A Review Of Detrital Zircon Data Treatment, And Launch Of A New Tool 'Dezirteer' Along With The Suggested Universal Workflow

Overwhelming numbers of detrital zircon (dZr) U-Pb ages have been acquired all over the globe in the last few decades, opening new horizons for employing big data approaches. However, several impediments such as frequent publication of heavily filtered data tables with omitted analyses, failure to cite the logic on "best age" computation, and generally the lack of uniformity among data treatment approaches delay the employment of this growing volume of data to the full extent. We have developed Dezirteer software to address some of the impediments. The program runs on any OS as a Python package and can be installed in Windows via an executable installer. Dezirteer imports U-Th-Pb LA-ICPMS geochronology data (either as .csv files from Iolite or Glitter, or from a manually-filled template). First, it allows the user to define and apply filters (by elevated discordance, analytical uncertainty, and U concentration). Next, the user defines computations (discordance calculation, type of Pb-correction, choosing "best age"), and Dezirteer applies them on dZr datasets. Finally, Dezirteer exports data and manipulations on them, unequivocally separated. Dezirteer permits to quickly assess the results of filters and computations visually (concordia, probability density curves, kernel density estimates, histograms, and cumulative diagrams) and numerically (Kolmogoroff - Smirnov statistics, Likeness, and Similarity coefficients, peaks locations). Thus, our program can be used as a substitute for several existing software packages and provides some unique functionality. We propose several guidelines for publishing U-Th-Pb dZr data to increase the data's trustworthiness and usability and improve the transparency of manipulations on these data. Most importantly, dZr data must be published before extensive filtering and interpretation, while all further filtering and computational aspects should be described. Such an approach will significantly simplify the use of global dZr data by the scientific community and step forward towards employing big data approaches in dZr studies.