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http://nimhstemcells.org/

Induced Pluripotent Stem Cell (iPSC) and Source Cells available for distribution for postnatal-to-adult human control and patient-derived cells and their reprogrammed derivatives in support of stem cell research relevant to mental disorders. This includes but is not limited to anxiety disorders, attention deficit hyperactivity disorder, autism spectrum disorders, bipolar disorder, borderline personality disorder, depression, eating disorders, obsessive-compulsive disorder, panic disorder, post-traumatic stress disorder, and schizophrenia. The capabilities of the repository range from derivation and banking of primary source cells from postnatal through adult human subject tissue to more comprehensive banking and validation of induced pluripotent stem cells (iPSCs) or similar reprogrammed / de-differentiated cells. Please send a message with the Contact page if you wish to contribute source cells or iPSC.

Proper citation: NIMH Stem Cell Center (RRID:SCR_006682) Copy   

•   Source: SciCrunch Registry

http://www.pediatricmri.nih.gov/

Data sets of clinical / behavioral and image data are available for download by qualified researchers from a seven year, multi-site, longitudinal study using magnetic resonance technologies to study brain maturation in healthy, typically-developing infants, children, and adolescents and to correlate brain development with cognitive and behavioral development. The information obtained in this study is expected to provide essential data for understanding the course of normal brain development as a basis for understanding atypical brain development associated with a variety of developmental, neurological, and neuropsychiatric disorders affecting children and adults. This study enrolled over 500 children, ranging from infancy to young adulthood. The goal was to study each participant at least three times over the course of the project at one of six Pediatric Centers across the United States. Brain MR and clinical/behavioral data have been compiled and analyzed at a Data Coordinating Center and Clinical Coordinating Center. Additionally, MR spectroscopy and DTI data are being analyzed. The study was organized around two objectives corresponding to two age ranges at the time of enrollment, each with its own protocols. * Objective 1 enrolled children ages 4 years, 6 months through 18 years (total N = 433). This sample was recruited across the six Pediatric Study Centers using community based sampling to reflect the demographics of the United States in terms of income, race, and ethnicity. The subjects were studied with both imaging and clinical/behavioral measures at two year intervals for three time points. * Objective 2 enrolled newborns, infants, toddlers, and preschoolers from birth through 4 years, 5 months, who were studied three or more times at two Pediatric Study Centers at intervals ranging from three months for the youngest subjects to one year as the children approach the Objective 1 age range. Both imaging and clinical/behavioral measures were collected at each time point. Participant recruitment used community based sampling that included hospital venues (e.g., maternity wards and nurseries, satellite physician offices, and well-child clinics), community organizations (e.g., day-care centers, schools, and churches), and siblings of children participating in other research at the Pediatric Study Centers. At timepoint 1, of those enrolled, 114 children had T1 scans that passed quality control checks. Staged data release plan: The first data release included structural MR images and clinical/behavioral data from the first assessments, Visit 1, for Objective 1. A second data release included structural MRI and clinical/behavioral data from the second visit for Objective 1. A third data release included structural MRI data for both Objective 1 and 2 and all time points, as well as preliminary spectroscopy data. A fourth data release added cortical thickness, gyrification and cortical surface data. Yet to be released are longitudinally registered anatomic MRI data and diffusion tensor data. A collaborative effort among the participating centers and NIH resulted in age-appropriate MR protocols and clinical/behavioral batteries of instruments. A summary of this protocol is available as a Protocol release document. Details of the project, such as study design, rationale, recruitment, instrument battery, MRI acquisition details, and quality controls can be found in the study protocol. Also available are the MRI procedure manual and Clinical/Behavioral procedure manuals for Objective 1 and Objective 2.

Proper citation: NIH MRI Study of Normal Brain Development (RRID:SCR_003394) Copy   

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http://www.broadinstitute.org/science/programs/genome-biology/computational-rd/somaticcall-manual

Software program that finds single-base differences (substitutions) between sequence data from tumor and matched normal samples. It is designed to be highly stringent, so as to achieve a low false positive rate. It takes as input a BAM file for each sample, and produces as output a list of differences (somatic mutations). Note: This software package is no longer supported and information on this page is provided for archival purposes only.

Proper citation: SomaticCall (RRID:SCR_001196) Copy   

•   Source: SciCrunch Registry

http://www.capitalbiosciences.com/

Biological products including Cell Immortalization Products, Clinically Defined Human Tissue, cDNA ORF Clones, Premade Adenoviruses, Purified Proteins, Viral Expression Systems and others as well as services like Custom Recombinant Adenovirus Production, Custom Recombinant Lentivirus Production, Protein Detection and Quantification and Stable Cell Line Production for academic and governmental research institutes, pharmaceutical and biotechnology industry. Capital Biosciences offers most types of human tissues, normal and diseased, with extensive clinical history and follow up information. Standard specimen format: Snap-frozen(flash-frozen), Formalin fixed and paraffin embedded (FFPE) tissues, Blood and blood products, Bone marrow, Total RNA, Genomic DNA, Total Proteins, Primary cell cultures, Viable frozen tissue. Tumor tissue samples include: Bladder cancer, Glioblastoma, Medulloblastoma, Breast Carcinoma, Cervical Cancer, Colorectal Cancer, Endometrial Cancer, Esophageal Cancer, Head and Neck (H&N) Carcinoma, Hepatocellular Carcinoma (HCC), Hodgkin's lymphoma, Kidney, Renal Cell Carcinoma, Lung Cancer, Non-Small Cell (NCSLC), Lung Cancer, Small Cell (SCLC), Melanoma, Mesothelioma, non-Hodgkin's Lymphoma, Ovarian Adenocarcinoma, Pancreatic Cancer, Prostate Cancer, Stomach Cancer.

Proper citation: Capital Biosciences (RRID:SCR_004879) Copy   

•   Source: SciCrunch Registry

https://github.com/ding-lab/msisensor

A C++ software program for automatically detecting somatic and germline variants at microsatellite regions. It computes length distributions of microsatellites per site in paired tumor and normal sequence data, subsequently using these to statistically compare observed distributions in both samples.

Proper citation: MSIsensor (RRID:SCR_006418) Copy   

•   Source: SciCrunch Registry

http://gmt.genome.wustl.edu/somatic-sniper/current/

Software program to identify single nucleotide positions that are different between tumor and normal (or, in theory, any two bam files). It takes a tumor bam and a normal bam and compares the two to determine the differences. It outputs a file in a format very similar to Samtools consensus format. It uses the genotype likelihood model of MAQ (as implemented in Samtools) and then calculates the probability that the tumor and normal genotypes are different. This probability is reported as a somatic score. The somatic score is the Phred-scaled probability (between 0 to 255) that the Tumor and Normal genotypes are not different where 0 means there is no probability that the genotypes are different and 255 means there is a probability of 1 ? 10(255/-10) that the genotypes are different between tumor and normal. This is consistent with how the SAM format reports such probabilities. It is currently available as source code via github or as a Debian APT package.

Proper citation: SomaticSniper (RRID:SCR_005108) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/gatk/gatkdocs/org_broadinstitute_sting_gatk_walkers_indels_SomaticIndelDetector.html

Tool for calling indels in Tumor-Normal paired sample mode.

Proper citation: SomaticIndelDetector (RRID:SCR_005107) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/MCCL

A comprehensive ontology on primary and established cell lines-both normal and pathologic. It covers around 400 cell lines. This ontology has been built to include the major domains in the field of biology like anatomy, bio-molecules, chemicals and drugs, pathological conditions and genetic variations around the cell lines. An extensive network of relations has been built across these concepts to enable different combinations of queries. The ontology covers all cell lines from major sources like ATCC, DSMZ, ECACC, ICLC etc. and is built in OWL format.

Proper citation: Cell Line Ontology by Mahadevan (RRID:SCR_010281) Copy   

•   Source: SciCrunch Registry

http://hippocampome.org

A curated knowledge base of the circuitry of the hippocampus of normal adult, or adolescent, rodents at the mesoscopic level of neuronal types. Knowledge concerning dentate gyrus, CA3, CA2, CA1, subiculum, and entorhinal cortex is distilled from published evidence and is continuously updated as new information becomes available. Each reported neuronal property is documented with a pointer to, and excerpt from, relevant published evidence, such as citation quotes or illustrations. Please note: This is an alpha-testing site. The content is still being vetted for accuracy and has not yet undergone peer-review. As such, it may contain inaccuracies and should not (yet) be trusted as a scholarly resource. The content does not yet appear uniformly across all combinations of browsers and screen resolutions.

Proper citation: Hippocampome.org (RRID:SCR_009023) Copy   

•   Source: SciCrunch Registry

http://bodymap.genes.nig.ac.jp/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. A taxonomical and anatomical database of latest cross species animal EST data, clustered by UniGene and inter connected by Inparanoid. Users can search by Unigene, RefSeq, or Entrez Gene ID, or search for Gene Name or Tissue type. Data is also sortable and viewable based on qualities of normal, Neoplastic, or other. The last data import appears to be from 2008

Proper citation: BodyMap-Xs (RRID:SCR_001147) Copy   

•   Source: SciCrunch Registry

http://srv00.recas.ba.infn.it/ASPicDB/

A database to access reliable annotations of the alternative splicing pattern of human genes, obtained by ASPic algorithm (Castrignano et al. 2006), and to the functional annotation of predicted isoforms. Users may select and extract specific sets of data related to genes, transcripts and introns fulfilling a combination of user-defined criteria. Several tabular and graphical views of the results are presented, providing a comprehensive assessment of the functional implication of alternative splicing in the gene set under investigation. ASPicDB also includes information on tissue-specific splicing patterns of normal and cancer cells, based on available EST data and their library source annotation.

Proper citation: ASPicDB (RRID:SCR_002102) Copy   

•   Source: SciCrunch Registry

https://sites.google.com/site/projectbci/

EEG motor activity data sets used for Brain Computer Interface research project in Matlab MAT format. * Dataset 1 - 1D motion: This subject is a 21 year old, right handed male with no known medical conditions. The EEG consists of actual random movements of left and right hand recorded with eyes closed. Each row represents one electrode. The order of the electrodes is FP1 FP2 F3 F4 C3 C4 P3 P4 O1 O2 F7 F8 T3 T4 T5 T6 FZ CZ PZ. The recording was done at 500Hz using Neurofax EEG System which uses a daisy chain montage. The data was exported with a common reference using Eemagine EEG. AC Lines in this country work at 50 Hz. This info is also included in the MAT file. * Dataset 2 - 2D motion: This subject is a 21 year old, right handed male with no known medical conditions. The EEG consists of actual random movements of left and right hand recorded with eyes closed. Each row represents one electrode. The order of the electrodes is FP1 FP2 F3 F4 C3 C4 P3 P4 O1 O2 F7 F8 T3 T4 T5 T6 FZ CZ PZ. The recording was done at 500Hz using Neurofax EEG System which uses a daisy chain montage. The data was exported with a common reference using Eemagine EEG. AC Lines in this country work at 50 Hz. This data consists of the following movements # Three trials left hand forward movement # Three trials left hand backward movement # Three trials left hand forward movement # Three trials left hand forward movement # 1 trial imagined left hand forward movement # 1 trial imagined left hand backward movement # 1 trial imagined right hand forward movement # 1 trial imagined right hand backward movement # 1 trial left leg movement # 1 trial right leg movement

Proper citation: Project BCI - EEG motor activity data set (RRID:SCR_001585) Copy   

•   Source: SciCrunch Registry

http://www.orgids.com/

Data sets resulting from glaucoma research including visual fields, various imaging modalities and other data from both glaucomatous and normal subjects. The Longitudinal Glaucomatous Visual Fields data set contains IOP (Intraocular pressure) measurements and 24-2 Full Threshold visual fields obtained with a Humphrey Field Analyzer (Zeiss). Data of both eyes of 139 patients over a mean period of over 9 years is included, with on average more than 17 fields per eye. Local threshold and total deviation values are included.

Proper citation: Open Rotterdam Glaucoma Imaging Data Sets (RRID:SCR_003540) Copy   

•   Source: SciCrunch Registry

http://www.radiologyresearch.org/HippocampusSegmentation.aspx

This dataset contains T1-weighted MR images of 50 subjects, 40 of whom are patients with temporal lobe epilepsy and 10 are nonepileptic subjects. Hippocampus labels are provided for 25 subjects for training. The users may submit their segmentation outcomes for the remaining 25 testing images to get a table of segmentation metrics.

Proper citation: MRI Dataset for Hippocampus Segmentation (RRID:SCR_009597) Copy   

•   Source: SciCrunch Registry

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

ART ''''acpcdetect'''' program for automatic detection of the AC and PC landmarks and the mid-sagittal plane on 3D structural MRI scans. ART ''''brainwash'''' program for automatic multi-atlas skull-stripping of 3D structural MRI scans. ART ''''3dwarper'''' program of non-linear inter-subject registration of 3D structural MRI scans. Software (art2) for linear rigid-body intra-subject inter-modality (MRI-PET) image registration. Data resource: The ART projects makes available corpus callosum segmentations of 316 normal subjects from the OASIS cross-sectional database. ART ''''yuki'''' program for fast, robust, and fully automatic segmentation of the corpus callosum on 3D structural MRI scans.

Proper citation: Automatic Registration Toolbox (RRID:SCR_005993) Copy   

•   Source: SciCrunch Registry

https://www.braintest.org/brain_test/BrainTest

A portal of online studies that encourage community participation to tackle the most challenging problems in neuropsychiatry, including attention-deficit / hyperactivity disorder, schizophrenia, and bipolar disorder. Our approach is to engage the community and try to recruit tens of thousands of people to spend an hour of their time on our site. You folks will provide data in both brain tests and questionnaires, as well as DNA, and in return, we will provide some information about your brain and behavior. You will also be entered to win amazon.com gift cards. While large collaborative efforts were made in genetics in order to discover the secrets of the human genome, there are still many mysteries about the behaviors that are seen in complex neuropsychiatric syndromes and the underlying biology that gives rise to these behaviors. We know that it will require studying tens of thousands of people to begin to answer these questions. Having you, the public, as a research partner is the only way to achieve that kind of investment. This site will try to reach that goal, by combining high-throughput behavioral assessment using questionnaires and game-like cognitive tests. You provide the data and then we will provide information and feedback about why you should help us achieve our goals and how it benefits everyone in the world. We believe that through this online study, we can better understand memory and attention behaviors in the general population and their genetic basis, which will in turn allow us to better characterize how these behaviors go awry in people who suffer from mental illness. In the end, we hope this will provide better, more personalized treatment options, and ultimately prevention of these widespread and extremely debilitating brain diseases. We will use the data we collect to try to identify the genetic basis for memory and impulse control, for example. If we can achieve this goal, maybe we can then do more targeted research to understand how the biology goes awry in people who have problems with cognition, including memory and impulse control, like those diagnosed with ADHD, Schizophrenia, Bipolar Disorder, and Autism Spectrum Disorders. By participating in our research, you can learn about mental illness and health and help researchers tackle these complex problems. We can''t do it without your help.

Proper citation: Brain Test (RRID:SCR_006212) Copy   

•   Source: SciCrunch Registry

http://embryoimaging.org/

Collection of high resolution images and movies of mouse and human embryos produced using high resolution episcopic microscopy (HREM). Each data set is a series of block-face images generated during sectioning through an entire embryo, typically cut at 2-3 micrometers. Datasets are organized by approximate developmental stage and each embryo has been assigned a specimen ID (SID) for identification. This is an ongoing project funded by the Wellcome Trust to provide comprehensive imaging of normal and mutant mouse embryos that will complement the standard anatomical texts and form the basis for systematic phenotyping. * Movies: A 3D reconstruction shows each embryo, and lower resolution movies created through each orthogonal plane enable you to quickly review the data set. * Image Stacks: In the stack viewer, you can step through the images in sequence, zoom in to see fine details and adjust the image contrast. * NEW: Embryo Comparison: Two image stacks can now be compared in the stack viewer.

Proper citation: Embryo Imaging (RRID:SCR_006329) Copy   

•   Source: SciCrunch Registry

http://www-personal.umich.edu/~brdsmith/Research.html

Data set of image collections and movies including Magnetic Resonance Imaging of Embryos, Human Embryo Imaging, MRI of Cardiovascular Development, and Live Embryo Imaging. Individual MRI slice images, three-dimensional images, animations, stereo-pair animations, animations of organ systems, and photo-micrographs are included.

Proper citation: Brad Smith Magnetic Resonance Imaging of Embryos (RRID:SCR_006300) Copy   

•   Source: SciCrunch Registry

http://irc.cchmc.org/software/pedbrain.php

Brain imaging data collected from a large population of normal, healthy children that have been used to construct pediatric brain templates, which can be used within statistical parametric mapping for spatial normalization, tissue segmentation and visualization of imaging study results. The data has been processed and compiled in various ways to accommodate a wide range of possible research approaches. The templates are made available free of charge to all interested parties for research purposes only. When processing imaging data from children, it is important to take into account the fact that the pediatric brain differs significantly from the adult brain. Therefore, optimized processing requires appropriate reference data be used because adult reference data will introduce a systematic bias into the results. We have shown that, in the in the case of spatial normalization, the amount of non-linear deformation is dramatically less when a pediatric template is used (left, see also HBM 2002; 17:48-60). We could also show that tissue composition is substantially different between adults and children, and more so the younger the children are (right, see also MRM 2003; 50:749-757). We thus believe that the use of pediatric reference data might be more appropriate.

Proper citation: CCHMC Pediatric Brain Templates (RRID:SCR_003276) Copy   

•   Source: SciCrunch Registry

http://www.humanconnectomeproject.org/

A multi-center project comprising two distinct consortia (Mass. Gen. Hosp. and USC; and Wash. U. and the U. of Minn.) seeking to map white matter fiber pathways in the human brain using leading edge neuroimaging methods, genomics, architectonics, mathematical approaches, informatics, and interactive visualization. The mapping of the complete structural and functional neural connections in vivo within and across individuals provides unparalleled compilation of neural data, an interface to graphically navigate this data and the opportunity to achieve conclusions about the living human brain. The HCP is being developed to employ advanced neuroimaging methods, and to construct an extensive informatics infrastructure to link these data and connectivity models to detailed phenomic and genomic data, building upon existing multidisciplinary and collaborative efforts currently underway. Working with other HCP partners based at Washington University in St. Louis they will provide rich data, essential imaging protocols, and sophisticated connectivity analysis tools for the neuroscience community. This project is working to achieve the following: 1) develop sophisticated tools to process high-angular diffusion (HARDI) and diffusion spectrum imaging (DSI) from normal individuals to provide the foundation for the detailed mapping of the human connectome; 2) optimize advanced high-field imaging technologies and neurocognitive tests to map the human connectome; 3) collect connectomic, behavioral, and genotype data using optimized methods in a representative sample of normal subjects; 4) design and deploy a robust, web-based informatics infrastructure, 5) develop and disseminate data acquisition and analysis, educational, and training outreach materials.

Proper citation: MGH-USC Human Connectome Project (RRID:SCR_003490) Copy   

•   Source: SciCrunch Registry