dataesr/scientific-paragraphs-categorization huggingface dataset:
ncbi. nlm. nih. gov/pubmed/), and Google Scholar (https://scholar. google. com. br/). We searched these databases for relevant articles using combinations of the following search terms within the title, abstract, and/or keywords: genus of Neotropical xenarthrans and location names (countries, states, and provinces or biomes and ecosystems) that lacked information in the first round of data compilation. Search terms were used in both English, Portuguese, and Spanish.
Research Methods All studies that report location coordinates of occurrence or abundance of xenarthrans were included in NEOTROPICAL XENARTHRANS, independent of sampling method or effort. All the recorded coordinates are in decimal degrees, and the datum is WGS 84. Although most of such data includes the precise location of species records, a small portion of the data refers to municipalities, roads, protected areas, or farms (i. e. , the location is not precise). However, we have a PRECISION attribute on the database that allows users to track the geographic coordinate precision and identify whether the level of precision satisfies their requirements. The focus of our data-paper is Neotropical regions, and we delineated this boundary considering the literature description of the realm boundary, encompassing Central America including Mexico, the Caribbean islands, and South America.
For the complete database, however, we also allowed contributors to send data for the transitional zone between the Neotropics and neighboring regions, both at northern and southern parts of the Neotropical range. Therefore, if users are interested in the stricto sensu Neotropical regions, we suggest that they use their boundaries of interest, overlap that data, and extract which data they want to use. We organized the entire dataset into two different sub-datasets, separated according to the data type: quantitative information (i. e. , more than one record per point) and qualitative information (i. e. , presence and absence and presence-only data; only one record per point). The quantitative information (Table  4 ) contains all quantitative data (i. e. number of records per point) obtained by different methods, such as linear transect, telemetry, and camera trapping.
Qualitative data (Table  5 ) includes all records of occurrence data (individual and occasional records of xenarthran species, independent of the method used), including interviews, museum collections, line transects, camera traps, roadkill, active searches, vestiges, and opportunistic visualization. All the missing information was labeled as "NA" in the dataset. Data were compiled from Darwin 1842,  Bard 1855 , Lahille 1899 , Miller 1899 , Allen 1905 , Thomas 1919 , Yepes 1938a , 1938b , 1939 , 1944 , Minoprio 1945 , Giai 1950 , Vieira 1953 , Zeballos 1960 , Roig 1965 , Rood 1970 , Crespo 1974 , 1982 , Olrog 1976 , 1979 , Orcés and Albuja 1976 , Wetzel and Avila-Pires 1980 , Daciuk et al. 1981 , Mares et al. 1981 , 1996 , McCarthy 1982 , Massoia and Chebez 1985 , Montgomery 1986 , Navone 1987 , 1988 , George et al.
2017)  led to uncertainty regarding the data that we received. In this case, after consulting key specialists on these species, we decided to adopt Cyclopes sp. , thus avoiding errors in the current distribution of the species. The same approach applies to the genus Dasypus. We followed a recent review of the taxonomic classification, which splits the species Dasypus kappleri into two other species, D. beniensis and D. pastasae  (Feijó et al. 2018). The geographical accuracy of data is another important topic which deserves attention. We received several types of coordinates, some of them referring to the municipality or cities, while others referred to the fragment or point sampled. This difference could represent a bias in our dataset. Therefore, we strongly recommended that co-authors add the precision (in meters).
A xenarthran survey may be complex and require alternative methods and significant efforts. In some cases, it may be necessary to use two or three different methods if questions aside from the presence of the species (e. g. , abundance or habitat use) are to be investigated. Despite these limitations, we compiled the largest and most-complete dataset of xenarthran species of the Neotropical region to date. The merit belongs to the researchers who have provided us with a great deal of information from different kinds of studies. With this data-paper, we are providing data often hidden in the gray literature, or in databases inaccessible to the public. We hope that the public who will use the database will be able to answer questions, such as: 1) general patterns of distribution, 2) gaps in xenarthran studies and which species deserve more attention, 3) poorly studied regions and where to focus more surveys.
Data anomalies: If no information is available for a given record, this is indicated as 'NA'. Mexico. All co-authors from the Bolivian medium to large sized mammal volume and associated database, especially Heidy Lopez-Strauss, Nohelia Mercado and Zulia Porcel. We dedicate this work to G. Gene Montgomery, M. E. Sunquist, Laurenz Pinder, and all the great names associated with Xenarthrans, that certainly contributed and inspired all current research with this incredible group. We also appreciate all the comments and suggestions from the reviewers and editor. We dedicate this work to the National Museum of Rio de Janeiro, which strongly contributes to research, education, and science not only in Brazil, but for the entire Neotropic realm and for the whole world.
Species information. Family, IUCN conservation status and number of records of the armadillos, anteaters and sloth species reported in the NEOTROPICAL XENARTHRANS dataset. Species without confirmed identification (sp. ; excluding Cyclopes sp. ) were not included in the species counting. DD=Data Deficient, NE=Not Evaluated, LC=Least Concern, NT=Near Threatened, VU=Vulnerable, CR=Critically Endangered.
Acknowledgements This paper is part of the  Neotropical, Amazonia, Atlantic , and Brazil series of data-papers. We thank the  NeoXen team , that made this work possible and to  Abigail Feuka  and  Emma Hanslowe  ( Colorado State University, USA ) for proof reading the manuscript. We thank the companies that provided sources and the colleagues that offered any kind of field assistance:  Daniel Martíne Nambo ,  Cristina López Fuerte ,  Ricardo Gaudarrama ,  Juína villages ,  Cintia Brazão ,  Luiz Gustavo ,  Carolina Potter , ICMBio workers from  Sena Madureira ,  Parque Nacional Chapada do Guimarães ,  Reserva Biológica das Araucárias and Parque Nacional dos Campos Gerais, RESEX Cazumbá-Iracema landowners ,  Tayanna Mendonça da Silva Godim ,  Fernanda Guedes da Silva ,  Marcela Alvares Oliveira ,  Marcela Alvares Oliveira ,  Armando Muniz Calouro ,  Associação de Defesa Etnoambiental Kanindé ,  Felipe B.
1) Table 4. Reference Information 2) Table 5. Quantitative information 3) Table 6. Qualitative information Class V -Supplemental Descriptors A. Data acquisition 1. Data request history: None 2. Data set updates history: None 3. Data entry/verification procedures B. History of dataset usage Valdênio Marques, Fernando de Miranda Ramos, FLONA de São Francisco de Paula workers. Companhia Paranaense de Energia (COPEL), Hugo C. M. Costa, Leandro A. Abade, Frederico Souza, Daniel G. Rocha, Caio F. M. Lima, Mr. Lenilson, Botanical Garden Benjamin Maranhão staff, UFAL staff, Adriana Ribeiro de Oliveira, Paula Vianna, RPPN SESC Pantanal, Instituto Estadual de Florestas de Minas Gerais, (IEF) for their support during fieldwork at Rio Doce State Park, the management of the Chandless State Park and the Extractive Reserve of Cazumbá Iracema, AMATA S.
Acknowledgements E. P. C. was supported by ELEVATE:  Irish Research Council   International Career Development Fellowship-co-funded by Marie Curie Actions , and is currently supported by the  European Commission Horizon   2020 INFRADEV-1-2017 LOFAR4SW  project, no. 777442. L. A. H. is supported is supported by  Enterprise Partnership Scheme studentship  from the  Irish Research Council (IRC) between Trinity College Dublin and Adnet System Inc. , S. A. M. is supported by the  Irish Research Council   Postdoctoral Fellowship Programme  and the  Air Force Office of Scientific Research  award number  FA9550-17-1-039. D. E. M. acknowledges external funding from  Met office ,  United Kingdom  and the  Finish Centre of Excellence in Research for Sustainable Space  ( Academy of Finland  grant no. 1312390 ).
We are grateful to the  SDO  and GOES teams for open access to their data. The NRH is funded by the  French Ministry of Education  and the  Région Centre. Orfées is part of the  FEDOME  project, partly funded by the  French Ministry of Defense. The authors acknowledge the  Nançay Radio Observatory/ Unité Scientifique de Nançay of the Observatoire de Paris (USR 704-CNRS , supported by  Université d'Orléans, OSUC, and Région Centre in France)  for providing access to NDA observations accessible online at http://www. obsnancay. fr. N. V. would like to acknowledge the support of  CNES  and of the  French Program on Solar-Terrestrial Physics of INSU (PNST). We would like to thank  Hamish Reid  for some useful discussion during the writing of this paper. We would like to thank the referees for their useful comments and suggestions.
Genotyping of SSRs and SNPs Six microsatellite markers isolated from A. bipindensis were amplified in two PCR multiplexes in all samples according to a previously published protocol  (Donkpegan et al. 2015). Amplified fragments were separated on an ABI 3730 sequencer (Applied Biosystems, Lennik, The Netherlands) and sized using the Genemapper software in comparison with the SYBR Safe (Invitrogen, Merelbeke, Belgium) size standard. Genotyping by Sequencing (GBS) was performed for a subset of individuals (39 A. africana and 14 A. quanzensis individuals) at the Institute for Genomic Diversity and Computational Biology Service Unit at Cornell University (Ithaca, NY) according to a published protocol  (Elshire et al. 2011). To select the best enzyme for the GBS protocol, one microgram of DNA of Afzelia bipindensis was used to build test libraries using three different enzymes: ApeKI (4.
5-base cutter), EcoT22I and PstI (both 6-base cutters). Libraries were checked for appropriate fragment sizes (<500bp) and distribution on an Experion automatic electrophoresis system (Bio-Rad, Laboratories, CA, USA). The enzyme EcoT22I, giving appropriate fragment sizes (<500bp) was selected. To limit the risk of uneven coverage across loci and samples when applying GBS data to organisms with large genome sizes, we built and sequenced two independent libraries per individual, or pooled several DNA extracts per individual. Before library construction, DNA extracts were purified using a ZR-96 DNA Clean up kit (Zymo Research, Orange, CA), DNA quality was checked on a 1. 5% agarose gel and DNA quantity was measured with Qbit HS (Invitrogen, USA). Overall, 95 GBS libraries were built corresponding to 154 DNA extractions obtained from 53 individuals of Afzelia.
All libraries were sequenced together on one Illumina lane (HiSeq2000 San Diego, CA, USA), using 100-bp Single Read chemistry. We used Sabre (https://github. com/najoshi/sabre) to demultiplex barcoded reads. After demultiplexing, sequence quality was evaluated with FastQC version 0. 11. 15  (Andrews, 2010). Low quality bases and adapter contamination were removed with TRIMMOMATIC version 0. 33  (Bolger et al. , 2014)  with the following options: ILLUMINACLIP 2:30:10, LEADING 3, TRAILING 3, SLIDINGWINDOW 4:15, MINLEN 36. First, a de novo assembly of GBS reads was carried out (including sequence reads of tetraploid African Afzelia species A. bella, A. pachyloba and A. bipindensis) using pyRAD v. 3. 0. 2 software  (Eaton & Ree, 2013)  to produce a catalogue of GBS loci (3749 contigs, approximate length of 100bp per contig).
africana and A. quanzensis separately. We ran STRUCTURE 10 times for each number K of genetic clusters, using K=1-5. We ran 1,000,000 iterations after a burn-in period of 100,000 iterations, using the admixture model with independent allele frequencies between clusters, without considering the population of origin of each individual. We estimated LnP(K) and ΔK using the Evanno method  (Evanno et al. , 2005)  implemented in STRUCTURE HARVESTER  (Earl and vonHoldt 2012)  to obtain the most likely value of K. We also used an alternative genetic clustering method implemented in the R package tess3r  (Caye et al. , 2016) , which takes into account spatial information (the sampling location of each individual) to derive individual ancestry estimates. The default values of the program were used and each run (K = 1-5) was replicated 10 times.
The optimal value of K was defined by the minimum of the cross-entropy criterion. For GBS-derived SNP data, we performed genetic clustering analysis using the sparse nonnegative matrix factorization (sNMF) software, implemented in the R package LEA  (Frichot et al. , 2014). We also computed a genetic covariance matrix for each species to perform principal components analysis (PCA) using SMARTPCA  (Patterson et al. , 2006; Price et al. , 2006)  implemented in the SNPRelate package  (Zheng et al. , 2012). Isolation by distance: Under Wright's isolation-by-distance (IBD) model, the relatedness between individuals and/or populations is expected to decay linearly with the logarithm of their geographic distance on a two-dimensional scale  (Hardy & Vekemans, 1999). To detect IBD within each species at large scales for SSR and SNP data, we calculated the kinship coefficient F ij between individuals i and j using the estimator of  Loiselle et al.
GBS-based SNP data: After filtering to retain only biallellic SNPs, we obtained VCF files with 8541 SNPs for A. africana and 8730 SNPs for A. quanzensis using the GBS catalogue produced for the genus Afzelia. These files were then filtered to retain polymorphic SNPs within each species to remove SNPs and individuals with ≥ 60% missing data. After applying all filters, we removed nine individuals in A. africana and two in A. quanzensis and obtained VCF files containing 2800 polymorphic SNPs and 30 individuals in A. africana and 3841 polymorphic SNPs and 12 individuals in A. quanzensis. The final set of A. africana genotypes had an average missing data rate of 13. 64% per sample with a mean depth of 40X. For A. quanzensis, the missing data rate was 28. 48% per sample and 34X for mean depth. Total nucleotide diversity was π=0.
ACKNOWLEDGMENTS This work received financial support from the  "Fonds pour la Formation à la Recherche dans l'Industrie et l'Agriculture (FRIA-FNRS, Belgium )" through a research grant to A. D. , from the  Marie Curie   FP7-PEOPLE-2012-IEF program  (project  AGORA ) awarded to R. P. , from the  Fonds de la Recherche Scientifique (F. R. S. -FNRS)  through project  J. 0292. 17F , the  Belgian Science Policy  (project  AFRIFORD ) and the  CGIAR Research Program on Forests, Trees and Agroforestry. This work has benefited from support of a grant from  Investissement d'Avenir grants  of the  ANR  ( CEBA : ANR-10-LABX-25-01 ). A. D. acknowledges a  Labex COTE Mobility  grant to INRA. The authors are grateful to  Nils Bourland  who helped us during field expeditions, through the project  PD 620/11 Rev.
The only conversion indexes above the background level were those of 4sU-labeled and alkylated RNAs of TBEV-and SMRV-infected cells at D1. The conversion indexes at D1 were 3. 3-and 17. 6-fold over the background of nonlabeled cells for TBEV and SMRV, respectively. Therefore, our results show that after 4Su labeling of cells, RNA-Seq was able to specifically identify newly synthesized viral RNAs with a high signal-to-background noise ratio in a one-shot test, without the need for an external reference sequence. Characterization of expression of SMRV sequences in Vero cells. With the reads mapping to SMRV-H, we derived a consensus full-length sequence, deposited in GenBank  (10)  as proviral DNA under the name SMRV-Vero (GenBank accession number a In each TBEV and SMRV cell, the first value (before the slash) and the second value (after the slash) correspond to the conversion rate or index based on the external consensus sequence derived from the D0 reads ("TBEV REFERENCE" and "SMRV REFERENCE") or from internal consensus sequences directly derived from the reads of the experimental condition ("INTERNAL REFERENCE"), respectively.
Agnostic bioinformatic analysis. The raw data reads were filtered to select high-quality and relevant reads. Raw data were sorted to suppress or cut duplicates, low-quality reads, and homopolymers (proprietary software; PathoQuest, Paris, France). Sequences introduced during the preparation of Illumina libraries (adapters, primers) were removed with Skewer  (29). Reads aligned with BWA (30) to (i) the human genome (reference GRCh37/hg19  [31] ) used as a proxy to mimic primate (Vero cells) host background or to (ii) bacterial rRNA were discarded. The bacterial rRNA database was initially downloaded from the EMBL-EBI ENA rRNA database (ftp://ftp. ebi. ac. uk/pub/databases/ena/rRNA/release) and reworked using in-house sequence cleaning and clustering processes. Remaining reads were considered sequences of interest and were assembled into contigs with CLC Assembly Cell solution (Qiagen Bioinformatics).
Resulting contigs and nonassembled reads (singletons) were aligned using BLAST alignment  (32)  on viral and comprehensive databases. Contigs and singletons were first aligned on a viral nucleotide database. Hits with an E value below 10 Ϫ3 were aligned on a comprehensive nucleotide database. If the best hit was still a viral taxonomy, hits were reported. The nucleotide viral and comprehensive databases were downloaded during November 2017 from the EMBL-EBI nucleotide sequence database (https://www. ebi. ac. uk/). The viral database is restricted to viral sequences of the EMBL-EBI nucleotide sequence database, including subgenomic sequences. Proprietary software was developed to remove duplicate and low-confidence sequences (e. g. , too short, multiple taxonomies, low-quality associated keywords).
Contigs without any viral nucleotide hits were similarly aligned successively on viral and comprehensive protein databases to check for more-distant viral hits. Protein viral and comprehensive databases were downloaded during November 2017 from the Uniref100 database (https://www. uniprot. org). Though the Uniref100 database is already nonredundant, we performed a taxonomic cleaning process to generate the final databases. The taxonomic assignation reported the best hit results with contigs not assigned after these two rounds of alignment being classified as unknown or nonviral species. The data from condition 1 ("DO no 4sU") enabled the identification of contigs covering the whole genome of TBEV (n ϭ 1; length ϭ 10850 bp) and SMRV (n ϭ 4; cumulated length ϭ 8639 bp). For SMRV, a new assembly of the reads with Megahit v1.
1. 3 (33)  gave a unique contig covering the whole genome. The resulting sequences, obtained at D0, were labeled "TBEV REFERENCE" and "SMRV REFERENCE. " Estimation of T¡C conversion ratio. In order to detect viral sequences with a very high number of T¡C conversions, the set of quality filtered reads was mapped back to TBEV/SMRV REFERENCE with minimap2  (34). The pileup module of the htsbox software (https://github. com/lh3/htsbox) was then used to detect all mismatches (with a base quality at least equal to 30) at every position of the TBEV or SMRV REFERENCE. The global variation profile was then analyzed by a proprietary script (PathoQuest, Paris, France) to define each nucleotide conversion rate. The proportion of converted nucleotides was compared to the total number of aligned nucleotides.
Exons were used from the following human genes (35) (RefSeq accession number): C1orf43 (NM_015449), CHMP2A (NM_014453), EMC7 (NM_020154), and GPI (NM_000175). We used these human exons in order to identify their equivalent in the Chlorocebus sabaeus genome, from where the Vero cells are derived. The complete assembly of Chlorocebus sabaeus (accession number GCF_000409795. 2) was retrieved from NCBI assembly database (https://www. ncbi. nlm. nih. gov/ assembly/). Selected human exons were mapped onto C. sabaeus assembly using minimap2  (34) , and the resulting bam file was converted to a bed file using the bamtobed module from the BEDTools utility  (36). Only hits with mapping quality higher than 30 were retained (41 exons), and the corresponding sequences were extracted from C. sabaeus assembly using the getfasta module from the BEDTools utility and indexed for analyses as the "CONTROL REFERENCE" sequence to calculate conversion rates and indexes.
ACKNOWLEDGMENTS We thank  Marielle Cochet  for her help in the SMRV investigations. The sponsor of the study was PathoQuest. Employees of PathoQuest (E. M. , J. C. , P. B. , and S. C. ) participated in writing the study report and in the decision to submit a manuscript for publication. E. M. , J. C. , P. B. , and S. C. are employees of PathoQuest. E. M. , J. C. , and M. E. hold shares in PathoQuest.
The CenW ecosystem model simulates carbon, water, and nitrogen cycles following ecophysiological processes and management practices on a daily basis. We tested and evaluated the model using five years eddy covariance measurements from two adjacent but differently managed grasslands in France. The data were used to independently parameterize CenW for the two grassland sites. Very good agreements, i. e. , high model efficiencies and correlations, between observed and modeled fluxes were achieved. We showed that the CenW model captured day-to-day, seasonal, and interannual variability observed in measured CO2 and water fluxes. We also showed that following typical management practices (i. e. , mowing and grazing), carbon gain was severely curtailed through a sharp and severe reduction in photosynthesizing biomass.
The reliability of these models highly depend on the quantity and quality of the data used for their calibration  [41] [42] [43]. Model parameters calibration aims to constrain the uncertainty in model parameter space and optimize the model output of ecosystem-atmosphere CO2 exchange  [44]. Measured CO2 fluxes were used to constrain model simulation through parameter calibration. Once parameterized, these models allow to simulate separately the constituents of NEE (soil, microbial, plant, and animal respiration and gross primary production) that cannot be measured directly by EC, or to interpolate and extrapolate CO2 fluxes in time and space. However, insufficient knowledge about underlying processes (e. g. , all the processes leading to observed carbon and water fluxes and flows that exists in the real world but are not or are only poorly understood and modeled) as well as parameters and initial conditions uncertainties can lead to bias and uncertainty in model simulations  [45].
Also, because interannual responses are usually less well captured by models than daily and seasonal dynamics  [46] [47] [48] , the availability and quality of long-term datasets are crucial to improve model performances  [49]. Process-based simulation models are therefore required to gain insight of processes and interactions between managed grasslands C dynamics, climate change, and management practices, in combination with experimental observations, especially for long-term analyses  [50, 51]. CenW (carbon, energy, nutrients, and water) is a process-based model, running at a daily time step. It was originally developed to simulate the carbon balance of forests over time  [52] [53] [54]. The soil organic matter module of the model was derived from the CENTURY model  [55] , which was originally developed for grasslands (more details are given in Section 2.