The software for rapid analysis and filtering of reduced (post-Iolite or Glitter) detrital zircon data
Download beta-version
Good: tons of detrital zircons (and counting).
Plot, illustrating the growth of number of publications in each year, starting in 2000, that are found in Google Scholar using the 'Detrital Zircon' as a search phrase.
Detrital zircon (dZ) U-Pb geochronology is a quickly developing field of geosciences; number of publications relying on dZ data is growing each year.
One of the most intriguing advances in dZ geochronology is studying our planet by manipulating big data from thousands of rock samples, of all ages, collected globally. For example, the largest, to our knowledge, compilation contains of about 200 thousand of dZ dates (Voice et al., 2011).
Bad: Challenging to compare data
Think they will treat their data similarly? Think again!
Apples need to be compared with apples, not with oranges. Kindergarten alumni know this.
This rule applies to dZr as well. However, how often do you see comparisons of different sets of dZr data that were filtered and analyzed differently? More than often' — should be an honest answer.
Let us examine a hypothetical example:
A researcher 'John' is interested in isolating all significant age populations in his sample that contains a wide variety of Mesozoic, Paleozoic and Precambrian grains. He also wants to compare his distribution to the one gathered by another researcher, 'Jane' from the same sedimentary formation. John is very careful not to filter out important populations, while Jane was particularly interested in young Mesozoic grains and did not care about older, mostly discordant populations. John used very weak discordant filters, while Jane used stringent ones. As a result, the two populations will not be statistically indistinguishable (which is a 'statistically-correct' expression to say that they will be, indeed, different).
What to do with data after Glitter/Iolite?
Quickly changing and plotting THESE easy as a pi, right?
In most cases raw data from ICPMS are reducted in either of the two data reduction packages: Iolite or Glitter. There, analyses are corrected for instrument drift and downhole fractionation; uncertainties are propagated; drill-throughs, zonal grains or other 'undesireables' are detected and such analyses omitted.
But what to do with reducted data? It still needs lots of filtering and computations. Non-conforming analyses (e.g., large degree of discordance, high analytical error, etc.) need to be filtered out; discordance need to be calculated (there are 2 way of doing it); 'best ages' need to be computed. Which age to use for the 'best age': ^{206}Pb/^{238}U or ^{207}Pb/^{206}Pb? When to switch between the systems?
Data also need to be plotted. Not once, not twice. Data need to be manipulated with, and immediately plotted, so that robustness of age populations are verified, and effects of any change are obvious.
In overwhelming amount of cases, MS-Excel is used for such 'games'. In best cases, some kind of macro's are used to help at least with filtering. In the worst: do it by hand, praying that you are attentive enough not to miss an analysis.
But, wait! What if you need to compare your data with someone else's? HOW do you do that? Should you use the same filtering that the other researcher did?
The solution: 'DeZirteer'
Dezirteer = 'DEtrital ZIRcons'
Simplicity: User imports Glitter/Iolite-reduced data, applies filters, sets algorithms, plots and export data. All in several clicks!
Python-powered: thus can be used in any OS with Python installed (yes, we do have a Windows installation for those who do not wish to bother with Python).
The Workflow
Import your data
Dezirteer imports data either right after reduction in Glitter or Iolite, or by simply copying and pasting someone else's data into a supplied spreadsheet.
Filtering
The following filters can be used for including/omitting analyses from a 'good' set: uranium concentration, analytical error, discordance (positive and negative)
Plot your data! And again, and again...
DeZirteer plots only those samples that passed the filters, after the application of discordance and 'best age' algorithms.
Data recognition
The program automatically parses the data into samples and analyses.
Algorithms
User tells the program how to calculate discordance, and the rules for best age calculation.
Exporting tables and graphs
Graphs can be exported in raster or vector formats. Data are exported in CSV-format, easily importable by MS-Excel.
Q&A
How is 'best age' calculated?
There are three choices of calculating the 'best age'; one with an age threshold, separating 207Pb/206Pb system from 206Pb/238U. In other two, the user defines, which of the two systems should be used for all analyses.
How is discordance calculated?
Discordance can be calculated by comparing (207Pb/206Pb)/(206Pb/238U) or (207Pb/235U)/(206Pb/238U) systems. User can also program the switch between the two, depending on the age.
Can I use common lead corrections?
Yes, you can! DeZirteer boasts having all 4 major corrections: 204Pb, 207Pb, 208Pb, Andersen.
Can age distributions be compared?
Of course. Check 'keep prev.' (so that multiple cumulative diagrams are plotted on top of each other) and 'show calc' checkboxes. After you press 'Draw' button, you will see the Kolmogoroff-Smirnov statistics for the current and the previous distribution
What graphs are available?
Three types of graphs are available:
(1) concordia/discordia (either conventional, or Tera-Wasserburg); (2) probability (PDP/KDE/Histogram); (3) Cumulative probability (PDP/KDE/Hist.).
Our contacts
Feel free to email us for any questions, complains, suggestions!