dataesr/scientific-paragraphs-categorization huggingface dataset:
We finally obtain the global optimal value b opt = (0. 330, 91. 236, 150. 000, 64. 722) in ([-], [µm], [GPa], [GPa]) with relative errors less than 4% for δ opt , opt , κ opt and µ opt with respect to the reference experimental value b meso exp = (0. 32, 80, 145, 67. 3) in ([-], [µm], [GPa], [GPa]) used to construct the numerically simulated "experimental" database, allowing to validate the proposed identification methodology in 3D linear elasticity for the resolution of the multi-objective optimization problem  (22)  at mesoscale. In terms of computational efficiency, we can see in Table  6  that the numbers of iterations n 1 , n 2 , n 3 required to achieve the desired convergence are relatively low (less than or equal to 4) for each of the 3 in silico test specimens yielding a number of calls to the deterministic numerical model at mesoscale of 150, 000.
Numerical Application of the Multiscale Identification Method to Real Beef Cortical Bone in Plane Stress Linear Elasticity In this section, we present a numerical application of the proposed multiscale identification methodology within the framework of 3D linear elasticity with 2D plane stress assumption by using a real experimental database made up of 2D multiscale optical measurements of displacement fields (obtained by DIC method) for only a single test specimen of cortical bone coming from a beef femur. The multiscale experimental test configuration corresponds to the one described in Section 3 and already considered in the 2D and 3D numerical validation examples presented in Section 10. Technical details concerning the experimental protocol (specimen preparation, measuring bench, optical image acquisition system and DIC method) for obtaining the multiscale field measurements (performed simultaneously at both macroscale and mesoscale) can be found in Reference  [145].
In line with this work, several perspectives could be addressed: (i) the multi-objective optimization problem could be solved by using machine learning based on artificial neural networks with a numerical database generated from the stochastic computational model to train an artificial neural network in an (offline) preliminary phase and to use the trained neural network to perform the statistical inverse identification in a computationally cheap (online) computing phase for further reducing the computational cost; (ii) the proposed methodology could be applied to real multiscale experimental measurements of full 3D displacement fields obtained for example by X-ray computed microtomography and digital volume correlation, and also to other types of random heterogeneous materials; (iii) the proposed methodology could be improved by identifying a posterior stochastic model of the non-Gaussian random elasticity (or compliance) field in high stochastic dimension at the mesoscale of an anisotropic heterogeneous linear elastic microstructure using the identified prior stochastic model.
Preprinting speeds up dissemination, meaning that findings are available sooner for sharing and reuse. Potential platforms for disseminating preprints include the Open Science Framework, biorXiv, or arXiv. Dissemination of other open science outputs that would usually remain hidden also not only helps to ensure the transparency and increased reproducibility of research  [24] , but also means that more research elements are released that can potentially impact upon others by creating network effects through reuse. Making FAIR (Findable, Accessible, Interoperable, Reusable) research data and code available enables reuse and remixing of core research outputs, which can also lead to further citations for projects  [25, 26, 27]. Published research proposals, protocols, and open notebooks act as advertisements for ongoing research and enable others to reuse methods, exposing the continuous and collaborative nature of scholarship.
For instance, you can upload your slides to a general-purpose repository such as Figshare or Zenodo and add a digital object identifier (DOI) to your presentation. This also makes it easier to integrate such outputs with other services like ORCID. You can also schedule tweets before and during any conferences, and use the conference hashtag to publicise your talk or poster. Finally, you can also add information about your contributions to email signatures or out-of-office messages  [28].
Rule 9: Find the right tools Innovative dissemination practices often require different resources and skills than traditional dissemination methods. As a result of different skills and tools needed, there may be higher costs associated with some aspects of innovative dissemination. You can find tools via a morecomplete range of sources, including the OpenUP Hub. The Hub lists a catalogue of innovative dissemination services, organised according to the following categories, with some suggested tools: • Visualising data: tools to help create innovative visual representations of data (e. g. , Nodegoat, DataHero, Plot. ly) • Sharing notebooks, protocols, and workflows: ways to share outputs that document and share research processes, including notebooks, protocols, and workflows (e. g. , HiveBench, Protocols.
g. , Open Science Framework, dokieli, ScienceMatters) • Archive and share: preprint servers and repositories to help you archive and share your texts, data, software, posters, and more (e. g. , BitBucket, GitHub, RunMyCode) The Hub here represents just one attempt to create a registry of resources related to scholarly communication. A similar project is the 101 Innovations in Scholarly Communication project, which contains different tools and services for all parts of a generalised research workflow, including dissemination and outreach. This can be broadly broken down into services for communication through social media (e. g. , Twitter), as well as those designed for sharing of scholarly outputs, including posters and presentations (e. g. , Zenodo or Figshare). The Open Science MOOC has also curated a list of resources for its module on Public Engagement with Science, and includes key research articles, organisations, and services to help with wider scientific engagement.
The mathematical models used for nuclear data evaluations contain a large number of theoretical parameters that are usually uncertain. These parameters can be calibrated (or improved) by the information collected from integral/differential experiments. The Bayesian inference technique is used to utilize measurements for data assimilation. The Bayesian approximation is based on the least-square or Monte-Carlo approaches. In this process, the model parameters are optimized. In the adjustment process, it is essential to include the analysis related to the influence of model parameters on the adjusted data. In this work, some statistical indicators such as the concept of Cook's distance; Akaike, Bayesian and deviance information criteria; effective degrees of freedom are developed within the CONRAD platform.
Test case and results In this work, for the adjustment process, the time-offlight experiment for radiative capture of 155 Gd in the energy range of  [1. 4, 3. 4]  eV is taken as an experiment for the adjustment. In Figure  1 , the experimental data (capture cross-section) at 2138 points in the energy range is shown. This experimental data is extracted from the CONRAD database. The uncertainty in the experimental data is 1% of the mean. Two peaks, first at 2. 008 eV and second at 2. 568 eV are observed. The corresponding resonance parameters (neutron width Γ n , radiation width Γ γ ) and associated uncertainties are shown in Table  1. These four parameters (Γ γ1 , Γ γ2 , Γ n1 , Γ n2 ) are considered here as prior parameters in the Bayesian inference for parameter adjustment. The case where all four parameters are used in the adjustment process can be considered as a model M.
Conclusions In this article, for 155 Gd, the influence of resonance parameters is evaluated using Cook's distance and is compared with other model selection techniques (AIC, BIC and DIC criteria) derived from information theory. Effective degrees of freedom is also estimated. It is concluded that all the approaches (AIC, BIC, DIC, and Cook's distance) discussed and developed in CONRAD give the same conclusion. Future work will concentrate on defining the best way to use these indicators to enhance the nuclear data assimilation process by reducing/discriminating the number of parameters.
In order to be founded on the best evidence available, it must still begin with an analysis of the literature, but by producing a systematic review that gathers the information in a computer-accessible manner that can be used for the assembly of the Treatabolome database, we can reduce the work that clinicians must do to find the information in future. A Solve-RD task force is currently working towards building that database by working with disease experts to manually capture gene and variant information from the literature by means of a systematic literature review, mapping it to existing phenotype, gene and classification ontologies and correlating it to existing specific therapies in a database developed by computer experts (Fig. 2 ). Although previous systematic reviews have been written about rare diseases and genetic conditions, the singularity of the Treatabolome is that they are centred on gene and variant specific treatments and will translate into a precision medicine endeavour.
The Treatabolome systematic reviews will share a common research question and are expert-led with direct involvement of specialists in each rare disease area, starting by experts from the four European Reference Networks  [9]  that participate in Solve-RD. However, our aim is to reach out to every ERN and invite experts from around the world to take the lead in their area of expertise. It is not hard to anticipate that in the future the sustainability of this project will depend on a "big data to knowledge" (BD2K) methodology that uses text-mining of scientific publications for accruing and updating the knowledge database  [10]. Therefore, the task force is working on a pilot of a text-mining tool to serve that purpose.
Aims The present paper aims to lay out the common ground and rules for all the Treatabolome expert-led systematic literature reviews (SLR) and incentivise the adoption of the FAIR-guiding principles for scientific data management and stewardship  [11]  as that later facilitate the translation of human-readable scientific papers into computer-actionable data. Under these FAIR principles, the data and metadata required for the translation of content, tools, algorithms, consents, and workflows described in the systematic reviews are collected into transparent, reproducible, and reusable digitallyaccessible datasets. Based on the interpretation of metadata, digital devices can take appropriate action on approved areas without the need for direct human intervention, a state denominated "machine-actionability.
Such digital resources can autonomously determine how Fig. 1  The Treatabolome is a project aiming at collecting and making freely available information about gene and variant-specific treatments for rare diseases. It starts by selecting a disease (or disease-group of interest) of interest and running a systematic literature review to identify the gene and variant-specific treatments that have been published. The resulting datasets will comply to the FAIR guiding principles of data stewardship to enable its easy translation to machine-readable data elements. The resulting database will be freely available online and will be made available for integration into existing diagnosis and management support tools, e. g. the GPAP platform of RD-Connect to act when presented with an unknown digital object as they are capable of determining the structure, reusability and intent of the object, through the interpretation of license, consent, and other data use constraints.
Methods The starting point within the Treatabolome task is to engage clinical experts in the production of state-of-theart systematic reviews of specific treatments for the rare diseases of their expertise. The critical addition to the workflow, in comparison with a traditional systematic review, is the capture of evidence linking treatment with specific genotype to enable the creation of a Treatabolome database flagging potentially treatable genes and variants. This information is mapped to different ontologies and classification systems. A checklist with recommendations has been composed to assist with the harmonisation of the different Treatabolome systematic reviews (Fig. 3 ). We describe below the proposed methodology for these SLRs, including an explanation of the checklist recommendations.
HGVS -Human Genome Variation Society. GPAP -Genome-Phenome Analysis Platform from RD-Connect Fig. 3  The Treatabolome Systematic Review Checklist quantitatively or qualitatively the evidence. We have elaborated a common research question to serve all the tretabolome systematic reviews: "What treatments have been described for this condition/gene/variant; on which specific genetic variants have they been tested; and what is the strength of the associated supporting evidence?" The question does not address only general treatment considerations but, more specifically, looks for treatments tested for specific genetic causes, which allows to establish gene and variant-specific treatment reports. Whenever this is not possible, other treatmentmeaningful genotype-phenotype associations are captured through the integration of the Treatabolome database with other existing resources.
Search strategy There is a need to build a search strategy and choose the databases/sources to be inquired to find the answers for the research question. Some examples of databases commonly employed are: The usual number of searched databases is between 1 to 5. The detailed documentation of the search process requires the production of a Search Log to enable replication of the Search Strategy. A Search Log is a document that indicates the date of search, the database or databases queried, the precise search terms and expressions employed, and the number of results returned. Examples of search logs are available online  [13] , some in spreadsheet format  [14] , and Additional file 2 contains a Search Log template for exemplification.
Exclusion criteria Cases without therapeutic intervention or regarding generic therapeutic interventions (e. g, the benefit of physiotherapy for a diverse group of patients) should be excluded. Studies that do not report outcomes should only be discussed in the narrative summary but not counted as evidence for the systematic review. The data capture form identifies the publication without ambiguity resorting to DOI and PMID pointers and using Harvard style citations of the paper. Cases without DOI or PMID have to include a URL address collected from the NLM online catalogue (https://www. ncbi. nlm. nih. gov/nlmcatalog) that indicates the web address of the publication's journal. Further instructions on how to format the bibliographic entries according to Harvard format can be retrieved online (https://www.
Patients with rare diseases are often risk-takers as a consequence of the scarcity of innovation and research in rare disease areas. For patients' safeguard, the Treatabolome opts for using a transparent and sensible approach for grading the quality of evidence and strength of recommendations in health care. This choice reflects our vision that is centred on the clinical significance of the findings and not exclusively on the statistical significance or effects size. We believe that is the right focus for establishing a health technology assessment for these treatments in a hopefully near future. We adopted the GRADE methodology for appraising the evidence collected and reaching the Treatabolome database treatment recommendations. The GRADE questionnaire is part of the Data Capture form supplied and is also available online through a resource that is free for academic institutions, the GRADE Pro website (https://gradepro.
Data synthesis The synthesis phase follows the extraction phase, and it addresses the quantitative and qualitative aspects separately. The qualitative analysis employs different tools depending on the objectives of the systematic review  [17] : narrative analysis extracts key concepts from a study narrative summary does a textual combination of data, namely when qualitative studies are heterogeneous (meta-synthesis) meta-ethnography synthesises the qualitative data to reach new theoretical understandings. The synthesis of quantitative data differs depending on the heterogeneity of studies. The meta-analysis, an operation that combines data from different studies, should only be performed if data from intervention studies are similar, and it is sensible to combine the studies. When it is feasible, meta-analysis enhances the degree of certainty when answering a research question as it boosts the power of data by aggregation of datasets  [18].
Conclusions In general, systematic reviews are still the best tool to guarantee that decisions regarding healthcare and research are guided by the best existing evidence to provide the best possible care to patients. Despite the efforts done for automation of systematic reviews  [21, 22] , the process is still mostly dependent on human effort, and the Treatabolome ones are characterised by the extra challenge of trying to find gene and variant-specific treatments while appraising their level of evidence through consecrated tools, as the GRADE system and the Oxford Evidence-Based Medicine Levels. It is our hope that the current paper assists researchers in developing Treatabolome systematic reviews and producing datasets for future incorporation in the Treatabolome knowledge base and diagnostic support tools.
Introduction Over the last years, the Web of data has received a tremendous increase, containing a huge number of RDF triples. Integrating data described in different RDF datasets and creating semantic links among them, has become one of the most important goals of RDF applications. These links express semantic correspondences between ontology entities, or semantic links between data such as owl:sameAs links. By comparing the number of resources published on the Web with the number of owl:sameAs links, the observation is that the goal of building a Web of data is not accomplished yet. Many approaches that aim to automatically discover owl:sameAs links (see  [9, 14]  for a survey) have been already proposed. Some of them are knowledge-based and guided by ontology axioms (i. e. , disjunctions, (inverse) functional properties, composite keys) or declared linkage rules.
A key expresses a set of properties whose values uniquely identify every resource of a dataset. Keys can be used as logical rules to clean or link data when a high Precision is needed  [12, 2, 1]  or to construct more complex similarity functions that can be used in linking platforms that support manually specified rules such as  [25, 15]. Moreover, keys can be also exploited by blocking methods that are defined to partition the data and reduce the search space for the linking step  [6, 21]. Nevertheless, in most of the datasets published on the Web, the keys are not available and it can be difficult, even for an expert, to determine them. Furthermore, when data are heterogeneous and incomplete, a large set of keys composed of different properties is needed to obtain a high Recall. Key discovery approaches have been proposed recently in the setting of the Semantic Web  [23, 5, 17, 22]  demonstrating the importance of keys in the data linking process.
These works differ not only in the method proposed to discover keys from a dataset but also in the way a key is defined. More precisely, three different key semantics have been proposed so far in the context of the Semantic Web, the S-keys  [23, 17] , the F-keys  [22]  and the SF-keys  [5]. The interpretation of the multivalued nature of the properties in the Semantic Web along with the incompleteness of the data has led to these three semantics. Data published on the Web are usually created automatically, thus may contain erroneous information or duplicates. When these data are exploited to discover keys, relevant keys can be lost. For example, let us con-Comment citer ce document : Symeonidou, D. , Armant, V. , Pernelle, N. (2020). BECKEY: Understanding, comparing and discovering keys of different semantics in knowledge bases.
Knowledge-Based Systems, 105708. , DOI : 10. 1016/j. knosys. 2020. 105708 sider a "dirty" dataset where two different people share the same social security number (SSN). In this case, SSN will not be considered as a key, since there exist two people sharing the same SSN. Allowing some exceptions can prevent the system from losing keys. Furthermore, there exist datasets where there exist only few or no sets of properties that uniquely identify every instance in a dataset. However, even if a set of properties is not a key, it can be very discriminative and provide many correct links. We call these keys with exceptions almost keys. For example, usually a telephone number corresponds to one restaurant. Nevertheless, there can be cases where two different restaurants located in the same place share phone numbers.
The core component of BECKEY is an efficient semantic agnostic approach based on different filtering and pruning techniques that are able to discover all three types of key semantics for large RDF datasets. In order to demonstrate the data linking capability of each semantic, an extended experimental comparison of the different key semantics takes place in this work. We illustrate through different examples under which conditions each semantic will be more likely to bring the best linking results. Therefore, this work provides not only a guide for understanding the difference among the semantics of keys but also yields a map for choosing a given semantic depending on the nature of the data to be linked. More precisely, our contributions are as follows: 1. a theoretical comparison of the three semantics of keys, the notion of exception in these semantics and the conditions under which a set of properties can be considered as a key 2.
a methodology for rewriting datasets into a common and compact structure that eases the automatic discovery for the three existing key semantics 3. a semantic agnostic algorithm that ensures the discovery of all three types of key semantics 4. an extended experimental comparison of the proposed semantics allowing to demonstrate in which cases each semantic is more appropriate to use. The paper is organised as follows. Section 2 discusses the related works on key discovery. Section 3 introduces first the data and ontology model and then it presents the notions of keys. Section 4 presents the semantic agnostic algorithm for the discovery of keys under all three semantics. Section 5 showcases our experiments before Section 6 concludes.
The import-by-query algorithm developed in  [2]   the rules to build SPARQL queries for importing from external sources the necessary data for resolving a link query while  [1]  allows to model and reason on uncertain RDF facts and rules, based on the semantics of probabilistic Datalog. In  [7] , the authors propose two algorithms that exploit more complex keys that can be expressed as graph patterns to link data. Since such keys are rarely available, some approaches focus on learning keys from the data. The problem of discovering Functional Dependencies (FD) and keys has been intensively studied in the relational databases field. However, the approaches that have been developed in the relational setting cannot be applied directly to knowledge graphs. Indeed, these approaches are geared towards relations that contain one single value for each subject while in knowledge graphs a property can contain several objects for the same subject.