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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.

Why bother about openness and licensing for data? After all they don't matter in themselves: what we really care about are things like the progress of human knowledge or the freedom to understand and share.

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.

Launched in October 2008, "On-call" Scientists connects scientists interested in volunteering their skills and knowledge with human rights organizations that are in need of scientific expertise.

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.

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.

This semantic wiki at OpenResearch.org aims at making the world of science more visible and accessible. Everybody can add his favorite events (e.g. conferences and workshops), co-workers, tools / datasets, community fora or journals. Pooled together these pieces of information constitute a vast knowledge base about who and what moves science forward.

WikiGenes is the first wiki system to combine the collaborative and largely altruistic possibilities of wikis with explicit authorship. In view of the extraordinary success of Wikipedia there remains no doubt about the potential of collaborative publishing, yet its adoption in science has been limited. Here I discuss a dynamic collaborative knowledge base for the life sciences that provides authors with due credit and that can evolve via continual revision and traditional peer review into a rigorous scientific

The establishment of open access literature makes it possible for knowledge to be extracted from scholarly articles and included in other resources. BioLit aims to extract database identifiers and rich meta-data from open access articles in the life sciences and integrate that information with existing biological databases. We have begun prototyping this effort using a clone of the RCSB Protein Data Bank, a database of macromolecular structures.