dataesr/scientific-paragraphs-categorization huggingface dataset:
To estimate the potential functions of microbial OTUs based on 16 S rDNA data, we used PICRUST software  18  on reference sequences, using KEGG orthologs  28  grouped into pathways (function categorize_by_function. py, level = 3). A matrix containing 329 pathways was obtained. We then removed all eukaryotic functions, for instance genes related to cardiovascular diseases and categories grouped as "organismal systems". NSTI values averaged 0. 04 ± 0. 02 and 0. 12 ± 0. 02 respectively in skin-associated and planktonic communities, indicating that OTUs sequences were close enough to the nearest 16 S rDNA of reference genomes to infer functions. Investigating the presence of pathogens. Two additional phylogenetic analyses were performed separately for the two genera Staphylococcus and Streptococcus to look for putative pathogenic bacteria on cetacean skin.
Near full-length 16 S rDNA sequences of well-known pathogenic and non-pathogenic species of these genera were downloaded from the SILVA database (ACC number provided in Supplementary S3). Reference sequences of the most abundant OTUs belonging to these two genera (i. e. 35 Staphylococci and 31 Streptococci sequences), as well as the SILVA sequences were aligned against the SILVA reference database using Mothur, and added into the SILVA reference phylogenetic tree using ARB software  29. The full phylogenetic tree was then pruned using the ape R-package  30  to remove all but the added sequences, while keeping the topology of the tree. We then visualized the phylogenetic tree to determine if OTUs from this study were close to the pathogenic species considered.
The taxonomic structural diversity was computed using the Shannon index  32  , expressed in Hill numbers  33  on abundance of OTUs. The phylogenetic structural diversity was measured using the Allen index  34. All diversity indices were computed using R software. The taxonomic alpha diversity indices were computed using our own functions (available at https://github. com/marlenec/chao), while the Faith PD and Allen index were calculated respectively using the picante and entropart packages  25, 35. Similarly, we used four complementary beta-diversity indices to assess the taxonomic and phylogenetic dissimilarity between pairs of microbiotas, according to their composition or structure. The compositional taxonomic dissimilarity was assessed based on presence/absence of OTUs, using the Sorensen index  36  computed with betapart package  37.
The structural taxonomic dissimilarity, taking into account the relative abundance of OTUs, was measured using the multiplicative decomposition of the Shannon index  9. The phylogenetic compositional and structural dissimilarities were computed using the unweighted and weighted versions of the Unifrac index  38, 39  , respectively, from the GUniFrac package  40. Kruskal-Wallis tests (KW) were performed on alpha-diversity indices to assess the effect of sample type (i. e. water vs. skin samples), species, individual, sex, or body zone on microbial alpha-diversity. When significant, the KW was followed by post-hoc pairwise comparisons among groups using the pgirmess package, which includes the correction for multiple tests from Siegel and Castellan  41, 42. The correlation between the age of the individual and its associated alpha-diversity was assessed using a Spearman's correlation test using stats R-package.
Beta-diversity values were visualized on PCoA plots using the ape package  30. The effect of sample type, species, individual, age, sex, and body zone on the structure and composition of microbial communities was assessed by performing separated one-factor PERMANOVAs with 999 permutations on beta-diversity values using vegan package  43. The number of identical OTUs between skin microbiota and planktonic communities was analyzed using an Euler Diagramm computed with venneuler R-package  44. To assess how each microbial clade contributed to the dissimilarity between planktonic and skin microbiotas, as well as between microbiotas of cetacean species, we performed a LefSe analysis  45. LefSe provides Linear Discriminant Analysis (LDA) scores for the bacteria clades contributing the most to the differences between cetacean species.
Comparing skin microbiota of cetaceans and other vertebrates. The skin microbiota of dolphins and killer whales was compared to the published skin microbiota of 11 terrestrial and marine vertebrates, namely Human  [46] [47] [48]  , pig  49  , humpback whale  10  and eight teleostean fish species  8, 9. Due to the different primers that were used for these different species, we could not directly reanalyze sequences from studies to assess OTUs abundance. Therefore, we extracted clades relative abundance from published figures and averaged across all individuals (i. e. 36 humans, 4 pigs and 57 humpback whales) for each mammalian species. In the case of marine fishes, as individual data was not available for all species, we chose to average clades relative abundances of all species to make a single "fish" category.
A metagenomics approach in surface seawater communities showed a similar trend towards dominance of flagellum assembly pathway and of membrane transporters, which is   consistent with the motile heterotrophic lifestyle of surface planktonic communities competing for nutrients  66. By contrast, skin-associated communities contained higher proportions of functions involved in protein folding, DNA replication, reparation and translation. Such functions may be driven by the need for skin-associated bacterial cells to grow rapidly on the skin of the animal to counter sloughing  10. However, in this study, microbial functions were estimated with the PICRUST software using phylogenetic affiliation of OTUs and a reference genome database. This assessment is limited to previously annotated genes (ignoring undiscovered functional genes), and do not account for potential differences in gene expression.
A good correspondence between the Ardley Lake guano/ fossil record and the sub-fossil record of former penguin colonies from across the AP exists during these phases, and until the end of guano phase GP-5, c. 1. 3 cal ka BP (Fig. 5 ). (a) Scatterplots of selected element/aluminium ratios of Ardley Lake and Yanou Lake sediments (this study), ornithogenic soils  49  and local bedrock  86. (b) Total Carbon/Total Nitrogen (TC/TN) and Total Carbon/Phosphorus (TC/P) ratios of Ardley Lake sediments, local lichens (for example, Usnea antarctica)  50  , liverworts (Cephalozia varians), mosses  50  , vascular plants (Deschampsia antarctica)  50  and different types of ornithogenic soils  49. The solid and the dashed lines show linear regression lines of samples from core ARD, and C/N and C/P ratios of phytoplankton according to the Redfield ratio, respectively (Supplementary Data 3).
Moss Palaeoenvironmental records. We then compared these five phases of elevated guano flux with a range of local and regional records of past environments to identify the major drivers of changes in penguin populations. First, we found no significant difference (5 or 10% level) in temperature data during guano and non-guano phases from cross-northern Peninsula 9-0 ka terrestrial temperature records, YAN-GDGT reconstructed mean summer air temperatures (MSAT) (Fig. 4a ) and JRI atmospheric temperature (Fig. 4f )  29. Overall, none of the guano phases were statistically related to warmer sea surface temperatures in the Palmer Deep (PD) record. Conversely, the non-guano phases were associated with significantly warmer sea surface temperatures in the Palmer Deep record (PD-SST 0-200 m; Fig. 4e )  48  (two-tailed Mann-Whitney U-tests: YAN-GDGT: P ¼ 0.
Electron Probe Micro-Analysis (EPMA) of 165 glass-shards from the most prominent T4-T7 tephra layers in the YAN, ARD and Beak 1 Lake records (Beak Island; Layers T a-e (ref. 63)) was used to determine the eruption characteristics and source of mid-late Holocene eruptions that likely had the biggest environmental impacts. Results were compared with glass-shard analyses from age-equivalent tephra layers in marine cores PC460/461 from the Scotia Sea (new data; this study) and similarly analysed data in Moreton and Smellie  23  from the Scotia Sea, northern Weddell Sea and Boyd Strait, and in Fretzdorff and Smellie  22  from the Bransfield Basin (see Supplementary Methods, Figs 20,21, Supplementary Table  10 ). For the YAN-GDGT reconstructed temperature record, temperature-sensitive glycerol dialkyl glycerol tetraethers (GDGTs) biomarkers were extracted from 41 samples using a microwave-assisted solvent extraction  76, 77.
4. 1) and GDGT-derived temperatures were calculated using the Antarctic and sub-Antarctic GDGT-temperature calibration  77  (see Data Analysis section). The Antarctic and sub-Antarctic GDGT-MSAT surface calibration data set (comprising 32 sites in total) includes surface sediments from Yanou Lake, Ardley Lake and three other lakes from Fildes Peninsula, along with two further lakes from Potter Peninsula and four lakes from the Trinity Peninsula, north-eastern AP (including Beak Island). Diatom analysis of the ARD and YAN records is as described in ref. 45 and summarized in Supplementary Fig. 14 , while Anvers Shelf marine core (ANVERS) diatom analysis and chronology are described in Supplementary Methods and summarized in Supplementary Fig. 15. Data analysis. Bio-element assemblages are immobile in (Antarctic) lake sediments  43  and guano input to sediments leads to the formation and preservation of stable phosphates, such as struvite (Mg(NH 4 )PO 4 x 6 H 2 O), leukoposphite and, in particular, hydroxylapatite (Ca 5 (PO 4 ) 3 (OH)), which is one of the dominant compounds found in ornithogenic soils/sediments on King George Island.
This also illustrates why the Ardley Lake guano phase colony was too small after c. 2,500 years for us to determine whether volcanic activity had a significant impact. Correlation analysis of geochemical data was undertaken using R 2. 15. 2 (R Foundation for Statistical Computing). Hierarchical R-mode cluster analyses were conducted with the R package 'Pvclust' (version 1. 2-2) using average linkage and a correlation-based dissimilarity matrix (see Supplementary Methods for software references). Based on multi-scale bootstrap resampling (number of bootstraps: 10,000), approximately unbiased P values were further calculated to assess the uncertainties in cluster analysis. We used constrained incremental sum of squares (CONISS) stratigraphically constrained cluster analysis with broken stick analysis to define Diatom Zones (D) in the ARD, YAN and ANVERS records, Geochemical Zones (GZ) in the ARD and YAN records, and Guano phase GP-1 to GP-5 boundaries in the ARD record.
Downcore cluster-zonation was undertaken on ecologically grouped diatom percentage data and square-root transformed geochemical data using the R packages vegan and rioja (see Supplementary Methods for software references). Downcore trends in diatom and geochemical data sets using Principal Components Analysis (PCA) were undertaken using square-root transformed percentage XRF data and m-XRF (Total Scatter Normalised (TSN)) percentage geochemical data and percentage diatom count data. For climate and sea-ice comparisons, the Yanou Lake reconstructed GDGT temperature (YAN-GDGT) data (grey plot, with RMSE errors shaded grey in Supplementary Fig. 16a ) and the Maxwell Bay measured TOC data (sea-ice proxy record)  52  (grey plot in Fig. 4c ) were smoothed using polynomial negative exponential regression analysis.
The YAN-GDGT o10 °C data set was used in 6-0 ka weighted mean temperature anomaly calculations, statistical analyses and hypothesis testing. The mean±1s 6-0 ka reconstructed YAN-GDGT o10 °C data set weighted mean MSAT value of þ 2. 39±2. 67 °C [95% confidence interval: 1. 52-3. 25  comm. ; SCAR-READER database: http://www. antarctica. ac. uk/met/READER/). On a regional to global-scale modern-day reconstructed lake sediment GDGTtemperatures broadly reflect changes in air temperature  77. At the local, small lake scale, water temperature might decouple from observed MSAT due to factors such as the thickness and length of seasonal lake ice-cover and the volume of meltwater input, both of which suppress lake water temperatures. Conversely, since water bodies store excess heat generated during extended ice-free seasons, small and/or shallow lakes (o10 m deep) reach temperatures of 6 °C or more (for example, Sombre Lake, Signy Island)  82.
It also corresponds to the time period covered by the terrestrial Yanou Lake GDGT-MSAT record. Using the Pre-industrial era (1,000-250 cal a BP) and its upper bound on the weighted mean value (95% confidence) produced similar timings for 'warm' and 'open-water' intervals. To compare variations in cross-Peninsula climate during guano and non-guano phases, we undertook normality tests and produced a set of descriptive statistics for the whole Ardley F o. sed. data set (weighted by 1s error), the YAN-GDGT o10 °C temperature data set, the JRI temperature anomaly data set  29  , and the PD 0-200 m sea-surface temperature (PD-SST) record  48  (weighted by published errors, where available). Using a ¼ 5% and 10% significance levels for three hypothesis test scenarios A-C shown in Supplementary Table  8 , we first performed Fisher's F-test Variance Ratio analysis.
Since some data sets were not normally distributed, we then used the Mann-Whitney U-statistic to test the hypothesis that temperature and temperature anomaly distributions in the YAN-GDGT o10 °C, JRI, PD-SST records were statistically different during the five guano-influenced phases (Scenario 1A in Supplementary Table  8 ) compared to the eight non-guano phases (Scenario 2A). We then repeated this process comparing guano phases of the mid-Holocene (8. 2-4. 2 ka; 1B) to those of the late Holocene (4. 2-0 ka; 2B), and for late Holocene guano phases (1C) versus non-guano phases (2C). We also undertook similar hypothesis testing using the Maxwell Bay sea-ice and Anvers Shelf openwater data sets to determine whether sea-ice conditions within guano and nonguano were statistically different in the three scenarios (A-C) described above.
Figure3| Scatterplots of element/aluminium ratios and comparison of C/N and C/P ratios. (a) Scatterplots of selected element/aluminium ratios of Ardley Lake and Yanou Lake sediments (this study), ornithogenic soils 49  and local bedrock 86. (b) Total Carbon/Total Nitrogen (TC/TN) and Total Carbon/Phosphorus (TC/P) ratios of Ardley Lake sediments, local lichens (for example, Usnea antarctica) 50  , liverworts (Cephalozia varians), mosses 50  , vascular plants (Deschampsia antarctica) 50  and different types of ornithogenic soils 49. The solid and the dashed lines show linear regression lines of samples from core ARD, and C/N and C/P ratios of phytoplankton according to the Redfield ratio, respectively (Supplementary Data 3).
Acknowledgements This study forms part of the  ESF -funded  IMCOAST  project, AP-6 led by S. J. R. , and the  EU  project  IMCONet  ( FP7404   IRSES , action no. 319718 ; coordinated by the  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Germany)  with additional funding from the  Natural Environmental Research Council (NERC) , and the  German Research Foundation  ( DFG  project no. BR 775/25-1 ) and logistic support from the  NERC-British Antarctic Survey (BAS) ,  HMS Endurance and 892 Naval Air Squadron , the  Alfred Wegner Institute (AWI)  and the  Instituto Antartico Argentino (IAA). We thank  NERC  for funding studentship  NE/J500173/1  ( BAS and Newcastle University ) to L. C. F. (supervisors S. J. R. , E. J. P. , D. A. H. , S. J. ). We thank colleagues at the  Chinese Great Wall Station  and  Russian Bellingshausen Bases on Fildes Peninsula  for their generous hospitality,  H.
Data availability. Key data sets for this study are in Supplementary Data files 1-7. All data produced by this study and associated IMCOAST/IMCONET projects are available from the corresponding author (sjro@bas. ac. uk) and the NERC Polar Data Centre (BAS) (https://www. bas. ac. uk/data/uk-pdc/) and Pangaea Data Publisher website (AWI) (https://www. pangaea. de/). Antarctic location maps and spatial data in Fig. 1  are the LIMA (Landsat Image Mosaic of Antarctica) downloaded from https://lima. usgs. gov/index. php and the SCAR Antarctic Digital Database http://www. add. scar. org/, used under a Creative Commons license. The basemap in Fig. 1a and Supplementary Figs 20,23 is the ESRI Ocean Basemap (http://goto. arcgisonline. com/maps/Ocean_Basemap) used under a BAS-NERC software license. Satellite images and image derivatives in Figs 1c,d and 6 and Supplementary Figs 2,22,23 are copyright of DigitalGlobe, Inc.
Bulk sediments were only dated where macrofossils were absent, while paired macrofossil or bone-collagen and bulk sediment samples were measured in the surface sediment and wherever present to check for any systematic offsets between ages obtained from different carbon sources (see Supplementary Methods, Supplementary Notes 5 and 6 for more details). Measured radiocarbon ages from samples shown in Supplementary Table  2  were calibrated using SH13 and MARINE13 calibration curves and age-depth models were generated using Bayesian age-depth modelling techniques (Fig. 2 , Supplementary Figs  3, 9 , Supplementary Methods and Supplementary Notes 5-7). All the 'as measured' (uncalibrated) radiocarbon age data shown in Supplementary Table  2  were used as input data for final age-depth model runs (ARD-M5, YAN-M4, ANVERS-M1, where model run number is indicated by the -M suffix).
The weighted mean basal age of the ARD core was 8,750 cal a BP [8,410-9, Sub-samples for carbon, nitrogen, XRF and ICP-MS (quantitative, dry mass) bio-element analyses were taken at 1 cm intervals from ARD and YAN lake sediment cores, lyophilized and ground with an agate ball mill and manually with an agate pestle and mortar. ARD samples were analysed for total carbon (TC), total sulphur (TS) and total nitrogen (TN) using a CNS analyser (vario EL Cube, Elementar, Germany) equipped with a solid-state infrared and a heat conductivity detector. TC and TS measurements were conducted on YAN sediments by means of an ELTRA CS analyser. For data replication and comparison purposes, the total inorganic carbon (TIC) of 69 (out of 385) samples from both cores was determined coulometrically by a CM 5012 CO 2 coulometer coupled to a CM 5130 acidification module (UIC, USA) while total organic carbon was then calculated as the difference between TC and TIC (TOC% ¼ TC% À TIC%).
The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim Reanalysis is presented. A number of different bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting those calculated from ERA-Interim to those based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed and relative humidity, available at either 3 or 6 h timescales over the period 1979-2014. This dataset is available 15 to anyone through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S), and can be accessed at present from (ftp://ecem. climate. copernicus. eu ). The benefit of performing bias-adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against observations.
Introduction Climate/weather information has been widely used in a number of climate-related impact sectors (e. g. agriculture, water and 20 energy) for decades. Increasingly, users are moving beyond the use of station observations to the use of gridded products, especially meteorological reanalysis datasets. These are reconstructions of past climates produced through the blending of observations with physical/numerical models which have been developed explicitly for climate monitoring and research  (Compo et al. , 2011; Dee et al. , 2011; Hersbach et al. , 2015). Reanalyses have the specific advantage of being complete through the process of physical/dynamic representation of the climate system which provides internally consistent fields across most 25 surface atmospheric variables as well as in the atmospheric column up to the stratosphere  (Compo et al.
, 2011). The present paper deals with the use of Reanalysis for the production of datasets of climate variables relevant to energy. The work took place within the European Climatic Energy Mixes (ECEM) project in the framework of the Copernicus Climate Change Service (C3S) Sectoral Information Service (SIS). This project is primarily focused on users in the energy sector who are interested in sub-daily (e. g. 6 h) and daily variability for the following variables at the near surface: air temperature, dewpoint temperature, precipitation, solar radiation, wind speed and relative humidity. Despite this choice of variables being of primary relevance to the energy sector, it is likely that the results will also be of use to other sectors (particularly water). Because reanalyses are computed on a model grid, inevitably there will be differences when compared to station observations.
20 Bias adjustment in the WATCH project  (Weedon et al. , 2011 (Weedon et al. , , 2014 ) was undertaken at the monthly average scale, with no multivariate dependence and for a number of hydrological variables necessary to calculate evapotranspiration, soil moisture and runoff (so including air temperature, precipitation, long-wave and short-wave/solar radiation, wind speed, specific humidity and surface pressure) and for the period of analysis  1958-2001 (1979-2013)   The aim of this paper is to present the construction of a bias-adjusted dataset of the climate variables listed above, by using ERA-Interim Reanalysis. This dataset is available to anyone through the CDS of C3S (currently 30 ftp://ecem. climate. copernicus. eu ). The benefit of performing bias-adjustment is demonstrated by comparing initial and biasadjusted data against station observations and gridded observation products.
The ERA-Interim Reanalysis and the gridded and station observation-based datasets used for bias adjustment are described in Section 2. Section 3 provides more information on the motivations for bias adjustment, together with some discussion on a number of proposed methods. As we are proposing bias adjustment at the daily and sub-daily timescales, we will consider whether we need multivariate schemes (as bias adjusting one variable may impact others), particularly whether this is essential in our Energy Sector context. Our selected techniques are discussed in Section 4. In Section 5, we also briefly discuss issues related to whether our bias adjustment is applicable to other sectors, or whether there is any technique that could be applicable across all possible user sectors. Different sectors have different user demands relating to variables required, timescales and the length of historical Reanalysis data needed.
ERA-Interim The development of ERA-Interim is described by  Dee et al. (2011). Surface air temperature, precipitation, wind speed at 10 m, surface solar irradiance and relative humidity were extracted from ERA-Interim on the native grid (~0. 7° by 0. 7° latitude/longitude grid). The period is 1979-2014 and the temporal resolution is either 3 h (forecast) or 6 h (analysis), depending on the variable (see  Dee et al. , 2011 for details). These five are Essential Climate Variables (ECVs) defined by the  Global 15  Climate Observing System  (Bojinski et al. , 2014). After extraction, the variables have been spatially interpolated to a more user-friendly 0. 5° by 0. 5° grid for the ECEM domain, by regridding ERA-Interim using a bi-linear interpolation technique. There are two principal reasons for this regridding: i) some of the observation datasets for the assessment of ERA-Interim are available on this regular latitude/longitude grid and ii) potential users of the datasets developed here requested regular latitude/longitude grids with cells size of 0.