dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis. Using Matlab, standard statistics were calculated for each recognition score (mean, standard deviation, range, minimum and maximum) for each experiment. Statistical analyses were performed using R Software, Version 2. 12. 2. To assess whether recognition scores were above chance level or not we calculated 95% confidence intervals (95% CI) using bootstrapping (10000 replications) and p (5%) values using binomial tests. We also calculated the number of subjects recognized per experiment with ratings superior than 10 times the response by chance. To provide detailed assessment of whether the obtained recognition score was moderated by one of the two contrasts of the testing conditions [known pictures (as opposed to unknown) and pictures shown randomly (as opposed to non-randomly)], we used a general linear mixed model-GLMM with the recognition score as the variable to be explained.
Acknowledgements This study was supported by grants from the  European Commission  (FP7: MICHELANGELO, n°  288241 ), the  Agence Nationale de la Recherche  ( SYNED-PSY  n°  ANR-12-SAMA-006 ,  DIRAC  n°  ANR-13-ASTR-018 ,  Labex Robotex  and  Labex SMART  n°  ANR-11-LABX-65  within the  Investissements d' Avenir n°   ANR-11-IDEX-0004-02 , the  Groupement de Recherche en Psychiatrie  ( GDR-3557 ) and the  National Science Foundation  ( SMA-1540619 ). Authors thank  Jean Xavier  and  Elodie Tilmont  for assessment of children with ASD,  Nicolas Bodeau  for statistical advice, and  Jacqueline Nadel  for early helpful comments on the manuscript.
A Selection of Computational Tools Here, we focus on the software tools used to generate results reported in the remaining sections. The web page http://colomoto. org/software/ provides a more comprehensive overview of available network modeling tools based on the logical framework. GINsim (http://ginsim. org) supports the definition of multivalued logical models, under the synchronous, asynchronous and priority updating schemes. Besides the explicit construction of STG (for reasonable sizes, i. e. , in the order of a few million states), GINsim provides a number of methods to analyze model properties and supports model exports into various formats, in particular for model checking (see Section 3. 1)  (Chaouiya et al. , 2012; Bérenguier et al. , 2013). Cell Collective (http://cellcollective.
org) is a web-based software with a user friendly interface for model construction, simulation and analyses in a collaborative fashion. Its model repository provides a way for users to directly use and/or expand any of the 50 or so available models. Cell Collective supports Boolean models, considers synchronous updates, stochastic input simulations, and semi-continuous dose-response (input-output) analyses as shown in Section 5. 2  (Helikar et al. , 2012 (Helikar et al. , , 2013b)). CellNetOptimizer (CellNOpt, http://www. cellnopt. org) permits to define models of signaling networks as Boolean synchronous models. It further supports constrained fuzzy logic  (Morris et al. , 2011)  and systems of differential equations  (Wittmann et al. , 2009). CellNOpt specificity is that, starting from a Prior Knowledge Network (i.
Identifying the Attractors and Analyzing Their Reachability As previously mentioned, properties of interest relate to attractors and their reachability properties. In small models (up to a dozen components), such properties can be easily recovered directly by constructing and analyzing the State Transition Graph (STG). However, for larger models, a variety of approaches based on different algorithmic techniques and efficient data structures have been proposed to handle the combinatorial explosion of the number of states. Stable states, which do not depend on updating schemes, are relatively easy to identify because they correspond to the fixed points of the transition function. The algorithm implemented in GINsim relies on (multi-valued) decision diagrams to represent the (Boolean) stability function of each component g i (which is true iff K i (g) = g i ).
Avatar allows to estimate the probability of reaching an attractor from an initial state or from any initial state (i. e. , sampling the state space) under the assumption of equiprobability of concurrent transitions. In turn, MaBoSS, mentioned in Section 2. 3, provides an estimation of state probabilities over time (cf. Section 5. 2), along with further characterizations of the whole dynamics. Model checking was proposed in the early 1980s to verify a (set of) specification(s) against very large models of hardware and software systems. Since then, methodologies have been improved as well as their ranges of applicability. Notably, in the mid 2000s, model checking started to be applied in Systems Biology, mainly to verify qualitative systems dynamics (e. g. ,  Chabrier and Fages, 2003; Batt et al.
Gene expression data can also be used  (Crespo et al. , 2013; Keller et al. , 2016). In addition to using experimental data for logical model construction, various types of data available in many databases can be exploited, in turn, to interpret simulation experiments and further validate the models and associated predictions. The advantage of dynamical models is that they can generate hypotheses about any targeted component, or about the system as a whole. For example,  Puniya et al. (2016)  interrogated a comprehensive signal transduction network model under all possible knock-in and knock-out perturbations, resulting in the identification and ranking of the most and least influential model components. These components were further mapped on various databases, resulting in the prediction of a new combinatorial drug target in a cancer setting.
Exchange Formats and Model Documentation As the popularity of logical modeling increases, standardization issues have to be tackled. To this intent, the informal consortium CoLoMoTo (http://www. colomoto. org) gathers researchers developing logical models, methods and tools  (Naldi et al. , 2015). The definition of a common file format was identified as a primary requirement to allow model exchange and software interoperability. Model encoding in a standard format facilitates model reuse for extension or composition. In the context of SBML Level 3 (Systems Biology Markup Language  Hucka et al. , 2003) , the SBML Qual (for qualitative) package has been defined to store logical models  (Chaouiya et al. , 2013 (Chaouiya et al. , , 2015)). This format is currently supported by a number of software tools, including GINsim, Cell Collective, CellNOpt, the tools mentioned in this paper.
Hence, models stored in the SBML qual format can be exchanged between these tools. Thanks to the LogicalModel library, GINsim also provides an export of Boolean models to MaBOSS, in addition to several other formats  (Chaouiya et al. , 2013). To allow reproducibility of in silico experiments, simulation settings must be specified along with the model itself. These settings include the initial condition(s) and a precise description of the updating scheme. Model perturbations may be also considered as specific simulation settings. For example, the software GINsim allows to store all this information in the form of a set of parameter settings (or simulation scenarios). Cell Collective stores simulation settings in a database. The Simulation Experiment Description Markup Language (SED-ML) has been defined as a standard format for encoding simulation experiments  (Waltemath et al.
, 2011). One objective of CoLoMoTo is to promote the use of such a format, possibly by extending it to support specificities of logical modeling. Proper documentation and annotation are crucial for reuse and expansion of computational models by the community. Often, published models lack information (evidence and/or clear assumptions) documenting model components, interactions and rules. Several efforts already exist to address this issue. The Minimum Information Requested In the Annotation of biochemical Models (MIRIAM)  (Le Novère et al. , 2005)  was developed to standardize the type of information (e. g. , connections to controlled vocabularies as well as to various databases) that should be included as model metadata. While MIRIAM (and other standards) provides minimal guidelines to ensure model reproducibility, additional efforts are needed to increase the overall quality (breadth and detail) of model documentation.
For instance, modelers and curators can provide detailed and exhaustive evidences supporting model components and interactions when available, or assumptions in the case of unavailable experimental observations. This is facilitated in Cell Collective, which provides a Knowledge Base for each model. Finally, BioModels database  (Chelliah et al. , 2015)  and other model repositories such as those of GINsim and Cell Collective are also essential to ensure that models are available to the community for reproducibility of the results as well as for model reuse.
Simulation results suggested that CAV1 expression regulates Ras-related C3 botulinum toxin substrate 1 (RAC1), B-cell lymphoma/leukemia 10 (BCL10), GATA-binding protein 3 (GATA3), CD26, and CD28. In addition to validating these predictions in Cav1 knock-out mice, model results were further successfully validated against gene expression signatures obtained from the Gene Expression Omnibus (GEO) database.
The labels associated with the arcs highlight the crucial transitions involved in the choice between the attractors (see Supplementary Figure  S1 ). Each stable state is annotated with the probability in red of being reached from Th22 subtype under the Treg polarizing condition, considering 1000 simulations (computed with the software Avatar). The components are ordered as follows: first the external input cytokines IL1B, IFNG, IL2, IL4, IL6, IL10, IL12, IL15, IL21, IL23, IL27, TGFB, IL36, IL33, IL18, IL25, IFNB, IFNA, IL1A, IL29, followed by the component representing the Antigen Presenting Cells, then the transcription factors TBET, GATA3, RORGT, FOXP3, BCL6, followed by the secreted cytokines IFNG, IL4, IL2, IL10, IL21, IL6, followed by the transcription factors STAT3 and PU1, then the secreted cytokine TGFB, followed by a node denoting the proliferation of Th cells and finally the secreted cytokine IL25.
This can be achieved first by providing model files, in BioModels database  (Chelliah et al. , 2015) , or in model repositories such as those provided by Cell Collective or GINsim (see Section 2. 3). Second, modeling assumptions and simulation settings should be precisely described. For example, we have underlined that model properties can vary depending on the adopted updating scheme (Section 2. 2). Furthermore, model extensions often simply refer to the addition of components, but it can also consists in refining the model with a stochastic extension (e. g. , with probabilistic input values as in Figure  6 ). The different formalism extensions evoked above together with many others still need to be precisely characterized, managed within a control vocabulary and supported in a future SBML qual version.
Each stable state is annotated with the probability in red of being reached from Th22 subtype under the Treg polarizing condition, considering 1000 simulations (computed with the software Avatar). The components are ordered as follows: first the external input cytokines IL1B, IFNG, IL2, IL4, IL6, IL10, IL12, IL15, IL21, IL23, IL27, TGFB, IL36, IL33, IL18, IL25, IFNB, IFNA, IL1A, IL29, followed by the component representing the Antigen Presenting Cells, then the transcription factors TBET, GATA3, RORGT, FOXP3, BCL6, followed by the secreted cytokines IFNG, IL4, IL2, IL10, IL21, IL6, followed by the transcription factors STAT3 and PU1, then the secreted cytokine TGFB, followed by a node denoting the proliferation of Th cells and finally the secreted cytokine IL25.
FUNDING WA has been supported by postdoctoral grants from the  LabEx MemoLife  and from the  Ecole Normale Supérieure. ENS team further acknowledges the support of the  French Plan Cancer  ( 2014-2017 ), in the context of the project entitled Modeling cell communication networks in breast cancer -CoMET. PM was supported by  FCT  grants  UID/CEC/50021/2013  and  IF/01333/2013. CC acknowledges the support of the  Fundação Calouste Gulbenkian.
Viticulture, like other fields of agriculture, is currently facing important challenges that will be addressed only through sustained, dedicated and coordinated research. Although the methods used in biology have evolved tremendously in recent years and now involve the routine production of large data sets of varied nature, in many domains of study, including grapevine research, there is a need to improve the findability, accessibility, interoperability and reusability (FAIR-ness) of these data. Considering the heterogeneous nature of the data produced, the transnational nature of the scientific community and the experience gained elsewhere, we have formed an open working group, in the framework of the International Grapevine Genome Program (www. vitaceae. org), to construct a coordinated federation of information systems holding grapevine data distributed around the world, providing an integrated set of interfaces supporting advanced data modeling, rich semantic integration and the next generation of data mining tools.
,  14  Doligez et al. 20  and Fournier-Level et al. 21 ) and manual field or greenhouse scoring by the use of more automated processes (for example, Marguerit et al. 9 and Coupel-Ledru et al. 22  ). The greatest value of these data sets depends on their integration to generate new knowledge, and therefore on the ability to combine the results of different experiments. To allow this, data should be Findable, Accessible, Interoperable and Reusable (FAIR principles  23  ). An emblematic model in the plant community is Arabidopsis thaliana, for which rich data sets are available and which has been used to derive working hypotheses for gene function in crop species. This has been supported by the TAIR portal (www. arabidopsis. org) and the more recent Arabidopsis Information Portal (www. araport.
org). However, in grapevine, the increasing wealth of data is highly dispersed and often poorly accessible, hindering its effective exploitation beyond the scope of its initial production. Moreover, in the absence of dedicated funding and sufficient international collaboration, there is no information portal targeted at the grapevine research community. Although large international repositories do exist for molecular biological data (for example, the European Nucleotide Archive, GenBank), these do not systematically capture the detailed knowledge related to genome function (for example, regulation networks, metabolic networks), the plant material used and any non-molecular phenotyping data that is the specific expertise of grape researchers. Instead, these data are at best published along with research papers and managed in regional and local databases, or at worst isolated on individual researcher's computers and completely inaccessible to the wider community.
There is a clear need for research policies that create incentives favoring data sharing to improve the quality of research results and foster scientific progress. 24 he interpretation of previously published data always requires additional 'metadata' to provide the appropriate context. In addition, both data and meta-data should also be formatted in standardized representations to enable its processing in an automated manner and avoid errors generated by manual manipulations, especially in the case of very large data sets. 23 his requires community-wide agreement on guidelines for annotation, tools for data preparation, and the dedicated custodianship of important/exemplar data. Although generic solutions exist for many data types individually, much grapevine data is still far from FAIR, and little support is available for community members to make it so.
To meet these objectives, we have devised a plan inspired in part by the experiences of the international WheatIS initiative that provides a portal for wheat data (http://www. wheatis. org/) and by the transPLANT infrastructure for plant genomic science (www. transplantdb. eu) that allows data integration from nine distinct European databases. The GrapeIS will comprise an open federation of independent information systems (nodes) interconnected by a central web portal (Figure  1 ), and will provide a toolset to reduce the costs of data publication and interrogation. This will provide a robust, cost-effective model for data integration by exploiting the expertise of existing resources, and best practice and data standards from related research communities grappling with similar problems.
REVIEW AND DISCUSSION Discovering data stored in distinct databases from a single entry point: interoperability of the infrastructures One model for providing integrated access to diverse data sources features a single data custodian, who takes comprehensive responsibility for the storage and integration of all relevant data. An alternative model is to provide an integrated query engine providing a common entry point to dispersed resources, each of which might contain different data (and have a different focus of interest). The second model has the advantage of exploiting (rather than replacing) existing resources (and their sources of funding). Such a common entry point should (i) allow the discovery of different data types (for example, omics data, phenotypic data, climatic data) or data sets of the same type (for example, multiple genome re-sequencing projects), (ii) facilitate their integration (for example, a catalog of all the genotypic and phenotypic evaluation data known for a given set of varieties) and (iii) facilitate the import of these data into diverse analysis or visualization tools.
Achieving this requires a commitment from all contributing resources to serving data in accordance with a set of common standards, such that it can be automatically interrogated in a standard way. The first step in providing FAIR data is 'findability'. A model for findability for plant-focused resources has been established by the transPLANT project. The transPLANT integrated search engine  26  operates using the generic SolR (http://lucene. apache. org/solr) search engine to provide search facilities over remote data files published by each participating resource conforming to a minimal standard schema (which allows for a faceted search to be provided, giving users the options to winnow large results sets based on commonly useful criteria). Access is provided through a common search portal and via RESTful web services.
Moreover, standard formats must be agreed for publishing such data; and appropriate forums identified for publicizing its availability. Standard formats already exist for many types of data: for example, General Feature Format (GFF3; http://gmod. org/wiki/ GFF3) and Genbank (GBK; https://www. ncbi. nlm. nih. gov/genbank/ samplerecord/) for genome and aligned data, Variant Call Format (VCF; http://vcftools. sourceforge. net/specs. html) for nucleotide sequence variants, Binary Alignment Format (BAM; http://www. htslib. org/) for next-generation sequence alignments, BioPAX (www. biopax. org) and Systems Biology Mark-up Language (SBML; http://sbml. org) for pathways and networks, PSI-MI XML standard for proteomic data (http://www. psidev. info/node/60#mipurpose)  28 and a suite of standards are being proposed by the Data Standards and Metabolite Identification Task Groups of the international Metabolomics Society for metabolites analysis (http://www.
With limited resources, a sensible strategy for the grapevine community is to promote the use of existing international repositories for common data types (for example, European Variation Archive, EBI Gene Expression Atlas, the Gene Expression Omnibus (GEO), MetaboLights, PRIDE and so on), which already require submission of standards-compliant data, and to utilize these data (alongside other grape-specific data) in specialized services targeted at the specific needs of grapevine researchers. This has been the strategy of the grapevine community from its start regarding molecular data (sequences, polymorphisms, proteomics, metabolomics). For instance, 3971 grapevine transcriptomic data sets have been so far submitted to the GEO database (for example, Moretto et al. 32  ). In turn, phenotypic data are not currently concentrated in any generic resource, nor is there an obvious repository to which submission can be recommended.
The grapevine community must therefore assist in the coordination of multiple resources and should contribute to the definition of international standards in the domain. As many of the data will have features in common with those produced by other crop communities, coordination with wider initiatives such as the European Plant Phenotyping Infrastructure (EMPHASIS, http://www. plant-phenotyping. org) is a sensible course. Capturing the data of the grapevine community in standard formats: toward data interoperability Looking backward, the grapevine community has been increasingly active in the production of data in the life science area, as shown by a very naive search of recent publications (using query terms 'grapevine' OR 'vitis') in the PubMed database (Figure  2 ). The data described in the papers are very diverse covering genomes, genotypes, genomic variation, genetic maps, QTLs, association genetics, transcriptomics, proteomics, metabolomics, phenotype characterizations; and rapidly developing, with the quantity of data produced by a single experiment increasing rapidly over time.
org/projects/minseqe/) and the Minimal Information About Proteomic Experiments (MIAPE),  34  the Metabolomic Standards Initiative has developed a standard for Core Information for Metabolomics Reporting. 35 Such papers have formed the basis for the subsequent development of exchange formats and databases. Others standards are still emerging like the Minimal Information for QTLs and Association Studies (MIQAS, http://mibbi. sf. net/projects/MIQAS. shtml), the Minimal Information about a Genotyping experiment (MIGen, http://migen. sourceforge. net/) or the Minimal Information About Plant Phenotyping Experiments  36  (MIAPPE, http://www. miappe. org/). Experimental metadata within-omics experiments can be conveniently standardized and shared with the ISA-Tab protocols. 37 The success of these standards obviously depends on their adoption by the community, which is determined by many factors, such as its enforcement by publishers and the existence and ease-of-use of an associated toolset.
It is of high importance that data always contain an unambiguous identification of the species, cultivar/variety and the accession from which the studied sample was derived. International coordination in this regard has been ongoing since the mid-seventies. The FAO/Biodiversity Multicrop Passport Descriptors  41  (MCPD; http://www. bioversityinternational. org/elibrary/publications/descriptors/) is widely recognized as the metadata standard for crop genetic resources (http://www. bioversityinternational. org/e-library/publications/detail/faobiover sity-multi-crop-passport-descriptors-v2-mcpd-v2/), and has been adopted by the curators of germplasm repositories and implemented in their information systems. In these, for a given crop, a pair value corresponding to the accession number and the genebank or laboratory holding it defines the entities (that is, a plant) to which accession-specific information is assigned.