institution, and the wider community, by these initiatives.

Performative digital asset management: To propose a framework and proof of concept model that effectively enables researchers to document, archive and curate their non-traditional research data

This cross disciplinary study was conducted as two research and development projects. The outcome is a multimodal and dynamic chronicle, which incorporates the tracking of spatial, temporal and visual elements of performative practice-led and design-led research journeys. The distilled model provides a strong new approach to demonstrate rigour in non-traditional research outputs including provenance and an 'augmented web of facticity'.

Open access spatial data and collaboration

On 19 June 2015, representatives from over 40 Australian research institutions gathered in Canberra to launch their Open Data Collections. The one day event, hosted by the Australian National Data Service (ANDS), showcased to government and a range of national stakeholders the rich variety of data collections that have been generated through the Major Open Data Collections (MODC) project. Colin Eustace attended the showcase for QUT Library and presented a poster that reflected the work that he and Jodie Vaughan generated through the project. QUT’s Blueprint 4, the University’s five-year institutional strategic plan, outlines the key priorities of developing a commitment to working in partnership with industry, as well as combining disciplinary strengths with interdisciplinary application. The Division of Technology, Information and Learning Support (TILS) has undertaken a number of Australian National Data Service (ANDS) funded projects since 2009 with the aim of developing improved research data management services within the University to support these strategic aims. By leveraging existing tools and systems developed during these projects, the Major Open Data Collection (MODC) project delivered support to multi-disciplinary collaborative research activities through partnership building between QUT researchers and Queensland government agencies, in order to add to and promote the discovery and reuse of a collection of spatially referenced datasets. The MODC project built upon existing Research Data Finder infrastructure (which uses VIVO open source software, developed by Cornell University) to develop a separate collection, Spatial Data Finder (https://researchdatafinder.qut.edu.au/spatial) as the interface to display the spatial data collection. During the course of the project, 62 dataset descriptions were added to Spatial Data Finder, 7 added to Research Data Finder and two added to Software Finder, another separate collection. The project team met with 116 individual researchers and attended 13 school and faculty meetings to promote the MODC project and raise awareness of the Library’s services and resources for research data management.

Investigating in-domain data requirements for PLDA training

This paper analyzes the limitations upon the amount of in- domain (NIST SREs) data required for training a probabilistic linear discriminant analysis (PLDA) speaker verification system based on out-domain (Switchboard) total variability subspaces. By limiting the number of speakers, the number of sessions per speaker and the length of active speech per session available in the target domain for PLDA training, we investigated the relative effect of these three parameters on PLDA speaker verification performance in the NIST 2008 and NIST 2010 speaker recognition evaluation datasets. Experimental results indicate that while these parameters depend highly on each other, to beat out-domain PLDA training, more than 10 seconds of active speech should be available for at least 4 sessions/speaker for a minimum of 800 speakers. If further data is available, considerable improvement can be made over solely out-domain PLDA training.

Model updating of real structures with ambient vibration data

It is important to develop reliable finite element models for real structures not only in the design phase but also for the structural health monitoring and structural maintenance purposes. This paper describes the experience of the authors in using ambient vibration model identification techniques together with model updating tools to develop reliable finite element models of real civil engineering structures. Case studies of two real structures are presented in this paper. One is a 10 storey concrete building which is considered as a non-slender structure with complex boundary conditions. The other is a single span concrete foot bridge which is also a relatively inflexible planar structure with complex boundary conditions. Both structures are located at the Queensland University of Technology (QUT) and equipped with continuous structural health monitoring systems.

Improving PLDA speaker verification using WMFD and linear-weighted approaches in limited microphone data conditions

This paper proposes the addition of a weighted median Fisher discriminator (WMFD) projection prior to length-normalised Gaussian probabilistic linear discriminant analysis (GPLDA) modelling in order to compensate the additional session variation. In limited microphone data conditions, a linear-weighted approach is introduced to increase the influence of microphone speech dataset. The linear-weighted WMFD-projected GPLDA system shows improvements in EER and DCF values over the pooled LDA- and WMFD-projected GPLDA systems in inter-view-interview condition as WMFD projection extracts more speaker discriminant information with limited number of sessions/ speaker data, and linear-weighted GPLDA approach estimates reliable model parameters with limited microphone data.

Using decision trees to understand structure in missing data

Objectives Demonstrate the application of decision trees – classification and regression trees (CARTs), and their cousins, boosted regression trees (BRTs) – to understand structure in missing data. Setting Data taken from employees at three different industry sites in Australia. Participants 7915 observations were included. Materials and Methods The approach was evaluated using an occupational health dataset comprising results of questionnaires, medical tests, and environmental monitoring. Statistical methods included standard statistical tests and the ‘rpart’ and ‘gbm’ packages for CART and BRT analyses, respectively, from the statistical software ‘R’. A simulation study was conducted to explore the capability of decision tree models in describing data with missingness artificially introduced. Results CART and BRT models were effective in highlighting a missingness structure in the data, related to the Type of data (medical or environmental), the site in which it was collected, the number of visits and the presence of extreme values. The simulation study revealed that CART models were able to identify variables and values responsible for inducing missingness. There was greater variation in variable importance for unstructured compared to structured missingness. Discussion Both CART and BRT models were effective in describing structural missingness in data. CART models may be preferred over BRT models for exploratory analysis of missing data, and selecting variables important for predicting missingness. BRT models can show how values of other variables influence missingness, which may prove useful for researchers. Conclusion Researchers are encouraged to use CART and BRT models to explore and understand missing data.

Japonica rice: Intellectual property, scientific publishing and data-sharing

This article examines a series of controversies within the life sciences over data sharing. Part 1 focuses upon the agricultural biotechnology firm Syngenta publishing data on the rice genome in the journal Science, and considers proposals to reform scientific publishing and funding to encourage data sharing. Part 2 examines the relationship between intellectual property rights and scientific publishing, in particular copyright protection of databases, and evaluates the declaration of the Human Genome Organisation that genomic databases should be global public goods. Part 3 looks at varying opinions on the information function of patent law, and then considers the proposals of Patrinos and Drell to provide incentives for private corporations to release data into the public domain.

Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling

Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9-85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large "mega-family". We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability.

Investigating brain connectivity heritability in a twin study using diffusion imaging data

Heritability of brain anatomical connectivity has been studied with diffusion-weighted imaging (DWI) mainly by modeling each voxel's diffusion pattern as a tensor (e.g., to compute fractional anisotropy), but this method cannot accurately represent the many crossing connections present in the brain. We hypothesized that different brain networks (i.e., their component fibers) might have different heritability and we investigated brain connectivity using High Angular Resolution Diffusion Imaging (HARDI) in a cohort of twins comprising 328 subjects that included 70 pairs of monozygotic and 91 pairs of dizygotic twins. Water diffusion was modeled in each voxel with a Fiber Orientation Distribution (FOD) function to study heritability for multiple fiber orientations in each voxel. Precision was estimated in a test-retest experiment on a sub-cohort of 39 subjects. This was taken into account when computing heritability of FOD peaks using an ACE model on the monozygotic and dizygotic twins. Our results confirmed the overall heritability of the major white matter tracts but also identified differences in heritability between connectivity networks. Inter-hemispheric connections tended to be more heritable than intra-hemispheric and cortico-spinal connections. The highly heritable tracts were found to connect particular cortical regions, such as medial frontal cortices, postcentral, paracentral gyri, and the right hippocampus.