dataesr/scientific-paragraphs-categorization huggingface dataset:
Demographical Data of Study Participants A final sample of 1056 participants, 354 psychiatrists, 511 psychologists, 147 psychiatry and psychology trainees, and 44 psychotherapists, from 52 countries, were included. The majority of the participants were female (71. 9%), and the mean age was 39. 9 (SD 10. 0) years. The majority of participants reported working in a hospital (n = 450, 42. 6%), followed by private practice (n = 233, 22. 1%), mental health outpatient services (n = 178, 16. 9%), rehabilitations centres (n = 64, 6. 1%), and day clinic (n = 20, 1. 9%). A further 10% reported working in institutions other than those listed above, and one participant did not specify their place of work. Table  1  gives an overview of the participant's country of occupation, grouped by country income level.
Data given in bold are the numbers of participants from each country who participated in the survey. Data given in italics are derived from the World Health Organization (WHO): GHO|By category|Human resources-Data by country. (2019). Retrieved 23 November 2020, from https://apps. who. int/gho/data/node. main. MHHR?lang=en (accessed on 23 November 2020); * Data source: Austrian Medical Chamber, 2020 ** Data source: Federal Ministry of Austria, List of health psychologists according to § 17 Psychologengesetz 2013, BGBl. I Nr. 182/2013 and List of clinical psychologists according to § 26 Psychologengesetz 2013, BGBl. I Nr. 182/2013, from https://www. sozialministerium. at/Themen/Gesundheit/Medizin-und-Gesundheitsberufe/Berufslisten. html (accessed on 3 December 2020). *** Data source: The College of Psychiatrists of Ireland Workforce Planning Report 2013-2023 December 2013.
All analysis and visualizations were performed with the R statistical programming languages (R Core Team, 2020) using the RStudio IDE 1 and the following packages: tidyverse  (Wickham et al. , 2019) , ggplot2  (Wickham, 2016, p. 2), rstatix  (Kassambara, 2020) , patchwork  (Pedersen, 2019) , and lme4  (Bates et al. , 2015). The code and raw data are available at https://gitlab. com/agoremix/oa-resistance.
ACKNOWLEDGMENTS We thank  Yasutaka Tsuchiya  and the technical staff at  Shimoda Marine Research Center  and aboard  RV Tsukuba II, University of Tsukuba  for their assistance and the  JF agencies of Izu/Shimoda  ( Shizuoka prefecture ) and  Nijima/Shikine Island  ( Tokyo prefecture ) for their support. We are grateful to the local populations of Normanby for the access to their reef, and to the  National Research Institute , the  Milne Bay Provincial Research Committee , and the  Conservation and Environment Protection Authority of Papua New Guinea  for permits. This publication contributes toward the  International Education and Research Laboratory Program ,  University of Tsukuba. Images used in Figures 1 and5 are courtesy of the  Integration and Application Network, University of Maryland Center for Environmental Science  (ian.
We tested this algorithm on a dataset of 137,726 weaned calves grouped into 9701 batches by a French organization. We also computed an index assessing the risks of developing BRD because of the batch composition by considering four pathogens involved in the BRD system. While increasing the heterogeneity of batches in calf bodyweight, which is not expected to strongly impact the performance, our algorithm successfully decreased the average number of origins in the same batch and their risk index. Both this algorithm and the risk index can be used as part of decision tool to assess and possibly minimize BRD risk at batch creation, but they are generic enough to assess health risk for other production animals, and optimize the homogeneity of selected characteristics.
Second, we tested the algorithm on a dataset of batches created by a French beef producer's organization. We compared the batches from the original dataset to the batches created by the algorithm in order to identify a decrease in the number of origins per batch and check the impact of the algorithm on other characteristics of the batches, namely the heterogeneity in the age or weight of the calves. Third, we computed an index for each calf of the dataset, representing its risk of developing symptoms because of four theoretical pathogens representing four of the main pathogens involved in BRD. We compared the risk index of the calves according to the batch composition given by the dataset to those according to the batch composition provided by the algorithm, in order to assess its impact on the estimated risk of developing BRD.
Presentation of the database We used a dataset provided by Terrena Production Bovine, a beef producer organization located in Western France, a region densely populated with cattle. The data includes 137,726 weaned calves managed by the organization between 2010 and 2018, with information about their breed, age and weight. Most calves were Charolais bulls (60%), although a large diversity of other breeds-23 in total-was represented. In addition, the origin of each animal (i. e. the cow-calf producer it comes from), the batch it was assigned to and the date of the assignment were also provided. All the calves in the database were managed by the same intermediate (Terrena Production Bovine). They came from 3675 different cow-calf producers and were sold to 1028 different fatteners. The cow-calf producers were very unevenly represented in the database: the average cow-calf producer provided 37.
From this database, we extracted the following information about the 9,701 batches these calves were assigned to: their size (i. e. the number of animals in each batch), their date of creation, the main breed represented in each batch. The batches were defined according to the database and corresponded to groups of all calves sent to a same fattener on the same day. The average batch included 14. 15 calves (standard deviation of 11. 9 calves) and 50% of the batches regrouped between 6 and 19 calves. We considered four classes of proportion of calves of the main breed in each batch, which are those used by Terrena Production Bovine: [0;0. 5], [0. 5;0. 9], [0. 9;1] and 1 (all the animals were of the main breed). Batches in the class [0;0. 5] were considered to have no main breed. Finally, we computed three additional characteristics for each batch: the number of different origins represented, the standard deviation in age and the standard deviation in weight of the animals.
Presentation of the optimization algorithm The management of beef calf batches by intermediates involves two sets of elements. On the one hand, there is a set of cow-calf producers, each characterized by the number of calves they can provide (its "size", noted X a for cow-calf producer a). On the other hand, there is a set of batches, each characterized by the number of calves they can receive (its "size", noted Y b for batch b). The sizes X a and Y b are fixed at values given in the database from Terrena Production Bovine. In this context, the objective of the algorithm is to assign calves to a single batch each, while minimizing the number of different cow-calf producers providing calves to a same batch. This functioning is akin to a wiring algorithm  [20] , although on a weighted bipartite graph.
For this study, we included two other constraints in order to recreate the batches of the database from Terrena Production Bovine: (i) assigning calves provided a given week to batches created the same week, and (ii) respecting the main breed and class of proportion of the batches generated. To respect the first constraint, the algorithm performed the assignments chronologically on a weekly basis. For each week, the sizes of each cow-calf producer and of each batch were set as the number of calves the provided or received on the given week. If there was an excess or deficit of calves provided, an option in the algorithm made it possible to carry the remaining cow-calf producers or the remaining batches over to the next week. Their sizes at the end of a weekly assignment were then simply added to their sizes at the next week.
haemolytica and its high virulence for naïve calves. Similarity, the value of α Mb reflected the much higher risk of being transmitted of M. bovis and the values of δ BRSV and δ PI-3 were null to reflect the absence of reactivation risks of the two viruses. The actual sanitary situation of the farms listed in the Terrena Production Bovine database was unknown. Therefore, we generated two sets of 10,000 sanitary situations (s) according to two scenarios of pathogen distribution across the farms. The first scenario assumed no correlation in the presence of pathogens at the cow-calf r i,s,c,k,naïve = β k E i,s,c,k 1 -Q i,s,k r i,s,c,k,reinf = γ k E i,s,c,k Q i,s,k r i,s,c,k,react = δ k Q i,s,k producers: each value of q j,k was randomly drawn according to a Bernoulli distribution of parameter π k (Table  1 ).
The second scenario assumed a positive correlation in the presence of the pathogens: each value of q j,k was randomly drawn according to a Bernoulli distribution whose parameter depended on the number of other pathogens present (see Additional file 2). In both scenarios, the average probability of presence of each pathogen across all farms was equal to its seroprevalence, as defined from the literature  [7, 22]. The average risk over all sanitary situations was noted R i,•,c. We considered two batch compositions: the historical one ( c = hist ) corresponded to the assignment of calves to batches as described in the Terrena Production Bovine database, while the optimized one ( c = opti ) cor- responded to the assignments performed by our algorithm. The difference between the risk indices for the two batch compositions was noted D i,s = R i,s,opti -R i,s,hist and its average over all sanitary situations was noted D i,•.
53 and 20. 07 (Figure  4B ). The distribution of R i,•,hist exhibited two peaks, respectively at low  (12)  and at high risk values  (26). The distribution of R i,•,opti only exhibited a low-risk peak, albeit lower, while the rest of the values was more evenly distributed. The value of p ' •,p (the proportion of calves expected to develop BRD with the optimized batch composition averaged over all sanitary situations) were systematically lower than p (the observed proportion of calves developing BRD) for any value of p between 0. 01 and 1 (Figure  4C ). The relative change in proportion of calves expected to develop symptoms after optimization was also strong, especially for lower values of p, with an average decrease of more than 20% if p ≤ 0. 57 and more than 30% if p ≤ 0. 29. To better understand how the algorithm impacted the risk index depending on the characteristics of the batches, we divided our set of 9701 batches into classes of (i) size, (ii) proportion of calves of the main breed, (iii) number of origins according to the database and (iv) weight homogeneity according to the database, and computed at the average values of R i,•,hist and R i,•,opti for each class (see Additional file 3).
Assuming that the observed proportion of BRD cases in the Terrena Production Bovine dataset corresponds to the incidence of 18. 5% estimated in a previous study concerning France  [7]  and that our risk index accurately represents the risks of developing BRD, we expect 12. 4% of cases with the optimized batch. Such a reduction of 35% would not only improve the health of cattle, but also their performance during fattening, as it is also impacted by BRD  [9]. Analysing more specifically how the algorithm affected the risk index depending on batch characteristics revealed two mains impacts (see Additional file 3). First, the algorithm preferentially improved the largest batches and the batches with the most calves of the main breed, which is consistent with the priority order in the creation of batches.
Second, it has also improved more batches with the greatest heterogeneity in weight and the most different origins according to the database from Terrena Bovine Production, i. e. those with a priori the greatest potential for improvement. However, the average risk index increased for the batches with the lowest number of origins (one or two) in the original database. This trade-off is consistent with the observed decrease in the number of batches with one or two origins, although the average risk index of these batches remained lower than those of the other classes of batch size. Besides, the improvement brought about by the algorithm remained visible for batches with as few as three different origins. This trade-off was also expected, and is at the core of a more general compromise between the goal of the cooperatives-globally decreasing the risks of developing BRD across all batches-and the individual goal of the fatteners, who are already minimizing the BRD risks in their own fattening units.
95 origins on average, while still manageable for a producers' organizations. The marginal improvement expected by further increasing the size of the window may therefore not be sufficient compared to the associated logistical costs of getting information from cow-calf producers further in advance. In this study, we tested our algorithm retrospectively on an existing database, and used the risk index we present to assess the expected impact of our algorithm on the probability of weaned calves developing BRD. However, these two tools are readily available, and could also be used by cooperatives and other intermediaries on a daily basis. On the one hand, the algorithm can be used as is, as part of decision support tools to design batches of young calves with less mixing of origins. On the other hand, the risk index can help quantifying the risks associated with given batch compositions, and possibly identify the batches most likely to present cases of BRD before the actual batches are even created.
Generally speaking, the lower the number of samples and the higher the ratio of features to sample size, the more a machine learning model will fit the noise in the data instead of a meaningful underlying pattern  (Jain & Chandrasekaran, 1982; Kanal & Chandrasekaran, 1971 ). In the case of our auditory spectrograms, algorithms would need to be trained/tested on a maximum of 120 samples with 4480 features each (as a benchmark: samples for each neural estimator in the main analyses were approximately 1600 and contained 1260 features each). Evidently, such disproportionate dataset is ill-suited for the same kind of estimators used on the ERPs: instability and overfitting would completely undermine the reliability (and therefore interpretability) of the outcome. Classifiers weights are projected onto the EEG sensor activation space.
This calls for (i) analysing 'congruent' models with potentially markedly different diversification dynamics but equal likelihood for any empirical tree  (69) , or (ii) implementing a solid hypothesis-driven approach, in which a small number of alternative hypotheses about the underlying mechanism are compared against data  (70). Based on an unparalleled comparative phylogenetic dataset containing 150 wellsampled species-level molecular phylogenies and 12,512 extant species, we evaluate the prevalence of macroevolutionary scenarios 1-4 (Fig. 1 ) as general explanations for Neotropical diversification at a continental scale (Q1), their drivers (Q2), and their variation across biogeographic units (Q3) and taxonomies (Q4). To address Q3, we previously identify evolutionary arenas of Neotropical diversification suitable for comparison.
Neotropical phylogenetic dataset We constructed a dataset of 150 time-calibrated clades of Neotropical tetrapods and plants derived from densely-sampled molecular phylogenies (Fig. 2  and Appendix 1). The dataset includes a total of 12,512 species, consisting of 6,222 species of plants, including gymnosperms and angiosperms (66 clades, representing 5-7% of the described Neotropical seed plants; Supporting Information Table  S1 ); 922 mammal species (12 clades, 51-77% of the Neotropical mammals; Table  S2 ); 2,216 bird species (32 clades, 47-59% of the Neotropical birds; Table  S3 ); 1,148 squamate species (24 clades, 30-33% of the Neotropical squamates; Table  S4 ); and 2,004 amphibian species (16 clades, 58-69% of the Neotropical amphibian diversity; Table  S5 ). Each clade in our dataset includes 7 to 789 species (mean=83.
4), with 53% of the phylogenies including more than 50% of the described taxonomic diversity (sampling fraction mean=57%). Clade ages range from 0. 5 to 88. 5 Myr (mean=29. 9; Fig. 2 , Fig. S1 ). Our dataset triples the data presented in previous meta-analyses of the Neotropics in terms of number of species, for example, 214 clades and 4,450 species in (4), and quadruples it in terms of sampling, with 20. 8 species per tree in  (4).
Variation of diversification dynamics across taxa, environmental drivers, and biogeographic units. We evaluated the prevalence of macroevolutionary scenarios 1-4 (Fig. 1 ) across environmental drivers (Q2), biogeographic units (Q3) and taxonomies (Q4) (see Methods). Table  2  summarize all the results. We found that species richness dynamics were related to particular environmental drivers (p=0. 003; Q2). Pairwise comparisons indicated that temperature-dependent models tended to best fit clades experiencing saturating (p=0. 049) and declining (p=0. 05) diversity dynamics. Meanwhile, uplift-and time-dependent models tended to best fit clades with exponentially increasing diversity (p=0. 03) (Fig. 5c ). In contrast, there is no evidence to suggest that species richness dynamics are related to particular geographic conditions when considering the whole dataset (Fig.
Many of the plant lineages fitting time-dependent models represent textbook examples of ongoing radiations; e. g. , centropogonids  (35) , Lupinus  (34) , or Inga  (96)  whose diversification has been associated with biotic drivers, such as the evolution of key adaptations or pollination syndromes. These factors are taxon-specific and were not evaluated in this study, where we focused on global phenomena. Our results therefore add support to the role of environmental and biotic factors as non-mutually exclusive drivers of macroevolutionary changes on Neotropical plants. Our plant dataset represents just a fraction (> 6,000 species, 66 clades; ~7% of the total species) of the vast diversity described in the region. As such, future investigations would be necessary to confirm the generality of the expanding trend for plants.
Data compilation Neotropical clades, representing independent radiations in the Neotropics, were pulled from large-scale time-calibrated phylogenies of frogs and toads  (95) , salamanders  (119, 120) , lizards and snakes  (121) , birds (56) (including only species for which genetic data was available), mammals  (122, 123) , and plants  (124). To identify independent Neotropical radiations, species in these large-scale phylogenies were coded as distributed in the Neotropicsdelimited by the World Wide Fund for Nature WWF (100) -or elsewhere using the R package speciesgeocodeR 1. 0-4 (125), and their geographical ranges extracted from the Global Biodiversity Information Facility "GBIF" (https://www. gbif. org/), the PanTHERIA database (https://omictools. com/pantheria-tool), BirdLife (http://www.
Estimating the tempo and mode of Neotropical diversification a) Diversification trends based on traditional diversification rates We compared a series of birth-death diversification models estimating speciation (λ) and extinction (μ) rates for each of the 150 phylogenies with the R package RPANDA 1. 9 (127). To make these results comparable with those derived from pulled diversification rates below, we followed a sequential approach by including models of increasing complexity. We first fitted a constant-rate birth-death model and compared it with a set of three models in which speciation and/or extinction vary according to time (78): λ(t) and μ(t). For time-dependent models, we measured rate variation for speciation and extinction rates with the parameters α and β, respectively: α and β > 0 reflect decreasing speciation and extinction toward the present, respectively, while α and β < 0 indicate the opposite, increasing speciation and extinction toward the present.
Neotropical bioregionalization We used a quantitative approach to identify geographic units of long-term Neotropical evolution. We divided the Neotropical region into 13 operational areas based on the WWF biome classification  (100)  and similar to other studies, e. g. , (4, 95) -Amazonia, Atlantic Forest, Bahama-Antilles, Caatinga, Central Andes, Cerrado, Chaco, Chocó, Guiana Shield, Mesoamerica, the Northern Andes, temperate South America, and an "elsewhere" region (see SI for detailed definitions) -and assessed the distribution in these areas of the 12,512 species included in our 150 phylogenies. Georeferenced records were downloaded for each species through GBIF using the R package rgbif 0. 9. 9  (131). We removed points with precision below 100 km, entries with mismatched georeference and country, duplicates, points representing country capitals or centroids, using the R package CoordinateCleaner 1.
0-7  (132). Then, we created 13 georeferenced polygons delimiting each operational area using the WWF terrestrial ecoregions annotated shapefile in QGIS, and species were assigned to each polygon according to coordinate observations using the R package speciesgeocodeR 1. 0-4. GBIF records can result in an overestimation of widespread ranges  (133) , so species distributions were manually inspected for completeness and accuracy with reference to databases (AmphibiaWeb 2018, Uetz et al. 2018, GBIF. org 2018, IUCN 2018). Based on the number of species belonging to each phylogenetic clade in the 13 ecoregions, we created a species abundance table (number of species per region per clade) that formed the basis for subsequent analyses. The number of species distributed in each region within each clade were transformed using Hellinger transformations to account for differences in species richness between clades, and the Morisita-horn distance metric was selected to quantify pairwise dissimilarities of regional assemblages using the R package vegan 2.
Note that for the elevation, in our dataset most clades fell into the mixed category, with montane species most often occurring within clades of lowland species, and rarely forming a clade of their own. To account for this pattern (and minimize the number of clades classified as "mixed"), we performed additional analyses pooling "lowland" and "montane" categories and considered a clade "mixed" only if contained species in lowlands, montane and highlands (lowland- montane [n=124], highland [n=6], mixed [n=20]). We assessed the phylogenetic signal of each multi-categorical trait (i. e. , biogeographic units, elevation, diversity dynamics, and environmental drivers) using the statistics (136) over a phylogeny including one tip for each of the 150 clades represented in this study. This tree was constructed using TimeTree  (137).
High -value indicates strong phylogenetic signal. can be arbitrarily large, and thus significance was evaluated by comparing inferred -values to the distribution of values when the trait was randomised along the phylogeny. We evaluated the phylogenetic signal of continuous traits (i. e. , diversification As any continuous (Kr=0. 06, p=0. 6; Kλ=0. 07, p=0. 4; Krp=0. 07, p=0. 6) or multicategorical trait displays phylogenetic signal (Fig. S10 ), suggesting that the distribution of trait values is not explained by the phylogeny itself, statistical tests were conducted without applying phylogenetic corrections to account for the non-independence of data points. Fisher's exact test was used in the analysis of contingency tables, performing pairwisecomparison with corrections for multiple testing  (140) , and Kruskal-Wallis Tests for comparing means between groups.