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This presentation will provide a brief overview of the Agile methodology as an introduction to our simplified approach to iteratively handling multiple projects across a small team. This iterative approach allows us to regularly re-evaluate requested enhancements against institutional priorities and more accurately estimate timelines for specific units of functionality. The presentation will highlight how we approach each development cycle (from planning to estimating to re-aligning) as well as some of the actual tools and techniques we use to manage work (like JIRA and Greenhopper). It will identify some challenges faced in applying an established development methodology to a small team of multi-tasking developers, the outcomes we’ve seen, and the areas we’d like to continue improving. These types of iterative planning/development techniques could be adapted by even a single developer to help manage a chaotic workplace.
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'''Talk Title'''
Public Datasets in the Cloud
'''Speaker name, affiliation and email address:'''
Rosalyn Metz, Wheaton College, metz_rosalyn@wheatoncollege.edu
Michael B. Klein, Oregon State University, Michael.Klein@oregonstate.edu
'''Abstract'''
When most people think about cloud computing (if they think about it at all), it usually takes one of two forms: Infrastructure Services, such as Amazon EC2 and GoGrid, which provide raw, elastic computing capacity in the form of virtual servers, and Platform Services, such as Google App Engine and Heroku, which provide preconfigured application stacks and specialized deployment tools.
Several providers, however, offer access to large public datasets that would be impractical for most organizations to download and work with locally. From a 67-gigabyte dump of DBpedia's structured information store to the 180-gigabyte snapshot of astronomical data from the Sloan Digital Sky Survey, chemistry and biology to economic and geographic data, these datasets are available instantly and backed by enough pay-as-you-go server capacity to make good use of them.
We will present an overview of currently-available datasets, what it takes to create and use snapshots of the data, and explore how the library community might push some of its own large stores of data and metadata into the cloud.
