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http://www.dbass.soton.ac.uk/

A database of new exon boundaries induced by pathogenic mutations in human disease genes.

Proper citation: DBASS (RRID:SCR_002107) Copy   

•   Source: SciCrunch Registry

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

Ontology that is a schema for information brokering and knowledge management in the complex field of rare diseases. On the one hand, the Dispedia schema uses semantic technologies to describe patients affected by rare diseases, and on the other hand, to record expertise concerning these particular diseases in machine-processable form. In addition to the differentiated attribution of facts to the individual need for information, Dispedia enables information brokering which adjusts to the type of recipient, its character as well as its personal attitude. The schema was initially geared to an exemplified disease, Amyotrophe Lateralsclerose (ALS).

Proper citation: Dispedia Core Ontology (RRID:SCR_010309) Copy   

•   Source: SciCrunch Registry

http://neurolog.i3s.unice.fr/public_namespace/ontology

An ontology for neuroimaging or medical imaging studies based on DOLCE (Descriptive Ontology for Linguistic and Cognitive Engineering), as the foundational ontology. Detailed description from web: Our aim is the design of a common semantic model providing a unified view on all data and tools to be shared between NeuroLOG partners. For this purpose, we built a multi-layered and multi-components formal ontology. We chose a design framework that structures the ontology at different levels of abstraction while respecting common conceptualization choices. At the highest level is a top-level ontology that includes abstract concepts and relationships valid across domains. We adopted DOLCE (Descriptive Ontology for Linguistic and Cognitive Engineering), as the foundational ontology. We then added Core ontologies, which provide generic, basic and minimal concepts and relations in a specific domain. By minimal we mean that core ontologies should include only the most reusable and widely applicable categories. These kinds of ontologies are essential for sharing intended meaning between different domains. We adopted I& DA (Information and Discourse Acts), a core ontology initially built for classifying documents as a function of their content.We use it to model medical images, which we consider as types of documents. Participant Roles is the core ontology we use to describe the modes of image participation in data processing. I& DA and Participant Roles are built according to DOLCE ontological commitments. On the basis of these two layers, we constructed our Domain ontology dedicated to conceptualizing a specific domain, in this case neuroimaging. Obviously, large domains such as neuroimaging can be divided into sub-domains for the sake of modularization.

Proper citation: OntoNeuroLOG (RRID:SCR_008957) Copy   

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http://www.uchicagoddrcc.org

Center whose goals include fostering collaboration among basic and clinical investigators, facilitating the use of new technologies in the study of treatment of digestive diseases, and providing education and training for improved treatment and diagnosis.

Proper citation: University of Chicago Digestive Diseases Research Core Center (RRID:SCR_015601) Copy   

•   Source: SciCrunch Registry

http://www.bsc.gwu.edu/dpp/index.htmlvdoc

Multicenter clinical research study aimed at discovering whether modest weight loss through dietary changes and increased physical activity or treatment with the oral diabetes drug metformin (Glucophage) could prevent or delay the onset of type 2 diabetes in study participants. At the beginning of the DPP, all 3,234 study participants were overweight and had blood glucose levels higher than normal but not high enough for a diagnosis of diabetesa condition called prediabetes. In addition, 45 percent of the participants were from minority groups-African American, Alaska Native, American Indian, Asian American, Hispanic/Latino, or Pacific Islander-at increased risk of developing diabetes. The DPP found that participants who lost a modest amount of weight through dietary changes and increased physical activity sharply reduced their chances of developing diabetes. Taking metformin also reduced risk, although less dramatically. In the DPP, participants from 27 clinical centers around the United States were randomly divided into different treatment groups. The first group, called the lifestyle intervention group, received intensive training in diet, physical activity, and behavior modification. By eating less fat and fewer calories and exercising for a total of 150 minutes a week, they aimed to lose 7 percent of their body weight and maintain that loss. The second group took 850 mg of metformin twice a day. The third group received placebo pills instead of metformin. The metformin and placebo groups also received information about diet and exercise but no intensive motivational counseling. A fourth group was treated with the drug troglitazone (Rezulin), but this part of the study was discontinued after researchers discovered that troglitazone can cause serious liver damage. The participants in this group were followed but not included as one of the intervention groups. In the years since the DPP was completed, further analyses of DPP data continue to yield important insights into the value of lifestyle changes in helping people prevent type 2 diabetes and associated conditions. For example, one analysis confirmed that DPP participants carrying two copies of a gene variant, or mutation, that significantly increased their risk of developing diabetes benefited from lifestyle changes as much as or more than those without the gene variant. Another analysis found that weight loss was the main predictor of reduced risk for developing diabetes in DPP lifestyle intervention group participants. The authors concluded that diabetes risk reduction efforts should focus on weight loss, which is helped by increased exercise.

Proper citation: Diabetes Prevention Program (RRID:SCR_001501) Copy   

•   Source: SciCrunch Registry

http://crispor.tefor.net

Web application that helps design, evaluate and clone guide sequences for the CRISPR/Cas9 system. This sgRNA design tool assists with guide selection in a variety of genomes and pre-calculated results for all human coding exons as a UCSC Genome Browser track.

Proper citation: CRISPOR (RRID:SCR_015935) Copy   

•   Source: SciCrunch Registry

http://www.humanmotorthalamus.com/

A research tool for clinical and experimental neuroscience, which provides images of human thalamus in the stereotactic planes of the coordinate system based on intercommissural line. Images include histological sagittal sections, sagittal maps, coronal maps, horizontal maps, and MRI images all from the same brain. The website also provides materials and methods section, references to the articles and presentations describing research data (on which the maps are based), and a bibliography on the synaptic relationships of subcortical and cortical afferents with projection and local circuit neurons in the motor thalamus.

Proper citation: Human Thalamus in 3D Stereotactic Coordinates (RRID:SCR_014214) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/iowa3/

Software for real-time parametric statistical analysis of functional MRI (fMRI) data. The system that combines a general architecture for sampling and time-stamping relevant information channels in fMRI (image acquisition, stimulation, subject responses, cardiac and respiratory monitors, etc.) and an efficient approach to manipulating these data, featuring incremental subsecond multiple linear regression. The advantages of the system are the simplification of event timing and efficient and unified data formatting. Substantial parametric analysis can be performed and displayed in real-time. Immediate (replay) and delayed off-line analysis can also be performed with the same interface. The system provides a time-accounting infrastructure that readily supports standard and innovative approaches to fMRI.

Proper citation: I/OWA (RRID:SCR_000858) Copy   

•   Source: SciCrunch Registry

http://www.radiology.ucsf.edu/cind

Biomedical technology research center that develops and validates new imaging methods for detecting brain abnormalities in neurodegenerative diseases, including Alzheimer's disease, vascular dementia, frontotemporal dementia, Parkinson's disease, as well as epilepsy, depression, and other conditions associated with nerve loss in the brain. As people around the globe live longer, the impact of neurodegenerative diseases is expected to increase further with dire social and economical consequences for societies if no effective treatments are developed soon. The development at CIND is aimed to improve magnetic resonance imaging (MRI). The ultimate goal of the scientific program is to identify imaging markers that improve accuracy in diagnosing neurodegenerative diseases at early stages, achieve more reliable prognoses of disease progression, and facilitate the discovery of effective treatment interventions. In addition to addressing the general needs for studying neurodegenerative diseases, another focus of CIND concerns brain diseases associated with military service and war combat, such as post traumatic stress disorder (PTSD), brain trauma, gulf war illness and the long-term effects of these conditions on the mental health of veterans. The symbiosis between CIND and the Veterans Administration Medical Center in San Francisco makes this program uniquely suited to serve military veterans.

Proper citation: Center for Imaging of Neurodegenerative Diseases (RRID:SCR_001968) Copy   

•   Source: SciCrunch Registry

http://rfmri.org/DPARSF

A MATLAB toolbox forpipeline data analysis of resting-state fMRI that is based on Statistical Parametric Mapping (SPM) and a plug-in software within DPABI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), fractional ALFF, degree centrality, voxel-mirrored homotopic connectivity (VMHC) results. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest. DPARSF basic edition is very easy to use while DPARSF advanced edition (alias: DPARSFA) is much more flexible and powerful. DPARSFA can parallel the computation for each subject, and can be used to reorient images interactively or define regions of interest interactively. Users can skip or combine the processing steps in DPARSF advanced edition freely.

Proper citation: DPARSF (RRID:SCR_002372) Copy   

•   Source: SciCrunch Registry

http://icr.coh.org/

Group of 10 academic laboratories provide pancreatic islets of cGMP-quality to eligible investigators for use in FDA approved, IRB-approved transplantation protocols in which isolated human islets are transplanted into qualified patients afflicted with type 1 diabetes mellitus; optimize the harvest, purification, function, storage, and shipment of islets while developing tests that characterize the quality and predict the effectiveness of islets transplanted into patients with diabetes mellitus; and provide pancreatic islets for basic science studies. The centers are electronically linked through an Administrative and Bioinformatics Coordinating Center (ABCC). The ABCC manages a system with objectively defined criteria that establishes the order of priority for islet distribution. It also provides database and other informatics to track the utilization of pancreata and all distributed clinical grade islets for transplant and basic research, and supports the Islet Cell Resource Centers Consortium so that the research community has a single entry point to the program. Qualified researchers from domestic institutions may request islets by submitting a written application to the director of the ABCC. The ICRs will distribute Islets as appropriate for either clinical or basic science protocol use to eligible investigators who have received a favorable review and subsequent approval by the ICR Steering Committee (SC). The Administrative and Bioinformatics Coordinating Center (ABCC) manages the distribution according to a priority list. The ABCC will give preference to investigators who have peer-reviewed, NIH-funded research support.

Proper citation: Islet Cell Resource Centers (RRID:SCR_002806) Copy   

•   Source: SciCrunch Registry

http://mindboggle.info

Mindboggle (http://mindboggle.info) is open source software for analyzing the shapes of brain structures from human MRI data. The following publication in PLoS Computational Biology documents and evaluates the software: Klein A, Ghosh SS, Bao FS, Giard J, Hame Y, Stavsky E, Lee N, Rossa B, Reuter M, Neto EC, Keshavan A. (2017) Mindboggling morphometry of human brains. PLoS Computational Biology 13(3): e1005350. doi:10.1371/journal.pcbi.1005350

Proper citation: Mindboggle (RRID:SCR_002438) Copy   

•   Source: SciCrunch Registry

https://www.ibdgc.org/

Repository of biospecimen and phenotype data collected from Crohn's disease and ulcerative colitis cases and controls recruited at six sites throughout North America that are available to the scientific community. Phenotyping is performed using a standardized protocol, and lymphoblastoid cell lines are established for each subject. Phenotype data for each subject are collected by the Consortium's Data Coordinating Center (DCC), and phenotype data for all subjects with DNA samples are available. The resulting DNA samples have already been utilized by the Consortium to complete various association studies, including genome-wide association studies using dense genotyping arrays. Researchers can obtain DNA samples and phenotype, genotype, and pedigree data through the Data Repository. GWAS data must be requested through dbGAP. The IBDGC is involved with independent genetic research studies and actively works with members of the IBD and genetic communities on collaborative projects. They are also members of the International IBD Genetics Consortium. Phenotype Tools: The Consortium Phenotype Committee, led by Dr. Hillary Steinhart designed and validated paper forms to collect extensive phenotype data on Crohn's Disease and ulcerative colitis. Consortium phenotype tools are available for use by non-Consortium members.

Proper citation: NIDDK Inflammatory Bowel Disease Genetics Consortium (RRID:SCR_001461) Copy   

•   Source: SciCrunch Registry

https://www.upf.edu/web/ntsa/downloads/-/asset_publisher/xvT6E4pczrBw/content/2012-nonrandomness-nonlinear-dependence-and-nonstationarity-of-electroencephalographic-recordings-from-epilepsy-patients

THIS RESOURCE IS NO LONGER IN SERVICE, documented November 23, 2020; EEG data set, source code, and results from 7500 signal pairs from 5 epilepsy patients analyzed in the manuscript, Andrzejak RG, Schindler K, Rummel C. Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys. Rev. E, 86, 046206, 2012. All Matlab source codes are included in the file ASR_Sources_2012_10_16.zip. The clinical purpose of these recordings was to delineate the brain areas to be surgically removed in each individual patient in order to achieve seizure control.

Proper citation: Bern-Barcelona EEG database (RRID:SCR_001582) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/sri24/

An MRI-based atlas of normal adult human brain anatomy, generated by template-free nonrigid registration from images of 24 normal control subjects. The atlas comprises T1, T2, and PD weighted structural MRI, tissue probability maps (GM, WM, CSF), maximum-likelihood tissue segmentation, DTI-based measures (FA, MD, longitudinal and transversal diffusivity), and two labels maps of cortical regions and subcortical structures. The atlas is provided at 1mm isotropic image resolution in Analyze, NIFTI, and Nrrd format. We are also providing an experimental packaging for use with SPM8.

Proper citation: SRI24 Atlas: Normal Adult Brain Anatomy (RRID:SCR_002551) Copy   

•   Source: SciCrunch Registry

https://www.epfl.ch/labs/mmspg/research/page-58317-en-html/bci-2/bci_datasets/

A portal containing EEG datasets (in MATLAB format) and the MATLAB software that were used to produce the results in the paper named in the title of this resource. The files published can also be used as a basis for individual research on P300-based brain-computer interfaces. The system is based on the P300 evoked potential and is tested with five severely disabled and four able-bodied subjects. For four of the disabled subjects classification accuracies of 100% are obtained. The bitrates obtained for the disabled subjects range between 10 and 25 bits/min. The effect of different electrode configurations and machine learning algorithms on classification accuracy is tested.

Proper citation: An efficient P300-based brain-computer interface for disabled subjects (RRID:SCR_001584) Copy   

•   Source: SciCrunch Registry

http://neuromuscular.wustl.edu/

Organization portal for neuromuscular disease community and contains comprehensive listing of biological and clinical aspects of neuromuscular disorders.This knowledge base contains information on the physiology, structure of ion channels, neurotransmitters, neuroreceptors, and associated diseases. Major categories include DISORDERS & SYNDROMES, INDEXES, NEUROMUSCULAR EVALUATION, ANTIBODY TESTING and NEUROMUSCULAR DIVISION.

Proper citation: Washington University Neuromuscular Disease Center (RRID:SCR_002059) Copy   

•   Source: SciCrunch Registry

http://clones.haplogen.org/

Collection of isogenic human cell lines that are deficient for the expression of single genes. The current collection is based on the human cell line KBM-7 (Kotecki et al. Experimental Cell Research 1999), which is haploid for all chromosomes except chromosome 8 and a small part of chromosome 15. In these cells, genes are disrupted by the means of a retroviral gene trap. The collection is being expanded to cover the majority of expressed genes. The Human Gene Trap Mutant Collection is generated as a public-private partnership between CeMM (the Research Center for Molecular Medicine of the Austrian Academy of Sciences) and Haplogen.

Proper citation: Human Gene Trap Mutant Collection (RRID:SCR_001634) Copy   

•   Source: SciCrunch Registry

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

Human brain atlases for adult, pediatric and elderly populations, by iterative joint deformable registration of training datasets into a single unbiased average image. Atlases packages include T1-weighted images, tissue priors (WM,GM,CSF), lobar parcellation maps and subcortical structures. Current available atlases: * Adult atlas: Symmetric atlas generated from 50+ healthy adult subjects (20-59 year old). * UNC-MNI Pediatric 1-year-old atlas: Symmetric atlas generated from 104 1-year-old subjects, combining children at high familial risk of autism and controls. * Pediatric 4-year-old atlas: Symmetric atlas generated from 10 4-year-old healthy subjects. * Elderly atlas: Atlas generated from 27 healthy elderly subjects (60+ years old). Additional information and acknowledgment for their usage can be found by clicking on the release notes.

Proper citation: UNC Human Brain Atlas (RRID:SCR_002606) Copy   

•   Source: SciCrunch Registry

http://aimlab.cs.uoregon.edu/NEMO/web/

THIS RESOURCE IS NO LONGER IN SERVICE. NIH tombstone webpage lists Project Period : 2009 - 2013. NIH funded project to create EEG and MEG ontologies and ontology based tools. These resources will be used to support representation, classification, and meta-analysis of brain electromagnetic data. Three pillars of NEMO are: DATA, ONTOLOGY, and DATABASE. NEMO data consist of raw EEG, averaged EEG (ERPs), and ERP data analysis results. NEMO ontologies include concepts related to ERP data (including spatial and temporal features of ERP patterns), data provenance, and cognitive and linguistic paradigms that were used to collect data. NEMO database portal is large repository that stores NEMO consortium data, data analysis results, and data provenance. EEG and MEG ontologies and ontology-based tools to support representation, classification, and meta-analysis of brain electromagnetic data. Raw EEG and ERP data may be uploaded to the NEMO FTP site., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Neural ElectroMagnetic Ontologies (NEMO) Project (RRID:SCR_002001) Copy   

•   Source: SciCrunch Registry