Group items tagged

SWAN (Semantic Web Applications in Neuromedicine) is a Web-based collaborative program that aims to organize and annotate scientific knowledge about Alzheimer disease (AD) and other neurodegenerative disorders. Its goal is to facilitate the formation, development and testing of hypotheses about the disease. The ultimate goal of this project is to create tools and resources to manage the evolving universe of data and information about AD in such a way that researchers can easily comprehend their larger context ("what hypothesis does this support or contradict?"), compare and contrast hypotheses ("where do these two hypotheses agree and disagree?"), identify unanswered questions and synthesize concepts and data into ever more comprehensive and useful hypotheses and treatment targets for this disease. The SWAN project is designed to allow the community of AD researchers to author, curate and connect a diversity of data and ideas about AD via secure personal and public SWAN workspaces, using the emerging Semantic Web paradigm for deep interconnection of data, information and knowledge. We are initially focusing on developing a fully public Web resource deployed as part of the Alzheimer Research Forum web site (www.alzforum.org). After the public resource has been launched, we will also develop secure personal workspaces (MySWAN) and semi-private lab workspaces (LabSWAN). An essential component of this project is development of an initial, core knowledge base within SWAN, which will provide immediate value to researchers at the time of deployment. This is a critically important part of our strategy to ensure that the SWAN system gains wide adoption and active participation by the AD research community. As part of our development strategy, we are also recruiting a "beta test" community of AD researchers to enter their own hypotheses, add commentaries and citations, and provide feedback on the technology and content. SWAN is being developed by a collaborative team from

The National Center for Biomedical Ontology is a consortium of leading biologists, clinicians, informaticians, and ontologists who develop innovative technology and methods allowing scientists to create, disseminate, and manage biomedical information and knowledge in machine-processable form. Our visionis that all biomedical knowledge and data are disseminated on the Internet using principled ontologies, such that they are semantically interoperable and useful for improving biomedical science and clinical care. Our resources include the Open Biomedical Ontologies (OBO) library, the Open Biomedical Data (OBD) repositories, and tools for accessing and using this information in research. The Center collaborates with biomedical researchers conducting Driving Biological Projects to enable outside research and stimulate technology development in the Center. The Center undertakes outreach and educational activities (Biomedical Informatics Program) to train future researchers to use biomedical ontologies and related tools with the goal of enhancing scientific discovery.

The Neuroscience Information Framework (NIF) is a dynamic inventory of web-based neurosciences data, resources, and tools that scientists and students can access via any computer connected to the Internet. An initiative of the NIH Blueprint for Neuroscience Research, the NIF will advance neuroscience research by enabling discovery and access to public research data and tools worldwide through an open source, networked environment.

Through its resource registry and concept based query system, NIF enhances neuroscience research by enabling discovery and access to research data and tools worldwide.

GenBioWiki is the student home page for the Genetics, Bioinformatics, and Computational Biology (GBCB) program at Virginia Tech. Bioinformatics and computational biology provide a research platform to acquire, manage, analyze, and display large amounts of data, which in turn catalyze a systems approach to understanding biological organisms, as well as making useful predictions about their behavior in response to environmental and other perturbations. Moreover, bioinformatics is the study of biological systems and large biological data sets using analytical methods borrowed from computer science, mathematics, statistics, and the physical sciences. This transdisciplinary approach to research requires graduates with extensive cross-cultural professional and technical training and provides ample employment opportunities for Ph.D. graduates. [1]

Open Journal Systems (OJS) is a journal management and publishing system that has been developed by the Public Knowledge Project through its federally funded efforts to expand and improve access to research.

ACADEMIC JOURNALS provides free access to research information to the international community without financial, legal or technical barriers. All the journals from this organization will be freely distributed and available from multiple websites.....ACADEMIC JOURNALS, breaking new frontiers in the world of journals.

BMC Biology - the flagship biology journal of the BMC series - publishes research and methodology articles of special importance and broad interest in any area of biology and biomedical sciences. BMC Biology (ISSN 1741-7007) is covered by PubMed, MEDLINE, BIOSIS, CAS, Scopus, EMBASE, Zoological Record, Thomson Reuters (ISI) and Google Scholar.

Sixty years of study have made Escherichia coli K-12 the most deeply understood organism at the molecular level. Much of what we know about cellular processes can be traced to fundamental discoveries in E. coli. In spite of its great importance as a model organism, information about E. coli is distributed among many online resources. EcoliHub uses web services that are being developed to make seamless bidirectional connections between E. coli resources, thereby enabling the full use of existing knowledge and supporting cutting-edge research into the molecular basis of life. Read More EcoliHub is being developed to serve the user community. Users can help teach us what is desirable in future releases by taking our User Survey.

OpenCV is an open source computer vision library originally developed by Intel. It is free for commercial and research use under a BSD license. The library is cross-platform, and runs on Mac OS X, Windows and Linux. It focuses mainly towards real-time image processing, as such, if it finds Intel's Integrated Performance Primitives on the system, it will use these commercial optimized routines to accelerate itself. This implementation is not a complete port of OpenCV. Currently, this library supports : * real-time capture * video file import * basic image treatment (brightness, contrast, threshold, …) * object detection (face, body, …) * blob detection Future versions will include more advanced functions such as motion analysis, object and color tracking, multiple OpenCV object instances … For more information about OpenCV visit the Open Source Computer Vision Library Intel webpage, the OpenCV Library Wiki, and the OpenCV Reference Manual (pdf).

Our community guide allows you to set up your own MPI (Message Passing Interface) based supercomputer cluster with the Playstation 3. This guide was co-written by Gaurav Khanna, based on his previous work on the Gravity Grid and is a current run-time environment for the research of co-author (Chris Poulin), based on his current work in distributed pattern recognition. As such, we currently utilize the Fedora Core for this infrastructure and illustrate a "how-to" below. NOTE: We focus on the Fedora 8 distribution, due to prevalence of Fedora and its Cell SDK (3.0) compatibility. Finally, this content should be considered open source, and here is the license.

The advent of the World Wide Web has led to an explosion in the number of diverse resources available to neuroscientists. Despite the availability of powerful search engines, locating these diverse resources has become increasingly difficult and time consuming. The NIF project utilizes both advanced machine-based search technologies and old-fashioned human legwork to provide access to neuroscience-relevant resources on the Web. Resources include research materials, Web pages, software tools, data sets, literature and general information. The NIF has developed technologies that allow a user to search across these different types of resources, all from a single interface. A unique feature of the NIF is the ability to issue direct queries against multiple databases simultaneously, retrieving content that is largely hidden from traditional search engines. A second unique feature is an extensive vocabulary covering major neuroscience domains for describing and searching these resources. The NIF takes advantage of advances in knowledge engineering to broaden and refine searches based on related concepts. The NIF beta test site was developed to gain feedback on the NIF search interface and content. Users will be asked to search the NIF, explore the vocabularies, and answer a questionnaire about their experience.

UCSF Chimera is a highly extensible program for interactive visualization and analysis of molecular structures and related data, including density maps, supramolecular assemblies, sequence alignments, docking results, trajectories, and conformational ensembles. High-quality images and animations can be generated. Chimera includes complete documentation and several tutorials, and can be downloaded free of charge for academic, government, non-profit, and personal use. Chimera is developed by the Resource for Biocomputing, Visualization, and Informatics and funded by the NIH National Center for Research Resources (grant P41-RR01081).

BioMart is a query-oriented data management system developed jointly by the Ontario Institute for Cancer Research (OiCR) and the European Bioinformatics Institute (EBI). The system can be used with any type of data and is particularly suited for providing 'data mining' like searches of complex descriptive data. BioMart comes with an 'out of the box' website that can be installed, configured and customised according to user requirements. Further access is provided by graphical and text based applications or programmatically using web services or API written in Perl and Java. BioMart has built-in support for query optimisation and data federation and in addition can be configured to work as a DAS 1.5 Annotation server. The process of converting a data source into BioMart format is fully automated by the tools included in the package. Currently supported RDBMS platforms are MySQL, Oracle and Postgres. BioMart is completely Open Source, licensed under the LGPL, and freely available to anyone without restrictions.

Welcome to the NIH WikiProject, a collaboration area and group of editors dedicated to improving Wikipedia's coverage of National Institutes of Health. This is a new WikiProject, so please join!! (For more information on WikiProjects, please see Wikipedia:WikiProject and the Guide to WikiProjects). Goals * Improve Wikipedia's current coverage of the NIH and deepen the coverage with more pages. Scope * Cover all of the Institutes all the way down to individual laboratories/units.