DAY ONE | 14 October 2026
1:00pm – 5:00pm
Dr Kiel Hards
Half-Day Training Session
Since its beta release in 2024, Positron, the latest Data Science IDE from Posit and the spiritual successor to RStudio, has taken the open-source data science community by storm. The IDE combines the many well-loved features of RStudio with the modern interface and extensibility of VS Code, providing first-class support for both R and Python out of the box. The pace of development has been rapid: 2026 alone has brought a fully redesigned Notebook Editor, inline Quarto output, an integrated Packages pane, first-class R debugging with breakpoints, and a new unified AI assistant purpose-built for data science. Join us for an interactive training on what Positron offers including a showcase of its latest features, and an honest discussion of how to navigate the switch from RStudio and avoid common frustrations. If you aren’t set up already we can help you install Positron live during the session and will work through hands-on demonstrations of how to customise and elevate your data science workflows.
6:30pm
DAY TWO | 15 October 2026
8:30am
9:00am
Dr Uli Muellner & Dr Petra Muellner | Epi
9:15am
Dr Shelly Lachish | Queensland Health
The Queensland Chief Health Officer’s Health of Queenslanders Report (CHO Report) has evolved from a static, printed, magazine-style publication into a web-based digital resource featuring interactive visualisations and dashboards. In this talk, I’ll share that journey from book to browser, focusing on the technical, cultural, and organisational aspects of moving a large, complex public health report into the digital space.
I’ll describe how the CHO production team rapidly adopted a new way of working: moving from STATA to R, embracing literate programming with R Markdown (more recently Quarto), building reusable R packages and data pipelines, standardising data structures, introducing version control, and developing interactive visualisations. Together, these changes have made it easier to manage a diverse collection of datasets, analyses and reporting requirements, while creating content that is faster to produce, more consistent and increasingly reproducible.
Along the way, I’ll reflect on some practical realities of digital transformation: navigating organisational IT barriers, “moving at the speed of trust” when introducing new tools, and discovering that some processes stubbornly resist automation.
This is a practical look at modernising public health reporting in a real-world setting.
9:40am
Nathan Dunn | Cancer Alliance Queensland
Analysts are often asked to rerun the same analysis for different cohorts, time periods, or parameters. While each request may be small, the cumulative effort can be substantial.
At Cancer Alliance Queensland, we have increasingly used Shiny to reduce this burden by enabling clinicians, managers, and analysts to explore data themselves. Interactive applications allow users to answer routine questions, test assumptions, and investigate trends without requiring a new report or analysis each time.
This presentation will demonstrate how Shiny has been used to support collaborative data exploration in a healthcare setting, from analyst-facing tools to dashboards used by clinical stakeholders. I will share examples, lessons learned, and the benefits we have seen in stakeholder engagement, efficiency, and reproducibility.
10:05am
Dr Kiel Hards | Epi
Interactive spatial visualizations are a cornerstone for understanding and communicating complexity in almost all technical and scientific disciplines and are commonly embedded in reports or interactive dashboards. As the amount of data grows as well the pressure to create operational intelligence grows existing tools used to generate and display maps hit their performance ceiling. They often lack modern features essential for complex datasets such as those arising from large scale or multi-year projects. MapLibre GL JS is an open-source JavaScript/TypeScript library for rendering interactive maps in the browser using WebGL (and soon WebGPU) that is built from the ground up for responsiveness and scale. To enable report and dashboard developers in R to leverage this powerful library, the team at Epi developed Toro: a R package providing bindings to MapLibre GL JS, allowing users to create interactive maps that can easily be integrated into both Quarto and the R Shiny dashboard framework. In this talk, we will discuss the technical challenges we had to overcome during the development of the package and provide examples of how Toro enabled spatial visualisation and exploration of data that otherwise would have been limited, too slow or too hard to scale using traditional tools.
10:35am
10:45am
11:30am
Taking your automated reporting game to the next level with Quarto
Hosted by Dr Kiel Hards
Quarto is one of Posit’s latest multi-language reporting offerings. It is an open-source scientific and technical publishing system that allows users to publish reproducible, production quality articles, presentations and dashboards. Having been rigorously developed by the team at Posit as the ‘successor’ to R Markdown, the framework offers full backwards compatibility with the original features of R Markdown, along with new features such as language support for Python and Julia, formats such as presentations, PDF, Microsoft Word, even website and ebook formats! You can even use Quarto documents as an environment for your data science experimentation, thanks to the multi-language integration which allows you to run code directly from the Quarto document itself. The framework uses a rich Markdown syntax to provide access to powerful technical and scientific notations such as equations, citations, figures, cross-references, advanced layouts and more. Quarto documents can easily be automated and published to online services such as Posit Connect or Confluence whenever your data changes. Join us to learn how to harness the power of Quarto to dynamically create and share high quality reports and presentations, fit these reports into your existing R or Python workflow and have all your favourite analytics built straight in!
Open source data science in restricted environments
Hosted by Dr Uli Muellner
Open-source tooling offers capabilities that are crucial for data manipulation, exploration, visualisation, modelling, and machine learning. However a common frustration is that they are often “un-officially” in use within organisations, and access restrictions hinder the development of a fully established data science infrastructure for technical and scientific data. In this talk we will explore common pain points relating to accessing open-source data science coding tools and the deployment of data products like Quarto or Shiny in different organisations. We explain when (and when not) the Posit Enterprise products Workbench (for coding), Connect (for deployment) and Package Manager (for secure access to R/Py packages) are needed and how to get the most out of the licenses. We will also dive into emerging capabilities, such managing AI and LLM workflows with the Model Context Protocol (MCP) support in Posit Connect and how local LLMs can be paired with Posit tools to sandbox and explore AI capability in a secure environment.
1:00pm
2:15pm
Adam Sparks | Curtin University
The Pesticide Resistance Integrated Mapping (PRIM) tool was originally launched in 2024 using Python and Google Earth Engine to display fungicide resistance surveillance data. PRIM 2.0 rebuilds that work as a privacy-preserving R platform for reproducible pipelines, structured Shiny development, and reliable deployment.
The second-generation platform combines {targets}, {golem}, and {Shiny} with fast loading {qs2} objects, {data.table} joins, {mapgl} maps, and {echarts4r} graphics to support responsive fungicide and herbicide surveillance workflows.
The core design separates data processing from application delivery: records are ingested, validated, standardised, classified against resistance thresholds, spatially summarised, and exported as lightweight app-ready data before Shiny consumes them.
This separation supports privacy and performance by processing GPS-derived coordinates locally, converting them to de-identified hexagonal bins, retaining a single geometry object, and ensuring sensitive locations and project identifiers do not leave the local computer for the web server.
The architecture was tested through staged expansion from herbicide resistance to integrated fungicide resistance data, showing how shared data models and interface components can support multiple pathosystems and pesticide classes.
As a practical extensibility test, a new fungicide threshold classification was added upstream in the pipeline and propagated to the final application without changing app code, map filters, or user-facing controls.
This case study discusses the design decisions, trade-offs, and lessons learned in moving from a single-purpose mapping product to a reusable R architecture that reduces maintenance burden, improves speed and application size, protects sensitive data, and provides a stronger foundation for future decision-support tools.
For R developers, the broader lesson is that careful pipeline design can make Shiny applications faster, smaller, safer, and easier to extend as data products mature.
2:30pm
François Michonneau | Posit
Artificial intelligence is reshaping what we build, how we build it, and how we share the results. In a landscape shifting this rapidly, with new tools and practices coming out every week, staying oriented is not easy. This talk offers a practical map for this new landscape, walking through the end-to-end lifecycle of an AI-native application.
What we build. We will explore the open source packages that Posit maintains to develop tools and applications to interact with LLMs.
How we build. Effective AI assistance requires rich local context. We will show how embedded AI tools in Positron and RStudio tap directly into live session state (loaded data, active plots, and console history) giving developers agentic editing, planning modes, and session memory that general-purpose chatbots cannot match. Behind the scenes, enterprise infrastructure safely brokers model credentials and distributes vetted, version-pinned AI agent skills across teams just like standard software packages.
How we share. Moving from laptop to production requires secure delivery. We’ll cover how Posit Connect hosts the final application while managing user permissions and server-side Model Context Protocol (MCP) integrations.
You will leave this session with a clear snapshot of the current terrain, an understanding of how open-source and enterprise components fit together, and the practical knowledge to navigate what comes next.
3:00pm
Matt Fraser | Minderoo Foundation
eDNA data has the potential to improve our ability to make science-based decisions across a wide range of objectives – from community efforts to large scale ecological and conservation measures. However, if the insights created are fragmented, not accessible and do not connect with stakeholders and operational needs, eDNA will fail to live up to its potential. This presentation will showcase how Minderoo's Oceanomics team uses storytelling dashboards (built in R Shiny) in a variety of contexts to bridge these gaps to embed eDNA science into day-to-day decision making and stakeholder engagement. It will further demonstrate how operational intelligence and decision support can be provided for highly diverse users via a dedicated open platform that allows for custom queries, notifications and demonstrates the value of data sharing across institutional and country boundaries.
3:15pm
Rebecca Fisher | Australian Institute of Marine Science
Environmental regulation runs on numbers: the turbidity trigger that stops a dredge, the concentration limit in a discharge licence. They have to be defensible — to a regulator, to a proponent, occasionally in court — and increasingly they come out of open-source R packages. I am a quantitative ecologist at the Australian Institute of Marine Science, with a sound applied grasp of statistics and no formal training in programming. This talk traces fifteen years of accidental software development around one question: how far did the software travel?
Era one produced scripts, not software. ElicitN, published in 2012 with four co-authors, was 57 R scripts in a zip file, and effectively unreproducible within five years. None of us thought to ask whether it would still run.
Era two ran as two strands that never met. One was industry monitoring data — over 1,500 coral colonies scored fortnightly for 530 days — producing thresholds still in regulatory use, and code entangled with data I cannot distribute. The other was GitHub and packaging, FSSgam and epower: installable, public, and depended on by no regulation at all. The pattern is backwards. The higher the stakes, the less shareable the tooling, and I do not think our field has solved this.
Era three was CRAN, and then ssdtools: jointly evaluated by Australian and Canadian regulators and adopted as the default for deriving water quality guideline values in three countries. Open source was the precondition for that agreement, because three regulators cannot audit a black box. It closes on the last few months. I stopped writing code by hand in June. Rather than assert a productivity gain I measured it across 1,340 of my own commits, and will show where the change actually is — it is not in the typing.
3:30pm
Brooke Bond | The University of Western Australia, Indian Ocean Marine Research Centre
Effective research and monitoring depend on accurate, interoperable, and reproducible data, yet quality-control issues are often only discovered late in the analysis workflow. Fish surveys using stereo- and mono-video provide a useful example: these methods generate large volumes of metadata and annotation data, often produced across different projects, organisations, and software platforms. A national synthesis of video-based fish survey datasets identified numerous inconsistencies and errors in data collection and annotation, highlighting the need for systematic and reusable quality-assurance workflows.
We developed CheckEM, an open-source R package and Shiny application for standardising, cleaning, validating and visualising fish survey data. CheckEM combines rule-based validation with external reference data to identify potential errors in survey metadata and biological annotations. Checks include taxonomic validation, expected spatial distributions, outdated scientific names and, for stereo-video data, biologically implausible body-size measurements. The toolkit supports outputs from multiple annotation software platforms, helping researchers bring heterogeneous datasets into a consistent workflow.
The Shiny application provides a user-friendly interface for running checks and exploring results through interactive tables, maps, and visualisations, while the R package enables the same processes to be incorporated into scripted, reproducible data pipelines. Users can download cleaned datasets, summary outputs, and detailed error reports, allowing issues to be corrected iteratively and checks to be rerun as datasets evolve.
By bringing quality-control steps into an open-source R workflow, CheckEM improves data accuracy, transparency, interoperability, and reusability. Although developed for imagery-based fish surveys, the approach demonstrates how domain-specific validation rules, external reference datasets and interactive R tools can be combined to make quality assurance more accessible and reproducible across research workflows and help to build a collaborative research community.
3:45pm
Claire Davies & Jason Everett | CSIRO
The Biological Ocean Observer was developed to bridge the gap between observations and impact. Despite multi variable, long-term time series datasets being openly accessible, uptake was slow. I found myself repeatedly downloading, wrangling and analysing data to make similar products for stakeholders. People were overwhelmed by the data portal, didn’t possess the technical skills, or simply didn’t have the time to develop their own workflows. Then we discovered R Shiny.
The Biological Ocean Observer is an online R-Shiny application that integrates, analyses and visualises data from the Integrated Marine Observing System (IMOS) so that everyone can understand, query and interpret it. We use internationally recognised programming frameworks (R and Shiny), and all code are freely available, facilitating further development and collaboration within the community. The Shiny application is underpinned by an R-package (planktonr) - this is the engine, performing the heavy wrangling prior to visualisation which improves app performance. planktonr also allows power users to replicate the data and visualisations they access from the Biological Ocean Observer and to modify it for their specific use case.
The Biological Ocean Observer is designed to be used by a wide range of stakeholders including science researchers, natural resource managers, policy makers, educators, students and the general public – including you! It increases our ability to deliver impact by expanding the analysis and visualisation of IMOS data to a broad audience, accelerating the generation of scientific insights. Publication ready plots can be uploaded directly into policy documentation, for example, State of Environment.
The Biological Ocean Observer and planktonr provide a pipeline from observations to impact and make IMOS data discoverable by all.
3:45pm
4:00pm
4:10pm