dataesr/scientific-paragraphs-categorization huggingface dataset:
Dataset completeness. Assessing dataset completeness is crucial for identifying knowledge gaps and highlighting future data collection priorities. The overall completeness of the dataset can be assessed based on species coverage, geographic coverage, and trait resolution. The EuroBaTrait 1. 0 dataset has broad taxonomic coverage (Fig. 2 ). At the trait category level, the species coverage of trait information is (i) complete or nearly complete (i. e. at least one trait from these categories encompasses >95% of the species) for foraging habitat (100%), diet (100%), distribution (100%), acoustic signature (98%), roost type (98%), genetic composition (96%) and morphology (96%), (ii) moderately complete (between 75% and 95%) for life history (94%) and pathogens (72%), and (iii) at a low level of completeness (<75%) for spatial behaviour (66%), climatic associations (60%), phenology (60%) and physiology (55%).
3 ). Nevertheless, this bias is more evident for some traits (e. g. foraging habitat and spatial behaviour) than others (e. g. roost type and pathogens, Supplementary Material 4) because few traits and species have been comprehensively measured in many locations throughout Europe. Finally, when looking at the trait resolution, quantitative data (continuous and discrete) were available for 71% of the traits. This proportion, however, largely varies between trait categories (e. g. 0% for roost and 100% for morphology). Increasing species and geographic coverage as well as trait resolution (from categorical to quantitative data, whenever possible) remains a real challenge that needs to be overcome to implement a more robust trait-based approach  108. To that end, enhancing collaboration among researchers/practitioners is the way forward, as witnessed here with the collection of the largest morphological dataset on bats in Europe if not in the world (measurements on body mass of ca.
55,000 individuals from 37 species and forearm length of ca. 120,000 individuals from 39 species). Current limitations. We acknowledge that many traits could not be compiled for the EuroBaTrait dataset 1. 0 at their finest resolution. This is particularly the case for the diet trait category. There are many published and unpublished studies in different languages depicting European bat diets using traditional (e. g. microscopic faecal or stomach content analysis) or molecular (e. g. DNA metabarcoding) methods. However, gathering such a large amount of information at the highest resolution was not feasible at this stage, and we aim to provide a much more comprehensive treatment of this trait category in future releases of the EuroBaTrait dataset. Trait quality, here defined as the degree of confidence in a trait value, is another important component to consider when assessing dataset completeness.
technical Validation We employed two main strategies to ensure the accuracy in the data included in the EuroBaTrait 1. 0 dataset. First, we looked for erroneous data in the dataset using a series of plots (e. g. boxplots and frequency histograms to detect potential outliers across species in continuous data) alongside traditional statistical validation techniques (e. g. outlier test) in R v4. 1. 1  109. When an outlier was detected, we looked at the original sources and Fig. 3  Number of trait categories (genetic composition, physiology, morphology, acoustic signature, climatic associations, foraging habitat, roost type, diet, spatial behaviour, life history, pathogens, phenology, and distribution) provided at the country level across all species. For sake of clarity and for highlighting gaps in geographic coverage, we did not consider in this map traits provided across a given species' range or at regional level.
Details on each trait category are provided in Supplementary Material 4. cross-checked with other sources (e. g. Handbook of the Mammals of Europe) the plausibility and reliability of the observation. Second, we asked several experts to conduct data quality control and error detection in their field of expertise. Experts on a given bat species revised the trait associated with that species, while experts on a given trait category revised all traits from that category. This allowed us to cross validate the full dataset. Furthermore, as we provide the original references for each trait value collected during the literature and dataset search and describe in detail the methods used for the traits computed with data from the large-scale bat monitoring programs, users can assess the validity and accuracy of the original sources (note that >75% of our data sources are peer-reviewed articles published in indexed scientific journals) and method used.
Usage Notes overview. Here we present the first comprehensive dataset of functional traits in European bats (Fig. 4 ). The dataset has numerous applications in community ecology, macroecology, biogeography, conservation biology, ecophysiology, evolutionary biology, and virology/epidemiology for species and/or community approaches. For example, the dataset enables researchers to investigate functional redundancy and complementarity of coexisting bat species, to establish linkages among functional traits, as well as between functional traits and ecosystem functioning, and to assess trait-environment relationships and their impacts on bat distributional patterns  92. This in turn is relevant for addressing relevant questions in conservation and global change biology. The paucity of trait data on bats has, until now, prevented the testing of large-scale correlations between the intrinsic characteristics of species and major drivers of decline in this taxon  11, 110, 111.
The EuroBaTrait dataset will contribute to assessing and quantifying the role of each trait in determining bats' risk of extinction. EuroBaTrait is also designed to be used to investigate functional responses of bat assemblages to various environmental drivers of change and their impacts on ecosystem functions and services  5. This could be done by relating environmental variables or processes to traits, i. e. either by calculating complementary and integrative functional diversity metrics such as functional richness, evenness or dispersion  112  or by computing individual Community-Weighted Mean trait values  113. Such trait-based approaches allow the inference of several key properties of bat communities including effective functional originality based on both rarity and distinctiveness of species in functional trait spaces to better support bat conservation programmes  114, 115.
4] [45] [46] [47] [48]  ). These trait-based analyses constitute a methodological corpus that is still seldom used by bat ecologists, while it can provide a relevant standard analysis approach to study the response of bat functional diversity and trait assemblages to bioclimatic variables, or environmental degradation and conservation measures (Fig. 1 ; see for example  17, 20, 30, 32, 38, 41, 51, 67, 83, 89, 116  ). Finally, bats are increasingly acknowledged for performing key ecological functions in semi-natural habitats as well as in production forests and agroecosystems, that ultimately provide valuable ecosystem services  [117] [118] [119] [120]. As a result, the functional trait-based characterisation of bat communities to determine their role in ecological networks will greatly benefit from the EuroBaTrait dataset in future bat ecological research.
From a static to a dynamic trait dataset. The data descriptor was peer reviewed in 2023 based on the data available on the platform at the time  105. It is our intention to keep improving and enriching the dataset over time, so while the paper presents version 1, new versions may be released in the future. For this reason, we invite anyone to (i) share their data in a standardized way via the R Shiny app at https://jasja. shinyapps. io/ClimBats/ and (ii) cite the dataset stored at figshare according to the specific version used as well as this publication, when using all or part of the dataset. We also encourage the wider bat research community to build on this first version and engage in updating it via meetings and workshops during dedicated bat congress (e. g. European Bat Research Symposiums).
towards an open access individual-level trait dataset. We encourage researchers and practitioners to collect, store publicly and publish trait values at the individual level. As most functional traits tend to vary greatly among individuals according to age, sex, condition, state, gene pool and behavioural personality  121  , this individual-level variation may persist across time and space and upscale at the local community level (e. g. mean individual specialisation or mean home range size and composition)  122, 123. Natural populations of a given species are composed of sets of individuals that occupy subsets of the species' niche. For instance, several studies of bats provide evidence of individual specialisation in diet  124. 6] [127]  ). Within-individual data are particularly valuable in seasonal species like bats, which have to cope with reproduction, hibernation and/or migration.
While we acknowledge that some traits can only be measured at the species level, the lack of individual-level trait information we observed during data collection likely arises from the fact that: (i) only summarised information (e. g. mean/median, accompanied with an estimate of precision at best) has traditionally been provided in the published literature, (ii) researchers/practitioners and/or institutions have limited time and resources, as well as almost no formal recognition (although the situation is now improving in many countries), for publishing such data  128. We provide in Supplementary Material 5 trait information collected at the individual level, but we strongly encourage the bat expert community and associated institutions to share and open their data (FAIR Data principles) to enable the progression from a species-level trait dataset to an individual-level trait dataset.
Genetic composition Trait categories Representativeness Traits Genus Fig. 4  Representativeness of the EuroBaTrait 1. 0 dataset. Summary of the 13 trait categories included in the trait dataset and their coverage in terms of number of traits included under the category, number of species and genera with data for at least one of the traits under the category, number of countries with data for at least one trait under the category (regional or global data were not included in these calculations), number of samples (individuals, calls or nights) used to generate values for the traits, and number of data sources on which the trait values are based. references.
acknowledgements This article is based on work from  COST Action CA18107 'Climate change  and bats: from science to conservation -ClimBats' (https://climbats. eu/),supported by  COST (European Cooperation in Science and Technology). We deeply thank the  Vigie-Chiro volunteers  for data collection, and the  CC-IN2P3  and  PCIA-MNHN  for providing computing and storage facilities to process and archive in the long-term all the acoustic recordings of  Vigie-Chiro , and  Didier Bas  for his help in this process. We thank the  Bat Conservation Trust National Bat Monitoring Programme (NBMP)  for providing data, and especially  Phillip Briggs  for collating the data. We thank the  Groupe Mammalogique Breton ,  Office National des Forêts, Groupe Chiroptères Corse, Coordination mammalogique du Nord de la France, Chauve-Souris Auvergne, Ligue pour la Protection des Oiseaux -Rhône-Alpes, Groupe Chiroptères Poitou-Charente, Groupe d'Étude et de Protection des Mammifères d' Alsace, Labex ECOFECT (Université de Lyon ),  Groupe Chiroptères de Provence, Commission de Protection des Eaux, du Patrimoine, de l'Environnement, du Sous-sol et des Chiroptères de Lorraine, Commission de Protection des Eaux, du Patrimoine, de l'Environnement, du Sous-sol et des Chiroptères de Franche-Comté ,  Benjamin Allegrini ,  Gildas Monnier ,  Raphael Colombo ,  Arnaud Le Houédec ,  James Jean-Baptiste ,  Rémy Grignon ,  Marie-Jo Dubourg-Savage , and all bat workers who have participated in the CACCHI programs.
Funding We would like to acknowledge funding from the  EU Framework Horizon 2020  through the  COST Action CA18107 'Climate change  and bats: from science to conservation -ClimBats' (https://climbats. eu/). Jeremy Froidevaux  was funded by the  Région Bretagne  (SAD grant number  19041 ) and the  Leverhulme Trust  (grant number:  ECF-2020-571 ). Nia Toshkova  was funded by the  Bulgarian National Science Fund  ( CP-06-COST/15  from  16. 12. 2020 )and а PhD Fellowship from  Karoll Knowledge Foundation. Orly Razgour  was supported through a  Natural Environment Research Council   Independent Research Fellowship  ( NE/M018660/1 ).
Results: We have created an ontology-formatted in the Web Ontology Language and adhering to the practices of the Open Biomedical and Biological Ontology Foundrywhich provides a rigorous, extensible and machine-actionable framework that can improve biogeographic analyses and interoperability between systems. One main class and 20 individuals per class were implemented. Main Conclusions: BIOREALM encodes a model-theoretic view of endemism using semantic web approaches, offering new avenues to express and analyse biogeographic units. This approach offers a means to identify monophyletic biogeographic areas for conservation, based on specific combinations of monophyletic endemic taxa. Such an ontology provides knowledge representation solutions which supports interoperability along the FAIR (Findable, Accessible, Interoperable, Reusable) principles, thus fostering more consistent ecological informatics.
Thus, we propose that the application of ontological techniques, that is definition of terms and their relationships to the ambiguities in protected area semantics is a timely contribution to conservation. We suggest that a coherent semantic representation of the understandings of biogeographic areas and protected areas will provide more efficient and standardized informatics, supporting research and decision-making processes. we do not address the metaphysics of biogeographic areas and areas of endemism (see  Crother & Murray, 2011) , but we offer a formalization of knowledge for comparative biogeography. We Here we present an example of the implementation of this model. We applied these methods to a study and particularly, its associated dataset, published by  Murphy et al. , 2019. BIOREALM is expresses in OWL following the technical guidelines of the OBO foundry and Library.
| Individuals by class in BIOREALM We defined 20 individuals (i. e. instances of a class) corresponding to the 20 Australian sub-regions described in  Ebach et al. (2015). Each individual was asserted to be part of the ENVO: Australasia ecoregion. We added exact and related synonyms as described above, as well as URLs to related resources using the annotation database_cross_reference (http://www. geneo ntolo gy. org/forma ts/oboIn Owl#hasDb Xref) (e. g. the World Wide Fund (www. wwf. org), Ecoregions® (https://ecore gions. appsp ot. com/) and the Interim Biogeographic Regionalisation for Australia v7 https://data. gov. au/data/datas et/ inter im-bioge ograp hic-regio nalis ation -for-austr alia-ibra-versi on-7regions). In each instance, any monophyletic endemic organism noted is linked to its taxon profile in the NCBI taxonomy database.
Using a model of embedded (i. e. hierarchical) classes structured by the monophyly constraint, we reconcile elements of Crother and Murray's metaphysical Ontology on areas of endemism through formalization via logics and mathematics  (Guarino et al. , 1995). More practically, BIOREALM provides support to semantically describe and align data and databases in an open data and open model following the FAIR principles  (Wilkinson et al. , 2016). Further developments will focus on linking descriptions of biomes and other ecological units related to those biogeographic areas to extend and enhance both BIOREALM and ENVO. Moreover, we seek to apply these ontologies to augment software such as the Global Biotic Interactions platform (https://www. globa lbiot icint eract ions. org/) and more generic use of semantic web resources to annotate, mobilize and analyse data.
Constraining consists in setting bounds for some fluxes and restraining the possible solutions of the model to the ones compliant with the constraints. To find these constraints, fluxomic datasets can be obtained from different experimental flux determination methods. With isotopic labeling (like 13-C), one can follow the path of a nutrient in the metabolic network  [6]. With metabolomics methods, one can derive metabolic fluxes from metabolite concentrations and possibly in a time-resolved approach  [7]. With transcriptomics methods, RNA sequencing data is used as input for models estimating fluxes in an indirect fashion, which, recently has even been achieved at the single-cell level  [8]. In some cases, data integration of several -omics methods is possible, constituting a state-of-the-art multi-omics data integration for Metabolic Flux Analysis (MFA)  [9] [10]  [11].
Conversely, the latter does a good job predicting the outcomes of complex biological processes using large input/output datasets, but does not provide understanding of the underlying mechanisms. The pros of one are the cons of the other, suggesting that a hybrid approach should be developed towards enabling a symbiotic relationship between both. An emerging field, Scientific Machine Learning (SciML), aims to develop such hybrid models  [13]. The main advantage of hybrid modeling is to bring models that comply well with experimental results via ML, but that also take mechanistic insights from MM. Recently, Nilsson et al. [14]  showed good performances of such a model, developed for signaling networks, where they used the supposed topology of a signaling network as a platform for learning on experimental data, therefore displaying more insights than a black-box approach.
SBML model Size Ratio Method (1)   Measure (2)  Size NN type Architecture Timestep Nbr Param (3)  Nbr epoch Time per epoch (s) (4)   R 2 X-fold (5)   Q 2 IISVII 2 X-fold (5)   Q 2 IISVII 2 test (6)   Ecoli_core 10000 0. Once an AMN has been trained on a large dataset of pFBA simulations, we can exploit it in subsequent, further learning, this time on experimental data. Precisely, we used the iML1515 AMN of Table  1  for characterizing and predicting the growth of E. coli on many different media compositions. To that end, we grew E. coli DH5-alpha in 73 different media compositions, with M9-glycerol as a basis and a wide variety of possibly added nutrients (see Methods -Culture conditions). As already mentioned in the introduction section, the intake rates of E. coli nutrients (i. e. , the nutrient exchange reactions values), as well as their relation to external concentrations, remain largely unknown: the quantitative rate for each compound may vary between growth media, unlike in the FBA calculations, where the same upper bound (or zero, if a compound is absent) is used in all cases.
Discussion and conclusion In this study, we showed that metabolic networks can be used as a learning architecture for neural network approaches. Previous work on RNN for constrained optimization was re-used and adapted to enable machine learning methods (such as backpropagation of errors) within metabolic networks. For improved scalability and adaptability, we first trained an AMN-reservoir on large, simulated pFBA datasets. We then exploited the reservoir to improve the predictive power of MFA on growth rate datasets of E. coli. Figure  4  shows good predictive power of AMN, far outperforming those obtained by standard CBM solvers (like COBRApy pFBA solver in Fig. 2C  and 2D ). Determining the uptake rates is a core experimental work needed for making constraint-based MFA realistic. Here, we developed an approach that gets rid of such needs for reaching plausible fluxes distributions.
Generation of training sets with COBRApy Training sets of metabolic flux distributions were generated using models downloaded from the BiGG database  [25]. The models were used to generate data using COBRApy  [26]  following a precise set of rules. First, we identified essential exchange reactions for the models we used (E. coli core and iML1515). Precisely, if one of these reactions has its upper bound set to zero, the biomass reaction optimization is impossible, even if all other exchange reactions are set to a high value, e. g. , 1000. In other words, we identified the minimal uptake fluxes that enable growth, according to the models. During data generation, the upper bounds on these reactions were always set to 1000. Two carbon sources always had their exchange reaction turned on, glycerol and arabinose, which were given an upper bound of 10.
Then, we selected a set of 27 exchange reactions to be the variables of These constitute the so-called set of variable exchange reactions (VER). On these reactions, we first applied a binomial drawing rule B(n, p) with n=27 and p ∈ [0, 1], a tunable parameter related to the ratio of 'activated' VER (to have an upper bound larger than zero). Consequently, the mean number of activated variable exchange reactions was close to n x p. For each of those activated VER, one out of several levels were randomly drawn from a uniform law, to give the upper bound actual value. In the workflow, the number of levels and the maximum value are tunable parameters. To retrieve the FBAsolved fluxes, we used COBRApy optimization on the biomass reaction without any constraints on the network reactions. In the end, each point (x, y) in the dataset consisted of a vectors of values for all the VER (X), and all the fluxes obtained running FBA or pFBA with COBRApy (Y).
our training set: the 20 canonical proteinogenic amino acids (L-histidine, L-leucine, L-phenylalanine, Llysine, L-tyrosine, L-valine, L-leucine, L-methionine, L-isoleucine, L-glutamine, L-cysteine, L-serine, Lalanine, L-tryptophan, L-asparagine, L-proline, L-threonine, Glycine, L-arginine, L-aspartate), 4 sugars (Melibiose, Sucrose, Trehalose, Lactose) and 3 acids (Acetate, Pyruvate, Citrate). Note that these exchange reactions corresponding compounds are used as the possibly added compounds in the experimental datasets.
Acknowledgements JLF would like to acknowledge funding provided by the  ANR  funding agency grant numbers  ANR-18-CE44-0015  ( SynBioDiag project ) and  ANR-21-CE45-0021-01  ( AMN  project). LF is supported by  INRAE's MICA department  and the by  INRAE's metaprogram BIOLPREDICT. BM is supported by an  Ecole Normale Supérieure Scholarship. JLF thanks  Aymeric Gaudin  ( CentraleSupélec Engineering School ) for early development in reservoir computing with AMN, and  Ivan Radkevich  ( University of Paris Saclay Systems & Synthetic Biology master program student ) for his work on custom RNN cells loading and saving.
1 1 = 1. 11 Fig. 1. Computing steady-state fluxes with stoichiometric and neural models. (A) Simple stoichiometric model. The model corresponds to the upper Glycolysis pathways of E. coli (iML1515 model extracted from the BiGG database [25]). The model is unidirectional, all fluxes values are positive. (B) Steady-state solution fluxes maximizing biomass production. At steady-state, the reaction fluxes (  ) must satisfy stationarity conditions that guarantee mass balance of all metabolites, this is depicted by the equation  = 0, where  is the stoichiometric matrix representing the connectivity of the model and  the vector of fluxes to be calculated. 0 is the medium represented by a vector of nutrient uptake fluxes (here  1 = TPI = 0. 1, symbol "-" indicates that no value is provided). The steady-state solution   is calculated by solving a linear program maximizing the objective    =  3 = BIOMASS, here we used the COBRApy package [26] to compute.