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Coastal water level measurements represent one of the earliest geophysical measurements and allow an assessment of historical sea level rise and trends in tides, river flow and storm surge. However, recovery and digitization of archival tidal records have been much less widespread and systematic than, for example meteorological records. In this contribution, we discuss data rescue efforts and lessons learned in France, the United States and the United Kingdom, countries with early and extensive tide gauge networks by the mid-19th century. We highlight the importance of (a) cataloguing the historical gauge records, as a first step towards locating them; (b) locating data in archives, and then recovering and saving data by any means necessary, including photographs and scanning; (c) obtaining metadata, including both quantitative survey records, gauge checks and clock data, but also qualitative records such as gauge notes, letters and reports; and (d) quantitative statistical analysis of data and datum quality, using both standard data-entry checks but also tools that leverage the unique predictability of tide measurements. Methods for digitizing original analogue records are also discussed, including semi-automatic, computer-based methods of digitizing tidal charts (marigrams). Although the current best practice is described, future improvements are desirable and needed to make the more than estimated 10,000 station years of unused, undigitized records available to the scientific community. 

This paper describes a major update to the quasi-global, higher-frequency sea-level dataset known as GESLA (Global Extreme Sea Level Analysis). Versions 1 (released 2009) and 2 (released 2016) of the dataset have been used in many published studies, across a wide range of oceanographic and coastal engineering-related investigations concerned with evaluating tides, storm surges, extreme sea levels, and other related processes. The third version of the dataset (released 2021), presented here, contains double the number of years of data, and nearly four times the number of records, compared to Version 2. The dataset consists of records obtained from multiple sources around the world. This paper describes the assembly of the dataset, its processing, and its format, and outlines potential future improvements