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| Resource Name | Proper Citation | Abbreviations | Resource Type |
Description |
Keywords | Resource Relationships | ||||||||||||||
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Open Connectome Project Resource Report Resource Website 1+ mentions |
Open Connectome Project (RRID:SCR_004232) | Open Connectome Project | analysis service resource, data access protocol, data analysis service, data or information resource, data repository, data set, image repository, production service resource, service resource, software resource, source code, storage service resource, web service | THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 9, 2023. Connectomes repository to facilitate the analysis of connectome data by providing a unified front for connectomics research. With a focus on Electron Microscopy (EM) data and various forms of Magnetic Resonance (MR) data, the project aims to make state-of-the-art neuroscience open to anybody with computer access, regardless of knowledge, training, background, etc. Open science means open to view, play, analyze, contribute, anything. Access to high resolution neuroanatomical images that can be used to explore connectomes and programmatic access to this data for human and machine annotation are provided, with a long-term goal of reconstructing the neural circuits comprising an entire brain. This project aims to bring the most state-of-the-art scientific data in the world to the hands of anybody with internet access, so collectively, we can begin to unravel connectomes. Services: * Data Hosting - Their Bruster (brain-cluster) is large enough to store nearly any modern connectome data set. Contact them to make your data available to others for any purpose, including gaining access to state-of-the-art analysis and machine vision pipelines. * Web Viewing - Collaborative Annotation Toolkit for Massive Amounts of Image Data (CATMAID) is designed to navigate, share and collaboratively annotate massive image data sets of biological specimens. The interface is inspired by Google Maps, enhanced to allow the exploration of 3D image data. View the fork of the code or go directly to view the data. * Volume Cutout Service - RESTful API that enables you to select any arbitrary volume of the 3d database (3ddb), and receive a link to download an HDF5 file (for matlab, C, C++, or C#) or a NumPy pickle (for python). Use some other programming language? Just let them know. * Annotation Database - Spatially co-registered volumetric annotations are compactly stored for efficient queries such as: find all synapses, or which neurons synapse onto this one. Create your own annotations or browse others. *Sample Downloads - In addition to being able to select arbitrary downloads from the datasets, they have also collected a few choice volumes of interest. * Volume Viewer - A web and GPU enabled stand-alone app for viewing volumes at arbitrary cutting planes and zoom levels. The code and program can be downloaded. * Machine Vision Pipeline - They are building a machine vision pipeline that pulls volumes from the 3ddb and outputs neural circuits. - a work in progress. As soon as we have a stable version, it will be released. * Mr. Cap - The Magnetic Resonance Connectome Automated Pipeline (Mr. Cap) is built on JIST/MIPAV for high-throughput estimation of connectomes from diffusion and structural imaging data. * Graph Invariant Computation - Upload your graphs or streamlines, and download some invariants. * iPad App - WholeSlide is an iPad app that accesses utilizes our open data and API to serve images on the go. | human, primary visual cortex, data sharing, male, electron microscopy, mri, connectome, annotation, image collection, array tomography |
is listed by: NeuroImaging Tools and Resources Collaboratory (NITRC) is related to: CATMAID is related to: neurodata is parent organization of: Rambo3D |
Johns Hopkins University; Maryland; USA ; JHU Applied Research Laboratory IRAD ; JHU Whiting School of Engineering ; Dean's Award ; NIBIB 1RO1EB016411-01 (CRCNS); DARPA N66001-14-1-4028 (GRAPHS); NSF ACI-1261715; NSF OCI-1040114; NIDA 1R01DA036400-01 |
PMID:23707591 | THIS RESOURCE IS NO LONGER IN SERVICE | SciRes_000189, nlx_143645 | http://openconnecto.me, http://www.nitrc.org/projects/ocp/ | SCR_004232 | SciCrunch Registry | openconnectomeproject, Open Connectome Project: Collectively reverse-engineering the brain one synapse at a time., Open Connectome Project: Collectively reverse-engineering the brain one synapse at a time | 2026-10-24 12:45:09 | 7 | ||||
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Viking Viewer for Connectomics Resource Report Resource Website 10+ mentions |
Viking Viewer for Connectomics (RRID:SCR_005986) | collaboration tool, data management software, data processing software, software application, software resource | A web-compliant application that allows connectomics visualization by converting datasets to web-optimized tiles, delivering volume transforms to client devices, and providing groups of users with connectome annotation tools and data simultaneously via conventional internet connections. Viking is an extensible tool for connectomics analysis and is generalizable to histomics applications. | annotation, 2d image, microscopy image, volume, serial section, 3d reconstruction, segmentation, microscopy, visualization, optical imaging, connectomics, synapse, retina, brain |
is listed by: NeuroImaging Tools and Resources Collaboratory (NITRC) is listed by: 3DVC has parent organization: University of Utah; Utah; USA |
Research to Prevent Blindness ; University of Utah; Utah; USA ; Graduate Research Fellowship ; Utah Science Technology and Research Initiative ; NEI R01 EY02576; NEI R01 EY015128; NEI P01 EY014800; NSF 0941717; NIDCD T32DC008553; NIBIB EB005832 |
PMID:21118201 | Open source | nlx_151360 | http://www.nitrc.org/projects/viking_viewer | SCR_005986 | SciCrunch Registry | Viking, Viking Connectome Annotation System, Viking Annotation System | 2026-10-24 12:45:35 | 16 | |||||
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nelpy Resource Report Resource Website 1+ mentions |
nelpy (RRID:SCR_016209) | data analysis software, data processing software, data visualization software, software application, software resource, software toolkit | Software toolkit for neuroelectrophysiology object modeling and data analysis in Python. Open source Python package for analysis of neuroelectrophysiology data. | model, modelling, python, neuroelectrophysiology, object, neuroimaging, analysis, neurophysiology, electrophysiology, BRAIN Initiative | is recommended by: BRAIN Initiative | Human Frontiers Science Program RGY0088; NSF CBET-1351692; NSF IOS-1550994 |
Free, Available for download, Freely available | SCR_016209 | SciCrunch Registry | Nelpy (Neuroelectrophysiology) | 2026-10-24 12:47:33 | 3 | ||||||||
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PetaVision Resource Report Resource Website |
PetaVision (RRID:SCR_017451) | OpenPV | simulation software, software application, software resource | Open source software neural simulation toolbox. C++ library for designing and deploying large scale neurally inspired computational models. Object oriented neural simulation toolbox optimized for high performance multi core, multi node computer architectures. | Neural, simulation, computational, model, object, oriented, BRAIN Initiative | is recommended by: BRAIN Initiative | NSF 1734980 | Free, Available for download, Freely available | http://petavision.github.io/ | SCR_017451 | SciCrunch Registry | 2026-10-24 12:49:14 | 0 | |||||||
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BehaviorDEPOT Resource Report Resource Website 1+ mentions |
BehaviorDEPOT (RRID:SCR_023602) | software resource, source code | Software tool for automated behavioral detection based on markerless pose tracking. Behavioral analysis tool to first compile and clean point-tracking output from DeepLabCut, and then classify behavioral epochs using custom behavior classifiers. Used to detect frame by frame behavior from video time series and can analyze results of common experimental assays, including fear conditioning, decision-making in T-maze, open field, elevated plus maze, and novel object exploration. Calculates kinematic and postural statistics from keypoint tracking data from pose estimation software outputs. | OpenBehavior, automated behavioral detection, markerless pose tracking, detect frame by frame behavior, video time series, kinematic and postural statistics, |
is listed by: OpenBehavior is related to: SLEAP, LEAP and MotionMapper project works with: DeepLabCut |
Brain and Behavior Research Foundation ; Brain Research Foundation ; NIMH K01MH116264; NIMH K08MH116125; NIMH T32MH073526; NSF ; Simonsen Foundation ; Whitehall Foundation |
PMID:35997072 | Free, Available for download, Freely available | https://edspace.american.edu/openbehavior/project/behaviordepot/ | SCR_023602 | SciCrunch Registry | 2026-10-24 12:53:19 | 2 | |||||||
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PyMVPA Resource Report Resource Website 100+ mentions |
PyMVPA (RRID:SCR_006099) | PyMVPA | software application, software resource, software toolkit | A Python package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run. Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. This Python-based, cross-platform, open-source software toolbox software toolbox for the application of classifier-based analysis techniques to fMRI datasets makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. | python, machine learning, fmri, eeg, neuroimaging, image analysis, scripting, multivariate pattern analysis, brain, meg, extracellular recording, algorithm, reusable library, analyze, c, console (text based), eeg, meg, electrocorticography, frequency domain, independent component analysis, linear, modeling, magnetic resonance, multivariate analysis, nifti, nonlinear, os independent, pet, spect, principal component analysis, python, regression, spatial transformation, statistical operation, temporal transformation, workflow |
is listed by: NeuroImaging Tools and Resources Collaboratory (NITRC) is related to: neurodebian is related to: CoSMoMVPA has parent organization: Dartmouth College; New Hampshire; USA has parent organization: Otto-von-Guericke University Magdeburg; Saxony-Anhalt; Germany |
German Academic Exchange Service PPP-USA D/05/504/7; NIMH MH080526; NSF SBE 0751008; James McDonnell Foundation 220020127 |
PMID:19184561 PMID:19212459 PMID:20582270 |
MIT License | nlx_151596 | http://www.nitrc.org/projects/pymvpa | SCR_006099 | SciCrunch Registry | Python MVPA, Multivariate Pattern Analysis in Python, PyMVPA - Multivariate Pattern Analysis in Python | 2026-10-24 12:51:27 | 144 | ||||
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MITRE Neuroinformatics Resource Report Resource Website |
MITRE Neuroinformatics (RRID:SCR_006508) | MITRE Neuroinfomatics | data analysis software, data distribution software, data processing software, data transfer software, data visualization software, simulation software, software application, software resource | This resource''s long-term goal is to develop informatics methodologies and tools that will increase the creativity and productivity of neuroscience investigators, as they work together to use shared human brain mapping data to generate and test ideas far beyond those pursued by the data''s originators. This resource currently has four major projects supporting this goal: * Database tools: The goal of the NeuroServ project is to provide neuroscience researchers with automated information management tools that reduce the effort required to manage, analyze, query, view, and share their imaging data. It currently manages both structural magnetic resonance image (MRI) datasets and diffusion tensor image (DTI) datasets. NeuroServ is fully web-enabled: data entry, query, processing, reporting, and administrative functions are performed by qualified users through a web browser. It can be used as a local laboratory repository, to share data on the web, or to support a large distributed consortium. NeuroServ is based on an industrial-quality query middleware engine MRALD. NeuroServ includes a specialized neuroimaging schema and over 40 custom Java Server Pages supporting data entry, query, and reporting to help manage and explore stored images. NeuroServ is written in Java for platform independence; it also utilizes several open source components * Data sharing: DataQuest is a collaborative forum to facilitate the sharing of neuroimaging data within the neuroscience community. By publishing summaries of existing datasets, DataQuest enables researchers to: # Discover what data is available for collaborative research # Advertise your data to other researchers for potential collaborations # Discover which researchers may have the data you need # Discover which researchers are interested in your data. * Image quality: The approach to assessing the inherent quality of an image is to measure how distorted the image is. Using what are referred to as no-reference or blind metrics, one can measure the degree to which an image is distorted. * Content-based image retrieval: NIRV (NeuroImagery Retrieval & Visualization) is a work environment for advanced querying over imagery. NIRV will have a Java-based front-end for users to issue queries, run processing algorithms, review results, visualize imagery and assess image quality. NIRV interacts with an image repository such as NeuroServ. Users can also register images and will soon be able to filter searches based on image quality. | brain, data, diffusion tensor image, distorted, human, imagery, image, informatics, investigator, laboratory, magnetic resonance image, mapping, neuroscience, structural, visualization, neuroimaging | Human Brain Project ; MITRE Technology Program ; NSF ; NIMH R01-MH64417 |
nif-0000-10469 | http://neuroinformatics.mitre.org/ | SCR_006508 | SciCrunch Registry | Neuroinfomatics at MITRE, Neuroinformatics: Exploring the Human Brain | 2026-10-24 12:45:45 | 0 |
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