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Found 274 result(s)
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.
Brainlife promotes engagement and education in reproducible neuroscience. We do this by providing an online platform where users can publish code (Apps), Data, and make it "alive" by integragrate various HPC and cloud computing resources to run those Apps. Brainlife also provide mechanisms to publish all research assets associated with a scientific project (data and analyses) embedded in a cloud computing environment and referenced by a single digital-object-identifier (DOI). The platform is unique because of its focus on supporting scientific reproducibility beyond open code and open data, by providing fundamental smart mechanisms for what we refer to as “Open Services.”
Tropicos® was originally created for internal research but has since been made available to the world’s scientific community. All of the nomenclatural, bibliographic, and specimen data accumulated in MBG’s electronic databases during the past 30 years are publicly available here.
The Drosophila Synthetic Population Resource (DSPR) consists of a new panel of over 1700 recombinant inbred lines (RILs) of Drosophila melanogaster, derived from two highly recombined synthetic populations, each created by intercrossing a different set of 8 inbred founder lines (with one founder line common to both populations). Complete genome sequence data for the founder lines are available, and in addition, there is a high resolution genetic map for each RIL. The DSPR has been developed as a community resource for high-resolution QTL mapping and is intended to be used widely by the Drosophila community.
WikiPathways was established to facilitate the contribution and maintenance of pathway information by the biology community. WikiPathways is an open, collaborative platform dedicated to the curation of biological pathways. WikiPathways thus presents a new model for pathway databases that enhances and complements ongoing efforts, such as KEGG, Reactome and Pathway Commons. Building on the same MediaWiki software that powers Wikipedia, we added a custom graphical pathway editing tool and integrated databases covering major gene, protein, and small-molecule systems. The familiar web-based format of WikiPathways greatly reduces the barrier to participate in pathway curation. More importantly, the open, public approach of WikiPathways allows for broader participation by the entire community, ranging from students to senior experts in each field. This approach also shifts the bulk of peer review, editorial curation, and maintenance to the community.
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Datanator is an integrated database of genomic and biochemical data designed to help investigators find data about specific molecules and reactions in specific organisms and specific environments for meta-analyses and mechanistic models. Datanator currently includes metabolite concentrations, RNA modifications and half-lives, protein abundances and modifications, and reaction kinetics integrated from several databases and numerous publications. The Datanator website and REST API provide tools for extracting clouds of data about specific molecules and reactions in specific organisms and specific environments, as well as data about similar molecules and reactions in taxonomically similar organisms.
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/
OrtholugeDB contains Ortholuge-based orthology predictions for completely sequenced bacterial and archaeal genomes. It is also a resource for reciprocal best BLAST-based ortholog predictions, in-paralog predictions (recently duplicated genes) and ortholog groups in Bacteria and Archaea. The Ortholuge method improves the specificity of high-throughput orthology prediction.
The NCBI Short Genetic Variations database, commonly known as dbSNP, catalogs short variations in nucleotide sequences from a wide range of organisms. These variations include single nucleotide variations, short nucleotide insertions and deletions, short tandem repeats and microsatellites. Short Genetic Variations may be common, thus representing true polymorphisms, or they may be rare. Some rare human entries have additional information associated withthem, including disease associations, genotype information and allele origin, as some variations are somatic rather than germline events. ***NCBI will phase out support for non-human organism data in dbSNP and dbVar beginning on September 1, 2017***
ASAP (a systematic annotation package for community analysis of genomes) is a relational database and web interface developed to store, update and distribute genome sequence data and gene expression data collected by or in collaboration with researchers at the University of Wisconsin - Madison. ASAP was designed to facilitate ongoing community annotation of genomes and to grow with genome projects as they move from the preliminary data stage through post-sequencing functional analysis. The ASAP database includes multiple genome sequences at various stages of analysis, and gene expression data from preliminary experiments.
>>>!!!<<< as stated 2017-06-09 MPIDB is no longer available under URL http://www.jcvi.org/mpidb/about.php >>>!!!<<< The microbial protein interaction database (MPIDB) aims to collect and provide all known physical microbial interactions. Currently, 24,295 experimentally determined interactions among proteins of 250 bacterial species/strains can be browsed and downloaded. These microbial interactions have been manually curated from the literature or imported from other databases (IntAct, DIP, BIND, MINT) and are linked to 26,578 experimental evidences (PubMed ID, PSI-MI methods). In contrast to these databases, interactions in MPIDB are further supported by 68,346 additional evidences based on interaction conservation, protein complex membership, and 3D domain contacts (iPfam, 3did). We do not include (spoke/matrix) binary interactions infered from pull-down experiments.
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.
<<<!!!<<< This repository is no longer available>>>!!!>>>. Although the web pages are no longer available, you will still be able to download the final UniGene builds as static content from the FTP site https://ftp.ncbi.nlm.nih.gov/repository/UniGene/. You will also be able to match UniGene cluster numbers to Gene records by searching Gene with UniGene cluster numbers. For best results, restrict to the “UniGene Cluster Number” field rather than all fields in Gene. For example, a search with Mm.2108[UniGene Cluster Number] finds the mouse transthyretin Gene record (Ttr). You can use the advanced search page https://www.ncbi.nlm.nih.gov/gene/advanced to help construct these searches. Keep in mind that the Gene record contains selected Reference Sequences and GenBank mRNA sequences rather than the larger set of expressed sequences in the UniGene cluster.
The HomoloGene database provides a system for the automated detection of homologs among annotated genes of genomes across multiple species. These homologs are fully documented and organized by homology group. HomoloGene processing uses proteins from input organisms to compare and sequence homologs, mapping back to corresponding DNA sequences.
Human Protein Reference Database (HPRD) has been established by a team of biologists, bioinformaticists and software engineers. This is a joint project between the PandeyLab at Johns Hopkins University, and Institute of Bioinformatics, Bangalore. HPRD is a definitive repository of human proteins. This database should serve as a ready reckoner for researchers in their quest for drug discovery, identification of disease markers and promote biomedical research in general. Human Proteinpedia (www.humanproteinpedia.org) is its associated data portal.
CDC.gov is the Centers for Disease Control and Prevention primary online communication channel. CDC.gov provides users with credible, reliable health information on Data and Statistics, Diseases and Conditions, Emergencies and Disasters, Environmental Health, Healthy Living, Injury, Violence and Safety,Life Stages and Populations, Travelers' Health, Workplace Safety and Health
The Harvard Dataverse is open to all scientific data from all disciplines worldwide. It includes the world's largest collection of social science research data. It is hosting data for projects, archives, researchers, journals, organizations, and institutions.
Content type(s)
>>>!!!<<< The NCI Cancer Models Database, caMOD, was retired on December 24, 2015. Information about many of the mouse models hosted in caMOD was obtained from the Jackson Laboratory Mouse Tumor Biology (MTB) Database and can be accessed through that resource http://tumor.informatics.jax.org/mtbwi/index.do . See caMOD Retirement Announcement https://wiki.nci.nih.gov/display/caMOD/caMOD+Retirement+Announcement >>>>!!<<< Query the Cancer Models database for models submitted by fellow researchers. Retrieve information about the making of models, their genetic description, histopathology, derived cell lines, associated images, carcinogenic agents, and therapeutic trials. Links to associated publications and other resources are provided.
>>> !!! the repository is offline !!! <<< More information see: https://dknet.org/about/NURSA_Archive All NURSA-biocurated transcriptomic datasets have been preserved for data mining in SPP through an enhanced and expanded version of Transcriptomine named Ominer. To access these datasets, dkNET provides users with the information of 527 transcriptomic datasets that contain data related to nuclear receptors and nuclear receptor coregulators in the NURSA Datasets table view and redirects users to the current SPP dataset page. Once users find the specific dataset of research interest, users can download the dataset by clicking DOI and then clicking the Download Dataset button at the Signaling Pathways Project webpage. See https://www.re3data.org/repository/r3d100013650
OpenWorm aims to build the first comprehensive computational model of the Caenorhabditis elegans (C. elegans), a microscopic roundworm. With only a thousand cells, it solves basic problems such as feeding, mate-finding and predator avoidance. Despite being extremely well studied in biology, this organism still eludes a deep, principled understanding of its biology. We are using a bottom-up approach, aimed at observing the worm behaviour emerge from a simulation of data derived from scientific experiments carried out over the past decade. To do so we are incorporating the data available in the scientific community into software models. We are engineering Geppetto and Sibernetic, open-source simulation platforms, to be able to run these different models in concert. We are also forging new collaborations with universities and research institutes to collect data that fill in the gaps All the code we produce in the OpenWorm project is Open Source and available on GitHub.
Genome track alignments using GBrowse on this site are featured with: (1) Annotated and predicted genes and transcripts; (2) QTL / SNP Association tracks; (3) OMIA genes; (4) Various SNP Chip tracks; (5) Other mapping fetures or elements that are available.
<<<!!!<<< 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.