domains:start [The ICE-D (Informal Cosmogenic-nuclide Exposure-age Database) wiki]

Table of Contents

ICE-D science applications

Unfortunately the phrase “science application” is pretty vague. But, basically, if you want to have a project to organize and use cosmogenic-nuclide data, one approach is just to say, “OK, we are organizing all cosmogenic-nuclide data globally and it's going to be all in one place.” That's great – really it is – but what is going to happen is that fairly soon you are going to wind up with what might be objectively a lot of data, but that doesn't contain enough data relevant to any specific science question to answer that question. You have kind of a large hodgepodge of random bits of stuff that aren't focused enough to actually do anything.

To prevent that from happening, this project is building out the global data set by focusing, one at a time, on smaller data sets that comprise the total amount of data needed to address a single important science application. It started by compiling all known cosmogenic-nuclide data from Antarctica, which include about 4000 measurements on about 3000 samples. That's a relatively small amount of data that can be compiled without too much suffering, but it's the complete set of data needed for useful and significant projects such as, for example, reconstructing changes in Antarctic ice volume and their impact on global sea level change during the last 25,000 years. This is an important thing to be able to do, and it's not that much data that needs to be compiled, so we were able to go from nothing to a useful data set fairly quickly. The relationship between effort and reward is much less daunting than it would be for a vaguely defined goal of assembling a global data set.

The Antarctica project has been followed by several other smaller-scale compilation projects aimed at specific applications, which are described in detail in the list below. The aim is to continue building useful segments of the total data set one at a time, thus breaking the task of compiling all data from everywhere into manageable chunks with decent effort-reward ratios.