In this week’s ebiquity meeting (10:00am EDT Wed 2/25, ITE 325), Curt Tilmes will talk on “Persistent Identifiers for Earth Science Provenance“.

Historically, published scientific research could include a description of an experiment that an independent party could use to reproduce the experiment with the same results, confirming the research. Modern research in the field of earth science often depends on terrabytes of data captured from remote sensing instruments, complex computer algorithms that undergo numerous changes over the year. A single result could be the result of the work of hundreds of individuals over decades. The representation of the measurements, algorithms and all the other artifacts of experimentation leading to that result becomes a daunting problem. A key to handling this representation is a good scheme for persisent identifiers.

Persistent identifiers seem like a simple problem. Just make a good URL and don’t change it [1]. This sounds good in theory, but is difficult to maintain forever. Many other schemes have been proposed to attack various aspects of the problem of identification, with various advantages and disadvantages. I will introduce this topic and briefly describe some of the concerns with using identifiers specifically in the context described above, and some of the characteristics of various identifier schemes.

The presentation will be streamed live via ustream.tv

References and some identifier schemes

[1] Cool URIs Don’t Change
[2] Naming and Addressing: URIs, URLs, …
[3] Object Identifer (OID)
[4] The Digital Object Identifier (DOI) System
[5] Persistent Uniform Resource Locator
[6] A Universally Unique IDentifier (UUID) URN Namespace
[7] XRI (Extensible Resource Identifier)

David Chapman will defend his MS thesis, A General Algorithm for Gridding Earth Sensing Scanning Instruments, at 10:00am Monday May 5 in room 325 ITE. The abstract is below.

Gridding in remote sensing must re-project observations from their original coordinate system based on satellite orbit and attitude to a grid defined by Earth coordinates. Primitive methods assume that observations are located at points on Earth and typically average observations in grid cells, or interpolate geolocated observations. These approaches are inaccurate, because they do not make use of the instrument’s footprint geometry, and spatial response. Observation Coverage (Obscov) gridding techniques make use of the satellite optics and geometry to more accurately describe coverage of a footprint on within each grid cell. Obscov gridding provides significant accuracy improvements exceeding 1 Kelvin Brightness Temperature over most regions on Earth for a 12 micron window channel on-board the Atmospheric Infrared Sounder (AIRS). Existing Obscov algorithms are only applicable to specific instruments and depend heavily on implicitly defined spatial response functions. We make use of raycasting and adaptive grid numerical integration to compute Obscov for the spatial response function of any instrument while processing streaming satellite observation data faster than 400 Megabits/second on a 6 machine cluster. We discuss the quality benefits of our algorithm by analyzing the results of gridded AIRS infrared sensor data with 324 operational spectral channels. We also address parallel processing issues to integrate AIRS Obscov gridding with SOAR, an on demand climate processing system built on a 122 processor blade server.