dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and discussion In this section we report the results of the BMH and POTH frequency models for the extreme storm surges frequency es-timation and analysis applied to the La Rochelle station. All the developments and simulations were carried out within the R environment. The packages "evd"  (Stephenson, 2012)  and "Renext"  (Deville, 2013) , which have specifically been developed for extreme events frequency analysis, were used. As shown in Figs. 5-6, the surge frequency analysis was performed considering only one scenario for each method: 30 systematic surges and 1 exact historical observation for the BMH frequency model and 106 systematic POT surges and 1 OTS data for the POTH model. Whatever the frequency model settings, the historical period considered in these two cases starts when the first historical data was observed (75 years long).
For each model, the evaluation of the impact of additional HI (by comparing quantiles with no historical data and those obtained using the whole 1866-2010 data set, including both systematic and HI), constitutes the main result of this paper, and it is highlighted later in the present section. These results are presented in form of probability and diagnostic plots (Figs. 7 8 ) and in form of tables summarizing the numerical estimates of the return levels of interest and uncertainty parameters. Fitting the GEV/GP distributions to the data yields the following parameters, the absolute and relative widths ( CI and CI/S T , respectively) of the confidence intervals of the representative surge quantiles which are presented in Tables  2  and 3. Since high return periods are needed for the safety of nuclear installations, the main focus was set to the 100, 500 and 1000-year surge quantiles.
In the next sub-sections, we present the results of each frequency model separately. However, we can afford to analyze first in the present sub-section the results when no HI is included. It can be seen that the fit with no historical data included, presented in Figs. 6  and 7 , is poor at the right tail of the distribution and more particularly at the outlier. It is observed that the POT model gives lower surge estimates for the La Rochelle station than the AM model. Quantiles corresponding to 100, 500 and 1000-year return periods given by the POT model are approximately 6 cm lower than those obtained by the AM method. The corresponding confidence intervals are very large when the annual maxima data are used, and it is much narrower in the case the POT frequency model is used. This was expected because the robustness of the re-  sults greatly depends on the amount of available information and since the POT method generally uses more data than the AM one.
It can be seen that this formula allows better positioning of the outliers in the systematic data and gives to these events more reasonable return periods. It can then be concluded that the fit with empirical frequencies of large surges (at the right tail of the distribution and especially at the outlier) is more satisfactory when using HI. Including HI reduces the relative width of the confidence bounds. This finding is underpinned by the values of the absolute and relative widths of the confidence intervals of surge quantiles presented in Table  2. This is the case when more data are included in the parameters estimation (if the extra data are consistent with previous ones). The relative widths of the confidence bounds for the BMH frequency model with no HI involved are 2. 4 times larger than those obtained with both systematic and historical surges.
The results given in Table  3  indicate that the values of relative confidence intervals widths with no HI involved are 1. 65 times narrower than those obtained with both systematic and historical surges. In other words, the user of this method is more confident in its parameter and quantile estimates when using HI. As with the BMH model, return levels given by the POTH frequency model with HI are also lower than those obtained with no historical data included. information One of the most important questions still remains is about the robustness: what is the impact of the use of the HI on the BMH and POTH models results (for high return levels)? And what are the shortcomings and the advantages of the two frequency models? In order to compare the two approaches, the two model settings were chosen to be as close as possible.
This is why for the POTH model we have considered one period ending where the systematic period began (the same period as that used in the BMH model). The POTH historical data can be handled differently; one could use for instance the four available historical data (Table  1 ) over the corresponding period of 12 years without making assumptions about the nongauging period. From Tables  2  and 3 , the convergence of the AM quantiles towards the POT ones is clearly observed when the HI is used. 100, 500 and 1000-year quantiles obtained by the POTH method are close to those given by the BMH one (with a difference of only 1. 8, 1. 6, and 1. 4 % respectively). This convergence can be seen as tangible evidence of the robustness of the two models results. Unlike the POTH model, the BMH one gave a relatively better visual fit not only at the outlier but also at the other large empirical frequencies.
In addition, the fact that the empirical probabilities are better positioned with the exceedance formula can be a misleading element in our assessment of the fits and the uncertainty. This section deals with the impact of additional extreme value data, which occurred during both the systematic and historical periods, on the robustness and the uncertainties in both the frequency estimation of the Xynthia event and the extrapolated levels corresponding to other desired return periods. As mentioned in Sect. 4. 2, additional extreme surges are available. Breilh et al. (2014)  have constructed a historical database of storm-induced coastal floods that affected the La Rochelle region over the last 5 centuries. The analysis of the documented events (some flooding events described as "comparable to Xynthia") led the authors to propose observed and modeled maximum water levels possibly reached during these events (Table  4 ), in particular at La Pallice, where the systematic data were measured.
Conclusion In this paper, two methods for how to use historical surges into the local frequency analysis have been presented and applied to the La Rochelle site located on the French Atlantic coast. The first method is based on the presence of a perception threshold using annual maxima data (BMH model) and the second method is based on the use of historical data periods and POT surges (POTH model). Two adapted likelihood functions and different class of plotting positions formulas are then built to properly handle the information on historical surges. Several types of HI can be considered in the frequency analysis when using these methods. Unlike the BMH frequency model, systematic events that exceeded the historical threshold are not considered as historical data in the POTH model. Consequently their empirical probabilities are different and fitting to the observations was improved especially with the BMH frequency model, but the likelihood remains the same with similar hypotheses for the historical information.
Although the two approaches are physically different, the use of historical information has close impacts when using the two methods with the available data and the applied settings. The comparison of the theoretical distributions fitted to AM and POT extreme events have indicated that, at high return periods (greater than 100 years), the two approaches give comparable predictions of surge magnitudes when historical information is used. This can be interpreted differently; the amount of information is more critical and important than the choice of the approach. Care is always needed when modeling data with the aim of extrapolating whatever the model used (BMH or POTH) and regardless of whether or not HI is used. As indicated by the results of the case study and reasoning in terms of high return levels and associated uncertainty, we have two models that handle differently the impact of the use of HI on high quantiles estimation.
In such an exercise in which extreme values are used with the aim of extrapolating, it is very important to account for the underlying error with which we make our inferences. Despite the improvement of the uncertainty degree resulting from the use of HI in the BMH model, this uncertainty remains high. The distribution parameters and quantiles estimates are qualified with a much more appropriate degree of uncertainty when using the POTH frequency model, because more systematic data are available with this model. On the other hand, how the theoretical distribution is representative of the observations is also an important is-Y. Hamdi et al. : Use of historical information in extreme-surge frequency estimation sue. Overall observations are relatively well fitted except at the outlier when the POTH frequency model is used.
The relative confidence intervals widths decrease as more historical data are used. However additional historical data do not necessarily lead to more robust estimates, the high return levels being very sensitive to the historical values. Then it is absolutely necessary to have historical data the more complete possible, in order to have both robust estimates and reduced uncertainties. During our research that led to the writing of this paper we noted three perspectives. The first one is related to the kinds of historical information, which are different in the two models (lower and/or upper bounds, threshold of perception, etc. ). The BMH and POTH models could be adapted to consider similar HI, and the plotting positions for the POTH model could be calculated with the exceedance formula. In addition, given the importance of the exhaustiveness of the implemented historical data, another kind of HI considering "at least k events above the threshold of perception" would be very interesting for the two models, then partially answering to the exhaustiveness issue.
Acknowledgements. Authors are grateful to the editor for the importance he gave to the present work. Special thanks are addressed to  Xavier Bertin  as well as all members of the research unit (LIENSs) at the  University of La Rochelle  for providing the historical information. The authors also thank  Luc Hamm  and  Franck Mazas  from  ARTELIA  for providing the historical data collected by  T. Gouriou  for the La Rochelle site. Edited by: I. Didenkulova Reviewed by:  F. Mazas  and one anonymous referee.
Data centres are facilities with large amount of machines (i. e. , servers) and hosted processes (e. g. , virtual machines). Managers of data centres (e. g. , operators, capital allocators, CRM) constantly try to optimise them, reassigning 'better' machines to processes. These managers usually see better/good placements as a combination of distinct objectives, hence why in this paper we define the data centre optimisation problem as a multi-objective machine reassignment problem. While classical solutions to address this either do not find many solutions (e. g. , GRASP), do not cover well the search space (e. g. , PLS), or even cannot operate properly (e. g. , NSGA-II lacks a good initial population), we propose GeNePi, a novel hybrid algorithm. We show that GeNePi outperforms all the other algorithms in terms of quantity of solutions (nearly 6 times more solutions on average than the second best algorithm) and quality (hypervolume of the Pareto frontier is 106% better on average).
Experimental Setups The ROADEF challenge 2012 is particularly suited to our needs, as it is rather realistic (proposed by Google, who claim it represents accurately some of their data centres) and it is quite comprehensive: lot of resources for the machines/processes while many papers in the area only consider two (namely, RAM and CPU), reasonably high number of machines and processes, complex dependencies and constraints on the services and processes which make the assignments not straight-forward. The ROADEF dataset distinguishes three categories of instances (a 1 are considered 'easy', a 2 'medium' and b 'hard'). In this paper, we pick up 14 instances (see Figure  1 ), leaving only the biggest ones. We have added variables α m and β m to each machine m ∈ M, and γ l for every location l ∈ L in order to include electricity consumption.
Recent advances in 'omic' technologies have created unprecedented opportunities for biological research, but current software and database resources are extremely fragmented. OMICtools is a manually curated metadatabase that provides an overview of more than 4400 web-accessible tools related to genomics, transcriptomics, proteomics and metabolomics. All tools have been classified by omic technologies (next-generation sequencing, microarray, mass spectrometry and nuclear magnetic resonance) associated with published evaluations of tool performance. Information about each tool is derived either from a diverse set of developers, the scientific literature or from spontaneous submissions. OMICtools is expected to serve as a useful didactic resource not only for bioinformaticians but also for experimental researchers and clinicians.
There is an urgent need of organizing the bioinformatics resources  (12). Among the existing efforts to solve the problem are the SEQanswers wiki  (13) , the NAR online Molecular Biology Database Collection  (14) , the Bioinformatics Links Directory  (15)  and the SIB bioinformatics resource portal  (16). Except the SEQanswers wiki, a specific database of tools for NGS analysis, these projects do not focus on the different analysis steps of 'omic' applications despite the wide range of interested users. Furthermore, these resources do not provide visual guidance for biomedical researchers/life scientists with little computing experience. It is thus important to identify better ways of disseminating useful information to the scientific community. To help remedy these deficiencies, we present OMICtools, the first open-access didactic directory that provides an overview of >4400 software tools and databases with particular attention to NGS, microarray, MS and NMR data analysis.
All tools have been classified and detailed information provided. Furthermore, published evaluations of tool performance have been added to provide guidance in the choice of programs or databases. Finally, an interface has been established to allow anyone to rate a tool, ask a question and report a problem for a specific tool. By making this resource available, we aim to help experimental researchers/clinicians find appropriate tools for their needs and developers to stay up to date and to avoid redundancy. The directory can also be used by life scientists as an educational or quick-reference support.
Database construction OMICtools (http://omictools. com/) is a metadatabase with particular attention to NGS, microarray, polymerase chain reaction (PCR), MS and NMR technologies. The directory includes >4400 tools. Content is presented in a three-level classification format. At the first level, specific icons label the technologies (sequencing, microarray, MS, etc. ; Figure  1A ). At the second level, a didactic scheme labels the applications (DNA-seq, gene expression microarray, MS-based proteomics, NMR-based metabolomics, etc. ). For each application, not only software tools are supplied but also associated databases. At the third level, the analytical steps specific to each application are used for classification (quality control, spliced alignment, de novo assembly, etc. ; Figure  1B ).
For each analytical step, published evaluations of tool performance and data analysis methods are provided when they are available (Figure  1C ). An exhaustive list of the different categories is available at http://omic tools. com/allcategories. html. Analytical software and databases are linked to the directory, and clicking on each tool leads to a structured description of the tool (Figure  1C ). In addition, an interface has been developed to allow anyone to report a problem, ask a question or rate a given tool, in an attempt to develop an interactive community (Figure  1D ).
Data description Analytical tools have been collected either by spontaneous submissions or by the authors from original articles, reviews, company Web sites and tool repositories (Figure  2 ). Submitters do not have to register, and submission is free. Developers and users can submit software or databases to the most appropriate category. Upon request, editors can manually add tools to other categories (up to three cross-references). To improve the clarity of OMICtools, we suggest that submitters add tools with multiple applications to the common tool category. Mandatory information is kept to a minimum: tool name, tool description, Web site address and webmaster's e-mail address. However, submitters are encouraged to add more specifications: original reference, PubMed link, conditions of use (free to all users, free to academic users, license purchase required), associated biological technology, type of tools (database, link to literature, program), user interface, operating system, program language, parallel computation and licensing.
Tools are reviewed by editors within two business days. OMICtools is also continually updated by the authors to classify and include the very latest analytical tools (Figure  2 ). To facilitate these curation tasks, authors use an RSS feed reader, which aggregates web content such as original articles, reviews and tool repositories in one location for easy viewing. Tool specifications are then extracted and incorporate into the metadatabase. Importantly, it is manually curated with special attention to the quality of links. To ensure this, OMICtools has an automatic link checker to avoid dead links. Obsolete tools are not eliminated but transferred in the sections named 'Deprecated tools'. A unique Research Resource Identifiers (RRIDs) have been added for each tool and transfer to the Neuroscience Information Framework (NIF) registry (see Discussion section; Figure  2 ).
Implementation To facilitate future development and updates of software and databases, the OMICtools database structure and software architecture has been flexibly designed. The directory is powered by a free and open-source directory script named Arfooo. The Arfooo Directory has been developed using the latest technologies [MVC architecture, Javascript/Ajax (jQuery), PHP 5] and uses UTF8 encoding. The ability to share to Twitter and Googleþ is provided. An RSS feed of the latest added tools is also supplied.
Discussion To profitably exploit the latest 'omic' research techniques, it is essential to retrieve information existing in the literature. Since its launch in 2013, OMICtools has been providing a metadatabase for software and database dissemination. OMICtools is not the only online resource providing an extensive catalog of tools  (13) (14) (15) (16) (17) (18). The majority of these directories focus either on software tools or databases and on a particular application  (13). OMICtools differs in scope. It is the first metadatabase that combines software tools and databases and extends to all high-throughput technologies. Many directories have used wikis after the launch of popular Wikipedia in 2001  (19). Wikis are Web sites where communities of users can collaborate online to build content and discuss progress.
nature. com/huhbyr). Starting in February, about 25 journals, including the Journal of Neuroscience and Neuron, have introduced editorial measures to address the problem by improving the consistency and quality of reporting on software tools and databases in life-sciences articles. Scientific reproducibility is dependent on many attributes of the scientific method, but one of the most fundamental is to know which materials or tools are being used. Being able to the uniquely identify these tools requires at least a unique identifier for each tool. The Resource Identification Initiative is designed to help researchers cite the key resources used to produce the scientific findings reported in the biomedical literature  (21). In collaboration with the NIF  (22, 23) , OMICtools has already added unique RRIDs for each software tool and database.
During the development of OMICtools, we encountered two main problems. Firstly, it is often the case that basic program specifications (Table  1 ) are difficult to find or not supplied. We encourage developers to provide these details in their publications and on their Web sites. Secondly, Web sites containing deprecated tools are often simply closed without any indication. It would be useful to keep a web page indicating whether the program is temporarily or permanently unavailable. Such initiatives should be warmly acknowledged, as they promote the use of standards that could help the work of curators in the future.
Conclusions and future work New research tools become available and existing tools are refined all the time. The aim of OMICtools is to provide the community with continuously updated information  OMICtools can also help funding agencies to ensure that the submitted projects are high value-added. OMICtools is an on-going project with many possibilities for interactions. Future plans include the addition of an advanced rating system, a tag search option, to convert the Web site to a responsive design and to create a free application for smartphones. Comments, questions and information about missing software are most welcome.
INTRODUCTION Relationships between tree-ring proxies (width, density, isotopes) and climate have usually been estimated with statistical approaches  (Fritts, 1976; Cook and Kairiukstis, 1990). More or less complex statistical relationships have been calibrated to large datasets either to understand the response of the proxy to climatic changes or to reconstruct past climates. In both cases, these statistical approaches often reduce the problem to linear relationships; they assume that there were no major changes in the tree environment other than the climatic factors considered; finally they consider the underlying processes as a black box. Process-based models developed for tree-growth  (Fritts et al. , 1991; Berninger et al. , 2004; Misson, 2004; Rathgeber et al. , 2005; Anchukaitis et al.
TOWARD A MORE GENERAL DATA ASSIMILATION SCHEME Data assimilation sensu stricto has been used extensively in various disciplines (meteorology, oceanography. ), where many observational datasets, both in-situ and remotely sensed, are available. A spectacular application can be found in climatology with the so-called climate reanalysis  (Kalnay et al. , 1996). As ecology cannot call on such massive datasets, more appropriate techniques are those based on Bayesian statistics. Dubinkina et al. (2011)  introduced particle filters in paleoclimatological modeling. We propose here to use a similar approach in dendroecology with MAIDENiso. Models need to state the initial conditions to start the runs. MAIDENiso  (Danis et al. , 2012)  needs several years of daily climate data (minimum and maximum temperature, precipitation).
ACKNOWLEDGMENTS Support for  Joël Guiot  and  Guillermo Gea-Izquierdo  was provided by the  Labex OT-Med  ( ANR-11-LABEX-0061 ) funded by the  "Investissements dAvenir ,"  French Government program  of the  French National Research Agency (ANR)  through the A *  Midex  project ( ANR-11-IDEX-0001-02 ). Support for  Etienne Boucher  was provided by the  Fonds de Recherche Quebecois -Nature et Technologies (FRQNT)  and the  ARCHIVES  project.