dataesr/scientific-paragraphs-categorization huggingface dataset:
The study identifiers (ENA IDs) are assigned by ENA and added to the samples by the Data Steward in LIMS. The Bioinformatician then analyses the data and produces the genotyping results. Afterwards, the Data Steward prepares these data for transmission by linking them to the already created sample data from the LIMS and extracting the required metadata and adding it to the header of the Variant Call Format (VCF) file. If the reference genome used for genotyping is not yet available in public repositories, it will now be transferred by the Data Steward to one of the International Nucleotide Sequence Database Collaboration (INSDC) databases. Otherwise, the metadata-enriched VCF file can be registered and submitted to the European Variation Archive (EVA). The identifiers assigned by EVA are then transmitted back and the Principal Investigator can approve the publication of the data.
This study received funding from  ELIXIR , the research infrastructure for life-science data, through the  ELIXIR Implementation Study: FONDUE -FAIR-ification of Plant Genotyping Data  and its linking to Phenotyping using ELIXIR Platforms. ML and SW received funding for the  AGENT  project from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  862613. US received funding for the de. NBI  project from the  German BMBF  under the  FKZ 031A536A. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data availability An example VCF conforming to the metadata recommendations presented here and comprising a barley genotyping experiment with 22626 accessions has been deposited at EVA under PRJEB51851 and is accessible via the study browser: https://www. ebi. ac. uk/eva/?eva-study=PRJEB51851. See the FAIR Cookbook under the recipe https://w3id. org/faircookbook/FCB061 for step-by-step instructions on how to submit data according to the recommendations in this manuscript.
Crises response systems form the majority of today's complex systems; they generate situations characterized by harmful consequence, low probability, and short decision time. The decision-making in these systems constitute a challenging issue and must be conscientiously supported. The aim of this paper is twofold, we derive a referential architecture for crisis response system-of-systems in one hand, the role of communication and the characteristics of constituent systems that are needed to adapt their dynamic behavior are added to the architecture standard of IEEE. In the other hand, we define a formal model on the basis of the Maude Strategy language, to deal with the dynamic reconfiguration of these system types.
In order to design SCRSoS, and to execute and simulate its behaviours and reconfigurations, a formal model is needed. This model should be enough generic, thus it will be inspired from a reference architecture derived from the ISO/IEC/IEEE 42010 standard (ISO/IEC/IEEE, 2011) and also based on ArchSoS, an existing Architecture Description Language (ADL) dedicated to SoS software architectures. Maude is used to specify, implement and execute this model. The aim of this paper is twofold, we derive a referential architecture for crisis response systemof-systems in one hand, the role of communication and the characteristics of constituent systems that are needed to adapt their dynamic behavior are added to the architecture standard of IEEE. In the other hand, we define a formal model on the basis of the Maude Strategy language, to deal with the dynamic reconfiguration of these system types.
Maudes strategy language offers additional specifications and control over the Maude model execution. SoSs are known by their evolutionary nature; either on the structural level by changing the SoS hierarchy and its actual constituent systems, or on the behavioural level by defining dynamic interactions and collaborations between them, leading to flexible reconfigurations. This evolutionary nature makes it difficult to capture SoS behaviours, and how they are conducted relatively to external events that may occur. We should specifiy how SoS missions are achieved, in a coherent and autonomous way. This is the case of Smart SoS; no central control on their evolution. Smart SoS have autonomous constituent systems that can adapt to external events, and evolve to fulfil an SoS mission. This paper addresses this challenging issue, by investigating and focusing on SoS reconfigurations.
It defines a formal approach to guide and constrain SoS behaviours. We use a Maude extension, known as the Maude strategy language (Martí-Oliet et al. , 2009)  (Rubio et al. , 2022)  to define deterministic behaviours of SoS in ArchSoS, as there is no control on how Maude rewrite theories are executed. This will ensure that the SCRSoS constituents can be reconfigured according to specific events, in order to achieve the SoS missions.
The usage of both these modules covers both the structural and behavioural aspects of a system. Maude modules extensions may be defined to make its specifications more expressive. For instance, we may cite Object-Oriented Maude  (Clave et al. , 2000)  which models systems as a multiset of entities represented as objects with unique identities, communicating with message passing. Real Time Maude  ( Ölveczky and Meseguer, 2007)  is another Maude extension used to define real-time/embedded systems, equipped with timed rewriting for simulations. In this paper, we are interested by Maude's strategy language that separates between rewrite rules of a Maude system module, and their rewrite guidance using strategies. This language has its own operational semantics, allowing the manipulation of strategies without the need of being familiar with the reflective concepts of Maude META-LEVEL module.
Reconfiguration Strategies and rewrite rules combination using operators. Verification Property Maude LTL Property Prop i (R ArchSoS , SM ArchSoS )} It describes SoS architectures expressed in Arch-SoS, while specifying their semantics via a set of Maude strategy modules : • Σ ArchSoS and (E ∪ A) ArchSoS represent the ArchSoS syntax using an equational theory giving to each ArchSoS entity (SoS, sub-systems, roles, links, events), its rigorous semantics (meaning). • The semantic S ArchSoS is defined by the tuple (R ArchSoS , SM ArchSoS ): -R ArchSoS identifies a set of rewrite rules representing actions that may affect SSoS behaviours, and a set of predicates that constraints the application of these rewrite rules. -SM ArchSoS is a set of strategies that may be combined via operators to define ArchSoS possible reconfigurations.
FORMAL ANALYSIS Formal models of SoS specified with this ArchSoS extension have been executed and prototyped through Maude system, in order to show how we deal with dynamic reconfiguration of Smart SoS. In this section, we exploit the model-checker tool of Maude to express and analyse SoS inherent properties. We develop a new Maude module called ArchSoSAnalysis containing a set properties defined using LTL syntax (more details can be found on  [Rozier, 2011] ) : • inactivity : Checks if an SoS role is inactive, stated by the word 'null'. • roles-violation : Checks if a role combination has occurred, despite having combined roles that are not compatible. • links-violation : Checks if two constituent systems are linked while being incompatible on that specific link; • vivacity : It ensures that something good will always happen.
CONCLUSIONS In this paper, we have extended ArchSoS, an ADL dedicated to describing SoS architectures in order to deal with Smart SoS. First, we have defined a conceptual model for SoS by adapting ArchSoS concepts to the ISO/IEC/IEEE 42010 standard. Then, we have formalized the extended version of ArchSoS using rewriting logic. Smart SoS architectures and their behaviors have been defined through a set of Maude modules allowing the formal execution and analysis of these models. Rewriting rules and predicates have been proposed to give a natural semantics to SoS behavior evolution. Dynamic reconfiguration of Smart SoS due to many possible external events was well specified using the concept of strategy provided by the Maude Strategy language. Three strategies have been defined for the considered case study SCRSoS, their execution has shown that two crisis scenarios may be applied simultaneously to achieve two SoS missions at the same time.
In addition, we have proposed a set of properties that we have checked positively using the model-checker LTL of Maude. Thus, we have shown the relevance of Maude strategies while validating some properties that remain not valid with the previous model of Arch-SoS. In future, we plan to equip ArchSoS with temporal constraints in order to check non-functional properties. Other case studies are also possible to better illustrate the contributions of this language.
The generalized half-logistic distribution is ideal to fit the lifetime of some products, such as ball bearings and electrical insulation. In this paper, we aim to extend this scope by creating a motivated bivariate version. We thus introduce the bivariate generalized half-logistic distribution using the Farlie Gumbel Morgenstern (FGM) copula, which is called the FGM bivariate generalized half-logistic distribution (FGMBGHLD for short). In particular, the FGMBGHLD finds application in describing bivariate lifetime datasets that have weak correlations between variables. Some statistical properties and functions of our new distribution, such as the product moments, moment generating function, reliability function, and hazard rate function, are derived. We discuss the maximum likelihood estimation method of the FGMBGHLD parameters.
In this paper, we are interested in economics, where X and Y are household income and consumption, and R = P(Y < X) is a measure of household financial affordability. This paper is organized as follows. In Section 2, the FGMBGHLD is described. In Section 3, we derive some statistical properties of the FGMBGHLD. In Section 4, we exploit the copula approach to take into account the dependence of stress and strength variables in evaluating R. In Section 5, the ML estimation method for the FGMBGHLD parameters is discussed. In Section 6, point and interval estimations for R are elaborated. In Section 7, a Monte Carlo simulation study is performed to study the behavior of different estimates. In Section 8, the estimation of R is applied to KSA data (year 2018) to measure the household financial affordability for Saudi households by administrative region, with comparison to a modern bivariate Weibull distribution.
Size of Tumors in Lung Cancer Patients A swelling or tumor arises when the cells in the lungs expand at an abnormally fast rate, which can lead to lung cancer. It is possible to identify that and see if its spread to other organs based on a variety of indicators. One of these characteristics is tumor stage, which aids doctors in determining the best treatment for their patients. The tumor size is used to determine the staging system. The data show the tumor size of 76 lung cancer patients at Tanta University's chest hospital, sixty of whom are in stage I, seven in stage II, and the rest in stage III. A summary of measures of descriptive statistics is provided in Table  6  with the box and TTT plots plotted in Figure  6. 6 , we see that the data are right-skewed and leptokurtic. This is proved in a graphical display of the box plot in Figure  6.
Methods: The mapping review was based on comprehensive searches in electronic databases and software repositories to obtain relevant literature and records, followed by screening for eligibility based on titles, abstracts, and full text by two reviewers. The data extraction consisted of listing and extracting the name and basic characteristics of the included tools, for example a tool's applicability to the various SR stages, pricing options, open-source availability, and type of software. These tools were classified and graphically represented to facilitate the description of our findings. Results: A total of 9653 studies and 585 records were obtained from the structured searches performed on selected bibliometric databases and software repositories respectively. After screening, a total of 119 descriptions from publications and records allowed us to identify 63 tools that assist the SR process using ML techniques.
They can be broadly classified as supervised learning (trained on labelled data), unsupervised learning (trained without labelled data) and semi-supervised learning (trained by a small, labelled dataset and a large unlabelled data set) algorithms  [53]. As mentioned above, these algorithms/software are more and more used for article screening in systematic reviews  [53, 55, 56] , with some examples being the programs Abstrack ®  [39] , ASReview ® [56], Colandr ®  [33] , EPPI-Reviewer ®  [57]  and Rayyan ®  [33] , all easily accessible online. Other steps of the SR process such as data extraction  [54, 58]  or risk of bias assessment  [59]  have been also been exploring whether ML tools can facilitate the work. As an example, the software RobotReviewer ® is able to assign low, high or unclear risk of bias to randomized control trials (RCTs)  [59].
0 • Yale MeSH Analyser Reference management • BibDesk • Citavi • Colwiz • Docear • EndNote • JabRef • KBibTex • Mendeley • Paperpile • Papersapp • Qiqqa • Refbase • Wikindx • Zotero Figures • ALBATROSS • Gephi • PRISMA 2020 • Prismadia- gramR • PRISMAstate- ment • ROSES flowchart • yEd Graph Editor SR database • BEME • Campbell Collaboration • Cochrane library • Epistemonikos • JBI EBP • MAGICapp • PEDro Screening • Abstrackr • ArticleNet • ASReview • BioReader • Colandr • Covidence • DistillerSR • DoCTER • EPPI-Reviewer • Metagear • Papers-review • PICO Portal • RAx • Rayyan • Revtools • RobotAnalyst • RobotSearch • Screen-IT • SRA • Subscreen • SWIFT-Active Screener • SWIFT-Review • SyRF • Sysrev Data extraction • CERC • Cochrane_ scraper • Colandr • ContentMine • Covidence • DAA • Distiller SR • EPPI-Reviewer • EvidenceSET • ExaCT • Graph2Data • Import.
Methods We conducted a Mapping Review to identify existing ML tools to assist during the SR process. A protocol was registered in the Open Science Forum (OSF) platform  [62]  (Available at https:// osf. io/ wmy7n/? view_ only= c501b 501ed e84b9 6b3c3 353e3 e81de b0), since the International Prospective Register of Systematic Reviews (PROSPERO) does currently not accept registrations for mapping reviews. To identify all relevant ML tools comprehensive searches in several bibliographic databases and software repositories were performed. Additionally searching in software repositories allowed us to identify tools not mentioned in journal articles, conference abstracts, or similar technical literature.
Search and selection A tailored search strategy was developed in collaboration with an information specialist (TL) to search for relevant publications in the electronic databases MED-LINE (through PubMed), EMBASE and Web of Science. Database specific terms (MeSH and Emtree) and keywords for the concepts of "Systematic review" and "Machine learning (ML)" were combined with Boolean operators to produce tailored search strings for each database. Multiple variations of search terms were combined to produce different sets of results. Final search strategies are available as supplementary material (See Additional file 1). In addition, a structured search was conducted to identify ML tools in the following repositories of software: The Comprehensive Perl Archive Network (CPAN)  [63] , The Comprehensive R Archive Network (CRAN)  [64] , GitHub  [65] , The National Centre for Text Mining (NaCTeM)  [66] , The PHP Extension Community Library (PECL)  [67] , The Python Package Index (PyPI)  [68] , SourceForge [69] and the Systematic Review Toolbox (SR toolbox)  [70].
Eligibility criteria Any publication or repository record describing a ML software to assist the SR process in any field was considered eligible. All publications or records with sufficient technical description (reporting at least the software name, a short tool description and its purpose) were included when reporting in English, Spanish, German, or French, and when developed or updated within the last 10 years. Reference management software and tools that were not accessible to the reviewers (i. e. , downloadable, importable, or executable from their source) were excluded. Publication records retrieved by the search in electronic databases were imported into EndNote ® and duplicates were removed. Records were screened for eligibility based on their titles and abstracts by two reviewers (RCJ and BII) independently.
Full text PDFs were obtained for all abstracts deemed relevant for inclusion and further assessed against the inclusion criteria by the same two reviewers independently to obtain a final number of included publications. Discrepancies were resolved by consensus. Records retrieved by the search in repositories were registered in an ad hoc developed data base using Microsoft Excel ® and duplicates were removed. Records were screened for eligibility based on their titles and summaries provided by the first reviewer (RCJ). A second reviewer (BII) revised the selection, resolving disagreement by consensus.
Data extraction and synthesis A data extraction form was developed, piloted, and refined to capture basic information for each identified tool. Extracted data included: computer science method applied for the development of the tool (e. g. , natural language processing, supervised learning), stage of systematic review process the tool assists with, tool release date or publication date of the description, date of last update, licensing and pricing details (free access or requiring payment for the complete version), as well as being open-source (the source code is available and repurposed) or not, and the source to access/download the tool (e. g. , software repository, hyperlink). In addition, the retrieved tools were categorised according to how much background in programming is needed to use the software, considering mainly whether the tool implementation needs modifications that require advanced programming skills (e.
Results A total of 9653 studies were retrieved from the structured searches in bibliometric databases (PubMed, Embase and Web of Science), 1491 of which were duplicates. Of the remaining 8162 items that were screened, 7970 were excluded during a review of title and abstract (see Fig. 2 ). The remaining 192 publications were assessed by full text, resulting in 105 studies being excluded for reasons as not reporting on a ML tool  [15] , not providing a link to the tool  [47] , not using ML methods  [23] , using ML but not being adapted for SR  [16] , or not being published in previously defined languages  [4]. 86 publications were identified describing ML tools and included. In addition, a total of 585 records were obtained from the selected software repositories (CPAN, CRAN, GitHub, NaCTeM, PyPI, SourceForge and SR toolbox), 17 of which were duplicates.
The remaining 568 records were screened, excluding 536 and selecting 32 that described ML tools. After the screening process, a total of 119 descriptions of ML tools from publications and repository records were included for the data extraction process (See Fig. 2 ). From those descriptions 63 tools that assist the SR process using ML techniques were identified (Table  1 ). We described basic information for each of the identified ML tools, including the step of the SR where it operates, pricing options, open-source availability, computational    1. To facilitate the tools description in the table they were grouped by category of application (e. g. , algorithms, web-applications). In addition, we graphically represented the identified ML tools into blocks depending on the covered SR step (see Fig.
3 ). Among the 63 tools identified, the majority were cited between 1 and 5 times (52, 83%) within the screening of the 119 descriptions obtained from publications and repository records. The remaining tools  (11, 18%)  obtained between 6 and 23 citations, being Abstrackr ®  [23] , EPPI-Reviewer ® [57], Rayyan ® [33], RobotReviewer ®  [14] , and RobotAnalyst ®  [11]  the top 5 most referenced tools in this review. All the identified software tools assist early stages of the SR process, with the 63 (100%) tools addressing automation of the steps of literature search, screening, and data extraction; 30 (47%), 35 (55%) and 11 (17%) tools respectively. Whereas late SR stages have been less wellexploited, with 1 tool count. In terms of updates over time most identified tools have been updated or released over the last 5 years (62, 98%), whereas only 1 (2%) case was considered nearly out of date due to older updates (see Table  1 ).
However, the majority of identified tools (83%) were cited less than 5 times, suggesting that despite the increasing development of software to assist the SR process, new tools will have to compete with a few dominant well-known tools. Efforts to improve the diffusion of the newly developed tools is therefore key, together with an improvement of currently applied methodology. We believe that collaboration to improve already available ML tools may yield well adapted software that can provide a wide range of functionalities needed for systematic reviews, as shown by the already existing variety of ML tools and the recent acceleration in the launch of new and updated version. There are projects such as Metaverse  [76] , where developers collaborate to collect, integrate, and expand available functions, following open-source principles and making the tools freely accessible to the evidence synthesis community.
Other projects as SR-Accelerator  [28] , integrate several tools in a suite to assist in more than one step of the SR procedure, aiming to produce software that guides and assists the reviewer during the whole process. Additionally software repositories or toolboxes such as the SR toolbox  [42, 70]  exist to promote and share already available tools that assist the SR process. Databases or repositories with specific training sets are also a resource that helps the community of developers perform collaborative work, providing the necessary platforms for the sharing of data, information, and expertise. Following this successful development, more efforts should be undertaken to facilitate communication and knowledge exchange among developers and users, so that usability and functionality of already existing tools can be improved and adapted to the needs of different systematic review projects.
Nevertheless, the iterative search process in these repositories combined with the sensitive search strategy applied in the bibliographic databases strengthen the completeness of our findings, and the high number of records screened makes this mapping review highly reliable. However, due to fast evolution of the targeted field, new potential tools have been developed since the performance of this project, being the Elicit tool  [78]  an example of a tool that the developed search strategy haven't considered. Despite the fact that the applied methodology does not provide a synthesis of the findings or a critical appraisal of the methodological quality of the retrieved publications, our mapping exercise has value and pertinence. Our description of available tools, visually summarized in two comprehensive infographics provide a decision support tool for reviewers, researchers and other decision-makers conducting and funding evidence synthesis projects.