dataesr/scientific-paragraphs-categorization huggingface dataset:
LecoS (Landscape ecology Statistics) is a plugin developed by  Jung (2016)  to calculate the spatial metrics usually employed in the field of landscape ecology. Although other methods can be implemented in QGIS to calculate these metrics, the LecoS plugin is the best-known QGIS tool for this purpose. All the relevant information about the plugin is available at the official website (see Table  1 ). The R Processing Provider allows the R software capabilities to be integrated into QGIS. Full documentation on the plugin is available at the official website (see Table  1 ). Users can also find extra information on the plugin and the way the R language can be integrated into QGIS in the official documentation on QGIS. 1  To find out more about how to integrate R into QGIS, users should consult Sect.
Integrating R into QGIS Some of the exercises presented in this book use R, a free, open-source statistical software. QGIS enables the R environment to be integrated into the software, making it easier for any QGIS user to take full advantage of the tools available through R. QGIS does not have the required tools to compute all the validation tools and methods that have been reviewed in this book. We have therefore had to implement some of them in QGIS through the R processing environment. Users wishing to find out more about R and its integration into QGIS, with practical exercises about how to use both software packages in combination, should consult the manual by  Islam (2018). To integrate R into QGIS, users must begin by downloading the R software. R and any of its associated data can be downloaded from a comprehensive file network, from which users must select the mirror closest to their location at https://cran.
r-project. org/mirrors. html. Once downloaded and installed, users must also install a series of packages in R to execute the different tools and methods included in the book (Table  2 ). This step cannot be carried out through the QGIS interface. Users must open R and manually install the different packages. To do this, select Packages > Install Package(s)… from the menu (Fig. 3 ). In the window that opens, select the mirror from which to download the packages (Fig. 4 ). Finally, select the package to be installed (Fig. 5 ). Installation of the package may take a little while to complete. Installation is complete when the R console allows the user to write new code (Fig. 6 ). Table  2  lists the packages required to do the different exercises appearing in this book. In the table, next to each package name, we offer a link to the website with all the information about the package: description, download link, reference manual, etc.
The landscapes are now made up above all of mosaics of agricultural lands, cattle pastures and human settlements. Detailed information on the LUCC modelling exercises developed for the Asturias Central Area and Ariège Valley can be found in studies by  García-Álvarez et al. (2019)  and  Bounoua and Le Campion (2019). The LUC maps for these two areas were obtained from two different datasets: COR-INE Land Cover, SIOSE. The LUC map for the Marqués de Comillas municipality was obtained through the classification of satellite imagery. We will now briefly describe the LUC datasets and maps that form part of the database for each study area. At the end of this section, there is a table with all the files used in this book. CORINE Land Cover (CLC) is a pan-European dataset of LUC information available for five different dates from 1990 to 2018.
It provides detailed, coherent LUC information for most of the countries in Europe. It is usually carried out by photointerpretation in vector format at a scale of 1:100,000, with a Minimum Mapping Unit (MMU) of 5-25 ha and a Minimum Mapping Width (MMW) of 100 m. Detailed information about this dataset can be found in Chap. " General Land Use Cover datasets for Europe" of this book. A simpler version of CLC is used in the Ariège Valley (Fig. 11 ) and the Asturias Central Area (Fig. 12 ) case studies. In the latter, CLC is available in both vector and raster format. Although CLC is officially distributed in raster format at a spatial resolution of 100 m, the CLC rasters for the study areas in this book are provided at a different spatial resolution: 50 m for Asturias and 15 m for Ariège. These rasters were obtained after rasterizing the CLC vector layers.
SIOSE (Sistema de Información sobre Ocupación del Suelo de España) is a Spanish dataset in vector format that provides very detailed LUC information. It was obtained by photointerpretation of aerial imagery at 1:25,000, with a MMU of 0. 5-2 ha and a MMW of 10 m. It follows a specific data model aimed at objects, which means that all the land uses and covers in a polygon are described by a specific code. This means that instead of being assigned to a specific LUC category, each polygon is described by a code detailing its LUC composition. Some of the maps in the Asturias Central Area case study were obtained after simplification of the SIOSE database. The maps were obtained after the classification of each SIOSE polygon into a single category and after the rasterization at 50 m of the original vector dataset (Fig.
12 ). More information on how this operation was performed can be found in  García-Álvarez (2018). Extra information about the characteristics of SIOSE can be found in  Valcárcel et al. (2008)  and  García-Álvarez and Camacho Olmedo (2017). The Marques de Comillas LUC map (Fig. 13 ) is part of a database on Land Cover and Land Cover/Land Use Changes The maps were computed via a supervised classification of 2019 Sentinel-2 imagery. They were subsequently photo-interpreted to correct errors from the supervised stage as well as to include information on agricultural land uses. The map contains eight thematic categories describing levels of forest conservation, and other land uses; the approximate scale is 1:40,000, with an MMU of one ha. More information can be found at the following link: https:// bosqueschiapasdemo.
List of repositories and web portals distributing LUC information at global and supra-national scales Web portal Description Copernicus Land Monitoring Service Web portal for the thematic land monitoring service provided by the Copernicus programme. It offers information on land monitoring at global, pan-European and local scales for the European Union FAO GeoNetwork Catalog of spatial datasets developed and maintained by the Food and Agriculture Organization (FAO). It has a specific section on LUC information Geo-Wiki Platform developed to collect LUC information via crowdsourcing. Its viewer hosts different LUC datasets, either from external projects or developed through the Geo-Wiki project and similar crowdsourcing approaches Google Earth Engine Catalogue of spatial datasets to be used as part of Google Earth Engine.
There is a specific section on LUC datasets Land Processes Distributed Active Archive Center (LP DAAC) Archive of spatial data managed in partnership by the USGS and NASA, which distributes most of the information produced by these institutions Wekeo Copernicus Data and Information Access Service (DIAS), which provides a cloud-based platform to access and process Copernicus data. It includes a catalogue of data with many LUC products produced within the context of the Copernicus programme FROM-GLC Web portal developed and maintained by experts from Tsinghua University, which distributes all the LUC datasets produced by the team associated with this university since the FROM-GLC project.
rst Simulation CORINE Asturias Central Area 2011 Simulation_C11_SIOSEDemands. rst Simulation CORINE 2 Asturias Central Area 2011 Simulated_changes_CORINE_05_11. rst Simulated CORINE changes Asturias Central Area 2005- 2011 C_Suitability _factor_Urban_Fabric. rst Urban fabric suitability map -CORINE model Marqués de Comillas Projection: WGS84/UTM 15N (EPSG: 32615) Associated files: Marques_LUC_datasets (Word document file): dataset description and legend File name Name in the book LandCoverMarques2019. tif Marqués de Comillas Land Use Land Cover Map 2019 RandomSample_Buffer. shp Photo-interpreted reference dataset -Marqués de Comillas Ariège Valley (Val d'Ariège) RandomSample. shp 2019 Centroids of sample sites -Marqués de Comillas random_sample_points. shp Marqués de Camilla random sample points from Mexico (2019) Associated files: BD_Val_Ariege (Word document file): Projection: WGS84/UTM 31N (EPSG: 32631) limit.
shp Boundaries of Marqués de Comillas study area explanation and legend 6 Review of Land Use Cover Datasets File name Name in the book CORINE Land Cover CLC_2000 CORINE Land Cover Map Val d'Ariège 2000 CLC_2012 CORINE Land Cover Map Val d'Ariège 2012 CLC_2018 CORINE Land Cover Map Val d'Ariège 2018 Model Drivers Roads_dist Distance to roads Simulation output CLC_predict_2018 Simulation LCM Val d'Ariège 2018 CLC_predict_2018_soft_UTM Soft prediction LCM Val d'Ariège 2018 00_12_18_transition_2_to_1 Transition potential map from agricultural to artificial areas 00_12_18_transition_3_to_1 Transition potential map from forests to artificial areas Markov18_class1_utm Markovian probability map for artificial areas Ariège Valley.
A random sample of 1000 HCW from the Human Resources department database was extracted to identify the participants. Selected HCW were contacted telephonically following the list order. After explaining the study and assess the inclusion and exclusion criteria, the participants were invited to participate. After that, interested participants signed the informed consent. In case of no participation, the reasons were recorded. Eligible participants on quarantine were visited at their homes by study personnel. After informed consent was obtained, relevant demographic, clinical and epidemiological information were collected in the standardized case report form (CRF) and samples were also collected (oral swabs and blood depending on the study visit). Exclusion criteria included: (a) absenteeism from workplace in the last 30 days (i.
At the M9 visit, 64 participants had already received one dose of the BNT162b2 (Comirnaty, Pfizer/BioNTech) or mRNA-1273 (Spikevax, Moderna) mRNA vaccines, with various times post vaccination. By M12, most of the participants had already received two doses of either vaccine and were invited to come for a cross-sectional visit 2 weeks (window 12-19 days) after the second dose was administered (N=342, BNT162b2 N=271, mRNA-1273 N =71). Finger prick blood was collected from the subset who visited at M9, and 10 mL of venous blood was collected from all participants at M12. Plasma was isolated and cryopreserved at -80°C. Data on the vaccination dates, COVID-19 infection and symptoms-confirmed by the Occupational Health department at Hospital Cl ınicwere retrospective collected. Self-reported related adverse events (AEs) were recorded at the time of recruitment.
Information on new SARS-CoV-2 infection episodes until 6 months after vaccination (M18) were also collected through the Occupational Health department at the HCB. Demographic data and information on comorbidities (heart and liver disease, diabetes, chronic respiratory and renal disease, cancers and autoimmune and other immunological disorders), chronic medication and smoking habits had been previously collected. Data for each participant were collected and managed using REDCap version 8. 8. 2 hosted at ISGlobal through a standardized electronic questionnaire as previously described. 38 REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies,  41, 42  providing 1) an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for data integration and interoperability with external sources.
Neutralizing antibodies For feasibility reasons, we selected 163 samples from the study visit M12 with a balanced representation of BNT162b2 and mRNA-1273 vaccinees and non-vaccinated participants (previously exposed and naive individuals) (Table  S1 ). We already had pre-vaccination neutralization data from 33 of the selected 163 individuals. 39 Plasma neutralizing capacity was assessed as the percentage of inhibition of RBD binding to ACE2 receptor and was measured through a flow cytometricbased assay that correlates with a validated pseudovirus neutralization assay. 39 Briefly, a murine stable cell line expressing the ACE2 receptor was incubated with RBD-mFc fusion protein, composed of RBD fused to the Fc region of murine IgG1, previously exposed to the different plasma samples at a 1:400 dilution.
Analysis of antibody levels after the second dose included only data from samples collected 12-19 days after vaccination, while for the first dose we included data from all samples collected 7 or more days after vaccination, as no previous visit window was established. Groups were compared using the Wilcoxon Sum Rank test for continuous non-parametric variables and with the Wilcoxon Signed-Sum Rank Test for paired continuous data. Correlations between continuous variables were analyzed using linear regression models and Spearman's rank test. Locally estimated scatterplot smoothing (LOESS) plots were used to visualize trends in antibody levels over days post vaccination, days postsymptom onset (PSO) or post rRT-PCR diagnosis. Univariable and multivariable linear regression models were fitted to assess factors associated with antibody responses to SARS-CoV-2 RBD and S antigens and their neutralization capacity (%) after vaccination among exposed and naive individuals, and overall.
Predictor variables that had a P-value of 0¢2 or lower in the univariable models were selected for stepwise multivariable models performed with the function stepAIC (R package MASS). The b obtained in each model for each of the predictor variables were transformed into a percentage of antibody increase for easier interpretation. For continuous log 10 -transformed variables (log-log model) the b transformed value (%) was calculated with the formula ((10( b*log 10 (1. 1)))-1)*100. This represents the effect (in percentage) on IgG levels of a 10% increase in the corresponding predictor variable. For categorical predictor variables (log-linear models), the b transformed value (%) was calculated with the formula ((10^b)-1)*100. This gives the difference (in percentage) in IgG levels between the reference and the study group.
Characteristics of study participants From the 578 participants recruited at baseline, 446 came to visits at M9 and/or M12, with 414 participants sampled at M12 (Figure  1 ). We measured the levels of IgA, IgG and IgM to SARS-CoV-2 antigens in blood samples from both visits. Of the 414 HCW visited at M12, 360 (81%) had received one (N= 18, 1 BNT162b2 and 17 mRNA-1273) or two doses (N= 342) of the mRNA vaccines. Seventy-six percent of the 360 HCW received BNT162b2 and 24% mRNA-1273 (Table  1 ). Most of the study participants were females (73%) and had a mean age of 42¢7 (SD 11¢65) years. Around 20% had underlying comorbidities and 22% were under chronic medication (Table  1 ). Thirty-two per cent of all participants and 22% of those vaccinated had previously been infected by SARS-CoV-2 according to rRT-PCR or serology data (Table  1 ).
Acknowledgments We thank the participation of HCW who are committed to this study and are key personnel facing the pandemic. We are grateful to  Pau Cister  o,  Chenjerai Jairoce ,  Selena Alonso ,  Rebeca Santano ,  Sarah Williams ,  Montserrat Lamoglia ,  Neus Rosell ,  Angeline Cruz , Eug enia Ch oliz, Ant ıa  Figueroa-Romero ,  Mikel J. Mart ınez ,  Patricia Sotomayor  and  Sara Torres  who participated in the field and/or laboratory work during previous visits. We also thank the administrative department in ISGlobal, and  Gemma Ruiz-Olalla , and  Sergi Sanz  for statistical advice.
Funding This work was supported by  Institut de Salut Global de Barcelona (ISGlobal)  internal funds, in-kind contributions from  Hospital Cl ınic de Barcelona , the  Fundaci o Privada Daniel Bravo Andreu , and  European Institute of Innovation and Technology (EIT) Health  (grant number  20877 ), supported by the  European Institute of Innovation and Technology , a body of the European Union receiving support from the  H2020 Research and Innovation Programme. We acknowledge support from the  Spanish Ministry of Science and Innovation and State Research Agency  through the " Centro de Excelencia Severo Ochoa 2019-2023" Program  ( CEX2018-000806-S) , and support from the  Generalitat de Catalunya  through the  CERCA Program. L. I. work was supported by  PID2019-110810RB-I00  grant from the  Spanish Ministry of Science & Innovation.
Natural selection on floral scent composition is a key element of the hypothesis that pollinators and other floral visitors drive scent evolution. The measure of such selection is complicated by the high-dimensional nature of floral scent data and uncertainty about the cognitive processes involved in scent-mediated communication. We use dimension reduction through reduced-rank regression to jointly estimate a scent composite trait under selection and the strength of selection acting on this trait. To assess and compare variation in selection on scent across species, time and space, we reanalyse 22 datasets on six species from four previous studies. The results agreed qualitatively with previous analyses in terms of identifying populations and scent compounds subject to stronger selection but also allowed us to evaluate and compare the strength of selection on scent across studies.
| Theory: phenotypic-selection analysis with reduced-rank regression Reduced-rank regression  (Anderson, 1951; Izenman, 1975)  achieves dimension reduction in multivariate problems by projecting an original set of covariates onto a reduced set of composite variables that best explains variance in the response variable. In selection analysis, this translates into the reduced set of phenotype axes that best explains relative fitness and, thus, is under selection. In the following analyses, we used the Bayesian reduced-rank regression implementation of the Hmsc 3. 0 R package  (Ovaskainen & Abrego, 2020; Tikhonov et al. , 2020). In the Hmsc model, the linear predictor for the fixed effects is written as L F ij = ∑ k x ik kj , where x ik is the value of covariate k for observation i, and kj is the regression slope of response variable j on covariate k.
| Study systems We analysed 22 datasets (population-year combinations) compiled from four previous studies. These include one population of Gymnadenia conopsea (Orchidaceae) from Sweden  (Chapurlat et al. , 2019)  seven populations belonging to three subspecies of Anacamptis coriophora (Orchidaceae) from France  (Joffard et al. , 2020)  and one population of Penstemon digitalis (Plantaginaceae) from Canada  (Parachnowitsch et al. , 2012). As a case study of spatio-temporal variation in selection on floral scent, we focused on Gymnadenia odoratissima. Four of the eight study populations were located in the lowland and four in the mountains. Of these, three lowland populations and two mountain populations were studied in 2 years. The phenotypic data include three morphological traits (flower number, plant height and inflorescence length) and 22 floral volatiles.
| Selection analyses We analysed each of the 22 datasets (population-year combinations) separately and refer to these as 'studies'. In all analyses, individual plants were treated as sampling units, and female reproductive success (fruit production) as a fitness proxy. All datasets included abundances of scent compounds (volatiles hereafter) as well as morphological traits, and some included a phenological trait (flowering time). We fitted Hmsc models to each dataset with relative fitness as response variable and Gaussian error distribution. As fixed effects, we included the morphological and phenological traits as 'standard' covariates (specified by the XData argument in Hmsc), while the volatiles were reduced into a single 'scent selection axis' through reduced-rank regression specified through the XRRRData argument in Hmsc.
The models did not include any random effect. The R code implementing all analyses is available on GitHub; github. com/oyste iop/Scent Selec tion). We obtained mean-(β μ ) and variance-scaled (β σ ) linear selection gradients for the standard traits and the scent selection axis by multiplying the regression slope on each covariate by its mean and standard deviation, respectively  (Hereford et al. , 2004). Because the scent selection axis is not on a ratio scale, mean-scaling is not meaningful  (Hereford et al. , 2004; Houle et al. , 2011)  and we report only variance-scaled selection gradients for the scent selection axis. After projecting the estimated selection gradient on the scent selection axis back onto the original volatiles to facilitate interpretation, we expressed inferred selection on each volatile as mean-scaled selection gradients.
(2021) , in which the among-dataset variation is computed as where c is the variance of the selection-gradient estimates among datasets, and SE 2 is the sampling variance of each selection-gradient estimate. In the current Bayesian framework, we used the variance of the posterior distribution as an estimate of the sampling variance (squared standard error). For mean-scaled selection gradients, this measure can be interpreted as the mean dispersion of the selection estimates in units of the strength of selection on fitness itself.
g. Chapurlat et al. , 2015; Herrera et al. , 2006; Opedal, 2021; Paudel et al. , 2016; Soteras et al. , 2020). While variation in selection on fit traits is expected to arise from variation in the fit of local pollinators to flowers, variation in selection on scent could well arise from variation in the scent preferences of local pollinators  (Ramírez et al. , 2011; Suinyuy et al. , 2015). Further tests of this hypothesis could leverage, for ex- For this dataset, the original analysis was practically identical to our multiple-regression analysis, and the compound-specific selection gradients so inferred were strongly correlated to those inferred by our reduced-rank regression approach (r = 0. 89, Table  1 ). Gross et al. (2016)  and  Joffard et al. (2020)  chose instead to include all detectable volatile compounds and instead reduced dimensionality through principal component regression.
DATA AVA I L A B I L I T Y S TAT E M E N T All data and R-code are available in GitHub (github. com/oyste iop/ Scent Selec tion), doi: 10. 5281/zenodo. 7085996. O RCI D Øystein H. Opedal https://orcid. org/0000-0002-7841-6933 Nina Sletvold https://orcid. org/0000-0002-9868-3449 R E FE R E N C E S.
EukRibo is a manually curated, public reference database of small-subunit ribosomal RNA gene (18S rDNA) sequences of eukaryotes, specifically aimed at taxonomic annotation of high-throughput metabarcoding datasets. Unlike other reference databases of ribosomal genes, it is not meant to exhaustively capture all publicly available 18S rDNA sequences from the INSDC repositories, but to represent a subset of highly trustable sequences covering the whole known diversity of eukaryotes. EukRibo strives to include only sequences with verified, up-to-date taxonomic identifications, with a strong focus on protists, and relatively low genetic redundancy, to keep the database compact yet comprehensive. Environmental clone sequences representing previously identified novel diversity are accepted as reference sequences only if they have a precise lineage designation, useful for taxonomic annotation.