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| # Histology analysis using napari and BrainGlobe | ||
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| For full details, please see the [BrainGlobe website](https://brainglobe.info/community/courses/dec-2023/index.html). | ||
| For full details, please see the [BrainGlobe website](https://brainglobe.info/community/courses/scheduled/dec-2023/index.html). |
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@@ -13,12 +13,7 @@ an SWC username and password and be able to | |
| If you require any assistance, please contact | ||
| <a href="mailto:[email protected]?subject=SWC/GCNU Software Skills">Adam Tyson</a> in advance of the course. | ||
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| ## Instructors | ||
| * [Adam Tyson](https://github.com/adamltyson) | ||
| * [Niko Sirmpilatze ](https://github.com/niksirbi) | ||
| * [Laura Porta](https://github.com/lauraporta) | ||
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| ## Materials | ||
| * [Slides](https://neuroinformatics.dev/course-behaviour-hpc) | ||
| * [GitHub repository](https://github.com/neuroinformatics-unit/course-behaviour-hpc) | ||
| * [GitHub repository](https://github.com/neuroinformatics-unit/course-behaviour-hpc) | ||
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| # General software skills for systems neuroscience | ||
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| ## Overview | ||
| Held over seven days, this course will provide researchers with some basic computational skills for systems | ||
| neuroscience research. This will include general programming, software development best practices and data analysis | ||
| using leading open-source software tools. This course will also include some general microscopy lectures. | ||
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| ## Schedule | ||
| | Date | Time | Event | Location | | ||
| |-----------------------|----------------|--------------------------------------------------------------|------------------------------------| | ||
| | **Monday, September 30th** | 13:00-14:00 | Introduction to the course | SWC Brasserie Seminar Room | | ||
| | | 14:00-17:00 | Introduction to Python (1) | SWC Brasserie Seminar Room | | ||
| | **Tuesday, October 1st** | 10:00-11:00 | Data management and sharing (1) | SWC Ground Floor Lecture Theatre | | ||
| | | 15:00-17:00 | Data management and sharing (2) | SWC Ground Floor Lecture Theatre | | ||
| | **Wednesday, October 2nd** | 10:00-12:30 | Introduction to Python (2) | GCNU Seminar Room | | ||
| | | 13:30-17:00 | Version control and software development best practices | GCNU Seminar Room | | ||
| | **Thursday, October 3rd** | 10:00-13:00 | Video-based analysis of animal behaviour (1) | SWC Brasserie Seminar Room | | ||
| | | 14:00-17:00 | Video-based analysis of animal behaviour (2) | SWC Brasserie Seminar Room | | ||
| | **Friday, October 4th** | 14:00-17:00 | Linux and high-performance computing | SWC Ground Floor Lecture Theatre | | ||
| | **Monday, October 7th** | 11:00-12:00 | General microscopy: basics | SWC Brasserie Seminar Room | | ||
| | | 13:00-14:30 | General microscopy: applications | SWC Brasserie Seminar Room | | ||
| | | 14:30-18:00 | Histology analysis (1) | SWC Brasserie Seminar Room | | ||
| | **Tuesday, October 8th** | 10:00-11:00 | Histology analysis (2) | SWC Ground Floor Lecture Theatre | | ||
| | | 14:00-17:00 | Histology analysis (3) | SWC Brasserie Seminar Room | | ||
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| ## Introduction to Python | ||
| The aim of the course is to introduce basic concepts of programming in Python, and establish a community of Python users. It is a hands-on course, in which we will guide you | ||
| through the process of writing your first Python script and teach you some best practices. | ||
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| The course will run over two days, 2.5-3h per day with a break in between. | ||
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| Instructors: Igor Tatarnikov, Sofía Miñano | ||
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| **Day 1** | ||
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| Part 1 | ||
| * Aims | ||
| * Why learn Python? | ||
| * How to work with Python? | ||
| * Basics of programming (1/2) | ||
| - Variables | ||
| - Data types | ||
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| Part 2 | ||
| * Basics of programming (2/2) | ||
| - Loops | ||
| - Conditional statements | ||
| - List comprehensions | ||
| * Writing your first Python script | ||
| * Loading and saving data | ||
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| **Day 2** | ||
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| Part 1 | ||
| * Recap and Q&A | ||
| * Using third party libraries | ||
| * Functions and methods | ||
| * Classes and objects | ||
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| Part 2 | ||
| * Errors and exceptions | ||
| * Organising your code | ||
| * Documenting your code | ||
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| ### Course materials | ||
| - [Slides](https://docs.google.com/presentation/d/11URuWOxi5TMbeJNo4R1NWfBQ_ZPNFpe6YU6dvLAVcYQ/edit?usp=sharing) | ||
| - [Site for the 2023/2024 course](https://software-skills.neuroinformatics.dev/courses/intro-software-dev.html) | ||
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| ### Additional resources | ||
| - [Miniforge installers](https://github.com/conda-forge/miniforge#download) | ||
| - [PyCharm](https://www.jetbrains.com/pycharm/) | ||
| - [VS Code](https://code.visualstudio.com/) | ||
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| ## Data Management and Sharing | ||
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| This course will introduce best-practices in neuroscience data management | ||
| and cover recent standardisation initiatives. The primary take-away from this course | ||
| will be to leave with a strong schema in mind for clean organisation of experimental data. | ||
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| Full details can be found on the [course webpage](https://software-skills.neuroinformatics.dev/courses/data-management.html) | ||
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| ## Version control and software development best practices | ||
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| The aims of the course are | ||
| * to learn how to keep track of changes to your code with Git and GitHub, | ||
| * to learn how to work with others on code | ||
| * and to get an initial idea of how structure, document and test your code. | ||
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| [Git and Github slides for 2023/2024 course](https://docs.google.com/presentation/d/1HmTqmgB34deJILvPOQtwuaQR_iwGp5AwEwGf7tmx5hE/edit?usp=sharing) | ||
| [Site for software development best practice](collaborative-coding) | ||
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| Instructors: Stephen Lenzi, Laura Porta, Alessandro Felder | ||
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| ## Video-based analysis of animal behaviour | ||
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| This course will introduce the theory and practice of tracking animals in videos to quantify their behaviour. | ||
| Participants will get to train pose estimation models | ||
| with [SLEAP](https://sleap.ai/), analyse pose tracks | ||
| with [movement](https://movement.neuroinformatics.dev/), and extract behavioural syllables with [keypoint-moseq](https://keypoint-moseq.readthedocs.io/en/latest/index.html). | ||
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| Full details can be found on the [course webpage](video-analysis). | ||
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| Instructors: Niko Sirmpilatze, Chang Huan Lo, Sofía Miñano | ||
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| ## Linux and high-performance computing | ||
| This course will introduce some basic principles of using Linux, and high-performance computing in general. Most of | ||
| the course will be centered around learning to run a specific workflow (pose estimation) on the SWC HPC system. | ||
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| Full details can be found on the [course webpage](hpc-behaviour). | ||
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| Instructors: Niko Sirmpilatze, Igor Tatarnikov, Adam Tyson | ||
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| ## General microscopy | ||
| This course will introduce some basic concepts about microscopy (optics, fluroescence etc.) to help better understand | ||
| the following course on histology analysis. | ||
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| Instructors: Rob Campbell, Adam Tyson, Alessandro Felder, Igor Tatarnikov | ||
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| ## Histology analysis | ||
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| Full details can be found on the [course webpage](https://brainglobe.info/community/courses/scheduled/oct-2024/index.html). |
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| (#video-analysis)= | ||
| # Video-based analysis of animal behaviour | ||
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| ## Overview | ||
| This will be an introductory course on analysing animal behaviour from video data. The course will cover: | ||
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| - Motivation | ||
| - Overview of animal tracking methods and terminology | ||
| - Pose estimation and tracking | ||
| - Overview of existing tools | ||
| - Labeling animal body parts | ||
| - Training a model | ||
| - Predicting poses | ||
| - Evaluating performance | ||
| - Analysing pose tracks with Python | ||
| - Loading and saving data | ||
| - Filtering/smoothing | ||
| - Visualising tracks | ||
| - Time spent in regions of interest | ||
| - Overview of animal tracking methods and terminology. | ||
| - **Pose estimation and tracking with [SLEAP](https://sleap.ai/)**. This includes hands-on practice with training a pose estimation model, evaluating its performance, and using it to predict pose tracks in new videos. | ||
| - **Analysing pose tracks with [movement](https://movement.neuroinformatics.dev/)**. This includes loading the predicted pose tracks in Python, filtering and smoothing them, computing kinematic variables, and visualising the results. | ||
| - **Extracting behavioural syllables with [keypoint-moseq](https://keypoint-moseq.readthedocs.io/en/latest/index.html)**. | ||
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| :::{note} | ||
| Training and prediction with pose estimation models are | ||
| GPU-intensive tasks. | ||
| Since many students will not have access to a GPU on their own machine, | ||
| we highly recommend that they also attend the follow-up course on | ||
| [Running pose estimation on the SWC HPC system](./hpc-behaviour). | ||
| This will cover how to run pose estimation at scale, using the GPUs of the SWC HPC cluster. | ||
| ::: | ||
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| ## Instructors | ||
| * [Niko Sirmpilatze ](https://github.com/niksirbi) | ||
| * [Chang Huan Lo](https://github.com/lochhh) | ||
| * [Sofía Miñano](https://github.com/sfmig) | ||
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| ## Prerequisites | ||
| Make sure to follow the [steps outlined here](https://github.com/neuroinformatics-unit/course-behavioural-analysis#prerequisites) which will guide you through | ||
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| If you encounter issues with any of these steps please contact | ||
| <a href="mailto:[email protected]?subject=SWC/GCNU Software Skills">Niko Sirmpilatze</a> | ||
| in advance of the course. | ||
| You may also drop by our office hours at the SWC Library (5th floor) on **Friday Nov 24th, 13:30-16:00**. | ||
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| ## Materials | ||
| - [GitHub repository](https://github.com/neuroinformatics-unit/course-behavioural-analysis) | ||
| - [Slides](https://neuroinformatics.dev/course-behavioural-analysis/#/title-slide) | ||
| - [Sample data](https://www.dropbox.com/scl/fo/ey7b6yrqax2olqyv1th7j/h?rlkey=u4wh2gxtbbn4g5o3s55zbx6pp&dl=0) - link expires on 2023-12-22 | ||
| - [Sample data](https://www.dropbox.com/scl/fo/ey7b6yrqax2olqyv1th7j/h?rlkey=u4wh2gxtbbn4g5o3s55zbx6pp&st=zolupk4i&dl=0) | ||
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| Useful links: | ||
| - [miniconda](https://docs.conda.io/en/latest/miniconda.html) | ||
| - [SLEAP](https://sleap.ai/) | ||
| - [DeepLabCut](https://www.mackenziemathislab.org/deeplabcut) | ||
| - [LightningPose](https://github.com/danbider/lightning-pose) | ||
| - [movement](https://movement.neuroinformatics.dev/) | ||
| - [keypoint-moseq](https://keypoint-moseq.readthedocs.io/en/latest/index.html) | ||
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| Recommended readings: | ||
| - [Neuroscience Needs Behavior: Correcting a Reductionist Bias](https://www.sciencedirect.com/science/article/pii/S0896627316310406?via%3Dihub) | ||
| - [Neuroscience Needs Behavior: Correcting a Reductionist Bias](http://dx.doi.org/10.1016/j.neuron.2016.12.041) | ||
| - [Quantifying behavior to understand the brain](https://www.nature.com/articles/s41593-020-00734-z) | ||
| - [Open-source tools for behavioral video analysis: Setup, methods, and best practices](https://elifesciences.org/articles/79305) | ||
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