dataesr/scientific-paragraphs-categorization huggingface dataset:
k. LAB and ARIES The software stack k. LAB  (Villa et al. , 2017 (Villa et al. , , 2014) )  aims at providing an infrastructure to integrate distributed scientific knowledge and data. It relies on the "semantically integrated modelling", a scientific practice that reconciles rigorous formal semantics with the production, use and curation of scientific artifacts such as datasets, mathematical models and computational services. The key feature of Integrated Modelling is that it distinguishes the process of "observation" from the resulting "data" -consequently, the logical representation of the "observation" remains distinct from both the actual data produced, and the functional knowledge usually implemented in mathematical models and algorithms. ARIES (ARtificial Intelligence for Ecosystem Services,  (Villa et al.
, 2014) ) is the flagship project based on the k. LAB technology, addressing decision-makers and researchers. As such, k. LAB and ARIES implement recommendations for all user profiles considered. The SRs editing environment included in k. LAB allows for export in OWL2, a standard W3C language (R1), it is licensed as an open source software (R2) and supports best practices to realize the FAIR principles (R3). Thorough documentation for the platform will be made available in the next release (R4), while metadata and documentation for the SRs are automatically generated both in formal and natural language (R6). k. LAB includes a web-based portal, k. Explorer, geared towards non-technical users (R5). In developing SRs, users are guided by the Integrated Modelling methodology, also supported by automatic check for logical constraints and quality (R7, R8, R9).
Discussion and Conclusions Semantics is part of any data-related task. All data scientists know the importance of good and unambiguous definitions of data dimensions, crucial to all phases of data analysis. However, semantics is often left implicit in the data, the semantic resources used to create the data are not easily accessible, or available in non-standard formats, non(easily) machine-readable -all factors hampering the possibility of reusing data in information systems or integrating it with other datasets and ultimately limiting the interoperability of data. The work reported in this paper aims at contributing to agricultural data interoperability, by providing operational suggestions to the user profiles involved in the entire data lifecycle, with a special focus on creators and users of semantic resources.
d. ;  Wilkinson et al. , 2016) , as shown in Table  2. For example, recommendation (R14) explicitly urges semantic professionals to make their SRs FAIR, by providing persistent identifiers to resources, sharing them in public repositories and catalogues, and reusing common metadata schemes. However, some aspects of our recommendations extend the FAIR data principles: e. g. , develop awareness and training for semantics. One of the points of convergence between our recommendations and the FAIR principles is the role of community agreements for achieving an acceptable level of "FAIRness" (e. g. , FAIR Principle R1. 3), for example for what concerns the adoption of specific metadata schemes. Community agreement is important to minimize the effort associated to data reuse, and to avoid "isolation" of resources -e.
g. in those cases when different level of richness of metadata reflect different technical or organizational capacity of the data curator (see  (David et al. , 2020; Schultes, 2019) ). However, the recommendations elaborated from the Agrisemantics WG also cover aspects not explicitly considered within the FAIR data principles, such as the importance of promotion and training (R19, R23, R24, R39)) or the recommendations addressed to policy makers (R35, R36, R38). In our set of recommendations, we have also drawn an emphasis on tools intelligibility and desired functionalities to address multiple aspects of FAIR in the SR lifecycle (especially R1-R14). Also, our recommendations stress the needs of different user profiles not only data stewards which are the primary targets of the FAIR principles.
Acknowledgements The Agrisemantics Working Group thanks RDA and  RDA Europe  for their support. Caterina Caracciolo was supported by the  Bill and Melinda Gates Foundation , the  Food and Agriculture Organization of the UN  and the  RDA Europe Ambassador Programme. The views expressed in this publication are those of the author and do not necessarily reflect the views or policies of the  Food and Agriculture Organization of the United Nations , nor those of the other funding bodies. Brandon Whitehead acknowledges with thanks the support of the  CABI Development Fund. CABI  is an international intergovernmental organization and we gratefully acknowledge the core financial support from our member countries (and lead agencies) including the  United Kingdom (Department for International Development), China (Chinese Ministry of Agriculture) ,  Australia (Australian Center for International Agricultural Research) ,  Canada (Agriculture and Agri-Food Canada) ,  Netherlands  ( Directorate-General for International Cooperation ), and  Switzerland (Swiss Agency for Development and Cooperation ).
Experimental setup The scenario is focused on the French airspace with 24 hours' traffic, which includes 6,255 planned flights, 15 ACCs, 1,511 configurations, 164 elementary sectors and 431 operating sectors. The airspace related information is retrieved from the DDR2 database for a typical day in February in 2017. The flight trajectories are produced using an in-house trajectory planning tool  (Dalmau et al. , 2018)  based on the initial flight demand, meteorological conditions, and hotspot avoidance information (if any). We consider 1 min as the unit time step, and 60 min as the time scale (which is used in the same step for sector reconfiguration as for demand counting). The above settings correspond to the generic setup for all case studies. Nevertheless, some specific setting variations are present in some models.
R1-10 It should be noted that, due to Eq. (  4 ) in Model DCB, and Eq. (  11 ) in Model C-DCB, SC-DCB-I and SC-DCB-II, the majority of the x j f,t or x k,j f,t variables are simply auxiliary variables. Concretely, since delay can be only conducted at the first position, namely ground holding, all the subsequent positions will be propagated with the same amount of delay. Therefore, a small amount of x j f,t or x k,j f,t act as the decision variables while the rest are dependent on them, thus being the auxiliary variables. The Gurobi solver that we adopted has an advanced presolve algorithm, which can effectively compact these variables and relevant equations (see the presolved model dimensions in Table  3 ) and solve it quite efficiently.
SIMULATIONS AND RESULT ANALYSIS 5. 1. Assumption and Parameters This paper uses Matlab R2014a as a simulation platform for LEACH protocol and for the two improved protocols (VSG-LEACH and VHG-LEACH). Initially, 100 nodes are deployed uniformly in an M x M square area. The simulations are perfomed for two base station positions which are: outside the sensing field at coordinates (50, 175) and the center of the sensing field at coordinates (50, 50). The following assumptions are made for these simulations:  The base station is located at fixed position and its location is known by each node.  The location of sensor nodes are known and fixed after deployment.  All the sensor nodes in the network are given the same amount of energy and cannot be recharged. Used parameters are given in Table  1.
MicroRNA alignment and quantification processes Sequenced data were processed with the bioinformatic OptimiR pipeline  17  in order to detect and quantify miRNAs. Briefly, OptimiR aligned miRNAs to a library composed of mature miRNA references sequences from miRBase 21. 18  For miRNA integrating genetic variants in their sequence (called polymiRs), the reference library was upgraded by OptimiR with sequences integrating alternative alleles. Ambiguous alignments were resolved using a scoring algorithm that keeps only the most likely alignment while considering the frequent post-transcriptional modifications that miRNAs can undergo. 19 Reads aligned on polymiRs were kept if they were consistent with the sample's genotype, otherwise, they were discarded. 17 rom the resulting miRNA abundances, we performed several quality assessments in order to discard unreliable data.
Any variable influencing another variable in the network is referred to as a parental node for this later variable. In the following, we will mainly focus on terminal nodes assuming that such nodes, as integrating the cumulative upstream effects of other variables, would serve as more relevant and powerful endophenotypes to be tested in relation to some outcomes of interest. In that context, BN analysis can also be viewed as a data reduction technique since, instead of testing the association of all initial variables with a given outcome, only the terminal nodes will be tested for association, reducing then the multiple testing burden. In this article, BNs will be constructed with the «bnlearn» package  24  that implements the relatively fast tabu search algorithm handling both discrete and continuous variables.
In the current application, BNs will be created from all expressed miRNAs but also with the age and sex variables. These two latter variables have been shown to have strong influence on circulating miRNA levels  25, 26  and their integration in the BN analysis can then add information to more efficiently model the dependencies and conditional independence between some miRNAs. Because tabu search is a greedy search algorithm, it may end up into a local optimum. To overcome such situation and to assess the stability of the BN analysis in identifying robust terminal nodes, we generated 2000 bootstrapped datasets composed of 95% of the initial samples and for each boostrapped datasets, we randomly shuffled the way the input variables were ordered in the initial dataset. For each shuffled bootstrapped dataset, a BN was constructed and the terminal nodes identified.
After 2000 bootstrap, we calculated the number of times a given variable was identified as terminal node. In order to assess whether the observed distribution of the number of terminal node's occurrences deviates from the null hypothesis of no correlation structure between miRNAs, a permutation strategy was adopted. For each permutation, we randomly selected at least 40 variables whose values were permuted between individuals in order to break down the original data correlation structure. We generated 2000 of such permuted datasets and constructed a BN on each of them. From these permuted BNs, we counted the maximum number of times a given variable (that could be any miRNA, age, or sex) was identified as a terminal node and used this maximum value as a cut-off to identify robust terminal miRNAs in the unpermuted analysis above.
4. Finally, we combined the results of our miR-eQTL analysis with those previously described by Nikpay et al. 31   available at https://zenodo. org/record/2560974 in order to identify additional single nucleotide polymorphism (SNP) Â miRNA associations. For this, a random-effect model-based meta-analysis was adopted as implemented in the GWAMA software. 32 SNP Â miRNA associations were considered as cis effects when the SNP maps ± 1 Mb from the mature miRNA position. Otherwise, they were considered as trans. Any association with P-value <3. 2 Â 10 À10 corresponding to the Bonferroni threshold corrected for the number of tested SNP Â miRNA associations was considered as genome-wide significant. We also used a miRNAwide threshold of P < 5 Â 10 À8 , the standard statistical threshold generally advocated in the context of a single GWAS, to identify additional suggestive associations.
Results The MARTHA microRNA cohort After the application of the OptimiR workflow, 162 miRNAs were found expressed in the 344 MARTHA participants. Full miRNA data are provided in Supplementary material online, Table  S1. The most expressed miRNA was the hsa-miR-122-5p (Supplementary material online, Figure  S1 ), a miRNA known to be mainly expressed in liver and that was previously shown to be amongst the most abundant plasma miRNAs. 33 Additional highly expressed miRNAs were hsa-miR-486-5p, hsa-miR-92a-3p, and hsa-miR-451a (Supplementary material online, Figure  S1 ). Of note, the 25 most expressed miRNAs accounted for >90% of all sequenced reads that were aligned to miRNA mature sequences.
40 ne trans-eQTL located in the long non-coding RNA (lncRNA) LINC01849 was associated with hsa-miR-330-3p. The identified trans miSNP, rs1554362, is also an eQTL for  the PDCL3 transcript levels in different tissues according to the GTeX database. 41 Another intronic miSNP located in the NELL1 gene was associated with hsa-miR-320d levels. The seven other trans eQTL are located in intergenic regions. We sought to in silico replicate these miSNP associations using the results from Nikpay et al. 31  who scanned for genetic polymorphisms associated with miRNA levels in 710 plasma samples. Unfortunately, as the Nikpay et al. study relied on a genotyping array focusing mainly on coding regions and used a very stringent imputation quality criterion (r 2 > 0. 9), it was not possible to assess all our candidate associations.
Third, polymorphisms (rs970280, rs11070216) in the promoter region of the THBS1 gene were found associated with plasma levels of hsa-miR-222-3p. This miRNA has been previously reported to associate with the of VTrecurrence  13  and has a suggestive association (P ¼ 0. 049) in our study (Supplementary material online, Table  S3 ), where it positively correlated with antithrombin levels (ρ ¼ 0. 21, P ¼ 8. 8 Â 10 À4 ) (Supplementary material online, Table  S2 ). THBS1 encodes Thrombospondin-1 and is known to be involved in angiogenesis and platelet aggregation. 44, 45 inally, we performed a random-effect meta-analysis of both datasets in order to discover additional miSNPs. At the 5 Â 10 À8 statistical threshold, we identified seven new cis and five new trans miSNP associations (Table  5 ). None of these miSNP associations appeared to involve loci with documented link with thrombosis related traits.
These associations could help deciphering the genomic architecture of complex diseases where miRNAs are involved. For example, plasma levels of hsa-miR-143-3p were found to be associated with the rs4734879 mapping to ZFPM2, a gene known to associate with platelet function  43  and VT risk. 42 We also observed a strong association of rs12473206 with plasma levels of hsa-miR-4433b-3p, a miRNA whose serum levels have recently shown to be associated with stroke. 54 The impact of this SNP on stroke risk deserves to be further and deeply investigated. The results of our GWAS on miRNA levels were combined with those obtained by Nikpay et al. 31  and freely available at https:// zenodo. org/. However, only SNPs with imputation quality greater than 0. 90 are available at this resource, which has hampered our ability to replicate some of the main associations observed in the MARTHA miRNA study.
Funding F. T. , G. M. , and M. G. were financially supported by the  GENMED Laboratory of Excellence on Medical Genomics  ( ANR-10-LABX-0013 ). D. A. T. was financially supported by the «EPIDEMIOM-VTE» Senior Chair from the  Initiative of Excellence of the University of Bordeaux. MiRNA sequencing in the MARTHA study was performed on the iGenSeq platform ( ICM Institute, Paris ) and supported by a grant from the  European Society of Cardiology for Medical Research Innovation. Bioinformatics  and statistical analyses benefit from the  CBiB computing centre of the University of Bordeaux. This paper was published as part of a supplement supported by an educational grant from  Boehringer Ingelheim.
Model Federative Approach The model federative approach was proposed in Reference  [16]  to build a unique database where all the data could be stored. Thereafter, a partial projection is executed to generate different models for each tool. Thramboulidis in Reference  [17]  proposed a framework to implement the model federative approach using the powerful and rich semantics of SysML language. They considered the SysML view as the main view that allowed generating the respective viewpoints under study. Although this approach allows ensuring global consistency and maintaining traceability between models in the context of developing complex systems, it presents several practical limitations. The approach raises security problems related to the use of a single database by all actors. In addition, the manipulation of a single database contains information on each version of each component of a mechatronic system.
The Use of Profiles or SysML extensions This technique allows the customization of SysML metamodels to define new semantics specific to a domain or a language. Many profiles are defined to enrich SysML with external information. For example, Pop et al. in Reference  [18]  proposed a UML (Unified Modeling Language) profile for Modelica language called ModelicaML. Moreover, Paredis et al. [19]  suggested another profile called SysML4Modelica to link SysML and Modelica. Another profile named "SafeSysE" was developed in Reference  [20]  to enable the extension of system models with some safety properties. With these extensions, the system models are used to generate safety artifacts that would be used by safety experts.
The Emergence of Model Transformation Languages Model transformation is based on creating a program to transform a model from one representation to another. The approach has been implemented in a large number of languages specialized in model transformation, like QVT  [11]  and Triple Graph Grammars (TGGs)  [21] , among others. Therefore, the approach has been applied to various research studies. Adourian et al. in Reference  [22]  used a TGG tool to construct links of the interconnections between geometric models (CAD) and the corresponding dynamic simulation models (Modelica). A bidirectional transformation was then performed to build models in a CAD tool and to synchronize the changes based on a dynamic simulation model. Anwar et al. [23]  also used TGG to formalize a model composition activity, which is used to produce a composed model that would represent an integrated view of different domain-specific views.
Model transformation is an interesting approach used to manage interoperability between modeling languages, allowing each designer to use his or her own language and tool. However, the major drawback of this technique is that there is a rapid increase in the number of tools and design actors involved into the design process. Consequently, this approach requires considerable programming development to implement each transformation process. Moreover, this approach considers orienting relations encoded in the transformation rule. For instance, a SysML model can be transformed into a Modelica model but a change in a Modelica model cannot automatically be transformed back into a SysML model if the transformation rules consider unidirectional model transformation. Therefore, consistency between models cannot always be guaranteed.
Modelica metamodel We express the concepts integrated into a Modelica model through the metamodel shown in Figure  2b. The metamodel is defined with regards to the specifications of Modelica language described in Reference  [28]. A Modelica metamodel consists of a fundamental structuring unit called a class, which provides the structure of objects. A class can be connected to zero or more classes. A connection between two classes is introduced by a connection equation. These connections can be presented in two types: causal connection and acausal connection.
Altarica metamodel Altarica uses a System Modeling Language (S2ML)  [29]  as a structuring paradigm to provide a sufficient set of constructs to build hierarchical models. We defined the metamodel of an Altarica model based on the S2ML language. The Altarica metamodel is shown in Figure  2c. Altarica has two concepts to structure models: block and class. Classes and blocks are the basic units of Altarica language since Altarica applies a prototype-oriented approach. A class or a block has interaction points defined by flow variables. These variables are used to model the propagation of the information between classes or blocks. The connections are represented by assertions. The assertion can be causal or acausal assertions.
Digraph metamodel A digraph is composed of a set of nodes (i. e. , vertices) and edges (Figure  2d ). Each node defines a set of leaving and entering edges. The graph is called directed because, for each edge, we define the source and the target nodes. In Reference  [26] , we define the different mapping between the entities of modeling languages (i. e. , SysML, Modelica, and Altarica) and the common representational formalism.
Model Management We implement the model management process in the Eclipse platform  [35]  since we apply the model-driven approach. We use the technical environment Eclipse Modeling Framework (EMF) as it is adapted to modeling, metamodeling, and model transformation. The choice of the transformation language is based on specific criteria for our approach. Indeed, the tool must be integrated into the EMF environment to be easily used with the modeling and metamodeling tools. Thus, we used the QVT operational mapping often called QVTo language  [11]. We have two types of transformations: the direct transformation and the indirect transformation.
Therefore, the project manager chooses the physical architecture models for the analysis (i. e, IBD in SysML), and they consider the physical architecture of the Modelica and Altarica models). Therefore, we guarantee that the entry models of the comparison process are defined at the same level of abstraction. The comparison process can be applicable in our scenario. We use NetworkX  [37] , which is "a python 3 package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks", as a query engine to demonstrate the comparison process. In the first step, we import the different graphs representing our abstracted models as shown in Listing 1, lines 3-8. Then, we can display the properties of graphs such as the type of graphs (e. g. , digraph, multigraph, etc.
Control and Representation Within the control and representation layer of our demonstrator, inconsistencies can be managed. Then, the changes can be visualized using a general-purpose diagramming program labeled "yEd"  [38]. Besides, this layer allows exchanging metadata information between various tools used to implement our methodology. We use the XMI format to exchange metadata between the QVTo language and the yEd tool. We use also the GraphML format to exchange the graph structure data between the yEd tool and the NetworkX language.
System Behavior Analysis (Modelica) Modelica  [28]  is an object-oriented and equation-based modeling language. Its main objective is to model the dynamic behavior of large, complex, and heterogeneous physical systems. These systems consist of mechanical, electrical, thermal, and hydraulic, among other components. To predict the dynamic behavior of the EMA system on Modelica, we designed a closed-loop system as represented in Figure  6. The DC motor receives a regulated signal voltage from the PID (Proportional-Integral-Derivative) controller that employs feedback from the angle sensor. Then, the ideal gear adapts the mechanical rotational power that will be transformed into mechanical transitional power via a set of components (i. e. , ideal Gear R2T, prismatic, bodybox2, and bodybox3). The universal joint represents the connection between the wing and the EMA system.