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http://connectomics.org/viewer
Extensible, scriptable, pythonic software tool for visualization and analysis in structural neuroimaging research on many spatial scales. Employing the Connectome File Format, diverse data such as networks, surfaces, volumes, tracks and metadata are handled and integrated. The field of Connectomics research benefits from recent advances in structural neuroimaging technologies on all spatial scales. The need for software tools to visualize and analyze the emerging data is urgent. The ConnectomeViewer application was developed to meet the needs of basic and clinical neuroscientists, as well as complex network scientists, providing an integrative, extensible platform to visualize and analyze Connectomics data. With the Connectome File Format, interlinking different datatypes such as hierarchical networks, surface data, volumetric data is easy and might provide new ways of analyzing and interacting with data. Furthermore, ConnectomeViewer readily integrates with: * ConnectomeWiki: a semantic knowledge base representing connectomics data at a mesoscale level across various species, allowing easy access to relevant literature and databases. * ConnectomeDatabase: a repository to store and disseminate Connectome files.
Proper citation: ConnectomeViewer: Multi-Modal Multi-Level Network Visualization and Analysis (RRID:SCR_008312) Copy
Public archive providing a comprehensive record of the world''''s nucleotide sequencing information, covering raw sequencing data, sequence assembly information and functional annotation. All submitted data, once public, will be exchanged with the NCBI and DDBJ as part of the INSDC data exchange agreement. The European Nucleotide Archive (ENA) captures and presents information relating to experimental workflows that are based around nucleotide sequencing. A typical workflow includes the isolation and preparation of material for sequencing, a run of a sequencing machine in which sequencing data are produced and a subsequent bioinformatic analysis pipeline. ENA records this information in a data model that covers input information (sample, experimental setup, machine configuration), output machine data (sequence traces, reads and quality scores) and interpreted information (assembly, mapping, functional annotation). Data arrive at ENA from a variety of sources including submissions of raw data, assembled sequences and annotation from small-scale sequencing efforts, data provision from the major European sequencing centers and routine and comprehensive exchange with their partners in the International Nucleotide Sequence Database Collaboration (INSDC). Provision of nucleotide sequence data to ENA or its INSDC partners has become a central and mandatory step in the dissemination of research findings to the scientific community. ENA works with publishers of scientific literature and funding bodies to ensure compliance with these principles and to provide optimal submission systems and data access tools that work seamlessly with the published literature. ENA is made up of a number of distinct databases that includes the EMBL Nucleotide Sequence Database (Embl-Bank), the newly established Sequence Read Archive (SRA) and the Trace Archive. The main tool for downloading ENA data is the ENA Browser, which is available through REST URLs for easy programmatic use. All ENA data are available through the ENA Browser. Note: EMBL Nucleotide Sequence Database (EMBL-Bank) is entirely included within this resource.
Proper citation: European Nucleotide Archive (ENA) (RRID:SCR_006515) Copy
Web server for macromolecular structure model optimisation and databank of optimised structure models. Focuses on automating final steps of crystallographic process: optimisation of structure model through refinement, rebuilding, and validation. Can automatically optimise most of crystallographic structure models based on input model and diffraction data. Web server works on user provided data, databank has updated versions of Protein Data Bank entries based on original experimental data that were deposited with atomic coordinates in PDB.
Proper citation: PDB-REDO (RRID:SCR_018936) Copy
FINDbase Worldwide is an online repository of information about the frequency of different mutations leading to inherited disorders in various populations around the globe. Frequency data about 32 disorders, 25 genes within 98 populations covering 1226 mutations is now available. 28 curators worldwide contributed to this database containing data from 37 submissions.
Proper citation: FINDbase Worldwide (RRID:SCR_012744) Copy
Website for analyzing microarray data. Software toolbox for storing, analyzing and integrating microarray data and related genotype and phenotype data. The site is particularly suited for combining QTL and microarray data to search for candidate genes contributing to complex traits. In addition, the site allows, if desired by the investigators, sharing of the data. Investigators can conduct in-silico microarray experiments using their own and/or shared data. There are five major sections of the site: Genome/Transcriptome Data Browser, Microarray Analysis Tools, Gene List Analysis Tools, QTL Tools, and Downloads. The genome/transcriptome data browser combines a genome browser with all the microarray, RNA-Seq, and Genomic Sequencing data. This provides an effective platform to view all of this data side by side. Source code is available on GitHub.
Proper citation: PhenoGen Informatics (RRID:SCR_001613) Copy
Repository for toxicogenomics data, including study design and timeline, clinical chemistry and histopathology findings and microarray and proteomics data. Data derived from studies of chemicals and of genetic alterations, and is compatible with clinical and environmental studies. Data relating to environmental health, pharmacology, and toxicology. It is not necessary to have microarray data, but study design and phenotypic anchoring data are required.CEBS contains raw microarray data collected in accordance with MIAME guidelines and provides tools for data selection, pre-processing and analysis resulting in annotated lists of genes of interest. Biomedical Investigation Database is another component of CEBS system. used to load and curate study data prior to export to CEBS, in addition to capturing and displaying novel data types such as PCR data, or additional fields of interest, including those defined by the HESI Toxicogenomics Committee. BID has been shared with Health Canada and the US Environmental Protection Agency.
Proper citation: Chemical Effects in Biological Systems (CEBS) (RRID:SCR_006778) Copy
A center that works with the Oregon Alzheimer's Disease Center's Data Core, and collects and stores tissue samples, family history and genotype data of various populations. These include samples and data from subjects from the following sources: OADC clinical studies, the Oregon Brain Aging Study, the Community Brain Donor Program, the Preventing Cognitive Decline with Alternative Therapies program (informally called the Dementia Prevention Study or DPS), the African American Dementia and Aging Project, and the Klamath Exceptional Aging Project. The collected data samples include genomic DNA, lymphoblast cell lines, genome-wide and candidate region SNP marker data, APOE, AD candidate gene markers.
Proper citation: Layton Center Biomarkers and Genetics (RRID:SCR_008824) Copy
https://www.itntrialshare.org/
Immune tolerance data management and visualization portal for studies sponsored by Immune Tolerance Network (ITN) and collaborating investigators. Data from published studies are accessible to any user; data from current in-progress studies are accessible to study investigators and collaborators. Includes links to published Figures, tools for visualization and analysis of data, and ability to query study data by subject, group, or any other study parameter.
Proper citation: Immune Tolerance Network TrialShare (RRID:SCR_013699) Copy
https://www.ebi.ac.uk/metabolights/
A cross-species, cross-technique database for metabolomics experiments, data, and derived information. It includes metabolite structures and their reference spectra, their biological roles, locations and concentrations, and experimental data from metabolic experiments.
Proper citation: MetaboLights (RRID:SCR_014663) Copy
DDMoRe project to develop common standards and tools to allow scientists to share their models. Developed model repository which enables access to curated and shared knowledge for benefit of model informed drug discovery.
Proper citation: DDMoRe (RRID:SCR_003851) Copy
Database of glycans 3D structural data and information that can be downloaded or used with Re-Glyco to rebuild glycoproteins from the RCSB PDB or EMBL-EBI AlphaFold repositories. Glycan structure database and toolbox designed to restore glycoproteins to their native and functional form.
Proper citation: GlycoShape (RRID:SCR_028443) Copy
A web-based neuroimaging and neuropsychology software suite that offers versatile, automatable data upload/import/entry options, rapid and secure sharing of data among PIs, querying and export all data, real-time reporting, and HIPAA and IRB compliant study-management tools suitable to large institutions as well as smaller scale neuroscience and neuropsychology researchers. COINS manages over over 400 studies, more than 265,000 clinical neuropsychological assessments, and 26,000 MRI, EEG, and MEG scan sessions collected from 18,000 participants at over ten institutions on topics related to the brain and behavior. As neuroimaging research continues to grow, dynamic neuroinformatics systems are necessary to store, retrieve, mine and share the massive amounts of data. The Collaborative Informatics and Neuroimaging Suite (COINS) has been created to facilitate communication and cultivate a data community. This tool suite offers versatile data upload/import/entry options, rapid and secure sharing of data among PIs, querying of data types and assessments, real-time reporting, and study-management tools suitable to large institutions as well as smaller scale researchers. It manages studies and their data at the Mind Research Network, the Nathan Kline Institute, University of Colorado Boulder, the Olin Neuropsychiatry Research Center (at) Hartford Hospital, and others. COINS is dynamic and evolves as the neuroimaging field grows. COINS consists of the following collaboration-centric tools: * Subject and Study Management: MICIS (Medical Imaging Computer Information System) is a centralized PostgreSQL-based web application that implements best practices for participant enrollment and management. Research site administrators can easily create and manage studies, as well as generate reports useful for reporting to funding agencies. * Scan Data Collection: An automated DICOM receiver collects, archives, and imports imaging data into the file system and COINS, requiring no user intervention. The database also offers scan annotation and behavioral data management, radiology review event reports, and scan time billing. * Assessment Data Collection: Clinical data gathered from interviews, questionnaires, and neuropsychological tests are entered into COINS through the web application called Assessment Manager (ASMT). ASMT's intuitive design allows users to start data collection with little or no training. ASMT offers several options for data collection/entry: dual data entry, for paper assessments, the Participant Portal, an online tool that allows subjects to fill out questionnaires, and Tablet entry, an offline data entry tool. * Data Sharing: De-identified neuroimaging datasets with associated clinical-data, cognitive-data, and associated meta-data are available through the COINS Data Exchange tool. The Data Exchange is an interface that allows investigators to request and share data. It also tracks data requests and keeps an inventory of data that has already been shared between users. Once requests for data have been approved, investigators can download the data directly from COINS.
Proper citation: Mind Research Network - COINS (RRID:SCR_000805) Copy
https://github.com/ledancs/hFigures
A Javascript library that aims to deliver a starting point for interactive health data visualization. Examples and demos are available on the site. hFigures was built with d3.js and a copy of the library is included in this repository. All rights and license terms apply to the d3.js library accordingly.
Proper citation: hFigures (RRID:SCR_014201) Copy
https://www.thieme.de/en/thieme-chemistry/home-51399.htm
Publishing company which provides access to its four journals SYNTHESIS, SYNLETT, SynOpen, and SYNFACTS; their reference works Science of Synthesis and Pharmaceutical Substances; and the paired encyclopedia ROEMPP.
Proper citation: Thieme Chemistry (RRID:SCR_014699) Copy
http://www.mitre.org/news/digest/archives/2002/neuroinformatics.html
This resource''s long-term goal is to develop informatics methodologies and tools that will increase the creativity and productivity of neuroscience investigators, as they work together to use shared human brain mapping data to generate and test ideas far beyond those pursued by the data''s originators. This resource currently has four major projects supporting this goal: * Database tools: The goal of the NeuroServ project is to provide neuroscience researchers with automated information management tools that reduce the effort required to manage, analyze, query, view, and share their imaging data. It currently manages both structural magnetic resonance image (MRI) datasets and diffusion tensor image (DTI) datasets. NeuroServ is fully web-enabled: data entry, query, processing, reporting, and administrative functions are performed by qualified users through a web browser. It can be used as a local laboratory repository, to share data on the web, or to support a large distributed consortium. NeuroServ is based on an industrial-quality query middleware engine MRALD. NeuroServ includes a specialized neuroimaging schema and over 40 custom Java Server Pages supporting data entry, query, and reporting to help manage and explore stored images. NeuroServ is written in Java for platform independence; it also utilizes several open source components * Data sharing: DataQuest is a collaborative forum to facilitate the sharing of neuroimaging data within the neuroscience community. By publishing summaries of existing datasets, DataQuest enables researchers to: # Discover what data is available for collaborative research # Advertise your data to other researchers for potential collaborations # Discover which researchers may have the data you need # Discover which researchers are interested in your data. * Image quality: The approach to assessing the inherent quality of an image is to measure how distorted the image is. Using what are referred to as no-reference or blind metrics, one can measure the degree to which an image is distorted. * Content-based image retrieval: NIRV (NeuroImagery Retrieval & Visualization) is a work environment for advanced querying over imagery. NIRV will have a Java-based front-end for users to issue queries, run processing algorithms, review results, visualize imagery and assess image quality. NIRV interacts with an image repository such as NeuroServ. Users can also register images and will soon be able to filter searches based on image quality.
Proper citation: MITRE Neuroinformatics (RRID:SCR_006508) Copy
http://www.emouseatlas.org/emage
A database of in situ gene expression data in the developing mouse embryo and an accompanying suite of tools to search and analyze the data. mRNA in situ hybridization, protein immunohistochemistry and transgenic reporter data is included. The data held is spatially annotated to a framework of 3D mouse embryo models produced by EMAP (e-Mouse Atlas Project). These spatial annotations allow users to query EMAGE by spatial pattern as well as by gene name, anatomy term or Gene Ontology (GO) term. The conceptual framework which houses the descriptions of the gene expression patterns in EMAGE is the EMAP Mouse Embryo Anatomy Atlas. This consists of a set of 3D virtual embryos at different stages of development, as well as an accompanying ontology of anatomical terms found at each stage. The raw data images can be conventional 2D photographs (of sections or wholemount specimens) or 3D images of wholemount specimens derived from Optical Projection Tomography (OPT) or confocal microscopy. Users may submit data using a Data submission tool or without.
Proper citation: EMAGE Gene Expression Database (RRID:SCR_005391) Copy
https://www.ebi.ac.uk/pdbe/emdb/empiar/
Public archive for raw electron microscopy image data. Resource for raw 2D electron microscopy images. Used to browse, upload and download raw images to build 3D structure. Provides access to raw data to facilitate methods development and validation. Complements Electron Microscopy Data Bank where 3D volumes are stored, and uses Aspera platform for data transfers.
Proper citation: Electron Microscopy Public Image Archive (RRID:SCR_019237) Copy
http://gwas.biosciencedbc.jp/cgi-bin/hvdb/hv_top.cgi
A repository database to achieve continuous and intensive management of GWAS data and variation data identified by next generation sequencing (NGS) and data-sharing among researchers. In this database, variations including short/long insertions / deletions and structural variations related to disease susceptibility, virus resistance, and drug response are registered along with statistical genetic results and simple clinical characteristics to clarify the locus specific characteristics. Currently this database contains information extracted from scientific papers and next generation sequencing results and other small scale experimental results of several research laboratories. Mutation data submission is greatly appreciated.
Proper citation: Human Variation DB (RRID:SCR_009014) Copy
Clearinghouse and exchange portal for gene variant (mutation) data produced by diagnostics laboratories, offering users a portal through which to announce, discover and acquire a comprehensive listing of observed neutral and disease-causing gene variants in patients and unaffected individuals. Cafe Variome is not a ''''database'''' for the hosting/display/release of data, but a shop window for finding data. As such, it holds only core info for each record, and uses this merely to enable holistic searching across resources. Diagnostics laboratories routinely assess DNA samples from patients with various inherited disorders, and so produce a great wealth of data on the genetic basis of disease. Unfortunately, those data are not usually shared with others. To address this gross deficiency, a novel system has been developed that aims to facilitate the automated transfer of diagnostic laboratory data to the wider community, via an internet based Cafe for routinely exchanging genetic variation data. The flow of research data concerning the genetic basis of health and disease is critical to understanding and developing treatments for a range of genetic diseases. Overall, the project aims to lower the barriers and provide incentives for a willing community to share data, and thereby facilitate the broader exploitation of diagnostic laboratory data. Cafe Variome aims to address the above data flow problems by: # Minimizing the effort required to publish variant data # Ensuring attribution for data creators working in diagnostic laboratories Key elements of the project strategy are: * Data publication will be automated by endowing standard analysis tools used by laboratories with an online data submission function. Submissions will be received by a central Internet depot, which will serve as a place where published datasets are advertised, and subsequently discovered by diverse 3rd parties. * Each dataset will be unambiguously linked with the data submitter''''s identity, and systems devised to facilitate citation of published variant datasets so they can be cited in the literature. Data creators will thus be credited for their contributions. Data submitters can use Cafe Variome to simply announce or publicize their data to the world. To enable this, only core, non-identifiable data is submitted to the central repository, enabling users to search and discover records of interest in the source repository. The data are not automatically handed on to the user (unless intended by the submitters). Hence, the concept is used to deal with the challenge of maximally sharing data whilst fully respecting ethico-legal considerations.
Proper citation: cafe variome (RRID:SCR_006162) Copy
Database of biological collections in natural history museums, herbaria, and other biorepositories resulting from a merger of Index Herbariorum (IH), Biodiversity Collections Index (BCI) and biorepositories.org. It contains more than 14,000 records for biorepository institutions, their collections, and staff members. Their two main goals are to improve access to information about biorepositories, the collections and specimens they house, and the researchers and collection managers who work there; and to facilitate electronic linkages to this information through web services that will rely on unique identifiers assigned to biorepositories and collections. The Consortium for the Barcode of Life (CBOL) has developed and will manage GRBio in collaboration with IH and BCI and in consultation with GBIF and NCBI. GRBio includes four categories of data records that provide information on: * Institutional repositories such as museums, herbaria, botanical gardens, zoos, biomedical research institutes and culture centers; * Institutional collection records such as the bird, algal or insect collections within an institutional repository; * Personal collections such as field samples held by a researcher before they have been accessioned into an institutional collection, or privately owned specimens held by non-researchers; and * Staff members at institutional repositories GRBio operates as a moderated community-curated resource. The community is invited to check and update their records and to register institutions, collections and staff members that have not already been registered. GRBio offers registration of institutional collections, "personal" research collections that have not yet been accessioned into an institutional repository, and privately owned collections.
Proper citation: GRBio (RRID:SCR_002228) Copy
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