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EcoCommons
EcoCommons

Empowering Ecological Modelling with Scalable, Collaborative Digital Infrastructure

The project is powered by
TYPE OF SUPPORT

Research Background

Researchers working in ecology and environmental science often need to run complex models using large, diverse datasets — from climate projections to species observations. However, these tasks are frequently limited by fragmented data sources, inconsistent modelling approaches, limited computing power, and technical barriers to setting up reproducible workflows.


This lack of accessible, scalable infrastructure hinders collaboration, slows research progress, and makes it harder to translate science into evidence-based policy and management.


EcoCommons was developed to address these challenges by providing a national platform that brings together data, models, and computing power in one place to support reproducible, transparent, and policy-relevant ecological and environmental modelling.


EcoCommons is a co-investment partnership with the Australian Research Data Commons (ARDC) through the Planet Research Data Commons (DOI: doi.org/10.3565/chbq-mr75). The ARDC is enabled by the Australian Government’s National Collaborative Research Infrastructure Strategy (NCRIS).

QCIF Role

QCIF’s Sustainable Futures team is responsible for the ongoing maintenance, management, and development of new functionalities for the EcoCommons platform. 


The team leads the creation and expansion of an extensive library of scalable, reusable Jupyter notebooks that support a wide range of ecological modelling workflows.


Beyond platform development, QCIF plays a key role in fostering and growing the EcoCommons community of practice by providing training, documentation, and direct support to researchers, enabling collaborative and reproducible science across Australia.

Research Outcome & Impact

EcoCommons supports thousands of active users nationwide who collectively run millions of ecological models through the platform. The project currently offers a growing library of over 10 scalable, reusable Jupyter notebooks, with new workflows released regularly to address emerging research needs.


The EcoCommons community actively engages through monthly “Hacky Hours” — collaborative coding and problem-solving sessions that bring together researchers, government agencies, and NGO partners. This vibrant network of collaborators helps drive continuous improvement and innovation.


EcoCommons is co-designing customised workflows with key partners such as the Federal Department of Environment, Energy and Climate Change (DEECCW) and leads a national collaborative working group focused on advancing species distribution modelling standards and practices across Australia.


Together, these efforts empower reproducible, scalable ecological modelling that supports evidence-based biodiversity conservation, land management, and climate adaptation decisions nationwide.

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EcoCommons provides computational tools and resources for reproducible ecosystems research. As an evolution of the BCCVL and ecocloud projects, it supports the full spectrum of researchers, from the non-technical who want good results without the need to code, to those comfortable working ‘closer to the metal’ and building their own tools. EcoCommons offers an exciting opportunity to teach the next generation of ecosystem modellers.”Professor Shawn Laffan, UNSW.

Shawn Laffan, School of Biological, Earth & Environmental Sciences, UNSW

Data & Software

Abhimanyu (Raj) Singh

Data Ecologist

Data & Software

Jenna Wraith

Head of Sustainable Futures Department and Principal Data Scientist

Data & Software

Renuka Sharma

Data Scientist (Computer Vision and Ecology)

Data & Software

Ryan Newis

Software Developer (R Specialist)

Data & Software

Xiang Zhao (Zhao)

Software Developer and R Specialist

Collaborating Organisations

ARDC | TERN | ALA | University of Melbourne (CEBRA) | Queensland Government | CSIRO | UNSW | Macquarie University | Griffith University

Researchers & COLLABORATORS
Team behind the project
Data & Software
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