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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.

(last updated: Oct 12, 2019)

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Resource NameResource TypeDescriptionKeywordsResource IDProper CitationParent OrganizationRelated ConditionFunding AgencyRelationReferenceWebsite StatusAlternate IDsAlternate URLsOld URLs
RefGenesResource, analysis service resource, data analysis service, service resource, commercial organization, production service resourceTool for the identification of reliable and condition specific reference genes for RT-qPCR data normalization. RefGenes is available within Genevestigator.rt-qpcr, target gene, gene expression, reference gene, microarraySCR_003372(RefGenes, RRID:SCR_003372)Genevestigator listed by: OMICtoolsPMID:21418615Last checked upOMICS_02319
Stowers Original Data RepositoryResource, bibliography, database, service resource, storage service resource, data repository, data or information resourceOpen access repository of original, unprocessed data underlying work published by Stowers researchers to allow the scientific community to validate and extend the findings made by Stowers researchers. For papers first submitted for publication after November 1, 2011, the Stowers Institute requires its members to deposit original data files into the Stowers Original Data Repository or to repositories maintained by third parties at the time of publication. Access to the Stowers Original Data Repository is free, but you will be asked to register before you can download set, imageSCR_002640(Stowers Original Data Repository, RRID:SCR_002640)Stowers Institute for Medical Research related to: OME-TIFF FormatLast checked upnlx_156067
The Cancer Genome AtlasResource, biomaterial supply resource, biospecimen repository, data set, service resource, portal, storage service resource, material storage repository, material resource, data or information resourceProject exploring the spectrum of genomic changes involved in more than 20 types of human cancer that provides a platform for researchers to search, download, and analyze data sets generated. As a pilot project it confirmed that an atlas of changes could be created for specific cancer types. It also showed that a national network of research and technology teams working on distinct but related projects could pool the results of their efforts, create an economy of scale and develop an infrastructure for making the data publicly accessible. Its success committed resources to collect and characterize more than 20 additional tumor types. Components of the TCGA Research Network: * Biospecimen Core Resource (BCR); Tissue samples are carefully cataloged, processed, checked for quality and stored, complete with important medical information about the patient. * Genome Characterization Centers (GCCs); Several technologies will be used to analyze genomic changes involved in cancer. The genomic changes that are identified will be further studied by the Genome Sequencing Centers. * Genome Sequencing Centers (GSCs); High-throughput Genome Sequencing Centers will identify the changes in DNA sequences that are associated with specific types of cancer. * Proteome Characterization Centers (PCCs); The centers, a component of NCI's Clinical Proteomic Tumor Analysis Consortium, will ascertain and analyze the total proteomic content of a subset of TCGA samples. * Data Coordinating Center (DCC); The information that is generated by TCGA will be centrally managed at the DCC and entered into the TCGA Data Portal and Cancer Genomics Hub as it becomes available. Centralization of data facilitates data transfer between the network and the research community, and makes data analysis more efficient. The DCC manages the TCGA Data Portal. * Cancer Genomics Hub (CGHub); Lower level sequence data will be deposited into a secure repository. This database stores cancer genome sequences and alignments. * Genome Data Analysis Centers (GDACs) - Immense amounts of data from array and second-generation sequencing technologies must be integrated across thousands of samples. These centers will provide novel informatics tools to the entire research community to facilitate broader use of TCGA data. TCGA is actively developing a network of collaborators who are able to provide samples that are collected retrospectively (tissues that had already been collected and stored) or prospectively (tissues that will be collected in the future).genome, genome sequencing, breast, central nervous system, endocrine, gastrointestinal, gynecologic, head, neck, hematologic, skin, soft tissue, thoracic, urologic, clinical, genomic characterization, analysis, tumor genome, demographic, gene expression, copy number alteration, epigenetic, dna sequence, exome, snp, methylation, mrna, mirnaSCR_003193(The Cancer Genome Atlas, RRID:SCR_003193)National Cancer Institute Cancer, Tumor, Normal, Breast cancer, Central Nervous System cancer, Endocrine cancer, Gastrointestinal cancer, Gynecologic cancer, Head cancer, Neck cancer, Hematologic cancer, Skin cancer, Soft tissue cancer, Thoracic cancer, Urologic cancerNCIrelated to: Cancer3D, used by: Mutation Annotation and Genomic Interpretation, BioXpress, listed by: One Mind Biospecimen Bank ListingLast checked upnlx_156913
Educational Resources in NeuroscienceResource, catalog, database, data or information resource, training resourceA database that lists, reviews, and rates resources for teaching neuroscience at the graduate and undergraduate, neuroscience, training material, clinicalSCR_000169(Educational Resources in Neuroscience, RRID:SCR_000169)Society for Neuroscience NSFrelated to: Neuromuscular Disease Center, ReMoto, Neurofly, Free Statistical Software, used by: NIF Data Federation, lists: UCSC Genome Browser, Neuromuscular Disease Center, ReMoto, Neurofly, Free Statistical SoftwareLast checked upnlx_143786
VIVOResource, controlled vocabulary, software application, service resource, portal, software resource, ontology, community building portal, data or information resourceOpen source semantic web application that enables the discovery of research and scholarship across disciplines at a particular institution and across institutions by creating a semantic cloud of information that can be searched and browsed. Participants include institutions with local installations of VIVO or those with research discovery and profiling applications that can provide semantic web-compliant data. The information accessible through the national network''''s search and browse capability will therefore reside and be controlled locally within institutional VIVOs or other semantic web applications. The VIVO ontology provides a set of types (classes) and relationships (properties) to represent researchers and the full context of their experience, outputs, interests, accomplishments, and associated institutions. VIVO is populated with detailed profiles of faculty and researchers including information such as publications, teaching, service, and professional affiliations. It also supports browsing and a search function which returns faceted results for rapid retrieval of desired information. The rich semantically structured data in VIVO support and facilitate research discovery. Examples of applications that consume these rich data include: visualizations, enhanced multi-site search through VIVO Search, and applications such as VIVO Searchlight, a browser bookmarklet which uses text content of any webpage to search for relevant VIVO profiles, and the Inter-Institutional Collaboration Explorer, an application which allows visualization of collaborative institutional partners, among others. Institutions are free to participate in the national network by installing and using the application. The application provides linked data via RDF data making users a part of the semantic web! or any other application that provides linked data can be used. Users can also get involved with developing applications that provide enhanced search, new collaboration capabilities, grouping, finding and mapping scientists and their sharing, network, semantic web, linked data, rdf, owl, database, people resourceSCR_005246(VIVO, RRID:SCR_005246)University of Florida; Florida; USA NCRRrelated to: Clinical and Translational Science Awards Consortium, DataStaR, CTSAconnect, CTSA ShareCenter, listed by: BioPortalLast checked upnlx_144266
GeneMANIAResource, data analysis service, database, analysis service resource, production service resource, service resource, software resource, data or information resourceData analysis service to predict the function of your favorite genes and gene sets. Indexing 1,421 association networks containing 266,984,699 interactions mapped to 155,238 genes from 7 organisms. GeneMANIA interaction networks are available for download in plain text format. GeneMANIA finds other genes that are related to a set of input genes, using a very large set of functional association data. Association data include protein and genetic interactions, pathways, co-expression, co-localization and protein domain similarity. You can use GeneMANIA to find new members of a pathway or complex, find additional genes you may have missed in your screen or find new genes with a specific function, such as protein kinases. Your question is defined by the set of genes you input. If members of your gene list make up a protein complex, GeneMANIA will return more potential members of the protein complex. If you enter a gene list, GeneMANIA will return connections between your genes, within the selected datasets. GeneMANIA suggests annotations for genes based on Gene Ontology term enrichment of highly interacting genes with the gene of interest. GeneMANIA is also a gene recommendation system. GeneMANIA is also accessible via a Cytoscape plugin, designed for power users. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatiblegene, association data, protein interaction, genetic interaction, pathway, co-expression, co-localization, protein, software library, statistical analysis, term enrichment, analysis, browser, gene ontology, gene predicting, gene prioritization, database or data warehouse, other analysis, interaction browser, protein-protein interaction, interactionSCR_005709(GeneMANIA, RRID:SCR_005709)University of Toronto; Ontario; Canada Genome Canada, Ontario Ministry of Research and Innovationrelated to: Cytoscape, Gene Ontology, PSICQUIC Registry, listed by: Gene Ontology ToolsReferences (3)Last checked upnlx_149159
Integrated Earth Data ApplicationsResource, data analysis service, database, analysis service resource, production service resource, service resource, storage service resource, data repository, data or information resourceA community-based data facility to support, sustain, and advance the geosciences by providing data services for observational solid earth data from the Ocean, Earth, and Polar Sciences. IEDA systems enable these data to be discovered and reused by a diverse community now and in the future. Data services include data access, data analysis, data compliance, data publication, DOI search, and web services. Desktop apps GeoMapApp and Virtual Ocean are available to explore, visualize and analyze your own data within the context of hundreds of other earth science data from around the world. IEDA is a partnership between EarthChem and the Marine Geoscience Data System (MGDS). EarthChem and MGDS systems include the geochemical databases PetDB and SedDB, the geochemistry data network EarthChem, the Ridge2000 and MARGINS Data Portals, the Academic Seismic Portal field data collection, the Antarctic and Southern Ocean Data System, the Global Multi Resolution Topography synthesis, and the System for Earth Sample Registration, ocean, earth, polar, sciences, ocean sciences, earth sciences, polar sciences, doi, global geochemistry, marine geoscienceSCR_006739(Integrated Earth Data Applications, RRID:SCR_006739)Columbia University; New York; USA NSFLast checked upnlx_156096
GrameneResource, data analysis service, production service resource, analysis service resource, database, service resource, data or information resourceA curated, open-source, integrated data resource for comparative functional genomics in crops and model plant species to facilitate the study of cross-species comparisons using information generated from projects supported by public funds. It currently hosts annotated whole genomes in over two dozen plant species and partial assemblies for almost a dozen wild rice species in the Ensembl browser, genetic and physical maps with genes, ESTs and QTLs locations, genetic diversity data sets, structure-function analysis of proteins, plant pathways databases (BioCyc and Plant Reactome platforms), and descriptions of phenotypic traits and mutations. The web-based displays for phenotypes include the Genes and Quantitative Trait Loci (QTL) modules. Sequence based relationships are displayed in the Genomes module using the genome browser adapted from Ensembl, in the Maps module using the comparative map viewer (CMap) from GMOD, and in the Proteins module displays. BLAST is used to search for similar sequences. Literature supporting all the above data is organized in the Literature database. In addition, Gramene now hosts a variety of web services including a Distributed Annotation Server (DAS), BLAST and a public MySQL database. Twice a year, Gramene releases a major build of the database and makes interim releases to correct errors or to make important updates to software and/or data. Additionally you can access Gramene through an FTP site.crop, plant genome, genetic, blast, gene, genome, genetic diversity, pathway, protein, marker, quantitative trait locus, comparative map, phenotype, genomics, physiology, comparative, grain, expressed sequence tag, trait, mutation, environment, taxonomy, web serviceSCR_002829(Gramene, RRID:SCR_002829) Cornell University; New York; USA , Cold Spring Harbor Laboratory NSF, USDA, USDA IFAFSrelated to: AmiGO, Gene Ontology, PO, Trait Ontology, EnvO, BioCyc, used by: NIF Data Federation, listed by: re3data.orgReferences (3)Last checked upnif-0000-02926, nlx_65829, SCR_000532
CellFinderResource, data analysis service, production service resource, analysis service resource, database, service resource, data or information resourceDatabase of mapped validated gene and protein expression, phenotype and images related to cell types. The data allow characterization and comparison of cell types and can be browsed by using the body browser and by searching for cells or genes. All cells are related to more complex systems such as tissues, organs and organisms and arranged according to their position in development. CellFinder provides long-term data storage for validated and curated primary research data and provides additional expert-validation through relevant information extracted from text. Operated under the Open Source/Access model, community and scientific networking applications will allow users to store and retrieve their data and to explore cells and their interactions on singular and complex resolution levels. The involvement of stem cell registries and banks will allow direct access to selected cells. The set up the stem cell data repository will involve three lines of action: * the acquisition of scientific data and contents * the standardized description of this data, its organization with the help of ontologies and technical implementation * the integration of existing sources/logistics and to ensure sustainable long-term operationcell, gene expression, protein expression, phenotype, image, gene, stem cell, cellome, metadata standard, stem cell therapy, differential expression, protein, cell type, development, adrenal gland, gall bladder, intestine, inferior vena cava, kidney, liver, spleenSCR_006598(CellFinder, RRID:SCR_006598)Charite - Universitatsmedizin Berlin; Berlin; Germany BMBF, DFGPMID:23599415Last checked upnlx_153857
Europhenome Mouse Phenotyping ResourceResource, data analysis service, production service resource, analysis service resource, database, service resource, data or information resourceOpen source software system for capturing, storing and analyzing raw phenotyping data from SOPs contained in EMPReSS, it provides access to raw and annotated mouse phenotyping data generated from primary pipelines such as EMPReSSlim and secondary procedures from specialist centers. Mutants of interest can be identified by searching the gene or the predicted phenotype. You can also access phenotype data from the EMPReSSlim Pipeline for inbred mouse strains. Initially EuroPhenome was developed within the EUMORPHIA programme to capture and store pilot phenotyping data obtained on four background strains (C57BL/6J, C3H/HeBFeJ, BALB/cByJ and 129/SvPas). EUMORPHIA (European Union Mouse Research for Public Health and Industrial Applications) was a large project comprising of 18 research centers in 8 European countries, with the main focus of the project being the development of novel approaches in phenotyping, mutagenesis and informatics to improve the characterization of mouse models for understanding human molecular physiology and pathology. The current version of EuroPhenome is capturing data from the EUMODIC project as well as the WTSI MGP, HMGU GMC pipeline and the CMHD. EUMODIC is undertaking a primary phenotype assessment of up to 500 mouse mutant lines derived from ES cells developed in the EUCOMM project as well as other lines. Lines showing an interesting phenotype will be subject to a more in depth assessment. EUMODIC is building upon the comprehensive database of standardized phenotyping protocols, called EMPReSS, developed by the EUMORPHIA project. EUMODIC has developed a selection of these screens, called EMPReSSslim, to enable comprehensive, high throughput, primary phenotyping of large numbers of mice. Phenovariants are annotated using a automated pipeline, which assigns a MP term if the mutant data is statistically different to the baseline data. This data is shown in the Phenomap and the mine for a mutant tool. Please note that a statistically significant result and the subsequent MP annotation does not necessarily mean a true phenovariant. There are other factors that could cause this result that have not been accounted for in the analysis. It is the responsibility of the user to download the data and use their expert knowledge or further analysis to decide whether they agree or not. EuroPhenome is primarily based in the bioinformatics group at MRC Harwell. The development of EuroPhenome is in collaboration with the Helmholtz Zentrum Munchen, Germany, the Wellcome Trust Sanger Institute, UK and the Institut Clinique de la Souris, France.phenotype, gene, mutant mouse strain, inbred mouse strain, annotation, ortholog, high-throughput, phenovariant, disorder, c57bl/6j, c3h/hebfej, balb/cbyj, 129/svpasSCR_006935(Europhenome Mouse Phenotyping Resource, RRID:SCR_006935)MRC Mammalian Genetics Unit European Union FP6, MRC, National Genome Research Networkrelated to: European Mouse Phenotyping Resource of Standardised Screens, OMIM, Understanding Human Disease Through Mouse Genetics, European Conditional Mouse Mutagenesis Program, European Mouse Phenotyping Resource of Standardised Screens, Monarch InitiativeReferences (2)Last checked upnif-0000-30535
BrainscapeResource, data analysis service, production service resource, analysis service resource, database, service resource, storage service resource, image repository, data repository, data or information resourceTHIS RESOURCE IS NO LONGER IN SERVICE, documented on May 23, 2013. Database for resting state functional connectivity studies. Functional connectivity has shown tremendous promise in mapping the intrinsic functional topography of the brain, evaluating neuroanatomical models, and investigating neurological and psychiatric disease. Brainscape includes a repository of public and private data and an analysis engine for exploring the correlation structure of spontaneous fluctuations in the fMRI BOLD signal. (DICOM data is the image format that can be uploaded.) With Brainscape you can upload, analyze, and share your own data. You can search for, download, and analyze studies in the repository of shared data. The analysis engine works by selecting one or more studies, typing in the coordinates of a brain region of interest, and the seed-region correlation engine computes the correlation structure across the whole brain. (T1, T2 and EPI data are the scan types Brainscape can process.) You decide who can access your data. You can keep it to yourself, share with select colleagues, or share it with everyone. The Brainscape database and analysis tools are open source and freely available.functional connectivity, fmri bold signal, brain, neuroanatomy, region of interest, resting state, fmri, analysis, processing, dicom, dicom data, t1, t2, epi data, 4-dimensional floating point, raw, statistical comparison, functional topography, neurological, psychiatric, disease, mri, functional, statistical operation, correlationSCR_002962(Brainscape, RRID:SCR_002962)University of California at San Diego; California; USA listed by: NeuroImaging Tools and Resources Collaboratory (NITRC), BiositemapsLast checked upnif-0000-00501
BeeBaseResource, data analysis service, production service resource, analysis service resource, data set, service resource, data or information resourceGene sequences and genomes of Bombus terrestris, Bombus impatiens, Apis mellifera and three of its pathogens, that are discoverable and analyzed via genome browsers, blast search, and apollo annotation tool. The genomes of two additional species, Apis dorsata and A. florea are currently under analysis and will soon be incorporated.genome, gene set, sequence, bee, genomics, entomology, blast, annotation, pest, pathogen, honey, beehive, insect, bee pollen, bee product, bee culture, pollination, pollinatorSCR_008966(BeeBase, RRID:SCR_008966)University of Missouri; Missouri; USA Golden Heritage Foods and Sioux Honey Association, NHGRI, Texas Agricultural Experiment Station, USDAlisted by: re3data.orgPMID:21071397Last checked upnlx_152034
EnrichrResource, data analysis service, production service resource, analysis service resource, software application, service resource, software resourceA web-based gene list enrichment analysis tool that provides various types of visualization summaries of collective functions of gene lists. It includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes / proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries.bed, gene, software as a service, rna-seq, analyze, protein, function, gene list, visualizationSCR_001575(Enrichr, RRID:SCR_001575)Icahn School of Medicine at Mount Sinai; New York; USA PMID:23586463Last checked upSciRes_000171
TM4 Microarray Software Suite - TIGR MultiExperiment ViewerResource, data analysis software, data processing software, software application, data visualization software, software resource, software toolkitA desktop application for the analysis, visualization and data-mining of large-scale genomic data. It is a versatile microarray tool, incorporating sophisticated algorithms for clustering, visualization, classification, statistical analysis and biological theme discovery. MeV generates informative and interrelated displays of expression and annotation data from single or multiple experiments. A huge array of alrogithms are included in MeV modules, and are available at a button-click, such as K-means clustering, Hierarchical clustering, t-Tests, Significance Analysis of Microarrays, Gene Set Enrichment Analysis, and EASE. Extensive documentation is available for helping new users get started with MeV. A Quickstart Guide provides the tutorial a brand new person will need to get their first dataset loaded and displayed in the program. Returning MEV users will want to check out the release notes to see what new features are available in the latest versions of the program. Tutorials have been written about several of its more involved features.gene expression, analysis, annotation, classification, microarray, visualization, statistical analysis, clustering, biological theme, graphic, annotation, data miningSCR_001915(TM4 Microarray Software Suite - TIGR MultiExperiment Viewer, RRID:SCR_001915)Dana-Farber Cancer Institute , J. Craig Venter Institute , University of Washington; Seattle; USA NCRRrelated to: Gene Ontology, listed by: Gene Ontology ToolsReferences (2)Last checked upnif-0000-10486, SCR_005576, OMICS_00781
EMBOSSResource, data analysis software, data processing software, software application, sequence analysis software, software resource, software toolkitA software analysis package specially developed for the needs of the molecular biology (e.g. EMBnet) user community. The software automatically copes with data in a variety of formats and even allows transparent retrieval of sequence data from the web. Also, as extensive libraries are provided with the package, it is a platform to allow other scientists to develop and release software in true open source spirit. EMBOSS also integrates a range of currently available packages and tools for sequence analysis into a seamless whole. EMBOSS has: a properly constructed toolkit for creating robust bioinformatics applications or workflows; a comprehensive set of sequence analysis programs; all sequence and many alignment and structural formats are handled; extensive programming library for common sequence analysis tasks; and additional programming libraries for many other areas. Within EMBOSS are programs (applications) covering areas such as: sequence alignment, rapid database searching with sequence patterns, protein motif identification, nucleotide sequence pattern analysis, codon usage analysis for small genomes, rapid identification of sequence patterns in large scale sequence sets, and presentation tools for publication.SCR_008493(EMBOSS, RRID:SCR_008493)related to: BioExtractLast checked upnif-0000-30488
UCSF DataShareResource, database, catalog, service resource, storage service resource, data repository, data or information resourcePlatform to facilitate sharing, discovery, and secure access to UCSF biomedical data. It''s powered by the Dataverse Network platform, which supports a variety of data types, as well as attribution and licensing needs. Researchers may share datasets, discover data from other labs, and reuse data. Links to tools and information that help scientists properly organize, manage, and document their datasets are also provided.biomedical data, neuroimaging, mri, cognitive impairment, dementia, data sharing, data setSCR_004340(UCSF DataShare, RRID:SCR_004340)University of California at San Francisco; California; USA Aginguses: Dataverse Network ProjectLast checked upnlx_35596
ConnectomeDBResource, database, image collection, service resource, storage service resource, image repository, data repository, data or information resourceData management platform that houses all data generated by the Human Connectome Project - image data, clinical evaluations, behavioral data and more. ConnectomeDB stores raw image data, as well as results of analysis and processing pipelines. Using the ConnectomeDB infrastructure, research centers will be also able to manage Connectome-like projects, including data upload and entry, quality control, processing pipelines, and data distribution. ConnectomeDB is designed to be a data-mining tool, that allows users to generate and test hypotheses based on groups of subjects. Using the ConnectomeDB interface, users can easily search, browse and filter large amounts of subject data, and download necessary files for many kinds of analysis. ConnectomeDB is designed to work seamlessly with Connectome Workbench, an interactive, multidimensional visualization platform designed specifically for handling connectivity data. De-identified data within ConnectomeDB is publicly accessible. Access to additional data may be available to qualified research investigators. ConnectomeDB is being hosted on a BlueArc storage platform housed at Washington University through the year 2020. This data platform is based on XNAT, an open-source image informatics software toolkit developed by the NRG at Washington University. ConnectomeDB itself is fully open source.brain, connectivity, human, adult human, evaluation, clinical, behavior, data set, diffusion imaging, resting-state fmri, task-evoked fmri, t1-weighted mri, t2-weighted mri, structural mapping, myelin mapping, magnetoencephalography, electroencephalography, fmri, twinSCR_004830(ConnectomeDB, RRID:SCR_004830)Washington University in St. Louis; Missouri; USA Healthy, Twin, Non-twin siblingMcDonnell Center for Systems Neuroscience, NIH Blueprint for Neuroscience Research, NIMH, Washington University in St. Louis; Missouri; USArelated to: Connectome Workbench, XNAT - The Extensible Neuroimaging Archive Toolkit, listed by: NeuroImaging Tools and Resources Collaboratory (NITRC)PMID:22366334Last checked upnlx_143923
HUDSEN Human Gene Expression Spatial DatabaseResource, database, service resource, storage service resource, atlas, data repository, data or information resourceDatabase of a set of standard 3D virtual models at different stages of development from Carnegie Stages (CS) 12-23 (approximately 26-56 days post conception) in which various anatomical regions have been defined with a set of anatomical terms at various stages of development (known as an ontology). Experimental data is captured and converted to digital format and then mapped to the appropriate 3D model. The ontology is used to define sites of gene expression using a set of standard descriptions and to link the expression data to an ''''anatomical tree''''. Human data from stages CS12 to CS23 can be submitted to the HUDSEN Gene Expression Database. The anatomy ontology currently being used is based on the Edinburgh Human Developmental Anatomy Database which encompasses all developing structures from CS1 to CS20 but is not detailed for developing brain structures. The ontology is being extended and refined (by Prof Luis Puelles, University of Murcia, Spain) and will be incorporated into the HUDSEN database as it is developed. Expression data is annotated using two methods to denote sites of expression in the embryo: spatial annotation and text annotation. Additionally, many aspects of the detection reagent and specimen are also annotated during this process (assignment of IDs, nucleotide sequences for probes etc). There are currently two main ways to search HUDSEN - using a gene/protein name or a named anatomical structure as the query term. The entire contents of the database can be browsed using the data browser. Results may be saved. The data in HUDSEN is generated from both from researchers within the HUDSEN project, and from the wider scientific community. The HUDSEN human gene expression spatial database is a collaboration between the Institute of Human Genetics in Newcastle, UK, and the MRC Human Genetics Unit in Edinburgh, UK, and was developed as part of the Electronic Atlas of the Developing Human Brain (EADHB) project (funded by the NIH Human Brain Project). The database is based on the Edinburgh Mouse Atlas gene expression database (EMAGE), and is designed to be an openly available resource to the research community holding gene expression patterns during early human development.embryonic human, anatomy, developmental stage, development, brain, gene expression, optical projection tomography, carnegie stage, in situ hybridization, immunohistochemistry, gene, ontology, anatomical structure, protein expression, embryonic development, annotation, embryoSCR_006325(HUDSEN Human Gene Expression Spatial Database, RRID:SCR_006325)HUDSEN EU FP6 Research Infrastructure Action Structuring the European Research Area Programme, NIMH, SENECA Foundation, Spanish Ministry of Science and Innovationrelated to: EMAGE Gene Expression Database, Human Developmental Biology ResourcePMID:20979583Last checked upnlx_152026
XNAT CentralResource, database, service resource, storage service resource, image repository, data repository, data or information resourceOnline repository of open access images including MR Sessions, MRI, Freesurfer APARC, Freesurfer ASEGs, Clinical Assessments, Atlas Scaling Factors, and Fast Segmentations data. CENTRAL currently contains 374 Projects, 3808 Subjects, and 5174 Imaging Sessions (June 2014). Central is powered by XNAT (The Extensible Neuroimaging Archive Toolkit), an open source software platform designed to facilitate management and exploration of neuroimaging and related data. XNAT includes a secure database backend and a rich web-based user interface.magnetic resonance, pet, computed tomography, neuroimaging, mri, computer axial tomography imaging protocol, freesurfer aparc, freesurfer aseg, clinical assessment, atlas scaling factor, fast segmentation, image collection, clinicalSCR_006235(XNAT Central, RRID:SCR_006235)Washington University School of Medicine in St. Louis; Missouri; USA related to: Morphometry BIRN, XNAT - The Extensible Neuroimaging Archive Toolkit, used by: NITRC-IR, NIF Data Federation, Integrated Datasets, listed by: re3data.orgLast checked upnif-0000-04375
Digital Commons NetworkResource, data or information resource, databaseBibliographic database that brings together free, full-text scholarly articles from hundreds of universities and colleges worldwide. Curated by university librarians and their supporting institutions, the Network includes a growing collection of peer-reviewed journal articles, book chapters, dissertations, working papers, conference proceedings, and other original scholarly work. A central discipline wheel features ten color-coded disciplines: law, social and behavioral sciences, arts and humanities, life sciences, physical sciences and mathematics, education, engineering, medicine and health sciences, business, and architecture. The size of each color-coded area reflects the size of each discipline's collection relative to the rest of DCN. Users can click on any segment of any layer of the wheel, with the selected discipline, subdiscipline, or subject navigating users to their chosen commons area where they can then proceed to a list of full-text PDFs. To be clear, typing a couple of keywords into the Search Entire Network box, also located on the homepage, might be a more efficient method than mousing around on this graphical browsing element. If you would like to contribute your institution's research to the Digital Commons Network, Use the form provided, article, book chapter, dissertation, working paper, conference proceeding, architecture, art, humanities, business, education, engineering, law, life sciences, medicine, health sciences, physical sciences, mathematics, social sciences, behavioral sciences, bibliographySCR_002646(Digital Commons Network, RRID:SCR_002646)affiliated with: Teaching CommonsLast checked upnlx_156076
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