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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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://rsb.info.nih.gov/

Portal for NIH, NIMH, and NINDS scientific and computer resources including Mac sites, PC sites, Linux sites, intramural programs, intranet and the NIH JumpStart and Directory.

Proper citation: Research Services Branch National Institutes of Mental Health (RRID:SCR_001633) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://brancusi.usc.edu/bkms/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 7th, 2019. BAMS is an online resource for information about neural circuitry. The BAMS Nested Regions view focuses on the major brain regions and their relationships.

Proper citation: BAMS Nested Regions (RRID:SCR_000238) Copy   

•   Source: SciCrunch Registry

http://www.brainscanr.com/

A database of neuroscience-related concepts that utilizes visualization tools for the purpose of research, education and knowledge discovery. The data comes from PubMed abstracts and an algorithm that assumes related terms will appear together. The topics can include computational modeling, behavioral functions and neurological degeneration.

Proper citation: brainSCANr (RRID:SCR_000500) Copy   

•   Source: SciCrunch Registry

http://www.fz-juelich.de/ime/spm_anatomy_toolbox

A MATLAB toolbox which uses three dimensional probabilistic cytoarchitechtonic maps to correlate microscopic, anatomic and functional data of the cerebral cortex. Correlating the activation foci identified in functional imaging studies of the human brain with structural (e.g., cytoarchitectonic) information on the activated areas is a major methodological challenge for neuroscience research. We here present a new approach to make use of three-dimensional probabilistic cytoarchitectonic maps, as obtained from the analysis of human post-mortem brains, for correlating microscopical, anatomical and functional imaging data of the cerebral cortex. We introduce a new, MATLAB based toolbox for the SPM2 software package which enables the integration of probabilistic cytoarchitectonic maps and results of functional imaging studies. The toolbox includes the functionality for the construction of summary maps combining probability of several cortical areas by finding the most probable assignment of each voxel to one of these areas. Its main feature is to provide several measures defining the degree of correspondence between architectonic areas and functional foci. The software, together with the presently available probability maps, is available as open source software to the neuroimaging community. This new toolbox provides an easy-to-use tool for the integrated analysis of functional and anatomical data in a common reference space.

Proper citation: SPM Anatomy Toolbox (RRID:SCR_013273) Copy   

•   Source: SciCrunch Registry

http://gbrowse.csbio.unc.edu/cgi-bin/gb2/gbrowse/slep/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Database of genetic and gene expression data from the published literature on psychiatric disorders. Users can search the accumulated data to find the evidence in support of the involvement of a particular genomic region with a set of important psychiatric disorders, ADHD, autism, bipolar disorder, eating disorder, major depressive disorder, schizophrenia, and smoking behavior. It contains findings from manual reviews of 144 papers in psychiatric genetics, 136 primary reports and 8 meta-analyses. Disorders covered include schizophrenia (44 papers), autism (24 papers), bipolar disorder (24 papers), smoking behavior (24 papers), major depressive disorder and neuroticism (14 papers), ADHD (8 papers), eating disorders (3 papers), and a combined schizophrenia-bipolar phenotype (3 papers). The unbiased searches integrated into SLEP include genomewide linkage (117 papers), genomewide association (15 papers), copy number variation (9 papers), and gene expression studies of post-mortem brain tissue (3 meta-analyses courtesy of the Stanley Foundation). In total, SLEP captures 3,741 findings from these 144 papers. SLEP also contains over 70,000 SignPosts. These annotations derive from many different sources and are designed to try to capture current state of knowledge about disease associations in the human genome. SignPosts can be searched simultaneously with the psychiatric genetics literature in order to integrate these two bodies of knowledge. The SignPosts include: accumulated GWAS findings from the human genetics literature, the OMIM database, candidate gene association study literature, CNV location and frequency data, SNPs that influence gene expression in brain, genes expressed in brain, genes with evidence of imprinting and random monoalleleic expression, genes mutated in breast or colorectal cancer, and pathway data from BioCyc.

Proper citation: Sullivan Lab Evidence Project (RRID:SCR_000753) Copy   

•   Source: SciCrunch Registry

http://cng.gmu.edu/brava

A database of digital reconstructions of the human brain arterial arborizations from 61 healthy adult subjects along with extracted morphological measurements. The arterial arborizations include the six major trees stemming from the circle of Willis, namely: the left and right Anterior Cerebral Arteries (ACAs), Middle Cerebral Arteries (MCAs), and Posterior Cerebral Arteries (PCAs).

Proper citation: BraVa (RRID:SCR_001407) Copy   

•   Source: SciCrunch Registry

http://www.jcvi.org/mpidb

Database that collects and provides all known physical microbial interactions. Currently, 24,295 experimentally determined interactions among proteins of 250 bacterial species/strains can be browsed and downloaded. These microbial interactions have been manually curated from the literature or imported from other databases (IntAct, DIP, BIND, MINT) and are linked to 26,578 experimental evidences (PubMed ID, PSI-MI methods). In contrast to these databases, interactions in MPIDB are further supported by 68,346 additional evidences based on interaction conservation, co-purification, and 3D domain contacts (iPfam, 3did). (spoke/matrix) binary interactions inferred from pull-down experiments are not included.

Proper citation: MPIDB (RRID:SCR_001898) Copy   

•   Source: SciCrunch Registry

https://bams1.org/cells/list.php, https://bams1.org/cells/search_bams_ref.php, https://bams1.org/cells/search_by_brain_region.php

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 6, 2023.BAMS is an online resource for information about neural circuitry. The BAMS Cell view focuses on the major brain regions and which cells are contained therein.

Proper citation: BAMS Cells (RRID:SCR_003531) Copy   

•   Source: SciCrunch Registry

https://balsa.wustl.edu/

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://www.broad.mit.edu/node/305

The Connectivity Map aims to generate a detailed map that links gene patterns associated with disease to corresponding patterns produced by drug candidates and a variety of genetic manipulations. The Connectivity Map is the most comprehensive effort yet for using genomics in a drug-discovery framework. It allows researchers to screen compounds against genome-wide disease signatures, rather than a pre-selected set of target genes. Drugs are paired with diseases using sophisticated pattern-matching methods with a high level of resolution and specificity. To build a Connectivity Map, the Broad Institute brings together molecular biologists, genomics specialists, computational scientists, pharmacologists, chemists and chemical biologists, as well as expertise from across the breadth and depth of medicine.Connectivity map is a large public database of signatures of drugs and genes, and pattern-matching tools to detect similarities among these signatures.The parent site for the Broad Institute at MIT has a software library of software applications developed for use in genetic analysis.

Proper citation: National Institute of Mental Health (NIMH) Human Genetics Initiative (RRID:SCR_007436) Copy   

•   Source: SciCrunch Registry

http://www.nimh.nih.gov/health/publications/index.shtml

Publications put out by the National Institute of Mental Health. Publications are available by topic: Disorders: * Attention Deficit Hyperactivity Disorder (ADHD) * Anxiety Disorders * Autism * Bipolar Disorder * Borderline Personality Disorder * Depression * Eating Disorders * Generalized Anxiety Disorder * Obsessive-Compulsive Disorder (OCD) * Panic Disorder * Post-Traumatic Stress Disorder * Schizophrenia * Social Phobia Populations * Older Adults * Men''s Mental Health * Women''s Mental Health * Children and Adolescents Research * Basic Research * Clinical Research and Trials * Research Funding * Mental Health Services Research Other * Coping with Traumatic Events * Genetics * HIV/AIDS * Imaging * Medications * NIMH * Prevention * Statistics * Suicide Prevention * Treatments

Proper citation: NIMH Publications (RRID:SCR_008846) Copy   

•   Source: SciCrunch Registry

http://datasharing.net

The U.S. National Institutes of Health Final NIH Statement on Sharing Research Data (NIH-OD-03-032) is now in effect. It specifies that all high-direct-cost NIH grant applications include plans for sharing of research data. To support and encourage collegial, enabling, and rewarding data sharing for neuroscience and beyond, the Laboratory of Neuroinformatics at Weill Medical College of Cornell University has established this site. A source of, and portal to, tools and proposals supporting the informed exchange of neuroscience data.

Proper citation: Datasharing.net (RRID:SCR_003312) Copy   

•   Source: SciCrunch Registry

https://zenodo.org/records/11095105

Software label transfer tool for single-cell RNA sequencing analysis. Scalable, Interpretable Modeling for Single-cell RNA-seq data classification.

Proper citation: SIMS (RRID:SCR_025787) Copy   

•   Source: SciCrunch Registry

https://www.synapse.org/

Sage Bionetworks, Mount Sinai School of Medicine (MSSM), University of Pennsylvania (Penn), the National Institute of Mental Health (NIMH), and Takeda Pharmaceuticals Company Limited (TAKEDA) have launched a Public-Private Pre-Competitive Consortium, the CommonMind Consortium, to generate and analyze large-scale genomic data from human subjects with neuropsychiatric disease and to make this data and the associated analytical results broadly available to the public. This collaboration brings together disease area expertise, large scale and well curated brain sample collections, and data management and analysis expertise from the respective institutions. As many as 450 million people worldwide are believed to be living with a mental or behavioral disorder: schizophrenia and bipolar disorder are two of the top six leading causes of years lived with disability according to the World Health Organization. The burden on the individual as well as on society is significant with estimates for the health care costs for these individuals as high as four percent GNP. This highlights a grave need for new therapies to alleviate this suffering. Researchers from MSSM including Dr. Pamela Sklar, Dr. Joseph Buxbaum and Dr. Eric Schadt will join with Dr. Raquel Gur and Dr. Chang-Gyu Hahn from Penn to combine their extensive brain bank collections for the generation of whole genome scale RNA and DNA sequence data. Dr.Pamela Sklar, Professor of Psychiatry and Neuroscience at MSSM commented this is an exciting opportunity for us to use the newest genomic methods to really expand our understanding of the molecular underpinnings of neuropsychiatric disease, while Dr Raquel Gur, Professor of Psychiatry from Penn observed this will be a great complement to some of the large-scale genetic analyses that have been carried out to date because it will give a more complete mechanistic picture. The CommonMind Consortium is committed to generating an open resource for the community and invites others with common goals to contact us at info (at) CommonMind.org.

Proper citation: CommonMind Consortium (RRID:SCR_000139) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry