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THIS 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.
Proper citation: Brainscape (RRID:SCR_002962) Copy
https://github.com/INCF/swc-specification
Software repository contains files needed to build the standard and its supplementary documentation. Changes are automatically pushed and built. Information about the SWC file specification.
Proper citation: SWC format (RRID:SCR_027631) Copy
http://www.bioconductor.org/packages/release/bioc/html/ArrayExpress.html
Software to access the ArrayExpress Repository at EBI and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet
Proper citation: ArrayExpress (R) (RRID:SCR_000120) Copy
http://www.emdataresource.org/
Portal for deposition and retrieval of cryo electron microscopy (3DEM) density maps, atomic models, and associated metadata. Global resource for 3 Dimensional Electron Microscopy structure data archiving and retrieval, news, events, software tools, data standards, validation methods.
Proper citation: EMDataResource.org (RRID:SCR_003207) Copy
http://bioinfo2.ugr.es/NGSmethDB/
A dedicated database for the storage, browsing and data mining of whole-genome, single-base-pair resolution methylomes.
Proper citation: NGSmethDB (RRID:SCR_012847) Copy
Database of traceable, standardized, annotated gene signatures which have been manually curated from publications that are indexed in PubMed. The Advanced Gene Search will perform a One-tailed Fisher Exact Test (which is equivalent to Hypergeometric Distribution) to test if your gene list is over-represented in any gene signature in GeneSigDB. Gene expression studies typically result in a list of genes (gene signature) which reflect the many biological pathways that are concurrently active. We have created a Gene Signature Data Base (GeneSigDB) of published gene expression signatures or gene sets which we have manually extracted from published literature. GeneSigDB was creating following a thorough search of PubMed using defined set of cancer gene signature search terms. We would be delighted to accept or update your gene signature. Please fill out the form as best you can. We will contact you when we get it and will be happy to work with you to ensure we accurately report your signature. GeneSigDB is capable of providing its functionality through a Java RESTful web service.
Proper citation: GeneSigDB (RRID:SCR_013275) Copy
https://omictools.com/l2l-tool
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.. Documented on August 26, 2019.
Database of published microarray gene expression data, and a software tool for comparing that published data to a user''''s own microarray results. It is very simple to use - all you need is a web browser and a list of the probes that went up or down in your experiment. If you find L2L useful please consider contributing your published data to the L2L Microarray Database in the form of list files. L2L finds true biological patterns in gene expression data by systematically comparing your own list of genes to lists of genes that have been experimentally determined to be co-expressed in response to a particular stimulus - in other words, published lists of microarray results. The patterns it finds can point to the underlying disease process or affected molecular function that actually generated the observed changed in gene expression. Its insights are far more systematic than critical gene analyses, and more biologically relevant than pure Gene Ontology-based analyses. The publications included in the L2L MDB initially reflected topics thought to be related to Cockayne syndrome: aging, cancer, and DNA damage. Since then, the scope of the publications included has expanded considerably, to include chromatin structure, immune and inflammatory mediators, the hypoxic response, adipogenesis, growth factors, hormones, cell cycle regulators, and others. Despite the parochial origins of the database, the wide range of topics covered will make L2L of general interest to any investigator using microarrays to study human biology. In addition to the L2L Microarray Database, L2L contains three sets of lists derived from Gene Ontology categories: Biological Process, Cellular Component, and Molecular Function. As with the L2L MDB, each GO sub-category is represented by a text file that contains annotation information and a list of the HUGO symbols of the genes assigned to that sub-category or any of its descendants. You don''''t need to download L2L to use it to analyze your microarray data. There is an easy-to-use web-based analysis tool, and you have the option of downloading your results so you can view them at any time on your own computer, using any web browser. However, if you prefer, the entire L2L project, and all of its components, can be downloaded from the download page. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible
Proper citation: L2L Microarray Analysis Tool (RRID:SCR_013440) Copy
Software experimental data table management system to make research data accessible and available for reuse with minimal effort on part of data provider. Allows any scientist or data researcher to be able to explore dataset and then extract some or all of data according to their needs. Designed to manage experimental data tables in quick and easy way for users.
Proper citation: ODAM (RRID:SCR_018774) Copy
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.. Documented October 4, 2017.
A sub-project of the Cell Centered Database (http://ccdb.ucsd.edu) providing a public repository for animal imaging data sets from MRI and related techniques. The public AIDB website provides the ability for browsing, visualizing and downloading the animal subjected MRI data. The AIDB is a pilot project to serve the current need for public imaging repositories for animal imaging data. The Cell Centered Database (CCDB) is a web accessible database for high resolution 2D, 3D and 4D data from light and electron microscopy. The AIDB data model is modified from the basic model of the CCDB where microscopic images are combined to make 2D, 3D and 4D reconstructions. The CCDB has made available over 40 segmented datasets from high resolution magnetic resonance imaging of inbred mouse strains through the prototype AIDB. These data were acquired as part of the Mouse BIRN project by Drs. G. Allan Johnson and Robert Williams. More information about these data can be found in Badea et al. (2009) (Genetic dissection of the mouse CNS using magnetic resonance microscopy - Pubmed: 19542887)
Proper citation: Animal Imaging Database (RRID:SCR_008002) Copy
http://national_databank.mclean.org
THIS RESOURCE IS NO LONGER IN SERVICE, documented September 6, 2016. A publicly accessible data repository to provide neuroscience investigators with secure access to cohort collections. The Databank collects and disseminates gene expression data from microarray experiments on brain tissue samples, along with diagnostic results from postmortem studies of neurological and psychiatric disorders. All of the data that is derived from studies of the HBTRC collection is being incorporated into the National Brain Databank. This data is available to the general public, although strict precautions are undertaken to maintain the confidentiality of the brain donors and their family members. The system is designed to incorporate MIAME and MAGE-ML based microarray data sharing standards. Data from various types of studies conducted on brain tissue in the HBTRC collection will be available from studies using different technologies, such as gene expression profiling, quantitative RT-PCR, situ hybridization, and immunocytochemistry and will have the potential for providing powerful insights into the subregional and cellular distribution of genes and/or proteins in different brain regions and eventually in specific subregions and cellular subtypes.
Proper citation: National Brain Databank (RRID:SCR_003606) Copy
A research portal to share and obtain research data and journal articles openly accessible to all disciplines. Established to support the Open Access Policy, as set out by the ERC Scientific Council Guidelines for Open Access and the Open Access pilot launched by the European Commission.
Proper citation: OpenAIRE (RRID:SCR_013740) Copy
Software pipeline for single-nucleus RNAseq data generated by Smart-seq2 assays. Developed in collaboration with the BRAIN Initiative Cell Census Network (BICCN) to process single-nucleus RNAseq (snRNAseq) data generated by Smart-seq2 assays. Used to simultaneously process multiple libraries of single nuclei Smart-seq2 and Smart-seq4 data. For each library (nucleus), the pipeline trims paired FASTQ files, aligns trimmed reads to the genome, counts intronic and exonic reads, and calculates quality control metrics. Counts and metrics for all libraries are combined into merged Loom formatted count matrix.
Proper citation: Smart-seq2 Multi-Sample Pipeline (RRID:SCR_018920) Copy
Flagship initiative of Childrens Mercy Research Institute and Genomic Medicine Center in Kansas City. This research program is building pediatric data repository to facilitate search for answers and novel treatments.Goal is to collect genomic data and health information for 30,000 children and their families to create a database of genomes.
Proper citation: Genomic Answers for Kids (RRID:SCR_021840) Copy
An MRI data repository that holds a set of 7 Tesla images and behavioral metadata. Multi-faceted brain image archive with behavioral measurements. For each participant a number of different scans and auxiliary recordings have been obtained. In addition, several types of minimally preprocessed data are also provided. The full description of the data release is available in a dedicated publication. This project invites anyone to participate in a decentralized effort to explore the opportunities of open science in neuroimaging by documenting how much (scientific) value can be generated out of a single data release by publication of scientific findings derived from a dataset, algorithms and methods evaluated on this dataset, and/or extensions of this dataset by acquisition and integration of new data.
Proper citation: studyforrest.org (RRID:SCR_003112) Copy
https://knights-lab.github.io/MLRepo/
Machine learning repository for microbiome datasets.
Proper citation: ML Repo (RRID:SCR_017079) Copy
http://www.cancerimagingarchive.net/
Archive of medical images of cancer accessible for public download. All images are stored in DICOM file format and organized as Collections, typically patients related by common disease (e.g. lung cancer), image modality (MRI, CT, etc) or research focus. Neuroimaging data sets include clinical outcomes, pathology, and genomics in addition to DICOM images. Submitting Data Proposals are welcomed.
Proper citation: Cancer Imaging Archive (TCIA) (RRID:SCR_008927) Copy
http://www.ncbi.nlm.nih.gov/pmc/
Collection of full text archive of biomedical and life sciences journal literature at U.S. National Institutes of Health National Library of Medicine (NIH/NLM). With PubMed Central, NCBI is taking lead in preserving and maintaining open access to electronic literature. Value of PubMed Central, in addition to its role as an archive, lies in what can be done when data from diverse sources is stored in common format in single repository. All articles in PMC are free (sometimes on a delayed basis). Some journals go beyond free, to Open Access.
Proper citation: PubMed Central (RRID:SCR_004166) Copy
An interactive multiresolution brain atlas that is based on over 20 million megapixels of sub-micron resolution, annotated, scanned images of serial sections of both primate and non-primate brains and integrated with a high-speed database for querying and retrieving data about brain structure and function. Currently featured are complete brain atlas datasets for various species, including Macaca mulatta, Chlorocebus aethiops, Felis catus, Mus musculus, Rattus norvegicus, Tyto alba and many other vertebrates. BrainMaps is currently accepting histochemical, immunocytochemical, and tracer connectivity data, preferably whole-brain. In addition, they are interested in EM, MRI, and DTI data.
Proper citation: BrainMaps.org (RRID:SCR_006878) Copy
A large multi-site pediatric MRI and genetics data resource to facilitate studies of the genomic landscape of the developing human brain. It includes information about the developing mental and emotional functions of the children to understand the genetic basis of individual differences in brain structure and connectivity, cognition, and personality. Investigators on the project are studying 1400 children between the ages of 3 and 20 years so that links between genetic variation and developing patterns of brain connectivity can be examined. Investigators interested in the effects of a particular gene will be able to search the database for any brain areas or connections between areas that differ as a function of variation in a particular gene, and also to determine if the genes appear to affect the course of brain development at some point during childhood. A data exploration tool has been created for mapping and analyzing MRI data sets collected for PING and related developmental studies. Approved investigators will be able to view raw image sets and derived 3D brain maps of MRI and DTI data, conduct hypothesis testing, and graph brain area measures as they change across the time course of development. PING Cores * Coordinating Core: Functions include project management, screening of participants and maintaining the database * Neuroimaging Core: applying a standardized high-resolution structural MRI protocol involving 3-D T1-weighted scans, a T2-weighted volume, and a set of diffusion-weighted scans with multiple b values and diffusion directions, scans to estimate MRI relaxation rates, and gradient echo EPI scans for resting state fMRI. Importantly, adaptive motion compensation, using ����??PROMO����??, a novel real-time motion correction algorithm will be used. Specific PING protocols for each scanner manufacturer: ** PING MRI Protocol - GE ** PING MRI Protocol - Philips ** PING MRI Protocol - Siemens * Assessment Core: Cognitive assessments for the PING project are conducted using the NIH Toolbox for Cognition. * Genomics Core: functions as a central repository for receipt of saliva samples collected for each study participant. Once received, samples are catalogued, maintained, and DNA is extracted using state-of-the-field laboratory techniques. Ultimately, genome-wide genotyping is performed on the extracted DNA using the Illumina Human660W-Quad BeadChip. PING involves 10 sites throughout the country including UCSD, University of Hawaii, Scripps Genomics, UCLA, UC Davis, Kennedy Krieger Institute/Johns Hopkins, Sacker Institute/Cornell University, University of Massachusetts, Massachusetts General Hospital/Harvard, and Yale. Families who may want to participate in the study, or others who want to know more about it, may email questions to ping (at) ucsd.edu.
Proper citation: Pediatric Imaging Neurocognition and Genetics (RRID:SCR_008953) Copy
Web-based microarray data analysis and visualization system powered by CRC, or Chinese Restaurant cluster, a Dirichlet process model-based clustering algorithm recently developed by Dr. Steve Qin. It also incorporates several gene expression analysis programs from Bioconductor, including GOStats, genefilter, and Heatplus. CRCView also installs from the Bioconductor system 78 annotation libraries of microarray chips for human (31), mouse (24), rat (14), zebrafish (1), chicken (1), Drosophila (3), Arabidopsis (2), Caenorhabditis elegans (1), and Xenopus Laevis (1). CRCView allows flexible input data format, automated model-based CRC clustering analysis, rich graphical illustration, and integrated Gene Ontology (GO)-based gene enrichment for efficient annotation and interpretation of clustering results. CRC has the following features comparing to other clustering tools: 1) able to infer number of clusters, 2) able to cluster genes displaying time-shifted and/or inverted correlations, 3) able to tolerate missing genotype data and 4) provide confidence measure for clusters generated. You need to register for an account in the system to store your data and analyses. The data and results can be visited again anytime you log in.
Proper citation: CRCView (RRID:SCR_007092) Copy
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