dataesr/scientific-paragraphs-categorization huggingface dataset:
Data processing and measurements We collected one year of continuous seismic data, from August 2014 to August 2015, recorded by ~200 permanent broadband stations in the region ( Fig. 1  , red triangles), complemented with 55 temporary stations deployed during the AlpArray-EASI seismic experiment ( Fig. 1  , orange triangles). The continuous seismic ambient noise records were downloaded from the European Integrated Data Archive (EIDA, www. orfeuseu. org/data/eida/ ). We analyzed the continuous noise records from the vertical component and extracted the empirical Green's function following the processing method described in  Obermann et al. (2016 Obermann et al. ( , 2019) ). In particular, we applied in this order: instrumental correction; resampling of the data to a sampling frequency of 5 Hz; band-pass filtering between 1 s and 50 s; elimination of 2 h signal segments that show amplitudes greater than ten times the standard deviation of the daily trace; spectral whitening of the amplitude from 1 s to 50 s; one-bit amplitude normalization; elimination of 2 h signal segments that show amplitudes greater than three times the standard deviation of the daily trace (e.
Conclusions We have derived a new 3-D v S model that fits surface wave group dispersion curves between 3 and 35 s. Our model explains data from the permanent and the EASI AlpArray complementary experiment broadband seismic stations, with a significant reduction of surface-wave data variance. The strength and quality of this new information relies on the absolute velocity and the velocity gradient being continuously and unambiguously sampled along the Moho, only limited by the amount, quality and back-azimuthal distribution of the data available. Our preferred model consists of 3 crustal layers (sediment, upper and lower crust) with the shear velocity described by a linear gradient. The final 3D model (i) is a complete model that include the isotropic elastic parameters (v P , v S and density), (ii) can be considered an improvement of existing reference models like EPcrust  ( Molinari and Morelli, 2011 ) , which has the same vertical parametrization, (iii) incorporates a priori information of recent progress in terms of crustal structure from independent methods  ( Spada et al.
, 2013 ; Molinari and Morelli, 2011 )  and (iv) is, in principle, suited for many applications such as wave propagation modelling, crustal correction, gravity studies, etc. Despite the fact that during the time of writing, a new and dense dataset has been made available -the AlpArray seismic network data  ( Hetényi et al. , 2018c  )-, and recent models have been published  ( Lu et al. , 2020 ; Qorbani et al. , 2020 ; Magrin and Rossi, 2020 ) , we believe that our study yields valuable results in terms of the 3D structure of the Eastern Alpine region. In particular, we found: (a) high correlation with the well-known geological features up to 20 km depth; (b) we image the inner structure of the Molasse basin; (c) we found important variations of crustal thickness and velocities as a result of Alpine orogeny.
The relatively poor ray coverage in the Eastern part and the strong 3D variability of the orogenic structure suggest that the inclusion of the new high-resolution dataset from the AlpArray Seismic Network will help to improve the resolution, coverage and the model details. Moreover, the derivation of nine-component cross-correlation functions to extract Love wave signals, combined with the measurements of both group and Love velocities would allow an increase of the resolution (horizontal and on depth) and a further investigation of the anisotropy in the crust. Irene  Molinari, Marcus Herrmann, Saulé Zukauskaité, Paula Koelemeijer, Sascha Winterberg.
Acknowledgement The authors acknowledge the AlpArray Working Group ( http://www. alparray. ethz. ch ) for making the AlpArray initiative possible. AlpArray Switzerland is funded by the  Swiss-AlpArray   SIN-ERGIA  project  CRSII2_154434/1  by  Swiss National Science Foundation  (SNSF). GH acknowledges the support of the  SNSF  for project  OROG3NY  (grant  PP00P2_157627 ). Full funding details for project EASI are found in Hetényi et al. (2018b). Figures have been prepared using the Generic Mapping tools ( Wessel and Smith, 1998 ), Obspy ( Krischer et al. , 2015 ) and are made using perceptually uniform colormaps ( Crameri, 2018 ). For careful reviews that greatly helped to improve the manuscript, we thank  Giuliana Rossi  and an anonymous reviewer. The authors thanks the group of field operators, the  AlpArray-EASI Team :  Jaroslava Plomerová ,  Helena Munzarová ,  Ludek Vecsey ,  Petr Jedlicka ,  Josef Kotek ,  Götz Bokelmann ,  Irene Bianchi ,  Maria-Theresia Apoloner ,  Florian Fuchs ,  Patrick Ott ,  Ehsan Qorbani ,  Katalin Gribovszki ,  Peter Kolinsky ,  Peter Jordakiev ,  Hans Huber ,  Stefano Solarino ,  Aladino Govoni ,  Simone Salimbeni ,  Lucia Margheriti ,  Adriano Cavaliere ,  Edi Kissling ,  John Clinton ,  Roman Racine ,  Sacha Barman ,  Robert Tanner ,  Pascal Graf ,  Laura Ermert ,  Anne Obermann ,  Stefan Hiemer ,  Meysam Rezaeifar ,  Edith Korger ,  Ludwig Auer ,  Korbinian Sager ,  György Hetényi ,.
Data availability Waveform data from permanent network stations and from the EASI AlpArray complementary experiment stations are available through EIDA ( http://www. orfeus-eu. org/eida/). The permanent and temporary stations are contributed via the following networks codes: BW, CH ( Swiss Seismological Service (SED) at ETH Zurich, 1983 ), CR, CZ ( Institute of Geophysics, Academy of Sciences of the Czech Republic, 1973 ), HU, GE ( GEOFON Data.
As indicated earlier, the energy simulation was performed with a Transient System Simulation (TRNSYS v. 17, Thermal Energy System Specialists, LLC, Madison, WI, USA) software package  [71] , an extremely flexible graphic-environment based software package that is used to simulate energy flows within transitory systems, such as buildings. It consists of two parts: the first processes the input data, computes the system by iteration, determines convergence values, and lists the system variables. The second part is an extensive component library of use for modeling the functioning of any one part of the system. The chosen case of study is the University Hospital of Burgos (HUBU), situated in Burgos, in the North of Spain. It was inaugurated in 2012, making it a good example of a modern hospital.
Constructive Characteristics of the Building The exterior enclosures and the interior partitions were fitted in layers and are in agreement with TRNSYS  [71] , taking into account the physical characteristics of the materials of each layer. The convective heat-transfer coefficients of the building enclosures are detailed in Table  8 , and are in agreement with the Basic Document DA DB-HE/1, of the Spanish Building Code  [72]. The solar absorptance values of the building enclosures are detailed in Table  9 , and are in agreement with DA DB-HE/1  [72]. All the thermal bridges on both floors of the building were identified and processed for display with DA DB-HE/2  [73]. The values of their linear thermal transmittance are detailed in Table  10 , and are in agreement with the Spanish Building Code [74].
Hygro-Thermal Behaviour of the Mortar Block Exterior enclosures and interior partitions of the building were generated layer-by-layer, from the interior toward the exterior, with the Transient System Simulation (TRNSYS v. 17, Thermal Energy System Specialists, LLC, Madison, WI, USA) software package  [71]. The geometric and thermophysical characteristics of the three facades are shown in Tables  19 20 21. Some constructive sections are depicted in Figure  11. The general characteristics of the three façades are shown below, in Table  22.
9% in the period 2013-2016  [11, 13]. These data may suggest that new drugs, introduced during the last decade for the management of metastatic and locally advanced melanoma, appear to improve survival  [13, 14]. In the present study, we analysed the population-based age-specific data for cutaneous melanoma in the Catalan population. A thorough knowledge of the incidence of melanoma in Spain is necessary to improve public health policies.
Material and Methods This is a descriptive study with an age-period-cohort analysis of melanoma in the region of Catalonia, located in north-east Spain. We obtained the data on the Catalonian population from the Statistical Institute of Catalonia (IDESCAT)  [15] , and totals 16. 23% of the Spanish population with 7,496,276 inhabitants in 2017, and corresponds to a predominantly white population. The melanoma incidence data was obtained from the Catalan registry of melanoma (Xarxa-melanoma) for the period of 2008-2017. The Xarxa-melanoma is a collaborative prospective database in which a total of 19 hospitals participate, covering most of the population of Catalonia. Cases diagnosed and monitored exclusively in private centres were not included in the database. These cases are a minority in Catalonia since most melanoma patients are seen in public hospitals, especially in the case of invasive tumours.
The research protocol received approval from the Research Ethics Committee of the Hospital Clinic of Barcelona (IRB number: HCB/2015/0298). All data were collected prospectively during the time period. This study followed the "strengthening the reporting of observational studies in epidemiology" (STROBE) statement  [16]. We analysed all cases of invasive and in situ cutaneous melanoma, including all ages. To evaluate age periods, we used equally spaced two-year calendar periods to analyse differences in time. Category variables were compared using Pearson's x 2 test, or Fisher's exact test, when the expected observations were fewer than five. For continuous variables, we used the mean and standard deviation and linear model ANOVA test. Annual incidence rates were age-standardised to the European standardised population  [17]  by the direct method to remove the confounding effect of age and to make valid comparisons between the incidence rates from different countries and 95% confidence intervals for age-adjusted rates were calculated using the Fay and Feuer method  [18].
To examine the population trends in age-standardised incidence rate according to sex and histological subtype (invasive vs in situ), joinpoint regression models were used. We calculated the average annual percent change (AAPC) for each variable independently using a fixed period between 2008 and 2017 for an easier comparison of the subgroups. Moreover, we calculated the average annual percentage change (APC) with a maximum number of possible joinpoints set to one, based on the number of data points in the series  [19]. A permutation test was used to determine the location of the joinpoints, when the change in trend was statistically significant. The resulting slope was recorded as the APC. Statistical analyses were performed using the computing environment R and RStudio  [20, 21] , and the Joinpoint Regression Software, version 4.
Funding: The study in the  Melanoma Unit, Hospital Clínic, Barcelona  was supported in part by grants from  Fondo de Investigaciones Sanitarias P. I. 12/00840 ,  PI 15/00956 , and  PI 15/00716  Spain; by the  CIBER de Enfermedades Raras of the Instituto de Salud Carlos III, Spain , co-funded by " Fondo Europeo de Desarrollo Regional (FEDER). Unión Europea. Una manera de hacer Europa"; by the  AGAUR 2014_SGR_603  and  2017_SGR_1134  of the Catalan Government, Spain; by a grant from " Fundació La Marató  de  TV3 ,  201331-30 ", Catalonia, Spain; by the  European Commission  under the 6th Framework Programme, Contract n o :  LSHC-CT-2006-018702  (GenoMEL); by  CERCA Programme/Generalitat de Catalunya ; by a  Research Grant  from  "Fundación Científica de la Asociación Española Contra el Cáncer "  GCB15152978SOEN , Spain; by a grant from the  European Academy of Dermatology and Venereology (EADV)  ( PPRC-2017/19 ).
In the South Taranaki Bight (STB) (40. 42°S, 174. 50°E) and Cook Strait (41. 09°S, 174. 55°E), recorders were bottom-mounted in water depths shallower than 300 m. AMARs sampled with a duty cycle of 900 s: 630 s at 16 kHz (WAV format, 24 bit), 125 s at 250 kHz (WAV format, 16 bit) and 145 s of sleep. New Zealand data were collected during 2016; thus song from the closest breeding populations was obtained for the year prior (2015), the same year (2016) and the year after (2017). East Australian song was recorded approximately 1. 5 km off Peregian Beach, Queensland (26. 48°S, 153. 10°E)  [39, 50, 51]. While this is a migratory corridor, around 600 km south of the East Australian breeding grounds around the Great Barrier Reef  [52]  (figure  1 ), there is extensive evidence that breeding activities also occur near Peregian Beach  [25, 51, 53, 54].
Quantifying unit classification using random forest A total of 71 unit types were qualitatively described during transcription of the New Zealand, New Caledonia and East Australia songs (electronic supplementary material, table  S2. 1). To ensure unit classifications were robust and repeatable across the dataset, multiple acoustic parameters were measured for all units from one high-quality example of each phrase type that was present for each location/year combination. Following Dunlop et al. [58] , Raven Pro (spectrogram parameters supplied above) was used to calculate duration, bandwidth, peak frequency, high frequency and low frequency of the fundamental frequency of each unit. The start frequency, end frequency, frequency trend (start frequency ÷ end frequency), frequency range (high frequency ÷ low frequency), number of inflections, pulse repetition rate ( per second) and a qualitative name per unit were also recorded manually, following Dunlop et al.
[58]  and Garland et al. [42]. A random forest analysis (R package 'randomForest'  [59] ) was conducted in R  [60]  to test the agreement in qualitative unit classification using qualitative name as the dependent variable (mtry = 3, 1000 trees grown)  [42, 61]. The random forest classified each unit based on its quantitative parameter values and resulted in a confusion matrix revealing which units were classified together, what variable was most informative, which unit types were often mis-classified and how this corresponded to qualitative classification (electronic supplementary material, S2, table  S2. 2). The out-of-bag (OOB) error rate was 15. 84% indicating a high level of agreement between qualitative and quantitative unit classification, confirming that unit classifications were robust and repeatable.
The LSI was calculated in R using custom-written code (package 'leven', available at http://github. com/ ellengarland/leven) for every possible pair of phrase strings (i. e. a sequence of units), resulting in a theme similarity matrix (that documents within-group and between-group similarities) to verify qualitative phrase type assignments (electronic supplementary material, S3). Using the LSI to check phrase type assignment ensured a robust and repeatable classification method at this level within the song hierarchy. Hierarchical clustering of the LSI similarity matrix of phrase strings (n = 3183; electronic supplementary material, S3, Dataset_S1) was conducted to check phrase type assignments, and visually displayed as a dendrogram to show phrase connections. The cophenetic correlation coefficient (CCC) was calculated to compare dendrograms to determine the most appropriate clustering method for the data (considered 'good' if value greater than 0.
Matching song sequences across locations and years Once phrase assignments, and thus theme assignments, were confirmed, two analyses were conducted to match songs among locations, years and recordings: LSI using the ordering of themes; and Dice's similarity index (DSI) based on the presence and sharing of themes regardless of their sequence information (further details for the two methods are provided in Garland et al. [42] ). For the LSI analysis, the order of themes making up each song were compiled into strings, with each theme identified by the identification number of its constituent phrase type (electronic supplementary material, Dataset_S2). Each theme was entered into a song string once, in sequence, regardless of whether the constituent phrase was sung once or repeated multiple times.
The LSI was run on (i) the full dataset comprising all song strings to incorporate the variability (theme similarity matrix), and (ii) the median song string per recording to create a point estimate (median string similarity matrix) (electronic supplementary material, S4). Both results were average-linkage hierarchically clustered and bootstrapped (1000 times) to identify the similarities and connections among songs from each location/year/recording combination. In addition to the LSI, DSI was also used to examine relationships among songs. DSI takes into account the number of shared phrases out of the total number of phrases present between all pairs of location/year/recording combinations in order to consider their similarity-see Garland et al. [63]  and  [42]  for further details. Unlike the LSI, DSI does not consider the sequence of themes, merely their presence or absence in a song (electronic supplementary material, Dataset_S3).
Discussion Humpback whales on their northbound migration to western South Pacific breeding grounds passed through central New Zealand during June, July and early August 2016, as demonstrated by song presence. The recorded whales migrated along the east coast of the South Island of New Zealand, and continued north through Cook Strait and into the STB, or along the east coast of the North Island, although the latter route was supported by fewer song recordings. Similarities in song themes suggested that these whales most likely continued onto the breeding ground of New Caledonia, although connectivity with the breeding ground of East Australia could not be ruled out due to a data gap for northbound East Australia 2016 song. Song similarities between 2015 and 2016, and 2016 and 2017, indicated that song was most likely transmitted from East Australia to New Caledonia along shared migration routes and/or on shared feeding grounds during the austral summer.
The timing of humpback migration through New Zealand waters varies between years  [23] , and no acoustic data were available prior to June 2016, but passive acoustic monitoring has enhanced our understanding of the spatio-temporal extent of the northbound migration period of whales through these waters as they recover post-whaling. The location of the acoustic recorders in this study covered known historic and modern humpback whale migration paths through central New Zealand. The data suggest that the singing whales that passed the Kaikoūra recorder on the east coast of the South Island during their northbound migration primarily travelled through Cook Strait and into the STB, rather than continuing along the east coast of the North Island, past the Wairarapa recording location. This conclusion is based on the low numbers of song detections made at Wairarapa.
Indeed, humpback whales sampled in New Zealand are most genetically royalsocietypublishing. org/journal/rsos R. Soc. Open Sci. 7: 201084 similar to the breeding population in New Caledonia  [4, 24]  and photographic mark-recapture studies have shown strong links  [33]. That said, sample sizes in this study are small and no song data were available from the northbound migration in East Australia in 2016 to compare with song recorded in New Zealand in 2016. In 2015, song evolved in East Australia during the breeding season, and the same could have occurred during 2016, meaning that the 2016 East Australia song presented here, from the southbound migration, would have been dissimilar to the northbound 2016 East Australia song and potentially the same as the song recorded in central New Zealand.
The New Zealand migratory corridor has been connected to the East Australian breeding ground in recent times via song matching  [40]  and other methods  [8, 24, 31, 33, 34] , and it is possible that some whales that migrated through central New Zealand could have travelled on to the breeding ground of East Australia. Connectivity between the New Zealand migratory corridor and the breeding ground of East Australia cannot be conclusively investigated within the present study. The results presented here support the hypothesis that the singing humpback whales recorded during the northbound migration through central New Zealand travelled on to the breeding grounds of western Oceania, but in acknowledging the caveats of the data outlined above, there is also a possibility that some whales travelled to the breeding ground of East Australia.
Acknowledgements. Thanks to the New Zealand acoustic deployment and retrieval teams from the  National Institute of Water and Atmospheric Research, Wellington :  Sarah Searson ,  Mike Brewer ,  Olivia Price ,  Fiona Elliott  and the crews of the RVs Tangaroa, Kaharoa and Ikatere. Thanks to  Jennifer Allen  ( Cetacean Ecology and Acoustics Lab, University of Queensland ) for collecting the New Caledonian recordings, and the  Department of Environment of the Southern Province of New Caledonia  for their help in deploying and recovering the acoustic equipment. Further thanks to  Craig Radford  ( University of Auckland ) and  Kimberly Goetz  ( NOAA ) for feedback that improved the manuscript, and again to  Jennifer Allen  for insights into song coding. royalsocietypublishing. org/journal/rsos  R.
Funding. V. E. W. is funded by a  University of Auckland Doctoral Scholarship  and the  Woodside Marine Mammal Research Grant  awarded by  Woodside Energy. E. C. G. is funded by a  Royal Society University Research Fellowship. Data collection in New Zealand was conducted by the  National Institute of Water and Atmospheric Research (NIWA) ,  Wellington , supported by funding from  OMV New Zealand Ltd ,  Chevron New Zealand Holdings LLC and Marlborough District Council. New Caledonian recordings were funded by a grant to  Jennifer Allen  from the  Sea World Research and Rescue Foundation Inc. East Australian recordings were funded by the  Joint Industry Programme, E&P Sound  and  Marine Life  as part of the  BRAHSS  project (2015) and by the  Cetacean Ecology and Acoustics Lab  ( 2016 ,  2017 ).
Settings and questionnaire The survey was conducted through an online questionnaire that was created with the eval and go © software (Montpellier, France, 2009). The questionnaire included 46 questions regarding the following topics: acquired academic degrees and competencies, type of hospital (UHC, peripheral), emergency medicine activities (ED, UMAS, MICU), pediatric activities in-and out-of-hospital, types of difficulties encountered when taking care of children, as well as level of interest regarding pediatric emergencies. The questionnaire was developed by the study team especially for this survey and was inspired from a previously published questionnaire  [8]. The complete questionnaire is available in the supplemental material section. Respondents had to answer questions according to 5-point Likert-type scales (strongly agree, agree, neutral, disagree, or strongly disagree).
The survey technique was an adapted form of respondent-driven sampling. One physician was asked to serve as a node to recruit additional study participants. The chair of the ED was contacted by email in March 2017 and was asked to transfer this email to all of their senior physician colleagues working for the ED. The email contained a brief description of the study objectives and a web link to the online questionnaire. Each participant received a reminder of the study twice by email. All of the questionnaires were anonymized once received by the research team. Answers were collected and analyzed in June 2017 with Excel © software. The respondents were divided into two groups according to their answer to question 19 ("As part of your daily medical activities, do you encounter difficulties when taking care of children?"): group 1 reporting difficulties (strongly agree or agree) vs.