WEBINAR: AlphaFold: what's in it for me?
This record includes training materials associated with the Australian BioCommons webinar ‘WEBINAR: AlphaFold: what’s in it for me?’. This webinar took place on 18 April 2023.
Event description
AlphaFold has taken the scientific world by storm with the ability to accurately predict the...
Keywords: Bioinformatics, Machine Learning, Structural Biology, Proteins, Drug discovery, AlphaFold, AI, Artificial Intelligence, Deep learning
WEBINAR: AlphaFold: what's in it for me?
https://zenodo.org/records/7865494
https://dresa.org.au/materials/webinar-alphafold-what-s-in-it-for-me-4d1ea222-4240-4b68-b9ae-7769ac664ee0
This record includes training materials associated with the Australian BioCommons webinar ‘WEBINAR: AlphaFold: what’s in it for me?’. This webinar took place on 18 April 2023.
Event description
AlphaFold has taken the scientific world by storm with the ability to accurately predict the structure of any protein in minutes using artificial intelligence (AI). From drug discovery to enzymes that degrade plastics, this promises to speed up and fundamentally change the way that protein structures are used in biological research.
Beyond the hype, what does this mean for structural biology as a field (and as a career)?
Dr Craig Morton, Drug Discovery Lead at the CSIRO, is an early adopter of AlphaFold and has decades of expertise in protein structure / function, protein modelling, protein – ligand interactions and computational small molecule drug discovery, with particular interest in anti-infective agents for the treatment of bacterial and viral diseases.
Craig joins this webinar to share his perspective on the implications of AlphaFold for science and structural biology. He will give an overview of how AlphaFold works, ways to access AlphaFold, and some examples of how it can be used for protein structure/function analysis.
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.
Materials shared elsewhere:
A recording of this webinar is available on the Australian BioCommons YouTube Channel:
https://youtu.be/4ytn2_AiH8s
Melissa Burke (melissa@biocommons.org.au)
Morton, Craig (orcid: 0000-0001-5452-5193)
Bioinformatics, Machine Learning, Structural Biology, Proteins, Drug discovery, AlphaFold, AI, Artificial Intelligence, Deep learning
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
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
Principles Aligned Institutionally-Contextualised (PAI-C) RDM Training
This GitHub repository contains resources for an institution to contextualise a principles-based RDM training with its institution's research data management policies, processes and systems.
The adoption of PAI-C across institutions will contribute to a common baseline understanding of RDM...
Keywords: PAI-C, Training, Data Management
Principles Aligned Institutionally-Contextualised (PAI-C) RDM Training
https://github.com/Adrian-W-Chew/PAI-C-RDM-Training
https://dresa.org.au/materials/principles-aligned-institutionally-contextualised-pai-c-rdm-training
This GitHub repository contains resources for an institution to contextualise a principles-based RDM training with its institution's research data management policies, processes and systems.
The adoption of PAI-C across institutions will contribute to a common baseline understanding of RDM across institutions, which in turn will facilitate cross institutional management of data (e.g. when researchers move between institutions, and collaborate across institutions).
Dr Adrian W. Chew (w.l.chew@unsw.edu.au)
Dr Adrian W. Chew
Dr Adele Haythornthwaite
Brock Askey
Dr Jacky Cho
Dr Anesh Nair
Dr Kyle Hemming
Iftikhar Hayat
Joanna Dziedzic
Janice Chan
Kaitlyn Houston
Linlin Zhao
Caitlin Savage
Jessica Suna
Dr Emilia Decker
Sharron Stapleton
PAI-C, Training, Data Management
Research Data Management Techniques
Are you drowning in research data? Do you want to know where you should be storing your data? Are you required to comply with funding body data management requirements, but don’t know how?
This workshop is ideal for researchers who want to know how research data management can support...
Keywords: Data Management
Research Data Management Techniques
https://intersect.org.au/training/course/rdmt001
https://dresa.org.au/materials/research-data-management-techniques
Are you drowning in research data? Do you want to know where you should be storing your data? Are you required to comply with funding body data management requirements, but don’t know how?
This workshop is ideal for researchers who want to know how research data management can support project success and are interested in research data management services and support available at their institution. Combining slide-based background material, discussions, and case studies this workshop will equip participants with best practices for managing their valuable research data.
How to manage research data according to legal, statutory, ethical, funding body and university requirements
Approaches to planning, collecting, organising, managing, storing, backing up, preserving, and sharing your data
Services supporting research data at your institution
The course has no prerequisites.
training@intersect.org.au
Intersect Australia
Data Management