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on page 1 showing 20 out of 122 results from 1 sources

    Addgene

Cite this (Addgene, RRID:SCR_002037)

URL: http://www.addgene.org

Resource Type: Resource, catalog, data or information resource, database

A non-profit plasmid repository dedicated to helping scientists around the world share high-quality plasmids. They work with laboratories to assemble a high-quality library of published and useful plasmids and their associated cloning/sequence data for use in research and discovery. By linking plasmids with articles, scientists can always find data related to the materials they request. There is no cost to deposit plasmids to Addgene and it will store samples in triplicate (including one at an offsite backup facility), sequence key regions for validation, and handle the appropriate Material Transfer Agreements (MTAs) with institutions. Additionally, users can create a webpage that directs scientists to request plasmids. Material Transfer Agreements (MTAs) allow open exchange to occur because they offer intellectual property and liability protection for material providers. Institutions that have deposited materials at Addgene require a MTA for each transfer of material.

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Cite this (ADHD-200 Sample, RRID:SCR_005358)

URL: http://fcon_1000.projects.nitrc.org/indi/adhd200/index.html#

Resource Type: Resource, disease-related portal, data set, topical portal, portal, data or information resource

A grassroots initiative dedicated to accelerating the scientific community''''s understanding of the neural basis of ADHD through the implementation of open data-sharing and discovery-based science. They believe that a community-wide effort focused on advancing functional and structural imaging examinations of the developing brain will accelerate the rate at which neuroscience can inform clinical practice. The ADHD-200 Global Competition invited participants to develop diagnostic classification tools for ADHD diagnosis based on functional and structural magnetic resonance imaging (MRI) of the brain. Applying their tools, participants provided diagnostic labels for previously unlabeled datasets. The competition assessed diagnostic accuracy of each submission and invited research papers describing novel, neuroscientific ideas related to ADHD diagnosis. Twenty-one international teams, from a mix of disciplines, including statistics, mathematics, and computer science, submitted diagnostic labels, with some trying their hand at imaging analysis and psychiatric diagnosis for the first time. The data for the competition was provided by the ADHD-200 Consortium. Consortium members from institutions around the world provided de-identified, HIPAA compliant imaging datasets from almost 800 children with and without ADHD. A phenotypic file including all of the test set subjects and their diagnostic codes can be downloaded. Winner is presented. The ADHD-200 consortium included: * Brown University, Providence, RI, USA (Brown) * The Kennedy Krieger Institute, Baltimore, MD, USA (KKI) * The Donders Institute, Nijmegen, The Netherlands (NeuroImage) * New York University Medical Center, New York, NY, USA (NYU) * Oregon Health and Science University, Portland, OR, USA (OHSU) * Peking University, Beijing, P.R.China (Peking 1-3) * The University of Pittsburgh, Pittsburgh, PA, USA (Pittsburgh) * Washington University in St. Louis, St. Louis, MO, USA (WashU)

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Cite this (Allen Human Brain Atlas, RRID:SCR_007416)

URL: http://human.brain-map.org/

Resource Type: Resource, data processing software, database, software application, data visualization software, software resource, atlas, data or information resource

A multi-modal atlas of the human brain that integrates anatomic and genomic information, coupled with a suite of visualization and mining tools to create an open public resource for brain researchers and other scientists across a wide range of specialties. Data modalities incorporated into this resource include magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), histology and gene expression data derived from both microarray and in situ hybridization (ISH) approaches. Brain Explorer 2 is a desktop software application for viewing the human brain anatomy and gene expression data in 3D. It is available for download. After installing Brain Explorer 2, you can view gene expression data by performing a gene search from the Microarray page or from within Brain Explorer 2''s main window.

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Cite this (Allen Mouse Brain Atlas, RRID:SCR_002978)

URL: http://mouse.brain-map.org/

Resource Type: Resource, atlas, database, data or information resource

A genome-wide, three-dimensional map of gene expression in the adult mouse brain. Similar in scale to the Human Genome Project, the Atlas reveals the expression patterns of approximately 20,000 genes throughout the entire adult mouse brain down to the cellular level. The Allen Institute's inaugural project, the Atlas was completed in 2006. The Allen Brain Atlas of the mouse brain is an interactive, genome-wide image database of gene expression with ISH and Nissl images. A combination of RNA in situ hybridization data, detailed Reference Atlases and informatics analysis tools are integrated to provide a searchable digital atlas of gene expression. Together, these resources present a comprehensive online platform for exploration of the brain at the cellular and molecular level.

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Cite this (Allen Mouse Brain Connectivity Atlas, RRID:SCR_008848)

URL: http://connectivity.brain-map.org/

Resource Type: Resource, atlas, spatially referenced dataset, data or information resource

A high-resolution map of neural connections in the mouse brain, built on an array of transgenic mice genetically engineered to target specific cell types. In addition to the connectivity data, further information about the transgenic mouse lines and genetic tracers used to create the Atlas is also available, thereby allowing other researchers to expand upon the neural connectivity framework from the Atlas. Ultimately, the Atlas will consist of high resolution 2-D projectivity image data that can be viewed side-by-side with the associated reference atlas and other reference datasets. Tools will be developed to enable 3-D visualization and spatial/ontological search of connectivity models through a combination of manual and informatics analyses. Upon completion the Atlas is expected to include: * Datasets ** Projection Mapping: Axonal projections mapped from ~300 anatomical regions and diverse neuronal populations defined by ~100 different Cre driver lines, labeled by viral tracers and visualized using serial two-photon tomography. ** BDA vs. rAAV Comparison: Axonal projections from 20 representative brain regions labeled by both conventional and viral tracers and imaged using an epifluorescence microscope for direct comparison of the two tracing methods. ** Transgenic Characterization: Transgene expression pattern of ~100 Cre and other driver lines characterized in adult and several developmental time points by colorimetric in situ hybridization (CISH), fluorescence in situ hybridization (FISH) or other histological methods. ** Anatomic Reference: A collection of histology data and whole-brain neuroimaging data to better delineate cytoarchitectural details, including fiber tracts. * Key features ** Annotation of the injection sites for all tracer injected brains. ** Development of a new registration interface from perfused mouse brain and registration into the associated 3-D reference atlas. ** Signal detection, quantification or other informatics analyses of axonal projections. ** 2-D and 3-D interactive, relational database incorporating connectivity information from all brain regions and Cre mice. ** Search and visualization tools for exploring the connectivity data. Currently, the following data are available on the Allen Brain Atlas data portal: * Projection mapping from ~200 brain regions including images from more than 10 Cre driver lines. * BDA vs. rAAV comparison of more than 20 brain regions. * 5 series of histological reference datasets. * Characterization data from more than 100 Cre or other driver lines.

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Cite this (Arizona Biospecimen Locator, RRID:SCR_004151)

URL: https://abl.azdhs.gov

Resource Type: Resource, biomaterial supply resource, tissue bank, data set, cell repository, material resource, data or information resource

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.

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Cite this (ArtRepair for robust fMRI, RRID:SCR_005990)

URL: http://cibsr.stanford.edu/tools/human-brain-project/artrepair-software.html

Resource Type: Resource, image analysis software, data processing software, software application, data visualization software, software resource, software toolkit

A toolbox for SPM to improve fMRI analysis of high motion pediatric and clinical subjects. The toolbox includes special algorithms for motion adjustment, data repair, and noise filtering, and methods to find outlier subjects in group studies. Visualization tools are included for quality checking the data, including a movie format for viewing all data and all contrast estimates on every voxel of every subject. Methods are included to quantify results into percent signal change. * Operating System: OS Independent * Programming Language: MATLAB * Supported Data Format: ANALYZE, NIfTI-1 * execution requires: SPM

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Cite this (Automatic Registration Toolbox, RRID:SCR_005993)

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

Resource Type: Resource, image analysis software, data processing software, software application, image collection, software resource, software toolkit, image processing software, data or information resource

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.

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    BCO-DMO

Cite this (BCO-DMO, RRID:SCR_002191)

URL: http://www.bco-dmo.org/

Resource Type: Resource, service resource, data set, data repository, storage service resource, data or information resource

Accepts and provides access to marine biogeochemical and ecological data sets from NSF-funded research programs. BCO-DMO is also the data repository for the US GLOBEC and JGOFS programs.

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Cite this (Bern-Barcelona EEG database, RRID:SCR_001582)

URL: http://ntsa.upf.edu/downloads/andrzejak-rg-schindler-k-rummel-c-2012-nonrandomness-nonlinear-dependence-and

Resource Type: Resource, data set, source code, software resource, data or information resource

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.

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Cite this (brainmap.org, RRID:SCR_003069)

URL: http://brainmap.org/

Resource Type: Resource, software resource, software application, data or information resource, database

A community database of published functional and structural neuroimaging experiments with both metadata descriptions of experimental design and activation locations in the form of stereotactic coordinates (x,y,z) in Talairach or MNI space. BrainMap provides not only data for meta-analyses and data mining, but also distributes software and concepts for quantitative integration of neuroimaging data. The goal of BrainMap is to develop software and tools to share neuroimaging results and enable meta-analysis of studies of human brain function and structure in healthy and diseased subjects. It is a tool to rapidly retrieve and understand studies in specific research domains, such as language, memory, attention, reasoning, emotion, and perception, and to perform meta-analyses of like studies. Brainmap contains the following software: # Sleuth: database searches and Talairach coordinate plotting (this application requires a username and password) # GingerALE: performs meta-analyses via the activation likelihood estimation (ALE) method; also converts coordinates between MNI and Talairach spaces using icbm2tal # Scribe: database entry of published functional neuroimaging papers with coordinate results

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Cite this (BrainNet Europe, RRID:SCR_004461)

URL: http://www.brainnet-europe.org/

Resource Type: Resource, organization portal, biomaterial supply resource, biospecimen repository, consortium, tissue bank, narrative resource, service resource, brain bank, standard specification, storage service resource, portal, material storage repository, material resource, training service resource, data or information resource

Consortium of 19 brain banks across Europe with an aim to harmonize neuropathological diagnostic criteria and develop gold standards for quality, safety and ethics standards for brain banking. The consortium coordinates various inter-laboratory studies to harmonize neuropathological diagnostics throughout Europe. Consensus meetings take place where experts discuss their recent findings. Additionally, they have an extensive stock of human CNS tissue samples registered in a modern database, which is only accessible to BNE Members. Purpose of BNE: * To promote brain banking as a research resource for European neuroscience through the provision of high quality human brain tissue samples. * To determine the effect of pre- and post mortem parameters on preservation of DNA, RNA, proteins and neurochemical substances. * To determine the limits of usability of human post-mortem brain tissue for advanced molecular techniques. * To develop gold standards for tissue handling, tissue quality control and ethics leading to best practice guidelines for brain banking. * To provide training in brain banking and related methodology. * To reach out to neuroscience centers worldwide and promote future expertise in central nervous system (CNS) research. BrainNet Europe also contributes to research on rare diseases, such as: Pick''s disease or other rare forms of dementia, as well as to questions after the events in the aging brain. Anyone can be a donor - irrespective of disease of the central nervous system or not, because for research purposes, one does not only need tissue samples from ill donors, but also from healthy ones for comparison.

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Cite this (BrainSpan: RNA-Seq exons summarized to genes, RRID:SCR_005029)

URL: http://brainspan.org/rnaseq/downloads.html?format=html

Resource Type: Resource, atlas, data or information resource, expression atlas

BrainSpan, an atlas of the developing human brain, is designed as a foundational resource for studying transcriptional mechanisms involved in human brain development. One of the BrainSpan datasets, RNA-Seq exons summarized to genes, is presented. It is a downloadable archive of files containing normalized RNA-Seq expression values for analysis.

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Cite this (Characterizing unidirectional couplings between point processes and flows, RRID:SCR_001663)

URL: http://ntsa.upf.edu/downloads/andrzejak-rg-kreuz-t-2011-characterizing-unidirectional-couplings-between-point-processes

Resource Type: Resource, software resource, source code

Source code that allows you to calculate the different measures used in Andrzejak and Kreuz 2011, http://iopscience.iop.org/0295-5075/96/5/50012

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Cite this (Charles Stark Draper Laboratory, RRID:SCR_003736)

URL: http://www.draper.com/

Resource Type: Resource, organization portal, portal, data or information resource

A not-for-profit research and development laboratory focused on the design, development, and deployment of advanced technological solutions for the nation's most challenging and important problems in security, space exploration, healthcare, and energy. Their expertise includes the areas of guidance, navigation, and control systems; fault-tolerant computing; advanced algorithms and software solutions; modeling and simulation; and MEMS and multichip module technology. With a strong commitment to delivering working solutions to their sponsors, they apply their expertise to a variety of domains, including autonomous air, land, sea, and space systems; information integration; distributed sensors and networks; precision-guided munitions; and biomedical engineering; chemical/biological defense; and energy system modeling and management. When appropriate for the needs of their sponsors, they will work with commercial partners to transition their technology to commercial production.

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Cite this (CHASM/SNV-Box, RRID:SCR_006445)

URL: http://wiki.chasmsoftware.org/index.php/Main_Page

Resource Type: Resource, software resource, data or information resource, database

CHASM is a method that predicts the functional significance of somatic missense mutations observed in the genomes of cancer cells, allowing mutations to be prioritized in subsequent functional studies, based on the probability that they give the cells a selective survival advantage. SNV-Box is a database of pre-computed features of all possible amino acid substitutions at every position of the annotated human exome. Users can rapidly retrieve features for a given protein amino acid substitution for use in machine learning.

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Cite this (ConBBPRED, RRID:SCR_006194)

URL: http://bioinformatics.biol.uoa.gr/ConBBPRED/

Resource Type: Resource, analysis service resource, data analysis service, service resource, production service resource

A web tool for the Consensus Prediction of TransMembrane Beta-Barrel Proteins. Prediction of the transmembrane strands and topology of beta-barrel outer membrane proteins is of interest in current bioinformatics research. Several methods have been applied so far for this task, utilizing different algorithmic techniques and a number of freely available predictors exist. The methods can be grossly divided to those based on Hidden Markov Models (HMMs), on Neural Networks (NNs) and on Support Vector Machines (SVMs). In this work, we compare the different available methods for topology prediction of beta-barrel outer membrane proteins. We evaluate their performance on a non-redundant dataset of 20 beta-barrel outer membrane proteins of gram-negative bacteria, with structures known at atomic resolution. Also, we describe, for the first time, an effective way to combine the individual predictors, at will, to a single consensus prediction method. We assess the statistical significance of the performance of each prediction scheme and conclude that Hidden Markov Model based methods, HMM-B2TMR, ProfTMB and PRED-TMBB, are currently the best predictors, according to either the per-residue accuracy, the segments overlap measure (SOV) or the total number of proteins with correctly predicted topologies in the test set. Furthermore, we show that the available predictors perform better when only transmembrane beta-barrel domains are used for prediction, rather than the precursor full-length sequences, even though the HMM-based predictors are not influenced significantly. The consensus prediction method performs significantly better than each individual available predictor, since it increases the accuracy up to 4% regarding SOV and up to 15% in correctly predicted topologies.

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    DBASS

Cite this (DBASS, RRID:SCR_002107)

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

Resource Type: Resource, data or information resource, database

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

  • From Current Category

Cite this (Detecting event-related time-dependent directional couplings, RRID:SCR_001664)

URL: http://ntsa.upf.edu/downloads/andrzejak-rg-ledberg-deco-g-2006-detecting-event-related-time-dependent-directional

Resource Type: Resource, software resource, source code

Source code that allows you to calculate the different measures used in Andrzejak et al 2006, http://iopscience.iop.org/1367-2630/8/1/006

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Cite this (Digital Asset Management System, RRID:SCR_000491)

URL: http://libraries.ucsd.edu/about/digital-library/dams.html

Resource Type: Resource, service resource, image repository, data repository, storage service resource, software resource

A digital repository designed to store and manage digital assets of UC San Diego. The flexible architecture can accept a variety of data formats, schemas and web services when ingesting digital assets. It stores digital content files and allows for the creation, indexing and searching of associated metadata to locate and retrieve the content files. Content can be composed of files in any format, including text, sound, video, and images. The DAMS is also designed to facilitate the transfer and submission of the Library's digital assets to Chronopolis and the California Digital Library's Merritt, and can easily be extended to serve other purposes. Additionally, the DAMS is able to export data in many formats, including METS, HTML, OAI, RSS, CSV. Future plans include linking data sets with other universities and organizations. For more information about the DAMS, including the data model and technical diagram, see Github, https://github.com/ucsdlib: * DAMS Manual, https://github.com/ucsdlib/dams/wiki/DAMS-Manual * DAMS Hydra Head, https://github.com/ucsdlib/damspas * DAMS REST API, https://github.com/ucsdlib/dams/wiki/REST-API * DAMS Ontology, https://github.com/ucsdlib/dams/tree/master/ontology Additional information on Hydra is available at Project Hydra and Duraspace.

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