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Authors: Barlow, Melanie (orcid: 000...  or Bouquin, Daina (orcid: 0000...  or Bourke, Caitlin (orcid: 000...  or Tang, Titus 

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
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
MetaSat. An open, collaboratively-developed metadata toolkit to support the future of space exploration.

MetaSat is an open metadata toolkit for describing small satellite (and even large satellite) missions in a uniform and shareable way. Optimised for small satellite missions, MetaSat fills an informatics gap. Although there have been a number of relevant metadata sets, there has been a...

Keywords: Small satellites, metadata, vocabularies, training material

MetaSat. An open, collaboratively-developed metadata toolkit to support the future of space exploration. https://dresa.org.au/materials/metasat-an-open-collaboratively-developed-metadata-toolkit-to-support-the-future-of-space-exploration-49af7d4d-f0d1-4f95-9fbe-afbd45170a6a MetaSat is an open metadata toolkit for describing small satellite (and even large satellite) missions in a uniform and shareable way. Optimised for small satellite missions, MetaSat fills an informatics gap. Although there have been a number of relevant metadata sets, there has been a longstanding need for a vocabulary to span these community standards. A vocabulary to annotate the data and information outputs of these satellite missions, to enable search across disparate data repositories, and provide support for application of analytical services to retrieved datasets. A common problem among small satellite teams is finding information about how other small satellites were put together, what parts worked well, what weren't compatible, what were the mission goals and outcomes. A lot of this information can be found, but it's not usually described in a consistent and searchable way across projects. MetaSat helps by building a uniform language of description which can be embedded into small satellite databases and tools to connect information across projects. Although a relatively new vocabulary initiative, MetaSat has secured early adoption by SatNOGS, a global network of ground stations that collects, manages & enables access to satellite observations. Also partnering with NASA's Small Satellite Reliability Initiative, and in discussion with NASA concerning implementation of the vocabulary in other areas of its information infrastructure. You can watch the full presentation on YouTube here: https://www.youtube.com/watch?v=uaCOzNL1eh4 contact@ardc.edu.au Small satellites, metadata, vocabularies, 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
National skills ecosystem - call to action

In this Community Action session working groups will be formed based on the challenges/opportunities that were prioritised in Community Action session #4.

  • Skilled trainers / facilitators

  • National training registry

  • National training event calendar

  • Jointly developed training

  • Research...

Keywords: national skills initiatives, data skills, training, skills community, training material

National skills ecosystem - call to action https://dresa.org.au/materials/national-skills-ecosystem-call-to-action-ffd9b4ed-b557-496b-ac35-72467c03c71b In this Community Action session working groups will be formed based on the challenges/opportunities that were prioritised in Community Action session #4. - Skilled trainers / facilitators - National training registry - National training event calendar - Jointly developed training - Research support professionals: career/progression contact@ardc.edu.au national skills initiatives, data skills, training, skills community, training material
ARDC FAIR Data 101 self-guided

FAIR Data 101 v3.0 is a self-guided course covering the FAIR Data principles

The FAIR Data 101 virtual course was designed and delivered by the ARDC Skilled Workforce Program twice in 2020 and has now been reworked as a self-guided course.

The course structure was based on 'FAIR Data in the...

Keywords: training material, FAIR data, video, webinar, activities, quiz, FAIR, research data management

ARDC FAIR Data 101 self-guided https://dresa.org.au/materials/ardc-fair-data-101-self-guided-2d794a84-f0ff-4e11-a39c-fa8ea481e097 FAIR Data 101 v3.0 is a self-guided course covering the FAIR Data principles The FAIR Data 101 virtual course was designed and delivered by the ARDC Skilled Workforce Program twice in 2020 and has now been reworked as a self-guided course. The course structure was based on 'FAIR Data in the Scholarly Communications Lifecycle', run by Natasha Simons at the FORCE11 Scholarly Communications Institute. These training materials are hosted on GitHub. contact@ardc.edu.au training material, FAIR data, video, webinar, activities, quiz, FAIR, research data management
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 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
National skills ecosystem - call to action

In this Community Action session working groups will be formed based on the challenges/opportunities that were prioritised in Community Action session #4.

  • Skilled trainers / facilitators

  • National training registry

  • National training event calendar

  • Jointly developed training

  • Research...

Keywords: national skills initiatives, data skills, training, skills community, training material

National skills ecosystem - call to action https://dresa.org.au/materials/national-skills-ecosystem-call-to-action In this Community Action session working groups will be formed based on the challenges/opportunities that were prioritised in Community Action session #4. - Skilled trainers / facilitators - National training registry - National training event calendar - Jointly developed training - Research support professionals: career/progression contact@ardc.edu.au national skills initiatives, data skills, training, skills community, training material
MetaSat. An open, collaboratively-developed metadata toolkit to support the future of space exploration.

MetaSat is an open metadata toolkit for describing small satellite (and even large satellite) missions in a uniform and shareable way. Optimised for small satellite missions, MetaSat fills an informatics gap. Although there have been a number of relevant metadata sets, there has been a...

Keywords: Small satellites, metadata, vocabularies, training material

MetaSat. An open, collaboratively-developed metadata toolkit to support the future of space exploration. https://dresa.org.au/materials/metasat-an-open-collaboratively-developed-metadata-toolkit-to-support-the-future-of-space-exploration MetaSat is an open metadata toolkit for describing small satellite (and even large satellite) missions in a uniform and shareable way. Optimised for small satellite missions, MetaSat fills an informatics gap. Although there have been a number of relevant metadata sets, there has been a longstanding need for a vocabulary to span these community standards. A vocabulary to annotate the data and information outputs of these satellite missions, to enable search across disparate data repositories, and provide support for application of analytical services to retrieved datasets. A common problem among small satellite teams is finding information about how other small satellites were put together, what parts worked well, what weren't compatible, what were the mission goals and outcomes. A lot of this information can be found, but it's not usually described in a consistent and searchable way across projects. MetaSat helps by building a uniform language of description which can be embedded into small satellite databases and tools to connect information across projects. Although a relatively new vocabulary initiative, MetaSat has secured early adoption by SatNOGS, a global network of ground stations that collects, manages & enables access to satellite observations. Also partnering with NASA's Small Satellite Reliability Initiative, and in discussion with NASA concerning implementation of the vocabulary in other areas of its information infrastructure. You can watch the full presentation on YouTube here: https://www.youtube.com/watch?v=uaCOzNL1eh4 contact@ardc.edu.au Small satellites, metadata, vocabularies, 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 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
ARDC FAIR Data 101 self-guided

FAIR Data 101 v3.0 is a self-guided course covering the FAIR Data principles

The FAIR Data 101 virtual course was designed and delivered by the ARDC Skilled Workforce Program twice in 2020 and has now been reworked as a self-guided course.

The course structure was based on 'FAIR Data in the...

Keywords: training material, FAIR data, video, webinar, activities, quiz, FAIR, research data management

ARDC FAIR Data 101 self-guided https://dresa.org.au/materials/ardc-fair-data-101-self-guided-bba41a59-8479-4f4f-b9ee-337b9eb294bf FAIR Data 101 v3.0 is a self-guided course covering the FAIR Data principles The FAIR Data 101 virtual course was designed and delivered by the ARDC Skilled Workforce Program twice in 2020 and has now been reworked as a self-guided course. The course structure was based on 'FAIR Data in the Scholarly Communications Lifecycle', run by Natasha Simons at the FORCE11 Scholarly Communications Institute. These training materials are hosted on GitHub. contact@ardc.edu.au training material, FAIR data, video, webinar, activities, quiz, FAIR, research data management
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 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