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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.

Associate Professor Nic Geard
Director
Amanda Belton
Data Scientist
Kim Doyle
Research Data Specialist
Dr Noel Faux
Senior Research Data Specialist
Dr Emily Fitzgerald
Research Data Specialist
Dr Jair Garcia
Research Data Specialist (HASS teaching)
Zaher Joukhadar
Research Data Specialist
Meirian Lovelace-Tozer
Digital Skills Trainer
Kabir Manadhar Shrestha
Research Data Specialist
Damien Mannion
Research Data Specialist
Jeremy Metha
Digital Skills Trainer
Dr Aleks Michalewicz
Research Data Specialist
Dr Mel Mistica
Research Data Specialist
Dr Simon Mutch
Senior Research Data Specialist
Usha Nattala
Research Data Specialist
Thao Nguyen
Research Data Specialist (Web Developer)
Priyanka Pillai
Research Data Specialist
Dr Mar Quiroga
Research Data Specialist
Marlaina Read
Research Communications Specialist
Dr Daniel Russo-Batterham
Research Data Specialist
Alex Shermon
Digital Skills Trainer
Dr Andrew Siebel
Platform Manager
Dr Kristal Spreadborough
Research Data Specialist
Dr Edoardo Tescari
Senior Research Data Specialist
Karen Thompson
Senior Research Data Specialist
Dr Robert Turnbull
Research Data Specialist
Geordie Zhang
Research Data Specialist

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.

2023 Collaborations

Social network mapping social networks in disaster-affected communities
To address the challenges of climate change-related disasters, new concepts and capabilities are needed for measuring and harnessing the collective resilience of communities. The goal of this project is to develop a way to capture social networks of civic participation quickly and efficiently in disaster-prone communities.

The main objective is to develop a novel survey portal and visualisation tool that allows community members to record, merge, visualise, and analyse their common social connections to local groups and organisations, for example sports clubs, churches and progression groups.

Conducted in real time, this platform will provide a visually engaging experience, and will open immediate use of network data. In this way, network data become a knowledge translation tool, allowing disaster recovery practitioners and community members to better understand key connections within their community, as well as optimised strategies for harnessing those connections.

In this phase of the project, we aim to enhance the user experience of the network mapping website. We likewise aim to develop:
- data visualisation – for example network visualisations with spatial maps
- sampling capabilities – for example network snowball methods
- data analysis (network metrics)
- data physicalisation – to better understand how people view their own networks.
In addition to the knowledge translation benefits brought by these capabilities, the project will provide an important foundation for further scientific research into societal resilience and social network methodology.

This project builds on a prior collaboration with MDAP for the initial development of the data collection software in question, in partnership with Natural Hazards Research Australia. The proposed extension to the project will build on project members longstanding collaborations with government and local partners in disaster recovery. It will also closely support our current and ongoing work with recovery committees in communities affected by recent major disasters such as bushfires and floods.

Chief Investigator: Dr Colin Gallagher, MDHS – Melbourne School of Population and Global Health

MDAP team: Amanda Belton, Daniel Russo-Batterham, Emily Fitzgerald
Stories of Hope: A participatory narrative inquiry into the emergence of hope during university transition
The transition to university represents a critical developmental milestone in emerging adulthood. However, this is often characterised by elevated psychological stress and lowered wellbeing (Kulig & Persky, 2017). Much of the discourse focuses on the deficits and challenges students face. However, this does not paint a holistic picture of the resourcefulness that enables young people to navigate this transition successfully.

A sense or feeling of hope, for example, is a strong protective and enabling factor, with evidence suggesting that hope is important in predicting resilience in university students (Goodman et al., 2016). This makes hope a critical construct to explore further as we prepare our youth for a volatile, uncertain, complex, and ambiguous (VUCA) world.

However, a recent critical analysis of this topic suggests the need for a deepened understanding of how hope emerges. One that recognises and addresses the inherent complexities in the emergence of hope and which is grounded in the lived experience of young people (Colla et al., 2022).

The project aims to address some of these limitations by taking a dynamic systems approach to the study of hope – expanding the current meta-theoretical, theoretical, and methodological approaches that have underpinned the research to date.

In particular, the project aims to address two key issues:
1. Limitations in current theoretical models of hope and understanding how hope emerges for young people. Understanding the factors that support resilience and adaptive functioning, particularly post-pandemic, is fundamental to enhancing student experience at university.
2. Student voice is often missing from models drawn upon to support and increase their wellbeing. This project will position students as experts in explaining their own experiences in relation to hope.
The project uses an interdisciplinary methodological approach called Participatory Narrative Inquiry (PNI). The project includes some innovations to PNI that draws from the intersection of positive psychology, complex systems dynamics and digital storytelling, to facilitate the analysis of the dynamic interplay between factors that enable hope to emerge.

We will collaborate with MDAP to:
- Co-design a data analysis methodology that enables the analysis of rich and complex relationships in a time-efficient manner. For example, enabling system dynamics tools to be integrated into qualitative research.
- Create data visualisations that detail participants' stories of hope and theories of how this emerges.
- Support the development of a web resource.
Chief Investigator: Rachel Colla, Melbourne Graduate School of Education

MDAP team: Amanda Belton, Kabir Manandhar Shrestha, Emily Fitzgerald, Kristal Spreadborough
Computational methods for artwork authentication using hyperspectral imaging
Authentication of physical artworks is essential for safeguarding cultural heritage, enforcing artist’s intellectual property rights, ensuring consumer protection and the credibility of the art market. The creation of certificates of authenticity based on digital signatures is an innovative response to shortcomings in current authentication practices.

This research project will use hyperspectral imaging and computational methods to create unique digital signatures for physical artworks. New developments in hyperspectral imaging technology have made it possible to use a portable front facing hyperspectral camera to record the spatial and spectral features of an artwork, in a non-invasive manner.

Each hyperspectral image produces a ‘hypercube’ of approximately 5.3 million data points, most of which are in the non-visible spectrum. We will investigate algorithms, such as manifold learning, to condense the hypercube to a lower dimensional representation to find a unique digital signature for the given artwork. This signature can then be used to authenticate .

It will be necessary to allow for and define an acceptable deviation range or ‘sphere of authenticity’ to account for minor variations in the image-capture process and possible age-related deterioration of the artwork. The digital signature and sphere of authenticity will then be stored on an authentication registry supported by blockchain infrastructure. An associated non-fungible token (NFT) will be minted as a certificate of authenticity.

This ability to authenticate an artwork in a non-invasive manner would be valuable to artists, commercial art galleries, auction houses, art collectors, museums, and other cultural institutions. The project’s scope could be expanded to include other cultural objects, such as Indigenous artifacts. We could also use similar computational methods to create a ‘spectral library’ of an artist’s body of work. This could protect against unknown or unauthenticated artworks that may appear on the art market after the artist’s death.

Chief Investigator: Dr Guy Morrow (CI) & Dr David Challis (PL), Faculty of Arts, School of Culture and Communication

MDAP team: Karen Thompson, Kabir Manandhar Shrestha, Aleks Michalewicz, Simon Mutch
Increasing comparative genomic resources for amphibians
The genomics revolution has spurred movements to sequence the genomes of all life on earth. This has led to a flurry of genomes in the past five years – everything from redwood trees to gorillas. This revolution has also piqued the interest of conservation biologists who are fighting the ever-increasing extinction crisis.

Genomic information can be used for species management to ensure the best approaches are used for maintaining or increasing genetic diversity. More recently this information is being used for more directed approaches to help promote characteristics that allow species to persist with intractable threats like climate change and disease.

This MDAP collaboration will be used to build computational and genomics resources for amphibians. Amphibians are the most imperiled group of vertebrates and face many threats such as the devastating disease, chytridiomycosis. Despite their conservation value, the development of genomic resources for amphibians has lagged behind other vertebrates. This is because their unusually large genomes are difficult to assemble and work with. This collaboration, between Dr Kosch’s team at One Health Research Group and MDAP, will help build the critical resources to identify gene targets for genetic intervention to help restore amphibians threatened by chytridiomycosis to the wild.

Chief Investigator: Dr Tiffany Kosch, Faculty of Science, Veterinary Biosciences

MDAP team: Robert Turnbull, Priyanka Pillai, Edoardo Tescari
Trans-diagnostic assessment of neurodevelopment with wearable eye-tracking technology
The Barwon Infant Study is a world-leading birth cohort study of approximately 1,000 children located in Victoria, Australia. It is a multi-organisational research effort comprehensively measuring child health outcomes from pregnancy onwards.

The Barwon Infant Study aims to include objective trans-diagnostic assessments of neurodevelopment in its ‘Primary School Review’. Children are currently taking part in these assessments, with outcomes measured through clinical assessments, parent-reports, behavioral assessments, eye-tracking, vagal tone and neuro-retinal imaging.

Using technological advances – such as wearable eye-tracking technology – the study aims to provide child health researchers with cutting-edge assessments of neurodevelopment. The incorporation of this technology into a population health setting (i.e. >1,000 children) is a unique data-intensive challenge requiring multi-disciplinary research efforts.

To harness this data, we need to extract relevant metrics and create a database of novel, non-invasive neurodevelopmental metrics for child health researchers. Collaborating with MDAP, this project will support the analysis of data obtained from wearable eye-tracking glasses during the ‘Primary School Review’ of the Barwon Infant Study and will create a database of the resulting neurodevelopmental metrics.

This will provide child health researchers, both nationally and internationally, with precise objective assessments of neurodevelopment in a population health setting. This precision phenotyping will also directly bolster current at the Florey Institute of Neuroscience and Mental Health.

Investigating team: Dr Eduardo Velloso (CI) & Dr Katherine Drummond (PL), Faculty of Engineering and Information Technology, School of Computing and Information Systems

MDAP team: Simon Mutch, Mar Quiroga, Karen Thompson, Geordie Zhang
Analysing decisions with the LATER model: an open-source tool
Reaction time experiments are used to measure the brain's decision-making processes. Cambridge neurophysiologist Professor Roger Carpenter developed a simple but influential model for decision-making called LATER (Linear Approach to Threshold with Ergodic Rate). Scientists and clinicians use LATER to model and visualise a range of lab and clinical reaction time datasets.

Key to the usability of LATER is a user-friendly software package, meaning users do not need to have programming skills. However, following the death of Professor Carpenter, this software is no longer available. This makes the LATER model inaccessible to scientists without experience in writing analytical software from scratch.

This project will create an open-source analysis tool that would allow scientists and clinicians to analyse and visualise their data using LATER, without the need for programming skills.

MDAP collaborators will contribute data analytic and programming skills to create and test a robust open-source toolkit – most likely in the R programming language commonly used for statistical computing – making use of modern data analytics methods.

Main project aims:
- Create an open-source and customisable tool.
- Develop a user toolkit for analysing reaction time data using the LATER model.
- Allow data fitting of the LATER model using maximum likelihood methods.
- Publish results in scientific literature describing the tool and how it has been modernised to make use of modern fitting procedures. This would include data-driven examples demonstrating the tool's use.
Chief Investigator: Associate Professor Andrew Anderson, MDHS, Melbourne School of Health Sciences

MDAP team: Mar Quiroga, Simon Mutch, Edoardo Tescari
Infrastructure and methods for forensic audio transcription experiments
Audio recordings provide powerful forensic evidence in criminal trials. The problem is they are often of poor quality, to the extent the jury needs a transcript to understand the content.

The Research Hub for Language in Forensic Evidence (The Hub) aims to develop evidence-based scientific methods for creating reliable transcripts of indistinct forensic audio. This requires collecting and analysing large amounts of empirical data regarding how listeners interpret indistinct audio under various conditions.  

The current project aims to develop tailored infrastructure and methodological protocols to enable ongoing experimental research on this topic. 

Over the past ten years, many experiments have been conducted, and methods have evolved. We want to consolidate these methods to streamline and expand our ongoing research. To do this, this collaboration will focus on two major requirements:
1. Infrastructure to deploy audio to participants, allow participants to input their transcripts and enable researchers to add metadata to responses. Existing platforms are too restrictive for our purposes. Dedicated infrastructure will enable us to collect and analyse data more efficiently, with a better experience for participants. 
2. Streamlined methodology. Our experimental approach has evolved over the last decade. While this work has produced valuable results, the methods now need to be refined and rationalised into efficient protocols. 
Recently, the Hub has been working with the Language Testing Research Centre to develop our capabilities for scoring transcripts. This collaboration would further benefit from the increased interdisciplinarity offered by MDAP, for example in platform development, human sciences and data security. There is scope for outputs of the proposed project to be extended to other branches of linguistic research. 

Chief Investigator: Professor Helen Fraser, Faculty of Arts, School of Languages and Linguistics

MDAP team: Aleks Michalewicz, Kristal Spreadborough, Robert Turnbull
Forecasting risks to savanna ecosystems in northern Australia under global change
Sustainable land management requires new tools for decision-making in the face of rapid and uncertain global changes. This project develops spatial tools for robust decisions on land management across large areas that integrate risk and uncertainty in climate and economic drivers – for example fire management, cattle grazing and pasture improvement. This ensures that landscapes can continue to support livelihoods, biodiversity, and other vital ecosystem services.  
 
Our focal region – northern Australia's savannas – supports immense biodiversity and an extensive cattle industry. Sustainable management of these savannas depends on understanding:
- how management actions impact biodiversity and cattle production under uncertain climate and economic futures
- how systems of financial incentives and regulation can be designed to drive more sustainable management decisions.  
The essential elements of this project consist of an integrated systems model for northern Australia, along with a spatial optimisation for the allocation of financial incentives for land and biodiversity management. We will also develop, a web app to enable end users to explore and interrogate the results.   
 
MDAP staff would contribute high-level programming skills and computing resources in developing the systems model, spatial optimisation, and web app. For example, machine learning, dynamic links among submodels and parallel processing.
 
The aim of the project is to inform climate-smart policy and planning in northern Australia. This approach will be transferrable to other landscapes and communities facing similarly uncertain futures, by providing tools to help people and ecosystems adapt and thrive in a changing climate. 

Chief Investigator: Dr Rebecca Runting, Science, School of Geography, Earth and Atmospheric Sciences

MDAP team: Kabir Manandhar Shrestha, Mel Mistica, Robert Turnbull
Developing a convolutional neural network for the analysis of gonadal tissue
This project aims to develop a convolutional neural network (CNN) for the automated analysis of gonadal tissue slide scans. Our team has the largest single centre biobank of paediatric oncofertility tissue stored for fertility preservation via cryopreservation. During surgery, a small portion is kept for histopathology analysis.

Histopathology analysis can help predict the success of different fertility preservation procedures. Currently, a lab technician needs to analyse the histology slides manually. This is a slow process – either counting ovarian follicles and follicle type in ovarian tissue, or determining the stage of spermatogenesis and presence of sperm in testicular tissue. We want to train CNNs to automate this manual process.

Currently there is interest in developing CNNs for ovarian tissue analysis, but most of development is from adult tissue. Paediatric tissue has a much higher rate of abnormal follicles, which would allow for a more robust training of our proposed CNN. In addition, no groups are currently working on a testicular tissue CNN, as far as we are aware. We hope to develop not only the most robust ovarian tissue CNN analysis tool, but also the world's first testicular tissue CNN analysis tool.

This phase of our ongoing research project would use standard CNN training methods, which would have a very quick impact on our department and would directly lead to innovations in clinical care. MDAP collaborators will contribute through slide annotation software and machine learning development, as well through the provision of feedback, analysis and project review.

Key outcomes include:
- Ability for clinicians to quickly analyse patient slides for more personalised care. For example, knowledge of ovarian follicle density would help guide counselling around future fertility options, such as continue storing cryopreserved ovarian tissue with its associated costs.
- Easy access to the analysis of information to reduce inequities around different Australian and international centres – giving clinicians access to information that is mostly currently absent to the decision making.
- Digital analysis of tissue will allow researchers to better understand ovarian and testicular function for the development of better models of ovarian follicle and testicular dynamics.
We aiming to establish a national oncofertility biobank as well as a national oncofertility tissue slide repository. Having CNN tools would allow oncofertility centres around Australia, as well as globally, to quickly analyse tissue to make important clinical decisions for their patients.

Investigative team: Associate Professor Yasmin Jayasinghe (CI) & Dr Michael Assis (PL)

MDAP team: Noel Faux, Geordie Zhang, Priyanka Pillai
Modelling to assist Australia's transition to a more environmentally sustainable diet
With predicted global population growth and the urgent need to limit the rise in global temperatures, the transition to dietary patterns that are both healthy and environmentally sustainable is a key challenge of our time. By 2030, Australia has committed to a 43 per cent reduction in greenhouse gas emissions from 2005 levels. 

Leading organisations such as the Intergovernmental Panel on Climate Change (IPCC) have advised reducing intake of animal-source food such as meat and dairy, and increased consumption of plant-based foods to mitigate greenhouse gas emissions. Policy instruments under consideration to encourage populations to adopt more plant-based diets include:
- taxes on food products
- food regulations
- food labelling
- food procurement policies – for example, policies specifying criteria for foods served or sold in public settings or purchased with government funds such as at hospitals, schools, childcare facilities and sporting venues
- regulations around the marketing of food
- development of sustainable dietary guidelines and ‘green nudges’  
This project aims to apply state-of-the-art modelling techniques and data sources to build a dietary modelling tool. This tool can be used to predict the nutritional implications of policies and public health messages aimed at encouraging the Australian population to transition to a more environmentally sustainable diet.

The project brings together expertise in human nutrition, environmental sustainability and food, meat, and meat-analogue science with MDAP’s expertise in computational modelling and simulation, and modelling experience from other health-related disciplines. Outcomes from this research will include evidence to guide policy development and public health messages and improve nutritional adequacy as Australia transitions to a diet that is both more environmentally sustainable and healthy. 

Chief Investigator: Dr Anita Lawrence, Science, School of Agriculture and Food

MDAP team: Edoardo Tescari, Kim Doyle, Daniel Russo-Batterham
Facilitating cognitive research in clinical environments: Automated scoring of the Autobiographical Memory Task
This project aims to train a machine learning model to identify different types of text-based autobiographical memories, that is, memories of an individual's past experiences. An impaired ability to retrieve 'specific' memories such as "I went swimming last night" is a key predictor of the course of depression. Identifying memory deficits in individuals, and how these memory difficulties might influence response to psychological and pharmacological treatments, can help improve outcomes for mental illness.

The gold standard for assessing memory retrieval is the Autobiographical Memory Task (AMT). This is a cued-recall task in which individuals type out a memory in response to a cue word. Currently, categorising individual's reported memories is reliant on human reading and scoring of the response using a coding manual – with each AMT taking five minutes to score. This reliance on human scoring limits how widely we can use the AMT along with the accuracy of data.

In this project we will work with MDAP to produce a machine learning model able to score text-based memory responses on the AMT. The model will be trained on a training dataset (9,000 text-based memories) and test dataset (8,400 text-based memories), both of which have been annotated by human-raters. 

Developing this model will allow us to embed the memory task in the thousands of assessments taken by nation-wide treatment services. The model will also support digitisation of our memory-based intervention which has demonstrated treatment efficacy in paper-format, thus providing a platform for planned grant applications, and an intervention prototype to attract seed funding.   

Chief Investigator: Dr Caitlin Hitchcock, MDHS, Melbourne School of Psychological Sciences

MDAP team: Mel Mistica, Emily Fitzgerald, Aleks Michalewicz
Socio-economic change and the realisation of climate risk 
Climate change risk is unequally distributed across Australia. That risk, however, is increasingly realised as weather-related disasters occur more often and with greater intensity in certain locations. Parts of NSW and Queensland, for example, have been hit by multiple major disasters in recent years.

This context poses a risk of social upheaval, where households with more resources resettle in safer locations, leaving households with less resources concentrated in higher-risk locations. Research in the US confirms these trends.

While there is some evidence indicating similar social upheaval in Australia, there is little systematic empirical research on this phenomenon in Australia to date.

Establishing the degree and reason for the concentration of low socio-economic households in at-risk locations occurs is of urgent value to policymakers to prepare for the intensification of climate risk in hotspot locations.  

This project seeks to fill an important gap in the literature by examining socio-economic change in locations that are increasingly recognised as climatically vulnerable. The project also seeks to contribute to a better understanding of how households' perceptions of climate change are changing as well as how households respond to the realisation of climate risk.

We will collaborate with MDAP to map data from multiple flooding disasters in New South Wales (a state identified as particularly vulnerable to climate change) in terms of the statistical areas available in census data. This allows the analysis to compare socioeconomic change in disaster-hit locations with those locations observed as less vulnerable, using multiple waves of census data.

A key project aim is to compare key socio-economic indicators before and after a disaster event – including movements of households into and out of disaster-hit locations – by comparing geo-mapped census indicators from 2016 and 2022. 

Chief Investigator: Dr Antonia Settle, Faculty of Business and Economics, Melbourne Institute

MDAP team: Usha Natala, Mel Mistica

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

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