dataesr/scientific-paragraphs-categorization huggingface dataset:
We checked whether artificially injecting repetitions or swapping answers in ground-truth training data may lead to the same improvement, but we found this is not the case: more in-depth features of the machine-generated dialogues' structure make our "mixing" approach effective. Finally, to check that the generated dialogues were indeed effective also for human annotators and to rule out the hypothesis that language drift was behind the improvements described above, we run a human-based evaluation on a sample of the machine-machine dialogues. We found that human annotators not only successfully guess the target when reading dialogues generated with the new training regime, but they are also significantly more confident in their choice. We believe further work is required to check the effectiveness of our approach on other tasks/datasets, and to explore other kinds of perturbations on the input of generative neural dialogue systems.
Fig. A3  shows B ij , the BF comparisons between models of varying F 1 -score, f i and f j. In almost all cases, B ij > 10 50 when f i > f j. Recall that BF are a statistical measure of the relative ability of two models to explain the same observed dataset, Appendix C 2. Eq. (A11) shows the BF as the exponential of the difference between the log-likelihoods for Ĥi and Ĥj : the log-likelihood is a cumulative measure of the goodness-of-fit of each model, therefore the BF indicates which model best captures the dynamics of Q, since the dataset against which the BF is computed are measurements of the system after evolving for some time. Then, we can see from Fig. A3  that in a pairwise comparison, the model with higher F 1 -score is usually favoured by the BF, and therefore fits the data more strongly, confirming our expectation that models which share more terms with Ĥ0 are better capable of reproducing the dynamics of Q.
ACKNOWLEDGEMENTS The authors thank  Stefano Paesani ,  Chris Granade  and  Sebastian Knauer  for helpful discussions. BF acknowledges support from  Airbus  and  EPSRC  grant code  EP/P510427/1. This work was carried out using the computational facilities of the  Advanced Computing Research Centre, University of Bristol  -http://www. bristol. ac. uk/acrc/. The authors acknowledge support from the  Engineering and Physical Sciences Research Council (EPSRC) ,  Programme  Grant  EP/L024020/1 , from the  European project QuCHIP. The authors also acknowledge support from the  EPSRC Hub in Quantum Computing and Simulation  ( EP/T001062/1 ). A. L. acknowledges fellowship support from  EPSRC  ( EP/N003470/1 ).
ACKNOWLEDGMENTS This work is supported by the  ANR   KerStream  project ( ANR-16-CE25-0014-01 ). The work of the third author was partially supported by  National Science Foundation  Grant  OAC-1740218. Experiments presented in this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RE-NATER, and several Universities as well as other organizations (see http://www. grid5000. fr/).
Then, PubMed, Scopus and Google Scholar databases were searched to identify, classify and evaluate each ingredient. The final sample consisted of 46 organic toothpastes that included 156 ingredients; 139 (89. 1%) and 17 (10. 9%) were classified as active and inactive, respectively. Overall, 32 (20. 5%) ingredients were associated with known adverse events. The results of this study indicate that organic toothpastes are highly biocompatible with oral cavity tissues. Careful product selection may help consumers avoid potential adverse effects that can be caused by ingredients such as polymers (e. g. , polyethylene glycol) and carbomers, detergent agents (e. g. , sodium lauryl sulfate), and triclosan. The lack of clinical studies should encourage the development of sufficient evidence to provide consumers with recommendations for daily use, based on both efficacy and biocompatibility.
Selection criteria for the organic toothpaste database The products included in this study were analyzed based on the database from the largest Italian organic supermarket chain, NaturaSì ® , which has more than 240 shops in the country. They were considered representative of the European market due to the international distribution of most of the products in this field. We accessed the online database of their dental products (https://www. naturasi. it/prodotti/cura-dellapersona/igiene-orale) and extracted a list of available toothpastes. As this chain is restricted to organic products, all of the toothpastes surveyed had some European organic certifications. The first coder (MM) compiled the list of ingredients, and the second coder (AN) created a coding sheet to register all the ingredients of each toothpaste.
Identification of active and inactive ingredients The authors developed an Excel (Microsoft Corp. , Redmond, USA) database that included each ingredient for every selected formulation. Subsequently, each ingredient was classified as active or inactive, according to literature data obtained from PubMed, Scopus and Google Scholar databases from March 2021 to July 2021. In general, active ingredients are considered those typically present in toothpastes, such as emulsifying, buffering, preserving, and wetting agents, as well as thickeners, abrasives, solvents, absorbents, and antibacterial agents. Inactive ingredients include substances such as sweeteners, flavorings, colorants, and fragrance additives.
Ingredient toxicity All AEs associated with each ingredient were collected by searching PubMed, Scopus and Google Scholar databases. Adverse events included the following: burning sensation/irritation/swelling/sensitivity of the cheek, tongue, lips, gum, palate, or papillae; tooth sensitivity; peeling/exfoliation/roughness of the cheek, tongue, lips, or gum; presence of aphthous ulcer/wounds; itching/tingling/taste changes of the cheek, tongue or lips; gastrointestinal signs and symptoms; and eye irritation. Since AEs may be individual in nature, the scope of the analyzed literature included not only randomized controlled trials and case-control studies on human and animal subjects but also case reports.
Organic toothpaste database A total of 46 all-natural organic toothpastes sold by NaturaSì ® were identified. They were classified according to different countries' certifications; 44 of the 46 toothpastes (95. 7%) were marketed in more than 6 European countries. The certificates attesting to the organic nature of the analyzed toothpastes were NA-TRUE label, Biorganic DE, ICEA Eco Bio Cosmesi, BDIH, AIAB Bio Eco Cosmesi, Cosmebio, Ecocert, and Demeter. The Italian companies included Ecor, Natyr, Bioearth, Ecosì-Pierpaoli s. r. l. , Victor Philippe, Lycon Cosmetics, and Argital. The German companies included Sante Naturkosmetik, Lavera Naturkosmetik, Logona Naturkosmetik, and Neobio. There was one product from each of the following countries: Switzerland (Weleda), England (Dr. Hauschka), the Netherlands (Ecodenta), and France (Cattier).
A recent clinical study found that scaling and root planing with the aid of ozonated olive oil mouthwash were more effective on salivary matrix metalloproteinase-8 (MMP-8) reduction than scaling and root planing alone. 35 None of the examined formulations contained ozonated olive oil. The supplementary research database is available from the corresponding author on reasonable request. All of the listed single ingredients can be searched. In the light of the above, the authors of this article emphasize the need for direct comparisons between manufacturers and researchers. It is only through constant dialogue and inclusive cooperation between the manufacturers and those conducting the research that products requested by patients can be obtained. ] [38].
(MIAPPE, www. miappe. org) that handles general metadata organization and its companion web service API, the Breeding API (www. brapi. org). Both rely on two established data standards, the MultiCrop Passport Descriptors (MCPD) for identification of plant genetic resources and the Crop Ontology (www. cropontology. org) for trait documentation. Researcher communities' coordination and collaborative approaches have enabled the success and adoption of MIAPPE and led to a general data lifecycle description by ELIXIR Plant Sciences Community to identify gaps and needed developments. A priority has been placed on addressing the "first mile" of data publishing, i. e. the gathering and documentation of data by the researcher, which enables relevant data findability and reusability. Here we describe the existing ecosystem of tools and standards for plant scientists as well as their history, including their convergence through the use of MIAPPE for describing genotyping datasets.
The latter encompasses all the observations and measures that can be made on a precisely identified plant material in a characterized environment. This very general definition of phenomics  (Watt et al. , 2020)  includes diverse types of properties and variables measured at different physical  (Tardieu et al. , 2017)  and temporal scales, ranging from field observation of plant populations to molecular cell characterizations, and for some research communities includes metabolomics or gene expression. The acquisition of these data is conducted in various experimental facilities like greenhouses, fields, phenotyping networks, or natural sites. It can be done using many different devices from manual measurements to high throughput means. The resulting complex and heterogeneous datasets include all the environment and phenotypic variable values at each relevant scale (plant, micro plot, and so on) and very importantly the identification of the phenotyped germplasm, i.
e. , the plant material being experimented upon. In addition, there are often relationships between levels (i. e. physical scales such as microplot or plant individual and organs) inside datasets and between different datasets. The resulting rich wealth of data is usually formatted in a very heterogeneous manner and is difficult to integrate manually or automatically. It is therefore a necessity not only to produce scientific datafrom genetic and genomic to phenomic and environmental and up to systems biologybut also to develop the means for managing it, integrating it and therefore analysing it at high throughput dimensions, not only for model species such as Arabidopsis but also for crops and trees. This management is therefore a direct application of the FAIR (Findable, Accessible, Interoperable, Reusable) data principles  (Wilkinson et al.
The Plant Data Life Cycle The plant sciences community of the European Infrastructure for life sciences ELIXIR  (Harrow et al. , 2021)  has been structured in recent years by funded activities such as the ELIXIR EXCELERATE Horizon 2020 project as well as its collaboration with the EMPHASIS European Infrastructure for Plant Phenotyping. With a switch from big structuring projects to a funding model that relies on the coordination of many smaller projects, including national projects such as ELIXIR implementation studies or European Open Science Cloud (EOSC) demonstrations, it has been necessary to create a roadmap to organize and coordinate the necessary activities. This roadmap  (Pommier et al. , 2021)  needed first a general description of its objectives, through the definition of a data life cycle underlying the needs of plant science.
These activities are both bottom-up, with concrete use cases and demonstration datasets used as the basis for discussions by the persons in charge of actually running data related activities, and top-down, with strategic decisions made by principal investigators and node representatives to increase collaboration between communities and infrastructures. The existence of this bottom-up approach, mobilising concrete elements and utilising real datasets to demonstrate the validity of the data standards and the data life cycle elements, has been instrumental to trigger interest and collaboration within the ELIXIR plant community. Last but not least, the fact that the elements of the roadmap and our objectives have been included as deliverables of many projects ensures that they will be realised.
Fig. 1  Schema of the data lifecycle, that begins with data gathering, then preparation for analysis through processing and integration, finally publication and sharing for knowledge extraction. Note that the integration encompasses both statistical data integration and normalization as well as data linking and mapping The activities described within the Roadmap are designed with the goal of enabling successful data handling across the complete data life cycle (Fig. 1 ). As a community, we will focus on tailoring data findability both at the level of describing the generated datasets in order to be discoverable as well as via developing tools for data retrieval. We will also define pipelines for efficient data pre-processing, integration, analysis and visualisation to enable successful biological interpretation of results.
, 2012)  formalized the Trait-Method-Scale model that has subsequently been embedded in MIAPPE. This model is aligned with the practices and approaches commonly applied by agronomists and phenotyping researchers, especially in terms of the terminology, organisation and range of descriptors for documenting observed and measured variables. Such semantic standards make researchers' life easier by providing a description framework and common vocabularies. They are therefore mostly driven by biologists. However, they don't answer the problem of data organisation. Structure standards allow the organisation of datasets through schemas of metadata descriptors, i. e. , sets of fields, possibly grouped hierarchically, including mandatory and recommended information. They allow the description of data and defining of the interrelations between the different data files that gather all the measures and analyses done (something that is especially challenging in multilocal and/or multiannual experimental networks).
Indeed, for a biostatistician or a data scientist, integrating means normalizing the data, reducing it to make its analysis and understanding possible. But from a data management point of view, integrating datasets is about finding links and common keys among several datasets. Since plant science relies heavily on the integration between phenomic, environmental and omics data, it is necessary to link them using common pivot objects  (Pommier et al. , 2019) , i. e. common keys. The work of the ELIXIR implementation study FONDUE sets up such common keys between databases and datasets. The concepts and descriptions from MIAPPE are used to describe the environment and the plant material in genotyping experiments published in EMBL-EBI data repositories, especially Biosamples, the European Nucleotide Archive (ENA) and the European Variation Archive (EVA).
Plant Data Standards History, Use and Adoption The community management and gathering around the data standards described above has been highly collaborative. The history of the use by ELIXIR of the Crop Ontology, initiated by Bioversity International of the CGIAR, is a good example of how we decided to join forces to avoid creating new data standards when possible. Giving up existing institution-specific standards in favor of international ones, e. g. Crop Ontology, allowed to avoid unnecessary competition. Furthermore, through common workshops, like the PhenoHarmonIS series of conferences, and projects, such as ELIXIR EXCELERATE, new communities have been invited to actively contribute to the Crop Ontology, either through dedicated sub-ontologies or at the level of the ontology's formal concepts.
Therefore, the evolution of MIAPPE relies on the willingness of self-elected workgroups that will hammer down all the details of their propositions through meetings and workshops. It is noteworthy that some of the most important evolutions of MIAPPE were made thanks to several EU projects that funded a group of people to work on a given problem, and were required to deliver a working solution. The available time of members of the focus groups has been critical here. As a consequence, the decisionmaking process takes a lot of time but ensures both the quality of the evolutions and improvements, which are tested with real datasets, as well as the fairness of the decisions, to ensure any stakeholder constraints will be taken into account. The building of BrAPI took a similar path. Indeed, in the BrAPI community, initiated by the CGIAR and Cornell University, several groups from the European infrastructures and the CGIAR brought forward their use cases with the set of necessary specifications and web service calls.
php) in order to foster good practices within one-stop community web portals. There are also activities that promote the use of standards in the frame of general data management and stewardship practices, such as the ELIXIR Research Data Management (RDM) toolkit developed within ELIXIR CONVERGE (https:// rdmkit. elixir-europe. org). It contains, among other things, guides for using specialised tool assemblies like the Plant Genomics Assembly (https://rdmkit. elixir-europe. org/plant_genomics_assembly. html) in order to promote the use of community standards, and supplements this with prominent data management tools and best practices. The RDM Toolkit is an interesting example of how data sharing practices can be brought to researchers, at a minimal and therefore affordable cost of invested time, and by putting its content directly in the hands of the researchers that will ultimately use it.
The example of the INRAE (National Research Institute for Agriculture, Food and Environment, France) forest tree community is interesting because it shows how the data lifecycle can be updated to cope with the constraints of data acquisition and data sharing through the use of the plant data standards described here. An automated data flow was set up to synchronize data shared by multiple information systems. Data produced by research teams are managed in local experimental information systems, each platform having its own. The system facilitates the daily management of those data (raw, analysed or inventory data). Publication and sharing is not done at this local level, however, but through a global information system using an automated workflow. This makes it possible to improve the datasets' visibility and interoperability in order to share this knowledge and enhance data quality, reuse and enrichment.
The First Mile Challenge The first step of the data life cycle is to gather and organize the data needed to answer a given scientific question. This can be achieved by documenting the data during experimentation or by adding and organizing the necessary metadata to existing datasets. For phenomics, this documentation process relies on dedicated tools and laboratory information management systems (LIMS) such as PIPPA  (Coppens et al. , 2017) , Breedbase  (Fernandez-Pozo et al. , 2015; Agbona et al. , this volume)  or PHIS  (Neveu et al. , 2018). For omics data, equivalent LIMS systems exist to run an experiment. But while those systems are commonly available with high throughput experimental platforms, there is a need for another tool both for classical experimentation management and for managing the data obtained from integration and reduction of the experimental datasets.
The solution came from a joint activity between ELIXIR, EOSC and EMPHASIS to add MIAPPE to the FAIRDOM-SEEK  (Wolstencroft et al. , 2017)  data management system. From a community point of view, this collaboration went smoothly thanks to the quality of the existing software, the willingness of all partners and the existence of an accepted and published standard, MIAPPE. Indeed, there hasn't been any extended discussion on the selection of metadata and fields, something that commonly occurs in those types of development projects; MIAPPE was simply selected and implemented in FAIRDOM-SEEK. The fact that both MIAPPE and FAIRDOM-SEEK share the common backbone of the ISA tools helped a lot.
The Findability Challenge for Dispersed Community Data Data discovery, i. e. the ability for researchers to find any dataset suitable for their scientific questions, is a very active domain nowadays, with many solutions. Indeed, to find data, one can either (i) query all relevant data repositories one after the other, (ii) use one or many data discovery web portals, or (iii) use general search engines such as google data search. The first solution isn't appropriate in our era of big data. The third approach might be too general, hence lacking specificity without the help of dedicated markups such as bioschemas. org. The second approach leads to building community global portals to cross data repository boundaries. The plant community has in particular built two of them: the WheatIS (http://wheatis.
org/Search. php) and FAIDARE (https://urgi. versailles. inrae. fr/faidare/). The WheatIS  (Sen et al. , 2020)  is an interesting example from a community management point of view. Indeed, it showed that the success of this portal relies on several key points: (i) keeping data distributed in a global federation rather than gathering all of it in a single global data repository, (ii) sharing at least one critical need, (iii) having clear leadership that ensures mutual benefit and relies on engaged people from several institutions, and (iv) gathering experts and making it easy to join the data federation through technical simplicity. FAIDARE goes one step beyond this; first, by extending the WheatIS species range to more crops and, second, by enabling MIAPPE and BrAPI data standards. This brings two main benefits: Including in the FAIDARE federation new BrAPI data repositories at no additional cost and providing refined search based on the MIAPPE/BrAPI metadata.
Conclusion Sharing data to ensure its useful reuse is complex and poses major technical, social and scientific challenges. The present paper has shown how some international communities, through their social interactions, managed to build technical solutions to enable FAIR data management throughout the data lifecycle. We have seen in particular that the most time-consuming challenge is the community management, not only to formalize the standards, but also to build adoption and train users in the long term. This can be done in a sustainable way if first adopters become trainers too, through 'train the trainers' initiatives, and ensure adoption by other research networks. The solutions presented in this paper are already helping a lot, but more work is needed to allow easy data management to be realised.
Finite element modeling The thermal loading was not axisymmetric during the experiments. The temperature and the displacements are only known in the region of interest (ROI) at the external surface of the sample. It follows that only this ROI was modeled to numerically 260 duplicate the experiments. The DIC surface mesh is "extruded" toward the radial direction to obtain a 3D mesh as shown in Figure  2. Two elements at the boundary of the correlation mesh are not extruded, in order to limit boundary errors and compute accurately the surface normal. For all the tests, the FE mesh of the 265 ROI represents a section of about 40°of arc length and 9 mm in height. The commercial software Abaqus is used to perform the FE analysis and to account for the geometric non-linearity. Six incompatible mode eight-node brick "C3D8I" elements  [26]  are used through the thickness to obtain a converged mesh.