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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Jeltsch-Thömmes, Aurich; Tran, Giang; Lienert, Sebastian; Keller, David; +2 Authors
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Nissen, Cara;

    This data set belongs to "21st-century environmental change decreases habitat overlap of Antarctic toothfish (Dissostichus mawsoni) and its prey" by Cara Nissen, Jilda Alicia Caccavo and Anne L. Moree (to be submitted to "Frontiers in Marine Science") Contact: cara.nissen@colorado.edu The data provided here are post-processed from the raw FESOM1.4-REcoM2 model output which can be obtained at the World Data Center for Climate (WDCC): https://www.wdc-climate.de/ui/project?acronym=HighRes_highLat_SO simA (historical simulation): https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vCsimA-ssp126: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s126_vA_vCsimA-ssp245: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s245_vA_vCsimA-ssp370: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s370_vA_vCsimA-ssp585: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vCsimB (control simulation): https://doi.org/10.26050/WDCC/FESOM14-REcoM2_B_1921_cA_cC The raw model output was first post-processed with MASTER_toothfish_postprocessing_AGI_save_netcdf_files_monthly_with_drift_correction.ipynb to get fields of in-situ temperature (t_insitu) and partial pressure of oxygen (pO2) on the regular grid used in this study (see Mesh_ancillary_information_v20220919.nc). Subsequently, these data are post-processed with reduce_depth_levels_drift_corr_files.sh. 2-dimensional distribution data of Antarctic Toothfish prey used in this study can be accessed viahttps://doi.org/10.5281/zenodo.10598488 The following files are provided here: Monthly climatological pO2 and t_insitu 1995-2014 (used to compute preferred temperature, pO2 threshold and critical AGI of each species):- pO2_fesom_simA_monthly_clim_1995_2014_v2.nc- t_insitu_fesom_simA_monthly_clim_1995_2014_v2.nc Annual pO2 and t_insitu for the historical period 1995-2014:- pO2_fesom_historical_1995_2014_annual_mean_AGImesh.tar.gz- t_insitu_historical_1995_2014_annual_mean_AGImesh.tar.gz Drift-corrected annual pO2 and t_insitu 2091-2100 for four emission scenarios: - pO2_fesom_ssp126_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz- pO2_fesom_ssp245_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz- pO2_fesom_ssp370_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz- pO2_fesom_ssp585_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz- t_insitu_fesom_ssp126_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz- t_insitu_fesom_ssp245_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz- t_insitu_fesom_ssp370_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz- t_insitu_fesom_ssp585_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz Sensitivity to chosen future time period / Drift-corrected annual pO2 and t_insitu 2081-2100 and 2098-2100 for the highest-emission scenario SSP5-8.5: - pO2_fesom_ssp585_2081_2100_drift_corrected_annual_mean_AGImesh.tar.gz- pO2_fesom_ssp585_2098_2100_drift_corrected_annual_mean_AGImesh.tar.gz- t_insitu_fesom_ssp585_2081_2100_drift_corrected_annual_mean_AGImesh.tar.gz- t_insitu_fesom_ssp585_2098_2100_drift_corrected_annual_mean_AGImesh.tar.gz Attributing change to warming and deoxygenation / Drift-corrected annual pO2 2091-2100 at clim. t_insitu for the highest-emission scenario SSP5-8.5:- pO2_fesom_ssp585_2091_2100_drift_corrected_at_clim_t_insitu_annual_mean_AGImesh.tar.gz Information on model drift (used to correct the above files): - oxygen_fesom_simB_1995_2014_2091_2100_monthly.tar.gz- pO2_fesom_simB_1995_2014_2091_2100_monthly.tar.gz- salinity_fesom_simB_1995_2014_2091_2100_monthly.tar.gz- t_insitu_fesom_simB_1995_2014_2091_2100_monthly.tar.gz

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lombard, Fabien; Guidi, Lionel; Brandão, Manoela C.; Coelho, Luis Pedro; +21 Authors

    Datasets are provided in 3 different layers. 1) Raw datasets as obtained from Ecotaxa exports (imaging devices) and as either fcs files for Accuri flowcytometry or concentration, size and biovolumes for Facscalibur flowcytometry. They are provided as separate .zip files for -Fascalibur flowcytometry -Accuri flowcytometry -eHFCM (H5) from the 5µm nets -eHFCM (H20) from the 20µm nets -Flowcam (both from Niskin bottles and bongo 20µm net (b20) -Imaging flowcytobot (IFCB); one file every 5 minutes -Zooscan from the WP2 (200µm) net -Zooscan from the bongo (300µm) net -Zooscan from the Regent (680µm) net -UVP data 2) Mergedbase.mat : one line per station Pre-processed (https://github.com/ecotaxa/ecotaxatoolbox ) and merged datasets where quality checked for potential errors in volume sampled against initial log sheets and corrected if needed, all concentrations have been calculated, biovolume calculated for every individual image obtained and normalized biovolume size spectra calculated assuming 3 different calculations (plain area biovolume, extruded area biovolume and ellipsoidal equivalent biovolume assuming prolate ellipsoids). Data are organized within a structured matlab file with a common architecture for every stations. The architecture of this file is similar for every station and instrument and includes in addition to every relevant metadata (position, date, volumes etc) the features described below for the different fractions of a sample (d1, d2... dx fractions; when scanned in several scans) in tot (sum of dx fractions); or in regrouped (further grouped in living/non-living// functional groups// trophic groups). Each levels contains the following derived datasets: - Ab: abundance per groups (ind.m-3) - Bv: biovolume per groups (mm3.m-3) Calculated from ellipsoidal estimate - Ybv_Plain_Area_BV_spectra: NBSSbiovolume per groups per size class (mm3.mm-3.m-3) from Area estimated ESD - Ybv_Riddled_Area_BV_spectra: NBSSbiovolume per groups per size class (mm3.mm-3.m-3) from Area_extruded estimated ESD - Ybv_Ellipsoid_BV_spectra: NBSSbiovolume per groups per size class (mm3.mm-3.m-3) from ellipsoidal estimate - X is the Middle of each biovolume size class (caution: it is log transformed here) (log(mm3)) - X1 is the amplitude of each biovolume size class - used for de-normalizing NBSS (mm3) - ESD vector is the conversion of X in ESD (µm this time) - ESDquartilesmoyenne are the 5 25 50 75 95 % quartiles of ESD (+ mean and std) of each groups This file is enriched with various environmental measures and with proportions of the used functional types and trophic levels from metabarcoding 3) MergedbaseC.mat A similar file but where all three NBSS have been converted to carbon units to obtain a normalized biomass size spectra (mgC.mm-3.m-3) 4) “metaplankton(…).mat” one line per station. For every station, all different observations have been selected depending on several data availability to provides a coherent measurement across stations. The different files diverge in the data collected (day or night), the fact that they are un biovolume or carbon units, or the addition of Facsalibur flowcytometry measurements (not size resolved): metaplankton.mat contains only Day data in biovolume (all data in mm3/m3 or mm3/mm3/m3 for NBSS) metaplanktonC.mat contains only Day data in Carbon content (all data in mgC/m3 or mgC/mm3/m3 for NBSS) metaplanktonnight.mat contains only night data in biovolume (all data in mm3/m3 or mm3/mm3/m3 for NBSS) metaplanktonfacs.mat contains only day data in biovolume (all data in mm3/m3 or mm3/mm3/m3 for NBSS) + addition of Facscalibur flowcytometer data (not finely size resolved) metaplanktonCfacs.mat contains only day data in Carbon content (all data in mgC/m3 or mgC/mm3/m3 for NBSS)+ addition of Facscalibur flowcytometer data (not finely size resolved) each of those files contains always the same type of data structure and content: If several observations have been made (often the case for UVP or IFCB) a mean observation is provided for one station (either day or night). For each instrument several NBSS (ellipsoid estimate only) are present either with the different tested corrections across instruments: -“raw” are uncorrected data -“trunked” are NBSS spectra where extreme data have been removed to ignore regions of incorrect efficiency (too small organisms or too large, rare ones) and calculate a slope (b) and the intercept (a) of the NBSS -“interceptadjusted” are correction of the intercept on the basis of WP2 measurments -“adjusted_site” are corrections of the intercept based on concurrent measurments of similar size class by several instruments (when possible) -“adjusted” are corrected by the median of correction between instruments (median of every “adjusted_site” corrections. -“final” are the final correction after manual QC of the different possibility of corrections above Those data are present either as total living organisms, and regrouped within their original taxonomic identification (“original”), in different functional groups (“pft”) or trophic (“trophic”) groups. For each of them the name of the groups is provided (nameoriginal, namepft). Trophic groups are organized such at position 1 is primary producers (trophic position in matrix: 1); 1.5 is mixotrophs (trophic position in matrix: 2) ; 2 is herbivorous (trophic position in matrix: 3); 2.5 is omnivorous (trophic position in matrix: 4) ; 3 is carnivorous (trophic position in matrix: 5) ; 3.5 is undefined trophic mode (trophic position in matrix: 6) For quality control purpose a figure is generated for each station and for each of the above coherent compilation of datasets allowing to control the different corrections and the different final products obtained. Those are saved under the name metaplankton.pdf, metaplanktonC.pdf, metaplanktonnight.pdf, metaplanktonfacs.pdf and metaplanktonCfacs.pdf Those single instruments measurements have afterward been merged in several subproducts: -merged (not recommended because of the heterogeneity of number of instruments merged ): all possible instruments have been combined for every stations (mostly used for diagnostics plots and systematic inspection of data. Called "Meta-Plk heterogeneous” in the manuscript. -merged4devicesmin: spans organisms ranging from 0.8 µm to several cm (including flow-cytometry, IFCB, Flowcam, Zooscan from several nets and UVP and with a complete coverage in between), it is only available from the Arctic ecosystem and covers 20 sites. Called " Meta-Plk >0.8 µm” in the manuscript. -mergedpft4devicesincomplete: is similar from the above but adds few stations which miss some observation for particular organism sizes, notably due to the absence of Flowcam analyses from the 20 µm net fractions. Called " Meta-Plk >0.8 µm incomplete” in the manuscript. -merged2netsUVPH20: spans organisms ranging from 20 µm to several cm (using e-HFCM with 20 µm net, Zooscan from WP2 and Regent nets and UVP). It covers both TO and TOPC parts of the expedition and includes 63 stations. Called " Meta-Plk >20 µm” in the manuscript. Some other trials (using only two nets : WP2 and Regent; merged2nets; or also including the UVP; merged2netsUVP) but not used in the manuscript are also included. Each of those “merged” products contains the full NBSS spectra for each functional groups (“datapft”) and in BSS spectra (ie. biovolume per size class; “datapft_denorm”), the total biovolume of each functional groups (“donut”) the same done for trophic levels (“datatrophic”; “datatrophic_denorm”) and the cumulated abundance and biovolume of the different trophic levels (“trophicpyramidnb”; “trophicpyramidbv”) The same grouping into functional groups and trophic groups was done from metabarcoding (18S, V9) genomic data for the size fraction 180-2000µm (pft180 and trophic180) or from the 20-200µm fraction (pft20 and trophic20) slopes and intercepts of the NBSS size spectra (slopesbss, ie. non normalized size spectra are also provided) relationship for every instrument and every merged datasets in the order: (1) Accuri flowcytometry (2) IFCB (3) Flowcam-Niskin (4) Flowcam b20 (5) Zooscan-WP2 net (6) Zooscan-bongo net (7) Zooscan-Regent net (8) UVP-in situ (9) E-HFCM-5µm net (10) E-HFCM-20µm net (11) Meta-Plk >200µm (ie. “merged2nets” product) (12) Meta-Plk >200µm-UVP (ie. “merged2netsUVP” product) (13) Meta-Plk >0.8µm (ie. “merged4devicesmin” product) (14) Meta-Plk >0.8µm- incomplete (ie. “merged4devicesminincomplete” product) (15) Meta-Plk heterogeneous (ie. “merged” product) (16) Meta-Plk >20µm (ie. “merged2netsUVPH20” product)

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Kalhori, Aram; Wille, Christian; Pia, Gottschalk; Li, Zhan; +3 Authors

    Rewetting drained peatlands is recognized as a leading and effective natural solution to curb greenhouse gas emissions. However, rewetting creates novel ecosystems whose emission behaviors are not adequately captured by currently used emission factors. These emission factors are applied immediately after rewetting, thus do not reflect the temporal dynamics of greenhouse gas emissions during the period wherein there is a transition to a rewetted steady-state. Here, we provide long-term data showing a mismatch between actual emissions and default emission factors and revealing the temporal patterns of annual carbon dioxide and methane fluxes in a rewetted peatland site in northeastern Germany. We show that site-level annual emissions of carbon dioxide and methane approach the IPCC default emission factors and those suggested for the German national inventory report only between 13 to 16 years after rewetting. Over the entire study period, we observed a source-to-sink transition of annual carbon dioxide fluxes with a decreasing trend of −0.36 t CO2-C ha−1 yr−1 and a decrease in annual methane emissions of −23.6 kg CH4 ha−1 yr−1. Our results indicate that emission factors should represent the temporally dynamic nature of peatlands post-rewetting and consider the effect of site characteristics to better estimate associated annual emissions. This project is supported by the WET HORIZONS project.

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    ZENODO
    Other ORP type . 2024
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Other ORP type . 2024
      License: CC BY
      Data sources: ZENODO
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    Authors: Morée, Anne L.; Caccavo, Jilda A.; Nissen, Cara;

    Habitat files of 28 Antarctic Toothfish (Dissostichus mawsoni) prey species and the Antarctic Toothfish itself, regridded to the FESOM-REcoM model grid, in NetCDF format. For use in article '21st-century environmental change decreases habitat overlap of Antarctic toothfish (Dissostichus mawsoni) and its prey'. This dataset is a collection of the following: 1) 2-dimensional distribution data for 3 squid species, derived from Raymond et al. (2015) 2) 2-dimensional distribution data for 25 prey species and the Antarctic Toothfish, derived from AquaMaps distribution data 3) the model grid file. A species is present in a certain gridcell when its value is 1, elsewhere the species is considered absent. 1) Original files for the 3 squid species galiteuthis glacialis, kondakovia longimana and mesonychoteuthis hamiltoni are taken from previously published data of Raymond et al. (2015) and regridded to the FESOM-REcoM model grid. These squid species are considered to have their habitat anywhere where habitat suitability in the original Raymond et al. (2015) dataset exceeds the habitat suitability thresholds of 0.228 for Galiteuthis glacialis, 0.281 for Kondakovia longimana and 0.121 for Mesonychoteuthis hamiltoni (threshold values as in Xavier et al. (2016), personal communication with Ben Raymond 16.01.2023 to get exact values). These data have an original resolution of 0.1x0.1 degrees (regular longitude-latitude grid). 2) The 25 files taken from AquaMaps start with Default_* or Reviewed_* and are the native predicted range data as provided by Kaschner et al. (2019). They were shared by Kathleen Kesner-Reyes from AquaMaps in March and April 2023 after being reviewed by her for correctness and actuality. The files that start with Reviewed_* have had adjustments made to the original Default_* based on this review, which is described in more detail on the AquaMaps website. These data have an original resolution of 0.5x0.5 degrees (regular longitude-latitude grid). 3) The model grid file 'Mesh_ancillary_information_v20220919.nc' contains the longitude and latitude data needed for regridding to the FESOM-REcoM model grid. Regridding was done using CDO version 1.9.6 (http://mpimet.mpg.de/cdo; developed by U. Schulzweida) and CDO function 'remaplaf', which performs largest area fraction remapping. References Xavier, J.C., Raymond, B., Jones, D.C. et al. Biogeography of Cephalopods in the Southern Ocean Using Habitat Suitability Prediction Models. Ecosystems 19, 220–247 (2016). https://doi.org/10.1007/s10021-015-9926-1 Raymond, B., Xavier, J., Griffiths, H., Jones, D. (2015) Habitat suitability predictions for 15 species of cephalopods in the Southern Ocean, Ver. 1, Australian Antarctic Data Centre - doi:10.4225/15/563AC33450A28, Accessed: 2023-01-16 Kaschner, K., Kesner-Reyes, K., Garilao, C., Segschneider, J., Rius-Barile, J. Rees, T., & Froese, R. (2019, October). AquaMaps: Predicted range maps for aquatic species. Retrieved from https://www.aquamaps.org.

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    ZENODO
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    ZENODO
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      ZENODO
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    Authors: Ballantyne, John;

    We introduce DarkNews, a lightweight Python -based Monte-Carlo generator for beyond-the-Standard-Model neutrino-nucleus scattering. The generator handles the production and decay of heavy neutral leptons via additional vector or scalar mediators, as well as through transition magnetic moments. DarkNews samples pre-computed neutrino-nucleus upscattering cross sections and heavy neutrino decay rates to produce dilepton and single-photon events in accelerator neutrino experiments. We present two case studies with differential distributions for models that can explain the MiniBooNE excess. The aim of this code is to aid the neutrino theory and experimental communities in performing searches and sensitivity studies for new particles produced in neutrino upscattering.

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    Authors: Martinez, Enoc; BAÑOS, POL; Prat, Oriol; FRANCESCANGELI, MARCO; +2 Authors

    Underwater Image dataset of fishes, macrofauna, acquired at Obsea during ANERIS project integrated into MINKA Citizen Science Platform.

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    ZENODO
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    ZENODO
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      ZENODO
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      ZENODO
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    Authors: Bald, Carlos; Domínguez, Haizea; Iñarra, Bruno;

    Aquaculture has grown exponentially during the last decades, even overcoming traditional fishing in volume since2012 (Iñarra et al, 2018). The rise of the production of fish involves the rise of fish by-products, that in case ofbeing disposed could suppose an environmental risk. Fish viscera are part of the by-products used to producefishmeal and are the 10-18 % of the whole fish weight.Fish silage or acid autolysis is commonly used in areas with high fisheries rates and consists of liquefactionand stabilization of minced fish at room temperature, normally adding formic acid until reaching a pH between3.5 and 4.5 to prevent microbial growth. Hydrolysis of proteins occurs thanks to the endogenous acid proteasesthat are located at the fish viscera, which enable to get low molecular weight peptides and amino acids (Toppe etal, 2018). The resulting protein hydrolysates could be used as fertilisers. However, silage can take several days toachieve a high hydrolysis degree. In this work, autolysis has been carried out simulating the conditions ofenzymatic hydrolysis but only working with the endogenous enzymes from fish viscera with the aim ofaccelerating a typical silage to get free amino acids while saving costs derived from the use of commercialenzymes

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    ZENODO
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    Authors: Nakayama, Yoshihiro; Jourdain, Nicolas; van Caspel, Mathias; Timmermann, Ralph; +1 Authors

    Observational data gathered and reprocessed to facilitate the evaluation of ocean and ice-sheet models in the Amundsen Sea sector as part of MISOMIP2. __________________________________________ This entire dataset should be cited as: the MISOMIP2 MIPkit-A dataset (http://zenodo.org/doi/10.5281/zenodo.10062355) that includes data collected through multiple cruises of Nathaniel B. Palmer (United States Antarctic Program), James C. Ross (British Antarctic Survey and Natural Environment Research Council), Araon (Korea Polar Research Institute), Oden (Swedish Polar Research) and Polarstern (Alfred Wegener Institute, Germany), as well as regridded glaciological data from the MeaSUREs, MeaSUREs ITS_LIVE and CPOM projects For more specific use of some of the MIPkit-A data, we encourage people to cite the original data referenced below. __________________________________________ IceSurfVel_MIPkitA_2000-2019 : annual maps of ice surface velocity Surface-parallel velocity vectors (in m/year) between 2000 and 2019 are provided on the common MISOMIP2 grid with regular grid spacing of 1 km. Data were calculated as a weighted average of all available Earth observation data from the MeaSUREs project (Rignot et al. 2014 and Mouginot et al. 2017) and MeaSUREs ITS_LIVE project (Gardner et al. 2022), with weights corresponding to the inverse square error of the original datasets. Propagated errors and a mask indicating the original data sources for each grid point are also included. __________________________________________ IceSurfElChange_MIPkitA_1992-2019 : annual maps of surface elevation change Surface elevation changes (in m) between 1992 and 2019, relative to 16-Dec-2013, are provided on the common MISOMIP2 grid with regular grid spacing of 1 km. Data were calculated as a weighted average of available Earth observation data from CPOM (Otosaka al. 2023) and MeaSUREs ITS_LIVE (Nilsson et al. 2023) for the grounded ice and MeaSUREs ITS_LIVE data (Paolo et al. 2023) for floating ice. Propagated errors and a mask indicating the original data sources for each grid point are also included. __________________________________________ Oce3d_MIPkitA_* : 3-dimensional temperature and salinity (horizontal slices every 100m) The hydrographic properties provided on horizontal sections at 15 depths come from the CTD measurements obtained during cruises of the following icebreaker research vessels: Nathaniel B. Palmer (United States Antarctic Program), James C. Ross (British Antarctic Survey and Natural Environment Research Council), Araon (Korea Polar Research Institute), Oden (Swedish Polar Research) and Polarstern (Alfred Wegener Institute, Germany). In this MIPkit, we have gathered data for the first months of 1994, 2000, 2007, 2009 (Jacobs, 1994, 2000, 2007, 2009), 2010 (Swedish Polar Research Secretariat 2010; Gohl 2015), 2012 (Kim et al. 2012), 2014 (Heywood 2014; Ha et al. 2014), 2016 (Kim et al. 2016), 2017 (Gohl 2017), 2018 (Kim et al. 2018), 2019 (Larter et al. 2019) and 2020 (Wellner, 2020). __________________________________________ OceSec_MIPkitA_* : vertical sections The first vertical (OceSec1) section where we provide hydrographic data in the Amundsen Sea starts across the continental shelf break and follows the Eastern Pine Island Trough southward until Pine Island Ice Shelf. This section was monitored by the following cruises: N.B. Palmer in January 2009, Polarstern in March 2010 and Araon in February-March 2012 (Jacobs et al. 2011; Gohl 2015; Dutrieux et al. 2014). The second vertical section (OceSec2) starts across the continental shelf break and follows the Dotson-Getz Trough southward until the Dotson Ice Shelf. It was monitored by the aforementioned Araon expeditions in 2010–2011 and early 2012 (Kim et al. 2017). The files OceSec_model_lon_lat.csv contain the coordinates (longitude, latitude) at which model data should be interpolated to be compared to the observational sections. __________________________________________ OceMoor_MIPkitA_* : moorings The first mooring site (OceMoor1) is located near the northern part of the Pine Island ice shelf front (102.07°W, 74.87°S) and captures the thermocline variability from 2012 to 2018 ("iSTAR-8" in NERC iSTAR program, and "pig-n" in NERC Ocean Forcing Ice Change Program). The second mooring site (OceMoor2)is located near the southern part of the Pine Island ice shelf front (102.15°W, 75.05°S), was monitored between 2009 and 2016, then in 2019–2020 through the following moorings: "BSR-5" (Buoy Supported Riser; Jacobs 2009), "iSTAR-9" (NERC iSTAR Program), and "pig-s" (NERC Ocean Forcing Ice Change Program). This second site experienced a strong deepening of the thermocline in 2012–2013 (Webber et al. 2017), then a more moderate deepening in 2016. These two mooring sites are located only 20 km from each other, show distinct mean thermocline depth and more consistent variability (Joughin et al. 2021). The third mooring observation (OceMoor3, "trough-e" in NERC Ocean Forcing Ice Change Program) used in MISOMIP is at the eastern Pine Island trough (102.55°W, 71.33°S). The eastern trough is considered to be the entrance of mCDW reaching the Pine Island Ice Shelf (Jacobs et al. 2011; Nakayama et al. 2013; Webber et al. 2017) but only two years of mooring observation was conducted from 2014-2015 due to important sea ice cover. The fourth mooring site (OceMoor4) used in MISOMIP is at the western Pine Island trough (113.05°W, 71.56°S). Several mooring observations were conducted within 2 km of each other, allowing us to observe thermocline variability from 2009 to 2016 with one year gap in 2011: "BSR-12" (Jacobs 2009), "iSTAR-1" (NERC iSTAR Program), and "trough-w" (NERC Ocean Forcing Ice Change Program). __________________________________________ The archive example_routines.zip contains example of Matlab routines that were used to prepare the MIPkit-A ocean data, as well as GenerateData_MIPkit_Ice.m and writeNC_MIPkit_Ice.m that were used to process the MIPkit-A glaciological data. __________________________________________ References Dutrieux, P., De Rydt, J., Jenkins, A., Holland, P. R., Ha, H. K., Lee, S. H., Steig, E. J., Ding, Q., Abrahamsen, E. P., and Schröder, M.: Strong sensitivity of Pine Island ice-shelf melting to climatic variability, Science, 343, 174–178, 2014. Gohl, K.: Station list and links to master tracks in different resolutions of POLARSTERN cruise ANT-XXVI/3, Wellington - Punta Arenas, 2010-01-30 - 2010-04-05, Tech. rep., Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, https://doi.org/10.1594/PANGAEA.847944, 2015. Gohl, K.: The Expedition PS104 of the Research Vessel POLARSTERN to the Amundsen Sea in 2017, Reports on polar and marine research, Tech. rep., Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, http://doi.org/10.2312/BzPM_0712_2017, 2017. Ha, H. K., Kim, T. W., Lee, H. J., Kang, C. Y., Hong, C. S., Wåhlin, A. K., Rolandsson, J., Karen, O., and Miles, T.: The Amundsen Sea Expedition (ANA04B): IBRV Araon, 24 December 2013 – 25 January 2014 – Chapther 1: Physical Oceanography, Tech. rep., Korea Polar Research Institute, Incheon, https://repository.kopri.re.kr/handle/201206/4605, 2014. Heywood, K. 690 J.: JR294/295 Cruise Report, Ice Sheet Stability Programme (iSTAR), RRS James Clark Ross, 26th February – 8th March 2014, Amundsen Sea, Tech. rep., Natural Environment Research Council (NERC), https://www.bodc.ac.uk/resources/inventories/cruise_inventory/report/13405/, 2014. Jacobs, S.: Cruise NBP9402, RVIB Nathaniel B. Palmer, Feb 14 – Apr 5 1994, Tech. rep., United States Antarctic Program. http://doi.org/10.7284/905397, 1994. Jacobs, S.: Cruise NBP0001, RVIB Nathaniel B. Palmer, Feb 15 – Apr 1 2000, Tech. rep., United States Antarctic Program. http://doi.org/10.7284/905450, 2000. Jacobs, S.: Cruise NBP0702, RVIB Nathaniel B. Palmer, Feb 03 – Mar 25 2007, Tech. rep., United States Antarctic Program. http://doi.org/10.7284/905530, 2007. Jacobs, S.: Cruise NBP0901, RVIB Nathaniel B. Palmer, Jan 05 – Feb 26 2009, Tech. rep., United States Antarctic Program, http://doi.org/10.7284/905547, 2009. Jacobs, S. S., Jenkins, A., Giulivi, C. F., and Dutrieux, P.: Stronger ocean circulation and increased melting under Pine Island Glacier ice shelf, Nature Geoscience, 4, 519–523, 2011. Joughin, I., Shapero, D., Smith, B., Dutrieux, P., and Barham, M.: Ice-shelf retreat drives recent Pine Island Glacier speedup, Science Advances, 7, eabg3080, 2021. Kim, T. W., H, H. K., and Hong, C. S.: The Amundsen Sea Expedition (ANA02C): IBRV Araon, 31 January 2012 – 20 March 2012 – Chapther 1: Hydrographic Survey, Tech. rep., Korea Polar Research Institute, Incheon, https://repository.kopri.re.kr/handle/201206/4603, 2012. Kim, T. W., Cho, K. H., Kim, C. S., Yang, H. W., La, H. S., Lee, J. H., Kim, D. K., Jung, J. H., Wåhlin, A. K., Assmann, K. M., Darelius, E., Abrahamsen, E. P., and Waite, N.: The Amundsen Sea Expedition (ANA06B): IBRV Araon, 6 January – 23 February 2016 – Chapther 1: Physical Oceanography in Amundsen Sea, Tech. rep., Korea Polar Research Institute, Incheon, https://ftp.nmdc.no/nmdc/UIB/Mooring/20181213/ANA06B_cruise_report.pdf, 2016. Kim, T.-W., Ha, H. K., Wåhlin, A. K., Lee, S., Kim, C.-S., Lee, J. H., and Cho, Y.-K.: Is Ekman pumping responsible for the seasonal variation of warm circumpolar deep water in the Amundsen Sea?, Continental Shelf Research, 132, 38–48, 2017. Kim, T. W., Cho, K. H., Park, T. W., Yang, H. W., Kim, Y., Assmann, K. M., Rolandsson, J., Dutrieux, P., Gobat, J., Beem, L., Richter, T., Buhl, D., and Durand, I.: The Amundsen Sea Expedition (ANA08B): IBRV Araon, 21 December 2017 – 13 February 2018 – Chapther 1: Physical Oceanography, Tech. rep., Korea Polar Research Institute, Incheon, https://repository.kopri.re.kr/handle/201206/9441, 2018. Larter, R., Barham, M., Boehme, L., Braddock, S., Graham, A., Hogan, K., Mazur, A., Minzoni, R., Queste, B., Sheehan, P., Spoth, M., Wåhlin, A., Bortolotto-d'Oliveira, G., Clark, R. W., Fitzgerald, V., Karam, S., Kirkham, J., Stedt, F., Zheng, Y., Beeler, C., Goodell, J., Rush, E., Snow, T., Welzenbach, L., Andersson, J., and Rolandsson, J.: Cruise NBP1902, RVIB Nathaniel B. Palmer, Jan 29 – Mar 25 2019, Tech. rep., United States Antarctic Program, http://doi.org/10.7284/908147, 2019. Nakayama, Y., Schröder, M., and Hellmer, H. H.: From circumpolar deep water to the glacial meltwater plume on the eastern Amundsen Shelf, Deep Sea Res. I, 77, 50–62, 2013. Swedish Polar Research Secretariat: Oden Southern Ocean 2009/10 - Conductivity-Temperature-Depth (CTD) Data Collected Onboard Icebreaker Oden during February through March 2010, Tech. rep., Swedish Polar Research, http://snd.gu.se/en/catalogue/dataset/ecds0220-1, 2010. Webber, B. G. M., Heywood, K. J., Stevens, D. P., Dutrieux, P., Abrahamsen, E. P., Jenkins, A., Jacobs, S. S., Ha, H. K., Lee, S. H., and Kim, T. W.: Mechanisms driving variability in the ocean forcing of Pine Island Glacier, Nature Communications, 8, 1–8, 2017. Wellner, J.: Cruise NBP2002, RVIB Nathaniel B. Palmer, Jan 25 2020 – Mar 08 2020, Tech. rep., United States Antarctic Program, http://doi.org/10.7284/908803, 2019.

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    Authors: Ballantyne, John;

    We present a collection of tools efficiently automating the computation of large sets of theory predictions for high-energy physics. Calculating predictions for different processes often require dedicated programs. These programs, however, accept inputs and produce outputs that are usually very different from each other. The industrialization of theory predictions is achieved by a framework which harmonizes inputs (runcard, parameter settings), standardizes outputs (in the form of grids), produces reusable intermediate objects, and carefully tracks all meta data required to reproduce the computation. Parameter searches and fitting of non-perturbative objects are exemplary use cases that require a full or partial re-computation of theory predictions and will thus benefit of such a toolset. As an example application we present a study of the impact of replacing NNLO QCD K-factors with the exact NNLO predictions in a PDF fit.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Jeltsch-Thömmes, Aurich; Tran, Giang; Lienert, Sebastian; Keller, David; +2 Authors
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    ZENODO
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    ZENODO
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      ZENODO
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    Authors: Nissen, Cara;

    This data set belongs to "21st-century environmental change decreases habitat overlap of Antarctic toothfish (Dissostichus mawsoni) and its prey" by Cara Nissen, Jilda Alicia Caccavo and Anne L. Moree (to be submitted to "Frontiers in Marine Science") Contact: cara.nissen@colorado.edu The data provided here are post-processed from the raw FESOM1.4-REcoM2 model output which can be obtained at the World Data Center for Climate (WDCC): https://www.wdc-climate.de/ui/project?acronym=HighRes_highLat_SO simA (historical simulation): https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vCsimA-ssp126: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s126_vA_vCsimA-ssp245: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s245_vA_vCsimA-ssp370: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s370_vA_vCsimA-ssp585: https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vCsimB (control simulation): https://doi.org/10.26050/WDCC/FESOM14-REcoM2_B_1921_cA_cC The raw model output was first post-processed with MASTER_toothfish_postprocessing_AGI_save_netcdf_files_monthly_with_drift_correction.ipynb to get fields of in-situ temperature (t_insitu) and partial pressure of oxygen (pO2) on the regular grid used in this study (see Mesh_ancillary_information_v20220919.nc). Subsequently, these data are post-processed with reduce_depth_levels_drift_corr_files.sh. 2-dimensional distribution data of Antarctic Toothfish prey used in this study can be accessed viahttps://doi.org/10.5281/zenodo.10598488 The following files are provided here: Monthly climatological pO2 and t_insitu 1995-2014 (used to compute preferred temperature, pO2 threshold and critical AGI of each species):- pO2_fesom_simA_monthly_clim_1995_2014_v2.nc- t_insitu_fesom_simA_monthly_clim_1995_2014_v2.nc Annual pO2 and t_insitu for the historical period 1995-2014:- pO2_fesom_historical_1995_2014_annual_mean_AGImesh.tar.gz- t_insitu_historical_1995_2014_annual_mean_AGImesh.tar.gz Drift-corrected annual pO2 and t_insitu 2091-2100 for four emission scenarios: - pO2_fesom_ssp126_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz- pO2_fesom_ssp245_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz- pO2_fesom_ssp370_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz- pO2_fesom_ssp585_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz- t_insitu_fesom_ssp126_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz- t_insitu_fesom_ssp245_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz- t_insitu_fesom_ssp370_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz- t_insitu_fesom_ssp585_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz Sensitivity to chosen future time period / Drift-corrected annual pO2 and t_insitu 2081-2100 and 2098-2100 for the highest-emission scenario SSP5-8.5: - pO2_fesom_ssp585_2081_2100_drift_corrected_annual_mean_AGImesh.tar.gz- pO2_fesom_ssp585_2098_2100_drift_corrected_annual_mean_AGImesh.tar.gz- t_insitu_fesom_ssp585_2081_2100_drift_corrected_annual_mean_AGImesh.tar.gz- t_insitu_fesom_ssp585_2098_2100_drift_corrected_annual_mean_AGImesh.tar.gz Attributing change to warming and deoxygenation / Drift-corrected annual pO2 2091-2100 at clim. t_insitu for the highest-emission scenario SSP5-8.5:- pO2_fesom_ssp585_2091_2100_drift_corrected_at_clim_t_insitu_annual_mean_AGImesh.tar.gz Information on model drift (used to correct the above files): - oxygen_fesom_simB_1995_2014_2091_2100_monthly.tar.gz- pO2_fesom_simB_1995_2014_2091_2100_monthly.tar.gz- salinity_fesom_simB_1995_2014_2091_2100_monthly.tar.gz- t_insitu_fesom_simB_1995_2014_2091_2100_monthly.tar.gz

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    ZENODO
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    Authors: Lombard, Fabien; Guidi, Lionel; Brandão, Manoela C.; Coelho, Luis Pedro; +21 Authors

    Datasets are provided in 3 different layers. 1) Raw datasets as obtained from Ecotaxa exports (imaging devices) and as either fcs files for Accuri flowcytometry or concentration, size and biovolumes for Facscalibur flowcytometry. They are provided as separate .zip files for -Fascalibur flowcytometry -Accuri flowcytometry -eHFCM (H5) from the 5µm nets -eHFCM (H20) from the 20µm nets -Flowcam (both from Niskin bottles and bongo 20µm net (b20) -Imaging flowcytobot (IFCB); one file every 5 minutes -Zooscan from the WP2 (200µm) net -Zooscan from the bongo (300µm) net -Zooscan from the Regent (680µm) net -UVP data 2) Mergedbase.mat : one line per station Pre-processed (https://github.com/ecotaxa/ecotaxatoolbox ) and merged datasets where quality checked for potential errors in volume sampled against initial log sheets and corrected if needed, all concentrations have been calculated, biovolume calculated for every individual image obtained and normalized biovolume size spectra calculated assuming 3 different calculations (plain area biovolume, extruded area biovolume and ellipsoidal equivalent biovolume assuming prolate ellipsoids). Data are organized within a structured matlab file with a common architecture for every stations. The architecture of this file is similar for every station and instrument and includes in addition to every relevant metadata (position, date, volumes etc) the features described below for the different fractions of a sample (d1, d2... dx fractions; when scanned in several scans) in tot (sum of dx fractions); or in regrouped (further grouped in living/non-living// functional groups// trophic groups). Each levels contains the following derived datasets: - Ab: abundance per groups (ind.m-3) - Bv: biovolume per groups (mm3.m-3) Calculated from ellipsoidal estimate - Ybv_Plain_Area_BV_spectra: NBSSbiovolume per groups per size class (mm3.mm-3.m-3) from Area estimated ESD - Ybv_Riddled_Area_BV_spectra: NBSSbiovolume per groups per size class (mm3.mm-3.m-3) from Area_extruded estimated ESD - Ybv_Ellipsoid_BV_spectra: NBSSbiovolume per groups per size class (mm3.mm-3.m-3) from ellipsoidal estimate - X is the Middle of each biovolume size class (caution: it is log transformed here) (log(mm3)) - X1 is the amplitude of each biovolume size class - used for de-normalizing NBSS (mm3) - ESD vector is the conversion of X in ESD (µm this time) - ESDquartilesmoyenne are the 5 25 50 75 95 % quartiles of ESD (+ mean and std) of each groups This file is enriched with various environmental measures and with proportions of the used functional types and trophic levels from metabarcoding 3) MergedbaseC.mat A similar file but where all three NBSS have been converted to carbon units to obtain a normalized biomass size spectra (mgC.mm-3.m-3) 4) “metaplankton(…).mat” one line per station. For every station, all different observations have been selected depending on several data availability to provides a coherent measurement across stations. The different files diverge in the data collected (day or night), the fact that they are un biovolume or carbon units, or the addition of Facsalibur flowcytometry measurements (not size resolved): metaplankton.mat contains only Day data in biovolume (all data in mm3/m3 or mm3/mm3/m3 for NBSS) metaplanktonC.mat contains only Day data in Carbon content (all data in mgC/m3 or mgC/mm3/m3 for NBSS) metaplanktonnight.mat contains only night data in biovolume (all data in mm3/m3 or mm3/mm3/m3 for NBSS) metaplanktonfacs.mat contains only day data in biovolume (all data in mm3/m3 or mm3/mm3/m3 for NBSS) + addition of Facscalibur flowcytometer data (not finely size resolved) metaplanktonCfacs.mat contains only day data in Carbon content (all data in mgC/m3 or mgC/mm3/m3 for NBSS)+ addition of Facscalibur flowcytometer data (not finely size resolved) each of those files contains always the same type of data structure and content: If several observations have been made (often the case for UVP or IFCB) a mean observation is provided for one station (either day or night). For each instrument several NBSS (ellipsoid estimate only) are present either with the different tested corrections across instruments: -“raw” are uncorrected data -“trunked” are NBSS spectra where extreme data have been removed to ignore regions of incorrect efficiency (too small organisms or too large, rare ones) and calculate a slope (b) and the intercept (a) of the NBSS -“interceptadjusted” are correction of the intercept on the basis of WP2 measurments -“adjusted_site” are corrections of the intercept based on concurrent measurments of similar size class by several instruments (when possible) -“adjusted” are corrected by the median of correction between instruments (median of every “adjusted_site” corrections. -“final” are the final correction after manual QC of the different possibility of corrections above Those data are present either as total living organisms, and regrouped within their original taxonomic identification (“original”), in different functional groups (“pft”) or trophic (“trophic”) groups. For each of them the name of the groups is provided (nameoriginal, namepft). Trophic groups are organized such at position 1 is primary producers (trophic position in matrix: 1); 1.5 is mixotrophs (trophic position in matrix: 2) ; 2 is herbivorous (trophic position in matrix: 3); 2.5 is omnivorous (trophic position in matrix: 4) ; 3 is carnivorous (trophic position in matrix: 5) ; 3.5 is undefined trophic mode (trophic position in matrix: 6) For quality control purpose a figure is generated for each station and for each of the above coherent compilation of datasets allowing to control the different corrections and the different final products obtained. Those are saved under the name metaplankton.pdf, metaplanktonC.pdf, metaplanktonnight.pdf, metaplanktonfacs.pdf and metaplanktonCfacs.pdf Those single instruments measurements have afterward been merged in several subproducts: -merged (not recommended because of the heterogeneity of number of instruments merged ): all possible instruments have been combined for every stations (mostly used for diagnostics plots and systematic inspection of data. Called "Meta-Plk heterogeneous” in the manuscript. -merged4devicesmin: spans organisms ranging from 0.8 µm to several cm (including flow-cytometry, IFCB, Flowcam, Zooscan from several nets and UVP and with a complete coverage in between), it is only available from the Arctic ecosystem and covers 20 sites. Called " Meta-Plk >0.8 µm” in the manuscript. -mergedpft4devicesincomplete: is similar from the above but adds few stations which miss some observation for particular organism sizes, notably due to the absence of Flowcam analyses from the 20 µm net fractions. Called " Meta-Plk >0.8 µm incomplete” in the manuscript. -merged2netsUVPH20: spans organisms ranging from 20 µm to several cm (using e-HFCM with 20 µm net, Zooscan from WP2 and Regent nets and UVP). It covers both TO and TOPC parts of the expedition and includes 63 stations. Called " Meta-Plk >20 µm” in the manuscript. Some other trials (using only two nets : WP2 and Regent; merged2nets; or also including the UVP; merged2netsUVP) but not used in the manuscript are also included. Each of those “merged” products contains the full NBSS spectra for each functional groups (“datapft”) and in BSS spectra (ie. biovolume per size class; “datapft_denorm”), the total biovolume of each functional groups (“donut”) the same done for trophic levels (“datatrophic”; “datatrophic_denorm”) and the cumulated abundance and biovolume of the different trophic levels (“trophicpyramidnb”; “trophicpyramidbv”) The same grouping into functional groups and trophic groups was done from metabarcoding (18S, V9) genomic data for the size fraction 180-2000µm (pft180 and trophic180) or from the 20-200µm fraction (pft20 and trophic20) slopes and intercepts of the NBSS size spectra (slopesbss, ie. non normalized size spectra are also provided) relationship for every instrument and every merged datasets in the order: (1) Accuri flowcytometry (2) IFCB (3) Flowcam-Niskin (4) Flowcam b20 (5) Zooscan-WP2 net (6) Zooscan-bongo net (7) Zooscan-Regent net (8) UVP-in situ (9) E-HFCM-5µm net (10) E-HFCM-20µm net (11) Meta-Plk >200µm (ie. “merged2nets” product) (12) Meta-Plk >200µm-UVP (ie. “merged2netsUVP” product) (13) Meta-Plk >0.8µm (ie. “merged4devicesmin” product) (14) Meta-Plk >0.8µm- incomplete (ie. “merged4devicesminincomplete” product) (15) Meta-Plk heterogeneous (ie. “merged” product) (16) Meta-Plk >20µm (ie. “merged2netsUVPH20” product)

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    Authors: Kalhori, Aram; Wille, Christian; Pia, Gottschalk; Li, Zhan; +3 Authors

    Rewetting drained peatlands is recognized as a leading and effective natural solution to curb greenhouse gas emissions. However, rewetting creates novel ecosystems whose emission behaviors are not adequately captured by currently used emission factors. These emission factors are applied immediately after rewetting, thus do not reflect the temporal dynamics of greenhouse gas emissions during the period wherein there is a transition to a rewetted steady-state. Here, we provide long-term data showing a mismatch between actual emissions and default emission factors and revealing the temporal patterns of annual carbon dioxide and methane fluxes in a rewetted peatland site in northeastern Germany. We show that site-level annual emissions of carbon dioxide and methane approach the IPCC default emission factors and those suggested for the German national inventory report only between 13 to 16 years after rewetting. Over the entire study period, we observed a source-to-sink transition of annual carbon dioxide fluxes with a decreasing trend of −0.36 t CO2-C ha−1 yr−1 and a decrease in annual methane emissions of −23.6 kg CH4 ha−1 yr−1. Our results indicate that emission factors should represent the temporally dynamic nature of peatlands post-rewetting and consider the effect of site characteristics to better estimate associated annual emissions. This project is supported by the WET HORIZONS project.

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