dataesr/scientific-paragraphs-categorization huggingface dataset:
Related Work To the best of our knowledge, there are only few solutions that have dealt with NoSQL databases conceptual modeling. Chevalier et al. (Chevalier, 2015)  defined a set of rules to map a multidimensional model into two NoSQL models: column-oriented and document-oriented. The links between facts and dimensions have been converted using imbrications. Although the transformation process proposed by authors start from a conceptual level (multidimensional model), this specific model is different from the UML standard; it contains facts, dimensions and one type of links only. Other studies investigate the process of transforming relational databases into a NoSQL model. Li  (Li, 2010)  proposed an approach for transforming a relational database into HBase; the relationships between tables (foreign keys) are converted by adding new columns-families that contain references.
Vajk et al. (Vajk, 2013)  propose a mapping from a relational model to document-oriented model using MongoDB. However, the relational model does not present the semantic richness of UML class diagram (especially through the several types of relationships that exist between classes: association, aggregation, composition, generalization, etc. ). Only few works have presented approaches to implement UML conceptual models in NoSQL databases. Li et al. (Li, 2014)  propose a MDA-based approach to transform UML class diagram into HBase. After building the meta-models of UML class diagram and HBase, the authors have proposed mapping rules to realize the transformation from the conceptual level to the physical level. These rules are applicable to HBase, only. Gwendal et al. (Daniel, 2016)  describe the mapping between UML conceptual models and graph databases via an intermediate graph meta-model.
Target: Column-Oriented Logical Model A column-oriented database consists of a set of tables. Each table is a container of a collection of rows with variable length; each row is identified by a unique identifier called "Row-Key". By default, we store the database in a single table that we call T. T is comprised of a set of column-families {f1,…fp}. The schema of a column-family f is a tuple (N, COL, Id) where: f. N is the column-family name; f. COL = {col 1 ,…,col q } is a set of q columns. The schema of each column is a triplet (N, T, TS) where "col. N" is the column name, "col. T" the column type and "col. TS" the TimeStamp. In this paper, we do not consider the TimeStamp parameter; F. Id is a unique column-family identifier whose type is called "Row-Key". We present these concepts through the meta-model of figure  4.
Transformation Rules For each transformation rule, we try to justify our choice based on the features and limits of the column-oriented storage. Note that at this stage of our work, we do not take into account the optimized solutions obtained through analysis queries.  R1: Package to Table NoSQL systems are not designed for the purpose of joining data from multiple tables. Even though, it is possible to support the join operation in some NoSQL systems, it's still a complicated process  (Cattell, 2011). Therefore, we chose to store the database (the package) in a single target table.  R2: Class to Column-Family All the attributes of a class are grouped into the same column-family. Indeed, all the data in a single column-family will be stored in the same file on the disk. That will enable the possibility of processing large amount of data faster and more cost effectively  (Abadi, 2008 ).
This rule generalizes the process of transforming association links; it applies to any association regardless of its degree (binary, ternary, quaternary, etc. ) and cardinalities.  R5: Composition/aggregation relationship Composition relationship can be represented as references or nested data. As the columns oriented databases don't support nested data (column containing other columns), we use references to tackle this issue. Therefore, each composition/aggregation relationship is transformed by creating a new column whose type is called "set Row-Key" in the column-family corresponding to the container class; this column is used to reference the column-family (ies) corresponding to the contained class (es). We note that number of values that will be contained in this new column depends on the maximum cardinality of composition relationship.
Conceptual PIM to Logical PIM Transformation Before proceeding to the implementation of the transformation rules, first, we created Ecore metamodels corresponding to the source (figure  3 ) and the target (figure  4 ). The next step is to create an XMI instance of the source meta-model (Figure  6 ). In parallel, we used Operational QVT plugin provided within EMF to implement the QVT rules. Finally, we tested the transformation by running the QVT script. The execution of this script transforms the UML class diagram (figure  6 ) into the NoSQL table corresponding to the logical model (figure  7 ).
Logical PIM to PSMs Transformation Choosing column-oriented model at logical level does not imply a specific target platform. Consequently, several implementation platforms could be used. In this paper, we choose to consider two PSMs that correspond to HBase and Cassandra platforms.  Cassandra PSM Cassandra is a columns-oriented database. It consists of one data container named Keyspace. The Keyspace is associated to a set of columns families; each column-family is identified by a PrimaryKey and contains a set of columns that must be declared up front at schema definition time. We note that the concepts of "Table " and "Row-Key" used at the logical level will be replaced respectively by "Keyspace" and 'PrimaryKey".  HBase PSM HBase is a column-oriented database built on top of Hadoop  (Grover, 2015).
CONCLUSION AND PERSPECTIVES In this paper we have presented a MDA-based approach to implement UML conceptual model describing Big Data in column-oriented NoSQL systems. Our approach consists of a chain of transformations that generate a column-oriented logical model independent of a particular NoSQL platform; this independence makes it easier to implement conceptual models into several columnoriented databases such as Cassandra and HBase regardless of their specific technical details. As future work, we plan to complete our transformation process and propose a mapping for OCL expressions defined in the conceptual model; queries languages provided by NoSQL databases (such as CQL, HiveQL …) could be used for this. Another ongoing work concerns the validation of the proposed transformation process on scientific medical applications.
Identification of ePKs and aPKs in the C. gigas genome The identification of eukaryotic protein kinases (ePKs) and atypical protein kinases (aPKs) was based on predicted proteins of Crassostrea gigas, as it was done similarly in Caenorhabditis elegans  [11] , microsporidia species  [19]  and Dictyostelium  [30]. C. gigas predicted proteins were retrieved from version 9 of the complete genome and downloaded from NCBI (http://www. ncbi. nlm. nih. gov/bioproject/PRJNA70283) [5]. Initially, putative protein kinases were detected using a Hidden Markov Model (HMM) profile based on known ePKs, in order to screen the 26,086 predicted C. gigas proteins. To assess the first selection, we also identified protein kinases using ePKs PFAM (PF00069)  [31]. For the final catalog, we compared the annotation as protein kinase (using HMM and PFAM) with the automatic annotation of the proteome  [5]  using an in-house non-stringent E-value cut-off of 10 -2.
Phylogenetic analysis A phylogenetic tree was built using the C. gigas ePK domains using the neighbor joining method. Sequences analysis was performed using the maximum likelihood method of PhyML  [33]. For the RGC group, a tree was built from protein sequences aligned against the entire PF00069 domain with hmmalign  [34] , manually refined using Jalview  [35] , then significant blocks were selected. The oyster RGC phylogenetic tree was inferred with the maximum likelihood method of PhyML with 1000 bootstrap replicates, using the complete set of human, urchin, nematode, fruit fly genes encoding RGC kinases, and insulin-related peptide receptor (EKC21734. 1) as outgroup. Both trees were visualized using the Figtree program (http://tree. bio. ed. ac. uk/software/figtree).
Data mining of available RNA-seq data for C. gigas Information regarding down and up-regulation of gene expression was extracted from C. gigas RNA-seq data (49 bp single-end Illumina reads) available on the NCBI website  [5]. Gene expression levels were measured by RPKM (reads per kilobase per million mapped reads)  [5]. To minimize the influence of sequencing depth between samples, the total read number was normalized by multiplying a normalization factor  [36]. This strategy introduces a scaling factor called Trimmed Mean of M-values (TMM), which aims at representing the "global foldchange"  [36]. The RNA-seq data used were obtained from several developmental stages: egg, two cell stage, four cell stage, morula, blastula, gastrula, trochophore, D-larva, umbo larva, pediveliger, and two additional datasets consisting of spat (day 22 post fertilization) and juveniles (day 215 post fertilization)  [5].
Expression of protein kinases under environmental stressors We performed an in silico analysis to identify down-and up-regulated kinases genes in oysters subjected to 8 different potential sources of stress: temperature, salinity, exposure to air, and to five metals (cadmium, copper, mercury, lead and zinc)  [5]. Among the 371 genes encoding protein kinases, we found that 177 genes (48% of the C. gigas kinome) were differentially expressed under at least one factor relative to the compared condition (Table  3 ). This corresponds to around 3% of the 5,844 genes identified from transcriptome datasets that were modulated (up or down-regulated) by these factors  [5]. Exposure to air is a serious stressor, causing hypoxia and leading to a decrease of ATP concentration in the hemolymph  [116]. Among the different potential sources of stress, air exposure affected the expression of the largest number of protein kinase genes  (110) , corresponding to 30% of the oyster kinome (Table  3 ).
Conclusion The characterization of the C. gigas kinome and the identification of differentially expressed protein kinases was made possible thanks to the recently published oyster genome and a rich transcriptome datasets. The C. gigas kinome might have an original evolutionary position, because the number, classification and distribution of protein kinases is closer to deuterostomes (sea urchin and human) than to protostomes (nematode and fly). Kinase gene redundancy in the C. gigas kinome concerns about 30% of the genes. The lack of redundant protein kinases isoforms in several groups suggests that the Pacific oyster C. gigas could be a good species for the development of functional research dealing with protein kinases, for example, to study developmental processes, given that most protein kinases are expressed during the first stages of embryogenesis.
Acknowledgments The authors are grateful to  Fanny Marquer  for her help in data mining of the Crassostrea gigas genome. We thank  Arnaud Huvet  and  Fabrice Pernet  for helpful discussions. We thank  Helen McCombie  of the  Brest University Translation Bureau (BTU)  for her English editing services. This works contributes to the  'Laboratoire d'Excellence '  LabexMER  ( ANR-10-LABX-19 ). We additionally thank  Dr. Jessica Moss Small  from the  Aquaculture Genetics and Breeding Technology Center of the Virginia Institute of Marine Science, College of William & Mary (VA, USA)  for her help with editing the English.
During the Asian summer monsoon, the circulation in the upper troposphere/lower stratosphere (UTLS) is dominated by the Asian monsoon anticyclone (AMA). Pollutants convectively uplifted to the upper troposphere are trapped within this anticyclonic circulation that extends from the Pacific Ocean to the Eastern Mediterranean basin. Among the uplifted pollutants are ozone (O 3 ) and its precursors, such as carbon monoxide (CO) and nitrogen oxides (NO x ). Many studies based on global modeling and satellite data have documented the source regions and transport pathways of primary pollutants (CO, HCN) into the AMA. Here, we aim to quantify the O 3 budget by taking into consideration anthropogenic and natural sources. We first use CO and O 3 data from the MetOp-A/IASI sensor to document their tropospheric distributions over Asia, taking advantage of the useful information they provide on the vertical dimension.
These satellite data are used together with MOZAIC tropospheric profiles recorded in India to validate the distributions simulated by the global GEOS-Chem chemistry transport model. Over the Asian region, UTLS monthly CO and O 3 distributions from IASI and GEOS-Chem display the same large-scale features. UTLS CO columns from GEOS-Chem are in agreement with IASI, with a low bias of 11 ± 9 % and a correlation coefficient of 0. 70. For O 3 , the model underestimates IASI UTLS columns over Asia by 14 ± 26 % but the correlation between both is high (0. 94). GEOS-Chem is further used to quantify the CO and O 3 budget through sensitivity simulations. For CO, these simulations confirm that South Asian anthropogenic emissions have a more important impact on enhanced concentrations within the AMA (∼ 25 ppbv) than East Asian emissions (∼ 10 ppbv).
During the October-May period, the subtropical westerly jet (SWJ) is located between 25 and 30 • N, promoting deep STE over the southern Himalayas. During the ASM, the SWJ is pushed northwards of the Tibetan Plateau by the AMA, and STE to the central Himalayas are blocked. Previous studies  (Kunhikrishnan et al. , 2004 (Kunhikrishnan et al. , , 2006 ) have therefore dealt with the O 3 budget in the Indian troposphere, but the O 3 budget of the AMA has not yet been addressed in detail. In particular, it is not yet known to what extent the different NO x sources are responsible for an increase in the O 3 concentrations within this upper-level large-scale circulation characterized by rather low O 3 concentrations. Furthermore, satellite data from the IASI sensor have been available since 2007 but they have not yet been used to document the ASM.
These data are complementary to MLS data that have been extensively used in the region  (Park et al. , 2009; Barret et al. , 2008)  because, although they have a coarse vertical resolution, they cover both the troposphere and the UTLS. Here, we aim to characterize the impact of STE and of NO x emissions from the different sources and regions on the O 3 budget in the South Asian UTLS during the monsoon season. We also use CO as a tracer of surface pollution that brings direct information about the origin of the air masses. We focus on the AMA in order to determine the role of its dynamical structure and isolation upon the regional upper-tropospheric O 3 budget. The second section of this paper is dedicated to the description of the observations (IASI and MOZAIC) and of the chemistry transport model GEOS-Chem (GC) that are used in our study.
In Sect. 3, we make use of IASI and MOZAIC O 3 and CO data to validate their distributions simulated by the GC model over Asia. In Sect. 4, we discuss the dynamical and chemical characteristics of the AMA and the role of convection in controlling the distributions of CO and O 3 during the ASM. Finally, in Sect. 5 the model is used to determine the impact of regional pollution uplift, LiNO x and STE upon the CO and O 3 concentrations within this upper-level AMA. Section 6 provides a summary and conclusions.
, 2008; Barret et al. , 2011)  and CO  (George et al. , 2009; De Wachter et al. , 2012). In the present study, we use data provided by the Software for a Fast Retrieval of IASI Data (SOFRID) presented in  Barret et al. (2011)  for O 3 and in De  Wachter et al. (2012)  for CO. In their study,  Barret et al. (2011)  showed that IASI enabled the independent retrieval of O 3 in the lower-middle troposphere (surface-225 hPa) and in the UTLS (225-70 hPa) in the tropics. Moreover, comparisons of SOFRID O 3 data with data from O 3 sondes have shown that the agreement is especially good for the UTLS column (225-70 hPa) with correlation coefficients of 0. 8 (resp. 0. 95) and biases of 17. 5 ± 20 % (resp. 10 ± 10 %) in  Dufour et al. (2012)  (resp. Barret et al. , 2011). The ability of SOFRID to capture O 3 daily variations in the tropical upper troposphere has also been demonstrated and validated against MOZAIC cruise data in  Tocquer et al.
MOZAIC O 3 and CO observations The MOZAIC program was set up to provide routine measurements of reactive gases on long-distance commercial aircraft  (Marenco et al. , 1998). In 1994, five airliners were equipped with O 3 and relative humidity instruments, and a CO analyzer was successfully added in December 2001. MOZAIC aircraft operations have stopped at the end of 2014. However, since 2011, a new set of instruments has been flying on commercial in-service aircraft in the frame of the IAGOS (In-service Aircraft for a Global Observing System) Research Infrastructure. IAGOS builds on the scien-tific and technological experience gained within the two predecessors programs: MOZAIC and CARIBIC (http://www. caribic-atmospheric. com). The MOZAIC and IAGOS data follow the same calibration and quality control procedures and are freely accessible for scientific use at http://www.
iagos. fr. The MOZAIC and IAGOS data are freely accessible for scientific use at http://www. iagos. org. These measurements are carried out with a 30 (4) s response time corresponding to a resolution of about 7 (1) km at cruise altitude and a vertical resolution of about 300 (30) m during ascents and descents, with a reported precision of ±5 (1) ppbv for CO  (Nedelec et al. , 2003) (O 3 ; Thouret et al. , 1998). For the present study, we used MOZAIC profiles measured at take off and landing near Hyderabad (17. 2 • N, 78. 3 • E) in central India from May to October 2009. CO data were available for each month but for O 3 no data were produced in September and October following an instrument failure. For both gases, we could use from 10 to 16 profiles for each month with available data.
(2000). STE are diagnosed with tagged O 3 simulations including a stratospheric O 3 tracer. Convection is parameterized with the relaxed Arakawa-Schubert scheme  (Moorthi and Suarez, 1992)  in GEOS-5. Turbulent mixing in the planetary boundary layer is described in  Wu et al. (2007). The simulations are performed on a regular 2 • × 2. 5 • horizontal grid and on 47 hybrid sigma-pressure levels from the surface up to 0. 01 hPa. Emissions from biomass burning (BB) come from the monthly Global Fire Emissions Database version 2 (GFED-v2) (van der  Werf et al. , 2010). The global anthropogenic emissions are taken from the EDGAR v. 4. 1 inventory, which provides annual global emissions of greenhouse gases and ozone precursors on a 1 • × 1 • horizontal grid, but typically the anthropogenic emissions are overwritten by data from various regional inventories.
Data availability The IASI-SOFRID CO and O 3 research data used in this publication are publicly available at http://thredds. sedoo. fr/ iasi-sofrid-o3-co/. The MOZAIC-IAGOS data are available via http://www. iagos. fr/. IASI L1c and L2-EUMETSAT data have been downloaded from the Ether French atmospheric database (http://ether. ipsl. jussieu. fr). MERRA data used in this study were provided by the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center through the NASA GES DISC online archive.
Acknowledgments We acknowledge the support from the  research federation FRABio  ( CNRS FR3688 ), " Structural and Functional Biochemistry of Biomolecular Assemblies ". JdR acknowledges funding from the  Nord-Pas-de-Calais Regional Council  and  M de Wergifosse  for fruitful discussion about ONIOM method. RB and MFL acknowledge financial support from the  French Agence Nationale de Recherche , project " Fluctuations in Structured Coulomb Fluids ", grant number  ANR-12-BSV5-0009-01. MFL is grateful to the  French Agence Nationale de Recherche  for his grant number  ANR-13-BSV8-0002-0.
There is increasing recognition that the ICT4S community needs to take a role in both developing education and conducting research on how to educate people in using ICT to make urban areas more sustainable. In this paper we explore the question how can a Massive Open Online Course (MOOC) be used to engage citizens in learning about smart cities and teach them how to co-create a smart cities project in their community. We discuss our experiences of designing and delivering a Smart Cities MOOC, a multidisciplinary course targeted at an international audience and built upon the pedagogical approach of social learning that is supported through FutureLearn, the MOOC platform on which the course is delivered. We use data gathered from the first presentation of the course to evaluate the motivations, demographics, online participation and experiences of our learners.
Whilst the possibility of using MOOCs to support citizen learning and participation in smart cities has received some attention in the literature  [17, 18] , the opportunity is still poorly understood. This paper contributes to the ICT4S community by providing an empirical study that evaluates the design and delivery of a Smart Cities MOOC  [19] , which been developed as part of MK:Smart  [20] , a large smart city initiative led by the Open University (OU). The paper uses data gathered from the first presentation to evaluate the success of the course and provides insights into the demographic profile and motivations of the learners, as well as their online participation and feedback on the course materials. An initial analysis of social learning interactions also reveals insights for the ICT4S community into citizens' views on smart cities.
Week 2 sets the scene for learning objective 2, focused around how to co-create a smart cities project. In this week learners explore the role of citizens considering issues such as the needs of different groups, engagement approaches and trust. They are also introduced to design thinking (see Fig 1. ), a process that they follow to co-create their projects, with the course taking them as far as the prototyping stage. At the end of Weeks 2 and 4 there are quizzes to check understanding of the core concepts that have been taught. Weeks 3 and 4 introduce learners to a wide variety of smart city initiatives and is focused on the ideate stage of the design thinking process. They explore the role of smart infrastructure and sensors in cities, learn about the role of data, debate the challenges of privacy and security, and look the smart city market, finance and also consider the data economy, civic hacking and digital social innovation.
A. Methodology The first MOOC presentation ran from September 28th 2015 for 6 weeks and 8005 people enrolled on the course. Five further presentations are scheduled between 2016 and spring 2017. In order to evaluate the design and delivery of the Smart Cities MOOC we use data analytics and qualitative data sets from the first presentation to understand who are learners are, their motivations, demographics, how they used the course and to ascertain their feedback on the learning materials and to identify what issues concerned them about smart cities. The FutureLearn platform has a statistics dashboard for the MOOC that presents course run measures that are updated daily and include: • Joiners -people enrolled on the course • Learners -joiners who start to use the course • Fully participating learners -completed at least 50% of the available steps • Social learners -posted at least one comment Weekly course run measures are also available such as the number of learners visiting steps, completed steps and comments, as well as downloadable datasets which include information on enrolments, step activity and comments.