dataesr/scientific-paragraphs-categorization huggingface dataset:
The size and the complexity of the data that can be exploited to study the resulting databases of scientific publications require the development of quantitative methods for the detection of emerging topics by bibliometric methods, applying relatively simple statistical techniques as growth curves, or more sophisticated ones, such as automatic classification and analysis of networks  [21]   [4]  [19]  [13]. Another concern is also to provide tools capable of producing outputs exploitable by the end user. These outputs should be descriptive, intelligible and viewable. Therefore, we separate our analysis into two parts. In the first part, we describe the quantitative and automatic methods that allow the extraction of relevant information from a corpus. In particular, these techniques offer to detect characteristic keywords from documents, or underlying topics -and keywords that describe them -referred in the documents.
We also discuss of the visual exploitation which may be made of these methods. In a second part, we detail the methods for studying topic changes within a corpus whose data are anchored in time. We insist in particular on diachronic analysis methods, particularly effective to track these changes in a step by step and in a synthetic way. Finally, in a last part, we detail Diachronic'Explorer, our open source tool for the production and viewing of diachronic analysis results. We show the effectiveness of the extraction methods that we offer through complementary and dynamic visualizations using recent technologies.
Feature maximization for feature selection The method To introduce the feature maximisation metric and process  [16] , we first use an example. We then explain its use in our application framework. In Table  3 , we present sample data collected from a panel of M en (M ) and W omen (W ) described by three features (N ose Size (N ), Hair Length (C), Shoes Size (S)). The problem of supervised classification in computer science is to learn to discriminate the class of M en of the class of W omen automatically by using these features. To achieve this, it is worthwhile for the algorithms to exploit the features that best separate the M en from the W omen. The process of feature maximization is comparable to a feature selection process. This process is based on the feature F-measure. The feature F-measure F F c (f ) of a feature f associated with a cluster c (M or W in our example) is defined as the harmonic mean of the feature recall F R c (f ) and of the feature predominance F P c (f ), themselves defined as follows: F R c (f ) = Σ d∈c W f d Σ c ∈C Σ d∈c W f d F P c (f ) = Σ d∈c W f d Σ f ∈Fc,d∈c W f d (1) with where W f d represents the weight 4  of the feature f for the data d and F c represents all the features present in the dataset associated with the class c.
Diachronic analysis Diachronic analysis, which consists in comparing data or results by time step, is extensively used. In linguistics, Perea uses this technique to follow the evolution of the Catalan language through time  [25]. Cardon and al. study the evolution of blogs and their importance on the web in a diachronic way  [3]. Similarly, the activity of bloggers and the evolution of their interests are studied in a diachronic way by  [12]. The work of Wang et al, more connected to our field of applications, analyzes the thematic evolution of the research in such a way  [28]. In our case, we develop a parameter-free method, directly exploitable by the user and based on feature maximization  [16]  to identify and describe the topics of a corpus. This approach allows identification of keywords issued from the full text content of documents which are characteristic of each topics, conversely to methods based on keywords indexed by the publication databases  [28]   [23].
Furthermore, the absence of parameters in the process allows to completely automate the task of indexing the documents passing through the detection of topics and up to the visualization of their evolution. Figure  13  shows the progress of the complete method up to visualization. The whole process is also detailled hereafter: 1. We query a bibliographic database in order to build up a corpus covering several years of publication on a given topic. 2. The full text of each obtained document is treated with a conventional PoS tagging tool to extract index terms (keywords). 3. The documents and their extracted keywords are then grouped into classes corresponding to their year of publication and a feature selection based on feature maximization is applied on the keywords of each of the document group.
Diachronic'Explorer tool We now introduce Diachronic'Explorer 6 , our open source tool for the production and visual exploitation of diachronic analysis results. Diachronic'Explorer is composed of two modules. The first module allows to use descriptions of the topics produced by the clustering and the feature maximization (Section 3. 2) to track these topics over time and detect the changes and similarities between time periods. The second module is dedicated to the visualization of the results produced by the first. Designed in the form of web platform using modern technology, the tool offers the possibility to explore the 6 A demo version of the tool can be found at URL: http://github. com/nicolasdugue/istex-demonstrateur corpus through various complementary visualizations each representing a different level of granularity in the exploration of this corpus.
Conclusion In this paper, using a detailed state of the art on work done for analysis of textual data, and particularly that of scientific data, we have highlighted in a first time, the strategic importance of the diachronic analysis of such data, as well as the difficulties and complexities related to this type of treatment, whether it's the lack of available metadata or the parameters settings and scope problems related to usual methods of analysis, especially those of topic detection. We have also shown that there are many interesting alternatives with regard to the visualization of analysis results. This discussion has enabled us to propose, as a second step, a new methodology of analysis based on feature maximization. This methodology has many benefits to the existing, which be without parameters, to be applicable at different scale levels, from the corpus to the document, with ease of calculation, and finally, to have strong capabilities of synthesis, which makes her results easily interpretable, even for complex problems and large corpora.
The ISTEX project (Initiative d'Excellence pour l'Information Scientifique et Technique) fits in the "Investment for the future" program, initiated by the French Ministry of Higher Education and Research (MESR), whose ambition is to strengthen research and French higher education on the world level. The ISTEX project main objective is to offer to the whole of the community of higher education and research, online access to the retrospective collections of scientific literature in all disciplines by engaging a national policy of massive acquisition of documentation: archives of journals, databases, corpus of texts.
, 2005) , the northeast American CREST Lidar Network (CLN)  (Hoff et al. , 2009) , and the Latin America Lidar Network LA-LINET  (Antuña et al. , 2012) , monitor aerosol vertical distributions in the atmosphere over vast regions of the Earth. The Global Atmosphere Watch (GAW) Aerosol Lidar Ob-servation Network (GALION), also known as the "network of networks" (e. g. Bösenberg and Hoff, 2007) , was established under the aegis of GAW to coordinate lidar activity all over the world. The outcome of the lidar observations are presented in the lidar network databases as vertical profiles of aerosol backscatter and extinction coefficients. Aerosol columnar properties from AERONET and aforementioned vertical profiles of aerosol parameters from lidar networks are complementary pieces of information characterizing aerosol properties.
The idea of combined lidar and radiometer sounding (LRS) for retrieving vertical distributions of aerosol characteristics was first proposed by  Chaikovsky et al. (2002) , and it gave rise to the development of the lidar-radiometer synergetic algorithms (e. g. Chaikovsky et al. , 2004a, b). Later, in 2012 under the ACTRIS Research Infrastructure project within the European Union Seventh Framework Programme, the algorithm and software package, named LIRIC (LIdar-Radiometer Inversion Code), was developed for processing data of EARLINET measurements. LIRIC is based on processing co-located lidar and radiometer measurements by using a two-step sequential inversion. First, the radiometer data was processed according to the standard AERONET inversion algorithm. Then, first-step results are used as a priori constrains on aerosol properties for lidar data processing.
A. Chaikovsky et al. : Lidar-Radiometer Inversion Code (LIRIC) desirable to perform the lidar sounding from the lowest possible altitude. Likewise, the contribution of the bottom layer (which is not observed by lidar) to the columnar optical parameters must be small enough to be modelled by a homogeneous layer with the same aerosol parameters as at the lowest level of lidar sounding. In practice, the lower sounding limit for most of the lidar measurements in EARLINET is about 200 m or more that can be too high especially for low boundary layers in winter seasons. It should be decreased in winter to compensate reduction of the boundary layer height. Therefore, lidar measurements in the lower layer have to be carried out by a second, complementary receiving system with smaller objective and larger field of view or by sounding the atmosphere along a slant trajectory.
The algorithm is divided into several rather independent modules to provide flexibility of the software package. Module 1 (preprocessing of lidar data) creates a set of smoothed and normalized lidar signals, L * , covariance matrix, L , and setting parameters (type of lidar measurement, sounding wavelength, geographical coordinates of lidar station and date of measurement, etc. ) for modelling aerosol and molecular layers. Module 2 (recalculation of radiometer data) estimates columnar parameters of the aerosol model for lidar sounding wavelengths. Level 1. 5 or Level 2. 0 AERONET data are acceptable as input data in LIRIC. (These data are inputs to Module 2). Initial profiles of the aerosol-mode concentrations, c 0 k (h), as well as molecular (Rayleigh) extinction, σ r (λ, h), and molecular backscatter coefficients, β r (λ, h), are generated by Module 3 (atmospheric model).
The algorithms to determine the Lagrange multipliers by finding a reasonable compromise between the solution quality and the closeness of the measured and model signals are described in  Hansen (2001) ,  Vogel (2002) , and  Doicu et al. (2010). The set of Lagrange multipliers is provided to LIRIC's users along with software package. However, we do not consider this set as the ultimate one, and we allow it to be modified to meet user's specifications. The final step of the retrieval procedure is calculation of the concentration profiles c k (h i ) for each aerosol mode. Initial approximations c 0 k (h i ) are set and stepwise improved to provide the minimum of the objective function (Eq. 25). Increments are calculated by means of the Levenberg-Marquardt method  (Levenberg, 1944; Marquardt, 1963).
Program package for processing combined lidar and radiometer data Figure  2  shows the structure of the software package that implements the LIRIC algorithm. A set of specific programs are joined in three sub-packages. The sub-package LiOpt implements module (2) of the LIRIC algorithm (Fig. 1 ), which provides preprocessing of the AERONET retrieval products. Program AERLID recalculates the columnar optical characteristics for the lidar sounding wavelengths, including the elements of the scattering matrices for the spherical and non-spherical particles as well as for fine and coarse aerosol modes. Then, this code transfers data to the radiometer database. The preprocessing of lidar data is carried out by the Sig-nalSuite sub-package. It contains several programs. Among them are the following: -ULIS -an operational program that provides measurement procedures and record of raw lidar data to Microsoft ACCESS database; -nc2mdb -a program to convert EARLINET standard raw-lidar nc (network common data form) files into mdb (database file used by Microsoft Access) files to process by LIRIC; -Synthesizer -a program to average the series of lidar signals, converts the profiles to the optimal altitude scale and, then, "glues" signals (i.
e. synthesizes single signal) for the upper and lower troposphere, which were measured with different receiving systems, as well as provides the "dead-time" correction, i. e. the correction for the finite time resolution of the photo-counting system; -Tropoexport -a program to calculate a normalized smoothed lidar signal and its variance, and generates molecular and aerosol atmospheric models; this program aims at implementing modules 2 and 3 of the algorithm. Finally, the main sub-package "ProfileRetriever" implements the LIRIC inversion procedure. The program "ConcentRetriever" retrieves profiles c k,m V (h) of the aerosol mode concentrations and writes data down to ACCESS database, DBprocessed. The module "inversion setting & errors modelling" generates a set of noise-corrupted input data files by adding white noise and amplitude distortions to the initial lidar signals and perturbing aerosol model parameters retrieved from radiometer measurements in order to provide the error sensitivity analysis.
Verification of LIRIC program package: EARLI09 intercomparison experiment EARLI09 intercomparison experiment was held in May 2009 at Leibniz Institute for Tropospheric Research in Leipzig, Germany  (Wandinger et al. , 2015). This campaign provided an excellent opportunity to validate the LRS technique for network measurements. The results of the LIRIC data processing for simultaneous measurements by seven lidars of different scientific teams on 25 May 2009 in Leipzig were compared. Total optical depth distribution (Fig. 3a ) and backtrajectory analysis (Fig. 3b ) indicates that LRS measurements were carried out during the Saharan dust event in the Leipzig region and the dust was transported in the layer above 2 km. the same AERONET information, as well as their root mean square deviations and relative deviations for the two types of input data set, namely, with and without depolarization measurements.
6. 1. 2 ), and their rms-deviations, rms_dev(1) and rms_dev(2). Profiles were retrieved from the data measured in Lille, 19 May 2010, 09:17-09:58 UTC. Profiles D(1) and rms_dev(1) are the results of the direct calculation of depolarization ratio and their rmsdeviations from lidar measurements, as well as D(2) and rms_ev(2) were calculated from retrieved aerosol mode concentrations, c k (h). A mixture of spherical and non-spherical particles constitutes the aerosol layer at the height of about 2000 m. The profile of particle depolarization ratio at 532 nm and its deviation have been calculated from the retrieved aerosol mode It should be noted that the lidar measurements included additional calibration measurement that was not used by the retrieval procedure. Profiles D(1) and D(2) show rather close agreement magnitude and vertical structure that confirm the efficiency of the aerosol modelling used in this study.
Discussion and conclusions The active process of dissemination of the LIRIC in EAR-LINET started in 2012. Nowadays, 11 EARLINET teams participate in implementation of LRS technique (see Fig. 13 ). Retrieval of the aerosol parameters from the LRS measurements is an "ill-posed" inverse problem, and its solution should be tested on stability to the measurement errors and variations of the regularization parameters, which are set by the module "Inversion setting and errors moduling" of the software package (Fig. 2 ). Results of the EARLI09 intercomparison experiment presented in Sect. 6. 1 demonstrate rather small scatter in c k (h) profiles that were retrieved from the data of different lidar systems with significantly corrupted input lidar signals and big uncertainties of the aerosol lidar ratio.
, 2013)  and anthropogenic pollution  (Granados-Muñoz et al. , 2014). LIRIC has become a tool for validation of the modelling of aerosol transport in atmosphere  (Binietoglou et al. , 2015; Granados-Muñoz et al. , 2015b). EARLINET teams form the data-base of the results of combined lidar and radiometer sounding. The list of lidar teams that take advantage of LIRIC is still expanding. The LIRIC software package is open and distributed both within the EARLINET community and beyond it. The EARLINET teams provide continuous improvement of the software and cooperate on the implementation of the LRS measurements at new sites.
Measurement errors Optical signals, detected by the lidar data acquisition system consists of backscatter P * j,i and background B * j components. A suitable algorithm for estimating the measurement errors is described by  Slesar et al. (2013 Slesar et al. ( , 2015)). Regardless of the type of the photo-receiving sensor, three factors determine the measurement errors: -non-linearity of the recording channel, which consist of nonlinearity of the photodetector and electronic units; -"non-synchronous" noise (non-correlated with the sounding pulse); -"synchronized" noise (correlated with the sounding pulse). Non-linearity of a receiving channel basically originates from saturation of an output signal at high incident light because of photo-sensor or electronic unit limitations. Likewise, deviations of an amplifier gain cause linear distortions of the detecting signal within the working range of photoreceiving module.
Acknowledgements. The financial support by the  European Union 's  Horizon 2020 research and innovation programme  ( ACTRIS-2 , grant agreement no. 654109 ) is gratefully acknowledged. The background of LIRIC algorithm and software was developed under the  ACTRIS Research Infrastructure  project, grant agreement no. 262254 , within the  European Union Seventh Framework Programme , which financial support is gratefully acknowledged. The authors are very thankful to AERONET community for establishing and maintaining the radiometer network and its information system that provides the excellent tools for aerosol studying and became the base for our work. The authors also thank  Tom L. Kucsera  ( GESTAR/USRA ) for back trajectories available at the http://aeronet. gsfc. nasa. govwebsite. The authors gratefully acknowledge the  Naval Research Laboratory (NRL)  for the provision of the NAAPS results and the  NOAA Air Resources Laboratory (ARL)  for the provision of the HYSPLIT transport and dispersion model used in this publication.
Linked Data initiatives have fostered the publication of more than one thousand of datasets in the Linking Open Data (LOD) cloud from a large variety of domains, e. g. , Life Sciences, Media, and Government. Albeit large in volume, Linked Data is essentially read-only and most collaborative tasks of cleaning, enriching, and reasoning are not dynamically available. Collaboration between data producers and consumers is essential for overcoming these limitations, and for fostering the evolution of the LOD cloud into a more participative and collaborative data space. In this paper, we describe the role that collaborative infrastructures can play in creating and maintaining Linked Data, and the benefits of exploiting knowledge represented in ontologies as well as the main features of Semantic Web technologies to effectively assess the LOD cloud's evolution.
Introduction Over the last decade, there has been a rapid increase in the numbers of users signing up to be part of Web-based social networks. Hundreds of millions of new members are now joining the major services each year. A large amount of content is being shared on these networks, and around tens of billions of content items are shared each month. In parallel, similar collaborative spaces are being leveraged in both private intranets and enterprise environments; these collaborative spaces have features mirroring those on the public Web. With this growth in usage and data being generated, there are many opportunities to discover the knowledge that is often inherent but somewhat hidden in these networks. Web mining techniques are being used to derive this hidden knowledge. In addition, Semantic Web technologies, including Linked Data initiatives to connect previously disconnected datasets, are making it possible to connect data from across various social spaces through common representations and agreed upon terms for people, content items, etc.
One of the largest semantic wikis is Semantic MediaWiki, based on the popular MediaWiki system. In the context of domain-specific applications, Kaliszyk and Urban  [5]  provide an overview of collaborative systems for collecting and sharing mathematical knowledge. One of the problems to be solved by these systems is the visualisation of formal proofs for people, while providing assistance with the translation of informal statements into formal mathematics. Different problems regarding disagreements between mathematicians, e. g. , in terms of the lack of a unique formal language and axiomatic systems, have complicated the development of such formalisation frameworks, necessitating collaborative work like the one implemented in wiki applications such as Wikipedia. These collaborative systems or formal wikis allow for formal verification, the implementation of formal libraries to provide a unified terminology and theorems, versioning of the proofs, semantic resolution of ambiguities, collaborative editing, and other semantic tools.
Exploiting Semantics in Collaborative Spaces Semantics encoded in ontologies and Linked Data sets can have a positive impact on the behaviour of applications built on top of collaborative spaces. The main challenge to be addressed is how to efficiently extract the knowledge represented in existing Linked Data sets and effectively use this knowledge to enhance existing collaborative spaces. Improvements can be of diverse types, e. g. , inferred facts from DBpedia can be used for quality assessment of collaborative space contents; thus, diverse methods to uncover data quality problems should be defined. Additionally, queries against these Linked Data sets have to be crafted in such a way that query answers will provide the insights to discover the missing or faulty content in the collaborative space.
If Semantic Web technologies have allowed many data providers to publish datasets with their own ontologies, consuming these datasets requires aligning different ontologies and performing entity matching. The Semantic Web community has developed sophisticated tools to tackle this problem, but a generic automatic reliable solution is still an open research problem. Human-machine collaboration can be effective to promote reuse of contextual trusted ontology mappings. This special issue also includes work by Bottali et al. [2]  on Okkam, a collaborative tool designed to share ontology mappings. Okkam allows users to disagree on mappings, but also to rank mappings according to their contextual sharedness. Consequently, Okkam allows users to quickly find mappings adapted to their usage, knowing their level of agreement for a given particular context.
Acknowledgements. Pascal Molli has been supported by the  ANR   Kolflow Project  ( ANR-10-CORD-0021 ),  University of Nantes. John G. Breslin  has been supported by  Science Foundation Ireland (SFI)  under Grant Number  SFI/12/RC/2289  (Insight). Maria-Esther Vidal  thanks the  DID-USB (Decanato de Investigación y Desarrollo de la Universidad Simón Bolívar ).