dataesr/scientific-paragraphs-categorization huggingface dataset:
From the untreated material, 25 uncoated grids were made (10 with 300 Mesh copper grids, 5 with hexagonal 75 Mesh copper grids, 10 with single slot Nickel or copper grids), all devoid of uranyl acetate and lead citrate staining. Among available chemical elements, Cu and Al were not considered in the results as belonging to the grid, Os is part of the embedding technique, Si is part of the oils used in the TEM and C and O are also major parts of the EPON embedding resin. Of the remaining chemical elements, measurements (5 for each check) were evaluated with the Mann Withney test, using XLSTAT version 2018. 2 software, providing diagrams of the results included in this paper. The EDS was also performed through SEM, enabling the comparisons and locations of the chemical elements obtained with EDS by TEM using three resin blocks of untreated material used for TEM sections, 10 nm of thickness carbon-coated, with a BALZERS MED010.
SEM is a Zeiss Merlin compact 10 kv, where EDS analysis is using Oxford X-max 50 mm2, AZTEC software. For  TEM and EDS statistics, XLSTAT 2019. 1 was used (Addinsoft (2019). XLSTAT statistical and data analysis solution. Long Island, NY, USA. https://www. xlstat. com. ) All the samples of Brachyphyllum garciarum sp. nov. are deposited in the Paleobotany Collection of the Regional Museum Padre M. J. Molina (MPM), Río Gallegos, Santa Cruz province, Argentina, under the acronym MPM-PB. The following terms and abbreviations are used in most parts of the text where it is relevant as in the tables, figures and appendices: OEC = ordinary epidermal cell cuticle; SC and GC = subsidiary cell and guard cell, respectively; CM = cuticular membrane (CP + CL); CP = cuticle proper (A = A2 granular layer); CL = cuticular layer (B = B1 fibrilous layer).
EDS analysis Two EDS analyses were performed by TEM and SEM. Firstly, using the EDS system (Fig. 2 ), and because of the insignificant differences from the Mann-Whitney test for five measurements, quantitative data of the cuticle elements obtained allowed us to select 14 ratios among all possibilities: N/S, N/Cl, N/K, N/Ca, N/NA, S/Cl, S/K, S/Ca, S/ Na, Cl/K, Cl/Ca, Cl/Na, K/Ca, K/Na (Table  2 , supplementary material Appendix B). These selected ratios concern the two layers (A2, B1), and cell wall and hypodermis of the ordinary epidermal cell lower and upper cuticles, all having homogeneous values of resin to provide similar eventual errors due to the elements in the resin, though usable for comparisons of statistical significance. Added to the confidence interval values, all of these cuticle ratios were also determined to be significantly different or not by the Mann-Whitney test (Fig.
Acknowledgements This research was partially supported by grants  CONICET PIP 212/12 ,  PICT 528/12  and  PICT 2206/2015. The authors are grateful to  Antonello Bartiromo  and the anonymous reviewer for all their valuable suggestions that have greatly improved the manuscript. We express our special thanks to  Orlando Cárdenas  for his unconditional help and support during the field trips and technical assistance in cuticles processing. Also to  Marcelo Miñana  for his help in many aspects of the field logistics. We thank the owners and employees of the  Estancia Suyai (Vladimiro Macharashvili, Santiago  and  Miguel Crozzoli ,  Griselda Garrido ,  Raquel Cuevas ),  Estancia El Correntoso  ( Nélida and Emilio Cvjetanovic ) and  Estancia El Salitral (Élida and Sebastián García)  who kindly gave us permission to prospect and visit the fossiliferous localities situated in their properties.
Species composition To compare species composition between habitats, nonmetric multidimensional scaling (NMDS) ordination was employed, using the metaMDS function (Vegan package), with Bray-Curtis dissimilarity measure and 1000 permutations. Five primary forest sites from the upper elevational limit were removed as no species were recorded. Communities were standardised using the Wisconsin transformation (Vegan package), and species data were converted into a proportion of the total number of occurrences by site, therefore, accounting for differences among sites in the total number of ant occurrences  (Woodcock et al. 2011). To test for differences in composition among habitat types, a permutational multivariate analysis of variance with 999 permutations, using the nonparametric ADONIS2 function (Vegan package), was utilised.
Primary forest species' responses To assess changes in species richness of primary forest species in response to habitat disturbance, we compared the occurrences of each species in primary forest against occurrences in secondary forests (summed young and mature) and pastures. We could not use a literature-based approach to define primary forest species because the majority of identifications were morpho-species. Therefore, we used the capture data to define primary forest species and focus the discussion. We defined a primary forest species as one with at least 1/2, 2/3 or 3/4 of its occurrences being in a primary forest site (excluding all species with a single occurrence). We present the results for three capture thresholds because any criteria are somewhat arbitrary and this enabled an assessment of how changing the criterion might impact the interpretation.
Acknowledgements We thank staff at the  Instituto de Investigacion de Recursos Biologicos Alexander von Humboldt , particularly  Fernando Forero  for logistical support. For field access permissions, we thank  Fundacion Colibri  ( Reserva Mesenia-Paramillo ),  Fundacion ProAves (Reserva Las Tangaras ) and  Leopoldina Tapasco  ( Cerro Montezuma ). We thank  Yesenia Tapasco  and other local assistants for help with data collection. Funding was provided to TH and DPE by the  Research Council of Norway , Grant number  208836. MGH was funded by the  Grantham Centre for Sustainable Futures. CMOS acknowledges the CPER Research Project CLIMBIO for her financial support. This is publication #17 of the  Biodiversity, Agriculture and Conservation in Colombia/Biodiversidad, Agricultura, y Conservacion en Colombia (BACC)  project.
In this study, we analyzed data from a long-term field experiment carried out from 1967 to 2016 in western Switzerland to assess (i) the evolution of climatic parameters, wheat yield and SOC content over a 50-year period; (ii) the combined effects of crop succession (monoculture vs. crop rotation) and soil tillage (minimum tillage vs. plough) on wheat yield, SOC content and other soil properties; and (iii) the relative contributions of climatic parameters, wheat phenology and agricultural practices on wheat yield variability.
Climatic parameters Climatic data was collected from the MeteoSwiss station of Changins (46°24′E, 06°14′N; 435 m a. s. l. ), 450 m away from the trial site. The resolution and the variables measured were limited during the first part of the study  (1967) (1968) (1969) (1970) (1971) (1972) (1973) (1974) (1975) (1976). Therefore, two datasets were collected. The first dataset, from 1967 to 1976, included variables measured at an annual resolution: mean temperature (°C), minimum temperature (°C), maximum temperature (°C) and total rainfall (mm). The second dataset, from 1977 to 2016, included variables measured at a daily resolution: mean temperature (°C), maximum temperature (°C), minimum temperature (°C), cumulative rainfall (mm) and solar radiation (Mj m -²). This higher resolution of the climatic data enabled a more precise assessment of how rotation and soil tillage impacted wheat yield under various climatic conditions.
Data analysis Statistical analyses were performed using the R Studio software (R Development Core Team, 2014). When necessary, data were transformed, and the normality and homoscedasticity of the distribution of residuals of models was visually verified. The change in annual temperature (mean temperature, maximum temperature and minimum temperature) and total annual rainfall through time was tested using a Mann-Kendall trend test (R package "  Kendall", McLeod, 2011). The evolution of SOC content (0-20 cm) through time was first tested using a Mann-Kendall trend test. In a second step, we used a linear mixed-effects model approach to explain the differences in SOC content across treatments for the period 1977-2016, with crop succession and soil tillage as fixed factors and sampling year as random factor following the R syntax: SOC ˜crop succession × soil tillage + 1|year.
Variety Year Growing season (GS) Period from sowing date to heading date (P1) Period during 4 weeks after heading (P2) Period from end P2 to harvest date (P3) (1) (2) (3) (4) (5) (6) (7) (8) (5) (9) (7) (5) (9) (7) (5) ** Rainfall, solar radiation and minimum/maximum temperatures were not available for 1977, so the climatic conditions from sowing to heading was calculated with the data collected from 1 December 1978 (values in italics). */** Heading dates were not recorded during these years in this study, however, as approximation, heading dates of the same wheat variety in a nearby study within the same site were used.
Phenotypic data The (elite × exotic) progeny was evaluated in 2016. A total of 187 individuals were evaluated in combination with a tester MSF1 and compared to four commercial hybrids and elite accessions as checks. The entire progeny was evaluated in a lattice design with two replicates for productivity and impurity traits in nine locations: AVE607, BAR601, BEL601, BER601, DOM601, MEM601, and PIE601 in France, DAW601 in Great Britain, and UPI601 in Belgium. The three measured impurity traits were the sodium content (Na, meq/100 g) and potassium content (K, meq/100 g) measured by a flame photometer, and the -amino nitrogen content (N, meq/100 g) measured by colorimetry. The measured traits linked with productivity were the root yield (RY, tons/ha) and the sucrose content (S, %) measured by refractometry.
Phenotypic data analysis Spatial effects on each of the nine progeny environments were adjusted with the R package SpATS  Rodríguez-Álvarez et al. (2017) , available from CRAN (https ://CRAN. R-proje ct. org/packa ge=SpATS ). The SpATS method allows us to consider the local trends, thanks to a smooth bivariate function f(u, v) represented by 2D P splines, where u is the numeric vector of rows and v is the numeric vector of columns. For this experiment, additional terms were included in the SpATS model to account for other sources of environmental variation and genotype effects. We assumed a model including random independent factors for rows ( c r ), columns ( c c ), and genotypes ( c g ), and also fixed factors for genetic checks ( t ), repetitions ( n ), and the spatially independent error term e ∼ N(0, 2 e I).
The freeze-dried leaves were subsequently ground using a MM400 Retsch grinder (http://www. retsc h. com) for 150 s at 30 frequencies per s Magnetic bead DNA extraction was performed on a robotized platform. All individuals were genotyped using the GeneTitan®microarray automated scanner following the manufacturer's recommendations (http://www. affym etrix. com/suppo rt/techn ical/bypro duct. affx?produ ct=genet itan). Genotyping crude data were analyzed with the Axiom ® 1. 1. analysis suite software package (http://www. affym etrix. com/ suppo rt/techn ical/bypro duct. affx?produ ct=axiom analy sissu ite). For further analyses, only the highest quality SNPs corresponding to "Poly High Resolution" and "No Minor Homozygote" categories were used. Only SNPs whose parental allele provenance was known without ambiguity were kept.
The missing genomic data were then imputed for each linkage group using Beagle software  (Browning and Browning 2009) , leading to 1638 SNPs. After this imputation, some SNPs had exactly the same genetic information. The redundancy of information was not useful for further GWAS analyses; on the contrary, it increased the computational burden and could skew the calculation of the relatedness between hybrids, decreasing the power in regions with many redundant markers (Rincent 2014). Thus, redundant SNPs were discarded. Then, a minor allele frequency (MAF) filter was used to remove SNPs with MAF less than 0. 03. Finally, only SNPs with three genotypic classes were retained, coded as 0, 1, or 2 for homozygous for the elite allele, heterozygous, or homozygous for the exotic allele, respectively.
Phenotypic data analysis Phenotypes were first adjusted per environment, and then the mean phenotype was calculated as described in supplementary material of  Mangin et al. (2019). These mean phenotypes were used as the observed phenotypes for further analyses. The structure of the elite panel was also analyzed. The optimal cluster number of the hierarchical clustering on principle components analysis was set to two. Clusters contained 676 (Panel A) and 1425 individuals (Panel B). Boxplots of potassium content, -amino nitrogen content and sodium content in each panel and in the entire population were plotted in supplementary material (see in supplementary material Figs. S5 to S7). Phenotypic variabilities were similar in both panels and in the entire population.
These 12 SNPs were removed. Chromosome maps were built using the CarthaGène software  (De Givry et al. 2005). The datasets of the four populations were merged using the "dsmergor" command. The redundant markers were merged with "mrkdouble" and "mrkmerges. " Then, the maps were built using the command line "buildfw 0 0 1. " See CarthaGène documentation for more information. This produced a map per chromosome and per population. Marker order was common across populations but distances were distinct. Consensus distances were calculated to simplify the use of the maps. For each map, absent markers were projected on the map. Then, the distances between markers were averaged across the four populations to produce the consensus distances. Three aberrant individuals appeared to have tens of recombinations per chromosome on the maps.
Among the 61,657 probes spotted on the array, 43,688 (71%) were expressed, and 36,473 (59%) were annotated (corresponding to 12,272 genes) based on the NCBI EntrezGene database and Galgal5 chicken genome assembly. Based on quantitative histological characterization and a multidimensional scaling plot of gene expression, three samples (i. e. , the two samples previously identified in the histological analysis as misclassified into groups according to the macroscopic examination of defects and another sample from the FG-C group) were considered outliers and were not kept in the differential analysis. The analysis of 21 samples was performed using a linear model and pairwise comparisons and revealed that 10,482 of the annotated genes were differentially expressed (DE) genes with a fold-change |FC| ≥ 1.
Functional analysis of DE genes. All expressed genes on the array were annotated by Gene Ontology for Biological Process (BP) based on NCBI EntrezGene chicken IDs (taxonomic id: 9031) and orthologues. Among the 12,272 expressed genes on the array, 6,232 were annotated with at least one BP GO term (51%). The enrichment analysis was performed independently for each DE genes cluster with all expressed genes used as the background (Supplementary Data S1). Two hundred and sixty-seven GO terms were enriched in at least one cluster (p-value < 0. 01), and 264 were unique, indicating that clusters of genes were very specific and different from each other (Table  3 ). The number of enriched GO terms in each cluster varied between 8 (cluster c3) and 128 (cluster c5).
Microarray expression profiling. RNA extraction. Total RNA was extracted from pectoralis major muscle samples using the RNeasy ® Mini kit (Qiagen, Valencia, CA, USA). Residual genomic DNA was removed by DNase I treatment (Qiagen, Valencia, CA, USA). RNA concentration was measured using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), and RNA integrity was checked according to an RNA Integrity Number (RIN > 8) using RNA 6000 Nano chips run on a Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA). Labelling and hybridization. Muscle transcriptional profiling was performed using an 8 × 60 K Agilent Gallus gallus custom-commercial array (Agilent Technologies, Santa Clara, CA, USA) containing 61,657 probes (Platform GPL20588 in the U. S. National Center for Biotechnology Information Gene Expression Omnibus (GEO) microarray database).
RNA labelling and microarray processing were performed by the @BRIDGe platform (INRA, UMR GABI, Jouy-en-Josas) using the procedure described in Jacquier et al. 54. The microarray datasets were submitted to the GEO microarray database with the accession number GSE127806. Re-annotation of the microarray chip on the current Galgal5 chicken genome. To compare the DE genes identified in this study and the genetic markers of WS provided by our previous study  18  , we re-annotated the chip. The transcriptome alignment on the Galgal5 chicken assembly (EntrezGene database) of all the probes deposited on the chip was carried out using the blastn algorithm (hybridization tolerance of two mismatches and a single probe-associated gene as parameters), which is available in the BLAST + suite (ncbi-blast-2.
Functional enrichment analysis. All the genes expressed on the chip were annotated by Gene Ontology  59, 60  for Biological Process categories according to the NCBI EntrezGene database using orthologues. Functional enrichment tests were performed using the ViSEAGO R package, which is available at https:// forgemia. inra. fr/umr-boa/viseago. Enrichment for functions within each cluster of genes was tested using a Fisher's exact test and the "elim" algorithm (p < 0. 01) with all expressed genes used as the background.
Acknowledgements We thank the  @BRIDGe platform (INRA, UMR GABI, Jouy-en-Josas)  for performing the RNA labelling and microarray processing. We also thank the  PEGASE Genetics and Genomics team (INRA ,  UMR1348 ,  Rennes, France)  for developing the 8 × 60 K Agilent custom platform. Finally, we thank the staff of  PEAT INRA Poultry Experimental Facility  (2018, https://doi. org/10. 15454/1. 5572326250887292E12 )and of the  Avian Research Unit (UMR BOA ,  INRA ,  Université de Tours, 37380, Nouzilly, France ) for their technical assistance. The phenotyping of the birds used for transcriptomic analysis and microarray processing was performed by the  @BRIDGe platform (INRA, UMR GABI, Jouy-en-Josas)  and funded by the  TECNOVIA program , which is supported by the  Bretagne and Pays de la Loire regions (France).
Soil organisms, including earthworms, are a key component of terrestrial ecosystems. However, little is known about their diversity, their distribution, and the threats affecting them. We compiled a global dataset of sampled earthworm communities from 6928 sites in 57 countries as a basis for predicting patterns in earthworm diversity, abundance, and biomass. We found that local species richness and abundance typically peaked at higher latitudes, displaying patterns opposite to those observed in aboveground organisms. However, high species dissimilarity across tropical locations may cause diversity across the entirety of the tropics to be higher than elsewhere. Climate variables were found to be more important in shaping earthworm communities than soil properties or habitat cover. These findings suggest that climate change may have serious implications for earthworm communities and for the functions they provide.
) We collated 180 datasets from the literature and unpublished field studies (164 and 16, respectively) to create a dataset spanning 57 countries (all continents except Antarctica) and 6928 sites (Fig. 1A ). We explored spatial patterns of earthworm communities and determined the environmental drivers that shape earthworm biodiversity. We then used the relationships between earthworm community metrics and environmental drivers (table  S1 ) to predict local earthworm communities across the globe. Three generalized linear mixed-effects models were constructed, one for each of the three community metrics: species richness (calculated within a site), abundance per m 2 , and biomass per m 2. Each model contained 12 environmental variables as main effects (table  S2 ), which were grouped into six themes; "soil," "precipitation," "temperature," "water retention," "habitat cover," and "elevation" [habitat cover and some soil variables were measured in the field; the remaining variables were extracted from global data layers based on the geographic coordinates of the sites  (14) ].
Acknowledgments We thank all the reviewers who provided thoughtful and constructive feedback on this manuscript. We thank  M. Winter  and the sDiv team for their help in organizing the sWorm workshops, and the  Biodiversity Informatics Unit (BDU) at iDiv  for their assistance in making the data open access. In addition, the data providers thank all supervisors, students, collaborators, technicians, data analysts, land owners/managers, and anyone else involved with the collection, processing, and/or publication of the primary datasets. ( ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement no. 677232  to N. E. ). K. S. R. and W. H. v. d. P. were supported by  ERC-ADV  grant  323020  to W. H. v. d. P. Also supported by  iDiv  ( DFG FZT118 ) Flexpool proposal 34600850 (C.
A. G. and N. E. ); the  Academy of Finland  ( 285882 ) and the  Natural Sciences and Engineering Research Council of Canada  (postdoctoral fellowship and  RGPIN-2019-05758 ) (E. K. C. ); DOB Ecology (T. W. C. , J. v. d. H. , and D. R. );  ERC-AdG 694368  (M. R. ); and the  TULIP Laboratory of Excellence  ( ANR-10-LABX-41 ) (M. L. ). In addition, data collection was funded by the  Russian Foundation for Basic Research  ( 12-04-01538 -a,  12-04-01734-a ,  14-44-03666-r_center_a ,  15-29-02724 -ofi_m,  16-04-01878-a 19-05-00245 );  Tarbiat Modares University ;  Aurora Organic Dairy; UGC(NERO)  ( F. 1-6/Acctt. /NERO/2007-08/1485 ); Natural Sciences and Engineering Research Council  ( RGPIN-2017-05391 );  Slovak Research and Development Agency  ( APVV-0098-12 );  Science for Global Development through Wageningen University ;  Norman Borlaug LEAP Programme  and  International Atomic Energy Agency (IAEA) ;  São Paulo Research Foundation -FAPESP  ( 12/22510-8 );  Oklahoma Agricultural Experiment Station ;  INIA -Spanish Agency  ( SUM 2006-00012-00-0 );  Royal Canadian Geographical Society ;  Environmental Protection Agency (Ireland)  ( 2005-S-LS-8 );  University of Hawai'i at Mānoa  ( HAW01127H ;  HAW01123M );  European Union   FP7  (FunDivEurope, 265171);  U.
S. Department of the Navy ,  Commander Pacific Fleet  ( W9126G-13-2-0047 );  Science and Engineering Research Board  ( SB/SO/ AS-030/2013 )  Department of Science and Technology, New Delhi, India ;  Strategic Environmental Research and Development Program (SERDP)  of the  U. S. Department of Defense  ( RC-1542 );  Maranhão State Research Foundation (FAPEMA) ;  Coordination for the Improvement of Higher Education Personnel (CAPES) ;  Ministry of Education, Youth and Sports of the Czech Republic  ( LTT17033 );  Colorado Wheat Research Foundation ;  Zone Atelier Alpes ,  French National Research Agency  ( ANR-11-BSV7-020-01 ,  ANR-09-STRA-02-01 ,  ANR 06 BIODIV 009-01 );  Austrian Science Fund  ( P16027 ,  T441 );  Landwirtschaftliche Rentenbank Frankfurt am Main ;  Welsh Government  and the  European Agricultural Fund for Rural Development (Project Ref.