National Collaborative Research Infrastructure Strategy Research Articles

Deep learning framework with Bayesian data imputation for modelling and forecasting groundwater levels

Although traditional physical models have been used to analyse groundwater systems, the emergence of novel machine learning models can improve the accuracy of the predictions. Deep learning has been prominent in environmental and climate change problems. In this paper, we present a framework for utilising deep learning models to predict groundwater levels based on nearby streamflow and rainfall data. We address the missing data problem using a Bayesian linear regression model within the deep learning framework. Our deep learning framework utilises models such as long-short term memory (LSTM) networks and convolutional neural networks (CNN) for multi-step ahead time series prediction. We examine the fluctuations in groundwater levels at various boreholes located near Middle Creek in New South Wales, Australia. We use the National Collaborative Research Infrastructure Strategy (NCRIS) groundwater database and utilise Bayesian linear regression to impute missing data. We investigate the accuracy of the selected models for individual and regional basins and univariate and multivariate strategies. Our results show that the LSTM-based regional model with multivariate strategy using rainfall data provided the best accuracy.

Digital soil mapping of lithium in Australia

Abstract. With a higher demand for lithium (Li), a better understanding of its concentration and spatial distribution is important to delineate potential anomalous areas. This study uses a digital soil mapping framework to combine data from recent geochemical surveys and environmental covariates that affect soil formation to predict and map aqua-regia-extractable Li content across the 7.6×106 km2 area of Australia. Catchment outlet sediment samples (i.e. soils formed on alluvial parent material) were collected by the National Geochemical Survey of Australia at 1315 sites, with both top (0–10 cm depth) and bottom (on average ∼60–80 cm depth) catchment outlet sediments sampled. We developed 50 bootstrap models using a cubist regression tree algorithm for each depth. The spatial prediction models were validated on an independent Northern Australia Geochemical Survey dataset, showing a good prediction with a root mean square error of 3.32 mg kg−1 (which is 44.2 % of the interquartile range) for the top depth. The model for the bottom depth has yet to be validated. The variables of importance for the models indicated that the first three Landsat 30+ Barest Earth bands (red, green, blue) and gamma radiometric dose have a strong impact on the development of regression-based Li prediction. The bootstrapped models were then used to generate digital soil Li prediction maps for both depths, which could identify and delineate areas with anomalously high Li concentrations in the regolith. The predicted maps show high Li concentration around existing mines and other potentially anomalous Li areas that have yet to be verified. The same mapping principles can potentially be applied to other elements. The Li geochemical data for calibration and validation are available from de Caritat and Cooper (2011b; https://doi.org/10.11636/Record.2011.020) and Main et al. (2019; https://doi.org/10.11636/Record.2019.002), respectively. The covariate data used for this study were sourced from the Terrestrial Ecosystem Research Network (TERN) infrastructure, which is enabled by the Australian Government's National Collaborative Research Infrastructure Strategy (NCRIS; https://esoil.io/TERNLandscapes/Public/Products/TERN/Covariates/Mosaics/90m/, last access: 6 December 2022; TERN, 2019). The final predictive map is available at https://doi.org/10.5281/zenodo.7895482 (Ng et al., 2023).

Enhanced Zinc Ion Storage Capability of V2O5 Electrode Materials with Hollow Interior Cavities

AbstractThis work was supported by National Natural Science Foundation of China (No. 52172218) and the JST‐ERATO Materials Space‐Tectonics Project (JPMJER2003). Z.A. is grateful to the Researchers Supporting Project (RSP‐2021/1), King Saud University, Riyadh, Saudi Arabia. This work was performed in part at the Queensland node of the Australian National Fabrication Facility, a company established under the National Collaborative Research Infrastructure Strategy to provide nano and microfabrication facilities for Australia′s researchers.

The Atlas of Living Australia: History, current state and future directions.

The Atlas of Living Australia (ALA) is Australia’s national biodiversity database, delivering data and related services to more than 80,000 Australian and international users annually. Established under the Australian Government’s National Collaborative Research Infrastructure Strategy to provide trusted biodiversity data to support the research sector, its utility now extends to government, higher education, non-government organisations and community groups. These partners provide data to the ALA and leverage its data and related services. The ALA has also played an important leadership role internationally in the biodiversity informatics and infrastructure space, both through its partnership with the Global Biodiversity Information Facility and through support for the international Living Atlases programmes which has now delivered 24 instances of ALA software to deliver sovereign biodiversity data capability around the world. This paper begins with a historical overview of the genesis of the ALA from the collections, museums and herbaria community in Australia. It details the biodiversity and related data and services delivered to users with a primary focus on species occurrence records which represent the ALA's primary data type. Finally, the paper explores the ALA's future directions by referencing results from a recently completed national consultation process.

Benchmarking Computational Performance of Virtual Machines Within the Secure Unified Research Environment Versus Physical Computers

IntroductionThe Secure Unified Research Environment (SURE) is a high-powered computing environment located within Sax Institute (Sydney, Australia). SURE was established through the financial support of the Australian Government National Collaborative Research Infrastructure Strategy (NCRIS) as part of the Population Health Research Network (PHRN). SURE is approved by the Australian Government as the only secure platform for analysing unit record level sensitive health and other Australian Government data, providing computational resources and secure infrastructure in a form of virtual machines (VMs) accessible by approved researchers in Australia and overseas.
 Objectives and ApproachWe aim to compare computational performance of SURE VMs of different configurations with the performance of physical computers by running a series of standardised computational tasks involving different numbers of central processingunit (CPU) cores available on each computer. The approach utilised the benchmark test maintained by the H2O.ai group (https://h2oai.github.io/db-benchmark/). The results were measured over the datasets of different sizes, ranging from 500MB to 50GB in Random Access Memory (RAM).
 ResultsOur benchmarking outcomes have revealed that computational efficiency of physical computers uniformly outperform the efficiency of the current standard SURE VM configuration offerings, sometimes demonstrating a nearly double performance. For the range of typical analytical tasks assessed, computational performance greatly benefits from extending the number of computational cores available on a machine.
 Conclusion / ImplicationsSURE is a highly valuable tool enabling research and collaborations involving confidential population-based data. The shortage of RAM and CPU cores can be a major bottleneck even for moderately large datasets. VMs currently offered by SURE yet fall short of reaching computational performance of physical desktop computers. The results are to guide the funders and providers of secure remote access data laboratories responsible for providing Research Infrastructure as a Service (IaaS) tailored to meet the needs of participating research groups.

Virtual Laboratories for Biodiversity Modelling: An Australian perspective

Recent technologies have enabled consistent and continuous collection of ecological data at high resolutions across large spatial scales. The challenge remains, however, to bring these data together and expose them to methods and tools to analyse the interaction between biodiversity and the environment. These challenges are mostly associated with the accessibility, visibility and interoperability of data, and the technical computation needed to interpret the data. Australia has invested in digital research infrastructures through the National Collaborative Research Infrastructure Strategy (NCRIS). Here we present two platforms that provide easy access to global biodiversity, climate and environmental datasets integrated with a suite of analytical tools and linked to high-performance cloud computing infrastructure. The Biodiversity and Climate Change Virtual Laboratory (BCCVL) is a point-and-click online platform for modelling species responses to environmental conditions, which provides an easy introduction into the scientific concepts of models without the need for the user to understand the underlying code. For ecologists who write their own modelling scripts, we have developed ecocloud: a new online environment that provides access to data connected with command-line analysis tools like RStudio and Jupyter Notebooks as well as a virtual desktop environment using Australia’s national cloud computing infrastructure. ecocloud is built through collaborations among key facilities within the ecosciences domain, establishing a collective long-term vision of creating an ecosystem of infrastructure that provides the capability to enable reliable prediction of future environmental outcomes. Underpinning these tools is an innovative training program, ecoEd, which provides cohesive training and skill development to enhance the translation of Australia’s digital research infrastructures to the ecoscience community by educating and upskilling the next generation of environmental scientists and managers. Both of these platforms are built using a best-practice microservice model that allows for complete flexibility, scalability and stability in a cloud environment. Both the BCCVL and ecocloud are open-source developments and provide opportunities for interoperability with other platforms (e.g. Atlas of Living Austalia). In Australia, the same technical infrastructure is also used for a platform for the humanities and social science domain, indicating that the underlying technologies are not domain specific. We therefore welcome collaborations with other organisations to further develop these platforms for the wider bio- and ecoinformatics community. This presentation will showcase the tools, services, and underpinning infrastructure alongside our training and engagement framework as an exemplar in building platforms for next generation biodiversity science.

Earth’s field MRI for the non-invasive detection of fouling in spiral-wound membrane modules in pressure vessels during operation

R. Ujihara acknowledges scholarship funding from the Forrest Research Foundation. The authors acknowledge the facilities and scientific and technical assistance of the National Imaging Facility, a National Collaborative Research Infrastructure Strategy (NCRIS) capability, at the Centre for Microscopy, Characterisation and Analysis, The University of Western Australia, and especially the assistance of Dr. Kirk Feindel.

Colloidal crystal based plasma polymer patterning to control Pseudomonas aeruginosa attachment to surfaces

Event Abstract Back to Event Colloidal crystal based plasma polymer patterning to control Pseudomonas aeruginosa attachment to surfaces Hitesh Pingle1, Peng-Yuan Wang1, 2, Helmut Thissen2, Sally Mcarthur1 and Peter Kingshott1 1 Swinburne University Of Technology,Hawthorn, Department of Chemistry and Biotechnology, Australia 2 CSIRO Manufacturing Flagship, Bay view Avenue, Clayton,, Australia Introduction: Infections from biofilm formation on medical devices is a global problem with risks to human health and huge costs to health care worldwide[1],[2]. Bacteria have unique capabilities to generate resistance against antimicrobial agents. Recent studies have shown that extra-cellular DNA is a key molecule that helps biofilms form[3],[4], but the precise mechanisms of how bacteria and DNA interact at surfaces remains unknown. It has been shown that micro- and nanotopography and chemistry on surfaces can influence initial biomolecule adsorption, and subsequent bacterial adhesion[5]-[7]. However, there are fewer reports using such approaches for selectively grafting of DNA to understand the mechanisms of initial bacterial attachment responsible for biofilm formation. Material and Methods: Colloidal self-assembly was used to pattern surfaces with colloidal crystals to use as masks against allylamine plasma polymer (AAMpp) and acrylic acid plasma (AACpp) deposition to generate highly ordered patterns from the micro- to the nanoscale. PEG-aldehyde was grafted to the plasma regions via ‘cloud point’ grafting to prevent the attachment of bacteria on the plasma patterned surface regions, thereby controlling the adhesive sites by choice of the colloidal crystal morphology. Salmon sperm DNA was physically or covalently attached on both patterned and flat surfaces. P. aeruginosa was chosen to study the bacterial interactions with these chemically patterned surfaces. Scanning electron microscope (SEM), x-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), and Epi-fluorescence microscopy were used for pattern characterization, surface chemical analysis, and imaging of attached bacteria. Results: The AAMpp, AACpp, PEG and eDNA patterns were developed using the colloidal lithography approach. XPS results confirm the successful grafting of PEG and eDNA on the plasma coated surfaces. The AAMpp surface showed increased bacterial attachment compared to control while PEG and eDNA coated surface showed reductions in bacterial attachment as highlighted in Figures 1 and 2. FIG. 1. Epifluorescence images of DAPI stained P. aeruginosa attachment to (a) a 5 µm AAMpp pattern (b) a 5 µm AAMpp-DNA pattern (c) a 5 µm AAMpp_PEG pattern (d) a 5 µm AAMpp-PEG_DNA pattern. All scale bars: 50µm. FIG. 2. SEM images of P. aeruginosa attachment onto (a) a 5 µm AAMpp pattern (b) a 5 µm AAMpp-DNA pattern (c) a 5 µm AAMpp-PEG pattern (d) a 5 µm AAMpp-PEG-DNA pattern All scale bars: 10µm. Discussion: The bacterial attachment increase on the AAMpp surface is most likely due to the amine groups displaying a positive charge that attract the negatively charged extra-cellular polysaccharides on the bacterial cell surfaces. AACpp and DNA both contain negatively charged groups thus repulsion of bacteria is the most likely cause for the reduced bacterial attachment. Micron sized plasma patterns were able to trap bacteria due to the different chemistries presented. Different sized plasma patterns were generated through the use of different sized colloidal particles used as masks for plasma deposition. Conclusion: Surface charge plays an essential role in initial attachment of bacteria. Different chemical patterns made from colloidal crystal masking are easy to fabricate and could be useful in further applications in biomaterials research. The results showed that PEG and DNA patterns can be easily fabricated with exquisite control of pattern size and spacing and this is a useful tool to help understand how bacterial attach to surfaces. The Australian Research Council is acknowledged for funding a PhD scholarship for Hitesh Pingle through the Discovery.; The Scientific Industrial Endowment Fund (SIEF) is acknowledged for providing a John Stocker Postdoctoral Research Fellowship for Peng Yuan Wang.; This work was supported in part at both the Bio-interface Engineering Hub at Swinburne and the Melbourne Centre for Nanofabrication as part of the Victorian Node of the Australian National Fabrication Facility, a company established under the National Collaborative Research Infrastructure Strategy to provide nano and micro-fabrication facilities for Australia’s researchers.

TissueStack: an Open Source HTML5 web based imaging viewer

Event Abstract Back to Event TissueStack: an Open Source HTML5 web based imaging viewer Andrew Janke1*, Meng Kuan Lin1, Oliver Nicolini1, Harald Waxenegger1, Jeremy Ullmann1 and Graham J. Galloway1 1 University of Queensland, Australia Web based viewing of large 3D datasets is now a critical part of imaging research. This has been driven by multiple efforts to make available large collections of histological, optical and electron Microscopy data. It is critical that the web applications to view and serve this data are robust, available on multiple platforms, have a scalable view of data and are intuitive to use for novel users. Existing tools that are capable of presenting tri-planar views of large datasets rely upon custom web extensions such as Adobe Flash, have restrictive dataset size limitations and as such are not tractable on mobile devices. The TissueStack project was born from these goals and is now a mature product. The original use case was a large multi-site study with geographically distant generators of imaging data. This leads to the problem in which no one is sure where or how to access the current version of all the data without having to download TB's of data and trawl through it. This problem is not unique to the neuroimaging field and as such in TissueStack we made extensice use of techniques from the very closely related field of GIS (Graphical Information Systems – ie: online mapping). The server and viewing application have a number of features: Image tiles can be either generated on the fly or Pre-tiling can be initiated via the interface. The tool is HTML5 based and thus runs on mobile devices (iOS, Android, etc). We have tested the TissueStack server on 3D datasets up to 12GB in size over a mobile data link with no significant increase in rendering time (See Figure). It supports embedding, direct links to dataset features, dataset colour mapping, intensity range adjustments, multiple views of datasets, overlays and load balancing across multiple servers. The current server has a plug in architecture and can load files in either MINC or NifTI format. An example server running on a virtual appliance in the Australian Research Cloud can be viewed at http://caivm1.qern.qcif.edu.au. Tissuestack has so far been successfully used for neuroimaging, chemical engineering, electron microscopy, digital curation by museums and animal atlasing data. The main website and more information is available at www.tissuestack.org TissueStack is an Open Source project and is available on GitHub (github.com/NIF-au/TissueStack). All code is licensed via the GPL v3. Figure 1 Figure 2 Acknowledgements This project is supported by the Australian National Data Service (ANDS). ANDS is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative. Keywords: digital atlasing, HTML5, Web service, Neuroimaging, image view Conference: Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013. Presentation Type: Demo Topic: Digital atlasing Citation: Janke A, Lin M, Nicolini O, Waxenegger H, Ullmann J and Galloway GJ (2013). TissueStack: an Open Source HTML5 web based imaging viewer. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00120 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 29 Apr 2013; Published Online: 11 Jul 2013. * Correspondence: Dr. Andrew Janke, University of Queensland, Brisbane, Australia, a.janke@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Andrew Janke Meng Kuan Lin Oliver Nicolini Harald Waxenegger Jeremy Ullmann Graham J Galloway Google Andrew Janke Meng Kuan Lin Oliver Nicolini Harald Waxenegger Jeremy Ullmann Graham J Galloway Google Scholar Andrew Janke Meng Kuan Lin Oliver Nicolini Harald Waxenegger Jeremy Ullmann Graham J Galloway PubMed Andrew Janke Meng Kuan Lin Oliver Nicolini Harald Waxenegger Jeremy Ullmann Graham J Galloway Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

TissueStack: a new way to view your imaging data

Event Abstract Back to Event TissueStack: a new way to view your imaging data Andrew Janke1*, Harald Waxenegger1, Jeremy Ullmann1 and Graham Galloway1 1 Center for Advanced Imaging, Australia It's no secret that the increases in resolution possible with modern imaging equipment has led to an explosion in data. This increase is not always something that is easy to deal with, especially in studies that involve multiple modalities and sites. University and research networks while continually improving still do not allow those in the neuroinformatics field to seamlessly share multi-terrabye images. This leads to the problem in multi-site studies and often in smaller projects in which no one is sure where or how to access the current version of all the data without having to download TB's of data, make a small change and then upload again. This problem is not unique to the neuroimaging field and as such we have endeavoured to make use of techniques from the very closely related field of GIS (Graphical Information Systems) in this project. There has been some work done in this area, most notably with the web interfaces of the Allen Brain atlas and CATMAID. Both of these however are primarilly written for the viewing of multiple 2D images, typically of histology. TissueStack is an Open Source project that is currently available on GitHub ( http://github.com/NIF-au/TissueStack ) and at the time of this abstract is in alpha release phase for comment. Its purpose is to allow researchers to view multi-TB imaging datasets online vai an interface style that most people are familiar with (online mapping) at reasonable speed. The application uses HTML5 Canvas elements and thus will work equally well on mobile devices (tablets, phones, etc). The current proof of concept can be viewed at http://www.imaging.org.au/tissuestack and is displaying a 850MB 30um c57/bl mouse model. The application generates multi-scale tiled images from an input dataset in order to optimise viewing speed for a given network connection. Data input formats currently include MINC, NiFTI and OpenSlide formats. Future versions of the application will allow the federation of data from multiple sites and the overlay of pre-aligned muti-modality data such Histology and MRI. Acknowlegements: This project is supported by the Australian National Data Service (ANDS). ANDS is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative. Keywords: General neuroinformatics Conference: 5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012. Presentation Type: Poster Topic: Neuroinformatics Citation: Janke A, Waxenegger H, Ullmann J and Galloway G (2013). TissueStack: a new way to view your imaging data. Front. Neuroinform. Conference Abstract: 5th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2013.08.00003 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Mar 2013; Published Online: 27 Nov 2013. * Correspondence: Dr. Andrew Janke, Center for Advanced Imaging, Brisbane, Australia, andrew.janke@cai.uq.edu.au Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Andrew Janke Harald Waxenegger Jeremy Ullmann Graham Galloway Google Andrew Janke Harald Waxenegger Jeremy Ullmann Graham Galloway Google Scholar Andrew Janke Harald Waxenegger Jeremy Ullmann Graham Galloway PubMed Andrew Janke Harald Waxenegger Jeremy Ullmann Graham Galloway Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Data linkage infrastructure for cross-jurisdictional health-related research in Australia.

BackgroundThe Centre for Data Linkage (CDL) has been established to enable national and cross-jurisdictional health-related research in Australia. It has been funded through the Population Health Research Network (PHRN), a national initiative established under the National Collaborative Research Infrastructure Strategy (NCRIS). This paper describes the development of the processes and methodology required to create cross-jurisdictional research infrastructure and enable aggregation of State and Territory linkages into a single linkage “map”.MethodsThe CDL has implemented a linkage model which incorporates best practice in data linkage and adheres to data integration principles set down by the Australian Government. Working closely with data custodians and State-based data linkage facilities, the CDL has designed and implemented a linkage system to enable research at national or cross-jurisdictional level. A secure operational environment has also been established with strong governance arrangements to maximise privacy and the confidentiality of data.ResultsThe development and implementation of a cross-jurisdictional linkage model overcomes a number of challenges associated with the federated nature of health data collections in Australia. The infrastructure expands Australia’s data linkage capability and provides opportunities for population-level research. The CDL linkage model, infrastructure architecture and governance arrangements are presented. The quality and capability of the new infrastructure is demonstrated through the conduct of data linkage for the first PHRN Proof of Concept Collaboration project, where more than 25 million records were successfully linked to a very high quality.ConclusionsThis infrastructure provides researchers and policy-makers with the ability to undertake linkage-based research that extends across jurisdictional boundaries. It represents an advance in Australia’s national data linkage capabilities and sets the scene for stronger government-research collaboration.

Editorial: Australian Chemistry under the Spotlight

Editorial: Australian Chemistry under the Spotlight

Making Research Data Available in Australia

Governmental and private initiatives in Australia are trying to make public-sector data and research data more available and of increased value for more purposes. Data is becoming more freely and openly available in the Commonwealth of Australia as a result of initiatives that span the public sector as well as the research and innovations sector. At the national level, the Commonwealth's Gov 2.0 agenda has resulted in the Declaration of Open Government, “built on better access to and use of government-held information.”In the research sector, a National Collaborative Research Infrastructure Strategy (NCRIS; http://ncris.innovation.gov.au) has operated on the assumption that research data is an integral part of research infrastructure. The Australian National Data Service (ANDS) has been funded by the Commonwealth Government to create the infrastructure to enable Australian researchers to easily publish, discover, access, and use research data.

New geodetic infrastructure for Australia

In November 2006, the Australian Federal Government announced $15.8M in funding for geospatial research infrastructure through the National Collaborative Research Infrastructure Strategy (NCRIS). NCRIS is an initiative under the Australian Government's Backing Australia's Ability package with a number of key principles, including maximising the contributions of the R&D system to economic development, national security, social wellbeing and environmental sustainability. Here we outline why particular components of geospatial infrastructure are required in Australia to advance (equip) geospatial research over the next 20 years. We describe some of the scientific objectives that required an upgrade and densification of Australia's geospatial infrastructure. This paper is the perspective from a subset of University researchers involved in the AuScope Geospatial component, and so does not necessarily encompass the opinions of all those involved in AuScope Geospatial.

Present and Active: Digital Publishing in a Post-print Age

Digital publishing is changing the way in which research, particularly in the humanities, is being undertaken and it offers a range of transformative possibilities and challenges to traditional disciplines and to the nature of the printed text. The technical manipulation of information and the construction of new content that can be customised, modified, disseminated, and accessed within a global, distributed network provide new opportunities for the humanities researcher. As a way of illustrating our argument, we discuss the Children's Literature Digital Resources (CLDR) project, with with we are involved, and explore a range of emerging new media and their impact on research in the humanities and education.

Population health and clinical data linkage: the importance of a population registry

The Australian National Collaborative Research Infrastructure Strategy supports development of a national research capability in population health and clinical data linkage. This paper illustrates the importance of incorporating a population registry within such a system using an example provided by the Manitoba Injury Outcome Study (MIOS) that quantified the long-term burden of mortality attributable to injury in working-age adults. MIOS is a population-based matched cohort study that used administrative health data from Manitoba, Canada. An inception cohort of injured cases (ICD-9-CM 800-995) aged 18-64 years was identified from all Manitoba hospital admissions between 1988 and 1991. A matched non-injured comparison group was randomly selected from the total provincial population using the Manitoba Population Registry. Mortality outcomes were obtained by linking the two cohorts with the deaths data over 10 years. Mortality rate ratios (MRRs) were calculated to compare the injured and non-injured cohorts. A total of 21,032 matched pairs were identified. Using the population registry, the 10-year adjusted all-cause MRR comparing injured and non-injured cohort was 1.80 (95% CI 1.65-1.98). Without the registry, the unadjusted standardised morality ratio was 2.76 (95% CI 2.52-3.02). The effect of injury on mortality outcomes was over-estimated using only the injured cases, without use of the population registry. Use of the population registry enabled the selection of a matched non-injured group for comparison purposes, ensured comprehensive follow-up of almost all participants, and provided more accurate estimates of exposure time, incidence of mortality and relative risk.