Software Architectures Expressly Designed to Promote Open Source Development: Using the Hyrax Data Server as a Case Study

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Presented at the AGU Fall Meeting 2009


Data providers are continually looking for new, faster, and more functional ways of providing data to researchers in varying scientific communities. To help achieve this, OPeNDAP has developed a modular framework that provides the ability to pick and chose existing module plug-ins, as well as develop new module plug-ins, to construct customizable data servers. The data server framework uses the Data Access Protocol as the basis of its network interface, so any client application that can read that protocol can read data from one of these servers. In this poster/presentation we explore three new capabilities recently developed using new plug-in modules and how the framework's architecture enables considerable economy of design and implementation for those plug-in modules. The three capabilities are to return data packaged in a specific file format, regardless of the original format in which the data were stored; combining an existing data set with new metadata information without modifying the original data; and building and returning an RDF representation for data. In all cases these new features are independent of the data's native storage format, meaning that they will work both with all of the existing format modules as well as modules as yet undeveloped. In addition, we discuss how this architecture has characteristics that are very desirable for a highly distributed open source project where individual developers have minimal (or no) person-to-person contact. Such a design enables a project to make the most of open source development's strengths.


DateCreated ByLink
August 17, 2014
Patrick WestDownload

Related Projects:

Description: Tasks for various TWC projects related to data access and the OPeNDAP software products.

Related Research Areas:

Data Science
Lead Professor: Peter Fox
Description: Science has fully entered a new mode of operation. Data science is advancing inductive conduct of science driven by the greater volumes, complexity and heterogeneity of data being made available over the Internet. Data science combines of aspects of data management, library science, computer science, and physical science using supporting cyberinfrastructure and information technology. As such it is changing the way all of these disciplines do both their individual and collaborative work.

Data science is helping scienists face new global problems of a magnitude, complexity and interdisciplinary nature whose progress is presently limited by lack of available tools and a fully trained and agile workforce.

At present, there is a lack formal training in the key cognitive and skill areas that would enable graduates to become key participants in escience collaborations. The need is to teach key methodologies in application areas based on real research experience and build a skill-set.

At the heart of this new way of doing science, especially experimental and observational science but also increasingly computational science, is the generation of data.

Concepts: eScience