dataesr/scientific-paragraphs-categorization huggingface dataset:
Data selection We selected all articles published between January 2004 and December 2010 that reported cohort, case-control, or crosssectional studies. We did not include non-original studies, experimental and basic science studies, meta-analyses, letters, or studies in categories having their own reporting guidelines, namely, diagnostic and genetic studies (STARD and STREGA, respectively). To identify eligible studies, we conducted a PubMed search of Medline and we manually searched all issues of each journal published during the study period. The indexing terms used for the electronic search were ((''Case-Control Studies''[Mesh] OR ''Cohort Studies''[Mesh] OR ''Cross-Sectional Studies''[Mesh]) AND (''the British Journal of Dermatology'' [journal] OR ''the Journal of the American Academy of Dermatology'' [journal] OR ''the Archives of Dermatology'' [journal] OR ''the Journal of Investigative Dermatology'' [journal]) NOT ''Randomized Controlled Trial''[Publication Type]) with limits: ''humans, only items with abstracts, English''.
Data abstraction To standardise the data abstraction process and to determine whether further clarification of STROBE item scoring was needed, all of us performed a pilot experiment consisting in abstracting data from 25 articles. All articles were then allocated at random to pairs of investigators; each pair was composed of a physician specialised in clinical epidemiology (PhD) and a dermatologist. Discrepancies were reviewed within the pairs and resolved by consensus if possible; if not, one of us (SBG) served as the arbitrator. To avoid potential bias due to working in pairs and to ensure consistency in the review process throughout the study, a permutation scheme was used to modify the pairs. Permutation scheme permitted to disseminate learning and to improve pair performance. The investigators abstracted the 22 items of the STROBE checklist by answering 57 questions (online supplement) adapted from those used by Langan et al.
[11]  Four response options were available for each of the 57 questions: 'yes', 'in part or unclear', 'no', and 'not applicable'. The acceptance year and publication year of each article were extracted by one of us (SBG), who was blinded to the checklist answers. Data were collected using an electronic case-report form established specifically for the study (CleanWEB ß , Telemedicine Technologies S. A. -2007).
Data analysis Quantitative variables are reported as median (interquartile range) and qualitative variables as number (percentage). Uncontrolled before-after analysis. We used the Mann-Whitney test with Bonferroni's correction to compare STROBE scores of articles published in 2004-5 (early pre-STROBE period) and 2008-10 (post-STROBE period). Then, we compared the early and immediate pre-STROBE periods (2004-5 versus 2006-7) and the immediate pre-STROBE and post-STROBE periods (2006-7 versus 2008-10). We assessed the a posteriori power. Interrupted time series analysis. We used a segmented linear regression model to determine the impact of STROBE over time  [18, 19, 20]. We considered two periods, the pre-STROBE period (from the first semester of 2004 to the second semester of 2007) and the post-STROBE period (from the first semester of 2008 to the second semester of 2010).
Before-after analysis The STROBE score increased significantly from the early pre-STROBE period to the post-STROBE period (median score 2004- 05 48% versus median score 2008-10 58%, p,0. 001) and between the two pre-STROBE periods (Table  2 ). Conversely, STROBE scores did not change significantly between the immediate pre-STROBE period (2006-7) and the post-STROBE period (median score 2006- 07 58% versus median score 2008-10 58%, p = 0. 42). Table  2. Quality of the reports of observational studies as assessed using the STROBE score over time. Similar results were obtained with the sensitivity-STROBE scores, and with the post-STROBE period restricted to 2009-10 (data not shown).
Limitations We did not analyse agreement between the pairs of reviewers, but the permutation scheme used to modify the pairs limited potential bias related to working in pairs while ensuring consistency in the review process throughout the study. We used a global score for each article to provide a measure of overall reporting. In choosing this method, we do not suggest that all items are of equal importance. We built two sensitivity-STROBE scores; the consistency of the sensitivity analysis results with the main analysis supports the robustness of our findings. The factor with the strongest influence on the quality of time series analysis is the number of data points collected in the preintervention period (estimation of trend) and in the post intervention period (estimation of the intervention effect)  [19, 20].
Acknowledgments We thank the study sponsor,  Departement de la Recherche Clinique et du Developpement (Assistance Publique-Hopitaux de Paris, APHP) ;  S. Langan, MD  for supplying the STROBE checklist transformed into a series of questions;  F. Gillaizeau  for helpful statistical advice;  F. Fakotondratafika  and  T. Vesval  for reviewing the reports;  C. Viallette  for data management;  M. Blachier  and  F. About  for helping with data abstraction;  E. Audureau, MD, PhD  for critical review, and  A. Wolfe MD , for revising the manuscript. EDEN Group Members:  Jan-Nico Bouwes Bavinck, MD, PhD ;  7 Pieter-Jan Coenraads, MD, PhD , MPH; 8  Thomas Diepgen, MD, PhD ;  9 Peter Elsner, MD, PhD ; 10  Ignacio Garcıa-Doval, MD, MSc , PhD; 11  Jean Jacques Grob, MD, PhD ; 12  Sinead Langan, MD, MSc , PhD; 13  Luigi Naldi , MD; 14  Tamar Nijsten, MD, PhD ; 15  Jochen Schmitt, MD , MPH; 16 A ˚ke Svensson, MD, PhD; 17 and  Hywel Williams ,  MSc, PhD , FRCP 18.
This sensitivity corresponds to the contribution of this parameter to the error at the output of the network. This contribution is determined by the partial derivative of the network output z with respect to the parameter 2 i w being considered: P 1 p ) p ( x w ) p ( z ) p ( S 1 i 2 i w 2 i 1 = = ∂ ∂ = , ( 5 ) where P is the number of data patterns from the learning database. Similarly, the sensitivity of the network output z to the input 0 h x (h=1…n 0 ) is obtained by performing the partial derivative of the output with respect to the input 0 h x under consideration: ( ) ( ) P. 1 p w. ) p ( x 1. w w. ) p ( z ' g. w ) p ( x ) p ( z. ) p ( z ) p ( x. ) p ( x ) p ( z ) p ( x ) p ( z ) p ( S 1 ih 2 1 i n 1 i 2 i 1 ih 1 i n 1 i 2 i 0 h 1 i 1 i 1 i n 1 i 1 i 0 h x 1 1 1 0 h = 2 2 3 4 5 5 6 7 - = = ∂ ∂ ∂ ∂ ∂ ∂ = ∂ ∂ = 1 1 1 = = =.
C. Database and initial learning For this, the available input data of the process are required  [8]. First, each log is scanned at the input of the canter line. This information relates to the product dimension, with length (Lg) and three values for timber diameter (diaPB, diaGB and diaMOY). These variables are used to control the path of the log to the RQM4 or RQM5 queue. This choice is an additional information (RQM). In addition to this dimensional information, the process variables must be characterized at the time of the log's arrival, so the input stock of the trimmer (Q_trim), the utilization rate of the trimmer (U_trim) and the number of logs present in the process between the inputs of RQM4 or RQM5 and the exit of the canter line (Q_RQM) must be measured. The last type of information is related to the cutting plan of the logs.
The last information is the thickness (in mm) of the product, which is also the reference. In this case, only two references are considered: main products are 75 mm and secondary products are 25 mm (ref). Consequently, the neural networks input variables are Lg, diaGB, diaMOY, diaPB, ref, type_piece, Q_trim, U_trim, Q_RQM and RQM. In this application, 12,775 products were simulated. The objective is to estimate the delay (∆T) corresponding to the throughput time for the 12,775 products, between the process input time and the trimmer queue input time. In practice, ∆T is the output of the neural network: 1 1 = = + 2 2 3 4 5 5 6 7 + = ∆ 1 n 1 i 10 1 h 1 i 0 h 1 ih 2 i b b x. w g. w T. ( 22 ) The learning of the network is supervised, so it is necessary to divide the database into learning and validation data sets.
In the most cases, it uses knowledge of human operators to develop the controller, synthesizing the human operator actions. Secondly, its characteristic is the simplicity integration of subjective data in the controller. Its utilization is recommended when the drive system is imprecise. This work aims to assess the states lifetime of a DEVS model by a fuzzy controller. A case study of forest fire propagation is done. Our example is based on this remark: "the duration of a wildfire spread at dry and windy time is necessarily shorter than that of a rainy and calm weather". Starting from this remark, we have tried to translate this observation by a fuzzy controller and simulated it via DEVS formalism. The remainder of this paper is organized as follows. First, the section 2 briefly reviews a background on fuzzy logic and the DEVS formalism.
Fuzzy Reasoning The proposed architecture is a classical DEVS framework. Our challenge is to keep the DEVS formalism unchanged and to improve it without modifying its components. Our contribution is the addition of the FIS module whose function is to assess the lifetime of each state according to the input parameters: wind velocity (V) and humidity (H). Initially, we fill the fuzzy rules base gotten from firemen reasoning. Each fuzzy rule is composed of two parts. The premise part, initially obtained from a generator, and the consequent part which represents a state variable of the rule's DEVS atomic model. The generator is a DEVS atomic model; it provides two kinds of values: spatial-temporal and environmental data. The spatial-temporal data are fed into forest coupled model, they supply the fire trigger event, while the environmental data, are fed into the FIS coupled model to compute the duration of fire consumption (Figure  12 ).
Results and Discussion To compare the simulation performance between the conventional DEVS lifetime state and the fuzzy one, two experiments on forest fire propagation are executed using the parameters described in section 5. 1. The difference concerns the manner to obtain the duration of each state. The simulations were carried out on a Dell System GX280 with Intel ® Pentium (R) IV, CPU 2. 80GHz processor,2G DDR2 SDRAM memory and Linux 2. 6. 32-5-686 operating system. The Table  3  summarizes some important results. The model ForestFireSimZ uses a conventional lifetime while ForestFireSim uses our approach. In the latter model, an atomic model was added in order to compute the duration of the cells fire consumption. This addition ensures the obtaining of the duration depending on weather changes.
Numerical Model of Reference The numerical model is based on numerical simulations using FLAC3D software. It is shown in Fig. 2. The model dimensions are 50 3 40 3 26 m in the transversal, longitudinal, and vertical directions, respectively, for a tunnel diameter D 5 10 m and a cover-to-depth ratio C=D 5 1. The vertical dimension may be modified to deal with different C=D ratios. The soil is assigned an elastic perfectly plastic constitutive model based on Mohr-Coulomb failure criterion. An associated flow rule was used to make the comparison meaningful, but it should be noted that the influence of the dilation angle on the critical collapse and blowout pressure is rather limited  (Mollon 2010). The use of the present numerical model in a probabilistic framework is very inconvenient because its computation time is close to 50 h.
Probabilistic Design The different theoretical approaches presented in this paper (the deterministic M1 and M2 models and the probabilistic method CSRSM) were gathered in a common platform to create the Tunnel Face Stability Software (TFSS) program. This software operates in a MATLAB environment, and is available for free download at the website, http://guilhem. mollon. free. fr. It is also given in the Journal website. The motivation for releasing this program is that it does not seem possible to provide a general and complete chart for the probabilistic design of the retaining face pressure. Indeed, the very large number of parameters of the problem (the tunnel geometry, the statistical characteristics of the random variables, means, COV, types of PDF, correlations between variables, and so on) prevents the plotting of such a design chart.
On the other hand, it would be useful to employ this program as a preliminary design tool to identify the most influential parameters that have a significant contribution in the variability of the system response. This has the great advantage of allowing one to undertake an intensive experimental campaign on only the most influential parameters, which may lead to an economic design, as will be shown later in this section. To demonstrate the capabilities of the TFSS program, two examples (called Example 1 and Example 2) are proposed. They are described in Table  6. Example 1 corresponds to a frictional and cohesive soil, while Example 2 corresponds to a purely cohesive soil. Illustrative probabilistic scenarios were chosen with four random variables for each example. These scenarios (mean values, COV, and type of PDF of the random variables) as well as the problem geometry and some additional tuning parameters (such as the discretization parameters of the deterministic models, the order of the PCE used in CSRSM, or the number of samples used in the Monte Carlo samplings) were introduced in the TFSS program.
Because Example 1 corresponds to a tunnel excavated in a frictional soil, the failure by blowout was ignored and only collapse was considered. Indeed, preliminary computations performed by TFSS (using the M1 model) showed that the deterministic values of the collapse and blowout pressures were 27 and 1,993 kPa, indicating that the latter failure mode may be considered as unlikely because this value of applied pressure is out of reach of most pressurized shields. Some of the probabilistic outputs of the program are provided in    6. Design Examples because this example deals with a purely cohesive soil. This figure shows that, in the probabilistic scenario defined for Example 2, the global probability of failure (defined as the sum of the probabilities of collapse and blowout) cannot reach a satisfactory value.
The TFSS program provides directly the Sobol's indices of each variable as an output, and showed that, in the present case, the variability of the cohesion was  actually responsible for 91. 8% (respectively, 90. 7%) of the variability of the critical collapse (respectively, blowout) pressure. This information is very valuable for a designer because it clearly indicates the most influential parameters that should be looked for thoroughly to reduce their level of uncertainty. The COV of the cohesion in Example 2 was 25%, for a mean value of 38 kPa. Let us imagine that, based on the preceding results, the designer would chose to drive a more intensive campaign of soil recognition to increase his/her knowledge about the soil cohesion, and would obtain a corrected mean value of 34 kPa with a much smaller COV of 10%.
An efficient probabilistic method called CSRSM was used to assess the uncertainty propagation through the deterministic models. Six illustrative configurations that cover a wide range of situations were studied using this method. CSRSM makes it possible (for a given soil configuration and a given probabilistic scenario) to compute efficiently and accurately the probability of collapse and blowout of the tunnel face. Also, a probabilistic design is possible using this method. For this purpose, a MATLAB software called TFSS that makes use of CSRSM and the different deterministic models was provided and illustrated in this paper for practical use in geotechnical engineering. The main findings of this paper can be summarized as follows: • The probabilistic scenario has a very large influence on the COV of the critical pressures: an optimistic scenario leads to a small variability of these pressures, while a pessimistic scenario increases by a lot their dispersion.
We analyze the entire publication database of the American Physical Society generating longitudinal (50 years) citation networks geolocalized at the level of single urban areas. We define the knowledge diffusion proxy, and scientific production ranking algorithms to capture the spatio-temporal dynamics of Physics knowledge worldwide. By using the knowledge diffusion proxy we identify the key cities in the production and consumption of knowledge in Physics as a function of time. The results from the scientific production ranking algorithm allow us to characterize the top cities for scholarly research in Physics. Although we focus on a single dataset concerning a specific field, the methodology presented here opens the path to comparative studies of the dynamics of knowledge across disciplines and research areas.
O ver the last decade, the digitalization of publication datasets has propelled bibliographic studies allowing for the first time access to the geospatial distribution of millions of publications, and citations at different granularities  [1] [2] [3] [4] [5] [6] [7] [8]  (see Ref. 9 for a review). More precisely, authors' name, affiliations, addresses, and references can be aggregated at different scales, and used to characterize publications and citations patterns of single papers  10, 11  , journals  12, 13  , authors  [14] [15] [16]  , institutions 17 , cities  18  , or countries  19. The sheer size of the datasets allows also system level analysis on research production and consumption 20 , migration of authors  21, 22  , and change in production in several regions of the world as a function of time  5, 6  , just to name a few examples.
At the same time those analyses have spurred an intense research activity aimed at defining metrics able to capture the importance/ ranking of authors, institutions, or even entire countries  14, 15, 17, [23] [24] [25] [26] [27] [28] [29]. Whereas such large datasets are extremely useful in understanding scholarly networks and in charting the creation of knowledge, they are also pointing out the limits of our conceptual and modeling frameworks 30 and call for a deeper understanding of the dynamics ruling the diffusion and fruition of knowledge across the the social and geographical space. In this paper we study citation patterns of articles published in the American Physical Society (APS) journals in a fifty-year time interval (1960-2009)   31. Although in the early years of this period the dataset was obviously biased toward the scholarly activity within the USA, in the last twenty years only about 35% of the papers are produced in the USA.
The same amount of production has been observed in databases that include multiple journals, and disciplines  7, 19. Indeed the journals of the APS are considered worldwide as reference publication venues that well represent the international research activity in Physics. Furthermore this dataset does not bundle different disciplines and publication languages, providing a homogeneous dataset concerning Physics scholarly research. For each paper we geolocalize the institutions contained in the authors' affiliations. In this way we are able to associate each paper in the database with specific urban areas. This defines a time resolved, geolocalized citation network including 2,307 cities around the world engaged in the production of scholarly work in the area of Physics. Following previous works  8, 17  we assume that the number of given or received citations is a proxy of knowledge consumption or production, respectively.
Results We focus our analysis on the APS dataset  31. It contains all the papers published by the APS from 1893 to 2009. We consider only the last 50 years due to the incomplete geolocalization information available for the early years. During this period, the large majority of indexed papers, 97. 47%, contain complete information such as authors name, journal of publication, day of publication, list of affiliations and list of citations to other articles published in APS journals. We geolocalized 96. 97% of papers at urban area level with an accuracy of 98. 5%. We refer the reader to the Methods section and to the Supplementary Information (SI) for the detailed description of the dataset and the techniques developed to geolocalize the affiliations. In total, only 43% of papers has been produced inside the USA.
Interestingly, over time this fraction has decreased. For example, in the 60's it was 85. 59%, while in the last 10 years decreased to just 36. 67%. While one might assume that the APS dataset is biased toward the USA scientific community, the percentage of publications contributed by the USA in APS journals after 1990 is almost the same as in other publication datasets  7, 19. These alternative datasets contain journals published all over the world and mix different scientific disciplines. This supports the idea that the APS journals are now attracting the worldwide physics scientific community independently of nationality, and fairly represent the world production and consumption of Physics. It is not possible to provide quantitative analysis of possible nationality bias and disentangle it by an actual change of the dynamic of knowledge production.
We do not have information on citations received or assigned to papers outside this dataset. These limitations certainly affect the count of citations of each city, potentially creating biases in our results. However, our findings, while limited to a particular dataset, are aligned with different observations reported by other studies focused on other datasets and fields. For example, we identify major US cities (e. g. Boston and San Francisco areas), as the most important sources of Physics. Similar observations have been done by Bo ¨rner et al. 17     Batty  4  at both institution and country level considering the Institute for Scientific Information (ISI) HighlyCited database. We also find that some European, Russian and Japanese cities have gradually improved their productivities and ranks in recent twenty years.
Similarly, such growth in scientific production has been observed by  King 19  in the ISI database. As discussed in detail in the SI, by aggregating citations of cities to their respective countries, we find the same correlation between the number of citations, as well as the number of papers, and the GDP invested on Research and Development of several countries as reported by Pan et al. 7  based on the ISI database. This analogy between our results, and many others in the literature, suggests that the APS dataset, although limited, is representative of the overall scientific production of the largest countries and cities in the recent 20 years. The methodology proposed in this paper could be readily extended to larger datasets for which the geolocalization of multiple affiliation is possible.
Methods Dataset. The dataset of the American Physical Society journals, considering papers published between 1893 and 2009 of which 450, 655 papers include a list of affiliations  31. Each of paper may have multiple affiliations. In total there are 945, 767 affiliation strings. In order to geolocalize the articles, we parse the city names from the affiliation strings for each article. First, we process each affiliation string and try to match country or US state names from a list of known names and their variations in different languages. We crosscheck the results with Google Map API obtaining validated location information for 97. 7% of affiliation strings, corresponding to 445, 223 articles. It is worth noticing that we do not use Google Map API (or other map APIs like Yahoo! or Bing) directly for geocoding because, to our best knowledge, there are no accuracy guarantees to these API results.