dataesr/scientific-paragraphs-categorization huggingface dataset:
The first set represents 23 bacterial species isolated from human stool samples. The second set represents 23 closely related species from other environments (Table  1 , Fig  1 ). We refer to these sets of genomes as "gut" and "non-gut", respectively. The majority of the selected species belongs to the class Clostridia, order Clostridiales (19 gut species and 14 nongut species). To evaluate the functional composition of the species studied, we attributed the predicted protein complement of each genome to orthologous groups (bactNOGs) using the eggNOG v. 3. 0 database  [18]. Orthologous groups of proteins have proven useful for functional analyses, as orthologs tend to have equivalent functions  [20]. The fraction of the proteins that could be attributed to bactNOGs varied from 48 to 81% depending on the species (Table  2 ), and was generally higher for non-gut species than for gut species (71% vs 60%, respectively, on an average).
A closer look at the catalog of bactNOGs and their functional descriptions revealed that these observations could (in part) be explained by the fact that many functions are represented by several bactNOGs, i. e. several bactNOGs carry identical function descriptions. This is due to All bacteria belong to the Firmicutes phylum, the sequencing status of their respective genomes is "complete" and sequences are publicly available (GOLD database: http://www. genomesonline. org/cgi-bin/GOLD/index. cgi). doi:10. 1371/journal. pone. 0159030. t001 the way the non-supervised orthologous groups (NOGs) were constructed  [18] , where the authors chose for a high resolution, i. e. small precisely defined NOGs containing very similar proteins, to improve accuracy with the consequence that less similar proteins with the same function are attributed to different bactNOGs.
We therefore decided to perform a second comparison of our sets of gut and non-gut genomes at the level of "groups of bactNOGs" where we considered bactNOGs with identical function descriptions as one group (in our dataset, a "group of bactNOGs" may contain from 1 to 606 bactNOGs, the latter representing "transcriptional regulator proteins"). By doing so, all the information on bactNOGs that had no description, accounting for 31. 2% of the bactNOGs in our dataset, was lost. Other methods of protein clustering and protein family construction that are out of the scope of this study may be instructive in revealing the potential importance of these unclassified bactNOGs beyond the level of the single bactNOG comparisons described above  [12, 28, 29]. 166 functional "groups of bactNOGs" were shared among all the genomes in the two sets (data not shown).
These organism have both MotB and MotD. With MotC (a MotA homolog) forms the ion channels that couple flagellar rotation to proton/sodium motive force across the membrane and forms the stator elements of the rotary flagellar machine. Either MotAB or MotCD is sufficient for swimming, but both are necessary for swarming motility protein PyrR uracil phosphoribosyltransferase; regulates pyrimidine biosynthesis by binding to the mRNA of the pyr genes, also has been shown to have uracil phosphoribosyltransferase activity [RNA chaperone that binds small regulatory RNA (sRNAs) and mRNAs to facilitate mRNA translational regulation in response to envelope stress, environmental stress and changes in metabolite concentrations. Also binds with high specificity to tRNAs protein functional descriptions were grouped in our dataset.
Groups of bactNOGs are presented for which the number of non-gut genomes where bactNOGs are represented exceeds the number of gut genomes where bactNOGs are represented by at least 12. Abundance, % of species where bactNOG is represented, in the eggNOG v. 3. 0 database (shown only for functional groups containing one bactNOG), in 23 gut genomes (GUT), or in 23 non-gut genomes (NONGUT), respectively. NA-non-assigned. doi:10. 1371/journal. pone. 0159030. t004 set of gut bacteria as opposed to 4. 3% (SEM 0. 3) for the non-gut bacteria (not shown). Interestingly, this difference appears to be explained by the presence of relatively low and stable numbers of secreted proteins across differently sized gut bacterial genomes, while in the closely related non-gut bacteria the number of secreted proteins clearly correlates with the total number of proteins encoded in the genome (Fig 5).
(Continued) CYT, MEMB, predicted numbers of potentially surface exposed, secreted, cytoplasmic and membrane proteins, respectively, per genome. Total, total number of proteins encoded per genome. eggNOG and eggNOG %, number and % of proteins, respectively, assigned to bactNOGs in the eggNOG v. 3. 0 database. Gut bacteria are grouped in the upper part of the table, non-gut bacteria in the lower part.
Abundance, % of species where bactNOG is represented, in the eggNOG v. 3. 0 database (shown only for functional groups containing one bactNOG), in 23 "gut" genomes (GUT), or in 23 "non-gut" genomes (NONGUT), respectively. NA-non-assigned. doi:10. 1371/journal. pone. 0159030. t003.
Open Science has gained a lot of attention not only within the academic community but also among policy-makers. Some international publishers have been active in moving towards open access publications and research data, but, overall, modest results have been achieved so far. In this context, the digital piracy engines emerge as vital actors in disseminating and determining the impact of research. This study examines the Sci-Hub downloads data in order to uncover patterns of piracy in European Studies research. We identify journals and the subjects of articles that have been pirated the most. We also study the geographical distribution of download requests. The analysis reveals that the readers are mostly interested in subjects reflecting the current major European challenges, specifically populism and the economic crisis.
DATA We use the data comprising all download requests received by the Sci-Hub servers between October 2015 and February 2016  (Elbakyan and Bohannon 2016). This represents a total of 22,915,621 download requests. The data have been anonymised in order to protect the identity of the user. For this the IP addresses have been aggregated to the nearest city location. Thus the data contains the city and the country from where the download request was received. The data contains the Digital Object Identifier (DOI) of the article requested. There is no other information about the requested article. Therefore, identifying the articles from the ES field represents a challenge. Clearly, all ES articles cannot be identified. Therefore, we followed Babutsidze (2016) and proceeded as follows. We relied on information provided by UACES, the University Association for Contemporary European Studies; UACES provided the list of ES journals, which comprises 25 journals.
We identified the articles published in these six journals that were downloaded through Sci-Hub service between October 2015 and February 2016. To identify the articles from the selected set of journals, we analysed the DOI assignment procedures by publishers of each of the journals. We identified unique character strings contained by digital object identifiers for each of the six journals that allowed us to separate ES publications from the rest of the observations. This reduced the working dataset drastically to 2,390 observations. This represents only 0. 01 per cent of the whole dataset. Following Babutsidze (2016), before carrying out the analysis, we removed duplicate downloads from the raw data by  Bohannon (2016). The original data contains all page load requests received by Sci-Hub servers.
When the user refreshes the browser that is in the process of loading the article, the server registers an additional download request. If we had the original IP data, these kinds of downloads could have been perfectly screened out. However, given the anonymised data we had to work with download time -download location pair of variables -in order to identify duplicates. To screen out multiple records for one actual download, we identified groups of downloads for the same paper that occurred from the same city within five minutes of one another. For each of these identified groups we retained only one download in our final dataset. This eliminated 80 observations and left us with the final dataset of 2,310 downloads for 1,537 distinct papers.
For this we require the number of researchers engaged in ES in each of the countries. Such data is not readily available. However, we can proxy the number of ES researchers by the data on UACES membership. One of the 13 countries in Table  3 , Brazil, has no UACES members. The data on the remaining 12 countries is taken into account in the third column of Table  3. Financial background and the likelihood of the illegally downloaded scholarly articles are expected to be highly correlated. The well-established Northern European universities, for example, have sufficient financial resources to provide legal access to electronic resources to its staff and students. Moreover, these institutions have a larger number of ES courses and ES programmes  (Timus and Cebotari 2014; Timuș, Cebotari and Hosein 2016).
Subgraph isomorphism search is a basic task in querying graph data. It consists to find all embeddings of a query graph in a data graph. It is encountered in many real world applications that require the management of structural data such as bioinformatics and chemistry. However, Subgraph isomorphism search is an NPcomplete problem which is prohibitively expensive in both memory and time in massive graph databases. To tackle this problem, we propose a new approach based on concepts widely different from existing works. Our approach relies on a summarized representation of the graph database that minimizes both the amount space required to store data graphs and the processing time of querying them. Experimental results show that our approach performs well compared to the most efficient approach of the literature.
INTRODUCTION Graphs are naturally used to represent data in several domains and applications such as protein interactions, regions of images, 2D and 3D shape recognition, etc. Graphs are also a first class solution for massive data representation. In fact, graph databases enable us to build interesting models that map closely to the problem domain. In this context, querying graphs is a fundamental problem studied by a large research community. Subgraph isomorphism search is the basic type of graph queries. Given a query graph Q and a data graph G, the subgraph isomorphism problem is to find all embeddings of Q in G. However, this problem is NP-complete  (Garey and Johnson, 1979)  and the problem of finding practical solutions for massive graph databases is a challenge. Subgraph isomorphism knows a lot of works that consist in finding the best way to match a query graph with the data graphs.
The inexact approach knows a flourishing research activity in several application domains such as databases and pattern recognition relying on different tools such as genetic algorithms  (Khoo and Suganthan, 2001) , neural networks  (Micheli, 2009) , etc. Within this approach, we generally compute a distance between the graphs. This distance reflects the degree of similarity or dissimilarity of the graphs. We focus here on exact subgraph isomorphism search where existing algorithms can be classified within 3 categories: 1. Backtracking-based algorithms such as Ullman's  (Ullmann, 1976) , VF2  (Cordella et al. , 2004) , QuickSI  (Shang et al. , 2008) , GraphQL  (He and Singh, 2008) , GADDI  (Zhang et al. , 2009) , and SPath  (Zhao and Han, 2010). This approach constructs a space search tree whose internal nodes correspond to partial solutions and leaves correspond to embeddings.
, 2008) , SwiftIndex  (Shang et al. , 2008) , and C-Tree  (He and Singh, 2006). In this approach, indexes are used to minimize the number of candidate graphs in the database. Then, a subgraph isomorphism search is launched on the candidates. 3. Candidate Region selecting algorithms such as Turbo ISO  (Han et al. , 2013). In this approach, the idea is to target specified regions on the same graph for subgraph isomorphism search. These regions are selected according to the properties of the query. A candidate region for a query graph Q is a subgraph of the data graph G which may contain embeddings of the query graph. So, performing subgraph isomorphism search on all candidate regions will ensure that all embeddings can be obtained. However, minimizing the number of candidate regions and the size of each region is obviously important for faster matching.
SUBGRAPH SEARCH The subgraph search phase takes as inputs a query C (Q) and a set s = {m 1 , m 2 , • • • , m j } of modules that are likely to contain an embedding of the query. It returns all the embeddings of the query in these modules. An embedding is represented by a set P of pairs  (u, v) , where u is a query vertex and v is the data vertex that match u. Algorithm 2 shows all the details of this phase. For each vertex u in C (Q), Subgraph-Search first finds the set of candidate vertices C u from the vertices of the modules of the set s. A vertex v of the data graph matches u if it has the same label as u and all the neighbors of u are matched to neighbors of v. This is verified by a call to function IsJoinable (detailed in Algorithm 3). Given two vertices u ( from the query) and v (from the data graph) to be matched, function IsJoinable returns TRUE if the neighbors of vertex u are matched to neighbors of vertex v in the match P.
As in any backtracking-based algorithm, SubgraphSearch uses recursion to complete the partial match until it meets the query. When a match fails, the procedure backtrack to the preceding state by removing the match. Algorithm 2: SubgraphSearch. Data: A set of modules from the data graph s = {m 1 , m 2 , • • • , m j }, the compressed query C (Q) and a partial embedding P. Algorithm 3: Verify that two vertices to be matched have the same adjacency (IsJoinable). Result: All embeddings of Q in s. begin if |P| = |V (C (Q))| then Report P; else Choose a non matched vertex u from Leaves(m), m ∈ C (Q); C u ← { non matched v ∈ Leaves(m i ) such that m i ∈ s and ℓ(v) = ℓ(u) and IsJoinable(u, v, P)}; foreach v ∈ C u do P ← P ∪ {(u, v)}; SubgraphSearch(C (Q), Data: Two vertices u and v to be matched. Result: True is the the vertices have the same adjacency.
EXPERIMENTAL RESULTS We evaluate the execution time performance of our algorithm, Sum ISO , over different type of graphs and size of queries. We also compared it with the most efficient state of the art algorithm, called Turbo ISO and presented in  (Han et al. , 2013). We recall that Turbo ISO is itself compared to the other existing solutions in  (Han et al. , 2013)  and showed to be superior to them. We first describe the datasets used in the experiments, then we present our results.
Datasets We use the same datasets considered in  (Han et al. , 2013)  for proving the superiority of Turbo ISO against the other algorithm of the literature described in Section 1. These datasets are referred to as AIDS, NASA, and Human. Their description is as follows: • AIDS database: This dataset consists of graphs representing molecular compounds. It contains 10, 000 small graphs of 27 edges. The number of unique labels in AIDS is 51. We present a summary of these graph databases in Table  2. Besides the average number of vertices and edges of the graphs in the dataset, we also give the average compression rate of each dataset. Given a graph G and its compressed graph C (G), the compression rate of G is given by: CR  Graphs within the three datasets were preliminarily compressed using an extension of the algorithm proposed in  (Capelle et al.
, 2002; Habib et al. , 2004)  that computes the modular decomposition of a graph in linear time. So, we compress an input graph in O(n + m) time, where n is the number of vertices and m the number of edges of the graph. (G) = |E(C (G)|)| |E(G)| • 100%. To show the storage saving obtained by compressing the datasets, Table  3  reports the size on disk of each dataset before and after compression. We also provide the time necessary to compress each dataset. The experiments are performed on a 2. 40 GHz Intel(R) Core(T M) i5 -4210U 64 bits laptop with 8 GB of RAM running windows 7. The algorithm is implemented in C++. We use the same query sets as in  (Han et al. , 2013). These queries are constructed as follows  (Han et al. , 2013) : • AIDS and NASA query sets: For each of these datasets,  (Han et al.
, 2013)  constructed 6 query sets (Q4, Q8, Q12, Q16, Q20, Q24), each of which contains 1,000 query graphs of the same size. Additionally, each query Q i is contained in a query Q i+1. Each query is a subgraph of a graph in the dataset. • Human Query sets: For this dataset,  (Han et al. , 2013)  generated three kind of queries: 1. Subgraph queries as for the Aids and Nasa datasets. In this case, we have 10 query sets obtained by varying the number of query sizes from 1 to 10. 2. Clique queries where the query subgraph is a complete graph. For biological datasets, such as Human, a clique Query corresponds to a protein complex  (He and Singh, 2008). 3. Path queries where the query subgraph is a path. A path query corresponds to transcrip- tional or signaling pathways  (He and Singh, 2008). The time performance reported in the results is the average time computed over the sets of queries of the same size.
Results Figure  5  shows the experimental results for AIDS. We can clearly see that the time performed by Turbo ISO decreases when the query size increases. This is explained in  (Han et al. , 2013)  by the containment relationship among the query sets in AIDS. We can also observe the same behavior with Sum ISO which achieves better than Turbo ISO. In our case, this can be explained by important compression rate of AIDS that yields a small number of candidates to be considered. Figure  6  shows the experimental results for NASA. For this dataset, Sum ISO achieves significantly better than Turbo ISO for all the queries. Figure  7  shows the results of subgraph queries over the human dataset. The superiority of Sum ISO over Turbo ISO is clearly observable as soon as the query size is greater than 8.
CONCLUSIONS In this paper, we presented a new approach to subgraph isomorphism search in massive graph databases. In our approach, data graphs are summarized to minimize storage requirement. Our subgraph isomorphism search algorithm, Sum ISO , finds all the embeddings of a query graph in a summarized data graph without decompressing the graph. Our experimentations show that the proposed approach achieves good performance on both time processing of queries and space storage of data graphs. However, several enhancement issues are possible and merit investigation. First, more experiments are needed to attest the efficiency of the approach. In fact, we used the same datasets as in  (Han et al. , 2013)  for our evaluation to compare with the Turbo ISO algorithm. These datasets, namely AIDS, NASA and HUMAN are highly compressible with more than 40% of compression rate.
However, not all the graphs are compressible with the same rate. So, it is interesting to study the behavior of the approach behaves with less compressible graphs and determine a compression rate threshold that conditions the use of compression. Second, it is interesting to compare the two approaches on larger graphs. Third, it is interesting to see if it is feasible to ran such an approach on a graph database such as Neo4j and also investigate how it can be implemented in a MapReduce framework. Finally, we have not used pruning methods in the SubgraphSearch phase and it may be possible to define some rules to prune the explored compressed paths by relying on the properties of the compression.
Computing the set of neighbors of a vertex in a compressed graph (Neighbors). Data: A vertex u and a compressed graph C (G). Algorithm 4: Result: The set of neighbors of u in g. begin N ← / 0; z ← u; x ← Father(u); while x ̸ = root(C (G)) do switch type of x do case a series module foreach child y ̸ = z of x do N ← N ∪ Leaves(y); end case a Neighborhood module foreach edge (z, y) ∈ x do N ← N ∪ Leaves(y); end endsw z ← x; x ← Father(x); end return N end • HUMAN database: This dataset consists of one large graph representing a protein interaction net- work. This graph has 4, 675 vertices and 86, 282 edges. The number of unique labels in the dataset is 90. • NASA database: This dataset contains 36, 790 trees with an average size of 32, and a number of unique labels of 117, 302.
Further, published studies on water consumption patterns in the USA  (5, 7, 8)  , Mexico  (9)  , Germany  (10, 11)  , Belgium  (11, 12)  and France  (11, 13)  have not always been compared with national or international recommendations and guidelines. The EFSA-recommended values are 1600 ml/d for boys and girls aged 4-8 years; 1900 ml/d for girls and 2100 ml/d for boys aged 9-13 years  (1). These EFSA values may be used as goals for individual adequate intakes  (1). Based on water-to-energy ratio, the desirable total water intake should be in the range of 1•0-1•15 litres/1000 kcal (4184 kJ)  (1). EFSA has also used urine osmolality as an index of adequate hydration  (14) (15) (16). In the present study, the nationally representative INCA 2 database (Étude Individuelle Nationale des Consommations Alimentaires 2006-2007)  (17, 18)  was used to assess total water consumption among French children aged 4-13 years.
The INCA 2 study is the flagship national dietary survey, conducted by the French National Agency for Food Safety (ANSES). The INCA 2 data are the most recent publicly available for France and are used to inform national food and nutrition policy, most notably the National Program for Nutrition and Health  (19). The present goal was to compare how close French children came to meeting the EFSA Dietary Reference Values for water. Additional analyses estimated total water sources (plain water, beverages and foods) and the waterto-energy ratio (litres/kcal) in relation to desirable norms. Analyses were conducted by sex and age group and by socio-economic status, eating occasion and location. The contributions of beverages and foods to total energy intake were also examined.
Dietary intake databases The INCA 2 study was conducted by the French National Agency for Food Security and Safety (AFSSA) between December 2005 and April 2007  (17, 18). INCA 2 provides data from a nationally representative sample of 4119 persons aged 3-79 years. Dietary intake assessment was based on 7 d food records for all foods and beverages consumed, including plain water  (20). Participants were asked to describe all of the foods and beverages consumed and estimate the amounts consumed, aided by a photographic atlas of portion sizes and a list of household measures. For children aged 3-10 years, food records were completed by responsible parents or caregivers. For children aged >10 years, the child was the primary source of dietary recall information, but could be assisted by an adult who had knowledge of the child's diet.
Plain water and beverage consumption Plain water and beverages were classified into nine categories: (i) tap water; (ii) bottled water; (iii) milks (including flavoured); (iv) sodas (regular and diet); (v) 100 % fruit juices; (vi) hot beverages (coffee and tea); (vii) fruit drinks; (viii) sports drinks; and (ix) flavoured waters. The INCA 2 food records for each respondent provided information on the amount in grams of each food and beverage consumed  (17, 18, 20). The water content of beverages and the moisture content of foods were established using the CIQUAL (Centre d'Information sur la Qualité des Aliments) 2013 database developed by the ANSES. Food and beverage amounts were converted to energy (kcal; 1 kcal = 4•184 kJ) using the CIQUAL database and standard procedures. The comparisons with the EFSA total water guidelines presented herein were for water content from different sources, including plain water, beverages and foods, calculated in ml/d.