dataesr/scientific-paragraphs-categorization huggingface dataset:
Values list and description can be added if necessary. Similarly, Annotations are populated as specified Figure  4. "e. dmfs" and "t. dmfs" are marked as "Experimental Question". Next, the "Breast Cancer Transcriptome Template" must be linked to the "GSE7390 (Desmedt et al. )" project. This is done from the project view by editing the template field. Once done, 5 Characteristics and 6 Annotation(s) were added to the project. The final step is to populate Characteristics and Annotations. The project contains 198 samples, making the process of manual annotation very lengthy. To ease the process, we wrote an annotation file that can be imported directly within Djeen (available from Djeen documentation page at http://sourceforge. net/projects/ djeen/files). However, it has to be formatted to be imported, especially to fit the sample database Ids.
This is done by generating an empty annotation file by exporting current file annotation data using "Files"-> "Save the file to annotation. txt", and editing "annotation. txt" with a spreadsheet and importing data from the "Desmedt-annotations. txt" file contained in the Affymetrix. zip archive. This file can then be imported directly with Djeen by the 'Import' and 'Save' functions. The following message should appear: 198 file(s) correctly edited -3168 file annotation(s) updated. At this point, the project has been correctly annotated. The annotation structure can be reused in other datasets if necessary. The last step consists in setting up proper permissions to share datasets. Read and Edition parameters can be set from the Permission tab. Also, specific Read and Edition parameters can be set for user groups.
The project is now completely loaded within Djeen and can be further edited and shared. Data can then be downloaded from Djeen. Individual samples can be downloaded from the "File" tab while the whole dataset can be downloaded by performing a snapshot and downloading the resulting archive. value is specified, this value will be set up by default when importing data if no other data is mentioned. Otherwise, a list of values can be specified, to limit the range of possible data. When importing data, the first value will be specified by default if no other data is specified. All metadata (characteristics and annotations) are edited with a similar interface.
Conclusion Djeen is a new Research Information Management System designed to answer laboratories's growing needs for flexibility in data management. It allows the sharing, secure storing and annotation of heterogeneous data types, streamlines repetitive annotations tasks and features import functions. Djeen organizes projects within a natural hierarchy similar to files and folders organization on disk. Templates (optionally used) allow automation and MI Information. These features were demonstrated in this report with the demonstration of a real use case for microarray data. Djeen features a user-friendly installation procedure and web interface. It is conceived as a Joomla! component, has a fast learning curve and can be deployed over MySQL or PostgreSQL. Future development include setting up an advanced interface for data requests, a scripting module to perform data processing directly from the UI, and an advanced authentication system using LDAP.
Figure2Djeen data organization model. Djeen separates Projects hierarchy, Templates and Annotations on one side (these are stored in the Djeen relational database) and the data itself (stored in the file system). Data stored into the database are divided into several objects, each of them being represented by a database table, linked to a class model in the MVC architecture. Project is the primary Djeen organizational element. It allows the construction of a hierarchical tree of sub-Projects and includes metadata about itself and stored files. The File object is the most basic and central piece of information within Djeen, since it represents the lowest granularity level in most high-throughput analyses. This project/files hierarchical tree is structurally mirrored into the file system. Metadata are divided into Characteristics (related to Projects) and Annotations (related to Files).
Acknowledgements We would like to thank  Dr Françoise Birg  and  Wahiba Gherraby  for their valuable comments on the manuscript. We would also thank the  Marseille proteomics core  for their involvement. Funding This project has been funded by an  Institut National du Cancer/Institut National de la santé et de la Recherche Médicale  grant to G. B. Additional support for  Olivier Stahl  was obtained from  Aix-Marseille University  and from the  Canceropôle Provence-Alpes-Côte d'Azur. The Djeen server is funded by a  Fondation pour la Recherche Médicale  grant to G. B. and currently maintained by the Datacentre for  IT and Scientific Computing of CRCM. Support for Hugo Duvergey  is given partly by the  Région Languedoc-Roussillon  (GEPETOS 2007 contract). OV is supported by the  Centre National de la Recherche Scientifique.
Availability and requirements Project name: Database for Joomla!'s Extensible Engine Project home page: https://sourceforge. net/projects/ djeen Operating system(s): Platform independent Programming language: PHP/Javascript/SQL/Perl Other requirements: A Linux server running Apache2/Perl 5. x//PHP 5. 3+/MySQL or PostgreSQL License: CeCILL Any restrictions to use by non-academics: Please contact directly the authors for additional information.
As frequently observed, such rankings suffer from a self-serving predisposition bias  (Beattie & Goodacre, 2006). It was pretty clear that the rankings depended at least partly on who served on the list committee and on how powerful the lobbyists pushing for a given journal were. In spite of hard data carefully documenting the number of submissions, acceptance rates, circulation, etc. , M@n@gement kept its bottom ranking when the list was revised one year later. We had to concur with Grey (2010: 683) that "the constitution of journals as 'top journals' is clearly an accomplishment of power" and acknowledged that M@n@gement was in need of a strong sponsor  (Williams, 2008). As luck would have it, AIMS, the French-based scholarly society for management and strategy, was at the same time pondering launching a new journal or endorsing an existing one.
EPJ Web of Conferences Application to the 55 25 Mn nucleus The 55 25 Mn (n,2n) cross section is measured at N = 11 energies in the range 12 MeV to 20 MeV with n = 1. The experimental data, extracted from  [3] , are ploted with their uncertainty on the figure  1 (a) (green). We build a SVM regression model on the experimental data (red curve figure  1(a) ). We generate r = 1 pseudo-measurements (blue circles figure  1(a) ) per energie with a gaussian noise N (h(E i ), σ 2 i ) for i = 1,. , N. The N = 400 experimental data (with n = 1) (green circles) of the total 55 25 Mn cross section extracted from the EXFOR database 3 , the SVM regression model (red curve) and the r = 1 pseudo-measurement (blue circles) are ploted on the figure  2(a).
Space Missions For space missions, health monitoring is managed through a FDIR hierarchical approach in which several levels of faults are de¦ned from local component/equipment up to global system failures. Depending on the mission needs, FDIR functions are combined with other functions (data processing, orbitography, event-based commanding, and dynamic reprogramming) to achieve a desired level of availability, safety, and autonomy  [6 8 ]. The FDIR strategy can be divided between all levels: detection and local recon¦guration in the subsystems, fault diagnosis and global recon¦guration at the operational level, prevention at the decisional level (detect in advance plans that no longer consistent with the actual resource usage and may lead to further failures, etc. ). The validation assumes testing all possible cross-path situations which becomes costly as the complexity of inboard hardware and software architectures increases.
In  [59] , the authors present a work related to the certi¦cation of a pilot application of advanced FDIR software at Ames Research Center and at the Jet Propulsion Laboratory (NASA). The authors underline the stringent requirements in terms of test e¨ort and the value of rethinking veri¦cation and validation when novel technologies are being deployed. For space missions, one can mention the precursor NASA£s New Millennium Program  [60] : here, the so-called Deep Space One (DS1) remote-agent experiment was initiated to demonstrate onboard fault-protection capabilities, including failure diagnosis and recovery, onboard replanning following otherwise unrecoverable failures, and system-level fault protection  [61]. The FDD challenges for aircraft §ight control systems are being investigated within the European project ADDSAFE  [62].
This allows us to identify and rank parameters that most influence model outputs and whose uncertainty should be tested against management measures in priority. Once enough model runs have been performed, we split combinations of model parameters between those giving output variables equal to or above our management goals and those that do not allow reaching management goals. So as to identify combinations of management parameters and ecological parameters needed to reach management goals, supervised classification is performed by means of classification trees on the dataset obtained with sensitivity analysis. This classification allows us to identify management parameter values that are most desirable so as to reach management goals, and what level of uncertainty on environmental parameters can be tolerated without compromising the achievement of management objectives.
Very powerful methods have been developed for machine learning that allow identifying the hyperplane separating input values from a dataset in two (or more) groups depending on resulting outputs, but these method are either black boxes or provide results that are too difficult to interpret for our needs. Therefore we chose to focus on classification trees  [53] , that allow for simpler representations of results by means of successive univariate splits of the set of input parameter values. A classification tree is built step by step. At each step, a split is performed on a parameter belonging to the set of parameters on Figure  1. The general approach used to identify areas of interest in the input parameters subspace. Once the model has been built and parameters identified (step 0) output variables to be studied and thresholds corresponding to these variables can be chosen and the input space to explore defined (step1).
Then the input parameter is explored and important parameters identified (step 2). Classification trees are used to classify input parameters values depending on the output values they gave when exploring the input parameter space (step 3). doi:10. 1371/journal. pone. 0077566. g001 which the tree is built. A split separates the dataset (values of R(q,u) simulated by the model and associated parameters) into two parts according to values of this parameter. When building a tree, the rule to perform a split is that some measure of discrepancy between the two datasets given by the split is maximized. Therefore the tree is built from the most important node (i. e. combination of a splitting parameter and a splitting value for this parameter) to the least important one. Classification stops when some criteria defined by the user are reached.
For instance, classification can stop when the information gain given by a split (i. e. the increase in discrepancy between the two datasets obtained) is lower than a defined threshold. The terminal nodes of a tree are small datasets that are parts of the initial dataset on which the tree was built. They are called leaves. For a given tree, the path leading from a node (usually the first node) to a leaf is called a branch of the tree (Fig. 2 ). Our goal being to determine the extent to which management measures can allow reaching management goals robustly, we perform classification in two successive steps for each output variable (Fig. 2 ). Let fR 1 ,R 2 ,. ,R M g be the set of all output variables studied. Let Q be the set of all management parameters in the model: Q~fX q,1 ,X q,2 ,. ,X q,n g: Let U be the set of parameters corresponding to important states of Nature (fish biology and fleet characteristics) identified at step 1: U~fX u,1 ,X u,2 ,.
The method we chose to build classification trees is that of conditional trees  [54]  that allows overcoming usual problems of possible overfitting, selection bias, or input parameters scaling. We assessed tree and subtrees instability (i. e tree structure changing when slightly modifying the dataset used to build it) by means of re-sampling techniques. For the main tree and subtrees corresponding to leaves of the main tree, we built sets of 500 trees with subsets containing 95% of the dataset. From all these trees, we identified the tree appearing most often and focused on it, making the hypothesis that all trees would converge to this tree type provided the dataset is big enough. We also compute average splitting values and standard deviations from the 500 replicates so as to have clear indicators of tree variability (see Supporting Information S1 for more details about this method).
Conditional Tree Analysis Conditional trees can be built according to the previously presented method for all output variables studied in the sensitivity analysis. However, only the most important variable for conservation, spawning biomass, will be discussed here for populations of sole and plaice in area 7D. The management goal, for both sole and plaice, is that spawning biomass must remain above 8000t. Sole 7D. The tree structure obtained when building the main tree (Fig. 4 ) on the entire training dataset corresponds to that identified when creating a large number of trees on a smaller subset (see Supporting Information S2 for more details about the tree-building process). Therefore we can infer that our dataset is big enough to ensure good tree stability and that all trees built from subsets would converge to that particular tree provided we had enough data.
Caveats The main limit of techniques we used is that many model simulations are needed. Otherwise reliability of sensitivity analysis results decreases, as large parts of the input space can be left unexplored, and results obtained with classification trees can become highly unstable if they cannot be trained properly. Here, we could perform many simulations because we used a ''simple'' model (one run takes about one minute on one core), but this method could be harder to apply to a more complex model with longer simulation times. However, the quick increase in available computing power opens very interesting prospects concerning the exploration of complex models input parameters spaces. Another limit of the method we used is that we built classification subtrees from the main tree obtained when using the whole training dataset.
The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) within the  Ocean of Tomorrow call  under Grant Agreement No. 266445  for the project Vectors of Change in Oceans and  Seas Marine Life , Impact on Economic Sectors (VECTORS) and the  Region des Pays de la Loire. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
e. , the radiation simulations and three-dimensional geometry. At CERN, the FLUKA Monte Carlo simulation package  [13, 14]  is used for radiation protection studies, as FLUKA has its roots in this field and is thus the most appropriate choice for these studies  [15]. FLUKA is well benchmarked in this area  [16] [17] [18] [19] [20]. It will thus be necessary for our application to be able to deal with data that is the output of a FLUKA simulation, and with the geometry data that is given as input to FLUKA. In Figure  2 , this is expressed by the two uppermost use cases. Data from manual measurements and/or robotic measurements are to be considered in a further phase of the development. These data will not have the dense nature that the simulation data has, and will thus need interpolation. This interpolation is however far from trivial  [21] , and much research will be needed to make this feasible.
Another important factor is that, using third-party tools, Python code can be very easily packaged into stand-alone executable programs, and that Python interpreters are available for many operating systems. For the visualization library, the Visualisation ToolKit (VTK)  [25, 26]  was selected. VTK is a well-known, open-source and freely available software system for three-dimensional computer graphics, image processing and visualization. VTK consists of a C++ class library and includes a Python interface layer, is cross-platform and runs on Linux, Windows, Mac and Unix platforms.
User need For the development of the graphical user interface (GUI), we choose to make use of wxPython  [27]. Because major attention has to be paid to the requirement of an intuitive graphical user interface allowing fast and flexible visualization, trajectory creation and reporting, the user interface (UI) is as much as possible decoupled from the back-end of the software  [28]. During this phase of the software development, many implementation iterations were run through. Each time, a prototype version of the software was tested by several users. The resulting prototype test results were used as an input for a new iteration of implementation. This phase of prototype testing distinguishes itself from the phase of usability testing described in the next section, in that intermediate prototype versions of the software were tested for practical reasons, i.
The resulting application The core of the resulting application is the visualization capacity for FLUKA simulation results and the geometry that comes with it. Due to the nature of FLUKA simulation data and the requirement of a clear visualization of the working conditions, volume rendering is the natural choice for visualizing the radiation levels augmented on the facility geometry. As we want to be able not only to see the radiation levels on certain positions in the three-dimensional space of the facility, but also inside the volume that makes up the facility, volume rendering is the only feasible choice. We consider volume rendering to be a very intuitive volume visualization technique, compared to, for example, volume slicing. Volume rendering has been around for many years  [37, 38]  and recently the development and improvement of off-the-shelve GPUs has led to the proposition of several interactive advanced volumetric illumination models  [39].
Architecturally, the application consists of two main packages, and a number of supporting modules. It makes use of a number of well-known design patterns, such as the Façade, Observer and Iterator patterns  [28]. The two main packages are a framework package for the processing of the facility geometry and radiation (simulation) data, and a GUI (Graphical User Interface) package. In the context of this paper the architecture will not be fully discussed, but it suffices to point out that by using an iterative development methodology, embedded in a rigorous systems engineering life cycle, an elegant design can be obtained. Arguably one of the most important aspects of the software, certainly in the context of this particular software project and as outlined before, is the user interface. A screenshot of this interface, the GUI of the resulting application, can be seen in Figure  3.
The settings are preset to values that have been empirically proven to be meaningful for the CERN cases that we have been provided with as test cases. 4. A possible future direction: robotics integration So far we have described a systems engineering life cycle for the development of an interactive intervention planner with human and computer actors. In the future, there may however also be a need to include robotic actors. In this context, we discuss a particular use case that could be part of an extended systems engineering life cycle for the development of the intervention planning methodology in general, namely, the validation of the latter methodology using programmable mobile radiation-measuring robots. Until now the software has relied on FLUKA simulation data for its operation. This is justified because FLUKA was extensively validated for use in radiation protection around high-energy accelerators  [16] [17] [18] [19] [20].
By integrating the software with a mobile robot equipped for radiation detection, which is under development  [9] [10] [11] ], the validation of individual intervention scenarios constructed with the software tool will become feasible. With the availability of a radiation-detecting mobile robot, a use case can be envisaged where the trajectory generated with the intervention planning software is used as the input for the programming of a trajectory of the robotic device. The robot shall therefore be equipped with a suitable radiation sensor, so that it can measure radiation levels while covering the trajectory. With the results of the measurements taken by the robot, both the FLUKA simulation data and the interactive intervention planner can be validated in a fine-grained way, taking into account all (possibly hidden) variables that come into play when planning the intervention.
Acknowledgements This research project has been supported by a  Marie Curie Fellowship  of the European Community's  Seventh Framework Programme  under contract number (PITN-GA-2010-264336-PURESAFE). The authors would like to thank  Chris Theis ,  Keith Kershaw  and  Pierre Bonnal  for the valuable discussions, as well as  Mathieu Baudin  for the tedious task of beta-testing.
Measured and calculated parameters Several parameters were determined during the experiments conducted on the test bench. The first set of parameters concerns the thermal behaviour of the polymer samples during thermal degradation. The ignition time T ignition (delay between application of heat flux and self-ignition of fuel) and mass loss rate per surface area (fuel mass burned per second and per surface area S fuel ) were computed based on the data recorded by the weighing machine used in the test bench. Parameters associated with aerosol release were also measured or computed during each test. According to previous authors  [9, 12] , aerosol releases during thermal degradation of PMMA samples are characterized by significant airborne releases during the first instants of the test, particularly prior to fuel ignition.
Computational Syllogism For implementing these rules, when comparing any two premises, first their terms x, y, z are compared. If any of the terms in one premise matches with a term in the other premise, concluding a new proposition is possible. To this end, it is checked whether the types of the premises match one of the mentioned rules; if yes, the rule is executed to produce a new proposition, which itself can serve as an additional premise, and so on. If a new proposition is already included in the set of existing premises, it is not added as a new premise; the iteration process ends if no new propositions are found. While  [6]  describes a computational syllogism web page implemented on a php server, we have implemented our own DOMINO reasoner in pure Javascript. The implementation has a small footprint, does not depend on server side computing and can be run in any browser (even as local html file) or used as a library for W3C or other HTML widgets or apps for the WAC  [9]  runtime, the Firefox OS or Google Chrome.