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Keywords: fish  or reproducibility  or Machine Learning 

WORKSHOP: Machine learning in the life sciences

This record includes training materials associated with the Australian BioCommons workshop ‘Machine Learning in the Life Sciences’. This on 11 June 2024.
Event description
Machine learning promises to revolutionise life science research by speeding up data analysis, enabling prediction of...

Keywords: Bioinformatics, Life Science, Machine Learning

WORKSHOP: Machine learning in the life sciences https://dresa.org.au/materials/workshop-machine-learning-in-the-life-sciences This record includes training materials associated with the Australian BioCommons workshop ‘Machine Learning in the Life Sciences’. This on 11 June 2024. Event description Machine learning promises to revolutionise life science research by speeding up data analysis, enabling prediction of biological patterns and modelling complex biological systems. But what exactly is machine learning and when should you use it? This hands-on online workshop provides a high-level introduction to machine learning: what it is, its advantages and disadvantages compared to traditional modelling approaches and the types of scenarios where it may be the right tool for the job. Using example datasets and basic machine learning pipelines we contrast a few commonly used algorithms for constructing predictive models and explore some of their trade-offs. We discuss common pitfalls in how machine learning is applied and evaluated, with a focus on its application in the life sciences, to help you recognise overly optimistic results. We discuss how and why such errors arise and strategies to avoid them. Lead trainer:  Dr Benjamin Goudey, Research Fellow, Florey Department of Neuroscience and Mental Health Facilitators: Dr Erin Graham, Queensland Cyber Infrastructure Foundation (QCIF) / James Cook University William Pinzon Perez, Queensland Cyber Infrastructure Foundation (QCIF) Dr Giorgia Mori, Sydney Informatics Hub, University of Sydney  Joseph McConnell, University of Adelaide Jessica Chung, Melbourne Bioinformatics 0000-0002-0627-0955 Host: Dr Melissa Burke, Australian BioCommons. Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Schedule (PDF): Schedule describing the timing of sessions for the in person and online events Materials shared elsewhere:   This workshop follows the Google Colab Notebook developed by Dr Benjamin Goudey: https://github.com/bwgoudey/IntroMLforLifeScienceWorkshopR Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Life Science, Machine Learning
WORKSHOP: Introduction to Machine Learning in R - from data to knowledge

This record includes training materials associated with the Australian BioCommons workshop ‘Introduction to Machine Learning in R - from data to knowledge’. This workshop took place over one, 4 hour sessions on 09 December 2024.
Event description
With the rise in high-throughput sequencing...

Keywords: Bioinformatics, Machine Learning

WORKSHOP: Introduction to Machine Learning in R - from data to knowledge https://dresa.org.au/materials/workshop-introduction-to-machine-learning-in-r-from-data-to-knowledge This record includes training materials associated with the Australian BioCommons workshop ‘Introduction to Machine Learning in R - from data to knowledge’. This workshop took place over one, 4 hour sessions on 09 December 2024. Event description With the rise in high-throughput sequencing technologies, the volume of omics data has grown exponentially. A major issue is to mine useful knowledge from these heterogeneous collections of data. The analysis of complex high-volume data is not trivial and classical tools cannot be used to explore their full potential. Machine Learning (ML), a discipline in which computers perform automated learning without being programmed explicitly and assist humans to make sense of large and complex data sets, can thus be very useful in mining large omics datasets to uncover new insights that can advance the field of bioinformatics. This hands-on workshop will introduce participants to the ML taxonomy and the applications of common ML algorithms to health data. The workshop will cover the foundational concepts and common methods being used to analyse omics data sets by providing a practical context through the use of basic but widely used R libraries. Participants will acquire an understanding of the standard ML processes, as well as the practical skills in applying them on familiar problems and publicly available real-world data sets. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Lead trainers: Dr Fotis Psomopoulos, Senior Researcher,  Institute of Applied Biosciences (INAB), Center for Research and Technology Hellas (CERTH) Facilitators: Dr Giorgia Mori, Australian BioCommons Dr Eden Zhang, Sydney Informatics Hub Dr Erin Graham, Queensland Cyber Infrastructure Foundation (QCIF) Infrastructure provision: Uwe Winter, Australian BioCommons Host: Dr. Giorgia Mori, Australian BioCommons Training materials Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Files and materials shared elsewhere: Training materials webpage Data and documentation     Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Machine Learning
7 Steps towards Reproducible Research

This workshop aims to take you further down your reproducibility path, by providing concepts and tools you can use in your everyday workflows. It is discipline and experience agnostic, and no coding experience is needed.

We will also examine how Reproducible Research builds business continuity...

Keywords: reproducibility, Reproducibility, reproducible workflows

Resource type: full-course, tutorial

7 Steps towards Reproducible Research https://dresa.org.au/materials/7-steps-towards-reproducible-research This workshop aims to take you further down your reproducibility path, by providing concepts and tools you can use in your everyday workflows. It is discipline and experience agnostic, and no coding experience is needed. We will also examine how Reproducible Research builds business continuity into your research group, how the culture in your institute ecosystem can affect Reproducibility and how you can identify and address risks to your knowledge. The workshop can be used as self-paced or as an instructor Amanda Miotto - a.miotto@griffith.edu.au reproducibility, Reproducibility, reproducible workflows phd support
WEBINAR: A practical guide to AI tools for life scientists

This record includes training materials associated with the Australian BioCommons webinar ‘A practical guide to AI tools for life scientists’. This webinar took place on 8 May 2024.
Event description
The widespread availability and application of AI tools like ChatGPT have fundamentally...

Keywords: Bioinformatics, Machine Learning, Artificial Intelligence, ChatGPT

WEBINAR: A practical guide to AI tools for life scientists https://dresa.org.au/materials/webinar-a-practical-guide-to-ai-tools-for-life-scientists This record includes training materials associated with the Australian BioCommons webinar ‘A practical guide to AI tools for life scientists’. This webinar took place on 8 May 2024. Event description The widespread availability and application of AI tools like ChatGPT have fundamentally transformed our approach to work, creativity, learning, and communication. In the realm of scientific research, the impact of AI extends far beyond mere promises, already catalysing significant advances and discoveries. This talk will explore how AI is reshaping scientific exploration and innovation. We explore how AI can accelerate research processes, from data analysis and code writing to hypothesis development. We will present some of the available and emerging AI and how we might effectively leverage these tools while acknowledging their limitations. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Speaker: Dr Michael Kuiper, Principal Research Scientist in Computational Biology and acting Group Leader of the Computational Modelling (CM) group at Data61 of CSIRO.  Host: Dr Patrick Capon, Australian BioCommons Training materials Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. Kuiper_May2024_b_version: A PDF copy of the slides presented during the webinar. Q_and_A_AI-life-scientists: PDF copy of questions and answers from the webinar Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/NbYvq3OLEfo   Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Machine Learning, Artificial Intelligence, ChatGPT
WEBINAR: AlphaFold: what's in it for me?

This record includes training materials associated with the Australian BioCommons webinar ‘WEBINAR: AlphaFold: what’s in it for me?’. This webinar took place on 18 April 2023.

Event description 

AlphaFold has taken the scientific world by storm with the ability to accurately predict the...

Keywords: Bioinformatics, Machine Learning, Structural Biology, Proteins, Drug discovery, AlphaFold, AI, Artificial Intelligence, Deep learning

WEBINAR: AlphaFold: what's in it for me? https://dresa.org.au/materials/webinar-alphafold-what-s-in-it-for-me-4d1ea222-4240-4b68-b9ae-7769ac664ee0 This record includes training materials associated with the Australian BioCommons webinar ‘WEBINAR: AlphaFold: what’s in it for me?’. This webinar took place on 18 April 2023. Event description  AlphaFold has taken the scientific world by storm with the ability to accurately predict the structure of any protein in minutes using artificial intelligence (AI). From drug discovery to enzymes that degrade plastics, this promises to speed up and fundamentally change the way that protein structures are used in biological research.  Beyond the hype, what does this mean for structural biology as a field (and as a career)? Dr Craig Morton, Drug Discovery Lead at the CSIRO, is an early adopter of AlphaFold and has decades of expertise in protein structure / function, protein modelling, protein – ligand interactions and computational small molecule drug discovery, with particular interest in anti-infective agents for the treatment of bacterial and viral diseases. Craig joins this webinar to share his perspective on the implications of AlphaFold for science and structural biology. He will give an overview of how AlphaFold works, ways to access AlphaFold, and some examples of how it can be used for protein structure/function analysis. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/4ytn2_AiH8s Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Machine Learning, Structural Biology, Proteins, Drug discovery, AlphaFold, AI, Artificial Intelligence, Deep learning
How can software containers help your research?

This video explains software containers to a research audience. It is an introduction to why containers are beneficial for research. These benefits are standardisation, portability, reliability and reproducibility. 

Software Containers in research are a solution that addresses the challenge of a...

Keywords: containers, software, research, reproducibility, RSE, standard, agility, portable, reusable, code, application, reproducible, standardisation, package, system, cloud, server, version, reliability, program, collaborator, ARDC_AU, training material

How can software containers help your research? https://dresa.org.au/materials/how-can-software-containers-help-your-research-ca0f9d41-d83b-463b-a548-402c6c642fbf This video explains software containers to a research audience. It is an introduction to why containers are beneficial for research. These benefits are standardisation, portability, reliability and reproducibility.  Software Containers in research are a solution that addresses the challenge of a replicable computational environment and supports reproducibility of research results. Understanding the concept of software containers enables researchers to better communicate their research needs with their colleagues and other researchers using and developing containers. Watch the video here: https://www.youtube.com/watch?v=HelrQnm3v4g If you want to share this video please use this: Australian Research Data Commons, 2021. How can software containers help your research?. [video] Available at: https://www.youtube.com/watch?v=HelrQnm3v4g DOI: http://doi.org/10.5281/zenodo.5091260 [Accessed dd Month YYYY]. contact@ardc.edu.au Martinez, Paula Andrea (type: ProjectLeader) Sam Muirhead (type: Producer) The ARDC Communications Team (type: Editor) The ARDC Skills and Workforce Development Team (type: ProjectMember) The ARDC eResearch Infrastructure & Services (type: ProjectMember) The ARDC Nectar Cloud Services team (type: ProjectMember) containers, software, research, reproducibility, RSE, standard, agility, portable, reusable, code, application, reproducible, standardisation, package, system, cloud, server, version, reliability, program, collaborator, ARDC_AU, training material
CheckEM User Guide

CheckEM is an open-source web based application which provides quality control assessments on metadata and image annotations of fish stereo-imagery. It is available at marine-ecology.shinyapps.io/CheckEM. The application can assess a range of sampling methods and annotation data formats for...

Keywords: stereo-video, fish, annotation

CheckEM User Guide https://dresa.org.au/materials/checkem-user-guide CheckEM is an open-source web based application which provides quality control assessments on metadata and image annotations of fish stereo-imagery. It is available at marine-ecology.shinyapps.io/CheckEM. The application can assess a range of sampling methods and annotation data formats for common inaccuracies made whilst annotating stereo imagery. CheckEM creates interactive plots and tables in a graphical interface, and provides summarised data and a report of potential errors to download. brooke.gibbons@uwa.edu.au stereo-video, fish, annotation
EventMeasure Annotation Guide

EventMeasure annotation guide for baited remote underwater stereo video systems (stereo-BRUVs) for count and length

Keywords: fish, stereo-video, annotation

EventMeasure Annotation Guide https://dresa.org.au/materials/eventmeasure-annotation-guide EventMeasure annotation guide for baited remote underwater stereo video systems (stereo-BRUVs) for count and length tim.langlois@uwa.edu.au fish, stereo-video, annotation
Fundamentals of Machine Learning

This is the first of four modules in our exciting new machine learning workshop series by the Sydney Informatics Hub (SIH).

Module 2: https://youtu.be/HVAFflj2PS0
Module 3:...

Keywords: Machine Learning, training material

Fundamentals of Machine Learning https://dresa.org.au/materials/fundamentals-of-machine-learning This is the first of four modules in our exciting new machine learning workshop series by the Sydney Informatics Hub (SIH). **Module 2**: [https://youtu.be/HVAFflj2PS0](https://youtu.be/HVAFflj2PS0) **Module 3**: [https://github.com/Sydney-Informatics-Hub/Module3R](https://github.com/Sydney-Informatics-Hub/Module3R) *The Sydney Informatics Hub is a Core Research Facility at The University of Sydney, enabling excellence in research* [https://sydney.edu.au/informatics-hub](https://sydney.edu.au/informatics-hub) sih.training@sydney.edu.au Machine Learning, training material
Stereo-video workflows for fish and benthic ecologists

Stereo imagery is widely used by research institutions and management bodies around the world as a cost-effective and non-destructive method to research and monitor fish and habitats (Whitmarsh, Fairweather and Huveneers, 2017). Stereo-video can provide accurate and precise size and range...

Keywords: stereo-video, fish, sharks, habitats

Resource type: tutorial

Stereo-video workflows for fish and benthic ecologists https://dresa.org.au/materials/stereo-video-workflows-for-fish-and-benthic-ecologists Stereo imagery is widely used by research institutions and management bodies around the world as a cost-effective and non-destructive method to research and monitor fish and habitats (Whitmarsh, Fairweather and Huveneers, 2017). Stereo-video can provide accurate and precise size and range measurements and can be used to study spatial and temporal patterns in fish assemblages (McLean et al., 2016), habitat composition and complexity (Collins et al., 2017), behaviour (Goetze et al., 2017), responses to anthropogenic pressures (Bosch et al., 2022) and the recovery and growth of benthic fauna (Langlois et al. 2020). It is important that users of stereo-video collect, annotate, quality control and store their data in a consistent manner, to ensure data produced is of the highest quality possible and to enable large scale collaborations. Here we collate existing best practices and propose new tools to equip ecologists to ensure that all aspects of the stereo-video workflow are performed in a consistent way. tim.langlois@uwa.edu.au stereo-video, fish, sharks, habitats
10 Reproducible Research things - Building Business Continuity

The idea that you can duplicate an experiment and get the same conclusion is the basis for all scientific discoveries. Reproducible research is data analysis that starts with the raw data and offers a transparent workflow to arrive at the same results and conclusions. However not all studies are...

Keywords: reproducibility, data management

Resource type: tutorial, video

10 Reproducible Research things - Building Business Continuity https://dresa.org.au/materials/9-reproducible-research-things-building-business-continuity The idea that you can duplicate an experiment and get the same conclusion is the basis for all scientific discoveries. Reproducible research is data analysis that starts with the raw data and offers a transparent workflow to arrive at the same results and conclusions. However not all studies are replicable due to lack of information on the process. Therefore, reproducibility in research is extremely important. Researchers genuinely want to make their research more reproducible, but sometimes don’t know where to start and often don’t have the available time to investigate or establish methods on how reproducible research can speed up every day work. We aim for the philosophy “Be better than you were yesterday”. Reproducibility is a process, and we highlight there is no expectation to go from beginner to expert in a single workshop. Instead, we offer some steps you can take towards the reproducibility path following our Steps to Reproducible Research self paced program. Video: https://www.youtube.com/watch?v=bANTr9RvnGg Tutorial: https://guereslib.github.io/ten-reproducible-research-things/ a.miotto@griffith.edu.au; s.stapleton@griffith.edu.au; i.jennings@griffith.edu.au; Sharron Stapleton Isaac Jennings reproducibility, data management masters phd ecr researcher support