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Birdhouse is a collaborative project open for the community to participate. It is a software framework containing a collection of Web Processing Services (WPS). The deployed algorithms are focusing on Earth Systems and environmental data processing with the philosophy of streamlining the software development and deployment. By supporting climate, earth observation and biodiversity data and processes, Birdhouse can be used in a wide array of Earth sciences projects and workflows. The core benefit of this project is to allow the seamless use of climate services developed by a diverse network of national meteorological offices, regional climate service providers, academics, not-for-profit research centers and private industry. As governments move toward open-data policies, there will be a need for analytical services that extract value out of the deluge of information. Using an interoperable software architecture, institutions can provide both data and services allowing users to process the data remotely from a laptop, instead of having to acquire and maintain large storage infrastructures. [ABSTRACT FROM AUTHOR]

A paper published by Anagnostopoulos et al. in volume 55 of the Hydrological Sciences Journal (HSJ) concludes that climate models are poor based on temporal correlation between observations and individual simulations. This interpretation hinges on a common misconception, that climate models predict natural climate variability. This discussion underlines fundamental differences between hydrological and climatological models, and hopes to clear misunderstandings regarding the proper use of climate simulations. Citation Huard, D. (2011) A black eye for the Hydrological Sciences Journal. Discussion of ‘A comparison of local and aggregated climate model outputs with observed data’ by G.G. Anagnostopoulos et al. (2010, Hydrol. Sci. J. 55 (7), 1094–1110). Hydrol. Sci. J. 56(7), 1330–1333. [ABSTRACT FROM PUBLISHER]

Background and Purpose: Several studies have been undertaken to validate quantitative methods of evaluating cervical spinal stenosis. This study was performed to assess the degree of interobserver and intraobserver agreement in the qualitative evaluation of cervical spinal stenosis on CT myelograms and MR images., Methods: Cervical MR images and CT myelograms of 38 patients were evaluated retrospectively. Six neuroradiologists with various backgrounds and training independently assessed the level, degree, and cause of stenosis on either MR images or CT myelograms. Unknown to the evaluators, 16 of the patients were evaluated twice to determine intraobserver variability., Results: Interobserver agreement among the radiologists with regard to level, degree, and cause of stenosis on CT myelograms showed kappa values of 0.50, 0.26, and 0.32, respectively, and on MR images showed kappa values of 0.60, 0.31, and 0.22, respectively. Intraobserver agreement with regard to level, degree, and cause of stenosis on CT myelograms showed mean kappa values of 0.69, 0.41, and 0.55, respectively, and on MR images showed mean kappa values of 0.80, 0.37, and 0.40, respectively., Conclusion: MR imaging and CT myelographic evaluation of cervical spinal stenosis by using current qualitative methods results in significant variation in image interpretation.