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Age–depth relationships are the key elements in paleoenvironmental studies to place proxy measurements into a temporal context. However, potential influencing factors of the available radiocarbon data and the associated modeling process can cause serious divergences of age–depth relationships from true chronologies, which is particularly challenging for paleolimnological studies in Arctic regions. This paper provides geoscientists with a tool-assisted approach to compare outputs from age–depth modeling systems and to strengthen the robustness of age–depth relationships. We primarily focused on the development of age determination data from a data collection of high-latitude lake systems (50 to 90∘ N, 55 sediment cores, and a total of 602 dating points). Our approach used five age–depth modeling systems (Bacon, Bchron, clam, hamstr, Undatable) that we linked through a multi-language Jupyter Notebook called LANDO (“Linked age and depth modeling”). Within LANDO we implemented a pipeline from data integration to model comparison to allow users to investigate the outputs of the modeling systems. In this paper, we focused on highlighting three different case studies: comparing multiple modeling systems for one sediment core with a continuously deposited succession of dating points (CS1), for one sediment core with scattered dating points (CS2), and for multiple sediment cores (CS3). For the first case study (CS1), we showed how we facilitate the output data from all modeling systems to create an ensemble age–depth model. In the special case of scattered dating points (CS2), we introduced an adapted method that uses independent proxy data to assess the performance of each modeling system in representing lithological changes. Based on this evaluation, we reproduced the characteristics of an existing age–depth model (Lake Ilirney, EN18208) without removing age determination data. For multiple sediment cores (CS3) we found that when considering the Pleistocene–Holocene transition, the main regime changes in sedimentation rates do not occur synchronously for all lakes. We linked this behavior to the uncertainty within the dating and modeling process, as well as the local variability in catchment settings affecting the accumulation rates of the sediment cores within the collection near the glacial–interglacial transition.