dataesr/scientific-paragraphs-categorization huggingface dataset:
Background scenario of LID In this work, new highly resolved data δ 15 N on EDC ice core are presented over Terminations II, III, IV, V and VI (Fig. 7a ). The available δ 15 N data can be sorted out in two groups: δ 15 N measured by  Grisart (2023)  and  Bréant et al. (2019)  at high temporal resolution (between 333 and 1,375 years, see Table  1 ) and the older measurements  (Bazin et al. , 2013)  used to estimate LID in AICC2012, characterised by a lower sampling resolution (between 1,400 and 2,400 years, see Table  1 ). The measurements of  Bazin et al. (2013)  and  Bréant et al. (2019)  have been shifted down by 0. 04 ‰ to account for calibration errors. The new dataset permits to extend the record around 1100 m and between 1700 and 2500 m and to improve the resolution over Terminations II to VI.
Outliers are discarded if they show an anomaly superior to 0. 045 ‰ when compared to the smoothed record (Savitzky-Golay algorithm with 25 points). This results in the rejection of 25 datapoints out of 475 measurements for the new dataset (see Fig. 7 ). The two δ 15 N datasets are merged and the compiled record is interpolated every 100 years. Then, assuming that the firn is solely a diffusive zone (i. e. no convection layer at the top) at EDC during the last 800 kyr, in agreement with current observations  (Landais et al. , 2006) , past LID is calculated as per the first order estimate of the barometric equation: 𝐿𝐼𝐷 ≃ 𝛿 15 𝑁 𝑔𝑟𝑎𝑣 ⋅ 𝑅𝑇 𝑔 (5) with 𝑇 the temperature at EDC estimated from combined measurements of ice δ 18 O and δD after correction of the influence of the sea water δ 18 O  (Landais et al.
, 2017). The 2 kyr shift of the new AICC2023 chronology towards older ages permits to improve the coherency between NALPS, NGRIP and BMS1 timescales over the GI-25a onset (traceable in the δ 18 O series, Fig. 11 ). The age discrepancy is reduced from ~3,600 years (between AICC2012 and BMS1 timescale) to 1,640 years (between AICC2023 and BMS1 timescale, Table  7 ). Between 128 and 103 ka BP, the comparison between the AICC2012 timescale and the novel Dome Fuji ice core DF2021 chronology indicates that AICC2012 is likely too young by up to 4 kyr. Here, thanks to new highly resolved δO 2 /N 2 data and to the alignment of δ 18 O atm and δ 18 O calcite records, we improve the consistency between AICC2023 and DF2021, now agreeing within 1. 7 kyr over MIS 5e (Fig. 12 ). With the new chronologies, the records of δ 18 O atm and δO 2 /N 2 from Dome Fuji and EDC ice cores show synchronous variations between 140 and 115 ka BP although the δO 2 /N 2 measurements from EDC are more scattered than DF data due to the use of smaller samples (see Supplementary material).
In particular, the links between the variability of TAC and δO 2 /N 2 records and their orbital targets are not obvious over the 800 -600 ka BP period (Fig. 1 ). This may be due to bad quality of the ice and/or diffusion of gases through the ice matrix  (Bereiter et al. , 2009). The imprecision of the signal may also be partially explained by the limited temporal resolution of the existing dataset in this deep section. To address these issues, highly resolved TAC and δO 2 /N 2 measurements are needed in the lowermost section of EDC ice core. In addition, δO 2 /N 2 from ice samples over the period covering TVI should also be analyzed to investigate the mismatch between old and new datasets (Fig. 1 ). A final important aspect would be to further extend the Paleochrono dating experiment by implementing other ice cores such as Dome Fuji, WAIS (West Antarctic Ice Sheet) Divide and NEEM (North Greenland Eemian), for which a large amount of chronological and glaciological information is now available.
Acknowledgements The research leading to these results has received funding from the  European Research Council  under the  European Union   H2020 Programme  ( H2020/20192024 )/ERC grant agreement no. 817493  ( ERC ICORDA ). Krypton analysis has been supported by the  Innovation Program for Quantum Science and Technology   2021ZD0303101 , and by the  National Natural Science Foundation of China  ( 41727901 ). Development of the Paleochrono model was funded by  CNRS/INSU/LEFE  projects  IceChrono  and  CO2Role. EC and EL acknowledge the financial support from the  French National Research Agency  under the " Programme d'Investissements d'Avenir"  ( ANR-19-MPGA-0001 ), through the  Make Our Planet Great Again HOTCLIM  project as well as the financial support from the  AXA Research Fund. We also acknowledge the assistance from the  European Union   FP5-EESD Programme  grant agreement no.
EVK2-CT-2000-00077  (EPICA), the  French National Research Agency Programme  " NEANDROOTS " ( ANR-19-CE27-0011 ) and the  French Polar Institute  project no. 902  ( GLACIOLOGIE CONCORDIA ). Our special thanks go to  Markus Grimmer ,  Marcel Haeberli ,  Daniel Baggenstos ,  Jochen Schmitt ,  Matthias Baumgartner ,  Hubertus Fischer ,  Kenji Kawamura  and  Ikumi Oyabu  for https://doi. org/10. 5194/egusphere-2023-1081 Preprint. Discussion started: 26 May 2023 c Author(s) 2023. CC BY 4. 0 License.
Data by Landais et al. (2012)  (shown by purple squares on Fig. 1 and S2) are consistent with the highly resolved https://doi. org/10. 5194/egusphere-2023-1081 Preprint. Discussion started: 26 May 2023 c Author(s) 2023. CC BY 4. 0 License. data presented here, supporting the relevance of the new dataset over this period. Over Termination VI (from 550 to 510 ka BP), the old dataset continuously increases while the novel dataset shows a brief maximum at around 525 ka BP followed by a minimum at around 520 ka BP. These newly revealed variations seem in phase with insolation variations, suggesting that the new dataset shows an improved agreement with insolation. Still, highly resolved measurements are needed in the lowermost part of the ice core where noise is significantly altering the temporal signal.
The filter is then applied to the local summer solstice insolation curve to check that it does not induce the shift of extrema positions by more than 100 years. This condition is verified over the last 800 kyr, except for the peaks located at the endpoints of the record (respectively around 107 and 788 ka BP) which are then not used for tie points determination. Outliers in the raw δO 2 /N 2 dataset are discarded if they show an anomaly superior to 3. 2 ‰ when compared to the low-pass filtered signal. Five outliers are rejected out of 294 points. The δO 2 /N 2 is interpolated and filtered again after removal of the outliers.
To this point the only other O. bengalensis fulllength sequences recovered from GenBank had little contextual information apart from location. For the sequences from Georgia and San Diego USA, temperature records from other sources for the period of collection indicated the water in May would be $24 C, reaching 29 C by July (Georgia), and averaging 20 C in July (San Diego, although the sequence may be from an enclosed tank, or the natural environment based on the Genbank nr record). To further examine the environmental distribution of the novel species, we turned to TARA Oceans V9 data, but found this variable region unsuitable for resolving Ostreococcus species, as reported previously  (Monier et al. , 2016). We then turned to V4 18S rRNA amplicon data from Ocean Sampling Day, TARA Oceans, and Malaspina to examine the distribution of O.
After acquiring full-length 16S rRNA gene sequences for the LCA taxon seen in amplicon data, phylogenetic analyses demonstrated it forms a statistically supported, independent Ostreococcus clade, Clade E. It should be noted that PhytoRef has a sequence (FJ744977) that is placed within the O. bengalensis clade in our phylogenetic analysis of partial-length sequences (Figure  S10 ) but is misnamed "O. tauri" in that database. Other lines of evidence supporting the new Bay of Bengal clade being an own species came from our environmental 18S-ITS1-5S-ITS2-to partial 28S rRNA clone sequencing and phylogenetic analysis (Figures  5A  and S11 ). Because Bathycoccus and Ostreococcus have an additional helix located between universal helices 3 and 4 (Figure  5B ) not present in other eukaryotic phytoplankton  (Marin & Melkonian, 2010) , we further validated our findings with analysis of the ITS2 secondary structure which showed Clade E sequences contained this helix, and possessed a unique nucleotide composition compared to other Ostreococcus clades (Figure  5C ).
Oceanographic sampling Samples were collected from the RV Roger Revelle between 24 August 2015 and 18 September 2015 at 18 hydrographic stations using a CTD Niskin Rosette as well as 11 samples collected by bucket after loss of the rosette (Table  S1A -E). Vertical profiles of temperature, salinity, fluorescence, and nutrients were measured at 0. 5 m intervals. Continuous thermosalinograph underway data was consulted to derive parameters not directly measured for samples collected by bucket. Seawater was collected at four to six specific depths ranging from 2 to 100 m depth corresponding to surface, ML, SCM, below SCM, base of the oxycline, and anoxic layer. DNA samples were collected by filtering 500 ml seawater onto 0. 2 μm pore size polyethersulfone membrane filters (Supor 200, Pall Gelman).
Flow cytometry Samples were analysed on a BD Influx flow cytometer equipped with a 488 nm 200 mW laser and running on 0. 2 μm filtered 1Â PBS as sheath fluid. Scatter and fluorescence signals were collected using the BD FACS Software (software v1. 0. 0. 650) with FALS (a proxy for cell size) set as the trigger parameter. Prior to measurement, fluorescent polystyrene beads (0. 75 μm yellow-green, Polysciences) were added as internal standards. Each sample was run for 8 min at 25 μl min À1 , as measured with an inline flow sensor Sensirion SLG-1430 (Sensirion AG) following a 2 min pre-run, and the corresponding volume was calculated by weighing the sample before and after the run. Cytograms were analysed using WinList 7. 1 (Verity Software House) with populations defined and enumerated based on FALS and chlorophyll autofluorescence (captured by a 692/40 nm bandpass) for Prochlorococcus and photosynthetic eukaryotes, and additionally phycoerythrin autofluorescence (captured by a 572/27 nm bandpass) for Synechococcus.
Generation of full-length sequence and additional phylogenetic analyses After an initial analysis of amplicons using PhyloAssigner with published reference alignments  (Choi et al. , 2020; Sudek et al. , 2015)  we noted that, at several key nodes, there were multiple amplicons assigned to LCA. These indicated presence of taxa within Ostreococcus and Prochlorococcus in our data that were highly abundant but for which sequences from closest extant taxa were not present in the existing alignments. Therefore, we targeted these groups to gain full-length sequences that could be used to improve taxonomic representation in the reference alignments. For Ostreococcus, DNA from the surface waters at stations S1 and S2 were used to recover fulllength 18S, ITS and partial 28S, and 16S rRNA gene sequence, where an unusual Ostreococcus 16S rRNA gene amplicon had highest relative abundances.
For recovery of sequences from Prochlorococcus ecotypes HLVI and LLVII, the 16S rRNA gene, ITS and partial 23S were amplified using universal forward primer 27f for the bacterial 16S rRNA gene and a reverse primer designed to recover the ITS/23S fragment of Prochlorococcus and Synechococcus (23S-1608r: 5 0 -CYACCTGTGTCGGTTT-3 0 )  (Rocap et al. , 2002). PCR reactions used HotStar Taq polymerase (Qiagen) and thermal cycling conditions as follows: 95 C for 15 min, followed by 30 cycles of 95 C for 30 s, 60 C for 30 s, and 72 C for 2 min; final extension was 72 C for 7 min. The resulting products were cloned and a total of 7 Ostreococcus and 6 Prochlorococcus clones were bidirectionally Sanger sequenced; sequences were checked with manual inspection after soft-trimming for potential vector contamination using the NCBI UniVec database and have been deposited under GenBank accessions OP909992-OP909998 and OP910119-OP910124, respectively.
Using the new Ostreococcus sequences and data from GenBank, we performed a phylogenetic reconstruction of the 18S rRNA, 18S rRNA + ITS1 + 5. 8S rRNA + ITS2 + partial 28S rRNA as well as 16S rRNA genes. Phylogenetic inferences were made by Maximum Likelihood methods implemented in RAxML under gamma corrected GTR model of evolution with 1000 bootstrap replicates based on 1568, 2203, and 1294 homologous positions for 18S rRNA, 18S rRNA + ITS1 + 5. 8S rRNA + ITS2 + partial 28S rRNA, and 16S rRNA gene trees, respectively, as well as with PhyML. Additional phylogenetic reconstructions were performed in MrBayes with the parameters of lset nst = 6 rates = invgamma ncat = 6, and ngenval = 10,000,000 samplefreqval = 1000 and tempval = 0. 200, and the final tree was produced with assistance from FigTree 1.
4. 0 (http://tree. bio. ed. ac. uk/software/figtree) and topology from RAxML (Figure  S10 ). Since the ITS region is not available for many eukaryotic phytoplankton, we also performed an analysis of the 18S rRNA gene alone. To this end, we collected nearly complete (>1500 bp) 18S rDNA sequences from Mamiellophyceae and prasinophyte sister clades using NCBI Nucleotide database searches and publicly available genome sequences. Prediction of 18S rDNA sequences from genome sequences was supported using the Basic Rapid Ribosomal RNA Predictor  (Seemann, 2018). The collected sequences were aligned using MAFFT with default parameters and gaps were masked using trimAl v1. 4. 1. Phylogenetic analyses were made using Maximum Likelihood and Bayesian methods as implemented in IQ-TREE v. 2. 2. 0  (Minh et al.
, 2020). Cyanobacteria amplicons were next analysed using a cyanobacteria reference alignment  (Sudek et al. , 2015)  and Prochlorococcus was further analysed with reference alignment developed herein after recognizing presence of atypical clades. Reference alignments used herein are available on github. The Prochlorococcus optimized alignment used 52 near full-length Prochlorococcus 16S rRNA gene sequences retrieved from SILVA database (release 132, 13 December 2017, https://www. arb-silva. de/ documentation/release-132), through BLASTN searches against GenBank nr, and the three HLVI and three LLVII full-length 16S rRNA gene sequences generated herein. Note that several LLVII sequences were also recovered from GenBank nr, specifically, JN166198, JN166205, KJ589744, KJ589747 from HOT157 125 m and Arabian Sea OMZ DCM.
4. 0 (http://tree. bio. ed. ac. uk/ software/figtree) and topology from RAxML. We also constructed a preliminary tree for parsing diatom sequences, although plastid sequences from known species are limiting. A total of 371 diatom sequences were recovered from GenBank nr using Blastn with known query sequences, as well as four rhodophytes for an outgroup. Sequences were aligned using MAFFT  (Katoh & Standley, 2013)  with default parameters and alignment gaps masked using trimAl v1. 4  (Capella-Gutiérrez et al. , 2009). The phylogenetic reference tree was created using Maximum Likelihood methods implemented in FastTree 2  (Price et al. , 2010)  under gammacorrected GTR model of evolution with 1000 bootstrap replicates based on 1253 homologous positions. To characterize distributions of Ostreococcus we turned to data from Ocean Sampling Day  (Kopf et al.
, 2015) , Malaspina  (Logares et al. , 2020) , and TARA Oceans  (Ibarbalz et al. , 2019)  deposited in metaPR2  (Vaulot et al. , 2022). Read counts for ASVs assigned to the genus Ostreococcus through metaPR2 were extracted and their taxonomic assignment to species level was validated through alignment to reference sequences, including the novel O. bengalensis, currently misidentified as O. tauri in metaPR2, due to the lack of O. bengalensis reference sequences (now provided herein). The global abundance of O. lucimarinus, O. OII, and O. bengalensis was visualized using R and Natural Earth map data from rnaturalearth (https:// github. com/ropensci/rnaturalearth).
Oceanographic study context. (A) Worldwide phytoplankton observations indicating global spatial biases in biological oceanographic sampling intensity as derived from PHYTOBASE database (https://doi. org/10. 1594/PANGAEA. 904397) by normalization of observations per area of 1 grid. Note that this database is of cyanobacteria and eukaryotic phytoplankton, but is largely from microscopy and flow cytometry with less sequence data; hence it has not been updated with relevant data such as that of (Larkin et al. , 2020)  on surface Prochlorococcus ecotype composition in the Indian Ocean, including Bay of Bengal. (B) Sampling sites and the salinity profile in the Bay of Bengal during the study period.
spp. -5 seqs Prasinodermophyceae -4 seqs Pyramimonadophyceae -13 seqs Mantoniella antarctica [AB017128] Mantoniella antarctica CCMP1436 [KF899842] Mantoniella squamata K0284 [KU600447] Micromonas sp. clone UEPACOp3 [AF525866] Micromonas sp. clone BL000921. 10 [AY425318] Micromonas sp. RCC1109 [KF501020] Micromonas bravo CCMP1646 [AY954996] Micromonas bravo M1681 CCAC1681 B [FN562452] Micromonas bravo RCC434 [AY425316] Micromonas sp. SAG5 [GCA 0096180551] Micromonas bravo CCMP1646 [AY954995] Micromonas bravo type strain RCC434 [KU244636] Micromonas sp. SAG8 [GCA 0096181951] Micromonas bravo RCC418 [KF501015] Micromonas polaris CCMP2099 [AY954999] Micromonas polaris RCC2308 [JN934683] Micromonas polaris CCMP2099 [DQ025753] Micromonas polaris CCMP2099 [AY955000] Micromonas polaris RCC2304 [KT860843] Micromonas polaris RCC2306 [JF794057].
ACKNOWLEDGEMENTS We thank the captain and crew of the RV  Roger Revelle  and the  ONR departmental research initiative, ASIRI , for support of the cruise, to  Amala Mahadevan  and  Gualtiero Spiro Jaeger  ( N000141310451 ). AZW thanks  Andrew Knoll  and the  Dept. of Organismic and Evolutionary Biology at Harvard University  for a visiting scholar position that facilitated completion of this research. We also thank  Huiwen Deng  for assistance during manuscript preparation and exceptional anonymous reviewers of the submitted manuscript. This research was supported by the  Gordon and Betty Moore Foundation  ( GBMF3788 ), and  NSF   Dimensions 2230811  to  Alexandra Z. Worden , and  Monterey Bay Aquarium Research Institute. Fabian Wittmers was supported by  BIOS-SCOPE  ( Simons Foundation International).
DATA AVAILABILITY STATEMENT Reference alignments used to taxonomically assign amplicon sequences in this study are available on github at (link to repository will follow). Amplicon sequences have been deposited at the Sequence Read Archive under BioProject PRJNA905425. The 16S and 18S rRNA gene sequences generated in this study have been deposited to DDBJ/EMBL/GenBank under accession numbers OP909992-OP909998 and OP910119-OP910124, respectively. All other data that support the findings of this study are available from the corresponding author upon reasonable request.
Very sparsely vegetated pixels with a long-term average NDVImax below 0. 15 showed inconsistent annual peaks of greenness and were discarded. Densely vegetated pixels with a NDVImax above 0. 75 were also removed because the sensitivity of NDVImax to canopy greenness decreases when biomass is high, due to saturation issues  (Huete et al. , 2002). The final dataset https://doi. org/10. 5194/bg-2023-74 Preprint. Discussion started: 2 May 2023 c Author(s) 2023. CC BY 4. 0 License. comprised 227,318 pixels. According to the 100 m resolution land cover product of Copernicus Global Land Service (CGLS)  (Buchhorn et al. , 2020) , the selected pixels were a majority of herbaceous vegetation and a minority of sparsely vegetated areas (Supplement Table  1 ). I calculated pairwise dissimilarities between NDVI time series using Euclidean distance and used the Partition Around Medoid algorithm of the Cluster R package  (Maechler et al.
Snow and climate datasets I used CHELSA v2. 1 and CHELSA-BIOCLIM+ datasets  (Brun et al. , 2022; Karger et al. , 2021)  to retrieve the climate averages of the reference period 1981-2010. CHELSA is a high resolution climatology that provides downscaled surface variable estimates at a horizontal resolution of 30 arc sec  (Karger et al. , 2017). The following variables were used to characterize the summer (June, July and August) climate in the study area: mean daily air temperatures (bio10), mean monthly precipitation amount (bio18), surface downwelling shortwave radiation (rsds) and vapour pressure deficit (vpd). To identify climate subregions, I computed pairwise dissimilarities between observations using Euclidean distance and performed a cluster analysis with the Partition Around Medoid algorithm of the Cluster R package  (Maechler et al.
, 2022). I varied the number of prescribed clusters from 4 to 9. For the sake of simplicity, results are only presented for 6 clusters. This was a fair compromise between a fine scale regionalization and a sufficient number of pixels per cluster for further analyses. I used ERA5-Land gridded datasets to estimate early summer meteorological conditions during the MODIS period. ERA5land is a process-based, climate re-analysis providing 0. 1° resolution variables related to the energy and water balance of land surfaces  (Muñoz-Sabater et al. , 2021). The study area encompassed 1053 ERA5-land cells (Supplement Table  1 ). I extracted the pre-calculated monthly means of air temperature (T2M), monthly accumulation of precipitation (PRE) and potential evapotranspiration (PET) for the months of June and July, i.
e. the early growing season. In the absence of available data on soil water capacity, I calculated the difference between PRE and PET, which represents atmospheric water balance used as a proxy of plant water availability during the early summer, and which is hereafter referred to as WBA. Using daily products, I also computed a Heat Wave Index (HWI) for the June-July period. This index, proposed by  (Russo et al. , 2014) , accounts for the magnitude and duration of heat waves. It was calculated following the simplified methodology described in  (Corona-Lozada et al. , 2019). Briefly, heat waves corresponded to sequences of at least three consecutive days during which the maximum daily air temperature was above the 8th decile for the reference period. For each heat wave, I computed the difference between the daily maximum air temperature and the 8 th decile and summed all these differences for the June-July period.
Variability of MODIS-derived NDVImax The study exploits MOD09Q1/Terra collection 6 products, which consist of 250 m resolution 8-day composite of the Moderate Resolution Spectro Radiometer (MODIS) sensor onboard the Terra satellite. This product has proven relevant to capture abrupt greenness changes during the short growing season above the treeline  (Choler, 2015). Data are distributed by the Land Processes Distributed Active Archive Center (https://e4ftl01. cr. usgs. gov/). I downloaded images from 18 February 2000 to 31 December 2022 for the tiles h18. v4 and h19v04 to cover the European Alps. Reflectance values (ρ) in the red and infrared bands that were produced with high quality (according to the MOD09Q1 Quality Control flag) were used to generate time series of raw NDVI, i. e. the ratio (ρ NIR -ρ RED)/(ρ NIR + ρ RED).
Finally, I extracted the following annual metrics from the denoised time series and for each pixel: the start of the season (SOS) as the first date of the year when NDVI surpasses 15% of NDVImax and the maximum NDVI of the growing season (NDVImax). I used the MODIStsp R package  (Busetto and Ranghetti, 2016)  to download the native hdf MOD09Q1 files and the Phenofit R package  (Kong et al. , 2023)  to process raw NDVI times series. As this study focuses on variability, an important initial step consisted of detrending NDVImax time series. The NDVImax variability for a given data span is defined as the residue of the NDVImax after the removal of the trend  (Wu et al. , 2007). For each pixel, I fitted a monotonic function of NDVImax over time using both the whole data span (22 years) and subperiods of 13 years.