dataesr/scientific-paragraphs-categorization huggingface dataset:
They need to be solved when possible and the project should develop (i) capacity building activities and (ii) improve technical support which is underlined by 27% of respondents as unsatisfactory or not satisfactory at all. • increase the number of users so that 100% of beneficiaries have more than 3 users (right now, 47% of respondents have less than 3 users). Many members have more than three users which shows that this is a reasonable target: this would increase the overall impact of EO data on decision making. To do so, it is fundamental to foster cross communication among GMES Africa members in order to share good practices for reaching new users. • increase and standardize communication between beneficiaries and users in order to (i) better assess their needs across the whole program, (ii) monitor impacts of EO data use, detect potential for specific win-win cooperation (need for a very specific tool etc.
Acknowledgments: The authors wish to thank all the European and African partners of the  GMES & Africa  project for promoting the fruitful partnership between the  European Commission  and the African Commission in the establishment of the eStation. Special thanks also to the partners who responded to the survey and made themselves available. The authors would also like to thank  Brice Montfraix  ( AUC-TAT ) for his support and for producing the partner maps. Finally, the authors would like to thank the two anonymous reviewers for their fruitful comments.
Use of historical data in clinical trial design and analysis has shown various advantages such as reduction of within-study placebo-treated number of subjects and increase of study power. The meta-analytic-predictive (MAP) approach accounts with a hierarchical model for between-trial heterogeneity in order to derive an informative prior from historical (often control) data. In this paper, we introduce the package RBesT (R Bayesian Evidence Synthesis Tools) which implements the MAP approach with normal (known sampling standard deviation), binomial and Poisson endpoints. The hierarchical MAP model is evaluated by MCMC. The numerical MCMC samples representing the MAP prior are approximated with parametric mixture densities which are obtained with the expectation maximization algorithm. The parametric mixture density representation facilitates easy communication of the MAP prior and enables via fast and accurate analytical procedures to evaluate properties of trial designs with informative MAP priors.
e. gemtc uses JAGS for arm-based network meta-analysis  (van Valkenhoef and Kuiper 2016) , pcnetmeta is based on contrast-based network meta-analysis  (Lin et al. 2017)  and nmaINLA employs the integrated nested Laplace approximations  (Guenhan et al. 2018). In this R package, R Bayesian evidence synthesis tools, RBesT, we implement the MAP approach tailored for the design and analysis of clinical trials as conducted during (usually early phases of) drug development. The focus on trial design leads to a number of considerations specific to this application. For example, the limited sample sizes involved may not warrant that the often used normal approximation holds well enough such that in addition to the common normal endpoint a binomial and a Poisson endpoint are supported by RBesT. As the package facilitates trial design and clinical trial protocol writing, a key step, which makes RBesT unique, is that the derived MAP prior can be easily documented in parametric form and, most importantly, the implications of actually using an informative prior for the trial design can be readily evaluated.
For each endpoint specific conjugate mixture densities are estimated. This allows the use of accurate and fast analytical results. RBesT supports trial design evaluation of one or two sample situations with flexible success criterions. In addition to standard frequentist operating characteristics, the probability of success or derivation of critical values is supported. All of these subsequent steps after MAP prior derivation operate in RBesT on the parametric mixture densities such that a large fraction of the package is devoted to evaluation of mixture densities. The organization of this paper is as follows. We will first give an overview of the package key functionality and describe the details of one motivating example from a real clinical study. In Section 3, theoretical background on MAP approach and robustification of MAP will be explained, followed by how these approaches can be applied via RBesT to the example.
Historical control in Phase II One of the main use-cases of RBesT is the use of historical information in the analysis of clinical trials. The goal is to reduce the trial (usually the control group) sample size while maintaining its target power under the assumed true effect size of an alternative hypothesis. The RBesT package facilitates the (i) prior derivation using MCMC, (ii) parametric (mixture) approximation of the MAP prior and finally (iii) evaluation of the clinical trial design. Prior derivation Once relevant data for the prior derivation has been compiled, the first step is to use the gMAP function which performs a random-effects meta-analysis using MCMC implemented by rstan (Stan Development Team 2018). The syntax follows standard R modeling conventions and the returned analysis object can be modified for sensitivity analyses with the update command or queried with the generic modeling functions.
The EM algorithm requires a pre-specified number of components, which is chosen from automixfit automatically through the use of AIC. The function fits parametric mixture models of increasing complexity with up to four components and then selects the one with the lowest AIC. The output below shows the log-likelihood results for the selected mixture model, as well as the mixture components of the beta mixture. To consider the quality of the EM fit we recommend to compare the MCMC sample with the parametric mixture density in a graphical manner. In Figure  3  the output of the plot method is shown for the generated mix plot, which overlays the fitted mixture density marginal with a histogram of the MCMC sample. In rare cases the response scale can be inadequate to compare the parametric mixture density appropriately (for example, if the response rate is very small or very large): Once the user has derived a parametric mixture representation for the MAP prior, the RBesT package provides additional functions to further investigate as shown in the overview Table  2.
Summary In this paper, we introduced the RBesT package which implements the MAP approach via MCMC sampling algorithms for a number of common sampling and prior distributions. Incorporating historical data in clinical studies has various advantages, such as reducing the number of subjects randomized to a control arm or getting more precise information for decision making. Incorporation of historical data should lead to more ethical and efficient clinical trials. The MAP approach is a hierarchical modeling method allowing heterogeneity between historical trials, which can incorporate historical data in a meta analytic framework. The RBesT package allows for easier implementation of MAP priors. After selection of appropriate historical information, RBesT facilitates the prior derivation using MCMC, the parametric (mixture) approximation of the MAP prior and finally the evaluation of the clinical trial design.
The dead-time enforcement was obtained by combining an Ortec CO4020 input logic with a Philips 794 gate and delay generation. The resultant nonextending dead-times were measured precisely using the two-oscillator method  (Gostely, 1978). The two signals were sent to the integrated 32-bits counter/timer PCI-6602 device from National Instruments, to count the number of γ-rays above an energy threshold set in the single-channel analyzer. An automated acquisition program using Lab-VIEW software recorded the number of counts in each channel, as well as the starting time and counting time of each measurement. The counting time is calculated by counting the frequency pulses from the DCF77 frequency standard type 860 (DCF77, 2021), which gives a high-stability frequency at 10 MHz, 1 MHz and 100 kHz with an accuracy of 10 -8 for 10 s measuring time.
, 2011). Therefore a double exponential function was fitted to the measured current: I(t) = A⋅e -λLu-177 ⋅t + B⋅e -λLu-177m ⋅t [2] where A, B and λ Lu-177 are free parameters and λ Lu-177m is fixed at its corresponding value from the nuclear database, 160. 4(3) days  (Kondev, 2019). The measurement data and corresponding fit and residuals are presented in Fig. 2. The measured 177 Lu half-life is 6. 640(9) days, which is in good agreement with the NDS value of 6. 6443(  9 ) days. The corresponding uncertainty was estimated in a similar way as described in section 4. 1 by following the approach adopted by Pommé  (Pommé et al. , 2008). The residuals of the fit, the background, timing and uncertainty on the current measurement were the more significant components taken into account.
Two types of indicator, considering visual and content characteristics, were developed: System Indicators for the entire document and Substantive and Formal Component Indicators for the fields it contains. They were formalized using five types of source: the literature, expert knowledge, opinions of residents, analysis of a DICRIM database and a questionnaire survey. The aggregation of indicators provides suitability scores and feedback on what should be improved in the DICRIM. Validation sessions were performed with five risk analysis experts. The advantage of the model is that it can be used by the town halls and design offices of any municipality without the need to call on experts or significant human or financial resources.
These included, for example, the total number of pages or sections recommended for the document, and useful data to be mentioned in the form of more definitions or suggestions for more attractive visuals. In the event of missing information from the various sources, the use of a database composed of about fifty DICRIMs from all over France and with various characteristics (number of pages, presentation format, quantity of information contained, etc. ), made extrapolation possible in order to construct definitions and scales. Finally, we decided to conduct a questionnaire survey specifically dedicated to the design of the cover page. Indeed, some of the document's characteristics were difficult to formalize because of the subjectivity involved in their analyses as diverse styles of presentation are possible: visuals that are figurative, humorous, modern, refined, simple, inspire a feeling of fear, etc.
The SI9 indicator is used to evaluate the main added items frequently encountered in the DICRIMs of our database. This regularly involves the addition of a multi-risk map that presents all the hazards of the municipality concerned on the same map. This has been identified as a particularly relevant tool for the communication on multi-risk  (Curt, 2021). As this map has no regulatory specifications, we developed SI11 to assess its suitability (cf. Table  2 ). -a scale fine enough for the observer to locate points of interest while encompassing the entire city; -an adapted semiology so that the extension areas of each risk can be identified; -colors for each risk identical to those used on each of the maps dedicated to a single risk present in the risk components; -sharpness allowing a clear legibility; -a scale clearly informing the observer about the meaning of each figure on the map; -information conveyed that can be understood by the largest number of people; 7: one of the above characteristics are not provided; 5: two of the above characteristics are not provided; 2: more than two of the above characteristics are not provided.
The associations and communities (Town Hall and Prefecture) we met were receptive to our approach and our results, considering them relevant. It noteworthy that this model is the first approach and that although it provided positive results during the verification session, it should be improved in future work. Future work will need to consider clarifying and improving the milestones of some indicators in an attempt to reduce subjectivity. It would also be appropriate to propose updates, for instance the safety instructions in the "Data utility" indicator of the Risk component, by calling on other experts such as firefighters and first-aid workers. Indeed, we esteem that some instructions are missing in the DICRIM, such as instructions for smartphone owners, since smartphones are now widely used to obtain information.
formalize the suitability indicators, i. e. provide the definition and define the milestones of the scale (anchorage points), various sources may be used: the literature, expert knowledge, opinions of residents, analysis of databases, survey, etc. The literature in the fields of Communication Sciences, Cartography, Risk Geography, Sociology, Cognitive Sciences, etc. allows identifying existing recommendations to promote effective communication. In our study, five sources were used to formalize the suitability indicators: the literature e. g. Doak et al. (1996), Dransch et al. (2010), Dubay (2004), Hagemeier-Klose and Wagner (2009), Macpherson-Krutsky et al. (2020), Serna Sans et al. (2018); expert knowledge; opinions of residents obtained from an exploratory survey; analysis of a DICRIM database; and finally a questionnaire survey for a specific analysis on the first page of the document.
ACKNOWLEDGMENTS This work was funded through the  ILLUSTRARE grant  by both the  Austrian Science Fund (FWF) :  I 2953-N31 , and the  French National Research Agency (ANR) :  ANR-16-CE91-0011-01. This work was also funded under the ILLVISATION grant by  WWTF  ( VRG11-010 ). The research was further supported by the  King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) , under award numbers  OSR-2019-CPF-4108  and  BAS/1/1680-01-01. This paper was partly written in collaboration with the  VRVis Competence Center. We thank  Nanographics GmbH  (nanographics. at) for providing the Marion Software Framework. We also thank  Haichao Miao  for his help with the video as well as the anonymous reviewers and the associate editor for their comments to improve the article.
Literature Review on The Benefits and The Risks of STT The topic of ST has been researched across various disciplines that can be grouped into three domains: technological (IS, computing science, etc. ), medical, and social sciences (marketing, sociology, etc. )  (De Moya & Pallud, 2017). The researchers performed a literature search on STT adoption using several databases, namely Business Source Premier, Science Direct, Web of Science, IEEExplore, and Google Scholar. As STT is a generic term, several adjacent keywords were also used for the query, such as: "activity tracking device," "fitness tracker," "physical activity tracking," "healthcare device," "mhealth," "wristband," and "wearable. " These keywords were used in conjunction with the terms "adoption," "appropriation," "acceptance," "perception," "continuity," "commitment," "attitude," and "motivation.
data Collection Procedure The authors conducted an online survey from two different sources. A portion of their sample came from Facebook. Using a social network has the advantage of returning a wide range of profiles through the snowball mechanism  (Biernacki & Waldorf, 1981)  in a relatively short period of time. The questionnaire was posted online from November 1 st to November 30 th , 2018, and received 210 responses. The second data source was a European university, where the authors administered the questionnaire to 118 students. They, therefore, received a total of 328 responses. Having these two different sources for data gathering offers more diversity in terms of the age range. The questionnaire began with a description of the tracking tools and cited examples of products, such as Fitbit and Apple Watch.
dATA ANALySIS The data were filtered to keep only those questionnaires that were completed in their original form. As a result, 83 questionnaires were deleted. Then, we removed all the questionnaires from the respondents with no knowledge of self-monitoring technologies. Nine questionnaires were thus withdrawn. The usable sample consists of 236 questionnaires in total. The authors employed SmartPLS 3. 0 software to analyze the data (C. Ringle et al. , 2015). Several reasons led us to favor the Partial List Squares technique. First, the model is composed of formative constructs. This type of construct is particularly adapted to a PLS approach. Second, the model is complex due to its 11 constructs. Finally, the size of our sample is small, and the data do not respect a normal distribution.
The effect of Unobservable Heterogeneous Groups The authors performed a final test to assess unobservable heterogeneity. This test is still absent in existing IS studies  (Becker et al. , 2013; Fosso Wamba et al. , 2017). Its application allows interesting conclusions to be drawn and avoids interpretation bias  (Becker et al. , 2013). The authors used the REBUS-PLS algorithm  (Esposito Vinzi et al. , 2008)  implemented in XLSTAT-PLS version 2019. 1. 1 to detect the groups. They tested the reflexive version of the model since the algorithm only works in this mode. The execution of the algorithm allowed us to detect three groups of 88, 65, and 83 individuals, which is put back into the SmartPLS software to evaluate the three models. The results are presented in Table  11. Table  12  shows the different R 2 values for each group.
Recently, Fitbit inc. launched a study conducted with the users of its wearables to contribute to the early detection of COVID-19 and the flu  (Etherington, 2020). Thus, in the medium term, there should be a convergence of medical and ST data. Manufacturers should then work more on storage solutions that are secure yet open enough to allow exchange with other applications. We advise designers to take this aspect into consideration and vendors to communicate their ability to maintain user security and privacy. For instance, Apple does it for its health app that stores Apple Watch data (Apple, 2018), as can be read on their website (www. apple. com/ios/health/). Furthermore, with COVID-19, several countries such as Singapore, South Korea, and France have developed and sometimes enforce the usage of a mobile application to encourage citizens to signal any early symptoms.
It has ability to keep all the context picture consistent, standardized, shared and scalable  [23]. The ontology intended also used the previous knowledge to render few concepts reuse or generalize. As for instantiation of the adaptive user interface, the Mobile-Phone interfaces are the test subject of the research. Thus, by considering the issues, some competency questions are defined to develop user context ontology for adaptive mobile phone interfaces. This paper presents a context based ontological model using Web Ontology Language (OWL) for adaptive mobile devices. It models user's context, device parameters, environment (location and time) and activity. Classes and subclasses have been created for the given parameters to establish the relationships between these concepts. The proposed ontological model is needed to preserve context in terms of context elements and concepts forming these elements.
8) Context knowledge representation through ontology The developed context ontology formally defines the usage context. It provides answer to the first competency question that requires a formal description for the usage context of smart devices. It consists of all the classes and individuals for the provided scenarios. These classes and individuals include Device, User, Environment and Activity properties that reflect the solution to CQ2 and sub-questions CQ2. 1 to CQ2. 4. This Ontology also preserves the semantic relations and constraints among defined classes and instances. With the provided results through consistency checking by the Reasoner, the suitable response to the CQ3 is also ensured in developed ontology. Furthermore, the ontology has been developed and verified through SPARQL query language.
The OWL DL and OWL2 are ontology languages to capture the context of elements of interest (e. g. persons, events, activities, locations) and their appropriate relations by mapping the information to respective class properties  [61]. SPARQL can be used to express queries across diverse data sources, optional graph patterns along with their conjunctions and disjunctions. SPARQL also supports aggregation, sub-queries, negation, creating values by expressions, extensible value testing, and constraining queries by source RDF graph. The results of SPARQL queries can be result sets or RDF graphs  1. The sample sets of queries performed through SPARQL have been taken from the defined semantic.
BioContainers is an open-source project that aims to create, store, and distribute bioinformatics software containers and packages. The BioContainers community has developed a set of guidelines to standardize the software containers including the metadata, versions, licenses, and/or software dependencies. BioContainers supports multiple packaging and containers technologies such as Conda, Docker, and Singularity. Here, we introduce the BioContainers Registry and Restful API to make containerized bioinformatics tools more findable, accessible, interoperable, and reusable (FAIR). BioContainers registry provides a fast and convenient way to find and retrieve bioinformatics tools packages and containers. By doing so, it will increase the use of bioinformatics packages and containers while promoting replicability and reproducibility in research.
Introduction The BioContainers community has created a complete ecosystem that enables bioinformatics software to be installed and executed in an isolated and controlled environment  [2, 8]. Also, it provides infrastructure and basic guidelines to create and distribute bioinformatics containers focusing especially on omics technologies  [1]. By 1/9 August 2020, BioContainers provides over 9,000 tools, 29,000 software versions, and 130,000 packages and containers. It gives access to containers for multiple technologies including Conda  [3] , Docker, and Singularity  [5]. The FAIR Guiding Principles for scientific data management provide recommendations on how to make research data findable, accessible, interoperable, and reusable (FAIR)  [9]. In 2017, Jimenez et al. [4]  proposed that the FAIR principles for the software involved: i) Findable: the software should be easy to discover by providing software metadata such as title, general description, publication, license, and versions; ii) Accessible: the link to the code or binaries should be available; iii) Interoperable: the metadata should be exchangeable between major software registries; and iv) Reusable: involving the adoption of a license, helping to define to which extent the community can reuse the tool.
BioContainers BioContainers infrastructure builds, stores, and releases packages and containers from three different technologies: Conda, Docker, and Singularity. For every bioinformatics software, a Bioconda recipe can be created and the corresponding Conda package built  [8]. The new package will be added to the Bioconda channel and the user will be ready to use it in a virtual environment (Figure  1 ). For example, the Conda package for samtools (Figure  1 ) can be installed from the following registry (samtools): conda install -c bioconda samtools ==0. 1. 19  For every Bioconda package, a container is generated automatically and pushed into quay. io. For example, the Docker container for samtools can be retrieved from https://quay. io/repository/biocontainers/samtools (Figure  1 ). The user can use the samtools containers without needs to install any additional software: docker pull quay.