dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenomic inferences To infer the Carnivora phylogenetic relationships, all carnivoran genomes available on Genbank, the DNAZoo website (https://www. dnazoo. org), and the OrthoMaM database  (Scornavacca et al. , 2019)  as of February 11th, 2020 were downloaded (Table  S10 ). In cases where more than one genome was available per species, the assembly with the best BUSCO scores was selected. Then, we annotated our two reference genome assemblies and the other unannotated assemblies using MAKER2  (Holt and Yandell, 2011)  following the recommendations of the DNAZoo (https://www. dnazoo. org/post/the-first-million-genes-arethe-hardest-to-make-r). In the absence of available transcriptomic data, this method allowed to leverage the power of homology combined with the thorough knowledge accumulated on the gene content of mammalian genomes.
As advised, a mammal-specific subset of UniProtKB/Swiss-Prot, a manually annotated, non-redundant protein sequence database, was used as a reference for this annotation step  (Boutet et al. , 2016). Finally, the annotated coding sequences (CDSs) recovered for the Southern aardwolf (P. c. cristata) were used to assembled those of the Eastern aardwolf (P. c. septentrionalis) by mapping the resequenced Illumina reads using BWA-MEM  (Li, 2013). Orthologous genes were extracted following the orthology delineation process of the OrthoMaM database (OMM)  (Scornavacca et al. , 2019). First, for each orthologous gene alignment of OMM, a HMM profile was created via hmmbuild using default parameters of the HMMER toolkit  (Eddy, 2011)  and all HMM profiles were concatenated and summarized using hmmpress to construct a HMM database.
Then, for each CDS newly annotated by MAKER, hmmscan was used on the HMM database to retrieve the best hits among the orthologous gene alignments. For each orthologous gene alignment, the most similar sequences for each species were detected via hmmsearch. Outputs from hmmsearch and hmmscan were discarded if the first hit score was not substantially better than the second (hit 2 < 0. 9 hit 1 ). This ensures our orthology predictions for the newly annotated CDSs to be robust. Then, the cleaning procedure of the OrthoMaM database was applied to the set of orthologous genes obtained. This process, implemented in a singularity image  (Kurtzer et al. , 2017)  named OMM_MACSE. sif (Ranwez et al. , 2020)  is composed of several steps including nucleotide sequence alignment at the amino acid level with MAFFT  (Katoh and Standley, 2013) , refining alignments to handle frameshifts with MACSE v2  (Ranwez et al.
, 2018) , cleaning of non homologous sequences, and masking of erroneous/dubious part of gene sequences with HMMcleaner (Di  Franco et al. , 2019). Finally, the last step of the cleaning process was to remove sequences that generated abnormally long branches during gene tree inferences. This was done by reconstructing gene trees using IQ-TREE v1. 6. 8  (Nguyen et al. , 2014)  with the MFP option to select the best fitting model for each gene. Then, the sequences generating abnormally long branches were identified and removed by PhylteR (https://github. com/damiendevienne/phylter). This software allows detecting and removing outliers in phylogenomic datasets by iteratively removing taxa in genes and optimizing a concordance score between individual distance matrices. Phylogenomic analyses were performed using maximum likelihood (ML) using IQ-TREE 1.
, to test whether the two pairs of allopatric subspecies diverged synchronously or in two different time periods, Bayesian molecular dating inferences were performed on the 142-taxon ML mitogenomic tree. The resulting divergence times were slightly different depending on the clock model used (strict clock [CL], autocorrelated [LN or TK02] and uncorrelated [UGAM or UCLM]) (Supplementary materials). Cross-validation analyses resulted in the selection of the LN and UGAM models as the models with the best fit based on a higher cross-likelihood score than that of CL (LN and UGAM versus CL mean scores = 35 ± 8). Unfortunately, these two statistically indistinguishable models provided different divergence times for the two pairs of subspecies, with LN favouring a synchronous divergence (approximately 1 Mya [95% credibility interval (CI) : 6.
Acknowledgements We would like to thank  Rachid Koual  and  Amandine Magdeleine  for technical help with DNA extractions and library preprations,  Aude Caizergues  and  Nathalie Delsuc  for fieldwork assistance,  Christian Fontaine ,  Jean-Christophe Vié  ( Faune Sauvage ,  French Guiana) ,  Corine Esser  ( Fauverie du Mont Faron, Toulon, France ),  François Catzeflis  ( ISEM Mammalian Tissue Collection ),  Adam Ferguson  and  Bruce Patterson  ( Field Museum of Natural History, Chicago, USA ),  Lily Crowley (Hamerton Zoo Park, UK) , and  Andrew Kitchener  ( National Museum of Scotland, Edinburgh, UK ) for access to tissue samples. We also acknowledge  Pierre-Alexandre Gagnaire  for helpful discussion on genetic differentiation index and  Brian Chase  for providing references on African paleoclimate.
Funding This work was supported by grants from the  European Research Council  ( ERC-2015-CoG-683257   ConvergeAnt  project),  Investissements d'Avenir of the Agence Nationale de la Recherche  ( CEMEB :  ANR-10-LABX-0004 ;  ReNaBi-IFB :  ANR-11-INBS-0013 ;  MGX :  ANR-10-INBS-09 ).
Finally , to test whether the two pairs of allopatric subspecies diverged synchronously or in two different time periods, Bayesian molecular dating inferences were performed on the 142-taxon ML mitogenomic tree. The resulting divergence times were slightly different depending on the clock model used (strict clock [CL], autocorrelated [LN or TK02] and uncorrelated [UGAM or UCLM]) (Supplementary materials). Cross-validation analyses resulted in the selection of the LN and UGAM models as the models with the best fit based on a higher cross-likelihood score than that of CL (LN and UGAM versus CL mean scores = 35 ± 8). Unfortunately, these two statistically indistinguishable models provided different divergence times for the two pairs of subspecies, with LN favouring a synchronous divergence (approximately 1 Mya [95% credibility interval (CI) : 6.
Introduction Enterprise Modelling (EM) is the process of producing models, i. e. abstract representations, that can be used by humans or machines to support understanding, analysis, (re)design, reasoning, control and even learning about various aspects of interest of an enterprise  [1] [2] [3] [4] [5]. Enterprise Modelling can be defined as the art of externalising knowledge in the form of models about the structure, functionality, behaviour, organisation, management, operations and maintenance of whole or part of an enterprise, or of an enterprise network, as well as the relationships with its environment. EM is an art, meaning an engineering discipline and not a scientific discipline. It emerged both as a concept and a set of tools and methods in the 80's from the needs coming from three main streams: -The needs of software and information system engineers, who required structured approaches to analyse and formalise business transactions/processes and data structures to build ever more complex enterprise applications and their supporting database systems.
2 EM must not be confused with, or reduced to, business process modelling  [6] [7] [8] [9]  or business process management  [10]. Business process modelling is central to EM but is only one of its many facets. Other aspects of the enterprise, or network of enterprises, to be analysed must be covered by EM as illustrated by Fig. 1. These aspects, such as functional, information, resource, organisation or economic aspects for examples, are separately modelled and analysed from different angles or various viewpoints, called 'modelling views. '  1  An enterprise model is therefore the sum of the models obtained in each view. These models can be stored in enterprise repositories or databases of EM tools and represent essential components of an Enterprise Architecture (EA). Indeed, Enterprise Modelling and Enterprise Architecting 2  are heavily intertwined  [11, 3].
e. building blocks defined as templates or object classes, usually with associated graphical symbols, and characterised by a set of descriptive properties or attributes. They represent enterprise elements depicted in the EML, e. g. 'organisation unit', 'activity', 'process', 'event', 'product' or 'resource' constructs. To fix ideas, simplified templates of major EM constructs, classified by views and adapted from CIMOSA or ISO 19440:2007, are given in the Appendix of the paper. The 'finality' of a model is a crucial element. Indeed, the intended use or purpose of the model will always determine what needs to be modelled, i. e. the aspects to be covered (or model scope), at what level of details (or degree of precision) and in which form, i. e. with which language (or model expressiveness).
o Implementation description: It corresponds to the level of system implementors who must define the implementation solutions using machine languages of physical devices, programming languages or physical database systems and take account of all technical constraints of the business entity. Models at this level are executable. -Separation of enterprise behaviour and enterprise functionality: 'Enterprise behaviour' describes how the enterprise performs its work. This is usually represented in terms of 'business processes' and how the enterprise reacts to occurrences of expected or unexpected 'events. ' 'Enterprise functionality' describes the actual work to be performed in terms of tasks or 'activities'. A clear distinction between activity and process implementation can foster enterprise agility, making easier business entity reengineering because enterprise functionality can be changed without affecting enterprise behaviour and vice versa.
Enterprise Modelling at the age of 35 To fairly assess the state of play of Enterprise Modelling, it is necessary to review its development and evolution since its origins. The history of EM can be characterised by different phases. Recently, Vallespir and Ducq identified two main periods  [40] : a brainstorming or divergence era producing initial methods and a convergence era made of four phases: multi-language modelling frameworks, convergence at the macro or framework level, convergence at the micro or language level and standardisation phase. Another characterisation could consist in classifying methods in three classes: namely, early or activitycentred methods (IDEF, MERISE, GRAI…), business process-centred methods (CIMOSA, ARIS, IEM…) and enterprise knowledge-centred methods (TOVE, AKM, DEMO, enterprise ontologies…).
On the IT side, the needs mostly dealt with defining requirements and specifying computer programmes and software applications as well as related data structures for data stores (file systems) or database systems. The aim was to automate business processes simply defined as chains of data processing steps called transactions  [41]. Thus, two main streams of methods and tools appeared in the mid-70's to propose structured system analysis techniques, one concentrating on functional aspects and one concentrating on data aspects. The best representative methods of each stream were SADT and ERM, respectively. SADT (Structured Analysis & Design Technique) is a method originally proposed in 1977 by Ross to analyse a functional domain by breaking it into sub-functions down to its elementary procedures in order to design software programmes in a systematic and structured way  [42].
Originally proposed as a software engineering method, the genericity of SADT was soon recognised as an excellent top-down design method for analysing any complex system thanks to:  (1)   [45, 46]. In addition, many structured methodologies for information system analysis and database design, such as MERISE, NIAM and M*  [47, 18] , have been elaborated in the 80's based on the previous modelling and analysis techniques. Their aim was to cover the analysis of functional and information aspects in one consistent method and at several abstraction levels (conceptual, logical and physical) in order to elaborate software programmes on one hand and associated database schemata on the other hand. Most of them are based on the so-called extended entity-relationship model, an ERM formalism augmented with two abstraction mechanisms: (1) the 'generalisation/ specialisation mechanism' (or 'Is-a' link used to generalise specialised classes into a more general super-class) and (2) the 'aggregation mechanism' (or 'Part-of' link used to form a composite class from component classes).
At first, software engineering methods just cited above were applied for their modelling. It soon became obvious that richer methods with additional capabilities than just coping with functional and informational aspects were needed. Indeed, industrial systems deal with concepts of physical flows, technical resources, temporal aspects or organisational aspects such as decision systems, absent in SADT and ERM. Prominent approaches which have impacted CIM planning and development and have played a pioneering role in Enterprise Modelling include the IDEF Method and the GRAI Method. The IDEF Method was developed for the US Air Force ICAM (Integrated Computer-Aided Manufacturing) Project at the beginning of the 80's  [50]. IDEF stands for ICAM DEFinition Method. In its original form, it comprised three methods set side by side, IDEF0, IDEF1 and IDEF2 (Fig.
4 ). IDEF0 was in fact SADT used for function modelling  [50]. IDEF1 was an adapted ERM formalism used for data modelling. It will become IDEF1x in a subsequent version  [51]. IDEF2 was proposed to model dynamic aspects with a graphical notation based on the queueing model of the SLAM simulation language. IDEF2 has since then been abandoned and replaced by IDEF3  [52]. The IDEF Method (www. idef. com) is still in use in industry, especially in North America, and is made of additional methods (IDEF3, 4 and 5 and even more). The GRAI (Graph with Results and Activities Inter-related) Method has been developed by Profs. Pun and Doumeingts at the University of Bordeaux I, France since 1975. Originally, the aim was to analyse the decision system of flexible manufacturing and CIM systems from a production management perspective  [53] [54] [55].
3. 2). Finally, the IBM Integrated Manufacturing Modelling System (IMMS) project must be cited  [57]. Although not widely known in the literature, IMMS played a key role in conceptualising EM because it has highly influenced the definition of the AMICE project. The objective of the IMMS project was to build a generic software package for manufacturing modelling. The package offered interactive model building, testing and experimentation, including a simulation engine for performance simulation (with output such as process yield, resource utilisation or work in process) in graphic form. The project served as proof of concept and introduced the notion of 'building block' for developing modelling languages and tools. These ideas have largely been reused, adapted and expanded in the CIMOSA architecture of AMICE for developing enterprise-wide modelling, model-based engineering and enterprise model enactment  [21, 22].
pera. net/). CIMOSA, PERA, the Zachman Framework and the like such as GIM have been compared and generalised into GERAM (Generalised Enterprise Reference Architecture and Methodology)  [70, 11] , which combines the best features of these frameworks to propose the most generic framework for EM and EA (See Sect. 3. 3). CIMOSA is described in the next section, but because PERA and the Zachman Framework do not provide any specific modelling language they are not. They fall in the category of EAFs rather than EMFs.
Enterprise Modelling Frameworks A fair presentation of the most prominent EMFs published at the turn of the 90's and reviewed hereafter would each deserve a full textbook. It is therefore a challenge to summarise them in a few words to point out their salient features. CIMOSA, ARIS, IEM, GIM and IDEF suite of languages are discussed in turn. CIMOSA  [71, 72, 22, 61 ] is an open system architecture for CIM (i. e. an EAF for manufacturing enterprises), which includes an enterprise modelling framework, namely the CIMOSA Modelling Framework or "CIMOSA cube" (Fig. 5 ). Its creation started in 1985 by the ESPRIT  5  Consortium AMICE, a group of 21 European companies, to define requirements and building blocks, specify a migration method for CIM implementation and promote standardisation activities for CIM and enterprise integration solutions.
The CIMOSA Modelling Framework is based on three dimensions: -The dimension of architectural levels or instantiation axis: It is made of three levels (Generic Level containing all modelling language constructs and building blocks, the Partial Level providing libraries of reference models, i. e. reusable partially instantiated models, and the Particular Level storing all the fully instantiated specific models of the enterprise). The generic and partial levels form the CIMOSA Reference Architecture while the particular level corresponds to the actual Enterprise Architecture of a given enterprise. -The dimension of modelling views or generation axis: It is made of, but not limited to, four views (Function, Information, Resource and Organisation) as defined in Sect. 2. 4. -The dimension of modelling levels or derivation axis: It corresponds to the concept of modelling levels defined in Sect.
2. 4 and is limited to three levels (Requirements Definition for the "What", Design Specification for the "How" and Implementation Description for the "Do It"). This framework became a European standard in 1991 (ENV 40003)  [73]  and, together with PERA and others (see hereafter), has been the basis for the development of the reference architecture (GERA) of GERAM and the ISO 19439:2006 standard (Fig. 5 ). The CIMOSA cube is not an empty shell as it has sometimes been written. Especially, the generic layer offers the specification of all the constructs of the CIMOSA languages at all modelling levels, organised by views and respecting the principles stated in Sect. 2. 4. For instance, CIMOSA adopted the concept of 'business process' from the beginning, defining an executable language of its control flow (in terms of 'procedural rule set')  [21, 74].
Many other constructs populated the CIMOSA views such as 'domain', 'event', 'enterprise activity', 'enterprise object' or 'resource' to name a few (Fig. 6 ). Another originality of CIMOSA, in addition to introducing the principles of multi-view modelling and late binding of resources to process steps, was to propose an 'event-driven process-based' modelling language  [74]. Indeed, while the business process approach was gaining wider attention at the turn of the 90's, due to the rise of Business Process Reengineering (BPR)  [75] , a process orientation alone would only give a sequential or deterministic vision of the enterprise. In addition, business and industrial systems are subject to perturbations or random events and will exhibit reactive behaviour in the case of unexpected happenings or stimuli.
e. switch from a normal behaviour to an exception behaviour in the case of reaction to perturbations or to random event occurrences. For the interested reader, the CIMOSA languages comprise a set of 12 core constructs organised within the four views (function, information, resource and organisation) and derived along the three modelling levels (ending up to a total of 31). They have first been defined in  [76, 77]  and then revised and enriched to get their final form as given in  [78, 79, 1]  (See Appendix for details on some of them). Concerning the architecture, two special issues of Computers in Industry were devoted to CIMOSA: "Validation of CIMOSA"  [72]  and "Enterprise Engineering and Integration"  [61]. Tools based on CIMOSA were CimTool (for education), PrimeObjects  [80]  and FirstStep  [81]  from Interfacing Technologies for decision makers (www.
ARIS was supported by ARIS ToolSet, the number one EM tool in terms of commercial sales, and it has for a long time been the modelling tool recommended by SAP for planning the implementation of its ERP product. ARIS ToolSet is now marketed as ARIS Architect & Designer by Software AG (www. softwareag. com), in which BPMN is an alternative to the EPC language and a Product/Service View has been added. A free-ofcharge tool named ARIS Express can be downloaded from the Net (www. ariscommunity. com/arisexpress). IEM (Integrated Enterprise Modelling) is a framework, the development of which started around 1990 at the Fraunhofer Institute IPK in Berlin, for integrated manufacturing systems  [84] [85] [86]. The kernel of IEM was based on the ICOM box of SADT to model process steps, called actions, but the originality of the method was to take advantage of the object-oriented approach, both in the modelling language and in the implementation of the supporting tool (Fig.
7 ). Thus, all inputs/outputs of an 'action' (input, control, output and resource), represented as an ICOM box, were 'objects', i. e. enterprise objects, and could either be of the 'order', 'product' or 'resource' type. Then, actions were connected in the form of business processes using the following control flow structures: 'sequence' for direct flow, 'case distinction' for conditional branching of flow, 'parallel' for launching simultaneous branches of flow, 'uniting' for resynchronisation of branches of flow and 'loop' which was constructed with a case distinction and a uniting structures. IEM was supported by a commercial tool, called MO 2 GO  [87] , still available at https://moogo. ipk. fraunhofer. de/. Figure  7. Main elements of the IEM modelling language GIM, initially standing for GRAI-IDEF0-MERISE  [88] , has been defined as an extension of the GRAI method to become GRAI-GIM or simply GIM (GRAI Integrated Methodology) within the framework of two ESPRIT projects, Open CAM system and IMPACS  [89] [90] [91] 55].
The modelling formalisms of the GIM language are those of the GRAI Method for decision system modelling, namely GRAI grid and GRAI nets, IDEF0 and stock/resource for physical system modelling, ERM for information system modelling and IDEF0 for functional system modelling. A companion method ECOGRAI has been developed for implementing performance indicators. Applications of GRAI, GIM and ECOGRAI have been demonstrated in industrial cases  [33]. A computerised support tool known as CAGIM (Computer Aided GIM) was developed within the framework of the IMPACS project to support the GRAI-GIM method. A suite of tools, named GraiTools 1. 0, was then developed and marketed by GraiSoft in the late 90's but is no more available on the marketplace. When a UOB is an elementary step in an IDEF3 process (i.
It can be used for complex systems, since the language can model involvement of two or more participants per process and allows interactions among several processes, depicted in the form of collaboration diagrams. It should be noted that BPMN and UML are extensively used nowadays by practitioners, especially by business analysts, either to internally document systems or to express requirements for IT service suppliers. More specifically, the overall application or system at hand is drafted as a set of use cases, business process descriptions are given using BPMN and data structures or enterprise objects are described as UML object classes. UML has even been considered as a technique per se for enterprise Modelling  [128]. Also, pro's and con's of using UML as a complementary approach of IDEF for EM have been analysed  [129].