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WEBINAR: DOME - Machine Learning Best Practices & Recommendations

This record includes training materials associated with the Australian BioCommons webinar ‘DOME - Machine Learning Best Practices & Recommendations’. This webinar took place on 5 December 2024.
Event description 
As the adoption of Artificial Intelligence (AI) and Machine Learning (ML)...

Keywords: Bioinformatics http://edamontology.org/topic_0091, Machine Learning http://edamontology.org/topic_3474

WEBINAR: DOME - Machine Learning Best Practices & Recommendations https://dresa.org.au/materials/webinar-dome-machine-learning-best-practices-recommendations This record includes training materials associated with the Australian BioCommons webinar ‘DOME - Machine Learning Best Practices & Recommendations’. This webinar took place on 5 December 2024. Event description  As the adoption of Artificial Intelligence (AI) and Machine Learning (ML) accelerates across life science research, the demand for standardised practices has become crucial to ensure transparency, reproducibility, and adherence to FAIR principles. In response to these needs, DOME (Data Optimization Model Evaluation) has been developed as a key solution - a set of community-wide recommendations designed to guide supervised ML analysis reporting in biological studies. DOME offers broad, field-agnostic guidelines to enhance the impact of ML applications while ensuring reproducibility. This framework not only supports robust model evaluation but also serves as a valuable resource for training and capacity building in life sciences.  Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Lead Trainer: Dr Fotis Psomopoulos, Institute of Applied Biosciences (INAB), Center for Research and Technology Hellas (CERTH) Host: Dr. Giorgia Mori, Australian BioCommons Training materials Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. DOME_Webinar (PDF): A PDF copy of the slides presented during the webinar.   Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://www.youtube.com/watch?v=ijFg3VbO2VM     Melissa Burke (melissa@biocommons.org.au) Bioinformatics http://edamontology.org/topic_0091, Machine Learning http://edamontology.org/topic_3474
WORKSHOP: Machine learning in the life sciences

This record includes training materials associated with the Australian BioCommons workshop ‘Machine Learning in the Life Sciences’. This on 11 June 2024.
Event description
Machine learning promises to revolutionise life science research by speeding up data analysis, enabling prediction of...

Keywords: Bioinformatics, Life Science, Machine Learning

WORKSHOP: Machine learning in the life sciences https://dresa.org.au/materials/workshop-machine-learning-in-the-life-sciences This record includes training materials associated with the Australian BioCommons workshop ‘Machine Learning in the Life Sciences’. This on 11 June 2024. Event description Machine learning promises to revolutionise life science research by speeding up data analysis, enabling prediction of biological patterns and modelling complex biological systems. But what exactly is machine learning and when should you use it? This hands-on online workshop provides a high-level introduction to machine learning: what it is, its advantages and disadvantages compared to traditional modelling approaches and the types of scenarios where it may be the right tool for the job. Using example datasets and basic machine learning pipelines we contrast a few commonly used algorithms for constructing predictive models and explore some of their trade-offs. We discuss common pitfalls in how machine learning is applied and evaluated, with a focus on its application in the life sciences, to help you recognise overly optimistic results. We discuss how and why such errors arise and strategies to avoid them. Lead trainer:  Dr Benjamin Goudey, Research Fellow, Florey Department of Neuroscience and Mental Health Facilitators: Dr Erin Graham, Queensland Cyber Infrastructure Foundation (QCIF) / James Cook University William Pinzon Perez, Queensland Cyber Infrastructure Foundation (QCIF) Dr Giorgia Mori, Sydney Informatics Hub, University of Sydney  Joseph McConnell, University of Adelaide Jessica Chung, Melbourne Bioinformatics 0000-0002-0627-0955 Host: Dr Melissa Burke, Australian BioCommons. Training materials 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. Schedule (PDF): Schedule describing the timing of sessions for the in person and online events Materials shared elsewhere:   This workshop follows the Google Colab Notebook developed by Dr Benjamin Goudey: https://github.com/bwgoudey/IntroMLforLifeScienceWorkshopR Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Life Science, Machine Learning
WORKSHOP: Genetic Outlier Analysis

This record includes training materials associated with the Australian BioCommons workshop ‘Genetic Outlier Analysis’. 
These workshops took place on:

27 - 28 February 2024: Online via Zoom
10 - 11 April 2024: In person in Melbourne
4 - 5 July 2024: In person in Sydney 

Event description
There...

Keywords: Bioinformatics, Genetics

WORKSHOP: Genetic Outlier Analysis https://dresa.org.au/materials/workshop-genetic-outlier-analysis This record includes training materials associated with the Australian BioCommons workshop ‘Genetic Outlier Analysis’.  These workshops took place on: 27 - 28 February 2024: Online via Zoom 10 - 11 April 2024: In person in Melbourne 4 - 5 July 2024: In person in Sydney  Event description There are many interesting patterns that you can extract from genetic variant data. This can include patterns of linkage, balancing selection, or even inbreeding signals. One of the most common approaches is to find sites on the genome that are under selection.  This workshop introduces the basics of genetic selection analysis. It will step you through the process of identifying signals of selection using your own data (or an example genomic dataset) using the outlier analysis method.  Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. This workshop is presented by the Australian BioCommons and the Genetics Society of AustralAsia Lead trainer:  Dr Katarina Stuart, Research Fellow, University of Auckland.  Facilitators: Adele Barugahare, Monash Genomics and Bioinformatics Platform  Dr Georgina Samaha, Sydney Informatics Hub, University of Sydney  Dr Ching-Yu Lu, Sydney Informatics Hub, University of Sydney Soleille Miller, University of NSW Dr Nandan Deshpande, Sydney Informatics Hub, University of Sydney   Infrastructure provision: Audrey Stott, Pawsey Supercomputing Research Centre Host: Dr Melissa Burke, Australian BioCommons Training materials Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Schedules describing the timing of sessions for the in person and online events Materials shared elsewhere: These workshops followed the materials developed by Dr Katarina Stuart https://github.com/katarinastuart/Ev1_SelectionMetaAnalysis   Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Genetics
WORKSHOP: Making sense of gene and protein lists with functional enrichment analysis

This record includes training materials associated with the Australian BioCommons workshop ‘Making sense of gene and protein lists with functional enrichment analysis’. This workshop took place over two, 3 hour sessions on 20, 21 November 2024.
Event description
Omics experiments often generate...

Keywords: Bioinformatics http://edamontology.org/topic_0091, Analysis http://edamontology.org/operation_2945, Enrichment analysis http://edamontology.org/operation_3501

WORKSHOP: Making sense of gene and protein lists with functional enrichment analysis https://dresa.org.au/materials/workshop-making-sense-of-gene-and-protein-lists-with-functional-enrichment-analysis This record includes training materials associated with the Australian BioCommons workshop ‘Making sense of gene and protein lists with functional enrichment analysis’. This workshop took place over two, 3 hour sessions on 20, 21 November 2024. Event description Omics experiments often generate long lists of genes or proteins. Functional enrichment analysis identifies biological trends in the data by assessing these lists against gene ontology and pathway information. This can help interpret the experimental results in the context of larger biological systems. This workshop continues from our introductory webinar and provides a practical introduction to functional enrichment analysis. Using example data you will get hands-on with some of the most commonly used databases and tools for over representation (ORA) and gene set enrichment analysis (GSEA) and for identifying enriched biological functions in a list of genes or proteins. We’ll focus on tools available online and in R. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Lead Trainers: Dr Hossein Valipour Kahrood, Bioinformatician, Monash Genomics and Bioinformatics Platform Dr Cali Willet, Senior Research Bioinformatician, Sydney Informatics Hub, University of Sydney Facilitators: Georgina Samaha, Australian BioCommons Laura Perlaza-Jimenez, Monash Genomics and Bioinformatics Platform Infrastructure provision: Uwe Winter, Australian BioCommonsHost: Dr. Melissa Burke, Australian BioCommons Training materials Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. R notebooks (zip): R markdown and html rendered files, input files. Materials shared elsewhere: Training materials webpage Melissa Burke (melissa@biocommons.org.au) Bioinformatics http://edamontology.org/topic_0091, Analysis http://edamontology.org/operation_2945, Enrichment analysis http://edamontology.org/operation_3501
WORKSHOP: Introduction to Machine Learning in R - from data to knowledge

This record includes training materials associated with the Australian BioCommons workshop ‘Introduction to Machine Learning in R - from data to knowledge’. This workshop took place over one, 4 hour sessions on 09 December 2024.
Event description
With the rise in high-throughput sequencing...

Keywords: Bioinformatics, Machine Learning

WORKSHOP: Introduction to Machine Learning in R - from data to knowledge https://dresa.org.au/materials/workshop-introduction-to-machine-learning-in-r-from-data-to-knowledge This record includes training materials associated with the Australian BioCommons workshop ‘Introduction to Machine Learning in R - from data to knowledge’. This workshop took place over one, 4 hour sessions on 09 December 2024. Event description With the rise in high-throughput sequencing technologies, the volume of omics data has grown exponentially. A major issue is to mine useful knowledge from these heterogeneous collections of data. The analysis of complex high-volume data is not trivial and classical tools cannot be used to explore their full potential. Machine Learning (ML), a discipline in which computers perform automated learning without being programmed explicitly and assist humans to make sense of large and complex data sets, can thus be very useful in mining large omics datasets to uncover new insights that can advance the field of bioinformatics. This hands-on workshop will introduce participants to the ML taxonomy and the applications of common ML algorithms to health data. The workshop will cover the foundational concepts and common methods being used to analyse omics data sets by providing a practical context through the use of basic but widely used R libraries. Participants will acquire an understanding of the standard ML processes, as well as the practical skills in applying them on familiar problems and publicly available real-world data sets. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Lead trainers: Dr Fotis Psomopoulos, Senior Researcher,  Institute of Applied Biosciences (INAB), Center for Research and Technology Hellas (CERTH) Facilitators: Dr Giorgia Mori, Australian BioCommons Dr Eden Zhang, Sydney Informatics Hub Dr Erin Graham, Queensland Cyber Infrastructure Foundation (QCIF) Infrastructure provision: Uwe Winter, Australian BioCommons Host: Dr. Giorgia Mori, Australian BioCommons Training materials Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Files and materials shared elsewhere: Training materials webpage Data and documentation     Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Machine Learning
WORKSHOP: Hello Nextflow!

This record includes training materials associated with the Australian BioCommons workshop ‘Hello Nextflow’. This workshop took place over two sessions on 24 - 25 September 2024.
Event description
The rise of big data has made it essential to be able to analyse and perform experiments on large...

Keywords: Bioinformatics, Workflows, Nextflow

WORKSHOP: Hello Nextflow! https://dresa.org.au/materials/workshop-hello-nextflow This record includes training materials associated with the Australian BioCommons workshop ‘Hello Nextflow’. This workshop took place over two sessions on 24 - 25 September 2024. Event description The rise of big data has made it essential to be able to analyse and perform experiments on large datasets in a portable and reproducible manner. Nextflow is a popular bioinformatics workflow orchestrator that makes it easy to run data-intensive computational pipelines. It enables scalable and reproducible scientific workflows using software containers on any infrastructure. It allows the adaptation of workflows written in most languages and provides the ability to customise and optimise workflows for different computational environments, types and sizes of data. This workshop will put you on the path to writing your own reproducible and scalable scientific workflows using Nextflow. You will learn how to use core Nextflow components to build, run and troubleshoot a scalable multi-step workflow. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event.   Lead trainers:  Dr Chris Hakkaart, Developer Advocate, Seqera Labs. Fred Jaya, Senior Bioinformatician (Australian BioCommons), Sydney Informatics Hub, University of Sydney. Dr Georgie Samaha - Product Owner of the Australian BioCommons BioCLI Project and Bioinformatics Group Lead at the Sydney Informatics Hub, The University of Sydney. Facilitator: Dr Ziad Al Bkhetan, Australian BioCommons Infrastructure provision: Uwe Winter, Australian BioCommons Host: Dr Melissa Burke, Australian BioCommons   Training materials Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Materials shared elsewhere: The materials were developed by the Sydney Informatics Hub, University of Sydney in partnership with Seqera. The workshop was enabled through the Australian BioCommons - BioCLI Platforms Project (NCRIS via Bioplatforms Australia). Training materials webpage: https://sydney-informatics-hub.github.io/hello-nextflow/   Data and documentation:  https://github.com/Sydney-Informatics-Hub/hello-nextflow Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Workflows, Nextflow
WORKSHOP: Fungal Genomics with Galaxy

This record includes training materials associated with the Bioplatforms Australia “Fungal Genomics with Galaxy Workshop”. This workshop took place over three days from 29-31 October 2024.
Event description
A three-day workshop organised by Bioplatforms Australia to introduce bioinformatics...

Keywords: Bioinformatics, Analysis, Fungi, Genome assembly, Genome annotation

WORKSHOP: Fungal Genomics with Galaxy https://dresa.org.au/materials/workshop-fungal-genomics-with-galaxy This record includes training materials associated with the Bioplatforms Australia “Fungal Genomics with Galaxy Workshop”. This workshop took place over three days from 29-31 October 2024. Event description A three-day workshop organised by Bioplatforms Australia to introduce bioinformatics theory and practice to researchers, citizen scientists, and industry involved in Bioplatforms fungi-themed National Initiatives: Australian Functional Fungi Initiative, and Plant Pathogen Omics Initiative. An in-person workshop, held over three days from ~9am-5pm.  The workshop included a series of interleaved presentations about fungal genomics theory and practical tutorials using the Galaxy Australia analysis platform, as well as discussion sessions and presentations from some of the attendees.  A breakdown of timings and topics is provided in the schedule. Registration was open to project representatives across the Australian Functional Fungi, and Plant Pathogen Omics Initiative.  Participation was free for registrants, and was supported by Bioplatforms National Initiatives.  Number of participants = 38 Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Lead trainers:  Prof. Benjamin Schwessinger (Australian National University) Dr Alistair McTaggart (Psymbiotika Lab) Dr Gareth Price / Dr Anna Syme (Galaxy Australia Presenters: Dr Mareike Moeller (Australian National University) Rita Tam (Australian National University) Zhenyan Luo (Australian National University) Coordination: Dr Kelly Scarlett (BioPlatforms Australia) Dr Mabel Lum (BioPlatforms Australia) Dr Sophie Mazard (BioPlatforms Australia)    Files and materials included in this record: Event metadata (PDF): Information about the event including description, learning objectives, prerequisites, etc. Schedule of program inlcuding links to training materials and resources (PDF) Slide sets as PDFs (PDF) Table of links to Galaxy workflows and example histories (PDF) Copy of Galaxy workflows developed as examples for fungal data analysis (as .ga files)   Materials shared elsewhere: This workshop includes tutorials from the Galaxy Training Network which are linked in the schedule document.    Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Analysis, Fungi, Genome assembly, Genome annotation
AWS Ramp-Up Guide: Academic Research

AWS Ramp-Up Guides offer a variety of resources to help you build your skills and knowledge of the AWS Cloud. Each guide features carefully selected digital training, classroom courses, videos, whitepapers, certifications, and more. AWS now offers four ramp-up guides that help academic...

Keywords: Machine learning, machine learning, aws, AWS, cloud, Cloud computing, cloud computing, training material, HPC training, HPC, training registry, training partnerships

AWS Ramp-Up Guide: Academic Research https://dresa.org.au/materials/aws-ramp-up-guide-academic-research AWS Ramp-Up Guides offer a variety of resources to help you build your skills and knowledge of the AWS Cloud. Each guide features carefully selected digital training, classroom courses, videos, whitepapers, certifications, and more. AWS now offers four ramp-up guides that help academic researchers who use AI, ML, Generative AI, and HPC in their research activities, as well as the essential AWS knowledge for Statistician Researchers and Research IT professionals. The guides help learners decide where to start, and how to navigate, their learning journey. Some resources will be more relevant than others based on each learner’s specific research tasks. AI, ML, Generative AI ramp-up guide (page 2) is for academic researchers who are exploring using AWS AI, ML, and Generative AI tools to improve efficiency and productivity in their research tasks. This course introduces seven components on AI and ML and ten components on Generative AI. The course starts with an introduction to AI, and covers AWS AI/ML services, such as Amazon SageMaker. The Generative AI content covers topics such as planning a Generative AI project, responsible AI Practices, security, compliance, and governance for AI solutions. The Generative AI topics also cover how to get started with Amazon Bedrock. Recommended prerequisites: basic understanding of Python. High Performance Computing ramp-up guide (page 3) is designed for academic researchers who seek to use HPC on AWS. In this course, you will be introduced to eleven components that are essential about Higher Performance Computing on AWS. The course starts with an overview of HPC on AWS, followed by topics including AWS ParallelCluster and Research HPC Workloads on AWS Batch. Recommended prerequisites: complete AWS Cloud Essentials. Statistician Researcher ramp-up guide (page 4) is specifically catered for researchers in the fields of statistics and quantum analysis. The course covers topics such as building with Amazon Redshift clusters, getting started with Amazon EMR, Machine Learning for Data Scientists, authoring visual analytics using Amazon QuickSight, Batch analytics on AWS, and Amazon Lightsail for Research. Recommended prerequisites: complete AWS Cloud Essentials. Research IT ramp-up guide (page 5) is an extension of the Foundational Researcher Learning Plan, and enables Research IT leaders and professionals to dive deeper into specific topics. The goal of this extension for Research IT professionals is to dive deeper on fundamentals, understand management capabilities and implementing guardrails, cost optimization for research workloads, become familiar with platforms for research and research partners, and learn more about AWS Landing Zone and AWS Control Tower for Research. Recommended prerequisites: Foundational Researcher Learning Plan. emmarrig@amazon.com Machine learning, machine learning, aws, AWS, cloud, Cloud computing, cloud computing, training material, HPC training, HPC, training registry, training partnerships
Amazon Braket - Knowledge Badge Readiness Path

This Learning Path helps you build knowledge and technical skills to use Amazon Braket. This Learning Path presents domain-specific content and includes courses, knowledge checks, a pre-assessment and a knowledge badge assessment. This path is a guide and presents learning in a structured order,...

Keywords: quantum, cloud, AWS, aws, Cloud computing, cloud computing

Amazon Braket - Knowledge Badge Readiness Path https://dresa.org.au/materials/amazon-braket-knowledge-badge-readiness-path This Learning Path helps you build knowledge and technical skills to use Amazon Braket. This Learning Path presents domain-specific content and includes courses, knowledge checks, a pre-assessment and a knowledge badge assessment. This path is a guide and presents learning in a structured order, it can be used as presented or you can select the content that is most beneficial. Intended Audience This path is created to help Quantum-curious developers, Solutions Architects and Enterprise technology evaluators program quantum computers and explore their potential applications. Learning Objectives After completing this learning path, you will be able to: Summarize the key benefits of Amazon Braket Explain the key concepts of Amazon Braket Explain the typical use cases for Amazon Braket Explain how to run Amazon Braket on an On-Demand Simulator and QPU Illustrate the business value of quantum technology with Amazon Braket List the key stages of quantum program development Describe how to plan the journey through the key features of Amazon Braket Create Amazon Braket quantum tasks using the Amazon Braket SDK and third-party plugins Identify the Amazon Braket resources for building on top of existing Amazon Braket deployments Differentiate between local and on-demand simulators based on appropriate use cases and project needs Examine QPU properties using both the AWS console and the Amazon Braket SDK Identify the QPU access paradigms available on Amazon Braket Express the pricing scheme for QPUs and estimate costs prior to running tasks Find and parse quantum task performance Access AWS Management Console interfaces for monitoring and managing quantum tasks, jobs, and their costs Differentiate between quantum tasks and hybrid jobs Describe the concepts of Braket Pulse Explain how to create Analog Hamiltonian Simulation programs Use error mitigation to deploy it with Amazon Braket AWS Knowledge Badge To verify your knowledge, or identify any gaps that you might have, take the knowledge badge assessment. Score 80% or higher and earn an AWS Knowledge badge that you can share with your network. The assessment is based on the courses in the learning path so we recommend completing these courses as needed. Already have some knowledge on Amazon Braket? Go directly to the assessment, test your knowledge. The score report will identify your areas of strength and direct you to the courses where you can improve any knowledge gaps. emmarrig@amazon.com quantum, cloud, AWS, aws, Cloud computing, cloud computing
AWS Foundational Researcher Learning Plan

Foundational Researcher Learning Plan is designed for researchers and research IT professionals who want to become more proficient in optimizing research on AWS. Learn how to use the right storage medium, remove heavy lifting with managed services, and reproduce research with containers and...

Keywords: cloud, cloud computing, AWS, aws, training material

AWS Foundational Researcher Learning Plan https://dresa.org.au/materials/aws-foundational-researcher-learning-plan Foundational Researcher Learning Plan is designed for researchers and research IT professionals who want to become more proficient in optimizing research on AWS. Learn how to use the right storage medium, remove heavy lifting with managed services, and reproduce research with containers and software-defined infrastructure. Foundational Researcher LP is suitable for academic researchers who need to acquire skills in: job roles such as Cloud architects, DevOps engineers, Operations staff, Developers, business decision makers; all tech roles interested in AWS Cloud Storage, Cloud architects, Storage administrators, Application developers, Data scientists, Machine Learning (ML) and a ML process, artificial intelligence, Application development. emmarrig@amazon.com cloud, cloud computing, AWS, aws, training material
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://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 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
WEBINAR: Making sense of gene and protein lists with functional enrichment analysis

This record includes training materials associated with the Australian BioCommons webinar ‘Making sense of gene and protein lists with functional enrichment analysis’. This webinar took place on 23 October 2024.
Topic description
Do you have a long list of genes or proteins from omics experiments...

Keywords: Bioinformatics, Enrichment analysis

WEBINAR: Making sense of gene and protein lists with functional enrichment analysis https://dresa.org.au/materials/webinar-making-sense-of-gene-and-protein-lists-with-functional-enrichment-analysis This record includes training materials associated with the Australian BioCommons webinar ‘Making sense of gene and protein lists with functional enrichment analysis’. This webinar took place on 23 October 2024. Topic description Do you have a long list of genes or proteins from omics experiments that you don’t know what to do with? This webinar explains how functional enrichment analysis can be used to understand what these lists mean by employing gene ontology and pathway information to highlight the underlying biology. We’ll discuss the statistics that underpin enrichment analysis methods and some of the most commonly used tools, databases and workflows. Speakers: Dr Hossein Valipour Kahrood, Bioinformatician, Monash Genomics and Bioinformatics Platform Dr Cali Willet, Senior Research Bioinformatician, Sydney Informatics Hub, The University of Sydney Host: Dr Melissa Burke, Australian BioCommons   Training materials 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. Functional_enrichment_webinar: A PDF copy of the slides presented during the webinar.   Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://www.youtube.com/watch?v=AvpH2WMNXxA Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Enrichment analysis
Ten Simple Rules for Researchers: Upskilling for a Rapidly Evolving Workforce

The following recommendations were inspired by the Australian Research Data Commons (ARDC) Digital Research Skills Summit 2023, that brought together Researchers, Learning Designers, Skills Trainers, and Librarians in productive discussions on how to run effective researcher skills training....

Keywords: Training, Training Material, Short Format Training, Digital Skills, Researcher Training, Learning

Ten Simple Rules for Researchers: Upskilling for a Rapidly Evolving Workforce https://dresa.org.au/materials/ten-simple-rules-for-researchers-upskilling-for-a-rapidly-evolving-workforce The following recommendations were inspired by the Australian Research Data Commons (ARDC) Digital Research Skills Summit 2023, that brought together Researchers, Learning Designers, Skills Trainers, and Librarians in productive discussions on how to run effective researcher skills training. These rules outline how to think about skills learning for researchers, plan training sessions, and efficiently maximize learning. We offer recommendations on how to design and develop learner-centered training programs (Rules 1 and 2), foster outreach, and connect with trainer communities (Rules 3 and 4). We then provide tips to manage and optimize training (Rules 5, 6, and 7), and conclude with valuable insights on preparing for uncertainty and the importance of post-training operations and continued learning (Rules 8, 9, and 10). contact@ardc.edu.au Training, Training Material, Short Format Training, Digital Skills, Researcher Training, Learning
WORKSHOP: Online data analysis for biologists

This record includes training materials associated with the Australian BioCommons workshop ‘Online data analysis for biologists’. This workshop took place on 21 August 2024.
Topic description
Galaxy is a web-based platform that lets you conduct accessible, reproducible, and transparent...

Keywords: Bioinformatics, Data analysis, Galaxy

WORKSHOP: Online data analysis for biologists https://dresa.org.au/materials/workshop-online-data-analysis-for-biologists This record includes training materials associated with the Australian BioCommons workshop ‘Online data analysis for biologists’. This workshop took place on 21 August 2024. Topic description Galaxy is a web-based platform that lets you conduct accessible, reproducible, and transparent computational biological research. Widely used by researchers world wide, Galaxy gives you access to 1000’s of popular tools for analysis and processing of biological data. It is perfect for working with a wide range of big and small datasets including genome assembly, annotation, epigenetics, metabolomics, metagenomics, proteomics, statistics, transcriptomics, variant analysis and visualisation. This workshop provides an introduction to using Galaxy and available tools. Using an example dataset, you’ll practice uploading data, choosing and running tools, and viewing the results. We’ll share our top tips for managing your experiments and speeding up your analysis with workflows. Lead trainer: Dr Gareth Price, Galaxy Australia Facilitator: Mike Thang, Galaxy Australia / QCIF Infrastructure provision: Galaxy Australia Host: Dr Melissa Burke, Australian BioCommons   Training Materials 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_Online_data_analysis_for_biologists_210824 (PDF): Information about the event logistics including, description, event URL, learning objectives, prerequisites, technical requirements etc. Schedule_Online_data_analysis_for_biologists_210824 (PDF): Schedule for the workshop providing a breakdown of topics and timings   Materials shared elsewhere: This workshop is based on the Galaxy Training Network tutorial ‘Galaxy basics for everyone’: https://training.galaxyproject.org/training-material/topics/introduction/tutorials/galaxy-intro-101-everyone/tutorial.html A recording of this workshop is available on the Australian BioCommons YouTube Channel: https://www.youtube.com/watch?v=PF39KjOvreM Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Data analysis, Galaxy
Randomised Controlled Trials with REDCap

REDCap is a powerful and extensible application for managing and running longitudinal data collection activities. In this course, learn how to manage a Randomised Controlled Trial using REDCap, including the randomisation module, adverse event reporting and automated participant withdrawals. This...

Keywords: REDCap

Randomised Controlled Trials with REDCap https://dresa.org.au/materials/randomised-controlled-trials-with-redcap REDCap is a powerful and extensible application for managing and running longitudinal data collection activities. In this course, learn how to manage a Randomised Controlled Trial using REDCap, including the randomisation module, adverse event reporting and automated participant withdrawals. This course will introduce some of REDCap’s more advanced features for running randomised trials, and builds on the material taught in REDCAP201 – Longitudinal Trials with REDCap. - Create Data Access Groups (DAGs) and assign users to manage trial sites - Build randomisation allocation table  - Enable and implement participant randomisation module - Design an adverse reporting system using Automated Survey Invitations and Alerts - Create an automated participant withdrawal process - Customise record dashboards Learners should have a solid understanding of REDCap and be familiar with the content of [Data Capture and Surveys with REDCap](https://intersectaustralia.github.io/training/REDCAP101/) and [Longitudinal Trials with REDCap](https://intersectaustralia.github.io/training/REDCAP201/). training@intersect.org.au REDCap
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://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 Laughlin, Greg (type: Editor) Appleyard, Baden (type: Editor) Martinez, Paula Andrea (type: ProjectLeader) Software citation, Software licensing, Software, research software, licence, License, training material
ARDC Research Data Rights Management Guide

A practical guide for people and organisations working with data, about rights information and licences, and to raise awareness of the implications of not having licences on data.

Who is this for? This guide is primarily directed toward members of the research sector, particularly data rights...

Keywords: data, rights, management, licence, licensing, research, policy, guide, training material

ARDC Research Data Rights Management Guide https://dresa.org.au/materials/ardc-research-data-rights-management-guide A practical guide for people and organisations working with data, about rights information and licences, and to raise awareness of the implications of not having licences on data. Who is this for? This guide is primarily directed toward members of the research sector, particularly data rights holders users and suppliers. Some general reference is made to characteristics and management of government data, acknowledging that this kind of data can be input to the research process. Government readers should consult their agency’s data management policies, in addition to reading this guide. contact@ardc.edu.au Laughlin, Greg (type: Editor) Appleyard, Baden (type: Editor) data, rights, management, licence, licensing, research, policy, guide, training material
WEBINAR: Global data resources for human genomics and health

This record includes training materials associated with the Australian BioCommons webinar ‘Global data resources for human genomics and health’. This webinar took place on 20 September 2024.
Event description
Dr Mallory Freeberg takes us on a whirlwind tour of the human genomics data resources...

Keywords: Bioinformatics, Genomics

WEBINAR: Global data resources for human genomics and health https://dresa.org.au/materials/webinar-global-data-resources-for-human-genomics-and-health This record includes training materials associated with the Australian BioCommons webinar ‘Global data resources for human genomics and health’. This webinar took place on 20 September 2024. Event description Dr Mallory Freeberg takes us on a whirlwind tour of the human genomics data resources available at EMBL's European Bioinformatics Institute (EMBL-EBI). Used by scientists across the world, these resources enable the discovery and exploration of genes, variants and their impact on human health and disease. Resources that will be covered in this webinar include: Ensembl: a genome browser that supports research in comparative genomics, evolution, sequence variation and transcriptional regulation  DECIPHER: an interactive web-based database and suite of tools that aid the clinical interpretation of genomic variants Federated EGA: a global resource for discovery and access of sensitive human omics and associated data consented for secondary use Perturbation Catalogue: a new project from Open Targets that is building a catalogue of harmonised and curated human gene, variant, and expression data EMBL-EBI’s comprehensive range of freely available and up-to-date molecular data resources are used by scientists globally. The teams running these resources collaborate closely with international experts and are key partners in human genomic data sharing communities including the Global Alliance for Genomics and Health.  Speaker: Dr Mallory Freeberg, Human Genomics Team Leader, EMBL European Bioinformatics Institute Host: Dr Melissa Burke, Australian BioCommons Training materials 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. 20240920_Australian-Biocommons-Webinar_MFreeberg: A PDF copy of the slides presented during the webinar. Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/5FAj1KffYnY   Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Genomics
7 Steps towards Reproducible Research

This workshop aims to take you further down your reproducibility path, by providing concepts and tools you can use in your everyday workflows. It is discipline and experience agnostic, and no coding experience is needed.

We will also examine how Reproducible Research builds business continuity...

Keywords: reproducibility, Reproducibility, reproducible workflows

Resource type: full-course, tutorial

7 Steps towards Reproducible Research https://dresa.org.au/materials/7-steps-towards-reproducible-research This workshop aims to take you further down your reproducibility path, by providing concepts and tools you can use in your everyday workflows. It is discipline and experience agnostic, and no coding experience is needed. We will also examine how Reproducible Research builds business continuity into your research group, how the culture in your institute ecosystem can affect Reproducibility and how you can identify and address risks to your knowledge. The workshop can be used as self-paced or as an instructor Amanda Miotto - a.miotto@griffith.edu.au reproducibility, Reproducibility, reproducible workflows phd support
Data Entry, Exploration, & Analysis in SPSS

This hands-on training is designed to familiarize you with the interface and basic data processing functionalities in SPSS. We will examine several “must know” syntax commands that can help streamline data entry and processing. In addition, we will explore how to obtain descriptive statistics in...

Keywords: SPSS

Data Entry, Exploration, & Analysis in SPSS https://dresa.org.au/materials/data-entry-exploration-analysis-in-spss This hands-on training is designed to familiarize you with the interface and basic data processing functionalities in SPSS. We will examine several “must know” syntax commands that can help streamline data entry and processing. In addition, we will explore how to obtain descriptive statistics in SPSS and perform visualization. This workshop is recommended for researchers and postgraduate students who are new to SPSS or Statistics; or those simply looking for a refresher course before taking a deep dive into using SPSS, either to apply it to their research or to add it to their arsenal of eResearch skills. - Navigate SPSS Variable and Data views. - Create and describe data from scratch. - Import Data from Excel. - Familiarise yourself with exploratory data analysis (EDA), including: - Understand variable types, identity missing data and outliers. - Visualise data in graphs and tables. - Compose SPSS Syntax to repeat and store analysis steps. - Generate a report testing assumptions of statistical tests. - Additional exercises: - Check assumptions for common statistical tests. - Make stunning plots. In order to participate, attendees must have a licensed copy of SPSS installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. training@intersect.org.au SPSS
WEBINAR: What exactly is bioinformatics?

This record includes training materials associated with the Australian BioCommons webinar ‘What exactly is bioinformatics?' This webinar took place on 7 August 2024.
Event description
‘Doing’ bioinformatics to extract, process, analyse, and interpret experimental results is something that all...

Keywords: Bioinformatics

WEBINAR: What exactly is bioinformatics? https://dresa.org.au/materials/webinar-what-exactly-is-bioinformatics This record includes training materials associated with the Australian BioCommons webinar ‘What exactly is bioinformatics?' This webinar took place on 7 August 2024. Event description ‘Doing’ bioinformatics to extract, process, analyse, and interpret experimental results is something that all life scientists do as part of their research. But what exactly is bioinformatics? And is there a right (or a wrong) way to do it? In this webinar, Dr Georgie Samaha welcomes you to the vast world of bioinformatics. Georgie will illuminate key concepts including: What does a typical experiment look like? What kind of data will I work with? What is involved in data-preprocessing? What’s involved in data analysis? Where can I do bioinformatics? We explore common experimental use cases and share essential - but easy to overlook - practical tips for accessing data, software, and computing resources you need to get your research done. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Speaker:  Dr Georgie Samaha - Product Owner of the Australian BioCommons BioCLI Project and Bioinformatics Group Lead at the Sydney Informatics Hub, The University of Sydney.  Host: Dr Patrick Capon, Australian BioCommons Training materials 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. Samaha_2024_what_is_bioinformatics_webinar: A PDF copy of the slides presented during the webinar. Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/wmy2C-S-rMU  Melissa Burke (melissa@biocommons.org.au) Bioinformatics
Cleaning Biodiversity Data in R

This book is a practical guide for cleaning geo-referenced biodiversity data using R. It focuses specifically on the processes and challenges you’ll face with biodiversity data. As such, this book isn’t a general guide to data cleaning but a targeted resource for those working with or interested...

Keywords: R, Data cleaning, Biodiversity data, Rstats, Ecology, Reproducibility, Beginner R coding, data wrangling, Coding

Cleaning Biodiversity Data in R https://dresa.org.au/materials/cleaning-biodiversity-data-in-r This book is a practical guide for cleaning geo-referenced biodiversity data using R. It focuses specifically on the processes and challenges you’ll face with biodiversity data. As such, this book isn’t a general guide to data cleaning but a targeted resource for those working with or interested in ecology, evolution, and geo-referenced biodiversity data. Atlas of Living Australia support@ala.org.au R, Data cleaning, Biodiversity data, Rstats, Ecology, Reproducibility, Beginner R coding, data wrangling, Coding
ALA Labs

ALA Labs provides resources and articles from the Atlas of Living Australia's Science and Decision Support team. On the website, you can find:

  • Posts: Code, articles, analyses and visualisations that will hopefully help you in your own work
  • Research: Highlighted summaries of scientific...

Keywords: Ecology, R, Python, Rstats, Biodiversity data, Open science, Reproducibility, Coding, Data cleaning, Data visualisation, Species Distribution Modelling, Beginner R coding

ALA Labs https://dresa.org.au/materials/ala-labs ALA Labs provides resources and articles from the Atlas of Living Australia's Science and Decision Support team. On the website, you can find: - Posts: Code, articles, analyses and visualisations that will hopefully help you in your own work - Research: Highlighted summaries of scientific research that has used data from the Atlas of Living Australia - Software: R & Python packages that the Science & Decision Support team manage - Books: Long-form resources with best-practice data wrangling and visualisation - Gallery: Showcasing external work that uses tools from ALA Labs Atlas of Living Australia support@ala.org.au Ecology, R, Python, Rstats, Biodiversity data, Open science, Reproducibility, Coding, Data cleaning, Data visualisation, Species Distribution Modelling, Beginner R coding
Secondary use of clinical trials data in health research: A Practical Guide

This document presents a theoretical framework for the use of clinical trials and other health data for secondary research purposes, which was derived from research papers, consultation with stakeholders and the research community.  Four overall scenarios for data reuse were identified; scenario...

Keywords: Secondary Data Use, Clinical Trials, Training Material, HeSANDA, Health Data Australia, HDA

Secondary use of clinical trials data in health research: A Practical Guide https://dresa.org.au/materials/secondary-use-of-clinical-trials-data-in-health-research-a-practical-guide This document presents a theoretical framework for the use of clinical trials and other health data for secondary research purposes, which was derived from research papers, consultation with stakeholders and the research community.  Four overall scenarios for data reuse were identified; scenario 1: evidence synthesis, scenario 2: secondary analyses, scenario 3: reproducibility, replication and validation, and scenario 4: education and methods development. contact@ardc.edu.au Secondary Data Use, Clinical Trials, Training Material, HeSANDA, Health Data Australia, HDA
Geophysical Research Data Processing and Modelling for 2030 Computation

The Cross-NCRIS National Data Assets program co-funded the ‘Geophysics 2030: Building a National High-Resolution Geophysics Reference Collection for 2030 Computation’ (Geophysics2030) project. At completion, Geophysics2030 i) trialled publishing vertically integrated geophysical datasets, making...

Keywords: Geophysics, Applied mathematics, Physical sciences, Computer and information sciences

Resource type: presentation

Geophysical Research Data Processing and Modelling for 2030 Computation https://dresa.org.au/materials/geophysical-research-data-processing-and-modelling-for-2030-computation The Cross-NCRIS National Data Assets program co-funded the ‘Geophysics 2030: Building a National High-Resolution Geophysics Reference Collection for 2030 Computation’ (Geophysics2030) project. At completion, Geophysics2030 i) trialled publishing vertically integrated geophysical datasets, making both raw datasets and successive levels of derivative data products available online in a new international self-describing data standard (first published in 2022); ii) co-located these datasets/data products with HPC computing resources required to process datasets at scale; and iii) developed new community software and environments allowing researchers to exploit the new data sets at high-resolution on a continental-scale. This ARDC, AuScope, NCI and TERN-funded project created new high-performance dataset and introduced a new, world-leading community platform that allows researchers to combine high-performance computing, high-resolution datasets, and flexible software workflows. The world-leading innovation was evidenced by new projects in collaboration with leading international researchers, including Jared Peacock, the United States Geological Survey-based leader of the new standards for Magnetotelluric (MT) data and Karl Kappler, DIAS Geophysics, who leads the development of ‘Aurora’, a National Science Foundation (USA) funded open-source software package for processing MT data using the new MTH5 standards. This Community Connect project, in partnership with NCI and AuScope, proposed to develop, deliver, and distribute a 2-day ‘Geophysical Research Data Processing and Modelling for 2030 Computation’ workshop in 2023. The training packages will consist of two parts, i) the utilisation of NCI for Geophysics processing and modelling, and ii) developing workflows for coupling Geophysical software, compute environments and datasets. Through previous engagement with the Geophysics community, we knew users of the 2030 Geophysics Collection were experts in their fields of geophysics data acquisition, processing and modelling. The community had high levels of computer literacy and deep technical skills in geophysics and research expertise. The workshop was targeted to support this advanced community and facilitate the usage of large co-located datasets and high-performance computing at the NCI HPC/cloud platform. rebecca@auscope.org.au Geophysics, Applied mathematics, Physical sciences, Computer and information sciences
Sharing a Trove List as a CollectionBuilder exhibition

You’ve been collecting and annotating items relating to your research project in a Trove List. You’d like to display the contents of your list as an online exhibition for others to explore. CollectionBuilder creates online exhibitions using static web...

Keywords: Trove, Trove List, CollectionBuilder, collection, GLAM Workbench, exhibition, HASS

Resource type: tutorial

Sharing a Trove List as a CollectionBuilder exhibition https://dresa.org.au/materials/sharing-a-trove-list-as-a-collectionbuilder-exhibition You’ve been collecting and annotating items relating to your research project in a Trove List. You’d like to display the contents of your list as an online exhibition for others to explore. [CollectionBuilder](https://collectionbuilder.github.io/) creates online exhibitions using static web technologies. But how do you get your List data from Trove into CollectionBuilder? This tutorial from the Trove Data Guide walks through the complete process step-by-step. Tim Sherratt (tim@timsherratt.au) ARDC Community Data Lab Trove, Trove List, CollectionBuilder, collection, GLAM Workbench, exhibition, HASS
Create a layer in the Gazetteer of Historical Australian Placenames using metadata from Trove’s digitised maps

Trove includes thousands of digitised maps, created and published across the last few centuries. You want to create a collection of maps relating to your area of interest and explore it using the Gazetteer of Historical Australian Placenames (GHAP). You know it’s possible to add layers to GHAP,...

Keywords: Trove, maps, Gazetteer of Historical Australian Placenames (GHAP), GLAM Workbench, geospatial, HASS

Resource type: tutorial

Create a layer in the Gazetteer of Historical Australian Placenames using metadata from Trove’s digitised maps https://dresa.org.au/materials/create-a-layer-in-the-gazetteer-of-historical-australian-placenames-using-metadata-from-trove-s-digitised-maps Trove includes thousands of digitised maps, created and published across the last few centuries. You want to create a collection of maps relating to your area of interest and explore it using the Gazetteer of Historical Australian Placenames (GHAP). You know it’s possible to add layers to GHAP, but how do you get the data from Trove in a format that can be uploaded as a layer? This tutorial from the Trove Data Guide walks through the complete process step-by-step. Tim Sherratt (tim@timsherratt.au) ARDC Community Data Lab Trove, maps, Gazetteer of Historical Australian Placenames (GHAP), GLAM Workbench, geospatial, HASS
Comparing manuscript collections from Trove in Mirador

You want to compare the contents of two digitised manuscript collections and examine individual documents side-by-side. The Mirador viewer can be configured as a flexible, research workspace that displays multiple images from different sources, but how do you get...

Keywords: Trove, images, manuscripts, GLAM Workbench, IIIF, HASS, Mirador

Resource type: tutorial

Comparing manuscript collections from Trove in Mirador https://dresa.org.au/materials/comparing-manuscript-collections-in-mirador You want to compare the contents of two digitised manuscript collections and examine individual documents side-by-side. The [Mirador viewer](https://projectmirador.org/) can be configured as a flexible, research workspace that displays multiple images from different sources, but how do you get manuscript collections from Trove to Mirador? This tutorial from the Trove Data Guide walks through the complete process step-by-step. Tim Sherratt (tim@timsherratt.au) ARDC Community Data Lab Trove, images, manuscripts, GLAM Workbench, IIIF, HASS, Mirador
Working with a Trove collection in Tropy

You want to be able to work on a collection of digitised images from Trove on your desktop – adding notes, transcriptions, and annotations. Tropy is a useful tool for managing collections of research images, but how do you import a collection of images from Trove into...

Keywords: Trove, images, Tropy, IIIF, GLAM Workbench, HASS

Resource type: tutorial

Working with a Trove collection in Tropy https://dresa.org.au/materials/working-with-a-trove-collection-in-tropy You want to be able to work on a collection of digitised images from Trove on your desktop – adding notes, transcriptions, and annotations. [Tropy](https://tropy.org/) is a useful tool for managing collections of research images, but how do you import a collection of images from Trove into Tropy? This tutorial from the [Trove Data Guide](https://tdg.glam-workbench.net/home.html) walks through the complete process step-by-step. Tim Sherratt (tim@timsherratt.au) ARDC Community Data Lab Trove, images, Tropy, IIIF, GLAM Workbench, HASS
Analysing keywords in Trove’s digitised newspapers

You want to explore differences in language use across a collection of digitised newspaper articles. The Australian Text Analytics Platform provides a Keywords Analysis tool that helps you...

Keywords: text analysis, Australian Text Analytics Platform (ATAP), Trove, GLAM Workbench, Trove Newspaper and Gazette Harvester, newspapers, HASS

Resource type: tutorial

Analysing keywords in Trove’s digitised newspapers https://dresa.org.au/materials/analysing-keywords-in-trove-s-digitised-newspapers You want to explore differences in language use across a collection of digitised newspaper articles. The [Australian Text Analytics Platform](https://www.atap.edu.au/) provides a [Keywords Analysis tool](https://github.com/Australian-Text-Analytics-Platform/keywords-analysis) that helps you examine whether particular words are over or under-represented across collections of text. But how do get data from Trove’s newspapers to the keyword analysis tool? This tutorial from the [Trove Data Guide](https://tdg.glam-workbench.net/home.html) walks through the complete process step-by-step. Tim Sherratt (tim@timsherratt.au) ARDC Community Data Lab text analysis, Australian Text Analytics Platform (ATAP), Trove, GLAM Workbench, Trove Newspaper and Gazette Harvester, newspapers, HASS