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http://nipy.org

Community site to make brain imaging research easier that aims to build software that is clearly written, clearly explained, a good fit for the underlying ideas, and a natural home for collaboration.

Proper citation: Neuroimaging in Python (RRID:SCR_013141) Copy   

•   Source: SciCrunch Registry

http://datasets.datalad.org/

DataLad data distribution. Super dataset collating DataLad datasets from various sources including OpenNeuro, CRCNS, etc., to provide unified access to over 200TB of neural data.

Proper citation: datasets.datalad.org (RRID:SCR_019089) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/lwdp/

A lightweight framework for setting up dependency-driven processing pipelines. The tool is essentially a configurable shell script (sh/bash), which can be included in other scripts and primarily provides a small number of utility functions for dependency checking and NFS-safe file locking for cluster processing.

Proper citation: Lightweight Data Pipeline (RRID:SCR_014135) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/score/

A collection of methods for comparing the performance of different image algorithms. These methods generate quantitative scores that measure divergences to a standard.

Proper citation: SCORE (RRID:SCR_014165) Copy   

•   Source: SciCrunch Registry

http://mrir.med.miami.edu:8000/midas

Software for processing, display, and analysis of magnetic resonance spectroscopic imaging data. MIDAS supports a "whole-brain" MRSI acquisition method that has been implemented on MRI systems from three major manufacturers., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: MIDAS (RRID:SCR_015704) Copy   

•   Source: SciCrunch Registry

http://www.iu.edu/~beca/

Visualization and analysis software for interactive visual exploration and mining of fiber-tracts and brain networks with their genetic determinants and functional outcomes. BECA includes an fMRI and Diseases Analysis version as well as a Genome Explorer version.

Proper citation: BECA (RRID:SCR_015846) Copy   

•   Source: SciCrunch Registry

https://www.icpsr.umich.edu/icpsrweb/content/addep/index.html

Provides access to data including wide range of topics related to disability. ADDEP data can be used to better understand and inform the implementation of Americans with Disabilities Act and other disability policies.

Proper citation: Archive of Data on Disability to Enable Policy (ADDEP) (RRID:SCR_016315) Copy   

•   Source: SciCrunch Registry

http://www.med.unc.edu/bric/ideagroup/free-softwares/unc-infant-0-1-2-atlases

3 atlases dedicated for neonates, 1-year-olds, and 2-year-olds. Each atlas comprises a set of 3D images made up of the intensity model, tissue probability maps, and anatomical parcellation map. These atlases are constructed with the help of state-of-the-art infant MR segmentation and groupwise registration methods, on a set of longitudinal images acquired from 95 normal infants (56 males and 39 females) at neonate, 1-year-old, and 2-year-old.

Proper citation: UNC Infant 0-1-2 Atlases (RRID:SCR_002569) Copy   

•   Source: SciCrunch Registry

http://mialab.mrn.org/data/index.html

An MRI data set that demonstrates the utility of a mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12-71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described, provide a useful baseline for future investigations of brain networks in health and disease.

Proper citation: MIALAB - Resting State Data (RRID:SCR_008914) Copy   

•   Source: SciCrunch Registry

http://nac.spl.harvard.edu/

Biomedical Technology Resource Center that develops image processing and analysis techniques for basic and clinical neurosciences. The NAC research approach emphasizes both specific core technologies and collaborative application projects. The core activity of the center is the development of algorithms and techniques for postprocessing of imaging data. New segmentation techniques aid identification of brain structures and disease. Registration methods are used for relating image data to specific patient anatomy or one set of images to another. Visualization tools allow the display of complex anatomical and quantitative information. High-performance computing hardware and associated software techniques further accelerate algorithms and methods. Digital anatomy atlases are developed for the support of both interactive and algorithmic computational tools. Although the emphasis of the NAC is on the dissemination of concepts and techniques, specific elements of the core software technologies have been made available to outside researchers or the community at large. The NAC's core technologies serve the following major collaborative projects: Alzheimer's disease and the aging brain, morphometric measures in schizophrenia and schizotypal disorder, quantitative analysis of multiple sclerosis, and interactive image-based planning and guidance in neurosurgery. One or more NAC researchers have been designated as responsible for each of the core technologies and the collaborative projects.

Proper citation: Neuroimage Analysis Center (RRID:SCR_008998) Copy   

•   Source: SciCrunch Registry

https://github.com/QTIM-Lab/DeepNeuro

Software Python package for neuroimaging data. Framework to design and train neural network architectures. Used in medical imaging community to ensure consistent performance of networks across variable users, institutions, and scanners.

Proper citation: DeepNeuro (RRID:SCR_016911) Copy   

•   Source: SciCrunch Registry

https://yeatmanlab.github.io/pyAFQ/

Software package focused on automated delineation of major fiber tracts in individual human brains, and quantification of tissue properties within the tracts.Software for automated processing and analysis of diffusion MRI data. Automates tractometry.

Proper citation: Automated Fiber Quantification in Python (RRID:SCR_023366) Copy   

•   Source: SciCrunch Registry

http://web.mit.edu/spectroscopy/facilities/lbrc.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. Biomedical technology research center that develops basic scientific understanding and new techniques required for advancing clinical applications of lasers and spectroscopy. LBRC merges optical spectroscopy, imaging, scattering, and interferometry techniques to study biophysics and biochemistry of healthy and diseased biological structures from subcellular to entire-organ scale.

Proper citation: Laser Biomedical Research Center (RRID:SCR_000106) Copy   

•   Source: SciCrunch Registry

http://rover.bsd.uchicago.edu/lfepr/

Biomedical technology research center that develops instrumentation, analysis techniques, spin probes and spin traps, and methodologies for imaging physiologically relevant aspects of tissue fluids, including high-resolution oxygen maps, with very low frequency electron paramagnetic resonance imaging (EPRI). Novel bridges and high-access, low-field magnet/gradient systems have produced physiologically relevant measurements and accommodate a number of resonant structures. The Center is a consortium between the University of Chicago, the University of Denver, the University of Maryland and Novosibirsk Institute of Organic Chemistry (NIOC), Russia.

Proper citation: Center for EPR Imaging in Vivo Physiology (RRID:SCR_001410) Copy   

•   Source: SciCrunch Registry

https://bli.uci.edu/laser-microbeam-program/

Biomedical technology research center dedicated to the use of lasers and optics in biology and medicine with activities in technological research and development, collaborative research, service, training, and dissemination. One of the primary goals of LAMMP is to facilitate translational research by rapidly moving basic science and technology discoveries from blackboard to benchtop to bedside. This is accomplished by combining state of the art optical technologies with specialized resource facilities for cell and tissue engineering, histopathology, pre-clinical animal models, and clinical care. The resource center has been organized into 3 cores: * Microscopy and Microbeam Technologies (MMT) for high-resolution functional imaging and manipulation of living cells and tissues * Medical Translational Technologies (MTT) for non- and minimally-invasive monitoring, treating, and imaging pre-clinical animal models and human subjects, and * Virtual Photonics Technologies (VPT) for developing computational models and methods that advance the performance of biophotonic technologies, and enhance the information content derived from optical measurements. LAMMP cores contain complementary technologies that are capable of quantitatively characterizing, imaging, and perturbing structure and biochemical function in cells and tissues with scalable resolution and depth sensitivity ranging from micrometers to centimeters.

Proper citation: Laser Microbeam and Medical Program (RRID:SCR_001409) Copy   

•   Source: SciCrunch Registry

http://www.cmu.edu/nmr-center/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on March 19,2024. Biomedical Technology Research Center that develops methodologies for the acquisition of morphological, biochemical, cellular, and functional information in living animals using nuclear magnetic resonance imaging (MRI) and spectroscopy (MRS). Novel techniques utilizing multidimensional MR imaging, magnetic resonance microscopy (MRM), and multinuclear in vivo spectroscopy are being applied to a wide range of problems in the biomedical sciences.

Proper citation: Pittsburgh NMR Center for Biomedical Research (RRID:SCR_001408) Copy   

•   Source: SciCrunch Registry

http://www.radiology.ucsf.edu/research/labs/hyperpolarized-mri-tech

Biomedical technology research center developing, investigating, and disseminating new hyperpolarized MR techniques, new 13C agents and specialized analysis open-source software for data reconstruction and interpretation. The Technology Research & Development projects will leverage the extensive DNP facilities and experience of the project leaders to develop improved, robust hyperpolarized MRI methods. These technology developments will be driven by Collaborative Projects led by outstanding clinical and basic scientists who aim to use hyperpolarized 13C MRI to accomplish the scientific goals of their funded research. These technical developments will also be disseminated to the Service Project investigators for extramural feedback and then widely to the scientific community via a dedicated website and onsite training. This center will provide state-of-the-art training in this new metabolic imaging field and sponsor a yearly symposium focused on hyperpolarized MR technology development.

Proper citation: Hyperpolarized MRI Technology Resource Center (RRID:SCR_001405) Copy   

•   Source: SciCrunch Registry

http://www.ncigt.org/

Biomedical Technology Resource Center that serves as a national resource for all aspects of research into medical procedures that are enhanced by imaging. Its common goal is to provide more effective patient care. The center is focused on the multidisciplinary development of innovative image-guided intervention technologies to enable effective, less invasive clinical treatments that are not only more economical, but also produce better results for patients. The NCIGT is helping to implement this vision by serving as a proving ground for some of the next generation of medical therapies.

Proper citation: National Center for Image-Guided Therapy (RRID:SCR_001419) Copy   

•   Source: SciCrunch Registry

http://www.raraf.org/

Biomedical technology research center dedicated for radiobiological research with available ionizing radiations such as protons, alpha particles, and neutrons. RARAF is well-established and highly user-friendly. The focus of RARAF is the development of high-throughput single-cell/single-particle microbeams, which can deliver defined amounts of ionizing radiation into individual cells with a spatial resolution of a few microns or better. The ability of a microbeam to put double strand break damage at any specific known location in a given cell has allowed new approaches to the study of damage signaling.

Proper citation: Radiological Research Accelerator Facility (RRID:SCR_001425) Copy   

•   Source: SciCrunch Registry

http://www.civm.duhs.duke.edu/

Biomedical technology research center dedicated to the development of novel imaging methods for the basic scientist and the application of the methods to important biomedical questions. The CIVM has played a major role in the development of magnetic resonance microscopy with specialized MR imaging systems capable of imaging at more than 500,000x higher resolution than is common in the clinical domain. The CIVM was the first to demonstrate MR images using hyperpolarized 3He which has been moved from mouse to man with recent clinical trials performed at Duke in collaboration with GE. More recently the CIVM has developed the molecular imaging workbench---a system dedicated to multimodality cardiopulmonary imaging in the rodent. Their collaborators are employing these unique imaging systems in an extraordinary range of mouse and rat models of neurologic disease, cardiopulmonary disease and cancer to illuminate the underlying biology and explore new therapies.

Proper citation: Center for In Vivo Microscopy (RRID:SCR_001426) Copy   

•   Source: SciCrunch Registry