dataesr/scientific-paragraphs-categorization huggingface dataset:
Considering the comparison of the observed to simulated interquartile range (Fig. S9 ), we see that the GIU site is amongst the most poorly simulated sites. However, we found that the simulation could be improved for both metrics of all four benchmarks through the use of modifiers (Table  1 , third row), despite the difference in simulated and observed stand structure (Fig. S10 ). In addition, we also found that inconsistencies between big-tree data and all-tree-based benchmarks may appear even when the simulated mean TRWs approach observed means (Figs. S4-S7). This suggests that (1) ITRDB data can be used as a first approximation to benchmark the growth of young and mature trees in LSMs, and (2) as the model improves, the need for unbiased datasets will increase, as biases in observed stand structure and growth rates could hamper the use of youngand mature-tree benchmarks in particular.
Outlook Tree-ring records of incremental growth that are suitably described in terms of benchmarks might complement wellestablished but short-term benchmarks for LSMs  (Randerson et al. , 2009) , such as forest inventory data  (Bellassen et al. , 2010; Naudts et al. , 2015) , eddy covariance measurements  (Blyth et al. , 2010; Williams et al. , 2009) , free-air CO 2 enrichment experiments  (De Kauwe et al. , 2013) , and satellite observations of vegetation activity  (Chen et al. , 2011; Demarty et al. , 2007). The novel benchmarks proposed here may also provide new targets for evaluating LSMs' performance, as the metrics could be used in the objective function of any data assimilation technique  (Peylin et al. , 2016)  to rigorously account for the information contained in TRW datasets. The value of tree-ring records for LSM verification might be further enhanced by (i) developing new, unbiased networks, such as the European biomass network, to both complement and identify biases in the ITRDB; (ii) adding their stable isotope ratios to verification benchmarks that may be simulated by isotope-enabled LSMs  (Levesque et al.
Conclusion We have proposed and evaluated the use of four benchmarks and two metrics that leverage observed demographics to provide more nuanced verification targets for LSMs that simulate both demographic responses and their environmental forcing on decadal to centennial timescales. Using small but relatively unbiased European biomass network datasets, we identify the extent to which presumed biases in the much larger ITRDB might degrade the validation of LSMs. We find that size, mature-tree, and extreme-growth verification benchmarks are relatively insensitive to big-tree bias, but the use of young-tree benchmarks for verification of LSMs may require the development of new unbiased observational TRW datasets and/or the innovative use of independent verification data. Code and data availability.
In line with GMD requirements, the model code has been archived and made accessible: https://doi. org/10. 14768/20200228001. 1  (Luyssaert, 2019). The scripts required for reproducing the figures, the ORCHIDEE simulations, and the intermediate results are available at https://doi. org/10. 5281/zenodo. 4899742 (Jeong et al. , 2021). The BACI dataset is freely available online at http://www. baci-h2020. eu/ (last access: 16 August 2021, BACI, 2020) but requires registration by email. The ITRDB dataset can be accessed through https: //www. ncei. noaa. gov/products/paleoclimatology/tree-ring, last access: 15 September 2021, National Oceanic and Atmospheric Administration, 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-14-5891-2021-supplement.
Figure4. Using virtual trees to account for challenges related to the use of ITRDB datasets when evaluating land surface. (a) Data-model comparison may overcome the big-tree selection bias by comparing only the simulated largest diameter class (bold blue line) for evaluation rather than all diameter classes (thin blue lines), with the compiled average virtual tree (black dotted line). Grey lines represent individual trees from observations. (b) The observed tree-ring records are a mixture of relatively slow-growing trees (light grey lines) and fast-growing trees (dark grey lines). Fast-growing trees do not attain the same age as slow-growing trees because they tend to die earlier. Using all trees without further consideration in the calculation of the average virtual tree (black dotted line) would lead to an underestimation of tree growth at the time of stand establishment, resulting in a flawed comparison with the simulated tree growth (blue lines).
Acknowledgements. Michael Neil Evans acknowledges insights arising from work with the PAGES/Data Assimilation and Proxy System Modeling Working Group. Stefan Klesse co-developed the European biomass network and provided management information. Sebastiaan Luyssaert would like to thank  Antonio Lara  ( Universidad Austral de Chile ) for early discussions on the study topic. Valerie Daux (Laboratoire des Sciences du Climat et de l'Environnement)  is acknowledged for comments on a previous version of the paper, which improved its clarity. Financial support. This research has primarily been supported by the  Horizon 2020   VERIFY  project (grant no. 776810 ). Additional support was received from the  Centre National de la Recherche Scientifique (CNRS) of France  through the  "Make Our Planet Great Again" programme ; the  Earth Systems and Climate Change Hub , funded by the  Australian Government's National Environmental Science Program  (grant no.
Truc Vert beach surveys A continuous beach survey program has been operational since 2003 at Truc Vert. The resulting monthly to bi-monthly beach morphology dataset is detailed and made available in  Castelle et al. (2020). The alongshore coverage of the surveys increased over time, exceeding 600 m in 2009 before stabilizing at c. 2200 m since early 2016 (Figure  3b ). Figure  3c  shows the time series of 1. 5-m elevation shoreline proxy cross-shore position S1. 5m, which has been used as the primary shoreline proxy in previous studies (e. g. ,  Castelle et al. , 2014; Splinter et al. , 2014)  as it best correlates with the beach-dune volume  (Robinet et al. , 2016). It is defined as the intersection of the alongshoreaveraged profile with the 1. 5-m AMSL elevation datum, where AMSL is obtained at Truc Vert by.
Publicly available satellite images and waterline detection algorithm We used the python toolkit CoastSat  (Vos et al. , 2019b)  which is freely-available on GitHub (https://github. com/kvos/CoastSat). The overall approach is described in detail in  Vos et al. (2019a). Briefly, the toolkit allows extracting waterlines from publicly available optical satellite data through Google Earth Engine. Landsat 5, 7 & 8 (L5, L7, L8, 30-m spatial resolution) and Sentinel-2 (S2, 10-m spatial resolution) images are retrieved to a user-defined region of interest before pre-processing to remove cloudy pixels and enhance spatial resolution. A generic waterline detection algorithm is then applied, consisting of two main steps: (1) an image classification into the four classes of 'sand', 'water', 'white-water' and 'other' is performed based on a Neural Network classifier algorithm trained on five training sites along the New South Wales coast; and (2) a sub-pixel resolution border segmentation based on the Modified Normalized Difference Water Index (MNDWI), which is widely used to discriminate water from land features in many applications  (Xu, 2006).
Finally, as per the waterline detection, all these errors are considered conservative due to the comparison window (< 10 days between the satellite image the beach survey used for comparison). Figure  10  shows the time series of shoreline deviation from the mean for the entire satellite image dataset, starting from April 21, 1984. Figure  10b  shows the results using our optimal approach, which is here disregarding hindcasted non-tidal residuals as they are not available prior to 2006 (and including these did not represent significant improvement anyway). The corresponding longterm trend using images with ηtr > 0. 2 m is 0. 50 m/yr, which is very similar to that computed for the 2009-2019 period (Figure  10a ). The long-term trend computed with shoreline is also similar (+0. 60 m/yr), while that using for images with ηt > 0.
Acknowledgments BC, VM and SB funded by  Agence Nationale de la Recherche (ANR)  grant number  ANR-17-CE01-0014 ; GM and TS funded by the  NERC   BLUEcoast  project ( NE/N015525/1 ); CS and AK funded by  Coastal and Marine Applied Research. This study includes the monitoring site of Truc Vert labelled by the  Service National d'Observation (SNO) Dynalit  (https://www. dynalit. fr). The  Observatoire de la Côte Aquitaine (OCA)  and  Observatoire Aquitain des Sciences de l'Univers (OASU)  provide additional financial support for the surveys. NORGAS-UG wave hindcast data provided by  LOPS-Ifremer. French Centre d'Archivage National de Données de Houle In Situ (CANDHIS)  buoy network operated by  French Centre d'Etudes et d'expertise sur les Risques , l'Environnement, la  Mobilité  et l'Aménagement (CEREMA).
Funding information The Bavarian Research Alliance (via the BayIntAn scheme) and the Bayreuth Center of Ecology and Environmental Research (BayCEER) funded the initial GrassPlot workshop during which the database was established and the current paper was initiated (grants to JDe). WU acknowledges support from the Polish National Science Centre (grant 2017/27/B/NZ8/00316). IB, JAC and IG-M were funded by the Basque Government (IT936-16). GF carried out the research in the frame of the MIUR initiative "Department of excellence" (Law 232/2016). SBa was supported by the GINOP-2. 3. 2-15-2016-00019 project. CM was supported by the Czech Science Foundation (grant no. 19-28491X) and the Basque Government (IT936-16). ID was supported by the Polish National Science Centre (grant DEC-2013/09/N/ NZ8/03234) and by a Swiss Government Excellence Scholarship for Postdocs  (ESKAS No.
Moreover, such grasslands occur under very diverse site conditions (e. g. from sea level to more than 5,000 m a. s. l. , from very wet to very dry sites) and management regimes (e. g. natural, semi-natural, intensified;  Dengler et al. , 2020b). Since Palaearctic grasslands are known to exhibit extreme variation in small-scale species richness, from monospecific systems to the world records in vascular plant species richness below 100 m²  (Wilson et al. , 2012; Dengler et al. , 2016a) , we expect that fine-grain β-diversity values will also cover a broad range. Here, we use the extensive GrassPlot database  (Dengler et al. , 2018) , which provides multi-scale species richness data of grasslands and other non-forested habitats across the whole Palaearctic biogeographic realm, to test how fine-grain β-diversity (measured as z-values of nested-plots SARs) is related to multiple potential drivers.
| Vegetation-plot data We used plot data from the collaborative vegetation-plot database GrassPlot  (Biurrun et al. , 2019; Dengler et al. , 2018 ; https://edgg. org/datab ases/Grass Plot) registered as EU-00-003 in the Global Index of Vegetation-Plot Databases (GIVD;  Dengler et al. , 2011). GrassPlot assembles vegetation-plot data, together with methodological, environmental and structural information from grasslands and other non-forest vegetation types (rocks and screes, deserts, ruderal communities etc. ) from the Palaearctic biogeographic realm. GrassPlot specifically collects multi-scale data sets from nested-plot sampling schemes (e. g. Dengler et al. , 2016b)  with areas from 0. 0001 to 1,024 m². We retrieved all nested-plot series from GrassPlot (v. 2. 04 on 20 March 2020) that contained at least four different grain sizes (4,654 series, consisting of 164,578 individual plots).
As biogeographic characteristics, we used two variables related to major biogeographic theories (latitude and elevation) and four major climatic variables (mean annual temperature, temperature seasonality, mean annual precipitation, precipitation seasonality). While latitude and most of the elevation data were provided by the original data set collectors, missing elevation data and the other four variables were derived from external sources using the plot coordinates (for details, see Appendix  S3 ). The stress-productivity variables refer to the stress-productivity axis of  Grime (1977; productivity in Huston, 2014) : We used soil pH and soil depth mean as soil-related stress measures, assuming a U-shaped relationship of stress with soil pH (nutrient uptake is limited at both high and low pH, with additional toxicity effects at low pH; see  Lambers et al.
8%. Reasons for the relatively low explained variance in fine-grain macroecological studies include the possible effects of other unmeasured factors, such as legacy effects, influences of the surroundings, and interspecific interactions, and a spatial mismatch between the environmental predictors (mostly derived from coarse-or at best medium-grain global databases) and the fine-grain biodiversity response variables. In this respect, analyses based on GrassPlot have the advantage that, unlike those in  Bruelheide et al. (2018; based on sPlot) , they contain numerous well-curated in-situ determined predictor variables (soil, microtopography, heterogeneity, land use, vegetation structure), which coincides with the relatively higher explained variance in our case. However, for climatic variables, we also had to rely on coarser-grain data, despite it being known that temperature can strongly vary across short distances, particularly in mountains  (Opedal et al.
for z-values andDengler et al. (2020a)  for shapes of SARs. From a statistical point of view multiple regressions, which analyse a multitude of predictors simultaneously, including potential interactions, might be considered advantageous. However, in our case such an approach would have drastically reduced the spatial coverage or forced us to restrict ourselves to those variables that can be retrieved from global databases at coarser grains instead of using our in-situ determined fine grain data. For the continuous variables (see AppendixS3), we used bivariate linear regressions to test for their potential influence on the z-values of the three taxonomic groups and complete vegetation. To account for potential hump-shaped or U-shaped relationships, we implemented a second-order polynomial function but removed the quadratic term if non-significant.
DATA AVA I L A B I L I T Y S TAT E M E N T The data used in this paper are derived from the collaborative vegetation-plot database GrassPlot  (Dengler et al. , 2018; Biurrun et al. , 2019) , version 2. 02. They can be requested from GrassPlot with a project proposal following the GrassPlot Bylaws (see https:// edgg. org/datab ases/Grass Plot).
Methods Study design and population. The data were extracted from the HELENA multicentric and cross-sectional study containing a total sample of 4356 adolescents (51. 6% females), aged 11-19 years old, from 10 European cities located in separated geographical points in Europe in 2006-2007. Their size of the cities was large enough to ensure participants diversity  23. The main objective of the HELENA study was to obtain comparable data of a large sample of European adolescents on nutrition and health-related parameters by a standardised procedure  24. More so, the study was performed following the ethical guidelines of the Declaration of Helsinki 1964 (revision of 2013), the Good Clinical Practice, and the legislation about clinical research in humans in each of the participating countries and was approved by the Ethics Committee of each city participating in the study  25.
DNA was extracted from white blood cells with the Puregene kit (QIAGEN, Courtaboeuf, France) and stored at -20 °C. The genotyping was done by an Illumina system (Illumina, Inc, San Diego, California) using the Golden-Gate technology (Sampling procedure scheme, GoldenGate; Software, Inc, San Francisco, California). In terms of gene selection within the HELENA study, a candidate gene approach was used. First, a certain number of behaviour and metabolic pathways related to the adolescence´s health were identified. These included included food intake, eating behaviour, food choices and preferences, energy and adipose tissue metabolism, glucose, insulin, lipid and lipoprotein metabolism among others. Finally, SNPs playing a key role in genes coding for the abovementioned pathways were selected.
The HapMap database was used to select tag and independent SNPs. SNPs were selected with a minor allele frequency (MAF) above 0. 1 and tag SNPs with r 2 above 0. 8. If tag SNPs described for a single gene exceeded in number (more than ~ 20), only SNPs significantly associated with appropriate phenotypes in previous studies were selected, if available. Lastly, SNPs from the NCBI database were used when a limited number of SNPs were available in the HapMap database. Statistical analysis. Descriptive characteristics by sex are shown as median and interquartile range (IQR) for continuous variables and as absolute and relative frequency for categorical ones. The statistical tests used to compare differences by sex were Pearson´s chi-square for categorical variables and Mann-Whitney-Wilcoxon test for continuous variables.
Development of the genetic risk score. Candidate gene approach was the procedure based to select the genes in the HELENA study. First, relevant adolescent´s behaviours and metabolic pathways related to health were identified. Second, key proteins that, according to the literature, play a role in these pathways were also identified. Third, a selection of SNPs coding for these proteins was performed. Fourth, in order to select and tag SNPs independently, the HapMap database (2007 release) was used. SNPs with a minor allele frequency (MAF) above 0. 1 and tag SNPs with r 2 above 0. 8 were selected. If too many tag SNPs described for a single gene (more than ~ 20) were identified, only SNPs significantly associated with appropriate phenotypes in previous publications were selected, if available.
Finally, SNPs from the NCBI database were included when a too limited number of SNPs were available in the HapMap database. Based on the above, a total of 611 SNPs related to obesity and obesity-related phenotypes available in the HELENA dataset  30  were used to build a GRS considering BMI as obesity-related variable in order to predict a major predisposition of overweight/obesity in European adolescents  24. Each SNP was recoded as 0, 1, or 2 depending on the number of risk alleles defined in previous literature, respectively. A further selection of SNPs was performed using generalised linear model (GLM) to establish an initial cut off point (p < 0. 20) to refine the search to 104 SNPs. Then, a step by step algorithm was applied to select the significant SNPs under the p < 0. 05 threshold in a multivariate model to shortlist a final number of 21 SNPs significantly associated with BMI.
e. , non-OW vs. OW/ OB). Selection of uGRS over wGRS to proceed with the design of the final model was performed by the higher value of the AUC compared using the Delong test. The model was internally validated performing tenfold cross validation analysis. For this analysis, the whole dataset was divided in 10 groups, using 9 of them to build the predictive model and the one to remains to validate this model. This procedure was repeated taking into account all possible ways to select the 9 subgroups, ensuring different forms to validate GRS with data not used in the building model process. Moreover, we evaluated the distribution of uGRS and wGRS values for NON-OW and OW/OB in a boxplot to graphically analyse the performance of the GRS. In order to provide the best cut-off for the use of the GRS as a dichotomic variable, the maximisation of the Youden index  33  was explored (see Table  3 ).
Supplementary Fig. 2  shows the calibration curves analysing the correspondence between probabilities of overweight and the real outcome. It can be observed that there is a good agreement between predicted and actual probabilities,thus, the panel of SNPs shows a good adjustment in order to predict OW/OB. Using the predictABEL R package  34  , from the multivariate logistic regression model built to predict OW/ OB, an uGRS and a wGRS were derived. The predictive ability of the GRS by means of the ROC curve, AUCs and Youden index of uGRS and wGRS models are displayed in Fig. 2. The results of the GRSs´ AUC (uGRS: 0. 7198,wGRS: 0. 7336) showed a moderate ability to discriminate OW/OB status. AUC´s comparisons displayed statistically significant differences between uGRS and wGRS to predict OW/OB (p = 0.
This finding could be due to the higher number of GRSs developed in other ethnicities in comparison to the number of GRS performed in European population. Also, we used the same data in the SNPs selection process and in the building model, thus little bias can be produced. Therefore, the results showed in the present study should be considered carefully. Further studies with larger sample size could provide key information of this potential genetic predisposition to obesity. On the other hand, the present study has also some strengths. The multicentric design of HELENA study involved the participation of adolescents from 10 European cities. This allowed the researchers to use a large database with relevant and diverse information from different populations across Europe. Additionally, only few GRSs to predict the overweight and obesity risk have been developed particularly in European adolescents, an understudied population from the early treatment and prevention perspective  21, 22.
Acknowledgements This work was part of the  HELENA Study  (http://www. helenastud y. com/). We gratefully acknowledge financial support of the  European Community Sixth RTD Framework Programme  (contract  FOOD-CT-2005-007034 ). The data for this study were gathered under the auspices of the  HELENA  project, and further analysis was additionally supported by the  Spanish Ministry of Economy and Competitiveness  (Grants  RYC-2010-05957  and  RYC-2011-09011 ), the  Instituto de Salud Carlos III ,  Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERObn). Miguel Seral-Cortes , the corresponding author, has received funding from the  Iberus Talent Pre -doctoral fellowships 2018, under the European Union's  H2020 research and innovation programme  under  Marie Sklodowska-Curie  grant agreement No  801586.
MV-NLPs resemble eukaryotic nucleosomes Single-particle cryogenic electron microscopy (cryo-EM) was used to visualize crosslinked MV-NLP 207. GraFix was required as the majority of untreated MV-NLPs dissociate during plunge freezing. Raw images and 2D classes show that MV-NLPs share many characteristic features of eukaryotic nucleosomes (Figure  S3A ), with defined segments of extending linker DNA visible in many classes. After 3D classifications and refinement, we obtained a structure of the MV-NLP 207 at 6. 1 A ˚(Figure  S3A ; Table  S3 ). A second dataset obtained with GraFix-treated particles reconstituted onto 147 bp DNA (MV-NLP 147 ) yielded an improved overall resolution of $4. 0 A Table (Figure  S3B ; Table  S3 ). In both structures, electron density for the DNA and histone helices are clearly distinguishable (Videos S1 and S2), which allowed the assignment of $120 bp of bound DNA and all histone chains (Figure  4A ; Video S3).