Pygoscelis adeliae, is one of the most abundant predators in the Antarctic. Its survival depends on numerous factors, including the presence of prey, closely related to sea ice. By changing sea-ice dynamics, climate change could affect its diet and recruitment. The aim of this study is to investigate, by means of stable isotope analysis of their faeces, penguin chicks’ diets in four colonies characterised by differing sea-ice persistence. In order to investigate any differences in diet between chicks and adults, the faeces of the latter were also collected and analysed. The study found that the contribution of krill to the chicks’ diets is generally greater than that of fish but that the proportion depends on the spatiotemporal variability of sea-ice dynamics. In addition, where sea ice was more persistent, the contribution of fish was lower in chicks than in adults. The less negative δ13C values of adults than chicks suggest that adults catch prey inshore for themselves and offshore for chicks. The expected variations in sea-ice dynamics and thus prey availability due to climate change might therefore modify the penguins’ diet and thus the role of this dominant endemic species in the Antarctic food web.The Adélie penguin, Pygoscelis adeliae, depends on sea-ice dynamics. By affecting cycles of sea-ice formation and melt, climate change could thus affect penguin diet and recruitment. In the light of climate change, this raises concerns about the fate of this dominant endemic species, which plays a key role in the Antarctic food web. However, few quantitative studies measuring the effects of sea-ice persistence on the diet of penguin chicks have yet been conducted. The purpose of this study was to fill this gap by comparing penguin diets across four penguin colonies in the Ross Sea and evaluating latitudinal and interannual variation linked to different sea-ice persistence. Diet was evaluated by analysing the δ13C and δ15N values of penguin guano, and sea-ice persistence by means of satellite images. Isotopic values indicate that penguins consumed more krill in colonies with longer sea-ice persistence. In these colonies, the δ13C values of chicks were lower and closer to the pelagic chain than those of adults, suggesting that the latter apparently catch prey inshore for self-feeding and offshore for their chicks. The results indicate that sea-ice persistence is among the principal factors that influence the spatiotemporal variability of the penguins’ diet.In Antarctica, prey availability for the mesopredator Adélie penguin,  Pygoscelis adeliae; Hombron and Jacquinot, 1841) is an endemic Antarctic species and one of two penguin species living in the Ross Sea [Euphausia genus and on the Antarctic silverfish (Pleuragramma antarctica) and bald rockcod (Pagothenia borchgrevinki), whose availability has been found to be associated with sea-ice dynamics [Rising global temperatures represent a major threat to ecosystems since they induce alterations in species composition and interactions within communities ,2. AntarRoss Sea ,15. It iRoss Sea ,17,18. Idynamics ,25,26,27dynamics ,28,29 andynamics ,27,30.Adults reproduce during the summer. After hatching, they feed their chicks, which are unable to catch prey on their own, by regurgitation for about three months ,32. Duri13C/12C) and nitrogen (15N/14N) to analyse the diet of Adélie penguin chicks in four colonies in two breeding seasons differing in terms of the timing of seasonal sea-ice break-up in the Ross Sea. We also compared the diet of chicks with that of adults along the latitudinal break-up gradient in the year with the earlier sea-ice break-up, associated with anomalous warmer temperatures.The purpose of our research was to fill these gaps, and to this end we used Stable Isotope Analysis (SIA) of carbon  is located in Terra Nova Bay and it is bounded to the East by Hells Gate and Evans Cove and to the West by the Nansen pack ice. On the eastern side of the island there is a polynya, an area of open water surrounded by sea ice for several months of the year ,71. Thiswww.scar.org (accessed on 23 January 2023), surface temperatures from the Mario Zucchelli, McMurdo and Scott Base stations). This trend has also been recorded on a larger scale in the broader Antarctic region (Copernicus Climate Change Service (C3S) Climate Bulletin, 2018, https://climate.copernicus.eu/ESOTC/2018 (accessed on 23 January 2023)).The sampling activities were carried out in the crèche phase (between 17 January and 3 February 2017 and between 20 and 28 January 2018) during the 32nd and 33rd expeditions of the PNRA . The mean monthly air temperatures recorded in the study area in the 2016/2017 Antarctic summer  were −3.58 ± 1.16 °C, quite high compared to both those recorded during the same months of the Antarctic 2017/2018 summer (−5.71 ± 1.09 °C) and those recorded from 1987 to 2018 . This allowed us to classify each sample as an independent observation . In 2018, due to the low accessibility to the shore due to the presence of sea-ice blocks, it was not possible to obtain an adequate number of adult faeces samples to make comparisons.Pleuragramma antarctica were found at Site A and Site C and were pooled, since their isotopic values did not vary. Since no specimens of the cryopelagic fish Pagothenia borchgrevinki were sampled in this study, its isotopic values were obtained from the literature. Samples of other potential preys, which included amphipods, the benthic fish Trematomus bernacchii and the benthic-pelagic Chionodraco hamatus [Where possible, in order to assess site-specific diet, penguin preys were collected from the vomited undigested food of adult penguins found near the crèches. Undigested krill was recovered from vomit at all penguin colonies in both years, except at Cape Hallett in 2018, and was analysed separately to account for the isotopic values of the two site-specific species. Regarding fish, samples of  hamatus ,27, were hamatus ,27,30, tSamples were stored in refrigerated containers until transport to the Mario Zucchelli Italian Research Station, enumerated and stored at −20 °C until transport to Italy for isotopic analysis.P. antarctica, T. bernacchii and C. hamatus, one sample of dorsal muscle was taken from each fish [In order to reduce any alteration during the nitrogen isotopic analysis, samples of faeces were cleaned by removing the residues of uric acid, rich in inorganic nitrogen, with a scalpel . Samplesach fish .13C; δ15N) as parts per-thousand (‰) deviations from international standards: Vienna Pee Dee Belemnite (PDB) for C and atmospheric N2 for N. Isotopic ratios were computed in accordance with the Equation (1)R is the heavy-to-light isotope ratio of the respective element . Results were calibrated to International Atomic Energy Agency reference materials . Caffeine IAEA-600 (C8H10N4O2) was used as the internal laboratory standard (δ15N = 1.00‰ and δ13C = −27.77‰). Measurement errors associated with the linearity and stability of the mass spectrometer were typically smaller than 0.05‰, while the standard deviation of repeated measurements of lab standard material  was ±0.02‰ for δ13C and ±0.07‰ for δ15N.The samples of faeces, krill, amphipods and fish were separately freeze-dried (−45 °C) for 24 h and pulverised to a fine homogeneous powder using a ball mill . Aliquots of 1.0–1.5 mg were then weighed and pressed into tin capsules for the Stable Isotope Analysis. Samples were analysed using an Elementar Vario Micro-Cube elemental analyser  coupled with an IsoPrime100 continuous flow mass spectrometer . Each sample was analysed twice, and values were averaged. Carbon (C) and Nitrogen (N) isotopic signatures were expressed in δ units (δhttp://www.usgs.gov/ (accessed on 23 January 2023)), were analysed. For each year and colony, the date of sea-ice break-up was calculated  and TIRS  satellite images, freely available on the USGS website . NR indicates the population’s degree of omnivory, and CR gives information about the range of basal resources consumed [Population-wide niche metrics were applied to adult and chick isotopic data using stable isotope Bayesian ellipses in R (SIBER) ,75. Nitrconsumed . The bi-consumed ,77. Baseconsumed . In ordeconsumed ,79. The consumed . SpecifiE. superba and E. crystallorophias), and to improve the accuracy of the Bayesian mixing models for the comparison between penguin colonies, we used site-specific krill isotopic values.The model required three inputs: (1) the isotopic signature of the target consumer; (2) the isotopic signatures (mean and standard deviation) of each of its potential food sources; and (3) the Trophic Enrichment Factor , i.e., the difference between a consumer and its diet. Krill was characterised by greater spatial isotopic variability than the other prey here considered . Thus, t13C and δ15N values of each sampled species were used in the input model. The δ13C and δ15N values of amphipods were averaged, as they did not differ significantly between colonies and were consistent with those reported in the literature for the Ross Sea [Since the isotopic signatures of the fish species are stable in space and time in the Ross Sea Region ,82,83,84Ross Sea . The TEFRoss Sea . Indeed,Ross Sea ,85.13C and δ15N values of the faeces and prey (krill and fish) of the penguins were tested, respectively, for normality and homogeneity with the Shapiro-Wilks test and the Levene test. Given the normality of the data, parametric tests were used for all comparisons. Differences between prey species and between chicks and adults in terms of δ13C and δ15N values were tested using one-way ANOVA and Tukey post hoc comparisons. Two-way ANOVAs were also performed to test the effects of site, year and their interaction on the δ13C and δ15N values of (1) krill and (2) chick faeces. The results were considered significant when the p value was less than 0.05.The δTo ensure that the isotopic differences between the penguins could be ascribed to the different proportions of krill and fish in their diet and not to krill’s acknowledged space-time isotopic variability ,87, befoWe used the proportional consumption of each resource, obtained from mixing models, to calculate the trophic niche width of Adélie penguin chicks using the Shannon-Wiener diversity index (Hs). The linear regression model was used to test the relationship between sea-ice persistence and the variety of the penguin chick diet (Hs).Beta regressions were used to test the relationship between sea-ice persistence and the percentage of krill, pelagic fish and amphipods in the Adélie penguin chicks’ diet. All statistical analyses were performed using R version 3.6.2 . The betThe timing of seasonal sea-ice break-up followed a latitudinal gradient, from the southern site (Site A), near the polynya, to the northern site (Site D), which was characterised by the longest sea-ice persistence. The earliest sea-ice break-up occurred at Site A in 2016/2017 , while differences between amphipods and krill were seen only in δ15N values .Significant differences in δ13C and δ15N values of the silverfish P. antarctica were higher than those of krill, except for δ13C in site C. The δ13C values of T. bernacchii and amphipods were higher than those of the other prey. The δ15N values of fish were higher than those of crustaceans, and those of T. bernacchii and C. hamatus were the highest of all fish .More specifically, the δ13C and δ15N values of krill differed between sites . In addition, the δ15N values of krill in both site A and site B were lower in 2018 than in 2017 .The δen sites . Notably13C and δ15N of chick faeces differed among colonies , and the δ15N values were significantly lower in chicks than in adults in the two northern colonies .In 2016/17, the δThe chicks had narrower isotopic niches than the adults, as indicated by CR, TA and SEAc .The niche overlap (SEAb) was highest near the polynya (Site A) and lowest at the northernmost site Site D; . NotablyT. bernacchii or the benthic-pelagic fish C. hamatus, since these fish lay outside the mixing space  than other resources, including fish  and amphipods , in all cases, although the exact percentages varied greatly between colonies and between years , where the chicks consumed less fish than adults. Krill consumption increased in 2018 , above all in the southern colonies  and in proportion to the delay in sea-ice break-up with respect to 2017  and was negligible for chicks in the northernmost colony , some faPygoscelis adeliae) chicks in the Ross Sea.In highly dynamic systems such as Antarctica, penguins have developed responses to the space-time heterogeneity of food availability ,62,89. FDue to strong katabatic wind and sea currents, seasonal sea-ice break-up proceeds northwards from the Terra Nova Bay polynya off Inexpressible Island, where sea ice is absent for most of the year, towards Cape Hallett, where sea ice persists longer in summer ,70,90,9115N of their faeces where the sea ice was more persistent indicate that under those conditions they received less fish than adults.Specifically, the results of this study show that: (1) During summer in the Ross Sea, krill make a larger contribution to the chicks’ diet than fish but in variable proportions depending on the persistence of sea ice. In detail, high consumption of krill was observed when long delays in the sea-ice break-up occurred, almost to the point of saturating the chicks’ diet. (2) Chicks and adults had similar diets, but their isotopic niche diverged as sea-ice persistence increased. The narrower isotopic niche of chicks and the lower δ13C and δ15N values than chicks and adults from the other colonies. In accordance with other studies’ findings [E. superba and E. crystallorophias have lower δ13C and δ15N values than the pelagic and cryopelagic fish P. antarctica and P. borchgrevinki. The increase in krill consumption towards the northern colony, furthest from the polynya, is supported by the results of the Bayesian mixing models. It has also been highlighted in adults by stomach content analysis [Regarding the first point, chicks and adults from the northernmost colony, which is not affected by the polynya, had significantly lower δfindings ,43,50,92analysis  and moreanalysis ,25,27.P. antarctica and P. borchgrevinki has been observed in ice-free open waters such as polynyas [While krill made the greatest contribution to the diets of chicks and adults in all colonies, these diets were complemented by pelagic and cryopelagic fish. In contrast, amphipods accounted for only a small proportion, and the contributions of benthic and bentho-pelagic fish were negligible. Although the Adélie penguin is considered a krill-dependent species ,94, the polynyas ,100,101.polynyas  observedpolynyas ,103. Thepolynyas ,103.13C and δ15N of chicks’ faeces in the southernmost colonies were typical of E. crystallorophias, while the more depleted values observed in the northernmost colony were typical of E. superba [E. crystallorophias) is abundant near the southern coast of Terra Nova Bay, in the innermost areas that are more affected by the presence of the polynya, while Antarctic krill (E. superba), a pelagic species inhabiting offshore areas, is more common on the northern continental shelf and slope [E. superba, a study conducted in East Antarctica by Riaz et al. [The enriched δ superba . This isnd slope ,104,105.z et al.  found inOur comparisons between years indicate that sea-ice persistence also determines variability in the trophic ecology of chick penguins over time. More specifically, in the southern colonies (Inexpressible Island and Adélie Cove) the chicks’ faeces had lower isotopic nitrogen values in 2018 than in 2017, which is explained by the lower fish consumption and the increased contribution of krill. This may be associated with the long delay in sea-ice break-up in 2018 compared with 2017. These between-year differences in diet were not observed in the northern colonies , where sea ice persisted longer than in the other colonies and the diet was based almost exclusively on krill, as also observed by Tabassum et al. .E. superba and E. crystallorophias [Interestingly, the wide Carbon Range of faeces observed in 2017 in Edmonson Point indicates that chicks relied on two food chains , which may have been favoured by the early ice break-up. This is the northernmost colony still under the influence of the polynya and is considered a sink population  occupyinorophias ,107. By 15N values were observed between adults and chicks, indicating the broadly similar composition of their diets. This is consistent with other studies carried out in East Antarctica using both DNA and stable isotope analysis [Regarding the second point, slight differences in δanalysis ,36. Howeanalysis ,34,40, aanalysis ,39. Howeanalysis ,108,109.analysis ,26 when The reduced provision of fish to chicks where sea ice is more persistent could be explained by the higher levels of energy spent by adults on longer foraging trips requiring locomotion on the pack ice and swimming in the sea ,100,110.13C differences between chicks and adults, which can be explained by the specific trophic strategy of adult penguins that prey on resources for themselves and for their offspring in different parts of the sea [13C between adults and chicks, which consume krill and fish from the pelagic chain, in the northernmost colonies, where the sea ice persists longer, the different δ13C values suggest consumption of krill from two separate food chains (sympagic vs. pelagic). The results suggest that adults self-feed in coastal waters (inshore) and catch food for their offspring in pelagic waters (offshore). However, the full description of this specific feeding behaviour deserves further investigation by coupling stable isotope analysis of faeces and individual tracking data. Individual tracking will also help clarify the dietary differences observed between individuals. Although outside the scope of this study, differences in the supply of fish with respect to krill may be related to the offspring’s sex, while differences in adult diets can be related to breeders and non-breeders, the latter having no parental costs and no influence on chicks’ diets [The former is supported by the δ the sea ,65. Acco the sea ,64,65, a the sea ,65. Whet the sea ,34. Whils’ diets .While our study was limited to the crèche period, sea-ice persistence can have various effects during chick rearing, and the guard period should also be taken into account to understand the effects of sea ice on penguin populations. Indeed, the population dynamics, i.e., growth/survival and chick recruitment, of Adélie penguins are highly complex mechanisms that depend on numerous factors, including rearing, predation and seasonal variation in climatic and sea-ice conditions ,35. The Other parameters such as chick growth and survival can be considered more direct proxies for both assessing the effects of environmental perturbations and understanding whether and how penguin population dynamics are changing . HoweverBy quantitatively describing the relationship between krill consumption and sea-ice persistence, this study shows that the space-time variations in the diet and foraging ecology of Adélie penguin chicks in the Ross Sea are determined by variations in sea ice persistence, which is known to be a key driver of resource availability and thus of adult foraging. The contribution of krill to diet decreases when sea-ice break-up occurs earlier. This is important, since krill is a resource not only for penguins but also for other species including pelagic fish, which are alternative prey for the penguins. In contrast, a strong delay in sea-ice break-up could affect the growth and survival of chicks, as the parents may increase their consumption of fish to the detriment of the abundance and quality of food provided to the chicks.This information adds to our knowledge of the effects of climate on the trophic ecology and reproductive success of the Adélie penguin ,114. CliStable isotope studies have highlighted an abrupt shift towards lower trophic level prey since the 19th century, which shows that penguins have only recently begun to rely on krill as an important part of their diet ,116. TheIn the light of these possible variations and due to the potential overlap between this predator’s activity and human fishing, identifying the factors associated with the Adélie penguin’s dietary composition is crucial for the sustainable management of Antarctic marine resources and food webs . The est


In this study, changes in quality parameters of rainbow trout croquettes treated with dill extracts obtained by brewing (BDA) and distillation (DDA) methods were investigated during 61 days of refrigerated storage (+2 ± 1°C). Physicochemical, microbial, and sensory parameters of the rainbow trout croquettes were analyzed once every 4 days. It was observed that the precooking process and additives influenced the nutrient composition and color change of the croquettes. Total volatile base nitrogen and thiobarbituric acid values did not exceed the limit values in any group during refrigerated storage and these values were found as 23.10 ± 0.89 mg/100 g and 3.13 ± 0.08 mg MDA/kg in the control group (C), 21.00 ± 0.92 mg/100 g and 2.77 ± 0.14 mg MDA/kg in BDA group, and 21.70 ± 0.92 mg/100 g and 2.85 ± 0.07 mg MDA/kg in DDA group, respectively. Extract treatments, especially DDA, resulted in a significant reduction in the counts of total aerobic mesophilic and psychotropic bacteria in trout croquettes compared to the control group. It was determined that the sensory scores of all croquette groups decreased during storage and the acceptability scores of the C, BDA, and DDA groups reached on the 33rd, 57th, and 61st days of storage, respectively. According to the results, this work demonstrated that the dill extracts both distilled and brewed can be used as a natural additive to improve the quality and extend the shelf life of rainbow trout croquettes. It has been determined that dill extract obtained by different methods restricts microbiological growth, inhibits lipid oxidation, and improves sensory taste in refrigerated trout croquettes. The results showed that the addition of dill extract extended the shelf life by 24–28 days compared with the control samples. This work demonstrated that the dill extracts both distilled and brewed in can be used as a natural additive to improve the quality and extend the shelf life of rainbow trout croquettes. With thTrout, which is one of the most widely cultivated species in Turkey, constitutes 35.12%  of Turkey's aquaculture production in 2021 and 20.71% of the total production TUIK, , and it Since ancient times, medicinal and aromatic plants have been used to increase and maintain the taste, smell, aroma, and quality characteristics of foods, as well as their antiseptic and pain reliever properties , a rich source of flavonoids, phenolics, tannins, saponins, terpene, and cardiac glycosides, is native to Mediterranean countries and Southeastern Europe  to make croquettes from rainbow trout as a ready‐made food product in order to increase the consumption of seafood and (2) to determine the effect of dill extracts obtained by different methods on the quality changes and shelf life of these croquettes during refrigerated storage.2Oncorhynchus mykiss) with an average length of 27.13 ± 1.13 cm and a weight of 272 ± 13.1 g were used for making croquettes. Fresh rainbow trout were purchased from a local fish farmer and they were transferred in insulated box containing ice within 1 h to the Fish Processing Technology laboratory. Dill (Anethum graveolens L.) was purchased from a local market in Rize, Turkey.A total of 60 rainbow trout  control: 1.45% added distilled water; (2) BDA: 1.45% added brewed dill extract; (3) DDA: 1.45% distilled dill extracts. Thus, the ratio of croquette dough was adjusted to 100%. Then, the croquette dough was given a round shape and coated  at 105°C until a constant weight  of 0.1 mol equi/L HCI used and W is the weight of trout croquette (g).Determination of total volatile basic nitrogen (TVB‐N) was performed according to the Lücke and Geide method  analysis was carried out to determine the degree of lipid oxidation of samples according to the method described by Tarladgis et al. . For TBAaw) was determined using dew point water activity meter Aqualab Series 4TE (Decagon) at 23–24°C. The color analysis of the homogenized samples was determined using CR‐14 Color Reader (Konica Minolta). The lightness (L* = 0 black to L* = 100 white), redness (+a*)/greens (−a*), and yellowness (+b*)/blueness (−b*) values were determined according to the CIE Lab coordinates.The pH of the croquette was measured using a digital pH meter  by making homogeneous mixtures of fish and distilled water   and psychrophilic (TAPB) bacteria counts and total coliforms (TC) test was carried out according to the method recommended by Halkman . Twenty‐2.7p < .05 using JMP software  method followed by the smallest significant difference (LSD) test was used to determine the differences among the treatments at 33.1p > .05). Crude fat values were found to be 5.51% in the fresh trout, and this value increased after precooking (6.25%) and spice additive (6.96%) processes (p < .05). Crude fat values of C, BDA, and DDA groups were 11.37%, 11.53%, and 10.19%, respectively. No statistically significant difference was found among the fat contents of the croquette groups (p < .05). Moisture content was found as 76.70% in the fresh. Depending on the precooking process, the trout lost water and the moisture content decreased to 72.47% (p > .05). Moisture contents of ST, C, BDA, and DDA were found to be significantly lower compared to fresh (p < .05). However, no significant differences were detected among the croquette groups (p > .05). The ash content found as 1.40% in the fresh sample did not show any significant (p > .05) changes despite the applied treatments and this value was determined in the range of 1.44%–1.71%. Similarly, Karsli, Caglak, and Kilic . Intragroup change in the decreasing scores of all sensory criteria during refrigerated storage was significant in all three croquette groups (p < .05). According to sensory evaluation results, all trout croquette groups were generally liked by consumer groups at the beginning of the refrigerated storage. However, compared to the dill extract‐treated groups, the scores of the control group were statistically lower from day 13 for appearance and from day 9 for odor, taste, texture, and general sensory scores (p < .05). Considering the acceptability limit of 3.9 points, the overall sensory scores of the croquette groups were below the consumable limit value on the 33rd day in the control group, on the 57th day in the BDA, and on the 61st day in the DDA  of trout croquette samples are given in Figure In this study, the results showed that dill extract additive increased the sensory properties and quality of croquette products and could be used as a natural antioxidant and antimicrobial additive in seafood products. Therefore, treatment with dill extracts has a potential to improve the storage quality of foods such as aquaculture. Similarly, Tümerkan et al.  reported3.3TBA, TVB‐N, pH, and water activity results of refrigerated fish croquette are given in Figure p < .05) to 3.13, 2.77, and 2.85 mg MDA/kg for C (33th day), BDA (57th day), and DDA (61st day) groups, respectively, at the end of refrigerated storage  during storage. There was no significant difference in TBA values between the BDA and DDA groups (p > .05). The increase in TBA value on the first day of cold storage has thought to be due to the temperature and additives applied in the croquette preparation steps. The lower TBA values of croquette treated with dill extracts may be attributed to the antioxidant components such as phenols, flavonoids, and proanthocyanidins  according to storage time . Especially, croquette group (DDA) treated with dill extract obtained by distillation method was found lower compared to other groups (p < .05). Even during 61 days storage period, the TVB‐N value of DDA did not exceed the acceptability limit of 30 mg/100 g recommended by Connell . During the refrigerated storage periods, fluctuations were observed in the pH values of the croquette products. Dill extract did not show much effect on the pH of the croquettes, however; pH of the dill extract added samples was consistently lower than that of control samples (p < .05). The lower pH values recorded for dill extract‐treated croquettes are confirmed by the antimicrobial effect of dill extracts, inhibiting bacteria that cause protein breakdown and the production of key compounds responsible for the pH increase. The low pH value in croquettes treated with dill extract is probably due to the pH stabilizing property of dill extract  is an important factor in controlling microbial activity and in terms of organoleptic quality parameters . During the study, the aw ranged between 0.98 and 0.99 in the control (C) group and 0.96 and 0.97 in the BDA and DDA groups. Similarly, it was stated that the aw values of the fish croquettes made from Barbus esocinus were between 0.95 and 0.98 during the 21‐day cold storage period , a* (9.95), and b* (7.55) values were found to be 26.80, 9.95, and 7.55, respectively , while a* value was decreased significantly (p < .05). A fluctuating change in L*, a*, and b* values of the croquette groups was observed throughout the refrigerated storage. Also, the dill extract treatments did not have a significant effect on the L*, a*, and b* values of the croquettes compared to the control group. However, it was thought that the significant changes observed on the first day of storage were caused by both precooking and adding spice processes. Karsli, Caglak, and Kilic , a* 9.9, and b* nd Kilic  reported3.4p < .05) during the refrigerated storage and reached 4.85, 3.52, and 3.48 log CFU/g in the C, BDA, and DDA, respectively, at the end of the storage period. The count of TAPB increased more than the count of TAMB and it was determined as 5.13 log CFU/g in C, 4.85 log CFU/g in BDA, and 4.78 log CFU/g in DDA at the end of each group's storage. During entire storage period, the TC values of croquette groups were found below the detectable limit values, and therefore all data were not shown due to low values of TC. The croquettes treated with dill extract had lower TVC compared to the control samples during the entire storage, indicating that dill extracts, especially DDA, showed an inhibitory effect against total viable bacteria growth. In addition, the TVC values of the BDA and DDA groups were found to be statistically lower than the value of the control group on the 57th and 61th days, respectively (p < .05). According to these results, all groups without exception did not exceed the acceptability upper limit value of 7 log CFU/g recommended by ICMSF ; investigation ; supervision (lead); writing – review and editing . Barış Karsli: Conceptualization ; data curation (lead); formal analysis (lead); investigation ; visualization (lead); writing – original draft (lead).The authors declare that they have no conflict of interest.


This study compares the accuracy and safety of pedicle screw placement using a 3D navigation template with the free-hand fluoroscopy technique in scoliotic patients. Fifteen scoliotic patients were recruited and divided into a template group (eight cases) and a free-hand group (seven cases). All patients received posterior corrective surgeries, and the pedicle screw was placed using a 3D navigation template or a free-hand technique. After surgery, the positions of the pedicle screws were evaluated using CT. A total of 264 pedicle screws were implanted in 15 patients. Both the two techniques were found to achieve satisfactory safety of screw insertion in scoliotic patients (89.9% vs. 90.5%). In the thoracic region, the 3D navigation template was able to achieve a much higher accuracy of screw than the free-hand technique (75.3% vs. 60.4%). In the two groups, the accuracy rates on the convex side were slightly higher than on the concave side, while no significance was seen. In terms of rotational vertebrae, no significant differences were seen in Grades I or II vertebrae between the two groups. In conclusion, the 3D navigation template technique significantly increased the accuracy of thoracic pedicle screw placement, which held great potential for extensively clinical application. Scoliosis is a complex, three-dimensional deformity of the spine with lateral deviation in the coronal plane >10°), the alternation of kyphosis or lordosis in the sagittal plane and the rotation of the vertebrae in the axial plane 0°, the a. For sevThe use of the 3D navigation template technique was first proposed in the 1990s , and theScoliosis typically occurs more often in the thoracic spine than in the other spinal regions. Previous studies have suggested that transpedicular screw fixation may not be suitable for most midthoracic regions because of the smaller pedicles and adjacent vital structures ,24; hencTherefore, as mentioned above, the main aim of this retrospective comparative analysis was to investigate the accuracy and safety of pedicle screw insertion using a 3D navigation template and the free-hand technique to correct the scoliotic spine, especially in the thoracic region. The second aim of this study was to investigate the difference in the accuracy of pedicle screw insertion with 3D navigation templates between the concave side and convex side in scoliosis correction surgery, and the third aim was to determine the accuracy of pedicle screw insertion with 3D navigation templates in vertebrae with different rotational angles.www.chictr.org.cn, accessed on 7 April 2023) with the registration number ChiCTR2300070277. All patients signed informed consent forms for the operation. A retrospective study was performed to analyse the data obtained for the scoliotic patients who underwent surgical treatment between January 2014 and December 2021. Inclusion criteria: (1) patients diagnosed with scoliosis who underwent primary posterior correction surgery, (2) the pedicle screw insertion was carried out using the free-hand technique or the 3D navigation template technique and (3) the postoperative data were complete. Exclusion criteria: (1) patients who received revision surgery or combined anterior–posterior surgery, (2) the pedicle screws were placed via other techniques and (3) other spinal surgeries were performed simultaneously.This study was approved by the Research Ethics Committee of Peking University Shenzhen Hospital (2022-041) and was carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki). Additionally, this study was registered at Chinese Clinical Trial Registry  were used for evaluation and measurement. As illustrated in The patients’ CT data were stored in DICOM format and imported into Mimics software  to generate a 3D reconstruction spine model. The optimal pedicle screw entry point and trajectory were designed with the aim of avoiding any penetration of the wall of the pedicle or vertebrae. The model (STL format) was then imported into 3-Matics software to design the matched virtual 3D navigation templates; the exported file of the 3D navigation template 3-Matics design results was imported into the 3D printer and the 3D navigation template was printed on a 3D printer  provided by Shenzhen Excellent Technology Company Research Institution using photosensitive polyamide (layer thickness = 16 um). As shown in All surgeries were performed using the posterior surgical approach after general anaesthesia. For congenital scoliosis, hemivertebra resection and short segment fusion were performed in all patients. In the thoracic region, due to the anatomical variance of pedicels, transpedicular screw fixation may not be suitable for most thoracic pedicles. An alternative method involving extrapedicular screw fixation within the PRU was evaluated and found to be anatomically feasible . AlthougIn the free-hand group, the entry point and direction were confirmed by anatomical landmarks and perioperative radiographs. After entering and widening the pedicle with an awl, the trajectory hole was explored with a ball-tipped probe to verify the integrity of the pedicular walls and exclude any cortical penetration. After the placement of the pedicle screw, anteroposterior and lateral fluoroscopies were performed to confirm the position of the screw in the free-hand group if necessary. In the template group, after removing the soft tissues surrounding the facet joint and lamina, the 3D navigation templates were tightly attached to the posterior bone surface of the laminae and upper articular processes. The surgeons used an electric bar to prepare the screw trajectory with the guidance of templates. During the drilling procedure, the surgeons pressed the connecting bar to keep the template stable, and the pedicle trajectory was enlarged using an awl before a pedicle screw was inserted along the trajectory, fluoroscopies were carried out after all screws were placed. The postoperative treatments and rehabilitation were similar between the two groups.The radiographs of the whole spine and a standard-dose 3D CT scan for each patient were performed postoperatively, and all pedicle screws were evaluated using PACS (Picture Archiving and Communication Systems) software. As described previously , the post-test was used to compare the preoperative measurements. Furthermore, the comparisons of accuracy and safety between the two groups were made using the χ2 test (the chi-squared test). p-values less than 0.05 were considered significant.Statistical analysis was performed using SPSS Statistics 25.0 software . All continuous variables are presented as the mean and standard deviation. A two-tailed Student’s Fifteen patients were recruited in this study , and n = 38) and 14.2 ± 1.8 mm (n = 48), respectively; and in the free-hand group, the values were 11.2 ± 2.1 mm (n = 31) and 13.6 ± 2.0 mm (n = 41), respectively. In the two groups, the transverse PRU width in lower thoracic spine was larger than it in middle thoracic spine (p< 0.05). The numbers of vertebrae with different rotations were recorded according to the Nash–Moe method. The number of vertebrae at Grades I and II were 39 and 10 in the template group, respectively, and 21 and 15 in the free-hand group, respectively  and lower thoracic spine (T9-T12) were 11.5 ± 1.5 mm (ectively .p = 0.028). The accuracy of the pedicle screw on the convex side was higher than that of the concave side in the template group or free-hand group, but neither showed a statistical difference . In the template group, 38 screws were placed in the middle thoracic spine (T5–T8) and 49 screws were placed in the lower thoracic spine (T9–T12), compared to 31 and 41 in the free-hand group, respectively; the accuracy in the lower thoracic spine was higher than in the middle thoracic spine , but no significant differences were observed. As shown in The most commonly reported complication was screw malposition. There were 89 perforations in the two groups (template vs. free-hand: 41 vs. 48); most frequently, these were medial perforations (template vs. free-hand: 26 vs. 28), followed by anterolateral perforations of the cortex of the vertebral body (template vs. free-hand: 7 vs. 15) and lateral perforations (template vs. free-hand: 4 vs. 4). Other perforations included four lateral-medial perforations and one inferior perforation. In the template group, two patients had CSF  leakage and one had a malpositioned screw (free-hand group) with asymptomatic aortic abutment, but screw revision was not necessary.p > 0.05). Additionally, no significant difference in postoperative hospital stay was observed between the two groups .As shown in As illustrated in Pedicle screw insertion is a challenging procedure in scoliosis correction surgery and misplacement of the screw may cause severe complications. This study investigated the accuracy and safety rate of the 3D navigation template in scoliosis surgery and found that this technique enhanced the accuracy of the pedicle screw in the thoracic region compared to the free-hand technique. Additionally, both 3D navigation template and free-hand technique were able to achieve high safety rates of pedicle screws during scoliosis correction surgery. According to the above results, the 3D navigation template has promising potential for improving the accuracy of pedicle screws in scoliotic corrections and thus deserves extensive application in clinics.Due to the anatomic variations of deformed spines, pedicle screw misplacement is more likely to occur during corrective surgery . Intraopp = 0.007) [The 3D navigation template technique, which has mainly been used in recent spinal corrective surgeries, improves the accuracy of pedicle screw placement based on clinical and cadaveric studies ,40,41. A= 0.007) , the accAxial vertebral rotation is an important part of deformity in the scoliotic spine, which makes pedicles converge and increases the difficulty of placing screws precisely. A previous study confirmed that as the rotation angle increased, the accuracy of the pedicle screw declined in the lumbar spine . In thisp < 0.05) [The spinal cord always remains tethered on the concave side, even during correction, and a slightly malpositioned screw may have a catastrophic consequence on the neurologic outcome; thus, much more attention must be paid when the pedicle screw is placed on the concave side. For instance, a previous study with a sample of 976 pedicle screws observed that more misplaced pedicle screws were seen on the concave side in scoliosis correction  . In this < 0.05) .In this study, the malposition rates were 29.7% and 38.1% in template group and free-hand group, respectively; the deformed pedicles, residual soft tissue, intervertebral movements during screw insertion might lead to this result. Nevertheless, the safety rates were high in two groups, and no postoperative revisions were necessary. After surgery, two cases of CSF leakage were found in the template group and the drainage tubes were removed after conservative treatments; CSF leakage was not found during surgery and the Ponte osteotomy in severe idiopathic scoliosis and a hemivertebrae resection in congenital scoliosis might contribute to the delayed CSF leakage.The 3D navigation template has many other advantages. A previous study confirmed that template technique was able to shorten the mean time for pedicle insertion and total surgical time, the mean time for one pedicle screw insertion in template group and in free-hand group were 6 min and 7 min, respectively . IntraopThe design of the template should be optimised based on surgical and mechanical considerations to reduce soft tissue removal but not sacrifice stability. In our study, the 3D navigation template was designed with two substrates and a connecting bar, which did not increase the contact area and allowed surgeons to hold it to maintain the stability of the template. Moreover, preoperative and postoperative CT scans mean more radiation exposure to patients, a low-dose CT scan might be a better choice to reduce radiation exposure. In addition, the cost of spine model was about USD 400 per model, and the cost of the navigation template was about USD 105 per template, which were covered by our research fund. Nevertheless, we should acknowledge that there are several limitations to this study. First, the sample size was too small and the basic characteristics of the patients in two groups did not match well; the difference in accuracy between the navigation template and the free-hand technique could be more evident if a larger randomized controlled trial (RCT) could be conducted in the future. Furthermore, the distribution of screws on the concave and convex sides, or in different rotational vertebrae, should also be investigated. Third, surgeons did not record the radiation exposure and mean screw insertion time during surgery, so that we could not truly describe whether the using of the 3D navigation template decreased the radiation exposure and shortened the screw insertion time. Last but not least, we did not examine the follow-up outcomes of the included patients as this was not our major objective.In this retrospective study, both techniques were found to achieve the satisfactory safety of screw insertion in scoliotic patients, and the 3D navigation template was demonstrated to increase the accuracy of pedicle screw placement in the thoracic region. Notably, screw malposition was more likely to occur on the concave side or in rotational pedicles; thus, more attention needed to be paid during screw insertion. In future studies, a larger RCT would help to further identify the value of the 3D navigation template in scoliotic corrections.


To this end, we have gathered a dataset of in-situ observations of sea ice drift and waves in ice. A total of 15 deployments were performed over a period of 5 years in both the Arctic and Antarctic, involving 72 instruments. These provide both GPS drift tracks, and measurements of waves in ice. The data can, in turn, be used for tuning sea ice drift models, investigating waves damping by sea ice, and helping calibrate other sea ice measurement techniques, such as satellite based observations.Variability in sea ice conditions, combined with strong couplings to the atmosphere and the ocean, lead to a broad range of complex sea ice dynamics. More  For example, a strong nonlinear coupling exists between ocean conditions and sea ice extent in the Arctic, due to the effect of ocean waves4. Indeed, as sea ice extent decreases in the Arctic basin, a new area of open ocean emerges between the polar ice cap and the surrounding continental landmasses. This, in turn, creates a new region of fetch where waves can grow larger before interacting with the sea ice. As a consequence of these larger waves, more sea ice breaks which also accelerates melting6, leading to a positive feedback loop. Similar couplings exist due to, for example, the albedo differences between open ocean and sea ice7. These changes are exacerbated by the polar amplification of climate change9.Sea ice is a major component of the global Earth ecosystem: it covers around 7% of the global oceans, averaged over a year18. This is due to the diversity of both sea ice and weather conditions that can be found in the polar regions, as well as the wide range of physical mechanisms at stake, including sea ice breaking, melting, freezing, collisions between ice floes, drifting, wave diffraction, reflection, viscoelastic effects in the ice, and turbulence in the water under the ice30. This makes the development of fully coupled ocean-atmosphere-sea ice-waves models a challenging task34.Unfortunately, accurately predicting sea ice dynamics under the influence of waves, winds, and currents, remains a challenging task35. Gathering in-situ data in this region is made challenging due to the statistically short lifetime of instrumentation under dynamic sea ice conditions, while remote sensing is, at present, challenging to use in a systematic way, and not yet able to provide all the information needed to calibrate models, especially regarding wave properties36.As a consequence, careful validation and calibration of sea ice models over a wide range of sea ice conditions is critical. Unfortunately, calibration data about sea ice drift and waves in ice are scarce, especially in the Marginal Ice Zone (MIZ), i.e. the sea ice area closest to the open water, which is most influenced by waves from the open ocean5. In these conditions, we deploy instruments close to the limit between the MIZ and the continuous ice, in order to study the relationship between incoming wave energy and continuous ice breakup.To help close the gap in sea ice data in the MIZ and other regions affected by waves propagating from the open ocean, we have focused on deploying instruments in both the Arctic and Antarctic. These instruments collect both drift information and wave energy spectrum. Two situations are of particular interest. The first corresponds to sea ice in the MIZ, where broken ice floes drift and break up under the influence of currents, winds, and waves. In these conditions, we usually deploy several instruments perpendicular to the MIZ edge, going successively deeper into the ice, so that we can quantify the effect of sea ice concentration on drift patterns, as well as wave damping into the MIZ, as illustrated in Fig. in-situ data are ground observations necessary to help calibrate satellite derived algorithms used in remote sensing of the sea ice.In the present dataset, we release data collected over the last 5 years by 13 groups, which corresponds to 15 deployments, involving a total of 72 instruments, covering both the Arctic, and several regions in the Antarctic. These data can, in turn, be used for investigating the effect of winds, currents, and waves on sea ice, to both identify physical processes, and help further develop and calibrate fully coupled models, in particular, regarding sea ice drift, waves in ice, and sea ice breakup. Moreover, these 37, the instrument “v2021”39, the Sofar Spotter buoy40, commercial Global Positioning System (GPS) drifters with Iridium communication ability, and the Ice Wave Rider (IWR)41. All instruments use GPS to measure geographical location. The instrument v2018, v2021, and IWR, use acceleration measurements from inertial measurement units to measure wave motion. By contrast, the Sofar Spotter uses GPS to measure the wave motion.In the course of the data collection, several instrument models have been used. These are, the instrument “v2018”41. Therefore, and in addition to the detailed self-contained methodology description we provide here, the open source codes  can also be used as a source for technical details around the methodology used by the instruments v2018 and v2021. These data cover locations and times when no other direct, in-situ observations that we know of are available, and are, therefore, potentially useful for a wide range of sea ice and marginal ice zone studies.In the following, we describe how measurements of drift and waves are performed, and we pinpoint differences between the instruments when relevant. The methodology used by the Sofar Spotter buoy is only partially known, as this is a close source, black box instrument. By contrast, the firmware and post processing code for the v2018, v2021 are fully open source, while the IWR is simply performing logging of an inertial measurement unit, with known configurationIn the case of the v2018, the full GPS National Marine Electronics Association (NMEA) output string is transmitted through Iridium. In addition, all the data collected by the instrument v2018 are stored on an internal SD card, so that, if the instrument v2018 is collected at the end of the deployment, the same GPS data are available there. The GPS measurements are performed with a period of approximately 3 hours.In the case of the v2021, only the UTC date and time as well as the latitude and longitude are transmitted. Other information is not transmitted, to save memory and cut satellite transmission costs. However, a higher GPS position sampling rate is applied, typically every 30 minutes.Similarly, the Sofar Spotter transmits GPS UTC time, latitude and longitude as a part of its iridium communications.The IWRs log GPS UTC time and geographic coordinates hourly to the internal SD card, without performing satellite transmission. Such routine is possible since IWRs deployments are either in ice camps or on shore-fast ice, and the data are retrieved together with the instruments at the end of the deployments.Commercial drifters also transmit GPS UTC time, latitude, and longitude.Drift tracks are measured using a GPS receiver. No processing of any form is applied on the output produced by the GPS module. The GPS module has built-in hardware and software that ensure that only valid GPS positions are produced.40. According to the datasheet delivered by Sofar, the typical resolution for significant wave height measurements is +/−2cm, depending on the conditions and sky view. The details of the implementation regarding how the raw data are filtered and how the wave spectrum is computed are, as far as the authors know, not available for this commercial close source instrument. The produced outputs include both wave statistics , full wave spectra, and directional information . The Sofar Spotter is considered a well-tested instrument, with several thousands units deployed according to the manufacturer, and it has been used in a number of peer reviewed measurement campaigns in the sea ice45.The Sofar Spotter uses GPS data in order to directly measure 2D wave surface displacement at 2.5 Hz in the North-South and East-West directions. These data are used to compute the wave properties assuming that the underlying signal corresponds to the circular wave orbital velocity41 that go deeper into the exact implementation, or to the code implementing the data processing .The instruments v2018, v2021, and IWR, use Inertial Measurement Units (IMUs) to measure the wave motion. In the following paragraphs, we outline the main lines of the data acquisition and the wave processing algorithms, though the reader curious of the exact, in-depth technical details regarding the open source instruments, is referred to the technical papers37, all the data collection and Kalman filtering process is implemented by the thermally calibrated Vectornav VN100 Inertial Measurement Unit (IMU) that is used in the instrument48. The VN100 is an industry-grade IMU, and the details of the Kalman filter and signal processing algorithms running on it are proprietary and close source. The VN100 IMU outputs a number of variables, including accelerations measured in the Earth frame of reference . The Down (D) component of the acceleration, accD, is obtained from the VN100 at a frequency of 10 Hz and used for evaluating waves statistics. This 10 Hz value is obtained by applying a Kalman filter to the raw sensor output , which translates into a 800 Hz processed output. After that, a running average over 80 samples is performed to obtain the final 10 Hz value. In the case when instruments are collected at the end of deployment, the full time series of the VN100 output at 10 Hz, is available on the internal SD card. This includes both raw sensor measurements, Kalman filter state, heading and orientation output, and acceleration in the Earth frame of reference.In the case of the v201838, the temperature-compensated 9-degree-of-freedom (9dof) sensor measures the accelerations, angular rates, and the local magnetic field, each over 3 axis . These raw measurements are performed at 800 Hz. Averaging and n-sigma filtering over 8 raw samples is used to downsample the signal from 800 Hz to 100 Hz, reject possible outliers, and reduce noise. An open source Kalman filter implementation is then used to fuse the data at 100 Hz update frequency. The Kalman filter produces a unit quaternion estimate q of the absolute orientation of the sensor relative to the Earth frame of reference. This information, combined with the acceleration measurements accrefSensor =  in the sensor frame of reference, allows to retrieve the acceleration of the sensor in the Earth frame of reference accrefEarth = , by applying the rotation described by the quaternion q on accrefSensor. At present, magnetometer calibration is not performed carefully enough to trust directional information relative to the magnetic North direction (this is ongoing work for further deployments). Therefore, the only data used at the moment are the vertical accelerations. These are averaged from the 100 Hz accD output into a 10 Hz wave acceleration signal.In the case of the v2021th value at the rate of 10 Hz. Later, to improve data quality, the sensors were reprogrammed to average sequences of 80 values, which were then saved at 10 Hz. Data output differed between the deployments, but always included  along with yaw pitch roll angles of the IWR. The accelerations were output with constant vertical gravity acceleration removed.Similar to the v2018 instrument, the IWR uses the Vectornav VN100 IMU. Processing and settings similar to the ones used with the instrument “v2018” are used by the IWR. However, the IWR records wave acceleration continuously, and stores it on-the-fly on an attached SD card, so that the time series of the wave displacement are available. Initial deployments were storing every 80The first step in measuring waves is to record the wave signal, i.e acceleration due to the wave motion. A duration of 20 minutes is used as the default time segment used to produce one wave spectrum and its associated statistics, and we used a wave acceleration sample frequency of 10 Hz unless specified otherwise. In order to gather this wave signal:in-situ processing of the data).At this stage, the vertical acceleration, which is the superposition of the gravity and wave acceleration , is available at 10 Hz for further processing in the case of either the v2018 or the v2021  is the half-cosine taper function:f is the frequency, fc is the Nyquist frequency, and f1 = 0.02 Hz and f2 = 0.03 Hz are the half-cosine taper corner frequencies, similar to49.In the case of the instrument v2018, details of the methodology are presented in the technical paper describing the instrumentη(t) at 10 Hz, the wave elevation spectrum S(f) is estimated using the Welch method on 12000 samples (20 minutes at 10 Hz), using a Hanning window of length 1024 samples per segment, and 50% overlap. In addition, the spectral moments m0, m1, m2, m4 of the wave spectrum are computed, following:Following the calculation of the time series for the wave elevation Hst = 4std(η), the significant wave height calculated from the spectral moment Tp corresponding to the maximum of the spectrum, the zero-upcrossing period This allows to compute the usual wave statistics, i.e. the significant wave height calculated from the time series, 37, and to implement their own quality checks if they want to use directional information.In addition to these data, some directional spread estimates are computed, though these are not as carefully validated and their accuracy may be lower. The reader who wants to use these is invited to read about the technical details in the technical paper38, a slightly simpler methodology is used. The Power Spectral Density (PSD) for the vertical wave acceleration PSDaccD is directly calculated from the vertical wave acceleration data at 10 Hz, by applying the Welch method on 20.48 minutes of data . Each segment for the Welch method has a length of 2048 samples, 75% overlap is used between the segments, and an energy-preserving Hanning window is used.In the case of the instrument v2021The 10 Hz wave acceleration component alongside the Down (D) direction in the Earth frame of reference is then used to compute the wave spectrum and its statistics. For this, two variants of the same core methodology are applied in the case of the instrument v2018 and v2021.S(f) can be retrieved as:From there, the spectrum for the wave vertical elevation Hs, Tz, and Tc, can be calculated in the same way as for the instrument v2018.At this stage, the spectral moments, as well as S(f) is downsampled into 25 logarithmically equally spaced bins between 0.05 Hz and 0.25 Hz, and transmitted together with the wave statistical parameters.In the case of the instrument v2018, the wave spectrum PSDaccD(f) between 0.05 Hz and 0.3 Hz is transmitted, alongside with the wave statistical parameters, though these are merely a cross validation of the spectrum, in the sense that they can be derived directly from the transmitted full spectrum. A simple script, which performs no further processing of the data, is then used to decode the binary SBD messages and apply the translation from PSDaccD(f) to S(f) following Eqn. In the case of the instrument v2021, the full PSD of the wave vertical acceleration In the case of the Sofar Spotter, a variety of modes are available, in which either the full spectrum, or only integrated quantities, are transmitted. The data are available as JSON files, which are provided directly by Sofar by unpacking the binary data.Data are transmitted from the sea ice by the instruments v2018, v2021, and the Sofar Spotter, using the Iridium communication network and the Short Burst Data (SBD) protocol, which allows messages of size up to 340 bytes.Commercial iridium trackers provide the track information data through a web-based API that abstract the details of the data transmission and processing protocol.https://adc.met.no/datasets/10.21343/azky-0X4451. Extensive metadata are provided, so that the different data fields are self documenting, following the FAIR data principle52. In addition, all the raw iridium data transmissions (hex-string binary data), and scripts for reading these raw data files and processing them into netCDF files, as well as example scripts of how the netCDF datasets can be read, are available on Github in the folder structure of the following repository: https://github.com/jerabaul29/data_release_sea_ice_drift_waves_in_ice_marginal_ice_zone_2022.All our high level, processed data are provided as netCDF4-CF files on the THREDDS data server of the Norwegian Meteorological Institute through the Arctic Data Center (ADC) database, at the following address : https://github.com/jerabaul29/data_release_sea_ice_drift_waves_in_ice_marginal_ice_zone_2022/tree/master/Data, and the figures therein. The time series obtained are of variable duration and some of them have holes in the data. This is due, to the best of our knowledge, to the harsh conditions found on sea ice, rather than technical issues with the instruments. Factors such as heavy snowfalls, polar bear destroying equipment, sea ice breakup, ridging and rafting, are all susceptible to interrupt iridium communications or destroy prematurely the instruments altogether.In the following, we provide an overview of the different deployments, what kinds of instruments and how many of them were used in each of these, and some background information about the sea ice conditions and the data of interest. As there are too many buoys and deployments involved, it is not feasible to provide a per-buoy description of the data here, nor to illustrate every individual deployment. The reader who wants a quick overview of the data available through figures displaying what information is available from each deployment, is referred to the github repository 53. We only summarize the main lines of the corresponding study here, and we refer the reader curious of more details to the full study53. The trackers could float and, therefore, the internal temperature records were used to determine the date and time when they entered water, as the temperature transitioned from a larger diurnal cycle when exposed to air on top of the ice, to a significantly more muted diurnal temperature variation when floating in open water. An analysis of the trackers’ internal temperature records and the Svalbard regional daily ice maps from the Norwegian Meteorological Institute Ice Service led to the determination that the beacons most likely began falling into open water sometime on May 2, 201753. The dispersion of the ice floes was dominated by strong shearing within the local ice pack, coinciding with a rapid increase in the speeds of the local tidal currents, which was soon followed by a rapid increase in the wave energy. Each of the six tracked ice floes increased their observed drift speeds in sync with the increase in the local tidal current speeds at different times for each floe, but at approximately the same decrease in water depth as they reached the northern edge of Spitsbergen Bank. The rapid increase in the tidal currents was linked to the topographic enhancement of tidal motion near Hopen Island in the shallower waters of Spitsbergen Bank. The last transmission from the trackers was received 2017-07-15.2017-04: Arctic deployment in the MIZ that contains drift data in the Barents Sea. 8 GPS ice trackers were deployed on 6 ice floes in the Barents Sea South of Svalbard. The trackers send GPS data every 30 minutes, and were deployed during April 24–26 2017 from the Research Vessel Polarsyssel. Trackers 4610 and 8650 were deployed on the same ice floe, and trackers 5630 and 2470 were deployed on the same ice floe, different from the previous one. The other trackers were deployed each on their respective ice floe. An overview of the deployment time for the different trackers is provided in Table 55.2018-03a: Arctic deployment in the MIZ, in the East Greenland Sea. The primary aim of the cruise was to monitor the production of seal pups. For that, 5 GPS and iridium trackers were deployed on large ice floes in the dense MIZ and drifted Southwestwards, following the East Greenland Current. GPS data sampling rate was 30 minutes. The trackers were deployed between March 20th, 2018, and March 23rd, 2018, and the last tracker stopped transmitting on April 25th, 2018, though communications were unreliable  after April 6th, 2018, possibly indicating that the trackers were covered by snow and ice. The trackers were not equipped with floatability equipment, so that the trackers are guaranteed to be on an ice floe for all the trajectory duration. More information is available in56.2018-03b: Arctic deployment on pack ice in the Beaufort Sea during ICEX2018, U.S. Navy exercises. Two IWRs were deployed for 4 days near an ice camp on 2018-03-17. One instrument (IWR2) was located on level ice near the air strip. It recorded numerous events characterized by strong, high-frequency oscillations produced by planes landing and taking off. The events matched the camp’s logbook. The second instrument was placed in an ice rubble field nearby. It also measured several cases of strong accelerations, not coinciding with the first instrument observations. Their origin is unclear. The instruments were recovered on 2018-03-21. More information is available in57.2018-04: Arctic deployment in the MIZ and drift ice in the Barents Sea. An ice tracker by Oceanetic Measurements Ltd was deployed on an ice floe to monitor the floe drift. GPS location was sent with an interval of 10 minutes. The tracker was deployed on April 27th, 2018 and transmitted until February 27th, 2019. The tracker drifted in the region of the Spitsbergen Bank for approximately 6 months, though it was on an ice floe only until around May 3rd, 2018 according to ice charts from cryo.met.no, and after this date, the tracker was floating in ice-free waters. More information are available in58. The instruments worked for around 2 weeks. However, a strong storm took place around 2018-09-24, and after this time, the spectra reported by the v2018 are very noisy with a lot of high frequency wave energy. This indicates that the supporting ice floes broke into small pieces and started to drift in very open water, which is confirmed by SIC from models . Therefore, the GPS drift tracks can be trusted over the whole duration of the dataset, but spectra dominated by high frequency signal correspond to small, broken ice floes drifting in very open water and experiencing erratic oscillations, rather than more classical waves in ice.2018-09: Arctic deployment in the MIZ in the Barents Sea, in the context of the Nansen Legacy project, Physical Process Cruise 2018. In this deployment, a total of 4 instruments v2018 were deployed while the icebreaker R/V Kronprins Haakon was traveling into the ice. The first instrument was deployed on a lone ice floe in the outer MIZ, while sea ice concentration (SIC) was about 1/10. The second instrument was deployed on an ice floe in dense drift ice, SIC about 5/10. The third instrument was deployed at the very start of the close pack ice, SIC about 9/10. The fourth instrument was deployed in the close pack ice, SIC about 10/10. All instruments were deployed on 2018-09-19. The cruise report is available if more details are needed59, and some additional information are available there.2020-03a: Arctic deployment on landfast ice in Grønfjorden, Svalbard. Three v2018 were deployed along the main axis of the fjord on continuous landfast ice between 10 and 13 March 2020. The instruments were deployed at a distance of 500 m, 1100 m and 1800m from the unbroken ice edge , respectively. At the time of deployment, the unbroken sea ice covered approximately half the length of the fjord . The other half was covered by broken ice and extended up to the entrance of the fjord. Ice thickness was measured at various locations near the instruments between 30 and 40 cm. Instruments were recovered after approximately two weeks on the 28th of March, 2020. At the time of retrieval all instruments were still deployed on unbroken ice and recording data. These data were used in some previous works56.2020-03b: Arctic deployment on pack ice in the Beaufort Sea during ICEX2020. The deployment strategy was similar to the ICEX2018 campaign (see 2018-04). Deployment took place on 2020-03-08, and the instruments were collected on 2020-03-18. Unlike the previous ICEX deployment, the instrument close to the air strip (IWR6) did not capture events associated with landing planes. No logging of arriving and leaving planes was made. Both IWRs recorded two events of propagating flexural-gravity waves presumably forced by strong winds during passing storms. Figure 60 In this deployment, a total of 6 instruments v2018 were deployed over the summer 2020 on drift ice, SIC ranging from approximately 3/10 to 10/10. One instrument was deployed 2020-07-15, and transmitted until 2020-07-31. One instrument was deployed 2020-07-21, and transmitted until 2020-09-03. However, the energy content in the high frequency part of the spectrum of this instrument indicates that it likely drifted on a small, isolated, broken ice floe from around 2020-08-19, as illustrated in Fig. 2020-07: Arctic deployment in the MIZ in the Barents Sea over the Yermak Plateau, in the context of the Fram-2020 hovercraft expedition. The cruise report is available at61. 3 instruments v2018 were deployed on the way into the ice, for SIC increasing from around 5/10, 9/10 and 10/10. All the other instruments were deployed in close pack ice, for SIC of 10/10. The instruments gradually stopped transmitting as ice broke up, with the last transmission on ice taking place in late April.2021-02: Arctic deployment in the MIZ in the Barents Sea, East of Svalbard, in the context of the Nansen Legacy project, PC-2 Winter Process Cruise 2021. In this deployment, a total of 6 instruments v2018, and 11 prototypes of the instrument v2021 (of which 6 were equipped with wave measurements) were deployed from the icebreaker R/V Kronprins Haakon on its way up and down the East coast of Svalbard, in close drift ice. The details of the deployment are reported in the cruise report56.2021-03: Arctic deployment on broken pack ice in the Beaufort Sea to accompany measurements during Sea Ice Dynamic Experiment (SIDEx2021). The instruments were deployed on 2021-03-06. In total six IWR instruments were installed surrounding the ice camp, but only three were recovered. Due to battery problems, the amount of collected data varied. The shortest observational interval was 2 weeks and the longest one and a half month (it was collected on 2021-04-22). More information is available in62.2021-04: Arctic deployment on landfast ice near Utqiagvik, Alaska as a part of Integrated System for Operations in Polar Seas project. Six buoys were placed in different locations on landfast sea ice. The deployment took place in late April-early May of 2021. The operation period of the IWRs varied. Some instruments were collected earlier to prevent instrument and data loss in cases of worsening ice stability. Ice conditions varied between rubber fields and level ice of a refrozen lead. Ice thickness ranged between 0.6 m to several meters with areas of smooth ice being about 1 m thick. All buoys were located in the deformed ice. IWR 35 was distinguished by being placed behind an iceberg frozen into the lead. IWRs 30&33 were the closest to the landfast ice edge. The measurements did not exhibit any clear sign of waves. However, a wave event was detected via ground-based radar interferometry positioned on the iceberg. Respective vertical accelerations above IMU’s noise level were also found in the IWRs . More on the deployment and results can be found in63, and the reader is referred to the corresponding work for further details about the deployment and the data collected. The last transmission included in the dataset took place on 2021-09-29.2021-09: Arctic deployment in the MIZ in the Laptev Sea, in the context of the NABOS campaign. Two buoys, one Sofar Spotter and one instrument v2021 packed in a Zeni floating enclosure, were deployed adjacent to the ice edge on 2021-09-15. The Sofar Spotter battery life at these latitudes and without solar input is only around 10 days, but by contrast, the v2021 instrument functioned for over 2 weeks. Several swell events were measured during the deployment, and the distance between the buoys allows to observe clear attenuation of the incoming swells by sea ice. The data are used in64.2022-03: Arctic deployment in the MIZ in the East Greenland Sea, in the context of the seal pup monitoring cruise 2022. In this deployment, 2 instruments v2021 were installed on 2 separate, neighboring medium size drifting ice floes  from the icebreaker R/V Kronprins Haakon on March 27th, 2022. In addition, 5 commercial GPS and iridium drifters that only measured sea ice drift were deployed on large ice floes in the dense MIZ (SIC around 9/10). The instruments drifted following the East Greenland current. The trajectories of the 2 instruments v2021 remained close  for around 2 weeks, before drifting slightly further apart from each other but heading in the same general direction. As a consequence, tracks and wave spectra are initially very similar, before clear differences in the wave spectra due to being at different depths in the MIZ get visible after around 2 weeks of drift. One of the instrument started to transmit data unreliably after around 4 weeks of activity, likely due to being covered by a layer of snow and ice that blocked iridium communications . The second instrument worked uninterrupted for over 4.5 weeks. The trajectories of the 5 commercial gps and iridium drifters remained close for most of the deployment. The last transmission was received on 2022-05-22. More information is available in the cruise reportThe deployments in the Arctic are, in chronological order:45, and some additional information are available there.2020-01: Antarctic deployment on landfast ice on the eastern rim of the Amery Ice Shelf. Four instruments, two v2018 and two Sofar Spotter, were deployed along a transect perpendicular to the unbroken ice edge on 7 December 2019. The first Spotter was deployed 100–200 m from the unbroken ice edge, while a v2018 and the second Sofar Spotter (40 m apart from each other) were deployed at a distance of about 3.7 km from the edge, and one more v2018 was deployed about 9.3 km from the unbroken ice edge. At the time of deployment, the ice was measured between 1 and 1.2 m thick. No wave events or drift was recorded until the 2nd of January 2020 when a large section of the fast ice broke and started drifting northward. The first v2018 stopped transmitting on 22 January 2020, while the two Sofar Spotters stopped transmitting on 1 February 2020 . The last message successfully transmitted by the final v2018 was on 10 March 2020. These data were used in papers24.2020-11: Antarctic deployment on landfast ice north of Casey Station. Deployment consisted of two v2018, deployed 1.9 km apart in October 2020. Instruments were recovered after about 3–4 weeks. Ice thickness was measured 1.1 m thick during the deployment and 1.3 m during retrieval. As the deployment site was separated from the Southern Ocean open water by roughly 300 km of broken sea ice, only limited wave energy was observed. The sea ice at the deployment site remained unbroken throughout the deployment. More information is available inThe deployments in the Antarctic are, in chronological order:A summary of the deployments is provided in Table There is no need for validation regarding GPS position measurements, as these are well established sensors with a well known accuracy .46 for the test and validation performed for the VN100), or indirectly . In practice, however, raw data from the instrument are only available for a couple of deployment of the instruments v2018 (when the SD card could be recovered), and for the IWR deployments, for which the data provided are always the timeseries of the IMU output.Regarding wave data, we are only using well established, longstanding methodologies. The sensors used are thermally calibrated over a range that typically exceeds the range of conditions found in the field in the MIZ . Therefore, we consider that the data acquisition for the wave acceleration by itself does not need additional validation, and users should refer to the datasheets of the corresponding sensors for further information. As a side note, validation of the accelerometer data was performed, either directly  to decode these raw data. The fact that both the firmware of the instruments , and the binary decoders, are open source, guarantees long term accessibility of the data, as well as full insight into the technical details associated.In addition, the full raw Iridium transmissions are available at the Github repository that supports the data release Two kinds of data processing levels are provided to the user:No additional processing is needed to use either the GPS or the wave data, and the data are ready to use as-is in Python or similar.lat and lon fields, containing the latitude and longitude in degree decimal format,frequency, containing the list of frequencies corresponding to each bin of the spectra, in Hz,trajectory_id, corresponding to a unique identifier for each of the instruments present in individual netCDF files,message_king, which indicates if the entry with the corresponding index is a GPS fixes message (kind ‘G’), or a wave spectrum message (kind ‘W’). Only ‘G’ messages will contain a valid GPS fix (lat and lon), and only a ‘W’ message will contain a valid wave spectrum entry (wave_spectrum and other wave statistics),wave_spectrum, which contains the wave elevation spectrum at the bins of frequencies described in the frequency field. For the instrument v2018, these bins are downsampled from the actual Welch transform, meaning that to reduce the size of the messages, the transmitted frequencies are different from the actual Welch spectrum output frequencies. The downsampling from the Welch spectrum output to the downsampled output is performed by means of a trapezoidal integration. For more information about the details of the downsampling, see37. For the instrument v2021, the bins are a subset of the Welch transform output bins, and no downsampling is applied. The unit for the wave elevation spectrum is m2.s , which is the standard unit for such spectra.in-situ by the instruments and transmitted over iridium in addition to the spectra. These quantities are redundant with the information available in the wave_spectrum variable, and are described in the metadata of the netCDF files. In particular, we transmit the significant wave height estimated from the wave elevation (instrument v2018 only) following the formula swh = 4std(η), where η is the wave elevation (computed on board by the instrument v2018), and estimated from the zeroth-order moment :https://github.com/jerabaul29/data_release_sea_ice_drift_waves_in_ice_marginal_ice_zone_2022.Examples of how these files can be read and plotted in the Python programming language are provided on the Github repository hosting the data, A proper use of the presented data for the analysis of wave damping will also critically depend on the availability of high-quality sea-ice concentration, thickness, and floe size distribution maps, as well as the wave frequency spectra in the open ocean adjacent to the sea-ice edge. While providing these data is, in general, outside of our scope, as there are a number of concurrent, different possible sources and models that produce such data, and these data are generated independently of the field measurements and rely on entirely different techniques, codes, and operational products, we highlight a few different sources that could be of interest to the reader.67. New multi-sensor products are becoming available, combining sensor information of passive and active microwave and Synthetic Aperture Radar (SAR) sensors70. These products reach resolutions of the order of 1 to 10 km. In addition, manual sea-ice charts can be used which cover scales down to a typical order of magnitude of 100 m and are available on a daily basis from the national ice services . Various pan Arctic sea-ice thickness satellite products are available with resolutions of typically around 25 km and are considered reliable in the thickness range from 0.5 to 4 m71. Approaches to produce satellite based information on the sea-ice floe size distribution are emerging but to our knowledge no operational products are available yet72. Some national ice services (e.g. the Danish Sea Ice Service) include information on floe sizes as part of the manual ice charts73. In addition to the sea-ice information, wave information on the wave properties adjacent to the sea-ice edge are needed. A number of wave hindcasts and reanalysis are available, however, often the energy frequency spectra are not disseminated (e.g.74); we are currently providing this feedback to the modeling community, and hope that more detailed information from numerical models will be available in the future.Sea-ice concentration products are available from passive microwave sensors with a footprint of around 20 km42, has adopted a “data first” approach to characterize specific waves and ice conditions that can lead to sea ice breakup59, has presented an in-depth analysis and validation of existing waves in ice damping models compared to field observations57, have presented case studies of sea ice drift properties and tuned models to in-situ observations56, has discussed observations of waves in ice deep into the continuous ice pack63, has analysed the shortcomings of existing sea ice models in thin ice conditions. In addition, we envision that these data can be used to further test, calibrate, and validate both sea ice drift and waves in ice models, as well as satellite processing algorithms, by serving as reference ground truth to compare against these methods.Regarding more specifically the possible uses of the data we release, these can be part of a number of studies about sea ice drift, waves damping by sea ice, and sea ice breakup. This has already been done, to some extent, by some of the authors and initial owners of some of the corresponding datasets. For example