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Found 59 result(s)
The Gene database provides detailed information for known and predicted genes defined by nucleotide sequence or map position. Gene supplies gene-specific connections in the nexus of map, sequence, expression, structure, function, citation, and homology data. Unique identifiers are assigned to genes with defining sequences, genes with known map positions, and genes inferred from phenotypic information. These gene identifiers are used throughout NCBI's databases and tracked through updates of annotation. Gene includes genomes represented by NCBI Reference Sequences (or RefSeqs) and is integrated for indexing and query and retrieval from NCBI's Entrez and E-Utilities systems.
The PAIN Repository is a recently funded NIH initiative, which has two components: an archive for already collected imaging data (Archived Repository), and a repository for structural and functional brain images and metadata acquired prospectively using standardized acquisition parameters (Standardized Repository) in healthy control subjects and patients with different types of chronic pain. The PAIN Repository provides the infrastructure for storage of standardized resting state functional, diffusion tensor imaging and structural brain imaging data and associated biological, physiological and behavioral metadata from multiple scanning sites, and provides tools to facilitate analysis of the resulting comprehensive data sets.
The ENCODE Encyclopedia organizes the most salient analysis products into annotations, and provides tools to search and visualize them. The Encyclopedia has two levels of annotations: Integrative-level annotations integrate multiple types of experimental data and ground level annotations. Ground-level annotations are derived directly from the experimental data, typically produced by uniform processing pipelines.
The CancerData site is an effort of the Medical Informatics and Knowledge Engineering team (MIKE for short) of Maastro Clinic, Maastricht, The Netherlands. Our activities in the field of medical image analysis and data modelling are visible in a number of projects we are running. CancerData is offering several datasets. They are grouped in collections and can be public or private. You can search for public datasets in the NBIA (National Biomedical Imaging Archive) image archives without logging in.
The Cancer Genome Atlas (TCGA) Data Portal provides a platform for researchers to search, download, and analyze data sets generated by TCGA. It contains clinical information, genomic characterization data, and high level sequence analysis of the tumor genomes. The Data Coordinating Center (DCC) is the central provider of TCGA data. The DCC standardizes data formats and validates submitted data.
The Brain Transcriptome Database (BrainTx) project aims to create an integrated platform to visualize and analyze our original transcriptome data and publicly accessible transcriptome data related to the genetics that underlie the development, function, and dysfunction stages and states of the brain.
A place where researchers can publicly store and share unthresholded statistical maps, parcellations, and atlases produced by MRI and PET studies.
BiGG is a knowledgebase of Biochemically, Genetically and Genomically structured genome-scale metabolic network reconstructions. BiGG integrates several published genome-scale metabolic networks into one resource with standard nomenclature which allows components to be compared across different organisms. BiGG can be used to browse model content, visualize metabolic pathway maps, and export SBML files of the models for further analysis by external software packages. Users may follow links from BiGG to several external databases to obtain additional information on genes, proteins, reactions, metabolites and citations of interest.
This interface provides access to several types of data related to the Chesapeake Bay. Bay Program databases can be queried based upon user-defined inputs such as geographic region and date range. Each query results in a downloadable, tab- or comma-delimited text file that can be imported to any program (e.g., SAS, Excel, Access) for further analysis. Comments regarding the interface are encouraged. Questions in reference to the data should be addressed to the contact provided on subsequent pages.
This project is an open invitation to anyone and everyone to participate in a decentralized effort to explore the opportunities of open science in neuroimaging. We aim to document how much (scientific) value can be generated from a data release — from the publication of scientific findings derived from this dataset, algorithms and methods evaluated on this dataset, and/or extensions of this dataset by acquisition and incorporation of new data. The project involves the processing of acoustic stimuli. In this study, the scientists have demonstrated an audiodescription of classic "Forrest Gump" to subjects, while researchers using functional magnetic resonance imaging (fMRI) have captured the brain activity of test candidates in the processing of language, music, emotions, memories and pictorial representations.In collaboration with various labs in Magdeburg we acquired and published what is probably the most comprehensive sample of brain activation patterns of natural language processing. Volunteers listened to a two-hour audio movie version of the Hollywood feature film "Forrest Gump" in a 7T MRI scanner. High-resolution brain activation patterns and physiological measurements were recorded continuously. These data have been placed into the public domain, and are freely available to the scientific community and the general public.
STOREDB is a platform for the archiving and sharing of primary data and outputs of all kinds, including epidemiological and experimental data, from research on the effects of radiation. It also provides a directory of bioresources and databases containing information and materials that investigators are willing to share. STORE supports the creation of a radiation research commons.
Project Achilles is a systematic effort aimed at identifying and cataloging genetic vulnerabilities across hundreds of genomically characterized cancer cell lines. The project uses genome-wide genetic perturbation reagents (shRNAs or Cas9/sgRNAs) to silence or knock-out individual genes and identify those genes that affect cell survival. Large-scale functional screening of cancer cell lines provides a complementary approach to those studies that aim to characterize the molecular alterations (e.g. mutations, copy number alterations) of primary tumors, such as The Cancer Genome Atlas (TCGA). The overall goal of the project is to identify cancer genetic dependencies and link them to molecular characteristics in order to prioritize targets for therapeutic development and identify the patient population that might benefit from such targets. Project Achilles data is hosted on the Cancer Dependency Map Portal (DepMap) where it has been harmonized with our genomics and cellular models data. You can access the latest and all past datasets here: https://depmap.org/portal/download/all/
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DataverseNO is a curated, FAIR-aligned national generic repository for open research data from all academic disciplines. DataverseNO commits to facilitate that published data remain accessible and (re)usable in a long-term perspective. The repository is owned and operated by UiT The Arctic University of Norway. DataverseNO accepts submissions from researchers primarily from Norwegian research institutions. Datasets in DataverseNO are grouped into institutional collections as well as special collections. The technical infrastructure of the repository is based on the open source application Dataverse (https://dataverse.org), which is developed by an international developer and user community led by Harvard University.
The UniPROBE (Universal PBM Resource for Oligonucleotide Binding Evaluation) database hosts data generated by universal protein binding microarray (PBM) technology on the in vitro DNA binding specificities of proteins. This initial release of the UniPROBE database provides a centralized resource for accessing comprehensive data on the preferences of proteins for all possible sequence variants ('words') of length k ('k-mers'), as well as position weight matrix (PWM) and graphical sequence logo representations of the k-mer data. In total, the database currently hosts DNA binding data for 406 nonredundant proteins from a diverse collection of organisms, including the prokaryote Vibrio harveyi, the eukaryotic malarial parasite Plasmodium falciparum, the parasitic Apicomplexan Cryptosporidium parvum, the yeast Saccharomyces cerevisiae, the worm Caenorhabditis elegans, mouse, and human. The database's web tools (on the right) include a text-based search, a function for assessing motif similarity between user-entered data and database PWMs, and a function for locating putative binding sites along user-entered nucleotide sequences
The Fungal Genetics Stock Center has preserved and distributed strains of genetically characterized fungi since 1960. The collection includes over 20,000 accessioned strains of classical and genetically engineered mutants of key model, human, and plant pathogenic fungi. These materials are distributed as living stocks to researchers around the world.
>>>>!!!!<<<< The Cancer Genomics Hub mission is now completed. The Cancer Genomics Hub was established in August 2011 to provide a repository to The Cancer Genome Atlas, the childhood cancer initiative Therapeutically Applicable Research to Generate Effective Treatments and the Cancer Genome Characterization Initiative. CGHub rapidly grew to be the largest database of cancer genomes in the world, storing more than 2.5 petabytes of data and serving downloads of nearly 3 petabytes per month. As the central repository for the foundational genome files, CGHub streamlined team science efforts as data became as easy to obtain as downloading from a hard drive. The convenient access to Big Data, and the collaborations that CGHub made possible, are now essential to cancer research. That work continues at the NCI's Genomic Data Commons. All files previously stored at CGHub can be found there. The Website for the Genomic Data Commons is here: https://gdc.nci.nih.gov/ >>>>!!!!<<<< The Cancer Genomics Hub (CGHub) is a secure repository for storing, cataloging, and accessing cancer genome sequences, alignments, and mutation information from the Cancer Genome Atlas (TCGA) consortium and related projects. Access to CGHub Data: All researchers using CGHub must meet the access and use criteria established by the National Institutes of Health (NIH) to ensure the privacy, security, and integrity of participant data. CGHub also hosts some publicly available data, in particular data from the Cancer Cell Line Encyclopedia. All metadata is publicly available and the catalog of metadata and associated BAMs can be explored using the CGHub Data Browser.
<<<!!!<<< OFFLINE >>>!!!>>> A recent computer security audit has revealed security flaws in the legacy HapMap site that require NCBI to take it down immediately. We regret the inconvenience, but we are required to do this. That said, NCBI was planning to decommission this site in the near future anyway (although not quite so suddenly), as the 1,000 genomes (1KG) project has established itself as a research standard for population genetics and genomics. NCBI has observed a decline in usage of the HapMap dataset and website with its available resources over the past five years and it has come to the end of its useful life. The International HapMap Project is a multi-country effort to identify and catalog genetic similarities and differences in human beings. Using the information in the HapMap, researchers will be able to find genes that affect health, disease, and individual responses to medications and environmental factors. The Project is a collaboration among scientists and funding agencies from Japan, the United Kingdom, Canada, China, Nigeria, and the United States. All of the information generated by the Project will be released into the public domain. The goal of the International HapMap Project is to compare the genetic sequences of different individuals to identify chromosomal regions where genetic variants are shared. By making this information freely available, the Project will help biomedical researchers find genes involved in disease and responses to therapeutic drugs. In the initial phase of the Project, genetic data are being gathered from four populations with African, Asian, and European ancestry. Ongoing interactions with members of these populations are addressing potential ethical issues and providing valuable experience in conducting research with identified populations. Public and private organizations in six countries are participating in the International HapMap Project. Data generated by the Project can be downloaded with minimal constraints. The Project officially started with a meeting in October 2002 (https://www.genome.gov/10005336/) and is expected to take about three years.