dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This research was supported by the My-Ocean2 European project and Mercator Océan. The authors wish to thank collaborators who have contributed to development of the NEMO ocean code as part of the  NEMO consortium. We thank  Yann Drillet ,  Jean-Michel Lellouche  and  Marie Drévillon  for their useful comments and both reviewers for their very constructive suggestions and questions. Edited by:  R. Marsh.
The location of this earthquake, within the continental United States, and the large amount of high-quality seismic data available make it ideal for a detailed analysis to confirm the preliminary NEIC depth. We combine regional seismological estimates of the earthquake focal mechanism and depth with teleseismic depth phase observations from both individual broadband stations and from small-to-medium aperture multi-instrument arrays to present conclusive evidence in favour of a hypocentre located significantly below the base of the crust in this region, well into the lithospheric mantle. We then briefly discuss the regional context of this earthquake, and how it may impact on current models for the rheology of continental lithosphere.
Regional waveform inversion To determine a source mechanism, and for an initial estimate of the source depth, we employ a time-domain regional waveform inversion routine (based on that of  Herrmann, 2013). We select available data from broadband and high-gain seismometers within 600 km of the NEIC earthquake epicentre. Seismograms, with the station response removed, are subjected to a four-pole Butterworth filter, with a pass band in the range 0. 02-0. 08 Hz. This frequency range has the advantage of removing sensitivity to short-wavelength variations in the velocity structure which, as stated earlier, are not included in our regional velocity model. Greens functions are calculated by wavenumber integration for the velocity model described above for event-station distances based on the separation between available stations and the NEIC earthquake location (see Figure  1(b) ).
Analysis of the aftershock using Pinedale array data Finally, we make use of the location of the short-period array (vertical component only) and single broadband station (three-component) at Pinedale, WY, located on the south side of the Wind River Range (see Figure  1. b), and in close proximity to the earthquake epicentre (∼ 42 km). In particular, we use this array to examine the aftershock reported by the NEIC at 15:15:34 UTC, approximately two hours after the main Wind River earthquake, and with a similar catalogue location. Whilst the small magnitude of the aftershock (M W 3) makes is unsuitable for the analyses conducted so far in this paper, the proximity of Pinedale to both earthquakes means that a clear signal was recorded for both events. Figure  8 (a) shows the unfiltered threecomponent waveforms from the broadband seismometer at Pinedale, aligned by the P arrival, and clearly demonstrates that the delay time between P and S arrivals for the mainshock event (red waveforms) is virtually identical to that for the aftershock (blue waveforms).
A similar figure using all the shortperiod data from the Pinedale array is included in supplementary material (Figure  S4 ). Figure  8 (b) then shows the relative inter-station delay times for arrivals between short-period instruments within the Pinedale array. Delay times were calculated using picks for the initial peak, rather than the onset as for both earthquakes the onsets are low amplitude and difficult to pick meaning that onset picks could potentially be affected by variable noise levels across the array. The sampling interval for these instruments is 0. 05 seconds and all inter-channel delays are within one sample of being the same for both the mainshock and aftershock, indicating that the apparent vector slowness across the array is the same for both events. Given the similarities in the delay time between P and S arrivals (in effect, the event-station distance), and in the apparent vector slowness, it is highly likely that the two events occurred in close proximity to each other.
Conclusion We present a robust set of seismological analyses, taking advantage from a high-quality, globally distributed, dataset, demonstrating that the M W 4. 7 2013 Wind River earthquake occurred at a depth of 75 ± 8 km, with strike=55           (Pearce, 1977)  for the 21 September 2013 Wyoming earthquake showing the orientations of double-couples which are consistent with the observed polarities and amplitude bounds in Table  S2. The lower-hemisphere stereographic projection shows the focal mechanism with the lowest calculated misfit in the regional inversion which is consistent with the observed polarities and amplitude bounds (shaded quadrants show compressional polarity). The coordinate system used is that of  (Pearce, 1977). (b-d) Lower hemisphere stereographic projections showing: (b) Focal mechanisms which have a misfit within 10% of the minimum misfit in the regional inversion.
Acknowledgements The data used in this study were retrieved through the  Incorporated Research Institutions for Seismology Data Management Center (IRIS DMC) , and through the  International Monitoring System (IMS). We are grateful to the editor,  Peter Shearer , to  Sean Ford , and to one anonymous reviewer for their comments on this manuscript. We also thank  R. A. Sloan ,  K. Priestley  and  S. Roecker  for useful discussions. TJC was funded by a postdoctoral fellowship from  ENS Paris. We are also grateful to the  Saint Louis University Earthquake Center  for the availability of the Computer Programs in Seismology software package.
Spectroscopic and interferometric observations and data reduction are presented in Sect. 2. Section 3 reports results from UVES high-resolution spectroscopy, along with the derived stellar physical parameters and distance. Section 4 presents the results of our SINFONI observations. We present our interferometric results across the Br γ line, along with its geometrical and physical modelling in Sects. 5 and 6. Section 7 provides a discussion on the origin of the Br γ emitting region and of the asymmetries observed in the interferometric observables. Finally, our conclusions are presented in Sect. 8.
Observations and data reduction 2. 1. VLT/UVES spectroscopy HD 98922 was observed on 21 March 2005 with UVES  (Dekker et al. 2000) , the echelle spectrograph mounted on the VLT/UT2. The observations were performed with the blue arm, covering a spectral range between ∼3750 Å and ∼4990 Å. A slit width of 0 ′′. 4, which gives a spectral resolution of 80 000, was adopted, and the total integration time was 100 s. Wavelength calibration frames were taken with a long slit and a ThAr arc lamp. The data were reduced using the UVES pipeline v3. 2. 0  (Ballester et al. 2000)  available from the ESO Common Pipeline Library. We converted the wavelength scale to the heliocentric rest frame. No standard star is available to provide an accurate flux calibration. Owing to the very small photometric variability of HD 98922  (de Winter et al.
8 ′′ ×0. 8 ′′. To correct for atmospheric transmission, observations of a telluric standard star of spectral type A were also performed. The main data reduction process was done using the SINFONI data-reduction pipeline in GASGANO  (Modigliani et al. 2007) , i. e. dark and bad pixel masks, flat-field corrections, optical distortion correction, and wavelength calibration using arc lamps. As a result, we obtained four calibrated datacubes, one for each observed PA. We then measured the full width at half maximum (FWHM) of both target and standard star on the final datacubes to estimate the achieved spatial resolution, which is ∼60 mas. From each datacube the stellar spectrum was then extracted, and continuum and Brγ line images were created by collapsing the datacubes along the z-axis, from 2.
Stars HD 60276 and HD 103125 were observed with the same observational settings and were used as interferometric calibrators to derive the transfer function for the first and second run, respectively. The calibration datasets for the first and second runs consist of 200 and 980 calibrator interferograms, respectively. To reduce our interferograms, we used our own data reduction software based on the P2VM algorithm  (Tatulli et al. 2007) , which provides us with wavelength-dependent visibilities, wavelength-differential phases, closure phases, and wavelength calibrated spectra. The fringe tracking performance of FINITO typically differs during the observations of target and calibrator. Therefore, we used the archival low-resolution visibilities of HD 98922 (216 different visibility uv points, mostly taken during the AMBER's GTO) to calibrate the continuum visibilities at the six uv points of our HR observations.
In our analysis, we also included the intrinsic Brγ photospheric absorption feature of HD 98922, using a synthetic spectrum with the spectral type and surface gravity values as in Table 2. The size of the continuum was obtained using the AMBER GTO archival data at low resolution. By fitting a circular symmetric Gaussian model, we derived a Gaussian half width at half maximum (HWHM) radius of 1. 8±0. 3 mas (or ∼0. 79±0. 13 AU at a distance of 440 pc). A similar value of 1. 6±0. 3 mas (or ∼0. 70±0. 13 AU) was inferred by fitting a ring model with a ring width of 20% of the inner ring radius. To get reliable line visibility values, we derived the continuum-corrected (hereafter pure) line visibility only in the spectral regions where the line flux is higher than ∼10% of the continuum flux. The results are shown in Figure  5.
The large scale asymmetry is represented by the different brightness distribution between the NNE and SSW side of the disc (whose ratio is about 2. 5). This feature is detected at both VLTI (AMBER and PIONIER) and SINFONI spatial scales. The different emission between forward and backward scattering could be the simplest explanation. The fact that the brightness asymmetry lies approximately in the direction of the system axis also supports this hypothesis. On the other hand, this asymmetry is observed both at large (SINFONI) and small (VLTI) scales, and the SINFONI data are likely dominated by scattered light, whereas the VLTI data are possibly dominated by thermal emission. These considerations are in favour of an intrinsic perturbation of the disc. In principle, disc warping is the most plausible explanation.
Acknowledgements. We thank an anonymous referee for his/her comments, which improved the paper. We are also grateful to  Jaques Kluska  for providing us with information about the VLTI/PIONIER observations reported in Kluska et al. 2014. A. C. G. and R. G. L. were partially supported by the  Science Foundation  Article number, page 13 of 16 A&A proofs: manuscript no. astroph of Ireland, grant  13/ERC/I2907. L. V. T. was partially supported by the  Russian Foundation for Basic Research  (Project  15-02-05399 ). S. K. acknowledges support from an  STFC Ernest Rutherford fellowship  ( ST/J004030/1 ),  Ernest Rutherford Grant  ( ST/K003445/1 ), and  Marie Curie CIG  grant ( SH-06192 ). V. P. G. was supported by grant of the  Presidium of RAS   P41. This research has also made use of  NASA's Astrophysics Data System Bibliographic Services  and the SIMBAD database, operated at the  CDS, Strasbourg, France.
We analyze a Twitter dataset using three different weightings of the structural network meant to highlight these three community types, and then infer the communities associated with these weightings. We show that the communities obtained in the three weighted cases are highly different from each other, and from the communities obtained by considering only the unweighted structural network. Our results confirm that asking a precise question is an unavoidable first step in community detection in online social networks, and that different questions can lead to different insights about the network under study.
The Initial Dataset and Network Construction The dataset for this study consisted of the tweets of 15,000 Twitter users over a 9 week period (from April 25th to June 25th 2011). The users are embedded in a network collected by performing a breadth-first expansion from a random seed user. In particular, once the seed user was chosen, the network was expanded to include his/her followers, but only included users considered to be 'active' (i. e. , users who tweeted at least once per day over the past one hundred days). Network collection continued in this fashion by considering the active followers of the active followers of the seed, and so on until 15,000 users were added to the network. The only biasing steps in this procedure are the selection of the seed node and the filtering out of inactive users.
Topic-Based Communities and Hashtag Weighting The final community we consider is a topic-based or topical community, i. e. , a community defined by the content (topics) users discuss. In a topical community, users are defined to be a member of a community if they tweet about similar topics as other members of the community. In order to detect the topical communities, we weight the edges of the userfollower network through a measure based on the number of common hashtags between pairs of users. Hashtags are a good proxy for a tweet's content as hashtags are explicitly meant to be keywords indicating the topic of the tweet. Moreover they are widely used and straightforward to detect. To this end, we characterize each user u by a vector h(u) of length equal to the number of unique hashtags in the dataset, and whose elements are defined as h i (u) = φ i (u) log N n i (5) where φ i (u) is the frequency of hashtag i occuring in user u's tweets, N is the total number of users, and n i is the number of users that have used the hashtag i in their tweets.
There is increasing demand for information on predator-prey interactions in the ocean as a result of legislative commitments aimed at achieving sustainable exploitation. However, comprehensive data sets are lacking for many fish species and this has hampered development of multispecies fisheries models and the formulation of effective food-web indicators. This work describes a new compilation of stomach content data for five pelagic fish species (herring, blue whiting, mackerel, albacore and bluefin tuna) sampled across the northeast Atlantic and submitted to the PANGAEA open-access data portal (www. pangaea. de). We provide detailed descriptions of sample origin and of the corresponding database structures. We describe the main results in terms of diet composition and predator-prey relationships.
The feeding preferences of small pelagic fish (herring, blue whiting, mackerel) were sampled over a very broad geographic area within the North Atlantic basin, from Greenland in the west, to the Lofoten Islands in the east and from the Bay of Biscay northwards to the Arctic. This analysis revealed significant differences in the prey items selected in different parts of the region at different times of year. Tunas (albacore and bluefin) were sampled in the Bay of Biscay and Celtic Sea. Dominant prey items for these species varied by location, year and season. This data compilation exercise represents one of the largest and most wide-ranging ever attempted for pelagic fish in the North Atlantic. The earliest data included in the database were collected in 1864, whereas the most recent were collected in 2012.
A number of coordinated fish stomach databases do exist to help facilitate this task, but these typically encompass only a limited selection of species or cover a very discrete period of time. One of the more extensive data sets is the ICES "Year of the Stomach" database for the North Sea, which provides information on 35 species, although detailed data are only available for nine, primarily based on stomachs collected during sampling campaigns in 1981 and 1991. A similar coordinated ICES data set exists for cod in the Baltic Sea and has been documented in  ICES (1997). In the Barents Sea, a combined database exists for Norway and Russia  (Dolgov et al. , 2007) , but in all cases these sources offer limited information on pelagic fishes (i. e. those living at or near the surface of the ocean) and especially those with a wide geographic distribution.
The EU EURO-BASIN project aims to understand and predict the dynamics of plankton-feeding and pelagic fish species in the North Atlantic, and to assess the impacts of climate variability. This project has a particular focus on herring (Clupea harengus), mackerel (Scomber scombrus) and blue whiting (Micromesistius poutassou), which are the most abundant and widespread planktivorous fish species in the region, as well as bluefin tuna (Thunnus thynnus) and albacore (Thunnus alalunga), top predator species that inhabit the whole North Atlantic basin and carry out trans-oceanic migrations. In the present paper we provide details of newly digitized information on the diet of these five species, firstly from the UK DAPSTOM database (mackerel, herring and blue whiting), that incorporates information from recent research cruises by the Institute of Marine Research (IMR, Norway), Marine Research Institute (MRI-HAFRO, Iceland), Institut français de recherche pour l'exploitation de la mer (IFRE-MER, France), Marine Institute (Ireland), as well as historic data from the Centre for Environment, Fisheries and Aquaculture Science (Cefas) in the UK.
Data and methods The two data sets described here provide information on feeding preferences of 27 746 individual herring, 7423 mackerel, 1126 blue whiting, 1526 albacore and 689 bluefin tuna. Samples have been obtained from scientific research surveys using a variety of different fishing gears, from commercial fishery catches, and from individual fish voluntarily sampled by recreational fishermen. In the following two sections we describe the origins of the data and database structures as well as the geographical and temporal coverage.
The DAPSTOM database The DAPSTOM database has been in existence for 8 years, having been created in response to a "data-rescue" call from the EU "Network of Excellence" project EUROCEANS. The most recent version of the DAPSTOM data set (Version 4. 7, collated in January 2014) includes 226 407 records derived from 449 distinct research cruises, spanning the period 1837-2012. The database contains information from 254 202 individual predator stomachs and 188 species. As such, this represents one of the largest and most diverse compilations of food-web information anywhere in the world. A key component of the DAPSTOM programme has been an online data portal (www. cefas. defra. gov. uk/fisheries-information/ fish-stomach-records. aspx) through which outputs are made freely available to the wider scientific community.
In addition, a subset of the pelagic fish information contained in the full DAPSTOM database has now been made available via PANGAEA (doi:10. 1594/PANGAEA. 820041; Pinnegar et al. , 2013). As the DAPSTOM initiative has progressed, a relationaldatabase structure has evolved (in Microsoft Access) that can accommodate all formats of stomach content information (see  Hyslop, 1980) , including data collected at the level of individual fish, pooled samples of multiple fish stomachs, frequency of occurrence data as well as fully gravimetric information (prey weights or volumes). As a minimum, in Version 4. 7 of the DAPSTOM database, information on the predator species, geographic area and the number of stomachs examined was required for a data set to be included. Information on predator length (or size range) was also widely available.
Central to the relational-database structure is the "DAP-STOM" data table (Fig. 1 ). This includes much of the "raw" information about both the predator and prey. The "DAP-STOM" data table includes 23 information fields (Fig. 1 ), and a full definition of each field is provided by  Pinnegar (2014). The "HAULS" table contains all information about the geographic location from which the sample was derived. In most cases this includes ship name, dates and times, latitudes, longitudes, depth, gear type, ICES area and any additional information. Each "haul" has been assigned to a predefined "Sea" (e. g. North Sea, Irish Sea, W Ireland, Celtic Sea, Channel, Biscay etc. ) and ICES "Division" -a spatial sub-unit used by the International Council for the Exploration of the Sea. Note that Tables  1  and 2  do not provide detailed information about sampling time and date, but that this information is available in the underlying PANGAEA and DAPSTOM data sets that are freely available online.
In Figs. 3-5 we have not controlled for time of day or season, but this would be possible if desired. A new innovation within version 4. 7 of the DAPSTOM database is the inclusion of a "PROVENANCE" look-up table (Fig. 1 ). The purpose of this is to record the origin of the data, for example whether or not the data were derived from published sources or based upon "raw" data files from collaborating scientists. Two additional look-up tables have been created to help standardize the taxonomic information that is available to users. The "PREDATOR" look-up table expands on the 3-digit predator names in the "DAPSTOM" table and gives the predator's full Latin name, common name (in English), 10-digit NODC code and TSN identifier. The "PREY" look-up table aims to reduce the enormous number of potential prey names and descriptions to a manageable number of standardized names that can be used for analyses and collation.
The AZTI tuna stomach database The feeding ecology of temperate tunas (albacore Thunnus alalunga and bluefin tuna Thunnus thynnus) in the Bay of Biscay has been investigated through several projects in AZTI-Tecnalia since the mid-2000s. Although there has been little continuity between the projects dealing with the feeding ecology of temperate tunas, a total of 1525 stomachs have been sampled from albacore and 686 stomachs from bluefin tuna between 2004 and 2011. The sample origin and methodologies employed have been diverse. Samples were collected: 3. through opportunistic sampling by the canning industry for albacore  (2005, 2006, 2010, 2011)  and in wholesalers stores for bluefin tuna  (2009, 2010, 2011, 2012). In this case, the catch dates and estimated locations of each predator were re-constructed a posteriori using information contained in the logbook of each fishing vessel.
No catch hour was recorded in the case of these commercial catches, and the estimated locations correspond to the centres of the ICES statistical rectangles recorded in the logbooks. The gears used to catch each sampled tuna also differed considerably, and included: (1) rod and reel (RR) primarily used by recreational fishermen; (2) trolling gear (TR) used during albacore tagging surveys; (3) pole and line with live bait (BB), used for most bluefin tuna catches (commercial and surveys) and for part of the commercial albacore catch, (4) pelagic trawl (MWT) used for a further part of the commercial albacore catch. Due to the lower number of predators sampled in the case of tunas (in comparison with small pelagic fishes such as herring, blue whiting and mackerel), the relatively restrained geographical location of the samples (mostly Bay of Biscay) especially in the case of bluefin tuna, we chose not to include the tuna data in the DAPSTOM data portal but rather to build a simple database in XML format held at AZTI-Tecnalia, but also uploaded to PANGAEA   10 618 (7423) 1 (1)  10 (3)  (doi:10.
1594/PANGAEA. 826992; see  Goñi, 2014). The tuna stomach database is provided as a single table, each row corresponding to a single predator. For each predator, the information provided included the species, the sample origin (survey or opportunistic sampling), the catch date and location (either measured or assigned according to logbook information), the size and weight, and the gear used for the catch. 3 Results -data submitted to PANGAEA under the EU project EURO-BASIN.