dataesr/scientific-paragraphs-categorization huggingface dataset:
Programming Android GNSS service To simplify access to positioning data from Syslor applications, a GNSS "service" was developed in Android Studio (Google's software dedicated to mobile application development). A service is a stand-alone program that can be used from any Android application. This solution avoids unnecessary duplication of code in each Syslor applications. For this project, the service module will be dedicated to the communication of the data transmitted by Bluetooth between the daughter board and the smartphone. This service automatically processes three essential data flows (Figure  8 ). The service is the centre of exchanges, it performs several operations in parallel. First, it manages the Bluetooth connection with the GNSS box. Once the link is established, the service receives the positioning frames in NMEA format.
This represents an obvious waste, considering the time, energy and money involved in the production of these data. Furthermore, besides transcriptomics data management, there are other challenges to try and combine various types of data and to set up tools to easily access and visualize multi-source knowledge, with the corresponding sources of information precisely indicated. Numerous databases have been developed for plant species in the past years, a number of which gather transcriptomics data and offer tools to analyze them in different ways. For example, TENOR  (Kawahara et al. 2016)  and Plant/OryzaExpress  (Kudo et al. 2017b ) are dedicated to rice RNAseq and microarray-based data respectively, with tools to analyze gene expression networks. Similarly, RNAseq data and mining tools are found for tomato in TOMATOMICS  (Kudo et al.
2017a)  and TomExpress  (Zouine et al. 2017). For Arabidopsis thaliana, Araport  (Krishnakumar et al. 2015a ) and HRGRN  (Dai et al. 2016 ) integrate a lot of data, many of them collected from various remote sites. Thus, HRGRN uses graph-search empowered tools to analyze Arabidopsis signal transduction, metabolism and gene regulatory networks. For Medicago truncatula, a model legume species  (Kang et al. 2016) , a widely used gene atlas (MtGEA; https://mtgea. noble. org/v3/) gathers transcriptomic analyses performed with Affymetrix chips from a wide range of biological conditions  (Benedito et al. 2008; He et al. 2009) , while LegumeGRN generates gene network predictions, based on the Affymetrix data  (Wang et al. 2013). Another database, SYMbiMICS (https://iant. toulouse. inra. fr/symbimics/), presents RNAseq data obtained so far from roots and symbiotic nodules, notably laser-microdissected samples  (Roux et al.
2014; Jardinaud et al. 2016 ). Yet, other transcriptomics tools have been used, such as Mt6k  (Kuster et al. 2004 ), Mt16k  (Hohnjec et al. 2005)  and Nimblegen microarrays  (Verdier et al. 2013 ). Four M. truncatula genome sequences and corresponding gene annotations have been released, namely Mt3. 5  (Young et al. 2011) , JCVI Mt4. 0  (Tang et al. 2014) , Mt20120830  (Roux et al. 2014 ) and Mt5. 0  (Pecrix et al. 2018). Corresponding databases and genome browsers are the Medicago genome database [Mt3. 5 and Mt4. 0; http://www. medicagogenome. org/; (Krishnakumar et al. 2015b) ], the Legume Information System [https://legumeinfo. org/ ;  (Dash et al. 2016) ], the M. truncatula A17 r5. 0 genome portal [https://medicago. toulouse. inra. fr/MtrunA17r5. 0-ANR/; (Pecrix et al. 2018) ], as well as a portal to explore the natural diversity of M.
truncatula genomes [hapmap project; http://www. medicagohapmap2. org/ (Kang et al. 2015) ]. The Mt5. 0 genome browser integrates data from numerous sources for thorough and extended analyses: gene, transposon and long non-coding RNA annotations; protein annotation (blastp and blast2GO results, interproscan analyses); gene models from all previous genome sequence releases; positions of Affymetrix and Nimblegen probe sets, as well as Tnt1 insertions in the mutant collection generated at the Noble Foundation  (Cheng et al. 2014) ; natural diversity data [hapmap project;  (Kang et al. 2015) ]; mRNAseq, sRNAseq and methylome data. The LeGOO knowledge base presented here is based on different principles. It does not collect "raw" data but rather information manually extracted from publications. Thus, for transcriptomics data, only statistically relevant differentially regulated genes are used to feed LeGOO.
This allows reliable knowledge generated with a variety of acquisition tools to be considered and integrated. This also implies that correspondences between all gene IDs and gene probes (used for microarrays) must be defined, which was performed using the last release (Mt5. 0) of the M. truncatula genome sequence as the pivotal nomenclature. Furthermore, LeGOO is not limited to transcriptomics and incorporates a variety of data and information, e. g. obtained by genetics or molecular biology approaches. This information is modelled in a graph-oriented database  (Miller 2013) , similarly to HRGRN  (Dai et al. 2016) , with pairwise relations (edges) between objects (nodes). So far, data from about 300 publications, from different fields (symbiotic and pathogenic interactions, response to abiotic stress, plant developmental pathways) have been manually retrieved, leading to more than 200,000 relations available in LeGOO.
Tracking Medicago truncatula genes amongst different generations of datasets The exploration of the M. truncatula transcriptome was initiated years before the first release of the M. truncatula genome sequence, in contrast to Arabidopsis thaliana, for which a high quality genome sequence was available prior to the development of array technologies, thereby enabling the establishment and use of a stable nomenclature. Moreover, over almost two decades, several generations of macro and microarrays were produced along with several M. truncatula genome versions, with consequently several nomenclatures being used in articles reporting on mid to large-scale gene expression analyses. This led us to develop an "ID Converter" service to quickly find the different identifiers (IDs) representing a same gene.
The ID Converter tool is an essential part of LeGOO, and it can be used independently to find the various names and codes given to the same gene. The ID Converter service manages most datasets that have been used for global M. truncatula gene expression analyses. It includes datasets built from Sanger Expressed Sequence Tags (EST) [TIGR/Dana Faber M. truncatula Gene Indices (MtGI) releases 5 to 11, EST MENS releases MtCDJan2003  (Journet et al. 2002)  and MtSCDJun2006  (Godiard et al. 2007) ] and several sets of microarray probes, namely the Mt6k  (Kuster et al. 2004 ), Mt16k  (Hohnjec et al. 2005) , Affymetrix  (Benedito et al. 2008)  and Nimblegen microarrays  (Verdier et al. 2013). The database also includes all successive M. truncatula genome sequence and annotation releases, namely Mt3.
5  (Young et al. 2011) , JCVI Mt4. 0  (Tang et al. 2014 ), Mt20120830 (Roux et al. 2014 ) and Mt5. 0  (Pecrix et al. 2018). The current reference transcriptome from pea  (Alves-Carvalho et al. 2015) , Pisum sativum, a legume species closely related to M. truncatula, is also included to facilitate knowledge transfer between a model and a crop legume species. This particular dataset was processed like M. truncatula reference datasets with specific overlap parameters (see Methods). The method for establishing correspondences between all the IDs for a given gene (Table  1 ) was to map all corresponding sequences on the last reference genome sequence, Mt5. 0 (see Methods). All "synonymy" links (Fig. 1 ) were then loaded into the LeGOO database to enable knowledge inference across the different generations of sequence datasets.
Each pairwise comparison of ID sets is available as a spreadsheet table. It is also possible to search for all IDs of any single gene through the main search box or a dedicated search form (termed "ID converter"). The mapping criteria were defined to take into account the variable accuracy of the datasets (see Methods). Each M. truncatula dataset could be mapped over 88% with a global mapping rate of 95%. The most recent datasets (Mt4. 0, Mt20120830 and small secreted peptide genes [SSPs;  (de Bang et al. 2017) ] have the highest mapping rates, confirming at the same time their high quality and the completeness of the Mt5. 0 reference genome. We can also observe a good specificity of the mapping (more than 96% of mapped sequences are mapped at only one location) for all the datasets except for the Nimblegen array probes, probably because they also included a set of transposable elements.
Elaborating a knowledge base associated with M. truncatula genes The aim of the LeGOO base is to retrieve knowledge, i. e. published information focused on scientific questions and validated through the publication process (with ad hoc statistical analyses and cross-validations when necessary). Non-curated raw data (e. g. raw transcriptomics, proteomics or interactome data) are therefore not taken into consideration. Importantly, results of a variety of targeted approaches, such as forward and reverse genetics or molecular biology (e. g. quantitative RT-PCR analyses, protein-protein or protein-DNA interactions…) are used to feed the LeGOO knowledge base, in addition to omics data. The way LeGOO is conceived therefore implies managing heterogeneous objects / data sets and defining appropriate representation tools.
Content of the LeGOO knowledge base As of April 2019, data from 298 publications from peer-reviewed journals concerning M. truncatula were manually retrieved, the list of which is provided on the LeGOO site (KnowledgeBase / PubMed List). In total more than 200,000 relations have been entered in the database, using controlled vocabulary for objects, relations and processes (based on Gene Ontology terms whenever possible). Additional related information, especially details of experimental conditions, the magnitude of observed effects (e. g. fold change for induction or repression) and plant or interacting microbe species and genotypes, have also been indicated by the curators. Furthermore, continuing efforts have been made to compile gene names used in publications, whether defined from phenotypes, genetic screens or systematic surveys of gene families and to establish their correspondence with genomic loci (> 2,500 genes are currently listed with corresponding publication identifiers as well as Mt5.
0, Mt4. 0, Mt3. 5, Mt20120830, Affymetrix and Nimblegen probe IDs, a list which is regularly updated). This resource (termed Gene Acronyms vs. IDs, in the ID converter tool) is very valuable when analyzing any transcriptome or proteome data set, to quickly find relevant information on regulated genes of interest, and also to avoid renaming entities already reported in the literature. A large fraction (140) of the publications used to feed LeGOO deal with transcriptomics analyses, which generated 98% of the relations and involved 88,250 different biological entities. Symbiosis-related publications (N-fixing and arbuscular mycorrhizal symbioses) are the most represented in the knowledge base with 125 publications and 71,626 relations, due to the scientific interests of the curators, but other processes are also documented (Table  2 ).
Discovering new indirect links between genes LeGOO uses the shortest path algorithm (implemented in the Neo4J database engine) to provide an original service. This functionality aims at finding a shortest path between two biological entities without any a priori about their connection. To run it, the user selects two objects and the maximum number of paths that should be retrieved. The result is a knowledge graph representing all the paths and intermediate nodes that connect the two objects (Fig. 3 ), as well as a table indicating all paths when several are found. As with all LeGOO graphical outputs, additional information on any objects (e. g. list of best blastp hits in several legume and nonlegume plant genomes, Interpro domains, existence of Tnt1 insertion mutants, RNAseq and methylome data…) can be obtained via a link to the Mt5.
The system collects the set of corresponding synonyms and uses the API of Europe PMC to retrieve up to last 100 publications in which at least one of the synonyms appears. As an example of the usefulness of this tool, when using MtEFD as a query, three publications were found when searching in PubMed (as of May 29 th 2019) vs. 10 using LeGOO. Publications already curated and available in the LeGOO database are indicated with the number of relations that were extracted per publication by the curators.
Navigating through different plant model species In addition to the current reference transcriptome from pea (http://bios. dijon. inra. fr/FATAL/cgi/PsUniLowCopy. cgi ), LeGOO offers a service to search for potential orthologs or recent paralogs to M. truncatula proteins (from the latest annotation release  (Pecrix et al. 2018 )) of identifiers from reference organisms (Table  3 ) such as A. thaliana. This functionality provides a way to benefit from the knowledge acquired on M. truncatula for the purpose of translational research, or reciprocally to help curators and users to identify relations missing in the LeGOO system. For any other organism that is not available among the reference list, users can enter the system via a blast search against all M. truncatula datasets used for the ID Converter service.
corresponding biological objects were identified with the bedtools intersect command combined with custom Perl scripts to define the following spanning thresholds: minimal query coverage of 30% for EST and gene queries and 80% for oligonucleotide, Affymetrix and Nimblegen probe queries; minimal subject coverage of 30% for EST and genes (same orientation as queries). For the P. sativum transcriptome data set (assemblies from short RNAseq reads), the threshold for subject coverage (i. e. the P. sativum sequences) was limited to 150% to avoid artefactual gene fusions between co-localized paralog genes.
Knowledge extraction from publication and formatting The "knowledge" extracted from each publication was modelled as a relation between one source object (e. g. a gene, a protein or a biological process) and a list of targets (biological objects or processes). Each object is defined with a type (controlled vocabulary), a name, an organism and a genotype. Additional optional fields can be attached to the object, such as a subtype (e. g. mRNA or ncRNA for the RNA type), a description or a link to a database. The relation is labelled using a controlled vocabulary describing the type of the relation (e. g. Controlled vocabulary relies as much as possible on ontologies (Sequence Ontology  (Eilbeck et al. 2005)  for object types) and established terms from Gene Ontology  (Harris et al. 2004 ) to describe biological processes whenever possible.
Data wrangling and database setup The construction of a new release of the database is left to the LeGOO administrator. All files uploaded by curators, as well as lookup tables, reference files, and annotation files are collected. Automatic corrections are made to fix case errors or use common identifiers (using mapping files). Biological objects are identified through the calculation of a unique signature composed of the type, name, organism and genotype in which the object is observed. Synonymy links are automatically inserted to link analog objects in different genotypes, or to model the Gene -> Transcript -> Protein relations. The set of relations is loaded into a Neo4J graph database (https://neo4j. com/). Nodes and edges are annotated and indexed using ElasticSearch (https://www. elastic.
Acknowledgments We thank  Stefano Collela  and  Marc Lepetit  (LMGM, Montpellier),  Sandra Bensmihen ,  Françoise Jardinaud ,  Andreas Niebel  and  Nicolas Pauly  ( LIPM, Castanet Tolosan ) for valuable discussions on the LeGOO template and their contribution to the curation work. We also thank  Ludovic Legrand  and  Ludovic Cottret  ( LIPM ) for technical advice.
Discussion Here, we describe LeGOO, a database centered on information retrieved from publications, with a graph-based structuration of knowledge acquired by the M. truncatula community. LeGOO provides: a comprehensive ID converter that considerably facilitates the mining and comparison of data produced over about two decades with different tools; a list of gene acronyms used in publications, along with the corresponding gene identifiers in successive genome sequence releases and microarrays; a text mining tool to find publications where a gene of interest is cited (with a search of all synonymous IDs corresponding to the gene). The interests of a knowledgebase such as LeGOO are that: (i) information and data obtained by a variety of approaches can be integrated and represented; (ii) information is already filtered and validated by the publication process, which ensures quality standards and reliability, and decreases the level of "noise" caused by spurious data that may be found in raw data.
Thanks to LeGOO, the user may find out valuable information outside his/her field of expertise or data of direct interest that is buried in supplementary tables of publications. For example, while MtEFD is known to be important for the regulation of nodulation  (Vernié et al. 2008 ) and pathogenesis induced by the bacteria Ralstonia solanacearum  (Moreau et al. 2014) , data mining via LeGOO revealed that MtEFD expression is also activated by the oomycete Aphanomyces euteiches, inhibited during flower and fruit development, and positively or negatively impacted by phytohormone treatments (abscisic acid and jasmonic acid, respectively) (Fig. 2 ). LeGOO is currently updated and maintained by manual curation by biologists, which is quite time consuming. In the future it would be very interesting to develop semi-automated text mining methods to facilitate the regular integration of published results.
Computing of synonymy links The gene models (release 5. 1. 6) of the high quality reference genome  (Pecrix et al. 2018)  were used as pivotal sequences and nomenclature. All datasets were mapped on the Mt5. 0 genome using GMAP  (Wu and Watanabe 2005)  (gmap. version = 2017-09-05, gmap. parameters = --gff3-add-separators=0 --mapboth --npaths=10 --suboptimal-score=1. 0 -L 100000 --min-intronlength=35 -K 25000 --trim-end-exons=25). Then,.
The mooring data together with the CTD casts were used to produce a hydrodynamic model of the seamount  (Cooper and Spearman, 2017). The model domain extended over an area of 500 km × 500 km; all runs were done including tide and real-time wind values. Model outputs included a time series of salinity, temperature and current velocity at 49 layers through the water column (with higher resolution near the seabed and surface) at a horizontal resolution over the seamount of 100-800 m. Model simulations were used to obtain the maximum near-bed velocity of currents (at 1 m above the bed) to include in the distribution models. The current information was derived at a variable resolution from 500 to 600 m, changing across the study area. The layer was then interpolated (using ordinary kriging) to the same resolution as the bathymetric data and rest of the variables using the function "krige" from the 'gstat' package  (Pebesma, 2004).
Data Analysis The distribution of P. amadou at Tropic Seamount was modeled using three algorithms widely used in species distribution modeling  (González-Irusta et al. , 2015) : Maxent  (Phillips et al. , 2006) , GAMs  (Hastie and Tibshirani, 1986)  and Random Forest  (Breiman, 2001)  using R (R Core Team, 2018). The Maxent algorithm estimates species distribution by minimizing the distance between the probability density of species occurrence and the probability density of the covariates as they occur in the geographic space  (Elith et al. , 2011). Although it was developed as a new machine-learning algorithm,  Renner and Warton (2013)  demonstrated that it is actually equivalent to an inhomogeneous Poisson process. Recently,  Phillips et al. (2017)  have developed a new R package named 'maxnet'  (Phillips, 2017) , which uses this feature to fit Maxent models using the 'glmnet' package for regularized generalized linear models  (Friedman et al.
, 2010). The main advantage of the 'maxnet' package is that it allows one to fit a point process model while keeping all the derived feature classes (especially hinge features) and default tuned regularization values of the Maxent Java application  (Phillips et al. , 2017). This means that the raw output of Maxent can be used directly as a model of relative abundance. In this work we used all the default parameters of 'maxnet' but using real absences derived from the ROV data instead of pseudo-absences. General Additive Models (GAMs) are an extension of General Linear Models (GLMs) which allow one to model non-linear relationships between the response variable and the explanatory variables, using a smoothing function to link both. The distribution of many marine species and habitats have been successfully modeled using this non-parametric approach (e.
g. ,  González-Irusta and Wright, 2016; Parra et al. , 2017; Serrano et al. , 2017). The GAM models were fitted using the function "gam" in the 'mgcv' package  (Wood, 2011). We used a binomial GAM with logit function and constrained the smoother curves to 4 knots to avoid overfitting. The third model applied was Random Forest (RF) using the package 'randomForest'  (Liaw and Wiener, 2002). This modeling framework builds multiple classification trees, each generated with subsets of random data and using a randomly smaller set of the predictor variables  (Breiman, 2001). Unlike GAMs, RF offers the advantage of lacking any underlying assumption of the distribution of the response variable. The variable selection differed for each algorithm. Maxent was fitted using all the variables previously selected after checking for collinearity, whereas for GAMs the variable selection was performed using the Akaike Information Criteria (AIC) and the function "dredge" from the package 'MuMIn'  (Barton, 2018).
Model Evaluation The performance of the models was assessed by cross-validation using the function "get. block" from the 'EnMEVAL' package  (Muscarella et al. , 2014). This function finds the latitude and longitude that divides the number of occurrence localities into four similar groups. The objective of this method is to provide a larger level of spatial independence between the training and the evaluation data, reducing overestimation of model performance caused by a lack of spatial independence in the data  (Fourcade et al. , 2017). From the four original spatial blocks, we randomly selected one to evaluate the model (test subsample) and used the other three to build the models (training subsample). The ability of the training subsample to predict the probability of presence was tested using the test subsample in 10 runs.
Species Data From the initial dataset of 7,547 pictures distributed across the study area, 923 showed at least one specimen of P. amadou. The mean number of P. amadou sponges in the images in which they occurred was 11 individuals with a maximum of 121 individuals. The sponge seemed to be distributed throughout the four ridges sampled on the seamount. Analysis of the still frames showed wide presence of P. amadou in the eastern, southern and western ridges of the seamount. A brief ROV dive on the northern crest of the seamount recorded one specimen of P. amadou. The sponges occupied a distinct bathymetric belt ranging from 1,960 to 3,660 m. Different body sizes ranging from ∼5 to 55 cm in length were observed across all the stills. The sponges were found only on hard substrates corresponding to rocks, of which the majority had a ferromanganese crust.