Tutorial Videos =============== .. Maintainer notes ---------------- Tiles are sphinx-design cards (``grid-item-card``); see https://sphinx-design.readthedocs.io/en/latest/cards.html. Thumbnails live in ``docs/img/thumbnails`` (1280x720, no spaces in file names). * Publishing a content video: in its card, swap the ``*_greyscale-coming-soon.jpg`` thumbnail for the matching ``*_color.jpg``, add ``:link: `` and ``:link-type: url``, add the class ``video-card-available`` to ``:class-card:``, drop "(coming soon)" from ``:img-alt:``, and add a ``+++`` footer line "▶ Watch on YouTube". * Adding a tutorial video: copy a card from the "Tutorial videos" grid of section 1 into the section's grid (if the section has no grid yet, copy the whole ``.. grid::`` block) above the ``tutorial-placeholder`` card, and change ``More tutorial videos`` in that placeholder if it was the first one. * Example notebooks use nbsphinx's ``nbgallery`` so titles and thumbnails come from the notebooks themselves. conf.py detaches this page from the toctree hierarchy so the notebooks keep their usual parents in the navigation. This page collects Cedalion's training videos in thirteen topics that follow a typical fNIRS/DOT analysis, from installation to reproducible pipelines. Each topic contains: * **Content videos** (5–15 min): slide-based lectures on concepts, theory and intuition. * **Tutorial videos** (5–15 min): (screen) recordings that walk through working code in Jupyter notebooks. * **Example notebooks**: the executable notebooks from this documentation that cover the topic. Run them locally or in the cloud via the *Open in Colab* button (see :doc:`getting_started/colab_setup`). Videos are being released step by step. Tiles marked *Coming soon* will link to the recording once it is published. .. contents:: Topics :local: :depth: 1 0. Orientation & Architecture ----------------------------- Start here: what Cedalion is designed for, how the toolbox is organised into subpackages, and how versioned environments and modular processing blocks keep analyses reproducible. .. rubric:: Content videos .. grid:: 1 1 2 2 :gutter: 3 .. grid-item-card:: :img-top: img/thumbnails/0.1.C1_Content-Video_Cedalion-Philosophy-and-Scope_greyscale-coming-soon.jpg :img-alt: 0.1.C1 content video: Cedalion – Philosophy and Scope (coming soon) :class-card: video-card **0.1.C1** · Cedalion – Philosophy and Scope .. grid-item-card:: :img-top: img/thumbnails/0.2.C1_Content-Video_Overview-of-Toolbox-Architecture_greyscale-coming-soon.jpg :img-alt: 0.2.C1 content video: Overview of Toolbox Architecture (coming soon) :class-card: video-card **0.2.C1** · Overview of Toolbox Architecture .. grid-item-card:: :img-top: img/thumbnails/0.3.C1_Content-Video_Reproducibility-and-Workflows-Versioning-environments-and-dependency-management_greyscale-coming-soon.jpg :img-alt: 0.3.C1 content video: Reproducibility & Workflows: Versioning, environments and dependency management (coming soon) :class-card: video-card **0.3.C1** · Reproducibility & Workflows: Versioning, environments and dependency management .. grid-item-card:: :img-top: img/thumbnails/0.3.C2_Content-Video_Reproducibility-and-Workflows-Functional-blocks-and-Pipelines_greyscale-coming-soon.jpg :img-alt: 0.3.C2 content video: Reproducibility & Workflows: Functional blocks and Pipelines (coming soon) :class-card: video-card **0.3.C2** · Reproducibility & Workflows: Functional blocks and Pipelines .. rubric:: Tutorial videos .. card:: :class-card: tutorial-placeholder Tutorial videos for this topic will show here as soon as they are recorded. .. rubric:: Example notebooks No example notebook covers this topic yet. **Related documentation:** :doc:`Rationale ` · :doc:`Core concepts ` · :doc:`Environments ` · :doc:`Changelog ` · :doc:`All examples ` · :doc:`Bibliography ` · :doc:`Community ` 1. Installation, Execution & Data Access ---------------------------------------- How to install Cedalion locally with conda or run it on Google Colab, verify optional backends such as MCX and NIRFASTer, and download and cache the example datasets used throughout the documentation. .. rubric:: Content videos .. grid:: 1 1 2 2 :gutter: 3 .. grid-item-card:: :img-top: img/thumbnails/1.1.C1_Content-Video_Basics-of-Python-plus-Installation-Overview_greyscale-coming-soon.jpg :img-alt: 1.1.C1 content video: Basics of Python + Installation Overview (coming soon) :class-card: video-card **1.1.C1** · Basics of Python + Installation Overview .. rubric:: Tutorial videos .. grid:: 1 1 2 2 :gutter: 3 .. grid-item-card:: :img-top: img/thumbnails/1.1.T1_Tutorial-Video_Local-installation-using-conda-WIN.png :img-alt: 1.1.T1 tutorial video: Local installation using conda – Windows :link: https://www.youtube.com/watch?v=G5zQawG6GDI :link-type: url :class-card: video-card video-card-available **1.1.T1** · Local installation using conda – Windows +++ ▶ Watch on YouTube .. grid-item-card:: :img-top: img/thumbnails/1.1.T1_Tutorial-Video_Local-installation-using-conda-MAC.png :img-alt: 1.1.T1 tutorial video: Local installation using conda – macOS :link: https://www.youtube.com/watch?v=wcS69hFFUK4 :link-type: url :class-card: video-card video-card-available **1.1.T1** · Local installation using conda – macOS +++ ▶ Watch on YouTube .. card:: :class-card: tutorial-placeholder More tutorial videos for this topic will show here as soon as they are recorded. .. rubric:: Example notebooks .. nbgallery:: examples/getting_started_io/00_test_installation **Related documentation:** :doc:`Installation ` · :doc:`Google Colab setup ` · :doc:`Quick start ` 2. Data Structures & I/O ------------------------ Cedalion keeps data in labelled, unit-aware xarray DataArrays that are collected in a ``Recording`` container. This section introduces these data structures and shows how to read and write them using the SNIRF and BIDS standards. .. rubric:: Content videos .. grid:: 1 1 2 2 :gutter: 3 .. grid-item-card:: :img-top: img/thumbnails/2.1.C1_Content-Video_Introduction-to-XArrays_greyscale-coming-soon.jpg :img-alt: 2.1.C1 content video: Introduction to XArrays (coming soon) :class-card: video-card **2.1.C1** · Introduction to XArrays .. grid-item-card:: :img-top: img/thumbnails/2.2.C1_Content-Video_Overview-of-the-recording-container_greyscale-coming-soon.jpg :img-alt: 2.2.C1 content video: Overview of the recording container (coming soon) :class-card: video-card **2.2.C1** · Overview of the recording container .. grid-item-card:: :img-top: img/thumbnails/2.3.C1_Content-Video_File-formats-and-standards-Brief-introduction-to-SNIRF-and-BIDS_greyscale-coming-soon.jpg :img-alt: 2.3.C1 content video: File formats & standards: Brief introduction to SNIRF and BIDS (coming soon) :class-card: video-card **2.3.C1** · File formats & standards: Brief introduction to SNIRF and BIDS .. grid-item-card:: :img-top: img/thumbnails/2.4.C1_Content-Video_Overview-of-Data-I-O-Functionality_greyscale-coming-soon.jpg :img-alt: 2.4.C1 content video: Overview of Data I/O Functionality (coming soon) :class-card: video-card **2.4.C1** · Overview of Data I/O Functionality .. rubric:: Tutorial videos .. grid:: 1 1 2 2 :gutter: 3 .. grid-item-card:: :img-top: img/thumbnails/2.3.T3_Tutorial-Video_Using-the-SNIRF2BIDS-conversion-tool.jpg :img-alt: 2.3.T3 tutorial video: Using the SNIRF2BIDS conversion tool :link: https://www.youtube.com/watch?v=UYL3BUg_7xE :link-type: url :class-card: video-card video-card-available **2.3.T3** · Using the SNIRF2BIDS conversion tool Hands-on workshop: BIDS-ifying fNIRS – a Python-based community tool for open data sharing +++ ▶ Watch on YouTube .. card:: :class-card: tutorial-placeholder More tutorial videos for this topic will show here as soon as they are recorded. .. rubric:: Example notebooks .. nbgallery:: examples/getting_started_io/10_xarray_datastructs_fnirs examples/getting_started_io/11_recording_container examples/getting_started_io/12_read_snirf_files examples/getting_started_io/13_data_structures_intro examples/getting_started_io/14_snirf2bids examples/getting_started_io/34_store_hrfs_in_snirf_file **Related documentation:** :doc:`Data structures ` · :doc:`Data structures and I/O ` 3. Modified Beer-Lambert Law ---------------------------- Converting raw light intensities to optical density and then to changes in oxy- and deoxyhaemoglobin concentration with the modified Beer-Lambert law, including plausibility checks and the role of source-detector distances. .. rubric:: Content videos .. grid:: 1 1 2 2 :gutter: 3 .. grid-item-card:: :img-top: img/thumbnails/3.1.C1_Content-Video_Introduction-to-Optical-Density-and-the-mBLL_greyscale-coming-soon.jpg :img-alt: 3.1.C1 content video: Introduction to Optical Density and the mBLL (coming soon) :class-card: video-card **3.1.C1** · Introduction to Optical Density and the mBLL .. rubric:: Tutorial videos .. card:: :class-card: tutorial-placeholder Tutorial videos for this topic will show here as soon as they are recorded. .. rubric:: Example notebooks .. nbgallery:: examples/getting_started_io/10_xarray_datastructs_fnirs examples/getting_started_io/12_read_snirf_files **Related documentation:** :doc:`Core concepts ` · :doc:`Signal processing ` 4. Signal Quality & Preprocessing --------------------------------- How to quantify signal quality (e.g. SCI, PSP, SNR, GVTD), build and combine quality masks, detect and correct motion artefacts, and assemble a complete preprocessing pipeline for a raw fNIRS dataset. .. rubric:: Content videos .. grid:: 1 1 2 2 :gutter: 3 .. grid-item-card:: :img-top: img/thumbnails/4.1.C1_Content-Video_Signal-Quality-Concepts-and-Sources-of-Noise_greyscale-coming-soon.jpg :img-alt: 4.1.C1 content video: Signal Quality: Concepts and Sources of Noise (coming soon) :class-card: video-card **4.1.C1** · Signal Quality: Concepts and Sources of Noise .. grid-item-card:: :img-top: img/thumbnails/4.2.C1_Content-Video_Quality-Metrics-Overview-of-Frequently-Used-Methods_greyscale-coming-soon.jpg :img-alt: 4.2.C1 content video: Quality Metrics: Overview of Frequently Used Methods (coming soon) :class-card: video-card **4.2.C1** · Quality Metrics: Overview of Frequently Used Methods .. grid-item-card:: :img-top: img/thumbnails/4.3.C1_Content-Video_Sources-of-Artifacts-in-fNIRS-Motion-and-Physiology_greyscale-coming-soon.jpg :img-alt: 4.3.C1 content video: Sources of Artifacts in fNIRS: Motion and Physiology (coming soon) :class-card: video-card **4.3.C1** · Sources of Artifacts in fNIRS: Motion and Physiology .. grid-item-card:: :img-top: img/thumbnails/4.4.C1_Content-Video_Practical-Preprocessing-End-to-end-Example-of-A-Raw-Dataset_greyscale-coming-soon.jpg :img-alt: 4.4.C1 content video: Practical Preprocessing: End-to-end Example of a Raw Dataset (coming soon) :class-card: video-card **4.4.C1** · Practical Preprocessing: End-to-end Example of a Raw Dataset .. rubric:: Tutorial videos .. card:: :class-card: tutorial-placeholder Tutorial videos for this topic will show here as soon as they are recorded. .. rubric:: Example notebooks .. nbgallery:: examples/signal_quality/20_scalp_coupling_index examples/signal_quality/21_data_quality_and_pruning examples/signal_quality/22_motion_artefacts_and_correction examples/signal_quality/24_downweighting_noisy_channels examples/signal_quality/25_intro_quality_workshop examples/tutorial/3_signal_processing **Related documentation:** :doc:`Signal processing ` · :doc:`Physiology ` 5. General Linear Model (GLM) ----------------------------- Modelling the haemodynamic response with the general linear model: design matrices with HRF basis functions, drift and short-channel regressors, the available solvers and noise models, and statistical inference on the estimated β values. .. rubric:: Content videos .. grid:: 1 1 2 2 :gutter: 3 .. grid-item-card:: :img-top: img/thumbnails/5.1.C1_Content-Video_GLM-fundamentals-Short-Channels-Physiology-Regression-and-Introduction-to-the-General-Linear_greyscale-coming-soon.jpg :img-alt: 5.1.C1 content video: GLM fundamentals: Short Channels, Physiology Regression and Introduction to the General Linear Model (coming soon) :class-card: video-card **5.1.C1** · GLM fundamentals: Short Channels, Physiology Regression and Introduction to the General Linear Model .. grid-item-card:: :img-top: img/thumbnails/5.2.C1_Content-Video_GLM-architecture-in-Cedalion_greyscale-coming-soon.jpg :img-alt: 5.2.C1 content video: GLM architecture in Cedalion (coming soon) :class-card: video-card **5.2.C1** · GLM architecture in Cedalion .. grid-item-card:: :img-top: img/thumbnails/5.3.C1_Content-Video_GLM-Statistics-Statsmodels-in-Cedalion_greyscale-coming-soon.jpg :img-alt: 5.3.C1 content video: GLM Statistics: Statsmodels in Cedalion (coming soon) :class-card: video-card **5.3.C1** · GLM Statistics: Statsmodels in Cedalion .. rubric:: Tutorial videos .. card:: :class-card: tutorial-placeholder Tutorial videos for this topic will show here as soon as they are recorded. .. rubric:: Example notebooks .. nbgallery:: examples/modeling/31_glm_basis_functions examples/modeling/32_glm_fingertapping_example examples/modeling/33_glm_illustrative_example examples/modeling/35_statsmodels_overview examples/modeling/36_glm_workshop examples/tutorial/4_model_driven_analysis **Related documentation:** :doc:`Modeling and machine learning ` 6. Head Models & Forward Modeling --------------------------------- Head models describe the anatomy that light travels through. This section covers atlas-based and individual head models, coordinate systems, anatomical parcellations, and forward modelling with Monte Carlo (MCX) and finite-element (NIRFASTer) photon simulations. .. rubric:: Content videos .. grid:: 1 1 2 2 :gutter: 3 .. grid-item-card:: :img-top: img/thumbnails/6.1.C1_Content-Video_Head-models-in-Neuroimaging-Overview_greyscale-coming-soon.jpg :img-alt: 6.1.C1 content video: Head models in Neuroimaging: Overview (coming soon) :class-card: video-card **6.1.C1** · Head models in Neuroimaging: Overview .. grid-item-card:: :img-top: img/thumbnails/6.2.C1_Content-Video_Overview-of-standard-head-model-coordinate-systems_greyscale-coming-soon.jpg :img-alt: 6.2.C1 content video: Overview of standard head-model coordinate systems (coming soon) :class-card: video-card **6.2.C1** · Overview of standard head-model coordinate systems .. grid-item-card:: :img-top: img/thumbnails/6.3.C1_Content-Video_Forward-modeling-with-photon-simulations_greyscale-coming-soon.jpg :img-alt: 6.3.C1 content video: Forward modeling with photon simulations (coming soon) :class-card: video-card **6.3.C1** · Forward modeling with photon simulations .. grid-item-card:: :img-top: img/thumbnails/6.4.C1_Content-Video_Anatomical-Atlases-and-Parcellation-Schemes_greyscale-coming-soon.jpg :img-alt: 6.4.C1 content video: Anatomical Atlases and Parcellation Schemes (coming soon) :class-card: video-card **6.4.C1** · Anatomical Atlases and Parcellation Schemes .. rubric:: Tutorial videos .. card:: :class-card: tutorial-placeholder Tutorial videos for this topic will show here as soon as they are recorded. .. rubric:: Example notebooks .. nbgallery:: examples/head_models/43a_head_models_overview examples/head_models/43b_individualized_head_models examples/head_models/44_head_models_crs_scaling examples/head_models/42_1010_system examples/head_models/48_headmodel_landmarks_verification examples/head_models/51_spring_relaxation_registration examples/head_models/46_precompute_fluence examples/head_models/45_parcel_sensitivity examples/head_models/52_mni_atlas_labels_aal3_brodmann examples/tutorial/1_heads_and_fwm **Related documentation:** :doc:`Diffuse optical tomography ` 7. Photogrammetric Optode Co-Registration & Probe Design -------------------------------------------------------- Photogrammetry measures where the optodes actually sit on a participant's head. This section covers optode detection in 3D scans, manual quality control, and labelling the detected optodes by registering them to the probe layout. Tools for probe design are planned. .. rubric:: Content videos .. grid:: 1 1 2 2 :gutter: 3 .. grid-item-card:: :img-top: img/thumbnails/7.1.C1_Content-Video_Photogrammetry-and-co-registration-Motivation_greyscale-coming-soon.jpg :img-alt: 7.1.C1 content video: Photogrammetry & co-registration: Motivation (coming soon) :class-card: video-card **7.1.C1** · Photogrammetry & co-registration: Motivation .. grid-item-card:: :img-top: img/thumbnails/7.2.C1_Content-Video_Photogrammetric-Optode-Co-Registration-in-Cedalion_greyscale-coming-soon.jpg :img-alt: 7.2.C1 content video: Photogrammetric Optode Co-Registration in Cedalion (coming soon) :class-card: video-card **7.2.C1** · Photogrammetric Optode Co-Registration in Cedalion .. grid-item-card:: :img-top: img/thumbnails/7.3.C1_Content-Video_Probe-Design-in-Cedalion_greyscale-coming-soon.jpg :img-alt: 7.3.C1 content video: Probe Design in Cedalion (coming soon) :class-card: video-card **7.3.C1** · Probe Design in Cedalion .. rubric:: Tutorial videos .. grid:: 1 1 2 2 :gutter: 3 .. grid-item-card:: :img-top: img/thumbnails/7.1.T1_Tutorial-Video_Acquiring-and-inspecting-raw-photogrammetry-meshes.png :img-alt: 7.1.T1 tutorial video: Acquiring and inspecting raw photogrammetry meshes :link: https://www.youtube.com/watch?v=PMBUWHnLXUo :link-type: url :class-card: video-card video-card-available **7.1.T1** · Acquiring and inspecting raw photogrammetry meshes fNIRS/EEG photogrammetry tutorial with the Cedalion toolbox +++ ▶ Watch on YouTube .. card:: :class-card: tutorial-placeholder More tutorial videos for this topic will show here as soon as they are recorded. .. rubric:: Example notebooks .. nbgallery:: examples/head_models/41_photogrammetric_optode_coregistration examples/tutorial/2_photogrammetry examples/head_models/51_spring_relaxation_registration 8. DOT Image Reconstruction --------------------------- Diffuse optical tomography reconstructs images of cortical haemodynamic activity from channel-space measurements by inverting the sensitivity matrix. This section covers the inverse problem, regularisation choices, and aggregating images into brain parcels. .. rubric:: Content videos .. grid:: 1 1 2 2 :gutter: 3 .. grid-item-card:: :img-top: img/thumbnails/8.1.C1_Content-Video_Intro-to-DOT-Image-Reconstruction-and-Inverse-Problem_greyscale-coming-soon.jpg :img-alt: 8.1.C1 content video: Intro to DOT Image Reconstruction & Inverse Problem (coming soon) :class-card: video-card **8.1.C1** · Intro to DOT Image Reconstruction & Inverse Problem .. grid-item-card:: :img-top: img/thumbnails/8.2.C1_Content-Video_Overview-Image-Reconstruction-workflow-in-Cedalion_greyscale-coming-soon.jpg :img-alt: 8.2.C1 content video: Overview: Image Reconstruction workflow in Cedalion (coming soon) :class-card: video-card **8.2.C1** · Overview: Image Reconstruction workflow in Cedalion .. rubric:: Tutorial videos .. card:: :class-card: tutorial-placeholder Tutorial videos for this topic will show here as soon as they are recorded. .. rubric:: Example notebooks .. nbgallery:: examples/head_models/40_image_reconstruction examples/head_models/47_image_reconstruction_regularizations examples/head_models/45_parcel_sensitivity examples/tutorial/5_image_reconstruction **Related documentation:** :doc:`Diffuse optical tomography ` 9. Visualization & Interpretation --------------------------------- Cedalion's plotting building blocks for time series, probe layouts, scalp and brain surfaces and reconstructed images, together with interactive tools for inspecting data. .. rubric:: Content videos .. grid:: 1 1 2 2 :gutter: 3 .. grid-item-card:: :img-top: img/thumbnails/9.1.C1_Content-Video_fNIRS-DOT-visualization-blocks-in-Cedalion_greyscale-coming-soon.jpg :img-alt: 9.1.C1 content video: fNIRS/DOT visualization blocks in Cedalion (coming soon) :class-card: video-card **9.1.C1** · fNIRS/DOT visualization blocks in Cedalion .. rubric:: Tutorial videos .. card:: :class-card: tutorial-placeholder Tutorial videos for this topic will show here as soon as they are recorded. .. rubric:: Example notebooks .. nbgallery:: examples/plots_visualization/12_plots_example examples/head_models/43a_head_models_overview **Related documentation:** :doc:`Plotting and visualization ` 10. Multimodal & Data-Driven Analysis ------------------------------------- Data-driven methods that do not require a stimulus model: unimodal and multimodal source decomposition (ICA, the CCA family, mSPoC) and single-trial classification with scikit-learn. .. rubric:: Content videos .. grid:: 1 1 2 2 :gutter: 3 .. grid-item-card:: :img-top: img/thumbnails/10.1.C1_Content-Video_Multimodal-fusion-variance-vs-decoding_greyscale-coming-soon.jpg :img-alt: 10.1.C1 content video: Multimodal fusion: variance vs decoding (coming soon) :class-card: video-card **10.1.C1** · Multimodal fusion: variance vs decoding .. grid-item-card:: :img-top: img/thumbnails/10.2.C1_Content-Video_Source-Decomposition-Methods-Overview-CCA-ICA-SPoC-family_greyscale-coming-soon.jpg :img-alt: 10.2.C1 content video: Source Decomposition Methods Overview: CCA, ICA, SPoC family (coming soon) :class-card: video-card **10.2.C1** · Source Decomposition Methods Overview: CCA, ICA, SPoC family .. grid-item-card:: :img-top: img/thumbnails/10.3.C1_Content-Video_ML-fNIRS-DOT-Classification-Workflows_greyscale-coming-soon.jpg :img-alt: 10.3.C1 content video: ML fNIRS/DOT Classification Workflows (coming soon) :class-card: video-card **10.3.C1** · ML fNIRS/DOT Classification Workflows .. rubric:: Tutorial videos .. card:: :class-card: tutorial-placeholder Tutorial videos for this topic will show here as soon as they are recorded. .. rubric:: Example notebooks .. nbgallery:: examples/machine_learning/51_multimodal_source_decomposition_on_synthetic_fNIRS-EEG_data examples/machine_learning/52_ica_erbm_fingertapping_example examples/machine_learning/53_constrained_ICA_example examples/machine_learning/50_finger_tapping_lda_classification examples/machine_learning/50b_advanced_finger_tapping_lda_classification examples/tutorial/6_data_driven_analysis **Related documentation:** :doc:`Modeling and machine learning ` 11. Simulation & Data Augmentation ---------------------------------- Synthetic haemodynamic responses, motion artefacts and paired fNIRS-EEG data with known ground truth, for benchmarking algorithms and augmenting training data for machine learning. .. rubric:: Content videos .. grid:: 1 1 2 2 :gutter: 3 .. grid-item-card:: :img-top: img/thumbnails/11.1.C1_Content-Video_Synthetic-data-for-Benchmarking-and-Augmentation-Rationale_greyscale-coming-soon.jpg :img-alt: 11.1.C1 content video: Synthetic data for Benchmarking and Augmentation: Rationale (coming soon) :class-card: video-card **11.1.C1** · Synthetic data for Benchmarking and Augmentation: Rationale .. grid-item-card:: :img-top: img/thumbnails/11.2.C1_Content-Video_Synthetic-data-generation-and-augmentation-in-Cedalion_greyscale-coming-soon.jpg :img-alt: 11.2.C1 content video: Synthetic data generation and augmentation in Cedalion (coming soon) :class-card: video-card **11.2.C1** · Synthetic data generation and augmentation in Cedalion .. rubric:: Tutorial videos .. card:: :class-card: tutorial-placeholder Tutorial videos for this topic will show here as soon as they are recorded. .. rubric:: Example notebooks .. nbgallery:: examples/augmentation/62_synthetic_hrfs_example examples/augmentation/61_synthetic_artifacts_example examples/tutorial/7_data_augmentation examples/machine_learning/51_multimodal_source_decomposition_on_synthetic_fNIRS-EEG_data **Related documentation:** :doc:`Synthetic data ` 12. Pipelines, Scaling & Reproducibility ---------------------------------------- Moving from interactive notebooks to scripted, reproducible pipelines that process many datasets and can be shared alongside a publication. .. rubric:: Content videos .. grid:: 1 1 2 2 :gutter: 3 .. grid-item-card:: :img-top: img/thumbnails/12.1.C1_Content-Video_Pipeline-abstraction-From-Notebooks-to-Pipelines_greyscale-coming-soon.jpg :img-alt: 12.1.C1 content video: Pipeline abstraction: From Notebooks to Pipelines (coming soon) :class-card: video-card **12.1.C1** · Pipeline abstraction: From Notebooks to Pipelines .. grid-item-card:: :img-top: img/thumbnails/12.2.C1_Content-Video_Snakemake-integration-in-Cedalion-Concepts-and-Introduction_greyscale-coming-soon.jpg :img-alt: 12.2.C1 content video: Snakemake integration in Cedalion: Concepts and Introduction (coming soon) :class-card: video-card **12.2.C1** · Snakemake integration in Cedalion: Concepts and Introduction .. grid-item-card:: :img-top: img/thumbnails/12.3.C1_Content-Video_Reproducible-and-shareable-Analyses_greyscale-coming-soon.jpg :img-alt: 12.3.C1 content video: Reproducible and shareable Analyses (coming soon) :class-card: video-card **12.3.C1** · Reproducible and shareable Analyses .. rubric:: Tutorial videos .. card:: :class-card: tutorial-placeholder Tutorial videos for this topic will show here as soon as they are recorded. .. rubric:: Example notebooks No example notebook covers this topic yet. **Related documentation:** :doc:`Environments ` · :doc:`Changelog `