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.

在过去的几十年里，在全球范围内获得了大量碎屑锆石 (dZr) U-Pb 年龄，为采用大数据方法开辟了新的视野。然而，一些障碍，例如频繁发布带有遗漏分析的经过严格过滤的数据表，未能引用“最佳年龄”计算的逻辑，以及数据处理方法之间普遍缺乏统一性，延迟了将这种不断增长的数据量用于全范围。我们开发了 Dezirteer 软件来解决一些障碍。该程序作为 Python 包在任何操作系统上运行，并且可以通过可执行安装程序安装在 Windows 中。Dezirteer 导入 U-Th-Pb LA-ICPMS 地质年代学数据（来自 Iolite 或 Glitter 的 .csv 文件，或来自手动填充的模板）。第一的，它允许用户定义和应用过滤器（通过增加的不一致性、分析不确定性和 U 浓度）。接下来，用户定义计算（不一致性计算、铅校正类型、选择“最佳年龄”），然后 Dezirteer 将它们应用于 dZr 数据集。最后，Dezirteer 导出数据并对其进行操作，明确分开。Dezirteer 允许快速评估过滤器和计算的结果（协和、概率密度曲线、核密度估计、直方图和累积图）和数字（Kolmogoroff - Smirnov 统计、相似性和相似性系数、峰值位置）。因此，我们的程序可以用作几个现有软件包的替代品，并提供一些独特的功能。我们提出了一些发布 U-Th-Pb dZr 数据的指南，以提高数据的可信度和可用性，并提高对这些数据进行操作的透明度。最重要的是，dZr 数据必须在广泛过滤和解释之前发布，同时应描述所有进一步的过滤和计算方面。这种方法将大大简化科学界对全球 dZr 数据的使用，并朝着在 dZr 研究中采用大数据方法迈进。