dataesr/scientific-paragraphs-categorization huggingface dataset:
LanguaL (2020), a multilingual thesaurus using faceted classification, is used in major food composition databases, e. g. in the United States (USDA, 2020), Europe  (EuroFIR, 2020)  and  France (Ciqual, 2020)  to define a food item by a set of standard controlled terms. Moreover, FoodOn (Dooley et al. , 2018)  is an ontology, initially based on a conversion of the LanguaL thesaurus, integrated with other resources and aiming to be the open standard controlled vocabulary for food science. In order to be able to integrate these major FCDBs and terminologies, called food sources, this paper proposes to align on FoodOn (that is therefore used as a pivot) a given food using both its LanguaL and English terminological descriptions commonly available in all FCDBs. In this proposed scenario, two foods from two different food sources being indexed with the same LanguaL description and same terminological English description are assumed to represent the same food.
AVAILABLE FOOd SOURCES This section presents the food sources used in this paper to illustrate the proposed approach: LanguaL, a well-known multilingual thesaurus using faceted classification (see Section 2. 1), FoodOn, a new international food ontology (see Section 2. 2), Ciqual, the French food composition database (see Section 2. 3) and finally USDA SR Legacy, the North American food composition database (see Section 2. 4). The last two food sources are used as examples of national FCDBs to be linked, using FoodOn as a pivot.
LanguaL LanguaL (  2020 ) is a well-known multilingual thesaurus using faceted classification  (Ireland & Moller, 2000 , 2010). LanguaL stands for "Langua aLimentaria" or "language of food". More than 40,000 foods used in food composition databases are LanguaL described  (LanguaL Indexed Datasets, 2020). LanguaL is based on the following hypothesis: i) any food can be systematically described using a combination of characteristics; ii) these characteristics can be categorized into viewpoints, composed of one or several facets, and coded for computer processing; iii) the resulting food description codes can be used to retrieve food data from FCDBs. Table  1  shows the 14 facets of LanguaL. Thanks to LanguaL, a food may be described by a combination of descriptors belonging to the standardized vocabulary associated with each facet presented in Table  1.
A food can be described by several descriptors for one facet and it can also have no descriptor for a facet. Each descriptor is defined by a standard controlled term and a code. Each standard controlled term belongs to a specialization hierarchy, that is defined for each facet. An excerpt of the hierarchy of descriptors associated with facet A is presented in Figure  1. In Table  2 , the food Cooked pork shoulder, choice belonging to Ciqual database is described by 14 descriptors, i. e. a code associated with a controlled term, regrouped in 10 facets. For instance, this food has two descriptors for facet A, but is not described for facet K. The descriptor of code A0797 associated with the controlled term PRESERVED MEAT (EUROFIR) belongs to the specialization hierarchy associated with facet A.
FoodOn FoodOn  (Dooley et al. , 2018)  is a food ontology initially based on a conversion of the LanguaL thesaurus. For instance, each specialization terms' hierarchy associated with each LanguaL facet was translated in FoodOn into a specialization concepts' hierarchy. Additionally, FoodOn includes 9,500 food terms imported from the Scientific Information and Retrieval Exchange Network of the US Food and Drug administration (called SIREN in the paper) food database that are organized in.
Ciqual Ciqual (Ciqual (  2020 )) is the French food nutritional composition database. It is managed by ANSES, the French Agency for Food, Environmental and Occupational Health and Safety. Ciqual 2017 version used in this paper includes 2,807 foods, of which 1,797 are fully described using LanguaL (see an example in Table  1 ). Up to 61 nutritional values, corresponding to different constituents, may be associated with a food (see Figure  3 ). Values are given for 100g of comestible food (e. g. without bone for meat).
USdA SR Legacy USDA SR Legacy (2020) is the major source of food nutritional composition data in the United States. It is managed by USDA, the US department of agriculture. USDA SR Legacy 2018 version used in this paper includes 8,618 foods of which 5,137 have been fully described using LanguaL (see an example in Table  3 ). Up to 150 nutritional values, corresponding to different constituents may be associated with a food. Table  3  gives the descriptors for a food, Pork, cured, shoulder, arm picnic, separable lean and fat, roasted. Let us notice that six of its descriptors-A0279, B1136, F0014, H0253, P0024 and Z0043-are the same as those used in Table  2 , for Cooked pork shoulder, choice, but both foods are different.
Syntactic Similarity The definition of the syntactic similarity between a food concept f of the source ontology O fs 1 and each food concept of the target ontology O fs 2 , is based on their labels. First, with each food concept f is associated a set of lemmatized words denoted W f computed using the Python NLTK (Natural Language Toolkit from  Bird et al. 2009 ) and the English Pen Treebank tagset (English Pen Treebank tagset (2020)) as follows: w1 = PartOfSpeech_Tagging( θ f ( ) ) w2 = WorldNetLemmatizer(w1) using the WordNet Database W f ( ) = Delete_Stop_Words(w2) Example 2. Let us consider the food concept having for label Cooked pork shoulder, choice. This label is transformed by the pre-processing step into the set of lemmatized words {cook, pork, shoulder, choice}. The syntactic similarity between two food concepts f and g is computed using the cosine similarity: SyntSim f g cos W W f g , , ( ) = ( ).
Alignment Method Assessment The food sources alignment method presented in Section 3. 2 has been tested on Ciqual food database and the SIREN subpart of FoodOn ontology, the foods of both sources being described with LanguaL descriptions. Let us call O fs 1 the source ontology built from Ciqual food database using Definition 3, O fs 2 the extension of the SIREN subpart of FoodOn (that is already an ontology) also using Definition 3. It must be noticed that O fs 1 (resp. O fs 2 ) is a flat list of food concepts described their LanguaL facets. The alignment method takes into account the LanguaL description to compute food matches A set of alignments, called GS (for gold standard) especially designed for this work have been used to test the method. GS has been designed using a subset of 181 food products from Ciqual fully described in LanguaL and including different kinds of foods (fruits and vegetables, meats and fishes,  Table  4  provides comparison results in terms of precision, recall and F-measure between 5 methods: the method presented in Algorithm 1, the syntactic score alone (Def.
PRESENTATION ANd ASSESSMENT OF MULTIdB EXPLORER MultiDB explorer has been designed in the framework of the French national research project Meatylab involving academic and industrial partners. The need expressed by industrial partners was to be able to take benefit of external nutritional data sources to help them in their recipe formulation process. The first step is to identify the nutritional values for each ingredient either by its identification in an appropriate Food Composition DataBase (FCDB), or by designing a specific experimental analysis procedure. As the second solution is time-consuming and costly, common practice is to reuse existing data available in national FCDBs. Unfortunately, values associated with nutrients of interest may be lacking in the FCDB of the country in which the nutritional quality must be assessed.
For example, Ciqual and USDA databases provide access to their data sources using specific web querying interfaces or the Microsoft Access format. With this latter solution, users need to understand database structures which are quite different. In case when, for a given food, nutrient data of interest are not available in the FCDB of the country in which the nutritional quality must be assessed, specification 4 allows one to facilitate the search of this lacking value for a similar food in another FCDB. In Section 4. 1, the main functionalities of MultiDB explorer are briefly presented. Industrial partners provided a use case to assess the relevance of MultiDB explorer to manage incompleteness of a given FCDB. The results obtained using MultiDB explorer to deal with iron, Vitamin B12, Vitamin C nutrients incompleteness in Ciqual reusing USDA data source are presented in Section 4.
Architecture and Functionalities The first three system specifications are already implemented in EuroFIR FoodEXplorer tool (EUROFIR (2020). Unfortunately, this tool must be used as it is and cannot be extended to be able to implement specification 4. However, EuroFIR provides, under license, copies of its database which integrates in a homogeneous XML file format the content of a large set of national FCDBs. A relational database with a schema equivalent to the EuroFIR XML schema has been created and fulfilled with data provided by EuroFIR. In MultiDB explorer, 6 FCDBs are currently included (see Table  6 ). As presented in Figure  6 , MultiDB explorer is composed of two subsystems. The first one is the querying subsystem which interacts with the relational database including EuroFIR data to retrieve nutritional values associated with searched foods and to provide access to the list of foods and family alignments associated with a given food.
The second subsystem computes food sources alignments implementing the food sources alignment method presented in Section 3. The food sources alignment 16 engine must be run when a new version of EUROFIR XML file is integrated in the relational database to update food and family alignments. Implementation of specification 4 in MultiDB explorer, which is the original one compared to FoodEXplorer, is presented through illustrations in the following. Figure  7  presents MultiDB explorer search of the French term Courgette, purée which has been found in Ciqual FCDB. It may be noticed that the query may be done using the national language term or English term defined in the FCDB (here Courgettes, puree). A click on the result of the query, id 20264 in column Origfcdb (see Figure  8 ), allows one to retrieve the LanguaL description associated with Courgette, purée and a list of USDA foods associated with the same FoodOn family.
why Choosing FoodOn? As pointed out in  Ireland and Moller (2016) , there is general agreement on the importance of using a multifaceted approach for identifying foods in food databases. Moreover, the use of a common food classification and description system could provide a central linking system between existing systems to promote more accurate dietary exposure calculations in Europe and also at a worldwide level. In agriculture, the Global Agricultural Concept Scheme (GACS) (  2020 ) is a hub for concepts related to agriculture integrating three well known thesauri: AGROVOC Concept Scheme proposed by the Food and Agricultural Organization of the United Nations (FAO) (AGROVOC, 2020), the CAB Thesaurus (CABT, 2020), and NAL Thesaurus (NALT, 2020) of the National Agricultural Library of  the USA (NALT, 2020).
The three organizations selected the 10,000 concepts most frequently used in each respective thesaurus. These concepts were automatically mapped, mappings were checked by hand, and inconsistencies were resolved by discussion  (Baker et al. , 2016). However, the initial thesauri that were designed for general purposes in Agriculture are not precise enough for the task of food description and are not suitable for representing specific foods appearing in nutrient databases  (Ireland & Moller, 2016). In food domain, a long-term effort has been done by the International Network of Food Data Systems (INFOODS) including a template in Excel format to compile food nutritional composition using a standardized vocabulary for chemical components  (Charrondiere & Burlingame, 2011; Murphy et al. , 2016).
Unfortunately, until now, each national agency, e. g. USDA or ANSES, indexes its own composition tables using its specific food terminology. The recent initiative of the FoodOn consortium supports the development of the FoodOn ontology  (Dooley et al. 2018 ), aiming at proposing a standard food description vocabulary composed of term hierarchy facets. Reusing terms from LanguaL-the thesaurus used to index numerous agency databases and presented as a good potential choice for a central mapping structure-the FoodOn project provides a familiar and well-known hierarchy of concepts and associated terminology for professionals and researchers. This is the main reason why FoodOn was chosen as a pivot ontology in the proposed approach to map specific food terminologies used by national agencies.
This paper proposes to perform this task using the OWL ontologies knowledge representation model. This choice has several advantages: (1) the use of international standards established by the W3C consortium (https://www. w3. org/); (2) the availability of numerous tools based on those standards (OWL ontology portals, editors, validators and alignment tools, triple stores to manage RDF databases annotated by OWL ontologies) to facilitate the reuse of ontologies in an Open Science perspective; (3) the accuracy of OWL language operators to describe knowledge compared to interchange data languages as JSON classically used.
ACKNOwLEdGMENT This work was supported by the FUI Metyl@b Project financed by BPI France. Let us notice that we can identify the descriptors of a given facet by the specialization hierarchy to which belongs the concept associated with the SomeValuesFrom restriction. Let us also notice that, to facilitate understanding of the Appendix, actual URI associated with Langual descriptors in FoodOn have been replaced by an URI based on its english label. By example, the actual URI associated with #CURED_MEAT_US_CFR is http://purl. obolibrary. org/ obo/FOODON_03400279.
It is the reason why each food has been manually aligned with its closest family in O fs 2 , called family match. In the following, the set of 73 food products from Ciqual associated both with a food match and a family match in O fs 2 is called GS 73. The set of 181 food products from Ciqual only associated with a family match in O fs 2 is called GS 181. It must be noticed that GS 73 is included in GS 181. GS 73 and GS 181 are available in the dataset https://doi. org/10. 15454/BVXD7I stored in Excel format in the institutional INRAE dataverse. Those datasets include also the LanguaL description of Ciqual foods. The alignment results are presented and discussed in the following.
O fs 1 (resp. O fs 2 ) is available in the dataset https://doi. org/10. 15454/6CEYU3 (resp. dataset https://doi. org/10. 15454/5LLGVY) stored in OWL format in the institutional INRAE dataverse. O fs 1 (resp. O fs 2 ) is composed of 3863 (resp. 7862) classes and 24218 (resp. 53832) logical axioms.
It is the reason why each food has been manually aligned with its closest family in O fs 2 , called family match. In the following, the set of 73 food products from Ciqual associated both with a food match and a family match in O fs 2 is called GS 73. The set of 181 food products from Ciqual only associated with a family match in O fs 2 is called GS 181. It must be noticed that GS 73 is included in GS 181. GS 73 and GS 181 are available in the dataset https://doi. org/10. 15454/BVXD7I stored in Excel format in the institutional INRAE dataverse. Those datasets include also the LanguaL description of Ciqual foods. The alignment results are presented and discussed in the following. Example 6. The food beef chuck (raw) (resp. the food family beef food product) from O fs 2 is an example of food match (resp.
robustus, A. orientalis, A. tristrami), and the African erythrurus group (A. blanci, A. erythrurus) and pardalis group (A. boueti, A. margaritae, A. busacki, A. bedriagai, A. maculatus). The newly sequenced specimen of A. guineensis (ZFMK 59511 from Daroha, near Bobo Dioulasso, Burkina Faso; Fig. 2 ) for which all gene fragments were available, clustered closely with the previously sequenced specimen (A275 = JFT 4143 from Kouré, Niger) for which only two fragments were sequenced, and which therefore could not be reliably placed into the lacertid tree in previous studies. The second sub-Saharan Acanthodactylus, A. boueti was not recovered as sister taxon of A. guineensis based on the available sequences of 12S and COB from GenBank (accession number A276) but instead placed with high support (bootstrap value 99) as sister to the pardalis group within the Western clade.
1% of occupied habitats), but others are in the 'Montane Grasslands & Shrublands' and 'Tropical & Subtropical Moist Broadleaf Forests' biomes (Fig. 4c ). The one locality from the 'Mangroves' biome is the area where the type locality "Brass, mouths of the Niger"  40  is located, however, various authors have cast doubt on the correctness of the type locality and expect it to be further north  18, 41. This locality also stands out with experiencing the highest annual precipitation in our dataset (3515 mm; see Fig. 4a ), while all other environmental parameters recorded fall within the total range for this species. Almost half of the known populations (47. 2%) are found in the ecoregion 'West Sudanian savanna' , another 27. 8% are located in different forests and forest-savanna mosaics (Fig.
5a , Supplementary Fig. S5a online ). In addition, there is a high probability that A. guineensis finds suitable abiotic conditions in northwestern and central Benin, northern and central Togo, and northern including all literature and GBIF records and our own discoveries from this study (unpublished). In addition, we present all records from the genus Acanthodactylus (purple circle) as downloaded from GBIF. The specimen that we sequenced (ZFMK 59511  43  ), museum specimens studied herein (holotype E. mandjarum ZMB 25479, ZMB 31046, ZMB 84911), the previously unpublished country record for Benin  38  , and the A. guineensis specimen collected by JFT (4143, GenBank record A275  14  ) that we added to our phylogeny are separately labeled on the map. In the background, data on mean annual precipitation in mm is shown, with cooler colors representing wetter conditions and warmer colors representing drier conditions.
obs. ). Although Meinig and Böhme  44  did not find morphological variation among A. guineensis along an East-West transect that would allow recognition of subspecies, their dataset did not include the specimens from Central African Republic. Our extended dataset including the latter, underpinned with climate, altitude and additional (macro-) habitat data provide at least some indication that there is still hidden diversity among these lizards. Additional in-depth assessment of A. guineensis (and A. boueti) taxonomy is consequently still required. Our new phylogeny combined with environmental niche models suggests that the only two sub-Saharan species of the genus (A. guineensis, A. boueti) are not sister taxa despite occupying roughly similar niches in precipitation-rich and hot tropical environments, opposed to the mainly xeric adapted remaining 42 species of Acanthodactylus.
Subsequently, libraries were normalized and pooled, and pools quantified with a KAPA Library quantification kit for Illumina platforms (Roche Sequencing, Pleasanton, CA, USA) on a ABI StepOnePlus qPCR machine (Thermo Fisher Scientific Inc. , Waltham, MA, USA), then loaded on a SP flowcell and paired-end sequenced (2 × 150 bp) on an Illumina NovaSeq 6000 next generation sequencer (Illumina, San Diego, CA, USA), and a S2 flowcell for linked read library. Raw reads were deposited in the Sequence Read Archive (SRA) under BioProject ID PRJNA700414. All mitogenome assemblies and original alignments were deposited in Figshare under https:// doi. org/10. 6084/m9. figshare. 13754083. v1. Raw FASTQ sequenced reads were first assessed for quality using FastQC v0. 11. 5  61. The reads where then passed through Trimmomatic v0.
We used the quality filtered reads to assemble the mitogenomes of A. guineensis and A. schmidti using an iterative mapping strategy in MITObim v. 1. 9. 1  64. We used the Acanthodactylus aureus mitogenome (GB accession number xxxxx; assembly method is provided in the next paragraph) as seed for both samples; this rendered an initial mapping of a conserved region from a more distantly related individual unnecessary  64. We therefore applied the -quick option in MITObim and iterations were run until no additional reads could be incorporated into the assembly (14 in A. guineensis, eight in A. schmidti). We also assembled complete or nearly complete mitogenomes for additional twelve species of Gallotiinae and Eremiadini using anchored hybrid enrichment sequence data from Garcia-Porta et al. 6  (see Supplementary methods in  6  for extraction protocol and sequencing methods and Supplementary Table  S1  online in  6  ) with the Podarcis muralis complete mitogenome (GB accession number NC_011607) as reference sequence.
The raw data of each individual was quality filtered using Trimmomatic v0. 36  62  (parameter MINLEN:45) and assembled using MITObim v. 1. 9. 1. All multiple alignment files generated in the final MITObim iteration were imported to Geneious R11 (https ://www. genei ous. com) to check for assembly quality and coverage. The resulting assemblies were annotated with MITOS 65 using defaults settings with the vertebrata database as a reference. The annotated assemblies were imported into PhyloSuite 66 together with existing mitogenomic sequences of Lacertidae, and Blanus cinereus (Amphisbaenia; outgroup) available in GenBank (as of July 2020). Details of all mitogenomic sequences included in this study and the corresponding GenBank accession numbers are provided in Supplementary Table  S1  online.
Using PhyloSuite, we exported all protein coding sequences plus the two rRNAs. The resulting files were aligned with MAFFT 67 using "auto" settings. Alignments of coding sequences were refined with MACSE  68  to account for open reading frame structure during the alignments. The protein coding and rRNAs alignments were finally concatenated into a single alignment delimiting partitions by marker and, for the protein-coding genes, by codons within markers. Mitogenome phylogenies were inferred using a maximum likelihood approach with IQ-TREE v. 1. 5. 4 software  69  and Bayesian inference (BI) analysis with MrBayes 3. 2  70. For the maximum likelihood approach, best-fitting partitioning schemes and substitution models were selected based on the Akaike Information Criterion (AIC) using the heuristic algorithms implemented in the MFP + MERGE option (Supplementary Table  S3  online).
5 software  71. Samples corresponding to the initial phase of the Markov chain (25%) were discarded as burn-in and the remaining results were combined to obtain a majority rule consensus tree and the respective posterior probabilities of nodes. An additional, more comprehensive, alignment was produced containing all currently known Acanthodactylus species to obtain a higher resolution perspective on the phylogenetic placement of the target species. Since no nuclear data was available for A. guineensis, we extracted from the newly obtained sequences the four best represented mitochondrial gene fragments across Lacertidae, as compiled by Garcia-Porta et al. 6  , despite the known shortcomings of partial mitochondrial datasets to resolve lacertid relationships  9, 72, 73. The corresponding sequences of the ribosomal RNAs (12S, 16S), COB, and NADH-dehydrogenase subunit (ND4) were later added to the alignment of 6 using -add and -keeplength options in MAFFT (Supplementary Table  S5  online).