SSRI maintains a small shared cluster of two virtual machines each with 32 gigs of memory. Each machine is pre-loaded with Matlab, SAS9.4, StataMp, Anaconda Python, R, ArcGIS, Gephi and MPlus 6.1. These machines have been created for jobs that either require a platform to run for long periods of time or have a large memory footprint exceeding that of a desktop or laptop. Other applications are possible to install upon review. Larger memory machines are available with an additional cost, covering larger jobs and restricted data.
Virtual machine provisioning
The SSRI team has worked closely with OIT enterprise group to create a wide range of systems. We have met the needs of groups needing small traffic web servers with custom applications all the way up to multiple large memory VMs with database components for memory bound computation. Linux and windows are within our operating scope at the desktop and server levels. We have working knowledge of the protected network space and can accommodate environments needing restricted (not sensitive ) processing. Examples have been collaborative student work areas where data can be worked on, but not moved out. Each VM can be dynamically resized with increasing or diminishing memory ( for cost reduction ) and in some case are free at the lower end of the scale.
SSRI IT has had a long run of being involved in custom requests that include PHP applications for data collection, API coding against Box and online resources for data collection/automation, converting large text data as a database feed and conversion content from various hosting platforms ( Drupal/apache/AWS ) into new spaces. With this broad exposure, and the ability to request compute resources, we are poised to create or consult on a wide range of solutions.
Identifying public data resources & data massage
In an increasing data centric world it has become critical for researchers to request data from providers, collect it from the field, and shape it to fit the needs of research. While our group does not provide analytics on the data we are involved with, we assist in matching and validation of new updates to current data sets, broad restructuring of existing data into more manageable formats and are experimenting with presenting largish data in the form of database feeds in the interest of providing faster access time (odbc/sql).
With our proximity to the MIDS course work, as well as our own researchers, our team has become a very loose spot to visit in working through coding ideas or project development. With the teams and systems we connect with, it has become a natural occurrence that questions will come our way. We have our own students stop by, researchers walking though the connection space, and random questions within our organization. If you have a question, maybe about an obscure library, how to think about a data type, if we cannot answer it we will know who can.