Information technology staff at each university maintain hardware and software systems in support of the consortium's programs. A PISCO data manager works with university staff and scientists to ensure smooth operations. We use open-source software for data archival and access tools.
Servers and large-scale storage hardware are installed and maintained at PISCO-OSU and UCSB. The multiple Linux-based servers provide terabytes of long-term data storage for the PISCO archive and a platform for centralized databases and applications. These servers are the backbone of the PISCO data network, allowing data that is stored in our data catalog to be replicated. This PISCO network provides a level of internal redundancy that helps protect from data loss. As a participating member node of DataONE, data and metadata are also replicated to other network systems outside of PISCO such as the Knowledge Network for Biocomplexity (KNB).
Multiple software suites are managed and maintained at OSU, UCSC, and UCSB. Desktop-based file sharing services at each of the campuses provide easy access to our storage arrays and allow researchers to reliably store their current analysis work and ongoing monitoring data. Systems are backed-up on a nightly basis. Researchers have access to programs (such as Matlab) that run on our server systems and process large datasets (such as physical oceanographic data). PISCO web serving software also hosts multiple web sites in support of the research. These major software applications use an industry-standard personnel directory for signing-in and providing access control to files and the data stored in the PISCO catalog.
Due to the interdisciplinary nature of PISCO research, there is no “one-size-fits-all” technology to accommodate all data management needs. PISCO researchers work with large oceanographic data, small but complex biological data, genetics data, physiology data, Geographic Information Systems (GIS) layers, etc. Given the challenges presented by these different types of data, PISCO adopted a disciplinary team approach where data managers work with researchers to produce well-documented datasets and tailor storage needs. The details (i.e. metadata) are structured in a way that allows a user to search, parse, and manipulate the data contents such that the PISCO databases remain independent of the structure of any of the individual datasets. This allows our researchers flexibility in defining what is collected, without causing us to re-build our central databases for each new data type.