Analysis and Visualization Tools

This is a page is a collection of tools we are cataloging as a convenience reference for NWB users. This is not a comprehensive list of NWB tools. Many of these tools are built and supported by other groups, and are in active development. If you would like to contribute a tool, please see the instructions here.

Exploring NWB Files

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NWB Widgets is a library of widgets for visualization NWB data in a Jupyter notebook (or lab). The widgets make it easy to navigate through the hierarchical structure of the NWB file and visualize specific data elements. It is designed to work out-of-the-box with NWB 2.0 files and to be easy to extend. Source




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NWB Explorer is a web application developed by MetaCell for reading, visualizing and exploring the content of NWB 2 files. The portal comes with built-in visualization for many data types, and provides an interface to a jupyter notebook for custom analyses and open exploration. Online



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HDFView is a general visual tool written in Java for browsing and editing HDF5 files. HDFView does not provide NWB-specific functionality but is useful (mainly for developers) to debug and browse NWB HDF5 files. HDFView supports viewing the HDF5 file hierarchy in a tree structure and editing of groups, datasets, and attributes in HDF5 files. In addition, the HDF5 library ships with range of HDF5 command line tools that can be useful for developers (e.g., h5ls and h5dump to introspect, h5diff to compare, or h5copy and h5repack to copy HDF5 files). Docs Source.




Extracellular Electrophysiology Tools

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SpikeInterface is a collection of Python modules designed to improve the accessibility, reliability, and reproducibility of spike sorting and all its associated computations. Video tutorial Docs Source





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CellExplorer is a graphical user interface (GUI), a standardized processing module and data structure for exploring and classifying single cells acquired using extracellular electrodes. NWB Tutorial Intro Video Video Tutorial Docs Source.




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EcogVIS is a Python-based, time series visualizer for Electrocorticography (ECoG) signals stored in NWB files. EcogVIS makes it intuitive and simple to visualize ECoG signals from selected channels, brain regions, make annotations and mark intervals of interest. Signal processing and analysis tools will soon be added. Source.



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Neo Neo is a Python package for working with electrophysiology data in Python, together with support for reading and writing a wide range of neurophysiology file formats (see the list of supported formats). The goal of Neo is to improve interoperability between Python tools for analyzing, visualizing and generating electrophysiology data, by providing a common, shared object model. In order to be as lightweight a dependency as possible, Neo is deliberately limited to representation of data, with no functions for data analysis or visualization. Docs Neo NWBIO Docs Website Source.




Optical Physiology Tools

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CaImAn is a computational toolbox for large scale Calcium Imaging Analysis, including movie handling, motion correction, source extraction, spike deconvolution and result visualization. CaImAn now supports reading and writing data in NWB 2.0. NWB Demo Video Tutorial Docs Source.




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suite2p is an imaging processing pipeline written in Python . suite2p includes modules for 1) Registration, 2) Cell detection, 3) Spike detection, and a 4) Visualization GUI. Video Tutorial Docs Source.




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CIAtah (pronounced cheetah; formerly calciumImagingAnalysis) is a Matlab software package for analyzing one- and two-photon calcium imaging datasets. Video tutorial Docs Source.




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EXTRACT is a Tractable and Robust Automated Cell extraction Tool for calcium imaging, which extracts the activities of cells as time series from both one-photon and two-photon Ca2+ imaging movies. EXTRACT makes minimal assumptions about the data, which is the main reason behind its high robustness and superior performance. Source NWB tutorials Publication




Intracellular Electrical Physiology Tools

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MIES is a sweep based data acquisition tool written in Igor Pro. MIES has three primary user interfaces: 1) the WaveBuilder to generate stimulus sets 2) the DA_Ephys GUI to control and observe data acquisition in real time, and 3) the DataBrowser to browse acquired data. All three interfaces are intended to be operated in parallel. Video tutorial MIES NWB Module Docs Source.




Behavior

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DeepLabCut is an efficient method for 2D and 3D markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results (i.e. you can match human labeling accuracy) with minimal training data (typically 50-200 frames). We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Documentation




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SLEAP is an open source deep-learning based framework for multi-animal pose tracking. It can be used to track any type or number of animals and includes an advanced labeling/training GUI for active learning and proofreading. Documentation




Data Analysis Toolbox

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pynapple is a unified toolbox for integrated analysis of multiple data sources. Designed to be “plug & play”, users define and import their own time-relevant variables. Supported data sources include, but are not limited to, electrophysiology, calcium imaging, and motion capture data. Pynapple contains integrated functions for common neuroscience analyses, including cross-correlograms, tuning curves, decoding and perievent time histogram. Docs Source Twitter




Data Archive, Publication, and Management

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DANDI (Distributed Archives for Neurophysiology Data Integration) is the NIH BRAIN Initiative archive for publishing and sharing neurophysiology data including electrophysiology, optophysiology, and behavioral time-series, and images from immunostaining experiments. Online Archive Docs Source.

Note

Disclaimer: Reference herein to any specific product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not constitute or imply its endorsement, recommendation, or favoring by the NWB development team, United States Government or any agency thereof, or The Regents of the University of California. Use of the NeurodataWithoutBorders name for endorsements is prohibited.