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Increasing interest in the deployment of optical oxygen sensors, or optodes, on oceanographic moorings reflects the value of dissolved oxygen (DO) measurements in studies of physical and biogeochemical processes. Optodes are well-suited for moored applications but require careful, multi-step calibrations in the field to ensure data accuracy. Without a standardized set of protocols, this can be an obstacle for science teams lacking expertise in optode data processing and calibration. Here, we provide a set of recommendations for the deployment andin situcalibration of data from moored optodes, developed from our experience working with a set of 60 optodes deployed as part of the Gases in the Overturning and Horizontal circulation of the Subpolar North Atlantic Program (GOHSNAP). In particular, we detail the correction of drift in moored optodes, which occurs in two forms: (i) an irreversible, time-dependent drift that occurs during both optode storage and deployment and (ii) a reversible and pressure-and-time-dependent drift that is detectable in some optodes deployed at depths greater than 1,000 m. The latter is virtually unidentified in the literature yet appears to cause a low-bias in measured DO on the order of 1 to 3µmol kg⁻¹per 1,000 m of depth, appearing as an exponential decay over the first days to months of deployment. Comparisons of our calibrated DO time series against serendipitous mid-deployment conductivity-temperature-depth (CTD)-DO profiles, as well as biogeochemical (BGC)-ARGO float profiles, suggest the protocols described here yield an accuracy in optode-DO of ∼1%, or approximately 2.5 to 3µmol kg⁻¹. We intend this paper to serve as both documentation of the current best practices in the deployment of moored optodes as well as a guide for science teams seeking to collect high-quality moored oxygen data, regardless of expertise. 

This dataset is comprised of processed and edited temperature and salinity profiles from the OOI (Ocean Observatories Initiative) Global Irminger Sea Array Apex profiler mooring from September 2014 to May 2020 (https://oceanobservatories.org/array/global-irminger-sea-array/). The original dataset (including physical units) is available at https://ooinet.oceanobservatories.org/data_access/?search=GI02HYPM-WFP02-00-WFPENG000. Methods: Vertical profile data were gridded using 5 m depth intervals taking the mean of upward and downward profiles. Salinity spikes were removed using a threshold of 0.01 over 5 m. Any salinity bins fresher than 34.72 were then removed. Salinity biases were corrected by referencing to a potential temperature of 2.5C, which was the deepest, most consistently sampled potential temperature of the dataset (Toole et al. 2017). The reference salinity at the reference isotherm was chosen to be 34.93 based on calibrated shipboard CTD measurements taken near the mooring on the OOI mooring service cruises from 2014 to 2019 (dataset references below). Salinity was calibrated by first subtracting the reference salinity from the raw value at the reference isotherm and then finding the ratio between this difference and the reference salinity. This gain correction was then multiplied by the full profile to produce the final salinity. The salinity is anchored to the reference salinity at the deep reference isotherm and this dataset should be used knowing that this is not necessarily realistic in this area of deep water mass formation. Caution should be used looking at properties near the reference isotherm. However, the salinity biases in the raw data are significant and without correction this time series is discontinuous whenever the mooring is replaced. The patterns in the upper part of the water column agree with nearby measurements (de Jong et al., 2023). Final Data: All data are included in a single NetCDF file, ‘OOI_Irm_HYPM_cal_21Mar2021_NCEIannotated_15Nov2023.nc’. The included fields are: time [days since 2000-01-01], depth [meters], practical salinity ‘sal’ [psu], in-situ temperature ‘temp’ [C], latitude [degrees north], and longitude [degrees east]. The Gibbs Seawater oceanographic toolbox (McDougall et al., 2011) was used to calculate all variables. 

The field of oceanography is transitioning from data-poor to data-rich, thanks in part to increased deployment ofin-situplatforms and sensors, such as those that instrument the US-funded Ocean Observatories Initiative (OOI). However, generating science-ready data products from these sensors, particularly those making biogeochemical measurements, often requires extensive end-user calibration and validation procedures, which can present a significant barrier. Openly available community-developed and -vetted Best Practices contribute to overcoming such barriers, but collaboratively developing user-friendly Best Practices can be challenging. Here we describe the process undertaken by the NSF-funded OOI Biogeochemical Sensor Data Working Group to develop Best Practices for creating science-ready biogeochemical data products from OOI data, culminating in the publication of the GOOS-endorsed OOI Biogeochemical Sensor Data Best Practices and User Guide. For Best Practices related to ocean observatories, engaging observatory staff is crucial, but having a “user-defined” process ensures the final product addresses user needs. Our process prioritized bringing together a diverse team and creating an inclusive environment where all participants could effectively contribute. Incorporating the perspectives of a wide range of experts and prospective end users through an iterative review process that included “Beta Testers’’ enabled us to produce a final product that combines technical information with a user-friendly structure that illustrates data analysis pipelines via flowcharts and worked examples accompanied by pseudo-code. Our process and its impact on improving the accessibility and utility of the end product provides a roadmap for other groups undertaking similar community-driven activities to develop and disseminate new Ocean Best Practices.