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

A free software tool for Copy Number Variation (CNV) detection from SNP genotyping arrays. Currently it can handle signal intensity data from Illumina and Affymetrix arrays. With appropriate preparation of file format, it can also handle other types of SNP arrays and oligonucleotide arrays. PennCNV implements a hidden Markov model (HMM) that integrates multiple sources of information to infer CNV calls for individual genotyped samples. It differs form segmentation-based algorithm in that it considered SNP allelic ratio distribution as well as other factors, in addition to signal intensity alone. In addition, PennCNV can optionally utilize family information to generate family-based CNV calls by several different algorithms. Furthermore, PennCNV can generate CNV calls given a specific set of candidate CNV regions, through a validation-calling algorithm.

Proper citation: PennCNV (RRID:SCR_002518) Copy   

•   Source: SciCrunch Registry

http://www.matrics.ucla.edu/index.html

Cognitive deficits -- including impairments in areas such as memory, attention, and executive function -- are a major determinant and predictor of long-term disability in schizophrenia. Unfortunately, available antipsychotic medications are relatively ineffective in improving cognition. Scientific discoveries during the past decade suggest that there may be opportunities for developing medications that will be effective for improving cognition in schizophrenia. The NIMH has identified obstacles that are likely to interfere with the development of pharmacological agents for treating cognition in schizophrenia. These include: (1) a lack of a consensus as to how cognition in schizophrenia should be measured; (2) differing opinions as to the pharmacological approaches that are most promising; (3) challenges in clinical trial design; (4) concerns in the pharmaceutical industry regarding the US Food and Drug Administration''s (FDA) approaches to drug approval for this indication; and (5) issues in developing a research infrastructure that can carry out clinical trials of promising drugs. The MATRICS program will bring together representatives of academia, industry, and government in a consensus process for addressing all of these obstacles. Specific goals of the NIMH MATRICS are: * To catalyze regulatory acceptance of cognition in schizophrenia as a target for drug registration. * To promote development of novel compounds to enhance cognition in schizophrenia. * Leverage economic research power of industry to focus on important but neglected clinical targets. * Identify lead compounds and if deemed feasible, support human proof of concept trials for cognition in schizophrenia.

Proper citation: MATRICS - Measurement And Treatment Research to Improve Cognition in Schizophrenia (RRID:SCR_005644) Copy   

•   Source: SciCrunch Registry

http://www.patternlabforproteomics.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented July 5, 2018. Gene Ontology Explorer (GOEx) combines data from protein fold changes with GO over-representation statistics to help draw conclusions in proteomic experiments. It is tightly integrated within the PatternLab for Proteomics project and, thus, lies within a complete computational environment that provides parsers and pattern recognition tools designed for spectral counting. GOEx offers three independent methods to query data: an interactive directed acyclic graph, a specialist mode where key words can be searched, and an automatic search. A recent hack included in GOEx is to load the sparse matrix index file directly into GOEx, instead of going through the report generation using the AC/T-fold methods. This makes it easy for GOEx to analyze any list of proteins as long as the list follows the index file format (described in manuscript) . Please note that if using this alternative strategy, there will be no protein fold information. Platform: Windows compatible

Proper citation: GOEx - Gene Ontology Explorer (RRID:SCR_005779) Copy   

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http://fcon_1000.projects.nitrc.org/indi/abide/

Resting state functional magnetic resonance imaging (R-fMRI) datasets from 539 individuals with autism spectrum disorder (ASD) and 573 typical controls. This initiative involved 16 international sites, sharing 20 samples yielding 1112 datasets composed of both MRI data and an extensive array of phenotypic information common across nearly all sites. This effort is expected to facilitate discovery science and comparisons across samples. All datasets are anonymous, with no protected health information included.

Proper citation: ABIDE (RRID:SCR_003612) Copy   

•   Source: SciCrunch Registry

http://www.open-ephys.org/pulsepal

Open source pulse train generator that allows users to create and trigger software defined trains of voltage pulses with high temporal precision. Generates precisely timed pulse sequences for use in research involving electrophysiology or psychophysics.

Proper citation: Open Ephys: Pulse Pal (RRID:SCR_017203) Copy   

•   Source: SciCrunch Registry

http://www.nimh.nih.gov/funding/clinical-trials-for-researchers/datasets/nimh-procedures-for-requesting-data-sets.shtml

A listing of data sets from NIMH-supported clinical trials. Limited Access Datasets are available from numerous NIMH studies. NIMH requires all investigators seeking access to data from NIMH-supported trials held by NIMH to execute and submit as their request the appropriate Data Use Certification pertaining to the trial. The datasets distributed by NIMH are referred to as limited access datasets because access is limited to qualified researchers who complete Data Use Certifications.

Proper citation: Limited Access Datasets From NIMH Clinical Trials (RRID:SCR_005614) 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://fcon_1000.projects.nitrc.org/indi/pro/nyu.html

Datasets including a collection of scans from 49 psychiatrically evaluated neurotypical adults, ranging in age from 6 to 55 years old, with age, gender and intelligence quotient (IQ) information provided. Future releases will include more comprehensive phenotypic information, and child and adolescent datasets, as well as individuals from clinical populations. The following data are released for every participant: * At least one 6-minute resting state fMRI scan (R-fMRI) * * One high-resolution T1-weighted mprage, defaced to protect patient confidentiality * Two 64-direction diffusion tensor imaging scans * Demographic information (age, gender) and IQ-measures (Verbal, Performance, and Composite; Weschler Abbreviated Scale of Intelligence - WASI) * Most participants have 2 R-fMRI scans, collected less than 1 hour apart in the same scanning session. Rest_1 is always collected first.

Proper citation: NYU Institute for Pediatric Neuroscience Sample (RRID:SCR_010458) Copy   

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https://brainlife.io/docs/using_ezBIDS/

Web-based BIDS conversion tool to convert neuroimaging data and associated metadata to BIDS standard. Guided standardization of neuroimaging data interoperable with major data archives and platforms.

Proper citation: ezBIDS (RRID:SCR_025563) Copy   

•   Source: SciCrunch Registry

http://kimlab.io/brain-map/atlas/

Website to visualize and share anatomical labels. Franklin and Paxinos (FP) based anatomical labels in Allen Common Coordinate Framework (CCF). Cell type specific transgenic mice and MRI atlas were used to adjust and further segment labels. New segmentations were created in dorsal striatum using cortico-striatal connectivity data. Anatomical labels were digitized based on Allen ontology, and web-interface was created for easy visualization. These labels provide resource to isolate and identify mouse brain anatomical structures. Open source data sharing will facilitate further refinement of anatomical labels and integration of data interpretation within single anatomical platform.

Proper citation: Enhanced and Unified Anatomical Labeling for Common Mouse Brain Atlas (RRID:SCR_019267) Copy   

•   Source: SciCrunch Registry

http://www.epmba.org/

The Electronic Prenatal Mouse Brain Atlas, EPMBA, at present consists of two sets of annotated images of coronal sections from Gestational Day (GD) 12 heads and GD 16 brains of C57BL/6J mice. Ten micron thick sections were stained with hematoxylin and eosin. Images were prepared at various resolutions for annotations and for high resolution presentation. A subset of sections were annotated and linked to anatomical terms. Additionally, horizontal sections of a GD 12 head were aligned and re-assembled into a 3D volume for digital sectioning in arbitrarily oblique planes. These images were captured using a Nikon E800 stereomicroscope with a 10X objective. The resolution is 1.35 pixels/micrometer. The PC program used to grab the images, Microbrightfield's Neurolucida (version 6), stitched together a mosaic of between 10 and 50 high-res images for each tissue slice, while the user focused the scope for each mosaic tile. Since the nature of optic lenses is to focus on one central point, it was difficult to obtain a uniformly-focused field of vision; as such, small areas of these images are blurred. Images were then transferred to a Macintosh and processed in Adobe Photoshop (version 7). Color levels were adjusted for maximum clarity of the tissue, and areas surrounding the tissue were cleared of artifacts. Each image is approximately 3350 pixels wide by 2650 pixels high. A scale bar with a length of 1350 pixels/mm is visible in the lower right-hand corner of each image. The annotations have been completed for the Atlas of Developing Mouse Brain Gestational (Embryonic) Day 12 (7/5/07) as well as the Atlas of Developing Mouse Brain Embryonic Day 16 (4/26/07). The 3D EPMBA data set has been mounted on a NeuroTerrain Atlas Server (NtAS). (6/27/07).

Proper citation: EPMBA.ORG: Electronic Prenatal Mouse Brain Atlas (RRID:SCR_001882) Copy   

•   Source: SciCrunch Registry

http://mindboggle.info/data.html

Complete set of free, publicly accessible, downloadable atlases, templates, and individual manually labeled brain image data, the largest collection of publicly available, manually labeled human brains in the world! http://journal.frontiersin.org/article/10.3389/fnins.2012.00171/full

Proper citation: Mindboggle-101 atlases (RRID:SCR_002439) 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

https://community.brain-map.org/t/allen-human-reference-atlas-3d-2020-new/405

Parcellation of adult human brain in 3D, labeling every voxel with brain structure spanning 141 structures. These parcellations were drawn and adapted from prior 2D version of adult human brain atlas.

Proper citation: Allen Human Reference Atlas, 3D, 2020 (RRID:SCR_017764) Copy   

•   Source: SciCrunch Registry

https://github.com/xinhe-lab/GSFA

Software R package that performs sparse factor analysis and differential gene expression discovery simultaneously on single cell CRISPR screening data.

Proper citation: Guided Sparse Factor Analysis (RRID:SCR_025023) Copy   

•   Source: SciCrunch Registry

https://kimlab.io/brain-map/DevCCF/

Open access multimodal 3D atlases of developing mouse brain that can be used to integrate mouse brain imaging data for visualization, education, cell census mapping, and more. Atlas ages include E11.5, E13.5, E15.5, E18.5, P4, P14, and P56. Web platform can be utilized to visualize and explore the atlas in 3D. Downloadable atlas can be used to align multimodal mouse brain data. Morphologically averaged symmetric template brains serve as the basis reference space and coordinate system. Anatomical labels are manually drawn in 3D based on the prosomeric model. For additional references, the P56 template includes templates and annotations from the aligned Allen Mouse Brain Common Coordinate Framework (Allen CCFv3) and aligned Molecular Atlas of the Adult Mouse Brain.

Proper citation: 3D Developmental Mouse Brain Common Coordinate Framework (RRID:SCR_025544) Copy   

•   Source: SciCrunch Registry

https://kimlab.io/brain-map/DevATLAS/

Whole brain developmental map of neuronal circuit maturation. Generated by whole brain spatiotemporal mapping of circuit maturation during early postnatal development. Standard reference for normative developmental trajectory of neuronal circuit maturation, as well as high throughput platform to pinpoint when and where circuit maturation is disrupted in mouse models of neurodevelopmental disorders, such as fragile X syndrome.

Proper citation: DevATLAS (RRID:SCR_025718) Copy   

•   Source: SciCrunch Registry

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

A repository of schizophrenia neuroimaging data collected from over 450 individuals with schizophrenia, healthy controls and their respective siblings, most with 2-year longitudinal follow-up. The data include neuroimaging data, cognitive data, clinical data, and genetic data.

Proper citation: Northwestern University Schizophrenia Data and Software Tool (NUSDAST) (RRID:SCR_014153) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/tximeta.html

Software R package for reference sequence checksums for provenance identification in RNA-seq. Performs numerous annotation and metadata gathering tasks on behalf of users during the import of transcript counts and abundance from quantification tools such as salmon. Data are imported as SummarizedExperiment objects with associated GenomicRanges metadata. Correct metadata is added automatically via reference sequence digests, facilitating genomic analyses and assisting in computational reproducibility.

Proper citation: tximeta (RRID:SCR_028005) 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