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This dataset contains results from Niskin bottle samples collected during CTD casts taken for nutrient concentration as well as the N isotopic composition of nitrate (NO3-). Corresponding CTD data measured at the same depths as the bottle samples are also included within the primary data file. CTD casts were carried out at fixed stations along the ship’s track to calibrate float sensor results as well as document variability associated with mesoscale features.  The associated cruise on the R/V Sally Ride, cruise SR2114, took place between 21 Dec 2021 and 21 Jan 2022 along a track between Costa Rica and San Diego, USA, transecting the oxygen-deficient zone (ODZ) of the Eastern Tropical North Pacific.  This cruise was in support of an NSF-funded project to develop an autonomous float array to study nitrogen loss (N-loss) processes in this region. 

Abstract The eastern tropical North Pacific oxygen deficient zone (ETNP‐ODZ) exhibits a distinct physical and biological environment compared to other oxygenated water columns, leading to a unique scenario of particulate organic matter (POM) production and vertical transport. To elucidate these biological pump processes, we present the first comparison of δ¹⁵N values of nitrate, phenylalanine (Phe), and glutamic acid (Glu) within two distinct size fractions of particles collected along a productivity gradient in the ETNP‐ODZ. Low δ¹⁵N_Pheand δ¹⁵N_Gluvalues in both particle pools at sites with prominent secondary chlorophyll maximum (SCM), compared to the ambient δ¹⁵N‐NO₃⁻, suggest the presence of recycled N‐utilizing primary producers distinct from those at the primary chlorophyll maximum and their contribution to export. We observed reduced¹⁵N enrichment of Phe in small particles and a narrower δ¹⁵N_Phedisparity between the two particle size fractions compared to the results from oxic waters, likely due to slower heterotrophic microbial degradation of small particles. Unique δ¹⁵N_Pheand δ¹⁵N_Glusignatures of particles were found at the lower oxycline, potentially attributable to chemoautotrophic production and zooplankton mediation. These findings underscore the need for further investigations targeting particles generated at the SCM, their subsequent alteration by zooplankton, and the new production by chemoautotrophs. This will allow for a better evaluation of the efficiency of the biological pump in the globally expanding ODZs under contemporary climate change. 

Using standard calibration schemes commercial oxygen optode sensors typically yield oxygen concentrations in the range of 2-4 umol/kg under anoxic conditions. They are thus unable to detect the roughly 0.1 umol/kg levels of oceanic functional anoxia. Here, a modified Stern-Volmer equation is used to characterize and calibrate 26 optodes deployed on 16 autonomous floats in the Eastern Tropical Pacific (ETNP) oxygen deficient zone (ODZ) using a combination of manufacturers', laboratory, and in-situ data. Laboratory calibrations lasting several months and conducted over 2 years show that optodes kept under anoxic conditions drift at rates of order 0.2 umol/kg/yr, with much higher drifts in the first month. The initial transient is plausibly due to the degassing of plastic components of the optodes and might be reduced by replacing these with metal. Oxygen concentrations measured by these calibrated optodes in the nearly anoxic ODZ core of the ETNP deviated from both the laboratory calibrations and ship-based STOX measurements by similar amounts. Thus with current sensors, an in-situ anoxic oxygen calibration only once or twice a year is needed to maintain an accuracy close to 0.2 umol/kg. An algorithm to find the anoxic cores of the ETNP ODZ is developed and used to remove the drift in the float optodes to this accuracy. This is an order-of-magnitude improvement in the low oxygen performance of the optodes and could be implemented on the existing database of Argo oxygen floats to map the geography of functional anoxia. This dataset contains the raw float data, the float data calibrated using the manufacturers’ schemes and our new scheme. The calibration points and our final calibration constants, as well as the STOX data used to validate our new calibrations, are included. Data was collected on 10 custom-built profiling 'ODZ' floats equipped with oxygen optodes and gas tension devices and on 6 standard Argo floats with oxygen sensors. Argo data was processed by Argo and recalibrated at APL/UW. ODZ float data was processed at APL/UW as described in the associated manuscript. # Oxygen data from Eastern Tropical North Pacific cruises and floats 2021-2022 [https://doi.org/10.5061/dryad.8kprr4xwk](https://doi.org/10.5061/dryad.8kprr4xwk) ## Description of the data and file structure ## **ODZ Level2.zip** contains scientific data for the ODZ floats converted from raw data using nominal calibrations. Level_2 in NASAspeak. A README, Diagnostic plots, and a Matlab conversion program are included.  The script ***MRVFloatDecode_2023.m*** reads the raw files for the ODZ floats and puts them in a single Matlab file **xo110-.mat** where the first is the float number and the second is the boot number. It makes lots of plots, which I also include. Matlab substructures and variables are: ***CTD*** – Structure containing Seabird 41CT data * P, T, S – pressure [dbar], temperature [deg C], practica salinity as computed by Seabird [psu] * time, mtime – time in Matlab datetime and datenum formats * SA, CT, Sig0 – Absolute salinity [g/kg], conservative temperature [deg C], potential density [kg/m^3] * CC, W, Drag–estimated oil volume [cc], vertical velocity [m/s], Drag force (for ballasting) [N] ***GPS*** – position * time, mtime - time as Matlab datetime, Matlab datnum * lat,lon- location degrees latitude, degrees longitude * nsat, hdop – number of satellites, horizontal dilution of precision ***GTD*** – Gas Tension Sensor * time, mtime - time as Matlab datetime, Matlab datnum, * P, T, S, Sig0 – Pressure [dbar], temperature [deg C], practical salinity [psu], potential density [kg/m^3] * GT – gas tension [mbar] * Tgtd – temperature of GTD [ deg C]  * Ref- time [matlab datenum], temperature [deg C], pressure [mbar] for reference sensor * Other variables are calibration constants and check values. ***SBE5M1,SBE5M2*** - status of pumps. 1 is for optode(1) and GTD. 2 is for reference optod  ***oldGTD*** - One float had an old-style GTD for reference.  ***optode*** - SBE63 optodes (1) is water optode, (2) is reference optode * time, time -time as Matlab datum and date time * SN – optode serial number * red_amp, blue_amp- amplitudes of red and blue LEDs [counts] * red-phase, blue-phase- phases [microvolts] of fluorescence phase. * O2phase- their difference [microvolts] used to compute oxygen * T – optode temperature [deg C] * O2uM – optode’s computed oxygen concentration converted to uMol/kg. * Tctd, S, P, Sig0 – CTD interpolated to optode time - temperature [deg C], practical salinity [psu], pressure [dbar], potential density [kg/m^3] ***ADC, AirPump, AirValve, OilPump,*** ***OilValve*** - structures diagnosing the buoyancy system operations. Scientfically uninteresting. ## ***STOX Oxygen Profiles.zip*** Contains high precision oxygen profiles taken on the two Sally Ride cruises using STOX oxygen sensors. The data is provided as .txt and .mat formats along with miscellaneous data from the CTD. Oxygen measurements from the floats were referenced to STOX oxygen profiles taken from the ship on the two cruises because these provide much more stable and high precision measurements. STOX sensors are described in detail in Revsbech, N. P.; Larsen, L. H.; Gundersen, J.; Dalsgaard, T.; Ulloa, O. and Thamdrup, B. ( 2009) Determination of ultra‐low oxygen concentrations in oxygen minimum zones by the STOX sensor. Limnology and Oceanography: Methods, 7, pp.371-381. DOI:10.4319/lom.2009.7.371. And from their manufacturer [https://unisense.com/products/stox-microsensor/](https://unisense.com/products/stox-microsensor/) STOX data was collected on two cruises of the research vessel, Sally Ride, SR 2114 and SR2011. Data from each CTD cast with a STOX profile is in a separate folder in this archive. In each, the raw data is in a ****.txt*** file and the converted Matlab data is in a ****.mat*** file. MATLAB scripts to read the ****.mat*** file are included in each folder. Data names and units are: Ship Cruise Station Cast Year Month Day Hour Minute * Depth [m] * Latitude [deg]  * Longitude [deg] * Density [sigma-theta,kg/m^3]  * Temperature [ºC]  * Salinity * Beam Attenuation [1/m] * Fluorescence [mg Chla/m3]  * PAR [umol/m2/s] * Oxygen_SBE [µmol/kg]) * Oxygen_STOX [µmol/kg] * STOX_SD [µmol/kg] * STOX_n [µmol/kg] * NO3-Suna [uM] ## **Optode Calibration.zip**  Contains all of the calibration data used to calibrate the optodes including the anoxic laboratory points, the manufacturers' calibration points, and the coefficients of the calibration model for each optode.  **Seabird 63 Optodes** Anoxic calibration data and model fit are in ***AnoxicCalibration/SBE63/2020/*** and ***/2021/***. The 2020 data was used in the final calibration. * Files are *******Tau0model.mat*** where **** is the optode serial number * Variable ***meta*** explains each variable, repeated here. Calibration model is '1./Taup.*exp(-(Etau+Etau2.*(K-283.15).^2)/R/K )*(1+Drift *(days since start) )' Variables are * Taup: 'Phase [uS]' * Etau: 'Energy is Etau+Etau2*(T-10C) [J/mol] * Drift: 'Drift coefficient in the model [1/days] * Ttau: 'Time scale of drift [days] * Drift_uSday: 'Model Drift uS/day' * Dcal: 'Robust Drift. The drift line is Dcal(2)+ Dcal(1)*(Yearday of 2021) in uMol. Drift is Dcal(1) [uMol/day] * Drms: 'RMS drift fit error [uS] * Derr: 'Uncertainty in Dcal; Drift uncertainty is Derr(1) [uMol/day]'  Calibration points from the anoxic tank are in structure ***RawS.*** Variable ***meta*** explains each variable, repeated here. * K: 'Temperature [Kelvin]' * O2phase: 'O2 phase tau [uS]' * R: 'Gas constant [J/K/mol] * dyd: 'Time since start of record [days]' * TIME: 'Time [matlab datetime] * Omodel: 'Tau computed from model with drift [uS] * OmodelND: 'Tau computed from model with drift removed [uS] **Full Calibration/** contains the oxic calibration points and calibration coefficients Calibration points from Seabird supplied with optode are in **SBE63/*FactoryCalibration/ ****_dd_mmm_yyyy.mat ***where **** is the optode serial number. The calibration date follows. Variables are * Caltime - Calibration time [matlab datum] * ID - Serial number * O2in_mll - Oxygen in tank from winklers [ml/L] * O2out_mll - Oxygen computed from Seabird calibration [ml/L] * S - Salinity [psu] * T - Temperature [deg C] * resid_mll - model residual [ml/L] * tau_us - optode phase lag [microseconds] The oxic part of the optode model calibration coefficients are in ***SBE63/Calfiles/*** Calibration model, coefficients, and check values are in ***Calfiles/_oxic_model.mat*** where **** is the optode SN Data is in structure ***Kfile*** ***Kfile.meta*** explains the variables, repeated here. Model is pO2=eta/K(T) * (1 + a(T)*eta^2.3)^q(T) ; eta= tau0(T)/tau-1.  Note that tau0(T) is computed from *******_Tau0model.mat*** coefficients above. Variables are * Check: 'Test values of T, Tau, and pO2 from SBE cal' * Lk: 'K(T)=polyval(Lk, T) - Matlab call to compute K from Lk polynomial coefficients and T [deg C] * La: 'a(T)=polyval(La,T)' * Lq: 'q(T)=polyval(Lq,T)' **Aanderaa 4330 Optodes** **Anoxic calibration** data and model fit is in ***AnoxicCalibration/AA/*** \**                  **File names and formats are the same as for SBE63 optodes **Full Calibration/AA** **/Factory Calibrations** contains the calibration information supplied with the optodes Files are *******_dd-mmm-yyyy.mat*** with the same format as for the SBE63 The relevant variables are: * Caltime - Calibration time [matlab datenum] * ID - Serial number of optode * O2in_uMol - Calibration bath oxygen [uMol/L] * S - Salinity [psu] * T - Temperature from optode [deg C] * tau_deg - optode output phase [degrees] * meta - Misc information **/Calfiles/********_M0_oxic_model.mat** contain oxic part of the optode model calibration coefficients The format is the same as for SBE63, but there is an extra variable * eta_off: Add this to eta to account for drift since calibration [uS] ## **Calibrated Oxygen.zip** contains both uncalibrated and calibrated optode data for both the ODZ and Argo floats. A README file and Matlab processing programs are included. /***SBE63/xo110**-***.mat*** contain the calibrated data for **ODZ float xo110** Format and data is identical to that in the ***optode*** structure in ***ODZ_Level2_Mat,*** but with 2 extra variables * pO2 – partial pressure of oxygen [mbar] in uncalibrated data * Cal – a structure containing calibrated data -- FINAL DATA IS HERE * pO2: partial pressure of oxygen [mbar] in calibrated data  * Tau0m: Calibration model of anoxic phase [microsecond]. Includes offset. * Tau: Measured phase [microsecond] * Tauoff: offset in Tau from in situ calibration [uS] * eta: (Tau0m+Tauoff)/Tau-1 * O2uM: oxygen concentration [micromoles/kg] * O2umol: same Note that optode(1) is the water oxygen. Optode(2) is a reference optode, which is not of scientific interest.  **/SBE63/Reprocess_SBE63.m** is a MATLAB script showing how to combine calibration data and float data to make calibrated data for SBE63 optodes **/AA/Mat/*FloatID*/*FloatID_profilenum*.mat** contains Argo float data from float FloatID, profile number profilenum. Variables are Data from Argos float archive * mtime, time - time in datetime and datenum formats * lat, lon - GPS position latitude degrees and longitude degrees * P, T, S - CTD pressure [dbar], temperature [deg C], salinity [psu] * Optode - Optode serial number * O2T - Optode temperature [deg C] * O2phase - Optode phase [degrees] * O2umol - Optode oxygen [micromole/kg] Added variables * Kfile - Structure as in Optode Calibration files. Kfile.meta also has metadata * Cal - Structure containing calibrated optode data on the same timebase * Tau - measured phase [degrees] * Tau0m - Model anoxic phase [degrees] * Tauoff - Offset from laboratory calibration [degrees]. Includes offset & drift. * Drift - Drift [degrees/year] * mtime0 - base time for drift [matlab datenum format] * eta - Tau0m/Tau-1 * pO2 - Calibrated Oxygen partial pressure [mbar] * O2umol - Calibrated Oxygen concentration [micromole/kg] * meta - similar list to this one. * SN - same as Optode * Float - FloatID **/AA/Mat/*FloatID*/*FloatID_profilenum*.xls** contains the calibrated data in Excel format ***/AA/Reprocess_3_AA.m*** is a MATLAB script showing how to combine calibration data and float data to make calibrated data for AA optodes ## ***ODZ Raw\.zip*** contains the raw data from 9 custom-built ODZ floats. Level_1 in NASAspeak. They can be read by ***MRVFloatDecode_2023.m*** included in ***ODZ Level 2 files*** ## Code/Software Processing and reading scripts in Matlab (24.1.0.2628055 (R2024a) Update 4) are provided. 

Oxygen Deficient Zones (ODZs) of the world’s oceans represent a relatively small fraction of the ocean by volume (<0.05% for suboxic and<5% for hypoxic) yet are receiving increased attention by experimentalists and modelers due to their importance in ocean nutrient cycling and predicted susceptibility to expansion and/or contraction forced by global warming. Conventional methods to study these biogeochemically important regions of the ocean have relied on well-developed but still relatively high cost and labor-intensive shipboard methods that include mass-spectrometric analysis of nitrogen-to-argon ratios (N₂/Ar) and nutrient stoichiometry (relative abundance of nitrate, nitrite, and phosphate). Experimental studies of denitrification rates and processes typically involve eitherin-situorin-vitroincubations using isotopically labeled nutrients. Over the last several years we have been developing a Gas Tension Device (GTD) to study ODZ denitrification including deployment in the largest ODZ, the Eastern Tropical North Pacific (ETNP). The GTD measures total dissolved gas pressure from which dissolved N₂concentration is calculated. Data from two cruises passing through the core of the ETNP near 17 °N in late 2020 and 2021 are presented, with additional comparisons at 12 °N for GTDs mounted on a rosette/CTD as well as modified profiling Argo-style floats. Gas tension was measured on the float with an accuracy of< 0.1% and relatively low precision (< 0.12%) when shallow (P< 200 dbar) and high precision (< 0.03%) when deep (P > 300 dbar). We discriminate biologically produced N₂(ie., denitrification) from N₂in excess of saturation due to physical processes (e.g., mixing) using a new tracer – ‘preformed excess-N₂’. We used inert dissolved argon (Ar) to help test the assumption that preformed excess-N₂is indeed conservative. We used the shipboard measurements to quantify preformed excess-N₂by cross-calibrating the gas tension method to the nutrient-deficit method. At 17 °N preformed excess-N₂decreased from approximately 28 to 12 µmol/kg over σ_{0 =}24–27 kg/m³with a resulting precision of ±1 µmol N₂/kg; at 12 °N values were similar except in the potential density range of 25.7< σ₀< 26.3 where they were lower by 1 µmol N₂/kg due likely to being composed of different source waters. We then applied these results to gas tension and O₂(< 3 µmol O₂/kg) profiles measured by the nearby float to obtain the first autonomous biogenic N₂profile in the open ocean with an RMSE of ± 0.78 µM N₂, or ± 19%. We also assessed the potential of the method to measure denitrification rates directly from the accumulation of biogenic N₂during the float drifts between profiling. The results suggest biogenic N₂rates of ±20 nM N₂/day could be detected over >16 days (positive rates would indicate denitrification processes whereas negative rates would indicate predominantly dilution by mixing). These new observations demonstrate the potential of the gas tension method to determine biogenic N₂accurately and precisely in future studies of ODZs. 

Abstract Concentrations and elemental ratios of suspended particulate organic matter influence many biogeochemical processes in the ocean, including patterns of phytoplankton nutrient limitation and links between carbon, nitrogen and phosphorus cycles. Here we present direct measurements of cellular nutrient content and stoichiometric ratios for discrete phytoplankton populations spanning broad environmental conditions across several ocean basins. Median cellular carbon-to-phosphorus and nitrogen-to-phosphorus ratios were positively correlated with vertical nitrate-to-phosphate flux for all phytoplankton groups and were consistently higher for cyanobacteria than eukaryotes. Light and temperature were inconsistent predictors of stoichiometric ratios. Across nutrient-rich and phosphorus-stressed biomes in the North Atlantic, but not in the nitrogen-stressed tropical North Pacific, we find that a combination of taxonomic composition and environmental acclimation best predict bulk particulate organic matter composition. Our findings demonstrate the central role of plankton biodiversity and plasticity in controlling linkages between ocean nutrient and carbon cycles in some regions. 

RationaleNitrogen isotopic compositions (δ¹⁵N) of source and trophic amino acids (AAs) are crucial tracers of N sources and trophic enrichments in diverse fields, including archeology, astrobiochemistry, ecology, oceanography, and paleo‐sciences. The current analytical technique using gas chromatography‐combustion‐isotope ratio mass spectrometry (GC/C/IRMS) requires derivatization, which is not compatible with some key AAs. Another approach using high‐performance liquid chromatography‐elemental analyzer‐IRMS (HPLC/EA/IRMS) may experience coelution issues with other compounds in certain types of samples, and the highly sensitive nano‐EA/IRMS instrumentations are not widely available. MethodsWe present a method for high‐precision δ¹⁵N measurements of AAs (δ¹⁵N‐AA) optimized for canonical source AA‐phenylalanine (Phe) and trophic AA‐glutamic acid (Glu). This offline approach entails purification and separation via high‐pressure ion‐exchange chromatography (IC) with automated fraction collection, the sequential chemical conversion of AA to nitrite and then to nitrous oxide (N₂O), and the final determination of δ¹⁵N of the produced N₂O via purge‐and‐trap continuous‐flow isotope ratio mass spectrometry (PT/CF/IRMS). ResultsThe cross‐plots of δ¹⁵N of Glu and Phe standards (four different natural‐abundance levels) generated by this method and their accepted values have a linear regression slope of 1 and small intercepts demonstrating high accuracy. The precisions were 0.36‰–0.67‰ for Phe standards and 0.27‰–0.35‰ for Glu standards. Our method and the GC/C/IRMS approach produced equivalent δ¹⁵N values for two lab standards (McCarthy Lab AA mixture and cyanobacteria) within error. We further tested our method on a wide range of natural sample matrices and obtained reasonable results. ConclusionsOur method provides a reliable alternative to the current methods for δ¹⁵N‐AA measurement as IC or HPLC‐based techniques that can collect underivatized AAs are widely available. Our chemical approach that converts AA to N₂O can be easily implemented in laboratories currently analyzing δ¹⁵N of N₂O using PT/CF/IRMS. This method will help promote the use of δ¹⁵N‐AA in important studies of N cycling and trophic ecology in a wide range of research areas.