TY - GEN
T1 - Navigating oceans of data
AU - Maier, David
AU - Megler, V. M.
AU - Baptista, António M.
AU - Jaramillo, Alex
AU - Seaton, Charles
AU - Turner, Paul J.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - environmental data
KW - ocean observatories
KW - spatial-temporal data management
UR - http://www.scopus.com/inward/record.url?scp=84863485079&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-31235-9_1
DO - 10.1007/978-3-642-31235-9_1
M3 - Conference contribution
AN - SCOPUS:84863485079
SN - 9783642312342
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 19
BT - Scientific and Statistical Database Management - 24th International Conference, SSDBM 2012, Proceedings
T2 - 24th International Conference on Scientific and Statistical DatabaseManagement, SSDBM 2012
Y2 - 25 June 2012 through 27 June 2012
ER -