1. Home
  2. Materials
Register training material
12 materials found

Keywords: AI  or Phylogeny  or Ecoacoustics 

Open Ecoacoustics make your own recogniser

Includes the requirements and practical steps required to make your own automated call recogniser using a convolution neural network.

The "Requirements" section includes demo data and requirements for the data you should include to develop your own recogniser as well as links to Anaconda &...

Keywords: Ecoacoustics, call recogniser, convolutional neural network

Open Ecoacoustics make your own recogniser https://dresa.org.au/materials/open-ecoacoustics-make-your-own-recogniser Includes the requirements and practical steps required to make your own automated call recogniser using a convolution neural network. The "Requirements" section includes demo data and requirements for the data you should include to develop your own recogniser as well as links to Anaconda & Raven Lite software. The "Practical Steps" provides instructions to run the required Jupyter notebook to build a recogniser with CNN. * Note additional AI methods will be available soon https://openecoacoustics.org/contact/ Ecoacoustics, call recogniser, convolutional neural network
Open Ecoacoustics wrangling sound files

An introduction to slicing, dicing, chopping, resampling, compressing etc sound files with an introduction to command line and graphical tools.

A "Requirements" section with demo data, file dependencies, and required software.

A "Presentation" section with an online introduction to storing...

Keywords: Ecoacoustics, sound files, data wrangling

Open Ecoacoustics wrangling sound files https://dresa.org.au/materials/open-ecoacoustics-wrangling-sound-files An introduction to slicing, dicing, chopping, resampling, compressing etc sound files with an introduction to command line and graphical tools. A "Requirements" section with demo data, file dependencies, and required software. A "Presentation" section with an online introduction to storing data, repairing data and segmenting files. A "Practical" section inclusive of setup, Terminal use, manipulating files with FFmpeg, using the AnalysisPrograms audio cutter, run EMU software https://openecoacoustics.org/contact/ Ecoacoustics, sound files, data wrangling
Open Ecoacoustics acoustic indices

Provides an introduction to and generation of false-colour spectrograms and indices.

Includes a "Requirements" section where demo audio files, other dependencies and required software.

Includes a "Presentation" section providing an online presentation on false colour...

Keywords: Ecoacoustics, false-colour spectrograms, acoustic indices

Open Ecoacoustics acoustic indices https://dresa.org.au/materials/open-ecoacoustics-acoustic-indices Provides an introduction to and generation of false-colour spectrograms and indices. Includes a "Requirements" section where demo audio files, other dependencies and required software. Includes a "Presentation" section providing an online presentation on false colour spectrograms. Includes a "Practical" section that provides the setup, use of terminal, Analysis Programs software, and calculation of acoustic indices. https://openecoacoustics.org/contact/ Ecoacoustics, false-colour spectrograms, acoustic indices
Open Ecoacoustics recording and labelling

This module includes recommendations for deployment, recording and labelling sounds, playing those sounds and annotation using Audacity and Raven software.

The "Requirements" section includes downloads of example data, required dependencies and software.

The "Presentation" walks through an...

Keywords: Ecoacoustics, recording sound, labelling sound, spectrograms

Open Ecoacoustics recording and labelling https://dresa.org.au/materials/open-ecoacoustics-recording-and-labelling This module includes recommendations for deployment, recording and labelling sounds, playing those sounds and annotation using Audacity and Raven software. The "Requirements" section includes downloads of example data, required dependencies and software. The "Presentation" walks through an online presentation with recommendations recorder deployment recommendations, annotation, raven software, & manual validation The "Practical" includes setup, single species annotation of spectrograms, multi-species, and generating images https://openecoacoustics.org/contact/ Ecoacoustics, recording sound, labelling sound, spectrograms
Open Ecoacoustics sound basics

This online presentation provides a review of five key concepts related to ecoacoustics: 1. Decibels, 2. clipping and gain, 3. ADC: Sample rate & bit depth, 4. Fast Fourier Transform (FFT), and 5. Spectrograms: time / frequency trade off.

Keywords: Ecoacoustics, sound basics, decibels, gain, sample rate, FFT, spectrograms

Open Ecoacoustics sound basics https://dresa.org.au/materials/open-ecoacoustics-sound-basics This online presentation provides a review of five key concepts related to ecoacoustics: 1. Decibels, 2. clipping and gain, 3. ADC: Sample rate & bit depth, 4. Fast Fourier Transform (FFT), and 5. Spectrograms: time / frequency trade off. https://openecoacoustics.org/contact/ Ecoacoustics, sound basics, decibels, gain, sample rate, FFT, spectrograms
Ecoacoustics & EcoCommons Generalised Dissimilarity Modelling (GDM) use case

This example highlights how data collected with passive acoustic monitoring (PAM) can be used to examine spatial variation in species composition.

This example draws from an R package developed to make GDM more accessible: https://github.com/EcoCommons-Australia/community-modelling

Keywords: Generalised Dissimilarity Modelling, Ecoacoustics, EcoCommons

Ecoacoustics & EcoCommons Generalised Dissimilarity Modelling (GDM) use case https://dresa.org.au/materials/ecoacoustics-ecocommons-generalised-dissimilarity-modelling-gdm-use-case This example highlights how data collected with passive acoustic monitoring (PAM) can be used to examine spatial variation in species composition. This example draws from an R package developed to make GDM more accessible: https://github.com/EcoCommons-Australia/community-modelling https://openecoacoustics.org/contact/ Generalised Dissimilarity Modelling, Ecoacoustics, EcoCommons
EcoCommons & Open EcoAcoustics SDM use case

  1. Examples of code and the associated text summaries describe how open ecoacoustics https://openecoacoustics.org/ data can generate better SDM predictions. By using long-term monitoring data from https://acousticobservatory.org/ which allows analysts to infer absence locations, which does a much...

Keywords: Species Distribution Modelling, Ecoacoustics, Ecology, Owls, Mapping uncertainty

EcoCommons & Open EcoAcoustics SDM use case https://dresa.org.au/materials/ecocommons-open-ecoacoustics-sdm-use-case 1. Examples of code and the associated text summaries describe how open ecoacoustics https://openecoacoustics.org/ data can generate better SDM predictions. By using long-term monitoring data from https://acousticobservatory.org/ which allows analysts to infer absence locations, which does a much better job at predicting distributions than presence only methods, and which facilitate use of call frequency as a response variable rather than presence absence. The code and data used to generate these examples: https://github.com/andrew-1234/sdm-usecase-master 2. Shows one way to overlay areas with the least geographically and environmentally representative sampling in addition to the predicted probability of occurrence generated by an SDM. This shows how to spatially represent areas where additional acoustic sampling would increase representative sampling most. The code used in this example: https://github.com/EcoCommons-Australia/educational_material/tree/main/SDMs_in_R/Scripts/adding_uncertainty_to_the_map https://www.ecocommons.org.au/contact/ Species Distribution Modelling, Ecoacoustics, Ecology, Owls, Mapping uncertainty ugrad masters mbr phd
WEBINAR: Detection of and phasing of hybrid accessions in a target capture dataset

This record includes training materials associated with the Australian BioCommons webinar ‘Detection of and phasing of hybrid accessions in a target capture dataset’. This webinar took place on 10 June 2021.

Hybridisation plays an important role in evolution, leading to the exchange of genes...

Keywords: Phylogenetics, Bioinformatics, Phylogeny, Genomics, Target capture sequencing

WEBINAR: Detection of and phasing of hybrid accessions in a target capture dataset https://dresa.org.au/materials/webinar-detection-of-and-phasing-of-hybrid-accessions-in-a-target-capture-dataset-51cc7740-0da1-45f1-95de-f1a47f676053 This record includes training materials associated with the Australian BioCommons webinar ‘Detection of and phasing of hybrid accessions in a target capture dataset’. This webinar took place on 10 June 2021. Hybridisation plays an important role in evolution, leading to the exchange of genes between species and, in some cases, generate new lineages. The use of molecular methods has revealed the frequency and importance of reticulation events is higher than previously thought and this insight continues with the ongoing development of phylogenomic methods that allow novel insights into the role and extent of hybridisation. Hybrids notoriously provide challenges for the reconstruction of evolutionary relationships, as they contain conflicting genetic information from their divergent parental lineages. However, this also provides the opportunity to gain insights into the origin of hybrids (including autopolyploids). This webinar explores some of the challenges and opportunities that occur when hybrids are included in a target capture sequence dataset. In particular, it describes the impact of hybrid accessions on sequence assembly and phylogenetic analysis and further explores how the information of the conflicting phylogenetic signal can be used to detect and resolve hybrid accessions. The webinar showcases a novel bioinformatic workflow, HybPhaser, that can be used to detect and phase hybrids in target capture datasets and will provide the theoretical background and concepts behind the workflow. This webinar is part of a series of webinars and workshops developed by the Genomics for Australian Plants (GAP) Initiative that focuses on the analysis of target capture sequence data. In addition to two public webinars, the GAP bioinformatics working group is offering training workshops in the use of newly developed and existing scripts in an integrated workflow to participants in the 2021 virtual Australasian Systematic Botany Society Conference. The materials are shared under a Creative Commons 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. Nauheimer_hybphaser_slides (PDF): Slides presented during the webinar Materials shared elsewhere: A recording of the webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/japXwTAhA5U Melissa Burke (melissa@biocommons.org.au) Phylogenetics, Bioinformatics, Phylogeny, Genomics, Target capture sequencing
WEBINAR: Conflict in multi-gene datasets: why it happens and what to do about it - deep coalescence, paralogy and reticulation

This record includes training materials associated with the Australian BioCommons webinar ‘Conflict in multi-gene datasets: why it happens and what to do about it - deep coalescence, paralogy and reticulation’. This webinar took place on 20 May 2021.

Multi-gene datasets used in phylogenetic...

Keywords: Phylogenetics, Bioinformatics, Phylogeny, Genomics, Target capture sequencing

WEBINAR: Conflict in multi-gene datasets: why it happens and what to do about it - deep coalescence, paralogy and reticulation https://dresa.org.au/materials/webinar-conflict-in-multi-gene-datasets-why-it-happens-and-what-to-do-about-it-deep-coalescence-paralogy-and-reticulation-a6743550-b904-45e1-9635-4e481ee8f739 This record includes training materials associated with the Australian BioCommons webinar ‘Conflict in multi-gene datasets: why it happens and what to do about it - deep coalescence, paralogy and reticulation’. This webinar took place on 20 May 2021. Multi-gene datasets used in phylogenetic analyses, such as those produced by the sequence capture or target enrichment used in the Genomics for Australian Plants: Australian Angiosperm Tree of Life project, often show discordance between individual gene trees and between gene and species trees. This webinar explores three different forms of discordance: deep coalescence, paralogy, and reticulation. In each case, it considers underlying biological processes, how discordance presents in the data, and what bioinformatic or phylogenetic approaches and tools are available to address these challenges. It covers Yang and Smith paralogy resolution and general information on options for phylogenetic analysis. This webinar is part of a series of webinars and workshops developed by the Genomics for Australian Plants (GAP) Initiative that focused on the analysis of target capture sequence data. In addition to two public webinars, the GAP bioinformatics working group is offering training workshops in the use of newly developed and existing scripts in an integrated workflow to participants in the 2021 virtual Australasian Systematic Botany Society Conference. The materials are shared under a Creative Commons 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. Schmidt-Lebuhn - paralogy lineage sorting reticulation - slides (PDF): Slides presented during the webinar   Materials shared elsewhere: A recording of the webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/1bw81q898z8 Melissa Burke (melissa@biocommons.org.au) Phylogenetics, Bioinformatics, Phylogeny, Genomics, Target capture sequencing
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
Accelerating skills development in Data science and AI at scale

At the Monash Data Science and AI  platform, we believe that upskilling our research community and building a workforce with data science skills are key to accelerating the application of data science in research. To achieve this, we create and leverage new and existing training capabilities...

Keywords: AI, machine learning, eresearch skills, training, train the trainer, volunteer instructors, training partnerships, training material

Accelerating skills development in Data science and AI at scale https://dresa.org.au/materials/accelerating-skills-development-in-data-science-and-ai-at-scale-2d8a65fa-f96e-44ad-a026-cfae3f38d128 At the Monash Data Science and AI  platform, we believe that upskilling our research community and building a workforce with data science skills are key to accelerating the application of data science in research. To achieve this, we create and leverage new and existing training capabilities within and outside Monash University. In this talk, we will discuss the principles and purpose of establishing collaborative models to accelerate skills development at scale. We will talk about our approach to identifying gaps in the existing skills and training available in data science, key areas of interest as identified by the research community and various sources of training available in the marketplace. We will provide insights into the collaborations we currently have and intend to develop in the future within the university and also nationally. The talk will also cover our approach as outlined below •        Combined survey of gaps in skills and trainings for Data science and AI •        Provide seats to partners •        Share associate instructors/helpers/volunteers •        Develop combined training materials •        Publish a repository of open source trainings •        Train the trainer activities •        Establish a network of volunteers to deliver trainings at their local regions Industry plays a significant role in making some invaluable training available to the research community either through self learning platforms like AWS Machine Learning University or Instructor led courses like NVIDIA Deep Learning Institute. We will discuss how we leverage our partnerships with Industry to bring these trainings to our research community. Finally, we will discuss how we map our training to the ARDC skills roadmap and how the ARDC platforms project “Environments to accelerate Machine Learning based Discovery” has enabled collaboration between Monash University and University of Queensland to develop and deliver training together. contact@ardc.edu.au AI, machine learning, eresearch skills, training, train the trainer, volunteer instructors, training partnerships, training material
Monash University - University of Queensland training partnership in Data science and AI

We describe the peer network exchange for training that has been recently created via an ARDC funded partnership between Monash University and Universities of Queensland under the umbrella of the Queensland Cyber Infrastructure Foundation (QCIF). As part of a training program in machine learning,...

Keywords: data skills, training partnerships, data science, AI, training material

Monash University - University of Queensland training partnership in Data science and AI https://dresa.org.au/materials/monash-university-university-of-queensland-training-partnership-in-data-science-and-ai-8082bf73-d20f-4214-ad8c-95123e25a36c We describe the peer network exchange for training that has been recently created via an ARDC funded partnership between Monash University and Universities of Queensland under the umbrella of the Queensland Cyber Infrastructure Foundation (QCIF). As part of a training program in machine learning, visualisation, and computing tools, we have established a series of over 20 workshops over the year where either Monash or QCIF hosts the event for some 20-40 of their researchers and students, while some 5 places are offered to participants from the other institution. In the longer term we aim to share material developed at one institution and have trainers present it at the other. In this talk we will describe the many benefits we have found to this approach including access to a wider range of expertise in several rapidly developing fields, upskilling of trainers, faster identification of emerging training needs, and peer learning for trainers. contact@ardc.edu.au data skills, training partnerships, data science, AI, training material