dataesr/scientific-paragraphs-categorization huggingface dataset:
Biosharing catalogue currently provides a dedicated collection, which lists standards and databases relevant to the field: https://biosharing. org/collection/Meta bolomicsStandardsandDatabases. This is possibly a game changer as these initiatives provide a unique incentive for scientists to release their data in standard compliant fashion. In return? A higher visibility of the scientific output as data that can be trusted, mined, reused, mashed up and above all cited and acknowledged. However, the metabolomics community lags 10 years behind the transcriptomics and proteomics communities in terms of learning-curve, maturity and acceptance of its resources. Metabolomics repositories face the same arduous situation as ArrayExpress  (Brazma et al. 2003)  or GEO  (Edgar et al. 2002)  when they were launched.
MageML  (Spellman et al. 2002)  was the metadata scheme for transcriptomics experiments, but the lack of timely software support for this complex XML format led to the development of the simpler format MAGE-TAB. Many data standards in metabolomics such as ISA-Tab  (Sansone et al. 2012) , mzTab  (Griss et al. 2014 ) and the mwTab used by the NIH metabolomics workbench have been modelled on, and learned from, the earlier -Omics formats. The combination of 'arm twisting' by publishers and funding agencies and at the same time loosening the annotation requirements resulted in the US and European repositories growing considerably. Today, no one doubts the value of these resources, as exemplified in several meta-studies  (Chen et al. 2010) ,  (Rhodes and Chinnaiyan 2005)  and  (Dhanasekaran et al.
Examples where data re-use boosted research In metabolomics, as in other fields, the ability to download and use legacy data to demonstrate new or to compare existing data analysis approaches is where data standards and sharing excel. This was exemplified e. g. in  (Gromski et al. 2014) , where the authors used three different data sets from MetaboLights, including GC-MS and NMR datasets (MTBLS1, MTBLS24, MTBLS40) to investigate the effects of scaling metabolomics data prior to analysis with multivariate methods. The ability to use multiple data sets allows overall conclusions to be drawn on the most sensible scaling methods, which might then be generally applicable to similar metabolomics data. Another example for re-use probably not anticipated by the original depositors is the MTBLS38 study in Metabo-Lights, which is a collection of biologically-relevant plant metabolite standards which were measured for the development and validation of MassCascade  (Beisken et al.
2014). This data was used by M. Stravs (Eawag, CH), during a training workshop, to demonstrate the use of RMassBank  (Stravs et al. 2013)  to extract, annotate and recalibrate MS/MS data, and finally create 58 new reference spectra from MetaboLights  (Haug et al. 2013)  in MassBank  (Horai et al. 2010). The deposited data also helps in the development of novel computational approaches. Stanstrup and Vrhovs ˇek used metabolite data from nine studies MTBLS4/17/19/20/ 36/38/39/4/52 and MTBLS87 along with other data sets for the development and evaluation of the www. predret. org retention time mapping database  (Stanstrup et al. 2015). In all these cases, the availability of the data in a standard format simplified or enabled the re-use. This demonstrated again that it is critical that publicly funded datasets are made available to the scientific community for mining and meta-analysis in a reasonable time frame.
An additional aspect pertains to the didactics of science: it will make training of data scientists easier, if real datasets can be used in textbooks and training courses. This requires trust: trust in the fact that repository content will grow and data will be discoverable; trust in the fact that enough individuals and institutes will contribute; trust that contributions will be of good enough quality so as to enable reuse, and trust that few will have their discoveries scooped. On this one last point, it seems that very few, if any, such cases can be documented. On the other hand, unrestricted access to data leads to critical review and early detection of reproducibility issues.
By regularising how information is structured and reported, standards make it easier to distribute, disseminate and exchange information. Metabolomics repositories like the Metabolomics Workbench or MetaboLights are available to provide all data, and make it easy for scientists to fulfill the requirements of the journals to deposit research data associated with a manuscript. In related disciplines, annotation standards such as MIAME guidelines  (Brazma et al. 2001)  or the Gene Ontology  (Ashburner et al. 2000)  controlled vocabulary have become essential resources in modern molecular and computational biology. Standards are developed to ensure that scientific information is delivered consistently, efficiently and meaningfully to the benefit of the community. Building such infrastructure does not occur overnight, and requires investment from all parties and also appreciation from funding agencies and stakeholders to acknowledge that data management is a new, essential scientific activity.
This should be properly evaluated and factored in by funding agencies when supporting research efforts. Therefore, instead of being seen as a burden, standardisation efforts and standards should be in fact perceived as unique helping tools to enhance the impact of the work carried out by scientists. Indeed, the examples presented above have shown that new types of research are made possible by exploiting a growing 'data lake', for example making it easier to assemble virtual cohorts by retrieving Open Access datasets for testing and evaluating algorithms or to perform meta-analysis. Sometimes, it is simply about ''just doing it'', or as the old adage goes, ''where there is a will, there is a way''.
Acknowledgments PRS, SAS, DS, SN, RS, CS, TE acknowledge funding from the  European Commission COSMOS  Grant  EC312941. TE, SN, DS, RS, CS acknowledges funding from the  European Commission PhenoMeNal  Grant  EC654241. MN acknowledges funding from the  Institut Francais de Bioinformatique (IFB)  grant  PIA INBS 2012. KH and CS acknowledge funding from the  UK BBSRC  Grants  BB/I000933/1 ,  BB/L024152/1 , BB/K021125/1 as well as  MRC  Grant  MR/L01632X/1. SD acknowledges funding from the  Australian National Collaborative Research Infrastructure Strategy. SAS  acknowledges funding from  BBSRC   BB/L024101/1 ,  BB/ J020265/1  ( UK-China partnering award ),  BB/L005069/1  ( Delivering ELIXIR-UK)  and the  University of Oxford e-Research Centre. PRS acknowledges IMI eTRIKS and F. Hoffmann-La Roche AG, Basel, Switzerland.
Apparatus We used an horizontal 32'' 3M touchscreen 1  (Figure  3 left ). We merged fingers' onscreen touch position with the 3D position reported by 5 GameTrak 2  devices (Figure  3  right) attached to each fingertip via cords. We wrote a C++ software using the libgametrak 3  library, that establishes a correspondence between the tracked finger positions and multiple touch points registered on the multi-touch surface. It uses a homography for each finger. The homographies are determined by a calibration procedure in which 3D points are sampled at known positions in the display reference frame. Once the system is calibrated, the software associates to an onscreen touch the identification of the closest finger.
Methodology We have searched 3 databases (Pubmed, Scopus, Web of Science) using the following keywords: [Research outcome* OR research output* OR bibliometric* OR scientometric* OR scientific production] AND [indicator* OR index* OR evaluation OR metrics]. We included articles presenting, discussing or evaluating indicators measuring the scientific production of an institution. The search was conducted by two independent authors using a standardised data extraction form. For each indicator we extracted its definition, calculation, its rationale and its positive and negative points. In order to reduce bias, data extraction and analysis was performed by two independent authors.
Search strategy We searched in Pubmed, Scopus and Web of science, using the following terms: ["research outcome Ã " OR "research output Ã " OR "research impact Ã " OR bibliometric Ã OR scientometric Ã OR "scientific production Ã "] AND [indicator Ã OR index Ã OR evaluation Ã OR metric Ã OR "outcome Ã assessment"], as terms in the abstract, title or keywords, with no time limit. On those three databases we applied filters on language (including only articles written in French or English) and on type of document (including only articles and reviews). Through snowballing, we added articles that seemed relevant in the bibliography of selected articles. Two of us (FT and RB) undertook the search independently, assessed articles on the basis of title and abstract and compared our search results.
Information retrieved We developed and tested two data extraction forms to collect and organise data about (1) the type of articles selected and the information they produced and (2) details of indicators presented in the articles. The templates of the data extraction forms are available in annex 1 and annex 2 (S1 Appendix; S2 Appendix). Using the first form, we retrieved information about each article: the article presents the results of surveys to select one/several indicator(s) (Yes/No), the article relates the development of one/several indicator(s) (Yes/No), the article evaluates the feasibility of one/several indicator (s) (Yes/No), the article evaluates the validity of one/several indicator(s) (Yes/No), the article evaluates the impact of measuring one/several indicator(s) (Yes/No), the article presents any other form of evaluation of the indicator (Yes/No), number and names of indicators.
We also collected for each article the name of the journal in which it was published, the impact factor and type of journal, and the domain of research. Using the second data extraction form, we retrieved the following information for each article: name of the indicator, references in articles, definition of the indicator and details how it is calculated, rationale of the indicator (why it was created), in what context the indicator is used, positive and negative points of the indicator, impact or consequences of measuring the indicator and any additional comments. When the article presented a mix of relevant indicators and irrelevant indicators (example: inputs indicators), we only retrieved information about the relevant indicators. The full text of every article was read and data were extracted a first time by FT and proofread by different authors.
This study was funded by the  European Commission  under the  7th Framework Programme  ( FP7)  under grant agreement  n°260791  for the project entitled 'EurocanPlatform'. The URL of the funders is http://ec. europa. eu/index_en. htmand, URL of the  FP7  is http://ec. europa. eu/research/fp7/index_en. cfm and the URL of the  EurocanPltaform  project is http://eurocanplatform. eu. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manual.
Integration into the engineering design process As it was previously said, particular attention has been paid to inserting the methodology developed into a genuine engineering design process. Due to engineering requirements and, in particular, the coupling with other parts of the system that affect the loadings applied on the structure presented Fig. 1 , the actual design process is performed using the commercial software SAMCEF. It is important to note that a single optimisation of the structure with respect to geometric parameters should have certainly been done efficiently using the embedded SAM-CEF optimisation procedures. However, this work is the first step of a more complex design process in which some other parameters will be taken into account. Further works are in progress to take also into account various types of loadings that can be applied on the structure and, then, the total number of situations considered justify the offline building of a virtual chart of solutions.
The proposed PGD approach must, then, be included in the design workflow with a limited number of modifications to take advantage of the existing facilities. The main problem of the approach detailed in Subsect. 4. 2 is the high degree of intrusiveness of the PGD. Indeed, the linear system defined in (  14 ) is not a classic finite elements problem since the stiffness matrix and the load vector are averaged on the parameter space A. To limit the intrusiveness in the design workflow, different approaches have been envisaged, in collaboration with SAMTECH, the SIEMENS subsidiary which develops SAMCEF software. The first was a complete embedding of the PGD approach in SAMCEF, calculating the averaged stiffness matrices and averaged load vectors (see Eq. (  14 )): - A m i=1 m j=1 λ i (α i ) λ j k α j K (α) dA (35) - A m i=1 λ i (α i ) F T (α) dA (36) directly in the commercial code.
This approach has been given up because it would have need the development of a new structure of fields in SAMCEF which has been considered a too consequent job. Finally, the technique which has been chosen and is demonstrated herein is to "encapsulate" SAMCEF into an in-house MATLAB code that deals with the particular operators associated to PGD. To sum up, on the one hand, SAMCEFs utilities are used to defined the geometry, the partition, the loads, the mesh and the materials, as well as to solve the space finite elements systems mandatory to build the space modes i of the PGD. On the other hand, the MATLAB in-house code is used to build the specific PGD operators Eqs. (  35 ) and (36), as well as the problems corresponding to parametric modes λ i. The communication between the two codes has needed the development by SAMTECH of specific interface functions which takes advantage of the possibility offered by SAMCEF to stop it at a defined step and to restart it from this step afterwards.
He must be able to look over all the possible geometries and solutions in an easy and practical way. For that purpose, we exploit the plug-in PXDMF for PARAVIEW developed at Ecole Centrale de Nantes which allows to plot separated variables data  [8]. Given the modes associated to each considered field (displacement, strain. ), PARAVIEW reconstructs the full fields in real-time by doing summations and multiplications. It allows the visualisation of the evolution of the fields varying each geometric blueparameter. The criteria used for the design is the size of the plastic zones. For the sake of demonstration in this paper, the calculations performed were linear elastic and the plastic zones are roughly evaluated by defining a threshold and considering the elements whose strain is superior to this value.
Conclusions In this work, geometric parameters were introduced into the Proper Generalized Decomposition following  [3]  and extended to complex geometries defined by splines and axisymmetric modelling. The approach allows to efficiently build a virtual chart of the solution that can be used, afterwards, in an optimisation process. Particular attention has been paid to integrating the methodology developed into a engineering design process. Due to the high degree of intrusiveness of the PGD, SAMCEF was interfaced with an in-house MATLAB code. The specific PGD operators are now computed by the MATLAB code and the resolution is done by SAMCEF after receiving these operators. We recall that a single optimisation of the structure with respect to geometric parameters should have certainly been done efficiently using the embedded SAMCEF optimisation procedures.
We have noticed that in the context of this logic, few studies have treated unbalanced linguistic term sets  (Abchir, 2013,Chaoued and Borgi, 2015). The aim of this paper is to establish a methodology to represent and manage this kind of data. It is based on the work of  (Chaoued and Borgi, 2015)  that express unbalanced terms using a uniform multi-set. In the present work, we apply our proposal to an Information Retrieval System (IRS). The latter aims to retrieve a set of documents that satisfies a user query. This system is composed of three units  (Herrera-Viedma and López-Herrera, 2007 ): 1. A documentary archive or a database including a set of documents. They are represented by means of index terms describing their subject content. 2. A query subsystem presenting user needs by means of weighted queries.
If the values v i and l k are equal, the position of τ i in L M is that of τ k-1. If v i < l k , we check the closest term to v i between τ k-1 and τ k. In this case, a proportion error, denoted by α, exists and its value is the difference between the position of a term τ i and that of the closest term, i. e. τ k-1 or τ k. If these terms are equidistant to τ i , we choose the term with the smallest index. This approach is described in Algorithm 2. The inputs for this function are the position v i of the term τ i , the M terms of the unbalanced multiset S un and the normalized distances d k between its successive terms. As output, we obtain the couple (τ pos , α). To illustrate our approach, let us continue with the IRS already described. Let us suppose that a document D is described in the database with a set of 5 index t i (i ∈ {1,.
Research Questions The article research question was whether or not large-scale access to e-journals has an impact on the French researcher's publication activity? This question is particularly sensible for the French context where no study have been conducted on this topic before and where the renewal subscription to Elsevier platform last year was criticized 2. So that, the aim of the study was to explore the relations between three variables: e-journal downloads, published articles and the citations they received. And since we expected downloads and publication activity were to vary from one field to another, the study aimed to shed light on the ways in which the connections between variables behave from one discipline to another. Because of a lack of reliable data on detailed costs, the study excluded aspects relating to cost savings or income generation.
Publication Publication data was taken from Scopus 3 database and took author affiliations into account. Two points justify the choice of using Scopus: firstly, the database includes more of the French and European titles in which French researchers publish their articles. That made data collection more exhaustive than when using Web of Science -WOS 4 ; secondly, Scopus's coverage in the fields of HSS and European languages is more extensive than WOS  (Chadegani et al 2013). The previous tests conducted in the context of the ROI ELICO study allowed us to observe that Scopus counted 10% more articles than the WOS  (Boukacem-Zeghmouri et al. 2014).
Citation Data Citation data refers to article citations gathered over the course of the two years following publication. For instance, for articles published in 2003, we gathered citations from 2004 and 2005. This 2-year window corresponds to the expected impact of usage that took place in 2003. Complex equations 5  , based on previous work  (Bador and Lafouge 2005) , were run on Scopus to address affiliation problems of French institutions and to ensure the most exhaustive data collection.
Observing Raw Data The first step in our study was to lie out raw data of our variables in order to distinguish the main trends. This led us to create a table  (Table 2 below ) based on all the studied universities. During a second phase, we observed in table 3 the behaviour of the three variables when brought down to the sample. Concerning downloads; Lyon 1 took the lead and assumed the role of "super user" within the sample. In regards to publication, Paris 6 took the lead and assumed the role of "super producer". Lyon 1, Paris 5 and Nantes came way behind. In the case of citations, Paris 6 also takes the lead, with the largest amount of citations. To go further in the analysis of.
Limitations Two main limitations must be pointed out, without affecting the relevance of our findings:  The only data available in France to study usage are the COUNTER statistics. MESURE 9  , the national portal that collects usage data (the equivalent of the British JUSP 10  ) also relies on COUNTER statistics. As of yet, libraries have not performed deep logs analysis -used in other studies for instance  (Nicholas et al. 2003; Jung et al. 2013 )which would have allowed us to conduct a more precise and reliable count of HTML and PDF downloads. As shown by previous studies  (Davis and Price 2006; Bucknell 2012 ), the PDF HTML ratio varies substantially in the COUNTER statistics, depending on the publisher's platform. Furthermore, it has been established that ergonomics of the ScienceDirect platform was elaborated with the idea of downloading articles while navigating  (Ke et al.
2002). For instance, PDF icon appears prominently in the menu alongside the summary; the user, while navigating tends to click on the PDF to read the abstract. Number of downloads therefore artificially increases. Previous analyses have allowed us to confirm this phenomenon (Boukacem-Zeghmouri 2012). However, in the context of our study, this bias is restricted since we only took into account the ScienceDirect platform. We did not attempt to compare its usage with other platforms, which could have had different ergonomics and PDF HTML ratios and which therefore couldn't have been compared. Furthermore, the tests conducted in 2008 by the IT department at the Joseph Fourier University in Grenoble 11  were meant to compare a set of COUNTER data with data taken from logs. This comparison showed that the difference in PDF downloads wasn't very significant (between 2 and 5%).
 The usage data from other publisher platforms (IOP, Springer, Wiley) weren't significant. Such a phenomenon isn't new and isn't specific to our sample: the first studies that analyzed usage on publisher platforms across the network of university libraries showed the same thing. Between 2001 and 2005, Elsevier's traffic continued to increase compared to other publishers, going from 65 to 85% of downloads (Boukacem-Zeghmouri and Kamga 2008). In the present study, Elsevier represented between 80 and 93% of total downloads in our sample. This is therefore a phenomenon that extends over time and that is visible in our sample. The usage data on other publisher platforms are therefore not significant enough to be considered in our analysis.  Downloads considered for this article only included the Elsevier journals that had been acquired by Couperin (in the case of the Freedom Collection) and by libraries (on a title to title basis).
Galaxy instance, and both sufficient storage and grid computing power). Galaxy is used to handle and analyze the user's input data from loading, indexing, mapping, assembly and DB-searches. Interaction between our application and Galaxy is ensured by the BioBlend library, which gives API-based access to Galaxy's main features. Metadata about samples, runs, as well as the workflow results are stored in the LIMS. For metagenomic classification and exploration purposes, we show, as a proof of concept, that integration of intuitive exploratory tools, like Krona for representation of taxonomic classification, can be achieved very easily. In the trend of Galaxy, the interface enables the sharing of scientific results to fellow team members.