Designing and mining multi-terabyte astronomy archives: the Sloan Digital Sky Survey
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data Management: NetCDF: an Interface for Scientific Data Access
IEEE Computer Graphics and Applications
Google fusion tables: data management, integration and collaboration in the cloud
Proceedings of the 1st ACM symposium on Cloud computing
Finding haystacks with needles: ranked search for data using geospatial and temporal characteristics
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
(In?)extricable links between data and visualization: preliminary results from the VISTAS project
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
When big data leads to lost data
Proceedings of the 5th Ph.D. workshop on Information and knowledge
Proceedings of the 2012 BELIV Workshop: Beyond Time and Errors - Novel Evaluation Methods for Visualization
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Some science domains have the advantage that the bulk of the data comes from a single source instrument, such as a telescope or particle collider. More commonly, big data implies a big variety of data sources. For example, the Center for Coastal Margin Observation and Prediction (CMOP) has multiple kinds of sensors (salinity, temperature, pH, dissolved oxygen, chlorophyll A & B) on diverse platforms (fixed station, buoy, ship, underwater robot) coming in at different rates over various spatial scales and provided at several quality levels (raw, preliminary, curated). In addition, there are physical samples analyzed in the lab for biochemical and genetic properties, and simulation models for estuaries and near-ocean fluid dynamics and biogeochemical processes. Few people know the entire range of data holdings, much less their structures and how to access them. We present a variety of approaches CMOP has followed to help operational, science and resource managers locate, view and analyze data, including the Data Explorer, Data Near Here, and topical "watch pages." From these examples, and user experiences with them, we draw lessons about supporting users of collaborative "science observatories" and remaining challenges.