dataesr/scientific-paragraphs-categorization huggingface dataset:
In Kepler obtaining data from external sources like CSV files, spreadsheets, relational DBMSs and remote data sources are done by specific actors as metaphors to model the steps of workflows. The system includes a graphical user interface for composing workflows. VisTrails  [11]  is an open-source scientific workflow and provenance management system that provides support for simulations, data exploration and visualization  [3]. The provenance information is presented as XML files or as tables in a relational database. It allows users to navigate workflow versions, to undo changes, to visually compare workflows and their results, and to examine the actions that led to a result. It allows the combination of loosely-coupled resources, specialized libraries, grid and Web services. Taverna  [12]  is an open source scientific Workflow management tool suite to design and execute workflows.
III. MOTIVATION AND CONCEPT FOR AN ENVIRONMENTAL WORKFLOW Scientific Workflows present a managed combination of activities and computations in order to resolve scientific problems. In contrast to business Workflows that implement business processes involving different actors and systems, scientific workflows are used to realize computational experiments, possibly confirming or invalidating scientific hypotheses. Scientific Workflow systems maintain the execution of repetitive tasks such as data access, transformation and analysis  [1, 24]  data from heterogeneous sources, e. g. sensor systems, measuring instruments, text files, spreadsheets, databases, simulation devices, etc. The creation and exchange of scientific and environmental information increase the amount of data that should be processed, from one hand, as well as the possibilities for their interpretation, on the other hand.
This motivates many researchers and specialists to reconsider the existing engineering and network architectures, the database schemas, the algorithms and rules for data interpretation. Beside the hug size, the data are represented in a way, which does not allow processing by the traditional DBMS, because of their heterogeneity and specific characteristics. Sensor systems are usually used to monitor the state of the environment in the urban areas. The obtained measurements need to be stored in a database, which is very important for the development of schema-based data models. So, the data collected by the sensors are used in real time by different applications through procedures for control of large amount of data in spatiotemporal databases. The problem which arises is related to the information control, because of the specific characteristics of the collected data.
The space-time character of data requires the development of new approaches for structuring, exploitation and visualization of these data. Sensor networks and associated databases are used for monitoring and registration of various environmental phenomena, e. g. for the accurate prediction of the future values of these phenomena and for all stochastic-based data processing for environmental norm evaluation. Specific languages for scientific data description already exist. CDF and HDF are languages, which are used in the physics of thermonuclear synthesis, the geology and the astronomy. They represent data models, API, and file formats for storage and control of scientific data. These formats allow storing data as a simple table that is difficult to apply with a large amount of data that have a complex structure.
C. Relational Model of Scientific Data In our work we propose a scientific-environmental dataset as a simple relational database. So, the metadata was devised in three parts (sections). 1) General: metadata about the origin and access rights. This part contains also a general description of the data and references to specific procedures used to process them. 2) Semantics: contains elements describing the meaning of the data file. The main hypothesis was that most environmental data can be presented as one or as few tables containing two types of quantities: (1) independent and (2) dependent quantities. This way they can be examined as a relational table with a primary key consisting of independent quantities and the dependent quantities as non-key attributes. There are other data named parameters that are common for the whole dataset and characterize the environment of the experiment or the assumptions of the simulation as shown in Fig.
VI. CONCLUSION In this paper we proposed a concept of the framework for environmental data processing and stochastic models for prediction of environmental parameters. We analyzed environmental data structure, and modeled this data using a semantic-based method. Using this model, we designed and implemented a framework based on Web services for transformation between massive environmental text-based data and relational databases. We presented a mapping model for environmental data transformation to be used by application of Gaussian processes. For future work we emphasize for environmental risk management and data provenance linked to gas emissions and pollution of air in industrialized cities.
For example, in the study by Hu and colleagues, several markers were significantly related to dementia status but in the opposite direction across cohorts despite the use of the same analytic platform. Additionally, several studies have examined the Alzheimer's Disease Neuroimaging Initiative (ADNI) proteomic database with different protein signatures reported  [29] [30] [31]. The discrepant findings may be due to the approach employed as the ADNI cohort was utilized as the validation sample with the protein signatures being developed in other cohorts (i. e. University of Pennsylvania, Washington University, AIBL, TARCC). For comprehensive recent reviews of AD proteomic studies conducted across a broad range of cohorts see Lista and colleagues  [35]  and Kiddle and colleagues  [34]. Apart from the fact that different initial panels were tested (along with different study design), inconsistencies across study findings could be attributable to many preanalytical variables, both technical and biological, across studies which may have significant impact on the outcomes of the proteomic analyses.
Sid E. O'Bryant has multiple patients pending, submitted by the University of North Texas Health Science Center wherein he is an inventor and receives research grants from the National Institutes of Health, National Institute on Aging, award number R01AG039389 and P30AG12300. Veer Gupta reports not conflicts of interests. Kim Henriksen has research supported by the Danish Research Foundation and reports no conflicts of interests. Melissa Edwards reports no conflicts of interests. Andreas Jeromin holds affiliations with Atlantic Biomarkers, LLC and reports no conflict of interests. Chantal Bazenet reports no conflicts of interests. Holly Soares holds affiliations with Bristol-Myers Squibb and reports no conflicts of interests. Simon Lovestone reports no conflicts of interests. Harald Hampel is supported by the AXA Research Fund, the Fondation Université Pierre et Marie Curie and the Fondation pour la Recherche sur Alzheimer, Paris, France.
The research leading to these results has received funding from the program "Investissements d'avenir" ANR-10-IAIHU-06. Thomas Montine receives funding from the National Institutes of Health, award Number P50AG05136 and reports no conflict of interest. Kaj Blennow has research supported by the Swedish Research Council and has served on Advisory Boards for Innogenetics, Belgium. Tatiana Foroud reports no conflict of interest. Maria Carrillo reports no conflict of interest. Neill Graff-Radford reports no conflict of interest. Christoph Laske reports no conflict of interest. Monique Breteler reports no conflict of interest. Leslie Shaw reports no conflict of interest. John Q. Trojanowski may accrue revenue in the future on patents submitted by the University of Pennsylvania wherein he is co-Inventor and received revenue from the sale of Avid to Eli Lily as co-inventor on imaging related patents submitted by the University of Pennsylvania.
We focus works that proposed methods for solving maintenance activity problem and its impact to production activities. Several approaches have been developed to address MA problems. In  [14] , they used mathematical models solved with LINGO and MATLAB, to optimize integrated planning, taking into account preventive MA in production. In industrial systems, production programme could not be designed without regarding MA. This reality must be regarded too in health care systems. We have not found any papers solving together OT optimization and MA. In the present work, we proposed a model that combines the flows of planned surgical operations (SO) and preventive MA.
Introduction According to good clinical practice, it is expected that all funded research will lead to publication. With this in mind, researchers have investigated the fate of funded studies. Of the 198 clinical trials funded by NIH and completed by 1988, 93% had been published 9 years later  [1]. Another study on 1996 NIH grants found that each grant led to 7. 6 manuscripts  [2]. Moreover, the NIH requested in 2005 that all publications resulting from NIHfunded research be submitted to PubMed Central  [3]. In 1992, a French funding scheme called the ''Programme Hospitalier de Recherche Clinique'' (PHRC) was created to develop clinical research  [4]. This annual scheme is reserved for French hospitals. Each year, priority medical themes are defined by the French Ministry of Health and clinicians can apply for a grant for their projects.
The PHRC is dedicated to funding clinical research studies from initiation to completion. A panel of national experts review and select projects to be funded by the French Ministry of Health. Each year, nearly 600 projects are submitted to the PHRC funding scheme. Overall, 30% of the projects submitted receive PHRC funding and in 2000, 17 million euros of funding was provided. Follow-up information is available for all funded projects  [5] [6] [7] [8] , but there is no information on the projects which did not receive funding. In a French regional study, it was found that even projects rejected for a given source of funding resulted in publications  [9]. The fate of rejected projects is not known: do they apply for and/or receive funding from other sources or are they carried out without specific funding? The aim of this study was to describe the initial characteristics of, and to follow up on all projects submitted to the French national funding scheme PHRC in 2000, to assess project initiation, completion and publication status according to its source of funding.
Materials All clinical research projects submitted for the PHRC 2000 funding scheme were Each French tertiary teaching hospital has a research administration department which was invited to participate and to assign a research assistant to this study. All the research assistants attended a formal training session on abstracting study characteristics in January 2008 and on primary investigator survey in March 2008. They were locally responsible for collecting the initial project characteristics and obtaining follow-up data from the primary investigator for each project (either by interview or questionnaire).
Study design This is a retrospective cohort study conducted in 2 steps. -Description of initial characteristics: For this step, each research assistant had to collect the data from the project file as submitted to the PHRC 2000 (planned number of patients, design, duration, and topic). Coding for topic and design was centralised (ED and LH). -Follow-up of submitted projects: The primary investigator was contacted to obtain information on the source of funding (if received), the project's initiation, the project's completion and its publication status (in 2008-2010, i. e. 8 to 10 years after applying to the scheme). Some investigators might have submitted more than one project; however the statistical unit remained the project.
Definition The information on the obtainment of ''PHRC 2000'' funding was retrieved from the French Ministry of Health. This information was then completed by the data on other funding provided by the primary investigator (when available). ''PHRC 2000/other PHRC'' was considered when the protocol obtained funding either from the PHRC 2000 itself or when the investigator declared they had obtained previous or subsequent PHRC funding (other PHRC). ''PHRC 2000/other PHRC/other funding'' was considered when investigator obtained either the PHRC 2000 funding or PHRC funding in another year (other PHRC) or from a source of funding other than PHRC (other funding). When the article reference was provided, we classified articles in 5 categories according to the percentiles of the impact factor of the journal  [10].
Primary investigators survey Of the 481 projects studied, follow-up information was obtained for 366, giving a response rate of 76%. This response rate varied according to the obtainment of the PHRC 2000 grant (73% for non-funded projects and 86% for funded projects, p = 0. 01). Amongst the protocols with follow-up data available, 24% were funded by the PHRC 2000 (n = 87), 10% were funded by other PHRC (n = 36) and 10% declared at least one other source of funding (n = 37) (table 2); for the remaining projects, no funding was declared (56%).
Median time to publication was 6. 33 years (95% confidence interval: 4. 74-7. 59). There was no difference in time to publication for projects funded by the PHRC 2000 compared to other projects (figure  1 , log-rank p-value = 0. 38). When replacing missing publication, the analysis comprised 162 protocols; the median time to publication was 6. 88 years  (6. 33-7. 57 ). Among the 111 projects with at least one publication (excluding the two outliers), the average number of publications was not significantly different according to the PHRC 2000 funding (2. 9 for PHRC 2000 funded projects versus 2. 8 for other projects; Mann-Whitney p-value = 0. 67). Concerning the quality of publication, complete references were available for 78 projects and the average publication quality score was not significantly different according to PHRC 2000 funding  (14.
Acknowledgments We would like to thank  Monique Mourey  and  Peggy Falgon  for retrieving information; and the chairmen and directors of all the clinical research administrative departments of French university hospitals, as well as the research assistants who collected data and interviewed the primary investigators. We would also like to thank  Dr Jean-Pierre Duffet ,  Direction Generale de l'Organisation des Soins  ( French Ministry of Health ), for giving access to the original files.
a grant has been investigated, but there is no information on the projects which did not receive funding. The fate of these projects is not known: do they apply for and/or receive funding from other sources or are they carried out without specific funding? Purpose: The aim of the study was to describe all clinical research projects submitted to a French national funding scheme (PHRC 2000) and to assess project initiation, completion and publication status taking into account whether or not they received funding. initiated, the funding did not influence completion or publication. The study was supported by a grant from the  French ministry of Health  ( PHRC 2008 , SAFIRE). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
76 47 (34) 23 (50) those initiated) Yes 113 47 (63) 66 (61) 66 (63) 47 (60) 90 (66) 23 (50) Not provided 2 Study published (of No 31 14 (27) 17 (25) 1. 00 19 (26) 12 (27) 1. 00 23 (24) 8 (32) those completed) Yes 88 38 (73) 50 (75) 55 (74) 33 (73) 71 (76) 17 (68) Not provided 2 Values are n (%). *Information on PHRC 2000 funding was obtained from the French Ministry of Health, information on other funding was provided by the primary investigator (327 out of 366 provided information). doi:10. 1371/journal. pone. 0099561. t002.
The resolution of such equations induces very often parameters with unknown exact value. There are two kinds of unknown parameters: first, the ones subjected to lack of knowledge or variability (e. g turbulence rate, equipment temperature, etc. ), and second, the parameters that have to be estimated (e. g thermal contact resistance, thermal conductivity, heat dissipation, etc. ). The latter parameters involve additional information which is in practice composed of datasets provided from former aircrafts, real experiments, trials, etc.
Then, the heat dissipation coefficient φ should be estimated. The observable of interest we consider is the temperature around the equipment given from a post-processing after resolution of the system above. Thus, a simulated temperature can be seen as a real-valued function (x, θ) → h(x, θ), where x = (τ, ε) with ε = ( (x 1 ), • • • , (x M )), and θ = φ. As the variables x = (τ, ε) are uncertain, we can choose to model this uncertainty by a random vector X = (τ, ε) where τ and ε are seen as random variables drawn from some given distributions. Besides this modelling, in order to characterize the parameter θ = φ, one needs a database of temperatures of the equipment which can be obtained from sensors placed on it. Let us denote such database by Y 1 ,. , Y n. Finally, the estimation problem consists in estimating θ = φ from the (stochastic) simulation model (X, θ) → h(X, θ).
We then try to recover the distributions of the parameters by a SAEM approach (using Monolix software  [22] ). For this we first precompute solutions of the KPP equation on a regular or non regular mesh (see Figure  2 ), and then run SAEM algorithm using interpolations of the precomputed values of KPP equation (instead of the genuine KPP). u(T 0 , x) = α1 |x-x0|≤ε , (6) As illustrated in table 1, the results are of good quality. Adding some noise deteriorates the accuracy but the results are reasonable for practical applications. The main interest of this methodology is to tackle problem of parameters identification in complex PDE systems. The computational cost of the whole algorithm that mean generation of the mesh and SAEM computation, can be divided in two distinct parts: an offline time corresponding to the computation of the mesh, which can be done once and for all, and an online time corresponding to the estimation of the parameters for a given population.
The differential settlement between the two footings was used to represent the system response. The deterministic model used to compute the system response is based on numerical simulations using the commercial software FLAC. This paper is organized as follows: a brief review of the SS approach and the Karhunen-Loeve expansion method is first presented. Then, the iSS approach and its implementation in the case of random field problems are presented. This is followed by the illustration of the efficiency of the iSS approach through the probabilistic analysis of two neighboring strip footings resting on a spatially varying soil. The paper ends with a conclusion of the main findings.
It is presented herein for completeness. Based on this link, the algorithm of the iSS approach for the case of a spatially varying soil property can be described as follows: (1) Generate a vector of M standard normal random variables {n 1 ,. , n i ,. , n M } by MCS methodology. This vector must satisfy the condition that its norm is larger than a prescribed radius R h of a hypersphere centered at the origin of the standard space. different realizations are arranged in an increasing order in the vector G 0. Notice also that the subscript '0' refers to the first level (i. e. level 0). (  4 ) Evaluate the first failure threshold C 1 of the failure region F 1 as the ½ðN s Â p 0 Þ þ 1 th value in the increasing list of elements of the vector G 0 where p 0 is a prescribed value that represents the ratio between the number of realizations for which G < C 1 and the number of realizations N s (i.
Available software packages (Monolix, NLMIXED in SAS, saemix and nlme in R, nlmefitsa in Matlab, among others) allow to fit a wide range of generalized linear and nonlinear mixed models. For model selection purpose, the Bayesian Information Criterion (BIC) provides a consistent and easy-to-use method  [27]. Its generic form is the negative maximized log-likelihood penalized by a term that depends on the number of estimated parameters and the sample size. Surprisingly, the BIC expression differs from one software to another. The reason is that the effective sample size and the effective number of parameters are not clearly defined in this context. The aim of the paper is to clarify how the BIC should be defined in general mixed effects models.
, n i are independent, the effective sample size is the total number of observations n tot = N i=1 n i. In a pure random effects model where all the components of ψ i are random, the estimation of the fixed effects β is based on N independent random vectors (y 1 ,. , y N ). In such situation, the effective sample size is the number of subjects N. In a mixed effects model which combines fixed and random components of ψ i , the definition of the effective sample size is not straightforward. Yet, in the mixed effects model literature, the BIC penalty usually involves log n tot. From a practical point of view, the log n tot penalty is implemented in the R package nlme  [23]  and in the SPSS procedure MIXED  [28]  while the log N penalty is used in Monolix [18], saemix  [2]  or in the SAS proc NLMIXED  [25].
e. using different covariate models, different variance models and different designs. We have then simulated data under the 4 × 4 × 4 = 64 possible situations by combining the covariate models (M k , 1 ≤ k ≤ 4), the variance models (O ℓ , 1 ≤ ℓ ≤ 4) and four different designs obtained with different numbers of subjects For each of these 64 models, 50 datasets were simulated as follows: the n observation time points t i1 ,. , t in were equally spaced in [0, 10] and the residual error variance was fixed to σ 2 = 1. In order to consider different situations, the covariates (c i ) and the variances (ω 2 m , 0 ≤ m ≤ 2) where randomly drawn for each replicate: c i ∼ N (0, 1), µ 0 , α 1 , α 2 ∼ N (0. 01, 1), µ 1 ∼ N (0. 005, 1), µ 2 ∼ N (0. 0025, 1) and ω 2 m ∼ U [0. 01,1. 01] , m = 0, 1, 2. For each simulated dataset, the EM algorithm was used for estimating the population parameters and the observed likelihood was computed as a Gaussian likelihood, according to (3.
Application to the warfarin data We will use a classical clinical pharmacology study  [21]  for illustrating the proposed method with a real data example. This data is available with the Monolix software and all the results presented in this section can easily be reproduced. In this well known study, N = 32 healthy volunteers received a 1. 5 mg/kg single oral dose of warfarin, an anticoagulant normally used in the prevention of thrombosis. The warfarin plasma concentration C was then measured at different times for these patients and a total number of n tot = 251 measurements was obtained. We consider a one compartment model for this data: C(t, D, ka, V, Cl) = D ka V ka -Cl e -(Cl/V ) t -e -ka t , (4. 1) where D is the initial dose of drug, ka the absorption rate constant, V the volume of distribution and Cl the clearance of the drug.
Acknowledgement The authors thank two referees for helpful comments that improved the manuscript. The research leading to these results has received support from  Innovative Medicines Initiative Joint. Undertaking  under grant agreement n. 115156, resources of which are composed of financial contributions from the  European Union 's  Seventh Framework Programme  and EFPIA companies in kind contribution. The  DDMoRe  project is also financially supported by contributions from  Academic  and SME partners. The third author was partially supported by the  FMJH  and the labex  LMH (Government Program :  ANR-10-CAMP-0151-02  and  ANR-11-LABX-0056-LMH ).