dataesr/scientific-paragraphs-categorization huggingface dataset:
g. cell, tissues, organ, plant and population), and analyzing and evaluating the newly designed system with numerical experiment. To face these challenges, a common hosting platform should: 1. Find and store phenomics data with their associated metadata. translator, which allows harmonized phenomics dataset to directly be translated to model inputs  (Porter et al. 2014). 5. Develop connector tools to allow the connection between models. More than hundred plant and crop models have been created in the last two decades. All these models have the potential to be reused and combined, broadening the scope of their original uses. Connecting such models into integration networks has the potential to integrate more complex with isolated models  (Passot et al. 2019; Long 2019). The integration of several models should be used to generate new outputs (e.
g. integrated or more complex plant traits), improve model predictions or design new strategies (e. g. a multi-scale [from gene to globe] crop modelling framework;  Benes et al. 2020; Peng et al. 2020). However, the technological barriers introduced by differences in language, data formats, spatial and temporal scales, and units have slowed this progress. AgMIP has provided a necessary important first step in bringing disparate models of each major crop together  (Rosenzweig et al. 2013 ). The plant model community has also designed modelling platforms (e. g. OpenAlea;  Pradal et al. 2008 ) that make sharing of models increasingly feasible. Moreover, recent efforts have been made by the plant science and crop modelling communities, such as the Crop in silico project (Cis;  Marshall-Colon et al.
CON CLUSION In the present analysis, we identified a well-established connection between the plant phenotyping and the image analysis tool communities. However, the connection between phenomics and modelling communities was low despite their research topics compatibilities. We hypothesize that this lack of connection is attributed to a lack of awareness of the benefits promoted by each community, the heterogeneous terminology used by the communities and the lack of common platforms to enable transparent data exchange. In the framework of the EMPHASIS project, strategies were proposed to move towards better communication and collaboration between phenotyping and modelling communities. Firstly, we suggest raising the awareness of the diversity of models, phenomics datasets and phenotyping platforms that exist by maintaining and advertising online databases.
The Scopus search was conducted in June 2020. For this reason, 2020 papers were not yet completely introduced in the Scopus database and the 2020 papers were not considered in this database. • the plant models: structural* PRE/3 plant* PRE/3 model* OR functional* PRE/3 plant* PRE/3 model* OR structural-functional* PRE/2 model* AND plant* OR functional-structural* PRE/2 model* AND plant* AND NOT structural* PRE/1 equation* The proximity operator PRE/n specifies the proximity (n being the number of terms) between two terms in an exact order. For instance, 'crop PRE/2 model' will find documents in which 'crop' precedes 'model' with no more than two terms in between. The wildcard char- acter (*) was used to replace or represent one or more characters. For instance, 'model*' will find documents in which 'model' , 'models' , 'modelling', 'modeling', 'modelled' or 'modeled' appear.
Spectropolarimetric data from Narval and Neo-Narval We have been monitoring Betelgeuse in linear polarimetry since November 2013 with the Telescope Bernard Lyot at Pic du Midi (France,TBL). Until August 2019, the Narval spectropolarimeter was used, and this data set has been described by  Aurière et al. (2016) ;  Mathias et al. (2018)  and López  Ariste et al. (2018). In September 2019, Narval was significantly upgraded and became Neo-Narval, and the first data for Betelgeuse were collected by February 2020. Neo-Narval maintains the successful polarimeter of Narval  (Donati et al. 2006 ), but has been upgraded with a new, higher performance detector, the iXon CCD by ANDOR. The faster readout times of the new detector (2-3 s) at readout noises comparable to the old detector of Narval (2-3 electrons in the usual configuration) and similar quantum efficiencies allow a more efficient use of telescope time.
In order to introduce this third calibration beam, the camera optics and the cross-dispersing prisms were overhauled. As a result, the raw image of the spectral orders differs considerably from the previous one with Narval, and required an upgrade of the datareduction software. Rather than refurbishing the old Libre-Esprit code  Donati et al. (1997) , Neo-Narval engaged in providing a completely new code written from scratch. For the present work, the modifications in Neo-Narval concerning velocity measurements are of no importance. However, the new DRS had to be validated for our present purposes. Since January 2020, three major upgrades of the DRS have been implemented, and the data re-calibrated with the latest version of the code. Although the DRS still shows room for improvement in terms of velocimetry and the normalization of the intensity continuum signal, the tests with respect to Betelgeuse spectropolarimetry, which are our only concern here, show a continuity with Narval data.
Such high velocities of rising plasma seem to be justified by numerical simulations  (Chiavassa et al. 2011). Even if we can, from data, measure the maximum observed velocity V max of the convective flows, we cannot expect this maximum velocity to be present at a given moment on Betelgeuse. However, we cannot afford to have a different value of V max for every observing date: this would add an extra parameter to the model and we cannot ensure that there is a bright granule at disk center on every date to send a signal at the bluest wavelength that fixes such a parameter. As adding a free parameter for which information is probably not always available is impractical, we fix a constant V max for our whole dataset. The relationship that our model establishes between wavelength and position over the disk implies that any bright plasma at disk center is assumed to move at velocity V max.
Over our whole dataset we see this tendency broken in just a few cases: 14 out of 64 to be precise. But even when the signal is not observed to be strictly larger, it is seen to be equal, and never smaller, or otherwise hidden in noise, as in the Q signal from February 2021. Noise also hides the smaller polarization signals of the shallower lines, with line depths smaller than 0. 4, and because of this we only consider masks that stop at line depths of 0. 4. To interpret this order in the amplitude of the signal, we hypothesise that the observed polarization comes from just one scattering event in the formation of the continuum at that wavelength, followed by an immediate and in-place absorption of the polarized photon by an atom which re-emits it unpolarized. This unique event must happen at a given geometrical depth in the atmosphere of Betelgeuse.
Finally, with this approximation at work, the only inferred vertical structures would be those with a velocity that is constant with height. Figure  8  shows 2D cuts across the inferred images on the two selected dates. In both cases, we can easily identify structures bending toward the limb, and also structures rising radially. For the date of 2013, the image inferred from Narval data shows a disk center granule rising almost vertically if one looks at the brightest point at each layer (blue dot). The two structures on the sides of this center granule are, on the other hand, clearly bending toward their respective limbs. One might see a coincidence in this blossoming of the structures as seen from Earth: the granules may, in reality, be bending and they are doing so symmetrically as seen from Earth.
Not all datasets collected by Narval and Neo-Narval show the right ordering of polarization amplitudes that justify the two hypotheses of a gray atmosphere and single scattering. However, for the majority of dates (50 out of 64), this condition applies and we can use the polarization profiles to infer a 3D image of the photosphere of Betelgeuse. Here we present 3D images of the photosphere of Betelgeuse for two particular dates in 2013 and 2021. Given the many approximations and hypotheses required to infer such 3D images, any direct, filterless interpretation of them should be done with caution. The first date selected to create these 3D snapshots was also been studied by  Aurière et al. (2016) and López Ariste et al. (2018). These authors showed that the inferred 2D image was comparable to interferometric images made close in time.
Motivation: The automatic mining for bibliography exploitation in given contexts is a challenge according to the increasing number of scientific publications and new concepts. Several indexing systems were developed for biomedical literature. However, such systems have failed to produce contextualised research of genes and proteins and automatically group texts according to shared concepts. In this paper, we present OntoContext, a contextualization system crossing the use of biomedical ontologies to annotate texts containing terms related to cell populations, anatomical locations and diseases and to extract gene, RNA or protein names in these contexts. Results: OntoContext, a new python package contains two modules. The "annot" module for "annotation" function, is based on combination of morphosyntactic labelling and exact matching and on dictionaries derived from the Cell Ontology, the UBERON Ontology (anatomical context), the Human Disease Ontology and geniatagger, (which contains particular tags for gene-related names).
Several community challenges for text-2 mining in biology have been organized to cross-evaluate these tools  (Krallinger et al. , 2008; Ananiadou et al. , 2015; Huang and Lu, 2016). The annotation of biological entities in texts has emerged over the 1990s  (Ananiadou et al. , 2006). Biological advances, especially in genetics and molecular biology, have greatly contributed to increase publications and biological databases  (Kersey et al. , 2015). This has encouraged the emergence of new biology and\or computer science research fields including biotext mining  (Ananiadou et al. , 2006). In particular, algorithms were developed to respond to the needs for protein and gene names unambiguous identification  (Denys et al. , 1998)  and protein function annotation  (Andrade and Valencia, 1998). In the 2000s, approaches of exact matching were used for biotext annotation, benefiting from the stable development of databases serving to construct dictionaries.
The OBO annotator  (Groza et al. , 2015)  is an annotation tool developed on the basis of the "Human Phenotype Ontology" in order to annotate similar characters between patients in different textual sources. The National Center of Biomedical Ontology Annotator (NCBO Annotator) tool uses multiple sources (ontologies, terminologies and databases) for biotext annotation  (Jonquet et al. , 2009). NCBO Annotator, based on a learning and exact matching algorithm "Mgrep", and trained on limited sources and ontologies, shows average performances and is known as a reference for biotext annotation  (Shah et al. , 2009). In this paper, we present OntoContext, a new python Package for biotext annotation using Ontologies to define Context. OntoContext was developed to automatically identify genes and gene products involved in a particular context: for example, cell population in a given anatomical localisation related to a physiopathological state.
Morphosyntactic labelling OntoContext relies on the Part Of Speech Tagging morphosyntactic labelling technique  (Baud et al. , 1998; Brown, 2010)  to identify targets in the literature. This technique allows transforming an explicit text in its syntactic representation. We define the morphosyntactic structure for each term, using the NLTK (Natural Language ToolKit) python package  (Bird et al. , 2009).
Ontologies, databases and construction of dictionaries According to the context of our research, the OntoContext workflow relies on dictionaries (detailed in Supplementary Table  S1 ) derived from three ontologies: the Cell Ontology for the cellular context  (Bard et al. , 2005) , the UBERON Ontology for the anatomical context  (Mungall et al. , 2012) , and the Human Disease Ontology for the pathological context  (Kibbe et al. , 2014). We used the available versions of these ontologies downloaded on 13/04/2015. We also used additional dictionaries, derived from previous or reduced versions of the ontologies. For each ontology, we build: 1. a synonym table associating all concepts in the ontology to their synonyms and plural form 2. a term-label table (called "ontology-derived dictionary") containing all concepts, synonyms and plural form associated to their respective morphosyntactic label 3.
Text annotation Our annotation method is based on the following steps as schematized on Figure  1 : We use the NLTK modules to parse and label terms derived from ontologies (Figure  1A ) and text sentences (Figure  1B ). The imple-4 mented method takes text files as Input. After parsing and labelling sentences (step1), OntoContext looks for labels also found in the selected dictionary (step2). From these labels, OntoContext identify terms that match with the term-label table (step3, step4). This process is iterated for each candidate term (step5). For gene name retrieving, we use the geniatagger. 0. 1 package  (Tsuruoka et al. , 2005). The selected annotations are stored in a new table. These steps are coded into the "Annotation" function in OntoContext.
Text contextualization and Gene/RNA/Protein name retrieval After text annotation and matched concept identification with the dictionaries, children and synonyms are retrieved from each ontology. Concepts found at least once among the annotated texts are listed and offered for multiple selections. This step is guided by the indication of the concept frequency in the annotated texts (defined as the number of texts where the concept and its children are cited). Finally, we recover a list of generelated names from this group of contextualized texts by querying the "result" table. The user is provided with the OntoContext graphical module "crisscross" developed using the Tkinter Package  (Lundh, 1999) , to visualize and select terms of interest in order to contextualize texts across ontologies.
Results OntoContext allows automatic annotation and contextualisation of biomedical texts in order to recover gene, RNA and protein names (abbreviated as gene-related names below). OntoContext is designed as a two-module package: The "annot" module contains the "annotation" function, based on the combination of the morphosyntactic labelling and the exact matching between target texts and ontology-derived dictionaries. The combination of these methods is faster than exact matching alone (Supplementary Note S1). The presented algorithms and the complexity calculation demonstrate how the "annot" module can be faster than classical exact matching algorithms. The "annot" module takes successively as input the path to the folder containing the texts to annotate, the path to the geniatagger file  (Tsuruoka et al.
, 2005)  and Concept. db database, containing the Ontology-derived dictionaries provided by OntoContext. The user provides a name for the output table where the annotation results will be stored. These results consist in a list of concepts found in the annotated texts and their assignment to a given category (cell population, human disease, anatomical localization, gene-related names. Each row contains a concept, the related category and the text Id. The second module named "crisscross" uses the previous "annot" output as input. "crisscross" contains five main interrelated function and steps, and a friendly interface: (1) Option step: The user can choose to display the annotation sorted either by alphabetic order or by citation frequency. (2) Extension step: The interface displays all terms found in the texts ("annot" output) along with their child terms obtained by automatic extension, classified into three lists, according to their assignment to a given ontology.
OntoContext annotation of the BioText corpus In order to evaluate OntoContext for disease annotations, we used the BioText Medical corpus (141 abstracts)  (Rosario and Hearst, 2004)  and assessed its performances using two dictionaries: the BioText-derived dictionary established in 2004 by experts and used as reference for annotation and the 2015 Human Disease-derived dictionary (Supplementary Table  S1 ). The BioText-derived dictionary not being generated from an ontology and thus not organized hierarchically, the extension step of the "crisscross" module cannot be performed. On-toContext applied to the BioText-derived dictionary reveals that some annotations include two disease con-9 cepts: for example, "PROSTATE CANCER" is a disease but the included term "CANCER" is also a disease. Therefore, OntoContext will annotate this term twice, as "CANCER" and "PROSTATE CANCER".
Real Case study In order to further validate our method, we applied OntoContext to a corpus of abstracts generated from Pub-Med in order to identify genes, RNA and proteins associated with a specific context. We queried PubMed with 10 "(aging OR longevity) AND immune system", restricted to MeSH terms and filtered for abstracts since 1980. This query was launched on August 1st, 2016 and retrieved 9,930 abstracts composed by 142,613 sentences representing 2 642,035 words. Abstracts were downloaded and annotated using the OntoContext "annot" module with the 2015 Cell-derived dictionary, 2015 Human Disease-derived dictionary and the 2015 UBERON-derived dictionary along with the geniatagger. 1. 0 package. The results are summarized in Figure  4A. OntoContext detected 34,350 cell population concepts, 17,260 anatomical concepts and 81,160 pathologies, and 3,063 gene-related names.
Discussion In this paper, we present OntoContext, a python package developed for the automatic annotation of biomedical texts according to ontology-derived dictionaries describing a biomedical context such as cell population, anatomical location and diseases, in addition to protein, RNA or gene name information. Although, specialized ontologies and databases are available, little interrelations between them in terms of biological meaning is provided. Our aim was to sort texts based first on a PubMed search with MeSH terms, then their contexts of study derived from ontology concept labelling and to retrieve gene-related names cited in these contexts. The two modules "annot" and "crisscross" developed here allow the annotation of a text corpus against a concept database and the intersection between texts annotated by these concepts, respectively.
These two modules can be used separately or successively. Noteworthy, "crisscross" depends on the output of "annot", the performance of which is crucial for the global performance of OntoContext. For that reason, we focused our performance assessment on "annot". With our original objective in mind, we needed an organized and controlled vocabulary and thus chose to build dictionaries derived from ontologies. Ontologies represent standard and controlled vocabularies shared by the scientific community and hierarchically organized, hence allowing clustering. For gene name recognition, we selected the already available geniatagger. 0. 1 package  (Tsuruoka et al. , 2005). The exact matching method has the advantage to easily annotate with controlled vocabulary. By implementing an exact matching algorithm based on the morphosyntactic labelling of text and dictionary terms, we have here overcome the relative slowness of this method  (Navarro and Fredriksson, 2004) , and OntoContext appears faster than a classic exact matching algorithm, as demonstrated in Supplementary Note S1.
For the morphosyntactic labelling, we used the NLTK python package  (Bird et al. , 2009)  included into the "Annotation" function to label the dictionaries terms and texts. These labels may differ between the database and the sentences depending on several criteria such as the uppercase and the lowercase writing and the plural forms. For that reason, a pre-treatment step of data consisting in adding plural, synonyms, considering lower and uppercase improves OntoContext that has better performances as compared to NCBO annotator  (Jonquet et al. , 2009). However, both tools are still unable to recognize adjectives, contrarily to experts. The most important performance criteria seem related to the date at which the dictionary used for annotation relatively to the text corpus is generated. Indeed, new concepts are created along with the evolution of knowledge, leading to changes in vocabularies and creating difficulties for annotation.
These difficulties are not related to the method but rather to the quality of the vocabulary used comparatively to the reference. Indeed, we observed variations in OntoContext annotation results when we used different dictionaries to analyse the same text corpus. For the evaluation of an annotation method, it is important to use the same dictionary as a reference. We indeed assessed OntoContext using CRAFT-derived dictionary to annotate CRAFT. 1. 0 corpus  (Bada et al. , 2012)  and obtained more than 90% Accuracy. This result indicates good performance of our "annot" module compared to other methods, similar with that reported by  Kim et al. (Kim et al. , 2015) , when geniatagger accuracy is about 54% for cell population identification  (Tsuruoka et al. , 2005). We have to notice that previous tools did not use the same corpus for evaluation as OntoContext.
These tools being developed on a training dataset, their performances decrease when corpuses are changed. For OntoContext, we have developed a 12 method that overcomes these drawbacks since it is based on exact matching and morphosyntactic labelling, using ontology-derived dictionaries. For gene-related name annotation, many databases were developed but they are species dependent: the "gene name database" concerns human  (Povey et al. , 2001) , the "mouse genome informatics database" concerns mouse  (Bult et al. , 2016)  and the "WormBase" concerns nematode  (Harris et al. , 2004). Other limits may exist such as for the Protein Ontology that contains only coding gene names  (Natale et al. , 2014). The inclusion in the "annot" module of the geniatagger package  (Tsuruoka et al. , 2005)  overcomes these limits allowing to annotate genes, RNA and protein names from any species with good performances.
As a whole, OntoContext performance is influenced essentially by the quality and date of the dictionaries. Using newest version of ontologies is a guarantee for better annotation results, whatever the text corpus considered for annotation. In its present stage of development, the OntoContext package can be used for different purposes. Here we present an example of annotation of an abstract corpus from PubMed. We used PubMed abstracts and not full texts here because they are freely available and easily extractable, although we realize that using abstracts only imposes a limitation on the number of identifiable gene-related names. OntoContext can also be applied to annotate information from clinical texts or from specialized databases (transcriptomic, protein, genomic…) in order to generate new knowledge and life process modelling.
Funding This work was supported by the  Programme Doctoral International Modélisation des Systèmes Complexes (PDI MSC IRD-UPMC) , the  Agence Nationale de la Recherche  within the  Investissements d'Avenir program  ( LabEx Transimmunom   ANR-11-IDEX-0004-02 & RHU iMAP ANR-16-RHUS-0001_iMAP ) and the  Tunisian Ministry of Higher Education and Scientific Research.