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7 materials found

Authors: Barlow, Melanie (orcid: 000...  or Bonu, Tarun (orcid: 0000-00...  or Brian Ballsun-Stanton 


ML4AU: Trainings, trainers and building an ML community

This lightning talk provides an update on the current state of machine lerning training activities. Additionally, the talk will introduce the training portal on the ML4AU website, which has been created to address some of the challenges faced by the trainer community.

You can watch the YouTube...

Keywords: machine learning, training, skills, community of practice, trainers, training material

ML4AU: Trainings, trainers and building an ML community https://dresa.org.au/materials/ml4au-trainings-trainers-and-building-an-ml-community-891b095f-91c2-461d-8039-1c25f50f5857 This lightning talk provides an update on the current state of machine lerning training activities. Additionally, the talk will introduce the training portal on the ML4AU website, which has been created to address some of the challenges faced by the trainer community. You can watch the YouTube video here: https://youtu.be/cQS0guC5_Cg contact@ardc.edu.au machine learning, training, skills, community of practice, trainers, 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
A hands on introduction to Large Language Models like Bing Chat and ChatGPT

Event run 7 June at the MQ Incubator. Event description:

A two-hour hands-on workshop giving a brief history of the last 4 months of development of "Generative AI."

These tools, these Large Language Models, offer present promise and peril -- disruption -- to ways of working and of...

Keywords: Large Language Model, ChatGPT

A hands on introduction to Large Language Models like Bing Chat and ChatGPT https://dresa.org.au/materials/a-hands-on-introduction-to-large-language-models-like-bing-chat-and-chatgpt Event run 7 June at the MQ Incubator. Event description: A two-hour hands-on workshop giving a brief history of the last 4 months of development of "Generative AI." These tools, these Large Language Models, offer present promise and peril -- disruption -- to ways of working and of learning. Outside the "hype," these tools are "calculators for words" and allow the same manipulation and reflection of a user's words as a calculator offers for a user's numbers. The workshop will guide users into using various free and paid tools, and the effective use of Large Language Models through chain of thought prompting. Remember: a LLM is "Always confident and usually correct." OSF Description (LLM generated): This two-hour workshop provides a comprehensive introduction to the world of Large Language Models (LLMs), focusing on the recent advancements in Generative AI. Participants will gain insights into the development and functionality of prominent LLMs such as Bing Chat and ChatGPT. The workshop will delve into the concept of LLMs as "calculators for words," highlighting their potential to revolutionize ways of working and learning. The session will explore the principles of Prompt Engineering and Transactional Prompting, demonstrating how consistent prompts can yield reliable and reproducible results. Participants will also learn about the practical applications of LLMs, including editing and proofreading papers, generating technical documentation, recipe ideation, and more. The workshop emphasizes the importance of understanding the terms of use and the responsibilities that come with using these powerful AI tools. By the end of the session, participants will be equipped with the knowledge and skills to effectively use LLMs in various contexts, guided by the mantra that a LLM is "Always confident and usually correct." Brian Ballsun-Stanton (brian.ballsun-stanton@mq.edu.au) Large Language Model, ChatGPT researcher
Managing Active Research Data

In this train-the-trainer workshop, we will be exploring and discussing methods for active data management.

Participants will become familiar with cloud storage and associated tools and services for managing active research data. Learn how to organise, maintain, store and analyse active data,...

Keywords: RDM Training, CloudStor, cloud

Resource type: lesson

Managing Active Research Data https://dresa.org.au/materials/managing-active-research-data In this train-the-trainer workshop, we will be exploring and discussing methods for active data management. Participants will become familiar with cloud storage and associated tools and services for managing active research data. Learn how to organise, maintain, store and analyse active data, and understand safe and secure ways of sharing and storing data. Topics such as cloud storage, collaborative editing, versioning and data sharing will be discussed and demonstrated. Sara King RDM Training, CloudStor, cloud phd support masters ecr researcher
ML4AU: Trainings, trainers and building an ML community

This lightning talk provides an update on the current state of machine lerning training activities. Additionally, the talk will introduce the training portal on the ML4AU website, which has been created to address some of the challenges faced by the trainer community.

You can watch the YouTube...

Keywords: machine learning, training, skills, community of practice, trainers, training material

ML4AU: Trainings, trainers and building an ML community https://dresa.org.au/materials/ml4au-trainings-trainers-and-building-an-ml-community This lightning talk provides an update on the current state of machine lerning training activities. Additionally, the talk will introduce the training portal on the ML4AU website, which has been created to address some of the challenges faced by the trainer community. You can watch the YouTube video here: https://youtu.be/cQS0guC5_Cg contact@ardc.edu.au machine learning, training, skills, community of practice, trainers, 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
Research Data Management (RDM) Online Orientation Module (Macquarie University)

This is a self-paced, guided orientation to the essential elements of Research Data Management. It is available for others to use and modify.
The course introduces the following topics: data policies, data sensitivity, data management planning, storage and security, organisation and metadata,...

Keywords: research data, data management, FAIR data, training

Resource type: quiz, activity, other

Research Data Management (RDM) Online Orientation Module (Macquarie University) https://dresa.org.au/materials/macquarie-university-research-data-management-rdm-online This is a self-paced, guided orientation to the essential elements of Research Data Management. It is available for others to use and modify. The course introduces the following topics: data policies, data sensitivity, data management planning, storage and security, organisation and metadata, benefits of data sharing, licensing, repositories, and best practice including the FAIR principles. Embedded activities and examples help extend learner experience and awareness. The course was designed to assist research students and early career researchers in complying with policies and legislative requirements, understand safe data practices, raise awareness of the benefits of data curation and data sharing (efficiency and impact) and equip them with the required knowledge to plan their data management early in their projects. This course is divided into four sections 1. Crawl - What is Research Data and why care for it? Policy and legislative requirements. The Research Data Life-cycle. Data Management Planning (~30 mins) 2. Walk - Data sensitivity, identifiability, storage, and security (~60 mins) 3. Run - Record keeping, data retention, file naming, folder structures, version control, metadata, data sharing, open data, licences, data repositories, data citation, and ethics (~75 mins) 4. Jump - Best practice FAIR data principles (~45 mins) 5. Fight - Review - a quiz designed to review and reinforce knowledge (~15 mins) https://rise.articulate.com/share/-AWqSPaEI_jTbHwzQHdmQ43R50edrCl0 * *Password: "FAIR" *Password: "FAIR" Any queries or suggestions for course improvement can be directed to the Macquarie University Research Integrity Team: Dr Paul Sou (paul.sou@mq.edu.au) or Dr Shannon Smith (shannon.smith@mq.edu.au). Scorm files can be made available upon request. research data, data management, FAIR data, training