dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Extraction Data, which were downloaded online through the Web of Science, included authors' names, the paper title, the journal name, keywords, institutional affiliations, frequency of citation, subject category, and abstract. Before downloading the data, articles were sorted by the frequency of citation. "Author keywords" and "keywords plus" were both used in this research. Additionally, we also downloaded the citation reports which were created automatically by the Web of Science. All of these data were stored in Microsoft Excel. After that, we filter all the downloaded data by excluding the papers which are: (1) not original articles and reviews; and (2) not about child abuse. A total of 3013 papers were excluded after screening titles and abstracts (Figure  1 ). Table  1  shows the characteristics of the papers.
Data Analysis Data were analyzed including: the basic characteristics (number of authors, publication years, main category), keywords (most common keywords and co-occurrence keywords), most prolific authors (more than 60 papers, countries with significant among of publications (more than 75 papers), chief journals, and highly impactful papers (times cited-more than 600 times). A network graph illustrates the connection among countries by applying the specific threshold of 50 papers for each collaboration (Figure  2 ). To find the most frequent keywords appearing in the keyword plus list, we used Wordle (http://www. wordle. net/) as it helped us to create a word cloud online and download the results directly. The larger the words within the cloud, the more often the keywords were repeated. Besides, the author keyword co-occurrence network was also created, which reflects the frequency of the keywords and the proportion of co-words.
We used VOSviewer (version 1. 6. 8, Center for Science and Technology, Leiden University, the Netherlands), free software for creating a co-occurrence network. For countries network, we used Social Networks Visualiser (SocNetV (version 2. 4, the Free Software Foundation, Massachusetts, the United States)), a user-friendly and free software tool for Social Network Analysis and Visualization. Data on population were obtained from the World Bank website with the total population of 2016 (https://data. worldbank. org/indicator/SP. POP. TOTL). However, the 2016 population of Wales, Scotland and Northern Ireland was collected from the Office for National Statistics Great Britain (https://www. ons. gov. uk/peoplepopulationandcommunity/ populationandmigration/populationestimates).
We must discuss some weakness and limitations of our study. First, the Web of Science database was the only one we used to generate the scientific papers, including the characteristics of the data (such as the number of records, publication year or authors) and for examining the network of keywords and international collaboration. However, articles that belong to open access data not provided by the Web of Science may affect the search results  [58]. Secondly, the analysis of keywords co-occurrence depends on the chosen keywords; and this can be a source of bias toward a cohort of the Web of Science-indexed papers. Thirdly, due to the vastly different definitions of child abuse and neglect in different organizations and countries, it is sometimes difficult to choose the one which can satisfactorily cover all forms of child maltreatment.
The separation was performed using a HP5-FAPP column (30 m long, 0. 32 mm internal diameter and 0. 5 mm film thickness) under the following conditions. Injector temperature 180 • C, detector temperature 230 • C, initial temperature 70 • C, then ramped up at 5 • C min -1 to 120 • C, then ramped at 8 • C min -1 to a final temperature of 210 • C, which was maintained for 12 min. The mass spectra were analyzed with MassHunter 2017 by Agilent Technologies R , and compounds were preliminarily annotated with "NIST MS Search Program v. 2. 0g, " Library version 11, and AMDIS version 2. 71 from Agilent Technologies R , and then confirmed with authentic standards purchased from Sigma-Aldrich and Fluka Chemie (now Merck, purchased via Sigma-Aldrich, Toluca, Mexico).
ACKNOWLEDGMENTS We thank  Rosa María Adame-Álvarez  and  Enrique Ramírez-Chávez  for invaluable help with the volatile analyses,  Octavio Martínez de la Vega  for help with the statistical design and  Enrique Hurtado Bautista  for help with scientific illustrations. Philip S. Ward  kindly confirmed the identity of the ant species and  Madgalena Ríos  generously provided  Supplementary Video 1. CONACyT de México  is acknowledged for financial support (grant no. 301808  to OH-Z,  237052  to RR-B, and  251102  to MH).
Statistical analysis Data were analyzed using the Statistical Analysis System Program SPSS v. 16. 0. The model applied included the fixed effects of season. Data given in the tables are means ± SD. Differences in the mean values of two seasons (one-one) were tested by using the independent-samples T test or Mann-Whitney U test after evaluating normal distribution by the Shapiro-Wilk and Kolmogorov-Smirnov tests. Also, the differences in the mean values of three seasons were analyzed by one-way analysis of variance followed by ANOVA test. The test of the correlation of Pearson-s was used to interpret the relationship between the various studied parameters. A probability level (p) of 0. 05 was chosen as the limit for statistical significance in all tests.
ACKNOWLEDGEMENTS The authors are very much thankful to the staff of El Oued municipality slaughterhouse especially the veterinary inspectors for the help to carry out this research. We also thank  Mr. Khalifa Hamad  for assisting in meat sample collections. My sincere thanks go to the staff of the  Laboratory of Hygiene and Animal Pathology , at the  Institute of Veterinary Sciences, Ibn Khaldoun University of Tiaret, Algeria.
Methods: We gathered existing Culicoides data from Spain, France, Germany, Switzerland, Austria, Denmark, Sweden, Norway and Poland. In total, 31,429 Culicoides trap collections were available from 904 ruminant farms across these countries between 2007 and 2013. Results: The Obsoletus ensemble was distributed widely in Europe and accounted for 83% of all 8,842,998 Culicoides specimens in the dataset, with the highest mean monthly abundance recorded in France, Germany and southern Norway. The Pulicaris ensemble accounted for only 12% of the specimens and had a relatively southerly and easterly spatial distribution compared to the Obsoletus ensemble. Culicoides imicola Kieffer was only found in Spain and the southernmost part of France. There was a clear spatial trend in the accumulated annual abundance from southern to northern Europe, with the Obsoletus ensemble steadily increasing from 4000 per year in southern Europe to 500,000 in Scandinavia.
Several European countries have gathered and analysed entomological data at a national level to determine the presence and abundance of different species of Culicoides  [21] [22] [23] [24] [25]. In addition, vector activity during the winter has been assessed in an attempt to determine the existence of a "vector-free period"  [26, 27]. Determining a vector-free period might be useful for national veterinary authorities to authorize movements of test-negative ruminants. The number of national studies from European countries has increased during the last decade, but the need to quantify Culicoides vector dynamics at a continental level remains, as European legislation for vector-borne diseases is founded on joint decisions among the member states. The aim of the present study was to generate a joint entomological database for large parts of Europe comprising different climatic zones, using surveillance and research data collected during the period 2007-2013.
Culicoides database We gathered available Culicoides data from Spain  [28] , France  [9, 23] , Germany  [24] , Switzerland  [29, 30] , Austria  [31] , Denmark, Sweden  [32, 33] , Norway and Poland  [34]. The data originated from national surveillance systems and research projects carried out during one or more years during a 7-year period  (2007) (2008) (2009) (2010) (2011) (2012) (2013)  by national authorities and research groups. Culicoides biting midges were sampled from a total of 904 livestock farms (Fig. 1 ), with 31,429 trap collections comprising 8,842,998 specimens. For entomological sampling details from the different countries see  [9, 23, 24, [28] [29] [30] [31] [32] [33] [34]. Black light suction traps were placed outside stables or near animal resting sites and the coordinates of each farm were recorded.
To create a uniform database, we aggregated the species into ensembles. We use the term "ensemble" to refer to a group of sympatric species for which morphological identification is sometimes not possible or difficult, and without phylogenetic meaning. In this work, "Obsoletus ensemble" refers to the Obsoletus group and C. dewulfi together and includes the following species: C. obsoletus, C. scoticus, Culicoides montanus Shakirzjanova, Culicoides chiopterus (Meigen) and C. dewulfi. The Pulicaris ensemble includes Culicoides pulicaris (Linnaeus) and Culicoides punctatus (Meigen). As it is commonly used in the literature while authors differ in its composition, we refer to the Obsoletus group as a species group including C. obsoletus, C. scoticus, C. montanus and C. chiopterus. Based on the phylogenetic analysis of Schwenkenbecher et al.
Descriptive analysis and data management We calculated the week number of each collection using the start date of the trapping. We defined week 1 of each year as 1st to 7th of January. This was done to ensure that the same dates from different years were given the same week number. We calculated the weekly and monthly mean abundance of vectors for each year. Finally, we calculated the average weekly and monthly abundance for the entire seven-year period to derive estimates for an "average year". We conducted three different analyses. In the first analysis, we divided Europe into seven latitudinal bands (A-G) of 5°width, from 35°N to 70°N (Fig. 1 ). Latitude range G (> 65 °N) contained only two farms with just 9 observations from 4 weeks in August and September 2008, and was therefore not included in the latitudinal range analysis.
To compare the seasonal variation among the seven different latitude ranges, we log transformed the trap collection data [log 10 (x + 1)] and then calculated the mean of all the trap collections for each week number and at each latitude range, based on the data for the entire 7-year period (Fig. 1 ). To quantify the variation of the mean abundance for each week number, we calculated the 10th and 90th percentiles. For each latitudinal range, we also calculated the weekly average of the daily minimum, mean and maximum temperatures per week for the period from 1994 to 2004, and contrasted with the Culicoides seasonal variation. We estimated the number of vectors collected in an average year within each latitudinal range by calculating the annual cumulative sum of the weekly mean abundance and multiplying this by 7 days.
NUTS is a hierarchical system used to divide up the economic territory of the EU for statistical purposes. We calculated the average start of the season for the 7-year period. The start of the season was defined as the first month in which the average monthly abundance per area polygon was equal to or higher than one specimen of C. imicola, and equal to or higher than 5 specimens of the Obsoletus and Pulicaris ensemble. The threshold numbers used here were based on (but not identical to) the threshold numbers defined by the European Commission to determine the start of the season  [20]. While the EU thresholds are based on individual traps, we applied the thresholds to the average vector densities of all traps within a polygon. Polygons were classified as having "no data" if (i) the polygon did not have any sampled farms, (ii) if the mean abundance of the polygon did not reach the threshold during the year, or if (iii) there were no data available for the month prior the start of the season, thus making it impossible to detect whether the season might have started earlier.
Results The trap data made available for our analyses included a total of 8,842,998 biting midges that had been collected at 904 farms between 2007 and 2013 in nine European countries. Of these, 82. 8% belonged to the Obsoletus ensemble, 12. 0% to the Pulicaris ensemble and 5. 1% were C. imicola. Biting midges of the Obsoletus and Pulicaris ensemble were found in all countries that were sampled, while C. imicola was only found in Spain, along the southern coast of France and in Corsica.
Discussion The descriptive analysis presented here is based on the most extensive Culicoides dataset created for Europe to date, and represents the first combined description of Culicoides abundance and distribution for a large part of Europe. The data were gathered from a 4000 km long transect from southern Spain to the Arctic Circle in Sweden, with the most easterly collection sites in Poland. The primary aim of this descriptive analysis was to identify and quantify major geographical patterns and seasonal trends in the abundance of key Culicoides vector groups. The focus of the analysis was to identify patterns and trends important for decision making to prevent, surveillance and control of Culicoides-borne pathogens. Specimens of the Obsoletus and Pulicaris ensemble were found in all of the countries sampled.
Conclusions This is the first report in which a dataset this size and covering a large part of Europe has been analysed. We identified and quantified the main mean spatial and temporal differences of three Culicoides species groups. Understanding the spatial and seasonal patterns of key vector groups or species facilitates the planning of preventive strategies and allows the development of more cost-effective vector and disease surveillance programmes by veterinary authorities in the European Union. The monthly abundance of the Obsoletus ensemble increased gradually from northern Spain to mid Scandinavia. The vector season also became increasingly shorter toward the north, starting three months later in mid Scandinavia compared to southern Spain. Nevertheless, the annual accumulated abundance of the Obsoletus ensemble increased steadily with latitude to 500,000 vectors per trap per year in mid Scandinavia.
Acknowledgments We would like to thank the  Direction générale de  l'alimentation from the  French Ministry in charge of agriculture  for funding and the  Directions départementales de la protection des populations  for their support in collecting the biting midges during the survey. We thank the  Swiss Food Safety  and  Veterinary Office  and the  Vet-Austria  project for financial support to the Swiss and Austrian partners, respectively. We also thanks the  Ministerio de Agricultura y Pesca, Alimentación y Medio Ambiente  for providing data about the national surveillance of Culicoides in Spain and the MARS-Agri4cast at the  Institute for Environment and Sustainability, European Commission , for kindly providing temperature data of Europe.
TDS were obtained by calculating the difference between UV-vis absorbance spectra above (95 • C) and below (4 • C) the melting temperature of the DNA secondary structure. In this case, given the very high stability of the G4 structure considered (see below), the structure was only partially denatured at 95 • C, which did not affect the shape of the TDS. The final data was normalized for ease of comparison. Isothermal difference spectra. For IDS, similar to TDS, the data were obtained by taking the difference between the absorbance spectra of unfolded and folded oligonucleotides. For TDS unfolded samples were prepared by thermal denaturation which normally does not happen in a biological setting. For IDS, on the other hand, unfolded DNA was prepared in 10 mM LiCaco buffer pH 7. 2 and folding was induced by adding KCl.
DNA melting monitored by UV-vis spectroscopy Stability of DNA at different concentrations was determined in UV-vis melting experiments by monitoring the ab- M concentration in 5K buffer. All experiments were repeated two to three times. The data were processed by subtracting the signal at 335 nm from the signal at 295 nm. The resulting curves were smoothed using a Savitzky-Golay filter with a 13-point quadratic function in Origin 9. 0. The first derivative was then taken. The temperature of half transition, T 1/2 , corresponds to the trough or peak on the first derivative curve as read by eye (error ±0. 5 • C). For reversible melting T 1/2 equals (or is very close) to the melting temperature, T m. In that case, the data were fit using a two-state model with a temperature-independent enthalpy of unfolding, H  (24) , which yielded T m and H values and associated fitting errors.
Circular Dichroism (CD) CD wavelength scans were collected on annealed DNA samples at 2. 5-4. 5 M concentration in 1-cm quartz cuvettes at 4 or 25 • C using a JASCO J-815 spectropolarimeter with a 2 nm bandwidth, 500 nm/min scan speed, 220-330 nm window, and 1 nm step. The data were also recorded on an AVIV-435 instrument with a 2 nm bandwidth, 1 s averaging time, 220-330 nm window and 1 nm step. Five scans were recorded and averaged. Data were processed as described elsewhere  (25). CD scans were also collected on the samples used for crystallization. CD melting experiments were conducted in the 25-95 • C temperature range with 1 • C step, 1 • C/min temperature change rate, 15 s averaging time, and 5 s equilibration time. Ellipticity was monitored at 295 nm, which represents the wavelength of maximum signal in CD scans.
The structure was solved via molecular replacement using the Phenix MR procedure  (27). A variety of G4 models were tested including mono-, di-and tetrameric G4s as well as G4s with two, three and four G-quartets. The four Gquartet quadruplex from the Oxytricha nova telomeric sequence (Protein Data Bank, PDB, code 1JRN) without connecting loops provided a successful solution in one orientation. The original model was then extensively rebuilt and completed manually in Coot  (28). It was necessary to include three K + ions equidistant from four guanine bases of two adjacent quartets during the crystallographic refinement of each G4. The unit cell contains seven G4 chains, labeled A-F and Z. Three DNA chains show some degree of disorder in the loop regions, i. e. A 20 in chain B, T 11 -A 12 in chain D and T 11 to A 14 in chain Z were not built in the deposited model.
Planarity of G-quartets, helical twist, and torsion angles In order to calculate the planarity of each quartet and a A-T-A triad, we used principal component analysis (PCA), as described previously  (30). We calculated helical twist using an in-house script which extracted the coordinates of all atoms from the PDB file and represented every guanine base as a vector from C8 to the midpoint between N1 and C2  (31). The angle between the vectors corresponding to every pair of stacked guanines was then calculated. We verified the script using a structure for which helical twist is reported (PDB code 4U5M)  (31). For comparison, we also calculated the helical twist for the G4 from O. nova (PDB code 1JPQ). Finally, we calculated DNA backbone torsional angles using the 3DNA software system  (32).
NMR studies All data were collected on a Bruker Avance 700 MHz instrument. The samples were annealed at 2 mM in 10 mM potassium phosphate buffer at pH 6. 9 supplemented with 40 mM KCl (KPi) and diluted to 120-200 M in the same buffer with 10% (v/v) D 2 O for data collection. Data for 19wt were collected in the 278 to 353 K temperature range. All the mutants were examined under the same condition but at 298 K only.
Thermal stability data also suggest that alteration of some loop-loop interactions might lead to the formation of stabilizing non-canonical base pairs. It is important to recognize and understand the specific inter-  Biologically relevant monomolecular G4s with four Gquartets are sparse and only three examples of such structures can be found in the PDB. Phan's laboratory reported X-ray and solution NMR structures of the left-handed Z-G4 formed by a modified version of an anti-proliferative oligonucleotide AGRO100 dT(GGT) 4 TG(TGG) 3 TGTT (PDB codes 4U5M and 2MS9). This four-quartet all parallel G4 is formed by two G-blocks each with two quartets connected by single nucleotide T loops  (31). The second ex-ample is a solution NMR structure of d(GGGGCC) 4 from the C9orf72 non-coding region (involved in ALS/FTD neurodegenerative disorders) published by Plavec's laboratory (PDB code 2N2D).
The observed G4 is antiparallel with syn-anti-syn-anti glycosidic torsion angles within each quartet. The quartets are linked by three lateral C-C loops; one nucleotide of each loop is stacked onto a terminal Gquartet, yielding a stable, compact structure  (40). The third example is a solution NMR structure of two antiparallel four-quartet G4s formed by dG 4 T x G 4 T 4 G 4 A 2 G 4 (where x = 2 or 3) deposited in PDB by the Webba da Silva's group (PDB codes 2M6W and 5J6U, to be published). One more sequence could be added to this list, 36-nt RNA motif called sc1, identified via in vitro selection to bind to the human fragile X mental retardation protein. Sc1 consists of duplex and G4 components. The topology of the G4 is unique: the bottom two G-quartets are parallel-stranded while the third G-quartet is inverted; the fourth quartet is formed by U•A•U•G and serves as a transition between the G4 and the duplex parts of sc1 (PDB code 2LA5)  (41).
ACKNOWLEDGEMENTS We would like to thank  Barrett Powell  ( Swarthmore ) for his help with PCA analysis and the survey of the PDB for four-tetrad G4 structures. We also want to thank  Sebastien Fribourg  for the use of crystallography equipment and the IECB BPCS technical platform for NMR instrumentation. We acknowledge the  European Synchrotron Radiation Facility  and members for assistance in using beamline ID14-1 and ID29. We also acknowledge the  Synchrotron SOLEIL  for providing access to the Proxima-1 beamline. We thank  Natacha Perebaskine  and  Alexandra Carolin Seefeldt  for diffraction data collection.
This list also includes quadruplex-duplex hybrids, i-motifs, and tetrastranded structures composed of G•C•G•C quartets. The other 33 structures do not con- tain actual quadruplexes. A search of the Nucleic Acids Database produces a similar list (see Supporting Informa- tion). Upon visual examination of the PDB files, 59 of the structures, formed by 17 unique nucleotide sequences, con- tain four G-quartets. Among these entries we find a number of tetramolecular G4s formed by dTGGGGT or dGGGG (13-15). These G4s play important roles as model structures and find applications in DNA nanotechnology (39) but are less likely to be biologically relevant, at least at the genomic level. Thirty five out of 59 reported G4 structures with four G-tetrads are dimers formed by the O. nova telomeric re- peat dG 4 T 4 G 4 ; the structures were solved by NMR (rep- resentative examples include PDB codes 156D, 230D and 1K4X) and X-ray crystallography (examples include 1JB7, 1JRN, 1JPQ).
Estimation of the carbon addition rate at different grazing intensities in a long-term, no-till, integrated crop-livestock system (produced from data of  Assmann et al. , 2014). ( ) 0 2 ln SOC ln a K t - = Likewise, coefficient K 1 was estimated from K 2 and coefficient "b" in the linear equation-which represents annual changes in SOC stocks per unit of C input by aboveand below-ground crop biomass (pasture + soybean): ( ) 2 2 1 K t K b K 1 e - = - The annual CSR of atmospheric C in soil was calculated as the ratio of the difference between current SOC stocks ( ) t 0 SOC SOC CSR t - = SOC stocks were simulated using the following equation with SOC 0 and the previously estimated K 1 and K 2 values, and A being the annual C input: ( ) 2 2 K t 0 1 t K t 2 SOC e K A C K 1 e - - + = - The first term on the right-hand side of the equation represents the carbon loss from the soil prior to the experiment and the second SOC accumulation by effect of plant and animal inputs during the experiment.