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

Keywords: R software  or Ecoacoustics 

Tutorials to learn how to use STAN

Stan tutorials offer links to exceptional tutorial papers, videos and statistics to learn Bayesian statistical methods and applied statistics.

Keywords: Statistics, applied statistics, Bayesian statistics, R software, Python, MATLAB

Tutorials to learn how to use STAN https://dresa.org.au/materials/tutorials-to-learn-how-to-use-stan Stan tutorials offer links to exceptional tutorial papers, videos and statistics to learn Bayesian statistical methods and applied statistics. https://mc-stan.org/about/team/ Statistics, applied statistics, Bayesian statistics, R software, Python, MATLAB
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
Species Distribution Modelling in R

This set of scripts and videos provide an introduction to running SDMs in R and include some steps to consider that go beyond what's available in the EcoCommons SDM point-and-click tools.

Five videos include: 1. An introduction to SDM in R, 2. occurrence data, 3. environmental data, 4. fitting...

Keywords: Species Distribution Modelling, Ecology, R software, EcoCommons

Species Distribution Modelling in R https://dresa.org.au/materials/species-distribution-modelling-in-r This set of scripts and videos provide an introduction to running SDMs in R and include some steps to consider that go beyond what's available in the EcoCommons SDM point-and-click tools. Five videos include: 1. An introduction to SDM in R, 2. occurrence data, 3. environmental data, 4. fitting your model, 5. model evaluation Scripts and files are available here: https://github.com/EcoCommons-Australia/educational_material/tree/main/SDMs_in_R/Scripts Scripts for all four modules are here: https://www.ecocommons.org.au/wp-content/uploads/EcoCommons_steps_1_to_4.html https://www.ecocommons.org.au/contact/ Species Distribution Modelling, Ecology, R software, EcoCommons ugrad mbr phd
WORKSHOP: Single cell RNAseq analysis in R

This record includes training materials associated with the Australian BioCommons workshop ‘Single cell RNAseq analysis in R’. This workshop took place over two, 3.5 hour sessions on 22 and 3 August 2022.

Event description

Analysis and interpretation of single cell RNAseq (scRNAseq) data...

Keywords: Bioinformatics, Analysis, Transcriptomics, R software, Single cell RNAseq, scRNAseq

WORKSHOP: Single cell RNAseq analysis in R https://dresa.org.au/materials/workshop-single-cell-rnaseq-analysis-in-r-4f60b82d-2f1e-4021-9569-6955878dd945 This record includes training materials associated with the Australian BioCommons workshop ‘Single cell RNAseq analysis in R’. This workshop took place over two, 3.5 hour sessions on 22 and 3 August 2022. Event description Analysis and interpretation of single cell RNAseq (scRNAseq) data requires dedicated workflows. In this hands-on workshop we will show you how to perform single cell analysis using Seurat - an R package for QC, analysis, and exploration of single-cell RNAseq data.  We will discuss the ‘why’ behind each step and cover reading in the count data, quality control, filtering, normalisation, clustering, UMAP layout and identification of cluster markers. We will also explore various ways of visualising single cell expression data. This workshop is presented by the Australian BioCommons and Queensland Cyber Infrastructure Foundation (QCIF) with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative.   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. scRNAseq_Slides (PDF): Slides used to introduce topics scRNAseq_Schedule (PDF): A breakdown of the topics and timings for the workshop scRNAseq_Resources (PDF): A list of resources recommended by trainers and participants scRNAseq_QandA(PDF): Archive of questions and their answers from the workshop Slack Channel.   Materials shared elsewhere: This workshop follows the tutorial ‘scRNAseq Analysis in R with Seurat’ https://swbioinf.github.io/scRNAseqInR_Doco/index.html This material is based on the introductory Guided Clustering Tutorial tutorial from Seurat. It is also drawing from a similar workshop held by Monash Bioinformatics Platform Single-Cell-Workshop, with material here. Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Analysis, Transcriptomics, R software, Single cell RNAseq, scRNAseq
WORKSHOP: R: fundamental skills for biologists

This record includes training materials associated with the Australian BioCommons workshop ‘R: fundamental skills for biologists’. This workshop took place over four, three-hour sessions on 1, 8, 15 and 22 June 2022.

 

Event description

Biologists need data analysis skills to be able to...

Keywords: Bioinformatics, Analysis, Statistics, R software, RStudio, Data visualisation

WORKSHOP: R: fundamental skills for biologists https://dresa.org.au/materials/workshop-r-fundamental-skills-for-biologists-81aa00db-63ad-4962-a7ac-b885bf9f676b This record includes training materials associated with the Australian BioCommons workshop ‘R: fundamental skills for biologists’. This workshop took place over four, three-hour sessions on 1, 8, 15 and 22 June 2022.   Event description Biologists need data analysis skills to be able to interpret, visualise and communicate their research results. While Excel can cover some data analysis needs, there is a better choice, particularly for large and complex datasets.  R is a free, open-source software and programming language that enables data exploration, statistical analysis, visualisation and more. The large variety of R packages available for analysing biological data make it a robust and flexible option for data of all shapes and sizes.  Getting started can be a little daunting for those without a background in statistics and programming. In this workshop we will equip you with the foundations for getting the most out of R and RStudio, an interactive way of structuring and keeping track of your work in R. Using biological data from a model of influenza infection, you will learn how to efficiently and reproducibly organise, read, wrangle, analyse, visualise and generate reports from your data in R. Topics covered in this workshop include: Spreadsheets, organising data and first steps with R Manipulating and analysing data with dplyr Data visualisation Summarized experiments and getting started with Bioconductor   This workshop is presented by the Australian BioCommons and Saskia Freytag from WEHI  with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative. 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. Schedule (PDF): A breakdown of the topics and timings for the workshop Recommended resources (PDF): A list of resources recommended by trainers and participants Q_and_A(PDF): Archive of questions and their answers from the workshop Slack Channel. Materials shared elsewhere:   This workshop follows the tutorial ‘Introduction to data analysis with R and Bioconductor’ which is publicly available. https://saskiafreytag.github.io/biocommons-r-intro/ This is derived from material produced as part of The Carpentries Incubator project https://carpentries-incubator.github.io/bioc-intro/ Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Analysis, Statistics, R software, RStudio, Data visualisation
WORKSHOP: Working with genomics sequences and features in R with Bioconductor

This record includes training materials associated with the Australian BioCommons workshop ‘Working with genomics sequences and features in R with Bioconductor’. This workshop took place on 23 September 2021.

Workshop description

Explore the many useful functions that the Bioconductor...

Keywords: R software, Bioconductor, Bioinformatics, Analysis, Genomics, Sequence analysis

WORKSHOP: Working with genomics sequences and features in R with Bioconductor https://dresa.org.au/materials/workshop-working-with-genomics-sequences-and-features-in-r-with-bioconductor-8399bf0d-1e9e-48f3-a840-3f70f23254bb This record includes training materials associated with the Australian BioCommons workshop ‘Working with genomics sequences and features in R with Bioconductor’. This workshop took place on 23 September 2021. Workshop description Explore the many useful functions that the Bioconductor environment offers for working with genomic data and other biological sequences.  DNA and proteins are often represented as files containing strings of nucleic acids or amino acids. They are associated with text files that provide additional contextual information such as genome annotations. This workshop provides hands-on experience with tools, software and packages available in R via Bioconductor for manipulating, exploring and extracting information from biological sequences and annotation files. We will look at tools for working with some commonly used file formats including FASTA, GFF3, GTF, methods for identifying regions of interest, and easy methods for obtaining data packages such as genome assemblies.  This workshop is presented by the Australian BioCommons and Monash Bioinformatics Platform with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative. 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. Schedule (PDF): schedule for the workshop providing a breakdown of topics and timings   Materials shared elsewhere: This workshop follows the tutorial ‘Working with DNA sequences and features in R with Bioconductor - version 2’ developed for Monash Bioinformatics Platform and Monash Data Fluency by Paul Harrison. https://monashdatafluency.github.io/r-bioc-2/ Melissa Burke (melissa@biocommons.org.au) R software, Bioconductor, Bioinformatics, Analysis, Genomics, Sequence analysis
VOSON Lab Code Blog

The VOSON Lab Code Blog is a space to share methods, tips, examples and code. Blog posts provide techniques to construct and analyse networks from various API and other online data sources, using the VOSON open-source software and other R based packages.

Keywords: visualisation, Data analysis, data collections, R software, Social network analysis, social media data, Computational Social Science, quantitative, Text Analytics

Resource type: tutorial, other

VOSON Lab Code Blog https://dresa.org.au/materials/voson-lab-code-blog The VOSON Lab Code Blog is a space to share methods, tips, examples and code. Blog posts provide techniques to construct and analyse networks from various API and other online data sources, using the VOSON open-source software and other R based packages. robert.ackland@anu.edu.au visualisation, Data analysis, data collections, R software, Social network analysis, social media data, Computational Social Science, quantitative, Text Analytics researcher support phd masters