dataesr/scientific-paragraphs-categorization huggingface dataset:
897571  to C. C. F. S. L. R. was supported by an  ETH Zurich postdoctoral  fellowship  20-1 FEL-07. M. L. , L. M. C. and G. Z. were supported by  EMBL Core Funding  and the  German Research Foundation (DFG, Deutsche Forschungsgemeinschaft , project no. 395357507 ,  SFB 1371  to G. Z. ). M. B. S. was supported by the  NSF  grant OCE# 1829831. C. B. was supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement Diatomic, no. 835067 ). S. G. A. was supported by the  Spanish Ministry of Economy and Competitiveness  ( PID2020-116489RB-I00 ). M. K. and H. M. were funded by the  SNSF  grant  407540_167331  as part of the  Swiss National Research Programme 75 'Big Data '. M. K. , H. M. and A. K. are also partially funded by  ETH core funding  (to  G.
Data availability The metagenomic and metatranscriptomic data used in this study were downloaded from the European Nucleotide Archive (ENA) and a summary of their accessions is provided in Supplementary Table  1. Publicly available genomes were downloaded from Figshare (https:// doi. org/10. 6084/m9. figshare. 4902923) for manually curated MAGs from Tara Oceans, from ENA using the project accession PRJEB33281 for GORG and from https://mmp2. sfb. uit. no/databases/ for MarDB. The GEM MAGs were downloaded from https://portal. nersc. gov/GEM/. MAGs contained in the GTDB r89 were downloaded from https://data. gtdb. ecogenomic. org/releases/release89/. The MIBiG and BiG-FAM databases can be accessed at https://mibig. secondarymetabolites. org/ and https://bigfam. bioinformatics. nl/, respectively.
The data produced in this study, including metagenomic assemblies, bins and MAGs have been deposited at the ENA under the accession PRJEB45951 and a list of individual accession identifiers is provided in Supplementary Table  1. Other supporting data have been deposited at Zenodo (https:// doi. org/10. 5281/zenodo. 4474310), and the OMD can be interactively accessed online (https://microbiomics. io/ocean/). Additional material generated in this study is available on request.
Analysis of survival Survival of spat in each tray was estimated by counting the number of individuals present in photos taken during the experimental treatment. By taking photos, we reduced the handling time and stress for the spat. On days 3, 9, 16, 24 and 31 (approximately once per week) photographs were taken of each of the 36 trays. To have sufficient image resolution, three photographs were taken of each tray and were stitched together afterwards using GIMP (GIMP development team 1997). All shells in stitched composites (apart from clearly empty shells) were counted using ImageJ software  (Rasband 1997). Survival analysis was carried out with the coxme package  (Therneau 2020)  in the R statistical language (R Development Core Team 2019), with tray as a random factor to account for variation between trays within each experimental system.
The significance of population and environmental variable effects on survival were tested with Wald chi-square tests using the Anova function from the car package (Fox & Weisberg 2011). Because coxme does not allow more than two dependent variables to be tested simultaneously, we first tested for differences between French and Norwegian scallops, before comparing the effects of temperature and pCO2 (plus their interaction) on survival for each population separately. To reduce the effect of sample size differences on statistical power, a random number of French spat, equivalent to the smaller number of Norwegian spat, were included in the survival analysis. Spat (2-6 individuals per tray) were sampled near-daily during the adjustment period and weekly during the experimental treatment for a separate experiment (unpublished data).
We also used these measures to estimate the condition index (CI: soft tissue dry weight / shell dry weight ratio), which encapsulates the difference in resource allocation to these compartments  (Lucas & Beninger 1985). Spat were dissected and soft tissue was dried at 75 °C for 24 hours while both the left valve (flat) and right valve (curved) were air dried for at least 24 hours. The dry bodies, flat and curved valves were then weighed to the nearest 0. 0001 g using a digital balance (Mettler Toledo). Multiple high-resolution images of the flat valve at a range of focal depths were obtained using an AxioCam MRC 5 linked to a SteREO Lumar. V12 stereomicroscope (Carl Zeiss) equipped with a motorized stage: the resultant photomosaics were then assembled using AxioVision 4. 9. 1 software (Carl Zeiss).
From these images shell height was measured using ImageJ software. These images were also used to estimate height at the beginning of the experiment (following transport), as growth in the controlled conditions of the experimental facility could be clearly associated with an alteration in the colour of newly calcified shell. Due to the considerable variation in size among scallops from both populations, initial shell height was included as a covariate in whole organism phenotypic analyses. Although  Lucas and Beninger (1985)  recommend the use of cubed height as a covariate for mass measures (such that variation scales in the same number of dimensions), we found initial height alone (not raised to a power) better accounted for covariation in the data. Phenotypic measures and ratios were plotted against these initial measures as part of an inspection for outliers.
Four out of 431 samples were removed due to at least one trait showing extreme outlier values. Quantile-quantile plots were assessed to determine probability distributions. Dry body weight and total shell weight were subsequently log transformed to ensure normality. For all analyses, populations were analysed separately because of the strong differences in initial sizes. The dependency of whole organism phenotypes on temperature, pCO2, and initial shell dimensions, as well as their 2-way and 3-way interactions, was assessed using linear mixed-effects models in the lme4 package in R  (Bates et al. 2015) ; tray was included as a random effect. Backwards stepwise term deletion was used to test the importance of interactions and main effects. Statistics were obtained from minimal models fitted with restricted maximum likelihood and P-values were obtained using the lsmeans package  (Lenth 2016).
When either temperature or the interaction of temperature and pCO2 were significant, contrasts between groups were evaluated with pairwise post-hoc tests using the emmeans package  (Lenth 2020). For the interaction between temperature and pCO2, temperature differences at a given pCO2 and pCO2 differences at a given temperature were considered. Phenotypic responses to temperature and pCO2 were plotted using effect size plots in the jtools package  (Long 2020)  based on fully parameterised linear models (temperature, pCO2, initial shell dimensions, and all interactions). These account for covariate variation (including interactions), include confidence intervals, and can be mean centred. Although they do not account for random effect variance, these plots provide an intuitive means of visualising these data when combined with mixed-effects model statistics.
Strips were then deposited on a 15cm × 15cm lab-cast SDS-PAGE gel containing 12% acrylamide and migrated. Protein spots were stained with Coomassie Blue (PhastGel, GE Healthcare). Gels were bleached with baths of H2O/methanol/acetic acid (70/30/7) and photographed using G:BOX (SynGene). The 32 clearest gels were taken forward for analysis (4 per population per treatment) and protein spots in the resulting images were aligned using Progenesis SameSpots v3. 3 software (Nonlinear Dynamics, Newcastle upon Tyne, UK) and then manually verified. The effects of population, temperature, and pH were evaluated by running ANOVAs for each spot (combined population analysis). Due to the large number of tests involved, P values were adjusted using the false discovery rate (FDR), and fold change values were determined.
Tandem mass spectrometry (MS/MS) spectra were acquired in the positive MS/MS reflector mode by summarizing a maximum of 2500 single spectra (10 × 250) with a laser intensity of 4200. For the MS/MS experiments, the acceleration voltage applied was 1 kV and air was used as the collision gas. Gas pressure was set to medium. The fragmentation pattern was used to determine the sequence of the peptide. Database searching was performed using the MASCOT 2. 4. 0 program (Matrix Science). A custom database consisting of an EST database from a previous study was used  (Artigaud et al. 2014c ) and a compilation of the Uniprot database with Pecten maximus as the selected species. The variable modifications allowed were as follows: carbamidomethylation of cystein, K-acetylation, methionine oxidation, and dioxidation.
"Trypsin" was selected as enzyme, and three miscleavages were also allowed. Mass accuracy was set to 300 ppm and 0. 6 Da for MS and MS/MS mode, respectively. Protein identification was considered as unambiguous when a minimum of two peptides matched with a minimum score of 20. False discovery rates were also estimated using a reverse database as decoy. As well as carrying out analysis of variance for the two populations combined, we also ran separate analyses of variance for each population. The overall effect of temperature, pCO2 and their interaction on protein abundance in each population were tested through permutational multivariate analysis of variance (Permanova) using the adonis2 function in vegan  (Oksanen et al. 2019). Then separate ANOVAs were fitted for each protein considering the effects of temperature, pCO2 and their interaction, with P values adjusted using FDR.
Differential accumulation of proteins: combined population analysis We identified 279 proteins common to all gels using SameSpots. Of these, 103 differed significantly (FDR < 0. 05) between populations (n = 87), temperature treatments (n = 17) or pCO2 treatments (n = 3); three differed according to two or more of these variables. Following mass spectrometry of these proteins, 79 were identified based on comparison with the protein database: 71 differed according to population, 8 differed according to temperature and 2 differed according to pCO2 (two proteins differed according to two variables). These proteins are presented in table  S2  and figure   Seventy-one of the 79 proteins differed significantly between populations, and the majority of these (43) were different isoforms of actin: 24 isoforms were elevated among French spat, and 19 were elevated among Norwegian spat.
Funding This work was supported by a grant from the  Regional Council of Brittany , from the  European Funds (ERDF) , and by the  "Laboratoire d'Excellence" LabexMER  ( ANR-10-LABX-19 ) through a grant from the  French government  under the program " Investissements d'Avenir ". The project was also supported by state funding to the  ISblue interdisciplinary graduate school for the blue planet  ( ANR-17-EURE-0015 ), also under the program " Investissements d'Avenir" and within the framework of France 2030. Additional funding was provided by the  Institute of Marine Research  through the project " Ecological interactions of low trophic benthic production " ( 82120-01 ).
Grouping of bryophytes 3. 1 Previous grouping of bryophytes  During (1979 During ( , 1992) )  identified life history types to classify bryophytes according to life strategies (e. g. , life span and reproductive strategy, -age and -effort). Life history types may depend on the environment and life history traits are likely a key to understanding bryophyte population dynamics  (Austrheim et al. 2005) ; however, they do not provide full insights into bryophyte functional roles in the ecosystem. Currently, the majority of trait data for bryophytes occurring in the TRY database are on life history traits  (Kattge et al. 2020). The growth form classification has often been used in combination with life form and/or perichaetial position in the literature. La  Farge-England (1996)  distinguishes growth form, life form and perichaetial position and indicates which are environmentally modified versus genetically fixed.
Ecosystem functions of the BFGs We assessed whether our choice of field-identifiable bryophyte functional groups can lead to a more meaningful representation of tundra bryophytes in the study of their ecosystem function by investigating how the groups separate for one key trait that is frequently measured, the water holding capacity (WHC). As such, if BFGs separate into more than one cluster, we can conclude that the resolution for that trait is improved compared to the commonly used 'bryophytes'. Currently bryophyte trait data are poorly represented in global and regional trait databases such as the TRY  (Kattge et al. 2020 ) and the tundra plant specific Tundra Trait Team database  (Bjorkman et al. 2018) , which limits the possibility to fully test the BFGs. However, a wide set of ecosystem functions are ultimately linked to bryophyte water balance.
Water holding capacity data collection and analysis We collated existing data on water holding capacity (WHC) defined as maximum water held per gram of dry mass of bryophyte shoots or monospecific bryophyte colonies. The full dataset included 1360 observations of 59 species from both published and unpublished studies. All data were from tundra ecosystems, except one Norwegian coastal heathland study (Rui, Vandvik, Haugum unpubl. ). Although the method across studies did not follow any standardized protocol the studies could be grouped into three methods of measuring WHC: as "internally" (shoot int ) and "internal and externally" (shoot int+ext ) held water in shoots and for whole bryophyte colonies (see Appendix Table  A1  for the descriptions of methods for the individual studies). While colonies represent the most realistic field situation, measurements at the shoot level are less destructive and therefore possible to conduct in long-term experimental plots and often data will therefore exist in this form.
Data for WHC shoot int was therefore excluded from further analyses. The remaining dataset included 963 observations of 37 bryophyte species, which we assigned to eight different BFGs  (Fig 3, Fig A3)  and to existing grouping schemes, namely 'perichaetial position' (Liverworts grouped separately) (La Farge-England 1996), life form according to  Grace (1995)  and life form according to the BryForTrait database  (Bernhardt-Römermann et al. 2018). Differences in WHC between groups within the different grouping schemes were analyzed with a mixed-effect model followed by Tukey's HSD test (see Appendix). All data were handled and analyzed in R version 4. 0. 3 (R Core Team 2020).
Large variation between species within those groups (Fig A3 ) shows that not all our groups distinguish themselves from each other in terms of WHC. Particularly the groups 'Short unbranched turf' and 'Tall unbranched turf' displayed almost as much variation between species within groups as the entire spectrum of the dataset. A better separation between those groups might have been achieved by use of standardized protocols or a larger number of species representing each group. However, the groups possibly differ in other functional traits and thus represent functionally distinct species clusters, and this should be tested in future work. Four of our groups were not represented in the analyses. Of these, 'Thalloid' is, with its absence of leaves, the morphologically most distinct. From this group Marchantia foliacea and Monoclea forsteri from New Zealand forests had WHC of 20 and 10 g/g, respectively  (Green and Snelgar 1982) , which is within the upper end the spectrum covered in our study.
Further testing of the BFGs requires accessible functional trait data, which is currently limited. To allow robust analyses, these traits must be gathered using standardized protocols building on previous efforts  (Jónsdóttir et al. 1999; Cornelissen et al. 2007; Hill et al. 2007). In order to improve or include bryophyte representation in current trait databases such as TRY and Tundra Trait Team  (Kattge et al. 2020; Björkman et al. 2018)  bryophyte functional trait data need to be geographically and taxonomically diverse for good representation of species. Unlike vascular plants, bryophyte functional traits are rarely recorded in field experiments, as this requires skills in species identification. For instance, in the database TRY, moss shoot length only has 716 entries and none of the observations are georeferenced, whereas vascular plant height has 249,551 observations  (Kattge et al.
2020)  and Arctic plant traits are generally highly under-represented  (Bjorkman et al. 2018). Future challenges therefore lie in identifying and measuring bryophyte traits that underpin key ecological functions and to add these to existing trait databases. Importantly, species level identification cannot be circumvented for these trait studies. Bryophyte species identification will likely remain a struggle for many ecologists. This is further challenged by the lack of a comprehensive flora covering the Arctic region and poor representation of bryophytes in plant-identification mobile phone applications such as SEEK-iNaturalist or PictureMe (pers. obs. , the authors). To date, there is not a comprehensive bryophyte flora that covers Greenland and North America. Fennoscandia and the Russian Arctic are covered by several regional floras (Table  A2 ).
Acknowledgements This study was supported by a grant to SL from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie, Grant No. 797446 and by the Independent Research Fund Denmark, Grant no. 0135-00140B. Funding from the Academy of Finland (grant  or "not Polytrichales". Groups originating from orange bubbles can be collated to form broader functional groups. Note, in nature bryophytes often occur as a mixture of species, usually individuals occupy the same BFG, but if they do not, the most abundant BFG should be recorded. Soil removed. Soaked in water for 30 min. Suspended in sealed container with water at the bottom for 24 hours at 22 to allow excess of water drops and prevent evaporation. Then, weighed, oven-dried for 72h at 70°C and weighed again N: 30, Sample size: 1g dw.
Rb/Sr Sampling trenches were excavated to a depth of >0. 5 m from the freshly exposed surfaces for sedimentary logging and sampling. The abundance of rubidium (Rb) and strontium (Sr) were measured in the field with an Innov-X Systems X-ray fluorescence spectrometer in geochemistry mode using beam 1 (50 kv) and beam 2 (10 kv). The measurement time for each sample analysis was at least 50 s. A total of 945 data points were acquired with a measurement interval of 4 cm.
Spectral Analysis and Cross-Spectral Analysis Spectral analysis of the Rb/Sr data was done on the 40-81 m (corrected thickness is 40-77. 6 m after removing a gravel layer between 71. 6 and 75 m) interval of our section where fine-grained lacustrine deposits are prominent. The power spectra and sliding window spectral analysis of Rb/Sr in the thickness domain and time domain were calculated using Acycle 2. 0 software (M. S. Li et al. , 2019). Cross-spectral analysis results were generated using the "ARAND" program from Brown University, USA  (Howell et al. , 2006 ; see Text S4 in Supporting Information S1 for detailed descriptions).
Response of Northern Hemispheric Ice Sheet Expansion to ZHANG ET AL. et al. , 2018 , 2019 , 2021; Figure S3  in Supporting Information S1), and Pleistocene records that show more regional and temporal complexity and more prominent obliquity forcing (T. Li et al. , 2017; Sun, An, et al. , 2010). The shift to a more prominent obliquity signal after the 2. 75-2. 6 Ma interval is also corroborated by several other (non lacustrine) East Asian records. Notably, obliquity forcing significantly increases in MS records from Chinese loess-paleosol sequences at 2. 75-2. 59 Ma  (Sun, Clemens, et al. , 2006 , 2010). Spectral analysis of previously published carbonate content data in the Plio-Pleistocene eolian Red Clay at the Lantian section (K. X. Wang et al. , 2022 ; western part of Fenwei Basin) also shows a very similar evolution, with a prevalence of obliquity after 2.
Sample and session selection The present analysis aimed to predict future cognitive decline from baseline nonbrain (e. g. , age and clinical scores) and brain imaging data (regional brain volume and functional connectivity). We used data from the publicly available, longitudinal OASIS-3 project, a collection of data from several studies at the Washington University Knight Alzheimer Disease Research Center  ( LaMontagne et al. , 2019 ). OASIS-3 acquired data in different types of sessions ( clinical sessions: nonbrain data describing personal characteristics, cognitive and everyday functioning, health; neuropsychological sessions: nonbrain data from neuropsychological tests; MRI sessions: structural and functional MRI). The count and spacing between sessions varied between participants. To predict future cognitive decline, baseline sessions were used as input data and follow-up sessions as targets.
The study design required a matching approach to select: (1) baseline sessions (from clinical, neuropsychological, and MRI sessions) to be used as input data, and (2) follow-up clinical sessions to estimate the future cognitive decline. First, baseline data were established by matching sessions from the different types (clinical, neuropsychological, MRI). We matched each MRI session that had at least 1 T1w and 1 fMRI scan with the closest clinical session. For each participant, the first MRI-clinicalsession pair with an absolute time difference of < 1 year was selected as the baseline session. If no such pair was available, the participant was excluded from the analysis. Additionally, the closest neuropsychological session (within 1 year of the MRI baseline session) was also considered as baseline data.
Baseline information from neuropsychological testing, however, was considered optional, and not finding a matching neuropsychological session was not a criterion for exclusion. All data preceding the selected baseline sessions were disregarded for the analysis. Second, all clinical sessions after the baseline clinical session were included as follow-up sessions to estimate cognitive decline. To reliably estimate decline, participants were only included if they had at least 3 clinical sessions (baseline plus 2 follow-up sessions). This matching approach reduced the sample (N total = 1098) to 662 participants (302 male; Table  2  ). 1 The majority were cognitively healthy at baseline (509 healthy controls, 12 were diagnosed with MCI, and 111 with dementia; for 30 no diagnosis was available for the baseline session).
Nonbrain data Non-brain data described personal characteristics at baseline, such as demographics, cognitive, and everyday functioning, genetics, and health ( Table  3  shows the abbreviations of tests). For fur- 1 The selected participants and session can be found here: https://github. com/ fliem/cpr/tree/0. 1. 2/info. 2 https://github. com/NrgXnat/oasis-scripts. 3 https://central. xnat. org/. et al. , 2019 ; Morris et al. , 2006 ; Weintraub et al. , 2009 ). The specific measures included: 1. Demographic information: sex, age, education 2. clinical scores: MMSE  ( Folstein, Folstein, and McHugh, 1975 )  , CDR  ( Morris, 1993 ) , FAQ  ( Jette et al. , 1986 ) , NPI-Q ( Kaufer et al. , 20 0 0 ), GDS (Geriatric Depression Scale,  Yesavage et al. , 1982 )  3. neuropsychological scores: WMS-R  ( Elwood, 1991 ) , Word fluency, TMT  ( Bucks, 2013 ) , WAIS-R ( Franzen, 20 0 0 ), BNT  ( Borod, Goodglass, and Kaplan, 1980  ) 4.