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Keywords: eresearch skills  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
Astronomy Data And Computing Services - Upskilling the Australian astronomy community

The Astronomy Data And Computing Services (ADACS) initiative has been working with the Australian astronomy community for just over 3 years now. Our vision is to deliver astronomy-focused training, support and expertise to maximise the scientific return on investments in astronomical data &...

Keywords: astronomy, data skills, eresearch skills, skills, computational skills, training, skills gaps, astronomy-focused training, training material

Astronomy Data And Computing Services - Upskilling the Australian astronomy community https://dresa.org.au/materials/astronomy-data-and-computing-services-upskilling-the-australian-astronomy-community-57afa0b9-77da-4dc1-ad29-25089f19363d The Astronomy Data And Computing Services (ADACS) initiative has been working with the Australian astronomy community for just over 3 years now. Our vision is to deliver astronomy-focused training, support and expertise to maximise the scientific return on investments in astronomical data & computing infrastructure. During these last 3 years, we have delivered dozens of face-to-face, hands-on workshops and created several hours worth of online tutorial materials. This talk will focus on our journey to deliver this computational skills training to the community, exploring how we chose different delivery pathways and content, based both on community input as well as our professional expertise and understanding of existing skill gaps. Most importantly we will discuss our plans for the future and how we are working on actively including the community in developing new training material beyond the usual skills survey. Come along to this talk if you would like to hear about a national effort to deliver computational skills training and would like to know more about potential new avenues to provide just-in-time training and how to collaborate with ADACS.  contact@ardc.edu.au astronomy, data skills, eresearch skills, skills, computational skills, training, skills gaps, astronomy-focused training, training material
Successful data training stories from NCI

NCI Australia manages a multi-petabyte sized data repository, collocated with its HPC systems and data services, which allows high performance access to many scientific research datasets across many earth science domains.
An important aspect is to provide training materials that proactively...

Keywords: skills, training, eresearch skills, HPC training, domain-specific training, reproducible workflows, training material

Successful data training stories from NCI https://dresa.org.au/materials/successful-data-training-stories-from-nci-33f110e3-0c06-492e-9cc5-fa0f886ca1b8 NCI Australia manages a multi-petabyte sized data repository, collocated with its HPC systems and data services, which allows high performance access to many scientific research datasets across many earth science domains. An important aspect is to provide training materials that proactively engages with the research community to improve their understanding of the data available, and to share knowledge and best practices in the use of tools and other software. We have developed multiple levels of training modules (introductory, intermediate and advanced) to cater for users with different levels of experience and interest. We have also tailored courses for each scientific domain, so that the use-cases and software will be most relevant to their interests and needs. For our training, we combine brief lectures followed by hands-on training examples on how to use datasets, using working examples of well-known tools and software that people can use as a template and modify to fit their needs. For example, we take representative use-cases from some scientific activities, from our collaborations and from user support issues, and convert to Jupyter notebook examples so that people can repeat the workfIow and reproduce the results. We also use the training as an opportunity to raise awareness of growing issues in resource management. Some examples include a familiarity of the FAIR data principles, licensing, citation, data management and trusted digital repositories. This approach to both our online training materials and workshops has been well-received by PhD students, early careers, and cross disciplinary users. contact@ardc.edu.au skills, training, eresearch skills, HPC training, domain-specific training, reproducible workflows, training material
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
Data Fluency: a community of practice supporting a digitally skilled workforce

This presentation showcases the impact of the Monash Data Fluency Community of Practice upon digitally skilled Graduate Research students involved as learners and instructors in the program. The strong focus on building community to complement training, has fostered an environment of learning,...

Keywords: skills, training, eresearch skills, data skills, online learning, pedagogy, train the trainer, digitally skilled workforce, training material

Data Fluency: a community of practice supporting a digitally skilled workforce https://dresa.org.au/materials/data-fluency-a-community-of-practice-supporting-a-digitally-skilled-workforce-b911a1a8-0331-496e-95a6-0015a12acc34 This presentation showcases the impact of the Monash Data Fluency Community of Practice upon digitally skilled Graduate Research students involved as learners and instructors in the program. The strong focus on building community to complement training, has fostered an environment of learning, networking and sharing of expertise. Hear what the Graduate research students have to say about the value of skills training and how it has impacted their research; how the community has enabled them to network with a broad range of researchers and affiliate partner groups they would not ordinarily be in contact with; how their research journey has been enhanced by working as part of a multi-disciplinary team, as well as sharpening their teaching skills. The rapid refocus from face - face to online delivery, as a result of the pandemic, highlights the importance of the multi-faceted online approach including workshops, drop-in sessions, SLACK chat and online learning resources. As a result of the shift to online, the range of strategic external partner/affiliate groups has extended and demand for workshops and drop-ins has increased.  Learn how the instructors have altered their pedagogical approach to engage workshop and drop-in participants; how they have overcome some of the challenges of facilitating in an online environment; and how this is preparing them to become part of a digitally skilled workforce. contact@ardc.edu.au skills, training, eresearch skills, data skills, online learning, pedagogy, train the trainer, digitally skilled workforce, training material
ARDC Skills Landscape

The Australian Research Data Commons is driving transformational change in the research data ecosystem, enabling researchers to conduct world class data-intensive research. One interconnected component of this ecosystem is skills development/uplift, which is critical to the Commons and its...

Keywords: skills, data skills, eresearch skills, community, skilled workforce, FAIR, research data management, data stewardship, data governance, data use, data generation, training material

ARDC Skills Landscape https://dresa.org.au/materials/ardc-skills-landscape-56b224ca-9e30-4771-8615-d028c7be86a6 The Australian Research Data Commons is driving transformational change in the research data ecosystem, enabling researchers to conduct world class data-intensive research. One interconnected component of this ecosystem is skills development/uplift, which is critical to the Commons and its purpose of providing Australian researchers with a competitive advantage through data.   In this presentation, Kathryn Unsworth introduces the ARDC Skills Landscape. The Landscape is a first step in developing a national skills framework to enable a coordinated and cohesive approach to skills development across the Australian eResearch sector. It is also a first step towards helping to analyse current approaches in data training to identify: - Siloed skills initiatives, and finding ways to build partnerships and improve collaboration - Skills deficits, and working to address the gaps in data skills - Areas of skills development for investment by skills stakeholders like universities, research organisations, skills and training service providers, ARDC, etc.   contact@ardc.edu.au skills, data skills, eresearch skills, community, skilled workforce, FAIR, research data management, data stewardship, data governance, data use, data generation, training material