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Systematic reviews and meta-analyses are critical in health-related decision-making, and are considered the gold standard in research synthesis methods. However, with new trials being regularly published and with the development of increasingly rigorous standards of data synthesis, systematic reviews often require much expertise and long periods of time to be completed. Automation of some of the steps of evidence synthesis productions is a promising improvement in the field, capable of reducing the time and costs associated with the process. This article describes the development and main characteristics of a novel online repository of cognitive intervention studies entitled Cognitive Treatments Article Library and Evaluation (CogTale). The platform is currently in a Beta Release phase, as it is still under development. However, it already contains over 70 studies, and the CogTale team is continuously coding and uploading new studies into the repository. Key features include advanced search options, the capability to generate meta-analyses, and an up-to-date display of relevant published studies. Supplementary Information The online version contains supplementary material available at 10.1186/s13643-021-01787-2.

Big data technologies and a range of Government open data initiatives provide the basis for discovering new insights into cities; how they are planned, how they managed and the day-to-day challenges they face in health, transport and changing population profiles. The Australian Urban Research Infrastructure Network (AURIN --- www.aurin.org.au) project is one example of such a big data initiative that is currently running across Australia. AURIN provides a single gateway providing online (live) programmatic access to over 2000 data sets from over 70 major and typically definitive data-driven organizations across federal and State government, across industry and across academia. However whilst open (public) data is useful to bring data-driven intelligence to cities, more often than not, it is the data that is not-publicly accessible that is essential to understand city challenges and needs. Such sensitive (unit-level) data has unique requirements on access and usage to meet the privacy and confidentiality demands of the associated organizations. In this paper we highlight a novel geo-privacy supporting solution implemented as part of the AURIN project that provides seamless and secure access to individual (unit-level) data from the Department of Health in Victoria. We illustrate this solution across a range of typical city challenges in localized contexts around Melbourne. We show how unit level data can be combined with other data in a privacy-protecting manner. Unlike other secure data access and usage solutions that have been developed/deployed, the AURIN solution allows any researcher to access and use the data in a manner that meets all of the associated privacy and confidentiality concerns, without obliging them to obtain ethical approval or any other hurdles that are normally put in place on access to and use of sensitive data. This provides a paradigm shift in secure access to sensitive data with geospatial content.

The Australian Defence Forces (ADF) including the army, navy and air force often share resources. This is the case for helicopter training where the resources are often people, e.g. instructors. Understanding the current and future needs of the helicopter pilot training continuum is challenging. New students are continually entering the Defence services as trainees/cadets; passing or failing exams at specialist flying schools; progressing through or leaving the Defence services and potentially becoming trained pilots or instructors that can subsequently train future students. This continuum has an associated optimisation challenge. The ATHENA platform has been developed as a strategic discrete event simulation, optimisation and analysis system for manpower planning with specific focus on addressing the needs and demands of this helicopter pilot training continuum for the ADF. ATHENA has been developed as a simulation platform running on a Cloud infrastructure. This paper introduces ATHENA and describes the way in which the platform leverages container technologies to auto-scale across the Cloud with focus on the Kubernetes orchestration technology.

Cognition-oriented treatments (COTs), including cognitive stimulation and training, are ever more popular in the older adult population despite the ongoing debate regarding their benefits and potential to prevent cognitive and functional decline. Numerous systematic reviews on the topic have produced mixed findings and recommendations and there is a dearth of credible and unbiased resources that stakeholders can turn to for complete up-to-date evidence base. CogTale is a novel platform that aims to both accelerate the data synthesis process, and provide a range of stakeholder groups access to up to date and reliable evidence to improve decision making.

Many/most of the challenges facing urban researchers and indeed policy makers relate to discovery of, access to and subsequent use of heterogeneous urban data sets. These data are typically held by a myriad of government agencies (local, State and Federal), industry and academic research organisations. The Australian Urban Research Infrastructure Network (AURIN) project commenced in 2010 and was tasked specifically with addressing this issue for the Australian urban research community. Specifically it was tasked with developing an e-Infrastructure providing seamless, secure live access to an extensive, extensible, and diverse collection of (typically) pre-existing data from autonomous and definitive data providers, where here "live" implies that the data is held by organizations and programmatic access to the data in situ is supported. The AURIN platforms currently provides access to over 1800 data sets from over 60 major organisations across Australia with over 100 targeted tools offering best practice analytics and visualization of data. These data sets can be combined in a multitude of ways reflecting urban research challenges/needs. This paper describes the design of the platform and its support for data re-use, data integration and associated data analytics and visualisation. We illustrate the flexibility of the system through case studies highlighting typical aggregate-level data re-use and integration of data from multiple independent agencies and in supporting novel disaggregated (unit-level) data re-use.