dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , implying line rank changes) across environments, and (iii) the greater similarity of the MS environment with Marchouch than with Alger for GE interaction pattern. The latter result was confirmed by the genetic correlation for yield of the whole set of lines across pairs of sites, which was moderately positive between the MS environment and Marchouch (r g = 0. 50, p < 0. 001), and non-significant (p > 0. 05) between Alger and the MS environment (r g = -0. 02) or Marchouch (r g = -0. 14). Alger proved distinct from the other environments also because it displayed no phenotypic correlation of grain yield with onset of flowering (r p = -0. 09, p > 0. 10), in contrast with the negative correlation (p < 0. 001) observed for Marchouch (r p = -0. 45) and the MS environment (r p = -0. 81). All these results supported the redefinition of a GS model for grain yield under severe terminal drought based on pooled phenotypic data from the MS environment and Marchouch.
Predictive Ability of Genomic Selection Models (Experiments 1, 2, and 3) As expected, the number of available polymorphic SNP markers issued from the genotyping-by-sequencing (GBS) analysis increased as a function of the threshold of allowed genotype SNP missing data, reaching the highest value for the threshold of 50% (Data repository S1). This SNP missing data threshold implied 4364 markers for the SNP calling criterion requiring at least six aligned reads per locus, and 7521 for the criterion requiring at least four reads. Only results for the six-read criterion are reported hereafter, because this criterion provided predictive ability values that were about equal or slightly higher than those provided by the four-read criterion in all analyses. Polymorphic markers for this criterion were 165 for the SNP missing data threshold of 10%, 647 for 20%, 1713 for 30%, and 3018 for 40%.
The best-predicting GS model for line mean yield across Lodi's MS environment and Marchouch (which was selected for GS in Exp. 4, 5, and 6) was BL trained on pooled population data with 20% SNP missing data threshold, whose predictive ability was 4% higher than that of the BL model with the same configuration but trained on individual populations (which was the alternative GS model used for Exp. 4). In agreement with results of location similarity for GE interaction, the cross-environment predictive accuracy provided by best-predicting GS models was moderate for predicting Marchouch data from data of the MS environment or vice versa (range 0. 35-0. 46), and very low for predicting Alger data from MS environment data or vice versa (<0. 06) or for predicting Alger data from Marchouch data or vice versa (<0.
12) (Supplementary Table  S2 ). The predictive ability of the top-performing GS model constructed from line mean yields across the MS environment and Marchouch was about 0. 26 for Marchouch, 0. 71 for the MS environment, and 0. 03 for Alger (Table  2 ). Cross-population predictive ability was investigated for the hypothesis of GS models trained on data of either one or two other connected RIL populations, considering by turns all possible combinations of training and validation populations. The assessment focused on line mean yield across Lodi's MS environment and Marchouch (whose data were exploited for GS proof-of-concept experiment work), and line yield in Alger (whose response pattern contrasted with that observed in the other two environments). Top-performing GS models for cross-population predictive ability were generally Bayesian Lasso with 20% to 40% genotype SNP missing rate.
On average, the loss of prediction for the top-performing GS model passing from intra-population prediction (Table  3 ) to cross-population prediction (Table  4 ) was only 9% (0. 630 vs. 0. 692) for mean yield across Lodi's MS environment and Marchouch, and 18% (0. 151 vs. 0. 184) for yield in Alger, for models trained on joint data of two populations. The loss of prediction was distinctly greater, namely, 43% for mean yield across Lodi's MS environment and Marchouch, and 46% for yield in Alger, for models trained on one population. Table  4. Cross-population predictive ability of the top-performing of models constructed by Bayesian Lasso or Ridge Regression BLUP for breeding value of pea lines belonging to three connected RIL populations, for grain yield in the agricultural site of Alger (Algeria) and mean grain yield across a managed drought stress (MS) environment (Lodi, Italy) and the site of Marchouch (Morocco).
One Two Yield, Alger 0. 099 0. 151 Mean yield, MS Lodi, and Marchouch 1 0. 397 0. 630 Averaged across results for three RIL populations encompassing 288 lines overall, considering models with five possible thresholds of genotype SNP missing data (10%, 20%, 30%, 40%, 50%). 1 Using phenotypic data averaged across the two environments.
Actually, one such panel exhibited high GS prediction accuracy for pea seed weight and moderate accuracy for number of seeds per plant by using only 331 well-distributed markers  [31]. GS models constructed from data of severely drought-prone environments such as Marchouch and the MS environment displayed nearly no value for a moderate drought-stress site such as Alger and vice versa, indicating that GS could hardly alleviate the difficulty to cope with the large GE interaction across stress levels. Breeders could use GS (or PS) to breed for (i) specific adaptation to severe-stress or moderate-stress environments, in the presence of high rainfall variation between sites and only moderate within-site rainfall variation in their target region (as it may be the case for geographically large target regions); or (ii) wide adaptation, by selecting for average value of the breeding values predicted by one GS model for severe-stress environments and another for moderate-stress environments (or by parallel PS selection across severe-stress and moderate-stress environments), in the opposite situation.
Multi-Environment Phenotyping and Data Analysis of RIL Populations (Experiments 1, 2, and 3) Phenotyping data were generated for 288 semi-dwarf, semi-leafless lines belonging to three connected RIL populations originated by single-seed descent from paired crosses between Attika (a European cultivar described as a spring-type), Isard (a French winter-type cultivar), and Kaspa (an Australian cultivar). These parent cultivars displayed fairly similar phenology and cycle duration along with high and stable grain yield and other positive agronomic characteristics across environments of Northern and Southern Italy  [39, 57]. The RIL populations are coded henceforth as 'A × I', 'K × A', and 'K × I' from the initials of their respective parents. The 288 lines represented a large subset of the 315 lines, 105 for each RIL population, that were phenotyped by Annicchiarico et al.
2 and 3. Dry grain yield was measured on a plot basis after estimating seed moisture by oven-drying seed samples at 90 • C for four days. Onset of flowering (as the number of days from April 1 to when 50% of plants in the plot had at least one open flower) was also recorded. Grain yield and onset of flowering data of the RIL populations underwent a preliminary analysis of variance (ANOVA) that verified the occurrence of within-population variation for each experiment. Yield data of RIL material and the parent and control cultivars underwent a combined ANOVA including the factors genotype, environment, and block within environment. Experiment errors previously tested by Hartley's test proved to be not homogeneous (p < 0. 01), implying some loss of sensitivity for the F tests of genotype main effects and GE interaction in the combined ANOVA  [58]  that had no practical importance because of the high statistical significance (p < 0.
The latter, more conservative criterion aimed to minimize the risk of imperfect SNP calling arising from residual heterozygosity in the genotyped material. Markers that were monomorphic or with minor allele frequency <2. 5% were removed. The data set was filtered for increasing levels of allowed genotype SNP missing values, excluding markers whose missing rate exceeded fixed thresholds of 10%, 20%, 30%, 40%, and 50%. SNP missing data were estimated using the K-Nearest neighbors imputation algorithm (K = 4) coupled with the simple matching coefficient distance function  [67]. SNP marker data for the five thresholds of genotype missing data are provided in the Data repository S1. We considered two GS models for yield prediction that stood out for predictive ability in a previous model comparison for pea grain yield limited to Lodi's MS environment  [30] , i.
e. , Bayesian Lasso (BL;  [68] ) and Ridge regression BLUP (rrBLUP;  [69] ). While rrBLUP assumes that the effects of all loci have a common variance, BL assumes relatively few markers with large effects, allowing different markers to have different effects and variances  [70]. Bayesian models assign prior densities to markers effects, thereby inducing different types of shrinkage, obtaining the solution by sampling from the resulting posterior density  [68]. For each of the two SNP calling criteria, we assessed GS models trained either on pooled data of the three RIL populations or the individual populations, with five possible genotype SNP missing data thresholds (10%, 20%, 30%, 40%, 50%). BL and rrBLUP models with different combinations of data training and SNP missing data thresholds were assessed for intra-environment predictive ability, as well as for ability to predict line mean yields across MS and Marchouch environments (whose application for actual GS of drought-tolerant lines was supported by GE interaction analysis results).
Finally, we explored the cross-population predictive ability of GS by assuming model training on data of one connected RIL population or on pooled data of two connected populations for all possible combinations of training and validation populations, for two traits of practical interest represented by yield in Alger and mean yield across MS and Marchouch environments. GS regression modelling, cross-validations, and predictive ability estimation were performed using the R package GROAN  [72] , adopting 50 repetitions of a 10-fold stratified cross-validation scheme for each analysis. The MAS criterion for intrinsic drought tolerance was defined on the basis of the GWAS reported in  [30]  for 315 lines belonging to the same RIL populations, which found 10 linked SNP markers with association score ≥2.
GS was applied using either the top-performing model trained on joint data of the three RIL populations, or the models trained on data of each separate population (RIL population-specific models). The set of 288 RILs was split into two subsets. The former set included 30 randomly chosen lines per RIL population, which acted as independent lines for GS and PS. The latter set included the remaining lines of the three populations, whose data were used for GS model definition. For each GS model, we selected for each RIL population the three lines out of 30 that were top-ranking for predicted yield. Likewise, we selected phenotypically for each RIL population the three lines out of 30 that were top-ranking for mean yield across MS and Marchouch environments. In addition, we performed environment-specific PS for Lodi's MS environment and for Marchouch, by selecting the three top-yielding lines out of 30 separately for each environment.
GS-based predictions were exploited to select the two top-ranking lines, two mid-ranking lines (ranks 15 and 16), and the two bottom-ranking lines out of 30 lines within each cross. The 18 selected lines and the three parent lines were evaluated for grain yield, aerial biomass, and onset of flowering in Lodi's MS environment by an RCB experiment with four replications (Exp. 5) whose management was identical to Exp. 4. The data analysis contemplated two.
Acknowledgments: We are grateful to  E. C. Brummer ,  Y. Wei  and  B. Ferrari  for their contribution to generation of GBS data,  J. Kreplak  and  G. Aubert  for verifying the position of key GBS-generated markers on the draft genome of pea, and  P. Gaudenzi ,  M. R. Herma ,  A. Mebarkia ,  A. Passerini ,  S. Proietti , and  M. Redha El-Amine , for technical assistance.
Funding: The multi-environment study and the development of GS and MAS procedures were funded by the FP7-ArimNet project 'Resilient, water-and energy-efficient forage and feed crops for Mediterranean agricultural systems (REFORMA)'. The assessment of selections issued by GS and MAS was funded by the FP7 project 'Legumes for the agriculture of tomorrow (LEGATO)' (grant agreement No. 613551).
For example, appropriately chosen and defined traits can help identify lineages that share similar life-history strategies for a given environmental regime  12, 13. Documenting and understanding the diversity and composition of traits in ecosystems directly contributes to our understanding of organismal and ecosystem processes, functionality, productivity and resilience in the face of environmental change  [14] [15] [16] [17] [18] [19]. In light of the multiple applications of trait data to address challenges of global significance (Box 1), a central question remains: How can we most effectively advance the synthesis of trait data within and across disciplines? In recent decades, the collection, compilation and availability of trait data for a variety of organisms has accelerated rapidly. Substantial trait databases now exist for plants  [20] [21] [22] [23]  , reptiles  24, 25  , invertebrates  23, [26] [27] [28] [29]  , fish  30, 31  , corals 32 , birds  23, 33, 34  , amphibians 35 , mammals  23, [36] [37] [38]  and fungi  23, 39  , and parallel efforts are no doubt underway for other taxa.
Current barriers to global trait-based science Despite the recognized importance of traits, several common research practices limit our capacity for meaningful synthesis across the Tree of Life. These practices include failure to publish usable datasets alongside new findings  40  , missing or inadequate metadata  41  , minimal descriptions of methods used to collate, clean and analyse trait datasets in published works  42  , and inadequate coordination between researchers and institutions with common goals, such as filling strategic spatial or taxonomic gaps in trait knowledge  43, 44. Our limited ability to access and redistribute trait data contributes to the widespread reproducibility crisis within science  45. Any study relying on data that cannot easily be re-used introduces barriers to verifying the claims made by those studies and thereby questions the reproducibility of the science  46  , which is becoming of prime importance to many scientific journals.
Opportunities exist for expanding the spatial and taxonomic coverage of trait observations, particularly by strengthening interdisciplinary connections across single organismic groups. Despite certain plant traits (for example, growth form, height and leaf size) being carefully catalogued in taxonomic species descriptions  47  , these data have only recently been exchanged with large-scale databases such as TRY  21  or BIEN (http://bien. nceas. ucsb. edu/bien/). Although several informatics challenges in biodiversity science have now been overcome (for example, synthesizing global species occurrence information (https://www. gbif. org/) and sharing genetic data on individuals (https://www. ncbi. nlm. nih. gov/genbank/)), trait science lacks a vision for achieving global integration across all organisms.
Box 1 | Research programs dependent on comprehensive trait data across the Tree of Life Access to open data on the traits of all organisms will allow the pursuit of long-standing questions in ecological science, including: Defining major axes of strategy variation across the Tree of Life. Measurements of organismal traits can be used to identify the position of species along trade-off spectrums that shape fitness. For instance, traits (for example, leaf nitrogen content, leaf mass per area, seed mass and maximum height) have been used to capture trade-offs between plant species at global scales  91. With access to open data on the traits of a wide breadth of organisms, we will be able to identify major axes of functional specialization across the Tree of Life. Traits such as adult body mass, offspring mass at independence, mass-specific metabolic rate, and body temperature capture core differences in the ecological strategies within and across groups  [92] [93] [94].
Strengthening predictions of the effects of global change on biodiversity. Traits already provide valuable information for models predicting global change impacts on the biosphere. Access to large-scale data on plant traits allows the distribution of traits to be captured in Earth system models rather than only modelling competition among broad plant functional types  [101] [102] [103]. Species-based predictions from correlative niche models may also benefit from the integration of data on species biology to capture mechanistic links between function and the environment  [104] [105] [106] [107]. The next generation of Earth system models may also benefit from integration of open trait data for (at least) two types of organisms: (1) terrestrial vertebrates; and (2) microbes. Terrestrial vertebrates provide key ecological functions (for example, dispersal and disturbance  108  ) and their loss may result in significant changes to ecosystem function  109.
Openness reinforces the scientific process by allowing increased scrutiny of methods and results, resulting in the deeper exploration of findings and their significance  42, [48] [49] [50] [51]. The scope of trait science would increase if researchers and institutions: (1) made datasets available in machine-accessible formats under clear licensing arrangements; (2) created and adopted standardized protocols, handbooks or metadata formats for data collection, documentation and management (see refs. 48, 49 ); and (3) created human-centred networks to reduce the complexity of integrating existing data from disparate sources (for example, specimens, published literature, citizen-science initiatives  50, 51  and large-scale digitization efforts). These different sources exhibit systematic differences in error rates, validation, context, reproducibility and objectivity relative to field-collected trait observations.
Introducing the Open Traits Network The Open Traits Network (OTN) is a collaborative initiative for accelerating trait data synthesis. Specifically, it is a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across all organisms. We promote five main objectives built upon Open Science ideals that could transform trait science: (1) Openly sharing data, methods, protocols, codes and workflows. (2) Citing original data collectors and providing scholarly credit. (3) Providing appropriate metadata together with trait observations. (4) Collecting trait data following reproducible, standardized methods and protocols (when available), or committing to their development. (5) Providing training resources in trait collection and database construction using Open Science principles.
We envision a future for trait research where protocols for data exchange and re-use are transparent, research findings are reproducible, and all trait data (either newly collected or from legacy sources) are openly available to the research community and broader public. While several network models exist in trait research (Fig. 2 ), the OTN adopts a decentralized but connected structure with an emphasis on bringing people together through data and expertise. Often, groups building smaller-scale databases do so in isolation, using their own tools and workflows tailored to their research question; they are decentralized and disconnected (Fig. 2a ). Decentralization has certain advantages, including retaining the power to determine which traits are most useful in a study system and how they should be compiled.
Some successful large-scale initiatives have followed the centralized and connected network model (for example, the Global Biodiversity Information Facility (GBIF; https://www. gbif. org/) and GenBank (https://www. ncbi. nlm. nih. gov/genbank/)). These platforms mandate strict data exchange protocols to facilitate synthesis using standardized vocabularies (for example, the Darwin Core  58  and Humboldt Core  59  ). These protocols have been central to the explosive growth of biodiversity data as they facilitate the exchange of information using common data formats  [58] [59] [60]. Ontologies that provide unified terms and concepts necessary to represent traits have been developed (for example, Uberon, the multispecies anatomy ontology for animals  61  , and TOP, the Thesaurus of Plant characteristics  62  ).
Birds Plants Mammals Trait data were downloaded from refs. 25, 34, 87. We counted the number of traits present across these datasets for each species and mapped those onto phylogenies using posteriors  37, 88  and a random subset of plant species within a single phylogeny  89. Terminal branches (representing species) and ancestral lineages (using ancestral state reconstruction  90  ) were coloured according to the number of reconstructed traits. Note that this is an exploratory analysis conducted purely to show variation in the availability of trait data across taxonomic groups. These provide integration with other data types (for example, genetic and environmental) and their corresponding ontologies (for example, Gene Ontology  63  and Environmental Ontology  64  ). Despite these successes, we argue that a centralized and connected network structure will not facilitate trait data synthesis.
Trait observations are highly nuanced and hierarchical. Describing multiple aspects of a phenotype for any organism with traits is not amenable to a simplified set of exchange fields that apply across the Tree of Life. While the centralized and connected model (Fig. 2b ) does have benefits, it lacks the necessary flexibility to connect trait data where ontologies and exchange formats do not exist. The likely result is that established trait networks will remain isolated and disconnected. The decentralized but connected model (orange connections in Fig. 2c ) adopted by the OTN maintains the key advantages of a decentralized network (for example, taxon-or discipline-specific decision making) while enhancing the level of connectivity among groups, allowing for easier sharing of expertise, tools and data.
How (and why) to participate in the OTN The OTN seeks to broaden its membership by lowering barriers to inclusion and advocating for approaches to trait science that benefit data custodians. New members can join the OTN via our website (www. opentraits. org) through two mechanisms: (1) adding a member profile (for example, name, location, expertise and collaboration statement); and/or (2) registering their open-source (or embargoed) trait datasets in the OTN Trait Dataset Registry (see Activity 1). The registry contains metadata for trait datasets and links users to the open dataset. New entries to the registry will be reviewed by OTN members before being added. This step will facilitate interaction between new and established OTN members and encourage deeper collaboration. Once registered, members will receive regular updates about the OTN, including newly registered trait datasets, notifications about upcoming chances for face-to-face meetings, and funding opportunities.