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We are a team of research data and academic specialists enabling data-intensive and collaborative research across UoM

We work with researchers at all stages of the research lifecycle, from research design and data collection, all the way through to analysis, visualisation, and interpretation. With a broad range of backgrounds and experiences, we are uniquely placed to make the most of all the benefits that interdisciplinary research can offer!

We have several ways to help researchers with data and computational needs, from quick questions and short and informal consultations to 9-month formal collaborations and internal secondments. Engage with us.

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We hold technical expertise in:

Research study design
Data management
Data stewardship
Data collection methods
Web development
HPC and cloud computing
Digital infrastructure
Data analysis
Machine learning
Data visualisation
Image and video processing
Virtual/augmented reality
Natural language processing
Artificial intelligence
… and much more!

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Not what you are looking for? Try the Research Helpline: +61 3 834 40999

MDAP is enabled by the University’s Petascale Campus.

We encourage researchers across the University to engage with us at any stage of their research project through one or more of the following engagement avenues and funding arrangements.

Research collaborations

Research projects lasting at least 3 months with full or part-time commitment from the MDAP team. There are two main ways to work with us here.

Faculty supported collaborations

Annual competitive round opens in September for collaborations to start the following year.

Researcher funded engagements

MDAP staff can be engaged on a salary cost-recovery secondment model, or a team of data specialists can be engaged at Academic Level C1 rates.

Researcher-funded engagements FAQs

For more information, read the FAQs then get in touch via the form below.

Start here

Quick consultations, advice and short projects

Working effectively with data can be complex, and it’s easy to get stuck. We offer consultancy, expert advice, advocacy and involvement in shorter term research projects – whether you are planning a new project, deep within it, or wrapping up.

Depending on requirements and availability, we can provide hands-on assistance with small parts of your data-intensive project, either for free or at cost-recovery rates.

Faculty supported

Depending on capacity and capability, we can either help or redirect you to someone who can, including the Melbourne Coder Network.

Enquire here

Researcher funded

If you have research funding, you could consider paying for an engagement at cost-recovery rates (see above).

Start here

More about engagement types

Research collaborations

A significant fraction of MDAP is devoted to faculty-funded research collaborations. Once a year we run an open, merit-based based allocation scheme. Researchers can engage with us for 3 to 9 months with costs covered by their faculty.

For more info, watch our Introduction to Collaborations video or visit Faculty Supported Collaborations FAQs.

Researcher-funded engagements

We are available year-round to work with you on researcher-funded projects. There’s two main ways to do this:

You can engage individual MDAP staff through secondments, charged at a salary cost-recovery rate.

MDAP can assemble a multi-disciplinary team of data specialists to collaborate on any research topic, charged at Academic Level C1 rates.

There are no set limits on the timing or length of funded engagements, subject to staff availability.

For more information visit Researcher-funded Engagements FAQs.

And…

If we don’t have the expertise on hand, we can redirect to other individuals, groups and services across the University who may be able to help. For example the Melbourne Centre for Data Science, Research Computing Services, Digital Skills Training and the Melbourne Coder Network.

2022 Collaborations

Unlocking diverse herbarium specimen associated data to accelerate biodiversity and evolutionary research
This collaboration will increase access to herbarium-derived biodiversity data by creating tools to capture, analyse, and share herbarium specimen associated data.

Biodiversity data, such as those that accompany museum and herbarium specimens, comprise a massive global data resource. This data can be used for rapid biodiversity analyses and to inform conservation and policy decision making. To do this it needs to be accessible within an integrated biodiversity infrastructure, that retains links among spatial, trait, and evolutionary data for taxa.

This project will provide tools to support efficient mobilisation of high-quality specimen associated primary (e.g. taxon, location) and secondary (e.g. molecular) biodiversity data. It will generate a pipeline for collation and phylogenetic analyses of molecular data generated by University of Melbourne researchers and students. It will also create an online phylogenetic browser to enable exploration of native Australian plant and algal evolutionary trees. This will be hosted on the University of Melbourne Herbarium Collection Online for access by students, researchers, and the global public.

Chief Investigator: Jo Birch, School of Biosciences, Faculty of Science

MDAP Collaboration Leads: Robert Turnbull and Karen Thompson

Collaborators: MDAP, the Faculty of Science, and Royal Botanic Gardens Victoria
Screening whole genome sequencing datasets for the Eukaryome of globally distributed kelp species
This collaboration aims to screen whole genome sequencing datasets of globally distributed kelp species for host-associated eukaryotic taxa, filling a considerable gap in kelp holobiome biology.

Bacterial diversity of seaweed microbiomes has received considerable attention over the years. But far less is known about their eukaryotic component such as algae, fungi, and animals living in/on organisms – collectively called the Eukaryome. We need new diagnostic frameworks for characterising eukaryotic microbial diversity. This will allow us to meet increasing industry demands for certified seaweed products, and better understand ecologically significant kelp forest communities.

The collaboration will help establish a robust workflow for future genomic analyses of entire microbial communities. It will employ state-of-the art machine learning algorithms to classify sequences and enhance knowledge about kelp holobiomes.

Chief Investigator: Trevor Bringloe, School of Biosciences, Faculty of Science

MDAP Collaboration Lead: Noel Faux

Collaborators: MDAP, CSIRO, Sungkyunkwan University (South Korea), the Athlone Institute of Technology (Ireland), the National University of Ireland Galway, and the University of Victoria (Canada)
Infectious disease genomics: from database to phylodynamics
The ongoing SARS-CoV-2 pandemic has seen an unprecedented amount of genome data generated, with over 2 million complete genomes in the GISAID platform. These data have been pivotal to track the evolution of the virus on a global scale and to detect mutations of epidemiological importance, such as those in variants of concern.

Many important biological questions could be addressed with these data. However, there can be issues assessing data quality and suitability. The main limitation in accessing sequence data from public databases is that many genomes have substantial stretches of missing data and are thus not sufficiently reliable to infer mutational events. Others can lack important metadata, such as travel history or vaccination status.

This project has two key aims:
1. Develop a system where one can select thousands of sequences that meet certain criteria for quality control or the presence of metadata that are fit-for-purpose for epidemiological questions
2. Develop tools to specify Bayesian hierarchical models in an intuitive format that will use the above fit-for-purpose data.
Chief Investigator: Sebastian Duchene, Melbourne School of Biomedical Sciences, Faculty of Medicine, Dentistry & Health Sciences

MDAP Collaboration Leads: Simon Mutch & Priyanka Pillai
FATCAT: a focused pipeline to assess functional changes in coral-associated bacterial communities
This project will develop a Functional Annotation Tool for Coral-Associated bacterial Taxa (FATCAT) to better predict coral-associated bacterial functional profiles. FATCAT will expand beyond traditional GTDB and NCBI databases with the addition of coral-associated bacterial genomes.

There have been encouraging examples in the literature of efforts to make environment-specific databases. For example: CowPI, a functional inference tool specific to the rumen microbiome, and FAPROTAX, designed to assess microbial communities from aquatic samples.

We plan to update FATCAT in sync with the release of new coral-associated bacterial genomes. This will make FATCAT the standard to accurately predict bacterial functions from 16S rRNA gene based coral microbiome studies.

Chief Investigator: Ashley Dungan, School of Biosciences, Faculty of Science

MDAP Collaboration Lead: Bobbie Shaban
Machine learning and patterns of primary care utilisation in cancer diagnosis and outcomes
This project seeks to apply novel analytical and machine learning methods to the large-scale linked data sources. This will generate new hypotheses and further characterise how people with cancer engage with primary care services.

The project aims to identify patterns in various aspects of primary cancer attendances prior to a definitive cancer diagnosis. These could include:
- prescribing
- test requests and results
- semi-coded fields such as ‘reason for encounter’
- co-morbidities or other conditions captured in primary care management systems.
The project will identify a specific cancer type (i.e. Upper Gastrointestinal) with sufficient linkages between hospital diagnosis and primary care data. It will also apply tools and algorithms to detect patterns present within specific timeframes of diagnosis or treatment.

Chief Investigator: Jon Emery, Centre for Cancer Research, Faculty of Medicine, Dentistry & Health Sciences

MDAP Collaboration Lead: Zaher Joukhadar
Artificial intelligence-based image enhancement and segmentation
This project aims to convert low-resolution CT images of large samples to super-resolution by using artificial intelligence (AI)-based image processing algorithms. AI algorithms are supposed to be used to split connected particles in CT images. Then, the particle volume, particle surface area and interparticle contact areas will be computed from the enhanced super-resolution CT images. The values will be compared with their counterparts derived from the low-resolution CT images before enhancement.

Chief Investigator: Wenbin Fei, School of Electrical, Mechanical and Infrastructure Engineering, Faculty of Engineering and Information Technology

MDAP Collaboration Lead: Jonathan Garber
Pipeline for the refinement of predicted protein structures and evaluation of their accuracy
Most 3D crystal structures for proteins of eukaryotes come from human and model organisms such as mouse, fruit fly and yeast. Until recently, there has been no method to acquire 3D protein structure for other organism such as parasitic worms and other pathogens.

The program AlphaFold, developed by DeepMind and published in July 2021, is about to "close the gap" between the quality of predicted proteins structure to those acquired from crystal structures. However, despite the progress, many predicted protein structures do not exhibit sufficient accuracy for applications such as drug design. To address this we need to refine structures using molecular dynamics (MD) simulations.

This project will develop a software pipeline for the refinement of predicted protein structures and the evaluation of their accuracy.

Chief Investigator: Robin B. Gasser, Melbourne Veterinary School (Graduate School), Faculty of Veterinary and Agricultural Sciences

MDAP Collaboration Leads: Bobbie Shaban & Edoardo Tescari

The project is supported by Australian Research Council Linkage Project (LP180101085) – ‘Illuminating genomic dark matter to develop new interventions for parasites’
Tracking changes in conservative political rhetoric over time
This project seeks to develop tools to investigate changes in liberal and conservative political rhetoric in Australia over time. Researchers and the public will be able to undertake their own temporal and linguistic analysis using unique primary sources, such as raw text files, transcriptions, and pdf images.

The collaborative project will also develop online natural language analysis tools, using Python, that will make visualisation of the data possible via an online interface. This project is designed to be scalable, allowing for the input of more primary sources in the future. This will enable the University to partner with cultural institutions such as the National Library of Australia. The tools will be used in the Faculty of Arts for teaching purposes. For example, embedding analytics and research data management exercises in selected subjects. It will also provide an ongoing resource for postgraduate and early career researchers.

Chief Investigator: David Goodman, Digital Studio, School of Historical and Philosophical Studies, Faculty of Arts

MDAP Collaboration Lead: Mel Mistica
Using the capability of deep learning to explore the predictive power of 180,000 eye images for dementia outcomes
This project will examine the ability of non-invasive eye imaging to predict dementia-related outcomes, cognitive tests and structural changes in MRI images. To do this we will use the 180,000 eye images in UK Biobank, one of the world’s largest eye-imaging studies.

In contrast with the existing literature, we will use deep learning methodologies to improve the ability to extract information from structural eye imaging. We will focus on prediction of a range of cognition outcomes, reflecting real world usage. The UK Biobank also contains brain MRI imaging. We can use this to explore the ability of structural changes in the eye to predict structural changes in the brain.

Chief Investigator: Benjamin Goudey, School of Computing and Information Systems, Faculty of Engineering and Information Technology

MDAP Collaboration Leads: Daniel Russo-Batterham & Zaher Joukhadar
Designing and Implementing an Interactive Data Platform for the Monitoring and Evaluating Climate Communication and Education (MECCE) Project
The MECCE Project is a global collaboration of more than 80 leading researchers and agencies, including UNESCO, the UN Framework Convention on Climate Change, and the International Panel on Climate Change. It aims to increase the quality and quantity of climate communication and education to advance global climate literacy and action.

This collaboration will support the design and implementation of an open-source Interactive Data Platform (IDP), a key component of the MECCE Project research impact strategy. The IDP will act as a digital interface for datasets and research arising from the MECCE Project. The contributions from MDAP will include IDP scoping, design, development and release, and strategic planning to ensure the IDP has a long-term impact.

Chief Investigator: Marcia McKenzie, Social Transformations of Education, Melbourne Graduate School of Education

MDAP Collaboration Lead: Geordie Zhang
Improving outcomes for people with complex mental health issues: insights from SANE Australia’s online and phone services
SANE Australia runs a phone and online counselling service that provides peer support, counselling, support, information, and referrals. The service is used by adults who identify as having a complex mental health issue, complex trauma or high levels of psychological distress, as well as the family or friends who support them.

SANE has 12 months of data related to these calls. These data include details of calls as well as some details of call content. We will work with SANE to apply data analysis techniques to understand how people with complex mental health issues interact with SANE, and how SANE might better meet their needs.

Chief Investigator: Nicola Reavley, Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry & Health Sciences

MDAP Collaboration Lead: Aleks Michalewicz
Fit for Students … Using Canvas data to tailor disciplinary eLearning approaches
How can discipline-specific pedagogies be translated to eLearning platforms? This collaboration uses topic modelling and sentiment analysis of Canvas data to identify key approaches to teaching and learning practices across multiple disciplines.

Subject data will be contextualised using University handbook entries and related to student experiences via end of subject survey responses. Built Environments Learning + Teaching (BEL+T) ‘s Delivery, Interaction and Assessment (DIA) model will provide a framework for analysis, and findings will be tested with subject coordinators. Data-based heat maps will offer visual summaries of eLearning activity and inform refinements to teaching practices.

This project will offer the University and academics an overview of Canvas use in different disciplines. It will also provide a foundation for live feedback tools to inform improvements to online learning practices.

Chief Investigator: Kate Tregloan, Built Environment Learning and Teaching, Architecture, Building and Planning

MDAP Collaboration Leads: Kristal Spreadborough & Amanda Belton
Caring for data now and for the future: Indigenous artistic cultural heritage data management, curation for access, and sustainability
The Wilin Centre for Indigenous Arts and Cultural Development and Research Unit for Indigenous Arts and Cultures (RUIAC) hold several collections of data, including video, audio, photographs, and text-based documentation.

In common with many Indigenous cultural, arts, and language centres across the globe, Wilin/RUIAC faces challenges to ensure:
1. That this cultural heritage data is accessible in an appropriate form to current and future generations of cultural custodians, and to researchers
2. New knowledge generated by research use and community use is linked to and enriches source records.
These collections record and describe tangible and intangible cultural heritage created and performed by visiting Indigenous artists and artist-researchers at artistic research events at the Boonwurrung/Southbank campus of UoM.

This project addresses the complex question: How can rich, living, cultural heritage data be preserved and managed in a university environment in a way that:
- supports cultural vitality
- protects IP, ICIP, moral rights, and copyrights of creators and communities
- supports new community and research use?
Chief Investigator: Sally Treloyn and Tiriki Onus, Wilin Centre for Indigenous Arts and Cultural Development, Faculty of Fine Arts and Music

MDAP Collaboration Lead: Emily Fitzgerald

2021 Collaborations

Longitudinal Mapping of Residential Building Projects in Victoria
Chief Investigator: Dr Vidal Paton-Cole (ABP)
MDAP Collaboration Lead: Robert Turnbull

The proposed research intends to analyse trends in building construction projects in the State of Victoria from 1996 to 2019. The researchers have sourced historical building permit data from the Victorian Building Authority (VBA), which includes a broad range of information relating to over 1.4 million building projects issued with building permits between 1996 and 2019 (in Excel format). This includes information on project location, cost, type, builder, architect, materials used etc.. In collaboration with MDAP, this data will be visualised within an interactive location-based tool and the data analysed to identify trends over time, including material use, construction type, prevalence of solar hot water systems and rainwater tanks. This will help to understand the nature of housing stock across different municipalities in the state of Victoria and also identify relevant correlations between building project characteristics, such as the prevalence of rainwater tanks by site area or local government area, or solar hot water systems by number of storeys.
Sustainable Development Goals for City of Melbourne
Chief Investigator: Dr Alexei Trundle (ABP)
MDAP Collaboration Lead: Geordie Zhang

In partnership with the City of Melbourne, the Connected Cities Lab is developing an evidence-based framework for localising and embedding the UN Sustainable Development Goals (SDGs) in the City’s long-term strategic planning processes. Consisting of 17 Goals, 169 Targets and 231 unique indicators, the SDGs are an ambitious, overarching, globally agreed upon framework that aims to achieve worldwide sustainable development by the year 2030. The primarily research question for the project is “how can the UN SDGs be used to guide a city’s strategic planning while maintaining connectivity and comparability to other cities as well as the broader global sustainable development agenda?”.
The research methodology draws on global best practice in translating SDGs target and indicators for the local context, based on an extensive literature review and consultation led by the research team with leading global cities and peak multilateral bodies, with a focus on the Asia-Pacific region. Phase one of the project included a 6-month internal SDG strategic environment assessment process that mapped existing City of Melbourne plans against the targets to ascertain current organisational SDG alignment. Procedures were developed iteratively through parallel project teams within both the University and the City.
Through 2021 the project will focus on 'localising' SDG targets and associated indicators, aligning the existing city database of more than 600 indicators with the SDG framework, which will form the basis of reporting and prioritisation within the City's overarching integrated planning framework going forward. This will include city-to-city comparability and sub-municipal precinct dissagregation.
Linking song collections and communities: Open release and interoperability of a song database and linking tool.
Chief Investigator: A/Prof Sally Treloyn (FFAM)
MDAP Collaboration Lead: Dr Aleks Michalewicz

There has been an exponential rise in Indigenous community use of archival song data to support the revival of song practice and knowledges, particularly where lineages of intergenerational knowledge transmission have been ruptured by colonisation. Research is needed to: 1. remove barriers to access including incomplete metadata and dispersed metadata and collections; and, 2. ensure newly-created data produced by ethnomusicologists and others, and Indigenous community members, are linked to legacy data. Non- interoperability of database and content management systems used by archives, and content management systems such as Mukurtu CMS, Keeping Culture CMS, and other systems, used in Indigenous communities, is also a barrier.
The interdisciplinary team has previously developed a linking interface and database tool to ingest, record and link metadata associated with archival and newly-created records of Indigenous song using FileMakerPro: the Discovery Database Tool (DDT) 0.9.2 Beta. The resulting metadata and surrogate source-data aggregates are used to support community song revitalisation initiatives (e.g., here) that serve to repair ruptures in intergenerational knowledge transmission, and serve to enrich the archival record by linking collections at the level of recorded song item. The DDT seeks to consolidate and optimise data for use in communities and by researchers.
Collaborative research with data management specialists is now needed, to:
AIM 1. Critically review and revise the design and documentation of the DDT 0.9 Beta for public, open access release.
AIM 2. Explore and test the tool so it can be used to populate commonly used community content management systems such as Keeping Culture KMS and Mukurtu CMS.
AIM 3. Explore how the tool can be used to communicate linked metadata back to archival databases.
These serve future priorities of identifying a fair/open platform for the DDT and scopeing correspondence of the design thinking behind the system with other data environments in the university.
A Fair Day's Work: Detecting Wage Theft with Data
Chief Investigator: Professor John Howe (Law)
MDAP Collaboration Lead: Dr Kristal Spreadborough

The research team seek support to develop a set of data science driven tools to prevent the underpayment and exploitation of young workers (frequently referred to as 'wage theft'). The team are specifically looking for MDAP support in accessing and managing relevant datasets, and to prepare for the development of a new dataset, which will involve the use of natural language processing.
Young people (~15-24 years old) are especially vulnerable to wage theft, due in part to issues such as: a culture of wage theft in industries where young people make up the majority of employees; a lack of awareness among young employees of their workplace rights; reluctance to complain about exploitation because of fear of retribution, combined with lack of resourcing for proactive detection of non-compliance by the regulator. This last point, in turn, makes it difficult for regulators, unions, and other organisations to detect wage theft, let alone address it.
To address the disadvantage wage theft causes, the team proposes a multi-pronged approach that aims to first and foremost, support young people at risk of wage theft, while also providing data for regulators, policymakers and business to drive system change. The project will draw upon cross-disciplinary expertise in labour law and regulation, digital design, information science, UX design, data analysis and data ethics to design/develop three interlinked components: The Fair Day's Work portal; a Wage Theft Database and finally a Wage Theft Prediction Tool. At the core of these three components is developing a wage theft database from public and private datasets, and through a new dataset collected from employees and unions via the Fair Day's Work portal.
Constraining the thermal evolution of Earth's crust through machine learning: Development of fully automated digital fission track analysis
Chief Investigator: Dr Samuel C Boone (Science)
MDAP Collaboration Leads: Dr Noel Faux & Usha Nattala

Fission track thermochronology is a temperature-sensitive geological dating technique that provides unparalleled insights into the thermal and tectonic evolution of the Earth’s crust with widespread applications. The method is based on the formation of radiation damage zones, called fission tracks, from the decay of 238U in natural minerals. The retention and length of fission tracks are sensitive to temperatures common in the shallow parts of the Earth’s crust (up to 5km depth). Thus, by quantifying the number and length distribution of fission tracks through digital microscopy and determining the 238U content via mass spectrometry, the detailed thermal history of a rock can be determined.
Since 2003, the Melbourne Thermochronology Research Group has developed Fission Track Studio (https://fissiontrackstudio.com/), an image analysis software suite that has brought significantly increased automation to the laborious collection and analysis of fission track data. However, persistent difficulties remain in identifying certain classes of tracks and a considerable degree of analyst review is still required to correct for deficiencies in the present algorithms.
This team wishes to collaborate with MDAP to develop a radically new approach to digital fission track analysis based on machine learning. Using an existing database of more than 30,000 photomicrograph stacks with corresponding expert-reviewed image sets, an artificial neural network approach will enable fully automated fission track image analysis to be achieved. Such an advance will revolutionise the methodology by significantly reducing analytical time, removing the influence of observer bias and allowing the wider, non-specialist geoscience community to utilise this powerful technique.
Forecasting Bushfire Risk: Integrating a new ground-based sensor network, remote sensing, and weather data to forecast forest fuel dryness
Chief Investigator: A/Prof Gary Sheridan (Science)
MDAP Collaboration Lead: Usha Nattala

Bushfires in southern Australia have resulted in more than 825 deaths and destroyed more than 7400 houses since 1901. The economic cost of the 2020 bushfire season alone was estimated to be $100 billion. The “dryness” of leaves and litter (called fuel) within the forest is a strong determinant of the risk of bushfire, however this dryness is very difficult to adequately predict. This project aims to develop new tools for Australian bushfire managers to forescast and map fuel dryness and in doing so, better anticipate where, when, and how bushfires and prescribed burns will burn. This will save lives, protect property, and improve the allocation of limited firefighting resources.
This team's novel approach will develop machine learning algorithms to integrate a world-first network of 34 real-time “fuel dryness” sensors in forests, with spatial and temporal remote sensing and meteorological predictor variables, to forecast fuel dryness at high resolution across vast forested landscapes. The research group is an international leader in this research area, developing both biophysical and remote sensing-based models of fuel dryness. The team seeks to collaborate with MDAP (who will provide the technical knowledge of ML model development, and the computational resources to develop, train, and interact with new models) in the exploration and development of data-based models for landscape fuel dryness.
Machine learning approaches for delineation of bankfull stream channel dimensions from LiDAR data
Chief Investigator: Dr Kathryn Russell (Science)
MDAP Collaboration Lead: Jonathan Garber

Catchment urbanisation has profound effects on the physical form and functioning of stream channels, with far reaching economic, ecological and social implications for our cities and suburbs. In this overarching project, being undertaken in partnership with Melbourne Water (mwrpp.org), the aim is to develop statistical models for the expected extent and severity of stream channel change relative to the level of catchment urbanization across the Greater Melbourne region. The outcomes will assist stream managers to plan for geomorphic change, and to develop new ways of protecting streams from catchment urbanization.
A key input to this model is stream channel dimension data, which is challenging to gather across broad scales despite good coverage of LiDAR topographic data. Existing datasets are incomplete, low-quality and non-reproducible, and current methods to extend and improve them are labour-intensive, severely limiting our modelling. The team proposes a collaboration with MDAP to explore machine learning methods to identify bankfull channel extents from LiDAR digital elevation models.
If a machine-learning method can perform comparably to current methods, the potential research impact is considerable, both on this project and on river research globally. This collaboration may to lead to improved coverage and quality of channel dimension data, and hence improved models of pressure-response relationships in stream geomorphology (both here in Melbourne and worldwide). Ultimately, these advances are expected to lead to better stream protection, management and planning.
Visualising networks and mobilities in the architecture profession
Chief Investigator: Professor Julie Willis (ABP)
MDAP Collaboration Lead: Dr Emily Fitzgerald

This project seeks to visualise the movements and migrations of architects within and across the British Empire from the mid-nineteenth to the mid-twentieth century. At the heart of this project are the questions: ‘Where did architects come from and go to?’ and 'Where did they work?'
To date, architectural histories have largely been grounded in a single place, disregarding architects’ movements across jurisdictions. But architects have long been highly mobile professionals. Their careers, then as now, could span the globe. Nor were their journeys simply a trip from centre to periphery. Indeed, a good number of colonial architects had careers which spanned Australasia, East and Southeast Asia, Africa, and the United Kingdom. And in these places, they could work for multiple entities – themselves, other firms, public agencies – making their careers complex journeys.
The project uses disparate, primarily textual, sources – trade directories, newspapers, and other archival material – to trace the movements of hundreds of architects through various architectural firms, and through the colonies and concessions of the British Empire over the course of a century. By using database and visualisation tools, the project will enable new ways of understanding architectural history, new methods for synthesis and analysis of large and disparate data sets in design histories and new interfaces for the presentation of complex historical datasets that involve different geographical locations and movements, over long time frames in varying professional configurations.
Community-driven data initiatives for preventing HIV in Indonesia
Chief Investigator: Dr Benjamin Hegarty (Arts)
MDAP Collaboration Leads: Dr Kristal Spreadborough & Priyanka Pillai

This project aims to investigate the processes and practices of data collection and use in efforts to prevent and treat HIV in Indonesia among vulnerable populations, with a focus on 'community-driven data initiatives'. Drawing on the interdisciplinary expertise of anthropologists, data scientists and epidemiologists/public health experts in Indonesia and Australia, this project hopes to inform emergent paradigms of governing HIV through data. The outcome is anticipated to be enhanced capacity to understand the social and cultural impacts of data-driven forms of health governance. Benefits include new forms of data visualisation, and more collaborative ethical protocols for collecting and using health data collected in the course of HIV prevention and treatment activities. The larger project of which this collaboration forms part will be based on two case studies that investigate data collection as it relates to two common categories: “men who have sex with men” (MSM) and “housewives” (ibu rumah tangga). Tracing how “MSM” and “housewives” are quantified and visualized through data collection – from the community to the epidemiological level – provides the opportunity to understand the visibilities and invisibilities that data entails. The team will work with Indonesian counterparts to look at data in two ways: 1) processes of data collection and analysis. They will work with epidemiologists and other program workers at a range of sites, including civil society organisations, regional departments of HIV, national department of health. 2) the effects of data as it is used in politics and policy. They will investigate the role of data in shaping responses to HIV, including data visualisation. The MDAP collaboration of which this project is part, only focuses on studying and improving community-driven data initiatives developed by/for MSM communities. The project builds on ongoing collaborations with these communities by CI Hegarty, Davies and Praptoraharjo.
Tackling the canine microbiome in chronic enteropathy: characterising the functionally significant changes that occur with remission of disease
Chief Investigator: Professor Caroline Mansfield (FVAS)
MDAP Collaboration Lead: Dr Noel Faux

Dogs are the most popular companion animal in Australia, with over 4.2 million pet dogs. Gastrointestinal diseases are commonly diagnosed in dogs, with chronic enteropathy (CE) a group of disorders that causes gastrointestinal tract inflammation. Although the exact cause is not known, dysregulation of the resident gut microbiota has been implicated in development and/or exacerbation of CE.
Microbiomic community profiling using universal biological markers (primarily the 16S rRNA gene) coupled with high throughput sequencing has been used to assess bacterial phyla in a wide variety of vertebrates, including dogs with CE. Although this approach is powerful, it has to date demonstrated few consistent biological differences associated with canine CE, other than a general loss in species richness and diversity. However, CE is largely an inflammatory condition and a major limitation with current research is that it examines all bacteria rather than only those bacteria recognized by the host immune response. Additionally, it is the functional capacity of these organisms (determined by their gene repertoires) and not their taxonomic identity that defines their niche within the microbiome. It may well be that despite variation in the taxonomic structure of the microbiome among individuals the functional niches occupied by these various species may be static and consistent. This longitudinal clinical study will functionally characterise the faecal microbiome through an integrative ‘omics approach: metagenomics, transcriptomics and metabolomics and bacteria coated with immunoglobulins. This will provide a better understanding of the significance of the changes of the microbiota and potentially identify therapeutic targets.
Machine Learning and Patterns of Primary Care Utilisation in Cancer Diagnosis and Outcomes
Chief Investigator: Professor Jon Emery (MDHS)
MDAP Collaboration Leads: Dr Mar Quiroga and Zaher Joukhadar

The Victorian Comprehensive Cancer Centre-funded Data-Driven Research Program was the first in Australia to link large-scale primary care and hospital data for the purpose of enabling increased capabilities in health services research. Until now, traditional analytical and statistical methods have been utilised to examine and map patterns in primary care attendances and how these are associated with cancer diagnoses, treatment and outcomes. These are generally based on the existing evidence base to test prevailing hypotheses and apply these to the local context.
This project seeks to apply novel analytical and machine learning methods to the large-scale linked data sources in order to generate new hypotheses and further characterise how people with cancer engage with primary care services. Specifically, this project would aim to identify patterns in various aspects of primary cancer attendances prior to a definitive cancer diagnosis (as identified in linked data sources). These could include prescribing, test requests/results, semi-coded fields such as ‘reason for encounter’ and co-morbidities/other conditions captured in primary care management systems.
The project would identify a specific cancer type (i.e. Upper Gastrointestinal) with sufficient linkages between hospital diagnosis and primary care data and apply tools and algorithms to detect patterns present within specific timeframes of diagnosis or treatment.
Social Media Disinformation and the Papua Conflict: an Indonesian Language Investigation
Chief Investigator: Dr Dave McRae (Arts)
MDAP Collaboration Leads: Dr Daniel Russo-Batterham & Kim Doyle

Online discussion of the conflict for independence in Indonesia's two easternmost provinces – hereafter the Papua conflict – is highly fractious. The Indonesian-language online space is especially contested. Actors posting online in Indonesian occupy a complex spectrum of positions ranging between full support for the Indonesian government or for Papuan independence. Increasingly, contention between these actors includes disinformation tactics, harassment of those criticising and scrutinising the Indonesian government, and – on occasion – internet shutdowns to obstruct the free flow of information.
The increase in disinformation and pro-government interference in online discussion of the Papua conflict accords with what scholars have identified as a broader regional illiberal turn in the conduct of contentious politics via social media. Existing analyses have mapped the distribution of pro-government material pertaining to the Papua conflict by inauthentic accounts and coordinated campaigns in English and in Dutch. It is intended to extend these analyses by examining, what accounts are posting pro-government messages in Indonesian in social media, and what is the nature and content of these posts. By focusing on Indonesian language materials, the team seeks to understand how pro-government actors shape debate on the Papua conflict within Indonesian society – where the outcome of the Papua conflict will ultimately be decided – rather than scrutinising attempts to shape international perceptions of Papua.
Associate: Encoding manuscripts as primary research objects
Chief Investigator: A/Prof Nick Thieberger (Arts)
MDAP Collaboration Leads: Dr Daniel Russo-Batterham & Robert Turnbull

Professor Thieberger is working on several Text Encoding Projects and would benefit from extending work begun with Robert Turnbull and Daniel Russo-Batterham. The pages at https://bates.org.au are an example, and he now has another project (with Simon Musgrave at Monash) working to prepare Heath's grammar, texts, dictionary, and media of the Aboriginal language Nunggubuyu and put it online. In SCIP/Digital Studio he is working with Birgit Lang (German) on a manuscript that she has transcribed, a diary of a Freudian analyst that has not been published before. With a DS intern the transcript has been encoded as a first draft TEI document and it is now ready for final editing. However, each of these projects needs a IIIF server for the images, and some assistance with the TEI, that Robert and Daniel can provide.
Digitally benchmarking public attitudes to secondary use of health data, with NLP data extraction from general practices as a case study
Chief Investigator: A/Prof Mark Taylor (Law)
MDAP Collaboration Lead: Priyanka Pillai

Significant benefits can flow from re-using people's health data for research, including gaining new insights that can be used to improve preventive healthcare, diagnosis and treatment. Such data is sensitive, however, and individuals may have concerns about its secondary use even when data has been de-identified. Secondary use without appropriate consultation and justification has sometimes created public scandal that damages trust. We need better ways to understand people's views about different types of secondary use of health data so that the public interest can be better served and trust protected.
This project has two related aims:
The first is to establish a robust electronic method to gather evidence of public attitudes regarding secondary uses of health data. The team will scope and evaluate alternative electronic means of gathering and benchmarking public attitudes toward specific secondary uses of health data. Alternatives will be evaluated for cost-effectiveness, issues of inclusivity, representation and equity, and their ability to provide useful insight into public attitudes toward secondary uses of data. The most promising method will be tested in practice in relation to the second aim.
The second aim is to gain insight into public attitudes to data extraction from medical records for primary care research using different natural language processing (NLP) tools. Tools will be tested according to: (1) location (2) robustness of de-identification (3) types of health data. This will provide insight into public acceptability of working with industry-leading medical annotation companies that require medical text to be sent to on-line services.
Accelerating large dimensional stochastic simulation models
Chief Investigator: Dr Aaron Dodd (Science)
MDAP Collaboration Leads: Dr Edoardo Tescari & Dr Mar Quiroga

Bioeconomic simulation models are a critical tool used to estimate both the impacts that might be caused by pests or diseases and the relative value of the various interventions that we deploy to manage them. Typically, these models describe the impacts of a single species on a single asset at relatively small spatial and temporal scales. Management agencies, however, are required to take an ‘all hazards’ approach when determining how they might best protect assets from the negative impacts of pests and disease at state and national scales. The ‘value model,’ developed at the University of Melbourne, is the only model globally that is capable of simultaneously modelling the arrival, spread and impact of multiple biological hazards on multiple assets over time. However, in order to be effectively deployed within management agencies, or expanded for additional research use, the core architecture of the model needs to be faster. This project will explore a range of options for improving the performance of the model spanning: how the dispersal of organisms is modelled, how the model is encoded, how compute resources are utilised and how the result data is stored. Improvements in any (or all) of these areas will enable uptake of the model into real-world decision-making contexts ultimately delivering improved biosecurity outcomes for society.
The Heart
Chief Investigator: Dr Robert Walton (FFAM/MSE)
MDAP Collaboration Leads: Zaher Joukhadar

Can a building have a heart? Does a building feel and can it dream? Imagine if the life of Melbourne Connect (MC) as a building and a community could be revealed through the collection and visualisation of omputational data. We are creating a major permanent digital artwork called ‘The Heart’ for the foyer of MC that visualises what the building is ‘feeling’ through its thousands of live data gathering sensors. The Heart will beat for the duration of the building’s life – at least 42 years until the end of the current lease. Its pulse is extrapolated from the sensations of its body: the ‘smartest’ building in Parkville. The project is well underway and is being developed through the collaboration of a broad coalition of university researchers, professional staff from FFAM and MSE, students, leading external craftspeople, and the MC architects and builders. The artwork reveals building functions that are normally hidden ‘offstage’ to bring to mind the volume of data and work supporting the life and optimal functioning of the University community. The pulse of MC is derived from Building Management System data combined with electricity generation (solar and geothermal), external temperature and wind direction, zoned data and power usage, human movement and behaviour. We envision MDAP helping us complete the data pipeline for the project, using Machine Learning/AI to extrapolate an evolving, live ‘pulse’ from realtime building data including human heart rate monitors in the foyer.

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