Digital research tools can help today’s researchers meet ambitious goals and overcome significant challenges, such as

• Mining and analyzing a large data set
• Modelling that is too complex for desktop computing
• Identifying patterns or trends in spatial data or visual imagery
• Developing a web-based research/analysis tool

In this session, equip yourself with basic terms and concepts related to high performance (HPC), supercomputing and big data analytics, and an understanding of the computing power and expert support available to you through the Digital Research Alliance of Canada and ACENET. Learn how advanced computing is already serving researchers across the sciences, and how you can get started.

Are you interested in learning more about digital research methods, but don't know where to start? Do you have large data sets that you would like to mine and analyze in innovative ways? Do you want to look for patterns in visual imagery, reveal trends in spatial data, or perform quantitative analysis of digitized texts? Perhaps you want to build a web-based research or analytical tool and don’t know where to start. This orientation session introduces you to the ACENET organization and the research supports and resources available through ACENET and the Digital Research Alliance of Canada that are directly applicable for humanities and social sciences researchers.

Most of us have experience using a computer, whether for school, work, or entertainment, but how many of us have actually had an expert teach us how to use it? When one of your computing devices goes wrong, do you struggle to understand why? Perhaps you do an internet search in the hope that someone else who has had the exact same problem as you found a solution that also works for you.

It doesn't need to be (quite) this way. This talk doesn't teach you how to troubleshoot everything, but does give you insight to how media, programs and data are encoded and used by computers so you can make more sense of why computers behave the ways they do and solve some of your problems with greater efficiency and less frustration. We provide an approachable overview of how a computer works, by both looking at their history and breaking one down to explain individual components, before highlighting some of the tradeoffs to consider when buying a computer. We provide practical, simple, and actionable advice on digital security and show you a few "pro tips" on how to make the most of your workstation, phone, or whatever device you happen to use. 

Whether you have a lot or a little experience using your digital technology, if you want to learn how to use your devices more effectively, this workshop is for you!

What is High Performance Computing (HPC) and what can it do for me? How can ACENET help? Used by researchers across many disciplines to tackle analyses too large or complex for a desktop, or to achieve improved efficiency over a desktop, this session takes participants through the preliminary stages of learning about high performance computing (HPC) and computing clusters, and how to get started with this type of computing. It then reviews software packages available for applications, data analysis, software development and compiling code. Finally, participants will be introduced to the concept of parallel computing to achieve much faster results in analysis. This session is designed for those with no prior experience in HPC, and are looking for an introduction and overview.

YouTube Tutorial: (Video)

This session involves practical exercises.

This core session is designed to help new users at ACENET and the Digital Research Alliance of Canada get up and running.

Linux is the terminal interface used to enable you to use the ACENET and the Digital Research Alliance of Canada (the Alliance) HPC clusters from your desktop. It's the tool you need to get your data on the clusters, run your programs, and get your data back. In this session, learn how to get started with Linux and how to perform some basic tasks:

• create and navigate directories for your data
• load files
• manage your storage
• run programs on the computing clusters
• set file permissions 

This workshop is designed for those with no prior experience in working with a terminal interface.

YouTube Tutorial: (Video)

This session involves practical exercises.

YouTube Tutorial: (Video) 

This session involves practical exercises.

This core session is designed to help new users at ACENET and the Digital Research Alliance of Canada get up and running.

The national systems use a job scheduler called “Slurm”. In this session you will learn how Slurm works and how to:

• minimize wait time by framing reasonable requests
• optimize use of the resources to achieve efficiency
• increase throughput
• run more jobs simultaneously
• troubleshoot and address crashes

This workshop is designed for new HPC users, or for experienced users either transitioning to Slurm or seeking to improve efficiency with the scheduler. Prerequisites: Completion of Introduction to Linux and Introduction to Shell Scripting, or prior experience with both.

YouTube Tutorial: (Video)

This workshop involves practical exercises.

Software Carpentry teaches basic computational research skills. Using Software Carpentry materials, this hands-on workshop introduces you to the fundamentals of the Unix Shell, Version Control and Python. 

The workshop covers the fundamentals of Python, including data types, conditional statements, loops and functions, as well as program design, version control, data management, and task automation. Participants will be encouraged to help one another and to apply what they have learned to their own projects. The goal is to teach the practical knowledge needed to start programming, debugging and using Python in everyday tasks.

Of interest to: active computational researchers and their teams; companies using computational tools for R&D; anyone considering a research project that requires computational research; and students eager to enhance their career choices by expanding their skill set.

This workshop involves practical exercises.

Software Carpentry teaches basic computational research skills. Using software-carpentry.org materials this hands-on workshop introduces you to the fundamentals of the Unix Shell, Version Control with Git and R.

This hands-on workshop covers the fundamentals of R, including data types, conditional statements, loops and functions, as well as program design, version control, data management, and task automation. Participants are encouraged to help one another and to apply what they have learned to their own projects. The goal is to teach the practical knowledge needed to start programming, debugging and using R in everyday tasks.

Of interest to: active computational researchers and their teams; companies using computational tools for R&D; anyone considering a research project that requires computational research; and students eager to enhance their career choices by expanding their skill set.

This workshop involves practical exercises.

Software Carpentry teaches basic computational research skills. Using software-carpentry.org materials this workshop introduces you to the fundamentals of MATLAB.

This is a beginner level workshop that is hands-on, covering the fundamentals of MATLAB, including data types, conditional statements, loops and functions, as well as program design, version control, data management, and task automation. Participants are encouraged to help one another and to apply what they have learned to their own research problems. The goal is to teach the practical knowledge needed to start programming, debugging and using MATLAB in everyday tasks.

Of interest to: active computational researchers and their teams; companies using computational tools for R&D; anyone considering a research project that requires computational research; and students eager to enhance their career choices by expanding their skill set.

This session involves practical exercises

This is an introductory workshop for researchers and students who are interested in programming basics for humanities and social sciences applications. No prior experience in programming is necessary. Topics covered include an introduction to Unix Shell and Linux, websites and versioning with Git, regular expressions, Python for humanities and web scraping with Python. Applications include web scraping, survey field restriction, websites and text analysis. The workshop uses software-carpentry.org materials.

This session provides an introduction to the Digital Research Alliance of Canada cloud which is used to create and manage virtual machines. Virtual machines allow great flexibility but require knowledge and effort to configure them for your specific needs. Virtual machines can be used for diverse work flows, from processing particle physics data to running humanities and social sciences websites. Learn how to create a virtual machine and how to start using it.

This is an intermediate level series. Cloud computing provides great flexibility, allowing complete control of the computing environment. In addition, the environment can be copied, backed up, created and recreated in an automated way. In these lessons, we start you on the path towards making use of the great flexibility and power of cloud computing. We use the popular static website generator Jekyll. This is an in-depth workshop for those with no prior cloud experience, at the end of which you have a virtual machine and a Jekyll website.

This is a beginner to intermediate level session. Learn the best practices at the Digital Research Alliance of Canada for minimizing your risk for cyber attacks. Topics include SSH security, firewalls, using logs and creating backups; all applied to running and maintaining cloud projects at the Digital Research Alliance of Canada.

The Canadian sensitive data framework is complex, with many legal and regulatory bodies at the institutional, provincial, and federal levels governing how data involving human participants should be managed and safeguarded. This presentation provides a high-level overview of Canada's research data management landscape, focusing on sensitive data involving human participants. Various resources and tools at the institutional and federal levels are discussed, as well as best practices in managing sensitive data with a data management plan (DMP), data encryption, and guidance in preserving and sharing sensitive data.

This is an introductory workshop to research data management for Humanists and Social Scientists. The Tri-Agencies, including the Social Sciences and Humanities Research Council (SSHRC), require that researchers make their data openly available to the public. What does this mean for Humanists and Social Scientists that don’t work with traditional “data” and instead work with humans, books, or art? The session focuses on the importance of data management planning. Specifically, it covers the tools and services available to Atlantic Canadian researchers that can help you better manage your data, enhance the discoverability of your research, and ensure that your valuable research data are preserved for future reuse. Special attention is also be given to managing sensitive data, including FRDR’s Sensitive Data Pilot Project. This session is co-facilitated by a data librarian to foster a discussion of the role of research data management in the Humanities and Social Sciences.

This is an introductory lesson adapted from the Library Carpentry workshop on Introduction to working with Data that introduces people with library- and information-related roles, or those in the Humanities and Social Sciences professions that work with data, to using regular expressions. Regular expressions are a concept and an implementation used in many different programming environments for sophisticated pattern matching. They are an incredibly powerful tool that can amplify your capacity to find, manage, and transform data and files. The lesson provides background on the regular expression language and how it can be used to match and extract text and to clean data. No previous experience with programming is required.

This is a hands-on introductory workshop focused on fostering best practices for data organization in spreadsheets. Participants learn how to organize their data to prioritize clarity, reproducibility, and interoperability, such that they can seamlessly load their data later into an analysis program. The spreadsheet programs covered are Microsoft Excel and Google Sheets. The examples explored are from the field of Social Sciences, but the principles are relevant for any discipline that collects data in spreadsheets. No previous experience with spreadsheets or programming is required.

This Library Carpentry adapted lesson introduces people working in Humanities, Social Sciences, and library- and information-related roles to working with data in OpenRefine. OpenRefine can be used to standardize and clean data across your file, and is most useful when working with a comma separated values file (csv) or a tab delimited file (tsv). It can help you get an overview of a data set, resolve inconsistencies in a data set, help you split data up into more granular parts, and more. At the conclusion of the lesson you will understand what the OpenRefine software does and how to use the OpenRefine software to work with data files. This lesson is co-facilitated by an academic librarian who provides real life examples of using OpenRefine in their work. No previous experience with the software is required.

This is a beginner to intermediate level session. Learn the best practices at Compute Canada for minimizing your risk for cyber attacks. Topics include SSH security, firewalls, using logs and creating backups; all applied to running and maintaining cloud projects at Compute Canada.

A key concept in software engineering for decades, version control is integral to effective management and manipulation of evolving work in a digital environment. You may have heard of Git, but there are many competing version control tools available, and they all exist in a large “ecosystem” of related web services. Learn how to assess your options and choose a combination of version control tools and services for your work.

Version control is the practice of managing and sharing changes to documents, programming code, websites or any other files to keep track of what’s been changed, by whom, when and why. All previous versions of files are saved and you can even revert to a previous version. Git is a version control software.

Git-portal sites, like GitHub or GitLab, offer many useful features to facilitate collaborative development.

This beginner level session introduces you to Git. We show you how to create a repository, record changes to files, explore and restore from the recorded history and how to resolve conflicts (when one member overwrites another’s changes).

Lesson Material: http://github-pages.ucl.ac.uk/git-novice/

Version control is the practice of managing and sharing changes to documents, programming code, websites or any other files to keep track of what’s been changed, by whom, when and why. All previous versions of files are saved and you can even revert to a previous version. Git is a version control software.

Git-portal sites, like GitHub or GitLab, offer many useful features to facilitate collaborative development.

This session focuses on collaborative development workflows using Git-collaboration sites like GitHub, GitLab or Bitbucket and demonstrates how to work with branches, issue tracking, contribute to projects using pull-/merge-requests, code-review, how to run CI/CD-pipelines and use other common features of these platforms.

Lesson Material: https://acenet-arc.github.io/git-collaboration/

Scientific visualization can be applied across a number of areas, including engineering, material science, sensor/drone data, geospatial, and life sciences. We all know the advantages of seeing a bar chart with data instead of reading the numbers in text. The larger your dataset, the greater the value of seeing it visually and being able to pinpoint the critical areas for decisions.

This one hour webinar provides an overview of the power of scientific visualization during the innovation process. The session begins with a general introduction to supercomputing and scientific visualization. It then illustrates the decision points for a dataset size that fits on a typical desktop, before showing what can be done using a supercomputer for data that’s too large for a desktop.

There are no prerequisites and no technical knowledge is needed.

This session is a precursor to the Data Analysis & Visualization with Paraview workshop, a 3.5 hour hands-on course where participants will learn how to run scientific visualizations on their desktop.

Scientific visualization of large datasets allows you to identify critical areas and vulnerabilities during the design process so that you can focus efforts, cut design time and create a better product. It can be applied across a number of areas, including engineering, material science, sensor/drone data, geospatial, and life sciences.

This beginner, hands-on workshop will introduce participants to scientific visualization on both a conceptual and practical level, teaching you how to use Paraview visualization software on your own desktop. 

Content:

• Using scientific visualization in communication and decision-making
• Types of visualization
• Extracting data from your model
• Using the open-source Paraview visualization software on a desktop. Datasets will be provided.

No previous experience in scientific visualization is required.

When working with large sets of numbers, it is often more useful to display the information graphically using histograms, scatter plots, bar charts, box plots and other depictions.

This workshop teaches participants how to gain insights into data through visualization using R as the programming language.  Basic knowledge of R is recommended, although not mandatory.

Participants learn how to:

• Create simple scatterplots, histograms, and box plots
• Compare the plotting features of base R and the ggplot2 package
• Plot with ggplot2
• Plot time series data
• Arrange and export plots

Apache Spark is an open-source software for large-scale data processing and analysis. Using Apache Spark and Python (PySpark), this workshop is aimed at analyzing data sets that are too large to be handled and processed by a single computer. 
In a hands-on format, using PySpark, participants will learn to import data, and use functions to transform, reduce and compile the data. You will also learn how to produce parallel algorithms that can run on the national clusters.

Parallel computing is the business of breaking a large problem into tens, hundreds, or even thousands of smaller problems which can then be solved at the same time, possibly on more than one computer.  It can reduce processing time to a fraction of what it would have been, or enable you to tackle larger, more complex problems, or both. It’s widely used in big data mining, AI, time-critical simulations, and advanced graphics such as augmented or virtual reality. It’s used in fields as diverse as genetics, biotech, geographic information systems, computational fluid dynamics, medical imaging, drug discovery, and agriculture.

This session introduces the terminology and concepts of parallel programming. Learn about parallel computer architectures, approaches to parallel program design and performance measurement.

This is a one-hour crash course in the primary tool for writing message-passing parallel programs. It covers the basic concepts of MPI, including sending and receiving messages, coordination, and data synchronization. This workshop assumes that you have some programming experience with one or more languages and have take the ACENET Basics sessions. (1 hour)

Participants learn about the primary tool for writing shared-memory parallel programs. The session covers OpenMP and provides a short introduction to POSIX threads. You learn about the most common techniques, such as parallel for loops, barriers and critical sections. This workshop assumes that you have programming experience with one or more languages and have taken the ACENET Basics sessions. (1 hour)

Offered annually in spring

The school is designed for participants familiar with the Linux command line and who have some level of programming experience. Completion of the ACENET Basics Series, or equivalent experience, is strongly recommended. The mornings consist of lectures, with the afternoons following a lab format, where participants are given exercises, or can bring specific problems to instructors related to their research. Topics include general parallel computing, OpenMP, GPGPU, and Message Passing Interface (MPI). (3.5 days)

Are you curious about machine learning, but not sure where to start, or if the discipline is for you? Join ACENET for a survey and explanation of several methods used to make machines learn. From simple models like Naive Bayes, Regression and Decision Trees to an introduction to Support Vector Machines and Feed-Forward Neural Networks. This talk is geared to be approachable to a novice audience, curious about machine learning, but not necessarily math or computer science majors. Methods and techniques are explained using metaphors, examples, and clear language, without diving too deeply into the math and calculus on which these techniques are based.

What do you need to make a machine learning project successful? How can you make smart decisions about setup and execution of a machine learning project? How should you hire and support the staff working on the project? We hope to provide clear, thoughtful answers to these, and other common questions to get you thinking about whether machine learning is a technology that you and your company or group should think about investing in. We discuss topics like data collection, the trade-offs involved in choosing a model, and what to expect from a successful project, as well as how to salvage useful by-products and skills when projects don’t go as planned.

This is a beginner session oriented to business owners and project managers curious to learn more about machine learning, or who may have an idea that involves machine learning and want to know where to begin.

How do computers understand language? It seems impossible that zeroes and ones could ever add up to words that humans can understand, but machine language has come a long way in the past few years. Let us take you behind the code to explain how machines simulate language comprehension, and why it’s a far more complicated problem than “bonjour = hello”.

This talk is aimed at an audience who is not necessarily familiar with computers or language comprehension, but would like a primer to the field, and what it can realistically do. We explain natural language processing from the perspective of machines that cannot understand words, but capture semantic meaning by processing data.

Have you wondered how machine learning models can suddenly do so many different types of work? How is it that machines can learn things like language, vision and translation in such a short amount of time, and what has helped drive these kinds of improvements? The obvious answers - big data and big processors - are only part of the story, and to understand the full picture, we need to take a closer look at the models driving the AI revolution. This talk is aimed at people who are familiar with the basics of feed-forward neural networks, and involves an in-depth explanation of how information is represented for machines to learn on, how machines can make sense of information, and the challenges presented.

Many programs come as source code and a mysterious Makefile, with instructions like “make all; make install”. Here’s how it works, what can go wrong, how to fix it, and maybe even how to write your own. Participants must have some programming experience and have taken the ACENET Basics sessions.

This workshop introduces researchers to the theory, key ideas, and techniques of Molecular Dynamics. A practical application targeted at biosimulations is introduced using the GROMACS package.

This workshop is an introduction to the Fortran programming language. Fortran, one of the initial high-level programming languages, continues to be an excellent option for high-performance computing due to its superb performance. The newer versions of Fortran offer many modern features, including object-oriented programming capabilities to programmers. This course covers some of these features. Participants must be familiar with another programming language. 

A great deal of high-performance computing software is written in C, but few universities offer courses in the language any more. If you have to work with “legacy code” written in C, adding features, porting to a new machine, or patching errors, or if you want to write user-defined functions for engineering packages such as Fluent, then this workshop is for you.

A great deal of high-performance computing software is written in C++, but few universities offer courses in the language any more. If you have to work with “legacy code” written in C++, adding features, porting to a new machine, or patching errors, or if you want to extend packages like OpenFOAM which are written in C++, then this workshop is for you.

C++ was designed as an extension of the C language but has its own distinct idiom or style. This workshop assumes that you already know C to the level reached in the prerequisite ACENET workshop, “C as a Second Language”.