OpenSciInfo

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 Open Computer Vision Library has > 500 algorithms, documentation and sample code for real time computer vision. Tutorial documentation is in O'Reilly Book: Learning OpenCV http://www.amazon.com/Learning-OpenCV-Computer-Vision-Library/dp/0596516134

PubMed is a service of the U.S. National Library of Medicine that includes over 18 million citations from MEDLINE and other life science journals for biomedical articles back to 1948. PubMed includes links to full text articles and other related resources.

Using the Tiddler macro, contents of one tiddler can be embedded ("transcluded") from another tiddler:

myExperiment makes it really easy to find, use and share scientific workflows and other files, and to build communities.