HR: 13:55h
AN: SF43B-02    [Abstracts]
TI: The IRI Climate Data Library: translating between data cultures
AU: * Blumenthal, M B
EM: benno@iri.columbia.edu
AF: International Research Institute for Climate Prediction/Columbia University, 61 Route 9W, Palisades, NY 10964-8000 United States
AB: The IRI Climate Data Library is a library of datasets. By {\it library} we mean a collection of things, collected from both near and far, designed to make them more accessible for the library's users. Our datasets come from many different sources, many different {\it data cultures}, many different formats. By {\it dataset} we mean a collection of data organized as multidimensional dependent variables, independent variables, and sub-datasets, along with the metadata (particularly use-metadata) that makes it possible to interpret the data in a meaningful manner. Ingrid, which provides the infrastructure for the Data Library, is an environment that lets one work with datasets: read, write, request, serve, view, select, calculate, transform, ... . It hides an extraordinary amount of technical detail from the user, letting the user think in terms of manipulations to datasets rather that manipulations of files of numbers. Among other things, this hidden technical detail could be accessing data on servers in other places, doing only the small needed portion of an enormous calculation, or translating to and from a variety of formats and between data cultures. Our datasets have been primarily climate, both oceanographic and meterological, and are thus of that data culture. Our data is multi-dimensional, our geolocation has been mostly either gridded longitude/latitude, or point-locations longitude/latitude. In order to access and serve data from and to a broader community, we are expanding our holdings and tools in three new directions structurally: (Geographical Information Systems (GIS) image data (similar to most of our holdings except that geolocation frequently requires interpreting the projection), GIS vector data (geolocation is by specifying vector geometries, i.e. lines or polygons), and named locations (data georeferenced only by named location). Our multidimensional data structure permits us to organize and analyze sets of images easily, unlike most GIS software. On the other hand, adding the new geolocation methods gives our users access to data from many more sources. Finally, by translating these datasets from different data cultures into a common structure with standard use-metadata, we can translate between those cultures, and provide the infrastructure necessary for cross-disciplinary research.
UR: http://iridl.ldeo.columbia.edu/dochelp/topics/DATASETS/
DE: 9820 Techniques applicable in three or more fields
DE: 4200 OCEANOGRAPHY: GENERAL
DE: 3300 METEOROLOGY AND ATMOSPHERIC DYNAMICS
DE: 1694 Instruments and techniques
DE: 0845 Instructional tools
SC: Special Focus: Advances in Data Acquisition, Management, Analysis and Display [SF]
MN: 2004 AGU Fall Meeting