dataesr/scientific-paragraphs-categorization huggingface dataset:
We considered, as outliers, the genome windows presenting either a H P or an F ST value above the 99. 95th percentile of the related distribution. This led to the identification of 12 genome windows for each pool. Data were graphically represented via Manhattan plots. Pipelines were developed either in Python or in R ("manhattan" function of the "qqman" library  [61] ). Genomic windows were computed with Bedtools v. 2. 17. 0  [62].
Annotation of genome windows Each 100-kb window that displayed signatures of selection was annotated with the Bedtool v. 2. 17. 0 program by retrieving annotated protein coding genes from the Sscrofa11. 1 NCBI's GFF file. Moreover, we extended the annotation by including the genes that were located in the ± 200-kb flanking regions of each window. Allele frequencies of SNPs within each extended genome window were plotted in R using the libraries "gplots" (function: heatmap. 2) and "Sushi"  [63]. The functional relevance of the genes annotated in regions of signatures of selection was evaluated based on a detailed analysis of the scientific literature and Gene Cards information  [64]. Moreover, gene enrichment analysis over sets of human traits was carried out with Enrichr  [65]  via Fisher's exact test.
In total, 26,732,468 high-quality SNPs (autosomes and scaffolds) were used in further analyses. Summary statistics for these variants are in Additional file 1: Table  S3. A Ts/Tv ratio of 2. 40 was found for the sequence dataset. VEP annotation of the detected SNPs is summarized in Additional file 1: Table  S4. In total, 44,784,029 SNPannotation pairs (several annotations per SNPs were possible) were retrieved, covering 22,165 genes out of the 22,452 annotated genes on the Sscrofa11. 1 genome version. As expected, the largest number of SNPs was found in introns (~ 62%) and in intergenic regions (~ 35%). SNPs that impacted the gene at the protein level (i. e. start/stop gained/lost and missense SNPs) accounted for only 0. 34% of all point mutations. Allele frequencies estimated from DNA pool sequencing and SNP chip genotyping presented an average Pearson's correlation coefficient r = 0.
Comparative F ST analysis between domestic (local) breeds and wild boars Wild boar whole-genome resequencing data were compared first with data from all domestic breeds. Signatures of selection were identified on SSC1, 7, 8, 13 and 15 (see Additional file 1: Table  S14  and Additional file 2: Figure  S7 ). The SSC1 regions were close to two major genes, already mentioned above (MC4R and NR6A1; Fig. 6a ). Two signatures of selection were located on SSC7 (Fig. 6b, c ): one close to TCC6 and FOXA1, which were also identified in the single population analysis, and to SSTR1, which is involved in the metabolite levels of the 5-HIAA/MHPG ratio  [98]. The second SSC7 region contains the SUPT16H gene, which encodes a component of the chromatin transcription (FACT) complex and has been suggested to be involved in transcriptional suppression during virus infections and thus in the promotion of virus latency  [99] , The SSC8 window (Fig.
Availability of data and materials Sequence data generated and analysed in the current study are available in the EMBL-EBI European Nucleotide Archive (ENA) repository (http://www. ebi. ac. uk/ena), under the study accession PRJEB36830. SNPs were submitted to the EMBL-EBI European Variation Archive (EVA) under the study accession PRJEB36974, analyses ERZ1302242. The datasets generated and/or analysed during the current study are available in the Zenodo repository, https ://doi. org/10. 5281/zenod o. 36705 60, and from the corresponding author on reasonable request.
Funding SB received a fellowship from the Europe-FAANG COST Action. This work received funding from the University of Bologna RFO 2016-2019 programme and from the European Union's Horizon 2020 research and innovation programme under Grant agreement No. 634476 for the project with the acronym TREASURE. The content of this article reflects only the authors' view and the European Union Agency is not responsible for any use that may be made of the information it contains.
Acknowledgments We gratefully acknowledge funding from the  Ethics and Governance of Artificial Intelligence Initiative of the Miami Foundation  (DGR and GP), the  William and Flora Hewlett Foundation  (DGR and GP), the  Social Sciences and Humanities Research Council of Canada  ( GP ),  ANR  grant  ANR-17-EURE-0010  ( Investissementts d'Avenir program ,  BB ),  ANR Labex IAST (BB)  and the  Scientific Research Fund Flanders (FWO-Vlaanderen, BB).
However, considering only sea ice fragmentation is not sufficient for representing the full complexity of wave-sea ice interactions. In parallel, progress has also been made regarding the inclusion of the effects of waves in coupled ocean-sea ice models. Using a very simple parameterization,  Steele et al. (1989)  and  Perrie and Hu (1997)  have investigated the ef-fect of WRS on sea ice drift in the MIZ, only considering the attenuation of waves generated between the ice floes, and found a limited impact on the sea ice conditions. More recently,  Williams et al. (2017)  implemented a wave module in the semi-Lagrangian sea ice model neXtSIM  (Rampal et al. , 2016)  and found that high-wave conditions can cause a significant displacement of the sea ice edge. The implementation of FSDs in different sea ice models, as introduced by  Zhang et al.
Lateral melt A parameterization to account for the sea ice lateral melt is already implemented in LIM3. Its formulation follows  Steele (1992) : dc dt = -w lat π α D c, ( 9 ) where c is the sea ice concentration; w lat is the lateral-melt rate, which depends on the difference between sea ice and sea surface temperatures taken from  Maykut and Perovich (1987) ; and α is a coefficient which varies with the floe geometry. By default, α = 0. 66, which is the average value of the non-circularity of floes obtained by  Rothrock and Thorndike (1984). By default, D , which represents the average floe size, is a function of the sea ice concentration obtained empirically from observational data by  Lüpkes et al. (2012) : D = D min c * c * -c β , (10) where β = 1 and c * is introduced to avoid a singularity at c = 1 and is defined as c * = 1 1 -(D min /D min ) 1/β.
Acknowledgements. Part of this work has been carried out as part of the  Copernicus Marine Environment Monitoring Service (CMEMS) ArcticMix and WIzARd  projects. CMEMS is implemented by Mercator Ocean in the framework of a delegation agreement with the  European Union. We thank  Martin Vancoppenolle  for his valuable help as well as  Verena Haid  and  Xavier Couvelard  for their significant assistance in setting up the coupled framework. Financial support. This research has been supported by the  DGA , the  ANR  (grant nos. ANR-14-CE01-0012   MIMOSA  and  ANR-10-LABX-19-01 ), the  EU-FP7  (grant no. SWARP 607476 ), the  ONR  (grant no. N0001416WX01117 ), and the  CMEMS  (grant projects  ArcticMix and WIzARd). Review statement. This paper was edited by  Daniel Feltham  and reviewed by three anonymous referees.
We apply and compare three widely applicable methods for estimating ecosystem transpiration (T) from eddy covariance (EC) data across 251 FLUXNET sites globally. All three methods are based on the coupled water and carbon relationship, but they differ in assumptions and parameterizations. Intercomparison of the three daily T estimates shows high correlation among methods (R between. 89 and. 94), but a spread in magnitudes of T/ET (evapotranspiration) from 45% to 77%. When compared at six sites with concurrent EC and sap flow measurements, all three EC-based T estimates show higher correlation to sap flow-based T than EC-based ET. The partitioning methods show expected tendencies of T/ET increasing with dryness (vapor pressure deficit and days since rain) and with leaf area index (LAI). Analysis of 140 sites with high-quality estimates for at least two continuous years shows that T/ET variability was 1.
Partitioning the existing ET from FLUXNET would improve cross site comparisons of GPP to T dynamics, which have previously relied on filtering each site for periods after rain events to minimize the effect of E. Here we applied three recent methods for estimating T from EC datasets: the underlying water use efficiency (uWUE) method  (Zhou, Yu, Zhang, Huang, & Wang, 2016) , the Pérez-Priego method  (Perez-Priego et al. , 2018) , and the Transpiration Estimation Algorithm (TEA) method  (Nelson et al. , 2018). We focused on methods which utilize current EC datasets, such as FLUXNET and the associated regional networks, which include continuous measurements of CO 2 , sensible, and latent heat fluxes, as well as meteorological variables at half-hourly or hourly time steps. All three methods utilize GPP estimates to partition E and T from ET, as CO 2 uptake and water vapor losses from T are both regulated via stomata in higher plants and are thus inherently linked  (Cowan & Farquhar, 1977).
Note that other ET partitioning methods exist, including methods that only use EC datasets, which are not highlighted here. Such methods include  Scott and Biederman (2017) , which may not be applicable at non-water-limited sites, and  Li et al. (2019) , which requires ancillary data such as canopy height and soil moisture. As reviewed in  Anderson, Zhang, and Skaggs (2017) , other methods for estimating T are being developed, such as flux variance partitioning of high frequency data using water use efficiency (WUE) measured at the leaf scale  (Scanlon & Kustas, 2010; Scanlon, Schmidt, & Skaggs, 2019) , measurement of isotopes  (Berkelhammer et al. , 2016; Wang, Good, Caylor, & Cernusak, 2012) , carbonyl sulfide  (Whelan et al. , 2018) , or concurrent below and above canopy EC measurements  (Paul-Limoges et al.
| Objectives Here we present estimates of ET partitioning from three different methods across FLUXNET, providing a dataset of T estimates at ecosystem level from sub-daily to annual values and covering many climate zones and biomes. These three methods are first compared against each other to identify how well they agree. The partitioning methods are then compared to an independent data source (scaled-up sap flow measurements), both to demonstrate absolute performance and to ensure that T estimates are adding information compared to the original ET estimates. After initial assessment, we examine the T estimates for expected patterns, such as the seasonal covariation of LAI and T/ET as well as responses to dry conditions such as the expected decrease of WUE to high VPD and the increase of T/ET during dry down events.
| EC data Flux data from the FLUXNET2015 dataset  (Pastorello et al. , 2017)  were used. In some cases, sites were included from the previous La Thuile dataset when not available in the FLUXNET2015 release. An overview of the variables used in this study can be found in Table  S1. In all cases, GPP was estimated from the EC-measured NEE using the night time flux partitioning method  (Reichstein et al. , 2005). The sites used in this study are distributed widely across the globe and they represent diverse ecosystems from a variety of climatological conditions. However, the global distribution of observations is largely biased toward Western countries in the Northern Hemisphere, with most of the sites located in USA, Western Europe, and East Asia. All sites, as well as the plant function type (PFT) designation are listed in both table and map form in File S1.
Finally, the TEA method utilizes a nonparametric model, a version of Random Forest  (Breiman, 2001) , to predict WUE (GPP/T). The model is trained on the ecosystem WUE (WUEeco = GPP/ET) during periods in the growing season and when surfaces are likely to be dry, that is, where E/ET should be minimal. Periods likely to have wet surfaces are filtered based on precipitation input and ET in a shallow bucket, water balance scheme (see  Nelson et al. , 2018  for a full description). The RF, trained on WUEeco from the filtered periods, then predicts WUE (now GPP/T) for the full time series. To further compensate for the existence of E in the training dataset, a higher prediction percentile of WUE is output from the RF  (Meinshausen, 2006). Nelson et al. (2018)  determined that the 75th percentile was the most appropriate prediction percentile based on the best performance when assessed against synthetic data from three terrestrial biosphere models.
| Sap flow estimates Stand T was obtained by upscaling sap flow measurements (T SF ) from six forest sites in the SAPFLUXNET database  (Poyatos et al. , 2016)  which overlapped in time with the FLUXNET2015 dataset (Table  S2 ). SAPFLUXNET datasets contain sub-daily sap flow rates, scaled to the tree level according to site-specific procedures, which are documented within the dataset metadata  (Poyatos et al. , 2019). In order to obtain stand-level T, we first temporally aggregated the data to daily sap flow values per tree (kg/day) and retained only those days with sufficient coverage (80% of the sub-daily time steps). We then normalized sap flow per unit basal area of each tree and averaged the values for each species present in the datasets. In all datasets, the species in which sap flow was measured represented >90% of the stand basal area (Table  S2 ).
The value of species-specific sap flow per basal area was multiplied by the basal area of each species in the stand and then data from all species were summed to obtain stand-level T (mm/day). All the tree and stand-level variables needed for the upscaling were extracted from the metadata corresponding to each dataset  (Poyatos et al. , 2019). Tucker, 1979) , enhanced vegetation index (EVI;  Huete et al. , 2002) , and normalized difference water index (NDWI;  Jin & Sader, 2005). Low-quality Landsat pixels due to clouds, cloud shadows, snow, and ice were masked out  (Zhu, Wang, & Woodcock, 2015; Zhu & Woodcock, 2012). Finally, 4 day values (as the best pixel from a 4 day window) of LAI from the MCD15A3H version 6 MODIS product  (Myneni & Knyazikhin, 2015)  were used to analyze the relationship of LAI to T/ET (Figure  3 ).
| Gridded and remote sensing data For both Landsat and MODIS products, the data extraction and the preprocessing chains (i. e. , cloud, cloud shadow masking, and downloading) were implemented in the Google Earth Engine platform  (Gorelick et al. , 2017 ; https://earth engine. google. com/). Landsat (i. e. , NDVI, EVI, and NDWI) and MODIS (i. e. , MCD15A3H LAI) data were summarized for each site using the mean and 95th percentiles. Additionally as spatial covariates of T/ET, five soil properties for each site were estimated using the SOILGRIDS dataset  (Hengl et al. , 2017) : coarse fragment volume, soil pH, and percent of clay, sand, and silt. Soil properties were summarized for each site using a weighted mean for the full depth available. In all cases, spatial data were aggregated from an area within ≈1 km of the tower location by taking the mean for all good quality pixels in the selected area.
, 2005)  and day time  (Lasslop et al. , 2010)  based GPP, the resulting two T estimates have a higher correlation than the two GPP products do to each other. In other words, T estimates are consistent even when the underlying GPP estimate has changed. This is likely due both to averaging aspects of the methods (e. g. , parameters estimated in moving windows in the uWUE and Pérez-Priego methods) and that GPP is not directly used (e. g. , Pérez-Priego calculates T from stomatal conductance and VPD) or canceled out in the final step of T estimation (e. g. , TEA estimates T as GPP divided by predicted WUE thus canceling out GPP, i. e. , T = GPP (GPP/T) -1 , similar for uWUE). However, systematic biases in GPP and ET can affect the ET partitioning methods in different ways. For example, Figure  S3  shows the response of the estimated T after applying each method to a dataset with manipulations to either GPP or ET fluxes.
Remote sensing-derived variables come from either 1 LANDSAT, 2 TIP, or 3 MODIS (see Section 2 for a full description). Plant function type designations are site reported. Climate data come from site measurements. Soil data were derived from the SOILGRIDS dataset  (Hengl et al. , 2017)  the peak growing season have the highest impact on estimated T. Aside from GPP and ET, all three T estimation methods integrate many measurements and therefore require not only high EC measurements, but also high-quality radiation, temperature, humidity, wind, and precipitation data.
02. 1. 01/0. 0/0. 0/16_019/0000797). G. W. acknowledges support by the Austrian National Science Fund (FWF, project I03859) and the Province of South Tyrol ("Cycling of carbon and water in mountain ecosystems under changing climate and land use"). R. P. was supported by grants CGL2014-55883-JIN, RTI2018-095297-J-I00 (Spain), and by a Humboldt Research Fellowship for Experienced Researchers (Germany). This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: Ameri-Flux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The ERA-Interim reanalysis data are provided by ECMWF and processed by LSCE. The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster, AmeriFlux Management Project, and Fluxdata project of FLUXNET, with the support of CDIAC and ICOS Ecosystem Thematic Center, and the OzFlux, ChinaFlux, and AsiaFlux offices.
g. , in France) are compulsory, which reinforces the interest in studying men-mandatory conscription. Our paper is the first to formally investigate the political consequences of such a national experiment. A few papers have considered the impact of veteran status on political attitudes in the United States (US), 1  but French conscripts differ in several dimensions. In particular, US veterans directly participated in conflicts, and the results could be influenced by combat exposure. Moreover, the United States relies on an all-volunteer military, implying that veterans are from a specific subsample of the population. In the first part of the paper, we study the impact of military service on turnout. We use data on turnout collected during the presidential and legislative elections in 2002, 2012 and 2017.
Our identification strategy exploits the variation in compliance with the military service across cohorts. We collected data from the archives of the Ministry of Defense and computed the share of individuals who completed the service for each year-of-birth cohort born after 1952. The military service was compulsory for males born before 1979; thus, younger cohorts did not have to serve. The variation of the rate of compliance with military service is mostly driven by exogenous political decisions, namely, the suspension of mandatory conscription announced in 1996. The main challenge of the identification strategy is disentangling the effect of military service from other cohort effects. We propose two identification strategies. The first approach is restricting the sample to males and controlling for a linear function of year of birth in addition to the share of compliance with the military service.
Who are the conscripts? We collected data from the archives of the office of the French Ministry of Defense in charge of military service (Direction du service national), including detailed yearly information on men who completed military service. We collected data on the age of conscripts from 1970 to 2001. We merged these data with the census to determine the share of each birth cohort that complied. Thus, we could compute the probability of compliance for males born in 1952 and after ( Fig. 1  ). For cohorts born before 1973, the share of men who completed their service is approximately 70%. This share declines for cohorts born in 1974 and later. After the announcement of the suspension of military service, the number of conscripts In principle, military service was mandatory for all French men.
Turnout Our data on turnout are from the datasets "Study on Electoral Participation" collected by the French Statistical Institution (INSEE) in 2002, 2012, and 2017. 6 For each study, a representative sample of 40,0 0 0 individuals is drawn from the census. The sample is renewed for each wave. We merged the data from the three bases and removed the individuals born before 1952 because we could not compute their probability of compliance with service. The sample collected was 82,266 individuals 7  (in the first part of the analysis, the number of observations is much smaller because we aggregated the data at the cohort level). Our data include information on individual characteristics from the census (e. g. , sex, age, education, occupation, marital status) but not information related to military service.
Political preferences Very few surveys have included both data on political preferences and on military service. After the suspension, all sources collecting data on political behavior stopped including questions on military service. Therefore, we introduced a question on this topic in the French Electoral Survey conducted in 2017 by  Gougou and Sauger (2017). This survey comprised 1830 face-to-face interviews and included a wide range of questions related to political preferences. Approximately one fourth of all respondents had been conscripts. We report the translation of all questions in the supplementary material available online. We summarize the variables used for the analysis of political preferences in Table  3. 8.
Military service and turnout In this section, we demonstrate that military service has a large and positive impact on turnout. Fig. 2  provides a graphical intuition of the main result of the paper. We pooled our three INSEE databases on turnout and computed the average participation of each individual for a given election year. As a result, our variable takes a range of values from 0 for individuals who abstained from all elections to 1 if they participated in the four elections 9 Next, we plotted the average of this variable by sex and birth cohort ( Fig. 2  ). The red vertical line in Fig. 2  represents the last cohort for which men had to fulfill military obligations (1978 cohort). The greyed-out region indicates the cohorts from 1975 to 1978 for which we observed a graduate decrease in compliance in Fig.
This specification does not impose a specific trend in year of birth but assumes that cohort effects are the same for men and women. We begin the analysis with the first identification strategy where we focus on males in the INSEE databases on turnout. We consider the average turnout computed at the cohort level for each election year  (2002, 2012, and 2017)  and estimate the following specification: Ȳ ct = α + β S c + δ 1 Age ct + δ 2 Cohort + δ 3 Election _ Year f e + ct. (1) In Model 1 , the dependent variable is the average turnout of males in birth cohort c ∈ { 1952 ,. , 1999 } for election year t ∈ { 2002 , 2012 , 2017 }. In this aggregated approach, the parameter of interest β is the effect of the service rate S c computed for each male cohort with the data of the army ( Fig. 1  ). We control for cohort effects with a smooth function of year of birth.
Accounting for these effects would require controlling for year of birth by sex fixed effects, but the impact of military service cannot be identified in such a specification. Next, we depart from the estimation strategy of  Bedard and Deschênes (2006)  and  Imbens and Van Der Klaauw (1995)  and exploit the individual structure of our data to control for those alternative explanations. We estimate the following model: y i = α + β S sc + δ 1 Age f e + δ 2 Male × Age f e + δ 3 Cohort f e + δ 4 Election _ Year f e + δ 5 X i + i. (5) Instead of aggregating the data at the cohort level, we consider individual turnout as the independent variable to control for X i , the individual characteristics available in the INSEE databases. The 2002 election dataset does not contain most Ȳ sct = α + βPlacebo k + δ 1 Age fe + δ 2 Male × Age fe + δ 3 Cohort fe + δ 4 Election _ Year fe + sct for k ∈ { 1960 ,.