dataesr/scientific-paragraphs-categorization huggingface dataset:
INTRODUCTION Traveling is getting more accessible in the present era of progressive globalization. It has never been easier to travel, resulting in a significant increase of the volume of leisure trips and tourists around the world (see, for instance, the statistics of the last UNWTO reports  [1] ). Over the last fifty years, this increasing importance of the economic, social and environmental impact of tourism on a region and its residents has led to a considerable number of studies in the so-called geography of tourism  [2]. In particular, geographers and economists have attempted to understand the contribution of tourism to global and regional economy  [3] [4] [5] [6] [7]  and to assess the impact of tourism on local people  [8] [9] [10] [11] [12] [13]. These researches on tourism have traditionally relied on surveys and economic datasets, generally composed of small samples with a low spatio-temporal resolution.
However, with the increasing availability of large databases generated by the use of geolocated information and communication technologies (ICT) devices such as mobile phones, credit or transport cards, the situation is now changing. Indeed, this flow of information has notably allowed researchers to study human mobility patterns at an unprecedented scale  [14] [15] [16] [17] [18] [19]. In addition, once these data are recorded, they can be aggregated in order to analyze the city's spatial structure and function  [20] [21] [22] [23] [24] [25] [26] [27]  and they have also been successfully tested against more traditional data sources  [28] [29] [30]. In the field of tourism geography, these new data sources have offered the possibility to study tourism behavior at a very high spatiotemporal resolution  [18, [31] [32] [33] [34] [35] [36] [37].
In this work, we propose a ranking of touristic sites worldwide based on their attractiveness measured with geolocated data as a proxy for human mobility. Many different rankings of most visited touristic sites exist but they are often based on the number of visitors, which does not really tell us much about their attractiveness at a global scale. Here we apply an alternative method proposed in  [38]  to measure the influence of cities. The purpose of this method is to analyze the influence and the attractiveness of a site based on the average radius traveled and the area covered by individuals visiting this site. More specifically, we select 20 out of the most popular touristic sites of the world and analyze their attractiveness using a dataset containing about 10 million geolocated tweets, which have already demonstrated their efficiency as useful source of data to study mobility at a world scale  [18, 38].
MATERIALS AND METHODS The purpose of this study is to measure the attractiveness of 20 touristic sites taking into account the spatial distribution of their visitors' places of residence. To do so, we analyze a database containing 9. 6 million geolocated tweets worldwide posted in the period between September 10, 2010 and October 21, 2015. The dataset was built by selecting the geolocated tweets sent from the touristic places in the general streaming and requesting the time-lines of the users posting them. The touristic sites boundaries have been identified manually. Collective accounts and user exhibiting non-human behaviors have been removed from the data by identifying users tweeting too quickly from the same place, with more than 9 tweets during the same minute and from places separated in time and space by a distance larger than what is possible to be covered by a commercial flight (with an average speed of 750 km/h).
Their spatial distributions and that of the touristic sites can be seen in Figure  1. In order to measure the site attractiveness, we need to identify the place of residence of every user who have been at least once in one of the touristic sites. First, we discretize the space by dividing the world into squares of equal area (100 × 100 km 2 ) using a cylindrical equal-area projection. Then, we identify the place most frequented by a user as the cell from which he or she has spent most of his/her time. To ensure that this most frequented location is the actual user's place of residence the constraint that at least one third of the tweets has been posted from this location is imposed. The resulting dataset contains about 59, 000 users' places of residence. The number of valid users is shown in Table  1  for each touristic site.
site's visiting figures by country of residence We can also do the opposite by studying the touristic of the residents of each country. We extract the distribution of the number of visitors from each country to the touristic sites and normalize by the total number of visitors in order to obtain a probability distribution to visit a touristic site according to the country of origin. This distribution can be averaged over the 70 countries with the higher number of residents in our database (gray bars in Figure  4 ). The Eiffel Tower, Times Square and the London Tower welcome in average 50% of the visitors of each country. It is important to note that these most visited touristic sites are not necessarily the ones with the higher attractiveness presented in the previous section. That is the advantage of the method proposed in  [38] , which allows us to measure the influence and the power of attraction of regions of the world with different number of local and non-local visitors.
At country level, the top rankers are the Taj Mahal and the Pyramids of Giza exhibiting a low cell coverage but with residence cells distributed in many different countries. Our method to use social media as a proxy to measure human mobility lays the foundation for even more involved analysis. For example, when we cluster the sites by the country of the origin of their visitors, two main clusters emerge: one including the Americas and the Far East and the other with Europe, Oceania and South Africa. The relations between sites have been also investigated by considering users who visited more than one place. An undirected network was built connecting sites visited by the same users. The Eiffel Tower, Times Square, Zocalo and the London Tower are the most central sites of the network. In summary, this manuscript serves to illustrate the power of geolocated data to provide world wide information regarding leisure related mobility.
ACKNOWLEDGEMENTS Partial financial support has been received from the  Spanish Ministry of Economy (MINECO)  and  FEDER (EU)  under project  INTENSE@COSYP  ( FIS2012-30634 ), and from the  EU Commission  through project  INSIGHT. The work of ML has been funded under the  PD/004/2013  project, from the  Conselleria de Educación, Cultura y Universidades of the Government of the Balearic Islands  and from the  European Social Fund  through the  Balearic Islands ESF operational program  for 2013-2017. JJR acknowledges funding from the  Ramón y Cajal program of MINECO. BG was partially supported by the  French ANR  project  HarMS-flu  ( ANR-12-MONU-0018 ).
Acknowledgements. The authors are indebted to  Sang Hoang  for providing the direction-finding analysis of the Wind/Waves observations. They acknowledge helpful discussions within the team The  Connection Between Coronal Shock Wave Dynamics and Early SEP Production  led by  K. Kozarev  and  N. Nitta  at the  International Space Science Institute (ISSI) in Bern. C. S. -M. gratefully acknowledges the financial support of her doctorate studies by the  University of Costa Rica  and the  Ministry of Science, Technology and Telecommunications of Costa Rica (MICITT)  through the  National Council of Scientific and Technological Research (CONICIT). This research was also supported by the  Agence Nationale pour la Recherche (ANR/ASTRID, DGA)  project  Outils radioastronomiques pour la météorologie de l 'espace (ORME, contract No.
ANR-14-ASTR-0027 ) and by the  French space agency (CNES). A. P. R. acknowledges use of the tools made available by the  French plasma physics data centre (Centre de Données de la Physique des Plasmas ; CDPP; http://cdpp. eu/), CNES  and the space weather team in Toulouse (Solar-Terrestrial Observations and Modelling Service; STORMS). This includes the data-mining tools AMDA (http://amda. cdpp. eu/), the CLWEB tool (clweb. cesr. fr/)and the propagation tool (http://propagationtool. cdpp. eu). The catalogs used in the.
Old astrophotographic plates are precious sources of historical data for astronomical studies, especially regarding the improvement of natural satellite orbits. Today, with the advent of new, accurate techniques, these old data can be re-processed so as to give positions that are much more accurate than those initially obtained. Various recent projects, including our Chinese project, have involved measuring and reducing these old plates again. Here we present a method for measurement and reduction that involves the digitization of plates using an advanced commercial scanner, namely the EPSON 10000 XL. We selected a set of 27 plates of the satellites of Jupiter, Saturn and Uranus taken from 1987 to 1990. A total of 125 satellite positions were derived from the new measurement and reduction of these plates using the UCAC4 catalogue.
I N T RO D U C T I O N Before CCD detectors were widely employed in observational astronomy, the main method of detection was the use of astrophotographic plates. More than 2 million astrophotographic plates were taken throughout the world over more than a hundred years, including about 30 000 plates in China. Because these plates contain more than a century of valuable original astronomical data, they need to be preserved and the information they contain recorded. Thus, in 2000 the International Astronomical Union (IAU) founded a working group dedicated to the Preservation and Digitization of Photographic Plates (PDPP). This working group asked all observatories around the world to digitize their plates as soon as possible, The data are available in electronic form by e-mail, as Supplementary Material to the online version of the paper on Blackwell Synergy, at the CDS via Anonymous FTP to cdsarc.
New observed satellite astrometric positions We present here 125 new satellite astrometric positions obtained from 27 selected plates taken from 1987 to 1990. The numbers of positions for each satellite are given in Table  5. In Table  6  we present an extract from the list of the new observed positions. The first column gives the name of the satellite, and the next three columns provide the year, month and decimal day in UTC of the middle time of each observation. The next two columns list the right ascension, expressed in hours, and the declination, expressed in degrees. The final column provides the observatory code. These data are topocentric and refer to the J2000. 0 ICRF reference frame. Note that a large number of satellite positions are published for the first time in this work. They are noted in Table  6  with an asterisk following the corresponding satellite name.
However, there is a second galaxy with almost the same brightness in r that might be considered as a second BCG. The coordinates of this second galaxy are (19 h 00 03. 9, 54 • 42 37. 06), and its redshift is z = 0. 375. The black circle shows the location of the Planck SZ centre with a 5 diameter. Red and blue circles correspond to our confirmed clusters at z = 0. 070 and z = 0. 193, with 17 and 24 galaxy members, respectively. Small squares mark the BCG for each system. The SZ signal seems to be mainly associated with the southern cluster (inside the blue circle). PSZ1 G087. 25-41. 86 Fossil group, with a gap between the BGC and the second cluster member of more than 2 mag in r. The spectroscopic redshift quoted in Table 2 corresponds to the BCG and was obtained from the SDSS database. Re-evaluation of the photometric redshift suggested that this probably is a z > 0.
This target was also included in the RTT150 paper (Planck Collaboration Int. XXVI 2014), with a spectroscopic redshift of z = 0. 107. PSZ1 G138. 60-10. 85  Figure  9  shows the RGB image obtained with the WFC/INT. The optical counterpart for this SZ target is 4. 5 from the Planck centre, and we obtained four spectroscopic members with OSIRIS/GTC. The redshift of the BCG is z = 0. 702. As the galaxy members are very red and we lacked a Z -band image for this cluster, our original photometric redshift determination was quite uncertain (z phot > 0. 53). PSZ1 G146. 00-49. 42 Our WFC/INT image clearly shows Galactic cirrus contamination in the region. The proposed optical counterpart for this cluster is WHL J015128. 7+104912, with z phot = 0. 105  (Wen et al. 2012 ). Here we provide the spectroscopic confirmation, with four members at the same redshift from the SDSS database.
PSZ1 G153. 56+36. 23 Using the SDSS database, we identified a low-redshift counterpart for this cluster, 5. 3 from the nominal Planck position, with six spectroscopic members, all of them around z = 0. 130. There is no clear BCG for this cluster. The image shows three large galaxies with similar r magnitudes (16. 9-17. 1). The coordinates in Table  2  correspond to the geometrical centroid of all photometrically selected members. A search in NED around this position yields two galaxy clusters: MaxBCG J129. 79086+62. 44628 with photometric redshift z phot = 0. 146 (Koester et al. 2007), and GMBCG J129. 95605+62. 41001 with z phot = 0. 168 (Hao et al. 2010). However, the proposed BCGs for these two photometrically selected clusters are indeed at the same spectroscopic redshift of z = 0. 132, and therefore we conclude that they are all part of the same structure that we identify here.
This target is only 22 away from PSZ1 G153. 41+36. 58. PSZ1 G153. 87+41. 05 Using the SDSS database, we identify a counterpart for this cluster 7 from the Planck position, with a spectroscopic redshift of z = 0. 279. Although this distance is unusually large, the Compton y-map in this area (Planck Collaboration XXI 2014) supports this association. PSZ1 G165. 76+31. 15 All our redshift estimates for this candidate are based on SDSS data (both photometry and spectroscopy). In this region, we found two clusters overlapped in projection along the line of sight. The cluster reported in Table 2      2015 ), but the quoted photometric redshift was z phot = 0. 316. Here, we provide the spectroscopic redshift of the cluster, using the information contained in the SDSS DR10 database 10 for the BCG galaxy: z = 0.
Non-detections from the PSZ1 catalogue In five cases, our optical data are not sufficient to identify a counterpart of the Planck SZ source. In these cases, the most plausible explanations are either that there is no optical counterpart (i. e. a false SZ detection due to contamination by Galactic gas and dust emissions), or that the counterpart is at high redshift and our imaging is not deep enough. The first four cases were flagged as quality class 2 in the PSZ1 validation process (Planck Collaboration XXIX 2014) (meaning that they fulfil good-quality criteria for the SZ detection and for the associations and/or counterparts in ancillary data), while the last case is quality class 3 (low-reliability cluster candidate). We discuss these five cases in detail.
PSZ1 G105. 91-38. 39 There is no clear optical counterpart for this SZ source. Our imaging data were obtained with WFC/INT down to i = 25. 6. The optical image shows several Galactic cirrus features and is strongly affected by extinction and a bright star near the centre (see Fig. 13 ). We found two overdensities of red objects. The first, 9 from the Planck position, is unlikely to be associated with the SZ detection. The second is associated with WHL J235339. 7+223928, a possible galaxy cluster at a z phot = 0. 40, 6 from the Planck position. However, long-slit observations with ACAM/WHT rule out this second case as a galaxy cluster because the red galaxies have very different redshifts. We note that this target has quality class 2 in the PSZ1 catalogue (Planck Collaboration XXIX 2014), and it is confirmed as a cluster by the Arcminute Microkelvin Imager  PSZ1 G194.
The fourth overdensity of red objects in the region is associated with NSCS J124127+455030 in the NED database  (Lopes et al. 2004 ). However, our MOS observations with DOLORES/TNG for this clump do not find coincident redshifts for the potential members, therefore this candidate does not seem to be a real cluster. PLCK G134. 25-44. 24 There are no clear counterparts within 5 of the Planck coordinates, but there are two possible counterparts farther away. Taking into account the spatial distribution of the signal in the Compton y-map, we consider as the most probable candidate a low-redshift cluster located at ∼ 8. 9 from the Planck position. This cluster has 4 spectroscopically confirmed members at < z >= 0. 232, and more than 25 photometric members. In addition, there is a closer second candidate at (01 h 25 40.
Acknowledgements. The Planck Collaboration acknowledges the support of:  ESA ;  CNES , and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MINECO ,  JA  and  RES (Spain) ;  Tekes, AoF , and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ;  ERC  and  PRACE (EU). A description of the Planck Collaboration and a list of its members, indicating which technical or scientific activities they have been involved in, can be found at http://www. cosmos. esa. int/web/planck/planck-collaboration. This article is based on observations made with a) the  Gran Telescopio Canarias (GTC) , installed in the  Spanish Observatorio del Roque de los Muchachos (ORM)  of the  Instituto de Astrofísica de Canarias (IAC) , in the island of  La Palma ; b) the  Isaac Newton Telescope  and the  William Herschel Telescope  operated on the island of  La Palma  by the  ISAAC Newton Group of Telescopes  in the Spanish ORM of the IAC; c) the italian Telescopio Nazionale Galileo (TNG) operated on the island of  La Palma  by the  Fundación Galileo Galilei of the INAF (Istituto Nazionale di Astrofisica ) at the Spanish ORM of the IAC; d) the  Nordic Optical Telescope , operated on the island of  La Palma jointly by Denmark, Finland, Iceland, Norway , and Sweden, in the Spanish ORM of the IAC; and e) the IAC80 telescope operated on the island of Tenerife by the  IAC in the Spanish Observatorio del Teide.
the NED database, operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with NASA; SIMBAD, operated at  CDS, Strasbourg, France ; the SZ-Cluster Database operated by the  Integrated Data and Operation Center (IDOC)  at the IAS under contract with CNES and CNRS; and the  SDSS. Funding for the Sloan Digital Sky Survey (SDSS)  has been provided by the  Alfred P. Sloan Foundation , the Participating Institutions, the  National Aeronautics and Space Administration , the  National Science Foundation , the  U. S. Department of Energy , the  Japanese Monbukagakusho , and the  Max Planck Society. AS, RB, HL, and JARM acknowledge financial support from the  Spanish Ministry of Economy and Competitiveness (MINECO)  under the  2011 Severo Ochoa Program MINECO   SEV-2011-0187 , and the  Consolider-Ingenio  project  CSD2010-00064  ( EPI: Exploring the Physics of Inflation ).
Generally, across the region, its use is only for certain conditions (e. g. , asplenia, and for other persons at risk), as well as for selected populations, such as those performing the Hajj pilgrimage and travelers to endemic countries  [43]. Meningococcal disease is not a reportable disease in Bangladesh; therefore, there are no national data on prevalence and incidence. However, cases are captured from the ongoing surveillance at a network of multiple hospitals in urban and rural Bangladesh (Saha SK, et al, unpublished data, cited with permission). Antibiotic use prior to specimen collection presents a barrier to obtaining viable meningococcal isolates, and most cases are now detected by PCR, with only a few isolates recovered from blood samples. MenA was predominant (90%; 152/167) from 1994 to 2005.
It also underlines the importance of maintaining immunity in adolescents and young adults. Subcapsular MenB vaccines are now available and have been used effectively in outbreak control  [100, 102, 109] , and are also now included in routine infant vaccination recommendations in the UK. To evaluate the level of coverage provided by these vaccines, such as in an outbreak situation, accurate data on presence and expression of the various vaccine-related antigens in the active strain are required. For this, again, good-quality surveillance with access to MATS and DNA analysis technologies, especially WGS, are required. There is still uncertainty about the ability of these vaccines to eliminate MenB acquisition/carriage, and further data are needed in this area. The importance of raising awareness and of advocacy in promoting the prevention and control of MD was highlighted by the work of the MRF and the CoMO.
Presentations from the MRF and the CoMO showed how such organizations are key to ensuring continuation and growth in all of these areas. Updates to the GMI Global Recommendations for Meningococcal Disease were determined during the meeting based on the findings presented (Table  3 ). The GMI agreed that there is a need for a recommendation to enable access to WGS as part of surveillance programs and also for DNA sequence data to be publicly available. It was also agreed that guidance on the antigen expression criteria that indicate use of subcapsular vaccines should be included in the recommendations. Finally, the GMI agreed that support for MD advocacy and awareness campaigns should be included in the GMI Global Recommendations for Meningococcal Disease.
Acknowledgements We are grateful to  Maya Kiskinova  ( Sincrotrone Trieste ),  Jan Vogel  ( Institut Neel, Grenoble ),  Giancarlo Panaccione  ( CNR-IOM, Trieste ),  Fausto Sirotti  (Synchrotron SOLEIL),  Nicolas Moisan  ( LPS, Orsay ),  Michael Meyer  (European XFEL, Hamburg) and  Coryn F. Hague  ( LCPMR, Paris ) for useful discussions and suggestions. This research received financial support from the  European Community 7th Framework Programme  under grant agreement  n°312284 , and from  CNRS (France)  via the  PEPS_SASLELX program. The  FERMI  project at  Elettra-Sincrotrone Trieste  is supported by  MIUR  under grants  FIRB-RBAP045JF2  and  FIRB-RBAP06AWK3.
Structured follow-up in dedicated centers and guidelines for optimal treatment were shown to improve outcome  [12]. Although steady progress in survival was achieved, the median age at death in developed countries is still in the late twenties, mainly due to lung disease. This median age of death is even earlier when access to specific follow-up and treatment is difficult  [13]. These morbidity and mortality data are gathered from CF registries, including the European Cystic Fibrosis Society Patient Registry (ECFSPR) [https://www. ecfs. eu/projects/ecfs-patient-registry/intro]. This registry was developed around 2000 and now contains data from 35 EU and non EU countries and more than 29,000 patients  [18]. For rare diseases, registries are critical to learn the natural history of the disorder, to understand the major morbidities, and to observe the impact of specific follow up or new treatments.