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SciCrunch Registry is a curated repository of scientific resources, with a focus on biomedical resources, including tools, databases, and core facilities - visit SciCrunch to register your resource.
Software package for p value based multiple testing that also implements dependence test and p-value simulation.
Proper citation: Myriads (RRID:SCR_017447) Copy
https://github.com/namboodirilab/B-CALM
Open source system for behavioral control based on Arduino Mega microcontroller and MATLAB based graphical interface and analysis code. Behavior controller optimized and customized for associative learning and memory tasks. Provided software should be able to control many different types of hardware for different task configurations.
Proper citation: B-CALM (RRID:SCR_023884) Copy
http://code.google.com/p/lapdftext/
Software that facilitates accurate extraction of text from PDF files of research articles for use in text mining applications. It is intended for both scientists and natural language processing (NLP) engineers interested in getting access to text within specific sections of research articles. The system extracts text blocks from PDF-formatted full-text research articles and classifies them into logical units based on rules that characterize specific sections. The LA-PDFText system focuses only on the textual content of the research articles. The current version of LA-PDFText is a baseline system that extracts text using a three-stage process: * identification of blocks of contiguous text * classification of these blocks into rhetorical categories * extraction of the text from blocks grouped section-wise.
Proper citation: lapdftext (RRID:SCR_006167) Copy
http://www.cpc.unc.edu/projects/addhealth
Longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States during the 1994-95 school year. Public data on about 21,000 people first surveyed in 1994 are available on the first phases of the study, as well as study design specifications. It also includes some parent and biomarker data. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood. The restricted-use contract includes four hours of free consultation with appropriate staff; after that, there''s a fee for help. Researchers can also share information through a listserv devoted to the database.
Proper citation: Add Health (National Longitudinal Study of Adolescent Health) (RRID:SCR_007434) Copy
http://senselab.med.yale.edu/cellpropdb
A repository for data regarding membrane channels, receptor and neurotransmitters that are expressed in specific types of cells. The database is presently focused on neurons but will eventually include other cell types, such as glia, muscle, and gland cells. This resource is intended to: * Serve as a repository for data on gene products expressed in different brain regions * Support research on cellular properties in the nervous system * Provide a gateway for entering data into the cannonical neuron forms in NeuronDB * Identify receptors across neuron types to aid in drug development * Serve as a first step toward a functional genomics of nerve cells * Serve as a teaching aid
Proper citation: Cell Properties Database (RRID:SCR_007285) Copy
http://www.mbl.org/mbl_main/atlas.html
High-resolution electronic atlases for mouse strains c57bl/6j, a/j, and dba/2j in either coronal or horizontal section. About this Atlas: The anterior-posterior coordinates are taken from an excellent print atlas of a C57BL/6J brain by K. Franklin and G. Paxinos (The Mouse Brain in Stereotaxic Coordinates, Academic Press, San Diego, 1997, ISBN Number 0-12-26607-6; Library of Congress: QL937.F72). The abbreviations we have used to label the sections conform to those in the Franklin-Paxinos atlas. A C57BL/6J mouse brain may contain as many as 75 million neurons, 23 million glial cells, 7 million endothelial cells associated with blood vessels, and 3 to 4 million miscellaneous pial, ependymal, and choroid plexus cells (see data analysis in Williams, 2000). We have not yet counted total cell number in DBA/2J mice, but the counts are probably appreciably lower.The brain and sections were all processed as described in our methods section. The enlarged images have a pixel count of 1865 x 1400 and the resolution is 4.5 microns/pixel for the processed sections.Plans: In the next several years we hope to add several additional atlases of the same sort for other strains of mice. A horizontal C57BL/6J atlas and a DBA/2J coronal atlas were completed by Tony Capra, summer 2000, and additional atlases may be made over the next several years. As describe in the MBL Procedures Section is not hard to make your own strain-specific atlas from the high resolution images in the MBL.
Proper citation: Mouse Brain Atlases (RRID:SCR_007127) Copy
https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIRST
Software model based segmentation and registration tool. Used for segmentation of sub-cortical structures. Introduces basic segmentation and vertex analysis for detecting group differences.
Proper citation: FMRIB’s Integrated Registration and Segmentation Tool (RRID:SCR_024921) Copy
https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/MSM
Software tool for registering cortical surfaces. Capable of driving alignment using wide variety of descriptors of brain architecture, function and connectivity.
Proper citation: Multimodal Surface Matching (RRID:SCR_024929) Copy
Database for hosting and sharing neuroimaging and neuroanatomical datasets for human and primate species. Includes 1) curated, user created Study datasets, extensively analyzed neuroimaging data associated with published figures/manuscripts, 2) Reference datasets mapped to brain atlas surfaces and volumes in human and nonhuman primates for use as general resources (e.g., published cortical parcellations), and 3) ConnectomeDB powered by BALSA for distributing HCP-Young Adult and related HCP-style processed imaging and phenotypic datasets. Datasets in BALSA may include PMID and/or DOI that links them directly to relevant publications.
Proper citation: BALSA (RRID:SCR_022960) Copy
http://wwwchg.duhs.duke.edu/research/osa.html
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 19,2025. Software application that allows the researcher to evaluate evidence for linkage even when heterogeneity is present in a data set. This is not an unusual occurrence when studying diseases of complex origin. Families are ranked by covariate values in order to test evidence for linkage among homogeneous subsets of families. Because families are ranked, a priori covariate cutpoints are not necessary. Covariates may include linkage evidence at other genes, environmental exposures, or biological trait values such as cholesterol, age at onset, and so on.
Proper citation: OSA (RRID:SCR_002016) Copy
https://github.com/SciKnowEngine/kefed.io
Knowledge engineering software for reasoning with scientific observations and interpretations. The software has three parts: (a) the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b) the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c) a "neural connection matrix" interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. The KEfED model is designed to provide a lightweight representation for scientific knowledge that is (a) generalizable, (b) a suitable target for text-mining approaches, (c) relatively semantically simple, and (d) is based on the way that scientist plan experiments and should therefore be intuitively understandable to non-computational bench scientists. The basic idea of the KEfED model is that scientific observations tend to have a common design: there is a significant difference between measurements of some dependent variable under conditions specified by two (or more) values of some independent variable.
Proper citation: Knowledge Engineering from Experimental Design (RRID:SCR_001238) Copy
http://labs.nri.ucsb.edu/reese/benjamin/SA3D.html
A user-friendly, graphical user interface (GUI) that allows statistical and visual manipulations of real and simulated three-dimensional spatial point patterns. The analyses use files containing sets of X, Y, Z coordinates. These point patterns are frequently coordinates of cells of specific cell classes within in volumes of tissue derived from microscopy analyses. The analyses are scale independent so spatial analyses of coordinates from larger and smaller scale distributions are possible. The software can also generate sample sets of X, Y, Z coordinates for program exploration and modeling purposes.
Proper citation: Spatial Analysis 3D (RRID:SCR_002563) Copy
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.
Proper citation: PyMVPA (RRID:SCR_006099) Copy
The MiND: Metadata in NIfTI for DWI framework enables data sharing and software interoperability for diffusion-weighted MRI. This site provides specification details, tools, and examples of the MiND mechanism for representing important metadata for DWI data sets at various stages of post-processing. MiND framework provides a practical solution to the problem of interoperability between DWI analysis tools, and it effectively expands the analysis options available to end users. To assist both users and developers in working with MiND-formatted files, we provide a number of software tools for download. * MiNDHeader A utility for inspecting MiND-extended files. * I/O Libraries Programming libraries to simplify writing and parsing MiND-formatted data. * Sample Files Example files for each MiND schema. * DIRAC LONI''s Diffusion Imaging Reconstruction and Analysis Collection is a DWI processing suite which utilizes the MiND framework.
Proper citation: LONI MiND (RRID:SCR_004820) Copy
http://web.stanford.edu/group/barres_lab/brain_rnaseq.html
Database containing RNA-Seq transcriptome and splicing data from glia, neurons, and vascular cells of cerebral cortex. Collection of RNA-Seq transcriptome and splicing data from glia, neurons, and vascular cells of mouse cerebral cortex. RNA-Seq of cell types isolated from mouse and human brain.
Proper citation: Brain RNA-Seq (RRID:SCR_013736) Copy
http://www.oreganno.org/oregano/
Open source, open access database and literature curation system for community based annotation of experimentally identified DNA regulatory regions, transcription factor binding sites and regulatory variants. Automatically cross referenced against PubMED, Entrez Gene, EnsEMBL, dbSNP, eVOC: Cell type ontology, and Taxonomy database. Community driven resource for curated regulatory annotation.
Proper citation: Open Regulatory Annotation Database (RRID:SCR_007835) Copy
http://pdsp.med.unc.edu/snidd/
A database of imaging probes useful for preclinical and clinical studies. The National Institute of Mental Health (NIMH) and the Society for Non-Invasive Imaging in Drug Development (SNIDD) are in the process of creating a centralized, searchable PET, SPECT, and MRI tracer database as a resource for the scientific community. The goal of this effort is to promote the use of imaging probes in preclinical and clinical research and in drug discovery to accelerate the identification and validation of novel targets for therapeutic intervention in human diseases, especially those with central nervous system components. NIMH will maintain the tracer database as part of the Psychoactive Drug Screening Program (PDSP). The database will contain records for each radiotracer with relevant information such as target, research uses, pharmacology, pharmacokinetics, synthesis protocols, toxicology and safety data, dosimetry, other clinical data, IND info, permission to cross-reference pharmacology, toxicology, or safety data in a drug master file (if an IND exists), contact information, patent, etc. with appropriate safeguards in place to protect the intellectual property of proprietary compounds.
Proper citation: NIMH/SNIDD Tracer Database Initiative (RRID:SCR_008105) Copy
http://www.nimh.nih.gov/about/director/index.shtml
Blog by the NIMH Director, Thomas R. Insel, M.D. Users may sort posts by topic and/or subsribe to the RSS Feed, http://www.nimh.nih.gov/site-info/feed-directors-blog.atom
Proper citation: NIMH Director's Blog (RRID:SCR_008841) Copy
http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000674.v1.p1
Human genetics data from an immense (78,000) and ethnically diverse population available for secondary analysis to qualified researchers through the database of Genotypes and Phenotypes (dbGaP). It offers the opportunity to identify potential genetic risks and influences on a broad range of health conditions, particularly those related to aging. The GERA cohort is part of the Research Program on Genes, Environment, and Health (RPGEH), which includes more than 430,000 adult members of the Kaiser Permanente Northern California system. Data from this larger cohort include electronic medical records, behavioral and demographic information from surveys, and saliva samples from 200,000 participants obtained with informed consent for genomic and other analyses. The RPGEH database was made possible largely through early support from the Robert Wood Johnson Foundation to accelerate such health research. The genetic information in the GERA cohort translates into more than 55 billion bits of genetic data. Using newly developed techniques, the researchers conducted genome-wide scans to rapidly identify single nucleotide polymorphisms (SNPs) in the genomes of the people in the GERA cohort. These data will form the basis of genome-wide association studies (GWAS) that can look at hundreds of thousands to millions of SNPs at the same time. The RPGEH then combined the genetic data with information derived from Kaiser Permanente''s comprehensive longitudinal electronic medical records, as well as extensive survey data on participants'' health habits and backgrounds, providing researchers with an unparalleled research resource. As information is added to the Kaiser-UCSF database, the dbGaP database will also be updated.
Proper citation: Resource for Genetic Epidemiology Research on Adult Health and Aging (RRID:SCR_010472) Copy
http://fcon_1000.projects.nitrc.org/indi/pro/eNKI_RS_TRT/FrontPage.html
A test-retest dataset to assess the reliability of multiband resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI) scans prior to launch of the Enhanced Nathan Kline Institute - Rockland Sample (NKI-RS). The dataset is primarily composed of individuals from the initial NKI-RS - for these individuals psychiatric assessment information is available and included (participants were not excluded due to history of illness. In addition to R-fMRI and DTI, they included: 1) simple visual checkerboard stimulation fMRI scans to allow for assessment of traditional fMRI data quality metrics (e.g., contrast-to-noise ratio), 2) breath holding data to enable assessment of regional differences in vascular responsiveness, and 3) eye movement calibration scans to enable the assessment of eye-movement related artifacts which may be particularly troublesome for multiband sequences since several slices are acquired simultaneously.
Proper citation: NKI-RS Multiband Imaging Test-Retest Pilot Dataset (RRID:SCR_010460) Copy
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