There are many possible modeling endpoints related to solubility. Solubility can be measured in various solvents and at different temperatures. The interest of pharmaceutical researchers is biased towards the solubility in water and DMSO under normal conditions. However, for organic chemistry, many other solvents can be at the same level of importance. During synthesis and for the purification and crystallization of compounds, one needs to know solubility of compounds in various organic solvents. Our observations indicates that there is lack of solubility data measured in organic solvents, rather than water and DMSO . Because solubility is a temperature-dependent property, the solubility data for a number of temperatures is also important. Several solubility datasets currently exist covering a wide range of temperatures and solvents, but the number of measured compounds is not enough for efficient training of machine learning models . The aim of this work is to create a large dataset of organic compounds solubility values to supplement available data for further data-driven model development. For this purpose we searched for and collected data from 771 unique articles from 3 previously selected journals (Figure ). As a result, organic compounds solubilities in 138 different solvents were collected. This dataset includes solubility values for 839 unique organic compounds under temperatures from 243,15 K to 403,15 K at atmospheric pressure. The complete dataset contains 54273 solubility data records. We named this dataset BigSolDB.