ARDC Digital Research Capabilities and Skills Framework: The Framework and Its Components
The ARDC's Digital Research Capabilities and Skills Framework, released in 2022, provides a structure for training programs to develop essential and advanced digital research skills. It aims to help researchers and professionals identify the necessary skills they need to leverage emerging...
Keywords: training material, skills framework, ARDC skills framework, ARDC capabilities framework, national skills framework, learning path, role profile, capabilities, FAIR implementation, skills, data management, research software, data governance, digital research infrastructure
ARDC Digital Research Capabilities and Skills Framework: The Framework and Its Components
https://zenodo.org/records/14188836
https://dresa.org.au/materials/ardc-digital-research-capabilities-and-skills-framework-the-framework-and-its-components
The ARDC's Digital Research Capabilities and Skills Framework, released in 2022, provides a structure for training programs to develop essential and advanced digital research skills. It aims to help researchers and professionals identify the necessary skills they need to leverage emerging opportunities in data management, data analysis, data linking, AI, and machine learning. The framework aligns with technological advancements and encourages ongoing discussion and contributions to evolve the coverage of digital research skills.
The framework focuses on digital research skills, excluding broader professional skills, and is intended for a wide range of stakeholders. It provides a structured approach for project teams and organisations to develop and enhance their digital research skills through six main components: a skills taxonomy, a skills glossary, a list of generalised roles, roles and skills-related profiles, learning paths, and a skills and roles matrix. The skills taxonomy classifies digital research skills into four capability families: Governance, Data, Software, and Digital Research Infrastructure Management. It provides a standard terminology for identifying and describing these skills.
contact@ardc.edu.au
Unsworth, Kathryn (orcid: 0000-0002-5407-9987)
Russell, Keith (type: Editor)
Wong, Adeline (type: Editor)
Lyrtzis, Ellen (type: Editor)
training material, skills framework, ARDC skills framework, ARDC capabilities framework, national skills framework, learning path, role profile, capabilities, FAIR implementation, skills, data management, research software, data governance, digital research infrastructure
ARDC Research Software Rights Management Guide
How researchers may license their research software in order to share it with others.
It addresses the types of open‑source licences, and considerations you (as a researcher) should have in deciding which licence to adopt for sharing.
Keywords: Software citation, Software licensing, Software, research software, licence, License, training material
ARDC Research Software Rights Management Guide
https://zenodo.org/records/5003962
https://dresa.org.au/materials/ardc-research-software-rights-management-guide
How researchers may license their research software in order to share it with others.
It addresses the types of open‑source licences, and considerations you (as a researcher) should have in deciding which licence to adopt for sharing.
contact@ardc.edu.au
Australian Research Data Commons
Laughlin, Greg (type: Editor)
Appleyard, Baden (type: Editor)
Martinez, Paula Andrea (type: ProjectLeader)
Software citation, Software licensing, Software, research software, licence, License, training material
An open source textbook for research software engineering
Over the past year, a group of Carpentries instructors have been working on an open source textbook called Research Software Engineering with Python. The book is a ready-to-go university semester course aimed at helping learners go from writing code for themselves, to creating tools that help...
Keywords: data skills, software skills, open source, research software, research software engineering, training material
An open source textbook for research software engineering
https://zenodo.org/records/4287860
https://dresa.org.au/materials/an-open-source-textbook-for-research-software-engineering-f275be2a-698e-4e8a-94a4-5b701b26eef8
Over the past year, a group of Carpentries instructors have been working on an open source textbook called Research Software Engineering with Python. The book is a ready-to-go university semester course aimed at helping learners go from writing code for themselves, to creating tools that help their entire field advance. A physical version of the book will be published with Taylor & Francis in early 2021. During the review phase of the publication process, we are seeking feedback on the content and scope of the book from the digital skills training community.
contact@ardc.edu.au
Irving, Damien (orcid: 0000-0003-1258-5002)
data skills, software skills, open source, research software, research software engineering, 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://zenodo.org/records/4287746
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
Tang, Titus
AI, machine learning, eresearch skills, training, train the trainer, volunteer instructors, training partnerships, training material
ARDC Research Software Rights Management Guide 2021
How researchers may license their research software in order to share it with others.
It addresses the types of open‑source licences, and considerations you (as a researcher) should have in deciding which licence to adopt for sharing.
Keywords: Software citation, Software licensing, Software, research software, licence, License, training material
ARDC Research Software Rights Management Guide 2021
https://zenodo.org/records/5003962
https://dresa.org.au/materials/ardc-research-software-rights-management-guide-d147c836-6ba8-4c76-a258-88e4c293569c
How researchers may license their research software in order to share it with others.
It addresses the types of open‑source licences, and considerations you (as a researcher) should have in deciding which licence to adopt for sharing.
contact@ardc.edu.au
Australian Research Data Commons
Laughlin, Greg (type: Editor)
Appleyard, Baden (type: Editor)
Martinez, Paula Andrea (type: ProjectLeader)
Software citation, Software licensing, Software, research software, licence, License, 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://zenodo.org/records/4287864
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
Tang, Titus
data skills, training partnerships, data science, AI, training material
