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<p>The saturated soil hydraulic conductivity (<span class="inline-formula"><i>K</i>_sat</span>) is a key parameter in many hydrological and climate models. <span class="inline-formula"><i>K</i>_sat</span> values are primarily determined from basic soil properties and may vary over several orders of magnitude. Despite the availability of <span class="inline-formula"><i>K</i>_sat</span> datasets in the literature, significant efforts are required to combine the data before they can be used for specific applications. In this work, a total of 13 258 <span class="inline-formula"><i>K</i>_sat</span> measurements from 1908 sites were assembled from the published literature and other sources, standardized (i.e., units made identical), and quality checked in order to obtain a global database of soil saturated hydraulic conductivity (SoilKsatDB). The SoilKsatDB covers most regions across the globe, with the highest number of <span class="inline-formula"><i>K</i>_sat</span> measurements from North America, followed by Europe, Asia, South America, Africa, and Australia. In addition to <span class="inline-formula"><i>K</i>_sat</span>, other soil variables such as soil texture (11 584 measurements), bulk density (11 262 measurements), soil organic carbon (9787 measurements), moisture content at field capacity (7382), and wilting point (7411) are also included in the dataset. To show an application of SoilKsatDB, we derived <span class="inline-formula"><i>K</i>_sat</span> pedotransfer functions (PTFs) for temperate regions and laboratory-based soil properties (sand and clay content, bulk density). Accurate models can be fitted using a random forest machine learning algorithm (best concordance correlation coefficient (CCC) equal to 0.74 and 0.72 for temperate area and laboratory measurements, respectively). However, when these <span class="inline-formula"><i>K</i>_sat</span> PTFs are applied to soil samples obtained from tropical climates and field measurements, respectively, the model performance is significantly lower (CCC <span class="inline-formula">=</span> 0.49 for tropical and CCC <span class="inline-formula">=</span> 0.10 for field measurements). These results indicate that there are significant differences between <span class="inline-formula"><i>K</i>_sat</span> data collected in temperate and tropical regions and <span class="inline-formula"><i>K</i>_sat</span> measured in the laboratory or field. The SoilKsatDB dataset is available at <a href="https://doi.org/10.5281/zenodo.3752721">https://doi.org/10.5281/zenodo.3752721</a> <span class="cit" id="xref_paren.1">(<a href="#bib1.bibx47">Gupta et al.</a>, <a href="#bib1.bibx47">2020</a>)</span> and the code used to extract the data from the literature and the applied random forest machine learning approach are publicly available under an open data license.</p>